AuditRisk_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='green'>UCI Audit Risk Project Part 2: Regression\n",
    "### MIS 6V99.003 Machine Learning \n",
    "#### Abhishek Patil \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of Contents:\n",
    "1. [Data Pre-processing](#Data-Pre-processing)\n",
    "    * [Importing Datasets](#Importing-Datasets)\n",
    "    * [Exploring Data](#Exploring-Data)\n",
    "    * [Merging Dataframes](#Merging-Dataframes)\n",
    "    * [Data Cleaning](#Data-Cleaning)\n",
    "    \n",
    "    \n",
    "2. [Correlation Matrix](#Correlation-Matrix)\n",
    "\n",
    "\n",
    "3. [Regression](#Regression)\n",
    "    * [Regression: Train-Test Split](#Regression:-Train-Test-Split)\n",
    "    * [Regression: Feature Scaling](#Regression:-Feature-Scaling)\n",
    "    * [Regression Models](#Regression:-Models)\n",
    "        * [Bagging](#Bagging)\n",
    "            * [Desision Tree Regressor with Bagging](#Desision-Tree-Regressor-with-Bagging)\n",
    "            * [Linear Regression with Bagging](#Linear-Regression-with-Bagging)\n",
    "        * [Pasting](#Pasting)\n",
    "            * [KNN with Pasting](#KNN-with-Pasting)\n",
    "            * [Linear Regression with Pasting](#Linear-Regression-with-Pasting)\n",
    "        * [Adaboost](#Adaboost)\n",
    "            * [Ridge with Adaboost](#Ridge-with-Adaboost)\n",
    "            * [Lasso with Adaboost](#Lasso-with-Adaboost)\n",
    "        * [Gradient Boosting Regressor](#Gradient-Boosting-Regressor)\n",
    "        * [Boosting, Pasting and Bagging: Generating a Report table](#Boosting,-Pasting-and-Bagging:-Generating-a-Report-table)\n",
    "        * [Principal Component Analysis](#Principal-Component-Analysis)\n",
    "            * [KNN Regressor with PCA](#KNN-Regressor-with-PCA)\n",
    "            * [Linear Regression](#Linear-Regression)\n",
    "            * [Polynomial Regression with PCA](#Polynomial-Regression-with-PCA)\n",
    "            * [Ridge Regression with PCA](#Ridge-Regression-with-PCA)\n",
    "            * [Lasso Regression with PCA](#Lasso-Regression-with-PCA)\n",
    "            * [SVM Regressor with PCA](#SVM-Regressor-with-PCA)\n",
    "            * [SGD Regressor with PCA](#SGD-Regressor-with-PCA)\n",
    "        * [Generating a Report table: PCA](#Generating-a-Report-table:-PCA)\n",
    "        * [Neural Network Model](#Neural-Network-Model) \n",
    "        \n",
    "        \n",
    "4. [Model Selection](#Model-Selection)   \n",
    "        \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing all the required libraries\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('display.max_columns', 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "audit_risk = pd.read_csv(\"audit_risk.csv\")\n",
    "trial = pd.read_csv(\"trial.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sector_score</th>\n",
       "      <th>PARA_A</th>\n",
       "      <th>Score_A</th>\n",
       "      <th>Risk_A</th>\n",
       "      <th>PARA_B</th>\n",
       "      <th>Score_B</th>\n",
       "      <th>Risk_B</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>numbers</th>\n",
       "      <th>Score_B.1</th>\n",
       "      <th>Risk_C</th>\n",
       "      <th>Money_Value</th>\n",
       "      <th>Score_MV</th>\n",
       "      <th>Risk_D</th>\n",
       "      <th>District_Loss</th>\n",
       "      <th>PROB</th>\n",
       "      <th>RiSk_E</th>\n",
       "      <th>History</th>\n",
       "      <th>Prob</th>\n",
       "      <th>Risk_F</th>\n",
       "      <th>Score</th>\n",
       "      <th>Inherent_Risk</th>\n",
       "      <th>CONTROL_RISK</th>\n",
       "      <th>Detection_Risk</th>\n",
       "      <th>Audit_Risk</th>\n",
       "      <th>Risk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>775.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.0</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.184536</td>\n",
       "      <td>2.450194</td>\n",
       "      <td>0.351289</td>\n",
       "      <td>1.351029</td>\n",
       "      <td>10.799988</td>\n",
       "      <td>0.313144</td>\n",
       "      <td>6.334008</td>\n",
       "      <td>13.218481</td>\n",
       "      <td>5.067655</td>\n",
       "      <td>0.223711</td>\n",
       "      <td>1.152964</td>\n",
       "      <td>14.137631</td>\n",
       "      <td>0.290979</td>\n",
       "      <td>8.265434</td>\n",
       "      <td>2.505155</td>\n",
       "      <td>0.206186</td>\n",
       "      <td>0.519072</td>\n",
       "      <td>0.104381</td>\n",
       "      <td>0.216753</td>\n",
       "      <td>0.053608</td>\n",
       "      <td>2.702577</td>\n",
       "      <td>17.680612</td>\n",
       "      <td>0.572680</td>\n",
       "      <td>0.5</td>\n",
       "      <td>7.168158</td>\n",
       "      <td>0.393041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.319017</td>\n",
       "      <td>5.678870</td>\n",
       "      <td>0.174055</td>\n",
       "      <td>3.440447</td>\n",
       "      <td>50.083624</td>\n",
       "      <td>0.169804</td>\n",
       "      <td>30.072845</td>\n",
       "      <td>51.312829</td>\n",
       "      <td>0.264449</td>\n",
       "      <td>0.080352</td>\n",
       "      <td>0.537417</td>\n",
       "      <td>66.606519</td>\n",
       "      <td>0.159745</td>\n",
       "      <td>39.970849</td>\n",
       "      <td>1.228678</td>\n",
       "      <td>0.037508</td>\n",
       "      <td>0.290312</td>\n",
       "      <td>0.531031</td>\n",
       "      <td>0.067987</td>\n",
       "      <td>0.305835</td>\n",
       "      <td>0.858923</td>\n",
       "      <td>54.740244</td>\n",
       "      <td>0.444581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.667494</td>\n",
       "      <td>0.488741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.370000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.042000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.537500</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.583500</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.316700</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.890000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.175000</td>\n",
       "      <td>0.405000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.081000</td>\n",
       "      <td>1.370000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.018000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>2.214000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.555600</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.570000</td>\n",
       "      <td>2.480000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.488000</td>\n",
       "      <td>4.160000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.840500</td>\n",
       "      <td>7.707500</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.595000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>2.235000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>10.663500</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.249900</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59.850000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1264.630000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>758.778000</td>\n",
       "      <td>1268.910000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>935.030000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>561.018000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>801.262000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>961.514400</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "       Sector_score      PARA_A     Score_A      Risk_A       PARA_B  \\\n",
       "count    776.000000  776.000000  776.000000  776.000000   776.000000   \n",
       "mean      20.184536    2.450194    0.351289    1.351029    10.799988   \n",
       "std       24.319017    5.678870    0.174055    3.440447    50.083624   \n",
       "min        1.850000    0.000000    0.200000    0.000000     0.000000   \n",
       "25%        2.370000    0.210000    0.200000    0.042000     0.000000   \n",
       "50%        3.890000    0.875000    0.200000    0.175000     0.405000   \n",
       "75%       55.570000    2.480000    0.600000    1.488000     4.160000   \n",
       "max       59.850000   85.000000    0.600000   51.000000  1264.630000   \n",
       "\n",
       "          Score_B      Risk_B        TOTAL     numbers   Score_B.1  \\\n",
       "count  776.000000  776.000000   776.000000  776.000000  776.000000   \n",
       "mean     0.313144    6.334008    13.218481    5.067655    0.223711   \n",
       "std      0.169804   30.072845    51.312829    0.264449    0.080352   \n",
       "min      0.200000    0.000000     0.000000    5.000000    0.200000   \n",
       "25%      0.200000    0.000000     0.537500    5.000000    0.200000   \n",
       "50%      0.200000    0.081000     1.370000    5.000000    0.200000   \n",
       "75%      0.400000    1.840500     7.707500    5.000000    0.200000   \n",
       "max      0.600000  758.778000  1268.910000    9.000000    0.600000   \n",
       "\n",
       "           Risk_C  Money_Value    Score_MV      Risk_D  District_Loss  \\\n",
       "count  776.000000   775.000000  776.000000  776.000000     776.000000   \n",
       "mean     1.152964    14.137631    0.290979    8.265434       2.505155   \n",
       "std      0.537417    66.606519    0.159745   39.970849       1.228678   \n",
       "min      1.000000     0.000000    0.200000    0.000000       2.000000   \n",
       "25%      1.000000     0.000000    0.200000    0.000000       2.000000   \n",
       "50%      1.000000     0.090000    0.200000    0.018000       2.000000   \n",
       "75%      1.000000     5.595000    0.400000    2.235000       2.000000   \n",
       "max      5.400000   935.030000    0.600000  561.018000       6.000000   \n",
       "\n",
       "             PROB      RiSk_E     History        Prob      Risk_F       Score  \\\n",
       "count  776.000000  776.000000  776.000000  776.000000  776.000000  776.000000   \n",
       "mean     0.206186    0.519072    0.104381    0.216753    0.053608    2.702577   \n",
       "std      0.037508    0.290312    0.531031    0.067987    0.305835    0.858923   \n",
       "min      0.200000    0.400000    0.000000    0.200000    0.000000    2.000000   \n",
       "25%      0.200000    0.400000    0.000000    0.200000    0.000000    2.000000   \n",
       "50%      0.200000    0.400000    0.000000    0.200000    0.000000    2.400000   \n",
       "75%      0.200000    0.400000    0.000000    0.200000    0.000000    3.250000   \n",
       "max      0.600000    2.400000    9.000000    0.600000    5.400000    5.200000   \n",
       "\n",
       "       Inherent_Risk  CONTROL_RISK  Detection_Risk  Audit_Risk        Risk  \n",
       "count     776.000000    776.000000           776.0  776.000000  776.000000  \n",
       "mean       17.680612      0.572680             0.5    7.168158    0.393041  \n",
       "std        54.740244      0.444581             0.0   38.667494    0.488741  \n",
       "min         1.400000      0.400000             0.5    0.280000    0.000000  \n",
       "25%         1.583500      0.400000             0.5    0.316700    0.000000  \n",
       "50%         2.214000      0.400000             0.5    0.555600    0.000000  \n",
       "75%        10.663500      0.400000             0.5    3.249900    1.000000  \n",
       "max       801.262000      5.800000             0.5  961.514400    1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "audit_risk.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sector_score</th>\n",
       "      <th>PARA_A</th>\n",
       "      <th>SCORE_A</th>\n",
       "      <th>PARA_B</th>\n",
       "      <th>SCORE_B</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>numbers</th>\n",
       "      <th>Marks</th>\n",
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       "      <th>MONEY_Marks</th>\n",
       "      <th>District</th>\n",
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       "      <th>LOSS_SCORE</th>\n",
       "      <th>History</th>\n",
       "      <th>History_score</th>\n",
       "      <th>Score</th>\n",
       "      <th>Risk</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>776.000000</td>\n",
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       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>775.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "      <td>776.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.184536</td>\n",
       "      <td>2.450194</td>\n",
       "      <td>3.512887</td>\n",
       "      <td>10.799988</td>\n",
       "      <td>3.131443</td>\n",
       "      <td>13.218481</td>\n",
       "      <td>5.067655</td>\n",
       "      <td>2.237113</td>\n",
       "      <td>14.137631</td>\n",
       "      <td>2.909794</td>\n",
       "      <td>2.505155</td>\n",
       "      <td>0.029639</td>\n",
       "      <td>2.061856</td>\n",
       "      <td>0.104381</td>\n",
       "      <td>2.167526</td>\n",
       "      <td>2.702577</td>\n",
       "      <td>0.626289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.319017</td>\n",
       "      <td>5.678870</td>\n",
       "      <td>1.740549</td>\n",
       "      <td>50.083624</td>\n",
       "      <td>1.698042</td>\n",
       "      <td>51.312829</td>\n",
       "      <td>0.264449</td>\n",
       "      <td>0.803517</td>\n",
       "      <td>66.606519</td>\n",
       "      <td>1.597452</td>\n",
       "      <td>1.228678</td>\n",
       "      <td>0.184280</td>\n",
       "      <td>0.375080</td>\n",
       "      <td>0.531031</td>\n",
       "      <td>0.679869</td>\n",
       "      <td>0.858923</td>\n",
       "      <td>0.484100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.370000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.537500</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.890000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.405000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.370000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.570000</td>\n",
       "      <td>2.480000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.160000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.707500</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.595000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59.850000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1264.630000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1268.910000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>935.030000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sector_score      PARA_A     SCORE_A       PARA_B     SCORE_B  \\\n",
       "count    776.000000  776.000000  776.000000   776.000000  776.000000   \n",
       "mean      20.184536    2.450194    3.512887    10.799988    3.131443   \n",
       "std       24.319017    5.678870    1.740549    50.083624    1.698042   \n",
       "min        1.850000    0.000000    2.000000     0.000000    2.000000   \n",
       "25%        2.370000    0.210000    2.000000     0.000000    2.000000   \n",
       "50%        3.890000    0.875000    2.000000     0.405000    2.000000   \n",
       "75%       55.570000    2.480000    6.000000     4.160000    4.000000   \n",
       "max       59.850000   85.000000    6.000000  1264.630000    6.000000   \n",
       "\n",
       "             TOTAL     numbers       Marks  Money_Value  MONEY_Marks  \\\n",
       "count   776.000000  776.000000  776.000000   775.000000   776.000000   \n",
       "mean     13.218481    5.067655    2.237113    14.137631     2.909794   \n",
       "std      51.312829    0.264449    0.803517    66.606519     1.597452   \n",
       "min       0.000000    5.000000    2.000000     0.000000     2.000000   \n",
       "25%       0.537500    5.000000    2.000000     0.000000     2.000000   \n",
       "50%       1.370000    5.000000    2.000000     0.090000     2.000000   \n",
       "75%       7.707500    5.000000    2.000000     5.595000     4.000000   \n",
       "max    1268.910000    9.000000    6.000000   935.030000     6.000000   \n",
       "\n",
       "         District        Loss  LOSS_SCORE     History  History_score  \\\n",
       "count  776.000000  776.000000  776.000000  776.000000     776.000000   \n",
       "mean     2.505155    0.029639    2.061856    0.104381       2.167526   \n",
       "std      1.228678    0.184280    0.375080    0.531031       0.679869   \n",
       "min      2.000000    0.000000    2.000000    0.000000       2.000000   \n",
       "25%      2.000000    0.000000    2.000000    0.000000       2.000000   \n",
       "50%      2.000000    0.000000    2.000000    0.000000       2.000000   \n",
       "75%      2.000000    0.000000    2.000000    0.000000       2.000000   \n",
       "max      6.000000    2.000000    6.000000    9.000000       6.000000   \n",
       "\n",
       "            Score        Risk  \n",
       "count  776.000000  776.000000  \n",
       "mean     2.702577    0.626289  \n",
       "std      0.858923    0.484100  \n",
       "min      2.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.400000    1.000000  \n",
       "75%      3.250000    1.000000  \n",
       "max      5.200000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trial.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most columns from the 2 dataframes have similar feature names and also similar description. Some columns are in all capital letters, some have values in multiples of 10. The only columns in **'trial'** that are entirely differrent are **'Loss'** and **'Risk'**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Renaming columns\n",
    "trial.columns = ['Sector_score','LOCATION_ID', 'PARA_A', 'Score_A', 'PARA_B',\n",
    "       'Score_B',  'TOTAL', 'numbers', 'Marks',\n",
    "       'Money_Value', 'MONEY_Marks', 'District',\n",
    "       'Loss', 'LOSS_SCORE', 'History', 'History_score', 'Score', 'Risk_trial' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scaling data to match the data in the other dataframe\n",
    "trial['Score_A'] = trial['Score_A']/10\n",
    "trial['Score_B'] = trial['Score_B']/10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['History', 'LOCATION_ID', 'Money_Value', 'PARA_A', 'PARA_B',\n",
       "       'Score', 'Score_A', 'Score_B', 'Sector_score', 'TOTAL', 'numbers'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "same_columns = np.intersect1d(audit_risk.columns, trial.columns)\n",
    "same_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sector_score', 'LOCATION_ID', 'PARA_A', 'Score_A', 'Risk_A', 'PARA_B',\n",
       "       'Score_B', 'Risk_B', 'TOTAL', 'numbers', 'Score_B.1', 'Risk_C',\n",
       "       'Money_Value', 'Score_MV', 'Risk_D', 'District_Loss', 'PROB', 'RiSk_E',\n",
       "       'History', 'Prob', 'Risk_F', 'Score', 'Inherent_Risk', 'CONTROL_RISK',\n",
       "       'Detection_Risk', 'Audit_Risk', 'Risk', 'Marks', 'MONEY_Marks',\n",
       "       'District', 'Loss', 'LOSS_SCORE', 'History_score', 'Risk_trial'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge two Dataframes  on common columns  using outer join\n",
    "merged_df = pd.merge(audit_risk, trial, how='outer', on = ['History', 'LOCATION_ID', 'Money_Value', 'PARA_A', 'PARA_B',\n",
    "       'Score', 'Score_A', 'Score_B', 'Sector_score', 'TOTAL', 'numbers'])\n",
    "merged_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframes have been succesfully merged."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = merged_df.drop(['Risk_trial'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I deleted the 'Risk_trial' column which as originally 'Risk' from 'trial.csv' as it had some values that were different from the 'Risk' column from 'audit_risk.csv'. Also, since it is a regression task, the Risk column is not required.\n",
    "\n",
    "The paper provided by professor states that the values of 'Audit_Risk' being greater or equal to 1 are classified as 1 and 0 otherwise. \n",
    "\n",
    "This condition is being satisfied by the 'Risk' column in 'audit_risk.csv' and not by the 'Risk' column in 'trial.csv'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 810 entries, 0 to 809\n",
      "Data columns (total 33 columns):\n",
      "Sector_score      810 non-null float64\n",
      "LOCATION_ID       810 non-null object\n",
      "PARA_A            810 non-null float64\n",
      "Score_A           810 non-null float64\n",
      "Risk_A            810 non-null float64\n",
      "PARA_B            810 non-null float64\n",
      "Score_B           810 non-null float64\n",
      "Risk_B            810 non-null float64\n",
      "TOTAL             810 non-null float64\n",
      "numbers           810 non-null float64\n",
      "Score_B.1         810 non-null float64\n",
      "Risk_C            810 non-null float64\n",
      "Money_Value       809 non-null float64\n",
      "Score_MV          810 non-null float64\n",
      "Risk_D            810 non-null float64\n",
      "District_Loss     810 non-null int64\n",
      "PROB              810 non-null float64\n",
      "RiSk_E            810 non-null float64\n",
      "History           810 non-null int64\n",
      "Prob              810 non-null float64\n",
      "Risk_F            810 non-null float64\n",
      "Score             810 non-null float64\n",
      "Inherent_Risk     810 non-null float64\n",
      "CONTROL_RISK      810 non-null float64\n",
      "Detection_Risk    810 non-null float64\n",
      "Audit_Risk        810 non-null float64\n",
      "Risk              810 non-null int64\n",
      "Marks             810 non-null int64\n",
      "MONEY_Marks       810 non-null int64\n",
      "District          810 non-null int64\n",
      "Loss              810 non-null int64\n",
      "LOSS_SCORE        810 non-null int64\n",
      "History_score     810 non-null int64\n",
      "dtypes: float64(23), int64(9), object(1)\n",
      "memory usage: 215.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Money_Value has 1 missing value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing the missing value by the median of the column\n",
    "df['Money_Value'] = df['Money_Value'].fillna(df['Money_Value'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sector_score</th>\n",
       "      <th>PARA_A</th>\n",
       "      <th>Score_A</th>\n",
       "      <th>Risk_A</th>\n",
       "      <th>PARA_B</th>\n",
       "      <th>Score_B</th>\n",
       "      <th>Risk_B</th>\n",
       "      <th>TOTAL</th>\n",
       "      <th>numbers</th>\n",
       "      <th>Score_B.1</th>\n",
       "      <th>Risk_C</th>\n",
       "      <th>Money_Value</th>\n",
       "      <th>Score_MV</th>\n",
       "      <th>Risk_D</th>\n",
       "      <th>District_Loss</th>\n",
       "      <th>PROB</th>\n",
       "      <th>RiSk_E</th>\n",
       "      <th>History</th>\n",
       "      <th>Prob</th>\n",
       "      <th>Risk_F</th>\n",
       "      <th>Score</th>\n",
       "      <th>Inherent_Risk</th>\n",
       "      <th>CONTROL_RISK</th>\n",
       "      <th>Detection_Risk</th>\n",
       "      <th>Audit_Risk</th>\n",
       "      <th>Risk</th>\n",
       "      <th>Marks</th>\n",
       "      <th>MONEY_Marks</th>\n",
       "      <th>District</th>\n",
       "      <th>Loss</th>\n",
       "      <th>LOSS_SCORE</th>\n",
       "      <th>History_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.0</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>810.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.406716</td>\n",
       "      <td>2.348556</td>\n",
       "      <td>0.344938</td>\n",
       "      <td>1.294561</td>\n",
       "      <td>10.347643</td>\n",
       "      <td>0.308395</td>\n",
       "      <td>6.068334</td>\n",
       "      <td>12.665829</td>\n",
       "      <td>5.064815</td>\n",
       "      <td>0.222716</td>\n",
       "      <td>1.146543</td>\n",
       "      <td>13.526807</td>\n",
       "      <td>0.287160</td>\n",
       "      <td>7.918490</td>\n",
       "      <td>2.493827</td>\n",
       "      <td>0.205926</td>\n",
       "      <td>0.516049</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.216049</td>\n",
       "      <td>0.051358</td>\n",
       "      <td>2.674074</td>\n",
       "      <td>16.999643</td>\n",
       "      <td>0.567407</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.881682</td>\n",
       "      <td>0.379012</td>\n",
       "      <td>2.227160</td>\n",
       "      <td>2.871605</td>\n",
       "      <td>2.493827</td>\n",
       "      <td>0.028395</td>\n",
       "      <td>2.059259</td>\n",
       "      <td>2.160494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.760476</td>\n",
       "      <td>5.579501</td>\n",
       "      <td>0.173042</td>\n",
       "      <td>3.378180</td>\n",
       "      <td>49.067564</td>\n",
       "      <td>0.167741</td>\n",
       "      <td>29.461510</td>\n",
       "      <td>50.292444</td>\n",
       "      <td>0.259188</td>\n",
       "      <td>0.078789</td>\n",
       "      <td>0.526897</td>\n",
       "      <td>65.213233</td>\n",
       "      <td>0.157414</td>\n",
       "      <td>39.157042</td>\n",
       "      <td>1.219166</td>\n",
       "      <td>0.036732</td>\n",
       "      <td>0.287118</td>\n",
       "      <td>0.520174</td>\n",
       "      <td>0.066628</td>\n",
       "      <td>0.299533</td>\n",
       "      <td>0.851869</td>\n",
       "      <td>53.676423</td>\n",
       "      <td>0.437573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.871031</td>\n",
       "      <td>0.485441</td>\n",
       "      <td>0.787889</td>\n",
       "      <td>1.574145</td>\n",
       "      <td>1.219166</td>\n",
       "      <td>0.180464</td>\n",
       "      <td>0.367323</td>\n",
       "      <td>0.666277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.370000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.014000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.564500</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.312900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.890000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.164000</td>\n",
       "      <td>0.335000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.067000</td>\n",
       "      <td>1.290000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>2.116000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.470800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.570000</td>\n",
       "      <td>2.397500</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.438500</td>\n",
       "      <td>2.985000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.347000</td>\n",
       "      <td>7.337500</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.862500</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>9.210500</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.882700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59.850000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1264.630000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>758.778000</td>\n",
       "      <td>1268.910000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>935.030000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>561.018000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>801.262000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>961.514400</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sector_score      PARA_A     Score_A      Risk_A       PARA_B  \\\n",
       "count    810.000000  810.000000  810.000000  810.000000   810.000000   \n",
       "mean      21.406716    2.348556    0.344938    1.294561    10.347643   \n",
       "std       24.760476    5.579501    0.173042    3.378180    49.067564   \n",
       "min        1.850000    0.000000    0.200000    0.000000     0.000000   \n",
       "25%        2.370000    0.070000    0.200000    0.014000     0.000000   \n",
       "50%        3.890000    0.820000    0.200000    0.164000     0.335000   \n",
       "75%       55.570000    2.397500    0.600000    1.438500     2.985000   \n",
       "max       59.850000   85.000000    0.600000   51.000000  1264.630000   \n",
       "\n",
       "          Score_B      Risk_B        TOTAL     numbers   Score_B.1  \\\n",
       "count  810.000000  810.000000   810.000000  810.000000  810.000000   \n",
       "mean     0.308395    6.068334    12.665829    5.064815    0.222716   \n",
       "std      0.167741   29.461510    50.292444    0.259188    0.078789   \n",
       "min      0.200000    0.000000     0.000000    5.000000    0.200000   \n",
       "25%      0.200000    0.000000     0.430000    5.000000    0.200000   \n",
       "50%      0.200000    0.067000     1.290000    5.000000    0.200000   \n",
       "75%      0.400000    1.347000     7.337500    5.000000    0.200000   \n",
       "max      0.600000  758.778000  1268.910000    9.000000    0.600000   \n",
       "\n",
       "           Risk_C  Money_Value    Score_MV      Risk_D  District_Loss  \\\n",
       "count  810.000000   810.000000  810.000000  810.000000     810.000000   \n",
       "mean     1.146543    13.526807    0.287160    7.918490       2.493827   \n",
       "std      0.526897    65.213233    0.157414   39.157042       1.219166   \n",
       "min      1.000000     0.000000    0.200000    0.000000       2.000000   \n",
       "25%      1.000000     0.000000    0.200000    0.000000       2.000000   \n",
       "50%      1.000000     0.050000    0.200000    0.010000       2.000000   \n",
       "75%      1.000000     4.862500    0.200000    0.972500       2.000000   \n",
       "max      5.400000   935.030000    0.600000  561.018000       6.000000   \n",
       "\n",
       "             PROB      RiSk_E     History        Prob      Risk_F       Score  \\\n",
       "count  810.000000  810.000000  810.000000  810.000000  810.000000  810.000000   \n",
       "mean     0.205926    0.516049    0.100000    0.216049    0.051358    2.674074   \n",
       "std      0.036732    0.287118    0.520174    0.066628    0.299533    0.851869   \n",
       "min      0.200000    0.400000    0.000000    0.200000    0.000000    2.000000   \n",
       "25%      0.200000    0.400000    0.000000    0.200000    0.000000    2.000000   \n",
       "50%      0.200000    0.400000    0.000000    0.200000    0.000000    2.200000   \n",
       "75%      0.200000    0.400000    0.000000    0.200000    0.000000    3.200000   \n",
       "max      0.600000    2.400000    9.000000    0.600000    5.400000    5.200000   \n",
       "\n",
       "       Inherent_Risk  CONTROL_RISK  Detection_Risk  Audit_Risk        Risk  \\\n",
       "count     810.000000    810.000000           810.0  810.000000  810.000000   \n",
       "mean       16.999643      0.567407             0.5    6.881682    0.379012   \n",
       "std        53.676423      0.437573             0.0   37.871031    0.485441   \n",
       "min         1.400000      0.400000             0.5    0.280000    0.000000   \n",
       "25%         1.564500      0.400000             0.5    0.312900    0.000000   \n",
       "50%         2.116000      0.400000             0.5    0.470800    0.000000   \n",
       "75%         9.210500      0.400000             0.5    2.882700    1.000000   \n",
       "max       801.262000      5.800000             0.5  961.514400    1.000000   \n",
       "\n",
       "            Marks  MONEY_Marks    District        Loss  LOSS_SCORE  \\\n",
       "count  810.000000   810.000000  810.000000  810.000000  810.000000   \n",
       "mean     2.227160     2.871605    2.493827    0.028395    2.059259   \n",
       "std      0.787889     1.574145    1.219166    0.180464    0.367323   \n",
       "min      2.000000     2.000000    2.000000    0.000000    2.000000   \n",
       "25%      2.000000     2.000000    2.000000    0.000000    2.000000   \n",
       "50%      2.000000     2.000000    2.000000    0.000000    2.000000   \n",
       "75%      2.000000     2.000000    2.000000    0.000000    2.000000   \n",
       "max      6.000000     6.000000    6.000000    2.000000    6.000000   \n",
       "\n",
       "       History_score  \n",
       "count     810.000000  \n",
       "mean        2.160494  \n",
       "std         0.666277  \n",
       "min         2.000000  \n",
       "25%         2.000000  \n",
       "50%         2.000000  \n",
       "75%         2.000000  \n",
       "max         6.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Detection_Risk' and 'Risk_F' have the same values throughout the columns. Deleting these columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 810 entries, 0 to 809\n",
      "Data columns (total 31 columns):\n",
      "Sector_score     810 non-null float64\n",
      "LOCATION_ID      810 non-null object\n",
      "PARA_A           810 non-null float64\n",
      "Score_A          810 non-null float64\n",
      "Risk_A           810 non-null float64\n",
      "PARA_B           810 non-null float64\n",
      "Score_B          810 non-null float64\n",
      "Risk_B           810 non-null float64\n",
      "TOTAL            810 non-null float64\n",
      "numbers          810 non-null float64\n",
      "Score_B.1        810 non-null float64\n",
      "Risk_C           810 non-null float64\n",
      "Money_Value      810 non-null float64\n",
      "Score_MV         810 non-null float64\n",
      "Risk_D           810 non-null float64\n",
      "District_Loss    810 non-null int64\n",
      "PROB             810 non-null float64\n",
      "RiSk_E           810 non-null float64\n",
      "History          810 non-null int64\n",
      "Prob             810 non-null float64\n",
      "Score            810 non-null float64\n",
      "Inherent_Risk    810 non-null float64\n",
      "CONTROL_RISK     810 non-null float64\n",
      "Audit_Risk       810 non-null float64\n",
      "Risk             810 non-null int64\n",
      "Marks            810 non-null int64\n",
      "MONEY_Marks      810 non-null int64\n",
      "District         810 non-null int64\n",
      "Loss             810 non-null int64\n",
      "LOSS_SCORE       810 non-null int64\n",
      "History_score    810 non-null int64\n",
      "dtypes: float64(21), int64(9), object(1)\n",
      "memory usage: 202.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df = df.drop(['Detection_Risk', 'Risk_F'], axis = 1) \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LOCATION_ID has object datatype. However there are numerical values in the column. There must be non-numeric values present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['23', '6', '7', '8', '13', '37', '24', '3', '4', '14', '5', '20',\n",
       "       '19', '21', '22', '9', '11', '12', '29', '30', '38', '31', '2',\n",
       "       '32', '16', '33', '15', '36', '34', '18', '25', '39', '27', '35',\n",
       "       '40', '41', '42', '1', '28', 'LOHARU', 'NUH', 'SAFIDON', '43',\n",
       "       '44', '17'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Unique values in LOCATION_ID column\n",
    "df[\"LOCATION_ID\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These are the number of non-numeric values in LOCATION_ID:  3\n"
     ]
    }
   ],
   "source": [
    "print(\"These are the number of non-numeric values in LOCATION_ID: \", len(df[(df[\"LOCATION_ID\"] == 'LOHARU') | (df[\"LOCATION_ID\"] ==  'NUH') | (df[\"LOCATION_ID\"] == 'SAFIDON')]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deleting the rows with these 3 values as there is no information about the sequencing of the numbers present in the LOCATION_ID column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated number of rows in the dataset:  807\n"
     ]
    }
   ],
   "source": [
    "df = df[(df.LOCATION_ID != 'LOHARU')]\n",
    "df = df[(df.LOCATION_ID != 'NUH')]\n",
    "df = df[(df.LOCATION_ID != 'SAFIDON')]\n",
    "df = df.astype(float)\n",
    "print(\"Updated number of rows in the dataset: \",len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping duplicate values if any. Here the rows which intersected with both dataframes get deleted.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Updated number of rows in the dataset:  760\n"
     ]
    }
   ],
   "source": [
    "df = df.drop_duplicates(keep = 'first')\n",
    "print(\"Updated number of rows in the dataset: \",len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sector_score : 13\n",
      "LOCATION_ID : 42\n",
      "PARA_A : 362\n",
      "Score_A : 3\n",
      "Risk_A : 362\n",
      "PARA_B : 358\n",
      "Score_B : 3\n",
      "Risk_B : 360\n",
      "TOTAL : 470\n",
      "numbers : 5\n",
      "Score_B.1 : 3\n",
      "Risk_C : 5\n",
      "Money_Value : 327\n",
      "Score_MV : 3\n",
      "Risk_D : 327\n",
      "District_Loss : 3\n",
      "PROB : 3\n",
      "RiSk_E : 5\n",
      "History : 7\n",
      "Prob : 3\n",
      "Score : 17\n",
      "Inherent_Risk : 583\n",
      "CONTROL_RISK : 11\n",
      "Audit_Risk : 600\n",
      "Risk : 2\n",
      "Marks : 3\n",
      "MONEY_Marks : 3\n",
      "District : 3\n",
      "Loss : 3\n",
      "LOSS_SCORE : 3\n",
      "History_score : 3\n"
     ]
    }
   ],
   "source": [
    "#Number of unique values in each columns\n",
    "for i in range(0, len(df.columns)):\n",
    "    print(df.columns[i], \":\", df.iloc[:,i].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col5 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col6 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col7 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col8 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col9 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col10 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col11 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col12 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col13 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col14 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col15 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col16 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col17 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col18 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col19 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col20 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col21 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col22 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col23 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col24 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col25 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col26 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col27 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col28 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col29 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col30 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col0 {\n",
       "            background-color:  #a1c2cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col1 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col2 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col3 {\n",
       "            background-color:  #c5dae0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col4 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col5 {\n",
       "            background-color:  #6d9ead;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col6 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col7 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col8 {\n",
       "            background-color:  #73a2b1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col9 {\n",
       "            background-color:  #72a1b0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col10 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col11 {\n",
       "            background-color:  #6fa0af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col12 {\n",
       "            background-color:  #4f8a9c;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col13 {\n",
       "            background-color:  #b9d2d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col14 {\n",
       "            background-color:  #4d899b;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col15 {\n",
       "            background-color:  #408094;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col16 {\n",
       "            background-color:  #5b92a3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col17 {\n",
       "            background-color:  #498699;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col18 {\n",
       "            background-color:  #498699;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col19 {\n",
       "            background-color:  #5a91a2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col20 {\n",
       "            background-color:  #b7d0d8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col21 {\n",
       "            background-color:  #679baa;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col22 {\n",
       "            background-color:  #488598;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col23 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col24 {\n",
       "            background-color:  #bcd4db;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col25 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col26 {\n",
       "            background-color:  #b9d2d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col27 {\n",
       "            background-color:  #408094;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col28 {\n",
       "            background-color:  #5c93a4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col29 {\n",
       "            background-color:  #5b92a3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col30 {\n",
       "            background-color:  #5a91a2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col0 {\n",
       "            background-color:  #76a4b2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col1 {\n",
       "            background-color:  #6599a9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col2 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col3 {\n",
       "            background-color:  #edcdc4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col4 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col5 {\n",
       "            background-color:  #a1c2cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col6 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col7 {\n",
       "            background-color:  #a0c1cb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col8 {\n",
       "            background-color:  #c8dce2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col9 {\n",
       "            background-color:  #9bbec8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col10 {\n",
       "            background-color:  #a3c3cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col11 {\n",
       "            background-color:  #a1c2cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col12 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col13 {\n",
       "            background-color:  #ecf4f7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col14 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col15 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col16 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col17 {\n",
       "            background-color:  #92b8c3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col18 {\n",
       "            background-color:  #8cb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col19 {\n",
       "            background-color:  #a5c5ce;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col20 {\n",
       "            background-color:  #f6e5e1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col21 {\n",
       "            background-color:  #f7e9e6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col22 {\n",
       "            background-color:  #a3c3cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col23 {\n",
       "            background-color:  #aac8d1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col24 {\n",
       "            background-color:  #f8ebe7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col25 {\n",
       "            background-color:  #a3c3cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col26 {\n",
       "            background-color:  #ecf4f7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col27 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col28 {\n",
       "            background-color:  #6a9cac;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col29 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col30 {\n",
       "            background-color:  #a5c5ce;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col0 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col1 {\n",
       "            background-color:  #83adba;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col2 {\n",
       "            background-color:  #f4e0db;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col3 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col4 {\n",
       "            background-color:  #f3ddd8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col5 {\n",
       "            background-color:  #bdd5dc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col6 {\n",
       "            background-color:  #edcdc4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col7 {\n",
       "            background-color:  #bdd5dc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col8 {\n",
       "            background-color:  #d3e3e8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col9 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col10 {\n",
       "            background-color:  #cee0e6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col11 {\n",
       "            background-color:  #cbdee4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col12 {\n",
       "            background-color:  #acc9d2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col13 {\n",
       "            background-color:  #f2dbd5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col14 {\n",
       "            background-color:  #aac8d1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col15 {\n",
       "            background-color:  #82adba;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col16 {\n",
       "            background-color:  #7ca8b6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col17 {\n",
       "            background-color:  #8cb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col18 {\n",
       "            background-color:  #a1c2cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col19 {\n",
       "            background-color:  #c3d9df;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col20 {\n",
       "            background-color:  #dc9c8c;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col21 {\n",
       "            background-color:  #dfebef;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col22 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col23 {\n",
       "            background-color:  #a4c4cd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col24 {\n",
       "            background-color:  #e3b1a4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col25 {\n",
       "            background-color:  #cee0e6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col26 {\n",
       "            background-color:  #f2dbd5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col27 {\n",
       "            background-color:  #82adba;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col28 {\n",
       "            background-color:  #7aa7b5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col29 {\n",
       "            background-color:  #7ca8b6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col30 {\n",
       "            background-color:  #c3d9df;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col0 {\n",
       "            background-color:  #74a3b2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col1 {\n",
       "            background-color:  #679baa;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col2 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col3 {\n",
       "            background-color:  #eccac1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col4 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col5 {\n",
       "            background-color:  #a1c2cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col6 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col7 {\n",
       "            background-color:  #a1c2cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col8 {\n",
       "            background-color:  #cadde3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col9 {\n",
       "            background-color:  #9dbfc9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col10 {\n",
       "            background-color:  #a3c3cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col11 {\n",
       "            background-color:  #a3c3cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col12 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col13 {\n",
       "            background-color:  #edf5f7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col14 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col15 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col16 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col17 {\n",
       "            background-color:  #92b8c3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col18 {\n",
       "            background-color:  #8eb4c0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col19 {\n",
       "            background-color:  #a5c5ce;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col20 {\n",
       "            background-color:  #f6e4df;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col21 {\n",
       "            background-color:  #f7e9e6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col22 {\n",
       "            background-color:  #a3c3cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col23 {\n",
       "            background-color:  #acc9d2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col24 {\n",
       "            background-color:  #f7e9e6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col25 {\n",
       "            background-color:  #a3c3cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col26 {\n",
       "            background-color:  #edf5f7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col27 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col28 {\n",
       "            background-color:  #6a9cac;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col29 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col30 {\n",
       "            background-color:  #a5c5ce;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col0 {\n",
       "            background-color:  #8cb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col1 {\n",
       "            background-color:  #6a9cac;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col2 {\n",
       "            background-color:  #b3ced6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col3 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col4 {\n",
       "            background-color:  #b7d0d8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col5 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col6 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col7 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col8 {\n",
       "            background-color:  #c3573b;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col9 {\n",
       "            background-color:  #b4cfd7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col10 {\n",
       "            background-color:  #c1d7de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col11 {\n",
       "            background-color:  #bdd5dc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col12 {\n",
       "            background-color:  #91b7c2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col13 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col14 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col15 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col16 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col17 {\n",
       "            background-color:  #85aebb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col18 {\n",
       "            background-color:  #aac8d1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col19 {\n",
       "            background-color:  #d5e5ea;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col20 {\n",
       "            background-color:  #f8ece9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col21 {\n",
       "            background-color:  #e6b8ac;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col22 {\n",
       "            background-color:  #afcbd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col23 {\n",
       "            background-color:  #cf7761;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col24 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col25 {\n",
       "            background-color:  #c1d7de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col26 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col27 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col28 {\n",
       "            background-color:  #6a9cac;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col29 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col30 {\n",
       "            background-color:  #d5e5ea;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col0 {\n",
       "            background-color:  #76a4b2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col1 {\n",
       "            background-color:  #92b8c3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col2 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col3 {\n",
       "            background-color:  #e7baaf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col4 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col5 {\n",
       "            background-color:  #e0ecf0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col6 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col7 {\n",
       "            background-color:  #dfebef;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col8 {\n",
       "            background-color:  #edf5f7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col9 {\n",
       "            background-color:  #cbdee4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col10 {\n",
       "            background-color:  #dbe9ed;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col11 {\n",
       "            background-color:  #d7e6eb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col12 {\n",
       "            background-color:  #acc9d2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col13 {\n",
       "            background-color:  #eac3b9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col14 {\n",
       "            background-color:  #aac8d1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col15 {\n",
       "            background-color:  #6196a7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col16 {\n",
       "            background-color:  #7ca8b6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col17 {\n",
       "            background-color:  #6fa0af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col18 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col19 {\n",
       "            background-color:  #d3e3e8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col20 {\n",
       "            background-color:  #cb6e56;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col21 {\n",
       "            background-color:  #ecf4f7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col22 {\n",
       "            background-color:  #9bbec8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col23 {\n",
       "            background-color:  #a5c5ce;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col24 {\n",
       "            background-color:  #e2ad9f;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col25 {\n",
       "            background-color:  #dbe9ed;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col26 {\n",
       "            background-color:  #eac3b9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col27 {\n",
       "            background-color:  #6196a7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col28 {\n",
       "            background-color:  #7da9b7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col29 {\n",
       "            background-color:  #7ca8b6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col30 {\n",
       "            background-color:  #d3e3e8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col0 {\n",
       "            background-color:  #8cb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col1 {\n",
       "            background-color:  #6a9cac;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col2 {\n",
       "            background-color:  #b3ced6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col3 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col4 {\n",
       "            background-color:  #b7d0d8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col5 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col6 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col7 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col8 {\n",
       "            background-color:  #c3573b;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col9 {\n",
       "            background-color:  #b4cfd7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col10 {\n",
       "            background-color:  #c1d7de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col11 {\n",
       "            background-color:  #bcd4db;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col12 {\n",
       "            background-color:  #91b7c2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col13 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col14 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col15 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col16 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col17 {\n",
       "            background-color:  #85aebb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col18 {\n",
       "            background-color:  #aac8d1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col19 {\n",
       "            background-color:  #d5e5ea;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col20 {\n",
       "            background-color:  #f8ece9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col21 {\n",
       "            background-color:  #e6b8ac;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col22 {\n",
       "            background-color:  #afcbd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col23 {\n",
       "            background-color:  #cf7761;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col24 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col25 {\n",
       "            background-color:  #c1d7de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col26 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col27 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col28 {\n",
       "            background-color:  #6a9cac;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col29 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col30 {\n",
       "            background-color:  #d5e5ea;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col0 {\n",
       "            background-color:  #87b0bc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col1 {\n",
       "            background-color:  #6a9cac;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col2 {\n",
       "            background-color:  #d5e5ea;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col3 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col4 {\n",
       "            background-color:  #d7e6eb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col5 {\n",
       "            background-color:  #c3573b;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col6 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col7 {\n",
       "            background-color:  #c3573b;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col8 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col9 {\n",
       "            background-color:  #b8d1d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col10 {\n",
       "            background-color:  #c2d8df;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col11 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col12 {\n",
       "            background-color:  #a0c1cb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col13 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col14 {\n",
       "            background-color:  #9fc0ca;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col15 {\n",
       "            background-color:  #83adba;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col16 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col17 {\n",
       "            background-color:  #88b1bd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col18 {\n",
       "            background-color:  #adcad2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col19 {\n",
       "            background-color:  #d9e7ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col20 {\n",
       "            background-color:  #f5e3de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col21 {\n",
       "            background-color:  #e2ad9f;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col22 {\n",
       "            background-color:  #b2cdd5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col23 {\n",
       "            background-color:  #cf7761;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col24 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col25 {\n",
       "            background-color:  #c2d8df;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col26 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col27 {\n",
       "            background-color:  #83adba;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col28 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col29 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col30 {\n",
       "            background-color:  #d9e7ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col0 {\n",
       "            background-color:  #87b0bc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col1 {\n",
       "            background-color:  #6a9cac;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col2 {\n",
       "            background-color:  #aac8d1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col3 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col4 {\n",
       "            background-color:  #acc9d2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col5 {\n",
       "            background-color:  #b2cdd5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col6 {\n",
       "            background-color:  #d9e7ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col7 {\n",
       "            background-color:  #b0ccd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col8 {\n",
       "            background-color:  #b8d1d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col9 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col10 {\n",
       "            background-color:  #cc6f58;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col11 {\n",
       "            background-color:  #c76147;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col12 {\n",
       "            background-color:  #a5c5ce;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col13 {\n",
       "            background-color:  #f5e2dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col14 {\n",
       "            background-color:  #a5c5ce;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col15 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col16 {\n",
       "            background-color:  #699bab;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col17 {\n",
       "            background-color:  #98bcc7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col18 {\n",
       "            background-color:  #aac8d1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col19 {\n",
       "            background-color:  #b2cdd5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col20 {\n",
       "            background-color:  #efd1ca;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col21 {\n",
       "            background-color:  #cee0e6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col22 {\n",
       "            background-color:  #bcd4db;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col23 {\n",
       "            background-color:  #acc9d2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col24 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col25 {\n",
       "            background-color:  #cc6f58;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col26 {\n",
       "            background-color:  #f5e2dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col27 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col28 {\n",
       "            background-color:  #5a91a2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col29 {\n",
       "            background-color:  #699bab;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col30 {\n",
       "            background-color:  #b2cdd5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col0 {\n",
       "            background-color:  #82adba;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col1 {\n",
       "            background-color:  #6397a7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col2 {\n",
       "            background-color:  #adcad2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col3 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col4 {\n",
       "            background-color:  #afcbd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col5 {\n",
       "            background-color:  #b8d1d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col6 {\n",
       "            background-color:  #e3eef1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col7 {\n",
       "            background-color:  #b8d1d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col8 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col9 {\n",
       "            background-color:  #cc6f58;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col10 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col11 {\n",
       "            background-color:  #c4583c;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col12 {\n",
       "            background-color:  #b2cdd5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col13 {\n",
       "            background-color:  #f0d3cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col14 {\n",
       "            background-color:  #b0ccd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col15 {\n",
       "            background-color:  #98bcc7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col16 {\n",
       "            background-color:  #699bab;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col17 {\n",
       "            background-color:  #a0c1cb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col18 {\n",
       "            background-color:  #b2cdd5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col19 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col20 {\n",
       "            background-color:  #e9c2b8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col21 {\n",
       "            background-color:  #dae8ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col22 {\n",
       "            background-color:  #c7dbe1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col23 {\n",
       "            background-color:  #b9d2d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col24 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col25 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col26 {\n",
       "            background-color:  #f0d3cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col27 {\n",
       "            background-color:  #98bcc7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col28 {\n",
       "            background-color:  #5c93a4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col29 {\n",
       "            background-color:  #699bab;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col30 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col0 {\n",
       "            background-color:  #83adba;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col1 {\n",
       "            background-color:  #6397a7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col2 {\n",
       "            background-color:  #adcad2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col3 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col4 {\n",
       "            background-color:  #afcbd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col5 {\n",
       "            background-color:  #b4cfd7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col6 {\n",
       "            background-color:  #e0ecf0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col7 {\n",
       "            background-color:  #b4cfd7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col8 {\n",
       "            background-color:  #bcd4db;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col9 {\n",
       "            background-color:  #c76147;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col10 {\n",
       "            background-color:  #c4583c;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col11 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col12 {\n",
       "            background-color:  #afcbd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col13 {\n",
       "            background-color:  #f0d6cf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col14 {\n",
       "            background-color:  #afcbd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col15 {\n",
       "            background-color:  #97bbc6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col16 {\n",
       "            background-color:  #699bab;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col17 {\n",
       "            background-color:  #9fc0ca;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col18 {\n",
       "            background-color:  #b0ccd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col19 {\n",
       "            background-color:  #bbd3da;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col20 {\n",
       "            background-color:  #ebc6bd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col21 {\n",
       "            background-color:  #d7e6eb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col22 {\n",
       "            background-color:  #c3d9df;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col23 {\n",
       "            background-color:  #b4cfd7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col24 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col25 {\n",
       "            background-color:  #c4583c;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col26 {\n",
       "            background-color:  #f0d6cf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col27 {\n",
       "            background-color:  #97bbc6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col28 {\n",
       "            background-color:  #5b92a3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col29 {\n",
       "            background-color:  #699bab;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col30 {\n",
       "            background-color:  #bbd3da;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col0 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col1 {\n",
       "            background-color:  #528c9e;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col2 {\n",
       "            background-color:  #f9edea;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col3 {\n",
       "            background-color:  #e6f0f3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col4 {\n",
       "            background-color:  #f9edea;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col5 {\n",
       "            background-color:  #94b9c4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col6 {\n",
       "            background-color:  #c1d7de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col7 {\n",
       "            background-color:  #94b9c4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col8 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col9 {\n",
       "            background-color:  #adcad2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col10 {\n",
       "            background-color:  #bdd5dc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col11 {\n",
       "            background-color:  #bbd3da;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col12 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col13 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col14 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col15 {\n",
       "            background-color:  #6d9ead;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col16 {\n",
       "            background-color:  #679baa;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col17 {\n",
       "            background-color:  #74a3b2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col18 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col19 {\n",
       "            background-color:  #91b7c2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col20 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col21 {\n",
       "            background-color:  #d48571;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col22 {\n",
       "            background-color:  #88b1bd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col23 {\n",
       "            background-color:  #d3e3e8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col24 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col25 {\n",
       "            background-color:  #bdd5dc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col26 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col27 {\n",
       "            background-color:  #6d9ead;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col28 {\n",
       "            background-color:  #6498a8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col29 {\n",
       "            background-color:  #679baa;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col30 {\n",
       "            background-color:  #91b7c2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col0 {\n",
       "            background-color:  #5a91a2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col1 {\n",
       "            background-color:  #8cb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col2 {\n",
       "            background-color:  #dae8ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col3 {\n",
       "            background-color:  #eed0c8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col4 {\n",
       "            background-color:  #ddeaee;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col5 {\n",
       "            background-color:  #d4e4e9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col6 {\n",
       "            background-color:  #edcdc4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col7 {\n",
       "            background-color:  #d4e4e9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col8 {\n",
       "            background-color:  #e0ecf0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col9 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col10 {\n",
       "            background-color:  #f5e3de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col11 {\n",
       "            background-color:  #f6e6e2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col12 {\n",
       "            background-color:  #ecf4f7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col13 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col14 {\n",
       "            background-color:  #eaf3f6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col15 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col16 {\n",
       "            background-color:  #89b2be;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col17 {\n",
       "            background-color:  #8cb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col18 {\n",
       "            background-color:  #b9d2d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col19 {\n",
       "            background-color:  #dbe9ed;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col20 {\n",
       "            background-color:  #d8917f;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col21 {\n",
       "            background-color:  #f7e9e6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col22 {\n",
       "            background-color:  #b9d2d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col23 {\n",
       "            background-color:  #c3d9df;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col24 {\n",
       "            background-color:  #dda090;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col25 {\n",
       "            background-color:  #f5e3de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col26 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col27 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col28 {\n",
       "            background-color:  #85aebb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col29 {\n",
       "            background-color:  #89b2be;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col30 {\n",
       "            background-color:  #dbe9ed;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col0 {\n",
       "            background-color:  #91b7c2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col1 {\n",
       "            background-color:  #528c9e;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col2 {\n",
       "            background-color:  #f9edea;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col3 {\n",
       "            background-color:  #e6f0f3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col4 {\n",
       "            background-color:  #f9edea;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col5 {\n",
       "            background-color:  #94b9c4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col6 {\n",
       "            background-color:  #c1d7de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col7 {\n",
       "            background-color:  #94b9c4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col8 {\n",
       "            background-color:  #a8c6cf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col9 {\n",
       "            background-color:  #adcad2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col10 {\n",
       "            background-color:  #bdd5dc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col11 {\n",
       "            background-color:  #bbd3da;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col12 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col13 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col14 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col15 {\n",
       "            background-color:  #6d9ead;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col16 {\n",
       "            background-color:  #679baa;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col17 {\n",
       "            background-color:  #74a3b2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col18 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col19 {\n",
       "            background-color:  #91b7c2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col20 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col21 {\n",
       "            background-color:  #d48571;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col22 {\n",
       "            background-color:  #88b1bd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col23 {\n",
       "            background-color:  #d3e3e8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col24 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col25 {\n",
       "            background-color:  #bdd5dc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col26 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col27 {\n",
       "            background-color:  #6d9ead;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col28 {\n",
       "            background-color:  #6498a8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col29 {\n",
       "            background-color:  #679baa;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col30 {\n",
       "            background-color:  #91b7c2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col0 {\n",
       "            background-color:  #92b8c3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col1 {\n",
       "            background-color:  #448396;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col2 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col3 {\n",
       "            background-color:  #c7dbe1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col4 {\n",
       "            background-color:  #aac8d1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col5 {\n",
       "            background-color:  #87b0bc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col6 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col7 {\n",
       "            background-color:  #87b0bc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col8 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col9 {\n",
       "            background-color:  #9abdc7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col10 {\n",
       "            background-color:  #a5c5ce;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col11 {\n",
       "            background-color:  #a4c4cd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col12 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col13 {\n",
       "            background-color:  #b0ccd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col14 {\n",
       "            background-color:  #6fa0af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col15 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col16 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col17 {\n",
       "            background-color:  #cb6e56;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col18 {\n",
       "            background-color:  #7ca8b6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col19 {\n",
       "            background-color:  #88b1bd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col20 {\n",
       "            background-color:  #d9e7ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col21 {\n",
       "            background-color:  #91b7c2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col22 {\n",
       "            background-color:  #e7bcb1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col23 {\n",
       "            background-color:  #a4c4cd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col24 {\n",
       "            background-color:  #f6e5e1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col25 {\n",
       "            background-color:  #a5c5ce;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col26 {\n",
       "            background-color:  #b0ccd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col27 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col28 {\n",
       "            background-color:  #679baa;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col29 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col30 {\n",
       "            background-color:  #88b1bd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col0 {\n",
       "            background-color:  #98bcc7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col1 {\n",
       "            background-color:  #679baa;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col2 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col3 {\n",
       "            background-color:  #c8dce2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col4 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col5 {\n",
       "            background-color:  #79a7b4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col6 {\n",
       "            background-color:  #9fc0ca;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col7 {\n",
       "            background-color:  #78a6b4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col8 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col9 {\n",
       "            background-color:  #7ca8b6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col10 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col11 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col12 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col13 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col14 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col15 {\n",
       "            background-color:  #78a6b4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col16 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col17 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col18 {\n",
       "            background-color:  #8bb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col19 {\n",
       "            background-color:  #9bbec8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col20 {\n",
       "            background-color:  #ccdfe4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col21 {\n",
       "            background-color:  #87b0bc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col22 {\n",
       "            background-color:  #e2edf1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col23 {\n",
       "            background-color:  #78a6b4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col24 {\n",
       "            background-color:  #dae8ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col25 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col26 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col27 {\n",
       "            background-color:  #78a6b4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col28 {\n",
       "            background-color:  #c45a3f;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col29 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col30 {\n",
       "            background-color:  #9bbec8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col0 {\n",
       "            background-color:  #8cb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col1 {\n",
       "            background-color:  #488598;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col2 {\n",
       "            background-color:  #a5c5ce;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col3 {\n",
       "            background-color:  #cadde3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col4 {\n",
       "            background-color:  #a8c6cf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col5 {\n",
       "            background-color:  #85aebb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col6 {\n",
       "            background-color:  #85aebb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col7 {\n",
       "            background-color:  #85aebb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col8 {\n",
       "            background-color:  #8eb4c0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col9 {\n",
       "            background-color:  #9dbfc9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col10 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col11 {\n",
       "            background-color:  #a8c6cf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col12 {\n",
       "            background-color:  #72a1b0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col13 {\n",
       "            background-color:  #b8d1d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col14 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col15 {\n",
       "            background-color:  #cc6f58;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col16 {\n",
       "            background-color:  #ecf4f7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col17 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col18 {\n",
       "            background-color:  #8bb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col19 {\n",
       "            background-color:  #95b9c5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col20 {\n",
       "            background-color:  #e0ecf0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col21 {\n",
       "            background-color:  #91b7c2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col22 {\n",
       "            background-color:  #dfa495;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col23 {\n",
       "            background-color:  #a4c4cd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col24 {\n",
       "            background-color:  #f5e3de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col25 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col26 {\n",
       "            background-color:  #b8d1d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col27 {\n",
       "            background-color:  #cc6f58;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col28 {\n",
       "            background-color:  #ddeaee;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col29 {\n",
       "            background-color:  #ecf4f7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col30 {\n",
       "            background-color:  #95b9c5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col0 {\n",
       "            background-color:  #91b7c2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col1 {\n",
       "            background-color:  #4c889a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col2 {\n",
       "            background-color:  #a5c5ce;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col3 {\n",
       "            background-color:  #dfebef;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col4 {\n",
       "            background-color:  #a8c6cf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col5 {\n",
       "            background-color:  #afcbd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col6 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col7 {\n",
       "            background-color:  #afcbd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col8 {\n",
       "            background-color:  #b7d0d8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col9 {\n",
       "            background-color:  #b3ced6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col10 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col11 {\n",
       "            background-color:  #bcd4db;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col12 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col13 {\n",
       "            background-color:  #e2edf1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col14 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col15 {\n",
       "            background-color:  #7ca8b6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col16 {\n",
       "            background-color:  #82adba;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col17 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col18 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col19 {\n",
       "            background-color:  #d17e68;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col20 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col21 {\n",
       "            background-color:  #b2cdd5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col22 {\n",
       "            background-color:  #dc9c8c;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col23 {\n",
       "            background-color:  #d1e2e7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col24 {\n",
       "            background-color:  #ecf4f7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col25 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col26 {\n",
       "            background-color:  #e2edf1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col27 {\n",
       "            background-color:  #7ca8b6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col28 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col29 {\n",
       "            background-color:  #82adba;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col30 {\n",
       "            background-color:  #d17e68;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col0 {\n",
       "            background-color:  #8bb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col1 {\n",
       "            background-color:  #578fa1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col2 {\n",
       "            background-color:  #b8d1d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col3 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col4 {\n",
       "            background-color:  #b9d2d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col5 {\n",
       "            background-color:  #d5e5ea;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col6 {\n",
       "            background-color:  #e2edf1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col7 {\n",
       "            background-color:  #d4e4e9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col8 {\n",
       "            background-color:  #ddeaee;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col9 {\n",
       "            background-color:  #b4cfd7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col10 {\n",
       "            background-color:  #c7dbe1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col11 {\n",
       "            background-color:  #c2d8df;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col12 {\n",
       "            background-color:  #8cb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col13 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col14 {\n",
       "            background-color:  #8bb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col15 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col16 {\n",
       "            background-color:  #8eb4c0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col17 {\n",
       "            background-color:  #94b9c4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col18 {\n",
       "            background-color:  #d27f6a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col19 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col20 {\n",
       "            background-color:  #f5e2dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col21 {\n",
       "            background-color:  #d0e1e6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col22 {\n",
       "            background-color:  #e7bcb1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col23 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col24 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col25 {\n",
       "            background-color:  #c7dbe1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col26 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col27 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col28 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col29 {\n",
       "            background-color:  #8eb4c0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col30 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col0 {\n",
       "            background-color:  #558ea0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col1 {\n",
       "            background-color:  #87b0bc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col2 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col3 {\n",
       "            background-color:  #da9887;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col4 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col5 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col6 {\n",
       "            background-color:  #cc7059;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col7 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col8 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col9 {\n",
       "            background-color:  #f6e5e1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col10 {\n",
       "            background-color:  #efd1ca;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col11 {\n",
       "            background-color:  #f0d6cf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col12 {\n",
       "            background-color:  #cadde3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col13 {\n",
       "            background-color:  #d89281;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col14 {\n",
       "            background-color:  #c8dce2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col15 {\n",
       "            background-color:  #acc9d2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col16 {\n",
       "            background-color:  #97bbc6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col17 {\n",
       "            background-color:  #b9d2d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col18 {\n",
       "            background-color:  #d4e4e9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col19 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col20 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col21 {\n",
       "            background-color:  #f9eeeb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col22 {\n",
       "            background-color:  #e6f0f3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col23 {\n",
       "            background-color:  #d3e3e8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col24 {\n",
       "            background-color:  #d58875;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col25 {\n",
       "            background-color:  #efd1ca;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col26 {\n",
       "            background-color:  #d89281;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col27 {\n",
       "            background-color:  #acc9d2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col28 {\n",
       "            background-color:  #92b8c3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col29 {\n",
       "            background-color:  #97bbc6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col30 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col0 {\n",
       "            background-color:  #82adba;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col1 {\n",
       "            background-color:  #5890a2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col2 {\n",
       "            background-color:  #f6e4df;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col3 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col4 {\n",
       "            background-color:  #f5e3de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col5 {\n",
       "            background-color:  #e7bbb0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col6 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col7 {\n",
       "            background-color:  #e7bbb0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col8 {\n",
       "            background-color:  #e3afa2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col9 {\n",
       "            background-color:  #cadde3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col10 {\n",
       "            background-color:  #dae8ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col11 {\n",
       "            background-color:  #d7e6eb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col12 {\n",
       "            background-color:  #d58875;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col13 {\n",
       "            background-color:  #f1d8d2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col14 {\n",
       "            background-color:  #d58875;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col15 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col16 {\n",
       "            background-color:  #6fa0af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col17 {\n",
       "            background-color:  #87b0bc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col18 {\n",
       "            background-color:  #a4c4cd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col19 {\n",
       "            background-color:  #c8dce2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col20 {\n",
       "            background-color:  #f3dcd6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col21 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col22 {\n",
       "            background-color:  #aac8d1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col23 {\n",
       "            background-color:  #dea191;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col24 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col25 {\n",
       "            background-color:  #dae8ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col26 {\n",
       "            background-color:  #f1d8d2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col27 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col28 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col29 {\n",
       "            background-color:  #6fa0af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col30 {\n",
       "            background-color:  #c8dce2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col0 {\n",
       "            background-color:  #85aebb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col1 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col2 {\n",
       "            background-color:  #b0ccd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col3 {\n",
       "            background-color:  #ddeaee;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col4 {\n",
       "            background-color:  #b0ccd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col5 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col6 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col7 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col8 {\n",
       "            background-color:  #b2cdd5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col9 {\n",
       "            background-color:  #bcd4db;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col10 {\n",
       "            background-color:  #c8dce2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col11 {\n",
       "            background-color:  #c7dbe1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col12 {\n",
       "            background-color:  #7da9b7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col13 {\n",
       "            background-color:  #d9e7ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col14 {\n",
       "            background-color:  #7da9b7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col15 {\n",
       "            background-color:  #e9c1b6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col16 {\n",
       "            background-color:  #d3e3e8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col17 {\n",
       "            background-color:  #dfa596;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col18 {\n",
       "            background-color:  #dd9e8f;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col19 {\n",
       "            background-color:  #e8beb4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col20 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col21 {\n",
       "            background-color:  #afcbd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col22 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col23 {\n",
       "            background-color:  #dbe9ed;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col24 {\n",
       "            background-color:  #f5e2dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col25 {\n",
       "            background-color:  #c8dce2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col26 {\n",
       "            background-color:  #d9e7ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col27 {\n",
       "            background-color:  #e9c1b6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col28 {\n",
       "            background-color:  #bdd5dc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col29 {\n",
       "            background-color:  #d3e3e8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col30 {\n",
       "            background-color:  #e8beb4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col0 {\n",
       "            background-color:  #97bbc6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col1 {\n",
       "            background-color:  #4b879a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col2 {\n",
       "            background-color:  #c7dbe1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col3 {\n",
       "            background-color:  #e4eff2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col4 {\n",
       "            background-color:  #c8dce2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col5 {\n",
       "            background-color:  #ce765f;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col6 {\n",
       "            background-color:  #c2d8df;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col7 {\n",
       "            background-color:  #ce765f;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col8 {\n",
       "            background-color:  #ce755e;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col9 {\n",
       "            background-color:  #b9d2d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col10 {\n",
       "            background-color:  #cadde3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col11 {\n",
       "            background-color:  #c7dbe1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col12 {\n",
       "            background-color:  #d9e7ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col13 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col14 {\n",
       "            background-color:  #d7e6eb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col15 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col16 {\n",
       "            background-color:  #76a4b2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col17 {\n",
       "            background-color:  #afcbd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col18 {\n",
       "            background-color:  #d5e5ea;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col19 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col20 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col21 {\n",
       "            background-color:  #dc9c8c;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col22 {\n",
       "            background-color:  #e8f1f4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col23 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col24 {\n",
       "            background-color:  #e6f0f3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col25 {\n",
       "            background-color:  #cadde3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col26 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col27 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col28 {\n",
       "            background-color:  #6b9dad;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col29 {\n",
       "            background-color:  #76a4b2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col30 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col0 {\n",
       "            background-color:  #448396;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col1 {\n",
       "            background-color:  #7da9b7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col2 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col3 {\n",
       "            background-color:  #e3afa2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col4 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col5 {\n",
       "            background-color:  #c1d7de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col6 {\n",
       "            background-color:  #e7baaf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col7 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col8 {\n",
       "            background-color:  #d1e2e7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col9 {\n",
       "            background-color:  #d5e5ea;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col10 {\n",
       "            background-color:  #e9f2f5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col11 {\n",
       "            background-color:  #e4eff2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col12 {\n",
       "            background-color:  #bdd5dc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col13 {\n",
       "            background-color:  #dfa495;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col14 {\n",
       "            background-color:  #bcd4db;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col15 {\n",
       "            background-color:  #edf5f7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col16 {\n",
       "            background-color:  #9abdc7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col17 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col18 {\n",
       "            background-color:  #b7d0d8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col19 {\n",
       "            background-color:  #d0e1e6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col20 {\n",
       "            background-color:  #d68b78;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col21 {\n",
       "            background-color:  #eaf3f6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col22 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col23 {\n",
       "            background-color:  #aac8d1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col24 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col25 {\n",
       "            background-color:  #e9f2f5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col26 {\n",
       "            background-color:  #dfa495;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col27 {\n",
       "            background-color:  #edf5f7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col28 {\n",
       "            background-color:  #97bbc6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col29 {\n",
       "            background-color:  #9abdc7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col30 {\n",
       "            background-color:  #d0e1e6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col0 {\n",
       "            background-color:  #82adba;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col1 {\n",
       "            background-color:  #6397a7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col2 {\n",
       "            background-color:  #adcad2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col3 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col4 {\n",
       "            background-color:  #afcbd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col5 {\n",
       "            background-color:  #b8d1d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col6 {\n",
       "            background-color:  #e3eef1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col7 {\n",
       "            background-color:  #b8d1d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col8 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col9 {\n",
       "            background-color:  #cc6f58;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col10 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col11 {\n",
       "            background-color:  #c4583c;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col12 {\n",
       "            background-color:  #b2cdd5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col13 {\n",
       "            background-color:  #f0d3cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col14 {\n",
       "            background-color:  #b0ccd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col15 {\n",
       "            background-color:  #98bcc7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col16 {\n",
       "            background-color:  #699bab;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col17 {\n",
       "            background-color:  #a0c1cb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col18 {\n",
       "            background-color:  #b2cdd5;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col19 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col20 {\n",
       "            background-color:  #e9c2b8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col21 {\n",
       "            background-color:  #dae8ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col22 {\n",
       "            background-color:  #c7dbe1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col23 {\n",
       "            background-color:  #b9d2d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col24 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col25 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col26 {\n",
       "            background-color:  #f0d3cc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col27 {\n",
       "            background-color:  #98bcc7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col28 {\n",
       "            background-color:  #5c93a4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col29 {\n",
       "            background-color:  #699bab;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col30 {\n",
       "            background-color:  #bfd6dd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col0 {\n",
       "            background-color:  #5a91a2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col1 {\n",
       "            background-color:  #8cb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col2 {\n",
       "            background-color:  #dae8ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col3 {\n",
       "            background-color:  #eed0c8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col4 {\n",
       "            background-color:  #ddeaee;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col5 {\n",
       "            background-color:  #d4e4e9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col6 {\n",
       "            background-color:  #edcdc4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col7 {\n",
       "            background-color:  #d4e4e9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col8 {\n",
       "            background-color:  #e0ecf0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col9 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col10 {\n",
       "            background-color:  #f5e3de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col11 {\n",
       "            background-color:  #f6e6e2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col12 {\n",
       "            background-color:  #ecf4f7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col13 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col14 {\n",
       "            background-color:  #eaf3f6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col15 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col16 {\n",
       "            background-color:  #89b2be;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col17 {\n",
       "            background-color:  #8cb3bf;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col18 {\n",
       "            background-color:  #b9d2d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col19 {\n",
       "            background-color:  #dbe9ed;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col20 {\n",
       "            background-color:  #d8917f;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col21 {\n",
       "            background-color:  #f7e9e6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col22 {\n",
       "            background-color:  #b9d2d9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col23 {\n",
       "            background-color:  #c3d9df;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col24 {\n",
       "            background-color:  #dda090;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col25 {\n",
       "            background-color:  #f5e3de;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col26 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col27 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col28 {\n",
       "            background-color:  #85aebb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col29 {\n",
       "            background-color:  #89b2be;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col30 {\n",
       "            background-color:  #dbe9ed;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col0 {\n",
       "            background-color:  #92b8c3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col1 {\n",
       "            background-color:  #448396;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col2 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col3 {\n",
       "            background-color:  #c7dbe1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col4 {\n",
       "            background-color:  #aac8d1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col5 {\n",
       "            background-color:  #87b0bc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col6 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col7 {\n",
       "            background-color:  #87b0bc;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col8 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col9 {\n",
       "            background-color:  #9abdc7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col10 {\n",
       "            background-color:  #a5c5ce;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col11 {\n",
       "            background-color:  #a4c4cd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col12 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col13 {\n",
       "            background-color:  #b0ccd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col14 {\n",
       "            background-color:  #6fa0af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col15 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col16 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col17 {\n",
       "            background-color:  #cb6e56;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col18 {\n",
       "            background-color:  #7ca8b6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col19 {\n",
       "            background-color:  #88b1bd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col20 {\n",
       "            background-color:  #d9e7ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col21 {\n",
       "            background-color:  #91b7c2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col22 {\n",
       "            background-color:  #e7bcb1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col23 {\n",
       "            background-color:  #a4c4cd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col24 {\n",
       "            background-color:  #f6e5e1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col25 {\n",
       "            background-color:  #a5c5ce;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col26 {\n",
       "            background-color:  #b0ccd4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col27 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col28 {\n",
       "            background-color:  #679baa;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col29 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col30 {\n",
       "            background-color:  #88b1bd;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col0 {\n",
       "            background-color:  #9abdc7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col1 {\n",
       "            background-color:  #6a9cac;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col2 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col3 {\n",
       "            background-color:  #c8dce2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col4 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col5 {\n",
       "            background-color:  #79a7b4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col6 {\n",
       "            background-color:  #a0c1cb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col7 {\n",
       "            background-color:  #79a7b4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col8 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col9 {\n",
       "            background-color:  #6fa0af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col10 {\n",
       "            background-color:  #76a4b2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col11 {\n",
       "            background-color:  #74a3b2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col12 {\n",
       "            background-color:  #6fa0af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col13 {\n",
       "            background-color:  #bcd4db;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col14 {\n",
       "            background-color:  #6d9ead;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col15 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col16 {\n",
       "            background-color:  #c45a3f;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col17 {\n",
       "            background-color:  #e8f1f4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col18 {\n",
       "            background-color:  #79a7b4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col19 {\n",
       "            background-color:  #8eb4c0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col20 {\n",
       "            background-color:  #cadde3;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col21 {\n",
       "            background-color:  #85aebb;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col22 {\n",
       "            background-color:  #ccdfe4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col23 {\n",
       "            background-color:  #6fa0af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col24 {\n",
       "            background-color:  #d9e7ec;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col25 {\n",
       "            background-color:  #76a4b2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col26 {\n",
       "            background-color:  #bcd4db;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col27 {\n",
       "            background-color:  #70a1af;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col28 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col29 {\n",
       "            background-color:  #c45a3f;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col30 {\n",
       "            background-color:  #8eb4c0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col0 {\n",
       "            background-color:  #98bcc7;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col1 {\n",
       "            background-color:  #679baa;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col2 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col3 {\n",
       "            background-color:  #c8dce2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col4 {\n",
       "            background-color:  #90b6c1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col5 {\n",
       "            background-color:  #79a7b4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col6 {\n",
       "            background-color:  #9fc0ca;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col7 {\n",
       "            background-color:  #78a6b4;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col8 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col9 {\n",
       "            background-color:  #7ca8b6;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col10 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col11 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col12 {\n",
       "            background-color:  #70a1af;\n",
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       "            background-color:  #9bbec8;\n",
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       "            background-color:  #ccdfe4;\n",
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       "            background-color:  #78a6b4;\n",
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       "            background-color:  #dae8ec;\n",
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       "            background-color:  #8cb3bf;\n",
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       "            background-color:  #f2f2f2;\n",
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       "            background-color:  #d27f6a;\n",
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       "            background-color:  #c3553a;\n",
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       "            background-color:  #f5e2dd;\n",
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       "            background-color:  #d0e1e6;\n",
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       "            background-color:  #e7bcb1;\n",
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       "            background-color:  #f2f2f2;\n",
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       "            background-color:  #f2f2f2;\n",
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       "            background-color:  #c7dbe1;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col26 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col27 {\n",
       "            background-color:  #81acb9;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col28 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col29 {\n",
       "            background-color:  #8eb4c0;\n",
       "        }    #T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col30 {\n",
       "            background-color:  #c3553a;\n",
       "        }</style>  \n",
       "<table id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Sector_score</th> \n",
       "        <th class=\"col_heading level0 col1\" >LOCATION_ID</th> \n",
       "        <th class=\"col_heading level0 col2\" >PARA_A</th> \n",
       "        <th class=\"col_heading level0 col3\" >Score_A</th> \n",
       "        <th class=\"col_heading level0 col4\" >Risk_A</th> \n",
       "        <th class=\"col_heading level0 col5\" >PARA_B</th> \n",
       "        <th class=\"col_heading level0 col6\" >Score_B</th> \n",
       "        <th class=\"col_heading level0 col7\" >Risk_B</th> \n",
       "        <th class=\"col_heading level0 col8\" >TOTAL</th> \n",
       "        <th class=\"col_heading level0 col9\" >numbers</th> \n",
       "        <th class=\"col_heading level0 col10\" >Score_B.1</th> \n",
       "        <th class=\"col_heading level0 col11\" >Risk_C</th> \n",
       "        <th class=\"col_heading level0 col12\" >Money_Value</th> \n",
       "        <th class=\"col_heading level0 col13\" >Score_MV</th> \n",
       "        <th class=\"col_heading level0 col14\" >Risk_D</th> \n",
       "        <th class=\"col_heading level0 col15\" >District_Loss</th> \n",
       "        <th class=\"col_heading level0 col16\" >PROB</th> \n",
       "        <th class=\"col_heading level0 col17\" >RiSk_E</th> \n",
       "        <th class=\"col_heading level0 col18\" >History</th> \n",
       "        <th class=\"col_heading level0 col19\" >Prob</th> \n",
       "        <th class=\"col_heading level0 col20\" >Score</th> \n",
       "        <th class=\"col_heading level0 col21\" >Inherent_Risk</th> \n",
       "        <th class=\"col_heading level0 col22\" >CONTROL_RISK</th> \n",
       "        <th class=\"col_heading level0 col23\" >Audit_Risk</th> \n",
       "        <th class=\"col_heading level0 col24\" >Risk</th> \n",
       "        <th class=\"col_heading level0 col25\" >Marks</th> \n",
       "        <th class=\"col_heading level0 col26\" >MONEY_Marks</th> \n",
       "        <th class=\"col_heading level0 col27\" >District</th> \n",
       "        <th class=\"col_heading level0 col28\" >Loss</th> \n",
       "        <th class=\"col_heading level0 col29\" >LOSS_SCORE</th> \n",
       "        <th class=\"col_heading level0 col30\" >History_score</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row0\" class=\"row_heading level0 row0\" >Sector_score</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col1\" class=\"data row0 col1\" >-0.052</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col2\" class=\"data row0 col2\" >-0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col3\" class=\"data row0 col3\" >-0.42</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col4\" class=\"data row0 col4\" >-0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col5\" class=\"data row0 col5\" >-0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col6\" class=\"data row0 col6\" >-0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col7\" class=\"data row0 col7\" >-0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col8\" class=\"data row0 col8\" >-0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col9\" class=\"data row0 col9\" >-0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col10\" class=\"data row0 col10\" >-0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col11\" class=\"data row0 col11\" >-0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col12\" class=\"data row0 col12\" >-0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col13\" class=\"data row0 col13\" >-0.32</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col14\" class=\"data row0 col14\" >-0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col15\" class=\"data row0 col15\" >-0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col16\" class=\"data row0 col16\" >-0.086</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col17\" class=\"data row0 col17\" >-0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col18\" class=\"data row0 col18\" >-0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col19\" class=\"data row0 col19\" >-0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col20\" class=\"data row0 col20\" >-0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col21\" class=\"data row0 col21\" >-0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col22\" class=\"data row0 col22\" >-0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col23\" class=\"data row0 col23\" >-0.091</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col24\" class=\"data row0 col24\" >-0.39</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col25\" class=\"data row0 col25\" >-0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col26\" class=\"data row0 col26\" >-0.32</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col27\" class=\"data row0 col27\" >-0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col28\" class=\"data row0 col28\" >-0.082</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col29\" class=\"data row0 col29\" >-0.086</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row0_col30\" class=\"data row0 col30\" >-0.14</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row1\" class=\"row_heading level0 row1\" >LOCATION_ID</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col0\" class=\"data row1 col0\" >-0.052</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col1\" class=\"data row1 col1\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col2\" class=\"data row1 col2\" >-0.0067</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col3\" class=\"data row1 col3\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col4\" class=\"data row1 col4\" >-0.0044</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col5\" class=\"data row1 col5\" >0.0066</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col6\" class=\"data row1 col6\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col7\" class=\"data row1 col7\" >0.0062</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col8\" class=\"data row1 col8\" >0.0065</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col9\" class=\"data row1 col9\" >0.0067</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col10\" class=\"data row1 col10\" >-0.018</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col11\" class=\"data row1 col11\" >-0.015</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col12\" class=\"data row1 col12\" >-0.067</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col13\" class=\"data row1 col13\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col14\" class=\"data row1 col14\" >-0.067</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col15\" class=\"data row1 col15\" >-0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col16\" class=\"data row1 col16\" >-0.0034</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col17\" class=\"data row1 col17\" >-0.097</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col18\" class=\"data row1 col18\" >-0.082</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col19\" class=\"data row1 col19\" >-0.054</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col20\" class=\"data row1 col20\" >0.088</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col21\" class=\"data row1 col21\" >-0.047</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col22\" class=\"data row1 col22\" >-0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col23\" class=\"data row1 col23\" >-0.087</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col24\" class=\"data row1 col24\" >0.063</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col25\" class=\"data row1 col25\" >-0.018</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col26\" class=\"data row1 col26\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col27\" class=\"data row1 col27\" >-0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col28\" class=\"data row1 col28\" >0.0064</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col29\" class=\"data row1 col29\" >-0.0034</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row1_col30\" class=\"data row1 col30\" >-0.054</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row2\" class=\"row_heading level0 row2\" >PARA_A</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col0\" class=\"data row2 col0\" >-0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col1\" class=\"data row2 col1\" >-0.0067</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col2\" class=\"data row2 col2\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col3\" class=\"data row2 col3\" >0.49</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col4\" class=\"data row2 col4\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col5\" class=\"data row2 col5\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col6\" class=\"data row2 col6\" >0.36</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col7\" class=\"data row2 col7\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col8\" class=\"data row2 col8\" >0.27</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col9\" class=\"data row2 col9\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col10\" class=\"data row2 col10\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col11\" class=\"data row2 col11\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col12\" class=\"data row2 col12\" >0.45</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col13\" class=\"data row2 col13\" >0.28</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col14\" class=\"data row2 col14\" >0.45</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col15\" class=\"data row2 col15\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col16\" class=\"data row2 col16\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col17\" class=\"data row2 col17\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col18\" class=\"data row2 col18\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col19\" class=\"data row2 col19\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col20\" class=\"data row2 col20\" >0.42</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col21\" class=\"data row2 col21\" >0.48</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col22\" class=\"data row2 col22\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col23\" class=\"data row2 col23\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col24\" class=\"data row2 col24\" >0.38</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col25\" class=\"data row2 col25\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col26\" class=\"data row2 col26\" >0.28</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col27\" class=\"data row2 col27\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col28\" class=\"data row2 col28\" >0.044</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col29\" class=\"data row2 col29\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row2_col30\" class=\"data row2 col30\" >0.17</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row3\" class=\"row_heading level0 row3\" >Score_A</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col0\" class=\"data row3 col0\" >-0.42</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col1\" class=\"data row3 col1\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col2\" class=\"data row3 col2\" >0.49</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col3\" class=\"data row3 col3\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col4\" class=\"data row3 col4\" >0.5</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col5\" class=\"data row3 col5\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col6\" class=\"data row3 col6\" >0.57</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col7\" class=\"data row3 col7\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col8\" class=\"data row3 col8\" >0.3</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col9\" class=\"data row3 col9\" >0.24</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col10\" class=\"data row3 col10\" >0.27</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col11\" class=\"data row3 col11\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col12\" class=\"data row3 col12\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col13\" class=\"data row3 col13\" >0.47</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col14\" class=\"data row3 col14\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col15\" class=\"data row3 col15\" >0.086</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col16\" class=\"data row3 col16\" >0.091</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col17\" class=\"data row3 col17\" >0.1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col18\" class=\"data row3 col18\" >0.18</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col19\" class=\"data row3 col19\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col20\" class=\"data row3 col20\" >0.72</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col21\" class=\"data row3 col21\" >0.32</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col22\" class=\"data row3 col22\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col23\" class=\"data row3 col23\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col24\" class=\"data row3 col24\" >0.62</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col25\" class=\"data row3 col25\" >0.27</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col26\" class=\"data row3 col26\" >0.47</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col27\" class=\"data row3 col27\" >0.086</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col28\" class=\"data row3 col28\" >0.091</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col29\" class=\"data row3 col29\" >0.091</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row3_col30\" class=\"data row3 col30\" >0.26</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row4\" class=\"row_heading level0 row4\" >Risk_A</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col0\" class=\"data row4 col0\" >-0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col1\" class=\"data row4 col1\" >-0.0044</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col2\" class=\"data row4 col2\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col3\" class=\"data row4 col3\" >0.5</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col4\" class=\"data row4 col4\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col5\" class=\"data row4 col5\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col6\" class=\"data row4 col6\" >0.36</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col7\" class=\"data row4 col7\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col8\" class=\"data row4 col8\" >0.27</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col9\" class=\"data row4 col9\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col10\" class=\"data row4 col10\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col11\" class=\"data row4 col11\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col12\" class=\"data row4 col12\" >0.45</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col13\" class=\"data row4 col13\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col14\" class=\"data row4 col14\" >0.45</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col15\" class=\"data row4 col15\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col16\" class=\"data row4 col16\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col17\" class=\"data row4 col17\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col18\" class=\"data row4 col18\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col19\" class=\"data row4 col19\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col20\" class=\"data row4 col20\" >0.43</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col21\" class=\"data row4 col21\" >0.48</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col22\" class=\"data row4 col22\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col23\" class=\"data row4 col23\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col24\" class=\"data row4 col24\" >0.38</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col25\" class=\"data row4 col25\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col26\" class=\"data row4 col26\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col27\" class=\"data row4 col27\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col28\" class=\"data row4 col28\" >0.045</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col29\" class=\"data row4 col29\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row4_col30\" class=\"data row4 col30\" >0.17</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row5\" class=\"row_heading level0 row5\" >PARA_B</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col0\" class=\"data row5 col0\" >-0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col1\" class=\"data row5 col1\" >0.0066</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col2\" class=\"data row5 col2\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col3\" class=\"data row5 col3\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col4\" class=\"data row5 col4\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col5\" class=\"data row5 col5\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col6\" class=\"data row5 col6\" >0.35</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col7\" class=\"data row5 col7\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col8\" class=\"data row5 col8\" >0.99</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col9\" class=\"data row5 col9\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col10\" class=\"data row5 col10\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col11\" class=\"data row5 col11\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col12\" class=\"data row5 col12\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col13\" class=\"data row5 col13\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col14\" class=\"data row5 col14\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col15\" class=\"data row5 col15\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col16\" class=\"data row5 col16\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col17\" class=\"data row5 col17\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col18\" class=\"data row5 col18\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col19\" class=\"data row5 col19\" >0.32</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col20\" class=\"data row5 col20\" >0.4</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col21\" class=\"data row5 col21\" >0.65</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col22\" class=\"data row5 col22\" >0.19</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col23\" class=\"data row5 col23\" >0.89</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col24\" class=\"data row5 col24\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col25\" class=\"data row5 col25\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col26\" class=\"data row5 col26\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col27\" class=\"data row5 col27\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col28\" class=\"data row5 col28\" >0.044</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col29\" class=\"data row5 col29\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row5_col30\" class=\"data row5 col30\" >0.32</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row6\" class=\"row_heading level0 row6\" >Score_B</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col0\" class=\"data row6 col0\" >-0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col1\" class=\"data row6 col1\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col2\" class=\"data row6 col2\" >0.36</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col3\" class=\"data row6 col3\" >0.57</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col4\" class=\"data row6 col4\" >0.36</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col5\" class=\"data row6 col5\" >0.35</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col6\" class=\"data row6 col6\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col7\" class=\"data row6 col7\" >0.35</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col8\" class=\"data row6 col8\" >0.38</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col9\" class=\"data row6 col9\" >0.28</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col10\" class=\"data row6 col10\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col11\" class=\"data row6 col11\" >0.3</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col12\" class=\"data row6 col12\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col13\" class=\"data row6 col13\" >0.56</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col14\" class=\"data row6 col14\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col15\" class=\"data row6 col15\" >-0.0077</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col16\" class=\"data row6 col16\" >0.091</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col17\" class=\"data row6 col17\" >0.012</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col18\" class=\"data row6 col18\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col19\" class=\"data row6 col19\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col20\" class=\"data row6 col20\" >0.9</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col21\" class=\"data row6 col21\" >0.36</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col22\" class=\"data row6 col22\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col23\" class=\"data row6 col23\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col24\" class=\"data row6 col24\" >0.63</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col25\" class=\"data row6 col25\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col26\" class=\"data row6 col26\" >0.56</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col27\" class=\"data row6 col27\" >-0.0077</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col28\" class=\"data row6 col28\" >0.097</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col29\" class=\"data row6 col29\" >0.091</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row6_col30\" class=\"data row6 col30\" >0.31</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row7\" class=\"row_heading level0 row7\" >Risk_B</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col0\" class=\"data row7 col0\" >-0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col1\" class=\"data row7 col1\" >0.0062</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col2\" class=\"data row7 col2\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col3\" class=\"data row7 col3\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col4\" class=\"data row7 col4\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col5\" class=\"data row7 col5\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col6\" class=\"data row7 col6\" >0.35</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col7\" class=\"data row7 col7\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col8\" class=\"data row7 col8\" >0.99</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col9\" class=\"data row7 col9\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col10\" class=\"data row7 col10\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col11\" class=\"data row7 col11\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col12\" class=\"data row7 col12\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col13\" class=\"data row7 col13\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col14\" class=\"data row7 col14\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col15\" class=\"data row7 col15\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col16\" class=\"data row7 col16\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col17\" class=\"data row7 col17\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col18\" class=\"data row7 col18\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col19\" class=\"data row7 col19\" >0.32</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col20\" class=\"data row7 col20\" >0.4</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col21\" class=\"data row7 col21\" >0.65</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col22\" class=\"data row7 col22\" >0.19</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col23\" class=\"data row7 col23\" >0.89</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col24\" class=\"data row7 col24\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col25\" class=\"data row7 col25\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col26\" class=\"data row7 col26\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col27\" class=\"data row7 col27\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col28\" class=\"data row7 col28\" >0.044</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col29\" class=\"data row7 col29\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row7_col30\" class=\"data row7 col30\" >0.32</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row8\" class=\"row_heading level0 row8\" >TOTAL</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col0\" class=\"data row8 col0\" >-0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col1\" class=\"data row8 col1\" >0.0065</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col2\" class=\"data row8 col2\" >0.27</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col3\" class=\"data row8 col3\" >0.3</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col4\" class=\"data row8 col4\" >0.27</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col5\" class=\"data row8 col5\" >0.99</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col6\" class=\"data row8 col6\" >0.38</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col7\" class=\"data row8 col7\" >0.99</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col8\" class=\"data row8 col8\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col9\" class=\"data row8 col9\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col10\" class=\"data row8 col10\" >0.24</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col11\" class=\"data row8 col11\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col12\" class=\"data row8 col12\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col13\" class=\"data row8 col13\" >0.34</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col14\" class=\"data row8 col14\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col15\" class=\"data row8 col15\" >0.093</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col16\" class=\"data row8 col16\" >0.046</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col17\" class=\"data row8 col17\" >0.088</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col18\" class=\"data row8 col18\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col19\" class=\"data row8 col19\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col20\" class=\"data row8 col20\" >0.43</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col21\" class=\"data row8 col21\" >0.69</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col22\" class=\"data row8 col22\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col23\" class=\"data row8 col23\" >0.89</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col24\" class=\"data row8 col24\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col25\" class=\"data row8 col25\" >0.24</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col26\" class=\"data row8 col26\" >0.34</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col27\" class=\"data row8 col27\" >0.093</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col28\" class=\"data row8 col28\" >0.048</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col29\" class=\"data row8 col29\" >0.046</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row8_col30\" class=\"data row8 col30\" >0.33</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row9\" class=\"row_heading level0 row9\" >numbers</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col0\" class=\"data row9 col0\" >-0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col1\" class=\"data row9 col1\" >0.0067</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col2\" class=\"data row9 col2\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col3\" class=\"data row9 col3\" >0.24</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col4\" class=\"data row9 col4\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col5\" class=\"data row9 col5\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col6\" class=\"data row9 col6\" >0.28</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col7\" class=\"data row9 col7\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col8\" class=\"data row9 col8\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col9\" class=\"data row9 col9\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col10\" class=\"data row9 col10\" >0.91</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col11\" class=\"data row9 col11\" >0.96</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col12\" class=\"data row9 col12\" >0.19</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col13\" class=\"data row9 col13\" >0.45</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col14\" class=\"data row9 col14\" >0.19</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col15\" class=\"data row9 col15\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col16\" class=\"data row9 col16\" >0.035</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col17\" class=\"data row9 col17\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col18\" class=\"data row9 col18\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col19\" class=\"data row9 col19\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col20\" class=\"data row9 col20\" >0.5</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col21\" class=\"data row9 col21\" >0.27</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col22\" class=\"data row9 col22\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col23\" class=\"data row9 col23\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col24\" class=\"data row9 col24\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col25\" class=\"data row9 col25\" >0.91</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col26\" class=\"data row9 col26\" >0.45</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col27\" class=\"data row9 col27\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col28\" class=\"data row9 col28\" >-0.0024</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col29\" class=\"data row9 col29\" >0.035</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row9_col30\" class=\"data row9 col30\" >0.21</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row10\" class=\"row_heading level0 row10\" >Score_B.1</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col0\" class=\"data row10 col0\" >-0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col1\" class=\"data row10 col1\" >-0.018</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col2\" class=\"data row10 col2\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col3\" class=\"data row10 col3\" >0.27</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col4\" class=\"data row10 col4\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col5\" class=\"data row10 col5\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col6\" class=\"data row10 col6\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col7\" class=\"data row10 col7\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col8\" class=\"data row10 col8\" >0.24</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col9\" class=\"data row10 col9\" >0.91</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col10\" class=\"data row10 col10\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col11\" class=\"data row10 col11\" >0.99</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col12\" class=\"data row10 col12\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col13\" class=\"data row10 col13\" >0.51</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col14\" class=\"data row10 col14\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col15\" class=\"data row10 col15\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col16\" class=\"data row10 col16\" >0.036</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col17\" class=\"data row10 col17\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col18\" class=\"data row10 col18\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col19\" class=\"data row10 col19\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col20\" class=\"data row10 col20\" >0.57</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col21\" class=\"data row10 col21\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col22\" class=\"data row10 col22\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col23\" class=\"data row10 col23\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col24\" class=\"data row10 col24\" >0.35</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col25\" class=\"data row10 col25\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col26\" class=\"data row10 col26\" >0.51</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col27\" class=\"data row10 col27\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col28\" class=\"data row10 col28\" >0.0038</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col29\" class=\"data row10 col29\" >0.036</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row10_col30\" class=\"data row10 col30\" >0.25</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row11\" class=\"row_heading level0 row11\" >Risk_C</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col0\" class=\"data row11 col0\" >-0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col1\" class=\"data row11 col1\" >-0.015</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col2\" class=\"data row11 col2\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col3\" class=\"data row11 col3\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col4\" class=\"data row11 col4\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col5\" class=\"data row11 col5\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col6\" class=\"data row11 col6\" >0.3</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col7\" class=\"data row11 col7\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col8\" class=\"data row11 col8\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col9\" class=\"data row11 col9\" >0.96</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col10\" class=\"data row11 col10\" >0.99</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col11\" class=\"data row11 col11\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col12\" class=\"data row11 col12\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col13\" class=\"data row11 col13\" >0.49</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col14\" class=\"data row11 col14\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col15\" class=\"data row11 col15\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col16\" class=\"data row11 col16\" >0.035</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col17\" class=\"data row11 col17\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col18\" class=\"data row11 col18\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col19\" class=\"data row11 col19\" >0.24</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col20\" class=\"data row11 col20\" >0.55</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col21\" class=\"data row11 col21\" >0.3</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col22\" class=\"data row11 col22\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col23\" class=\"data row11 col23\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col24\" class=\"data row11 col24\" >0.34</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col25\" class=\"data row11 col25\" >0.99</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col26\" class=\"data row11 col26\" >0.49</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col27\" class=\"data row11 col27\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col28\" class=\"data row11 col28\" >0.0001</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col29\" class=\"data row11 col29\" >0.035</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row11_col30\" class=\"data row11 col30\" >0.24</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row12\" class=\"row_heading level0 row12\" >Money_Value</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col0\" class=\"data row12 col0\" >-0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col1\" class=\"data row12 col1\" >-0.067</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col2\" class=\"data row12 col2\" >0.45</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col3\" class=\"data row12 col3\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col4\" class=\"data row12 col4\" >0.45</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col5\" class=\"data row12 col5\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col6\" class=\"data row12 col6\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col7\" class=\"data row12 col7\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col8\" class=\"data row12 col8\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col9\" class=\"data row12 col9\" >0.19</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col10\" class=\"data row12 col10\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col11\" class=\"data row12 col11\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col12\" class=\"data row12 col12\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col13\" class=\"data row12 col13\" >0.39</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col14\" class=\"data row12 col14\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col15\" class=\"data row12 col15\" >0.028</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col16\" class=\"data row12 col16\" >0.031</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col17\" class=\"data row12 col17\" >0.032</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col18\" class=\"data row12 col18\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col19\" class=\"data row12 col19\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col20\" class=\"data row12 col20\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col21\" class=\"data row12 col21\" >0.83</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col22\" class=\"data row12 col22\" >0.069</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col23\" class=\"data row12 col23\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col24\" class=\"data row12 col24\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col25\" class=\"data row12 col25\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col26\" class=\"data row12 col26\" >0.39</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col27\" class=\"data row12 col27\" >0.028</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col28\" class=\"data row12 col28\" >0.024</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col29\" class=\"data row12 col29\" >0.031</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row12_col30\" class=\"data row12 col30\" >0.11</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row13\" class=\"row_heading level0 row13\" >Score_MV</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col0\" class=\"data row13 col0\" >-0.32</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col1\" class=\"data row13 col1\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col2\" class=\"data row13 col2\" >0.28</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col3\" class=\"data row13 col3\" >0.47</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col4\" class=\"data row13 col4\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col5\" class=\"data row13 col5\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col6\" class=\"data row13 col6\" >0.56</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col7\" class=\"data row13 col7\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col8\" class=\"data row13 col8\" >0.34</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col9\" class=\"data row13 col9\" >0.45</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col10\" class=\"data row13 col10\" >0.51</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col11\" class=\"data row13 col11\" >0.49</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col12\" class=\"data row13 col12\" >0.39</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col13\" class=\"data row13 col13\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col14\" class=\"data row13 col14\" >0.39</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col15\" class=\"data row13 col15\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col16\" class=\"data row13 col16\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col17\" class=\"data row13 col17\" >0.1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col18\" class=\"data row13 col18\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col19\" class=\"data row13 col19\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col20\" class=\"data row13 col20\" >0.76</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col21\" class=\"data row13 col21\" >0.48</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col22\" class=\"data row13 col22\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col23\" class=\"data row13 col23\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col24\" class=\"data row13 col24\" >0.69</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col25\" class=\"data row13 col25\" >0.51</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col26\" class=\"data row13 col26\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col27\" class=\"data row13 col27\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col28\" class=\"data row13 col28\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col29\" class=\"data row13 col29\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row13_col30\" class=\"data row13 col30\" >0.33</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row14\" class=\"row_heading level0 row14\" >Risk_D</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col0\" class=\"data row14 col0\" >-0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col1\" class=\"data row14 col1\" >-0.067</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col2\" class=\"data row14 col2\" >0.45</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col3\" class=\"data row14 col3\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col4\" class=\"data row14 col4\" >0.45</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col5\" class=\"data row14 col5\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col6\" class=\"data row14 col6\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col7\" class=\"data row14 col7\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col8\" class=\"data row14 col8\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col9\" class=\"data row14 col9\" >0.19</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col10\" class=\"data row14 col10\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col11\" class=\"data row14 col11\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col12\" class=\"data row14 col12\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col13\" class=\"data row14 col13\" >0.39</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col14\" class=\"data row14 col14\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col15\" class=\"data row14 col15\" >0.028</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col16\" class=\"data row14 col16\" >0.031</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col17\" class=\"data row14 col17\" >0.032</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col18\" class=\"data row14 col18\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col19\" class=\"data row14 col19\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col20\" class=\"data row14 col20\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col21\" class=\"data row14 col21\" >0.83</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col22\" class=\"data row14 col22\" >0.069</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col23\" class=\"data row14 col23\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col24\" class=\"data row14 col24\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col25\" class=\"data row14 col25\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col26\" class=\"data row14 col26\" >0.39</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col27\" class=\"data row14 col27\" >0.028</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col28\" class=\"data row14 col28\" >0.024</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col29\" class=\"data row14 col29\" >0.031</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row14_col30\" class=\"data row14 col30\" >0.11</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row15\" class=\"row_heading level0 row15\" >District_Loss</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col0\" class=\"data row15 col0\" >-0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col1\" class=\"data row15 col1\" >-0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col2\" class=\"data row15 col2\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col3\" class=\"data row15 col3\" >0.086</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col4\" class=\"data row15 col4\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col5\" class=\"data row15 col5\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col6\" class=\"data row15 col6\" >-0.0077</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col7\" class=\"data row15 col7\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col8\" class=\"data row15 col8\" >0.093</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col9\" class=\"data row15 col9\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col10\" class=\"data row15 col10\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col11\" class=\"data row15 col11\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col12\" class=\"data row15 col12\" >0.028</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col13\" class=\"data row15 col13\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col14\" class=\"data row15 col14\" >0.028</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col15\" class=\"data row15 col15\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col16\" class=\"data row15 col16\" >0.055</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col17\" class=\"data row15 col17\" >0.91</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col18\" class=\"data row15 col18\" >0.069</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col19\" class=\"data row15 col19\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col20\" class=\"data row15 col20\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col21\" class=\"data row15 col21\" >0.08</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col22\" class=\"data row15 col22\" >0.64</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col23\" class=\"data row15 col23\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col24\" class=\"data row15 col24\" >0.4</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col25\" class=\"data row15 col25\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col26\" class=\"data row15 col26\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col27\" class=\"data row15 col27\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col28\" class=\"data row15 col28\" >0.036</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col29\" class=\"data row15 col29\" >0.055</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row15_col30\" class=\"data row15 col30\" >0.083</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row16\" class=\"row_heading level0 row16\" >PROB</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col0\" class=\"data row16 col0\" >-0.086</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col1\" class=\"data row16 col1\" >-0.0034</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col2\" class=\"data row16 col2\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col3\" class=\"data row16 col3\" >0.091</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col4\" class=\"data row16 col4\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col5\" class=\"data row16 col5\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col6\" class=\"data row16 col6\" >0.091</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col7\" class=\"data row16 col7\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col8\" class=\"data row16 col8\" >0.046</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col9\" class=\"data row16 col9\" >0.035</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col10\" class=\"data row16 col10\" >0.036</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col11\" class=\"data row16 col11\" >0.035</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col12\" class=\"data row16 col12\" >0.031</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col13\" class=\"data row16 col13\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col14\" class=\"data row16 col14\" >0.031</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col15\" class=\"data row16 col15\" >0.055</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col16\" class=\"data row16 col16\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col17\" class=\"data row16 col17\" >0.41</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col18\" class=\"data row16 col18\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col19\" class=\"data row16 col19\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col20\" class=\"data row16 col20\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col21\" class=\"data row16 col21\" >0.052</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col22\" class=\"data row16 col22\" >0.34</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col23\" class=\"data row16 col23\" >0.073</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col24\" class=\"data row16 col24\" >0.18</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col25\" class=\"data row16 col25\" >0.036</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col26\" class=\"data row16 col26\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col27\" class=\"data row16 col27\" >0.055</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col28\" class=\"data row16 col28\" >0.98</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col29\" class=\"data row16 col29\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row16_col30\" class=\"data row16 col30\" >0.14</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row17\" class=\"row_heading level0 row17\" >RiSk_E</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col0\" class=\"data row17 col0\" >-0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col1\" class=\"data row17 col1\" >-0.097</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col2\" class=\"data row17 col2\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col3\" class=\"data row17 col3\" >0.1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col4\" class=\"data row17 col4\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col5\" class=\"data row17 col5\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col6\" class=\"data row17 col6\" >0.012</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col7\" class=\"data row17 col7\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col8\" class=\"data row17 col8\" >0.088</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col9\" class=\"data row17 col9\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col10\" class=\"data row17 col10\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col11\" class=\"data row17 col11\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col12\" class=\"data row17 col12\" >0.032</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col13\" class=\"data row17 col13\" >0.1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col14\" class=\"data row17 col14\" >0.032</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col15\" class=\"data row17 col15\" >0.91</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col16\" class=\"data row17 col16\" >0.41</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col17\" class=\"data row17 col17\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col18\" class=\"data row17 col18\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col19\" class=\"data row17 col19\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col20\" class=\"data row17 col20\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col21\" class=\"data row17 col21\" >0.082</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col22\" class=\"data row17 col22\" >0.73</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col23\" class=\"data row17 col23\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col24\" class=\"data row17 col24\" >0.41</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col25\" class=\"data row17 col25\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col26\" class=\"data row17 col26\" >0.1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col27\" class=\"data row17 col27\" >0.91</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col28\" class=\"data row17 col28\" >0.37</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col29\" class=\"data row17 col29\" >0.41</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row17_col30\" class=\"data row17 col30\" >0.12</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row18\" class=\"row_heading level0 row18\" >History</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col0\" class=\"data row18 col0\" >-0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col1\" class=\"data row18 col1\" >-0.082</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col2\" class=\"data row18 col2\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col3\" class=\"data row18 col3\" >0.18</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col4\" class=\"data row18 col4\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col5\" class=\"data row18 col5\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col6\" class=\"data row18 col6\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col7\" class=\"data row18 col7\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col8\" class=\"data row18 col8\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col9\" class=\"data row18 col9\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col10\" class=\"data row18 col10\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col11\" class=\"data row18 col11\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col12\" class=\"data row18 col12\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col13\" class=\"data row18 col13\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col14\" class=\"data row18 col14\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col15\" class=\"data row18 col15\" >0.069</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col16\" class=\"data row18 col16\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col17\" class=\"data row18 col17\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col18\" class=\"data row18 col18\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col19\" class=\"data row18 col19\" >0.86</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col20\" class=\"data row18 col20\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col21\" class=\"data row18 col21\" >0.18</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col22\" class=\"data row18 col22\" >0.76</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col23\" class=\"data row18 col23\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col24\" class=\"data row18 col24\" >0.24</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col25\" class=\"data row18 col25\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col26\" class=\"data row18 col26\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col27\" class=\"data row18 col27\" >0.069</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col28\" class=\"data row18 col28\" >0.06</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col29\" class=\"data row18 col29\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row18_col30\" class=\"data row18 col30\" >0.86</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row19\" class=\"row_heading level0 row19\" >Prob</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col0\" class=\"data row19 col0\" >-0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col1\" class=\"data row19 col1\" >-0.054</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col2\" class=\"data row19 col2\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col3\" class=\"data row19 col3\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col4\" class=\"data row19 col4\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col5\" class=\"data row19 col5\" >0.32</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col6\" class=\"data row19 col6\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col7\" class=\"data row19 col7\" >0.32</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col8\" class=\"data row19 col8\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col9\" class=\"data row19 col9\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col10\" class=\"data row19 col10\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col11\" class=\"data row19 col11\" >0.24</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col12\" class=\"data row19 col12\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col13\" class=\"data row19 col13\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col14\" class=\"data row19 col14\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col15\" class=\"data row19 col15\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col16\" class=\"data row19 col16\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col17\" class=\"data row19 col17\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col18\" class=\"data row19 col18\" >0.86</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col19\" class=\"data row19 col19\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col20\" class=\"data row19 col20\" >0.44</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col21\" class=\"data row19 col21\" >0.27</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col22\" class=\"data row19 col22\" >0.64</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col23\" class=\"data row19 col23\" >0.43</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col24\" class=\"data row19 col24\" >0.3</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col25\" class=\"data row19 col25\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col26\" class=\"data row19 col26\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col27\" class=\"data row19 col27\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col28\" class=\"data row19 col28\" >0.1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col29\" class=\"data row19 col29\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row19_col30\" class=\"data row19 col30\" >1</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row20\" class=\"row_heading level0 row20\" >Score</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col0\" class=\"data row20 col0\" >-0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col1\" class=\"data row20 col1\" >0.088</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col2\" class=\"data row20 col2\" >0.42</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col3\" class=\"data row20 col3\" >0.72</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col4\" class=\"data row20 col4\" >0.43</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col5\" class=\"data row20 col5\" >0.4</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col6\" class=\"data row20 col6\" >0.9</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col7\" class=\"data row20 col7\" >0.4</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col8\" class=\"data row20 col8\" >0.43</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col9\" class=\"data row20 col9\" >0.5</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col10\" class=\"data row20 col10\" >0.57</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col11\" class=\"data row20 col11\" >0.55</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col12\" class=\"data row20 col12\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col13\" class=\"data row20 col13\" >0.76</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col14\" class=\"data row20 col14\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col15\" class=\"data row20 col15\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col16\" class=\"data row20 col16\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col17\" class=\"data row20 col17\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col18\" class=\"data row20 col18\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col19\" class=\"data row20 col19\" >0.44</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col20\" class=\"data row20 col20\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col21\" class=\"data row20 col21\" >0.46</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col22\" class=\"data row20 col22\" >0.35</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col23\" class=\"data row20 col23\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col24\" class=\"data row20 col24\" >0.78</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col25\" class=\"data row20 col25\" >0.57</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col26\" class=\"data row20 col26\" >0.76</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col27\" class=\"data row20 col27\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col28\" class=\"data row20 col28\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col29\" class=\"data row20 col29\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row20_col30\" class=\"data row20 col30\" >0.44</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row21\" class=\"row_heading level0 row21\" >Inherent_Risk</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col0\" class=\"data row21 col0\" >-0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col1\" class=\"data row21 col1\" >-0.047</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col2\" class=\"data row21 col2\" >0.48</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col3\" class=\"data row21 col3\" >0.32</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col4\" class=\"data row21 col4\" >0.48</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col5\" class=\"data row21 col5\" >0.65</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col6\" class=\"data row21 col6\" >0.36</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col7\" class=\"data row21 col7\" >0.65</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col8\" class=\"data row21 col8\" >0.69</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col9\" class=\"data row21 col9\" >0.27</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col10\" class=\"data row21 col10\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col11\" class=\"data row21 col11\" >0.3</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col12\" class=\"data row21 col12\" >0.83</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col13\" class=\"data row21 col13\" >0.48</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col14\" class=\"data row21 col14\" >0.83</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col15\" class=\"data row21 col15\" >0.08</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col16\" class=\"data row21 col16\" >0.052</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col17\" class=\"data row21 col17\" >0.082</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col18\" class=\"data row21 col18\" >0.18</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col19\" class=\"data row21 col19\" >0.27</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col20\" class=\"data row21 col20\" >0.46</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col21\" class=\"data row21 col21\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col22\" class=\"data row21 col22\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col23\" class=\"data row21 col23\" >0.75</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col24\" class=\"data row21 col24\" >0.36</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col25\" class=\"data row21 col25\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col26\" class=\"data row21 col26\" >0.48</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col27\" class=\"data row21 col27\" >0.08</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col28\" class=\"data row21 col28\" >0.047</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col29\" class=\"data row21 col29\" >0.052</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row21_col30\" class=\"data row21 col30\" >0.27</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row22\" class=\"row_heading level0 row22\" >CONTROL_RISK</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col0\" class=\"data row22 col0\" >-0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col1\" class=\"data row22 col1\" >-0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col2\" class=\"data row22 col2\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col3\" class=\"data row22 col3\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col4\" class=\"data row22 col4\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col5\" class=\"data row22 col5\" >0.19</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col6\" class=\"data row22 col6\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col7\" class=\"data row22 col7\" >0.19</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col8\" class=\"data row22 col8\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col9\" class=\"data row22 col9\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col10\" class=\"data row22 col10\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col11\" class=\"data row22 col11\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col12\" class=\"data row22 col12\" >0.069</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col13\" class=\"data row22 col13\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col14\" class=\"data row22 col14\" >0.069</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col15\" class=\"data row22 col15\" >0.64</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col16\" class=\"data row22 col16\" >0.34</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col17\" class=\"data row22 col17\" >0.73</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col18\" class=\"data row22 col18\" >0.76</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col19\" class=\"data row22 col19\" >0.64</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col20\" class=\"data row22 col20\" >0.35</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col21\" class=\"data row22 col21\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col22\" class=\"data row22 col22\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col23\" class=\"data row22 col23\" >0.36</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col24\" class=\"data row22 col24\" >0.41</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col25\" class=\"data row22 col25\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col26\" class=\"data row22 col26\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col27\" class=\"data row22 col27\" >0.64</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col28\" class=\"data row22 col28\" >0.28</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col29\" class=\"data row22 col29\" >0.34</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row22_col30\" class=\"data row22 col30\" >0.64</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row23\" class=\"row_heading level0 row23\" >Audit_Risk</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col0\" class=\"data row23 col0\" >-0.091</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col1\" class=\"data row23 col1\" >-0.087</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col2\" class=\"data row23 col2\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col3\" class=\"data row23 col3\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col4\" class=\"data row23 col4\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col5\" class=\"data row23 col5\" >0.89</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col6\" class=\"data row23 col6\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col7\" class=\"data row23 col7\" >0.89</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col8\" class=\"data row23 col8\" >0.89</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col9\" class=\"data row23 col9\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col10\" class=\"data row23 col10\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col11\" class=\"data row23 col11\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col12\" class=\"data row23 col12\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col13\" class=\"data row23 col13\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col14\" class=\"data row23 col14\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col15\" class=\"data row23 col15\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col16\" class=\"data row23 col16\" >0.073</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col17\" class=\"data row23 col17\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col18\" class=\"data row23 col18\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col19\" class=\"data row23 col19\" >0.43</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col20\" class=\"data row23 col20\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col21\" class=\"data row23 col21\" >0.75</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col22\" class=\"data row23 col22\" >0.36</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col23\" class=\"data row23 col23\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col24\" class=\"data row23 col24\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col25\" class=\"data row23 col25\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col26\" class=\"data row23 col26\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col27\" class=\"data row23 col27\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col28\" class=\"data row23 col28\" >0.049</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col29\" class=\"data row23 col29\" >0.073</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row23_col30\" class=\"data row23 col30\" >0.43</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row24\" class=\"row_heading level0 row24\" >Risk</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col0\" class=\"data row24 col0\" >-0.39</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col1\" class=\"data row24 col1\" >0.063</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col2\" class=\"data row24 col2\" >0.38</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col3\" class=\"data row24 col3\" >0.62</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col4\" class=\"data row24 col4\" >0.38</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col5\" class=\"data row24 col5\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col6\" class=\"data row24 col6\" >0.63</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col7\" class=\"data row24 col7\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col8\" class=\"data row24 col8\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col9\" class=\"data row24 col9\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col10\" class=\"data row24 col10\" >0.35</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col11\" class=\"data row24 col11\" >0.34</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col12\" class=\"data row24 col12\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col13\" class=\"data row24 col13\" >0.69</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col14\" class=\"data row24 col14\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col15\" class=\"data row24 col15\" >0.4</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col16\" class=\"data row24 col16\" >0.18</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col17\" class=\"data row24 col17\" >0.41</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col18\" class=\"data row24 col18\" >0.24</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col19\" class=\"data row24 col19\" >0.3</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col20\" class=\"data row24 col20\" >0.78</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col21\" class=\"data row24 col21\" >0.36</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col22\" class=\"data row24 col22\" >0.41</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col23\" class=\"data row24 col23\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col24\" class=\"data row24 col24\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col25\" class=\"data row24 col25\" >0.35</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col26\" class=\"data row24 col26\" >0.69</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col27\" class=\"data row24 col27\" >0.4</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col28\" class=\"data row24 col28\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col29\" class=\"data row24 col29\" >0.18</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row24_col30\" class=\"data row24 col30\" >0.3</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row25\" class=\"row_heading level0 row25\" >Marks</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col0\" class=\"data row25 col0\" >-0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col1\" class=\"data row25 col1\" >-0.018</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col2\" class=\"data row25 col2\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col3\" class=\"data row25 col3\" >0.27</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col4\" class=\"data row25 col4\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col5\" class=\"data row25 col5\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col6\" class=\"data row25 col6\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col7\" class=\"data row25 col7\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col8\" class=\"data row25 col8\" >0.24</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col9\" class=\"data row25 col9\" >0.91</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col10\" class=\"data row25 col10\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col11\" class=\"data row25 col11\" >0.99</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col12\" class=\"data row25 col12\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col13\" class=\"data row25 col13\" >0.51</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col14\" class=\"data row25 col14\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col15\" class=\"data row25 col15\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col16\" class=\"data row25 col16\" >0.036</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col17\" class=\"data row25 col17\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col18\" class=\"data row25 col18\" >0.23</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col19\" class=\"data row25 col19\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col20\" class=\"data row25 col20\" >0.57</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col21\" class=\"data row25 col21\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col22\" class=\"data row25 col22\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col23\" class=\"data row25 col23\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col24\" class=\"data row25 col24\" >0.35</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col25\" class=\"data row25 col25\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col26\" class=\"data row25 col26\" >0.51</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col27\" class=\"data row25 col27\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col28\" class=\"data row25 col28\" >0.0038</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col29\" class=\"data row25 col29\" >0.036</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row25_col30\" class=\"data row25 col30\" >0.25</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row26\" class=\"row_heading level0 row26\" >MONEY_Marks</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col0\" class=\"data row26 col0\" >-0.32</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col1\" class=\"data row26 col1\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col2\" class=\"data row26 col2\" >0.28</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col3\" class=\"data row26 col3\" >0.47</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col4\" class=\"data row26 col4\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col5\" class=\"data row26 col5\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col6\" class=\"data row26 col6\" >0.56</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col7\" class=\"data row26 col7\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col8\" class=\"data row26 col8\" >0.34</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col9\" class=\"data row26 col9\" >0.45</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col10\" class=\"data row26 col10\" >0.51</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col11\" class=\"data row26 col11\" >0.49</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col12\" class=\"data row26 col12\" >0.39</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col13\" class=\"data row26 col13\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col14\" class=\"data row26 col14\" >0.39</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col15\" class=\"data row26 col15\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col16\" class=\"data row26 col16\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col17\" class=\"data row26 col17\" >0.1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col18\" class=\"data row26 col18\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col19\" class=\"data row26 col19\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col20\" class=\"data row26 col20\" >0.76</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col21\" class=\"data row26 col21\" >0.48</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col22\" class=\"data row26 col22\" >0.22</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col23\" class=\"data row26 col23\" >0.29</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col24\" class=\"data row26 col24\" >0.69</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col25\" class=\"data row26 col25\" >0.51</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col26\" class=\"data row26 col26\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col27\" class=\"data row26 col27\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col28\" class=\"data row26 col28\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col29\" class=\"data row26 col29\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row26_col30\" class=\"data row26 col30\" >0.33</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row27\" class=\"row_heading level0 row27\" >District</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col0\" class=\"data row27 col0\" >-0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col1\" class=\"data row27 col1\" >-0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col2\" class=\"data row27 col2\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col3\" class=\"data row27 col3\" >0.086</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col4\" class=\"data row27 col4\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col5\" class=\"data row27 col5\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col6\" class=\"data row27 col6\" >-0.0077</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col7\" class=\"data row27 col7\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col8\" class=\"data row27 col8\" >0.093</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col9\" class=\"data row27 col9\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col10\" class=\"data row27 col10\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col11\" class=\"data row27 col11\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col12\" class=\"data row27 col12\" >0.028</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col13\" class=\"data row27 col13\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col14\" class=\"data row27 col14\" >0.028</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col15\" class=\"data row27 col15\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col16\" class=\"data row27 col16\" >0.055</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col17\" class=\"data row27 col17\" >0.91</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col18\" class=\"data row27 col18\" >0.069</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col19\" class=\"data row27 col19\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col20\" class=\"data row27 col20\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col21\" class=\"data row27 col21\" >0.08</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col22\" class=\"data row27 col22\" >0.64</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col23\" class=\"data row27 col23\" >0.2</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col24\" class=\"data row27 col24\" >0.4</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col25\" class=\"data row27 col25\" >0.15</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col26\" class=\"data row27 col26\" >0.079</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col27\" class=\"data row27 col27\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col28\" class=\"data row27 col28\" >0.036</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col29\" class=\"data row27 col29\" >0.055</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row27_col30\" class=\"data row27 col30\" >0.083</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row28\" class=\"row_heading level0 row28\" >Loss</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col0\" class=\"data row28 col0\" >-0.082</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col1\" class=\"data row28 col1\" >0.0064</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col2\" class=\"data row28 col2\" >0.044</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col3\" class=\"data row28 col3\" >0.091</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col4\" class=\"data row28 col4\" >0.045</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col5\" class=\"data row28 col5\" >0.044</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col6\" class=\"data row28 col6\" >0.097</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col7\" class=\"data row28 col7\" >0.044</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col8\" class=\"data row28 col8\" >0.048</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col9\" class=\"data row28 col9\" >-0.0024</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col10\" class=\"data row28 col10\" >0.0038</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col11\" class=\"data row28 col11\" >0.0001</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col12\" class=\"data row28 col12\" >0.024</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col13\" class=\"data row28 col13\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col14\" class=\"data row28 col14\" >0.024</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col15\" class=\"data row28 col15\" >0.036</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col16\" class=\"data row28 col16\" >0.98</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col17\" class=\"data row28 col17\" >0.37</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col18\" class=\"data row28 col18\" >0.06</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col19\" class=\"data row28 col19\" >0.1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col20\" class=\"data row28 col20\" >0.16</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col21\" class=\"data row28 col21\" >0.047</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col22\" class=\"data row28 col22\" >0.28</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col23\" class=\"data row28 col23\" >0.049</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col24\" class=\"data row28 col24\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col25\" class=\"data row28 col25\" >0.0038</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col26\" class=\"data row28 col26\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col27\" class=\"data row28 col27\" >0.036</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col28\" class=\"data row28 col28\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col29\" class=\"data row28 col29\" >0.98</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row28_col30\" class=\"data row28 col30\" >0.1</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row29\" class=\"row_heading level0 row29\" >LOSS_SCORE</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col0\" class=\"data row29 col0\" >-0.086</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col1\" class=\"data row29 col1\" >-0.0034</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col2\" class=\"data row29 col2\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col3\" class=\"data row29 col3\" >0.091</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col4\" class=\"data row29 col4\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col5\" class=\"data row29 col5\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col6\" class=\"data row29 col6\" >0.091</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col7\" class=\"data row29 col7\" >0.042</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col8\" class=\"data row29 col8\" >0.046</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col9\" class=\"data row29 col9\" >0.035</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col10\" class=\"data row29 col10\" >0.036</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col11\" class=\"data row29 col11\" >0.035</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col12\" class=\"data row29 col12\" >0.031</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col13\" class=\"data row29 col13\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col14\" class=\"data row29 col14\" >0.031</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col15\" class=\"data row29 col15\" >0.055</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col16\" class=\"data row29 col16\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col17\" class=\"data row29 col17\" >0.41</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col18\" class=\"data row29 col18\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col19\" class=\"data row29 col19\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col20\" class=\"data row29 col20\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col21\" class=\"data row29 col21\" >0.052</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col22\" class=\"data row29 col22\" >0.34</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col23\" class=\"data row29 col23\" >0.073</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col24\" class=\"data row29 col24\" >0.18</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col25\" class=\"data row29 col25\" >0.036</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col26\" class=\"data row29 col26\" >0.13</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col27\" class=\"data row29 col27\" >0.055</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col28\" class=\"data row29 col28\" >0.98</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col29\" class=\"data row29 col29\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row29_col30\" class=\"data row29 col30\" >0.14</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2level0_row30\" class=\"row_heading level0 row30\" >History_score</th> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col0\" class=\"data row30 col0\" >-0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col1\" class=\"data row30 col1\" >-0.054</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col2\" class=\"data row30 col2\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col3\" class=\"data row30 col3\" >0.26</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col4\" class=\"data row30 col4\" >0.17</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col5\" class=\"data row30 col5\" >0.32</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col6\" class=\"data row30 col6\" >0.31</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col7\" class=\"data row30 col7\" >0.32</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col8\" class=\"data row30 col8\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col9\" class=\"data row30 col9\" >0.21</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col10\" class=\"data row30 col10\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col11\" class=\"data row30 col11\" >0.24</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col12\" class=\"data row30 col12\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col13\" class=\"data row30 col13\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col14\" class=\"data row30 col14\" >0.11</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col15\" class=\"data row30 col15\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col16\" class=\"data row30 col16\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col17\" class=\"data row30 col17\" >0.12</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col18\" class=\"data row30 col18\" >0.86</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col19\" class=\"data row30 col19\" >1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col20\" class=\"data row30 col20\" >0.44</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col21\" class=\"data row30 col21\" >0.27</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col22\" class=\"data row30 col22\" >0.64</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col23\" class=\"data row30 col23\" >0.43</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col24\" class=\"data row30 col24\" >0.3</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col25\" class=\"data row30 col25\" >0.25</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col26\" class=\"data row30 col26\" >0.33</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col27\" class=\"data row30 col27\" >0.083</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col28\" class=\"data row30 col28\" >0.1</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col29\" class=\"data row30 col29\" >0.14</td> \n",
       "        <td id=\"T_bbd1d930_691d_11e9_8e05_9cb6d08ec8d2row30_col30\" class=\"data row30 col30\" >1</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x182ac36bac8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corr = df.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')\n",
    "# 'RdBu_r' & 'BrBG' are other good diverging colormaps\n",
    "cm = sns.diverging_palette(220, 20, sep=20, as_cmap=True) \n",
    "corr.style.background_gradient(cmap=cm).set_precision(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, there are some interesting correlations. Deleting some variables that are highly correlated (0.8 and more) with each other to avoid overfitting the models and to avoid multicollinearity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col3 {\n",
       "            background-color:  #98bcc7;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col4 {\n",
       "            background-color:  #d3e3e8;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col5 {\n",
       "            background-color:  #91b7c2;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col6 {\n",
       "            background-color:  #e1aa9d;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col7 {\n",
       "            background-color:  #cbdee4;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col8 {\n",
       "            background-color:  #82adba;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col9 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col10 {\n",
       "            background-color:  #eccac1;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col11 {\n",
       "            background-color:  #7fabb8;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col0 {\n",
       "            background-color:  #a0c1cb;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col1 {\n",
       "            background-color:  #a9c7d0;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col2 {\n",
       "            background-color:  #f2f2f2;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col3 {\n",
       "            background-color:  #cee0e6;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col4 {\n",
       "            background-color:  #5a91a2;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col5 {\n",
       "            background-color:  #a0c1cb;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col6 {\n",
       "            background-color:  #e5b5a8;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col7 {\n",
       "            background-color:  #7aa7b5;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col8 {\n",
       "            background-color:  #a0c1cb;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col9 {\n",
       "            background-color:  #f0d3cc;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col10 {\n",
       "            background-color:  #c3553a;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col11 {\n",
       "            background-color:  #6a9cac;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col0 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col1 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col2 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col3 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col4 {\n",
       "            background-color:  #c3d9df;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col5 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col6 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col7 {\n",
       "            background-color:  #94b9c4;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col8 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col9 {\n",
       "            background-color:  #3f7f93;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col10 {\n",
       "            background-color:  #448396;\n",
       "        }    #T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col11 {\n",
       "            background-color:  #c3553a;\n",
       "        }</style>  \n",
       "<table id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Risk_A</th> \n",
       "        <th class=\"col_heading level0 col1\" >Risk_B</th> \n",
       "        <th class=\"col_heading level0 col2\" >Risk_C</th> \n",
       "        <th class=\"col_heading level0 col3\" >Risk_D</th> \n",
       "        <th class=\"col_heading level0 col4\" >RiSk_E</th> \n",
       "        <th class=\"col_heading level0 col5\" >Prob</th> \n",
       "        <th class=\"col_heading level0 col6\" >Score</th> \n",
       "        <th class=\"col_heading level0 col7\" >CONTROL_RISK</th> \n",
       "        <th class=\"col_heading level0 col8\" >Audit_Risk</th> \n",
       "        <th class=\"col_heading level0 col9\" >Risk</th> \n",
       "        <th class=\"col_heading level0 col10\" >MONEY_Marks</th> \n",
       "        <th class=\"col_heading level0 col11\" >Loss</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2level0_row0\" class=\"row_heading level0 row0\" >Risk_A</th> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row0_col1\" class=\"data row0 col1\" >0.16</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row0_col2\" class=\"data row0 col2\" >0.14</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row0_col3\" class=\"data row0 col3\" >0.45</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row0_col4\" class=\"data row0 col4\" >0.12</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row0_col5\" class=\"data row0 col5\" >0.17</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row0_col6\" class=\"data row0 col6\" >0.43</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row0_col7\" class=\"data row0 col7\" >0.15</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row0_col8\" class=\"data row0 col8\" >0.22</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row0_col9\" class=\"data row0 col9\" >0.38</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row0_col10\" class=\"data row0 col10\" >0.29</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row0_col11\" class=\"data row0 col11\" >0.045</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2level0_row1\" class=\"row_heading level0 row1\" >Risk_B</th> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row1_col0\" class=\"data row1 col0\" >0.16</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row1_col1\" class=\"data row1 col1\" >1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row1_col2\" class=\"data row1 col2\" >0.22</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row1_col3\" class=\"data row1 col3\" >0.12</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row1_col4\" class=\"data row1 col4\" >0.079</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row1_col5\" class=\"data row1 col5\" >0.32</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row1_col6\" class=\"data row1 col6\" >0.4</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row1_col7\" class=\"data row1 col7\" >0.19</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row1_col8\" class=\"data row1 col8\" >0.89</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row1_col9\" class=\"data row1 col9\" >0.25</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row1_col10\" class=\"data row1 col10\" >0.31</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row1_col11\" class=\"data row1 col11\" >0.044</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2level0_row2\" class=\"row_heading level0 row2\" >Risk_C</th> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row2_col0\" class=\"data row2 col0\" >0.14</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row2_col1\" class=\"data row2 col1\" >0.22</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row2_col2\" class=\"data row2 col2\" >1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row2_col3\" class=\"data row2 col3\" >0.21</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row2_col4\" class=\"data row2 col4\" >0.15</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row2_col5\" class=\"data row2 col5\" >0.24</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row2_col6\" class=\"data row2 col6\" >0.55</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row2_col7\" class=\"data row2 col7\" >0.25</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row2_col8\" class=\"data row2 col8\" >0.25</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row2_col9\" class=\"data row2 col9\" >0.34</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row2_col10\" class=\"data row2 col10\" >0.49</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row2_col11\" class=\"data row2 col11\" >0.0001</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2level0_row3\" class=\"row_heading level0 row3\" >Risk_D</th> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row3_col0\" class=\"data row3 col0\" >0.45</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row3_col1\" class=\"data row3 col1\" >0.12</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row3_col2\" class=\"data row3 col2\" >0.21</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row3_col3\" class=\"data row3 col3\" >1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row3_col4\" class=\"data row3 col4\" >0.032</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row3_col5\" class=\"data row3 col5\" >0.11</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row3_col6\" class=\"data row3 col6\" >0.29</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row3_col7\" class=\"data row3 col7\" >0.069</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row3_col8\" class=\"data row3 col8\" >0.33</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row3_col9\" class=\"data row3 col9\" >0.25</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row3_col10\" class=\"data row3 col10\" >0.39</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row3_col11\" class=\"data row3 col11\" >0.024</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2level0_row4\" class=\"row_heading level0 row4\" >RiSk_E</th> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row4_col0\" class=\"data row4 col0\" >0.12</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row4_col1\" class=\"data row4 col1\" >0.079</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row4_col2\" class=\"data row4 col2\" >0.15</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row4_col3\" class=\"data row4 col3\" >0.032</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row4_col4\" class=\"data row4 col4\" >1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row4_col5\" class=\"data row4 col5\" >0.12</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row4_col6\" class=\"data row4 col6\" >0.23</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row4_col7\" class=\"data row4 col7\" >0.73</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row4_col8\" class=\"data row4 col8\" >0.2</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row4_col9\" class=\"data row4 col9\" >0.41</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row4_col10\" class=\"data row4 col10\" >0.1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row4_col11\" class=\"data row4 col11\" >0.37</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2level0_row5\" class=\"row_heading level0 row5\" >Prob</th> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row5_col0\" class=\"data row5 col0\" >0.17</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row5_col1\" class=\"data row5 col1\" >0.32</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row5_col2\" class=\"data row5 col2\" >0.24</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row5_col3\" class=\"data row5 col3\" >0.11</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row5_col4\" class=\"data row5 col4\" >0.12</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row5_col5\" class=\"data row5 col5\" >1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row5_col6\" class=\"data row5 col6\" >0.44</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row5_col7\" class=\"data row5 col7\" >0.64</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row5_col8\" class=\"data row5 col8\" >0.43</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row5_col9\" class=\"data row5 col9\" >0.3</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row5_col10\" class=\"data row5 col10\" >0.33</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row5_col11\" class=\"data row5 col11\" >0.1</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2level0_row6\" class=\"row_heading level0 row6\" >Score</th> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row6_col0\" class=\"data row6 col0\" >0.43</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row6_col1\" class=\"data row6 col1\" >0.4</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row6_col2\" class=\"data row6 col2\" >0.55</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row6_col3\" class=\"data row6 col3\" >0.29</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row6_col4\" class=\"data row6 col4\" >0.23</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row6_col5\" class=\"data row6 col5\" >0.44</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row6_col6\" class=\"data row6 col6\" >1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row6_col7\" class=\"data row6 col7\" >0.35</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row6_col8\" class=\"data row6 col8\" >0.33</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row6_col9\" class=\"data row6 col9\" >0.78</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row6_col10\" class=\"data row6 col10\" >0.76</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row6_col11\" class=\"data row6 col11\" >0.16</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2level0_row7\" class=\"row_heading level0 row7\" >CONTROL_RISK</th> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row7_col0\" class=\"data row7 col0\" >0.15</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row7_col1\" class=\"data row7 col1\" >0.19</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row7_col2\" class=\"data row7 col2\" >0.25</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row7_col3\" class=\"data row7 col3\" >0.069</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row7_col4\" class=\"data row7 col4\" >0.73</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row7_col5\" class=\"data row7 col5\" >0.64</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row7_col6\" class=\"data row7 col6\" >0.35</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row7_col7\" class=\"data row7 col7\" >1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row7_col8\" class=\"data row7 col8\" >0.36</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row7_col9\" class=\"data row7 col9\" >0.41</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row7_col10\" class=\"data row7 col10\" >0.22</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row7_col11\" class=\"data row7 col11\" >0.28</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2level0_row8\" class=\"row_heading level0 row8\" >Audit_Risk</th> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row8_col0\" class=\"data row8 col0\" >0.22</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row8_col1\" class=\"data row8 col1\" >0.89</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row8_col2\" class=\"data row8 col2\" >0.25</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row8_col3\" class=\"data row8 col3\" >0.33</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row8_col4\" class=\"data row8 col4\" >0.2</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row8_col5\" class=\"data row8 col5\" >0.43</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row8_col6\" class=\"data row8 col6\" >0.33</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row8_col7\" class=\"data row8 col7\" >0.36</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row8_col8\" class=\"data row8 col8\" >1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row8_col9\" class=\"data row8 col9\" >0.22</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row8_col10\" class=\"data row8 col10\" >0.29</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row8_col11\" class=\"data row8 col11\" >0.049</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2level0_row9\" class=\"row_heading level0 row9\" >Risk</th> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col0\" class=\"data row9 col0\" >0.38</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col1\" class=\"data row9 col1\" >0.25</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col2\" class=\"data row9 col2\" >0.34</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col3\" class=\"data row9 col3\" >0.25</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col4\" class=\"data row9 col4\" >0.41</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col5\" class=\"data row9 col5\" >0.3</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col6\" class=\"data row9 col6\" >0.78</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col7\" class=\"data row9 col7\" >0.41</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col8\" class=\"data row9 col8\" >0.22</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col9\" class=\"data row9 col9\" >1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col10\" class=\"data row9 col10\" >0.69</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row9_col11\" class=\"data row9 col11\" >0.17</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2level0_row10\" class=\"row_heading level0 row10\" >MONEY_Marks</th> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col0\" class=\"data row10 col0\" >0.29</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col1\" class=\"data row10 col1\" >0.31</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col2\" class=\"data row10 col2\" >0.49</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col3\" class=\"data row10 col3\" >0.39</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col4\" class=\"data row10 col4\" >0.1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col5\" class=\"data row10 col5\" >0.33</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col6\" class=\"data row10 col6\" >0.76</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col7\" class=\"data row10 col7\" >0.22</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col8\" class=\"data row10 col8\" >0.29</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col9\" class=\"data row10 col9\" >0.69</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col10\" class=\"data row10 col10\" >1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row10_col11\" class=\"data row10 col11\" >0.12</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2level0_row11\" class=\"row_heading level0 row11\" >Loss</th> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col0\" class=\"data row11 col0\" >0.045</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col1\" class=\"data row11 col1\" >0.044</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col2\" class=\"data row11 col2\" >0.0001</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col3\" class=\"data row11 col3\" >0.024</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col4\" class=\"data row11 col4\" >0.37</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col5\" class=\"data row11 col5\" >0.1</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col6\" class=\"data row11 col6\" >0.16</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col7\" class=\"data row11 col7\" >0.28</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col8\" class=\"data row11 col8\" >0.049</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col9\" class=\"data row11 col9\" >0.17</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col10\" class=\"data row11 col10\" >0.12</td> \n",
       "        <td id=\"T_bbfa7bf8_691d_11e9_b19b_9cb6d08ec8d2row11_col11\" class=\"data row11 col11\" >1</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x182ac835518>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Keeping just the columns that are correlated with the target variable and not with other independent variables.\n",
    "df = df[['Risk_A', 'Risk_B', 'Risk_C', 'Risk_D','RiSk_E', 'Prob', 'Score', 'CONTROL_RISK',\n",
    "        'Audit_Risk', 'Risk', 'MONEY_Marks', 'Loss']]\n",
    "corr = df.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')\n",
    "# 'RdBu_r' & 'BrBG' are other good diverging colormaps\n",
    "cm = sns.diverging_palette(220, 20, sep=20, as_cmap=True) \n",
    "corr.style.background_gradient(cmap=cm).set_precision(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deleting 'Risk' column\n",
    "regression_df = df.drop(\"Risk\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "regression_X = regression_df.drop([\"Audit_Risk\"], axis = 1)\n",
    "regression_y = regression_df[\"Audit_Risk\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression: Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train_org, X_test_org, y_train, y_test = train_test_split(regression_X, regression_y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression: Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train_org)\n",
    "X_test  = scaler.transform(X_test_org)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MinMax scaling is used to avoid any feature to dominate the model. MinMax scaling scales all the data in the columns between 0 to 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Desision Tree Regressor with Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=0, splitter='best'),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'max_depth': [3, 5, 7, 9, 11, 15]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "#Base model\n",
    "dt = DecisionTreeRegressor(random_state = 0)\n",
    "\n",
    "#model param\n",
    "grid_param = {'max_depth': [3, 5, 7, 9, 11, 15]}\n",
    "\n",
    "#grid model\n",
    "dt_grid = GridSearchCV(dt, grid_param, cv = 5, n_jobs = -1)\n",
    "\n",
    "#train grid model\n",
    "dt_grid.fit(X_train_org, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for Decision Tree:  {'max_depth': 11}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for Decision Tree: \", dt_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=BaggingRegressor(base_estimator=DecisionTreeRegressor(criterion='mse', max_depth=11, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "         ...n_estimators=10, n_jobs=None, oob_score=False,\n",
       "         random_state=0, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'n_estimators': [100, 500, 1000], 'max_samples': [0.1, 0.5, 1.0]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "#base model\n",
    "dt = DecisionTreeRegressor(max_depth = 11, random_state=0)\n",
    "bag = BaggingRegressor(dt, random_state = 0)\n",
    "#model param\n",
    "grid_param = {'n_estimators': [100, 500, 1000],\n",
    "              'max_samples': [0.1, 0.5, 1.0]}\n",
    "\n",
    "#grid model\n",
    "bag_dt_grid = GridSearchCV(bag, grid_param, cv = 5, n_jobs = -1, return_train_score= True)\n",
    "\n",
    "#train grid model\n",
    "bag_dt_grid.fit(X_train_org, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for Decision Tree with Bagging:  {'max_samples': 1.0, 'n_estimators': 500}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for Decision Tree with Bagging: \", bag_dt_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingRegressor(base_estimator=DecisionTreeRegressor(criterion='mse', max_depth=11, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=0, splitter='best'),\n",
       "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=1.0, n_estimators=500, n_jobs=-1, oob_score=True,\n",
       "         random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag = BaggingRegressor(dt, n_estimators=500, max_samples=1.0, n_jobs = -1, bootstrap=True, random_state=0, oob_score=True)\n",
    "bag.fit(X_train_org, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree with Bagging Training Score:  0.9644569376939184\n",
      "Decision Tree with Bagging Testing Score:  0.1748916773967426\n"
     ]
    }
   ],
   "source": [
    "print(\"Decision Tree with Bagging Training Score: \", bag.score(X_train_org, y_train))\n",
    "print(\"Decision Tree with Bagging Testing Score: \", bag.score(X_test_org, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_1 = ['Decision Tree with Bagging', 'max_depth = 11, max_samples = 1.0, n_estimators = 500', bag.score(X_train_org, y_train),\n",
    "                                bag.score(X_test_org, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression with Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=BaggingRegressor(base_estimator=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False),\n",
       "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=1.0, n_estimators=10, n_jobs=None, oob_score=False,\n",
       "         random_state=0, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'n_estimators': [100, 500, 1000], 'max_samples': [0.1, 0.5, 1.0]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "\n",
    "bag = BaggingRegressor(lr, random_state = 0)\n",
    "#model param\n",
    "grid_param = {'n_estimators': [100, 500, 1000],\n",
    "              'max_samples': [0.1, 0.5, 1.0]}\n",
    "\n",
    "#grid model\n",
    "bag_lr_grid = GridSearchCV(bag, grid_param, cv = 5, n_jobs = -1, return_train_score= True)\n",
    "\n",
    "#train grid model\n",
    "bag_lr_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for Linear Regression with Bagging:  {'max_samples': 0.1, 'n_estimators': 1000}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for Linear Regression with Bagging: \", bag_lr_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingRegressor(base_estimator=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False),\n",
       "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=0.1, n_estimators=1000, n_jobs=-1, oob_score=True,\n",
       "         random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag = BaggingRegressor(lr, n_estimators=1000, max_samples=0.1, n_jobs = -1, bootstrap=True, random_state=0, oob_score=True)\n",
    "bag.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression with Bagging Training Score:  0.6102985544478863\n",
      "Linear Regression with Bagging Testing Score:  0.4650472632888174\n"
     ]
    }
   ],
   "source": [
    "print(\"Linear Regression with Bagging Training Score: \", bag.score(X_train, y_train))\n",
    "print(\"Linear Regression with Bagging Testing Score: \", bag.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_2 = ['Linear Regression with Bagging', 'max_samples = 0.1, n_estimators = 1000', bag.score(X_train, y_train),\n",
    "                                bag.score(X_test, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN with Pasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "          weights='uniform'),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'n_neighbors': [3, 5, 7, 11, 15]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knn = KNeighborsRegressor()\n",
    "\n",
    "knn_param ={'n_neighbors': [3,5,7,11,15]}\n",
    "knn_grid = GridSearchCV(knn, knn_param,cv = 5, n_jobs= -1)\n",
    "knn_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for KNN:  {'n_neighbors': 3}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for KNN: \", knn_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=BaggingRegressor(base_estimator=KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "          weights='uniform'),\n",
       "         bootstrap=False, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=1.0, n_estimators=10, n_jobs=None, oob_score=False,\n",
       "         random_state=0, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'n_estimators': [100, 500, 1000], 'max_samples': [0.1, 0.5, 1.0]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsRegressor(n_neighbors=3)\n",
    "bag = BaggingRegressor(knn, bootstrap=False, random_state = 0)\n",
    "#model param\n",
    "grid_param = {'n_estimators': [100, 500, 1000],\n",
    "              'max_samples': [0.1, 0.5, 1.0]}\n",
    "\n",
    "#grid model\n",
    "bag_knn_grid = GridSearchCV(bag, grid_param, cv = 5, n_jobs = -1, return_train_score= True)\n",
    "\n",
    "#train grid model\n",
    "bag_knn_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for KNN with Pasting:  {'max_samples': 1.0, 'n_estimators': 1000}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for KNN with Pasting: \", bag_knn_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingRegressor(base_estimator=KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "          weights='uniform'),\n",
       "         bootstrap=False, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=1.0, n_estimators=1000, n_jobs=-1, oob_score=False,\n",
       "         random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag = BaggingRegressor(knn, n_estimators=1000, max_samples=1.0, n_jobs = -1, bootstrap=False, random_state=0)\n",
    "bag.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN with Pasting Training Score:  0.8131837439024987\n",
      "KNN with Pasting Testing Score:  0.18511216741364478\n"
     ]
    }
   ],
   "source": [
    "print(\"KNN with Pasting Training Score: \", bag.score(X_train, y_train))\n",
    "print(\"KNN with Pasting Testing Score: \", bag.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_3 = ['KNN with Pasting', 'max_samples = 1.0, n_estimators = 1000', bag.score(X_train, y_train),\n",
    "                                bag.score(X_test, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression with Pasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=BaggingRegressor(base_estimator=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False),\n",
       "         bootstrap=False, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=1.0, n_estimators=10, n_jobs=None, oob_score=False,\n",
       "         random_state=0, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'n_estimators': [100, 500, 1000], 'max_samples': [0.1, 0.5, 1.0]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "\n",
    "bag = BaggingRegressor(lr, bootstrap=False, random_state = 0)\n",
    "#model param\n",
    "grid_param = {'n_estimators': [100, 500, 1000],\n",
    "              'max_samples': [0.1, 0.5, 1.0]}\n",
    "\n",
    "#grid model\n",
    "bag_lr_grid = GridSearchCV(bag, grid_param, cv = 5, n_jobs = -1, return_train_score= True)\n",
    "\n",
    "#train grid model\n",
    "bag_lr_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for Linear Regression with Pasting:  {'max_samples': 0.1, 'n_estimators': 1000}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for Linear Regression with Pasting: \", bag_lr_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingRegressor(base_estimator=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False),\n",
       "         bootstrap=False, bootstrap_features=False, max_features=1.0,\n",
       "         max_samples=0.1, n_estimators=1000, n_jobs=-1, oob_score=False,\n",
       "         random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag = BaggingRegressor(lr, n_estimators=1000, max_samples=0.1, n_jobs = -1, bootstrap=False, random_state=0)\n",
    "bag.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression with Pasting Training Score:  0.6024972743479569\n",
      "Linear Regression with Pasting Testing Score:  0.4756178696333738\n"
     ]
    }
   ],
   "source": [
    "print(\"Linear Regression with Pasting Training Score: \", bag.score(X_train, y_train))\n",
    "print(\"Linear Regression with Pasting Testing Score: \", bag.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_4 =  ['Linear Regression with Pasting', 'max_samples = 0.1, n_estimators = 1000', bag.score(X_train, y_train),\n",
    "                                bag.score(X_test, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaboost "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge with Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'alpha': [0.001, 0.01, 0.1, 0, 1]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge = Ridge()\n",
    "grid_param = {'alpha' : [0.001, 0.01, 0.1,0,1]}\n",
    "\n",
    "ridge_ada_grid = GridSearchCV(ridge, grid_param, cv= 5)\n",
    "ridge_ada_grid. fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for Ridge with Adaboost:  {'alpha': 1}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for Ridge with Adaboost: \", ridge_ada_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=AdaBoostRegressor(base_estimator=Ridge(alpha=1, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001),\n",
       "         learning_rate=1.0, loss='linear', n_estimators=50,\n",
       "         random_state=None),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'n_estimators': [100, 500, 1000], 'learning_rate': [0.1, 0.5, 1, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "ridge = Ridge(alpha = 1)\n",
    "\n",
    "ada = AdaBoostRegressor(ridge)\n",
    "\n",
    "param = {'n_estimators' : [100,500,1000],\n",
    "             'learning_rate': [0.1, 0.5, 1, 10]}\n",
    "\n",
    "ada_grid = GridSearchCV(ada, param, cv=5, n_jobs= -1)\n",
    "\n",
    "ada_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for Adaboost:  {'learning_rate': 0.1, 'n_estimators': 500}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for Adaboost: \", ada_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostRegressor(base_estimator=Ridge(alpha=1, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001),\n",
       "         learning_rate=0.5, loss='linear', n_estimators=500,\n",
       "         random_state=0)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada = AdaBoostRegressor(ridge, n_estimators=500, learning_rate=0.5, random_state=0)\n",
    "ada.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge with Adaboost Training Score:  0.6091384026074081\n",
      "Ridge with Adaboost Testing Score:  0.5987726066215993\n"
     ]
    }
   ],
   "source": [
    "print(\"Ridge with Adaboost Training Score: \", ada.score(X_train, y_train))\n",
    "print(\"Ridge with Adaboost Testing Score: \", ada.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_5 = ['Ridge with Adaboost', 'alpha = 1, learning_rate=0.5, n_estimators = 500', ada.score(X_train, y_train),\n",
    "                                ada.score(X_test, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso with Adaboost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'alpha': [0.001, 0.01, 0.1, 0, 1], 'max_iter': [500, 1000, 5000, 10000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "lasso = Lasso()\n",
    "param = {'alpha' : [0.001, 0.01, 0.1,0,1],\n",
    "               'max_iter': [500, 1000, 5000, 10000]}\n",
    "\n",
    "lasso_ada_grid = GridSearchCV(lasso, param, cv= 5)\n",
    "lasso_ada_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for Lasso with Adaboost:  {'alpha': 0.1, 'max_iter': 500}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for Lasso with Adaboost: \", lasso_ada_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=AdaBoostRegressor(base_estimator=Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=500,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False),\n",
       "         learning_rate=1.0, loss='linear', n_estimators=50,\n",
       "         random_state=None),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'n_estimators': [100, 500, 1000], 'learning_rate': [0.1, 0.5, 1, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso = Lasso(alpha=0.1, max_iter=500)\n",
    "\n",
    "ada = AdaBoostRegressor(lasso)\n",
    "\n",
    "param = {'n_estimators' : [100,500,1000],\n",
    "             'learning_rate': [0.1, 0.5, 1, 10]}\n",
    "\n",
    "ada_grid = GridSearchCV(ada, param, cv=5, n_jobs= -1)\n",
    "\n",
    "ada_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for Adaboost:  {'learning_rate': 1, 'n_estimators': 1000}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for Adaboost: \", ada_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostRegressor(base_estimator=Lasso(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=500,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False),\n",
       "         learning_rate=1, loss='linear', n_estimators=500, random_state=0)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada = AdaBoostRegressor(lasso, n_estimators=500, learning_rate=1, random_state=0)\n",
    "ada.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso with Adaboost Training Score:  0.5312509401768786\n",
      "Lasso with Adaboost Testing Score:  0.44034419221673093\n"
     ]
    }
   ],
   "source": [
    "print(\"Lasso with Adaboost Training Score: \", ada.score(X_train, y_train))\n",
    "print(\"Lasso with Adaboost Testing Score: \", ada.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_6 =  ['Lasso with Adaboost', 'alpha = 0.1, max_iter=500, learning_rate=1, n_estimators = 500',\n",
    "                                ada.score(X_train, y_train), ada.score(X_test, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_sampl...ate=0, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'max_depth': [4, 5, 7, 9, 11, 15], 'n_estimators': [100, 500, 1000], 'learning_rate': [0.01, 0.1, 0.5, 1.0]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from  sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gb = GradientBoostingRegressor(random_state=0)\n",
    "\n",
    "param = {'max_depth': [4,5,7,9,11,15],\n",
    "           'n_estimators': [100, 500, 1000],\n",
    "           'learning_rate': [0.01,0.1, 0.5, 1.0]}\n",
    "gb_grid = GridSearchCV(gb, param, cv = 5, return_train_score= True)\n",
    "gb_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for Gradient Boosting Regressor:  {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 1000}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for Gradient Boosting Regressor: \", gb_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=4, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "             n_estimators=1000, n_iter_no_change=None, presort='auto',\n",
       "             random_state=0, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb = GradientBoostingRegressor(max_depth=4, n_estimators=1000, learning_rate=0.1, random_state=0)\n",
    "gb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Training Score:  0.999999996334178\n",
      "Gradient Boosting Testing Score:  0.1947749225668023\n"
     ]
    }
   ],
   "source": [
    "print(\"Gradient Boosting Training Score: \", gb.score(X_train, y_train))\n",
    "print(\"Gradient Boosting Testing Score: \", gb.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_7 =  ['Gradient Boosting Regressor', 'learning_rate: 0.1, max_depth: 4, n_estimators: 1000',\n",
    "                                gb.score(X_train, y_train), gb.score(X_test, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boosting, Pasting and Bagging: Generating a Report table\n",
    "For comparing all the models, let's create a table and a plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table = pd.DataFrame(list(zip(report_table_1,\n",
    "             report_table_2,\n",
    "             report_table_3,\n",
    "             report_table_4,\n",
    "             report_table_5,\n",
    "             report_table_6,\n",
    "             report_table_7))).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Model Parameter</th>\n",
       "      <th>Training Score</th>\n",
       "      <th>Testing Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Decision Tree with Bagging</th>\n",
       "      <td>Decision Tree with Bagging</td>\n",
       "      <td>max_depth = 11, max_samples = 1.0, n_estimator...</td>\n",
       "      <td>0.964457</td>\n",
       "      <td>0.174892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Linear Regression with Bagging</th>\n",
       "      <td>Linear Regression with Bagging</td>\n",
       "      <td>max_samples = 0.1, n_estimators = 1000</td>\n",
       "      <td>0.610299</td>\n",
       "      <td>0.465047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN with Pasting</th>\n",
       "      <td>KNN with Pasting</td>\n",
       "      <td>max_samples = 1.0, n_estimators = 1000</td>\n",
       "      <td>0.813184</td>\n",
       "      <td>0.185112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Linear Regression with Pasting</th>\n",
       "      <td>Linear Regression with Pasting</td>\n",
       "      <td>max_samples = 0.1, n_estimators = 1000</td>\n",
       "      <td>0.602497</td>\n",
       "      <td>0.475618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge with Adaboost</th>\n",
       "      <td>Ridge with Adaboost</td>\n",
       "      <td>alpha = 1, learning_rate=0.5, n_estimators = 500</td>\n",
       "      <td>0.609138</td>\n",
       "      <td>0.598773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso with Adaboost</th>\n",
       "      <td>Lasso with Adaboost</td>\n",
       "      <td>alpha = 0.1, max_iter=500, learning_rate=1, n_...</td>\n",
       "      <td>0.531251</td>\n",
       "      <td>0.440344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Boosting Regressor</th>\n",
       "      <td>Gradient Boosting Regressor</td>\n",
       "      <td>learning_rate: 0.1, max_depth: 4, n_estimators...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.194775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Model Name  \\\n",
       "Model Name                                                       \n",
       "Decision Tree with Bagging          Decision Tree with Bagging   \n",
       "Linear Regression with Bagging  Linear Regression with Bagging   \n",
       "KNN with Pasting                              KNN with Pasting   \n",
       "Linear Regression with Pasting  Linear Regression with Pasting   \n",
       "Ridge with Adaboost                        Ridge with Adaboost   \n",
       "Lasso with Adaboost                        Lasso with Adaboost   \n",
       "Gradient Boosting Regressor        Gradient Boosting Regressor   \n",
       "\n",
       "                                                                  Model Parameter  \\\n",
       "Model Name                                                                          \n",
       "Decision Tree with Bagging      max_depth = 11, max_samples = 1.0, n_estimator...   \n",
       "Linear Regression with Bagging             max_samples = 0.1, n_estimators = 1000   \n",
       "KNN with Pasting                           max_samples = 1.0, n_estimators = 1000   \n",
       "Linear Regression with Pasting             max_samples = 0.1, n_estimators = 1000   \n",
       "Ridge with Adaboost              alpha = 1, learning_rate=0.5, n_estimators = 500   \n",
       "Lasso with Adaboost             alpha = 0.1, max_iter=500, learning_rate=1, n_...   \n",
       "Gradient Boosting Regressor     learning_rate: 0.1, max_depth: 4, n_estimators...   \n",
       "\n",
       "                               Training Score Testing Score  \n",
       "Model Name                                                   \n",
       "Decision Tree with Bagging           0.964457      0.174892  \n",
       "Linear Regression with Bagging       0.610299      0.465047  \n",
       "KNN with Pasting                     0.813184      0.185112  \n",
       "Linear Regression with Pasting       0.602497      0.475618  \n",
       "Ridge with Adaboost                  0.609138      0.598773  \n",
       "Lasso with Adaboost                  0.531251      0.440344  \n",
       "Gradient Boosting Regressor                 1      0.194775  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report_table.columns = ['Model Name', 'Model Parameter', 'Training Score', 'Testing Score']\n",
    "report_table.index = report_table['Model Name']\n",
    "report_table.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ax = report_table[['Training Score','Testing Score']].plot(kind='bar',\n",
    "            title = \"Comparison of Accuracies of Different Mods with Boosting, Bagging and Pasting\", figsize=(8, 8), fontsize = 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ridge Regression with Adaboost seems to be the best model amongst the others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=0.95)\n",
    "\n",
    "X_train_reduced = pca.fit_transform(X_train)\n",
    "X_test_reduced = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of PCA components:  6\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of PCA components: \", pca.n_components_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN Regressor with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "          weights='uniform'),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_neighbors': [3, 4, 5, 6, 7, 8, 9, 10, 15]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "params = {'n_neighbors':[3,4,5,6,7,8,9,10,15]}\n",
    "\n",
    "knn = KNeighborsRegressor()\n",
    "\n",
    "grid_knn_reg = GridSearchCV(knn, params, cv=5)\n",
    "grid_knn_reg.fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for KNN Regressor with PCA:  {'n_neighbors': 10}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for KNN Regressor with PCA: \", grid_knn_reg.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=None, n_neighbors=10, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_knn = KNeighborsRegressor(n_neighbors=10)\n",
    "pca_knn.fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Regressor with PCA Training Score:  0.5666341097112795\n",
      "KNN Regressor with PCA Testing Score:  0.1000626267050837\n"
     ]
    }
   ],
   "source": [
    "print(\"KNN Regressor with PCA Training Score: \", pca_knn.score(X_train_reduced, y_train))\n",
    "print(\"KNN Regressor with PCA Testing Score: \", pca_knn.score(X_test_reduced, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_report_table_1 = ['KNN Regressor with PCA', 'n_neighbors = 10', pca_knn.score(X_train_reduced, y_train), pca_knn.score(X_test_reduced, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "pca_lr = LinearRegression()\n",
    "pca_lr.fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression with PCA Training Score:  0.5420754521690085\n",
      "Linear Regression with PCA Testing Score:  0.6184445619391601\n"
     ]
    }
   ],
   "source": [
    "print(\"Linear Regression with PCA Training Score: \", pca_lr.score(X_train_reduced, y_train))\n",
    "print(\"Linear Regression with PCA Testing Score: \", pca_lr.score(X_test_reduced, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_report_table_2 = ['Linear Regression with PCA', 'Simple', pca_lr.score(X_train_reduced, y_train), pca_lr.score(X_test_reduced, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Polynomial Regression with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from  sklearn.preprocessing  import PolynomialFeatures\n",
    "\n",
    "lr = LinearRegression()\n",
    "poly = PolynomialFeatures(3)\n",
    "X_train_poly = poly.fit_transform(X_train_reduced)\n",
    "X_test_poly = poly.transform(X_test_reduced)\n",
    "lr.fit(X_train_poly, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polynomial Regression with PCA Training Score:  0.9941347063040205\n",
      "Polynomial Regression with PCA Testing Score:  -250.9681955536823\n"
     ]
    }
   ],
   "source": [
    "print(\"Polynomial Regression with PCA Training Score: \", lr.score(X_train_poly, y_train))\n",
    "print(\"Polynomial Regression with PCA Testing Score: \", lr.score(X_test_poly, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Writing the test score as 0 in the report table to make the plot of the table look proportionate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_report_table_3 =  ['Polynomial Regression with PCA', 'Degree = 3', lr.score(X_train_poly, y_train), 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regression with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'alpha': [0.001, 0.01, 0.1, 0, 1]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_ridge = Ridge()\n",
    "params = {'alpha' : [0.001, 0.01, 0.1,0,1]}\n",
    "\n",
    "ridge_pca_grid = GridSearchCV(pca_ridge, params, cv= 5)\n",
    "ridge_pca_grid. fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for Ridge Regression:  {'alpha': 1}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for Ridge Regression: \", ridge_pca_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_ridge = Ridge(alpha = 1)\n",
    "pca_ridge.fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Regression Training Score:  0.5397104035664451\n",
      "Ridge Regression Testing Score:  0.5616111495061841\n"
     ]
    }
   ],
   "source": [
    "print(\"Ridge Regression Training Score: \", pca_ridge.score(X_train_reduced, y_train))\n",
    "print(\"Ridge Regression Testing Score: \", pca_ridge.score(X_test_reduced, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_report_table_4 = ['Ridge Regression with PCA', 'alpha = 1', pca_ridge.score(X_train_reduced, y_train), pca_ridge.score(X_test_reduced, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Regression with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'alpha': [0.001, 0.01, 0.1, 1], 'max_iter': [500, 1000, 5000, 10000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_lasso = Lasso()\n",
    "params = {'alpha' : [0.001, 0.01, 0.1,1],\n",
    "         'max_iter': [500, 1000, 5000, 10000]}\n",
    "\n",
    "lasso_pca_grid = GridSearchCV(pca_lasso, params, cv= 5)\n",
    "lasso_pca_grid.fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for Lasso Regression:  {'alpha': 0.1, 'max_iter': 500}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for Lasso Regression: \", lasso_pca_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=500,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_lasso = Ridge(alpha = 0.1, max_iter = 500)\n",
    "pca_lasso.fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso Regression Training Score:  0.5420440325500699\n",
      "Lasso Regression Testing Score:  0.612067792658997\n"
     ]
    }
   ],
   "source": [
    "print(\"Lasso Regression Training Score: \", pca_lasso.score(X_train_reduced, y_train))\n",
    "print(\"Lasso Regression Testing Score: \", pca_lasso.score(X_test_reduced, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_report_table_5 = ['Lasso Regression with PCA', 'alpha = 0.1, max_iter = 500', pca_lasso.score(X_train_reduced, y_train), pca_lasso.score(X_test_reduced, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Regressor with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "       estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'kernel': ('linear', 'rbf', 'poly'), 'gamma': [0.01, 0.1, 1, 10], 'epsilon': [0.1, 0.3, 0.5, 1], 'C': [0.1, 1, 10, 100]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "params = {'kernel': ('linear', 'rbf', 'poly'), 'gamma': [0.01, 0.1, 1, 10],'epsilon':[0.1,0.3,0.5, 1], 'C':[0.1, 1, 10, 100]}\n",
    "pca_svr = svm.SVR()\n",
    "svr_pca_grid = GridSearchCV(pca_svr, params)\n",
    "svr_pca_grid.fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for SVM Regressor:  {'C': 100, 'epsilon': 0.1, 'gamma': 1, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for SVM Regressor: \", svr_pca_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=1,\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_svr = svm.SVR(C = 100, epsilon= 0.1, gamma= 1, kernel = 'rbf')\n",
    "pca_svr.fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR Regression Training Score:  0.6105266952839437\n",
      "SVR Regression Testing Score:  0.07743081880759926\n"
     ]
    }
   ],
   "source": [
    "print(\"SVR Regression Training Score: \", pca_svr.score(X_train_reduced, y_train))\n",
    "print(\"SVR Regression Testing Score: \", pca_svr.score(X_test_reduced, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_report_table_6 = ['SVR Regression with PCA', 'C = 100, epsilon = 0.1, gamma = 1, kernel =rbf', pca_svr.score(X_train_reduced, y_train), pca_svr.score(X_test_reduced, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SGD Regressor with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "       estimator=SGDRegressor(alpha=0.0001, average=False, early_stopping=False, epsilon=0.1,\n",
       "       eta0=0.01, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='invscaling', loss='squared_loss', max_iter=None,\n",
       "       n_iter=None, n_iter_no_change=5, penalty='l2', power_t=0.25,\n",
       "       random_state=0, shuffle=True, tol=None, validation_fraction=0.1,\n",
       "       verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'alpha': array([1.e-01, 1.e-02, 1.e-03, 1.e-04, 1.e-05, 1.e-06]), 'loss': ['squared_loss', 'huber'], 'penalty': ['l2', 'l1'], 'learning_rate': ['constant', 'optimal']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDRegressor\n",
    "\n",
    "sgd = SGDRegressor(random_state= 0)\n",
    "\n",
    "params = {\n",
    "    'alpha': 10.0 ** -np.arange(1, 7),\n",
    "    'loss': ['squared_loss', 'huber'],\n",
    "    'penalty': ['l2', 'l1'],\n",
    "    'learning_rate': ['constant', 'optimal'],\n",
    "}\n",
    "\n",
    "sgd_pca_grid = GridSearchCV(sgd, params)\n",
    "sgd_pca_grid.fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters for SGD Regressor:  {'alpha': 1e-05, 'learning_rate': 'optimal', 'loss': 'huber', 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Parameters for SGD Regressor: \", sgd_pca_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor(alpha=1e-05, average=False, early_stopping=False, epsilon=0.1,\n",
       "       eta0=0.01, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='huber', max_iter=None, n_iter=None,\n",
       "       n_iter_no_change=5, penalty='l2', power_t=0.25, random_state=None,\n",
       "       shuffle=True, tol=None, validation_fraction=0.1, verbose=0,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_sgd = SGDRegressor(alpha = 1e-05, learning_rate = 'optimal', loss = 'huber', penalty = 'l2')\n",
    "pca_sgd.fit(X_train_reduced, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SGD Regression Training Score:  0.41175881954318494\n",
      "SGD Regression Testing Score:  0.4012883962399182\n"
     ]
    }
   ],
   "source": [
    "print(\"SGD Regression Training Score: \", pca_sgd.score(X_train_reduced, y_train))\n",
    "print(\"SGD Regression Testing Score: \", pca_sgd.score(X_test_reduced, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_report_table_7 = ['SGD Regression with PCA', 'alpha = 1e-05, learning_rate = optimal, loss = huber, penalty = l2', pca_sgd.score(X_train_reduced, y_train), pca_sgd.score(X_test_reduced, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating a Report table: PCA\n",
    "For comparing all the models, let's create a table and a plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_report_table = pd.DataFrame(list(zip(pca_report_table_1,\n",
    "             pca_report_table_2,\n",
    "             pca_report_table_3,\n",
    "             pca_report_table_4,\n",
    "             pca_report_table_5,\n",
    "             pca_report_table_6,\n",
    "             pca_report_table_7))).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_report_table.columns = ['Model Name', 'Model Parameter', 'Training Score', 'Testing Score']\n",
    "pca_report_table.index = pca_report_table['Model Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Model Parameter</th>\n",
       "      <th>Training Score</th>\n",
       "      <th>Testing Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNN Regressor with PCA</th>\n",
       "      <td>KNN Regressor with PCA</td>\n",
       "      <td>n_neighbors = 10</td>\n",
       "      <td>0.566634</td>\n",
       "      <td>0.100063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Linear Regression with PCA</th>\n",
       "      <td>Linear Regression with PCA</td>\n",
       "      <td>Simple</td>\n",
       "      <td>0.542075</td>\n",
       "      <td>0.618445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polynomial Regression with PCA</th>\n",
       "      <td>Polynomial Regression with PCA</td>\n",
       "      <td>Degree = 3</td>\n",
       "      <td>0.994135</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge Regression with PCA</th>\n",
       "      <td>Ridge Regression with PCA</td>\n",
       "      <td>alpha = 1</td>\n",
       "      <td>0.53971</td>\n",
       "      <td>0.561611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso Regression with PCA</th>\n",
       "      <td>Lasso Regression with PCA</td>\n",
       "      <td>alpha = 0.1, max_iter = 500</td>\n",
       "      <td>0.542044</td>\n",
       "      <td>0.612068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVR Regression with PCA</th>\n",
       "      <td>SVR Regression with PCA</td>\n",
       "      <td>C = 100, epsilon = 0.1, gamma = 1, kernel =rbf</td>\n",
       "      <td>0.610527</td>\n",
       "      <td>0.0774308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SGD Regression with PCA</th>\n",
       "      <td>SGD Regression with PCA</td>\n",
       "      <td>alpha = 1e-05, learning_rate = optimal, loss =...</td>\n",
       "      <td>0.411759</td>\n",
       "      <td>0.401288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Model Name  \\\n",
       "Model Name                                                       \n",
       "KNN Regressor with PCA                  KNN Regressor with PCA   \n",
       "Linear Regression with PCA          Linear Regression with PCA   \n",
       "Polynomial Regression with PCA  Polynomial Regression with PCA   \n",
       "Ridge Regression with PCA            Ridge Regression with PCA   \n",
       "Lasso Regression with PCA            Lasso Regression with PCA   \n",
       "SVR Regression with PCA                SVR Regression with PCA   \n",
       "SGD Regression with PCA                SGD Regression with PCA   \n",
       "\n",
       "                                                                  Model Parameter  \\\n",
       "Model Name                                                                          \n",
       "KNN Regressor with PCA                                           n_neighbors = 10   \n",
       "Linear Regression with PCA                                                 Simple   \n",
       "Polynomial Regression with PCA                                         Degree = 3   \n",
       "Ridge Regression with PCA                                               alpha = 1   \n",
       "Lasso Regression with PCA                             alpha = 0.1, max_iter = 500   \n",
       "SVR Regression with PCA            C = 100, epsilon = 0.1, gamma = 1, kernel =rbf   \n",
       "SGD Regression with PCA         alpha = 1e-05, learning_rate = optimal, loss =...   \n",
       "\n",
       "                               Training Score Testing Score  \n",
       "Model Name                                                   \n",
       "KNN Regressor with PCA               0.566634      0.100063  \n",
       "Linear Regression with PCA           0.542075      0.618445  \n",
       "Polynomial Regression with PCA       0.994135             0  \n",
       "Ridge Regression with PCA             0.53971      0.561611  \n",
       "Lasso Regression with PCA            0.542044      0.612068  \n",
       "SVR Regression with PCA              0.610527     0.0774308  \n",
       "SGD Regression with PCA              0.411759      0.401288  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_report_table.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model Name</th>\n",
       "      <th>Model Name.1</th>\n",
       "      <th>Model Parameter</th>\n",
       "      <th>Training Score</th>\n",
       "      <th>Testing Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Regressor</td>\n",
       "      <td>KNN Regressor</td>\n",
       "      <td>n_neighbors = 3</td>\n",
       "      <td>0.813184</td>\n",
       "      <td>0.185112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>Simple</td>\n",
       "      <td>0.778848</td>\n",
       "      <td>0.502026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Polynomial Regression</td>\n",
       "      <td>Polynomial Regression</td>\n",
       "      <td>Degree = 3</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>alpha = 1</td>\n",
       "      <td>0.755743</td>\n",
       "      <td>0.456444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>alpha = 0.1, max_iter = 500</td>\n",
       "      <td>0.778212</td>\n",
       "      <td>0.496214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SVR Regression</td>\n",
       "      <td>SVR Regression</td>\n",
       "      <td>C = 1, epsilon = 0.1, gamma = 10, kernel = poly</td>\n",
       "      <td>0.999597</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SGD Regression</td>\n",
       "      <td>SGD Regression</td>\n",
       "      <td>alpha = 1e-06, learning_rate = optimal, loss =...</td>\n",
       "      <td>0.619898</td>\n",
       "      <td>0.396764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Model Name           Model Name.1  \\\n",
       "0          KNN Regressor          KNN Regressor   \n",
       "1      Linear Regression      Linear Regression   \n",
       "2  Polynomial Regression  Polynomial Regression   \n",
       "3       Ridge Regression       Ridge Regression   \n",
       "4       Lasso Regression       Lasso Regression   \n",
       "5         SVR Regression         SVR Regression   \n",
       "6         SGD Regression         SGD Regression   \n",
       "\n",
       "                                     Model Parameter  Training Score  \\\n",
       "0                                    n_neighbors = 3        0.813184   \n",
       "1                                             Simple        0.778848   \n",
       "2                                         Degree = 3        0.999998   \n",
       "3                                          alpha = 1        0.755743   \n",
       "4                        alpha = 0.1, max_iter = 500        0.778212   \n",
       "5    C = 1, epsilon = 0.1, gamma = 10, kernel = poly        0.999597   \n",
       "6  alpha = 1e-06, learning_rate = optimal, loss =...        0.619898   \n",
       "\n",
       "   Testing Score  \n",
       "0       0.185112  \n",
       "1       0.502026  \n",
       "2       0.000000  \n",
       "3       0.456444  \n",
       "4       0.496214  \n",
       "5       0.000000  \n",
       "6       0.396764  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report_table_without_pca = pd.read_csv('Report Table without PCA.csv')\n",
    "report_table_without_pca.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ax = pca_report_table[['Training Score','Testing Score']].plot(kind='bar',\n",
    "            title = \"Comparison of Accuracies of Different Mods with PCA\", figsize=(8, 8), fontsize = 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x182cb4105c0>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "ax = fig.add_axes([0, 0, 1, 1])\n",
    "\n",
    "# Hide spines, ticks, etc.\n",
    "ax.axis('off')\n",
    "\n",
    "# Display the image.\n",
    "img=mpimg.imread('report_table_without_pca.png')\n",
    "ax.imshow(img, aspect='auto')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Regression without PCA seems to be the best model amongst the others here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From d:\\software\\python36\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From d:\\software\\python36\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/40\n",
      "570/570 [==============================] - 0s 302us/step - loss: 276.4020 - mean_squared_error: 276.4020\n",
      "Epoch 2/40\n",
      "570/570 [==============================] - 0s 28us/step - loss: 215.6572 - mean_squared_error: 215.6572\n",
      "Epoch 3/40\n",
      "570/570 [==============================] - 0s 26us/step - loss: 184.2368 - mean_squared_error: 184.2368\n",
      "Epoch 4/40\n",
      "570/570 [==============================] - 0s 28us/step - loss: 161.2375 - mean_squared_error: 161.2375\n",
      "Epoch 5/40\n",
      "570/570 [==============================] - 0s 29us/step - loss: 148.3984 - mean_squared_error: 148.3984\n",
      "Epoch 6/40\n",
      "570/570 [==============================] - 0s 26us/step - loss: 152.5598 - mean_squared_error: 152.5598\n",
      "Epoch 7/40\n",
      "570/570 [==============================] - 0s 25us/step - loss: 121.1125 - mean_squared_error: 121.1125\n",
      "Epoch 8/40\n",
      "570/570 [==============================] - 0s 21us/step - loss: 113.9886 - mean_squared_error: 113.9886\n",
      "Epoch 9/40\n",
      "570/570 [==============================] - 0s 28us/step - loss: 104.7245 - mean_squared_error: 104.7245\n",
      "Epoch 10/40\n",
      "570/570 [==============================] - 0s 28us/step - loss: 97.0038 - mean_squared_error: 97.0038\n",
      "Epoch 11/40\n",
      "570/570 [==============================] - 0s 20us/step - loss: 89.3615 - mean_squared_error: 89.3615\n",
      "Epoch 12/40\n",
      "570/570 [==============================] - 0s 33us/step - loss: 88.8882 - mean_squared_error: 88.8882\n",
      "Epoch 13/40\n",
      "570/570 [==============================] - 0s 22us/step - loss: 79.8625 - mean_squared_error: 79.8625\n",
      "Epoch 14/40\n",
      "570/570 [==============================] - 0s 26us/step - loss: 76.3721 - mean_squared_error: 76.3721\n",
      "Epoch 15/40\n",
      "570/570 [==============================] - 0s 23us/step - loss: 69.1352 - mean_squared_error: 69.1352\n",
      "Epoch 16/40\n",
      "570/570 [==============================] - 0s 25us/step - loss: 60.4066 - mean_squared_error: 60.4066\n",
      "Epoch 17/40\n",
      "570/570 [==============================] - 0s 25us/step - loss: 67.3911 - mean_squared_error: 67.3911\n",
      "Epoch 18/40\n",
      "570/570 [==============================] - 0s 27us/step - loss: 60.8480 - mean_squared_error: 60.8480\n",
      "Epoch 19/40\n",
      "570/570 [==============================] - 0s 27us/step - loss: 60.3143 - mean_squared_error: 60.3143\n",
      "Epoch 20/40\n",
      "570/570 [==============================] - 0s 27us/step - loss: 54.9422 - mean_squared_error: 54.9422\n",
      "Epoch 21/40\n",
      "570/570 [==============================] - 0s 29us/step - loss: 50.4308 - mean_squared_error: 50.4308\n",
      "Epoch 22/40\n",
      "570/570 [==============================] - 0s 28us/step - loss: 57.9888 - mean_squared_error: 57.9888\n",
      "Epoch 23/40\n",
      "570/570 [==============================] - 0s 24us/step - loss: 54.3620 - mean_squared_error: 54.3620\n",
      "Epoch 24/40\n",
      "570/570 [==============================] - 0s 28us/step - loss: 59.3252 - mean_squared_error: 59.3252\n",
      "Epoch 25/40\n",
      "570/570 [==============================] - 0s 17us/step - loss: 49.4464 - mean_squared_error: 49.4464\n",
      "Epoch 26/40\n",
      "570/570 [==============================] - 0s 26us/step - loss: 50.7017 - mean_squared_error: 50.7017\n",
      "Epoch 27/40\n",
      "570/570 [==============================] - 0s 20us/step - loss: 49.3809 - mean_squared_error: 49.3809\n",
      "Epoch 28/40\n",
      "570/570 [==============================] - 0s 24us/step - loss: 42.9023 - mean_squared_error: 42.9023\n",
      "Epoch 29/40\n",
      "570/570 [==============================] - 0s 27us/step - loss: 44.9271 - mean_squared_error: 44.9271\n",
      "Epoch 30/40\n",
      "570/570 [==============================] - 0s 20us/step - loss: 44.6675 - mean_squared_error: 44.6675\n",
      "Epoch 31/40\n",
      "570/570 [==============================] - 0s 30us/step - loss: 47.4311 - mean_squared_error: 47.4311\n",
      "Epoch 32/40\n",
      "570/570 [==============================] - 0s 20us/step - loss: 46.9714 - mean_squared_error: 46.9714\n",
      "Epoch 33/40\n",
      "570/570 [==============================] - 0s 23us/step - loss: 41.2260 - mean_squared_error: 41.2260\n",
      "Epoch 34/40\n",
      "570/570 [==============================] - 0s 28us/step - loss: 42.2382 - mean_squared_error: 42.2382\n",
      "Epoch 35/40\n",
      "570/570 [==============================] - 0s 21us/step - loss: 38.9271 - mean_squared_error: 38.9271\n",
      "Epoch 36/40\n",
      "570/570 [==============================] - 0s 29us/step - loss: 42.2789 - mean_squared_error: 42.2789\n",
      "Epoch 37/40\n",
      "570/570 [==============================] - 0s 32us/step - loss: 73.9594 - mean_squared_error: 73.9594\n",
      "Epoch 38/40\n",
      "570/570 [==============================] - 0s 27us/step - loss: 44.1936 - mean_squared_error: 44.1936\n",
      "Epoch 39/40\n",
      "570/570 [==============================] - 0s 17us/step - loss: 39.6891 - mean_squared_error: 39.6891\n",
      "Epoch 40/40\n",
      "570/570 [==============================] - 0s 26us/step - loss: 52.6517 - mean_squared_error: 52.6517\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x182d6c57cc0>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,MaxPooling1D\n",
    "np.random.seed(0)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(10, input_dim = 10, activation = 'relu'))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(loss = 'mse', optimizer = 'sgd', metrics = ['mse'])\n",
    "\n",
    "model.fit(X_train, y_train, epochs = 40, batch_size = 70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "570/570 [==============================] - 0s 91us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[40.79327370074758, 40.79327370074758]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190/190 [==============================] - 0s 75us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1928.41079651682, 1928.41079651682]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NN Train Score:  0.847608338421235\n",
      "NN Test Score:  0.6332799907149307\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "\n",
    "print(\"NN Train Score: \",r2_score(y_train, y_train_pred))\n",
    "print(\"NN Test Score: \", r2_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amongst all the models Neural Network Regression model seems to be the better model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted value for 1st testing row:  [0.52922744]\n",
      "Original value for 1st testing row:  0.2808\n",
      "\n",
      "Predicted value for 5th testing row:  [11.069509]\n",
      "Original value for 5th testing row:  10.8756\n"
     ]
    }
   ],
   "source": [
    "y_predicted = model.predict(X_test)\n",
    "print(\"Predicted value for 1st testing row: \", y_predicted[0])\n",
    "print(\"Original value for 1st testing row: \", y_test.values[0])\n",
    "print(\"\")\n",
    "print(\"Predicted value for 5th testing row: \", y_predicted[4])\n",
    "print(\"Original value for 5th testing row: \", y_test.values[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model works pretty well on the test data as well.\n",
    "\n",
    "**Training and testing R2 score of around 0.84 and 0.63 was achieved using Neural Network for regression.** "
   ]
  }
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