Holmusk - Healthcare Data Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Healthcare Data Challenge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import os\n",
    "import copy\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing\n",
    "\n",
    "1. data type standardization\n",
    "2. encode categorical variables\n",
    "3. fill in missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read raw data\n",
    "bill_amount = pd.read_csv('data/bill_amount.csv', header=0, dtype={'bill_id': str,\n",
    "                                                                   'amount': float})\n",
    "bill_id = pd.read_csv('data/bill_id.csv', header=0, dtype={'bill_id': str,\n",
    "                                                           'patient_id': str,\n",
    "                                                           'date_of_admission': str})\n",
    "\n",
    "bill = bill_id.merge(bill_amount, on='bill_id', how='left')\n",
    "bill = bill.groupby(['patient_id', 'date_of_admission'], as_index=False).amount.sum()\n",
    "\n",
    "clinical_data = pd.read_csv('data/clinical_data.csv', header=0,\n",
    "                            dtype={'id': str,\n",
    "                                   'date_of_admission': str,\n",
    "                                   'date_of_discharge': str,\n",
    "                                   'weight': float,\n",
    "                                   'height': float})\n",
    "clinical_data.rename(columns={'id': 'patient_id'}, inplace=True)\n",
    "dmgr = pd.read_csv('data/demographics.csv', header=0,\n",
    "                   dtype={'patient_id': str,\n",
    "                          'gender': str,\n",
    "                          'race': str,\n",
    "                          'resident_status': str,\n",
    "                          'date_of_birth': str})\n",
    "\n",
    "# explore\n",
    "# print(bill_amount.describe())\n",
    "# print(bill_id.dtypes)\n",
    "# print(clinical_data.dtypes)  # inconsistent data types - require type standardization\n",
    "# for col in clinical_data.columns[3:-2]:\n",
    "#     print(col)\n",
    "#     print(clinical_data[col].unique().tolist())\n",
    "\"\"\"\n",
    "recoding, dtype standardization, missing values\n",
    "medical_history_2\n",
    "[1.0, 0.0, nan]\n",
    "medical_history_3\n",
    "['0', 'No', '1', 'Yes']\n",
    "medical_history_5\n",
    "[0.0, nan, 1.0]\n",
    "medical_history_2: missing values\n",
    "medical_history_3: dtype, recode\n",
    "medical_history_5: missing values\n",
    "\"\"\"\n",
    "# print(dmgr.dtypes)\n",
    "# for col in dmgr.columns[1:4]:\n",
    "#     print(col)\n",
    "#     print(dmgr[col].unique().tolist())\n",
    "\"\"\"\n",
    "require recoding\n",
    "gender\n",
    "['Female', 'f', 'Male', 'm']\n",
    "race\n",
    "['Indian', 'Chinese', 'Malay', 'chinese', 'India', 'Others']\n",
    "resident_status\n",
    "['Singaporean', 'Singapore citizen', 'PR', 'Foreigner']\n",
    "\n",
    "gender: recode\n",
    "race: recode\n",
    "residence_status: recode\n",
    "\"\"\"\n",
    "# preprocessing\n",
    "# clinical data\n",
    "# print(len(clinical_data))\n",
    "# print(len(clinical_data[pd.isnull(clinical_data.medical_history_2)]))\n",
    "# print(len(clinical_data[pd.isnull(clinical_data.medical_history_5)]))\n",
    "\n",
    "clinical_data['medical_history_3'] = clinical_data['medical_history_3'].replace({'1': 1,\n",
    "                                                                                 '0': 0,\n",
    "                                                                                 'Yes': 1,\n",
    "                                                                                 'No': 0})\n",
    "# fill in missing using mode\n",
    "clinical_data['medical_history_2'].fillna(clinical_data['medical_history_2'].mode()[0],\n",
    "                                          inplace=True)\n",
    "clinical_data['medical_history_5'].fillna(clinical_data['medical_history_2'].mode()[0],\n",
    "                                          inplace=True)\n",
    "# standardize data type\n",
    "clinical_data.iloc[:, 3:21] = clinical_data.iloc[:, 3:21].astype(int)\n",
    "\n",
    "# compute common clinical metrics\n",
    "clinical_data['days_of_stay'] = clinical_data.apply(lambda x: (pd.to_datetime(\n",
    "    x.date_of_discharge) - pd.to_datetime(x.date_of_admission)).days, axis=1)\n",
    "clinical_data['bmi'] = clinical_data.weight / ((clinical_data.height / 100) ** 2)\n",
    "\n",
    "# for col in clinical_data.columns[3:-2]:\n",
    "#     print(col)\n",
    "#     print(clinical_data[col].unique().tolist())\n",
    "\n",
    "# demographic\n",
    "# standardize categorical variables\n",
    "dmgr.loc[:, 'gender'] = dmgr.loc[:, 'gender'].replace({'Female': 'f', 'Male': 'm'})\n",
    "dmgr.loc[:, 'race'] = dmgr.loc[:, 'race'].replace({'Indian': 'in',\n",
    "                                                   'Chinese': 'cn',\n",
    "                                                   'Malay': 'my',\n",
    "                                                   'chinese': 'cn',\n",
    "                                                   'India': 'in',\n",
    "                                                   'Others': 'other'})\n",
    "dmgr.loc[:, 'resident_status'] = dmgr.loc[:, 'resident_status'].replace(\n",
    "    {'Singaporean': 'ctz',\n",
    "     'Singapore citizen': 'ctz',\n",
    "     'PR': 'pr',\n",
    "     'Foreigner': 'frn'})\n",
    "# compute age from date of birth\n",
    "dmgr['age'] = dmgr.apply(lambda x: (pd.datetime.today() - pd.to_datetime(\n",
    "    x.date_of_birth)).days / 365.0, axis=1)\n",
    "# print(dmgr.dtypes)\n",
    "# for col in dmgr.columns[1:4]:\n",
    "#     print(col)\n",
    "#     print(dmgr[col].unique().tolist())\n",
    "\n",
    "# check primary key\n",
    "# if bill_id.groupby('bill_id', as_index=False).size().sort_values(ascending=False)[0] == 1:\n",
    "#     print('no duplication')\n",
    "# else:\n",
    "#     print('duplication')\n",
    "# if clinical_data.groupby(['patient_id', 'date_of_admission'], as_index=False).size(\n",
    "# \n",
    "# ).sort_values(ascending=False)[0] == 1:\n",
    "#     print('no duplication')\n",
    "# else:\n",
    "#     print('duplication')\n",
    "# if dmgr.groupby(['patient_id'], as_index=False).size().sort_values(\n",
    "#         ascending=False)[0] == 1:\n",
    "#     print('no duplication')\n",
    "# else:\n",
    "#     print('duplication')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create joined dataset\n",
    "data = bill.merge(dmgr, on='patient_id', how='left')\n",
    "data = data.merge(clinical_data, on=['patient_id', 'date_of_admission'], how='left')\n",
    "\n",
    "data.to_csv('data/cost_of_care.txt', sep='|', header=True, index=False)\n",
    "data_backup = copy.deepcopy(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>date_of_admission</th>\n",
       "      <th>amount</th>\n",
       "      <th>gender</th>\n",
       "      <th>race</th>\n",
       "      <th>resident_status</th>\n",
       "      <th>date_of_birth</th>\n",
       "      <th>age</th>\n",
       "      <th>date_of_discharge</th>\n",
       "      <th>medical_history_1</th>\n",
       "      <th>...</th>\n",
       "      <th>symptom_3</th>\n",
       "      <th>symptom_4</th>\n",
       "      <th>symptom_5</th>\n",
       "      <th>lab_result_1</th>\n",
       "      <th>lab_result_2</th>\n",
       "      <th>lab_result_3</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>days_of_stay</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00225710a878eff524a1d13be817e8e2</td>\n",
       "      <td>2014-04-10</td>\n",
       "      <td>5190.566695</td>\n",
       "      <td>f</td>\n",
       "      <td>cn</td>\n",
       "      <td>ctz</td>\n",
       "      <td>1983-01-16</td>\n",
       "      <td>37.189041</td>\n",
       "      <td>2014-04-22</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>27.9</td>\n",
       "      <td>96.0</td>\n",
       "      <td>66.9</td>\n",
       "      <td>155.0</td>\n",
       "      <td>12</td>\n",
       "      <td>27.845994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0029d90eb654699c18001c17efb0f129</td>\n",
       "      <td>2012-11-07</td>\n",
       "      <td>22601.497872</td>\n",
       "      <td>f</td>\n",
       "      <td>cn</td>\n",
       "      <td>ctz</td>\n",
       "      <td>1943-10-14</td>\n",
       "      <td>76.473973</td>\n",
       "      <td>2012-11-20</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7</td>\n",
       "      <td>26.5</td>\n",
       "      <td>109.0</td>\n",
       "      <td>89.1</td>\n",
       "      <td>160.0</td>\n",
       "      <td>13</td>\n",
       "      <td>34.804687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0040333abd68527ecb53e1db9073f52e</td>\n",
       "      <td>2013-01-19</td>\n",
       "      <td>17447.181635</td>\n",
       "      <td>m</td>\n",
       "      <td>in</td>\n",
       "      <td>ctz</td>\n",
       "      <td>1972-08-26</td>\n",
       "      <td>47.586301</td>\n",
       "      <td>2013-01-31</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.8</td>\n",
       "      <td>25.2</td>\n",
       "      <td>96.0</td>\n",
       "      <td>79.5</td>\n",
       "      <td>172.0</td>\n",
       "      <td>12</td>\n",
       "      <td>26.872634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00473b58e3dc8ae37b3cb34069705083</td>\n",
       "      <td>2014-02-10</td>\n",
       "      <td>15285.883220</td>\n",
       "      <td>f</td>\n",
       "      <td>cn</td>\n",
       "      <td>ctz</td>\n",
       "      <td>1976-07-23</td>\n",
       "      <td>43.676712</td>\n",
       "      <td>2014-02-15</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>28.7</td>\n",
       "      <td>122.0</td>\n",
       "      <td>81.1</td>\n",
       "      <td>160.0</td>\n",
       "      <td>5</td>\n",
       "      <td>31.679687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0078662d1d983dde68ea057c42d5b5cf</td>\n",
       "      <td>2012-04-28</td>\n",
       "      <td>73477.869010</td>\n",
       "      <td>m</td>\n",
       "      <td>my</td>\n",
       "      <td>frn</td>\n",
       "      <td>1942-10-19</td>\n",
       "      <td>77.460274</td>\n",
       "      <td>2012-05-10</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.2</td>\n",
       "      <td>27.2</td>\n",
       "      <td>89.0</td>\n",
       "      <td>74.7</td>\n",
       "      <td>173.0</td>\n",
       "      <td>12</td>\n",
       "      <td>24.959070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         patient_id date_of_admission        amount gender  \\\n",
       "0  00225710a878eff524a1d13be817e8e2        2014-04-10   5190.566695      f   \n",
       "1  0029d90eb654699c18001c17efb0f129        2012-11-07  22601.497872      f   \n",
       "2  0040333abd68527ecb53e1db9073f52e        2013-01-19  17447.181635      m   \n",
       "3  00473b58e3dc8ae37b3cb34069705083        2014-02-10  15285.883220      f   \n",
       "4  0078662d1d983dde68ea057c42d5b5cf        2012-04-28  73477.869010      m   \n",
       "\n",
       "  race resident_status date_of_birth        age date_of_discharge  \\\n",
       "0   cn             ctz    1983-01-16  37.189041        2014-04-22   \n",
       "1   cn             ctz    1943-10-14  76.473973        2012-11-20   \n",
       "2   in             ctz    1972-08-26  47.586301        2013-01-31   \n",
       "3   cn             ctz    1976-07-23  43.676712        2014-02-15   \n",
       "4   my             frn    1942-10-19  77.460274        2012-05-10   \n",
       "\n",
       "   medical_history_1  ...  symptom_3  symptom_4  symptom_5  lab_result_1  \\\n",
       "0                  0  ...          1          0          0          13.4   \n",
       "1                  0  ...          0          0          1          16.7   \n",
       "2                  0  ...          0          0          1          14.8   \n",
       "3                  1  ...          1          1          0          14.9   \n",
       "4                  0  ...          0          1          1          14.2   \n",
       "\n",
       "   lab_result_2  lab_result_3  weight  height  days_of_stay        bmi  \n",
       "0          27.9          96.0    66.9   155.0            12  27.845994  \n",
       "1          26.5         109.0    89.1   160.0            13  34.804687  \n",
       "2          25.2          96.0    79.5   172.0            12  26.872634  \n",
       "3          28.7         122.0    81.1   160.0             5  31.679687  \n",
       "4          27.2          89.0    74.7   173.0            12  24.959070  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cost\n",
    "From below descriptions statistics, the mean of cost is around $21859 with median $20180, which suggests a slightly right skewed dataset. \n",
    "\n",
    "The average cost is much higher than $5407, the average hospital bill in Singapore (reference: TOSP hospital bill benchmark https://www.moh.gov.sg/cost-financing/fee-benchmarks-and-bill-amount-information). Based on TOSP hospital bill benchmarking, bills correspond to the majority of this sample (p25 to p75) are surgeries at private hospitals and unsubsidised public hospitals.\n",
    "\n",
    "The skewness in accordance with the histogram and medical expenditure in reality. Extremely high medical bills could be due to expense medication, surgeries, intensive care units, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     3400.000000\n",
      "mean     21859.071130\n",
      "std      10155.336753\n",
      "min       2946.100354\n",
      "25%      14793.370831\n",
      "50%      20180.020614\n",
      "75%      26680.292435\n",
      "max      98723.854994\n",
      "Name: amount, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a16ca24e0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAFNCAYAAAATl+fvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAGztJREFUeJzt3XuQnWd9H/Dvz16wbEyJbWImvklYAnuADIQ4DEkTGghYggQsM2lKxDQBwkVtVOymeDEmmZq2tMU0mQRI6npCaAgxkBImYbj4Qrk4qW0ltkNim+uuQIBNDJYQtiHIlv30j313OZZ2vbuyVruP9PnMnNlznvO8z+W9HH31nvfdrdZaAACgR0cs9wAAAGB/CbMAAHRLmAUAoFvCLAAA3RJmAQDoljALAEC3hFngkFFVX6mq5y7zGF5WVX+9nGMAOJwIswDLpKo+VVWvPMh9tqpadzD7BFhKwiwAAN0SZoFDzU9U1Wer6ttV9a6qWjX9RlW9qqomqmpnVX2oqk4ayn+qqu6sqlOH108dlj9ztg6Gs5uvraptw3JvrapZP0+Htv+2qr4z/PypofzNSX4myTuq6p6qesccy/90VV1bVbuq6mtV9bKh/DFV9e6q+lZVba+q35weQ1Wtq6pPD33eWVXvH8qvGZr9+6HPf7X41QuwsgizwKHmpUnWJ1mb5IlJfjNJquo5Sf5bkl9K8iNJtid5X5K01q5N8r+S/HFVHZ3kPUl+q7X2+Yfo59wkZyV5epJzkrxi7wpVdXySjyR5W5ITkvxOko9U1QmttTcm+askW1prx7bWtsyy/OokH0vy9iQ/nORpST4zvP32JI9JcnqSf5HkV5K8fHjvPye5KslxSU4Z6qa19qzh/acOfb7/IeYH0AVhFjjUvKO19rXW2s4kb07yy0P5S5P8UWvtptba7iRvSPKTVbVmeP/iTIXDv0lyW5Lfn6eft7TWdrbWvprkd0f6GfXzSb7UWvuT1tqe1tp7k3w+yQsXOJdNST7eWntva+2+1tqO1tpnqurIJC9J8obW2t2tta8k+e0k/3pY7r4kq5Oc1Fr7fmvNDWnAIUuYBQ41Xxt5vj3JScPzk4bXSZLW2j1JdiQ5eXh9X5L/neQpSX67tdb2s59RD+pzpO7J87Q97dQkk7OUPzbJI/Zqe7Td8SSV5G+q6taq2uesMcChQpgFDjWnjjw/Lcntw/PbM3W2MklSVY/K1Ff/tw2vT07yH5O8K8lvV9VR+9nPqAf1OVL3tuH5QgLz2lnK78wPzr7u025r7R9ba69qrZ2U5DVJ/sBvMAAOVcIscKj59ao6Zbhe9Y1Jpq8LfW+Sl1fV04ag+l+TbG2tfaWqKlNnZd+Z5NeSfCNT150+lAuq6rjhprHzRvoZ9dEkT6yqTVU1Ntxw9aQkHx7evyNT17zO5U+TPLeqfmlY/oSqelpr7f4kf5bkzVX16OHa2t/I1LW+qap/WVWnDG18O1Oh+YEF9gnQFWEWONRcnqmbn7Zl6iv6/5IkrbWPJ/mtJH+eqbC6NlPXnSbJa5OcmKmbvlqmbqR6eVX9zEP085dJbszUDVkfyVQQfpDW2o4kv5DkP2TqkobxJL/QWrtzqPJ7SX5x+M0Jb5tl+a8mecGw/M6hr6cOb/+7JN8d5vnXw7z/aHjvJ5Jsrap7knwoyXmttW3Dexdn6ka3XVX1Sw8xP4Au1PyXhQEwqqpakie01iaWeywAhztnZgEA6JYwCwBAt1xmAABAt5yZBQCgW8IsAADdGltM5cc+9rFtzZo1SzQUAABIbrzxxjtbaz+8kLqLCrNr1qzJDTfcsH+jAgCABaiqvf8U+JxcZgAAQLeEWQAAuiXMAgDQLWEWAIBuCbMAAHRLmAUAoFvCLAAA3RJmAQDoljALAEC3hFkAALolzAIA0C1hFgCAbgmzAAB0S5gFAKBbwiwAAN0SZgEA6JYwCwBAt4RZAAC6JcwCANAtYRYAgG4JswAAdEuYBQCgW8IsAADdEmYBAOjW2HIPgAPn0ksvzeTk5HIPI0ly++23J0lOOumkZR7JQ1u7dm02b9683MMAAPaTMHsImZyczOdv/myOz9HLPZTcle8lScZ27F7mkcxtZ/5puYcAADxMwuwh5vgcnefnics9jHwsX0ySFTGWuUyPEQDol2tmAQDoljALAEC3hFkAALolzAIA0C1hFgCAbgmzAAB0S5gFAKBbwiwAAN0SZgEA6JYwCwBAt4RZAAC6JcwCANAtYRYAgG4JswAAdEuYBQCgW8IsAADdEmYBAOiWMAsAQLeEWQAAuiXMAgDQLWEWAIBuCbMAAHRLmAUAoFvCLAAA3RJmAQDoljALAEC3hFkAALolzAIA0C1hFgCAbgmzAAB0S5gFAKBbwiwAAN0SZgEA6JYwCwBAt4RZAAC6JcwCANAtYRYAgG4JswAAdEuYBQCgW8IsAADdEmYBAOiWMAsAQLeEWQAAuiXMAgDQLWEWAIBuCbMAAHRLmAUAoFvCLAAA3RJmAQDoljALAEC3hFkAALolzAIA0K0VH2YvvfTSXHrppcs9DGAF8vkAwNhyD2A+k5OTyz0EYIXy+QDAij8zCwAAcxFmAQDoljALAEC3hFkAALolzAIA0C1hFgCAbgmzAAB0S5gFAKBbwiwAAN0SZgEA6JYwCwBAt4RZAAC6JcwCANAtYRYAgG4JswAAdEuYBQCgW8IsAADdEmYBAOiWMAsAQLeEWQAAuiXMAgDQLWEWAIBuCbMAAHRLmAUAoFvCLAAA3RJmAQDoljALAEC3hFkAALolzAIA0C1hFgCAbgmzAAB0S5gFAKBbwiwAAN0SZgEA6JYwCwBAt4RZAAC6JcwCANAtYRYAgG4JswAAdEuYBQCgW8IsAADdEmYBAOiWMAsAQLeEWQAAuiXMAgDQLWEWAIBuCbMAAHRLmAUAoFvCLAAA3RJmgW7dd999mZyczM6dOzMxMZFzzjknGzZsyAtf+MKcc845eeUrX5mNGzfmFa94RTZs2JBXvepVOe+88zI5OZnzzjsvW7Zsyfnnn5/Jycm87nWvy86dO3PjjTfm+c9/fjZv3pybbrop5557bjZv3pzNmzdn48aN2bJlS3bu3JkdO3bMLDNqYmIi5557brZs2TKz/JYtW/KpT30qL3jBC/LpT396ZrmJiYls3LgxGzduzLZt22banJyczPnnn5/XvOY1Offcc2feO++887J58+a86EUvyoYNG3LNNdfM9DvXeKbLb7rpprz4xS/OTTfdNGu92UxMTOTFL35xtm3bNm8/o/MeXZ8LsWPHjpx//vkz22Z62bn6mquNxfS5UEvV7mL7PtjzW0jf841pIWM+UHWWst3l3AcOtr3n2svcj7z44osXXPmyyy67+NWvfvXSjWYWV199dZLk7LPPPqj99ujqq6/Od7+5K0/ICcs9lExkR5KsiLHMZSI78qjHHWff6ti73/3u3HXXXdm9e3c++MEPznzgPvDAA7n//vtz1113Zc+ePbn77ruTJHfddVd27NiRW2+9dSYE33nnnbn11lszMTGR3bt35z3veU/uvffe7Nq1K1u3bs33vve97Nq1K7t27cqePXuyc+fO7N69OzfffHOuvfba7N69O894xjNmxnThhRdmx44d2blz58zyO3fuzPXXX5/7778/119/fe64446ZMe/YsSN79uzJLbfckjvuuCPXXnttbrnllkxOTuY73/nOg9677rrrsmvXrtx///1Jkuuuuy6bNm1Kkrzzne+cdTzT5Vu3bs13v/vdbN26Nbfddts+9WZz4YUX5s4778wtt9ySF77whQ/Zz+i8R9fnfH1Mt3ndddfNbJvpZedax3O1sdC6i7FU7S6278Wsi/3tY7b95qH6nm/dLGTdHag6S9nucu4DB9vec13Oub/pTW/6xsUXX3zZQuo6Mwt0aceOHfn2t7+dJLnyyivz1a9+dcHLbt++fZ/XrbVcccUVueeee2bKR5+PuuKKK3LVVVeltZarrrpqJkRPTEw8aByjy+/Zs2fmZ2ttnzFv3749V155ZVprs47viiuu2Gcce/bsyTXXXJMdO3bk6quv3mc8o+XTY7nnnnv2qTeb0bls37595uzwbP3sPe/p9TlfH9NjvOqqq/ZZ9sorr5x1Hc/VxmzjeriWqt3F9r2YdbG/fcy138zV93zrZiHr7kDVWcp2l3MfONj2nuvk5GQ3cx9b7gHM5/bbb8/3v//9XHDBBcs9lBVv27Ztabl3uYfRjbuzO/ds22bf6tRtt92W1lqSqcsNDoTpwLmQelWVZOos8OWXX54tW7bkkksuWXBfs435ofqfPhu7t0suuSTr16/PAw88sM94Lr/88pnyvY3Wm6vdUW95y1vylKc8ZdZ+5pr3fH0kyeWXXz7rvEfL5mtndJ4L6XOhlqrdxfa9mHWxv33Mtd/M1fd862Yh6+5A1VnovPan3eXcBw62ved6ySWXdDP3ec/MVtWrq+qGqrrhW9/61sEYE8C8du3ataz9TwfpPXv25BOf+ESSLOrs8IGyZ8+efPKTn3zQmd/p8YyWz7bcdL3Z7D2X7du3z9nPXPOer4/pMU6vy1GttVnX8VxtzDauh2up2l1s34tZF/vbx1z7zVx9z7duFrLuDlSdpWx3OfeBg23vuW7fvr2buc97Zra1dlmSy5LkrLPO2vcTZ4mddNJJSZK3vvWtB7vr7lxwwQX55s1fXu5hdOPROSonnv54+1an3v72t+cjH/nIsvVfVWmtZWxsLM95znOSJKeddtpBD7RjY2N59rOfnSuvvDJ79ux50HhGy2dbbrrebPaey+rVq/OUpzxl1n7mmvd8fUyP8aMf/eg+gXb6zPfe63iuNmYb18O1VO0utu/FrIv97WOu/WauvudbNwtZdweqzlK2u5z7wMG291xPPvnk3HbbbV3M3TWzQJc2bdo08w/tIx7xiAPS5tjYwq68Ghsbm6l7xBFHzNyENT4+vuC+ZhvzQ/V/5JFHzlo+Pj6eTZs25YgjjthnPKPlexutN1e7o17/+tfP2c9c856vj+kxzjbvudbxXG3MNq6Ha6naXWzfi1kX+9vHXPvNXH3Pt24Wsu4OVJ2lbHc594GDbe+5jo+PdzN3YRbo0gknnJDjjjsuSbJ+/fqcdtppC1529erV+7yuqmzYsCHHHnvsTPno81EbNmzI2WefnarK2WefneOPPz5Jsm7dugeNY3T56UAwNjaWqtpnzKtXr8769etTVbOOb8OGDfuMY2xsLM961rNywgkn5HnPe94+4xktnx7Lscceu0+92YzOZfXq1Tn99NPn7GfveU+vz/n6mB7j6G8UmV52/fr1s67judqYbVwP11K1u9i+F7Mu9rePufabufqeb90sZN0dqDpL2e5y7gMH295zXbt2bTdzF2aBbp144ok55phjsmnTpoyPj+eoo45KMnXW86ijjsopp5ySVatWzVyudOqpp+aMM87I+Ph4zjjjjKxbty5nnnlmxsfH8+QnPzmbNm3KG97whlRV1qxZk4suuihHH3101qxZkzVr1mTVqlVZt25dNm3alE2bNs0sM2p8fDxHH3101q1bN7P8unXr8rrXvS5HHHFELrjggpnlxsfHs2rVqqxatWrmzOeTn/zkjI+P58wzz8zq1atz9NFHz7x3xhlnZM2aNXnkIx8509e0ucYzXX7RRRflmGOOyUUXXTRrvdmMj4/nmGOOyetf//p5+xmd9+j6XIhNmzblzDPPnNk208vO1ddcbSymz4VaqnYX2/fBnt9C+p5vTAsZ84Gqs5TtLuc+cLDtPdde5l6zXXg/l7POOqvdcMMNSzicfU3fae66xvlNXzP7/DxxuYeSj+WLSbIixjKXj+WLOfFHXTPbM58PAIemqrqxtXbWQuo6MwsAQLeEWQAAuiXMAgDQLWEWAIBuCbMAAHRLmAUAoFvCLAAA3RJmAQDoljALAEC3hFkAALolzAIA0C1hFgCAbgmzAAB0S5gFAKBbwiwAAN0SZgEA6JYwCwBAt4RZAAC6JcwCANAtYRYAgG4JswAAdEuYBQCgW8IsAADdEmYBAOiWMAsAQLeEWQAAuiXMAgDQLWEWAIBuCbMAAHRLmAUAoFvCLAAA3RJmAQDoljALAEC3hFkAALolzAIA0C1hFgCAbgmzAAB0S5gFAKBbwiwAAN0SZgEA6JYwCwBAt4RZAAC6JcwCANAtYRYAgG4JswAAdEuYBQCgW8IsAADdEmYBAOjW2HIPYD5r165d7iEAK5TPBwBWfJjdvHnzcg8BWKF8PgDgMgMAALolzAIA0C1hFgCAbgmzAAB0S5gFAKBbwiwAAN0SZgEA6JYwCwBAt4RZAAC6JcwCANAtYRYAgG4JswAAdEuYBQCgW8IsAADdEmYBAOiWMAsAQLeEWQAAuiXMAgDQLWEWAIBuCbMAAHRLmAUAoFvCLAAA3RJmAQDoljALAEC3hFkAALolzAIA0C1hFgCAbgmzAAB0S5gFAKBbwiwAAN0SZgEA6JYwCwBAt4RZAAC6JcwCANAtYRYAgG4JswAAdEuYBQCgW8IsAADdEmYBAOiWMAsAQLeEWQAAuiXMAgDQLWEWAIBuCbMAAHRLmAUAoFvCLAAA3RJmAQDoljALAEC3hFkAALolzAIA0C1hFgCAbgmzAAB0a2y5B8CBtTP/lI/li8s9jOzM95JkRYxlLjvzTzlxuQcBADwswuwhZO3atcs9hBl7br89SXLiSSct80jmdmJW1joDABZPmD2EbN68ebmHAABwULlmFgCAbgmzAAB0S5gFAKBbwiwAAN0SZgEA6JYwCwBAt4RZAAC6JcwCANAtYRYAgG4JswAAdEuYBQCgW8IsAADdEmYBAOiWMAsAQLeEWQAAuiXMAgDQLWEWAIBuCbMAAHRLmAUAoFvCLAAA3RJmAQDoljALAEC3hFkAALolzAIA0C1hFgCAblVrbeGVq76VZPsCqz82yZ37MygOCbb/4cu2P3zZ9ocv2/7wtVTbfnVr7YcXUnFRYXYxquqG1tpZS9I4K57tf/iy7Q9ftv3hy7Y/fK2Ebe8yAwAAuiXMAgDQraUMs5ctYdusfLb/4cu2P3zZ9ocv2/7wtezbfsmumQUAgKXmMgMAALq1JGG2qjZU1ReqaqKqLlyKPlh6VXVqVX2yqj5bVbdW1XlD+fFVdXVVfWn4edxQXlX1tmG7/0NVPX2krV8d6n+pqn51pPzHq+rmYZm3VVUd/Jkym6o6sqr+rqo+PLx+fFVtHbbV+6vqkUP5UcPrieH9NSNtvGEo/0JVrR8p9xmxglXVD1XVB6rq81X1uar6Scf94aGq/v3weX9LVb23qlY59g9NVfVHVfXNqrplpGzJj/O5+nhYWmsH9JHkyCSTSU5P8sgkf5/kSQe6H4+lfyT5kSRPH54/OskXkzwpySVJLhzKL0zyluH5C5J8LEkleWaSrUP58Um2DT+PG54fN7z3N0PdGpZ9/nLP22Nm+/9GksuTfHh4/WdJXjI8vzTJvxme/9sklw7PX5Lk/cPzJw3H/1FJHj98LhzpM2LlP5L8cZJXDs8fmeSHHPeH/iPJyUm+nOTo4fWfJXmZY//QfCR5VpKnJ7llpGzJj/O5+ng4j6U4M/uMJBOttW2ttXuTvC/JOUvQD0ustfaN1tpNw/O7k3wuUx9252TqH7sMPzcOz89J8u425fokP1RVP5JkfZKrW2s7W2vfTnJ1kg3De/+stXZ9m9qr3z3SFsuoqk5J8vNJ/nB4XUmek+QDQ5W9t/v0/vCBJD831D8nyftaa7tba19OMpGpzwefEStYVT0mU//IvTNJWmv3ttZ2xXF/uBhLcnRVjSU5Jsk34tg/JLXWrkmyc6/ig3Gcz9XHfluKMHtykq+NvP76UEbHhq+PfizJ1iSPa619Y3jrH5M8bng+17Z/qPKvz1LO8vvdJONJHhhen5BkV2ttz/B6dFvNbN/h/e8M9Re7P7AyPD7Jt5K8a7jM5A+r6lFx3B/yWmu3JfkfSb6aqRD7nSQ3xrF/ODkYx/lcfew3N4Axr6o6NsmfJzm/tXbX6HvD/7j8SoxDSFX9QpJvttZuXO6xsCzGMvXV4/9srf1Yku9m6qvAGY77Q9Nw7eI5mfoPzUlJHpVkw7IOimVzMI7zA9XHUoTZ25KcOvL6lKGMDlXVIzIVZP+0tfbBofiO4SuEDD+/OZTPte0fqvyUWcpZXv88yYuq6iuZ+hrwOUl+L1NfK40NdUa31cz2Hd5/TJIdWfz+wMrw9SRfb61tHV5/IFPh1nF/6Htuki+31r7VWrsvyQcz9Xng2D98HIzjfK4+9ttShNm/TfKE4e7HR2bqovAPLUE/LLHh2qd3Jvlca+13Rt76UJLpOxZ/NclfjpT/ynDX4zOTfGf4KuHKJGdX1XHD//zPTnLl8N5dVfXMoa9fGWmLZdJae0Nr7ZTW2ppMHb+faK29NMknk/ziUG3v7T69P/ziUL8N5S8Z7nh+fJInZOqGAJ8RK1hr7R+TfK2qzhiKfi7JZ+O4Pxx8Nckzq+qYYdtMb3vH/uHjYBznc/Wx/w7EHXF7PzJ119sXM3XX4huXog+PpX8k+elMnf7/hySfGR4vyNQ1Uf83yZeSfDzJ8UP9SvL7w3a/OclZI229IlM3AUwkeflI+VlJbhmWeUeGP+ThsTIeSX42P/htBqdn6h+kiST/J8lRQ/mq4fXE8P7pI8u/cdi2X8jIHes+I1b2I8nTktwwHPt/kam7lB33h8EjyZuSfH7YPn+Sqd9I4Ng/BB9J3pupa6Pvy9Q3Mr92MI7zufp4OA9/AQwAgG65AQwAgG4JswAAdEuYBQCgW8IsAADdEmYBAOiWMAuwglXVxqp60nKPA2ClEmYBVraNSYRZgDkIswBzqKq/qKobq+rWqnr1UHZPVb11KPt4VT2jqj5VVduq6kVDnVVV9a6qurmq/q6qnj2Uv6yq3jHS/oer6mdH2n1zVf19VV1fVY+rqp9K8qIkb62qz1TV2oO+EgBWOGEWYG6vaK39eKb+ks1rq+qEJI/K1J/tfHKSu5P8lyTPS3Jukv80LPfrSVpr7UeT/HKSP66qVfP09agk17fWnprkmiSvaq1dm6k//XhBa+1prbXJAzw/gO6NLfcAAFaw11bVucPzUzP1N+bvTXLFUHZzkt2ttfuq6uYka4byn07y9iRprX2+qrYneeI8fd2b5MPD8xszFZABmIcwCzCL4ev/5yb5ydba96rqU5n6W/T3tR/8HfAHkuxOktbaA1U132fqnjz4G7HRs7Wj7d4fn88AC+IyA4DZPSbJt4cge2aSZy5i2b9K8tIkqaonJjktyReSfCXJ06rqiKo6NckzFtDW3UkevZiBAxxOhFmA2V2RZKyqPpfkvye5fhHL/kGSI4ZLD96f5GWttd1J/l+SLyf5bJK3JblpAW29L8kFw41kbgAD2Ev94FstAADoizOzAAB0S5gFAKBbwiwAAN0SZgEA6JYwCwBAt4RZAAC6JcwCANAtYRYAgG79f05hcK2DGAO/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of cost\n",
    "print(data.amount.describe())\n",
    "f= plt.figure(figsize=(15,5))\n",
    "data.amount.hist(bins=1000)\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.title('box plot cost')\n",
    "sns.boxplot(x='amount', data=data , orient='h', palette = 'magma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Correlation\n",
    "\n",
    "From below correlation heatmap , it can be observed that symptom 5 exhibits the highest correlation with cost of care, followed by age, medical history 1, medical history 6, symptom 1 to 4, and weight. As BMI is a calculated feature, it is excluded here to avoid influence of confounding variables, although displayed in the heatmap.\n",
    "\n",
    "Categorical variables are transformed using one-hot encoding. It can be observed that gender male and female has the same absolute correlation value, which implies the cost of care does not differ between female patients and male patients.\n",
    "\n",
    "In terms of race, Malaysian has relatively higher correlation (0.386) than other races, followed by Indian (0.06). As the majority of Singapore population are Chinese, this factor may be confounding with residence status, which will be further validated. Indeed, foreigners show higher correlation (0.436) than other residence status, followed by permanent residence, which is in accordance with the bill policy in Singapore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a182b3f98>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 10))\n",
    "corr = data.corr()\n",
    "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=\"YlGnBu\",\n",
    "            square=True, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3d20d550>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# encode categorical variables\n",
    "cols_to_encode = data.columns[3:6]\n",
    "X1 = pd.get_dummies(data[cols_to_encode], drop_first=False)\n",
    "X = pd.concat([X1, data['amount']], axis=1)\n",
    "f, ax = plt.subplots(figsize=(5, 5))\n",
    "corr = X.corr()\n",
    "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=\"YlGnBu\",\n",
    "            square=True, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>symptom_5</td>\n",
       "      <td>0.516790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>resident_status_frn</td>\n",
       "      <td>0.435734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>race_my</td>\n",
       "      <td>0.385877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>age</td>\n",
       "      <td>0.325104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>medical_history_1</td>\n",
       "      <td>0.226518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>symptom_3</td>\n",
       "      <td>0.183988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>symptom_2</td>\n",
       "      <td>0.157819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>weight</td>\n",
       "      <td>0.157511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>medical_history_6</td>\n",
       "      <td>0.141640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>symptom_4</td>\n",
       "      <td>0.130236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>symptom_1</td>\n",
       "      <td>0.128317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>resident_status_pr</td>\n",
       "      <td>0.104483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>race_in</td>\n",
       "      <td>0.060258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gender_m</td>\n",
       "      <td>0.057739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>medical_history_7</td>\n",
       "      <td>0.038951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>medical_history_5</td>\n",
       "      <td>0.036783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>medical_history_2</td>\n",
       "      <td>0.033585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>preop_medication_2</td>\n",
       "      <td>0.032263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>height</td>\n",
       "      <td>0.026057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>preop_medication_6</td>\n",
       "      <td>0.021606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>preop_medication_1</td>\n",
       "      <td>0.016112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>preop_medication_4</td>\n",
       "      <td>0.015196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>medical_history_3</td>\n",
       "      <td>0.011539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>preop_medication_3</td>\n",
       "      <td>0.007839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>preop_medication_5</td>\n",
       "      <td>0.000453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>lab_result_3</td>\n",
       "      <td>0.000092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>lab_result_2</td>\n",
       "      <td>-0.005537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>lab_result_1</td>\n",
       "      <td>-0.006518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>medical_history_4</td>\n",
       "      <td>-0.009356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>race_other</td>\n",
       "      <td>-0.012645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>gender_f</td>\n",
       "      <td>-0.057739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>resident_status_ctz</td>\n",
       "      <td>-0.325732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>race_cn</td>\n",
       "      <td>-0.357536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0         1\n",
       "0             symptom_5  0.516790\n",
       "1   resident_status_frn  0.435734\n",
       "2               race_my  0.385877\n",
       "3                   age  0.325104\n",
       "4     medical_history_1  0.226518\n",
       "5             symptom_3  0.183988\n",
       "6             symptom_2  0.157819\n",
       "7                weight  0.157511\n",
       "8     medical_history_6  0.141640\n",
       "9             symptom_4  0.130236\n",
       "10            symptom_1  0.128317\n",
       "11   resident_status_pr  0.104483\n",
       "12              race_in  0.060258\n",
       "13             gender_m  0.057739\n",
       "14    medical_history_7  0.038951\n",
       "15    medical_history_5  0.036783\n",
       "16    medical_history_2  0.033585\n",
       "17   preop_medication_2  0.032263\n",
       "18               height  0.026057\n",
       "19   preop_medication_6  0.021606\n",
       "20   preop_medication_1  0.016112\n",
       "21   preop_medication_4  0.015196\n",
       "22    medical_history_3  0.011539\n",
       "23   preop_medication_3  0.007839\n",
       "24   preop_medication_5  0.000453\n",
       "25         lab_result_3  0.000092\n",
       "26         lab_result_2 -0.005537\n",
       "27         lab_result_1 -0.006518\n",
       "28    medical_history_4 -0.009356\n",
       "29           race_other -0.012645\n",
       "30             gender_f -0.057739\n",
       "31  resident_status_ctz -0.325732\n",
       "32              race_cn -0.357536"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# putting everything together\n",
    "data_cor = pd.concat([X1, data['age'], data.iloc[:, 9:-2], data['amount']], axis=1)\n",
    "corr_ = []\n",
    "for i in data_cor.columns[:-1]:\n",
    "    corr_.append([i, data_cor[[i,'amount']].corr()['amount'][i]])\n",
    "pd.DataFrame(corr_).sort_values(by=1, ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3af84e48>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# putting everything together\n",
    "data_cor = pd.concat([data['amount'], X1, data['age'], data.iloc[:, 9:-2]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(10, 10))\n",
    "corr_all = data_cor.corr()\n",
    "sns.heatmap(corr_all, mask=np.zeros_like(corr_all, dtype=np.bool), cmap=\"YlGnBu\",\n",
    "            square=True, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age\n",
    "\n",
    "Age feature displays bimodal distribution, which could imply there are two underlying population of patients in this sample whose average age has observable difference. This is further confirmed using KMeans clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f916630>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f9a6780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.age.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "0    [[AxesSubplot(0.125,0.125;0.775x0.755)]]\n",
       "1    [[AxesSubplot(0.125,0.125;0.775x0.755)]]\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120480c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1204c6978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "km = KMeans(n_clusters=2)\n",
    "y_kmeans = km.fit_predict(data.age.values.reshape(-1,1))\n",
    "age_profile = pd.DataFrame({'age': data.age, 'cluster': y_kmeans})\n",
    "age_profile.groupby('cluster').hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a384bf4a8>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= plt.figure(figsize=(20,5))\n",
    "\n",
    "ax=f.add_subplot(121)\n",
    "sns.distplot(data[(data.age<60)]['amount'],color='b',ax=ax)\n",
    "ax.set_title('Distribution of cost for patients below 60 yo')\n",
    "\n",
    "ax=f.add_subplot(122)\n",
    "sns.distplot(data[(data.age >= 60)]['amount'],color='b',ax=ax)\n",
    "ax.set_title('Distribution of cost for patients above 60 yo')\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('box plot cost by age group')\n",
    "data_age = copy.deepcopy(data)\n",
    "data_age['age_group'] = 0\n",
    "data_age.loc[data_age.age >= 60, 'age_group'] = 1\n",
    "sns.boxplot(y='age_group', x='amount', data=data_age , orient='h', palette = 'magma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "except the tail of > 60 yo group is higher, no significant difference is observed in the two age groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Medical Histories\n",
    "- Patients with medical history 1 and 6 on average have higher cost than those without.\n",
    "- Patients with medical history 6 on average have higher cost than those without, but the difference is smaller than the medical history 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### medical history 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a17df5400>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= plt.figure(figsize=(20,5))\n",
    "\n",
    "ax=f.add_subplot(121)\n",
    "sns.distplot(data[(data.medical_history_1 == 1)]['amount'],color='r',ax=ax)\n",
    "ax.set_title('Distribution of cost for patients with medical history 1')\n",
    "\n",
    "ax=f.add_subplot(122)\n",
    "sns.distplot(data[(data.medical_history_1 == 0)]['amount'],color='b',ax=ax)\n",
    "ax.set_title('Distribution of cost for patients without medical history 1')\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('box plot cost by medical history 1')\n",
    "sns.boxplot(y='medical_history_1', x='amount', data=data , orient='h', palette = 'magma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medical History 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1864c320>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= plt.figure(figsize=(20,5))\n",
    "\n",
    "ax=f.add_subplot(121)\n",
    "sns.distplot(data[(data.medical_history_6 == 1)]['amount'],color='r',ax=ax)\n",
    "ax.set_title('Distribution of cost for patients with medical history 6')\n",
    "\n",
    "ax=f.add_subplot(122)\n",
    "sns.distplot(data[(data.medical_history_6 == 0)]['amount'],color='b',ax=ax)\n",
    "ax.set_title('Distribution of cost for patients without medical history 6')\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('box plot cost by medical history 6')\n",
    "sns.boxplot(y='medical_history_6', x='amount', data=data , orient='h', palette = 'magma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Symptoms\n",
    "- Patients with symptoms on average have higher cost than those without, especially for symptom 5.\n",
    "- For patients with symptom 5, 25% of cost is higher than 75% of those without.\n",
    "\n",
    "Following patterns can be observed from the table in below section:\n",
    "1. For symptom 1 to 4, as the number of symptoms increases, the average cost of care increases. This can be intepreted as when patients are diagnosed with one or more symptoms in the group of symptoms 1 to 4, the more symptoms a patients exhibits, the higher the cost of care tends to be, which could suggest that more symptoms imply higher severity and complexity of a certain type of disease.\n",
    "2. The influence of symptoms on the increase of average cost follow the sequence of 4 < 1 < 2 < 3 < 5, which could suggest the severity of disease and complexity of treatment correspond to diagnosis from the symptoms.\n",
    "3. Symptom 5 has larger influence on average cost than collective influence of symptoms 2 to 4, but smaller than the collective influence of symptom . This can be observed from the difference between mean cost of patients with only symptom 5 (18665.293012), patients with symptoms 2 to 4 (18441.017145), and patients with symptoms 1 to 4 (20788.698007).\n",
    "\n",
    "Implication to model design:\n",
    "Introduce an engineered feature to represent sum of occurence for symtom 1 to 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Symptom 1 as an example for symtom 1 to 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a181f0588>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= plt.figure(figsize=(20,5))\n",
    "\n",
    "ax=f.add_subplot(121)\n",
    "sns.distplot(data[(data.symptom_1 == 1)]['amount'],color='r',ax=ax)\n",
    "ax.set_title('Distribution of cost for patients with symptom_1')\n",
    "\n",
    "ax=f.add_subplot(122)\n",
    "sns.distplot(data[(data.symptom_1 == 0)]['amount'],color='b',ax=ax)\n",
    "ax.set_title('Distribution of cost for patients without symptom_1')\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('box plot cost by symptom_1')\n",
    "sns.boxplot(y='symptom_1', x='amount', data=data , orient='h', palette = 'magma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Symptom 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a181e1080>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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l6quvHlfTQQcdlLe+9a155StfOen2BxxwQL7//e+PWzZVO1dffXWOOOKIKedutJ7nPve52bZt29h8JtllTKPjOvzww8f10T83M+1zJiabp7lod38x0/kZxDxO12b/6xOPpYW8H72nB8O8Ajz8VdWG1tqM/pO6eMC1PD7J5r7nX0vytAH3OXSbN2/OAw88MBYYMXgjIyPZll3/w8XsfS8PZGRkxPt3gdldSJRMHmgk2SU42rFjR9avX5/WWnbu3LnL+tu2bUtVjbU5+njnzp258MILx7bpf2379u2Z7JcaF1xwQS6//PJJ61q/fv1YWzt37tylpp07d+a1r33tlOOdGBLtrp3169fn/PPPn7Kt0Xr652qq+Rwd10knnTSuj/65mWmfMzHZPM1Fu/uLmc7PIOZxujb7X9+xY8fYMbLQ96P39GCYV4CFZdBnFL0gyXNaa+d2z1+a5GmttVf1rfOKJK9IkqOPPvrkO+64Y2D1zJfTTjstDzzwQJYvXz7sUhaMkZGRbLt3e07+/n8edikP+zOKNhxwbQ581BLv3wVmw4YNc9bWIYcckiS5//7756zNqWzcuHHS5c9+9rPH9T9XNU3WziGHHJLPfvazu91uYj0z6Wd368+kz5mYbJ7mot39xUznZxDzOF2bu3tPLeT96D09GOYV4OFvXzqj6OtJlvY9P6pbNqa1dkmSS5LepWcDrmdeLF3aG7JLd+bP6KVn7L1H5JF54nKXni00J5988qRnAM3WokWLcsYZZ6S1liuuuGLSM2eqKq21VFWqKjt37sySJUty9NFH56tf/erY2USjr42uP9GyZcumrOP0008f63/JkiW71LRkyZIcfPDBueeee2Y8tqnaOeOMM6bd9vTTT88HP/jBGc3xsmXLctJJJ43ro39uZtrnTEw2T/zATOdnEPM4XZv9ry9atCittbTWFvx+9J4eDPMKsLAM+q+e/XOSJ1bVsVV1YJJfTXLVgPsEYJaOO+643b6+ZMmSLF686+8WJi5bvHhxVq9endWrV2fRol0/Yg488MCx+/P0t7lo0aKsXbt2bJv+15YsWTK2Tb+LL754ynr7+1+0aNEuNS1atChvfvObp9z+gAMO2GXZVO2sXr16ynb66+mfq6nGNDquiX30z81M+5yJPRnLQjLT+RnEPE7XZv/rixcvHns/LfT96D09GOYVYGEZaFDUWtuR5FVJrknypSTvb63dNMg+AZi9Rz/60eNCmonOPvvsnH322eOWLVu2bJdlZ511Vo444ogceeSROfPMM1NV4878Oeuss7JixYpU1bjHK1asyPLly8e2mbjeWWedtUvfU93IOsm4/lesWLFLTStWrMjTn/70cQHZYYcdNvb4l3/5l8deO+yww3bbzkxu6HrkkUdmxYoV4+Zz4pj6xzWxj/65mWmfM7EnY1lIZjo/g5jH6drsf33isbSQ96P39GCYV4CFZdCXnqW19tEkHx10PwDsneOOOy633XZb3vjGN+Y973lPbr/99iTJsccem9WrV6e1lhtvvDG33357qioXX3xxDj/88GzatGnscoT+3zKvXr06t956a84777ysXbt2bFlrLbfeeusuj/u3mWy9TZs25cEHH8w3vvGN3Z5NNLH/yWoaXbZ27dqcc845Ofroo/Oa17wmv/d7v5ejjz46q1evztlnn53Vq1fnLW95Sy655JLdtjMTq1evzqZNm8bNw+jc7dixY5dxTexjT/qcaV2DaHd/MdP5GcQ8TtfmVMfLQuc9PRjmFWDhGOjNrGfrlFNOaTfccMOwy9hro38tyj1e5s/oPYr2hRtIP9xvZn3TATfkiae4R9FC4/sWAADsv2ZzM+tB36MIAAAAgIcJQREAAAAASQRFAAAAAHQERQAAAAAkERQBAAAA0BEUAQAAAJBEUAQAAABAR1AEAAAAQBJBEQAAAAAdQREAAAAASQRFAAAAAHQERQAAAAAkERQBAAAA0BEUAQAAAJBEUAQAAABAR1AEAAAAQBJBEQAAAAAdQREAAAAASQRFAAAAAHQERQAAAAAkERQBAAAA0Fk87AL2R8uXLx92CQCz4vsWAACQCIoGYs2aNcMuAWBWfN8CAAASl54BAAAA0BEUAQAAAJBEUAQAAABAR1AEAAAAQBJBEQAAAAAdQREAAAAASQRFAAAAAHQERQAAAAAkERQBAAAA0BEUAQAAAJBEUAQAAABAR1AEAAAAQBJBEQAAAACdaYOiqvqNvsdHVdU/VtV3qupzVXX8YMsDAAAAYL7M5IyiV/U9/pMklyX54STrkvz5IIoCAAAAYP7N9tKz41trl7TWdrbWPpxeYAQAAADAfmDxDNY5qqrenqSSHFlVS1pr27vXlgyuNAAAAADm00yCojV9j29IcmiSb1fVf0py1UCqAgAAAGDeTRsUtdYunWL5fyS5YPR5VZ3fWnvTHNYGAAAAwDya7T2KdueFc9gWAAAAAPNsLoOimsO2AAAAAJhncxkUtTlsCwAAAIB55owiAAAAAJLMbVD0gTlsCwAAAIB5Nu1fPRtVVccm+e0kx/Rv11o7s/t68VwXB7Nxf+7NTQfcMOwycn/uTZJ9opY9MVo/AAAAC8+Mg6IkVyR5V5L/nWTnYMqBPbN8+fJhlzBm8+bNSZKlS5cOuZI9ty/NJwAAAPNnNkHR91prbx9YJbAX1qxZM+wSAAAA4GFvNkHR26rqD5P8Q5KHRhe21r4w51UBAAAAMO9mExSdmOSlSX42P7j0rHXPAQAAAHiYm01Q9MIkx7XWtg2qGAAAAACGZ9Es1v23JD80qEIAAAAAGK7ZnFH0Q0k2VdU/Z/w9is6c86oAAAAAmHezCYr+cGBVAAAAADB0Mw6KWmufrqrHJvnpbtHnW2t3DaYsAAAAAObbjO9RVFUvSvL59G5q/aIk11fVCwZVGAAAAADzazaXnl2Y5KdHzyKqqiOTfCLJ5YMoDAAAAID5NZu/erZowqVm35rl9gAAAADsw2ZzRtHHquqaJO/rnv9Kkr+f+5IAAAAAGIbZ3Mx6TVU9P8mzu0WXtNY+PJiyAAAAAJhvMw6KqurNrbXXJvnQJMsAAAAAeJibzT2GfmGSZb80V4UAAAAAMFzTnlFUVa9M8l+THFdV/zq6OMmhSf7PAGsDAAAAYB7N5NKzv03vptVvSvL7fcvvba3dPZCqAAAAAJh30wZFrbXvJvlukpVVdVJ6N7Nu6Z1NJCgCAAAA2E/M+B5FVfW6JJcmeUySI5K8p6r+YFCFAQAAADC/ZvxXz5L8WpInt9a+lyRV9UdJvpjkjYMoDAAAAID5NZu/evaNJI/oe35Qkq/PbTkAAAAADMtszij6bpKbqurj6d2j6BeSfL6q3p4krbVXD6A+AAAAAObJbIKiD3f/Rv3T3JYCAAAAwDDNOChqrV06yEIAAAAAGK7Z/NWz51bVxqq6u6ruqap7q+qeQRYHAAAAwPyZzaVnb03y/CQ3ttbagOoB9lHr1q3LyMjIsMvYa5s3b06SLF26dMiVDM/y5cuzZs2aYZcBAADsg2YTFG1O8m9CIliYRkZG8qV/+WKOPfTQYZeyV+67974kyfe2PTTkSobjK/fdN+wSAACAfdhsgqLzkny0qj6dZOx/WK21P5nzqoB90rGHHpo3nPSUYZexV173hY1J8rAfx54aHT8AAMBkZhMUrU1yX5JHJDlwMOUAAAAAMCyzCYoe11r7yYFVAgAAAMBQzfivnqV32dkvDqwSAAAAAIZqNkHRK5N8rKoerKp7qureqrpnUIUBAAAAML9mfOlZa+1RgywEAAAAgOGa8RlFVfXBqjq9qmZzFhIAAAAADxOzCX3+PMlLkny5qv6oqpYPqCYAAAAAhmDGQVFr7ROttZckOSnJ7Uk+UVWfq6pfr6olgyoQAAAAgPkxq8vIquoxSV6e5NwkG5O8Lb3g6ONzXhkAAAAA82rGN7Ouqg8nWZ7kvUme11q7s3vpsqq6YRDFAQAAADB/ZnNG0fuSPL219qYk51TVh6rqpCRprZ0ykOoAAAAAmDezCYr+oLV2T1U9O8nPJ3lXeje4BgAAAGA/MJug6Pvd1zOSXNJa+0iSA+e+JAAAAACGYTZB0der6i+S/EqSj1bVQbPcHgAAAIB92GyCnhcluSbJaa217yT54SRrBlIVAAAAAPNuxn/1rLX2QJIP9T2/M8mdU28BAAAAwMOJS8cAAAAASCIoAgAAAKAjKAIAAAAgiaAIAAAAgI6gCAAAAIAkgiIAAAAAOoIiAAAAAJIIigAAAADoCIoAAAAASCIoYh+wbt26rFu3bthlALCP8LkAADA8i4ddAIyMjAy7BAD2IT4XAACGxxlFAAAAACQRFAEAAADQERQBAAAAkERQBAAAAEBHUAQAAABAEkERAAAAAB1BEQAAAABJBEUAAAAAdARFAAAAACQRFAEAAADQERQBAAAAkERQBAAAAEBHUAQAAABAEkERAAAAAB1BEQAAAABJBEUAAAAAdARFAAAAACQRFAEAAADQERQBAAAAkERQBAAAAEBHUAQAAABAEkERAAAAAJ2BBkVV9e6ququq/m2Q/QAA+68tW7bknHPOydatW8ctW7lyZZ75zGdm5cqV2bp1azZt2pRTTz01119/fVatWpWVK1eO/Vu1atW47adqd3f9j4yMjH1dtWpVVq1aNbbsuuuuy6mnnpqbb755yrZH67vmmmvGrTtx/f7Ho9v0r9u//mi/H/jAB3LyySfn85///IzncCbrbdmyZZexTtfGdKYa057U3L/P56K23dXRv/8n62umczybPgcxHiZ/n+9r872nx+zebjtXdc319oPaR8Maz1xtz9zY3X5YqPto0GcU/WWS5wy4DwBgP7Z+/fps3Lgx69evH7ds06ZNefDBB7Np06asX78+F154Ye67776cd955ufHGG7Np06axfzfeeOO47adqd3f9X3jhhWNfb7zxxtx4441jy1772tfmvvvuywUXXDBl26P1ve51rxu37sT1+x+PbtO/bv/6o/2+6U1vys6dO7NmzZoZz+FM1lu/fv0uY52ujelMNaY9qbl/n89Fbburo3//T9bXTOd4Nn0OYjxM/j7f1+Z7T4/Zvd12ruqa6+0HtY+GNZ652p65sbv9sFD30UCDotbatUnuHmQfAMD+a8uWLbnqqqvSWsuVV145dsbNFVdcMW69D33oQ7ntttuSJPfcc8+kbV1xxRXjziCY2O50/d96661jX0eNLhvt89Zbb83111+/S9ubNm0aq2/79u1j6958883j+rjiiity5ZVXprWWD3/4w2PbjK47sabRfltrY2OfeFbRnoz1yiuvzMjISK688spdxrq7NqbTPw/9Y5qulsn662/rnnvu2evapqujf/9P7GumczzbPud6PEz+Pt/X5ntPj9nR7497uu1c1TXX2w/qmBjWeOZqe+bG7vbDQt5Hi4ddAGzevDkPPPBAzj333GGXwm6MjIzkwG3bhl0Ge+nOBx7MtpERxxv7tJGRkTzykY9M0vtN3s6dO5MkO3fuzPr169NaGwtbRu3YsWPadnfs2JH169fn/PPPn7Td888/f5dt+tebqfPOO2+Xtm+44YZJ173gggty0kknja2/Y8eOsdBn4hgvuOCCXH755dPWtGbNmnz605+edAwzHevOnTtz4YUX7lLDdG1M58ILL5x0TNPVMll/E9va29oms7u57u9rpnM82z7nejxM/j7f1+Z7T4/Z0e+Pe7rtdOPe2/fmnm4/qGNiWOOZq+2ZG7vbDwt5H9XoDyMD66DqmCRXt9Z+corXX5HkFUly9NFHn3zHHXcMtB72PaeddloeeOCBLF++fNilsBujQdE7n/3MYZeyV173hY1Jkjec9JQhVzIc5372c9l24IGON/Zpo0HRNddck2c/+9m5//77x1475JBDkmTcstk45JBD8tnPfnbSdj/72c/usv7E9fa0z921Md3r/TZu3DijmjZu3Dj2eBBjnaqN6TzlKbt+7+2vdapaJutvsrb2prbJTDcns30/7UmfczkeZr5Ph2lPj9nJvj/OZtvpxr2378093X5Qx8SwxjNX2zM3drcf9rd9VFUbWmunzGTdoZ9R1Fq7JMklSXLKKacMNrVin7R06dIkyTvf+c4hV8LunHvuufnerbd0mk44AAANgUlEQVQMuwz20o8+8uA8YtkTHG/s0/rPeDv99NNzxRVXZPv27VmyZEnOOOOMtNbygQ98YNbtLlq0KGecccaU7U6mf72ZOuyww/Lggw+Oa/uGG24Yu0yq37Jly3LSSSeN9bFo0aK01jLZL/KWLVs2o5oOO+ywKccw07EuWbIkRx99dG677bZdatldG9M57rjjxs3D6Jimq2Wy/ia2tbe1TVfHRP19zXSOZ9vnXI+Hyd/nX/3qV/ep+d7TY3b0++OebjtXdc319oM6JoY1nrnanrmxu/2wkPfRoG9mDQCwx1avXp1Fi3o/rixatCirV6/O6tWrs2TJknHrLV48/e++Fi9enNWrV0/Z7nT9z9Rb3vKWXdpeu3btpOtefPHF4/pYvHjx2NgmjvHiiy+eUU3r1q2bcgwzHeuiRYuydu3aXWqYro3pTJyH0TFNV8tk/U02p3tT23R17K6vmc7xbPuc6/Ew+ft8X5vvPT1mR78/7um2c1XXXG8/qGNiWOOZq+2ZG7vbDwt5Hw00KKqq9yX5/5Isr6qvVdU5g+wPANi/HHnkkTnzzDNTVVmxYkWOOOKIHHnkkTnrrLPGrff85z8/xx13XJJdz6gZddZZZ+WII46Yst3p+l+2bNnY11Gjy0b7XLZsWZ72tKft0vYJJ5wwVt9o+LJs2bIcf/zx4/o466yzsmLFilRVzj777LFtRtedWNNov1U1NvanPvWp087hTOZ6+fLlWbFixS5j3V0b0+mfh/4xTVfLZP31t3XYYYftdW3T1dG//yf2NdM5nm2fcz0eJn+f72vzvafH7Oj3xz3ddq7qmuvtB3VMDGs8c7U9c2N3+2Eh76NB/9Wzla21H22tLWmtHdVae9cg+wMA9j+rV6/OU57ylF1+y3fCCSfk4IMPzgknnDB21s6hhx6at7zlLTnxxBNzwgknjP078cQTd/lN4GTt7q7/tWvXjn098cQTc+KJJ44te/Ob35xDDz103Fk/E9sere8Nb3jDuHUnrt//eHSbiWfejK4z2u/555+fRYsW7XI20Z6Otf9MmYlj3dvfqE41pj2puX+fz0Vtu6ujf/9P1tdM53g2fS6k317Pp8ne5/vafO/pMbu3285VXXO9/aD20bDGM1fbMzd2tx8W6j4a+M2sZ+OUU05pU/1VEPZfo/eicM+UfdvoPYoe7jeBXug3s37dFza6RxH7PJ8LAABzazY3s3aPIgAAAACSCIoAAAAA6AiKAAAAAEgiKAIAAACgIygCAAAAIImgCAAAAICOoAgAAACAJIIiAAAAADqCIgAAAACSCIoAAAAA6AiKAAAAAEgiKAIAAACgIygCAAAAIImgCAAAAICOoAgAAACAJIIiAAAAADqCIgAAAACSCIoAAAAA6AiKAAAAAEgiKAIAAACgIygCAAAAIEmyeNgFwPLly4ddAgD7EJ8LAADDIyhi6NasWTPsEgDYh/hcAAAYHpeeAQAAAJBEUAQAAABAR1AEAAAAQBJBEQAAAAAdQREAAAAASQRFAAAAAHQERQAAAAAkERQBAAAA0BEUAQAAAJBEUAQAAABAR1AEAAAAQBJBEQAAAAAdQREAAAAASQRFAAAAAHQERQAAAAAkERQBAAAA0BEUAQAAAJBEUAQAAABAR1AEAAAAQBJBEQAAAAAdQREAAAAASQRFAAAAAHQERQAAAAAkERQBAAAA0Fk87AKAh4+v3HdfXveFjcMuY6985d77kuRhP4499ZX77suPD7sIAABgnyUoAmZk+fLlwy5hThy6eXOS5BFLlw65kuH48ew/+xIAAJh7giJgRtasWTPsEgAAABgw9ygCAAAAIImgCAAAAICOoAgAAACAJIIiAAAAADqCIgAAAACSCIoAAAAA6AiKAAAAAEgiKAIAAACgIygCAAAAIImgCAAAAICOoAgAAACAJIIiAAAAADrVWht2DWOqakuSO+awySOSbJ3D9mAhczzB3HE8wdxxPMHccCzB3NkXj6cfa60dOZMV96mgaK5V1Q2ttVOGXQfsDxxPMHccTzB3HE8wNxxLMHce7seTS88AAAAASCIoAgAAAKCzvwdFlwy7ANiPOJ5g7jieYO44nmBuOJZg7jysj6f9+h5FAAAAAMzc/n5GEQAAAAAztN8GRVX1nKoaqapbqur3h10P7AuqamlVfaqq/r2qbqqq13TLf7iqPl5VX+6+Ht4tr6p6e3cc/WtVndTX1su69b9cVS/rW35yVd3YbfP2qqr5HynMn6o6oKo2VtXV3fNjq+r67hi4rKoO7JYf1D2/pXv9mL42zu+Wj1TVaX3LfZaxYFTVD1XV5VW1qaq+VFXP8PkEs1dV/637Oe/fqup9VfUIn00wc1X17qq6q6r+rW/ZwD+PpupjGPbLoKiqDkjyv5L8UpKfSLKyqn5iuFXBPmFHkt9rrf1Ekqcn+a3u2Pj9JP/YWntikn/snie9Y+iJ3b9XJPnzpPdNLMkfJnlakqcm+cO+b2R/nmR133bPmYdxwTC9JsmX+p6/Ocn/bK09Icm3k5zTLT8nybe75f+zWy/dMfirSZ6U3vHyZ1345LOMheZtST7WWjshyZPTO658PsEsVNXjk7w6ySmttZ9MckB6nzE+m2Dm/jK7fkbMx+fRVH3Mu/0yKEpvR9zSWruttbYtyd8lWTHkmmDoWmt3tta+0D2+N70fwh+f3vFxabfapUnO6h6vSPJXree6JD9UVT+a5LQkH2+t3d1a+3aSjyd5TvfaYa2161rvBmh/1dcW7Heq6qgkZyR5Z/e8kvxsksu7VSYeT6PH2eVJfq5bf0WSv2utPdRa+0qSW9L7HPNZxoJRVY9O8p+TvCtJWmvbWmvfic8n2BOLkxxcVYuTPDLJnfHZBDPWWrs2yd0TFs/H59FUfcy7/TUoenySzX3Pv9YtAzrdqcVPSXJ9kse21u7sXvqPJI/tHk91LO1u+dcmWQ77q7cmOS/Jzu75Y5J8p7W2o3vefwyMHTfd69/t1p/tcQb7o2OTbEnynupdyvnOqjokPp9gVlprX0/yP5J8Nb2A6LtJNsRnE+yt+fg8mqqPebe/BkXAblTVoUk+mOR3Wmv39L/WJdv+HCJMo6qem+Su1tqGYdcC+4HFSU5K8uettackuT8TTrn3+QTT6y5tWZFe+Pq4JIfEZZYwp+bj82jYn3n7a1D09SRL+54f1S2DBa+qlqQXEv1Na+1D3eJvdqdBpvt6V7d8qmNpd8uPmmQ57I+eleTMqro9vVPvfza9e6z8UHe6fzL+GBg7brrXH53kW5n9cQb7o68l+Vpr7fru+eXpBUc+n2B2fj7JV1prW1pr25N8KL3PK59NsHfm4/Noqj7m3f4aFP1zkid2d/c/ML0bsV015Jpg6Lprzt+V5EuttT/pe+mqJKN34n9Zkiv7lq/q7ub/9CTf7U6HvCbJL1bV4d1vrn4xyTXda/dU1dO7vlb1tQX7ldba+a21o1prx6T3OfPJ1tpLknwqyQu61SYeT6PH2Qu69Vu3/Fe7vzxzbHo3Nfx8fJaxgLTW/iPJ5qpa3i36uST/Hp9PMFtfTfL0qnpk914fPZZ8NsHemY/Po6n6mHeLp1/l4ae1tqOqXpXezjkgybtbazcNuSzYFzwryUuT3FhVX+yWXZDkj5K8v6rOSXJHkhd1r300yenp3cDwgSS/niSttbur6g3p/bCQJP+9tTZ6w7f/mt5fCjg4yd93/2AheW2Sv6uqNybZmO7mvN3X91bVLendIPFXk6S1dlNVvT+9H+R3JPmt1tr3k8RnGQvMbyf5m+4/n7el95mzKD6fYMZaa9dX1eVJvpDeZ8rGJJck+Uh8NsGMVNX7kvxMkiOq6mvp/fWy+fj/0lR9zLvqBcYAAAAALHT766VnAAAAAMySoAgAAACAJIIiAAAAADqCIgAAAACSCIoAAAAA6AiKAACGoKrOqqqfGHYdAAD9BEUAAMNxVhJBEQCwTxEUAQALTlVdUVUbquqmqnpFt+y+qlrXLftEVT21qv6pqm6rqjO7dR5RVe+pqhuramNV/V/d8pdX1Tv62r+6qn6mr921VfUvVXVdVT22qp6Z5Mwk66rqi1W1bN4nAQBgEoIiAGAh+o3W2slJTkny6qp6TJJDknyytfakJPcmeWOSX0hydpL/3m33W0laa+3EJCuTXFpVj5imr0OSXNdae3KSa5Osbq19LslVSda01n6qtXbrHI8PAGCPLB52AQAAQ/Dqqjq7e7w0yROTbEvysW7ZjUkeaq1tr6obkxzTLX92kj9Nktbapqq6I8nx0/S1LcnV3eMN6YVPAAD7JEERALCgdJeE/XySZ7TWHqiqf0ryiCTbW2utW21nkoeSpLW2s6qm+5lpR8afqd1/llF/u9+Pn78AgH2YS88AgIXm0Um+3YVEJyR5+iy2/UySlyRJVR2f5OgkI0luT/JTVbWoqpYmeeoM2ro3yaNmUzgAwKAJigCAheZjSRZX1ZeS/FGS62ax7Z8lWdRdjnZZkpe31h5K8n+SfCXJvyd5e5IvzKCtv0uyprsptptZAwD7hPrBmdAAAAAALGTOKAIAAAAgiaAIAAAAgI6gCAAAAIAkgiIAAAAAOoIiAAAAAJIIigAAAADoCIoAAAAASCIoAgAAAKDz/wMec5BK82XbwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= plt.figure(figsize=(20,5))\n",
    "\n",
    "ax=f.add_subplot(121)\n",
    "sns.distplot(data[(data.symptom_5 == 1)]['amount'],color='r',ax=ax)\n",
    "ax.set_title('Distribution of cost for patients with symptom_5')\n",
    "\n",
    "ax=f.add_subplot(122)\n",
    "sns.distplot(data[(data.symptom_5 == 0)]['amount'],color='b',ax=ax)\n",
    "ax.set_title('Distribution of cost for patients without symptom_5')\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('box plot cost by symptom_5')\n",
    "sns.boxplot(y='symptom_5', x='amount', data=data , orient='h', palette = 'magma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>symptom_1</th>\n",
       "      <th>symptom_2</th>\n",
       "      <th>symptom_3</th>\n",
       "      <th>symptom_4</th>\n",
       "      <th>symptom_5</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7477.144841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11125.033912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11142.964366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11472.253122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12056.785573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14072.651009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14440.360427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14635.249814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15349.265161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15858.324287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16430.023063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16531.566120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16628.756878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17884.478309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18441.017145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18665.293012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19650.333473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20458.712959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20591.903092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20788.698007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22027.130802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23746.989376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24464.754145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25291.653416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25385.994059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25616.972210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27206.902715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27521.893883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28379.359118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28497.744720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30698.923568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31267.764273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    symptom_1  symptom_2  symptom_3  symptom_4  symptom_5        amount\n",
       "0           0          0          0          0          0   7477.144841\n",
       "2           0          0          0          1          0  11125.033912\n",
       "16          1          0          0          0          0  11142.964366\n",
       "8           0          1          0          0          0  11472.253122\n",
       "4           0          0          1          0          0  12056.785573\n",
       "18          1          0          0          1          0  14072.651009\n",
       "24          1          1          0          0          0  14440.360427\n",
       "20          1          0          1          0          0  14635.249814\n",
       "10          0          1          0          1          0  15349.265161\n",
       "12          0          1          1          0          0  15858.324287\n",
       "6           0          0          1          1          0  16430.023063\n",
       "22          1          0          1          1          0  16531.566120\n",
       "28          1          1          1          0          0  16628.756878\n",
       "26          1          1          0          1          0  17884.478309\n",
       "14          0          1          1          1          0  18441.017145\n",
       "1           0          0          0          0          1  18665.293012\n",
       "17          1          0          0          0          1  19650.333473\n",
       "3           0          0          0          1          1  20458.712959\n",
       "9           0          1          0          0          1  20591.903092\n",
       "30          1          1          1          1          0  20788.698007\n",
       "5           0          0          1          0          1  22027.130802\n",
       "19          1          0          0          1          1  23746.989376\n",
       "25          1          1          0          0          1  24464.754145\n",
       "21          1          0          1          0          1  25291.653416\n",
       "7           0          0          1          1          1  25385.994059\n",
       "11          0          1          0          1          1  25616.972210\n",
       "13          0          1          1          0          1  27206.902715\n",
       "27          1          1          0          1          1  27521.893883\n",
       "15          0          1          1          1          1  28379.359118\n",
       "23          1          0          1          1          1  28497.744720\n",
       "29          1          1          1          0          1  30698.923568\n",
       "31          1          1          1          1          1  31267.764273"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(['symptom_1', 'symptom_2', 'symptom_3',\n",
    "              'symptom_4', 'symptom_5'], as_index=False).amount.mean().sort_values(by='amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resident Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a17ee02e8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= plt.figure(figsize=(20,5))\n",
    "\n",
    "ax=f.add_subplot(131)\n",
    "sns.distplot(data[(data.resident_status == 'frn')]['amount'],color='r',ax=ax)\n",
    "ax.set_title('Distribution of cost for Foreigner')\n",
    "\n",
    "ax=f.add_subplot(132)\n",
    "sns.distplot(data[(data.resident_status == 'pr')]['amount'],color='b',ax=ax)\n",
    "ax.set_title('Distribution of cost for PR')\n",
    "\n",
    "ax=f.add_subplot(133)\n",
    "sns.distplot(data[(data.resident_status == 'ctz')]['amount'],color='b',ax=ax)\n",
    "ax.set_title('Distribution of cost for citizen')\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('box plot cost by resident status')\n",
    "sns.boxplot(y='resident_status', x='amount', data=data , orient='h', palette = 'magma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a17df52b0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= plt.figure(figsize=(20,5))\n",
    "\n",
    "ax=f.add_subplot(131)\n",
    "sns.distplot(data[(data.race == 'my')]['amount'],color='r',ax=ax)\n",
    "ax.set_title('Distribution of cost for Malaysian')\n",
    "\n",
    "ax=f.add_subplot(132)\n",
    "sns.distplot(data[(data.race == 'cn')]['amount'],color='b',ax=ax)\n",
    "ax.set_title('Distribution of cost for Chinese')\n",
    "\n",
    "ax=f.add_subplot(133)\n",
    "sns.distplot(data[(data.race == 'in')]['amount'],color='b',ax=ax)\n",
    "ax.set_title('Distribution of cost for Indian')\n",
    "\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title('box plot cost by race')\n",
    "sns.boxplot(y='race', x='amount', data=data , orient='h', palette = 'magma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>no_of_symptoms</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no_of_symptoms</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.306429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>0.306429</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                no_of_symptoms    amount\n",
       "no_of_symptoms        1.000000  0.306429\n",
       "amount                0.306429  1.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['no_of_symptoms'] = data[[\n",
    "    'symptom_1',\n",
    "    'symptom_2',\n",
    "    'symptom_3',\n",
    "    'symptom_4']].sum(axis=1)\n",
    "data[['no_of_symptoms','amount']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>no_of_med_history</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no_of_med_history</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.255893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>0.255893</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   no_of_med_history    amount\n",
       "no_of_med_history           1.000000  0.255893\n",
       "amount                      0.255893  1.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['no_of_med_history'] = data[[\n",
    "    'medical_history_1',\n",
    "    'medical_history_6']].sum(axis=1)\n",
    "data[['no_of_med_history','amount']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one-hot encoding\n",
    "cols_to_encode = data.columns[3:6]\n",
    "X1 = pd.get_dummies(data[cols_to_encode], drop_first=False)\n",
    "X2 = data['age']\n",
    "X3 = data.iloc[:, 9:]\n",
    "X = pd.concat([X1, X2, X3], axis=1)\n",
    "y = data['amount']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Exploration\n",
    "\n",
    "Following models are selected for benchmarking performance using cross validation. Since the prediction target is continuous, regression methods are used and score computed as coefficient of determination R^2 of the prediction.\n",
    "\n",
    "Grid search is used to optimize hyperparameters.\n",
    "\n",
    "1. Linear Regression\n",
    "2. Decision Tree\n",
    "3. Support Vector Machine Regression\n",
    "4. Random Forest\n",
    "5. Stochastic Gradient Descent Regressor\n",
    "6. Keras Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, PowerTransformer\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression\n",
    "\n",
    "Linear regression is the simpliest model to start with and despite its simplicity, many times could yield good result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv r2: 0.926\n",
      "cv mse: -7699212.553\n"
     ]
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "\n",
    "lr = LinearRegression() # no grid search required\n",
    "print('cv r2: {:.3f}'.format(np.mean(cross_val_score(lr, X_trans, y, cv=10, scoring='r2'))))\n",
    "print('cv mse: {:.3f}'.format(np.mean(cross_val_score(lr, X_trans, y, cv=10, scoring='neg_mean_squared_error'))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear regression cv score: 0.981\n",
      "cv mse: 1484305.260\n"
     ]
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "pt = PowerTransformer()\n",
    "\n",
    "X_trans = scaler.fit_transform(X)\n",
    "X_trans = pt.fit_transform(X_trans)\n",
    "\n",
    "y_trans = pt.fit_transform(y.values.reshape(-1,1))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_trans, y_trans, test_size=0.20, random_state=99)\n",
    "\n",
    "# R2\n",
    "lr = LinearRegression()\n",
    "print('linear regression cv score: {:.3f}'.format(np.mean(cross_val_score(lr, X_trans, y_trans, cv=10))))\n",
    "\n",
    "# MSE\n",
    "lr.fit(X_train, y_train)\n",
    "y_pred = lr.predict(X_test)\n",
    "y_pred = pt.inverse_transform(y_pred.reshape(-1,1)).flatten().tolist()\n",
    "y_test = pt.inverse_transform(y_test).flatten().tolist()\n",
    "\n",
    "mse = mean_squared_error(y_pred, y_test)\n",
    "print('cv mse: {:.3f}'.format(mse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.860574</td>\n",
       "      <td>0.919529</td>\n",
       "      <td>{'ccp_alpha': 0, 'criterion': 'mse', 'splitter...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.860574</td>\n",
       "      <td>0.919529</td>\n",
       "      <td>{'ccp_alpha': 0.1, 'criterion': 'friedman_mse'...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.860574</td>\n",
       "      <td>0.919529</td>\n",
       "      <td>{'ccp_alpha': 0.1, 'criterion': 'mse', 'splitt...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.860574</td>\n",
       "      <td>0.919529</td>\n",
       "      <td>{'ccp_alpha': 0.05, 'criterion': 'mse', 'split...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.860574</td>\n",
       "      <td>0.919529</td>\n",
       "      <td>{'ccp_alpha': 0.05, 'criterion': 'friedman_mse...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_test_score  mean_train_score  \\\n",
       "0         0.860574          0.919529   \n",
       "1         0.860574          0.919529   \n",
       "2         0.860574          0.919529   \n",
       "3         0.860574          0.919529   \n",
       "4         0.860574          0.919529   \n",
       "\n",
       "                                              params  rank_test_score  \n",
       "0  {'ccp_alpha': 0, 'criterion': 'mse', 'splitter...                1  \n",
       "1  {'ccp_alpha': 0.1, 'criterion': 'friedman_mse'...                1  \n",
       "2  {'ccp_alpha': 0.1, 'criterion': 'mse', 'splitt...                1  \n",
       "3  {'ccp_alpha': 0.05, 'criterion': 'mse', 'split...                1  \n",
       "4  {'ccp_alpha': 0.05, 'criterion': 'friedman_mse...                1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grid search\n",
    "dtr = DecisionTreeRegressor(random_state=99, min_samples_leaf=10)\n",
    "parameters = {'criterion': ['mse', 'friedman_mse', 'mae'], \n",
    "              'splitter': ['best', 'random'],\n",
    "              'ccp_alpha': [0, 0.05, 0.1]}\n",
    "\n",
    "gs_dtr = GridSearchCV(dtr, parameters, return_train_score=True, cv=10)\n",
    "gs_dtr.fit(X, y) # feature scaling not required\n",
    "result_cols = ['mean_test_score','mean_train_score', 'params', 'rank_test_score']\n",
    "gs_result = pd.DataFrame(gs_dtr.cv_results_)[result_cols].sort_values(by='rank_test_score').reset_index(drop=True)\n",
    "gs_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv r2: 0.861\n",
      "cv mse: -14095936.131\n"
     ]
    },
    {
     "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>features</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>symptom_5</td>\n",
       "      <td>0.289828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>resident_status_frn</td>\n",
       "      <td>0.212545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>race_my</td>\n",
       "      <td>0.188668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>age</td>\n",
       "      <td>0.103742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>no_of_symptoms</td>\n",
       "      <td>0.080345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              features  importance\n",
       "0            symptom_5    0.289828\n",
       "1  resident_status_frn    0.212545\n",
       "2              race_my    0.188668\n",
       "3                  age    0.103742\n",
       "4       no_of_symptoms    0.080345"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross validation using optmized parameters\n",
    "dtr = DecisionTreeRegressor(random_state=99, min_samples_leaf=10, criterion='mse', splitter='best', ccp_alpha=0)\n",
    "\n",
    "print('cv r2: {:.3f}'.format(np.mean(cross_val_score(dtr, X_trans, y, cv=10, scoring='r2'))))\n",
    "print('cv mse: {:.3f}'.format(np.mean(cross_val_score(dtr, X_trans, y, cv=10, scoring='neg_mean_squared_error'))))\n",
    "\n",
    "dtr.fit(X, y)\n",
    "feature_eval = pd.DataFrame({'features': X.columns.tolist(),\n",
    "                             'importance': dtr.feature_importances_}).sort_values(\n",
    "                            by='importance', ascending=False).reset_index(drop=True)\n",
    "feature_eval.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear regression cv score: 0.887\n",
      "cv mse: 13724995.118\n"
     ]
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "pt = PowerTransformer()\n",
    "\n",
    "X_trans = scaler.fit_transform(X)\n",
    "X_trans = pt.fit_transform(X_trans)\n",
    "\n",
    "y_trans = pt.fit_transform(y.values.reshape(-1,1))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_trans, y_trans, test_size=0.20, random_state=99)\n",
    "\n",
    "# R2\n",
    "dtr = DecisionTreeRegressor(random_state=99, min_samples_leaf=10, criterion='mse', splitter='best', ccp_alpha=0)\n",
    "print('linear regression cv score: {:.3f}'.format(np.mean(cross_val_score(dtr, X_trans, y_trans, cv=10))))\n",
    "\n",
    "# MSE\n",
    "dtr.fit(X_train, y_train)\n",
    "y_pred = dtr.predict(X_test)\n",
    "y_pred = pt.inverse_transform(y_pred.reshape(-1,1)).flatten().tolist()\n",
    "y_test = pt.inverse_transform(y_test).flatten().tolist()\n",
    "\n",
    "mse = mean_squared_error(y_pred, y_test)\n",
    "print('cv mse: {:.3f}'.format(mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv r2: 0.861\n",
      "cv mse: -14095936.131\n"
     ]
    }
   ],
   "source": [
    "# training the model with features of high importance\n",
    "features = feature_eval.features[:5].tolist()\n",
    "dtr = DecisionTreeRegressor(random_state=99, min_samples_leaf=10, criterion='mse', splitter='best', ccp_alpha=0)\n",
    "print('cv r2: {:.3f}'.format(np.mean(cross_val_score(dtr, X_trans, y, cv=10, scoring='r2'))))\n",
    "print('cv mse: {:.3f}'.format(np.mean(cross_val_score(dtr, X_trans, y, cv=10, scoring='neg_mean_squared_error'))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "tree.plot_tree(dtr.fit(X[features], y)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv r2: -0.023\n",
      "cv mse: -105234020.623\n"
     ]
    }
   ],
   "source": [
    "X_trans = scaler.fit_transform(X)\n",
    "svr = SVR()\n",
    "print('cv r2: {:.3f}'.format(np.mean(cross_val_score(svr, X_trans, y, cv=10, scoring='r2'))))\n",
    "print('cv mse: {:.3f}'.format(np.mean(cross_val_score(svr, X_trans, y, cv=10, scoring='neg_mean_squared_error'))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.015850</td>\n",
       "      <td>-0.012644</td>\n",
       "      <td>{'C': 2, 'epsilon': 0.1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.015850</td>\n",
       "      <td>-0.012644</td>\n",
       "      <td>{'C': 2, 'epsilon': 0.05}</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.015850</td>\n",
       "      <td>-0.012644</td>\n",
       "      <td>{'C': 2, 'epsilon': 0.025}</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.019542</td>\n",
       "      <td>-0.016328</td>\n",
       "      <td>{'C': 1.5, 'epsilon': 0.025}</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.019542</td>\n",
       "      <td>-0.016328</td>\n",
       "      <td>{'C': 1.5, 'epsilon': 0.05}</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_test_score  mean_train_score                        params  \\\n",
       "0        -0.015850         -0.012644      {'C': 2, 'epsilon': 0.1}   \n",
       "1        -0.015850         -0.012644     {'C': 2, 'epsilon': 0.05}   \n",
       "2        -0.015850         -0.012644    {'C': 2, 'epsilon': 0.025}   \n",
       "3        -0.019542         -0.016328  {'C': 1.5, 'epsilon': 0.025}   \n",
       "4        -0.019542         -0.016328   {'C': 1.5, 'epsilon': 0.05}   \n",
       "\n",
       "   rank_test_score  \n",
       "0                1  \n",
       "1                2  \n",
       "2                3  \n",
       "3                4  \n",
       "4                5  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_trans = scaler.fit_transform(X)\n",
    "svr = SVR()\n",
    "parameters = {'C': [1, 1.5, 2], \n",
    "              'epsilon': [0.025, 0.05, 0.1]}\n",
    "gs_svr = GridSearchCV(svr, parameters, return_train_score=True, cv=10)\n",
    "gs_svr.fit(X_trans, y)\n",
    "result_cols = ['mean_test_score','mean_train_score', 'params', 'rank_test_score']\n",
    "gs_result = pd.DataFrame(gs_svr.cv_results_)[result_cols].sort_values(by='rank_test_score').reset_index(drop=True)\n",
    "gs_result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Support vector machine is not suited in this problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.880079</td>\n",
       "      <td>0.920611</td>\n",
       "      <td>{'ccp_alpha': 0, 'criterion': 'mse'}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.880079</td>\n",
       "      <td>0.920611</td>\n",
       "      <td>{'ccp_alpha': 0.05, 'criterion': 'mse'}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.880079</td>\n",
       "      <td>0.920611</td>\n",
       "      <td>{'ccp_alpha': 0.1, 'criterion': 'mse'}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.839808</td>\n",
       "      <td>0.882845</td>\n",
       "      <td>{'ccp_alpha': 0.05, 'criterion': 'mae'}</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.839807</td>\n",
       "      <td>0.882848</td>\n",
       "      <td>{'ccp_alpha': 0, 'criterion': 'mae'}</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_test_score  mean_train_score                                   params  \\\n",
       "0         0.880079          0.920611     {'ccp_alpha': 0, 'criterion': 'mse'}   \n",
       "1         0.880079          0.920611  {'ccp_alpha': 0.05, 'criterion': 'mse'}   \n",
       "2         0.880079          0.920611   {'ccp_alpha': 0.1, 'criterion': 'mse'}   \n",
       "3         0.839808          0.882845  {'ccp_alpha': 0.05, 'criterion': 'mae'}   \n",
       "4         0.839807          0.882848     {'ccp_alpha': 0, 'criterion': 'mae'}   \n",
       "\n",
       "   rank_test_score  \n",
       "0                1  \n",
       "1                1  \n",
       "2                1  \n",
       "3                4  \n",
       "4                5  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr = RandomForestRegressor(random_state=99, min_samples_leaf=10)\n",
    "parameters = {'criterion': ['mse', 'mae'], \n",
    "              'ccp_alpha': [0, 0.05, 0.1]}\n",
    "gs_rfr = GridSearchCV(rfr, parameters, return_train_score=True, cv=5)\n",
    "gs_rfr.fit(X, y)\n",
    "result_cols = ['mean_test_score','mean_train_score', 'params', 'rank_test_score']\n",
    "gs_result = pd.DataFrame(gs_rfr.cv_results_)[result_cols].sort_values(by='rank_test_score').reset_index(drop=True)\n",
    "gs_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv r2: 0.889\n",
      "cv mse: -11288429.046\n"
     ]
    },
    {
     "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>features</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>symptom_5</td>\n",
       "      <td>0.292809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>resident_status_frn</td>\n",
       "      <td>0.212924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>race_my</td>\n",
       "      <td>0.181911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>age</td>\n",
       "      <td>0.112399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>no_of_symptoms</td>\n",
       "      <td>0.076175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              features  importance\n",
       "0            symptom_5    0.292809\n",
       "1  resident_status_frn    0.212924\n",
       "2              race_my    0.181911\n",
       "3                  age    0.112399\n",
       "4       no_of_symptoms    0.076175"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr = RandomForestRegressor(random_state=99, min_samples_leaf=10, criterion='mse', ccp_alpha=0)\n",
    "print('cv r2: {:.3f}'.format(np.mean(cross_val_score(rfr, X, y, cv=10, scoring='r2'))))\n",
    "print('cv mse: {:.3f}'.format(np.mean(cross_val_score(rfr, X, y, cv=10, scoring='neg_mean_squared_error'))))\n",
    "\n",
    "rfr.fit(X, y)\n",
    "feature_eval = pd.DataFrame({'features': X.columns.tolist(),\n",
    "                             'importance': rfr.feature_importances_}).sort_values(\n",
    "                            by='importance', ascending=False).reset_index(drop=True)\n",
    "feature_eval.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv r2: 0.919\n",
      "cv mse: 13187101.451\n"
     ]
    }
   ],
   "source": [
    "# power transform\n",
    "# cross validation score based on best set of parameters\n",
    "scaler = MinMaxScaler()\n",
    "pt = PowerTransformer()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "X_trans = pt.fit_transform(X_trans)\n",
    "y_trans = pt.fit_transform(y.values.reshape(-1,1))\n",
    "\n",
    "# R2\n",
    "rfr = RandomForestRegressor(random_state=99, min_samples_leaf=10, criterion='mse', ccp_alpha=0)\n",
    "print('cv r2: {:.3f}'.format(np.mean(cross_val_score(rfr, X_trans, y_trans, cv=10, scoring='r2'))))\n",
    "# mse = cross_val_score(sgdr, X_trans, y_trans, cv=10, scoring='neg_mean_squared_error')\n",
    "# mse_it = pt.inverse_transform(mse.reshape(-1,1))\n",
    "# print('cv mse: {:.3f}'.format(np.mean(mse_it)))\n",
    "\n",
    "# MSE\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_trans, y_trans, test_size=0.20, random_state=99)\n",
    "\n",
    "rfr.fit(X_train, y_train)\n",
    "y_pred = rfr.predict(X_test)\n",
    "y_pred = pt.inverse_transform(y_pred.reshape(-1,1)).flatten().tolist()\n",
    "y_test = pt.inverse_transform(y_test).flatten().tolist()\n",
    "\n",
    "mse = mean_squared_error(y_pred, y_test)\n",
    "print('cv mse: {:.3f}'.format(mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3631.4048866795342"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(13187101.451)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SGD Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>mean_test_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.925618</td>\n",
       "      <td>0.926737</td>\n",
       "      <td>{'l1_ratio': 1, 'loss': 'squared_epsilon_insen...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.925529</td>\n",
       "      <td>0.927350</td>\n",
       "      <td>{'l1_ratio': 0.25, 'loss': 'squared_loss'}</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.925511</td>\n",
       "      <td>0.927346</td>\n",
       "      <td>{'l1_ratio': 0.5, 'loss': 'squared_loss'}</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.925373</td>\n",
       "      <td>0.927132</td>\n",
       "      <td>{'l1_ratio': 0.25, 'loss': 'squared_epsilon_in...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.925320</td>\n",
       "      <td>0.927113</td>\n",
       "      <td>{'l1_ratio': 0, 'loss': 'squared_loss'}</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_test_score  mean_train_score  \\\n",
       "0         0.925618          0.926737   \n",
       "1         0.925529          0.927350   \n",
       "2         0.925511          0.927346   \n",
       "3         0.925373          0.927132   \n",
       "4         0.925320          0.927113   \n",
       "\n",
       "                                              params  rank_test_score  \n",
       "0  {'l1_ratio': 1, 'loss': 'squared_epsilon_insen...                1  \n",
       "1         {'l1_ratio': 0.25, 'loss': 'squared_loss'}                2  \n",
       "2          {'l1_ratio': 0.5, 'loss': 'squared_loss'}                3  \n",
       "3  {'l1_ratio': 0.25, 'loss': 'squared_epsilon_in...                4  \n",
       "4            {'l1_ratio': 0, 'loss': 'squared_loss'}                5  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {'l1_ratio': [0, 0.25, 0.5, 0.75, 1],\n",
    "              'loss': ['squared_loss', 'squared_epsilon_insensitive']}\n",
    "scaler = MinMaxScaler()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "sgdr = SGDRegressor(max_iter=1000, penalty='elasticnet')\n",
    "gs_sgdr = GridSearchCV(sgdr, parameters, return_train_score=True, cv=10)\n",
    "gs_sgdr.fit(X_trans, y)\n",
    "result_cols = ['mean_test_score','mean_train_score', 'params', 'rank_test_score']\n",
    "gs_result = pd.DataFrame(gs_sgdr.cv_results_)[result_cols].sort_values(by='rank_test_score').reset_index(drop=True)\n",
    "gs_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv r2: 0.92562\n",
      "cv mse: -7704227.87060\n"
     ]
    }
   ],
   "source": [
    "# cross validation score based on best set of parameters\n",
    "scaler = MinMaxScaler()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "sgdr = SGDRegressor(max_iter=1000, tol=1e-3, penalty='elasticnet', loss='squared_loss', l1_ratio=0.75)\n",
    "print('cv r2: {:.5f}'.format(np.mean(cross_val_score(sgdr, X_trans, y, cv=10, scoring='r2'))))\n",
    "print('cv mse: {:.5f}'.format(np.mean(cross_val_score(sgdr, X_trans, y, cv=10, scoring='neg_mean_squared_error'))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cv r2: 0.978\n",
      "cv mse: 1683170.104\n"
     ]
    }
   ],
   "source": [
    "# power transform\n",
    "# cross validation score based on best set of parameters\n",
    "scaler = MinMaxScaler()\n",
    "pt = PowerTransformer()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "X_trans = pt.fit_transform(X_trans)\n",
    "y_trans = pt.fit_transform(y.values.reshape(-1,1))\n",
    "\n",
    "# R2\n",
    "sgdr = SGDRegressor(max_iter=1000, tol=1e-3, penalty='elasticnet', loss='squared_loss', l1_ratio=0.75)\n",
    "print('cv r2: {:.3f}'.format(np.mean(cross_val_score(sgdr, X_trans, y_trans, cv=10, scoring='r2'))))\n",
    "# mse = cross_val_score(sgdr, X_trans, y_trans, cv=10, scoring='neg_mean_squared_error')\n",
    "# mse_it = pt.inverse_transform(mse.reshape(-1,1))\n",
    "# print('cv mse: {:.3f}'.format(np.mean(mse_it)))\n",
    "\n",
    "# MSE\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_trans, y_trans, test_size=0.20, random_state=99)\n",
    "\n",
    "sgdr.fit(X_train, y_train)\n",
    "y_pred = sgdr.predict(X_test)\n",
    "y_pred = pt.inverse_transform(y_pred.reshape(-1,1)).flatten().tolist()\n",
    "y_test = pt.inverse_transform(y_test).flatten().tolist()\n",
    "\n",
    "mse = mean_squared_error(y_pred, y_test)\n",
    "print('cv mse: {:.3f}'.format(mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1: without power transform\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "X_trans = scaler.fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_trans, y, test_size=0.20, random_state=99)\n",
    "sgdr = SGDRegressor(max_iter=1000, tol=1e-3, penalty='elasticnet', loss='squared_loss', l1_ratio=0.75)\n",
    "sgdr.fit(X_train, y_train)\n",
    "y_pred = sgdr.predict(X_test)\n",
    "mse = mean_squared_error(y_pred, y_test)\n",
    "\n",
    "result1 = pd.DataFrame({'predict': y_pred,\n",
    "                       'actual': y_test})\n",
    "result1['abs_diff'] = abs(result1.predict - result1.actual)\n",
    "\n",
    "# 2: with power transform\n",
    "# power transform\n",
    "# cross validation score based on best set of parameters\n",
    "scaler = MinMaxScaler()\n",
    "pt = PowerTransformer()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "X_trans = pt.fit_transform(X_trans)\n",
    "y_trans = pt.fit_transform(y.values.reshape(-1,1))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_trans, y_trans, test_size=0.20, random_state=99)\n",
    "\n",
    "sgdr.fit(X_train, y_train)\n",
    "y_pred = sgdr.predict(X_test)\n",
    "y_pred = pt.inverse_transform(y_pred.reshape(-1,1)).flatten().tolist()\n",
    "y_test = pt.inverse_transform(y_test).flatten().tolist()\n",
    "mse = mean_squared_error(y_pred, y_test)\n",
    "\n",
    "result2 = pd.DataFrame({'predict': y_pred,\n",
    "                       'actual': y_test})\n",
    "result2['abs_diff'] = abs(result2.predict - result2.actual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'With Power Transform')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= plt.figure(figsize=(20,5))\n",
    "\n",
    "ax=f.add_subplot(121)\n",
    "plt.scatter('actual', 'abs_diff', data=result1)\n",
    "ax.set_title('Without Power Transform')\n",
    "\n",
    "ax=f.add_subplot(122)\n",
    "plt.scatter('actual', 'abs_diff', data=result2)\n",
    "ax.set_title('With Power Transform')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Design and Evaluation\n",
    "By iteratively adding layers and experimenting with activation functions, the model architecture with the best performance is a 5 layer simple forward feeding neural network.\n",
    "\n",
    "- Model 1: baseline model with only 1 input layer and 1 output layer\n",
    "- Model 2: added a dense layer with 20 neurons and relu activator; performance improvemnet observed\n",
    "- Model 3: added a dense layer with 10 neurons and relu activator; performance improvement observed\n",
    "- Model 4: added a dense layer with 10 neurons and sigmoid activator; no performance improvement observed, and therefore rejecting sigmoid activator design\n",
    "- Model 5: added a dense layer with 5 neurons and relu activator; performance improvement observed\n",
    "- Model 6: added a dense layer with 5 neurons and relu activator; no performance improvement observed, and therefore stop adding more layers\n",
    "- Model 7: added a dropout layer with dropout rate 0.1; no performance improvement observed, and therefore rejecting adding dropout layer\n",
    "- Model 8: re-evaluate number of neurons in Model 5. Change to 16, 8, 4; converaging speed increased, but no performance improvement observed\n",
    "\n",
    "Model is evaluated using R2 and mean square error.\n",
    "\n",
    "In below section, code for model 5 is retained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import datetime, os\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "from tensorflow.keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tensorboard extension is already loaded. To reload it, use:\n",
      "  %reload_ext tensorboard\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Reusing TensorBoard on port 6006 (pid 8013), started 1 day, 16:02:32 ago. (Use '!kill 8013' to kill it.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-ba2119e5886aaa84\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-ba2119e5886aaa84\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          url.port = 6006;\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext tensorboard\n",
    "%tensorboard --logdir logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 37)                1406      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 38        \n",
      "=================================================================\n",
      "Total params: 1,444\n",
      "Trainable params: 1,444\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "8297736.943981611\n"
     ]
    }
   ],
   "source": [
    "##############################\n",
    "# baseline model: Model 1 NT #\n",
    "##############################\n",
    "\n",
    "# without transforming y NT: not transformed\n",
    "scaler = MinMaxScaler()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "# separate data into train, test, and validate\n",
    "X_train, X_validate, y_train, y_validate = train_test_split(X_trans, y, test_size=0.20, random_state=99)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.20, random_state=99)\n",
    "\n",
    "\n",
    "def coeff_determination(y_true, y_pred):\n",
    "    SS_res =  K.sum(K.square( y_true-y_pred )) \n",
    "    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) ) \n",
    "    return ( 1 - SS_res/(SS_tot + K.epsilon()) )\n",
    "\n",
    "\n",
    "def build_model():\n",
    "    model = Sequential()\n",
    "    n_input = len(X.columns)\n",
    "    model.add(Dense(n_input, input_dim=n_input, kernel_initializer='normal', activation='relu'))\n",
    "    model.add(Dense(1, kernel_initializer='normal')) \n",
    "    print(model.summary())\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', coeff_determination])\n",
    "    return model\n",
    "\n",
    "\n",
    "def train_model(model):\n",
    "    logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "    tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1, profile_batch=0)\n",
    "    model.fit(x=X_train, y=y_train, \n",
    "              batch_size=10, verbose=0,\n",
    "              epochs=300, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])\n",
    "    return model\n",
    "    \n",
    "trained_model_1 = train_model(build_model())\n",
    "\n",
    "# without power transformer\n",
    "y_pred = trained_model_1.predict(X_validate)\n",
    "mse = mean_squared_error(y_pred, y_validate)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_2 (Dense)              (None, 37)                1406      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 20)                760       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 2,187\n",
      "Trainable params: 2,187\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "1743697.1538016067\n"
     ]
    }
   ],
   "source": [
    "##############\n",
    "# model 2 NT #\n",
    "##############\n",
    "\n",
    "# without transforming y\n",
    "scaler = MinMaxScaler()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "# separate data into train, test, and validate\n",
    "X_train, X_validate, y_train, y_validate = train_test_split(X_trans, y, test_size=0.20, random_state=99)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.20, random_state=99)\n",
    "\n",
    "\n",
    "def coeff_determination(y_true, y_pred):\n",
    "    SS_res =  K.sum(K.square( y_true-y_pred )) \n",
    "    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) ) \n",
    "    return ( 1 - SS_res/(SS_tot + K.epsilon()) )\n",
    "\n",
    "\n",
    "def build_model():\n",
    "    model = Sequential()\n",
    "    n_input = len(X.columns)\n",
    "    model.add(Dense(n_input, input_dim=n_input, kernel_initializer='normal', activation='relu')) # model 1\n",
    "    model.add(Dense(20, activation='relu')) # added in model 2\n",
    "    model.add(Dense(1, kernel_initializer='normal')) # model 1\n",
    "    print(model.summary())\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', coeff_determination])\n",
    "    return model\n",
    "\n",
    "\n",
    "def train_model(model):\n",
    "    logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "    tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1, profile_batch=0)\n",
    "    model.fit(x=X_train, y=y_train, \n",
    "              batch_size=10, verbose=0,\n",
    "              epochs=300, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])\n",
    "    return model\n",
    "    \n",
    "trained_model_2 = train_model(build_model())\n",
    "\n",
    "# without power transformer\n",
    "y_pred = trained_model_2.predict(X_validate)\n",
    "mse = mean_squared_error(y_pred, y_validate)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_5 (Dense)              (None, 37)                1406      \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 20)                760       \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 10)                210       \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 2,387\n",
      "Trainable params: 2,387\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "1554210.4450980455\n"
     ]
    }
   ],
   "source": [
    "##############\n",
    "# model 3 NT #\n",
    "##############\n",
    "\n",
    "# without transforming y\n",
    "scaler = MinMaxScaler()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "# separate data into train, test, and validate\n",
    "X_train, X_validate, y_train, y_validate = train_test_split(X_trans, y, test_size=0.20, random_state=99)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.20, random_state=99)\n",
    "\n",
    "\n",
    "def coeff_determination(y_true, y_pred):\n",
    "    SS_res =  K.sum(K.square( y_true-y_pred )) \n",
    "    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) ) \n",
    "    return ( 1 - SS_res/(SS_tot + K.epsilon()) )\n",
    "\n",
    "\n",
    "def build_model():\n",
    "    model = Sequential()\n",
    "    n_input = len(X.columns)\n",
    "    model.add(Dense(n_input, input_dim=n_input, kernel_initializer='normal', activation='relu')) # model 1\n",
    "    model.add(Dense(20, activation='relu')) # added in model 2\n",
    "    model.add(Dense(10, activation='relu')) # added in model 3\n",
    "    model.add(Dense(1, kernel_initializer='normal')) # model 1\n",
    "    print(model.summary())\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', coeff_determination])\n",
    "    return model\n",
    "\n",
    "\n",
    "def train_model(model):\n",
    "    logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "    tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1, profile_batch=0)\n",
    "    model.fit(x=X_train, y=y_train, \n",
    "              batch_size=10, verbose=0,\n",
    "              epochs=300, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])\n",
    "    return model\n",
    "    \n",
    "trained_model_3 = train_model(build_model())\n",
    "\n",
    "# without power transformer\n",
    "y_pred = trained_model_3.predict(X_validate)\n",
    "mse = mean_squared_error(y_pred, y_validate)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_9 (Dense)              (None, 37)                1406      \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 20)                760       \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 10)                210       \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 10)                110       \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 2,497\n",
      "Trainable params: 2,497\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "563719201.4390745\n"
     ]
    }
   ],
   "source": [
    "##############\n",
    "# model 4 NT #\n",
    "##############\n",
    "\n",
    "# without transforming y\n",
    "scaler = MinMaxScaler()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "# separate data into train, test, and validate\n",
    "X_train, X_validate, y_train, y_validate = train_test_split(X_trans, y, test_size=0.20, random_state=99)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.20, random_state=99)\n",
    "\n",
    "\n",
    "def coeff_determination(y_true, y_pred):\n",
    "    SS_res =  K.sum(K.square( y_true-y_pred )) \n",
    "    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) ) \n",
    "    return ( 1 - SS_res/(SS_tot + K.epsilon()) )\n",
    "\n",
    "\n",
    "def build_model():\n",
    "    model = Sequential()\n",
    "    n_input = len(X.columns)\n",
    "    model.add(Dense(n_input, input_dim=n_input, kernel_initializer='normal', activation='relu')) # model 1\n",
    "    model.add(Dense(20, activation='relu')) # added in model 2\n",
    "    model.add(Dense(10, activation='relu')) # added in model 3\n",
    "    model.add(Dense(10, activation='sigmoid'))\n",
    "    model.add(Dense(1, kernel_initializer='normal')) # model 1\n",
    "    print(model.summary())\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', coeff_determination])\n",
    "    return model\n",
    "\n",
    "\n",
    "def train_model(model):\n",
    "    logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "    tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1, profile_batch=0)\n",
    "    model.fit(x=X_train, y=y_train, \n",
    "              batch_size=10, verbose=0,\n",
    "              epochs=300, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])\n",
    "    return model\n",
    "    \n",
    "trained_model_4 = train_model(build_model())\n",
    "\n",
    "# without power transformer\n",
    "y_pred = trained_model_4.predict(X_validate)\n",
    "mse = mean_squared_error(y_pred, y_validate)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_9\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_40 (Dense)             (None, 37)                1406      \n",
      "_________________________________________________________________\n",
      "dense_41 (Dense)             (None, 20)                760       \n",
      "_________________________________________________________________\n",
      "dense_42 (Dense)             (None, 10)                210       \n",
      "_________________________________________________________________\n",
      "dense_43 (Dense)             (None, 5)                 55        \n",
      "_________________________________________________________________\n",
      "dense_44 (Dense)             (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 2,437\n",
      "Trainable params: 2,437\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "1240195.596900131\n"
     ]
    }
   ],
   "source": [
    "##############\n",
    "# model 5 NT #\n",
    "##############\n",
    "\n",
    "# without transforming y\n",
    "scaler = MinMaxScaler()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "# separate data into train, test, and validate\n",
    "X_train, X_validate, y_train, y_validate = train_test_split(X_trans, y, test_size=0.20, random_state=99)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.20, random_state=99)\n",
    "\n",
    "\n",
    "def coeff_determination(y_true, y_pred):\n",
    "    SS_res =  K.sum(K.square( y_true-y_pred )) \n",
    "    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) ) \n",
    "    return ( 1 - SS_res/(SS_tot + K.epsilon()) )\n",
    "\n",
    "\n",
    "def build_model():\n",
    "    model = Sequential()\n",
    "    n_input = len(X.columns)\n",
    "    model.add(Dense(n_input, input_dim=n_input, kernel_initializer='normal', activation='relu')) # model 1\n",
    "    model.add(Dense(20, activation='relu')) # added in model 2\n",
    "    model.add(Dense(10, activation='relu')) # added in model 3\n",
    "    # model.add(Dense(10, activation='sigmoid')) # added in model 4 rejected due to bad performance\n",
    "    model.add(Dense(5, activation='relu')) \n",
    "    model.add(Dense(1, kernel_initializer='normal')) # model 1\n",
    "    print(model.summary())\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', coeff_determination])\n",
    "    return model\n",
    "\n",
    "\n",
    "def train_model(model):\n",
    "    logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "    tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1, profile_batch=0)\n",
    "    model.fit(x=X_train, y=y_train, \n",
    "              batch_size=10, verbose=0,\n",
    "              epochs=300, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])\n",
    "    return model\n",
    "    \n",
    "trained_model_5 = train_model(build_model())\n",
    "\n",
    "# without power transformer\n",
    "y_pred = trained_model_5.predict(X_validate)\n",
    "mse = mean_squared_error(y_pred, y_validate)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1116.8245208209228"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(1247297.0103068838)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "result1 = pd.DataFrame({'predict': y_pred.flatten().tolist(), 'actual': y_validate})\n",
    "result1['abs_diff'] = abs(result1.predict - result1.actual)\n",
    "result1.sort_values(by='abs_diff', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_30 (Dense)             (None, 37)                1406      \n",
      "_________________________________________________________________\n",
      "dense_31 (Dense)             (None, 20)                760       \n",
      "_________________________________________________________________\n",
      "dense_32 (Dense)             (None, 10)                210       \n",
      "_________________________________________________________________\n",
      "dense_33 (Dense)             (None, 5)                 55        \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 5)                 0         \n",
      "_________________________________________________________________\n",
      "dense_34 (Dense)             (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 2,437\n",
      "Trainable params: 2,437\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "2317346.7992870025\n"
     ]
    }
   ],
   "source": [
    "##############\n",
    "# model 7 NT #\n",
    "##############\n",
    "\n",
    "# without transforming y\n",
    "scaler = MinMaxScaler()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "# separate data into train, test, and validate\n",
    "X_train, X_validate, y_train, y_validate = train_test_split(X_trans, y, test_size=0.20, random_state=99)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.20, random_state=99)\n",
    "\n",
    "\n",
    "def coeff_determination(y_true, y_pred):\n",
    "    SS_res =  K.sum(K.square( y_true-y_pred )) \n",
    "    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) ) \n",
    "    return ( 1 - SS_res/(SS_tot + K.epsilon()) )\n",
    "\n",
    "\n",
    "def build_model():\n",
    "    model = Sequential()\n",
    "    n_input = len(X.columns)\n",
    "    model.add(Dense(n_input, input_dim=n_input, kernel_initializer='normal', activation='relu')) # model 1\n",
    "    model.add(Dense(20, activation='relu')) # added in model 2\n",
    "    model.add(Dense(10, activation='relu')) # added in model 3\n",
    "    # model.add(Dense(10, activation='sigmoid')) # added in model 4 rejected due to bad performance\n",
    "    model.add(Dense(5, activation='relu')) # added in model 5\n",
    "    # model.add(Dense(5, activation='relu')) # added in model 6; rejected due to no substantial improvement\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(1, kernel_initializer='normal')) # model 1\n",
    "    print(model.summary())\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', coeff_determination])\n",
    "    return model\n",
    "\n",
    "\n",
    "def train_model(model):\n",
    "    logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "    tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1, profile_batch=0)\n",
    "    model.fit(x=X_train, y=y_train, \n",
    "              batch_size=10, verbose=0,\n",
    "              epochs=300, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])\n",
    "    return model\n",
    "    \n",
    "trained_model_7 = train_model(build_model())\n",
    "\n",
    "# without power transformer\n",
    "y_pred = trained_model_7.predict(X_validate)\n",
    "mse = mean_squared_error(y_pred, y_validate)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_8\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_35 (Dense)             (None, 37)                1406      \n",
      "_________________________________________________________________\n",
      "dense_36 (Dense)             (None, 16)                608       \n",
      "_________________________________________________________________\n",
      "dense_37 (Dense)             (None, 8)                 136       \n",
      "_________________________________________________________________\n",
      "dense_38 (Dense)             (None, 4)                 36        \n",
      "_________________________________________________________________\n",
      "dense_39 (Dense)             (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 2,191\n",
      "Trainable params: 2,191\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "1575215.2558385958\n"
     ]
    }
   ],
   "source": [
    "##############\n",
    "# model 8 NT #\n",
    "##############\n",
    "\n",
    "# without transforming y\n",
    "scaler = MinMaxScaler()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "# separate data into train, test, and validate\n",
    "X_train, X_validate, y_train, y_validate = train_test_split(X_trans, y, test_size=0.20, random_state=99)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.20, random_state=99)\n",
    "\n",
    "\n",
    "def coeff_determination(y_true, y_pred):\n",
    "    SS_res =  K.sum(K.square( y_true-y_pred )) \n",
    "    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) ) \n",
    "    return ( 1 - SS_res/(SS_tot + K.epsilon()) )\n",
    "\n",
    "\n",
    "def build_model():\n",
    "    model = Sequential()\n",
    "    n_input = len(X.columns)\n",
    "    model.add(Dense(n_input, input_dim=n_input, kernel_initializer='normal', activation='relu')) # model 1\n",
    "    model.add(Dense(16, activation='relu')) # number of neuron changed in model 8\n",
    "    model.add(Dense(8, activation='relu')) # number of neuron changed in model 8\n",
    "    # model.add(Dense(10, activation='sigmoid')) # added in model 4 rejected due to bad performance\n",
    "    model.add(Dense(4, activation='relu')) # number of neuron changed in model 8\n",
    "    # model.add(Dense(5, activation='relu')) # added in model 6; rejected due to no substantial improvement\n",
    "    # model.add(Dropout(0.1)) # rejected due to bad performance\n",
    "    model.add(Dense(1, kernel_initializer='normal')) # model 1\n",
    "    print(model.summary())\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', coeff_determination])\n",
    "    return model\n",
    "\n",
    "\n",
    "def train_model(model):\n",
    "    logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "    tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1, profile_batch=0)\n",
    "    model.fit(x=X_train, y=y_train, \n",
    "              batch_size=10, verbose=0,\n",
    "              epochs=300, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])\n",
    "    return model\n",
    "    \n",
    "trained_model_8 = train_model(build_model())\n",
    "\n",
    "# without power transformer\n",
    "y_pred = trained_model_8.predict(X_validate)\n",
    "mse = mean_squared_error(y_pred, y_validate)\n",
    "print(mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 5 (No Power Transformation) Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standardized: 0.99 (0.00) coefficient of regression\n"
     ]
    }
   ],
   "source": [
    "##############\n",
    "# model 5 NT #\n",
    "##############\n",
    "# cross validation - r2\n",
    "\n",
    "def build_model():\n",
    "    model = Sequential()\n",
    "    n_input = len(X.columns)\n",
    "    model.add(Dense(n_input, input_dim=n_input, kernel_initializer='normal', activation='relu')) # model 1\n",
    "    model.add(Dense(20, activation='relu')) # added in model 2\n",
    "    model.add(Dense(10, activation='relu')) # added in model 3\n",
    "    # model.add(Dense(10, activation='sigmoid')) # added in model 4 rejected due to bad performance\n",
    "    model.add(Dense(5, activation='relu')) # added in model 5\n",
    "    # model.add(Dense(5, activation='relu')) # added in model 6 rejected due to worse performance than model 5\n",
    "    \n",
    "    model.add(Dense(1, kernel_initializer='normal')) # model 1\n",
    "    # print(model.summary())\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', coeff_determination])\n",
    "    return model\n",
    "\n",
    "estimators = []\n",
    "estimators.append(('minmaxscale', MinMaxScaler()))\n",
    "estimators.append(('mlp', KerasRegressor(build_fn=build_model, epochs=500, batch_size=10, verbose=0)))\n",
    "pipeline = Pipeline(estimators)\n",
    "results = cross_val_score(pipeline, X, y, cv=5, scoring='r2')\n",
    "print(\"Standardized: %.2f (%.2f) coefficient of regression\" % (results.mean(), results.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standardized: -756317.06 (190888.13) MSE\n"
     ]
    }
   ],
   "source": [
    "##############\n",
    "# model 5 NT #\n",
    "##############\n",
    "# cross validation - mse\n",
    "estimators = []\n",
    "estimators.append(('minmaxscale', MinMaxScaler()))\n",
    "estimators.append(('mlp', KerasRegressor(build_fn=build_model, epochs=500, batch_size=10, verbose=0)))\n",
    "pipeline = Pipeline(estimators)\n",
    "results = cross_val_score(pipeline, X, y, cv=5)\n",
    "print(\"Standardized: %.2f (%.2f) MSE\" % (results.mean(), results.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "869.6649124806635"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(756317.06)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model 5 (With Power Transformation) Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############\n",
    "# model 5 T #\n",
    "#############\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_trans = scaler.fit_transform(X)\n",
    "pt = PowerTransformer()\n",
    "y_trans = pt.fit_transform(y.values.reshape(-1,1))\n",
    "# separate data into train, test, and validate\n",
    "X_train, X_validate, y_train, y_validate = train_test_split(X_trans, y_trans, test_size=0.20, random_state=99)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.20, random_state=99)\n",
    "\n",
    "\n",
    "def coeff_determination(y_true, y_pred):\n",
    "    SS_res =  K.sum(K.square( y_true-y_pred )) \n",
    "    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) ) \n",
    "    return ( 1 - SS_res/(SS_tot + K.epsilon()) )\n",
    "\n",
    "\n",
    "def build_model():\n",
    "    model = Sequential()\n",
    "    n_input = len(X.columns)\n",
    "    model.add(Dense(n_input, input_dim=n_input, kernel_initializer='normal', activation='relu')) # model 1\n",
    "    model.add(Dense(20, activation='relu')) # added in model 2\n",
    "    model.add(Dense(10, activation='relu')) # added in model 3\n",
    "    # model.add(Dense(10, activation='sigmoid')) # added in model 4 rejected due to bad performance\n",
    "    model.add(Dense(5, activation='relu')) # added in model 5\n",
    "    # model.add(Dense(5, activation='relu')) # added in model 6 rejected due to worse performance than model 5\n",
    "    \n",
    "    model.add(Dense(1, kernel_initializer='normal')) # model 1\n",
    "    # print(model.summary())\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', coeff_determination])\n",
    "    return model\n",
    "\n",
    "\n",
    "def train_model(model):\n",
    "    logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "    tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1, profile_batch=0)\n",
    "    model.fit(x=X_train, y=y_train, \n",
    "              batch_size=10, verbose=0,\n",
    "              epochs=300, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])\n",
    "    return model\n",
    "    \n",
    "trained_model_t = train_model(build_model())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "560490.1695971142\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "748.6589140570719"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inverse transform prediction and get MSE\n",
    "y_pred = trained_model_t.predict(X_validate)\n",
    "y_pred = pt.inverse_transform(y_pred.reshape(-1,1)).flatten().tolist()\n",
    "y_validate = pt.inverse_transform(y_validate).flatten().tolist()\n",
    "mse = mean_squared_error(y_pred, y_validate)\n",
    "print(mse)\n",
    "np.sqrt(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "result2 = pd.DataFrame({'predict': y_pred, 'actual': y_validate})\n",
    "result2['abs_diff'] = abs(result2.predict - result2.actual)\n",
    "result2.sort_values(by='abs_diff', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standardized: 0.99 (0.00) coefficient of regression\n"
     ]
    }
   ],
   "source": [
    "#############\n",
    "# model 5 T #\n",
    "#############\n",
    "# cross validation - r2\n",
    "estimators = []\n",
    "estimators.append(('minmaxscale', MinMaxScaler()))\n",
    "estimators.append(('powertransform', PowerTransformer()))\n",
    "estimators.append(('mlp', KerasRegressor(build_fn=build_model, epochs=500, batch_size=10, verbose=0)))\n",
    "pipeline = Pipeline(estimators)\n",
    "results = cross_val_score(pipeline, X, y, cv=5, scoring='r2')\n",
    "print(\"Standardized: %.2f (%.2f) coefficient of regression\" % (results.mean(), results.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standardized: 0.43 (1.68) MSE\n"
     ]
    }
   ],
   "source": [
    "def build_model():\n",
    "    model = Sequential()\n",
    "    n_input = len(X.columns)\n",
    "    model.add(Dense(n_input, input_dim=n_input, kernel_initializer='normal', activation='relu')) # model 1\n",
    "    model.add(Dense(20, activation='relu')) # added in model 2\n",
    "    model.add(Dense(10, activation='relu')) # added in model 3\n",
    "    # model.add(Dense(10, activation='sigmoid')) # added in model 4 rejected due to bad performance\n",
    "    model.add(Dense(5, activation='relu')) # added in model 5\n",
    "    # model.add(Dense(5, activation='relu')) # added in model 6 rejected due to worse performance than model 5\n",
    "    \n",
    "    model.add(Dense(1, kernel_initializer='normal')) # model 1\n",
    "    # print(model.summary())\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse', coeff_determination])\n",
    "    return model\n",
    "# cross validation - mse\n",
    "estimators = []\n",
    "estimators.append(('minmaxscale', MinMaxScaler()))\n",
    "estimators.append(('powertransform', PowerTransformer()))\n",
    "estimators.append(('mlp', KerasRegressor(build_fn=build_model, epochs=500, batch_size=10, verbose=0)))\n",
    "pipeline = Pipeline(estimators)\n",
    "results = cross_val_score(pipeline, X, y, cv=5)\n",
    "print(\"Standardized: %.2f (%.2f) MSE\" % (results.mean(), results.std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sklearn's MSE cannot be directly used for NN evaluation after power transform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare Abosolute Error Distribution by Target Variable (No Power Transform vs Power Transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a3cfb87b8>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,5))\n",
    "ax1 = fig.add_subplot(1, 2, 1)\n",
    "ax2 = fig.add_subplot(1, 2, 2, sharex=ax1)\n",
    "ax1.set_ylim(0,10000)\n",
    "ax2.set_ylim(0,10000)\n",
    "ax1.scatter('actual', 'abs_diff', data=result1)\n",
    "ax2.scatter('actual', 'abs_diff', data=result2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}