ML-Overfitting-Lasso-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6912462c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import *\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ffae719f",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6330dc99",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv('overfittrain.csv')\n",
    "test=pd.read_csv('overfittest.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de4e9660",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>input1</th>\n",
       "      <th>input2</th>\n",
       "      <th>input3</th>\n",
       "      <th>input4</th>\n",
       "      <th>input5</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>27.50</td>\n",
       "      <td>114.0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>24.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52.5</td>\n",
       "      <td>55.5</td>\n",
       "      <td>53.5</td>\n",
       "      <td>26.75</td>\n",
       "      <td>111.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>45.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>45.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>4.25</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>36.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>78.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>54.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>27.50</td>\n",
       "      <td>114.0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>73.5</td>\n",
       "      <td>76.5</td>\n",
       "      <td>74.5</td>\n",
       "      <td>37.25</td>\n",
       "      <td>153.0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   input1  input2  input3  input4  input5  target\n",
       "0     7.0     5.0     7.0    7.00     1.5       1\n",
       "1    54.0    57.0    55.0   27.50   114.0      36\n",
       "2     9.0    12.0    10.0    5.00    24.0       6\n",
       "3    52.5    55.5    53.5   26.75   111.0      35\n",
       "4     4.0    11.0     5.0   45.00    12.0       2\n",
       "5    45.0     4.0     4.0    7.00     1.0       7\n",
       "6     7.5    10.5     8.5    4.25    21.0       5\n",
       "7    36.0    39.0    37.0   18.50    78.0      24\n",
       "8    54.0    57.0    55.0   27.50   114.0      36\n",
       "9    73.5    76.5    74.5   37.25   153.0      49"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6eacbf2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>input1</th>\n",
       "      <th>input2</th>\n",
       "      <th>input3</th>\n",
       "      <th>input4</th>\n",
       "      <th>input5</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>input1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.894441</td>\n",
       "      <td>0.814930</td>\n",
       "      <td>0.113240</td>\n",
       "      <td>0.958105</td>\n",
       "      <td>0.961522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>input2</th>\n",
       "      <td>0.894441</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.752368</td>\n",
       "      <td>0.100058</td>\n",
       "      <td>0.894043</td>\n",
       "      <td>0.887137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>input3</th>\n",
       "      <td>0.814930</td>\n",
       "      <td>0.752368</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.077787</td>\n",
       "      <td>0.832580</td>\n",
       "      <td>0.826593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>input4</th>\n",
       "      <td>0.113240</td>\n",
       "      <td>0.100058</td>\n",
       "      <td>0.077787</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.102200</td>\n",
       "      <td>0.138107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>input5</th>\n",
       "      <td>0.958105</td>\n",
       "      <td>0.894043</td>\n",
       "      <td>0.832580</td>\n",
       "      <td>0.102200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.968234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>0.961522</td>\n",
       "      <td>0.887137</td>\n",
       "      <td>0.826593</td>\n",
       "      <td>0.138107</td>\n",
       "      <td>0.968234</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          input1    input2    input3    input4    input5    target\n",
       "input1  1.000000  0.894441  0.814930  0.113240  0.958105  0.961522\n",
       "input2  0.894441  1.000000  0.752368  0.100058  0.894043  0.887137\n",
       "input3  0.814930  0.752368  1.000000  0.077787  0.832580  0.826593\n",
       "input4  0.113240  0.100058  0.077787  1.000000  0.102200  0.138107\n",
       "input5  0.958105  0.894043  0.832580  0.102200  1.000000  0.968234\n",
       "target  0.961522  0.887137  0.826593  0.138107  0.968234  1.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b58abf48",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=train.iloc[:,:-1]\n",
    "X_test=test.iloc[:,:-1]\n",
    "y_train=train.iloc[:,-1]\n",
    "y_test=test.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8c78ad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "15ea7ae9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ae4cc4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred_trains = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e17656cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54</td>\n",
       "      <td>47.391544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>57.050430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>61.440832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>61.440832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>45.635383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>92</td>\n",
       "      <td>80.758603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53</td>\n",
       "      <td>46.513464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>73</td>\n",
       "      <td>64.075074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>61</td>\n",
       "      <td>53.538108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>88</td>\n",
       "      <td>77.246281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      54  47.391544\n",
       "1      65  57.050430\n",
       "2      70  61.440832\n",
       "3      70  61.440832\n",
       "4      52  45.635383\n",
       "5      92  80.758603\n",
       "6      53  46.513464\n",
       "7      73  64.075074\n",
       "8      61  53.538108\n",
       "9      88  77.246281"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Actual':y_test,'Predicted':y_pred}).head(10)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "52a60deb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar',figsize=(13,7))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5436e80e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9533433466614624\n",
      "Adjusted R^2 : 0.9531086552462785\n",
      "Mean Absolute Error: 0.961819692069944\n",
      "Mean Squared Error: 9.40660646554726\n",
      "Root Mean Squared Error: 3.067019149850105\n"
     ]
    }
   ],
   "source": [
    "#R2 score on train data\n",
    "\n",
    "n = X_train.shape[0]\n",
    "p = X_train.shape[1]\n",
    "r2 = r2_score(y_train, y_pred_trains)\n",
    "print('R^2:' , r2)\n",
    "print('Adjusted R^2 :' , 1-(1-r2)*(n-1)/(n-p-1))\n",
    "print('Mean Absolute Error:' , mean_absolute_error(y_train, y_pred_trains))\n",
    "print('Mean Squared Error:' , mean_squared_error(y_train, y_pred_trains))\n",
    "print('Root Mean Squared Error:' , np.sqrt(mean_squared_error(y_train, y_pred_trains)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1ec39b5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.5853773941881704\n",
      "Adjusted R^2 : 0.5768810293149771\n",
      "Mean Absolute Error: 8.995151836843103\n",
      "Mean Squared Error: 83.92136678220125\n",
      "Root Mean Squared Error: 9.160860591789467\n"
     ]
    }
   ],
   "source": [
    "#R2 score on test data\n",
    "\n",
    "n = X_test.shape[0]\n",
    "p = X_test.shape[1]\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print('R^2:' , r2)\n",
    "print('Adjusted R^2 :' , 1-(1-r2)*(n-1)/(n-p-1))\n",
    "print('Mean Absolute Error:' , mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:' , mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:' , np.sqrt(mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cc1eb4f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Lasso(alpha=-3.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7cd86532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=-3.9)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "38097657",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred_trains = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "53eb2be6",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54</td>\n",
       "      <td>54.936259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>66.181242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>71.292598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>71.292598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52</td>\n",
       "      <td>52.891717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>92</td>\n",
       "      <td>93.782563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>53</td>\n",
       "      <td>53.913988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>73</td>\n",
       "      <td>74.359411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>61</td>\n",
       "      <td>62.092157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>88</td>\n",
       "      <td>89.693478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      54  54.936259\n",
       "1      65  66.181242\n",
       "2      70  71.292598\n",
       "3      70  71.292598\n",
       "4      52  52.891717\n",
       "5      92  93.782563\n",
       "6      53  53.913988\n",
       "7      73  74.359411\n",
       "8      61  62.092157\n",
       "9      88  89.693478"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Actual':y_test,'Predicted':y_pred}).head(10)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "690a442e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar',figsize=(13,7))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "21591060",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9526086525196978\n",
      "Adjusted R^2 : 0.9523702654599377\n",
      "Mean Absolute Error: 0.8510122991510984\n",
      "Mean Squared Error: 9.554730648693814\n",
      "Root Mean Squared Error: 3.091072734293681\n"
     ]
    }
   ],
   "source": [
    "#R2 score on train data\n",
    "\n",
    "n = X_train.shape[0]\n",
    "p = X_train.shape[1]\n",
    "r2 = r2_score(y_train, y_pred_trains)\n",
    "print('R^2:' , r2)\n",
    "print('Adjusted R^2 :' , 1-(1-r2)*(n-1)/(n-p-1))\n",
    "print('Mean Absolute Error:' , mean_absolute_error(y_train, y_pred_trains))\n",
    "print('Mean Squared Error:' , mean_squared_error(y_train, y_pred_trains))\n",
    "print('Root Mean Squared Error:' , np.sqrt(mean_squared_error(y_train, y_pred_trains)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3ce0d6e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9902006309823388\n",
      "Adjusted R^2 : 0.9899998242401736\n",
      "Mean Absolute Error: 1.3722392189978432\n",
      "Mean Squared Error: 1.9834336817093636\n",
      "Root Mean Squared Error: 1.4083443051006255\n"
     ]
    }
   ],
   "source": [
    "#R2 score on test data\n",
    "\n",
    "n = X_test.shape[0]\n",
    "p = X_test.shape[1]\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print('R^2:' , r2)\n",
    "print('Adjusted R^2 :' , 1-(1-r2)*(n-1)/(n-p-1))\n",
    "print('Mean Absolute Error:' , mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:' , mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:' , np.sqrt(mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2023567e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('overfitdata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7a0f322f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data.iloc[:, :-1]\n",
    "y_train = data.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "881d4136",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = [0,1,2,3,4]\n",
    "alpha = [1,2,3,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3b8a551e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tNFDXGxEMGTKEwMBAevbsSf/+/UlKqngmz4YNG874DICT1Ua0EUEdakRgjPkdVi/ibWdbrnZdi1LqAiYifLh2L08v2Uh+sZ1HB3Vi3CUX4ed77t+JyzYiuOKKK5xqROBOjr6t5ZoNlG00cK6NCObMmcNll13GzJkzK6yPPG7cOBYvXkzPnj0xxvDxxx+78rBKLVmyhKKiIr788ks2b95c6VSr04/T8bOPsb4tVNaIoDk1r2wjAjuw3JGEbsU60wRHIwLHz1U1IhiM1Yjgv6cvUKYRwRCxGhqsN8acbETwlWOxH0VkoePnyhoRJIpIoTFmFtaXgL9WdnByns0GjDENsBL6UyJy+t9ZOVUmW8c3mLIfTFvgTyLy0vkEqZQ6ZU/mcR7/KJVvt2bQo004zw1PoG3U2W8BOetkI4IrrriCOXPmnCx67xGhodYxZWdnl/a+LdtooLqNCHr16sWuXbsAqxHBpZdeWum+x40bx9ChQ5k1a1aVjQi++846QcvPt3LHyf6/ffv2rbI+sr+/P1dffTUzZ86kXbt2paUoyzr9OB0/20VEHD1lvbERQSesYxhBBcmWyhsRlL08UK1GBMYYtzUiMMYEYZ31rxKRv1S1fJVfmUVki4j8TkR+h3VD+Djw0fkGqpSCErvw9vc7ueqllazbncX0YXF8ML7XuSXaE1nW4zS1qRFBo0aNaNasGcnJyaWvJScnlzYa8KZGBMXFxWzfvr3C9+Li4s74DDh1pqaNCM5tXzXWiMAYUw9Y6IjtnrPEVKq616cux7ppvrua6ymlTrPtcA43v/4jTy3eSPc24Xz+x36M6t0GH58z7zueVfoWWPIwvBALP/7rjLdrWyOC0aNH8/TTT5OVlcXmzZt54403GDt2LFB3GxFs3ryZpUuXcuLECYqKipgzZw4rV66kX79+FS4/evRoXnjhBfbt28f+/fuZMWMGwBHQRgTU8kYEjvvY87C+HI0+7az/rAFWZ8TVW1jzryp6bzywBljTqlUrUUpVrLC4RP751a/S4fFPJfGpZbJg3Z5yo2+dUlIismWZyHvXiUxtIPLnKJGP7hc5kFrh4rWpEUF+fr7ccccdUr9+fWncuLHMmDGj3LbqYiOCjRs3So8ePSQ0NFTCwsIkKSlJFixYUPr+6Y0I7Ha7TJw4URo1aiSNGjWSiRMnaiMCqRuNCIB+js/oONbo7ZOPS862ntONCIwxAVin+XEicuhsy2ojAqUqlrr3GJPmp7DpQDaDbc14amgckaGV3z88Q0EOrP8PrH4dMrdD/WbQ/S7odgeERFa6mjYiqP20EYF3q85o5KuBdVUlWqXUmfKLSnjpy6288e0OIkICeH1UN66Ka+r8BjJ3wk+z4Jc5UJANzZNg+JvQeSj4BZx1VW1EoJTnVSfZ3oo1B0opVQ2rdxxh8oJUdmbkMSKpJY8P7kxYUMWNA8oRgZ0rYfVrsGUp+PhC3PXQ815o4dwJkDYiUNWltZHdw6mvucaYYOAKrMocSikn5OQX8eTCNEbMWkWx3c77d/fkrzfaqk60RSdg7Tvw6sXw3lDYsxou/T94KA2Gz3Y60cKpRgQ//PADDRqcPpvEdapTz3js2LEEBASUG+lcUlJyTtuC2lMb+aRvvvkGYwxTpkypdJlp06bh7+9f7jPAKowAaG3k2s7xd5NujMk2xiQbY84Y8HU6p5KtiBwXkQipYtKuUsry9ZbDXPXiSuas3s1dfS9i2UOX0qd95fdUATi2F76cBi90hsUPgvGFYf+ChzfCZVOgQcX9aj2tOvWMT5o0aVK5kc6+vr7V3lZtq40M1iX7Bx98kJ49q6xLz4gRI8p9BlgjZ0/S2shV78uTRZkeBJqJSAOswcFzjDFn/Q+qN3CUcqHMvEIe/u967nj7Z0Lq+TH/vot5ckgswQGV/F4Qgd9WwYdj4SUbfD8T2vSFsZ/Cvd9Cl5Hgf+4nAw888ACPPPJIudeuvfba0iINrrBixQqKi4t56KGHqFevHhMmTEBEWL58uVu31atXL+Lj47nvvvv44IMPWLp0KRMmTDhjue3btxMeHs66desA2L9/P5GRkaxYsaLa8VVlxowZXHnllXTq1Omct2GM6Qh0BaaKyAkRmY9VV3i4i8KsbL/1HAU1fIFkY8x2x+udjTErHCUMNxhjhpZZ5x1jzKvGmE+NMXnAgLPsQrAKWpwthmhjzMfGmExjzDZjzLgy700zxswzxswxxmQDYytYP8oYs8AR6y/GmBhjzLrTykueNxFJEWuq0cnj8scaCV0pTbZKuYCIsCRlP1e88A2Lk/cz4fIOLJnQl66tGlW8QnEBJH8As/rDW1fB9uXQ+36YsB5GzIE2faCCOr/VVRO1katbzxjglVdeITw8nG7dujF//qnpo3W5NvLu3bt56623+NOf/uTU8osXLyY8PJy4uDheffXVsm9pbeQ6UBvZsW4+1lzkFVhTXyultZGVOk+HsvOZsjCNLzYewtYijDl396Rzs0ruj+YehjVvwc9vQt5hiOwIg2dA4q0QcLbfQeemJmojV6eeMcCECROYMWMGYWFhfP7554wYMYKmTZvSp0+fOl0becKECUyfPr20JOXZ3HzzzYwfP54mTZqwevVqhg8fDla7NtDayHWiNrKIDHF8GRkIdJIqiltoslXqHIkI/1uzh6c/2URhsZ3Hr+nEnX0qaRyw/xdrbmzafCgphA5XWqOK2w4AN0/HcXdt5OrUMwbK9by95ppruP3221mwYAF9+vSps7WRFy9eTE5ODiNGnPUqaanY2NjSny+++GIefPBBHnvssZOXQbQ2cvl91crayI59FAFLjTEPGmO2i0il3+I02Sp1Dn47cpzJC1L4YfsRel4Uzl+H22gTedqZaUkxbF4Mq16DPasgIBS6jYUe90Bke5fHVGIvocheRKBf+Xu8I0eOJD4+3unayLt3V1yNdeTIkRWWbIyLi2PGjBlWlRzHZdyUlBQeeOABp+I2xpTWQj6XbZ1LbeThw4cTHh5e4bJlk+nJ7Va1/a+++oo1a9bQtKk1d/rYsWP4+vqSmprKokVV/64/7fJ3aW3kMoknEfhPlRtyvdLayGUSbivg1zLLOF0b2RjzAfCn046t7L7CT3vvXGsjH3e2NjKVl3P8Viop2VgBP6DdWZdwR7kvbR6vvFVxiV1mf7tDOk1ZKnF/+kzmrNolJSWnlVrMOyLy7QsiM2KtUoovJoj88LLIiaMujSX9eLos371cZq6dKXd9dpf0fL+nvLb+tQqXHThwoCQkJMgdd9zh0hhERAoKCqRVq1by0ksvSX5+vvzzn/+UVq1aSUFBQYXLf/jhh5KTkyMlJSWybNkyCQ0NLS3tWN1tOevOO++Um266SURExo0bV/pzVZwt15idnS0HDhwofdx8883y0EMPlZaIPN3ChQslMzNT7Ha7rF69WqKjowXYKadKAq7CmoITCFyP1Qw9Stzw+/r0B2XKNWJNR9oOTMYaBNQf6+y0kzhXrtEXaxTyZWVeWw2MrGT5b7HqKAdi1YM+BFwhzpdr/AR43PHzo1h1nR9x8efTCavIU5DjMxmJNZK861nXc8dfliZb5Y22HMyWYS9/J60fXSJ3vP2T7D96vPwCBzeIfDxBZHoTK8m+M0Rk0yciJcXnve/84nz55dAv8t6G92Tiioly1byrJP6deIl/J14S302Umz6+Sab/OF1+3P9jheu7uzby2eoZn14buW/fvtKgQQOpX7++2Gw2mTt3rtPbOhc1URv5dGPGjJEnnnii9PnptZFvueUWCQ8Pl5CQEImJiZGZM2dqbWSpM7WROzu+MOQ4vgT9XPYzqezhdG3k6tDayMqbFBbbeXXFdl7+eiv1A/2Zem0sQxOjrUt/djtsXQarXoWd34BfINhutu7HNjm3waMiwt6cvSRnJJOankpKegqbszZTbLdmGjQJboItyoYt0kZCVAKxEbEE+VXeyQe0NnJdoLWRvZves1XqLJL3HOXR+SlsPpjD0MRopl4bS0RoPcjPhvXvW4OesnZCg+Zw+VTrnmxwxfcCK5NdmE1aehopGSmkpKeQlpFGVoHVlzbIL4jYiFhGxY6ykmtkAk1CKh5JXBmtjayU52myVaoCJwpLePHLX5n97Q4a1w9k9ugkBsY2gSPbYeXrVqItzIWWPeHyP0Hna8G36nrHxfZitmZtJTUjleT0ZFIzUtl5bGfp+23D2tKvZT8SIhNIjEqkXcN2+Pmc+39TrY2sqktrI7uJO6756z1bVZf9sC1D+j2/XFo/ukQmz0+RY8cLRLZ9JTLnJpGpYSJPRYjMHy+yd22V2zqQe0A+3/W5zPh5hoz+dLR0n9O99F7rJXMvkQe+fEBeW/+a/LDvB8kuyHb/wbnJL7/8Il27dpWgoCDp2rWr/PLLL5UuW7afbZMmTc7oZ1udbYmI7Ny585zuq7rDSy+9JG3atJHg4GDp1KmTbNmypcLl7Ha7TJo0ScLDwyU8PLyyfrZfY/VM3UzN3rPdDgxz8TYvB7KBwJo6jhr6rE72tq30vvXJh57ZKuWQnV/Ec0s385/Vv9E6IpgPxibQK+cLePMuyNgCIVHQ71FIuhPqn3kp93jRcTYe2UhqhnWfNSUjhcPHDwPg7+NP5/DO3NDhBhIiE7BF2WgR2qLCikd1zcl6xg899BD3338/r7/+OsOGDWPr1q0EBJzZ/m/atGls3bqV3bt3c/DgQQYMGEBsbCyDBg2q1rZWrVrFV199VTq3deXKlXz33XcVFrU4V85O/QGYPXs2b775Jp988gmdO3dmx44dNGpUcQWxWbNmsXDhQpKTkzHGcMUVVwCUrX08F/gRq5bwNVi1kTuISPr5HI+Tzqs2soicMdVGRL4yxpysjeyShjbGGD85VTKxxjkKWszEGixVNXdkez2zVXXNlxsPSs9nvpSLJi+Rfy74SgqXPiHyl1bWqOLXLhH55T8iRfmly5fYS2R71nb5aOtH8tQPT8mNH98oie8mlp61XjXvKpn4zUT594Z/S/LhZCkoPr+pK+fq/vvvlz/+8Y/lXhsyZIi8+OKLLtvHsmXLJDo6Wuz2U1OgWrZsKUuXLq1w+ejoaFm2bFnp8ylTpsiIESPOaVsLFy6UgQMHSkxMjEyePFlycnLOWGbbtm3SqFEjWbvWuhKxb98+iYiIKJ1udDbOjkYuKSmRFi1ayJdfflnlsiIivXv3ltdff730+ezZswXIFetsqSNQANSXU2dQ31JmZK47HkA9rIIagjVlZrvj9c5YI6OPYiXhoWXWeQd4FfjUsU6lZ+BY9Z3/W0UM0cDHWKORtwHjyrw3DatU4xyss+S7K1g/CiuZH8UajRwDrANi3PB5TQaep4oR2ScfemarLmhHcgt4avFGPk7ex/CIPTwZ8w0NU5YBxroP2/NeaNWLrIKjpB5cbd1nTU8lLSONnCJr3n2ofyjxkfHcGX8niVGJxEfGExEU4dkDcxgzZgzXXXcdf/vb3/Dx8SmtjTx79uwKl7fZbPz2228Vvnfbbbfxyitn3nI7Wz3jQYMGlVs2KyuL/fv3k5iYWPpaYmIiCxcurPa2oPq1kdeuXeuW2sh79+5l7969pKWlMXbsWPz8/Bg9ejRTp06tcFDahg0bzvgMsOZtggdrIwOhxhjBKnm4rUxt5LeAK7HKKS4yxiTJqXrDt2GdsQ6hTJvAssrURm5rjAkRkbxKwpiLldCjseazfmGM2SFW1yCwphLdBIzG+nJwurK1kf8PJ2ojO46pIt9JJeUcjTGtgTuxGka8XMn65WiyVRckEeHj5P385eP19CtcyU8RX9M4bwuUNKSo9+/Z3KEfKfmHSNm9kNS109mTY1WJ8zE+dGjYgasuugpbpA1blI2Lwi7Cx9TOUb61rTayo5VcueXLLltXayPv3bsXgM8//5zU1FSOHj3KlVdeSYsWLRg3btwZy59+nI6ffYz1bUFrI9f+2sj/AJ4UkVxnbwU5lWyNMQ2B2UA81iWGO0Xkx3MMUimPOnDsBH/78Bta7/qApf7LyQvMZW2DtqR0GkaKKWLToU8oOrAQgKigKGxRNm7seCMJkQnERcQR7B/s2QOoptpUG/lkkf7s7GwCAwPPWLau1kYOCrJOSidNmlTaJemee+7h008/rTDZnn6cjp/tIiKONndaG/nUvmpVbWRjzLVYl/grOoZKOXtmOxOrtuWNxpgAoG79tlEKsNuFj5d9xK71/6JD4FY2RAdwXUg4mYQBBQQe20RsRCy3dbrNKhoRZaNJcJM6P4ipNtVGbtSoEc2aNSM5OfnkoCCSk5OJi4ur9rZOqg21kWNiYggICHD630pcXBzJycn06NEDsD4DTp2paW3k2l0b+XIgyRhz0PE8DCgxxiSIyLDK9lVlsjXGNAAuxdGoV0QKsepAVqpw5052jxpd1aaVcitBOFF8grzCXLKPHyGvKI9iI7QwACEM8qnHjQH1CfEPISQglCC/IHwQIAVIoQCrqGtdUq9zJ5qedhm1RYsWdO/enVGjRjF8+PDSs7CKnK0HbWX69++Pr68v//jHP7j33nt54403ALjssssqXH706NE8/fTTJCUlcejQId54443SOcDV3ZazHnzwQbp168bs2bMZP3489957L//73//Oa5tlBQcHM2LECJ5//nm6dOnCsWPHeOONN5g4cWKFy48ePZoXXniBa665BmMMM2bMADgCICK/GmPWA1ONMVOw6vDacHPz+Eqsxhr4NMkYMwOr/OG1QHdnVjbG+ALjOZU/dhpjUrDuvc4pu6yI7DHG/AD8xRjzf1gDxe7Cqj1cJREpNsZ8BowBnnXstxArmf5UyTrONhoo60nguTLPZ2J9UZh+tpWcudHUFkgH3jZW5/vZFTX/NcaMN8asMcasKSoqqkbcSrlGob2IrIIs9ubuZXPWFtYdWseGIxvYlbOb7OJcAkSI9AmlY1h7ujTuQoLjfmvj4MaE+AXjQ90+gz2bMWPGkJqayqhRo1y+7YCAABYuXMh7771Hw4YNeeutt1i4cGHpVJ3333+/9MwV4KmnnqJdu3a0bt2afv36MXHixNLBT1Vt61wsWrSIzz77rPSs/IUXXmDdunW8/35FvcfP3csvv0xoaCjR0dH07t2b2267jTvvvBOAb7/9tlyf23vuuYdrr72WhIQE4uPjGTx4MFi/Z0+6BevyaRbWL/YbpWam/ZTjOLkaipXwM4BXgNEistnJTQwBckRkeZnX5mIdX0VuxRpItR/4CJgqIl9UI+R7gQHGmLWOfVwJ3GGM+UM1tnFWIpIjIgdPPrCuSOSJSObZ1quyNrIxJgmrA0UfEVltjJkJZIvIk5Wto7WRlbvlF+ezKXOTNZ81PYXUjFQO5B0AwM/4EuMTTMLRQ9hOHKfgRBv2Rt3GLbfeRdOGF+YdEK2NXPtpbWTv5sw9273AXhE5OXF3Htb8IqVqhIiwO3t3uRKHv2b+SrFjPnt0SDSJkTZGRnTFtnsNnXatwdcE8GFRX971H8KYYYN40Naszt97PVdaG1kpz6sy2YrIQWPMHmNMjGOu0uVYbZaUcotjBcfKVWFKTU8lu9AauRnsF0x8ZDxj48dalZhCWxO5eSn8NAuO7qYwpDnv1hvDy0cv5rIuMcwaEkt4yLlfgqzrtDayUrWDs6OR/wC87xiJvAO4w30hqQtJkb2IX7N+tS4Fp6eSmpHKruxdABgM7Rq244rWV5SWOGwb1hZfH1/I2AqrX4P1c6Eoj5KWvVkQcQ+PbWxJVIMQXhqbwIBOjT17cLVA586dycurrH6AUqqmOJVsRWQ95ec6KVVtIsLBvIOlreRSM1LZeGQjBSUFAEQERmCLsjGs/TBskTbiIuMI8S8zFs9uh+3LYfWrsO1L8A2A+BtZ3/wW/rCihD2ZJxjZqxWPDupE/cCqO/AopVRN0QpSym2OFx1nw5ENpSUOUzJSyDiRAUCATwCxEbHcHHNzaSP0ZiGV3FctyIXkuVbv2CNbIbQJDHiC7LjbefabI3ywYA8XRYbw3/G96Nm2dpRJVEqpsjTZKpewi50dR3eUO2vddnQbdsc8+NYNWtOrWa/SPq0dG3XEv6r+r1m74Kc3YN2/oeAYRHeFG96A2Ov4fEsmU15PIyO3gHv6teXhgR0J9Pd1/4EqpdQ50GSrzsmRE0fKDWJKy0gjr8i6N1g/oD62SBuXtboMW6SNhMgEGgY2dG7DIrDrO+t+7JZPAQOxw6DXfdCiOxl5hUz7XxpLUg7QqWl9Zo9JwtbCyW0rpZSHaLJVVSosKSyd03rycvC+XKuCmp/xo0OjDgxpOwRblJVYWzdoXf3C/EUnIHWelWQPpUFQOPR9GJLugrDmiAgL1+/jqcUbOV5QwiNXdOTe/u3w99WpLEqp2k+TrSpHRNibs5fkjOTS0cGbMjdRbLfmtDYNaUpCZAK3drqVhMgEOkd0Jsiv8vJ/VcreDz/PhrXvwPEj0DgOhv4TEm4Cf2u7+46e4ImPUlmxJZ0urRry/HAbHZqcWZheKaVqK022F7jswmzSMtJK77OmpqeSVZAFQJBfEHERcYyOHW1dDo5KoHGwi6bT7PnZGlW8cRHYS6DTYKt3bJu+4BgkZbcL7//0G899ugm7wNRrYxnduw2+PhdmcQqlVN2lyfYCUmwvZtvRbaUlDlMyUth5bCdgzWltG9aWfi37lY4ObtewHX4+LvwnUlxoJdfVr8K+tVAvzEqwPcZBozblFt2Rnsvk+an8tCuTvu0j+csNCbQMvzBLLSql6j5Ntl7sUN6h0gpMyenJbMrcxIliq4tXeGA4tkgbQ9oOISEygfjIeOoHuOnSbG46rH0bfn4Tcg9CRHu45u+QeCvUCy23aHGJndnf7eTFL36lnp8Pz99o46ZuLS7YUotKKe+gydZLHC86zsYjG8uNED58/DAA/j7+dA7vzPAOw0srMTUPbe7+BHYgxRrwlDoPSgqg/UDo+TK0uxwqqNG7cX82k+Ynk7Yvm6vimjB9WDyNGwS6N0allKoBmmzrILvY2XVsV7k5rVuztlIiVo/kFqEtSGqSVDo6uFN4JwJ8a6g+cEmxNWVn9Wuw+3vwD4Guo6DHPRDVscJVCopLeHn5Nl5dsZ2Gwf68cntXro5vqmezSimvocm2DsjKzzp1xppuzWnNKcoBINQ/lPjIeO5KuKt0EFN4YHjNB3kiC9a9Bz/NhmO/QcNWcOXT0GUUBDWsdLW1uzOZNC+F7el53NC1OU8OjqXRBdw4QCnlnTTZ1jJFJUVsztxc7qx1T84eAHyMDx0admDQRYNKKzG1CWtT/TmtrpS+xTqLTf4Aio5Dm0tg0LMQcw34VF7RKa+gmL8t28K7P+4iOiyId+7oTv8YbRyglPJOmmw9SETYl7uv3H3WTUc2UWQvAqBxUGNsUTZu7HgjCZEJxEXEEexfC0bk2u2w7QsryW5fDr71wHaTNbK4aUKVq3+7NZ3HFqSyN+sEY3q3ZuKgToTW03+KSinvpb/halBuYS5pR9LKVWLKzM8EINA3kNiIWG7vfHvpIKamIU09HPFpCnJg/X+shgCZ26F+M7hsCnS7A0Iiq1z92PEinv5kIx+u3UvbqBA+vLc33dt44JK3UkrVME22blJiL7HmtGac6tO6/eh2BAGgTYM29G3et/Q+a4dGHfD3qaVt4TJ3Ws3Zf5kDBdnQojsMeNyqWVxVMwGHz9IO8OSiDWTmFXJ//3ZMuLyDNg5QSl0wnEq2xphdQA5QAhSLiPa2PU368fTSS8GpGamkZaSVzmkNqxeGLdLGlW2uxBZpIz4ynrB6YR6OuAoisPMb6yx2y1Lr/mvc9dDzPmjRzenNHM7JZ+qiDSxNO0hsswa8PbY78c1r+bErpZSLVefMdoCIZLgtkjokvzi/tDD/yQR7MO8gYBXmjwmP4br215UOYmpZv2XdmcZSeBxS/2cl2cMbITgSLp0ISXdCg2ZOb0ZEmL9uH9OXbOREUQkTr4ph/KVttXGAUuqCpJeRqyAi7M7eXTo6OCU9ha1ZWykWqzB/dEg0v4v6HQmdrfusncI7EehXBwsxHNt7qiHAiSxroNOwVyB+OPhX73j2Zh3n8Y/SWPlrOt1aN+Kvw220bxxa9YpKKeWlnE22AnxujBHgdRGZdfoCxpjxwHiAVq1auS7CGnas4FjplJuTf2YXZgMQ7BdMfGQ8Y+PHlg5iigyqemBQrSUCe1Zbo4o3fgyIoyHAfdD64tKGAM6y24V/r9rNXz/bDMBTQ+MY1as1Pto4QCl1gXM22fYRkf3GmMbAF8aYzSKysuwCjgQ8CyApKUlcHKdbFJUU8WvWr6WDmFIyUtidvRuwCvO3a9iOK1pfUZpY24a1xfcsc0frjOIC2PARrHoVDqyHwDDo/QB0vxsatT6nTW47nMvk+Sms2Z3FpR2jePb6eFo0qgXTlJRSqhZwKtmKyH7Hn4eNMR8BPYCVZ1+rdhERDuQdOJVY01PYlLmJgpICACICI7BF2biu/XXYIm3ERcYR4h/i4ahdLOfQqYYAeYchMgYGvwCJt0DAuR1rUYmdWSt3MPPLrQQF+PL3mxIZ3rUG6i4rpVQdUmWyNcaEAD4ikuP4+Urgz26P7DzlFeWxIWNDuUpMGSes8V0BPgHERsRyc8zN2CJt2KJsNAtp5r0JYv8vsOo12LAASgqhw5VWAYp2l1X7UnFZafuOMWleChsPZHNNQlOmDY2jcf06eL9aKaXczJkz2ybAR45E5Af8R0Q+c2tU1VRiL2HHsR3lKjFtP7odu9gBaN2gNb2a9SodHdyxUUf8nZwfWmeVFMPmxVaS3bMKAkKt4hM974GIdue16fyiEmZ+tZVZK3fQKDiA10Z2ZVC88yOVlVLqQlNlshWRHUBiDcTitIwTGaX3WFPTU0k7kkZeUR4A9QPqY4u0cXmry62CEZEJNAxs6NmAa9LxTFj3rtUQIHuv1ZT9qr9Al9ute7Pn6eddmTw6L4UdGXnc1K0FUwbHEhbs5V9clFLqPNX6qT8FJQVsOrKp3Ojgfbn7AGtOa4dGHRjSdkhpO7nWDVp7tjC/pxzaaI0qTvkfFJ+Ai/rB4L9bl4xdMKgrt6CY5z/bzHs/7qZFoyDeu7MHl3aMckHgSinl/WpVshUR9uTsOXWfNT2VzVmbKbZbc1qbhjQlITKBWzvdSkJkAp0jOhPkF+ThqD3Iboety6xRxTu/Ab9AsI2w7sc2iXXZblZsOcwTH6Wx/9gJxl7cholXxRCijQOUUsppHv2NmV2YTVp6GskZyaX1g48WHAUgyC+IuIg4RseOLq0f3DhYW7ABkJ8N69+3qjxl7YQGzeHyqdBtLAS7rrB/Vl4h0z/ZyIJ1+2gXFcK8e3vTrbU2DlBKqeqqsWRbbC9ma9bWcvWDdx7bCVhzWtuGtWVAywEkRCVgi7TRrmE7/Hz07KmcI9utBLv+fSjMhZa9YOBU6DTE6YYAzhARlqYd5E+L0jh6vIjfD2jP7y9rr40DlFLqHLktmx3MO3hqdHB6ChuPbCS/JB+A8MBwbJE2hrQdQkJkAvGR8dQPqO+uUOo2EdjxtTWqeOvn4ONnlVDsdS9Ed3H57g5n5/PkojSWbThEfPMGvHtnD+KitXGAUkqdDyPi+mJPDdo1kNZ/sioR+fv40zmic+nIYFuUjeahWvSgSoV5kPyBdSabsQVCoiDpLqshQP0mLt+diPDhmr08/clG8ovtPDywI+MuuQg/bRygVI0wxqzVjmreyy1ntsH+wUzuMRlbpI2Y8BgCfAPcsRvvdPQ3+OkNWPce5B+FZolw3WsQfwP41XPLLvdkHuexBal8ty2DHm3CeW54Am2jtHGAUkq5iluSbYvQFtze+XZ3bNo7icBvP1qjijcvAQx0vhZ63Qcte55XlaezKbEL7/6wi78t24KPgenXxXN7j1baOEAppVxMRyB5UlE+pM235sceTIGgRnDxBOgxDsJauHXXWw/l8Oj8FNb9dpT+MVE8c30CzRtewNOolFLKjTTZekLOQasZwJq34HgGRHWGa2dCws0Q4N5OOUUldl5bsZ1/Lt9GcD1fXhyRyHW/03voSinlTppsa9K+tY6GAB+BvRg6DrJGFV/Uz22XistK3XuMifOS2Xwwh8G2Zjw1NI7IUPfcB1ZKKXWKJlt3KymCjYusUcV7f4KA+tZl4u53n3dDAGflF5Xw4pe/8sbKHUSG1uP1Ud24Kq5pjexbKaWUJlv3yTtyqndszn4IbwtXPw+/uw3q1dyc4lU7jjB5fgq7jhxnRFJLHh/cmbAgbRyglFI1SZOtqx1MswY8pX4IxfnQdgBc+xK0vwJ8am7Oak5+Ec8t3cz7q3+jZXgQ79/dkz7tI2ts/0oppU7RZOsK9hLYstRKsru+Bf9g6wy2xz3QuFONh/P15sM8/lEqB7PzuavvRTxyZUeCA/SvWimlPMXp38DGGF9gDbBPRIa4L6Q65MRR+GUO/DQLju6GsJZwxZ+h62hrGk8Ny8wr5M+LN7Bw/X46NA5l/n0X07VVzcehlFKqvOqc7jwIbAIauCmWuiNjq3UWu34uFOVBq4vhyukQMxh8a/4MUkRYknKAaR9v4NiJIiZc3oEHBrSjnp82DlBKqdrAqcxgjGkBDAaeAf7o1ohqK7sdti+H1a/Cti/BNwASboKe91glFT3k4LF8pixM48tNh7C1CGPO3T3p3Ey/DymlVG3i7GnYS8AkoNJhtMaY8cB4gFatWp13YLVGQS4kz7Wm7hzZCqFNYMAT0O0OCI3yWFgiwgc/7+HZTzZRWGLn8Ws6cWcfbRyglFK1UZXJ1hgzBDgsImuNMf0rW05EZgGzAJKSklzfSqimZe1yNAT4NxQcg+bd4IbZEDsM/DzbWGH3kTwmz0/lxx1H6HlROH8dbqNNZIhHY1JKKVU5Z85s+wBDjTHXAIFAA2PMHBEZ6d7QPEAEdn1n3Y/d8ikYHyu59rwPWnb3dHSU2IW3v9/J3z/fgp+PD89en8At3Vtq4wCllKrlqky2IvIY8BiA48z2/7wu0RadgNR5VpI9lAZB4dD3YavKU4NoT0cHwJaDOUyan0LynqNc1qkxz1wfT7MwbRyglFJ1wYU9+TJ7P/w8G9a+A8ePQJN4GPoyJNwI/rUjkRUW23llxTb+9fU26gf6M/OW3zE0MVobByilVB1SrWQrIiuAFW6JpCbt+dkaVbxxkVWQotNg6HkvtOlbIw0BnLV+z1EenZfClkM5DE2MZuq1sURo4wCllKpzLpwz2+JC2LjQulS8by3UC7MSbI9x0KiNp6Mr50RhCS98sYU3v9tJ4/qBzB6dxMDYJp4OSyml1Dny/mSbm36qIUDuQYhoD9f8HRJvhXqhno7uDD9sz2Dy/FR+yzzObT1bMfnqTjQI1MYBSilVl3lvsj2Q4mgIMA9KCqD9QOj5L2h3WY02BHBWdn4Rf/l0M3N/+o3WEcH8Z1xPLm6njQOUUsobeFeyLSmGLZ9YDdp/+wH8Q6DrKKshQFRHT0dXqS83HuKJhamk5xQw/tK2PDywI0EBWmpRKaW8hXck2xNZsO49qwjFsT3QsBVc+Qx0GQlBDT0dXaWO5Bbw1OKNfJy8n5gm9Xl9VBK/a9nQ02EppZRysbqdbNO3WJeKkz+AouPQ5hIY9BzEXA0+tffMUET4OHk/0z7eQG5BMQ8P7Mh9/dsR4Ff7Lm8rpZQ6f3Uv2drtsO0LK8luXw6+9cB2kzWyuGmCp6Or0oFjJ5jyURpfbT5MYsuGPD/cRkzTSktOK6WU8gJ1J9kW5MD6/1gNATK3Q/1mcNmT0G0shNT+gUR2uzD359/4y6ebKbbbmTK4M3f0uQhfLbWolFJer/Yn28wd1r3YX+ZAQTa06A4DHrdqFvvWjSkxOzPymDw/hdU7M7m4XQTP3WCjVUSwp8NSSilVQ2pnshWBnd9Yo4p//cy6/xp3vdUQoEU3T0fntOISO299v5MZn/9KgK8Pz92QwIjuLbXUolJKXWBqV7ItPA6p/7MuFR/eCMGRcOlESLoTGjTzdHTVsulANo/OTyFl7zEGdm7C09fF0zQs0NNhKaWU8oDakWyP7XX0jn3XmsbTNAGGvQLxw8G/biWoguIS/vX1dl75ehthQf7889YuDLE107NZpZS6gHku2YrAntWw6lXYtBgQ6DQEet0HrXrXqoYAzlr3WxaPzkth6+Fcru/SnCeHxBIe4tlG80oppTyv5pNtcQFs+MhKsgfWQ2AY9H7AagjQsFWNh+MKxwuLmfH5r7z1/U6aNgjk7bHdGdCpsafDUkopVUvUXLLNOQRr3rIeeYchMgYGvwCJt0BASI2F4Wrfb8tg8oIU9mSeYGSvVjw6qBP1tXGAUkqpMqpMtsaYQGAlUM+x/DwRmer0Hvb/Yo0qTpsP9iLocBX0uhfaDqiTl4pPOnaiiGc/2cR/1+zhosgQ/ju+Fz3bRng6LKWUUrWQM2e2BcBlIpJrjPEHvjPGLBWRVZWvIo5Lxa/BnlUQEGqNKO55D0S0c1HonvP5hoNMWZhGRm4B9/SzGgcE+tfe8pBKKaU8q8pkKyIC5Dqe+jsectaVDm2ED8daTdmv+gt0ud26N1vHpecUMG3xBj5JOUCnpvWZPSYJW4uGng5LKaVULefUPVtjjC+wFmgP/EtEVlewzHhgPIAtOhhumQsdr6rVDQGcJSIsXL+PpxZv5HhBCY9c0ZF7+7fD31cbByillKqasU5cnVzYmIbAR8AfRCStsuWSkpJkzZo15x9dLbDv6Ame+CiVFVvS6dqqIc/faKN9Y20coJRyLWPMWhFJ8nQcyj2qNRpZRI4aY1YAg4BKk603sNuF91fv5rmlm7ELTL02ltG922jjAKWUUtXmzGjkKKDIkWiDgIHAX90emQftSM9l8vxUftqVSd/2kfzlhgRahmvjAKWUUufGmTPbZsC7jvu2PsD/RGSJe8PyjOISO298u5MXv/yVQD8fnr/Rxk3dWmipRaWUUufFmdHIKUCXGojFozbuz2bS/GTS9mVzVVwTpg+Lp3GDulWXWSmlVO1UOxoReFB+UQkvL9/Ga99sp2GwP6/c3pWr45vq2axSSimXuaCT7drdmUyal8L29DyGd23Bk0M60zBYGwcopZRyrQsy2eYVFPO3ZVt498ddRIcF8e6dPejXMcrTYSmllPJSF1yyXflrOo8tSGXf0ROM6d2aiYM6EVrvgvsYlFJK1aALJsscO17E9E82Mm/tXtpGhfDhvb3p3ibc02EppZS6AFwQyfaztAM8uWgDmXmF3N+/HRMu76CNA5RSStUYr062h3PymbpoA0vTDhLbrAFvj+1OfPO63xBBKaVU3eKVyVZEmL9uH9OXbOREUQkTr4ph/KVttXGAUkopj/C6ZLsn8ziPf5TKt1szSGrdiOeG22jfONTTYSmllLqAeU2ytduF937cxfPLtgDw1NA4RvVqjY82DlBKKeVhXpFstx3OZfL8FNbszuLSjlE8e308LRpp4wCllFK1Q51OtkUldmat3MHML7cSFODL329KZHjX5lpqUSmlVK1SZ5Nt2r5jTJqXwsYD2VyT0JRpQ+NoXF8bByillKp96lyyzS8qYeZXW5m1cgfhIQG8NrIrg+KbeTospZRSqlJ1Ktn+vCuTR+elsCMjj5u6tWDK4FjCgv09HZZSSil1VlUmW2NMS+A9oClgB2aJyEx3B1ZWbkExz3+2mfd+3E2LRkH8+64eXNJBGwcopZSqG5w5sy0GHhGRdcaY+sBaY8wXIrLRzbEBsGLLYZ74KI39x04w9uI2TLwqhhBtHKCUUqoOqTJricgB4IDj5xxjzCagOeDWZJuVV8j0TzayYN0+2kWFMO/e3nRrrY0DlFJK1T3VOkU0xrQBugCrK3hvPDAeoFWrVucckIiwNO0gf1qUxtHjRfzhsvY8MKC9Ng5QSilVZzmdbI0xocB84CERyT79fRGZBcwCSEpKknMJ5nB2Pk8uSmPZhkMkNA/jvTt7Ehvd4Fw2pZRSStUaTiVbY4w/VqJ9X0QWuDoIEeHDtXt5eslGCortTL66E3f3vQg/bRyglFLKCzgzGtkAbwKbROQFVwewJ/M4jy1I5bttGfRoE85zwxNoG6WNA5RSSnkPZ85s+wCjgFRjzHrHa4+LyKfns+MSu/DuD7v427It+BiYfl08t/dopY0DlFJKeR1nRiN/B7g0A249lMOj81NY99tR+sdE8cz1CTRvGOTKXSillFK1Ro1OWC0qsfPaiu38c/k2Qur58uKIRK77nTYOUEop5d1qLNmm7j3GxHnJbD6YwxBbM6YNjSMytF5N7V4ppZTyGLcn2/yiEl788lfeWLmDyNB6zBrVjSvjmrp7t0oppVSt4dZku3rHESYvSGVnRh63dG/JY9d0JixIGwcopZS6sLgl2dpFmLIwlTmrfqNleBDv392TPu0j3bErpZRSqtZzS7L99VAuR1f/xt19L+KPV3YkOEAbByillLpwuSUL+hiYf9/FdGnVyB2bV0oppeoUt9RD7NC4viZapZRSysEtyVanzSqllFKnaKV/pZRSys002SqllFJupslWKaWUcjNNtkoppZSbabJVSiml3EyTrVJKKeVmVSZbY8xbxpjDxpi0mghIKaWU8jbOnNm+AwxycxxKKaWU16oy2YrISiCzBmJRSimlvJLes1VKKaXczGXJ1hgz3hizxhizJj093VWbVUoppeo8lyVbEZklIkkikhQVFeWqzSqllFJ1nl5GVkoppdzMmak/c4EfgRhjzF5jzF3uD0sppZTyHlU2jxeRW2siEKWUUspb6WVkpZRSys002SqllFJupslWKaWUcjNNtkoppZSbabJVSiml3EyTrVJKKeVmmmyVUkopN9Nkq5RSSrmZJlullFLKzTTZKqWUUm6myVYppZRyM022SimllJtpslVKKaXcTJOtUkop5WaabJVSSik302SrlFJKuZlTydYYM8gYs8UYs80YM9ndQSmllFLepMpka4zxBf4FXA3EArcaY2LdHZhSSinlLZw5s+0BbBORHSJSCHwADHNvWEoppZT38HNimebAnjLP9wI9T1/IGDMeGO94WmCMSTv/8GqlSCDD00G4kR5f3abHV3fFeDoA5T7OJFtTwWtyxgsis4BZAMaYNSKSdJ6x1UrefGygx1fX6fHVXcaYNZ6OQbmPM5eR9wItyzxvAex3TzhKKaWU93Em2f4MdDDGXGSMCQBuAT52b1hKKaWU96jyMrKIFBtjfg8sA3yBt0RkQxWrzXJFcLWUNx8b6PHVdXp8dZc3H9sFz4iccftVKaWUUi6kFaSUUkopN9Nkq5RSSrmZS5OtN5d1NMa8ZYw57K3zh40xLY0xXxtjNhljNhhjHvR0TK5kjAk0xvxkjEl2HN9Tno7J1YwxvsaYX4wxSzwdi6sZY3YZY1KNMeu9cYqMMaahMWaeMWaz4/9gb0/HpFzLZfdsHWUdfwWuwJou9DNwq4hsdMkOPMwYcymQC7wnIvGejsfVjDHNgGYiss4YUx9YC1znRX9/BggRkVxjjD/wHfCgiKzycGguY4z5I5AENBCRIZ6Ox5WMMbuAJBHxyoIWxph3gW9FZLZj1kewiBz1cFjKhVx5ZuvVZR1FZCWQ6ek43EVEDojIOsfPOcAmrOphXkEsuY6n/o6H14wONMa0AAYDsz0di6oeY0wD4FLgTQARKdRE631cmWwrKuvoNb+sLyTGmDZAF2C1h0NxKcdl1vXAYeALEfGm43sJmATYPRyHuwjwuTFmraM0rDdpC6QDbztuA8w2xoR4OijlWq5Mtk6VdVS1mzEmFJgPPCQi2Z6Ox5VEpEREfodVBa2HMcYrbgcYY4YAh0VkradjcaM+ItIVq/vYA47bOt7CD+gKvCoiXYA8wKvGvCjXJlst61jHOe5lzgfeF5EFno7HXRyX6FYAgzwbicv0AYY67mt+AFxmjJnj2ZBcS0T2O/48DHyEddvKW+wF9pa50jIPK/kqL+LKZKtlHeswxwCiN4FNIvKCp+NxNWNMlDGmoePnIGAgsNmjQbmIiDwmIi1EpA3W/7vlIjLSw2G5jDEmxDFoD8fl1SsBr5kVICIHgT3GmJNdfy4HvGJgojrFma4/TjnHso51hjFmLtAfiDTG7AWmisibno3KpfoAo4BUx31NgMdF5FPPheRSzYB3HaPmfYD/iYjXTZHxUk2Aj6zvg/gB/xGRzzwbksv9AXjfcaKyA7jDw/EoF9NyjUoppZSbaQUppZRSys002SqllFJupslWKaWUcjNNtkoppZSbabJVSiml3EyTrVJKKeVmmmyVUkopN/t/QPKALhdNNuEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for l in alpha:\n",
    "    model = Lasso(alpha=l)\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    model_coeff = model.coef_\n",
    "    model_intercept = model.intercept_\n",
    "    y_pred = model.predict(pd.DataFrame(X_test))\n",
    "    \n",
    "    plt.figure(2)       \n",
    "    plt.plot(X_test, y_pred)\n",
    "    plt.ylim(ymin=0, ymax=9)\n",
    "    plt.xlim(xmin=0, xmax=6)\n",
    "    plt.text(X_test[-1], y_pred[-1],  \n",
    "             ' y = ' + \n",
    "             str('%.2f' %model_coeff) +\n",
    "             ' * x' + \n",
    "             ' + ' + \n",
    "             str('%.2f' %model_intercept) +\n",
    "             '      for \\u03BB or \\u03B1 = ' + str(l), fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7bc6aa4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, l in enumerate(alpha):\n",
    "    model = Lasso(alpha=l)\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    model_coeff = model.coef_\n",
    "    model_intercept = model.intercept_\n",
    "    y_pred = model.predict(pd.DataFrame(X_test))\n",
    "\n",
    "    plt.figure(1)    \n",
    "    plt.subplot(2,2,i+1)\n",
    "    plt.plot(X_test, y_pred)\n",
    "    plt.ylim(ymin=0, ymax=9)\n",
    "    plt.xlim(xmin=0, xmax=6)\n",
    "    plt.title(' y = ' + \n",
    "             str('%.2f' %model_coeff) +\n",
    "             ' * x' + \n",
    "             ' + ' + \n",
    "             str('%.2f' %model_intercept) +\n",
    "             ' for \\u03BB or \\u03B1 = ' + str(l), fontsize=12)\n",
    "    plt.tight_layout()"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}