Ridge And Lasso Practical.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c3b5cf5d",
   "metadata": {},
   "source": [
    "### Linear Regression Practical Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "26dd6c2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "048fc045",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c01140eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "b2c173df",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "3592e757",
   "metadata": {},
   "outputs": [
    {
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       "         7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
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       "         8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "         9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "        10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
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       " 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "        'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'),\n",
       " 'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\",\n",
       " 'filename': 'C:\\\\Users\\\\win10\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\boston_house_prices.csv'}"
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     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
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    "df"
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  {
   "cell_type": "code",
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   "id": "e6067ea6",
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   "source": [
    "dataset=pd.DataFrame(df.data)"
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   "cell_type": "code",
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     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "7ad3b490",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.columns=df.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "9cf167a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "8204ddd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Independent features and dependent features\n",
    "X=dataset\n",
    "y=df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "f05469ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
       "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
       "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
       "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
       "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
       "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
       "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
       "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
       "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
       "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
       "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
       "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
       "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
       "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
       "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
       "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
       "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
       "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
       "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
       "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
       "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
       "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
       "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
       "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
       "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
       "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
       "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
       "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
       "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
       "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
       "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
       "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
       "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
       "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
       "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
       "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
       "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
       "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
       "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
       "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
       "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
       "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
       "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
       "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "2dca59c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "## train test split \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9a76e705",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.02985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.430</td>\n",
       "      <td>58.7</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.12</td>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0.13158</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.547</td>\n",
       "      <td>6.176</td>\n",
       "      <td>72.5</td>\n",
       "      <td>2.7301</td>\n",
       "      <td>6.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>393.30</td>\n",
       "      <td>12.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.17142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.448</td>\n",
       "      <td>5.682</td>\n",
       "      <td>33.8</td>\n",
       "      <td>5.1004</td>\n",
       "      <td>3.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>396.90</td>\n",
       "      <td>10.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.05393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>5.935</td>\n",
       "      <td>29.3</td>\n",
       "      <td>4.4986</td>\n",
       "      <td>4.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>386.85</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>15.57570</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.580</td>\n",
       "      <td>5.926</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.9084</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>368.74</td>\n",
       "      <td>18.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>0.17120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.520</td>\n",
       "      <td>5.836</td>\n",
       "      <td>91.9</td>\n",
       "      <td>2.2110</td>\n",
       "      <td>5.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>395.67</td>\n",
       "      <td>18.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>0.29916</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.464</td>\n",
       "      <td>5.856</td>\n",
       "      <td>42.1</td>\n",
       "      <td>4.4290</td>\n",
       "      <td>3.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>388.65</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>0.01501</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.435</td>\n",
       "      <td>6.635</td>\n",
       "      <td>29.7</td>\n",
       "      <td>8.3440</td>\n",
       "      <td>4.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>390.94</td>\n",
       "      <td>5.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>11.16040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.740</td>\n",
       "      <td>6.629</td>\n",
       "      <td>94.6</td>\n",
       "      <td>2.1247</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>109.85</td>\n",
       "      <td>23.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.22876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.520</td>\n",
       "      <td>6.405</td>\n",
       "      <td>85.4</td>\n",
       "      <td>2.7147</td>\n",
       "      <td>5.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>70.80</td>\n",
       "      <td>10.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS   RAD    TAX  \\\n",
       "5     0.02985   0.0   2.18   0.0  0.458  6.430  58.7  6.0622   3.0  222.0   \n",
       "116   0.13158   0.0  10.01   0.0  0.547  6.176  72.5  2.7301   6.0  432.0   \n",
       "45    0.17142   0.0   6.91   0.0  0.448  5.682  33.8  5.1004   3.0  233.0   \n",
       "16    1.05393   0.0   8.14   0.0  0.538  5.935  29.3  4.4986   4.0  307.0   \n",
       "468  15.57570   0.0  18.10   0.0  0.580  5.926  71.0  2.9084  24.0  666.0   \n",
       "..        ...   ...    ...   ...    ...    ...   ...     ...   ...    ...   \n",
       "106   0.17120   0.0   8.56   0.0  0.520  5.836  91.9  2.2110   5.0  384.0   \n",
       "270   0.29916  20.0   6.96   0.0  0.464  5.856  42.1  4.4290   3.0  223.0   \n",
       "348   0.01501  80.0   2.01   0.0  0.435  6.635  29.7  8.3440   4.0  280.0   \n",
       "435  11.16040   0.0  18.10   0.0  0.740  6.629  94.6  2.1247  24.0  666.0   \n",
       "102   0.22876   0.0   8.56   0.0  0.520  6.405  85.4  2.7147   5.0  384.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  \n",
       "5       18.7  394.12   5.21  \n",
       "116     17.8  393.30  12.04  \n",
       "45      17.9  396.90  10.21  \n",
       "16      21.0  386.85   6.58  \n",
       "468     20.2  368.74  18.13  \n",
       "..       ...     ...    ...  \n",
       "106     20.9  395.67  18.66  \n",
       "270     18.6  388.65  13.00  \n",
       "348     17.0  390.94   5.99  \n",
       "435     20.2  109.85  23.27  \n",
       "102     20.9   70.80  10.63  \n",
       "\n",
       "[354 rows x 13 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "882e5902",
   "metadata": {},
   "outputs": [],
   "source": [
    "## standardizing the dataset\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "21f803c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "2dcdff63",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e7ff359",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "c88b6c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "##cross validation\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "572ff827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression=LinearRegression()\n",
    "regression.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "c52bdebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "mse=cross_val_score(regression,X_train,y_train,scoring='neg_mean_squared_error',cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "04a908f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-25.550660791660782"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "34f2244f",
   "metadata": {},
   "outputs": [],
   "source": [
    "##prediction \n",
    "reg_pred=regression.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "5cd2b597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([28.64896005, 36.49501384, 15.4111932 , 25.40321303, 18.85527988,\n",
       "       23.14668944, 17.3921241 , 14.07859899, 23.03692679, 20.59943345,\n",
       "       24.82286159, 18.53057049, -6.86543527, 21.80172334, 19.22571177,\n",
       "       26.19191985, 20.27733882,  5.61596432, 40.44887974, 17.57695918,\n",
       "       27.44319095, 30.1715964 , 10.94055823, 24.02083139, 18.07693812,\n",
       "       15.934748  , 23.12614028, 14.56052142, 22.33482544, 19.3257627 ,\n",
       "       22.16564973, 25.19476081, 25.31372473, 18.51345025, 16.6223286 ,\n",
       "       17.50268505, 30.94992991, 20.19201752, 23.90440431, 24.86975466,\n",
       "       13.93767876, 31.82504715, 42.56978796, 17.62323805, 27.01963242,\n",
       "       17.19006621, 13.80594006, 26.10356557, 20.31516118, 30.08649576,\n",
       "       21.3124053 , 34.15739602, 15.60444981, 26.11247588, 39.31613646,\n",
       "       22.99282065, 18.95764781, 33.05555669, 24.85114223, 12.91729352,\n",
       "       22.68101452, 30.80336295, 31.63522027, 16.29833689, 21.07379993,\n",
       "       16.57699669, 20.36362023, 26.15615896, 31.06833034, 11.98679953,\n",
       "       20.42550472, 27.55676301, 10.94316981, 16.82660609, 23.92909733,\n",
       "        5.28065815, 21.43504661, 41.33684993, 18.22211675,  9.48269245,\n",
       "       21.19857446, 12.95001331, 21.64822797,  9.3845568 , 23.06060014,\n",
       "       31.95762512, 19.16662892, 25.59942257, 29.35043558, 20.13138581,\n",
       "       25.57297369,  5.42970803, 20.23169356, 15.1949595 , 14.03241742,\n",
       "       20.91078077, 24.82249135, -0.47712079, 13.70520524, 15.69525576,\n",
       "       22.06972676, 24.64152943, 10.7382866 , 19.68622564, 23.63678009,\n",
       "       12.07974981, 18.47894211, 25.52713393, 20.93461307, 24.6955941 ,\n",
       "        7.59054562, 19.01046053, 21.9444339 , 27.22319977, 32.18608828,\n",
       "       15.27826455, 34.39190421, 12.96314168, 21.01681316, 28.57880911,\n",
       "       15.86300844, 24.85124135,  3.37937111, 23.90465773, 25.81792146,\n",
       "       23.11020547, 25.33489201, 33.35545176, 20.60724498, 38.4772665 ,\n",
       "       13.97398533, 25.21923987, 17.80946626, 20.63437371,  9.80267398,\n",
       "       21.07953576, 22.3378417 , 32.32381854, 31.48694863, 15.46621287,\n",
       "       16.86242766, 28.99330526, 24.95467894, 16.73633557,  6.12858395,\n",
       "       26.65990044, 23.34007187, 17.40367164, 13.38594123, 39.98342478,\n",
       "       16.68286302, 18.28561759])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "8ef9df8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x184d3e06a90>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A6iv0ZAyvKIBFAqRnbJaW6uDcgINFzRgK4LRTAIsExHwk3oSR3H8hSOLNGJqCSDcFsEhA9I/P4hyBGwFDvDFEW1KmnwJYJCCCtBH7Usl2ZDVjpJcCWCQgkj/itwRwCqKpqoS5SIyR6QW/S8kpKQWwmX3XzN5nZgpsEY+8MQIO3hREi/YF9kSqgfoV4FeBI2b2OTO72MOaRPJS7+gMlSUFVBQX+F3KmyT/Uegd1TxwOqUUwM65p5xzvwZcDZwAfmhm/2pmHzOz4LTsiGSxIC5BSzrbDTeuAE6nlKcUzGwD8FHg14G9wJeIB/IPPalMJM/0jc0GcgkavNGMoW649ErpZx0zexi4GPgb4FbnXG/i0ENmttur4kTySe/YDJe1VvldxrLCasbwRKqTTV91zj22+AUzK3bOzTnndnpQl0hemYtEGZycP7vzWBA1VZdoDjjNUp2C+K/LvPZsOgsRyWfJJoegbcSzWFN1CX2aA06r846AzawJaAVKzewqwBKHqoAyj2sTyRvJH+2DtBH7Ui3VJfzoUD/OOcxs5QtkRStNQdxI/MZbG/DFRa9PAH+40hc3s5uI36wLE5/G+NyS45Y4fgswDXzUObcncawG+CpwGeCAf++c06hbclIQN2Jfqqm6lNmFGKPTC9SWF/ldTk44bwA7574BfMPMftk5993VfGEzCwNfBt4DdAO7zOxR59zBRafdDGxL/Lqe+Hrj6xPHvgQ87pz7oJkVoRG35LCzjyIK8Bzw4o3ZFcDpsdIUxB3OuW8CHWb2fy897pz74jKXJV0HHHXOHUt8rQeB24DFAXwb8ICLN5g/Z2Y1ZtYMTAE/T3z0jXNuHtADqSRn9Y3NUlNWSGlR2O9SzunsxuzjM2xvCeZqjWyz0k248sTvFUDlMr/OpxXoWvR5d+K1VM7ZAgwA/7+Z7TWzr5pZOcswszvNbLeZ7R4YGFihJJFg6hmdoakquNMP8Maz4Xq0EiJtVpqC+KvE73+8hq+93Cz90q2UznVOAfEmj992zj1vZl8C7gX+8zI13g/cD7Bz505t1SRZqXtkhk0bgj3L1lBZTDhk2pYyjVLdjOe/m1mVmRWa2Y/MbNDM7ljhsm6gfdHnbUBPiud0A93OuecTr/898UAWyTnOObpGpmmrDe78LySaMSqL1YyRRqmuA36vc24ceD/xcLwQ+A8rXLML2GZmnYmbaLcDjy4551HgwxZ3AzDmnOt1zvUBXWZ2UeK8d/Gzc8ciOWNkeoHp+SjttcEeAUNyLbDakdMl1U645IY7twDfcs4Nr7QO0DkXMbN7gCeIL0P7unPugJndlTh+H/BY4mseJb4M7WOLvsRvA3+bCO9jS46J5Iyu4WkA2uuCH8DN1aUc6h33u4yckWoAf9/MXgVmgP/LzBqAFX8OSbQvP7bktfsWfeyAu89x7UuA2pwl53WNxAM46FMQEF8J8c+vnlEzRpqkuh3lvcBbgJ3OuQXiy8Ru87IwkXzRPRL/kT4bAripuoSZhSjjMxG/S8kJq9n5+RLi64EXX/NAmusRyTtdw9PUlBVSWRL8rbXPLkUbm6G6LPj1Bl2q21H+DbAVeAmIJl52KIBF1q1rZCYrbsDBG63SfWOzXNKsZoz1SnUEvBPY7vRIVJG06x6Z5uKmlfqaguGNZ8NpKVo6pLoM7RWgyctCRPJRLOboHpmhLUtGwA0VxYQMPRkjTVIdAdcDB83sBWAu+aJz7gOeVCWSJwYm55iPxGjPghtwAAXhEBsrS+jRCDgtUg3gz3pZhEi+6j67BC07RsAAzTUlakdOk5QC2Dn3EzPbDGxzzj1lZmXEmytEZB26huM/yrfXZccIGOJrgQ/3TfhdRk5IdS+I3yC+H8NfJV5qBR7xqCaRvJHsgsumEXBTVSm9Y7Ponvz6pXoT7m7gbcA4gHPuCLDRq6JE8kX3yAz1FcWUFGbPD5QtNSVMz0cZn1UzxnqlGsBziU3RAUg0Y+ifP5F16hqZzqrpB/jZtcCyPqkG8E/M7A+JP5zzPcB3gO97V5ZIfsimJWhJySdj9Ggp2rqlGsD3En9CxcvAbxLfYOc/eVWUSD6Ixhw9ozNZswQtKdmOrBHw+qW6CiJmZo8Ajzjn9NwfkTToHZshEnNZsQ3lYg2V8WYMdcOt33lHwImN0j9rZoPAq8BhMxswsz/KTHkiuSubdkFbrDAcoqGyWN1wabDSFMTvEV/9cK1zboNzro74Y+PfZmaf8Lo4kVx2diP2LJsDhvg0hEbA67dSAH8Y+JBz7njyhcRj5u9IHBORNToxNEU4ZLRm2QgY4jfiFMDrt1IAFzrnBpe+mJgH1magIutwYij+IM7CcKr3woOjqVrtyOmw0v/5+TUeE5EVnBicomNDud9lrElLdSmTcxHGZxf8LiWrrbQKYoeZLfcEPgNKPKhHJC845zgxOMW1HXV+l7Imi5sxqrLgSR5Bdd4Ads5lT3+kSBYZmJxjaj5Kx4bsuwEHi5oxRme4sDE7NpMPouybfBLJASeH4isgOuqzdAqiJn7jUDfi1kcBLOKD44NTAHRmaQBvrCwmHDJOj2gt8HoogEV8cGJwioKQ0VqTfUvQIP5kjKaqEnpGFcDroQAW8cHJoWna68ooyMIlaEmtNaWcVgCvS/b+3xfJYq8PTGbt9ENSS02JdkRbJwWwSIZFY47jg1NcsLHC71LWpaWmlL6xWaIxbQ2+VgpgkQzrGZ1hLhJja0O2j4BLWYg6BibmVj5ZlqUAFsmwowOTAGxtyO4RcPIGouaB104BLJJhr5/JkQBObCKklRBrpwAWybDXByapKy+itrzI71LWZXE3nKyNAlgkw14/M5X1878AlSWFVJUUaApiHRTAIhn2+sBk1k8/JLXUlGoEvA4KYJEMGpmaZ2hqPmcCuK22lNOj2g9irRTAIhn0Wv8EANsacyOANQJeHwWwSAYlA/iiptzYwrGlppSxmQUm5yJ+l5KVFMAiGXS4f4LKkgKaqnLjeQbJbSk1Cl4bBbBIBr3WP8lFjZWYmd+lpEVrTfwfEq2EWBsFsEiGOOd4rX+CC3Nk+gGgtSb+RA+NgNdGASySIQMTc4xOL3Bhlm/Cs1hDZTEFIVMAr5ECWCRDDiduwOXSCDgcMpqqS/RkjDVSAItkyOG+xAqIHHuIZXwpmtYCr4UCWCRDDvaOs7GymA0VxX6XklZ6MsbaKYBFMuRgzzjbW6r8LiPtWmtK6RufJRKN+V1K1lEAi2TAXCTK0TOTXNKcewHcVltKNOb0iPo1UACLZMDRM5NEYo7tORjA7XXxpWhdI9M+V5J9PA1gM7vJzA6b2VEzu3eZ42Zmf5E4vt/Mrl5yPGxme83sB17WKeK1gz3jADk5BdFeGw/g7mHNA6+WZwFsZmHgy8DNwHbgQ2a2fclpNwPbEr/uBL6y5PjvAoe8qlEkUw72jlNSGKJjQ/bvA7xUc00JIdMIeC28HAFfBxx1zh1zzs0DDwK3LTnnNuABF/ccUGNmzQBm1ga8D/iqhzWKZMTBnnEuaqoiHMqNFuTFCsMhmqtL6dZa4FXzMoBbga5Fn3cnXkv1nD8H/gA4761VM7vTzHab2e6BgYF1FSzihWjM8crpMXa0VftdimfaakvpGtYIeLW8DODl/ql3qZxjZu8HzjjnXlzpmzjn7nfO7XTO7WxoaFhLnSKeOjYwydR8lB1tNX6X4pn2ujJNQayBlwHcDbQv+rwN6EnxnLcBHzCzE8SnLn7BzL7pXaki3nmpaxSAHe25OwJury2jf3yO2YWo36VkFS8DeBewzcw6zawIuB14dMk5jwIfTqyGuAEYc871Ouc+5Zxrc851JK77Z+fcHR7WKuKZfd2jVBQXsKU+dzbhWaq9Lr4vsDriVqfAqy/snIuY2T3AE0AY+Lpz7oCZ3ZU4fh/wGHALcBSYBj7mVT0iftnXNcYVbdWEcvAGXNLZtcDD0znzvLtM8CyAAZxzjxEP2cWv3bfoYwfcvcLX+BfgXzwoT8RzswtRDvWO8xs/v8XvUjzVVhsfAXdpJcSqqBNOxEMHe8eJxFxO34ADaKwsoSgcolsrIVZFASzioX2JG3BXttf4WofXQiGjtVZrgVdLASzioX1dozRWFdNUnRsP4TyfttpSLUVbJQWwiIf2d49xRY5PPyS115WpGWOVFMAiHhmbXuDY4FTOTz8ktdeWMTK9wORcxO9SsoYCWMQj+0+PAuT8Dbik5FpgjYJTpwAW8cjeU6MAXJ7De0As1lb7xlpgSY0CWMQju04Mc3FTJdWlhX6XkhHtWgu8agpgEQ8sRGO8eHKE6zrr/C4lY+rKiygrCmsEvAoKYBEPHOgZZ3o+yvWdG/wuJWPMjPZarYRYDQWwiAdeOD4EwLWdtT5XklmbN5RxYmjK7zKyhgJYxAMvHB9mS305GytzvwFjsc76crqGZ4jGlm79LctRAIukWSzmeOH4cF7N/yZ11JczH43Ro20pU6IAFkmzw/0TjM9G8jOAEw8d1TREahTAImn2wvFhgPwM4Pr4WuATQ7oRlwoFsEiavXB8mNaa0rONCfmksbKEksIQJwY1Ak6FAlgkjZxzPJ+n878Q35ayY0O5AjhFCmCRNDo2OMXg5BzXduRnAEN8HlhzwKlRAIuk0TNHBgH4uQvqfa7EP5vry7QULUUKYJE0evrIAJvqyti0If/mf5M6N2gpWqoUwCJpshCN8ezrQ7x9W/6OfiG+Fhi0FC0VCmCRNNl7apSp+Shv39bgdym+6kwGsG7ErUgBLJImTx8ZIGTwlq35swHPcjZWFlNaGOb4oNYCr0QBLJImTx8Z5Mr2mrzZ//dczEyb8qRIASySBqPT8+zvHs376YekznotRUuFAlgkDf719SFijry/AZfUUV9O1/A0kWjM71ICTQEskgZPHxmksriAHXnyBOSVdGwoYyHq6Bmd9buUQFMAi6yTc46fvjbADVs3UBjWXynQrmip0p8WkXV6tW+C06MzvOvijX6XEhidDfEAfn1g0udKgk0BLLJOTx3sB+AXLlEAJzVUFFNdWsjRMwrg81EAi6zTU6+e4cr2mrx7/ND5mBnbNlZwRAF8XgpgkXU4Mz7Lvq5R3q3R75tsa6zQCHgFCmCRdfjRq2cAePf2Rp8rCZ5tGysZnppncHLO71ICSwEssg5PHeynrbaUixor/S4lcLY1VgBwpF+j4HNRAIus0cx8lGeODvLuSxoxM7/LCZxtG+P/KB09M+FzJcGlABZZo2eODjIXifHuSzT9sJzGqmIqiwt0I+48FMAia/TEgT4qiwvy9vlvKzEzLmis0BTEeSiARdZgLhLliQN9vPfSJooK9NfoXOJL0TQFcS76kyOyBj99bZCJ2Qjv39HsdymBtm1jJYOT8wxPzftdSiApgEXW4Af7e6gpK8zrh2+m4oLESgitB16eAlhklWbmo/zwYD83X9akzXdWcGFieZ6mIZanPz0iq/Tjw2eYno9y6xUtfpcSeC3VJZQXhXUj7hwUwCKr9P19PdRXFHP9lvx+9lsqzIwLdCPunBTAIqswORfhn189w/subyIcUvNFKi7YWKkR8DkogEVW4Z9e7mUuEuPWHZp+SNW2xgrOTMwxOq2VEEspgEVW4cFdXWxpKOeazbV+l5I1Lm6K34g71KtpiKU8DWAzu8nMDpvZUTO7d5njZmZ/kTi+38yuTrzebmY/NrNDZnbAzH7XyzpFUvFa/wQvnhzh9mvbtffDKmxvqQLgUO+4z5UEj2cBbGZh4MvAzcB24ENmtn3JaTcD2xK/7gS+kng9Avy+c+4S4Abg7mWuFcmoh3Z1URg2funqNr9LySobK0uoryjmoAL4TbwcAV8HHHXOHXPOzQMPArctOec24AEX9xxQY2bNzrle59weAOfcBHAIaPWwVpHzmotEeXhPN+/Z3kh9RbHf5WSdS5orOdijAF7KywBuBboWfd7Nm0N0xXPMrAO4Cng+/SWKpOaJA/2MTC9w+7Wb/C4lK21vqeLomUnmIzG/SwkULwN4uUkyt5pzzKwC+C7we865Zf/5NLM7zWy3me0eGBhYc7Ei5/PgC6dorSlV6/EabW+uYj4a01OSl/AygLuB9kWftwE9qZ5jZoXEw/dvnXMPn+ubOOfud87tdM7tbGhoSEvhIou92jfOv74+xIeuayektb9rsr05fiNO0xA/y8sA3gVsM7NOMysCbgceXXLOo8CHE6shbgDGnHO9Fr/F/DXgkHPuix7WKLKi+39yjNLCMHfcsNnvUrLWloYKSgvDvNIz5ncpgVLg1Rd2zkXM7B7gCSAMfN05d8DM7kocvw94DLgFOApMAx9LXP424N8BL5vZS4nX/tA595hX9Yos5/ToDI/u6+HfvWUzNWVFfpeTtcIh49KWKvZ3K4AX8yyAARKB+diS1+5b9LED7l7mumdYfn5YJKO+/sxxHPDrb9/idylZ74q2Gv7uhZNEojEKtIscoE44kXManZ7nWy+c4gM7WmitKfW7nKx3RVs1swsxjupG3FkKYJFz+JtnTzI9H+XOn9foNx0ub6sG0DTEIgpgkWWMTM1z/9PHeNfFG7kkcQdf1qdzQzkVxQW8rAA+SwEssoy//PFRpuYi/MFNF/tdSs4IhYzLW6vZ1z3qdymBoQAWWaJreJoHnj3BB69p46LETl6SHldtquFgzzizC1G/SwkEBbDIEl948jDhkPGJ91zodyk555rNtURiTvPACQpgkUX2d4/yvZd6+PjPddJcrZUP6XbVpvg+yi+eHPG5kmBQAIskRKIx/vAfXqahspjffMdWv8vJSXXlRWypL1cAJyiARRK+8exJXjk9zmdu3U5VSaHf5eSsqzfXsvfUCPE+rPymABYBekZn+LMnD/POixp43+XNfpeT067ZXMvQ1DzHB6f8LsV3CmDJe845/uh7B3AO/uS2y/S4IY9d31kHwPPHh32uxH8KYMl7j7x0mqcO9fOJ92yjva7M73JyXmd9ORsri3nu2JDfpfhOASx5rXtkmj965ADXddTx8Z9Ty3EmmBk3bNnAc8eG8n4eWAEseSsWc/z+t/fhgD/7lR2Etdl6xtywZQP943N5Pw+sAJa89dVnjvH88WE+c+t2TT1k2A1b4vPAz+b5NIQCWPLSod5xvvDEa9x4aSMfvEaPmc+0zvpyWqpLePq1Qb9L8ZUCWPLOXCTKJx56iarSQv70Fy/XqgcfmBnvuKiB/3V0kIVo/j4pWQEseefPnnyNV/sm+PwHr2BDRbHf5eStd1zYwMRchL2nRv0uxTcKYMkrz74+xF8/fYxfu34T77x4o9/l5LW3XlBPQcj4l8Nn/C7FNwpgyRvjswt88jv76NhQzqffd4nf5eS9qpJCrt5cy48PD/hdim8UwJI3Pvu9A/SNz/LFX9lBWZGnz6OVFL3nkkYO9Y7TNTztdym+UABLXvjH/b08vPc097zzgrNbIor/bry0CYAnDvT5XIk/FMCS8/rHZ/n0Iy+zo62ae37hAr/LkUU2bShje3MVj7+iABbJOc45PvmdfcwuRPniv72SwrD+yAfNjZc28eKpEfrHZ/0uJeP0p1Fy2gPPnuTpI4N8+n3b2dpQ4Xc5soz372jGOfj+vh6/S8k4BbDkrKNnJvnTxw7xjgsbuOP6TX6XI+ewtaGCHe01fHfPab9LyTgFsOSkuUiU33toL6VFYT7/wSvU7RZwv3x1K4d6xznYM+53KRmlAJac9N8fP8wrp8f5f3/5CjZWlfhdjqzg1itaKAwb397d5XcpGaUAlpzzo0P9fO2Z43zkLZvPLnOSYKstL+KWy5v5+xe7mZyL+F1OxiiAJaf0jc3yye/s45LmKj51i7rdsslH3trB5FyEh/d0+11KxiiAJWfMLkS565svMheJ8Ze/ehUlhWG/S5JVuKq9hh1t1fzP/3WCaCw/npShAJac4Jzj3u/u56WuUb74K1dqyVkWMjPuesdWjg1O8YP9+bEkTQEsOeErP3mdR17q4ZPvvZCbLtO8b7a68dImLm6q5Es/OpIXo2AFsGS9R/ae5vNPHObWHS3c/U61GmezUMj43Xdt49jAFA/tyv0VEQpgyWrf39fD739nHzd0btB63xxx02VNXNdZxxeePMzY9ILf5XhKASxZyTnH1545zu88uJdrNtXy1Y/s1E23HGFmfPbWSxmdnudPHzvkdzmeUgBL1hmdnud3HnyJ//KDg7x3eyMPfPw6you1v28u2d5SxW++YysP7e7iyRzeqlJ/aiVrzC5E+fbuLv78qSOMzSzwH268iN96x1ZCIU075KJPvPtCnj4ywB98dz8XNlbSUV/ud0lpZ87lzp3GnTt3ut27d/tdhqTJ1FyE3rEZDvVO8MyRQR4/0MfYzALXdtTyxx+4jO0tVX6XKB47NTTNbV9+htqyIr77W2+ltrzI75LWatlRggJYfNU9Ms2rvROcGJrixNAUPaOz9IzO0DM6w/jsGy2pFcUFvGd7I//22nau76zTzbY88sLxYe742vNsbajgb3/9euqyM4QVwOK/+UiMHx8+wxOv9PH88WFOj86cPVZVUkBbbRktNSU0V5fSXFNCS3UpWxrK2d5cRYE2U89bP31tgN94YDfN1SV87aPXZmOjjQJY/OGcY3/3GA/v6ebRfT2MTC9QV17EDVvquK6jjh3tNXTWl1NTlpUjG8mQ3SeG+c2/eZHZhSifuuUSfvW6Tdk0/68AlszqHZvhH/ae5uE9pzl6ZpKighDv3d7IL1/TxtsvqNeIVlbt9OgM//Hv9/PM0UFu2FLHn/7i5WzJjtGwAli85Zyja3iGpw7189jLvbx4agTn4NqOWn7p6jZuubyZ6tJCv8uULOec46FdXfy3fzzE9EKUX7qqld/+hW1s2lDmd2nnowD228TsAoOT80SiMSIxR2lhmI1VxZQV+b8acHo+wonBaQYn5xicnGNqLkI05og6CBkUhEMUhIxwyM7+DjA0OU//xCzHBqbYe2qUwck5AC5uquSWy5u57coWNm/IveVD4r+BiTnu+8nrfPO5k0Rjjpsua+Kjb+3gms21QbxJqwDOpL6xWV44MczuE8PsPTXKyaGpn7mrv1hFcQFbG8q5pLmK7S1VXL2ploubKj37EX0+EuPl06O8cHyEF0+O8GrfON0jMytfeA5F4RBttaVcuamGqzbV8ratG7Llx0LJAf3js/z1T4/x0O4uJmYjXNxUyS9e1cqtO1poqSn1u7wkBbBXYjHH6wOT7Doxwq4Tw+w6MXw20MqKwly1qYYt9RW01ZaysaqYglCIcMiYno8yMDFH//gsr/VPcKh3nJFE73tpYZgd7dVcs7mWazbXclV77ZrXQA5NzrGve5Q9J0d54cQw+7pGmYvEAOisL+fSlioubKxka0MFG6uKqa8opqK4gHDICJsRc45IzBGNOSKxWOJ3h3OODeXF1JQVBnHEIXlmej7CI3t7eGjXKfZ1jwFwXUcdt+5o5p0Xb6St1tcpiswHsJndBHwJCANfdc59bslxSxy/BZgGPuqc25PKtcvJVACPTs/zUtfoz/waTQRnfUUR13bUsbMjfof/kubUR7LOOXrGZtlzMj4y3XNqhAM942e35dvSUM5V7bVsaShnU10ZzdUllBcXUF5UQNQ5pucjTM5GOD06Q9fwDEfOTLCve5Su4fg/BuGQcWlLFdd21HFtRy07O+qoryj25j+SiI9ODE7x6L4evvfSaV4fmAJga0M5b7ugnqs31XLVpho21ZVlcuCQ2QA2szDwGvAeoBvYBXzIOXdw0Tm3AL9NPICvB77knLs+lWuXs5YAjkRjzEVizEfe+H1mIcrI9DwjU/OMTC8wODnHiaEpTg5Nc2JwiqGp+UT9sG1jBVe217Bzcx3XdtbRsSG9/1On5yPs7x5jz6kR9pwcYV/3GAMTcyteZwYt1aVc2V7DjvZqdrTVcFlrtfZMkLzinOPomUl+8toAP3ltgBdPjjA9HwXiP2V21Jezpb6cjvoyGqtKqC0roq68iJqyQsqLCiguDFEUDlFcGKYoHKIwbGv9+53xAH4L8Fnn3I2Jzz8F4Jz7fxad81fAvzjnvpX4/DDwfwAdK127nNUG8Ae/8q/sPjmS0rlNVSV01JfRsaGcjvpyrmit5vK2aipLMn9Xf2ouQtfINP3jc0zPRZiajxIOQWlhAeXFYVpqSmmtKdXuYCJLRKIxXuuf5KWuUV4fmOT44BTHB6c4NTyd0gbwlcUFvPzHN67lWy8bwF4Oh1qBxTsqdxMf5a50TmuK1wJgZncCdyY+nUyE+FrUA4PnOngSeH6NXzhgzvs+c4zea+7x/X3an6zpssedczctfdHLAF4u8Zf+E3Ouc1K5Nv6ic/cD96+utDczs93OuZ3r/TpBly/vE/Rec1GuvU8vA7gbaF/0eRuw9El75zqnKIVrRUSympe9oLuAbWbWaWZFwO3Ao0vOeRT4sMXdAIw553pTvFZEJKt5NgJ2zkXM7B7gCeJLyb7unDtgZncljt8HPEZ8BcRR4svQPna+a72qNWHd0xhZIl/eJ+i95qKcep851YghIpJNtB2ViIhPFMAiIj7J6wA2s/9iZvvN7CUze9LMWhYd+5SZHTWzw2a2ppXXQWJmnzezVxPv9x/MrGbRsVx7r//GzA6YWczMdi45lmvv9abEezlqZvf6XU86mdnXzeyMmb2y6LU6M/uhmR1J/F7rZ43rldcBDHzeOXeFc+5K4AfAHwGY2XbiKy8uBW4C/r9Ee3Q2+yFwmXPuCuJt3p+CnH2vrwC/BPx08Yu59l4TtX8ZuBnYDnwo8R5zxf8k/v9psXuBHznntgE/SnyetfI6gJ1z44s+LeeNZo/bgAedc3POuePEV2lcl+n60sk596RzLrkf5nPE11ZDbr7XQ8655Toic+29Xgccdc4dc87NAw8Sf485wTn3U2B4ycu3Ad9IfPwN4P/MZE3pltcBDGBm/83MuoBfIzEC5twt0rni3wP/lPg419/rYrn2XnPt/aSiMdErQOL3jT7Xsy45vzWWmT0FNC1z6NPOue855z4NfDqx4c89wGdYRSt0kKz0XhPnfBqIAH+bvGyZ83PivS532TKvBf69nkeuvZ+8k/MB7Jx7d4qn/h3wj8QDOJU26sBZ6b2a2UeA9wPvcm8sAM/J93oOWflezyPX3k8q+s2s2TnXa2bNwBm/C1qPvJ6CMLNtiz79APBq4uNHgdvNrNjMOoFtwAuZri+dEhvc/0fgA8656UWHcu69nkeuvdd8bNl/FPhI4uOPAOf6aScr5PwIeAWfM7OLgBjxHSeTbdIHzOzbwEHiP67f7ZyL+ldmWvwlUAz8MLGh9HPOubty8b2a2S8C/wNoAP7RzF5yzt2Ya+/Vp5b9jDGzbxHfH7zezLqJ/3T6OeDbZvZx4BTwb/yrcP3Uiiwi4pO8noIQEfGTAlhExCcKYBERnyiARUR8ogAWEfGJAlhExCcKYBERn/xvkQ+kXbE2bQIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.displot(reg_pred-y_test,kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "09da978f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e229dcb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "score=r2_score(reg_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "0276b604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.669370269149559"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e503cdc8",
   "metadata": {},
   "source": [
    "## Ridge Regression Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "915bf093",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "4d2dcfbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_regressor=Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "3f0fa493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "37053785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Ridge(),\n",
       "             param_grid={'alpha': [1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80,\n",
       "                                   90]},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters={'alpha':[1,2,5,10,20,30,40,50,60,70,80,90]}\n",
    "ridgecv=GridSearchCV(ridge_regressor,parameters,scoring='neg_mean_squared_error',cv=5)\n",
    "ridgecv.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "3c9c1798",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 10}\n"
     ]
    }
   ],
   "source": [
    "print(ridgecv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "e74eae4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-25.80722882229147\n"
     ]
    }
   ],
   "source": [
    "print(ridgecv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "175800b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge_pred=ridgecv.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "49d1b076",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x184d41da9d0>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.displot(ridge_pred-y_test,kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "178542d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "score=r2_score(ridge_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "bdfa8b77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6468557055633644"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "9f4f3eac",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Lasso Regression\n",
    "from sklearn.linear_model import Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "cf7f027b",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso=Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "b33e3a2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Lasso(),\n",
       "             param_grid={'alpha': [1, 2, 5, 10, 20, 30, 40, 50, 60, 70, 80,\n",
       "                                   90]},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters={'alpha':[1,2,5,10,20,30,40,50,60,70,80,90]}\n",
    "lassocv=GridSearchCV(lasso,parameters,scoring='neg_mean_squared_error',cv=5)\n",
    "lassocv.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "7aac0425",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 1}\n",
      "-31.153603752119\n"
     ]
    }
   ],
   "source": [
    "print(lassocv.best_params_)\n",
    "print(lassocv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "e0b0fd47",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso_pred=lassocv.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "28bc1b9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x184d3fcfac0>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.displot(lasso_pred-y_test,kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbf4bf18",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}