Lassoand Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge and Lasso Regression implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
       "         4.9800e+00],\n",
       "        [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
       "         9.1400e+00],\n",
       "        [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
       "         4.0300e+00],\n",
       "        ...,\n",
       "        [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "         5.6400e+00],\n",
       "        [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
       "         6.4800e+00],\n",
       "        [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "         7.8800e+00]]),\n",
       " 'target': 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]),\n",
       " '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 blacks 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\\\\HP\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\boston_house_prices.csv'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        0     1     2    3      4      5     6       7    8      9     10  \\\n",
      "0  0.00632  18.0  2.31  0.0  0.538  6.575  65.2  4.0900  1.0  296.0  15.3   \n",
      "1  0.02731   0.0  7.07  0.0  0.469  6.421  78.9  4.9671  2.0  242.0  17.8   \n",
      "2  0.02729   0.0  7.07  0.0  0.469  7.185  61.1  4.9671  2.0  242.0  17.8   \n",
      "3  0.03237   0.0  2.18  0.0  0.458  6.998  45.8  6.0622  3.0  222.0  18.7   \n",
      "4  0.06905   0.0  2.18  0.0  0.458  7.147  54.2  6.0622  3.0  222.0  18.7   \n",
      "\n",
      "       11    12  \n",
      "0  396.90  4.98  \n",
      "1  396.90  9.14  \n",
      "2  392.83  4.03  \n",
      "3  394.63  2.94  \n",
      "4  396.90  5.33  \n"
     ]
    }
   ],
   "source": [
    "dataset = pd.DataFrame(df.data)\n",
    "print(dataset.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.columns=df.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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",
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       "    <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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"Price\"]=df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
       "      <th>Price</th>\n",
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       "      <td>0.00632</td>\n",
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       "      <td>0.538</td>\n",
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       "      <td>0.02731</td>\n",
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       "      <td>7.07</td>\n",
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       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</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",
       "      <td>33.4</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",
       "      <td>36.2</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  Price  \n",
       "0     15.3  396.90   4.98   24.0  \n",
       "1     17.8  396.90   9.14   21.6  \n",
       "2     17.8  392.83   4.03   34.7  \n",
       "3     18.7  394.63   2.94   33.4  \n",
       "4     18.7  396.90   5.33   36.2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=dataset.iloc[:,:-1] ## independent features\n",
    "y=dataset.iloc[:,-1] ## dependent features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-37.131807467699026\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_regressor=LinearRegression()\n",
    "mse=cross_val_score(lin_regressor,X,y,scoring='neg_mean_squared_error',cv=5)\n",
    "mean_mse=np.mean(mse)\n",
    "print(mean_mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Ridge(),\n",
       "             param_grid={'alpha': [1e-15, 1e-10, 1e-08, 0.001, 0.01, 1, 5, 10,\n",
       "                                   20, 30, 35, 40, 45, 50, 55, 100]},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "ridge=Ridge()\n",
    "parameters={'alpha':[1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,30,35,40,45,50,55,100]}\n",
    "ridge_regressor=GridSearchCV(ridge,parameters,scoring='neg_mean_squared_error',cv=5)\n",
    "ridge_regressor.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 100}\n",
      "-29.905701947540386\n"
     ]
    }
   ],
   "source": [
    "print(ridge_regressor.best_params_)\n",
    "print(ridge_regressor.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3199.405343449699, tolerance: 3.9191485420792076\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4305.464760864917, tolerance: 3.3071316790123455\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4171.556985240626, tolerance: 2.813643886419753\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2556.41300153619, tolerance: 3.3071762123456794\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4435.007878243403, tolerance: 3.4809104444444445\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 1}\n",
      "-35.53158022069486\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "lasso=Lasso()\n",
    "parameters={'alpha':[1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,30,35,40,45,50,55,100]}\n",
    "lasso_regressor=GridSearchCV(lasso,parameters,scoring='neg_mean_squared_error',cv=5)\n",
    "\n",
    "lasso_regressor.fit(X,y)\n",
    "print(lasso_regressor.best_params_)\n",
    "print(lasso_regressor.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_lasso=lasso_regressor.predict(X_test)\n",
    "prediction_ridge=ridge_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.distplot(y_test-prediction_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Price', ylabel='Density'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.distplot(y_test-prediction_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}