RidgeRegression_HousePricePrediction.ipynb

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    "colab": {
      "name": "Ridge Regression.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6uglOG3Hxge"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a_jMFPegH7tY"
      },
      "source": [
        "df=pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mBzpLjm2H-AL",
        "outputId": "70a6941f-0bcb-4927-856b-cccd589d765f"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "      <th>medv</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</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1</td>\n",
              "      <td>296</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</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</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</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</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242</td>\n",
              "      <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</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222</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</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222</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  ...  tax  ptratio       b  lstat  medv\n",
              "0  0.00632  18.0   2.31     0  0.538  ...  296     15.3  396.90   4.98  24.0\n",
              "1  0.02731   0.0   7.07     0  0.469  ...  242     17.8  396.90   9.14  21.6\n",
              "2  0.02729   0.0   7.07     0  0.469  ...  242     17.8  392.83   4.03  34.7\n",
              "3  0.03237   0.0   2.18     0  0.458  ...  222     18.7  394.63   2.94  33.4\n",
              "4  0.06905   0.0   2.18     0  0.458  ...  222     18.7  396.90   5.33  36.2\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
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      "metadata": {
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          "height": 508
        },
        "id": "TD4xY5Q3LigX",
        "outputId": "812c0d83-d27e-41ae-9e2e-04d4ad1a95d7"
      },
      "source": [
        "corr = df.corr()\n",
        "corr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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",
              "      <th>medv</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>crim</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.200469</td>\n",
              "      <td>0.406583</td>\n",
              "      <td>-0.055892</td>\n",
              "      <td>0.420972</td>\n",
              "      <td>-0.219247</td>\n",
              "      <td>0.352734</td>\n",
              "      <td>-0.379670</td>\n",
              "      <td>0.625505</td>\n",
              "      <td>0.582764</td>\n",
              "      <td>0.289946</td>\n",
              "      <td>-0.385064</td>\n",
              "      <td>0.455621</td>\n",
              "      <td>-0.388305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>zn</th>\n",
              "      <td>-0.200469</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.533828</td>\n",
              "      <td>-0.042697</td>\n",
              "      <td>-0.516604</td>\n",
              "      <td>0.311991</td>\n",
              "      <td>-0.569537</td>\n",
              "      <td>0.664408</td>\n",
              "      <td>-0.311948</td>\n",
              "      <td>-0.314563</td>\n",
              "      <td>-0.391679</td>\n",
              "      <td>0.175520</td>\n",
              "      <td>-0.412995</td>\n",
              "      <td>0.360445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>indus</th>\n",
              "      <td>0.406583</td>\n",
              "      <td>-0.533828</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.062938</td>\n",
              "      <td>0.763651</td>\n",
              "      <td>-0.391676</td>\n",
              "      <td>0.644779</td>\n",
              "      <td>-0.708027</td>\n",
              "      <td>0.595129</td>\n",
              "      <td>0.720760</td>\n",
              "      <td>0.383248</td>\n",
              "      <td>-0.356977</td>\n",
              "      <td>0.603800</td>\n",
              "      <td>-0.483725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chas</th>\n",
              "      <td>-0.055892</td>\n",
              "      <td>-0.042697</td>\n",
              "      <td>0.062938</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.091203</td>\n",
              "      <td>0.091251</td>\n",
              "      <td>0.086518</td>\n",
              "      <td>-0.099176</td>\n",
              "      <td>-0.007368</td>\n",
              "      <td>-0.035587</td>\n",
              "      <td>-0.121515</td>\n",
              "      <td>0.048788</td>\n",
              "      <td>-0.053929</td>\n",
              "      <td>0.175260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nox</th>\n",
              "      <td>0.420972</td>\n",
              "      <td>-0.516604</td>\n",
              "      <td>0.763651</td>\n",
              "      <td>0.091203</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.302188</td>\n",
              "      <td>0.731470</td>\n",
              "      <td>-0.769230</td>\n",
              "      <td>0.611441</td>\n",
              "      <td>0.668023</td>\n",
              "      <td>0.188933</td>\n",
              "      <td>-0.380051</td>\n",
              "      <td>0.590879</td>\n",
              "      <td>-0.427321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rm</th>\n",
              "      <td>-0.219247</td>\n",
              "      <td>0.311991</td>\n",
              "      <td>-0.391676</td>\n",
              "      <td>0.091251</td>\n",
              "      <td>-0.302188</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.240265</td>\n",
              "      <td>0.205246</td>\n",
              "      <td>-0.209847</td>\n",
              "      <td>-0.292048</td>\n",
              "      <td>-0.355501</td>\n",
              "      <td>0.128069</td>\n",
              "      <td>-0.613808</td>\n",
              "      <td>0.695360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0.352734</td>\n",
              "      <td>-0.569537</td>\n",
              "      <td>0.644779</td>\n",
              "      <td>0.086518</td>\n",
              "      <td>0.731470</td>\n",
              "      <td>-0.240265</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.747881</td>\n",
              "      <td>0.456022</td>\n",
              "      <td>0.506456</td>\n",
              "      <td>0.261515</td>\n",
              "      <td>-0.273534</td>\n",
              "      <td>0.602339</td>\n",
              "      <td>-0.376955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dis</th>\n",
              "      <td>-0.379670</td>\n",
              "      <td>0.664408</td>\n",
              "      <td>-0.708027</td>\n",
              "      <td>-0.099176</td>\n",
              "      <td>-0.769230</td>\n",
              "      <td>0.205246</td>\n",
              "      <td>-0.747881</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.494588</td>\n",
              "      <td>-0.534432</td>\n",
              "      <td>-0.232471</td>\n",
              "      <td>0.291512</td>\n",
              "      <td>-0.496996</td>\n",
              "      <td>0.249929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rad</th>\n",
              "      <td>0.625505</td>\n",
              "      <td>-0.311948</td>\n",
              "      <td>0.595129</td>\n",
              "      <td>-0.007368</td>\n",
              "      <td>0.611441</td>\n",
              "      <td>-0.209847</td>\n",
              "      <td>0.456022</td>\n",
              "      <td>-0.494588</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.910228</td>\n",
              "      <td>0.464741</td>\n",
              "      <td>-0.444413</td>\n",
              "      <td>0.488676</td>\n",
              "      <td>-0.381626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tax</th>\n",
              "      <td>0.582764</td>\n",
              "      <td>-0.314563</td>\n",
              "      <td>0.720760</td>\n",
              "      <td>-0.035587</td>\n",
              "      <td>0.668023</td>\n",
              "      <td>-0.292048</td>\n",
              "      <td>0.506456</td>\n",
              "      <td>-0.534432</td>\n",
              "      <td>0.910228</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.460853</td>\n",
              "      <td>-0.441808</td>\n",
              "      <td>0.543993</td>\n",
              "      <td>-0.468536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ptratio</th>\n",
              "      <td>0.289946</td>\n",
              "      <td>-0.391679</td>\n",
              "      <td>0.383248</td>\n",
              "      <td>-0.121515</td>\n",
              "      <td>0.188933</td>\n",
              "      <td>-0.355501</td>\n",
              "      <td>0.261515</td>\n",
              "      <td>-0.232471</td>\n",
              "      <td>0.464741</td>\n",
              "      <td>0.460853</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.177383</td>\n",
              "      <td>0.374044</td>\n",
              "      <td>-0.507787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>-0.385064</td>\n",
              "      <td>0.175520</td>\n",
              "      <td>-0.356977</td>\n",
              "      <td>0.048788</td>\n",
              "      <td>-0.380051</td>\n",
              "      <td>0.128069</td>\n",
              "      <td>-0.273534</td>\n",
              "      <td>0.291512</td>\n",
              "      <td>-0.444413</td>\n",
              "      <td>-0.441808</td>\n",
              "      <td>-0.177383</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.366087</td>\n",
              "      <td>0.333461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lstat</th>\n",
              "      <td>0.455621</td>\n",
              "      <td>-0.412995</td>\n",
              "      <td>0.603800</td>\n",
              "      <td>-0.053929</td>\n",
              "      <td>0.590879</td>\n",
              "      <td>-0.613808</td>\n",
              "      <td>0.602339</td>\n",
              "      <td>-0.496996</td>\n",
              "      <td>0.488676</td>\n",
              "      <td>0.543993</td>\n",
              "      <td>0.374044</td>\n",
              "      <td>-0.366087</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.737663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>medv</th>\n",
              "      <td>-0.388305</td>\n",
              "      <td>0.360445</td>\n",
              "      <td>-0.483725</td>\n",
              "      <td>0.175260</td>\n",
              "      <td>-0.427321</td>\n",
              "      <td>0.695360</td>\n",
              "      <td>-0.376955</td>\n",
              "      <td>0.249929</td>\n",
              "      <td>-0.381626</td>\n",
              "      <td>-0.468536</td>\n",
              "      <td>-0.507787</td>\n",
              "      <td>0.333461</td>\n",
              "      <td>-0.737663</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             crim        zn     indus  ...         b     lstat      medv\n",
              "crim     1.000000 -0.200469  0.406583  ... -0.385064  0.455621 -0.388305\n",
              "zn      -0.200469  1.000000 -0.533828  ...  0.175520 -0.412995  0.360445\n",
              "indus    0.406583 -0.533828  1.000000  ... -0.356977  0.603800 -0.483725\n",
              "chas    -0.055892 -0.042697  0.062938  ...  0.048788 -0.053929  0.175260\n",
              "nox      0.420972 -0.516604  0.763651  ... -0.380051  0.590879 -0.427321\n",
              "rm      -0.219247  0.311991 -0.391676  ...  0.128069 -0.613808  0.695360\n",
              "age      0.352734 -0.569537  0.644779  ... -0.273534  0.602339 -0.376955\n",
              "dis     -0.379670  0.664408 -0.708027  ...  0.291512 -0.496996  0.249929\n",
              "rad      0.625505 -0.311948  0.595129  ... -0.444413  0.488676 -0.381626\n",
              "tax      0.582764 -0.314563  0.720760  ... -0.441808  0.543993 -0.468536\n",
              "ptratio  0.289946 -0.391679  0.383248  ... -0.177383  0.374044 -0.507787\n",
              "b       -0.385064  0.175520 -0.356977  ...  1.000000 -0.366087  0.333461\n",
              "lstat    0.455621 -0.412995  0.603800  ... -0.366087  1.000000 -0.737663\n",
              "medv    -0.388305  0.360445 -0.483725  ...  0.333461 -0.737663  1.000000\n",
              "\n",
              "[14 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3OzWHiEIBD1"
      },
      "source": [
        "X=df.drop(['medv'],axis=1)[['lstat']]\n",
        "y=df['medv']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtPMwFtxIDHj"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.33,random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qyNqgJGQIGnN"
      },
      "source": [
        "from sklearn.linear_model import Ridge "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3eFRdIYhILAk"
      },
      "source": [
        "rid=Ridge(alpha=0.01)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U05WvifBINjR"
      },
      "source": [
        "param_grid={'alpha':[1,0.5,0.01,3,5,100,7,42]}\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "grid=GridSearchCV(rid,param_grid)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BKQhv5SeIPjQ",
        "outputId": "252a24e7-d8c8-494c-fa86-894813becf93"
      },
      "source": [
        "grid.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=Ridge(alpha=0.01),\n",
              "             param_grid={'alpha': [1, 0.5, 0.01, 3, 5, 100, 7, 42]})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wqqZKgMeISDp",
        "outputId": "c978af4c-d639-4f36-b971-cb4b72a1b215"
      },
      "source": [
        "grid.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'alpha': 100}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cAlc-RH-IWOH",
        "outputId": "f70ea447-e117-4883-a385-384d3d997f2f"
      },
      "source": [
        "grid.best_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5459604712020253"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UmLH5uX-IYVf",
        "outputId": "b3c965ae-9ea8-4172-bf5e-0fbfe3fe087e"
      },
      "source": [
        "rid.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.01)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YEqhGd_LIasD",
        "outputId": "eab97784-ee53-43e8-c943-dbf568ccde27"
      },
      "source": [
        "rid.score(X_test,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4924573593562217"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fh5XLNVIIdkb",
        "outputId": "e5fb0768-644a-4832-f226-adecf9ed897d"
      },
      "source": [
        "rid.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.98053644])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P96_k8uiKhYr",
        "outputId": "31916500-7daf-4c70-e006-37f1f5f043b7"
      },
      "source": [
        "rid.intercept_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35.22515913648968"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "-TmMqF0dKjt3",
        "outputId": "43d910d3-31b7-4089-d8b9-f496002bce96"
      },
      "source": [
        "X_new =pd.DataFrame({'lstat':[df.lstat.min(),df.lstat.max()]})\n",
        "\n",
        "X_new"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>lstat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37.97</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   lstat\n",
              "0   1.73\n",
              "1  37.97"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D8VGZEYNMYS_",
        "outputId": "105fb0af-d34a-438a-af73-b98bc15793a4"
      },
      "source": [
        "preds=rid.predict(X_new)\n",
        "preds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([33.52883109, -2.00580965])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "W0DMql1xMdo2",
        "outputId": "e3a72ee0-4c2b-45ea-c8a7-bdf768c0053f"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "df.plot(kind=\"scatter\",x=\"lstat\",y=\"medv\")\n",
        "plt.plot(X_new,preds,c='r')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f61ed3daad0>]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
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      "metadata": {
        "id": "EbOWVHceMlkP"
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      "source": [
        ""
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}