Machine_Learning_Day_2.ipynb

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  "metadata": {
    "colab": {
      "name": "Machine Learning Day 1",
      "provenance": [],
      "toc_visible": true,
      "mount_file_id": "1bJcnfECOHUWYYXSdcV_vY3_kQuzDBvSP",
      "authorship_tag": "ABX9TyNRPTBcIPSmFnogtdRI/FGf",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zohaib304/MachineLearning/blob/main/Machine_Learning_Day_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jlhhfZG3NZWA"
      },
      "source": [
        "100 Days of Machine Learning\r\n",
        "\r\n",
        "# Day 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sBujp3aeNi-W"
      },
      "source": [
        "# Data Processing "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NSe5XwbYNp_f"
      },
      "source": [
        "## Step 1: Importing the required Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HkPOohhKMaAi"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TBfFfRfmOctR"
      },
      "source": [
        "  ## Step 2: Importing the Data Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4I3tODEHPR8P"
      },
      "source": [
        "# load the data\r\n",
        "data = pd.read_csv('/content/drive/MyDrive/Dataset/housing.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2uSioj3iho8P",
        "outputId": "0e3adfe5-6269-413d-a101-52cdfcf01b4b"
      },
      "source": [
        "data.isna().sum()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM       0\n",
              "ZN         0\n",
              "INDUS      0\n",
              "CHAS       0\n",
              "NOX        0\n",
              "RM         5\n",
              "AGE        0\n",
              "DIS        0\n",
              "RAD        0\n",
              "TAX        0\n",
              "PTRATIO    0\n",
              "B          0\n",
              "LSTAT      0\n",
              "MEDV       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "r5u1wfjfPp6F",
        "outputId": "edff0c47-2a6b-46fb-ae42-d10581c50d21"
      },
      "source": [
        "# let's have look at first 5 rows\r\n",
        "data.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CofwSWuPSBxv",
        "outputId": "6967ebe4-ee1d-4742-8fa0-09ae0f9f8606"
      },
      "source": [
        "# Dimension of the dataset\r\n",
        "data.shape "
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(506, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "2h346zDFTPu3",
        "outputId": "8dcdfb95-894f-45d3-f82b-9e83f99d4773"
      },
      "source": [
        "# Let's summarize the data\r\n",
        "data.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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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>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>501.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.613524</td>\n",
              "      <td>11.363636</td>\n",
              "      <td>11.136779</td>\n",
              "      <td>0.069170</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284341</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>3.795043</td>\n",
              "      <td>9.549407</td>\n",
              "      <td>408.237154</td>\n",
              "      <td>18.455534</td>\n",
              "      <td>356.674032</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>22.532806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.601545</td>\n",
              "      <td>23.322453</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.253994</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.705587</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.105710</td>\n",
              "      <td>8.707259</td>\n",
              "      <td>168.537116</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>91.294864</td>\n",
              "      <td>7.141062</td>\n",
              "      <td>9.197104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.006320</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.460000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.129600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.082045</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.190000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.884000</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>2.100175</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>375.377500</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>17.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.256510</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.690000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208000</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.207450</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>391.440000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>21.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.677082</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>18.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.625000</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>5.188425</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>666.000000</td>\n",
              "      <td>20.200000</td>\n",
              "      <td>396.225000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88.976200</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>27.740000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.126500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>711.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>396.900000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             CRIM          ZN       INDUS  ...           B       LSTAT        MEDV\n",
              "count  506.000000  506.000000  506.000000  ...  506.000000  506.000000  506.000000\n",
              "mean     3.613524   11.363636   11.136779  ...  356.674032   12.653063   22.532806\n",
              "std      8.601545   23.322453    6.860353  ...   91.294864    7.141062    9.197104\n",
              "min      0.006320    0.000000    0.460000  ...    0.320000    1.730000    5.000000\n",
              "25%      0.082045    0.000000    5.190000  ...  375.377500    6.950000   17.025000\n",
              "50%      0.256510    0.000000    9.690000  ...  391.440000   11.360000   21.200000\n",
              "75%      3.677082   12.500000   18.100000  ...  396.225000   16.955000   25.000000\n",
              "max     88.976200  100.000000   27.740000  ...  396.900000   37.970000   50.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FI7CDwrWTSJn"
      },
      "source": [
        "# Splitting the Dataset into the Independent Feature Matrix:\r\n",
        "# X = data.iloc[:, :-1].values\r\n",
        "X = data['RM']\r\n",
        "\r\n",
        "# Extracting the Dataset to Get the Dependent Vector\r\n",
        "y = data['MEDV']"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5n4NOO6Nk2f",
        "outputId": "6fcac0bf-5e7f-4312-eb4c-5d1265f2ee3a"
      },
      "source": [
        "type(X)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "2uaLARV7HdMQ",
        "outputId": "ab56d17a-30b4-451d-8c95-bea090102c62"
      },
      "source": [
        "plt.plot(X, y, 'o')\r\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyFFRQvKL4f2"
      },
      "source": [
        "X = pd.DataFrame(X)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SOtbrms7dRsM"
      },
      "source": [
        "## Step 3: Handling The Missing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nJxj16THeqEw"
      },
      "source": [
        "# from sklearn.impute import SimpleImputer\r\n",
        "\r\n",
        "# imputer_X = SimpleImputer(missing_values=np.nan, strategy='mean')\r\n",
        "# imputer_X = pd.DataFrame(imputer_X.fit_transform(X))"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l8AX22wZNQiY",
        "outputId": "6348bd3b-4435-401d-c6ee-8aea5d34ebb9"
      },
      "source": [
        "type(imputer_X)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IRWI77bnNhaW",
        "outputId": "ff9cd554-c74e-43c4-8600-437748629a9e"
      },
      "source": [
        "type(y)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "X8vWRoarOPzm",
        "outputId": "6a0aa3b2-5ec0-4636-88d0-334c178d44fe"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 15,
      "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>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>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>501.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.613524</td>\n",
              "      <td>11.363636</td>\n",
              "      <td>11.136779</td>\n",
              "      <td>0.069170</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284341</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>3.795043</td>\n",
              "      <td>9.549407</td>\n",
              "      <td>408.237154</td>\n",
              "      <td>18.455534</td>\n",
              "      <td>356.674032</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>22.532806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.601545</td>\n",
              "      <td>23.322453</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.253994</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.705587</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.105710</td>\n",
              "      <td>8.707259</td>\n",
              "      <td>168.537116</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>91.294864</td>\n",
              "      <td>7.141062</td>\n",
              "      <td>9.197104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.006320</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.460000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.129600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.082045</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.190000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.884000</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>2.100175</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>375.377500</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>17.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.256510</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.690000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208000</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.207450</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>391.440000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>21.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.677082</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>18.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.625000</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>5.188425</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>666.000000</td>\n",
              "      <td>20.200000</td>\n",
              "      <td>396.225000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88.976200</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>27.740000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.126500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>711.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>396.900000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             CRIM          ZN       INDUS  ...           B       LSTAT        MEDV\n",
              "count  506.000000  506.000000  506.000000  ...  506.000000  506.000000  506.000000\n",
              "mean     3.613524   11.363636   11.136779  ...  356.674032   12.653063   22.532806\n",
              "std      8.601545   23.322453    6.860353  ...   91.294864    7.141062    9.197104\n",
              "min      0.006320    0.000000    0.460000  ...    0.320000    1.730000    5.000000\n",
              "25%      0.082045    0.000000    5.190000  ...  375.377500    6.950000   17.025000\n",
              "50%      0.256510    0.000000    9.690000  ...  391.440000   11.360000   21.200000\n",
              "75%      3.677082   12.500000   18.100000  ...  396.225000   16.955000   25.000000\n",
              "max     88.976200  100.000000   27.740000  ...  396.900000   37.970000   50.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "abunykY3OKWr"
      },
      "source": [
        "X.fillna(6.265735,inplace=True)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "n9GKT4grM6h3",
        "outputId": "75a52dbb-1791-47fd-fb32-bc48a1f2040c"
      },
      "source": [
        "plt.plot(X, y, 'o')\r\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bX_vx2bQof3G"
      },
      "source": [
        "\r\n",
        "## Step 4: Splitting the dataset into test set and training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W95u-CjmozL6"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LU4Px9sauSgH"
      },
      "source": [
        "# X_train.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNGsUrBHuXXp"
      },
      "source": [
        "## Step 5: Feature Scalling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AP2IuE9hubWQ"
      },
      "source": [
        "# from sklearn.preprocessing import StandardScaler\r\n",
        "# norma = StandardScaler()\r\n",
        "# X_train = norma.fit_transform(X_train)\r\n",
        "# X_test = norma.fit_transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_IERHNdWu7OB"
      },
      "source": [
        "\r\n",
        "\r\n",
        "---\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AtGwie3oGPty"
      },
      "source": [
        "# Day 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lPVynVc3P1JN"
      },
      "source": [
        "Fitting simple linear regression model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZn1lkXYvgNT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67de57f1-f098-46f8-e011-5147ff28f30a"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\r\n",
        "model = LinearRegression()\r\n",
        "model.fit(X_train, y_train)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jdQ7JWmsP604"
      },
      "source": [
        "Predicting the result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbyaARDIJz8Z"
      },
      "source": [
        "y_predict = model.predict(X_test)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "uAdeFDFaPURu",
        "outputId": "037655e0-e5a3-41ea-f761-f4d8f5a1d782"
      },
      "source": [
        "plt.plot(X_test, y_predict)\r\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pYx_DlFtPeWe"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}