Machine_Learning_Day_1.ipynb

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  "metadata": {
    "colab": {
      "name": "Machine Learning Day 1",
      "provenance": [],
      "toc_visible": true,
      "mount_file_id": "1bJcnfECOHUWYYXSdcV_vY3_kQuzDBvSP",
      "authorship_tag": "ABX9TyM7HMmWgh2a88hqsHwzKwQ/",
      "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_1.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"
      ],
      "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": "4b4a790b-3aa6-4bf2-d026-4b2d5a31077b"
      },
      "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": "00f06fd3-49b5-47fb-f892-e2971e5a4b47"
      },
      "source": [
        "# let's have look at first 5 rows\r\n",
        "data.head()"
      ],
      "execution_count": 4,
      "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>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": "252a7672-dd68-43f1-d624-51e0948a5c6d"
      },
      "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": "c00a9f1b-380e-4195-a715-b5562d84f262"
      },
      "source": [
        "# Let's summarize the data\r\n",
        "data.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\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",
        "\r\n",
        "# Extracting the Dataset to Get the Dependent Vector\r\n",
        "y = data.iloc[:, -1].values"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4LibctvIh_8L",
        "outputId": "b3f254c7-235d-4088-e78f-da7da11362db"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(506, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1eFF5XVDhiNy"
      },
      "source": [
        "X = pd.DataFrame(X)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SOtbrms7dRsM"
      },
      "source": [
        "## Step 3: Handling The Missing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GYF--nQlc-sB"
      },
      "source": [
        "# Get the name of columns with missing values\r\n",
        "cols_with_missing = [col for col in X.columns\r\n",
        "                     if X[col].isnull().any()]"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lx7PbuEXm9gW",
        "outputId": "c4aa560f-1079-4606-e461-af31f709f552"
      },
      "source": [
        "print(cols_with_missing)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nJxj16THeqEw"
      },
      "source": [
        "from sklearn.impute import SimpleImputer\r\n",
        "\r\n",
        "imputer_X = SimpleImputer()\r\n",
        "imputer_X = pd.DataFrame(imputer_X.fit_transform(X))"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQ5UVW_0mZTy"
      },
      "source": [
        "imputer_X.columns = X.columns"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "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(imputer_X, y, test_size = 0.2, random_state = 42)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "LU4Px9sauSgH",
        "outputId": "62601c8a-728b-4881-fd37-e0c51575bd3f"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>477</th>\n",
              "      <td>15.02340</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6140</td>\n",
              "      <td>5.304</td>\n",
              "      <td>97.3</td>\n",
              "      <td>2.1007</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>349.48</td>\n",
              "      <td>24.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.62739</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5380</td>\n",
              "      <td>5.834</td>\n",
              "      <td>56.5</td>\n",
              "      <td>4.4986</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>395.62</td>\n",
              "      <td>8.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>332</th>\n",
              "      <td>0.03466</td>\n",
              "      <td>35.0</td>\n",
              "      <td>6.06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4379</td>\n",
              "      <td>6.031</td>\n",
              "      <td>23.3</td>\n",
              "      <td>6.6407</td>\n",
              "      <td>1.0</td>\n",
              "      <td>304.0</td>\n",
              "      <td>16.9</td>\n",
              "      <td>362.25</td>\n",
              "      <td>7.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>423</th>\n",
              "      <td>7.05042</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.6140</td>\n",
              "      <td>6.103</td>\n",
              "      <td>85.1</td>\n",
              "      <td>2.0218</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>2.52</td>\n",
              "      <td>23.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.72580</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5380</td>\n",
              "      <td>5.727</td>\n",
              "      <td>69.5</td>\n",
              "      <td>3.7965</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>390.95</td>\n",
              "      <td>11.28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           0     1      2    3       4   ...    8      9     10      11     12\n",
              "477  15.02340   0.0  18.10  0.0  0.6140  ...  24.0  666.0  20.2  349.48  24.91\n",
              "15    0.62739   0.0   8.14  0.0  0.5380  ...   4.0  307.0  21.0  395.62   8.47\n",
              "332   0.03466  35.0   6.06  0.0  0.4379  ...   1.0  304.0  16.9  362.25   7.83\n",
              "423   7.05042   0.0  18.10  0.0  0.6140  ...  24.0  666.0  20.2    2.52  23.29\n",
              "19    0.72580   0.0   8.14  0.0  0.5380  ...   4.0  307.0  21.0  390.95  11.28\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-D8Si_SouTsF"
      },
      "source": [
        ""
      ],
      "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": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_IERHNdWu7OB"
      },
      "source": [
        "\r\n",
        "\r\n",
        "---\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZn1lkXYvgNT"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}