EDA.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2GTpJUoq3t7b"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n",
        "!cp \"drive/MyDrive/Colab Notebooks/AutoInsurance.zip\" \"/content/AutoInsurance.zip\"\n",
        "\n",
        "!unzip AutoInsurance.zip\n",
        "!rm AutoInsurance.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MU0VEi4dEsbl",
        "outputId": "2e35dd85-e812-4989-837c-44c0af761124"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n",
            "Archive:  AutoInsurance.zip\n",
            "replace train(1).csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "FVFLkSdKo0s_"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "LyyysPoComeX",
        "outputId": "61ec0545-426b-4d4f-ec2b-6d427b84d425"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  target  ps_ind_01  ps_ind_02_cat  ps_ind_03  ps_ind_04_cat  \\\n",
              "0   7       0          2              2          5              1   \n",
              "1   9       0          1              1          7              0   \n",
              "2  13       0          5              4          9              1   \n",
              "3  16       0          0              1          2              0   \n",
              "4  17       0          0              2          0              1   \n",
              "\n",
              "   ps_ind_05_cat  ps_ind_06_bin  ps_ind_07_bin  ps_ind_08_bin  ...  \\\n",
              "0              0              0              1              0  ...   \n",
              "1              0              0              0              1  ...   \n",
              "2              0              0              0              1  ...   \n",
              "3              0              1              0              0  ...   \n",
              "4              0              1              0              0  ...   \n",
              "\n",
              "   ps_calc_11  ps_calc_12  ps_calc_13  ps_calc_14  ps_calc_15_bin  \\\n",
              "0           9           1           5           8               0   \n",
              "1           3           1           1           9               0   \n",
              "2           4           2           7           7               0   \n",
              "3           2           2           4           9               0   \n",
              "4           3           1           1           3               0   \n",
              "\n",
              "   ps_calc_16_bin  ps_calc_17_bin  ps_calc_18_bin  ps_calc_19_bin  \\\n",
              "0               1               1               0               0   \n",
              "1               1               1               0               1   \n",
              "2               1               1               0               1   \n",
              "3               0               0               0               0   \n",
              "4               0               0               1               1   \n",
              "\n",
              "   ps_calc_20_bin  \n",
              "0               1  \n",
              "1               0  \n",
              "2               0  \n",
              "3               0  \n",
              "4               0  \n",
              "\n",
              "[5 rows x 59 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>target</th>\n",
              "      <th>ps_ind_01</th>\n",
              "      <th>ps_ind_02_cat</th>\n",
              "      <th>ps_ind_03</th>\n",
              "      <th>ps_ind_04_cat</th>\n",
              "      <th>ps_ind_05_cat</th>\n",
              "      <th>ps_ind_06_bin</th>\n",
              "      <th>ps_ind_07_bin</th>\n",
              "      <th>ps_ind_08_bin</th>\n",
              "      <th>...</th>\n",
              "      <th>ps_calc_11</th>\n",
              "      <th>ps_calc_12</th>\n",
              "      <th>ps_calc_13</th>\n",
              "      <th>ps_calc_14</th>\n",
              "      <th>ps_calc_15_bin</th>\n",
              "      <th>ps_calc_16_bin</th>\n",
              "      <th>ps_calc_17_bin</th>\n",
              "      <th>ps_calc_18_bin</th>\n",
              "      <th>ps_calc_19_bin</th>\n",
              "      <th>ps_calc_20_bin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
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              "      <td>9</td>\n",
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              "      <td>7</td>\n",
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              "      <td>1</td>\n",
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              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>9</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 59 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5f435c9-bf23-48cc-8eef-b17a24e454ec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e5f435c9-bf23-48cc-8eef-b17a24e454ec button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e5f435c9-bf23-48cc-8eef-b17a24e454ec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df=pd.read_csv('train(1).csv')\n",
        "df[:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vipctl3i7nBS"
      },
      "source": [
        "###  Inferences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0rUeyd9Boyt6",
        "outputId": "c9cac60f-bc10-4f06-a33c-4f960672a54b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 595212 entries, 0 to 595211\n",
            "Data columns (total 59 columns):\n",
            " #   Column          Non-Null Count   Dtype  \n",
            "---  ------          --------------   -----  \n",
            " 0   id              595212 non-null  int64  \n",
            " 1   target          595212 non-null  int64  \n",
            " 2   ps_ind_01       595212 non-null  int64  \n",
            " 3   ps_ind_02_cat   595212 non-null  int64  \n",
            " 4   ps_ind_03       595212 non-null  int64  \n",
            " 5   ps_ind_04_cat   595212 non-null  int64  \n",
            " 6   ps_ind_05_cat   595212 non-null  int64  \n",
            " 7   ps_ind_06_bin   595212 non-null  int64  \n",
            " 8   ps_ind_07_bin   595212 non-null  int64  \n",
            " 9   ps_ind_08_bin   595212 non-null  int64  \n",
            " 10  ps_ind_09_bin   595212 non-null  int64  \n",
            " 11  ps_ind_10_bin   595212 non-null  int64  \n",
            " 12  ps_ind_11_bin   595212 non-null  int64  \n",
            " 13  ps_ind_12_bin   595212 non-null  int64  \n",
            " 14  ps_ind_13_bin   595212 non-null  int64  \n",
            " 15  ps_ind_14       595212 non-null  int64  \n",
            " 16  ps_ind_15       595212 non-null  int64  \n",
            " 17  ps_ind_16_bin   595212 non-null  int64  \n",
            " 18  ps_ind_17_bin   595212 non-null  int64  \n",
            " 19  ps_ind_18_bin   595212 non-null  int64  \n",
            " 20  ps_reg_01       595212 non-null  float64\n",
            " 21  ps_reg_02       595212 non-null  float64\n",
            " 22  ps_reg_03       595212 non-null  float64\n",
            " 23  ps_car_01_cat   595212 non-null  int64  \n",
            " 24  ps_car_02_cat   595212 non-null  int64  \n",
            " 25  ps_car_03_cat   595212 non-null  int64  \n",
            " 26  ps_car_04_cat   595212 non-null  int64  \n",
            " 27  ps_car_05_cat   595212 non-null  int64  \n",
            " 28  ps_car_06_cat   595212 non-null  int64  \n",
            " 29  ps_car_07_cat   595212 non-null  int64  \n",
            " 30  ps_car_08_cat   595212 non-null  int64  \n",
            " 31  ps_car_09_cat   595212 non-null  int64  \n",
            " 32  ps_car_10_cat   595212 non-null  int64  \n",
            " 33  ps_car_11_cat   595212 non-null  int64  \n",
            " 34  ps_car_11       595212 non-null  int64  \n",
            " 35  ps_car_12       595212 non-null  float64\n",
            " 36  ps_car_13       595212 non-null  float64\n",
            " 37  ps_car_14       595212 non-null  float64\n",
            " 38  ps_car_15       595212 non-null  float64\n",
            " 39  ps_calc_01      595212 non-null  float64\n",
            " 40  ps_calc_02      595212 non-null  float64\n",
            " 41  ps_calc_03      595212 non-null  float64\n",
            " 42  ps_calc_04      595212 non-null  int64  \n",
            " 43  ps_calc_05      595212 non-null  int64  \n",
            " 44  ps_calc_06      595212 non-null  int64  \n",
            " 45  ps_calc_07      595212 non-null  int64  \n",
            " 46  ps_calc_08      595212 non-null  int64  \n",
            " 47  ps_calc_09      595212 non-null  int64  \n",
            " 48  ps_calc_10      595212 non-null  int64  \n",
            " 49  ps_calc_11      595212 non-null  int64  \n",
            " 50  ps_calc_12      595212 non-null  int64  \n",
            " 51  ps_calc_13      595212 non-null  int64  \n",
            " 52  ps_calc_14      595212 non-null  int64  \n",
            " 53  ps_calc_15_bin  595212 non-null  int64  \n",
            " 54  ps_calc_16_bin  595212 non-null  int64  \n",
            " 55  ps_calc_17_bin  595212 non-null  int64  \n",
            " 56  ps_calc_18_bin  595212 non-null  int64  \n",
            " 57  ps_calc_19_bin  595212 non-null  int64  \n",
            " 58  ps_calc_20_bin  595212 non-null  int64  \n",
            "dtypes: float64(10), int64(49)\n",
            "memory usage: 267.9 MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gXOqBiU0SSrr",
        "outputId": "692e2103-5924-4fbd-d4eb-5a97e5c059af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(595212, 59)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.drop_duplicates()\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7c6-XiK_39C6"
      },
      "source": [
        "\n",
        "\n",
        "*   The dataset has 595212 rows and 59 columns\n",
        "*   All columns are of either int64(49 columns) or float64(10) datatype\n",
        "*   There are no null objects\n",
        "*   There are no duplicates\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xIZoLV6P7izT"
      },
      "source": [
        "### Checking the Data Balance or not (Target distribution)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1f9m_MW35wH",
        "outputId": "4cd35ec7-bab1-4678-99cc-549ffe80691a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    573518\n",
              "1     21694\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df['target'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4EasNnqjo8n9",
        "outputId": "9ca6d0a4-59cf-4b6e-be0b-8237b3cbbf7c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96.3552482140817"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df['target'].value_counts()[0]/(df['target'].value_counts()[0]+df['target'].value_counts()[1]) *100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xwhwE9Lo6g4l"
      },
      "source": [
        "\n",
        "\n",
        "*   The data is imbalanced as target data distribution is not balanced.\n",
        "*   96% of target data is of one class and only 4% data is of another class"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_jnqwAVv_X08"
      },
      "source": [
        "\n",
        "### Splitting Features and Handling Missing Values\n",
        "* Categorical Features \n",
        "* Binary Features \n",
        "* Handling Missing Values\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DynzZVg7ERlk",
        "outputId": "734c6f80-deb4-47a7-b1ba-072c75c2f549"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No of Outliers for column ps_reg_02 is 36793 and Percentage is 6.181494996740657\n",
            "No of Outliers for column ps_reg_03 is 120865 and Percentage is 20.30621022425623\n",
            "No of Outliers for column ps_car_12 is 15356 and Percentage is 2.579921103741188\n",
            "No of Outliers for column ps_car_13 is 26913 and Percentage is 4.521582226164795\n",
            "No of Outliers for column ps_car_14 is 58905 and Percentage is 9.896473861414085\n",
            "No of Outliers for column ps_car_15 is 27409 and Percentage is 4.604913879424474\n",
            "Binary features: 18\n",
            "Categorical features: 14\n",
            "Continuous featues: 10\n",
            "Ordinal_features: 17\n"
          ]
        }
      ],
      "source": [
        "bin_count=cat_count=continuous_count=ordinal_count=miss_count=0\n",
        "cat_cols=[]\n",
        "interval_cols=[]\n",
        "ordinal_cols=[]\n",
        "binary_cols=[]\n",
        "cols_to_be_dropped=[]\n",
        "outliers_indices=[]\n",
        "miss_count_dict=dict.fromkeys(df.columns,0)\n",
        "\n",
        "for i,column in enumerate(df.columns):\n",
        "  len=df[df[column]==-1].shape[0]\n",
        "  miss_count_dict[column]=len\n",
        "  last_3=column[-3:]\n",
        "  #Calculating Percentage of Missing values\n",
        "  Percent=round((len*100)/df.shape[0],2)\n",
        "  if last_3=='bin' or column == 'target':\n",
        "        bin_count+=1\n",
        "        binary_cols.append(column)\n",
        "        if Percent>40:\n",
        "            cols_to_be_dropped.append(column)\n",
        "        elif len>0:\n",
        "            df[column]=df[column].replace(-1,df[df[column]!=-1][column].mode()[0])\n",
        "  elif last_3=='cat':\n",
        "        cat_count+=1\n",
        "        cat_cols.append(column)\n",
        "        if Percent>40:\n",
        "            cols_to_be_dropped.append(column)\n",
        "        elif len>0: \n",
        "            df[column]=df[column].replace(-1,df[df[column]!=-1][column].mode()[0])\n",
        "  elif df[column].dtypes=='float64':\n",
        "        continuous_count+=1\n",
        "        interval_cols.append(column)\n",
        "        if Percent>40:\n",
        "            cols_to_be_dropped.append(column)\n",
        "        else:\n",
        "            Q1=df[column].quantile(0.25)\n",
        "            Q3=df[column].quantile(0.75)\n",
        "            IQR=Q3-Q1\n",
        "            outliers=df[column][(df[column] < (Q1-1.5*IQR))| (df[column] > (Q3 + 1.5 * IQR))]\n",
        "            if outliers.shape[0]>0:\n",
        "                outliers_indices.extend(outliers.index)\n",
        "                print(f\"No of Outliers for column {column} is {outliers.shape[0]} and Percentage is {(outliers.shape[0]*100)/df.shape[0]}\")#\\nOutliers Index: {outliers.index}\")\n",
        "                if len>0:\n",
        "                    #Replace outliers with median and rest with mean\n",
        "                    df[column]=df[column].replace(-1,round(df[column].median(),2))\n",
        "            elif len>0:\n",
        "                df[column]=df[column].replace(-1,round(df[column].mean(),2))\n",
        "  else:\n",
        "        ordinal_count+=1\n",
        "        ordinal_cols.append(column)\n",
        "        if Percent>40:\n",
        "            cols_to_be_dropped.append(column)\n",
        "        elif len>0:\n",
        "            df[column]=df[column].replace(-1,df[df[column]!=-1][column].mode()[0])\n",
        " \n",
        "print(f\"Binary features: {bin_count}\\nCategorical features: {cat_count}\\nContinuous featues: {continuous_count}\\n\\\n",
        "Ordinal_features: {ordinal_count}\")     \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wm1KuCzlXaXZ"
      },
      "source": [
        "\n",
        "*   There are 18 binary features\n",
        "*   There are 14 categorical features,     27continuous/ordinal features \n",
        "*   Categorical,Ordinal and Binary features having missing values less than 40% are filled with mode \n",
        "*   Continuous features having missing values less than 40% are filled with mean if there are no outliers.filled with median if there are outliers\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QatMqKspHLd2"
      },
      "source": [
        "###  Missing Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dn7vUHxtHReP",
        "outputId": "fd25f5b7-3ff8-4a64-9f1a-222f345d471d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values count :0\n"
          ]
        }
      ],
      "source": [
        "print(f\"Missing Values count :{miss_count}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0zxCJjKhYnGC",
        "outputId": "88237d68-20b3-4688-eee2-d1f23f0d6ed2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': 0,\n",
              " 'target': 0,\n",
              " 'ps_ind_01': 0,\n",
              " 'ps_ind_02_cat': 216,\n",
              " 'ps_ind_03': 0,\n",
              " 'ps_ind_04_cat': 83,\n",
              " 'ps_ind_05_cat': 5809,\n",
              " 'ps_ind_06_bin': 0,\n",
              " 'ps_ind_07_bin': 0,\n",
              " 'ps_ind_08_bin': 0,\n",
              " 'ps_ind_09_bin': 0,\n",
              " 'ps_ind_10_bin': 0,\n",
              " 'ps_ind_11_bin': 0,\n",
              " 'ps_ind_12_bin': 0,\n",
              " 'ps_ind_13_bin': 0,\n",
              " 'ps_ind_14': 0,\n",
              " 'ps_ind_15': 0,\n",
              " 'ps_ind_16_bin': 0,\n",
              " 'ps_ind_17_bin': 0,\n",
              " 'ps_ind_18_bin': 0,\n",
              " 'ps_reg_01': 0,\n",
              " 'ps_reg_02': 0,\n",
              " 'ps_reg_03': 107772,\n",
              " 'ps_car_01_cat': 107,\n",
              " 'ps_car_02_cat': 0,\n",
              " 'ps_car_03_cat': 411231,\n",
              " 'ps_car_04_cat': 0,\n",
              " 'ps_car_05_cat': 266551,\n",
              " 'ps_car_06_cat': 0,\n",
              " 'ps_car_07_cat': 11489,\n",
              " 'ps_car_08_cat': 0,\n",
              " 'ps_car_09_cat': 569,\n",
              " 'ps_car_10_cat': 0,\n",
              " 'ps_car_11_cat': 0,\n",
              " 'ps_car_11': 5,\n",
              " 'ps_car_12': 1,\n",
              " 'ps_car_13': 0,\n",
              " 'ps_car_14': 42620,\n",
              " 'ps_car_15': 0,\n",
              " 'ps_calc_01': 0,\n",
              " 'ps_calc_02': 0,\n",
              " 'ps_calc_03': 0,\n",
              " 'ps_calc_04': 0,\n",
              " 'ps_calc_05': 0,\n",
              " 'ps_calc_06': 0,\n",
              " 'ps_calc_07': 0,\n",
              " 'ps_calc_08': 0,\n",
              " 'ps_calc_09': 0,\n",
              " 'ps_calc_10': 0,\n",
              " 'ps_calc_11': 0,\n",
              " 'ps_calc_12': 0,\n",
              " 'ps_calc_13': 0,\n",
              " 'ps_calc_14': 0,\n",
              " 'ps_calc_15_bin': 0,\n",
              " 'ps_calc_16_bin': 0,\n",
              " 'ps_calc_17_bin': 0,\n",
              " 'ps_calc_18_bin': 0,\n",
              " 'ps_calc_19_bin': 0,\n",
              " 'ps_calc_20_bin': 0}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "miss_count_dict # Missing values initially in dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZamJDynHPfR"
      },
      "source": [
        "*   12 features have missing values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jY2NCfIPFhhi"
      },
      "source": [
        "#### Top Two features in terms of Missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFIfrup-ERlm",
        "outputId": "88d9bffe-83e5-46dc-a8d5-184ee44a061f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ps_car_03_cat : 411231\n",
            "ps_car_05_cat : 266551\n"
          ]
        }
      ],
      "source": [
        "x=list(miss_count_dict.values())\n",
        "x.sort(reverse=True)\n",
        "x=x[:2]\n",
        "for i in x:\n",
        "    for j in miss_count_dict.keys():\n",
        "        if(miss_count_dict[j]==i):\n",
        "            print(str(j)+\" : \"+str(miss_count_dict[j]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hz7725U6GrgN"
      },
      "source": [
        "*   ps_car_03_cat column,ps_car_05_cat are top 2 missing columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_z0NgfSUIUa"
      },
      "source": [
        "#### Handling Missing Values \n",
        "* Droping Unwanted columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2PQxS75IDJe",
        "outputId": "ed668f95-cdad-4832-f595-439054e85faf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ps_car_03_cat', 'ps_car_05_cat']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "cols_to_be_dropped"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GILcHCseiLxB",
        "outputId": "89c991ba-8f63-4184-8880-e4738cb20536"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(595212, 57)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.drop(cols_to_be_dropped,axis=1,inplace=True)  \n",
        "cat_cols.remove('ps_car_03_cat')\n",
        "cat_cols.remove('ps_car_05_cat')\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WM5egKClJAjI"
      },
      "source": [
        "*   Columns having missing values more than 40% are 2 and they are dropped\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PPVXsv0WCqxJ"
      },
      "source": [
        "### Inference about Interval(Continuous) variables \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "x72mAnVuIq17",
        "outputId": "c4888844-f052-4de5-8469-ad0e1c182493"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           ps_reg_01      ps_reg_02      ps_reg_03      ps_car_12  \\\n",
              "count  595212.000000  595212.000000  595212.000000  595212.000000   \n",
              "mean        0.610991       0.439184       0.862533       0.379947   \n",
              "std         0.287643       0.404264       0.319686       0.058300   \n",
              "min         0.000000       0.000000       0.061237       0.100000   \n",
              "25%         0.400000       0.200000       0.666615       0.316228   \n",
              "50%         0.700000       0.300000       0.720677       0.374166   \n",
              "75%         0.900000       0.600000       1.000000       0.400000   \n",
              "max         0.900000       1.800000       4.037945       1.264911   \n",
              "\n",
              "           ps_car_13      ps_car_14      ps_car_15     ps_calc_01  \\\n",
              "count  595212.000000  595212.000000  595212.000000  595212.000000   \n",
              "mean        0.813265       0.374355       3.065899       0.449756   \n",
              "std         0.224588       0.043963       0.731366       0.287198   \n",
              "min         0.250619       0.109545       0.000000       0.000000   \n",
              "25%         0.670867       0.353553       2.828427       0.200000   \n",
              "50%         0.765811       0.370000       3.316625       0.500000   \n",
              "75%         0.906190       0.396485       3.605551       0.700000   \n",
              "max         3.720626       0.636396       3.741657       0.900000   \n",
              "\n",
              "          ps_calc_02     ps_calc_03  \n",
              "count  595212.000000  595212.000000  \n",
              "mean        0.449589       0.449849  \n",
              "std         0.286893       0.287153  \n",
              "min         0.000000       0.000000  \n",
              "25%         0.200000       0.200000  \n",
              "50%         0.400000       0.500000  \n",
              "75%         0.700000       0.700000  \n",
              "max         0.900000       0.900000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-46000a17-4835-491b-a446-86b9983d1e96\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>ps_reg_01</th>\n",
              "      <th>ps_reg_02</th>\n",
              "      <th>ps_reg_03</th>\n",
              "      <th>ps_car_12</th>\n",
              "      <th>ps_car_13</th>\n",
              "      <th>ps_car_14</th>\n",
              "      <th>ps_car_15</th>\n",
              "      <th>ps_calc_01</th>\n",
              "      <th>ps_calc_02</th>\n",
              "      <th>ps_calc_03</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>595212.000000</td>\n",
              "      <td>595212.000000</td>\n",
              "      <td>595212.000000</td>\n",
              "      <td>595212.000000</td>\n",
              "      <td>595212.000000</td>\n",
              "      <td>595212.000000</td>\n",
              "      <td>595212.000000</td>\n",
              "      <td>595212.000000</td>\n",
              "      <td>595212.000000</td>\n",
              "      <td>595212.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.610991</td>\n",
              "      <td>0.439184</td>\n",
              "      <td>0.862533</td>\n",
              "      <td>0.379947</td>\n",
              "      <td>0.813265</td>\n",
              "      <td>0.374355</td>\n",
              "      <td>3.065899</td>\n",
              "      <td>0.449756</td>\n",
              "      <td>0.449589</td>\n",
              "      <td>0.449849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.287643</td>\n",
              "      <td>0.404264</td>\n",
              "      <td>0.319686</td>\n",
              "      <td>0.058300</td>\n",
              "      <td>0.224588</td>\n",
              "      <td>0.043963</td>\n",
              "      <td>0.731366</td>\n",
              "      <td>0.287198</td>\n",
              "      <td>0.286893</td>\n",
              "      <td>0.287153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.061237</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.250619</td>\n",
              "      <td>0.109545</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.666615</td>\n",
              "      <td>0.316228</td>\n",
              "      <td>0.670867</td>\n",
              "      <td>0.353553</td>\n",
              "      <td>2.828427</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.720677</td>\n",
              "      <td>0.374166</td>\n",
              "      <td>0.765811</td>\n",
              "      <td>0.370000</td>\n",
              "      <td>3.316625</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.906190</td>\n",
              "      <td>0.396485</td>\n",
              "      <td>3.605551</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.900000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>4.037945</td>\n",
              "      <td>1.264911</td>\n",
              "      <td>3.720626</td>\n",
              "      <td>0.636396</td>\n",
              "      <td>3.741657</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-46000a17-4835-491b-a446-86b9983d1e96')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-46000a17-4835-491b-a446-86b9983d1e96 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-46000a17-4835-491b-a446-86b9983d1e96');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df[interval_cols].describe()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[interval_cols].hist(figsize=(10,10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "0Kjr4QNXFD_O",
        "outputId": "661464c8-98d1-4015-9658-57ad1192012c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4dfef333d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4dfeef8820>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4dfeea9c40>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4dfee540d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4dfee8a460>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4dfee33790>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4dfee33880>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4dfeddbcd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4dfedbf4c0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4dfed708b0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4dfed1bc10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4dfeccc340>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=[15,15])\n",
        "for i,col in enumerate(interval_cols):\n",
        "  plt.subplot(4,3,i+1) \n",
        "  sns.distplot(df[col])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "id": "P8tQ8XaEGhkV",
        "outputId": "8ff63e3c-9caf-4417-c940-884e9a3b345c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "E2cXBGRSHzld",
        "outputId": "6ac8ba1c-9dd1-43aa-ed91-37d06f3ca012"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4df9a9ea00>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df[interval_cols].boxplot(rot=90)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "So0rv5ypERlo",
        "outputId": "e6e9991e-6644-49b0-f533-021933191069"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No of Outliers for column ps_reg_02 is 36793 and Percentage is 6.181494996740657\n",
            "No of Outliers for column ps_reg_03 is 35514 and Percentage is 5.966613576339187\n",
            "No of Outliers for column ps_car_12 is 15355 and Percentage is 2.579753096375745\n",
            "No of Outliers for column ps_car_13 is 26913 and Percentage is 4.521582226164795\n",
            "No of Outliers for column ps_car_14 is 30420 and Percentage is 5.110784056773049\n",
            "No of Outliers for column ps_car_15 is 27409 and Percentage is 4.604913879424474\n"
          ]
        }
      ],
      "source": [
        "outliers_indices=[]    \n",
        "for column in interval_cols:\n",
        "    Q1=df[column].quantile(0.25)\n",
        "    Q3=df[column].quantile(0.75)\n",
        "    IQR=Q3-Q1\n",
        "    outliers=df[column][(df[column] < (Q1-1.5*IQR))| (df[column] > (Q3 + 1.5 * IQR))]\n",
        "    if outliers.shape[0]>0:\n",
        "        outliers_indices.extend(outliers.index)\n",
        "        print(f\"No of Outliers for column {column} is {outliers.shape[0]} and Percentage is {(outliers.shape[0]*100)/df.shape[0]}\")#\\nOutliers Index: {outliers.index}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6pL5h76iIV9B",
        "outputId": "6a1040a2-8347-4b33-e6dc-75a0ebed28d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "117563\n"
          ]
        }
      ],
      "source": [
        "outliers_count=0\n",
        "for i in list(set(outliers_indices)):\n",
        "  outliers_count+=1\n",
        "print(outliers_count)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mH_waKs0A2l"
      },
      "source": [
        "*   All interval features values are in between 0 to 4\n",
        "*   All features having mean between 0 to 1 except for ps_car_15\n",
        "*   Standard Deviation lies between 0 to 0.7\n",
        "*   The data has outliers for 6 columns ps_reg_02 ,ps_reg_03,ps_car_12,\n",
        "pas_car_13,ps_car_14,ps_car_15\n",
        "*   117563 rows are outliers\n",
        "*   The columns not having outliers are in range of 0 to 1\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hS2wgVwwKs7g"
      },
      "source": [
        "### Inference about Ordinal Variables \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "S-I6BhAMpKH9"
      },
      "outputs": [],
      "source": [
        "# Dropping id column as too many ordinals to be handled and it assign higher rank for higher id.\n",
        "df.drop('id',axis=1,inplace=True)\n",
        "if 'id' in ordinal_cols:\n",
        "  ordinal_cols.remove('id')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "id": "iVaHsJ3NERlp",
        "outputId": "c6787273-32a8-4a57-f054-4a0f6a7354fd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "'''for column in ordinal_cols[1:]:\n",
        "    fig, ax = plt.subplots()\n",
        "    df[column].value_counts().plot(ax=ax, kind='bar',figsize=(3,3),title=  column+' VS Frquency')\n",
        "    plt.xticks(rotation='horizontal')\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Frquency')'''\n",
        "\n",
        "#https://stackoverflow.com/questions/64229894/how-to-fix-numpy-ndarray-object-has-no-attribute-get-figure-when-plotting-su\n",
        "fig,ax=plt.subplots(5,3)\n",
        "ax = ax.ravel()\n",
        "for i,column in enumerate(ordinal_cols[1:]):\n",
        "    df[column].value_counts().plot(ax=ax[i],kind='bar',figsize=(12,10),title=  column+' VS Frquency')\n",
        "    plt.xticks(rotation='horizontal')\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Frquency')\n",
        "plt.tight_layout()\n",
        "#As you can see, the axis labels in these subplots overlap one another. This is visually unappealing.\n",
        "#If you add the plt.tight_layout() statement to the end of this code block, this problem resolves itself."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LzUKuQTI0BiJ"
      },
      "source": [
        "\n",
        "\n",
        "* We can observe in all columns there is high to low range (rank wise) which indicates they are ordinal variables and in each column they are ranked \n",
        "* Since the values range between 0 to 24 we need to do scaling\n",
        " \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjk98ypvKu7T"
      },
      "source": [
        "### Inference about Binary Variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "viYf9fCK3QHo",
        "outputId": "cde429f5-936c-4882-d2ba-a6704118a9b0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "\n",
        "no_of_ones=[]\n",
        "no_of_zeros=[]\n",
        "count=0\n",
        "for _ in binary_cols:\n",
        "  count+=1\n",
        "for column in binary_cols:\n",
        "  no_of_ones.append(df[df[column]==1].shape[0])\n",
        "  no_of_zeros.append(df[df[column]==0].shape[0])\n",
        "  \n",
        "X_axis = np.arange(count)\n",
        "  \n",
        "plt.bar(X_axis - 0.2, no_of_ones, 0.4, label = 'Ones')\n",
        "plt.bar(X_axis + 0.2, no_of_zeros, 0.4, label = 'Zeros')\n",
        "  \n",
        "plt.xticks(X_axis, binary_cols,rotation='vertical')\n",
        "plt.xlabel(\"Binary Features\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.title(\"Binary variables in each feature\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p12LPyHW0Cf0"
      },
      "source": [
        "\n",
        "*   In ps_ind_10_bin,ps_ind_11_bin,ps_ind_12_bin,ps_ind_13_bin ones ratio is very less\n",
        "*   In most of Binary columns Zeros count is more\n",
        "*   Only in ps_ind_16_bin and ps_calc_16_bin,ps_calc_17_bin ones are more\n",
        "*   Mean is close to 0 and 75% is also 0 for most of columns.So we can conclude there are more zeros in majority of columns\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "77McvFnCe1eS"
      },
      "source": [
        "### Which Interval Variables having strong correlation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "5OUHwBKle1Ay",
        "outputId": "fb554e8c-20cf-442c-cb5d-3c014834bb4a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "'''fig, ax = plt.subplots(figsize=(10,10))\n",
        "sns.heatmap(df[interval_cols].corr(), cmap='YlGnBu', vmax=1.0, center=0, fmt='.2f',\n",
        "            square=True, linewidths=.5, annot=True, cbar_kws={\"shrink\": .80})\n",
        "plt.title(\"correlation of interval data types\")\n",
        "plt.show();'''\n",
        "corr_mat = df[interval_cols].corr() \n",
        "corr_mat_mask = np.array(corr_mat)\n",
        "corr_mat_mask[np.tril_indices_from(corr_mat)] = False \n",
        "fig, ax = plt.subplots(figsize=(10,10))\n",
        "sns.heatmap(corr_mat,mask=corr_mat_mask, cmap='YlGnBu', vmax=1.0, center=0, fmt='.2f',\n",
        "            square=True, linewidths=.5, annot=True, cbar_kws={\"shrink\": .80})\n",
        "plt.title(\"correlation of interval data types\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "IDnshY1jmIsA"
      },
      "outputs": [],
      "source": [
        "df.drop(['ps_reg_03','ps_car_12'],axis=1,inplace=True)\n",
        "if 'ps_reg_03' in interval_cols:\n",
        "  interval_cols.remove('ps_reg_03')\n",
        "if 'ps_car_12' in interval_cols:\n",
        "  interval_cols.remove('ps_car_12')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YzipmTMtgg_h"
      },
      "source": [
        "\n",
        "\n",
        "*   ps_reg_02 and ps_reg_03 are more correlated and slightly with ps_reg_01.Hence dropped ps_reg_03\n",
        "*   ps_car_12 ,pas_car_13 are highly correlated to each other and slightly with ps_car_15, ps_car_14.Hence dropped ps_car_12\n",
        "*   Rest has no good correlation\n",
        "*   Since ps_reg_02 and ps_reg_03 are highly correlated and both are having outliers and ps_reg_03 has 1lakh missing values.\n",
        "let's drop ps_reg_03 column\n",
        "*   Since ps_car_12,ps_car_13,ps_car_14,ps_car_15 are not highly correlated and though they are having outliers the outliers percentage is very less(2 to 4.5).So we are not dropping any column\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CsLG5D7pgjKD"
      },
      "source": [
        "###  Level Of Correlation among ordinal features\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "YYDF0ELfERlq",
        "outputId": "3b94edc8-da96-4645-ed11-aa4a7c4fcfd8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "'''fig, ax = plt.subplots(figsize=(10,10))\n",
        "sns.heatmap(df[ordinal_cols].corr(),cmap='RdYlGn',square=True,annot=True,fmt='.2f',cbar_kws={\"shrink\": .60})\n",
        "plt.title(\"correlation of interval data types\")\n",
        "plt.show();'''\n",
        "corr_mat = df[ordinal_cols].corr() \n",
        "corr_mat_mask = np.array(corr_mat)\n",
        "corr_mat_mask[np.tril_indices_from(corr_mat)] = False \n",
        "fig, ax = plt.subplots(figsize=(10,10))\n",
        "sns.heatmap(corr_mat,mask=corr_mat_mask, cmap='RdYlGn', square=True,annot=True,fmt='.2f',cbar_kws={\"shrink\": .60})\n",
        "plt.title(\"correlation of interval data types\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TlY1OkvCh3PX"
      },
      "source": [
        "*   ps_ind_01 and ps_ind_03 has small correlation\n",
        "*   Rest has no good correlation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QVD3mW5pcjfN"
      },
      "source": [
        "### Checking target data is proportionate or not?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RwJVnCHGawVx",
        "outputId": "032a1763-5f13-4cb8-f5ad-3bc56364df24"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96.3552482140817"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df['target'].value_counts()[0]/(df['target'].value_counts()[0]+df['target'].value_counts()[1]) *100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rvaHvsGlctMO"
      },
      "source": [
        "\n",
        "*   96% of target data is of one class and only 4% data is of another class\n",
        "*   In binary data One class is below than 30%\n",
        "*   So the target data is not proportionate \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VEyl5miKenLy"
      },
      "source": [
        "###  Prefered way to balance the data is achieve balance of 12% using Undersampling or OverSampling."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rbV3k6dtnHGd"
      },
      "source": [
        "#### As there are less than millions of records in our data let's go with oversampling and also check undersampling\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "u1J4wydbNZbp"
      },
      "outputs": [],
      "source": [
        "#!pip install imblearn\n",
        "from imblearn.over_sampling import SMOTE\n",
        "smote_obj=SMOTE(random_state=23)\n",
        "x_b=df.drop(['target'],axis=1)\n",
        "y_b=df['target']\n",
        "x_resampled,y_resampled = smote_obj.fit_resample(x_b,y_b)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfdePYaNRVst",
        "outputId": "03c7c6e2-17ec-4283-e59f-2afc7ace3796"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    573518\n",
              "1    573518\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "y_resampled.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "CwI69W52q4aA",
        "outputId": "ffa0632c-b630-4137-88ac-fac9b3d1e16d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4df1a1e3a0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "pd.Series(y_resampled).value_counts().plot(kind='bar', title='Class distribution after appying SMOTE', xlabel='buy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "gtSJkS4Qee-J",
        "outputId": "244d696d-36eb-4134-dcb4-b89ef15f8ef2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4df6f23b80>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "x_resampled[interval_cols].boxplot(rot=90)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VmYG6N2qqXC"
      },
      "source": [
        "### Undersampling achieving balance of 12%"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y4zhOqDUcsBq",
        "outputId": "228a20d5-8015-4326-d122-f126b56e2449"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.28 159089 21694 573518\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    159089\n",
              "1     21694\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "def UnderSamplingRate(desired_balance,no_of_ones,no_of_zeros):\n",
        "  return (1-desired_balance)*no_of_ones/(no_of_zeros*desired_balance)\n",
        "desired_balance=0.12\n",
        "no_of_ones=df[df['target']==1].shape[0]\n",
        "no_of_zeros=df[df['target']==0].shape[0]\n",
        "under_sampling_rate=UnderSamplingRate(desired_balance,no_of_ones,no_of_zeros)\n",
        "target_zero_records=round(under_sampling_rate*no_of_zeros)\n",
        "print(round(under_sampling_rate,2),target_zero_records,no_of_ones,no_of_zeros)\n",
        "\n",
        "zero_indexes = df[df.target == 0].index\n",
        "one_indexes = df[df.target == 1].index\n",
        "from sklearn.utils import shuffle\n",
        "undersampled_indexes = shuffle(zero_indexes,random_state=42,n_samples=target_zero_records)\n",
        "final_indexes=list(one_indexes)+list(undersampled_indexes)\n",
        "df_train=df.loc[final_indexes].reset_index(drop=True)\n",
        "df_train['target'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lUcGVcE2lN9h"
      },
      "source": [
        "* Dropped Id column\n",
        "* After OverSampling there is balanced dataset \n",
        "* After undersampling Now we have 159089 zeros and 21694 ones and achieved a balance of 12%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uwqpmLeAoXG0"
      },
      "source": [
        "### Training Records after Oversampling and Undersampling achieving balance of 12%"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M9VeiXuuoOYS",
        "outputId": "391531a1-ac6f-4b0e-b603-01bea3d7325e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Records After OverSampling: 1147036\n",
            "Records After UnderSampling: 180783\n"
          ]
        }
      ],
      "source": [
        "print(f\"Records After OverSampling: {x_resampled.shape[0]}\")\n",
        "#UnderSampling\n",
        "print(f\"Records After UnderSampling: { target_zero_records + no_of_ones}\") #159089+21694 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DzOX-QtNof7T"
      },
      "source": [
        "*  Records After OverSampling: 1147036\n",
        "*  Records After UnderSampling: 180783"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUnnsBARPwsz"
      },
      "source": [
        "###  HotEncoding For Categorical Features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i0WTxT2TERlt"
      },
      "source": [
        "#### One Hot Vector representation for categorical data before Resampling "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "0VYgiXluERlt"
      },
      "outputs": [],
      "source": [
        "x_resampled_encoding=pd.get_dummies(x_resampled,columns=cat_cols)\n",
        "df=pd.get_dummies(df,columns=cat_cols)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBPSJLlZuDnt"
      },
      "source": [
        "### Final Data for modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Zxu_sHsERlt"
      },
      "source": [
        "#### Initial data without Resampling and with Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "J5lmV4oeERlt",
        "outputId": "76075814-3ba3-4ecc-8d09-bb6594c2b137"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        target  ps_ind_01  ps_ind_03  ps_ind_06_bin  ps_ind_07_bin  \\\n",
              "0            0          2          5              0              1   \n",
              "1            0          1          7              0              0   \n",
              "2            0          5          9              0              0   \n",
              "3            0          0          2              1              0   \n",
              "4            0          0          0              1              0   \n",
              "...        ...        ...        ...            ...            ...   \n",
              "595207       0          3         10              0              0   \n",
              "595208       0          5          3              0              0   \n",
              "595209       0          1         10              1              0   \n",
              "595210       0          5          3              0              0   \n",
              "595211       0          0          8              1              0   \n",
              "\n",
              "        ps_ind_08_bin  ps_ind_09_bin  ps_ind_10_bin  ps_ind_11_bin  \\\n",
              "0                   0              0              0              0   \n",
              "1                   1              0              0              0   \n",
              "2                   1              0              0              0   \n",
              "3                   0              0              0              0   \n",
              "4                   0              0              0              0   \n",
              "...               ...            ...            ...            ...   \n",
              "595207              0              1              0              0   \n",
              "595208              0              1              0              0   \n",
              "595209              0              0              0              0   \n",
              "595210              1              0              0              0   \n",
              "595211              0              0              0              0   \n",
              "\n",
              "        ps_ind_12_bin  ...  ps_car_11_cat_95  ps_car_11_cat_96  \\\n",
              "0                   0  ...                 0                 0   \n",
              "1                   0  ...                 0                 0   \n",
              "2                   0  ...                 0                 0   \n",
              "3                   0  ...                 0                 0   \n",
              "4                   0  ...                 0                 0   \n",
              "...               ...  ...               ...               ...   \n",
              "595207              0  ...                 0                 0   \n",
              "595208              0  ...                 0                 0   \n",
              "595209              0  ...                 0                 0   \n",
              "595210              0  ...                 0                 0   \n",
              "595211              0  ...                 0                 0   \n",
              "\n",
              "        ps_car_11_cat_97  ps_car_11_cat_98  ps_car_11_cat_99  \\\n",
              "0                      0                 0                 0   \n",
              "1                      0                 0                 0   \n",
              "2                      0                 0                 0   \n",
              "3                      0                 0                 0   \n",
              "4                      0                 0                 0   \n",
              "...                  ...               ...               ...   \n",
              "595207                 0                 0                 0   \n",
              "595208                 0                 0                 0   \n",
              "595209                 0                 0                 0   \n",
              "595210                 0                 0                 0   \n",
              "595211                 0                 0                 0   \n",
              "\n",
              "        ps_car_11_cat_100  ps_car_11_cat_101  ps_car_11_cat_102  \\\n",
              "0                       0                  0                  0   \n",
              "1                       0                  0                  0   \n",
              "2                       0                  0                  0   \n",
              "3                       0                  0                  0   \n",
              "4                       0                  0                  0   \n",
              "...                   ...                ...                ...   \n",
              "595207                  0                  0                  0   \n",
              "595208                  0                  0                  0   \n",
              "595209                  0                  0                  0   \n",
              "595210                  0                  1                  0   \n",
              "595211                  0                  0                  0   \n",
              "\n",
              "        ps_car_11_cat_103  ps_car_11_cat_104  \n",
              "0                       0                  0  \n",
              "1                       0                  0  \n",
              "2                       0                  0  \n",
              "3                       0                  1  \n",
              "4                       0                  0  \n",
              "...                   ...                ...  \n",
              "595207                  0                  0  \n",
              "595208                  0                  0  \n",
              "595209                  0                  0  \n",
              "595210                  0                  0  \n",
              "595211                  0                  0  \n",
              "\n",
              "[595212 rows x 213 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-96c577fe-1455-4bd3-bd00-84c1787b0434\">\n",
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          "metadata": {},
          "execution_count": 34
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        "df"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJQlNQB-ERlu"
      },
      "source": [
        "#### Data with Resampling and without Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "Ko2aZ2qVERlu",
        "outputId": "977be3c3-e113-40b5-ab0d-a70a684420e8"
      },
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          },
          "metadata": {},
          "execution_count": 35
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      ],
      "source": [
        "x_resampled"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4lv1foaERlu"
      },
      "source": [
        "#### Data with Resampling and with Encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "BQU49NL0ERlu",
        "outputId": "a311fc50-0c7d-45b9-cd10-c6361d85775f"
      },
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1147031</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1147032</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1147033</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1147034</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>...</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1147035</th>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>1147036 rows × 212 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bce20528-5683-47c0-a00f-b9223b1052b3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-bce20528-5683-47c0-a00f-b9223b1052b3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bce20528-5683-47c0-a00f-b9223b1052b3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "x_resampled_encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "D16i_JUvERlu"
      },
      "outputs": [],
      "source": [
        "df.to_csv('original_data_with_encoding.csv',index=False)\n",
        "x_resampled.to_csv('resampled_without_encoding.csv',index=False)\n",
        "x_resampled_encoding.to_csv('resampled_with_encoding.csv',index=False)\n",
        "y_resampled.to_csv('y_resampled.csv',index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9JpgjMwJJIK"
      },
      "source": [
        "###  Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LsMt4RDvXdqP"
      },
      "source": [
        "*   Load the Data into DataFrame\n",
        "*   Inference about the data\n",
        "*   Finding the distribution of target variables\n",
        "*   Split features into Categorical,Binary,Ordinal,Interval\n",
        "*   Handled Missing Values\n",
        "*   Found Outliers in continuous variables\n",
        "*   Dropped Id column\n",
        "*   Inferences about continuous,ordinal,binary variables through Visualization\n",
        "*   Level of Correlation for interval,ordinal \n",
        "*   Keep only 1 column for columns having high correlation\n",
        "*   Balanced the data by OverSampling(Smote algorithm)\n",
        "*   OneHotEncoding for categorical variables\n",
        "*   Saved CSV Files for data with and without Encoding\n",
        "\n"
      ]
    }
  ],
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    "colab": {
      "provenance": []
    },
    "kernelspec": {
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