Data_Preparation_&_Exploration.ipynb

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      "name": "Data Preparation & Exploration.ipynb",
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      "authorship_tag": "ABX9TyMuj3YCEFFH9eGz/VAoYls5",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kwanhong66/KaggleShoveling/blob/master/Data_Preparation_%26_Exploration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w2w0FfHwmRM-",
        "colab_type": "text"
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      "source": [
        "## Kaggle kernal transcription study #4\n",
        "### Data Preparation & Exploration\n",
        "\n",
        "- Kernal 주소\n",
        "  * https://www.kaggle.com/bertcarremans/data-preparation-exploration\n",
        "- Competition 주소\n",
        "  * https://www.kaggle.com/c/porto-seguro-safe-driver-prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ohlQaNOmbpd",
        "colab_type": "text"
      },
      "source": [
        "## Dataset with Kaggle API"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzrc4DYPmQJO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip3 install -q kaggle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DrADehDZme2T",
        "colab_type": "code",
        "outputId": "47614d00-3b2a-4a96-83a0-c8caaa69ce05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "!wget 'https://raw.githubusercontent.com/kwanhong66/KaggleShoveling/master/token/kaggle.json'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-02-11 02:46:16--  https://raw.githubusercontent.com/kwanhong66/KaggleShoveling/master/token/kaggle.json\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 63 [text/plain]\n",
            "Saving to: ‘kaggle.json’\n",
            "\n",
            "\rkaggle.json           0%[                    ]       0  --.-KB/s               \rkaggle.json         100%[===================>]      63  --.-KB/s    in 0s      \n",
            "\n",
            "2020-02-11 02:46:16 (10.9 MB/s) - ‘kaggle.json’ saved [63/63]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmmBcP9qmg2l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9NESJwSmjz4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e90NcCGVmknZ",
        "colab_type": "code",
        "outputId": "ebe39f62-9368-4712-cf61-513f9718ffc7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        }
      },
      "source": [
        "!kaggle competitions list"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "ref                                            deadline             category             reward  teamCount  userHasEntered  \n",
            "---------------------------------------------  -------------------  ---------------  ----------  ---------  --------------  \n",
            "digit-recognizer                               2030-01-01 00:00:00  Getting Started   Knowledge       2384           False  \n",
            "titanic                                        2030-01-01 00:00:00  Getting Started   Knowledge      15931            True  \n",
            "house-prices-advanced-regression-techniques    2030-01-01 00:00:00  Getting Started   Knowledge       5052            True  \n",
            "connectx                                       2030-01-01 00:00:00  Getting Started   Knowledge        422           False  \n",
            "imagenet-object-localization-challenge         2029-12-31 07:00:00  Research          Knowledge         64           False  \n",
            "competitive-data-science-predict-future-sales  2020-12-31 23:59:00  Playground            Kudos       5678           False  \n",
            "flower-classification-with-tpus                2020-05-11 23:59:00  Playground            Kudos         12           False  \n",
            "deepfake-detection-challenge                   2020-03-31 23:59:00  Featured         $1,000,000       1618           False  \n",
            "cat-in-the-dat-ii                              2020-03-31 23:59:00  Playground             Swag        546           False  \n",
            "nlp-getting-started                            2020-03-23 23:59:00  Getting Started     $10,000       2701           False  \n",
            "bengaliai-cv19                                 2020-03-16 23:59:00  Research            $10,000       1212           False  \n",
            "google-quest-challenge                         2020-02-10 23:59:00  Featured            $25,000       1571           False  \n",
            "tensorflow2-question-answering                 2020-01-22 23:59:00  Featured            $50,000       1233           False  \n",
            "data-science-bowl-2019                         2020-01-22 23:59:00  Featured           $160,000       3497           False  \n",
            "pku-autonomous-driving                         2020-01-21 23:59:00  Featured            $25,000        864           False  \n",
            "santa-2019-revenge-of-the-accountants          2020-01-16 23:59:00  Playground             Swag        106           False  \n",
            "santa-workshop-tour-2019                       2020-01-15 23:59:00  Featured            $25,000       1620           False  \n",
            "nfl-big-data-bowl-2020                         2020-01-06 23:59:00  Featured            $75,000       2038           False  \n",
            "nfl-playing-surface-analytics                  2020-01-02 23:59:00  Analytics           $75,000          0           False  \n",
            "ashrae-energy-prediction                       2019-12-19 23:59:00  Featured            $25,000       3614           False  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vAtMwRtUm6X-",
        "colab_type": "code",
        "outputId": "387535db-5b93-48f6-ab10-07a3b28a4822",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "!kaggle competitions download 'porto-seguro-safe-driver-prediction'"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
            "Downloading sample_submission.csv.zip to /content\n",
            "  0% 0.00/2.12M [00:00<?, ?B/s]\n",
            "100% 2.12M/2.12M [00:00<00:00, 69.6MB/s]\n",
            "Downloading train.csv.zip to /content\n",
            " 50% 15.0M/30.0M [00:00<00:00, 30.6MB/s]\n",
            "100% 30.0M/30.0M [00:00<00:00, 75.7MB/s]\n",
            "Downloading test.csv.zip to /content\n",
            " 88% 39.0M/44.4M [00:00<00:00, 13.1MB/s]\n",
            "100% 44.4M/44.4M [00:01<00:00, 41.7MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kU-uKWb3nLpB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir input"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fGD7O58nzXn",
        "colab_type": "code",
        "outputId": "babaaf8a-72b6-498d-b921-8ed254f5ae28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "!unzip '*.zip' -d ./input/"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  sample_submission.csv.zip\n",
            "  inflating: ./input/sample_submission.csv  \n",
            "\n",
            "Archive:  train.csv.zip\n",
            "  inflating: ./input/train.csv       \n",
            "\n",
            "Archive:  test.csv.zip\n",
            "  inflating: ./input/test.csv        \n",
            "\n",
            "3 archives were successfully processed.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_3bESBkAoG0c",
        "colab_type": "text"
      },
      "source": [
        "## Introduction\n",
        "\n",
        "This notebook aims at getting a good insight in the data for the PorteSeguro competition. Besides that, it gives some tips and tricks to prepare your data for modeling. The notebook consists of the following main sections:\n",
        "\n",
        "1. Visual inspection of your data\n",
        "2. Defining the metadata\n",
        "3. Descriptive statistics\n",
        "4. Handling imbalanced classes\n",
        "5. Data quality checks\n",
        "6. Exploratory data visualization\n",
        "7. Feature engineering\n",
        "8. Feature selection\n",
        "9. Feature scaling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JTr6mA80oVaa",
        "colab_type": "text"
      },
      "source": [
        "## Loading packages\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gOMoMg-ioYsB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "# from sklearn.preprocessing import Imputer\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "pd.set_option('display.max_columns', 100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0F7jlLvrLA4",
        "colab_type": "text"
      },
      "source": [
        "## Loading data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "codBY9C3rMdj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = pd.read_csv('./input/train.csv')\n",
        "test = pd.read_csv('./input/test.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUqNVSYgrWU5",
        "colab_type": "text"
      },
      "source": [
        "## Data at first sight\n",
        "\n",
        "Here is an excerpt of the the data description for the competition:\n",
        "\n",
        "- Features that belong to **similar groupings are tagged** as such in the feature \n",
        "names (e.g., ind, reg, car, calc).\n",
        "- Feature names include the postfix **bin** to indicate binary features and **cat** to indicate categorical features.\n",
        "- Features **without these designations are either continuous or ordinal.**\n",
        "- Values of **-1** indicate that the feature was **missing** from the observation.\n",
        "- The **target** columns signifies whether or not a claim was filed for that policy holder.\n",
        "\n",
        "Ok, that's important information to get us started. Let's have a quick look at the first and last rows to confirm all of this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-IWAf8oxre-x",
        "colab_type": "code",
        "outputId": "4a25f473-877f-43a2-ed60-488ebeb0192a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <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",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>0.316228</td>\n",
              "      <td>0.641586</td>\n",
              "      <td>0.347275</td>\n",
              "      <td>3.316625</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>7</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.580948</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>104</td>\n",
              "      <td>1</td>\n",
              "      <td>0.374166</td>\n",
              "      <td>0.542949</td>\n",
              "      <td>0.294958</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>2</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.840759</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>82</td>\n",
              "      <td>3</td>\n",
              "      <td>0.316070</td>\n",
              "      <td>0.565832</td>\n",
              "      <td>0.365103</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>12</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",
              "</div>"
            ],
            "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  ps_ind_09_bin  \\\n",
              "0              0              0              1              0              0   \n",
              "1              0              0              0              1              0   \n",
              "2              0              0              0              1              0   \n",
              "3              0              1              0              0              0   \n",
              "4              0              1              0              0              0   \n",
              "\n",
              "   ps_ind_10_bin  ps_ind_11_bin  ps_ind_12_bin  ps_ind_13_bin  ps_ind_14  \\\n",
              "0              0              0              0              0          0   \n",
              "1              0              0              0              0          0   \n",
              "2              0              0              0              0          0   \n",
              "3              0              0              0              0          0   \n",
              "4              0              0              0              0          0   \n",
              "\n",
              "   ps_ind_15  ps_ind_16_bin  ps_ind_17_bin  ps_ind_18_bin  ps_reg_01  \\\n",
              "0         11              0              1              0        0.7   \n",
              "1          3              0              0              1        0.8   \n",
              "2         12              1              0              0        0.0   \n",
              "3          8              1              0              0        0.9   \n",
              "4          9              1              0              0        0.7   \n",
              "\n",
              "   ps_reg_02  ps_reg_03  ps_car_01_cat  ps_car_02_cat  ps_car_03_cat  \\\n",
              "0        0.2   0.718070             10              1             -1   \n",
              "1        0.4   0.766078             11              1             -1   \n",
              "2        0.0  -1.000000              7              1             -1   \n",
              "3        0.2   0.580948              7              1              0   \n",
              "4        0.6   0.840759             11              1             -1   \n",
              "\n",
              "   ps_car_04_cat  ps_car_05_cat  ps_car_06_cat  ps_car_07_cat  ps_car_08_cat  \\\n",
              "0              0              1              4              1              0   \n",
              "1              0             -1             11              1              1   \n",
              "2              0             -1             14              1              1   \n",
              "3              0              1             11              1              1   \n",
              "4              0             -1             14              1              1   \n",
              "\n",
              "   ps_car_09_cat  ps_car_10_cat  ps_car_11_cat  ps_car_11  ps_car_12  \\\n",
              "0              0              1             12          2   0.400000   \n",
              "1              2              1             19          3   0.316228   \n",
              "2              2              1             60          1   0.316228   \n",
              "3              3              1            104          1   0.374166   \n",
              "4              2              1             82          3   0.316070   \n",
              "\n",
              "   ps_car_13  ps_car_14  ps_car_15  ps_calc_01  ps_calc_02  ps_calc_03  \\\n",
              "0   0.883679   0.370810   3.605551         0.6         0.5         0.2   \n",
              "1   0.618817   0.388716   2.449490         0.3         0.1         0.3   \n",
              "2   0.641586   0.347275   3.316625         0.5         0.7         0.1   \n",
              "3   0.542949   0.294958   2.000000         0.6         0.9         0.1   \n",
              "4   0.565832   0.365103   2.000000         0.4         0.6         0.0   \n",
              "\n",
              "   ps_calc_04  ps_calc_05  ps_calc_06  ps_calc_07  ps_calc_08  ps_calc_09  \\\n",
              "0           3           1          10           1          10           1   \n",
              "1           2           1           9           5           8           1   \n",
              "2           2           2           9           1           8           2   \n",
              "3           2           4           7           1           8           4   \n",
              "4           2           2           6           3          10           2   \n",
              "\n",
              "   ps_calc_10  ps_calc_11  ps_calc_12  ps_calc_13  ps_calc_14  ps_calc_15_bin  \\\n",
              "0           5           9           1           5           8               0   \n",
              "1           7           3           1           1           9               0   \n",
              "2           7           4           2           7           7               0   \n",
              "3           2           2           2           4           9               0   \n",
              "4          12           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  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bcj9Cwh2sbJ3",
        "colab_type": "code",
        "outputId": "2a10a195-c2b8-4068-fb64-275accbdbf1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "train.tail()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>ps_ind_09_bin</th>\n",
              "      <th>ps_ind_10_bin</th>\n",
              "      <th>ps_ind_11_bin</th>\n",
              "      <th>ps_ind_12_bin</th>\n",
              "      <th>ps_ind_13_bin</th>\n",
              "      <th>ps_ind_14</th>\n",
              "      <th>ps_ind_15</th>\n",
              "      <th>ps_ind_16_bin</th>\n",
              "      <th>ps_ind_17_bin</th>\n",
              "      <th>ps_ind_18_bin</th>\n",
              "      <th>ps_reg_01</th>\n",
              "      <th>ps_reg_02</th>\n",
              "      <th>ps_reg_03</th>\n",
              "      <th>ps_car_01_cat</th>\n",
              "      <th>ps_car_02_cat</th>\n",
              "      <th>ps_car_03_cat</th>\n",
              "      <th>ps_car_04_cat</th>\n",
              "      <th>ps_car_05_cat</th>\n",
              "      <th>ps_car_06_cat</th>\n",
              "      <th>ps_car_07_cat</th>\n",
              "      <th>ps_car_08_cat</th>\n",
              "      <th>ps_car_09_cat</th>\n",
              "      <th>ps_car_10_cat</th>\n",
              "      <th>ps_car_11_cat</th>\n",
              "      <th>ps_car_11</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",
              "      <th>ps_calc_04</th>\n",
              "      <th>ps_calc_05</th>\n",
              "      <th>ps_calc_06</th>\n",
              "      <th>ps_calc_07</th>\n",
              "      <th>ps_calc_08</th>\n",
              "      <th>ps_calc_09</th>\n",
              "      <th>ps_calc_10</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>595207</th>\n",
              "      <td>1488013</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.692820</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>0.374166</td>\n",
              "      <td>0.684631</td>\n",
              "      <td>0.385487</td>\n",
              "      <td>2.645751</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595208</th>\n",
              "      <td>1488016</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.382027</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>63</td>\n",
              "      <td>2</td>\n",
              "      <td>0.387298</td>\n",
              "      <td>0.972145</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>3.605551</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595209</th>\n",
              "      <td>1488017</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.659071</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>0.397492</td>\n",
              "      <td>0.596373</td>\n",
              "      <td>0.398748</td>\n",
              "      <td>1.732051</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>6</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595210</th>\n",
              "      <td>1488021</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.698212</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>101</td>\n",
              "      <td>3</td>\n",
              "      <td>0.374166</td>\n",
              "      <td>0.764434</td>\n",
              "      <td>0.384968</td>\n",
              "      <td>3.162278</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595211</th>\n",
              "      <td>1488027</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>34</td>\n",
              "      <td>2</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.932649</td>\n",
              "      <td>0.378021</td>\n",
              "      <td>3.741657</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>8</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             id  target  ps_ind_01  ps_ind_02_cat  ps_ind_03  ps_ind_04_cat  \\\n",
              "595207  1488013       0          3              1         10              0   \n",
              "595208  1488016       0          5              1          3              0   \n",
              "595209  1488017       0          1              1         10              0   \n",
              "595210  1488021       0          5              2          3              1   \n",
              "595211  1488027       0          0              1          8              0   \n",
              "\n",
              "        ps_ind_05_cat  ps_ind_06_bin  ps_ind_07_bin  ps_ind_08_bin  \\\n",
              "595207              0              0              0              0   \n",
              "595208              0              0              0              0   \n",
              "595209              0              1              0              0   \n",
              "595210              0              0              0              1   \n",
              "595211              0              1              0              0   \n",
              "\n",
              "        ps_ind_09_bin  ps_ind_10_bin  ps_ind_11_bin  ps_ind_12_bin  \\\n",
              "595207              1              0              0              0   \n",
              "595208              1              0              0              0   \n",
              "595209              0              0              0              0   \n",
              "595210              0              0              0              0   \n",
              "595211              0              0              0              0   \n",
              "\n",
              "        ps_ind_13_bin  ps_ind_14  ps_ind_15  ps_ind_16_bin  ps_ind_17_bin  \\\n",
              "595207              0          0         13              1              0   \n",
              "595208              0          0          6              1              0   \n",
              "595209              0          0         12              1              0   \n",
              "595210              0          0         12              1              0   \n",
              "595211              0          0          7              1              0   \n",
              "\n",
              "        ps_ind_18_bin  ps_reg_01  ps_reg_02  ps_reg_03  ps_car_01_cat  \\\n",
              "595207              0        0.5        0.3   0.692820             10   \n",
              "595208              0        0.9        0.7   1.382027              9   \n",
              "595209              0        0.9        0.2   0.659071              7   \n",
              "595210              0        0.9        0.4   0.698212             11   \n",
              "595211              0        0.1        0.2  -1.000000              7   \n",
              "\n",
              "        ps_car_02_cat  ps_car_03_cat  ps_car_04_cat  ps_car_05_cat  \\\n",
              "595207              1             -1              0              1   \n",
              "595208              1             -1              0             -1   \n",
              "595209              1             -1              0             -1   \n",
              "595210              1             -1              0             -1   \n",
              "595211              0             -1              0             -1   \n",
              "\n",
              "        ps_car_06_cat  ps_car_07_cat  ps_car_08_cat  ps_car_09_cat  \\\n",
              "595207              1              1              1              0   \n",
              "595208             15              0              0              2   \n",
              "595209              1              1              1              2   \n",
              "595210             11              1              1              2   \n",
              "595211              0              1              0              2   \n",
              "\n",
              "        ps_car_10_cat  ps_car_11_cat  ps_car_11  ps_car_12  ps_car_13  \\\n",
              "595207              1             31          3   0.374166   0.684631   \n",
              "595208              1             63          2   0.387298   0.972145   \n",
              "595209              1             31          3   0.397492   0.596373   \n",
              "595210              1            101          3   0.374166   0.764434   \n",
              "595211              1             34          2   0.400000   0.932649   \n",
              "\n",
              "        ps_car_14  ps_car_15  ps_calc_01  ps_calc_02  ps_calc_03  ps_calc_04  \\\n",
              "595207   0.385487   2.645751         0.4         0.5         0.3           3   \n",
              "595208  -1.000000   3.605551         0.2         0.2         0.0           2   \n",
              "595209   0.398748   1.732051         0.4         0.0         0.3           3   \n",
              "595210   0.384968   3.162278         0.0         0.7         0.0           4   \n",
              "595211   0.378021   3.741657         0.4         0.0         0.5           2   \n",
              "\n",
              "        ps_calc_05  ps_calc_06  ps_calc_07  ps_calc_08  ps_calc_09  \\\n",
              "595207           0           9           0           9           1   \n",
              "595208           4           8           6           8           2   \n",
              "595209           2           7           4           8           0   \n",
              "595210           0           9           4           9           2   \n",
              "595211           3          10           4          10           2   \n",
              "\n",
              "        ps_calc_10  ps_calc_11  ps_calc_12  ps_calc_13  ps_calc_14  \\\n",
              "595207          12           4           1           9           6   \n",
              "595208          12           4           1           3           8   \n",
              "595209          10           3           2           2           6   \n",
              "595210          11           4           1           4           2   \n",
              "595211           5           4           4           3           8   \n",
              "\n",
              "        ps_calc_15_bin  ps_calc_16_bin  ps_calc_17_bin  ps_calc_18_bin  \\\n",
              "595207               0               1               1               0   \n",
              "595208               1               0               1               0   \n",
              "595209               0               0               1               0   \n",
              "595210               0               1               1               1   \n",
              "595211               0               1               0               0   \n",
              "\n",
              "        ps_calc_19_bin  ps_calc_20_bin  \n",
              "595207               1               1  \n",
              "595208               1               1  \n",
              "595209               0               0  \n",
              "595210               0               0  \n",
              "595211               0               0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FdUSLNunsf83",
        "colab_type": "text"
      },
      "source": [
        "We indeed see the following\n",
        "\n",
        "- binary variables\n",
        "- categorical variables of which the category values are integers\n",
        "- other variables with integer of float values\n",
        "- variables with -1 representing missing values\n",
        "- the target variable and ID variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vocJNajpscGH",
        "colab_type": "code",
        "outputId": "e9665e46-f578-4c42-8ad4-100ca3327817",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "train.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(595212, 59)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfvBxFdAs5Hf",
        "colab_type": "code",
        "outputId": "8a83c297-892d-4283-b819-0915a03690e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "train.drop_duplicates()\n",
        "train.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(595212, 59)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0wHbyfycs_oP",
        "colab_type": "code",
        "outputId": "09dd4eb4-b5af-4d63-ae33-bf7e8be29d5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "test.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(892816, 58)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sdIeyYUItJaP",
        "colab_type": "text"
      },
      "source": [
        "So later on we can create dummy variables for the 14 categorical variables. The bin variables are already binary and do not need dummification."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O5W7biB6tJ3G",
        "colab_type": "code",
        "outputId": "610fa1bb-933a-485f-8cb0-76153fbfe551",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 595212 entries, 0 to 595211\n",
            "Data columns (total 59 columns):\n",
            "id                595212 non-null int64\n",
            "target            595212 non-null int64\n",
            "ps_ind_01         595212 non-null int64\n",
            "ps_ind_02_cat     595212 non-null int64\n",
            "ps_ind_03         595212 non-null int64\n",
            "ps_ind_04_cat     595212 non-null int64\n",
            "ps_ind_05_cat     595212 non-null int64\n",
            "ps_ind_06_bin     595212 non-null int64\n",
            "ps_ind_07_bin     595212 non-null int64\n",
            "ps_ind_08_bin     595212 non-null int64\n",
            "ps_ind_09_bin     595212 non-null int64\n",
            "ps_ind_10_bin     595212 non-null int64\n",
            "ps_ind_11_bin     595212 non-null int64\n",
            "ps_ind_12_bin     595212 non-null int64\n",
            "ps_ind_13_bin     595212 non-null int64\n",
            "ps_ind_14         595212 non-null int64\n",
            "ps_ind_15         595212 non-null int64\n",
            "ps_ind_16_bin     595212 non-null int64\n",
            "ps_ind_17_bin     595212 non-null int64\n",
            "ps_ind_18_bin     595212 non-null int64\n",
            "ps_reg_01         595212 non-null float64\n",
            "ps_reg_02         595212 non-null float64\n",
            "ps_reg_03         595212 non-null float64\n",
            "ps_car_01_cat     595212 non-null int64\n",
            "ps_car_02_cat     595212 non-null int64\n",
            "ps_car_03_cat     595212 non-null int64\n",
            "ps_car_04_cat     595212 non-null int64\n",
            "ps_car_05_cat     595212 non-null int64\n",
            "ps_car_06_cat     595212 non-null int64\n",
            "ps_car_07_cat     595212 non-null int64\n",
            "ps_car_08_cat     595212 non-null int64\n",
            "ps_car_09_cat     595212 non-null int64\n",
            "ps_car_10_cat     595212 non-null int64\n",
            "ps_car_11_cat     595212 non-null int64\n",
            "ps_car_11         595212 non-null int64\n",
            "ps_car_12         595212 non-null float64\n",
            "ps_car_13         595212 non-null float64\n",
            "ps_car_14         595212 non-null float64\n",
            "ps_car_15         595212 non-null float64\n",
            "ps_calc_01        595212 non-null float64\n",
            "ps_calc_02        595212 non-null float64\n",
            "ps_calc_03        595212 non-null float64\n",
            "ps_calc_04        595212 non-null int64\n",
            "ps_calc_05        595212 non-null int64\n",
            "ps_calc_06        595212 non-null int64\n",
            "ps_calc_07        595212 non-null int64\n",
            "ps_calc_08        595212 non-null int64\n",
            "ps_calc_09        595212 non-null int64\n",
            "ps_calc_10        595212 non-null int64\n",
            "ps_calc_11        595212 non-null int64\n",
            "ps_calc_12        595212 non-null int64\n",
            "ps_calc_13        595212 non-null int64\n",
            "ps_calc_14        595212 non-null int64\n",
            "ps_calc_15_bin    595212 non-null int64\n",
            "ps_calc_16_bin    595212 non-null int64\n",
            "ps_calc_17_bin    595212 non-null int64\n",
            "ps_calc_18_bin    595212 non-null int64\n",
            "ps_calc_19_bin    595212 non-null int64\n",
            "ps_calc_20_bin    595212 non-null int64\n",
            "dtypes: float64(10), int64(49)\n",
            "memory usage: 267.9 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q59USuvptREu",
        "colab_type": "text"
      },
      "source": [
        "Again, with the info() method we see that the data type is integer or float. No null values are present in the data set. That's normal because missing values are replaced by -1. We'll look into that later."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qK34W9kEtTu2",
        "colab_type": "text"
      },
      "source": [
        "## Meatadata\n",
        "\n",
        "To facilitate the data management, we'll store meta-information about the variables in a DataFrame. This will be helpful when we want to select specific variables for analysis, visualization, modeling, ...\n",
        "\n",
        "Concretely we will store:\n",
        "\n",
        "* **role**: input, ID, target\n",
        "* **level**: nominal, interval, ordinal, binary\n",
        "* **keep**: True or False\n",
        "* **dtype**: int, float, str"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "td5TYDoAtRmm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = []\n",
        "for f in train.columns:\n",
        "  # Defining the role\n",
        "  if f == 'target':\n",
        "    role = 'target'\n",
        "  elif f == 'id':\n",
        "    role = 'id'\n",
        "  else:\n",
        "    role = 'input'\n",
        "  \n",
        "  # Defining the level\n",
        "  if 'bin' in f or f == 'target':\n",
        "    level = 'binary'\n",
        "  elif 'cat' in f or f == 'id':\n",
        "    level = 'nominal'\n",
        "  elif train[f].dtype == float:\n",
        "    level = 'interval'\n",
        "  elif train[f].dtype == int:\n",
        "    level = 'ordinal'\n",
        "\n",
        "  # Intialize keep to True for all variables except for id\n",
        "  keep = True\n",
        "  if f == 'id':\n",
        "    keep = False\n",
        "  \n",
        "  # Defining the data type\n",
        "  dtype = train[f].dtype\n",
        "\n",
        "  # Creating a Dict that contains all the metadata for the variables\n",
        "  f_dict = {\n",
        "      'varname': f,\n",
        "      'role': role,\n",
        "      'level': level,\n",
        "      'keep': keep,\n",
        "      'dtype': dtype\n",
        "  }\n",
        "  data.append(f_dict)\n",
        "\n",
        "meta = pd.DataFrame(data, columns=['varname', 'role', 'level', 'keep', 'dtype'])\n",
        "meta.set_index('varname', inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hEg3JoxnDIhq",
        "colab_type": "code",
        "outputId": "73378c4a-4c37-43f6-a5c6-4c1b7730fc76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "meta"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>role</th>\n",
              "      <th>level</th>\n",
              "      <th>keep</th>\n",
              "      <th>dtype</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>varname</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <td>id</td>\n",
              "      <td>nominal</td>\n",
              "      <td>False</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>target</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_01</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_02_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_03</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_04_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_05_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_06_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_07_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_08_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_09_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_10_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_11_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_12_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_13_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_14</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_15</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_16_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_17_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_ind_18_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_reg_01</th>\n",
              "      <td>input</td>\n",
              "      <td>interval</td>\n",
              "      <td>True</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_reg_02</th>\n",
              "      <td>input</td>\n",
              "      <td>interval</td>\n",
              "      <td>True</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_reg_03</th>\n",
              "      <td>input</td>\n",
              "      <td>interval</td>\n",
              "      <td>True</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_01_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_02_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_03_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_04_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_05_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_06_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_07_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_08_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_09_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_10_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_11_cat</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_11</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_12</th>\n",
              "      <td>input</td>\n",
              "      <td>interval</td>\n",
              "      <td>True</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_13</th>\n",
              "      <td>input</td>\n",
              "      <td>interval</td>\n",
              "      <td>True</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_14</th>\n",
              "      <td>input</td>\n",
              "      <td>interval</td>\n",
              "      <td>True</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_car_15</th>\n",
              "      <td>input</td>\n",
              "      <td>interval</td>\n",
              "      <td>True</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_01</th>\n",
              "      <td>input</td>\n",
              "      <td>interval</td>\n",
              "      <td>True</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_02</th>\n",
              "      <td>input</td>\n",
              "      <td>interval</td>\n",
              "      <td>True</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_03</th>\n",
              "      <td>input</td>\n",
              "      <td>interval</td>\n",
              "      <td>True</td>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_04</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_05</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_06</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_07</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_08</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_09</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_10</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_11</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_12</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_13</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_14</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_15_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_16_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_17_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_18_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_19_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ps_calc_20_bin</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>True</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  role     level   keep    dtype\n",
              "varname                                         \n",
              "id                  id   nominal  False    int64\n",
              "target          target    binary   True    int64\n",
              "ps_ind_01        input   ordinal   True    int64\n",
              "ps_ind_02_cat    input   nominal   True    int64\n",
              "ps_ind_03        input   ordinal   True    int64\n",
              "ps_ind_04_cat    input   nominal   True    int64\n",
              "ps_ind_05_cat    input   nominal   True    int64\n",
              "ps_ind_06_bin    input    binary   True    int64\n",
              "ps_ind_07_bin    input    binary   True    int64\n",
              "ps_ind_08_bin    input    binary   True    int64\n",
              "ps_ind_09_bin    input    binary   True    int64\n",
              "ps_ind_10_bin    input    binary   True    int64\n",
              "ps_ind_11_bin    input    binary   True    int64\n",
              "ps_ind_12_bin    input    binary   True    int64\n",
              "ps_ind_13_bin    input    binary   True    int64\n",
              "ps_ind_14        input   ordinal   True    int64\n",
              "ps_ind_15        input   ordinal   True    int64\n",
              "ps_ind_16_bin    input    binary   True    int64\n",
              "ps_ind_17_bin    input    binary   True    int64\n",
              "ps_ind_18_bin    input    binary   True    int64\n",
              "ps_reg_01        input  interval   True  float64\n",
              "ps_reg_02        input  interval   True  float64\n",
              "ps_reg_03        input  interval   True  float64\n",
              "ps_car_01_cat    input   nominal   True    int64\n",
              "ps_car_02_cat    input   nominal   True    int64\n",
              "ps_car_03_cat    input   nominal   True    int64\n",
              "ps_car_04_cat    input   nominal   True    int64\n",
              "ps_car_05_cat    input   nominal   True    int64\n",
              "ps_car_06_cat    input   nominal   True    int64\n",
              "ps_car_07_cat    input   nominal   True    int64\n",
              "ps_car_08_cat    input   nominal   True    int64\n",
              "ps_car_09_cat    input   nominal   True    int64\n",
              "ps_car_10_cat    input   nominal   True    int64\n",
              "ps_car_11_cat    input   nominal   True    int64\n",
              "ps_car_11        input   ordinal   True    int64\n",
              "ps_car_12        input  interval   True  float64\n",
              "ps_car_13        input  interval   True  float64\n",
              "ps_car_14        input  interval   True  float64\n",
              "ps_car_15        input  interval   True  float64\n",
              "ps_calc_01       input  interval   True  float64\n",
              "ps_calc_02       input  interval   True  float64\n",
              "ps_calc_03       input  interval   True  float64\n",
              "ps_calc_04       input   ordinal   True    int64\n",
              "ps_calc_05       input   ordinal   True    int64\n",
              "ps_calc_06       input   ordinal   True    int64\n",
              "ps_calc_07       input   ordinal   True    int64\n",
              "ps_calc_08       input   ordinal   True    int64\n",
              "ps_calc_09       input   ordinal   True    int64\n",
              "ps_calc_10       input   ordinal   True    int64\n",
              "ps_calc_11       input   ordinal   True    int64\n",
              "ps_calc_12       input   ordinal   True    int64\n",
              "ps_calc_13       input   ordinal   True    int64\n",
              "ps_calc_14       input   ordinal   True    int64\n",
              "ps_calc_15_bin   input    binary   True    int64\n",
              "ps_calc_16_bin   input    binary   True    int64\n",
              "ps_calc_17_bin   input    binary   True    int64\n",
              "ps_calc_18_bin   input    binary   True    int64\n",
              "ps_calc_19_bin   input    binary   True    int64\n",
              "ps_calc_20_bin   input    binary   True    int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XM0o0BszFqZR",
        "colab_type": "text"
      },
      "source": [
        "Example to extract all nominal variables that are not dropped"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E74ipGOtFq7j",
        "colab_type": "code",
        "outputId": "25cde0ae-0fe3-49b7-fd2f-30ba637cd76f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "meta[(meta.level == 'nominal') & (meta.keep)].index"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ps_ind_02_cat', 'ps_ind_04_cat', 'ps_ind_05_cat', 'ps_car_01_cat',\n",
              "       'ps_car_02_cat', 'ps_car_03_cat', 'ps_car_04_cat', 'ps_car_05_cat',\n",
              "       'ps_car_06_cat', 'ps_car_07_cat', 'ps_car_08_cat', 'ps_car_09_cat',\n",
              "       'ps_car_10_cat', 'ps_car_11_cat'],\n",
              "      dtype='object', name='varname')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJQF9DRyF_rb",
        "colab_type": "text"
      },
      "source": [
        "Below the number of variables per role and level are displayed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xp0I22psGAQd",
        "colab_type": "code",
        "outputId": "604743fd-79b5-46f7-d642-a7ee630f96af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "pd.DataFrame({'count': meta.groupby(['role', 'level'])['role'].size()}).reset_index()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>role</th>\n",
              "      <th>level</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id</td>\n",
              "      <td>nominal</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>input</td>\n",
              "      <td>binary</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>input</td>\n",
              "      <td>interval</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>input</td>\n",
              "      <td>nominal</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>input</td>\n",
              "      <td>ordinal</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>target</td>\n",
              "      <td>binary</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     role     level  count\n",
              "0      id   nominal      1\n",
              "1   input    binary     17\n",
              "2   input  interval     10\n",
              "3   input   nominal     14\n",
              "4   input   ordinal     16\n",
              "5  target    binary      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PcX_rzLDGfi_",
        "colab_type": "text"
      },
      "source": [
        "## Descriptive statistics\n",
        "\n",
        "We can also apply the describe method on the dataframe. However, it doesn't make much sense to calculate the mean, std, ... on categorical variables and the id variable. We'll explore the categorical variables visually later.\n",
        "\n",
        "Thanks to our meta file we can easily select the variables on which we want to compute the descriptive statistics. To keep things clear, we'll do this per data type."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U7YEav5_GlkC",
        "colab_type": "text"
      },
      "source": [
        "### Interval variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6s_ySnjFGuF3",
        "colab_type": "code",
        "outputId": "6465648a-4d60-4d0d-864b-4a425d7956a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "v = meta[(meta.level == 'interval') & (meta.keep)].index\n",
        "train[v].describe()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>0.551102</td>\n",
              "      <td>0.379945</td>\n",
              "      <td>0.813265</td>\n",
              "      <td>0.276256</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>0.793506</td>\n",
              "      <td>0.058327</td>\n",
              "      <td>0.224588</td>\n",
              "      <td>0.357154</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>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.250619</td>\n",
              "      <td>-1.000000</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>0.525000</td>\n",
              "      <td>0.316228</td>\n",
              "      <td>0.670867</td>\n",
              "      <td>0.333167</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>0.368782</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>"
            ],
            "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       0.551102       0.379945   \n",
              "std         0.287643       0.404264       0.793506       0.058327   \n",
              "min         0.000000       0.000000      -1.000000      -1.000000   \n",
              "25%         0.400000       0.200000       0.525000       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       0.276256       3.065899       0.449756   \n",
              "std         0.224588       0.357154       0.731366       0.287198   \n",
              "min         0.250619      -1.000000       0.000000       0.000000   \n",
              "25%         0.670867       0.333167       2.828427       0.200000   \n",
              "50%         0.765811       0.368782       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  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZbGUGKtIXcj",
        "colab_type": "text"
      },
      "source": [
        "**reg variables**\n",
        "* only ps_reg_03 has missing values\n",
        "* the range (min to max) differs between the variables. We could apply scaling (e.g. StandardScaler), but it depends on the classifier we will want to use\n",
        "\n",
        "**car variables**\n",
        "* ps_car_12 and ps_car_15 have missing values\n",
        "* again, the range differs and we could apply scaling.\n",
        "\n",
        "**calc variables**\n",
        "* no missing values\n",
        "* this seems to be some kind of ratio as the maximum is 0.9\n",
        "* all three *_calc* variables have very similar distributions\n",
        "\n",
        "**Overall**, we can see that the range of the interval variables is rather small. Perhaps some transformation (e.g. log) is already applied in order to anonymize the data?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pK76s1zyKB9m",
        "colab_type": "text"
      },
      "source": [
        "### Ordinal variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oH6deJyEKh5Y",
        "colab_type": "code",
        "outputId": "09055359-fdfd-4529-f2d1-8e31871158fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "v = meta[(meta.level == 'ordinal') & (meta.keep)].index\n",
        "train[v].describe()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ps_ind_01</th>\n",
              "      <th>ps_ind_03</th>\n",
              "      <th>ps_ind_14</th>\n",
              "      <th>ps_ind_15</th>\n",
              "      <th>ps_car_11</th>\n",
              "      <th>ps_calc_04</th>\n",
              "      <th>ps_calc_05</th>\n",
              "      <th>ps_calc_06</th>\n",
              "      <th>ps_calc_07</th>\n",
              "      <th>ps_calc_08</th>\n",
              "      <th>ps_calc_09</th>\n",
              "      <th>ps_calc_10</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",
              "    </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",
              "      <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>1.900378</td>\n",
              "      <td>4.423318</td>\n",
              "      <td>0.012451</td>\n",
              "      <td>7.299922</td>\n",
              "      <td>2.346072</td>\n",
              "      <td>2.372081</td>\n",
              "      <td>1.885886</td>\n",
              "      <td>7.689445</td>\n",
              "      <td>3.005823</td>\n",
              "      <td>9.225904</td>\n",
              "      <td>2.339034</td>\n",
              "      <td>8.433590</td>\n",
              "      <td>5.441382</td>\n",
              "      <td>1.441918</td>\n",
              "      <td>2.872288</td>\n",
              "      <td>7.539026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.983789</td>\n",
              "      <td>2.699902</td>\n",
              "      <td>0.127545</td>\n",
              "      <td>3.546042</td>\n",
              "      <td>0.832548</td>\n",
              "      <td>1.117219</td>\n",
              "      <td>1.134927</td>\n",
              "      <td>1.334312</td>\n",
              "      <td>1.414564</td>\n",
              "      <td>1.459672</td>\n",
              "      <td>1.246949</td>\n",
              "      <td>2.904597</td>\n",
              "      <td>2.332871</td>\n",
              "      <td>1.202963</td>\n",
              "      <td>1.694887</td>\n",
              "      <td>2.746652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>23.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           ps_ind_01      ps_ind_03      ps_ind_14      ps_ind_15  \\\n",
              "count  595212.000000  595212.000000  595212.000000  595212.000000   \n",
              "mean        1.900378       4.423318       0.012451       7.299922   \n",
              "std         1.983789       2.699902       0.127545       3.546042   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         0.000000       2.000000       0.000000       5.000000   \n",
              "50%         1.000000       4.000000       0.000000       7.000000   \n",
              "75%         3.000000       6.000000       0.000000      10.000000   \n",
              "max         7.000000      11.000000       4.000000      13.000000   \n",
              "\n",
              "           ps_car_11     ps_calc_04     ps_calc_05     ps_calc_06  \\\n",
              "count  595212.000000  595212.000000  595212.000000  595212.000000   \n",
              "mean        2.346072       2.372081       1.885886       7.689445   \n",
              "std         0.832548       1.117219       1.134927       1.334312   \n",
              "min        -1.000000       0.000000       0.000000       0.000000   \n",
              "25%         2.000000       2.000000       1.000000       7.000000   \n",
              "50%         3.000000       2.000000       2.000000       8.000000   \n",
              "75%         3.000000       3.000000       3.000000       9.000000   \n",
              "max         3.000000       5.000000       6.000000      10.000000   \n",
              "\n",
              "          ps_calc_07     ps_calc_08     ps_calc_09     ps_calc_10  \\\n",
              "count  595212.000000  595212.000000  595212.000000  595212.000000   \n",
              "mean        3.005823       9.225904       2.339034       8.433590   \n",
              "std         1.414564       1.459672       1.246949       2.904597   \n",
              "min         0.000000       2.000000       0.000000       0.000000   \n",
              "25%         2.000000       8.000000       1.000000       6.000000   \n",
              "50%         3.000000       9.000000       2.000000       8.000000   \n",
              "75%         4.000000      10.000000       3.000000      10.000000   \n",
              "max         9.000000      12.000000       7.000000      25.000000   \n",
              "\n",
              "          ps_calc_11     ps_calc_12     ps_calc_13     ps_calc_14  \n",
              "count  595212.000000  595212.000000  595212.000000  595212.000000  \n",
              "mean        5.441382       1.441918       2.872288       7.539026  \n",
              "std         2.332871       1.202963       1.694887       2.746652  \n",
              "min         0.000000       0.000000       0.000000       0.000000  \n",
              "25%         4.000000       1.000000       2.000000       6.000000  \n",
              "50%         5.000000       1.000000       3.000000       7.000000  \n",
              "75%         7.000000       2.000000       4.000000       9.000000  \n",
              "max        19.000000      10.000000      13.000000      23.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yhja82utK1Lw",
        "colab_type": "text"
      },
      "source": [
        "* Only one missing variable: ps_car_11\n",
        "* We could apply scaling to deal with the different ranges"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lhhbdAzxLCIr",
        "colab_type": "text"
      },
      "source": [
        "### Binary variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sux7_-4pLEI-",
        "colab_type": "code",
        "outputId": "66b2f894-7f8a-4453-b646-285fb740aae8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "v = meta[(meta.level == 'binary') & (meta.keep)].index\n",
        "train[v].describe()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>target</th>\n",
              "      <th>ps_ind_06_bin</th>\n",
              "      <th>ps_ind_07_bin</th>\n",
              "      <th>ps_ind_08_bin</th>\n",
              "      <th>ps_ind_09_bin</th>\n",
              "      <th>ps_ind_10_bin</th>\n",
              "      <th>ps_ind_11_bin</th>\n",
              "      <th>ps_ind_12_bin</th>\n",
              "      <th>ps_ind_13_bin</th>\n",
              "      <th>ps_ind_16_bin</th>\n",
              "      <th>ps_ind_17_bin</th>\n",
              "      <th>ps_ind_18_bin</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>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",
              "      <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>0.036448</td>\n",
              "      <td>0.393742</td>\n",
              "      <td>0.257033</td>\n",
              "      <td>0.163921</td>\n",
              "      <td>0.185304</td>\n",
              "      <td>0.000373</td>\n",
              "      <td>0.001692</td>\n",
              "      <td>0.009439</td>\n",
              "      <td>0.000948</td>\n",
              "      <td>0.660823</td>\n",
              "      <td>0.121081</td>\n",
              "      <td>0.153446</td>\n",
              "      <td>0.122427</td>\n",
              "      <td>0.627840</td>\n",
              "      <td>0.554182</td>\n",
              "      <td>0.287182</td>\n",
              "      <td>0.349024</td>\n",
              "      <td>0.153318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.187401</td>\n",
              "      <td>0.488579</td>\n",
              "      <td>0.436998</td>\n",
              "      <td>0.370205</td>\n",
              "      <td>0.388544</td>\n",
              "      <td>0.019309</td>\n",
              "      <td>0.041097</td>\n",
              "      <td>0.096693</td>\n",
              "      <td>0.030768</td>\n",
              "      <td>0.473430</td>\n",
              "      <td>0.326222</td>\n",
              "      <td>0.360417</td>\n",
              "      <td>0.327779</td>\n",
              "      <td>0.483381</td>\n",
              "      <td>0.497056</td>\n",
              "      <td>0.452447</td>\n",
              "      <td>0.476662</td>\n",
              "      <td>0.360295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              target  ps_ind_06_bin  ps_ind_07_bin  ps_ind_08_bin  \\\n",
              "count  595212.000000  595212.000000  595212.000000  595212.000000   \n",
              "mean        0.036448       0.393742       0.257033       0.163921   \n",
              "std         0.187401       0.488579       0.436998       0.370205   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         0.000000       0.000000       0.000000       0.000000   \n",
              "50%         0.000000       0.000000       0.000000       0.000000   \n",
              "75%         0.000000       1.000000       1.000000       0.000000   \n",
              "max         1.000000       1.000000       1.000000       1.000000   \n",
              "\n",
              "       ps_ind_09_bin  ps_ind_10_bin  ps_ind_11_bin  ps_ind_12_bin  \\\n",
              "count  595212.000000  595212.000000  595212.000000  595212.000000   \n",
              "mean        0.185304       0.000373       0.001692       0.009439   \n",
              "std         0.388544       0.019309       0.041097       0.096693   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         0.000000       0.000000       0.000000       0.000000   \n",
              "50%         0.000000       0.000000       0.000000       0.000000   \n",
              "75%         0.000000       0.000000       0.000000       0.000000   \n",
              "max         1.000000       1.000000       1.000000       1.000000   \n",
              "\n",
              "       ps_ind_13_bin  ps_ind_16_bin  ps_ind_17_bin  ps_ind_18_bin  \\\n",
              "count  595212.000000  595212.000000  595212.000000  595212.000000   \n",
              "mean        0.000948       0.660823       0.121081       0.153446   \n",
              "std         0.030768       0.473430       0.326222       0.360417   \n",
              "min         0.000000       0.000000       0.000000       0.000000   \n",
              "25%         0.000000       0.000000       0.000000       0.000000   \n",
              "50%         0.000000       1.000000       0.000000       0.000000   \n",
              "75%         0.000000       1.000000       0.000000       0.000000   \n",
              "max         1.000000       1.000000       1.000000       1.000000   \n",
              "\n",
              "       ps_calc_15_bin  ps_calc_16_bin  ps_calc_17_bin  ps_calc_18_bin  \\\n",
              "count   595212.000000   595212.000000   595212.000000   595212.000000   \n",
              "mean         0.122427        0.627840        0.554182        0.287182   \n",
              "std          0.327779        0.483381        0.497056        0.452447   \n",
              "min          0.000000        0.000000        0.000000        0.000000   \n",
              "25%          0.000000        0.000000        0.000000        0.000000   \n",
              "50%          0.000000        1.000000        1.000000        0.000000   \n",
              "75%          0.000000        1.000000        1.000000        1.000000   \n",
              "max          1.000000        1.000000        1.000000        1.000000   \n",
              "\n",
              "       ps_calc_19_bin  ps_calc_20_bin  \n",
              "count   595212.000000   595212.000000  \n",
              "mean         0.349024        0.153318  \n",
              "std          0.476662        0.360295  \n",
              "min          0.000000        0.000000  \n",
              "25%          0.000000        0.000000  \n",
              "50%          0.000000        0.000000  \n",
              "75%          1.000000        0.000000  \n",
              "max          1.000000        1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F5bTvTHtLXmy",
        "colab_type": "text"
      },
      "source": [
        "* A priori in the train data is 3.645%, which is strongly imbalanced.\n",
        "* From the means we can conclude that for most variables the value is zero in most cases."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Vz2fJIyLcsu",
        "colab_type": "text"
      },
      "source": [
        "## Handling imbalanced classes\n",
        "\n",
        "As we mentioned above the proportion of records with target = 1 is far less than target = 0. This can lead to a model that has great accuracy but does have any added value in practice. Two possible strategies to deal with this problem are:\n",
        "\n",
        "* oversampling records with target=1\n",
        "* undersampling records with target=0\n",
        "\n",
        "There are many more strategies of course and MachineLearningMastery.com gives a [nice overview](https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/). As we have a rather large training set, we can go for **undersampling**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cqsdixldMPOA",
        "colab_type": "code",
        "outputId": "4b0f7894-bf7d-4897-f5da-a5cc490a159d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "desired_apriori = 0.10\n",
        "\n",
        "# Get the indices per target value\n",
        "idx_0 = train[train.target == 0].index\n",
        "idx_1 = train[train.target == 1].index\n",
        "\n",
        "# Get original number of records per target value\n",
        "nb_0 = len(train.loc[idx_0])\n",
        "nb_1 = len(train.loc[idx_1])\n",
        "\n",
        "# Calculate the undersampling rate and resulting number of records with target=0\n",
        "undersampling_rate = ((1-desired_apriori)*nb_1) / (nb_0 * desired_apriori)\n",
        "undersampled_nb_0 = int(undersampling_rate*nb_0)\n",
        "print('Rate to undersample records with target=0: {}'.format(undersampling_rate))\n",
        "print('Number of records with target=0 after undersampling: {}'.format(undersampled_nb_0))\n",
        "\n",
        "# Randomly select records with target=0 to get at the desired a priori\n",
        "undersampled_idx = shuffle(idx_0, random_state=37, n_samples=undersampled_nb_0)\n",
        "\n",
        "# Construct list with remaining indices\n",
        "idx_list = list(undersampled_idx)+ list(idx_1)\n",
        "\n",
        "# Return undersample data frame\n",
        "train = train.loc[idx_list].reset_index(drop=True)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rate to undersample records with target=0: 0.34043569687437886\n",
            "Number of records with target=0 after undersampling: 195246\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uFuozzMxWNo9",
        "colab_type": "text"
      },
      "source": [
        "## Data Quality Checks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "knv8nwe1WPiP",
        "colab_type": "text"
      },
      "source": [
        "### Check missing values\n",
        "\n",
        "Missings are represented as -1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qnT6xXBkWU-O",
        "colab_type": "code",
        "outputId": "235e3a0d-dc58-47dc-9e1d-58f981440d03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        }
      },
      "source": [
        "vars_with_missing = []\n",
        "\n",
        "for f in train.columns:\n",
        "  missings = train[train[f] == -1][f].count()\n",
        "  if missings > 0:\n",
        "    vars_with_missing.append(f)\n",
        "    missings_perc = missings / train.shape[0]\n",
        "\n",
        "    print('Variable {} has {} records ({:.2%}) with missing values'.format(f, missings, missings_perc))\n",
        "\n",
        "print('In total, there are {} variables with missing values'.format(len(vars_with_missing)))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Variable ps_ind_02_cat has 103 records (0.05%) with missing values\n",
            "Variable ps_ind_04_cat has 51 records (0.02%) with missing values\n",
            "Variable ps_ind_05_cat has 2256 records (1.04%) with missing values\n",
            "Variable ps_reg_03 has 38580 records (17.78%) with missing values\n",
            "Variable ps_car_01_cat has 62 records (0.03%) with missing values\n",
            "Variable ps_car_02_cat has 2 records (0.00%) with missing values\n",
            "Variable ps_car_03_cat has 148367 records (68.39%) with missing values\n",
            "Variable ps_car_05_cat has 96026 records (44.26%) with missing values\n",
            "Variable ps_car_07_cat has 4431 records (2.04%) with missing values\n",
            "Variable ps_car_09_cat has 230 records (0.11%) with missing values\n",
            "Variable ps_car_11 has 1 records (0.00%) with missing values\n",
            "Variable ps_car_14 has 15726 records (7.25%) with missing values\n",
            "In total, there are 12 variables with missing values\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sx9XfZ-MXbOE",
        "colab_type": "text"
      },
      "source": [
        "* **ps_car_03_cat and ps_car_05_cat** have a large proportion of records with missing values. Remove these variables.\n",
        "* For the other categorical variables with missing values, we can leave the missing value -1 as such.\n",
        "* **ps_reg_03** (continuous) has missing values for 18% of all records. Replace by the mean.\n",
        "* **ps_car_11** (ordinal) has only 5 records with misisng values. Replace by the mode.\n",
        "* **ps_car_12** (continuous) has only 1 records with missing value. Replace by the mean.\n",
        "* **ps_car_14** (continuous) has missing values for 7% of all records. Replace by the mean."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_D8yxClYjkN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Dropping the variables with too many missing values\n",
        "vars_to_drop = ['ps_car_03_cat', 'ps_car_05_cat']\n",
        "train.drop(vars_to_drop, inplace=True, axis=1)\n",
        "meta.loc[(vars_to_drop), 'keep'] = False # Updating the meta\n",
        "\n",
        "# Imputing with the mean or mode\n",
        "mean_imp = SimpleImputer(missing_values=-1, strategy='mean')\n",
        "mode_imp = SimpleImputer(missing_values=-1, strategy='most_frequent')\n",
        "train['ps_reg_03'] = mean_imp.fit_transform(train[['ps_reg_03']]).ravel()\n",
        "train['ps_car_12'] = mean_imp.fit_transform(train[['ps_car_12']]).ravel()\n",
        "train['ps_car_14'] = mean_imp.fit_transform(train[['ps_car_14']]).ravel()\n",
        "train['ps_car_11'] = mode_imp.fit_transform(train[['ps_car_11']]).ravel()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qx7sHKeYappX",
        "colab_type": "text"
      },
      "source": [
        "### Checking the cardinality of the categorical variables\n",
        "\n",
        "Cardinality refers to the number of different values in a variable. As we will create dummy variables from the categorical variables later on, we need to check whether there are variables with many distinct values. We should handle these variables differently as they would result in many dummy variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9ZES56qa-82",
        "colab_type": "code",
        "outputId": "263d200a-20f4-4fd7-aaed-96193e38ae36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        }
      },
      "source": [
        "v = meta[(meta.level == 'nominal') & (meta.keep)].index\n",
        "\n",
        "for f in v:\n",
        "  dist_values = train[f].value_counts().shape[0]\n",
        "  print('Variable {} has {} distinct values'.format(f, dist_values))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Variable ps_ind_02_cat has 5 distinct values\n",
            "Variable ps_ind_04_cat has 3 distinct values\n",
            "Variable ps_ind_05_cat has 8 distinct values\n",
            "Variable ps_car_01_cat has 13 distinct values\n",
            "Variable ps_car_02_cat has 3 distinct values\n",
            "Variable ps_car_04_cat has 10 distinct values\n",
            "Variable ps_car_06_cat has 18 distinct values\n",
            "Variable ps_car_07_cat has 3 distinct values\n",
            "Variable ps_car_08_cat has 2 distinct values\n",
            "Variable ps_car_09_cat has 6 distinct values\n",
            "Variable ps_car_10_cat has 3 distinct values\n",
            "Variable ps_car_11_cat has 104 distinct values\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HIYOaEy0RZvf",
        "colab_type": "text"
      },
      "source": [
        "Only ps_car_11_cat has many distinct values, although it is still reasonable.\n",
        "\n",
        "EDIT: [nickycan](https://www.kaggle.com/nickycan) made an excellent remark on the fact that my first solution could lead to data leakage. He also pointed me to another kernel made by [oliver](https://www.kaggle.com/ogrellier) which deals with that. I therefore replaced this part with the kernel of oliver. All credits go to him. It is so great what you can learn by participating in the Kaggle competitions :)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsJutFJLbGeT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Script by https://www.kaggle.com/ogrellier\n",
        "# Code: https://www.kaggle.com/ogrellier/python-target-encoding-for-categorical-features\n",
        "def add_noise(series, noise_level):\n",
        "  return series * (1 + noise_level * np.random.randn(len(series)))\n",
        "\n",
        "def target_encode(trn_series=None,\n",
        "                  tst_series=None,\n",
        "                  target=None,\n",
        "                  min_samples_leaf=1,\n",
        "                  smoothing=1,\n",
        "                  noise_level=0):\n",
        "    \"\"\"\n",
        "    Smoothing is computed like in the following paper by Daniele Micci-Barreca\n",
        "    http://helios.mm.di.uoa.gr/~rouvas/ssi/sigkdd/sigkdd.vol3.1/barreca.pdf\n",
        "    trn_series : training categorical feature as a pd.Series\n",
        "    tst_series : test categorical feature as a pd.Series\n",
        "    target : target data as a pd.Series\n",
        "    min_samples_leaf (int) : minimum samples to take category average into account\n",
        "    smoothing (int) : smoothing effect to balance categorical average vs prior  \n",
        "    \"\"\" \n",
        "    assert len(trn_series) == len(target)\n",
        "    assert trn_series.name == tst_series.name\n",
        "    temp = pd.concat([trn_series, target], axis=1)\n",
        "    # Compute target mean\n",
        "    averages = temp.groupby(by=trn_series.name)[target.name].agg(['mean', 'count'])\n",
        "    # Compute smoothing\n",
        "    smoothing = 1 / (1 + np.exp(-(averages['count'] - min_samples_leaf) / smoothing))\n",
        "    # Apply average function to all target data\n",
        "    prior = target.mean()\n",
        "    # The biggeer the count the less full_avg is taken into account\n",
        "    averages[target.name] = prior * (1 - smoothing) + averages['mean'] * smoothing\n",
        "    averages.drop(['mean', 'count'], axis=1, inplace=True)\n",
        "    # Apply averages to trn and tst series\n",
        "    ft_trn_series = pd.merge(\n",
        "        trn_series.to_frame(trn_series.name),\n",
        "        averages.reset_index().rename(columns={'index': target.name, target.name: 'average'}),\n",
        "        on=trn_series.name,\n",
        "        how='left'\n",
        "    )['average'].rename(trn_series.name + '_mean').fillna(prior)\n",
        "    # pd.merge does not keep the index so restore it\n",
        "    ft_trn_series.index = trn_series.index\n",
        "    ft_tst_series = pd.merge(\n",
        "        tst_series.to_frame(tst_series.name),\n",
        "        averages.reset_index().rename(columns={'index': target.name, target.name: 'average'}),\n",
        "        on=tst_series.name,\n",
        "        how='left'\n",
        "    )['average'].rename(tst_series.name + '_mean').fillna(prior)\n",
        "    # pd.merge does not keep the index so restor it\n",
        "    ft_tst_series.index = tst_series.index\n",
        "    return add_noise(ft_trn_series, noise_level), add_noise(ft_tst_series, noise_level)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fGkd8EmXDd5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_encoded, test_encoded = target_encode(train['ps_car_11_cat'],\n",
        "                                            test['ps_car_11_cat'],\n",
        "                                            target=train.target,\n",
        "                                            min_samples_leaf=100,\n",
        "                                            smoothing=10,\n",
        "                                            noise_level=0.01)\n",
        "\n",
        "train['ps_car_11_cat_te'] = train_encoded\n",
        "train.drop('ps_car_11_cat', axis=1, inplace=True)\n",
        "meta.loc['ps_car_11_cat', 'keep'] = False # Updating the meta\n",
        "test['ps_car_11_cat_te'] = test_encoded\n",
        "test.drop('ps_car_11_cat', axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xWYQDh-ZYaO9",
        "colab_type": "text"
      },
      "source": [
        "## Exploratory Data Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jadQv3idYd4m",
        "colab_type": "text"
      },
      "source": [
        "### Categorical variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G61-hsILYgLw",
        "colab_type": "code",
        "outputId": "198fc7ba-a345-41d8-f28f-f90694d17f3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "v = meta.loc[(meta.level == 'nominal') & (meta.keep)].index\n",
        "\n",
        "for f in v:\n",
        "  plt.figure()\n",
        "  fig, ax = plt.subplots(figsize=(20, 10))\n",
        "  # Calculate the percentage of target=1 per category value\n",
        "  cat_perc = train[[f, 'target']].groupby([f], as_index=False).mean()\n",
        "  cat_perc.sort_values(by='target', ascending=False, inplace=True)\n",
        "  # Bar plot\n",
        "  # Order the bars descending on target mean\n",
        "  sns.barplot(ax=ax, x=f, y='target', data=cat_perc, order=cat_perc[f])\n",
        "  plt.ylabel('% target', fontsize=18)\n",
        "  plt.xlabel(f, fontsize=18)\n",
        "  plt.tick_params(axis='both', which='major', labelsize=18)\n",
        "  plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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evXv6XZmEWG9L8uEVfTrJPUne393vXWh1AAAAABzQ1gyvuvv6TA49T1U9Jskb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\nSVJvGV5JkiRJkiSpt/4fskOBh9dKRMYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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i3pbkBa21vfMbFwAAAICDtWaoC7fWbqmqy5OcW1XbklyX5KQkm5PckK//Nrzr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\nAyI3ScNDEictAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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31m6fsu6GJI+tqp1ba3fMaf9rklyc5N+S3JnkUUmOSfILVfVzrbUrtuXNAAAA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\nvWXySpIkSZIkSb1l8kqSJEmSJEm99b9Gy/6I42axDAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K9d/xqqlHAABgBTEKAGADZ7zryqlHOOT8xpkvmXoEAGCbcpseAAAAAMOIUQAA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\nYII=\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jyf2HtXsAAAAAttS2ZZ24qk7PLDjt7u6zF8ZvS3JpkmcmuWrq9VX1OUlek+Sy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\ngg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iFQAAAADDEq8AAAAAGNamRS8AAAAA9kVvvfi4RS/hLu+njr9y0UtgAK68AgAA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\nZmZmZmZm1rD+H/7vgrYikhZRAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YaeviM5OcnuQZVXVca+2Kcfubk7y9qo7MaOvi/57k8Un+aM0qBQAAAIBlLLui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\nwUSXJEmSJEmSBsFElyRJkiRJkgbh/wPOurasDAxWvwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zvtPWSkbh1ry1AAAAANwJrBl0dfflGR3enqo6JMmru/tvF7j+gUmmhVxJsn2i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\n6Fs5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XSc5LcijJzYuD3X2wqm6bH590fVWdn+TiJBd29/1VdQzbBwAAAGCrLfPKq9OS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\n/RuXjAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PPzTJjUm+muTGqrq4qo7apekBAAAA2HCLfObVIUlu6O5bltl2XZKHVtU+3X3r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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m67VXAj7cx1LBZbUWMK+q3hil7Slg+ySTqmphLy+WZB2aMOoBYPuu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\nRQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8f8hJsq0b4ps",
        "colab_type": "text"
      },
      "source": [
        "As we can see from the variables **with missing values**, it is a good idea to keep the missing values as a separate category value, instead of replacing them by the mode for instance. The customers with a missing value appear to have a much higher (in some cases much lower) probability to ask for an insurance claim."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CO3GQc09b-9g",
        "colab_type": "text"
      },
      "source": [
        "### Interval variables\n",
        "Checking the correlations between interval variables. A heatmap is a good way to visualize the correlation between variables. The code below is based on [an example by Michael Waskom](http://seaborn.pydata.org/examples/many_pairwise_correlations.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWaCoHL_cRd-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "outputId": "d7ac0b01-2af7-4352-faef-9c5f25db8309"
      },
      "source": [
        "def corr_heatmap(v):\n",
        "  correlations = train[v].corr()\n",
        "\n",
        "  # Create color map ranging between two colors\n",
        "  cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "\n",
        "  fig, ax = plt.subplots(figsize=(10,10))\n",
        "  sns.heatmap(correlations, cmap=cmap, vmax=1.0, center=0, fmt='.2f',\n",
        "              square=True, linewidth=.5, annot=True, cbar_kws={'shrink': .75})\n",
        "  plt.show()\n",
        "\n",
        "v = meta[(meta.level == 'interval') & (meta.keep)].index\n",
        "corr_heatmap(v)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a6wSl1J3Ae0CU1vri+w6U7YqG82qa71OPAbC3a58aTlJxTVcvJeWlKp/j7zDB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\nM7MSuIgyMzMzK8H/B5FFnw8DewkWAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RamKbbWdtNGS",
        "colab_type": "text"
      },
      "source": [
        "There are a strong correlations between the variables:\n",
        "\n",
        "* ps_reg_02 and ps_reg_03 (0.7)\n",
        "* ps_car_12 and ps_car_13 (0.67)\n",
        "* ps_car_12 and ps_car14 (0.58)\n",
        "* ps_car_13 and ps_car15 (0.67)\n",
        "\n",
        "Seaborn has some handy plots to visualize the (linear) relationship between variables. We could use a pairplot to visualize the relationship between the variables. But because the heatmap already showed the limited number of correlated variables, we'll look at each of the highly correlated variables separately.\n",
        "\n",
        "**NOTE:** I take a sample of the train data to speed up the process."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8-nn79htORo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s = train.sample(frac=0.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fj5kTWkXtnf5",
        "colab_type": "text"
      },
      "source": [
        "**ps_reg_02 and ps_reg_03**\n",
        "\n",
        "As the regression line shows, there is a linear relationship between these variables. Thanks to the hue parameter we can see that the regression lines for target=0 and target=1 are the same."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "REny52SAttuq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "f2bae066-1453-47ae-ba2e-4e5f954c9b6e"
      },
      "source": [
        "sns.lmplot(x='ps_reg_02', y='ps_reg_03', data=s, hue='target', palette='Set1', scatter_kws={'alpha': 0.3})\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hlEUz5pW1RjObtra5B5kv3NiAAMgX0Db3BMqKzg6c0VE/2ma+b4HW1dVwtM1K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\n",
            "text/plain": [
              "<Figure size 402.375x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cjgy_SXEuUH3",
        "colab_type": "text"
      },
      "source": [
        "**ps_car_12 and ps_car_13**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWc3Uje_ucb-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "4ca23e48-3543-45a9-aa4e-736a497e28d8"
      },
      "source": [
        "sns.lmplot(x='ps_car_12', y='ps_car_13', data=s, hue='target', palette='Set1', scatter_kws={'alpha': 0.3})\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tmRhyYAB7jqp7yvNmDoptW2n+zKSg0NXttDViuWdDPcr4B9g5zlHAry1nO7Tp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\nGJPX/wPg1V/Ec4t6zAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 402.375x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vCwR1mzEutwS",
        "colab_type": "text"
      },
      "source": [
        "**ps_car_12 and ps_car_14**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8aiG0vAduxwe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "bfc6f2e4-d319-4ed6-d655-020ca03b2c5e"
      },
      "source": [
        "sns.lmplot(x='ps_car_12', y='ps_car_14', data=s, hue='target', palette='Set1', scatter_kws={'alpha': 0.3})\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NQ6CYtmBRxya+omQbOc1N0u3D8uq4bAuvmQFER6DQOgoAEhvB6tHHDcB333+W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\n",
            "text/plain": [
              "<Figure size 402.375x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w_qeIjt5u67R",
        "colab_type": "text"
      },
      "source": [
        "**ps_car_13 and ps_car_15**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUdKzgCJvATZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "42af051b-99be-483d-cc0c-c545fe6423c4"
      },
      "source": [
        "sns.lmplot(x='ps_car_15', y='ps_car_13', data=s, hue='target', palette='Set1', scatter_kws={'alpha': 0.3})\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D+UlK8TS+beMFPqlqkUTUOukeiIWxHJwohKjRFlkOQRTc3j9Iw78GhkTkLeBl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\neHJtN4mPAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 402.375x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JHVWCT2svXi0",
        "colab_type": "text"
      },
      "source": [
        "Allright, so now what? How can we decide which of the correlated variables to keep? We could perform Principal Component Analysis (PCA) on the variables to reduce the dimensions. In the AllState Claims Severity Competition I made [this kernel](https://www.kaggle.com/bertcarremans/reducing-number-of-numerical-features-with-pca) to do that. But as the number of correlated variables is rather low, we will let the model do the heavy-lifting."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I7ve4Mwfvxzl",
        "colab_type": "text"
      },
      "source": [
        "### Checking the correlations between ordinal variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rdlCQLxMvmAY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "93af6be8-84c0-47d6-f538-59317a9d199e"
      },
      "source": [
        "v = meta[(meta.level == 'ordinal') & (meta.keep)].index\n",
        "corr_heatmap(v)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ad911FxMnTuTxxx+v8t4fqampREREYLVaiY2NvWT5+fPnmTdvHlarlSlTpnDy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\n6dOnGTduHBs3biQ1NRWAnTt3sn//fkaMGMHq1au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mz5/fPb8/VVVVpKWlkZqaysiRI8nJ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\nRBmGYRiGYUSABVGGYRiGYRgR8H8AmNvLiU/PP8MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DnJ6sSj-wG1r",
        "colab_type": "text"
      },
      "source": [
        "For the ordinal variables we do not see many correlations. We could, on the other hand, look at how the distributions are when grouping by the target value."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zhocmwSMwH__",
        "colab_type": "text"
      },
      "source": [
        "## Feature engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MPehTnlywK4o",
        "colab_type": "text"
      },
      "source": [
        "### Creating dummy variables\n",
        "\n",
        "The values of the categorical variables do not represent any order of multitude. For instance, category 2 is not twice the value of category 1. Therefore we can create dummy variables to deal with that. We drop the first dummy variable as this information can be derived from the other dummy variables generated for the categories of the original variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iqbOn07XwuzQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "abcc3e24-665a-4860-91fd-6df4b3d43b54"
      },
      "source": [
        "v = meta[(meta.level == 'nominal') & (meta.keep)].index\n",
        "print('Before dummification we have {} variables in train'.format(train.shape[1]))\n",
        "train = pd.get_dummies(train, columns=v, drop_first=True)\n",
        "print('After dummification we have {} variables in train'.format(train.shape[1]))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before dummification we have 57 variables in train\n",
            "After dummification we have 109 variables in train\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LX_MdGlOyXOi",
        "colab_type": "text"
      },
      "source": [
        "So, creating dummy variables adds 52 variables to the training set."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fLA0zrZqybNp",
        "colab_type": "text"
      },
      "source": [
        "### Creating interaction variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OvmHTiCqyZEL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "35ee132f-85d1-4e5c-af7d-3f7f0df63f70"
      },
      "source": [
        "v = meta[(meta.level == 'interval') & (meta.keep)].index\n",
        "poly = PolynomialFeatures(degree=2, interaction_only=False, include_bias=False)\n",
        "interactions = pd.DataFrame(data=poly.fit_transform(train[v]), columns=poly.get_feature_names(v))\n",
        "interactions.drop(v, axis=1, inplace=True) # Remove the original columns\n",
        "# Concat the intercation variables to the train data\n",
        "print('Before creating interactions we have {} variables in train'.format(train.shape[1]))\n",
        "train = pd.concat([train, interactions], axis=1)\n",
        "print('After creating interactions we have {} variables in train'.format(train.shape[1]))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before creating interactions we have 109 variables in train\n",
            "After creating interactions we have 164 variables in train\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hymJQIYZzlUB",
        "colab_type": "text"
      },
      "source": [
        "This adds extra interaction variables to the train data. Thanks to the get_feature_names method we can assign column names to these new variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3mrrQCnSzl0r",
        "colab_type": "text"
      },
      "source": [
        "## Feature selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tczz0F3EzpdL",
        "colab_type": "text"
      },
      "source": [
        "## Removing features with low or zero variance\n",
        "\n",
        "Personally, I prefer to let the classifier algorithm chose which features to keep. But there is one thing that we can do ourselves. That is removing features with no or a very low variance. Sklearn has a handy method to do that: **VarianceThreshold**. By default it removes features with zero variance. This will not be applicable for this competition as we saw there are no zero-variance variables in the previous steps. But if we would remove features with less than 1% variance, we would remove 31 variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WxlMYwG52dG1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "5e3735ce-6d49-48da-d257-8ae00c6c9914"
      },
      "source": [
        "selector = VarianceThreshold(threshold=.01)\n",
        "selector.fit(train.drop(['id', 'target'], axis=1)) # Fit to train without id and target variables\n",
        "\n",
        "f = np.vectorize(lambda x: not x) # Function to toggle boolean array elements\n",
        "\n",
        "v = train.drop(['id', 'target'], axis=1).columns[f(selector.get_support())]\n",
        "print('{} variables have too low variance.'.format(len(v)))\n",
        "print('These variables are {}'.format(list(v)))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "28 variables have too low variance.\n",
            "These variables are ['ps_ind_10_bin', 'ps_ind_11_bin', 'ps_ind_12_bin', 'ps_ind_13_bin', 'ps_car_12', 'ps_car_14', 'ps_car_11_cat_te', 'ps_ind_05_cat_2', 'ps_ind_05_cat_5', 'ps_car_01_cat_1', 'ps_car_01_cat_2', 'ps_car_04_cat_3', 'ps_car_04_cat_4', 'ps_car_04_cat_5', 'ps_car_04_cat_6', 'ps_car_04_cat_7', 'ps_car_06_cat_2', 'ps_car_06_cat_5', 'ps_car_06_cat_8', 'ps_car_06_cat_12', 'ps_car_06_cat_16', 'ps_car_06_cat_17', 'ps_car_09_cat_4', 'ps_car_10_cat_1', 'ps_car_10_cat_2', 'ps_car_12^2', 'ps_car_12 ps_car_14', 'ps_car_14^2']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OAsrjwsm9CPe",
        "colab_type": "text"
      },
      "source": [
        "We would lose rather many variables if we would select based on variance. But because we do not have so many variables, we'll let the classifier chose. For data sets with many more variables this could reduce the processing time.\n",
        "\n",
        "Sklearn also comes with other [feature selection methods](http://scikit-learn.org/stable/modules/feature_selection.html). One of these methods is SelectFromModel in which you let another classifier select the best features and continue with these. Below I'll show you how to do that with a Random Forest."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qnUpUjim9ISg",
        "colab_type": "text"
      },
      "source": [
        "### Selecting features with a Random Forest and SelectFromModel\n",
        "\n",
        "Here we'll base feature selection on the feature importance of a random forest. With Sklearn's SelectFromModel you can then specify how many variables you want to keep. You can set a threshold on the level of feature importance manually. But we'll simply select the top 50% best variables\n",
        "\n",
        "> The code in the cell below is borrowed from the [GitHub repo of Sebastian Raschka](https://github.com/rasbt/python-machine-learning-book/blob/master/code/ch04/ch04.ipynb). This repo contains code samples of his book Python Machine Learning, which is an absolute must to read."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JbKO6aGAmK7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "18d5c47b-c294-4b9a-e8fd-ea71d7571aa7"
      },
      "source": [
        "X_train = train.drop(['id', 'target'], axis=1)\n",
        "y_train = train.target\n",
        "\n",
        "feat_labels = X_train.columns\n",
        "\n",
        "rf = RandomForestClassifier(n_estimators=1000, random_state=0, n_jobs=-1)\n",
        "\n",
        "rf.fit(X_train, y_train)\n",
        "importances = rf.feature_importances_\n",
        "\n",
        "indices = np.argsort(rf.feature_importances_)[::-1]\n",
        "\n",
        "for f in range(X_train.shape[1]):\n",
        "    print(\"%2d) %-*s %f\" % (f + 1, 30,feat_labels[indices[f]], importances[indices[f]]))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " 1) ps_car_11_cat_te               0.021117\n",
            " 2) ps_car_13                      0.017387\n",
            " 3) ps_car_13^2                    0.017267\n",
            " 4) ps_car_12 ps_car_13            0.017220\n",
            " 5) ps_car_13 ps_car_14            0.017190\n",
            " 6) ps_reg_03 ps_car_13            0.017132\n",
            " 7) ps_car_13 ps_car_15            0.016789\n",
            " 8) ps_reg_01 ps_car_13            0.016778\n",
            " 9) ps_reg_03 ps_car_14            0.016213\n",
            "10) ps_reg_03 ps_car_12            0.015480\n",
            "11) ps_reg_03 ps_car_15            0.015166\n",
            "12) ps_car_14 ps_car_15            0.015094\n",
            "13) ps_car_13 ps_calc_01           0.014747\n",
            "14) ps_reg_02 ps_car_13            0.014695\n",
            "15) ps_car_13 ps_calc_03           0.014690\n",
            "16) ps_car_13 ps_calc_02           0.014681\n",
            "17) ps_reg_01 ps_reg_03            0.014679\n",
            "18) ps_reg_01 ps_car_14            0.014364\n",
            "19) ps_reg_03^2                    0.014247\n",
            "20) ps_reg_03                      0.014219\n",
            "21) ps_reg_03 ps_calc_03           0.013814\n",
            "22) ps_reg_03 ps_calc_02           0.013789\n",
            "23) ps_reg_03 ps_calc_01           0.013725\n",
            "24) ps_calc_10                     0.013626\n",
            "25) ps_car_14 ps_calc_02           0.013577\n",
            "26) ps_car_14 ps_calc_01           0.013539\n",
            "27) ps_car_14 ps_calc_03           0.013467\n",
            "28) ps_calc_14                     0.013459\n",
            "29) ps_car_12 ps_car_14            0.012992\n",
            "30) ps_ind_03                      0.012943\n",
            "31) ps_car_14^2                    0.012753\n",
            "32) ps_car_14                      0.012742\n",
            "33) ps_reg_02 ps_car_14            0.012701\n",
            "34) ps_calc_11                     0.012630\n",
            "35) ps_reg_02 ps_reg_03            0.012552\n",
            "36) ps_ind_15                      0.012151\n",
            "37) ps_car_12 ps_car_15            0.010932\n",
            "38) ps_car_15 ps_calc_03           0.010878\n",
            "39) ps_car_15 ps_calc_01           0.010874\n",
            "40) ps_car_15 ps_calc_02           0.010811\n",
            "41) ps_calc_13                     0.010451\n",
            "42) ps_car_12 ps_calc_01           0.010417\n",
            "43) ps_car_12 ps_calc_02           0.010318\n",
            "44) ps_car_12 ps_calc_03           0.010308\n",
            "45) ps_reg_02 ps_car_15            0.010215\n",
            "46) ps_reg_01 ps_car_15            0.010148\n",
            "47) ps_calc_02 ps_calc_03          0.010014\n",
            "48) ps_calc_01 ps_calc_03          0.010013\n",
            "49) ps_calc_01 ps_calc_02          0.009996\n",
            "50) ps_calc_08                     0.009808\n",
            "51) ps_calc_07                     0.009796\n",
            "52) ps_reg_01 ps_car_12            0.009461\n",
            "53) ps_reg_02 ps_car_12            0.009336\n",
            "54) ps_reg_02 ps_calc_01           0.009267\n",
            "55) ps_reg_02 ps_calc_03           0.009190\n",
            "56) ps_reg_02 ps_calc_02           0.009164\n",
            "57) ps_reg_01 ps_calc_03           0.009108\n",
            "58) ps_reg_01 ps_calc_02           0.009062\n",
            "59) ps_reg_01 ps_calc_01           0.009024\n",
            "60) ps_calc_06                     0.009023\n",
            "61) ps_calc_09                     0.008863\n",
            "62) ps_ind_01                      0.008552\n",
            "63) ps_calc_05                     0.008327\n",
            "64) ps_calc_04                     0.008158\n",
            "65) ps_reg_01 ps_reg_02            0.008075\n",
            "66) ps_calc_12                     0.008010\n",
            "67) ps_car_15^2                    0.006173\n",
            "68) ps_car_15                      0.006161\n",
            "69) ps_calc_03                     0.006005\n",
            "70) ps_calc_03^2                   0.006001\n",
            "71) ps_calc_02                     0.005938\n",
            "72) ps_calc_01^2                   0.005934\n",
            "73) ps_calc_01                     0.005926\n",
            "74) ps_calc_02^2                   0.005903\n",
            "75) ps_car_12                      0.005364\n",
            "76) ps_car_12^2                    0.005336\n",
            "77) ps_reg_02^2                    0.004984\n",
            "78) ps_reg_02                      0.004980\n",
            "79) ps_reg_01^2                    0.004165\n",
            "80) ps_reg_01                      0.004131\n",
            "81) ps_car_11                      0.003803\n",
            "82) ps_ind_05_cat_0                0.003580\n",
            "83) ps_ind_17_bin                  0.002823\n",
            "84) ps_calc_17_bin                 0.002691\n",
            "85) ps_calc_16_bin                 0.002596\n",
            "86) ps_calc_19_bin                 0.002561\n",
            "87) ps_calc_18_bin                 0.002480\n",
            "88) ps_car_01_cat_11               0.002424\n",
            "89) ps_ind_16_bin                  0.002404\n",
            "90) ps_ind_04_cat_0                0.002386\n",
            "91) ps_ind_04_cat_1                0.002367\n",
            "92) ps_ind_07_bin                  0.002325\n",
            "93) ps_car_09_cat_2                0.002298\n",
            "94) ps_ind_02_cat_1                0.002283\n",
            "95) ps_car_09_cat_0                0.002101\n",
            "96) ps_car_01_cat_7                0.002101\n",
            "97) ps_ind_02_cat_2                0.002084\n",
            "98) ps_calc_20_bin                 0.002064\n",
            "99) ps_ind_06_bin                  0.002042\n",
            "100) ps_calc_15_bin                 0.001985\n",
            "101) ps_car_06_cat_1                0.001975\n",
            "102) ps_car_07_cat_1                0.001970\n",
            "103) ps_ind_08_bin                  0.001933\n",
            "104) ps_car_09_cat_1                0.001818\n",
            "105) ps_car_06_cat_11               0.001808\n",
            "106) ps_ind_18_bin                  0.001738\n",
            "107) ps_ind_09_bin                  0.001712\n",
            "108) ps_car_01_cat_9                0.001612\n",
            "109) ps_car_01_cat_10               0.001602\n",
            "110) ps_car_06_cat_14               0.001572\n",
            "111) ps_car_01_cat_6                0.001546\n",
            "112) ps_car_01_cat_4                0.001541\n",
            "113) ps_ind_05_cat_6                0.001510\n",
            "114) ps_ind_02_cat_3                0.001444\n",
            "115) ps_car_07_cat_0                0.001361\n",
            "116) ps_car_02_cat_1                0.001343\n",
            "117) ps_car_08_cat_1                0.001338\n",
            "118) ps_car_01_cat_8                0.001338\n",
            "119) ps_car_02_cat_0                0.001327\n",
            "120) ps_car_06_cat_4                0.001224\n",
            "121) ps_ind_05_cat_4                0.001221\n",
            "122) ps_ind_02_cat_4                0.001151\n",
            "123) ps_car_01_cat_5                0.001144\n",
            "124) ps_car_06_cat_6                0.001127\n",
            "125) ps_car_06_cat_10               0.001071\n",
            "126) ps_ind_05_cat_2                0.001032\n",
            "127) ps_car_04_cat_1                0.001029\n",
            "128) ps_car_06_cat_7                0.001001\n",
            "129) ps_car_04_cat_2                0.000992\n",
            "130) ps_car_01_cat_3                0.000897\n",
            "131) ps_car_09_cat_3                0.000878\n",
            "132) ps_car_01_cat_0                0.000874\n",
            "133) ps_ind_14                      0.000855\n",
            "134) ps_car_06_cat_15               0.000840\n",
            "135) ps_car_06_cat_9                0.000788\n",
            "136) ps_ind_05_cat_1                0.000764\n",
            "137) ps_car_10_cat_1                0.000705\n",
            "138) ps_car_06_cat_3                0.000695\n",
            "139) ps_ind_12_bin                  0.000674\n",
            "140) ps_ind_05_cat_3                0.000672\n",
            "141) ps_car_09_cat_4                0.000632\n",
            "142) ps_car_01_cat_2                0.000551\n",
            "143) ps_car_04_cat_8                0.000543\n",
            "144) ps_car_06_cat_17               0.000521\n",
            "145) ps_car_06_cat_16               0.000468\n",
            "146) ps_car_04_cat_9                0.000437\n",
            "147) ps_car_06_cat_12               0.000427\n",
            "148) ps_car_06_cat_13               0.000399\n",
            "149) ps_car_01_cat_1                0.000388\n",
            "150) ps_ind_05_cat_5                0.000316\n",
            "151) ps_car_06_cat_5                0.000281\n",
            "152) ps_ind_11_bin                  0.000217\n",
            "153) ps_car_04_cat_6                0.000202\n",
            "154) ps_ind_13_bin                  0.000150\n",
            "155) ps_car_04_cat_3                0.000146\n",
            "156) ps_car_06_cat_2                0.000139\n",
            "157) ps_car_06_cat_8                0.000098\n",
            "158) ps_car_04_cat_5                0.000097\n",
            "159) ps_car_04_cat_7                0.000079\n",
            "160) ps_ind_10_bin                  0.000069\n",
            "161) ps_car_10_cat_2                0.000065\n",
            "162) ps_car_04_cat_4                0.000043\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bo00WotyGheV",
        "colab_type": "text"
      },
      "source": [
        "With SelectFromModel we can specify which prefit classifier to use and what the threshold is for the feature importances. With the get_support method we can then limit the number of variables in the train data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5gqXxoE5GqHD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "4dda5237-243f-4e19-bcdd-1788a81c7b9b"
      },
      "source": [
        "sfm = SelectFromModel(rf, threshold='median', prefit=True)\n",
        "print('Number of features before selection: {}'.format(X_train.shape[1]))\n",
        "n_features = sfm.transform(X_train).shape[1]\n",
        "print('Number of features after selection: {}'.format(n_features))\n",
        "selected_vars = list(feat_labels[sfm.get_support()])"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of features before selection: 162\n",
            "Number of features after selection: 81\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YHa21f3NH9xL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = train[selected_vars + ['target']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56vAoq4lIDUy",
        "colab_type": "text"
      },
      "source": [
        "## Feature scaling\n",
        "\n",
        "As mentioned before, we can apply standard scaling to the training data. Some classifiers perform better when this is done."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YT0SK8dFIki1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "485c034d-aad1-4c62-d840-f3fca8ce427e"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit_transform(train.drop(['target'], axis=1))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.45941104, -1.26665356,  1.05087653, ..., -0.72553616,\n",
              "        -1.01071913, -1.06173767],\n",
              "       [ 1.55538958,  0.95034274, -0.63847299, ..., -1.06120876,\n",
              "        -1.01071913,  0.27907892],\n",
              "       [ 1.05168943, -0.52765479, -0.92003125, ...,  1.95984463,\n",
              "        -0.56215309, -1.02449277],\n",
              "       ...,\n",
              "       [-0.9631112 ,  0.58084336,  0.48776003, ..., -0.46445747,\n",
              "         0.18545696,  0.27907892],\n",
              "       [-0.9631112 , -0.89715418, -1.48314775, ..., -0.91202093,\n",
              "        -0.41263108,  0.27907892],\n",
              "       [-0.45941104, -1.26665356,  1.61399304, ...,  0.28148164,\n",
              "        -0.11358706, -0.72653353]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    }
  ]
}