Fraud_Detection_Starter.ipynb

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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Fraud Detection in Electricity and Gas Consumption Challenge\n",
        "\n",
        "This is a simple starter notebook based on the tutorial prepared by Joy Wawira. Check out the article [here](https://zindi.africa/learn/fraud-detection-in-electricity-and-gas-consumption-challenge-tutorial) for a more detailed description of the steps taken.\n",
        "\n",
        "This notebook covers:\n",
        "- Downloading the data straight from Zindi and onto colab\n",
        "- Loading the data and carrying out simple EDA to understand the data and prepare for modelling \n",
        "- Preprocessing the data and feature engineering \n",
        "- Creating a simple LGBM model and predicting on the test set\n",
        "- Prepare submission file and save as csv\n",
        "- Some tips on how to improve model performance and your score"
      ],
      "metadata": {
        "id": "RqLQ-hbyb5jH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pre-Requisites"
      ],
      "metadata": {
        "id": "e6LfbE6PJbmp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IYR_e0SMJnpa"
      },
      "outputs": [],
      "source": [
        "DATA_DIR = '/content'\n",
        "\n",
        "TRAIN_DIR = f'{DATA_DIR}/train'\n",
        "TEST_DIR = f'{DATA_DIR}/test'\n",
        "\n",
        "OUTPUT_DIR = f'{DATA_DIR}/output'"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download and extract files"
      ],
      "metadata": {
        "id": "3qArEf-4JhbE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os.path\n",
        "from os import path\n",
        "\n",
        "for pth in [TRAIN_DIR, TEST_DIR, OUTPUT_DIR]:\n",
        "  if path.exists(pth) == False:\n",
        "    os.mkdir(pth)"
      ],
      "metadata": {
        "id": "GblUChAfGsaY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#only run this cell once, at the start\n",
        "import requests, os\n",
        "\n",
        "train_zip = \"train.zip\"\n",
        "test_zip = \"test.zip\"\n",
        "sample_sub = \"SampleSubmission.csv\""
      ],
      "metadata": {
        "id": "4GEpOaBQGvlB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip \"/content/train/train.zip\" -d \"/content/train/\"\n",
        "!unzip \"/content/test/test.zip\" -d \"/content/test/\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcqJHBiuIVp8",
        "outputId": "538c087d-1c8e-41df-9457-2a2a315b181a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/train/train.zip\n",
            "  inflating: /content/train/client_train.csv  \n",
            "  inflating: /content/train/invoice_train.csv  \n",
            "Archive:  /content/test/test.zip\n",
            "  inflating: /content/test/client_test.csv  \n",
            "  inflating: /content/test/invoice_test.csv  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Prep"
      ],
      "metadata": {
        "id": "Jy_tdygDJm-k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Libraries"
      ],
      "metadata": {
        "id": "GKfpS4UVJ4Wj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import lightgbm\n",
        "from lightgbm import LGBMClassifier\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')"
      ],
      "metadata": {
        "id": "OJMD_I3EJWP4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Read the Data"
      ],
      "metadata": {
        "id": "exEGyf6vJ_4T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "client_train = pd.read_csv(f'{TRAIN_DIR}/client_train.csv', low_memory=False)\n",
        "invoice_train = pd.read_csv(f'{TRAIN_DIR}/invoice_train.csv', low_memory=False)\n",
        "\n",
        "client_test = pd.read_csv(f'{TEST_DIR}/client_test.csv', low_memory=False)\n",
        "invoice_test = pd.read_csv(f'{TEST_DIR}/invoice_test.csv', low_memory=False)\n",
        "sample_submission = pd.read_csv(f'{DATA_DIR}/SampleSubmission.csv', low_memory=False)"
      ],
      "metadata": {
        "id": "I2oIx7IuKCJm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Understanding"
      ],
      "metadata": {
        "id": "cDRp9i5ZO9mz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#compare size of the various datasets\n",
        "print(client_train.shape, invoice_train.shape, client_test.shape, invoice_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h4zQzvLjKZ6E",
        "outputId": "deb14c51-2f5d-40cd-aa35-771477e044e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(135493, 6) (4476749, 16) (58069, 5) (4476749, 16)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#print top rows of dataset\n",
        "invoice_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "Tw1On2leQsxU",
        "outputId": "4bbe6376-2007-4ea4-cd6f-e53e4c0bcb33"
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      "execution_count": null,
      "outputs": [
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              "        client_id invoice_date  tarif_type  counter_number counter_statue  \\\n",
              "0  train_Client_0   2014-03-24          11         1335667              0   \n",
              "1  train_Client_0   2013-03-29          11         1335667              0   \n",
              "2  train_Client_0   2015-03-23          11         1335667              0   \n",
              "3  train_Client_0   2015-07-13          11         1335667              0   \n",
              "4  train_Client_0   2016-11-17          11         1335667              0   \n",
              "\n",
              "   counter_code  reading_remarque  counter_coefficient  consommation_level_1  \\\n",
              "0           203                 8                    1                    82   \n",
              "1           203                 6                    1                  1200   \n",
              "2           203                 8                    1                   123   \n",
              "3           207                 8                    1                   102   \n",
              "4           207                 9                    1                   572   \n",
              "\n",
              "   consommation_level_2  consommation_level_3  consommation_level_4  \\\n",
              "0                     0                     0                     0   \n",
              "1                   184                     0                     0   \n",
              "2                     0                     0                     0   \n",
              "3                     0                     0                     0   \n",
              "4                     0                     0                     0   \n",
              "\n",
              "   old_index  new_index  months_number counter_type  \n",
              "0      14302      14384              4         ELEC  \n",
              "1      12294      13678              4         ELEC  \n",
              "2      14624      14747              4         ELEC  \n",
              "3      14747      14849              4         ELEC  \n",
              "4      15066      15638             12         ELEC  "
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              "      <th></th>\n",
              "      <th>client_id</th>\n",
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              "      <td>2016-11-17</td>\n",
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      "cell_type": "code",
      "source": [
        "#print top rows of dataset\n",
        "client_train.head()"
      ],
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          "data": {
            "text/plain": [
              "   disrict          client_id  client_catg  region creation_date  target\n",
              "0       60     train_Client_0           11     101    31/12/1994     0.0\n",
              "1       69     train_Client_1           11     107    29/05/2002     0.0\n",
              "2       62    train_Client_10           11     301    13/03/1986     0.0\n",
              "3       69   train_Client_100           11     105    11/07/1996     0.0\n",
              "4       62  train_Client_1000           11     303    14/10/2014     0.0"
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>69</td>\n",
              "      <td>train_Client_1</td>\n",
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              "      <th>2</th>\n",
              "      <td>62</td>\n",
              "      <td>train_Client_10</td>\n",
              "      <td>11</td>\n",
              "      <td>301</td>\n",
              "      <td>13/03/1986</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>69</td>\n",
              "      <td>train_Client_100</td>\n",
              "      <td>11</td>\n",
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              "      <td>11/07/1996</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>62</td>\n",
              "      <td>train_Client_1000</td>\n",
              "      <td>11</td>\n",
              "      <td>303</td>\n",
              "      <td>14/10/2014</td>\n",
              "      <td>0.0</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 9
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      ]
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    {
      "cell_type": "code",
      "source": [
        "#Get a summary for all numerical columns\n",
        "invoice_train.describe()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "fFBTeG8YSicC",
        "outputId": "551f42dc-8b1c-42a9-9a60-f6dc9279911b"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "         tarif_type  counter_number  counter_code  reading_remarque  \\\n",
              "count  4.476749e+06    4.476749e+06  4.476749e+06      4.476749e+06   \n",
              "mean   2.012804e+01    1.230587e+11  1.724884e+02      7.321702e+00   \n",
              "std    1.347256e+01    1.657267e+12  1.338871e+02      1.571654e+00   \n",
              "min    8.000000e+00    0.000000e+00  0.000000e+00      5.000000e+00   \n",
              "25%    1.100000e+01    1.211080e+05  5.000000e+00      6.000000e+00   \n",
              "50%    1.100000e+01    4.945610e+05  2.030000e+02      8.000000e+00   \n",
              "75%    4.000000e+01    1.115161e+06  2.070000e+02      9.000000e+00   \n",
              "max    4.500000e+01    2.798115e+13  6.000000e+02      4.130000e+02   \n",
              "\n",
              "       counter_coefficient  consommation_level_1  consommation_level_2  \\\n",
              "count         4.476749e+06          4.476749e+06          4.476749e+06   \n",
              "mean          1.003040e+00          4.109795e+02          1.093225e+02   \n",
              "std           3.083466e-01          7.573080e+02          1.220123e+03   \n",
              "min           0.000000e+00          0.000000e+00          0.000000e+00   \n",
              "25%           1.000000e+00          7.900000e+01          0.000000e+00   \n",
              "50%           1.000000e+00          2.740000e+02          0.000000e+00   \n",
              "75%           1.000000e+00          6.000000e+02          0.000000e+00   \n",
              "max           5.000000e+01          9.999100e+05          9.990730e+05   \n",
              "\n",
              "       consommation_level_3  consommation_level_4     old_index     new_index  \\\n",
              "count          4.476749e+06          4.476749e+06  4.476749e+06  4.476749e+06   \n",
              "mean           2.030620e+01          5.292588e+01  1.776700e+04  1.834970e+04   \n",
              "std            1.574239e+02          8.754725e+02  4.036693e+04  4.095321e+04   \n",
              "min            0.000000e+00          0.000000e+00  0.000000e+00  0.000000e+00   \n",
              "25%            0.000000e+00          0.000000e+00  1.791000e+03  2.056000e+03   \n",
              "50%            0.000000e+00          0.000000e+00  7.690000e+03  8.192000e+03   \n",
              "75%            0.000000e+00          0.000000e+00  2.166000e+04  2.234300e+04   \n",
              "max            6.449200e+04          5.479460e+05  2.800280e+06  2.870972e+06   \n",
              "\n",
              "       months_number  \n",
              "count   4.476749e+06  \n",
              "mean    4.483095e+01  \n",
              "std     3.128335e+03  \n",
              "min     0.000000e+00  \n",
              "25%     4.000000e+00  \n",
              "50%     4.000000e+00  \n",
              "75%     4.000000e+00  \n",
              "max     6.366240e+05  "
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              "      <th></th>\n",
              "      <th>tarif_type</th>\n",
              "      <th>counter_number</th>\n",
              "      <th>counter_code</th>\n",
              "      <th>reading_remarque</th>\n",
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              "      <td>4.476749e+06</td>\n",
              "      <td>4.476749e+06</td>\n",
              "      <td>4.476749e+06</td>\n",
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              "      <td>7.321702e+00</td>\n",
              "      <td>1.003040e+00</td>\n",
              "      <td>4.109795e+02</td>\n",
              "      <td>1.093225e+02</td>\n",
              "      <td>2.030620e+01</td>\n",
              "      <td>5.292588e+01</td>\n",
              "      <td>1.776700e+04</td>\n",
              "      <td>1.834970e+04</td>\n",
              "      <td>4.483095e+01</td>\n",
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              "      <td>1.347256e+01</td>\n",
              "      <td>1.657267e+12</td>\n",
              "      <td>1.338871e+02</td>\n",
              "      <td>1.571654e+00</td>\n",
              "      <td>3.083466e-01</td>\n",
              "      <td>7.573080e+02</td>\n",
              "      <td>1.220123e+03</td>\n",
              "      <td>1.574239e+02</td>\n",
              "      <td>8.754725e+02</td>\n",
              "      <td>4.036693e+04</td>\n",
              "      <td>4.095321e+04</td>\n",
              "      <td>3.128335e+03</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.100000e+01</td>\n",
              "      <td>1.211080e+05</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>7.900000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.791000e+03</td>\n",
              "      <td>2.056000e+03</td>\n",
              "      <td>4.000000e+00</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.100000e+01</td>\n",
              "      <td>4.945610e+05</td>\n",
              "      <td>2.030000e+02</td>\n",
              "      <td>8.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.740000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>7.690000e+03</td>\n",
              "      <td>8.192000e+03</td>\n",
              "      <td>4.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000e+01</td>\n",
              "      <td>1.115161e+06</td>\n",
              "      <td>2.070000e+02</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>6.000000e+02</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.166000e+04</td>\n",
              "      <td>2.234300e+04</td>\n",
              "      <td>4.000000e+00</td>\n",
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              "      <th>max</th>\n",
              "      <td>4.500000e+01</td>\n",
              "      <td>2.798115e+13</td>\n",
              "      <td>6.000000e+02</td>\n",
              "      <td>4.130000e+02</td>\n",
              "      <td>5.000000e+01</td>\n",
              "      <td>9.999100e+05</td>\n",
              "      <td>9.990730e+05</td>\n",
              "      <td>6.449200e+04</td>\n",
              "      <td>5.479460e+05</td>\n",
              "      <td>2.800280e+06</td>\n",
              "      <td>2.870972e+06</td>\n",
              "      <td>6.366240e+05</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 10
        }
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    {
      "cell_type": "code",
      "source": [
        "#Get a summary for all numerical columns\n",
        "client_train.describe()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "egcBZ2ysSkji",
        "outputId": "37102972-554b-4063-ce3b-14dae74b3540"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             disrict    client_catg         region         target\n",
              "count  135493.000000  135493.000000  135493.000000  135493.000000\n",
              "mean       63.511222      11.512506     206.159809       0.055841\n",
              "std         3.354400       4.423761     104.207044       0.229614\n",
              "min        60.000000      11.000000     101.000000       0.000000\n",
              "25%        62.000000      11.000000     103.000000       0.000000\n",
              "50%        62.000000      11.000000     107.000000       0.000000\n",
              "75%        69.000000      11.000000     307.000000       0.000000\n",
              "max        69.000000      51.000000     399.000000       1.000000"
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>63.511222</td>\n",
              "      <td>11.512506</td>\n",
              "      <td>206.159809</td>\n",
              "      <td>0.055841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.354400</td>\n",
              "      <td>4.423761</td>\n",
              "      <td>104.207044</td>\n",
              "      <td>0.229614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>62.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>103.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>62.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>107.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>69.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>307.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>69.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>399.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57e24e19-7a75-4cb5-b920-f4d9c3d8706c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-57e24e19-7a75-4cb5-b920-f4d9c3d8706c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-57e24e19-7a75-4cb5-b920-f4d9c3d8706c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get concise information of each column in dataset\n",
        "invoice_train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-8gQmkajSnXs",
        "outputId": "fef429ad-2fd5-46e3-9d3b-32def757324c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4476749 entries, 0 to 4476748\n",
            "Data columns (total 16 columns):\n",
            " #   Column                Dtype \n",
            "---  ------                ----- \n",
            " 0   client_id             object\n",
            " 1   invoice_date          object\n",
            " 2   tarif_type            int64 \n",
            " 3   counter_number        int64 \n",
            " 4   counter_statue        object\n",
            " 5   counter_code          int64 \n",
            " 6   reading_remarque      int64 \n",
            " 7   counter_coefficient   int64 \n",
            " 8   consommation_level_1  int64 \n",
            " 9   consommation_level_2  int64 \n",
            " 10  consommation_level_3  int64 \n",
            " 11  consommation_level_4  int64 \n",
            " 12  old_index             int64 \n",
            " 13  new_index             int64 \n",
            " 14  months_number         int64 \n",
            " 15  counter_type          object\n",
            "dtypes: int64(12), object(4)\n",
            "memory usage: 546.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get concise information of each column in dataset\n",
        "client_train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VII8aa8CSoed",
        "outputId": "330bd35b-fd37-422b-b718-c34d354661db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 135493 entries, 0 to 135492\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   disrict        135493 non-null  int64  \n",
            " 1   client_id      135493 non-null  object \n",
            " 2   client_catg    135493 non-null  int64  \n",
            " 3   region         135493 non-null  int64  \n",
            " 4   creation_date  135493 non-null  object \n",
            " 5   target         135493 non-null  float64\n",
            "dtypes: float64(1), int64(3), object(2)\n",
            "memory usage: 6.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting unique values on the invoice train data\n",
        "for col in invoice_train.columns:\n",
        "    print(f\"{col} - {invoice_train[col].nunique()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iiJ3e_pFSOWg",
        "outputId": "d074cb2a-d009-471f-f124-466db367df68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "client_id - 135493\n",
            "invoice_date - 8275\n",
            "tarif_type - 17\n",
            "counter_number - 201893\n",
            "counter_statue - 12\n",
            "counter_code - 42\n",
            "reading_remarque - 8\n",
            "counter_coefficient - 16\n",
            "consommation_level_1 - 8295\n",
            "consommation_level_2 - 12576\n",
            "consommation_level_3 - 2253\n",
            "consommation_level_4 - 12075\n",
            "old_index - 155648\n",
            "new_index - 157980\n",
            "months_number - 1370\n",
            "counter_type - 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting unique values on the invoice train data\n",
        "for col in client_train.columns:\n",
        "    print(f\"{col} - {client_train[col].nunique()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e363f7b-35c4-45c2-c763-acf80585ef7c",
        "id": "uMC16gFETUAF"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "disrict - 4\n",
            "client_id - 135493\n",
            "client_catg - 3\n",
            "region - 25\n",
            "creation_date - 8088\n",
            "target - 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check for missing values\n",
        "invoice_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h10LPXsfVHhu",
        "outputId": "332ac9e1-02e3-4a3a-a8d6-1c8d706b9601"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "client_id               0\n",
              "invoice_date            0\n",
              "tarif_type              0\n",
              "counter_number          0\n",
              "counter_statue          0\n",
              "counter_code            0\n",
              "reading_remarque        0\n",
              "counter_coefficient     0\n",
              "consommation_level_1    0\n",
              "consommation_level_2    0\n",
              "consommation_level_3    0\n",
              "consommation_level_4    0\n",
              "old_index               0\n",
              "new_index               0\n",
              "months_number           0\n",
              "counter_type            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check for missing values\n",
        "client_train.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3XyoiIbVO-b",
        "outputId": "9200e12f-66fd-43a4-edf4-daa1e1cfdd11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "disrict          0\n",
              "client_id        0\n",
              "client_catg      0\n",
              "region           0\n",
              "creation_date    0\n",
              "target           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No missing values in train set"
      ],
      "metadata": {
        "id": "i3oZLTnSVSOT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualize fraudulent activities\n",
        "fraudactivities = client_train.groupby(['target'])['client_id'].count()\n",
        "plt.bar(x=fraudactivities.index, height=fraudactivities.values, tick_label = [0,1])\n",
        "plt.title('Fraud - Target Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "JOzSAlEWTR3j",
        "outputId": "97a46548-924e-44a4-9b85-e40d01979b99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Target is highly imbalanced with fewer cases of fraudulent activities"
      ],
      "metadata": {
        "id": "fQb7RZf_Ufn9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualize client distribution across districts and regions\n",
        "for col in ['disrict','region']:\n",
        "    region = client_train.groupby([col])['client_id'].count()\n",
        "    plt.bar(x=region.index, height=region.values)\n",
        "    plt.title(col+' distribution')\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "v8G8BPgMUrVW",
        "outputId": "886941f9-3a90-48c2-ddac-75728bd7b935"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Engineering"
      ],
      "metadata": {
        "id": "0-LOrxdIU7-h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#convert the column invoice_date to date time format on both the invoice train and invoice test\n",
        "for df in [invoice_train,invoice_test]:\n",
        "    df['invoice_date'] = pd.to_datetime(df['invoice_date'])"
      ],
      "metadata": {
        "id": "aVypp4ahU9tq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#encode labels in categorical column\n",
        "d={\"ELEC\":0,\"GAZ\":1}\n",
        "invoice_train['counter_type']=invoice_train['counter_type'].map(d)\n",
        "invoice_test['counter_type']=invoice_test['counter_type'].map(d)"
      ],
      "metadata": {
        "id": "sW2W5i63VYyK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#convert categorical columns to int for model\n",
        "client_train['client_catg'] = client_train['client_catg'].astype(int)\n",
        "client_train['disrict'] = client_train['disrict'].astype(int)\n",
        "\n",
        "client_test['client_catg'] = client_test['client_catg'].astype(int)\n",
        "client_test['disrict'] = client_test['disrict'].astype(int)"
      ],
      "metadata": {
        "id": "NldYnn0GWHAk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def aggregate_by_client_id(invoice_data):\n",
        "    aggs = {}\n",
        "    aggs['consommation_level_1'] = ['mean']\n",
        "    aggs['consommation_level_2'] = ['mean']\n",
        "    aggs['consommation_level_3'] = ['mean']\n",
        "    aggs['consommation_level_4'] = ['mean']\n",
        "\n",
        "    agg_trans = invoice_data.groupby(['client_id']).agg(aggs)\n",
        "    agg_trans.columns = ['_'.join(col).strip() for col in agg_trans.columns.values]\n",
        "    agg_trans.reset_index(inplace=True)\n",
        "\n",
        "    df = (invoice_data.groupby('client_id')\n",
        "            .size()\n",
        "            .reset_index(name='{}transactions_count'.format('1')))\n",
        "    return pd.merge(df, agg_trans, on='client_id', how='left')"
      ],
      "metadata": {
        "id": "Bt7LJHhyXJji"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#group invoice data by client_id\n",
        "agg_train = aggregate_by_client_id(invoice_train)"
      ],
      "metadata": {
        "id": "8gUjxEmKWZjp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(agg_train.shape)\n",
        "agg_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "5cIpY1tGWrkw",
        "outputId": "c57de3d5-3efe-4e8a-9a0c-7362f05fca4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(135493, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           client_id  1transactions_count  consommation_level_1_mean  \\\n",
              "0     train_Client_0                   35                 352.400000   \n",
              "1     train_Client_1                   37                 557.540541   \n",
              "2    train_Client_10                   18                 798.611111   \n",
              "3   train_Client_100                   20                   1.200000   \n",
              "4  train_Client_1000                   14                 663.714286   \n",
              "\n",
              "   consommation_level_2_mean  consommation_level_3_mean  \\\n",
              "0                  10.571429                   0.000000   \n",
              "1                   0.000000                   0.000000   \n",
              "2                  37.888889                   0.000000   \n",
              "3                   0.000000                   0.000000   \n",
              "4                 104.857143                 117.357143   \n",
              "\n",
              "   consommation_level_4_mean  \n",
              "0                   0.000000  \n",
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          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#merge aggregate data with client dataset\n",
        "train = pd.merge(client_train,agg_train, on='client_id', how='left')"
      ],
      "metadata": {
        "id": "beXOMOEMW97l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#aggregate test set\n",
        "agg_test = aggregate_by_client_id(invoice_test)\n",
        "test = pd.merge(client_test,agg_test, on='client_id', how='left')"
      ],
      "metadata": {
        "id": "y6SFawOVXFDX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.shape, test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dg0832jcXzkI",
        "outputId": "2c6ff557-0258-4310-d8f6-aad0627c9ddb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((135493, 11), (58069, 10))"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop redundant columns\n",
        "sub_client_id = test['client_id']\n",
        "drop_columns = ['client_id', 'creation_date']\n",
        "\n",
        "for col in drop_columns:\n",
        "    if col in train.columns:\n",
        "        train.drop([col], axis=1, inplace=True)\n",
        "    if col in test.columns:\n",
        "        test.drop([col], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "BwYBcKk8X3mr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelling"
      ],
      "metadata": {
        "id": "iJ3KlVOCYfnZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train LGBM *Classifier*"
      ],
      "metadata": {
        "id": "d1qXp8jYY30X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = train.drop(columns=['target'])\n",
        "y_train = train['target']"
      ],
      "metadata": {
        "id": "QTr8Wbu1ZKTM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LGBMClassifier(boosting_type='gbdt', num_iterationss=500)\n",
        "model.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iXjUC4PZYMgF",
        "outputId": "3f132cea-1ef5-45ce-bdf0-45c76a62c84c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(num_iterationss=500)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.disrict.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddn7FuuCZHqr",
        "outputId": "e6d5735b-9abf-4e99-e83d-b04b7e63ac44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([60, 69, 62, 63])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Make Predictions on test set"
      ],
      "metadata": {
        "id": "CKsrOiePadZ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nBww16tEajXs",
        "outputId": "a963cd4a-4aaf-4d1b-8524-0d7e67be1c48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['disrict', 'client_catg', 'region', '1transactions_count',\n",
              "       'consommation_level_1_mean', 'consommation_level_2_mean',\n",
              "       'consommation_level_3_mean', 'consommation_level_4_mean'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = model.predict(test)\n",
        "preds = pd.DataFrame(preds, columns=['target'])\n",
        "preds.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "SGIjyZfmaf7C",
        "outputId": "b4bdd7a5-a496-405c-9651-ba482afd1046"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target\n",
              "0     0.0\n",
              "1     0.0\n",
              "2     0.0\n",
              "3     0.0\n",
              "4     0.0"
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              "                                                     [key], {});\n",
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    {
      "cell_type": "code",
      "source": [
        "submission = pd.DataFrame(\n",
        "    {\n",
        "        'client_id': sub_client_id,\n",
        "        'target': preds['target']\n",
        "    }\n",
        ")\n",
        "\n",
        "submission.head()"
      ],
      "metadata": {
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      "execution_count": null,
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              "          client_id  target\n",
              "0     test_Client_0     0.0\n",
              "1     test_Client_1     0.0\n",
              "2    test_Client_10     0.0\n",
              "3   test_Client_100     0.0\n",
              "4  test_Client_1000     0.0"
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>test_Client_1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>test_Client_10</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>test_Client_100</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>test_Client_1000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-51c40ad3-3bbb-4bed-b3ca-804232763da5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-51c40ad3-3bbb-4bed-b3ca-804232763da5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-51c40ad3-3bbb-4bed-b3ca-804232763da5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "submission.to_csv(f'{OUTPUT_DIR}/submission.csv', index=False)"
      ],
      "metadata": {
        "id": "O7_ANcWUboUB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tips \n",
        "- Thorough EDA and incorporating domain knowledge\n",
        "- Re-grouping categorical features\n",
        "- More feature engineering(try utilizing some date-time features)\n",
        "- Target balancing - oversampling, undersampling, SMOTE, scale_pos_weight\n",
        "- Model ensembling\n",
        "- Train-test split or cross-validation\n"
      ],
      "metadata": {
        "id": "Msm0u0WXdQ2l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ******************* GOOD LUCK!!! ***************************"
      ],
      "metadata": {
        "id": "fgvYxyLXeEuU"
      }
    }
  ]
}