Amex_2020_Analyze_This_competition.ipynb

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  "metadata": {
    "colab": {
      "name": "Amex_2020_Analyze_This_competition.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "5ZDCbPKte8Wu",
        "CZYYACPHgTrG",
        "h5IwYndjgYhp",
        "G2XN1c8fxE0a",
        "5QGcFq5DR8Jv",
        "sNt76eOtaLJT",
        "469cID8kRwJu"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ZDCbPKte8Wu"
      },
      "source": [
        "# **Libraries Loading, Data Extraction, and cleaning**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CZYYACPHgTrG"
      },
      "source": [
        "## **Load Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZSA23abe5u7"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import roc_auc_score, roc_curve, mean_squared_error, classification_report\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, RepeatedStratifiedKFold, StratifiedKFold\n",
        "from collections import Counter\n",
        "import seaborn as sns\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h5IwYndjgYhp"
      },
      "source": [
        "## **Importing Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWJ9i0NRnnSe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e08b63f-619e-494d-cac1-3c86da789cb5"
      },
      "source": [
        "! git clone https://github.com/adityajaas/AMEX_2020_Competition"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'AMEX_2020_Competition' already exists and is not an empty directory.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6AwN19moa69"
      },
      "source": [
        "train_data = pd.read_csv('/content/AMEX_2020_Competition/Training_Data.csv')\n",
        "test_data = pd.read_csv('/content/AMEX_2020_Competition/Evaluation_Data.csv')\n",
        "data_info = pd.read_csv('/content/AMEX_2020_Competition/Data_Dictionary.csv', sep=';', encoding='cp1252')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asF8BBsgo-5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "6fad17dd-6d48-4c00-a0b2-9953fe708444"
      },
      "source": [
        "train_data.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cust_id</th>\n",
              "      <th>profitable_flag</th>\n",
              "      <th>count_accts</th>\n",
              "      <th>cm_age</th>\n",
              "      <th>flag_top_ed_spender</th>\n",
              "      <th>flag_cust_fee_paid_6m</th>\n",
              "      <th>pre6m_cust_spend</th>\n",
              "      <th>pre6m_cust_roc_cnt</th>\n",
              "      <th>pre6m_cust_non_disc_amt</th>\n",
              "      <th>pre6m_cust_non_disc_cnt</th>\n",
              "      <th>pre6m_cust_disc_amt</th>\n",
              "      <th>pre6m_cust_disc_cnt</th>\n",
              "      <th>pre6m_cust_outbound_amt</th>\n",
              "      <th>pre6m_cust_online_amt</th>\n",
              "      <th>pre6m_cust_online_cnt</th>\n",
              "      <th>pre6m_cust_travel_amt</th>\n",
              "      <th>pre6m_cust_travel_cnt</th>\n",
              "      <th>pre6m_cust_retail_amt</th>\n",
              "      <th>pre6m_cust_retail_cnt</th>\n",
              "      <th>pre6m_cust_myca_active</th>\n",
              "      <th>pre6m_cust_mob_logins</th>\n",
              "      <th>pre6m_total_mc_trs</th>\n",
              "      <th>acq_sub_chn</th>\n",
              "      <th>acq_type_grp</th>\n",
              "      <th>fee_type_grp</th>\n",
              "      <th>Cust_tenure</th>\n",
              "      <th>pre6m_spend_active_ind</th>\n",
              "      <th>highly_utilized_ind</th>\n",
              "      <th>min_pay_ind</th>\n",
              "      <th>paid_in_full_ind</th>\n",
              "      <th>sum_total_line_amt</th>\n",
              "      <th>direct_debit_ind</th>\n",
              "      <th>referrals</th>\n",
              "      <th>spillover</th>\n",
              "      <th>self_accts</th>\n",
              "      <th>Customer Low Quality indicator</th>\n",
              "      <th>cdss_most_rcnt_prob</th>\n",
              "      <th>cust_max_credit_12m_amt</th>\n",
              "      <th>cust_max_remit_12m_amt</th>\n",
              "      <th>cbr_3_score</th>\n",
              "      <th>cnsumr_chrg_actv_cust_cnt</th>\n",
              "      <th>cnsumr_chrg_avg_credit_12m_amt</th>\n",
              "      <th>cnsumr_lend_actv_cust_cnt</th>\n",
              "      <th>cnsumr_lend_tot_util_ratio</th>\n",
              "      <th>sow_revol_avg_paydown_pct</th>\n",
              "      <th>sow_tot_annual_ext_pmt_amt</th>\n",
              "      <th>sow_tot_revol_bal_amt</th>\n",
              "      <th>sow_tot_revol_cnt</th>\n",
              "      <th>sow_tot_trans_bal_amt</th>\n",
              "      <th>sow_tot_trans_cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>550391.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1,518</td>\n",
              "      <td>122.0</td>\n",
              "      <td>811</td>\n",
              "      <td>96.0</td>\n",
              "      <td>707</td>\n",
              "      <td>26.0</td>\n",
              "      <td>-</td>\n",
              "      <td>606</td>\n",
              "      <td>36.0</td>\n",
              "      <td>28</td>\n",
              "      <td>2.0</td>\n",
              "      <td>814</td>\n",
              "      <td>98.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>(2)</td>\n",
              "      <td>Unreferred</td>\n",
              "      <td>external</td>\n",
              "      <td>no_fee</td>\n",
              "      <td>63.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>80,000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>482</td>\n",
              "      <td>418</td>\n",
              "      <td>1376.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.034044</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>991</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>550392.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4,807</td>\n",
              "      <td>354.0</td>\n",
              "      <td>1,052</td>\n",
              "      <td>82.0</td>\n",
              "      <td>3,756</td>\n",
              "      <td>272.0</td>\n",
              "      <td>673</td>\n",
              "      <td>3,164</td>\n",
              "      <td>456.0</td>\n",
              "      <td>2,291</td>\n",
              "      <td>44.0</td>\n",
              "      <td>1,265</td>\n",
              "      <td>116.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15</td>\n",
              "      <td>In House</td>\n",
              "      <td>others</td>\n",
              "      <td>no_fee</td>\n",
              "      <td>203.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>140,000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2,165</td>\n",
              "      <td>2,165</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.044086</td>\n",
              "      <td>0.0877</td>\n",
              "      <td>2</td>\n",
              "      <td>24</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>550393.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2,361</td>\n",
              "      <td>204.0</td>\n",
              "      <td>1,351</td>\n",
              "      <td>118.0</td>\n",
              "      <td>1,010</td>\n",
              "      <td>86.0</td>\n",
              "      <td>237</td>\n",
              "      <td>693</td>\n",
              "      <td>152.0</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1,623</td>\n",
              "      <td>166.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>(20)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>full_fee</td>\n",
              "      <td>201.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>32,800</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>703</td>\n",
              "      <td>687</td>\n",
              "      <td>1376.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.039613</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>4,828</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>387</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>550394.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5,452</td>\n",
              "      <td>142.0</td>\n",
              "      <td>1,195</td>\n",
              "      <td>50.0</td>\n",
              "      <td>4,257</td>\n",
              "      <td>92.0</td>\n",
              "      <td>803</td>\n",
              "      <td>1,618</td>\n",
              "      <td>64.0</td>\n",
              "      <td>2,760</td>\n",
              "      <td>54.0</td>\n",
              "      <td>1,605</td>\n",
              "      <td>54.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>full_fee</td>\n",
              "      <td>286.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>100,000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1,225</td>\n",
              "      <td>1,225</td>\n",
              "      <td>1372.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>493</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.001200</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>4,598</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>295</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>550395.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>170</td>\n",
              "      <td>34.0</td>\n",
              "      <td>88</td>\n",
              "      <td>20.0</td>\n",
              "      <td>82</td>\n",
              "      <td>14.0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>(9)</td>\n",
              "      <td>Affiliates</td>\n",
              "      <td>spillover</td>\n",
              "      <td>no_fee</td>\n",
              "      <td>123.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20,800</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>51</td>\n",
              "      <td>35</td>\n",
              "      <td>1338.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.003378</td>\n",
              "      <td>0.0168</td>\n",
              "      <td>2,176</td>\n",
              "      <td>2,606</td>\n",
              "      <td>5.0</td>\n",
              "      <td>143</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>550396.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>46,428</td>\n",
              "      <td>644.0</td>\n",
              "      <td>5,108</td>\n",
              "      <td>116.0</td>\n",
              "      <td>41,323</td>\n",
              "      <td>528.0</td>\n",
              "      <td>12,620</td>\n",
              "      <td>28,522</td>\n",
              "      <td>820.0</td>\n",
              "      <td>8,155</td>\n",
              "      <td>108.0</td>\n",
              "      <td>34,480</td>\n",
              "      <td>460.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1,146</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>full_fee</td>\n",
              "      <td>179.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>149,600</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>11,555</td>\n",
              "      <td>11,555</td>\n",
              "      <td>1255.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.207708</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>550397.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12,431</td>\n",
              "      <td>442.0</td>\n",
              "      <td>5,679</td>\n",
              "      <td>232.0</td>\n",
              "      <td>6,753</td>\n",
              "      <td>210.0</td>\n",
              "      <td>608</td>\n",
              "      <td>4,205</td>\n",
              "      <td>248.0</td>\n",
              "      <td>2,801</td>\n",
              "      <td>56.0</td>\n",
              "      <td>8,376</td>\n",
              "      <td>330.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>(75)</td>\n",
              "      <td>Unreferred</td>\n",
              "      <td>spillover</td>\n",
              "      <td>full_fee</td>\n",
              "      <td>280.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>58,800</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3,316</td>\n",
              "      <td>3,316</td>\n",
              "      <td>1255.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.064085</td>\n",
              "      <td>0.3565</td>\n",
              "      <td>2,967</td>\n",
              "      <td>441</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1,866</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>550398.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22,606</td>\n",
              "      <td>472.0</td>\n",
              "      <td>10,796</td>\n",
              "      <td>182.0</td>\n",
              "      <td>11,812</td>\n",
              "      <td>290.0</td>\n",
              "      <td>332</td>\n",
              "      <td>4,155</td>\n",
              "      <td>352.0</td>\n",
              "      <td>1,673</td>\n",
              "      <td>80.0</td>\n",
              "      <td>17,909</td>\n",
              "      <td>312.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>(48)</td>\n",
              "      <td>Affiliates</td>\n",
              "      <td>spillover</td>\n",
              "      <td>full_fee</td>\n",
              "      <td>219.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>140,000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>6,503</td>\n",
              "      <td>6,287</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.143371</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>73,575</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5,833</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>550399.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3,127</td>\n",
              "      <td>116.0</td>\n",
              "      <td>1,090</td>\n",
              "      <td>58.0</td>\n",
              "      <td>2,037</td>\n",
              "      <td>58.0</td>\n",
              "      <td>25</td>\n",
              "      <td>14</td>\n",
              "      <td>4.0</td>\n",
              "      <td>293</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1,949</td>\n",
              "      <td>102.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>full_fee</td>\n",
              "      <td>461.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1,267</td>\n",
              "      <td>1,267</td>\n",
              "      <td>1281.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>482</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>550400.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2,770</td>\n",
              "      <td>258.0</td>\n",
              "      <td>901</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1,869</td>\n",
              "      <td>168.0</td>\n",
              "      <td>617</td>\n",
              "      <td>788</td>\n",
              "      <td>184.0</td>\n",
              "      <td>893</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1,103</td>\n",
              "      <td>160.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>185</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>full_fee</td>\n",
              "      <td>280.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>831</td>\n",
              "      <td>823</td>\n",
              "      <td>1376.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>421</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>9</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    cust_id  profitable_flag  ...  sow_tot_trans_bal_amt  sow_tot_trans_cnt\n",
              "0  550391.0              0.0  ...                   -                   3.0\n",
              "1  550392.0              0.0  ...                   -                   0.0\n",
              "2  550393.0              0.0  ...                   387                 3.0\n",
              "3  550394.0              0.0  ...                   295                 4.0\n",
              "4  550395.0              0.0  ...                   143                13.0\n",
              "5  550396.0              0.0  ...                   -                   0.0\n",
              "6  550397.0              0.0  ...                 1,866                 2.0\n",
              "7  550398.0              1.0  ...                 5,833                 6.0\n",
              "8  550399.0              0.0  ...                   -                   0.0\n",
              "9  550400.0              0.0  ...                   -                   2.0\n",
              "\n",
              "[10 rows x 50 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJ2j7lq4tbfv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a04f47e5-7308-48f8-c240-fe9790d0be30"
      },
      "source": [
        "print(train_data.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(19599, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G2XN1c8fxE0a"
      },
      "source": [
        "## **Preprocessing and Cleaning**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SROdvaf7x61O"
      },
      "source": [
        "# Replacing brackets for train data\n",
        "train_data['pre6m_total_mc_trs'] = (train_data['pre6m_total_mc_trs'].str.replace(r\"\\(\",\"\")).str.replace(r\"\\)\",\"\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLfCI45FGu6f"
      },
      "source": [
        "# Replacing brackets for test data\n",
        "test_data['pre6m_total_mc_trs'] = (test_data['pre6m_total_mc_trs'].str.replace(r\"\\(\",\"\")).str.replace(r\"\\)\",\"\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xsMjG4c9_vj"
      },
      "source": [
        "# Dropping the unnecessary cols from train\n",
        "train_data = train_data.drop(columns=['acq_sub_chn','acq_type_grp','fee_type_grp'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQD_99tTHQlk"
      },
      "source": [
        "# Dropping the channel of acquition of the first card by customer for test data\n",
        "test_data = test_data.drop(columns=['acq_sub_chn','acq_type_grp','fee_type_grp'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_W-4d928IZMZ"
      },
      "source": [
        "# processing numeric columns for train data\n",
        "cols = ['pre6m_cust_spend','pre6m_cust_non_disc_amt','pre6m_cust_disc_amt','pre6m_cust_outbound_amt','pre6m_cust_online_amt',\n",
        "        'pre6m_cust_travel_amt','pre6m_cust_retail_amt','pre6m_total_mc_trs','sum_total_line_amt','cust_max_credit_12m_amt','cust_max_remit_12m_amt',\n",
        "        'cnsumr_chrg_avg_credit_12m_amt','sow_tot_annual_ext_pmt_amt','sow_tot_revol_bal_amt','sow_tot_trans_bal_amt']\n",
        "\n",
        "for col in cols:\n",
        "  train_data[col] = train_data[col].str.replace(r\",\",\"\") "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JN93ozIhHwYp"
      },
      "source": [
        "# processing numeric columns for test data\n",
        "cols = ['pre6m_cust_spend','pre6m_cust_non_disc_amt','pre6m_cust_disc_amt','pre6m_cust_outbound_amt','pre6m_cust_online_amt',\n",
        "        'pre6m_cust_travel_amt','pre6m_cust_retail_amt','pre6m_total_mc_trs','sum_total_line_amt','cust_max_credit_12m_amt','cust_max_remit_12m_amt',\n",
        "        'cnsumr_chrg_avg_credit_12m_amt','sow_tot_annual_ext_pmt_amt','sow_tot_revol_bal_amt','sow_tot_trans_bal_amt']\n",
        "\n",
        "for col in cols:\n",
        "  test_data[col] = test_data[col].str.replace(r\",\",\"\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnLtez4RLzVU"
      },
      "source": [
        "# Converting dataframe to numeric cols for train data\n",
        "for col in train_data.columns:\n",
        "  train_data[col] = pd.to_numeric(train_data[col], errors='coerce')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jvTW4LtGH8Au"
      },
      "source": [
        "# Converting dataframe to numeric cols for test data\n",
        "for col in test_data.columns:\n",
        "  test_data[col] = pd.to_numeric(test_data[col], errors='coerce')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZwybZRs-gVu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "111a81e7-19c9-4aef-fc4d-4bb7db26e3cc"
      },
      "source": [
        "train_data.head(10)"
      ],
      "execution_count": null,
      "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>cust_id</th>\n",
              "      <th>profitable_flag</th>\n",
              "      <th>count_accts</th>\n",
              "      <th>cm_age</th>\n",
              "      <th>flag_top_ed_spender</th>\n",
              "      <th>flag_cust_fee_paid_6m</th>\n",
              "      <th>pre6m_cust_spend</th>\n",
              "      <th>pre6m_cust_roc_cnt</th>\n",
              "      <th>pre6m_cust_non_disc_amt</th>\n",
              "      <th>pre6m_cust_non_disc_cnt</th>\n",
              "      <th>pre6m_cust_disc_amt</th>\n",
              "      <th>pre6m_cust_disc_cnt</th>\n",
              "      <th>pre6m_cust_outbound_amt</th>\n",
              "      <th>pre6m_cust_online_amt</th>\n",
              "      <th>pre6m_cust_online_cnt</th>\n",
              "      <th>pre6m_cust_travel_amt</th>\n",
              "      <th>pre6m_cust_travel_cnt</th>\n",
              "      <th>pre6m_cust_retail_amt</th>\n",
              "      <th>pre6m_cust_retail_cnt</th>\n",
              "      <th>pre6m_cust_myca_active</th>\n",
              "      <th>pre6m_cust_mob_logins</th>\n",
              "      <th>pre6m_total_mc_trs</th>\n",
              "      <th>Cust_tenure</th>\n",
              "      <th>pre6m_spend_active_ind</th>\n",
              "      <th>highly_utilized_ind</th>\n",
              "      <th>min_pay_ind</th>\n",
              "      <th>paid_in_full_ind</th>\n",
              "      <th>sum_total_line_amt</th>\n",
              "      <th>direct_debit_ind</th>\n",
              "      <th>referrals</th>\n",
              "      <th>spillover</th>\n",
              "      <th>self_accts</th>\n",
              "      <th>Customer Low Quality indicator</th>\n",
              "      <th>cdss_most_rcnt_prob</th>\n",
              "      <th>cust_max_credit_12m_amt</th>\n",
              "      <th>cust_max_remit_12m_amt</th>\n",
              "      <th>cbr_3_score</th>\n",
              "      <th>cnsumr_chrg_actv_cust_cnt</th>\n",
              "      <th>cnsumr_chrg_avg_credit_12m_amt</th>\n",
              "      <th>cnsumr_lend_actv_cust_cnt</th>\n",
              "      <th>cnsumr_lend_tot_util_ratio</th>\n",
              "      <th>sow_revol_avg_paydown_pct</th>\n",
              "      <th>sow_tot_annual_ext_pmt_amt</th>\n",
              "      <th>sow_tot_revol_bal_amt</th>\n",
              "      <th>sow_tot_revol_cnt</th>\n",
              "      <th>sow_tot_trans_bal_amt</th>\n",
              "      <th>sow_tot_trans_cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>550391.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1518.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>811.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>707.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>606.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>814.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>80000.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>482.0</td>\n",
              "      <td>418.0</td>\n",
              "      <td>1376.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.034044</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>991.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>550392.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4807.0</td>\n",
              "      <td>354.0</td>\n",
              "      <td>1052.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>3756.0</td>\n",
              "      <td>272.0</td>\n",
              "      <td>673.0</td>\n",
              "      <td>3164.0</td>\n",
              "      <td>456.0</td>\n",
              "      <td>2291.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>140000.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2165.0</td>\n",
              "      <td>2165.0</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.044086</td>\n",
              "      <td>0.0877</td>\n",
              "      <td>2.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>550393.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2361.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>1351.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>1010.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>237.0</td>\n",
              "      <td>693.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1623.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>32800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>703.0</td>\n",
              "      <td>687.0</td>\n",
              "      <td>1376.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.039613</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>4828.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>387.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>550394.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5452.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>1195.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>4257.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>803.0</td>\n",
              "      <td>1618.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>2760.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>1605.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>286.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>100000.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1225.0</td>\n",
              "      <td>1225.0</td>\n",
              "      <td>1372.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>493.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.001200</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>4598.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>295.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>550395.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>123.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20800.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>51.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1338.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.003378</td>\n",
              "      <td>0.0168</td>\n",
              "      <td>2176.0</td>\n",
              "      <td>2606.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>550396.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>46428.0</td>\n",
              "      <td>644.0</td>\n",
              "      <td>5108.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>41323.0</td>\n",
              "      <td>528.0</td>\n",
              "      <td>12620.0</td>\n",
              "      <td>28522.0</td>\n",
              "      <td>820.0</td>\n",
              "      <td>8155.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>34480.0</td>\n",
              "      <td>460.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1146.0</td>\n",
              "      <td>179.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>149600.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>11555.0</td>\n",
              "      <td>11555.0</td>\n",
              "      <td>1255.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.207708</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>550397.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12431.0</td>\n",
              "      <td>442.0</td>\n",
              "      <td>5679.0</td>\n",
              "      <td>232.0</td>\n",
              "      <td>6753.0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>608.0</td>\n",
              "      <td>4205.0</td>\n",
              "      <td>248.0</td>\n",
              "      <td>2801.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>8376.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>58800.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>3316.0</td>\n",
              "      <td>3316.0</td>\n",
              "      <td>1255.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.064085</td>\n",
              "      <td>0.3565</td>\n",
              "      <td>2967.0</td>\n",
              "      <td>441.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1866.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>550398.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22606.0</td>\n",
              "      <td>472.0</td>\n",
              "      <td>10796.0</td>\n",
              "      <td>182.0</td>\n",
              "      <td>11812.0</td>\n",
              "      <td>290.0</td>\n",
              "      <td>332.0</td>\n",
              "      <td>4155.0</td>\n",
              "      <td>352.0</td>\n",
              "      <td>1673.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>17909.0</td>\n",
              "      <td>312.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>219.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>140000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>6503.0</td>\n",
              "      <td>6287.0</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.143371</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>73575.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5833.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>550399.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3127.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>1090.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>2037.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>293.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1949.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>461.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1267.0</td>\n",
              "      <td>1267.0</td>\n",
              "      <td>1281.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>482.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>550400.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2770.0</td>\n",
              "      <td>258.0</td>\n",
              "      <td>901.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1869.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>617.0</td>\n",
              "      <td>788.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>893.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1103.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>831.0</td>\n",
              "      <td>823.0</td>\n",
              "      <td>1376.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>421.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    cust_id  profitable_flag  ...  sow_tot_trans_bal_amt  sow_tot_trans_cnt\n",
              "0  550391.0              0.0  ...                    NaN                3.0\n",
              "1  550392.0              0.0  ...                    NaN                0.0\n",
              "2  550393.0              0.0  ...                  387.0                3.0\n",
              "3  550394.0              0.0  ...                  295.0                4.0\n",
              "4  550395.0              0.0  ...                  143.0               13.0\n",
              "5  550396.0              0.0  ...                    NaN                0.0\n",
              "6  550397.0              0.0  ...                 1866.0                2.0\n",
              "7  550398.0              1.0  ...                 5833.0                6.0\n",
              "8  550399.0              0.0  ...                    NaN                0.0\n",
              "9  550400.0              0.0  ...                    NaN                2.0\n",
              "\n",
              "[10 rows x 47 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uw3XxKnIIFyt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "f228a69b-ee50-4891-f7c7-37928e36a827"
      },
      "source": [
        "test_data.head(10)"
      ],
      "execution_count": null,
      "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>cust_id</th>\n",
              "      <th>count_accts</th>\n",
              "      <th>cm_age</th>\n",
              "      <th>flag_top_ed_spender</th>\n",
              "      <th>flag_cust_fee_paid_6m</th>\n",
              "      <th>pre6m_cust_spend</th>\n",
              "      <th>pre6m_cust_roc_cnt</th>\n",
              "      <th>pre6m_cust_non_disc_amt</th>\n",
              "      <th>pre6m_cust_non_disc_cnt</th>\n",
              "      <th>pre6m_cust_disc_amt</th>\n",
              "      <th>pre6m_cust_disc_cnt</th>\n",
              "      <th>pre6m_cust_outbound_amt</th>\n",
              "      <th>pre6m_cust_online_amt</th>\n",
              "      <th>pre6m_cust_online_cnt</th>\n",
              "      <th>pre6m_cust_travel_amt</th>\n",
              "      <th>pre6m_cust_travel_cnt</th>\n",
              "      <th>pre6m_cust_retail_amt</th>\n",
              "      <th>pre6m_cust_retail_cnt</th>\n",
              "      <th>pre6m_cust_myca_active</th>\n",
              "      <th>pre6m_cust_mob_logins</th>\n",
              "      <th>pre6m_total_mc_trs</th>\n",
              "      <th>Cust_tenure</th>\n",
              "      <th>pre6m_spend_active_ind</th>\n",
              "      <th>highly_utilized_ind</th>\n",
              "      <th>min_pay_ind</th>\n",
              "      <th>paid_in_full_ind</th>\n",
              "      <th>sum_total_line_amt</th>\n",
              "      <th>direct_debit_ind</th>\n",
              "      <th>referrals</th>\n",
              "      <th>spillover</th>\n",
              "      <th>self_accts</th>\n",
              "      <th>Customer Low Quality indicator</th>\n",
              "      <th>cdss_most_rcnt_prob</th>\n",
              "      <th>cust_max_credit_12m_amt</th>\n",
              "      <th>cust_max_remit_12m_amt</th>\n",
              "      <th>cbr_3_score</th>\n",
              "      <th>cnsumr_chrg_actv_cust_cnt</th>\n",
              "      <th>cnsumr_chrg_avg_credit_12m_amt</th>\n",
              "      <th>cnsumr_lend_actv_cust_cnt</th>\n",
              "      <th>cnsumr_lend_tot_util_ratio</th>\n",
              "      <th>sow_revol_avg_paydown_pct</th>\n",
              "      <th>sow_tot_annual_ext_pmt_amt</th>\n",
              "      <th>sow_tot_revol_bal_amt</th>\n",
              "      <th>sow_tot_revol_cnt</th>\n",
              "      <th>sow_tot_trans_bal_amt</th>\n",
              "      <th>sow_tot_trans_cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>569986</td>\n",
              "      <td>1</td>\n",
              "      <td>37</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>9868.0</td>\n",
              "      <td>760</td>\n",
              "      <td>777.0</td>\n",
              "      <td>174</td>\n",
              "      <td>9091.0</td>\n",
              "      <td>586</td>\n",
              "      <td>1705.0</td>\n",
              "      <td>2286.0</td>\n",
              "      <td>280</td>\n",
              "      <td>3219.0</td>\n",
              "      <td>268</td>\n",
              "      <td>2668.0</td>\n",
              "      <td>240</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>474</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>46000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.048</td>\n",
              "      <td>1880.0</td>\n",
              "      <td>1875.0</td>\n",
              "      <td>833.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.055513</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>569987</td>\n",
              "      <td>2</td>\n",
              "      <td>33</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5133.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>569988</td>\n",
              "      <td>2</td>\n",
              "      <td>40</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>302</td>\n",
              "      <td>1045.0</td>\n",
              "      <td>166</td>\n",
              "      <td>1357.0</td>\n",
              "      <td>136</td>\n",
              "      <td>289.0</td>\n",
              "      <td>483.0</td>\n",
              "      <td>96</td>\n",
              "      <td>220.0</td>\n",
              "      <td>18</td>\n",
              "      <td>986.0</td>\n",
              "      <td>176</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>69600.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>739.0</td>\n",
              "      <td>618.0</td>\n",
              "      <td>1251.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.029297</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>569989</td>\n",
              "      <td>1</td>\n",
              "      <td>34</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>9836.0</td>\n",
              "      <td>856</td>\n",
              "      <td>829.0</td>\n",
              "      <td>190</td>\n",
              "      <td>9007.0</td>\n",
              "      <td>666</td>\n",
              "      <td>2104.0</td>\n",
              "      <td>2545.0</td>\n",
              "      <td>292</td>\n",
              "      <td>2109.0</td>\n",
              "      <td>254</td>\n",
              "      <td>1921.0</td>\n",
              "      <td>274</td>\n",
              "      <td>6</td>\n",
              "      <td>20</td>\n",
              "      <td>170</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1864.0</td>\n",
              "      <td>1851.0</td>\n",
              "      <td>1004.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>829.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>569990</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2351.0</td>\n",
              "      <td>260</td>\n",
              "      <td>584.0</td>\n",
              "      <td>90</td>\n",
              "      <td>1767.0</td>\n",
              "      <td>170</td>\n",
              "      <td>63.0</td>\n",
              "      <td>282.0</td>\n",
              "      <td>56</td>\n",
              "      <td>127.0</td>\n",
              "      <td>26</td>\n",
              "      <td>1878.0</td>\n",
              "      <td>124</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9600.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>752.0</td>\n",
              "      <td>731.0</td>\n",
              "      <td>1293.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.200658</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>569991</td>\n",
              "      <td>3</td>\n",
              "      <td>42</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>18963.0</td>\n",
              "      <td>1508</td>\n",
              "      <td>2451.0</td>\n",
              "      <td>298</td>\n",
              "      <td>16513.0</td>\n",
              "      <td>1210</td>\n",
              "      <td>4286.0</td>\n",
              "      <td>8199.0</td>\n",
              "      <td>692</td>\n",
              "      <td>8392.0</td>\n",
              "      <td>508</td>\n",
              "      <td>4183.0</td>\n",
              "      <td>436</td>\n",
              "      <td>21</td>\n",
              "      <td>279</td>\n",
              "      <td>367</td>\n",
              "      <td>91.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>165000.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>3302.0</td>\n",
              "      <td>3276.0</td>\n",
              "      <td>1042.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>765.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.058062</td>\n",
              "      <td>0.0786</td>\n",
              "      <td>5083.0</td>\n",
              "      <td>9240.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>569992</td>\n",
              "      <td>2</td>\n",
              "      <td>36</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5784.0</td>\n",
              "      <td>872</td>\n",
              "      <td>945.0</td>\n",
              "      <td>312</td>\n",
              "      <td>4840.0</td>\n",
              "      <td>560</td>\n",
              "      <td>480.0</td>\n",
              "      <td>2877.0</td>\n",
              "      <td>388</td>\n",
              "      <td>1811.0</td>\n",
              "      <td>194</td>\n",
              "      <td>2078.0</td>\n",
              "      <td>456</td>\n",
              "      <td>15</td>\n",
              "      <td>475</td>\n",
              "      <td>28</td>\n",
              "      <td>57.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>72000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1781.0</td>\n",
              "      <td>1771.0</td>\n",
              "      <td>1304.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.087980</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>569993</td>\n",
              "      <td>3</td>\n",
              "      <td>34</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>4932.0</td>\n",
              "      <td>118</td>\n",
              "      <td>305.0</td>\n",
              "      <td>14</td>\n",
              "      <td>4627.0</td>\n",
              "      <td>104</td>\n",
              "      <td>1488.0</td>\n",
              "      <td>1660.0</td>\n",
              "      <td>108</td>\n",
              "      <td>2391.0</td>\n",
              "      <td>24</td>\n",
              "      <td>1527.0</td>\n",
              "      <td>62</td>\n",
              "      <td>18</td>\n",
              "      <td>48</td>\n",
              "      <td>184</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>21333.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.130</td>\n",
              "      <td>1483.0</td>\n",
              "      <td>1483.0</td>\n",
              "      <td>906.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.957510</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>4263.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1262.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>569994</td>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>286.0</td>\n",
              "      <td>18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>286.0</td>\n",
              "      <td>18</td>\n",
              "      <td>102.0</td>\n",
              "      <td>113.0</td>\n",
              "      <td>20</td>\n",
              "      <td>184.0</td>\n",
              "      <td>10</td>\n",
              "      <td>102.0</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.006</td>\n",
              "      <td>210.0</td>\n",
              "      <td>169.0</td>\n",
              "      <td>958.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.000480</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>621.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>569995</td>\n",
              "      <td>1</td>\n",
              "      <td>29</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1580.0</td>\n",
              "      <td>178</td>\n",
              "      <td>542.0</td>\n",
              "      <td>98</td>\n",
              "      <td>1038.0</td>\n",
              "      <td>80</td>\n",
              "      <td>137.0</td>\n",
              "      <td>592.0</td>\n",
              "      <td>52</td>\n",
              "      <td>382.0</td>\n",
              "      <td>8</td>\n",
              "      <td>678.0</td>\n",
              "      <td>112</td>\n",
              "      <td>7</td>\n",
              "      <td>41</td>\n",
              "      <td>29</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>16000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>282.0</td>\n",
              "      <td>281.0</td>\n",
              "      <td>1255.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.230783</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>7920.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>285.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   cust_id  count_accts  ...  sow_tot_trans_bal_amt  sow_tot_trans_cnt\n",
              "0   569986            1  ...                    NaN                0.0\n",
              "1   569987            2  ...                    NaN                NaN\n",
              "2   569988            2  ...                    NaN                0.0\n",
              "3   569989            1  ...                    NaN                0.0\n",
              "4   569990            1  ...                    NaN                0.0\n",
              "5   569991            3  ...                    NaN                0.0\n",
              "6   569992            2  ...                    NaN                0.0\n",
              "7   569993            3  ...                 1262.0                2.0\n",
              "8   569994            1  ...                    NaN                2.0\n",
              "9   569995            1  ...                  285.0                2.0\n",
              "\n",
              "[10 rows x 46 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-37ZcQ_nJPoS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c0c2a35-9f39-4284-d23b-80a4e8a81a09"
      },
      "source": [
        "train_data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['cust_id', 'profitable_flag', 'count_accts', 'cm_age',\n",
              "       'flag_top_ed_spender', 'flag_cust_fee_paid_6m', 'pre6m_cust_spend',\n",
              "       'pre6m_cust_roc_cnt', 'pre6m_cust_non_disc_amt',\n",
              "       'pre6m_cust_non_disc_cnt', 'pre6m_cust_disc_amt', 'pre6m_cust_disc_cnt',\n",
              "       'pre6m_cust_outbound_amt', 'pre6m_cust_online_amt',\n",
              "       'pre6m_cust_online_cnt', 'pre6m_cust_travel_amt',\n",
              "       'pre6m_cust_travel_cnt', 'pre6m_cust_retail_amt',\n",
              "       'pre6m_cust_retail_cnt', 'pre6m_cust_myca_active',\n",
              "       'pre6m_cust_mob_logins', 'pre6m_total_mc_trs', 'Cust_tenure',\n",
              "       'pre6m_spend_active_ind', 'highly_utilized_ind', 'min_pay_ind',\n",
              "       'paid_in_full_ind', 'sum_total_line_amt', 'direct_debit_ind',\n",
              "       'referrals', 'spillover', 'self_accts',\n",
              "       'Customer Low Quality indicator', 'cdss_most_rcnt_prob',\n",
              "       'cust_max_credit_12m_amt', 'cust_max_remit_12m_amt', 'cbr_3_score',\n",
              "       'cnsumr_chrg_actv_cust_cnt', 'cnsumr_chrg_avg_credit_12m_amt',\n",
              "       'cnsumr_lend_actv_cust_cnt', 'cnsumr_lend_tot_util_ratio',\n",
              "       'sow_revol_avg_paydown_pct', 'sow_tot_annual_ext_pmt_amt',\n",
              "       'sow_tot_revol_bal_amt', 'sow_tot_revol_cnt', 'sow_tot_trans_bal_amt',\n",
              "       'sow_tot_trans_cnt'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aABdf32INAzl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0189eb2c-ea19-4ec6-cd29-41dc1c65c2a5"
      },
      "source": [
        "# Imbalance present in the data\n",
        "train_data['profitable_flag'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    15951\n",
              "1.0     3644\n",
              "Name: profitable_flag, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnsolWfTcB72",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a31e9c78-1810-4f89-9096-172dc7b72c6c"
      },
      "source": [
        "# Dropping rows with cust_id null for train data\n",
        "drop_idx = train_data[train_data['cust_id'].isnull()].index.tolist()\n",
        "drop_idx"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[19595, 19596, 19597, 19598]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_IsEHbY7cbX7"
      },
      "source": [
        "train_data = train_data.drop(drop_idx)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bE88iDdNuPf"
      },
      "source": [
        "corr_mat = train_data.corr()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Q0_cqhANuCm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "outputId": "5a1b315b-e8a5-42bf-c3ce-c0ee7b6c96aa"
      },
      "source": [
        "# distribution plot for corr matrix\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.heatmap(corr_mat.values, xticklabels=corr_mat.columns, yticklabels=corr_mat.columns)\n",
        "plt.title('Correlation heatmap', fontsize=24)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Z23pBjmd7_O"
      },
      "source": [
        "# Doing random analysis"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9tomNH9cmHL"
      },
      "source": [
        "train = train_data.copy(deep=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXSkPsG50yiN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af0a8ace-1339-4120-cbc2-169328bdaeec"
      },
      "source": [
        "train.shape[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19595"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_1AOdGly7pa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "069a04f1-d373-4b8d-de61-5bc3ea3ab315"
      },
      "source": [
        "train.isna().sum().sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sow_tot_revol_bal_amt             16498\n",
              "cnsumr_chrg_avg_credit_12m_amt    15448\n",
              "sow_tot_trans_bal_amt             14089\n",
              "sow_tot_annual_ext_pmt_amt        10557\n",
              "pre6m_cust_outbound_amt            2246\n",
              "cust_max_remit_12m_amt             2002\n",
              "pre6m_cust_travel_amt              1974\n",
              "cust_max_credit_12m_amt            1853\n",
              "sum_total_line_amt                 1784\n",
              "pre6m_cust_online_amt              1249\n",
              "pre6m_cust_non_disc_amt            1192\n",
              "pre6m_cust_retail_amt               854\n",
              "pre6m_cust_disc_amt                 725\n",
              "pre6m_cust_spend                    615\n",
              "sow_tot_trans_cnt                   245\n",
              "cdss_most_rcnt_prob                 245\n",
              "cbr_3_score                         245\n",
              "cnsumr_chrg_actv_cust_cnt           245\n",
              "cnsumr_lend_actv_cust_cnt           245\n",
              "cnsumr_lend_tot_util_ratio          245\n",
              "sow_revol_avg_paydown_pct           245\n",
              "sow_tot_revol_cnt                   245\n",
              "Cust_tenure                         236\n",
              "pre6m_spend_active_ind              236\n",
              "highly_utilized_ind                 236\n",
              "self_accts                          236\n",
              "min_pay_ind                         236\n",
              "paid_in_full_ind                    236\n",
              "direct_debit_ind                    236\n",
              "referrals                           236\n",
              "Customer Low Quality indicator      236\n",
              "spillover                           236\n",
              "flag_top_ed_spender                  48\n",
              "profitable_flag                       0\n",
              "count_accts                           0\n",
              "cm_age                                0\n",
              "flag_cust_fee_paid_6m                 0\n",
              "pre6m_cust_roc_cnt                    0\n",
              "pre6m_cust_disc_cnt                   0\n",
              "pre6m_cust_non_disc_cnt               0\n",
              "pre6m_total_mc_trs                    0\n",
              "pre6m_cust_online_cnt                 0\n",
              "pre6m_cust_travel_cnt                 0\n",
              "pre6m_cust_retail_cnt                 0\n",
              "pre6m_cust_myca_active                0\n",
              "pre6m_cust_mob_logins                 0\n",
              "cust_id                               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xg4WYKYZYBPq"
      },
      "source": [
        "# dropping cols having heavy null values and low correlation cofficient with target variable\n",
        "null_col_drop = ['sow_tot_revol_bal_amt','sow_tot_trans_bal_amt','sow_tot_annual_ext_pmt_amt']\n",
        "train = train.drop(columns=null_col_drop)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBj1DAoWgWr6"
      },
      "source": [
        "# dropping cols from test data\n",
        "test_data = test_data.drop(columns=null_col_drop)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esm8nJy3YoD4"
      },
      "source": [
        "# Imputing missing values for the rest of the columns using median\n",
        "col = list(train.columns)\n",
        "imputer = SimpleImputer(strategy='median')\n",
        "train[col] = imputer.fit_transform(train[col])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "remeUV6IgnR5"
      },
      "source": [
        "# imputation in test data\n",
        "test_data['profitable_flag'] = 1\n",
        "test_data[col] = imputer.transform(test_data[col])\n",
        "test_data.drop(columns='profitable_flag', inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JMs17ZARy7an"
      },
      "source": [
        "corr_mat = train.corr()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCJdAlzzPEbF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "5a157ed6-d6de-41af-91c8-c1e813cd5ea8"
      },
      "source": [
        "# distribution plot for corr matrix after dropping out few columns\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.displot(corr_mat.values, legend= False, kind='hist')\n",
        "plt.title('Distribution of correlation coff values', fontsize=24)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtHahBm9sYX9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 773
        },
        "outputId": "0f9b73b1-31f0-4662-c44f-07034ad4cc8f"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(corr_mat.values, xticklabels=corr_mat.columns, yticklabels=corr_mat.columns)\n",
        "plt.title('Correlation heatmap', fontsize=16)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RV5bBqUy_ETa"
      },
      "source": [
        "target_corr = corr_mat.iloc[1,:].sort_values()\n",
        "target_corr = target_corr[:-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KgqZvqCP_Egi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 964
        },
        "outputId": "1936378c-2c53-4939-b795-3b3532909e51"
      },
      "source": [
        "plt.figure(figsize=(8,16))\n",
        "plt.barh(list(target_corr.index), list(target_corr.values))\n",
        "plt.title('Correlation values with target variable', fontsize=30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Correlation values with target variable')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2y6e7RS7CmHi"
      },
      "source": [
        "# Finding highly corr feature pairs\n",
        "col = list(train.columns)\n",
        "pairs = []\n",
        "for i in range(len(col)):\n",
        "  for j in range(i+1,len(col),1):\n",
        "    if i == j:\n",
        "      continue\n",
        "    if corr_mat.iloc[i,j] > 0.5:\n",
        "      pairs.append((col[i], col[j], corr_mat.iloc[i,j]))\n",
        "\n",
        "pairs.sort(key = lambda x : x[2], reverse=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4fWFRu9ClZu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee2c98bb-71cc-4cf0-8548-2a29dda3a697"
      },
      "source": [
        "# sorted list of pairs having high corr\n",
        "pairs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('cust_max_credit_12m_amt', 'cust_max_remit_12m_amt', 0.9861438012874116),\n",
              " ('pre6m_cust_spend', 'pre6m_cust_disc_amt', 0.9730325229649457),\n",
              " ('pre6m_cust_roc_cnt', 'pre6m_cust_disc_cnt', 0.9712875673139646),\n",
              " ('pre6m_cust_online_cnt', 'pre6m_cust_retail_cnt', 0.9390173240646688),\n",
              " ('pre6m_cust_disc_cnt', 'pre6m_cust_online_cnt', 0.8930653854209487),\n",
              " ('pre6m_cust_disc_cnt', 'pre6m_cust_retail_cnt', 0.8906160113910369),\n",
              " ('pre6m_cust_roc_cnt', 'pre6m_cust_retail_cnt', 0.8851087401594667),\n",
              " ('referrals', 'spillover', 0.8464940726172074),\n",
              " ('pre6m_cust_disc_amt', 'pre6m_cust_online_amt', 0.8397152662427706),\n",
              " ('pre6m_cust_roc_cnt', 'pre6m_cust_online_cnt', 0.8226164502350798),\n",
              " ('pre6m_cust_spend', 'pre6m_cust_online_amt', 0.8204378312604217),\n",
              " ('pre6m_cust_spend', 'pre6m_cust_retail_amt', 0.8055053205108511),\n",
              " ('pre6m_cust_spend', 'cust_max_remit_12m_amt', 0.7946861790611087),\n",
              " ('pre6m_cust_spend', 'cust_max_credit_12m_amt', 0.7895524812981357),\n",
              " ('pre6m_cust_disc_amt', 'cust_max_remit_12m_amt', 0.7645772704188786),\n",
              " ('pre6m_cust_disc_amt', 'cust_max_credit_12m_amt', 0.7576065241445409),\n",
              " ('pre6m_cust_disc_amt', 'pre6m_cust_retail_amt', 0.750946492451908),\n",
              " ('count_accts', 'cnsumr_lend_actv_cust_cnt', 0.6957619727418192),\n",
              " ('pre6m_cust_disc_amt', 'pre6m_cust_outbound_amt', 0.6686174477643668),\n",
              " ('pre6m_cust_spend', 'pre6m_cust_outbound_amt', 0.6493515416018351),\n",
              " ('pre6m_cust_retail_amt', 'cust_max_remit_12m_amt', 0.6396830613110163),\n",
              " ('pre6m_cust_retail_amt', 'cust_max_credit_12m_amt', 0.6358619762783109),\n",
              " ('pre6m_cust_online_amt', 'cust_max_remit_12m_amt', 0.6338440868639885),\n",
              " ('pre6m_cust_online_amt', 'cust_max_credit_12m_amt', 0.6322462175437847),\n",
              " ('pre6m_cust_online_amt', 'pre6m_cust_retail_amt', 0.6110589092307996),\n",
              " ('pre6m_cust_spend', 'pre6m_total_mc_trs', 0.6060798224276688),\n",
              " ('pre6m_cust_disc_amt', 'pre6m_total_mc_trs', 0.6050779661928332),\n",
              " ('count_accts', 'pre6m_cust_myca_active', 0.6019897996636869),\n",
              " ('pre6m_cust_disc_amt', 'pre6m_cust_travel_amt', 0.5973903635796366),\n",
              " ('pre6m_cust_spend', 'pre6m_cust_travel_amt', 0.5913195002562329),\n",
              " ('pre6m_cust_roc_cnt', 'pre6m_cust_non_disc_cnt', 0.5840622233683234),\n",
              " ('pre6m_cust_outbound_amt', 'pre6m_cust_online_amt', 0.581809154321342),\n",
              " ('pre6m_cust_roc_cnt', 'pre6m_cust_travel_cnt', 0.5723923167870659),\n",
              " ('pre6m_cust_outbound_amt', 'pre6m_total_mc_trs', 0.5708309347470593),\n",
              " ('pre6m_cust_outbound_amt', 'pre6m_cust_travel_amt', 0.5520194509024052),\n",
              " ('sow_revol_avg_paydown_pct', 'sow_tot_revol_cnt', 0.5475069815239867),\n",
              " ('pre6m_cust_non_disc_amt', 'pre6m_cust_retail_amt', 0.5419628571871039),\n",
              " ('pre6m_cust_disc_cnt', 'pre6m_cust_travel_cnt', 0.5418185477716987),\n",
              " ('pre6m_cust_travel_amt', 'cust_max_remit_12m_amt', 0.53872829263888),\n",
              " ('pre6m_cust_online_amt', 'pre6m_cust_online_cnt', 0.5382975149575837),\n",
              " ('pre6m_cust_travel_amt', 'cust_max_credit_12m_amt', 0.5349399694940136),\n",
              " ('highly_utilized_ind', 'cnsumr_lend_tot_util_ratio', 0.5331846467780719),\n",
              " ('pre6m_total_mc_trs', 'cust_max_remit_12m_amt', 0.5309490173218316),\n",
              " ('pre6m_cust_online_amt', 'pre6m_total_mc_trs', 0.5308811008068403),\n",
              " ('pre6m_total_mc_trs', 'cust_max_credit_12m_amt', 0.5295631946958861),\n",
              " ('pre6m_cust_outbound_amt', 'cust_max_remit_12m_amt', 0.5256916504648639),\n",
              " ('pre6m_cust_outbound_amt', 'cust_max_credit_12m_amt', 0.5253740567102336),\n",
              " ('pre6m_cust_spend', 'pre6m_cust_non_disc_amt', 0.5164232190503472),\n",
              " ('pre6m_cust_disc_cnt', 'pre6m_cust_online_amt', 0.5100804785529572),\n",
              " ('Cust_tenure', 'sum_total_line_amt', 0.5088328735171636),\n",
              " ('pre6m_cust_retail_amt', 'pre6m_cust_retail_cnt', 0.5006161404963732)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IuUG52-fci6I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "b2f4813e-af5c-45f0-8719-8d579cce2c06"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "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>cust_id</th>\n",
              "      <th>profitable_flag</th>\n",
              "      <th>count_accts</th>\n",
              "      <th>cm_age</th>\n",
              "      <th>flag_top_ed_spender</th>\n",
              "      <th>flag_cust_fee_paid_6m</th>\n",
              "      <th>pre6m_cust_spend</th>\n",
              "      <th>pre6m_cust_roc_cnt</th>\n",
              "      <th>pre6m_cust_non_disc_amt</th>\n",
              "      <th>pre6m_cust_non_disc_cnt</th>\n",
              "      <th>pre6m_cust_disc_amt</th>\n",
              "      <th>pre6m_cust_disc_cnt</th>\n",
              "      <th>pre6m_cust_outbound_amt</th>\n",
              "      <th>pre6m_cust_online_amt</th>\n",
              "      <th>pre6m_cust_online_cnt</th>\n",
              "      <th>pre6m_cust_travel_amt</th>\n",
              "      <th>pre6m_cust_travel_cnt</th>\n",
              "      <th>pre6m_cust_retail_amt</th>\n",
              "      <th>pre6m_cust_retail_cnt</th>\n",
              "      <th>pre6m_cust_myca_active</th>\n",
              "      <th>pre6m_cust_mob_logins</th>\n",
              "      <th>pre6m_total_mc_trs</th>\n",
              "      <th>Cust_tenure</th>\n",
              "      <th>pre6m_spend_active_ind</th>\n",
              "      <th>highly_utilized_ind</th>\n",
              "      <th>min_pay_ind</th>\n",
              "      <th>paid_in_full_ind</th>\n",
              "      <th>sum_total_line_amt</th>\n",
              "      <th>direct_debit_ind</th>\n",
              "      <th>referrals</th>\n",
              "      <th>spillover</th>\n",
              "      <th>self_accts</th>\n",
              "      <th>Customer Low Quality indicator</th>\n",
              "      <th>cdss_most_rcnt_prob</th>\n",
              "      <th>cust_max_credit_12m_amt</th>\n",
              "      <th>cust_max_remit_12m_amt</th>\n",
              "      <th>cbr_3_score</th>\n",
              "      <th>cnsumr_chrg_actv_cust_cnt</th>\n",
              "      <th>cnsumr_chrg_avg_credit_12m_amt</th>\n",
              "      <th>cnsumr_lend_actv_cust_cnt</th>\n",
              "      <th>cnsumr_lend_tot_util_ratio</th>\n",
              "      <th>sow_revol_avg_paydown_pct</th>\n",
              "      <th>sow_tot_revol_cnt</th>\n",
              "      <th>sow_tot_trans_cnt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>550391.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1518.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>811.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>707.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>410.0</td>\n",
              "      <td>606.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>814.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>80000.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>482.0</td>\n",
              "      <td>418.0</td>\n",
              "      <td>1376.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>395.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.034044</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>550392.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4807.0</td>\n",
              "      <td>354.0</td>\n",
              "      <td>1052.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>3756.0</td>\n",
              "      <td>272.0</td>\n",
              "      <td>673.0</td>\n",
              "      <td>3164.0</td>\n",
              "      <td>456.0</td>\n",
              "      <td>2291.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>140000.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>2165.0</td>\n",
              "      <td>2165.0</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.044086</td>\n",
              "      <td>0.0877</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>550393.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2361.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>1351.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>1010.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>237.0</td>\n",
              "      <td>693.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>903.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1623.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>32800.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>703.0</td>\n",
              "      <td>687.0</td>\n",
              "      <td>1376.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>395.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.039613</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>550394.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5452.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>1195.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>4257.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>803.0</td>\n",
              "      <td>1618.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>2760.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>1605.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>286.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>100000.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1225.0</td>\n",
              "      <td>1225.0</td>\n",
              "      <td>1372.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>493.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.001200</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>550395.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>903.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>123.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20800.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001</td>\n",
              "      <td>51.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1338.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>395.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.003378</td>\n",
              "      <td>0.0168</td>\n",
              "      <td>5.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    cust_id  profitable_flag  ...  sow_tot_revol_cnt  sow_tot_trans_cnt\n",
              "0  550391.0              0.0  ...                0.0                3.0\n",
              "1  550392.0              0.0  ...                2.0                0.0\n",
              "2  550393.0              0.0  ...                0.0                3.0\n",
              "3  550394.0              0.0  ...                0.0                4.0\n",
              "4  550395.0              0.0  ...                5.0               13.0\n",
              "\n",
              "[5 rows x 44 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBRdtNhlcOC3"
      },
      "source": [
        "# Aggregating columns having high correlation cofficients\n",
        "col_set1 = ['pre6m_cust_spend','pre6m_cust_non_disc_amt','pre6m_cust_disc_amt','pre6m_cust_outbound_amt','pre6m_cust_online_amt',\n",
        "            'pre6m_cust_travel_amt','pre6m_cust_retail_amt']\n",
        "col_set2 = ['pre6m_cust_roc_cnt','pre6m_cust_non_disc_cnt','pre6m_cust_disc_cnt','pre6m_cust_online_cnt','pre6m_cust_travel_cnt',\n",
        "            'pre6m_cust_retail_cnt']\n",
        "col_set3 = ['cust_max_credit_12m_amt','cust_max_remit_12m_amt']\n",
        "col_set4 = ['referrals','spillover']\n",
        "col_set5 = ['sow_tot_revol_cnt','sow_tot_trans_cnt']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZg2MgaacOU-"
      },
      "source": [
        "# (To-Do) Replace mean by the first of the column\n",
        "\n",
        "# train['pre6m_spend'] = train[col_set1].mean(axis=1)\n",
        "train['pre6m_spend'] = train['pre6m_cust_spend']\n",
        "train = train.drop(columns=col_set1)\n",
        "\n",
        "# train['pre6m_count'] = train[col_set2].mean(axis=1)\n",
        "train['pre6m_count'] = train['pre6m_cust_roc_cnt']\n",
        "train = train.drop(columns=col_set2)\n",
        "\n",
        "# train['cust6m_max_amt'] = train[col_set3].mean(axis=1) / 2\n",
        "train['cust6m_max_amt'] = train['cust_max_credit_12m_amt']/2\n",
        "train = train.drop(columns=col_set3)\n",
        "\n",
        "# train['reff_spill'] = train[col_set4].mean(axis=1)\n",
        "train['reff_spill'] = train['spillover']\n",
        "train = train.drop(columns=col_set4)\n",
        "\n",
        "train['sow_counts'] = train[col_set5].mean(axis=1)\n",
        "train = train.drop(columns=col_set5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-uAAxAThgUY"
      },
      "source": [
        "# for test data\n",
        "\n",
        "test_data['prem6m_spend'] = test_data['pre6m_cust_spend']\n",
        "test_data = test_data.drop(columns=col_set1)\n",
        "\n",
        "test_data['pre6m_count'] = test_data['pre6m_cust_roc_cnt']\n",
        "test_data = test_data.drop(columns=col_set2)\n",
        "\n",
        "test_data['cust6m_max_amt'] = test_data['cust_max_credit_12m_amt'] / 2\n",
        "test_data = test_data.drop(columns=col_set3)\n",
        "\n",
        "test_data['reff_spill'] = test_data['spillover']\n",
        "test_data = test_data.drop(columns=col_set4)\n",
        "\n",
        "test_data['sow_counts'] = test_data[col_set5].mean(axis=1)\n",
        "test_data = test_data.drop(columns=col_set5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZuWFBmXcNHN"
      },
      "source": [
        "# Additional columns to drop which have less corr coff with target\n",
        "drop_col = ['cm_age','pre6m_cust_mob_logins','sow_counts','cust_id','highly_utilized_ind','paid_in_full_ind','cnsumr_lend_tot_util_ratio',\n",
        "            'cnsumr_chrg_actv_cust_cnt','pre6m_cust_myca_active']\n",
        "train = train.drop(columns=drop_col)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lv7LngyClLQ"
      },
      "source": [
        "# for test data dropping cols\n",
        "cust_id = test_data['cust_id']\n",
        "test_data = test_data.drop(columns=drop_col)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otIJBeUQdquW"
      },
      "source": [
        "y = train['profitable_flag']\n",
        "train.drop(columns='profitable_flag', inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5HG0Lfd7o8OC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "adbeb0f5-6dc4-4f50-d1ae-f9eba9025329"
      },
      "source": [
        "train.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['count_accts', 'flag_top_ed_spender', 'flag_cust_fee_paid_6m',\n",
              "       'pre6m_total_mc_trs', 'Cust_tenure', 'pre6m_spend_active_ind',\n",
              "       'min_pay_ind', 'sum_total_line_amt', 'direct_debit_ind', 'self_accts',\n",
              "       'Customer Low Quality indicator', 'cdss_most_rcnt_prob', 'cbr_3_score',\n",
              "       'cnsumr_chrg_avg_credit_12m_amt', 'cnsumr_lend_actv_cust_cnt',\n",
              "       'sow_revol_avg_paydown_pct', 'pre6m_spend', 'pre6m_count',\n",
              "       'cust6m_max_amt', 'reff_spill'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5QGcFq5DR8Jv"
      },
      "source": [
        "# **Gradient Boosting Classifier with oversampling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLUgK9T2gq0m"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from imblearn.pipeline import Pipeline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UExMfCTqin--"
      },
      "source": [
        "over = SMOTE()\n",
        "#under = RandomUnderSampler(sampling_strategy=0.6)\n",
        "model = GradientBoostingClassifier()\n",
        "steps = [('o', over), ('model',model)]\n",
        "pipe = Pipeline(steps=steps)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VC18TeArclx5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "683b592f-3cd1-4838-d500-8374524f0ce2"
      },
      "source": [
        "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=42)\n",
        "scores = cross_val_score(pipe, train, y, scoring='roc_auc', cv=cv, n_jobs=-1)\n",
        "scores.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6347347994193349"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDDUcL8cclua",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c45d52ac-9898-4a5c-f7d1-ffd7b24eba49"
      },
      "source": [
        "k_values = [1,2,3,4,5,6,7]\n",
        "for k in k_values:\n",
        "  over = SMOTE(sampling_strategy=0.3, k_neighbors=k)\n",
        "  under = RandomUnderSampler(sampling_strategy=0.6)\n",
        "  model = GradientBoostingClassifier(random_state=42)\n",
        "  steps = [('o', over), ('u', under), ('model',model)]\n",
        "  pipe = Pipeline(steps=steps)\n",
        "  cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=42)\n",
        "  scores = cross_val_score(pipe, train, y, scoring='roc_auc', cv=cv, n_jobs=-1)\n",
        "  print('k = {}, score is {}'.format(k,scores.mean()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "k = 1, score is 0.6501385699718717\n",
            "k = 2, score is 0.6495523180191861\n",
            "k = 3, score is 0.6493172689924119\n",
            "k = 4, score is 0.6493891324568061\n",
            "k = 5, score is 0.6488179369534905\n",
            "k = 6, score is 0.6490852727577597\n",
            "k = 7, score is 0.6504426928871668\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5ZUluy0ok3j"
      },
      "source": [
        "# Since k=4 gives best score so, getting predictions for test data\n",
        "over = SMOTE(sampling_strategy=0.3, k_neighbors=4)\n",
        "under = RandomUnderSampler(sampling_strategy=0.6)\n",
        "steps = [('o', over), ('u',under)]\n",
        "pipe = Pipeline(steps=steps)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hX7kPWIuowwN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2de67053-28bc-469c-8214-9f0fbd16c6a1"
      },
      "source": [
        "sample_x, sample_y = pipe.fit_resample(train, y)\n",
        "\n",
        "counter = Counter(sample_y)\n",
        "print(counter)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Counter({0.0: 7975, 1.0: 4785})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UngJ4Sa2pu7p"
      },
      "source": [
        "xgb = GradientBoostingClassifier()\n",
        "xgb.fit(sample_x, sample_y)\n",
        "predx = xgb.predict(sample_x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RSBBuEOoweF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b715a82f-c6fc-4e05-93d3-7305441580da"
      },
      "source": [
        "roc_auc_score(sample_y, predx)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6620480668756531"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h38MJx-fChqv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8eed2776-6528-4b69-d0d0-871ece813fb6"
      },
      "source": [
        "print(\"Confusion Matrix:\\n\", confusion_matrix(sample_y, predx))\n",
        "print(\"Precision Score:\", precision_score(sample_y, predx))\n",
        "print(\"Recall score:\", recall_score(sample_y, predx))\n",
        "print(classification_report(sample_y, predx))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix:\n",
            " [[7443  532]\n",
            " [2915 1870]]\n",
            "Precision Score: 0.7785179017485429\n",
            "Recall score: 0.39080459770114945\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.72      0.93      0.81      7975\n",
            "         1.0       0.78      0.39      0.52      4785\n",
            "\n",
            "    accuracy                           0.73     12760\n",
            "   macro avg       0.75      0.66      0.67     12760\n",
            "weighted avg       0.74      0.73      0.70     12760\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tefdRIqrO0-y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "d9ca199b-2d5e-4814-859d-8bae29a9ab80"
      },
      "source": [
        "# generate a no skill prediction (majority class)\n",
        "ns_probs = [0 for _ in range(len(sample_y))]\n",
        "# predict probabilities\n",
        "lr_probs = xgb.predict_proba(sample_x)\n",
        "# keep probabilities for the positive outcome only\n",
        "lr_probs2 = lr_probs[:, 1]\n",
        "# calculate scores\n",
        "ns_auc = roc_auc_score(sample_y, ns_probs)\n",
        "lr_auc = roc_auc_score(sample_y, lr_probs2)\n",
        "# summarize scores\n",
        "print('No Skill: ROC AUC=%.3f' % (ns_auc))\n",
        "print('Gradient Boosting: ROC AUC=%.3f' % (lr_auc))\n",
        "# calculate roc curves\n",
        "ns_fpr, ns_tpr, _ = roc_curve(sample_y, ns_probs)\n",
        "lr_fpr, lr_tpr, _ = roc_curve(sample_y, lr_probs2)\n",
        "# plot the roc curve for the model\n",
        "plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')\n",
        "plt.plot(lr_fpr, lr_tpr, marker='.', label='Gradient Boosting')\n",
        "# axis labels\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Validation ROC Curve: AUC = 0.764', fontsize = 14)\n",
        "# show the legend\n",
        "plt.legend()\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No Skill: ROC AUC=0.500\n",
            "Gradient Boosting: ROC AUC=0.764\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45OLL7GVqRLR"
      },
      "source": [
        "predictions = xgb.predict(test_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQemcrBwu18n"
      },
      "source": [
        "submission = pd.DataFrame(list(zip(list(cust_id),list(predictions))), columns=['cust_id', 'profitable_flag'], dtype='int64')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKdOzIrFu2fo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "94fd93ae-3959-4e1b-fc42-e54ce842b942"
      },
      "source": [
        "submission.head()"
      ],
      "execution_count": null,
      "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>cust_id</th>\n",
              "      <th>profitable_flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>569986</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>569987</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>569988</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>569989</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>569990</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   cust_id  profitable_flag\n",
              "0   569986                1\n",
              "1   569987                0\n",
              "2   569988                0\n",
              "3   569989                1\n",
              "4   569990                0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ib5-IOwXu1vR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "e5db9804-d957-4e9b-da4c-0857cc116a82"
      },
      "source": [
        "#saving as xgb output\n",
        "submission.to_csv('submission_xgb.csv')\n",
        "\n",
        "from google.colab import files\n",
        "files.download('submission_xgb.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_5f0ab04c-1d6b-4762-865c-02fcf76e5632\", \"submission_xgb.csv\", 68915)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNt76eOtaLJT"
      },
      "source": [
        "# **Random Forest Classifier with oversampling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QkLUxX-5aOJp"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJst7f8phNwX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 863
        },
        "outputId": "f9ac4375-c042-45ec-e117-1490dcf5fa9e"
      },
      "source": [
        "x_train, x_val, y_train, y_val = train_test_split(train, y, test_size=0.2, stratify=y, random_state=42)\n",
        "\n",
        "#Create a Gaussian Classifier\n",
        "clf=RandomForestClassifier(n_estimators=100)\n",
        "\n",
        "#Oversampling\n",
        "over = SMOTE(sampling_strategy=0.3, k_neighbors=4)\n",
        "under = RandomUnderSampler(sampling_strategy=0.5)\n",
        "steps = [('o', over), ('u',under)]\n",
        "pipe = Pipeline(steps=steps)\n",
        "\n",
        "x_train_sampled, y_train_sampled = pipe.fit_resample(x_train, y_train)\n",
        "\n",
        "counter = Counter(y_train_sampled)\n",
        "print('For training:', counter)\n",
        "\n",
        "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
        "clf.fit(x_train_sampled,y_train_sampled)\n",
        "\n",
        "y_pred = clf.predict(x_val)\n",
        "\n",
        "print(\"Confusion Matrix:\\n\", confusion_matrix(y_val, y_pred))\n",
        "print(\"Precision Score:\", precision_score(y_val, y_pred))\n",
        "print(\"Recall score:\", recall_score(y_val, y_pred))\n",
        "print(classification_report(y_val, y_pred))\n",
        "\n",
        "# Visualizing important features\n",
        "feature_imp = pd.Series(clf.feature_importances_, index = train.columns).sort_values(ascending=False)\n",
        "\n",
        "# Creating a bar plot\n",
        "sns.barplot(x=feature_imp, y=feature_imp.index)\n",
        "# Add labels to your graph\n",
        "plt.xlabel('Feature Importance Score')\n",
        "plt.ylabel('Features')\n",
        "plt.title(\"Visualizing Important Features\")\n",
        "plt.show()\n",
        "\n",
        "# ROC plot\n",
        "# generate a no skill prediction (majority class)\n",
        "ns_probs = [0 for _ in range(len(y_val))]\n",
        "# predict probabilities\n",
        "lr_probs = clf.predict_proba(x_val)\n",
        "# keep probabilities for the positive outcome only\n",
        "lr_probs2 = lr_probs[:, 1]\n",
        "# calculate scores\n",
        "ns_auc = roc_auc_score(y_val, ns_probs)\n",
        "lr_auc = roc_auc_score(y_val, lr_probs2)\n",
        "# summarize scores\n",
        "print('No Skill: ROC AUC=%.3f' % (ns_auc))\n",
        "print('Random Forest Classifier: ROC AUC=%.3f' % (lr_auc))\n",
        "# calculate roc curves\n",
        "ns_fpr, ns_tpr, _ = roc_curve(y_val, ns_probs)\n",
        "lr_fpr, lr_tpr, _ = roc_curve(y_val, lr_probs2)\n",
        "# plot the roc curve for the model\n",
        "plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')\n",
        "plt.plot(lr_fpr, lr_tpr, marker='.', label='Random Forest')\n",
        "# axis labels\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Validation ROC Curve: AUC = 0.637', fontsize = 14)\n",
        "# show the legend\n",
        "plt.legend()\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For training: Counter({0.0: 7656, 1.0: 3828})\n",
            "Confusion Matrix:\n",
            " [[2990  200]\n",
            " [ 618  111]]\n",
            "Precision Score: 0.35691318327974275\n",
            "Recall score: 0.1522633744855967\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.83      0.94      0.88      3190\n",
            "         1.0       0.36      0.15      0.21       729\n",
            "\n",
            "    accuracy                           0.79      3919\n",
            "   macro avg       0.59      0.54      0.55      3919\n",
            "weighted avg       0.74      0.79      0.76      3919\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "No Skill: ROC AUC=0.500\n",
            "Random Forest Classifier: ROC AUC=0.626\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "469cID8kRwJu"
      },
      "source": [
        "#**Neural Network Classifier with oversampling**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stgSk-PaclrJ"
      },
      "source": [
        "import tensorflow as tf\n",
        "from keras.models import Sequential\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras import backend as K\n",
        "from keras.optimizers import SGD\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w0NyVKXReE8Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93357c23-7b77-44f8-8b28-33b72579a7ef"
      },
      "source": [
        "x_train, x_val, y_train, y_val = train_test_split(train, y, test_size=0.2, stratify=y, random_state=42)\n",
        "# x_train.head(5)\n",
        "# y_train.head(5)\n",
        "print('x_train shape:', x_train.shape)\n",
        "print('y_train shape:', y_train.shape)\n",
        "print('x_val shape:', x_val.shape)\n",
        "print('y_val shape:', y_val.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train shape: (15676, 20)\n",
            "y_train shape: (15676,)\n",
            "x_val shape: (3919, 20)\n",
            "y_val shape: (3919,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BKcrKtt9a5aG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "753b203a-a009-481f-87ab-1d4c7d78239e"
      },
      "source": [
        "over = SMOTE(sampling_strategy=0.3, k_neighbors=4)\n",
        "under = RandomUnderSampler(sampling_strategy=0.5)\n",
        "steps = [('o', over), ('u',under)]\n",
        "pipe = Pipeline(steps=steps)\n",
        "\n",
        "x_train_sampled, y_train_sampled = pipe.fit_resample(x_train, y_train)\n",
        "\n",
        "counter = Counter(y_train_sampled)\n",
        "print('For training:', counter)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For training: Counter({0.0: 7656, 1.0: 3828})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTYTS_cdbGcr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a81e5fc0-7df3-4948-9f5d-2ff6e0a9f828"
      },
      "source": [
        "over = SMOTE(sampling_strategy=0.3, k_neighbors=4)\n",
        "under = RandomUnderSampler(sampling_strategy=0.5)\n",
        "steps = [('o', over), ('u',under)]\n",
        "pipe = Pipeline(steps=steps)\n",
        "\n",
        "# x_val_sampled, y_val_sampled = pipe.fit_resample(x_val, y_val)\n",
        "x_val_sampled, y_val_sampled = np.array(x_val.values), np.array(y_val.values)\n",
        "\n",
        "counter = Counter(y_val_sampled)\n",
        "print('For validation:', counter)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For validation: Counter({0.0: 3190, 1.0: 729})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TITvBSbbSxfZ"
      },
      "source": [
        "N = y_train.shape[0]\n",
        "pos_freq = np.sum(y_train.values, axis=0) / N\n",
        "neg_freq = 1 - pos_freq\n",
        "pos_weights = neg_freq\n",
        "neg_weights = pos_freq"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZpY9_aBKU4I6"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(train)\n",
        "x_train_scaled = scaler.transform(x_train_sampled)\n",
        "x_val_scaled = scaler.transform(x_val_sampled)\n",
        "\n",
        "# y_train_array = np.array(y_train.values)\n",
        "y_train_tensor = K.constant(y_train_sampled)\n",
        "\n",
        "# y_val_array = np.array(y_val.values)\n",
        "y_val_tensor = K.constant(y_val_sampled)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m88AwKGqQ8y0"
      },
      "source": [
        "def get_weighted_loss(pos_weights, neg_weights, epsilon = 1e-7):\n",
        "  \n",
        "  \n",
        "  def weighted_loss(y_true, y_pred):\n",
        "        loss = 0.0\n",
        "        loss_pos = - K.mean(pos_weights * y_true * K.log(y_pred + epsilon))\n",
        "        loss_neg = - K.mean(neg_weights * (1 - y_true) * K.log(1 - y_pred + epsilon))\n",
        "        loss += loss_pos + loss_neg\n",
        "        \n",
        "        return loss\n",
        "\n",
        "\n",
        "  return weighted_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlNqR4Tgcln2"
      },
      "source": [
        "def get_model():\n",
        "  model = Sequential()\n",
        "  model.add(Dense(20, input_dim=20, activation='tanh', kernel_initializer='he_uniform'))\n",
        "  model.add(Dense(32, activation='tanh'))\n",
        "  model.add(Dropout(0.2))\n",
        "  model.add(Dense(1, activation='sigmoid'))\n",
        "  \n",
        "  opt = SGD(lr=0.01, momentum=0.9)\n",
        "  model.compile(loss='binary_crossentropy', \n",
        "                optimizer = opt, \n",
        "                metrics = [tf.keras.metrics.AUC()]\n",
        "                )\n",
        "  # model.compile(optimizer='adam', loss=get_weighted_loss(pos_weights, neg_weights), metrics=[tf.keras.metrics.AUC()])\n",
        "\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ihptDTM2ybO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "92ce3678-5624-4274-b6ba-841ebf0ae395"
      },
      "source": [
        "model = get_model()\n",
        "early_stopping = EarlyStopping(monitor='val_loss', \n",
        "                               mode='min', \n",
        "                               verbose = 1, # Shows progress of each epoch\n",
        "                               patience = 8, # Check for 8 more iterations after loss decrement stalls\n",
        "                               restore_best_weights = False\n",
        "                               )\n",
        "\n",
        "loss_history = model.fit(x_train_scaled, \n",
        "                         y_train_tensor, epochs=40, \n",
        "                         verbose=1, \n",
        "                         validation_data=(x_val_scaled, y_val_sampled), \n",
        "                         callbacks= [early_stopping]\n",
        "                         )\n",
        "\n",
        "model.summary() # Get model summary\n",
        "\n",
        "# early_stopping.stopped_epoch returns 0 if training didn't stop early.\n",
        "if early_stopping.stopped_epoch != 0:\n",
        "  print('Training stopped after',early_stopping.stopped_epoch,'epochs.')\n",
        "\n",
        "plt.plot(loss_history.history['loss'])\n",
        "plt.plot(loss_history.history['val_loss'])\n",
        "plt.title('Model Loss vs. Epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper right')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/40\n",
            "359/359 [==============================] - 1s 3ms/step - loss: 0.6305 - auc: 0.5962 - val_loss: 0.5089 - val_auc: 0.6422\n",
            "Epoch 2/40\n",
            "359/359 [==============================] - 1s 1ms/step - loss: 0.6123 - auc: 0.6329 - val_loss: 0.4986 - val_auc: 0.6436\n",
            "Epoch 3/40\n",
            "359/359 [==============================] - 1s 1ms/step - loss: 0.6109 - auc: 0.6378 - val_loss: 0.5140 - val_auc: 0.6524\n",
            "Epoch 4/40\n",
            "359/359 [==============================] - 1s 2ms/step - loss: 0.6081 - auc: 0.6439 - val_loss: 0.5175 - val_auc: 0.6530\n",
            "Epoch 5/40\n",
            "359/359 [==============================] - 1s 2ms/step - loss: 0.6075 - auc: 0.6444 - val_loss: 0.4914 - val_auc: 0.6501\n",
            "Epoch 6/40\n",
            "359/359 [==============================] - 1s 2ms/step - loss: 0.6062 - auc: 0.6469 - val_loss: 0.5097 - val_auc: 0.6565\n",
            "Epoch 7/40\n",
            "359/359 [==============================] - 1s 2ms/step - loss: 0.6035 - auc: 0.6540 - val_loss: 0.5138 - val_auc: 0.6576\n",
            "Epoch 8/40\n",
            "359/359 [==============================] - 1s 1ms/step - loss: 0.6024 - auc: 0.6549 - val_loss: 0.5292 - val_auc: 0.6585\n",
            "Epoch 9/40\n",
            "359/359 [==============================] - 1s 2ms/step - loss: 0.6038 - auc: 0.6532 - val_loss: 0.5114 - val_auc: 0.6544\n",
            "Epoch 10/40\n",
            "359/359 [==============================] - 1s 2ms/step - loss: 0.6016 - auc: 0.6581 - val_loss: 0.4991 - val_auc: 0.6528\n",
            "Epoch 11/40\n",
            "359/359 [==============================] - 1s 2ms/step - loss: 0.6012 - auc: 0.6589 - val_loss: 0.5100 - val_auc: 0.6601\n",
            "Epoch 12/40\n",
            "359/359 [==============================] - 1s 1ms/step - loss: 0.6003 - auc: 0.6604 - val_loss: 0.5059 - val_auc: 0.6582\n",
            "Epoch 13/40\n",
            "359/359 [==============================] - 1s 2ms/step - loss: 0.5998 - auc: 0.6607 - val_loss: 0.5091 - val_auc: 0.6574\n",
            "Epoch 00013: early stopping\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 20)                420       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 32)                672       \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 1,125\n",
            "Trainable params: 1,125\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Training stopped after 12 epochs.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QP3SPXeJ3-JC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "outputId": "bc6bd312-6b4e-4147-9b32-4a9628cc1544"
      },
      "source": [
        "y_pred_val = model.predict(x_val_scaled)\n",
        "y_pred_val = np.where(y_pred_val > 0.5, 1, 0).flatten()\n",
        "print(\"Confusion Matrix:\\n\",confusion_matrix(y_val_tensor, y_pred_val))\n",
        "print(\"Precision Score:\", precision_score(y_val_tensor, y_pred_val))\n",
        "print(\"Recall Score:\", recall_score(y_val_tensor, y_pred_val))\n",
        "print(classification_report(y_val_tensor, y_pred_val))\n",
        "\n",
        "# generate a no skill prediction (majority class)\n",
        "ns_probs = [0 for _ in range(len(y_val_tensor))]\n",
        "# predict probabilities\n",
        "lr_probs = model.predict_proba(x_val_scaled)\n",
        "# keep probabilities for the positive outcome only\n",
        "lr_probs2 = lr_probs[:]\n",
        "# calculate scores\n",
        "ns_auc = roc_auc_score(y_val_tensor, ns_probs)\n",
        "lr_auc = roc_auc_score(y_val_tensor, lr_probs2)\n",
        "# summarize scores\n",
        "print('No Skill: ROC AUC=%.3f' % (ns_auc))\n",
        "print('ANN: ROC AUC=%.3f' % (lr_auc))\n",
        "# calculate roc curves\n",
        "ns_fpr, ns_tpr, _ = roc_curve(y_val_tensor, ns_probs)\n",
        "lr_fpr, lr_tpr, _ = roc_curve(y_val_tensor, lr_probs2)\n",
        "# plot the roc curve for the model\n",
        "plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')\n",
        "plt.plot(lr_fpr, lr_tpr, marker='.', label='ANN')\n",
        "# axis labels\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Validation ROC Curve: AUC = 0.657', fontsize = 14)\n",
        "# show the legend\n",
        "plt.legend()\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix:\n",
            " [[3011  179]\n",
            " [ 629  100]]\n",
            "Precision Score: 0.35842293906810035\n",
            "Recall Score: 0.13717421124828533\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.83      0.94      0.88      3190\n",
            "         1.0       0.36      0.14      0.20       729\n",
            "\n",
            "    accuracy                           0.79      3919\n",
            "   macro avg       0.59      0.54      0.54      3919\n",
            "weighted avg       0.74      0.79      0.75      3919\n",
            "\n",
            "No Skill: ROC AUC=0.500\n",
            "ANN: ROC AUC=0.657\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3i64bQOFd5XA"
      },
      "source": [
        "# predicting for test using ANN\n",
        "test_scaled = scaler.transform(test_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1SvEBijxA7G"
      },
      "source": [
        "predictions = model.predict(test_scaled)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sflmHajjxHNY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f27cde0-2e96-43e3-a19b-8ab963af4fbf"
      },
      "source": [
        "predictions = np.where(predictions > 0.5, 1, 0).flatten()\n",
        "predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 646
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hVHj7qvAypO3"
      },
      "source": [
        "submission = pd.DataFrame(list(zip(list(cust_id),list(predictions))), columns=['cust_id', 'profitable_flag'], dtype='int64')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBvfsAMhzKfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "6a396d09-db99-406a-9d9c-c02906bf459b"
      },
      "source": [
        "submission.head()"
      ],
      "execution_count": null,
      "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>cust_id</th>\n",
              "      <th>profitable_flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>569986</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>569987</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>569988</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>569989</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>569990</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   cust_id  profitable_flag\n",
              "0   569986                1\n",
              "1   569987                0\n",
              "2   569988                0\n",
              "3   569989                1\n",
              "4   569990                0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 504
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lK__pLCAzNbn"
      },
      "source": [
        "# save predictions of ANN\n",
        "submission.to_csv('submission_ANN.csv')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}