CONSUMER_COMPLAINT_RESOLUTION_MODELJilsy.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JilsyXavier/Predictive_ML_Model_Consumer_Analysis-/blob/main/CONSUMER_COMPLAINT_RESOLUTION_MODELJilsy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "os4d-5gsroAb"
      },
      "source": [
        "**CONSUMER COMPLAINT RESOLUTION ANALYSIS**\n",
        "\n",
        "Product review is the most basic function/factor in resolving customer issues and\n",
        "increasing the sales growth of any product. We can understand their mindset toward our service\n",
        "without asking each customer.\n",
        "When consumers are unhappy with some aspect of a business, they reach out to customer\n",
        "service and might raise a complaint. Companies try their best to resolve the complaints that they\n",
        "receive. However, it might not always be possible to appease every customer.\n",
        "So Here, we will analyze data, and with the help of different algorithms, we are finding the best\n",
        "classification of customer category so that we can predict our test data.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bnrvB9iKsJX4"
      },
      "source": [
        "**Objective**\n",
        "\n",
        "The main objective is to predict whether our customer is disputed or not with the help of given\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gw-_spmmKord",
        "outputId": "4e9b4d5e-ce36-4d25-80de-5adbfe0d202f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "#import libraries required\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "import datetime\n",
        "from sklearn.impute import SimpleImputer\n",
        "import string\n",
        "from nltk.tokenize import wordpunct_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer, PorterStemmer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P47NR59wu3mh"
      },
      "outputs": [],
      "source": [
        "df1=pd.read_csv('Consumer_Complaints_train.csv')\n",
        "df2=pd.read_csv('Consumer_Complaints_test.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "pNYcLmzHvUKy",
        "outputId": "80b198c8-dcd9-41e9-a2bd-9bc920235bf4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Date received                  Product  \\\n",
              "0    2015-10-14         Credit reporting   \n",
              "1    2015-04-26  Bank account or service   \n",
              "2    2013-12-20              Credit card   \n",
              "3    2016-03-03          Debt collection   \n",
              "4    2015-01-30          Debt collection   \n",
              "\n",
              "                             Sub-product  \\\n",
              "0                                    NaN   \n",
              "1             Other bank product/service   \n",
              "2                                    NaN   \n",
              "3  Other (i.e. phone, health club, etc.)   \n",
              "4                                Medical   \n",
              "\n",
              "                                    Issue  \\\n",
              "0  Incorrect information on credit report   \n",
              "1                Deposits and withdrawals   \n",
              "2                                   Other   \n",
              "3         Disclosure verification of debt   \n",
              "4         Disclosure verification of debt   \n",
              "\n",
              "                              Sub-issue  \\\n",
              "0               Information is not mine   \n",
              "1                                   NaN   \n",
              "2                                   NaN   \n",
              "3  Not given enough info to verify debt   \n",
              "4  Not given enough info to verify debt   \n",
              "\n",
              "                        Consumer complaint narrative  \\\n",
              "0                                                NaN   \n",
              "1  RE : XXXX XXXX XXXX-PRIVILEGED AND CONFIDENTIA...   \n",
              "2                                                NaN   \n",
              "3                                                NaN   \n",
              "4                                                NaN   \n",
              "\n",
              "                             Company public response  \\\n",
              "0                                                NaN   \n",
              "1                                                NaN   \n",
              "2                                                NaN   \n",
              "3  Company has responded to the consumer and the ...   \n",
              "4                                                NaN   \n",
              "\n",
              "                                     Company State ZIP code Tags  \\\n",
              "0                                    Equifax    GA    30134  NaN   \n",
              "1                      Wells Fargo & Company    GA    319XX  NaN   \n",
              "2                                   Citibank    SC    29203  NaN   \n",
              "3       FAIR COLLECTIONS & OUTSOURCING, INC.    OH    43082  NaN   \n",
              "4  HCFS Health Care Financial Services, Inc.    CA    90036  NaN   \n",
              "\n",
              "  Consumer consent provided? Submitted via Date sent to company  \\\n",
              "0       Consent not provided           Web           2015-10-14   \n",
              "1           Consent provided           Web           2015-04-26   \n",
              "2                        NaN         Phone           2014-01-03   \n",
              "3                        NaN      Referral           2016-03-04   \n",
              "4                        NaN           Web           2015-01-30   \n",
              "\n",
              "      Company response to consumer Timely response? Consumer disputed?  \\\n",
              "0          Closed with explanation              Yes                 No   \n",
              "1          Closed with explanation              Yes                Yes   \n",
              "2  Closed with non-monetary relief              Yes                 No   \n",
              "3          Closed with explanation              Yes                 No   \n",
              "4          Closed with explanation              Yes                Yes   \n",
              "\n",
              "   Complaint ID  \n",
              "0       1605653  \n",
              "1       1347613  \n",
              "2        640394  \n",
              "3       1815134  \n",
              "4       1218613  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-65376a43-6f6c-44a0-ba38-4e1345d5e311\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date received</th>\n",
              "      <th>Product</th>\n",
              "      <th>Sub-product</th>\n",
              "      <th>Issue</th>\n",
              "      <th>Sub-issue</th>\n",
              "      <th>Consumer complaint narrative</th>\n",
              "      <th>Company public response</th>\n",
              "      <th>Company</th>\n",
              "      <th>State</th>\n",
              "      <th>ZIP code</th>\n",
              "      <th>Tags</th>\n",
              "      <th>Consumer consent provided?</th>\n",
              "      <th>Submitted via</th>\n",
              "      <th>Date sent to company</th>\n",
              "      <th>Company response to consumer</th>\n",
              "      <th>Timely response?</th>\n",
              "      <th>Consumer disputed?</th>\n",
              "      <th>Complaint ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-10-14</td>\n",
              "      <td>Credit reporting</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Incorrect information on credit report</td>\n",
              "      <td>Information is not mine</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Equifax</td>\n",
              "      <td>GA</td>\n",
              "      <td>30134</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Consent not provided</td>\n",
              "      <td>Web</td>\n",
              "      <td>2015-10-14</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1605653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-04-26</td>\n",
              "      <td>Bank account or service</td>\n",
              "      <td>Other bank product/service</td>\n",
              "      <td>Deposits and withdrawals</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RE : XXXX XXXX XXXX-PRIVILEGED AND CONFIDENTIA...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Wells Fargo &amp; Company</td>\n",
              "      <td>GA</td>\n",
              "      <td>319XX</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Consent provided</td>\n",
              "      <td>Web</td>\n",
              "      <td>2015-04-26</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1347613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013-12-20</td>\n",
              "      <td>Credit card</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Other</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Citibank</td>\n",
              "      <td>SC</td>\n",
              "      <td>29203</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Phone</td>\n",
              "      <td>2014-01-03</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>640394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-03-03</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Other (i.e. phone, health club, etc.)</td>\n",
              "      <td>Disclosure verification of debt</td>\n",
              "      <td>Not given enough info to verify debt</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Company has responded to the consumer and the ...</td>\n",
              "      <td>FAIR COLLECTIONS &amp; OUTSOURCING, INC.</td>\n",
              "      <td>OH</td>\n",
              "      <td>43082</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Referral</td>\n",
              "      <td>2016-03-04</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1815134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-30</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Medical</td>\n",
              "      <td>Disclosure verification of debt</td>\n",
              "      <td>Not given enough info to verify debt</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>HCFS Health Care Financial Services, Inc.</td>\n",
              "      <td>CA</td>\n",
              "      <td>90036</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Web</td>\n",
              "      <td>2015-01-30</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1218613</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-65376a43-6f6c-44a0-ba38-4e1345d5e311')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-65376a43-6f6c-44a0-ba38-4e1345d5e311 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-65376a43-6f6c-44a0-ba38-4e1345d5e311');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "id": "Z5yZx2Qhzctj",
        "outputId": "17e0c6e1-fd1e-4f81-e4c0-5b96139e82cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Date received          Product                            Sub-product  \\\n",
              "0    2015-01-17      Credit card                                    NaN   \n",
              "1    2016-06-22    Consumer Loan                             Title loan   \n",
              "2    2015-09-04      Credit card                                    NaN   \n",
              "3    2016-05-17    Consumer Loan                       Installment loan   \n",
              "4    2016-07-07  Debt collection  Other (i.e. phone, health club, etc.)   \n",
              "\n",
              "                                   Issue  \\\n",
              "0  Customer service / Customer relations   \n",
              "1           Payment to acct not credited   \n",
              "2          Credit line increase/decrease   \n",
              "3    Problems when you are unable to pay   \n",
              "4    Improper contact or sharing of info   \n",
              "\n",
              "                               Sub-issue  \\\n",
              "0                                    NaN   \n",
              "1                                    NaN   \n",
              "2                                    NaN   \n",
              "3                                    NaN   \n",
              "4  Contacted employer after asked not to   \n",
              "\n",
              "                        Consumer complaint narrative  \\\n",
              "0                                                NaN   \n",
              "1                                                NaN   \n",
              "2  I WANT TO REQUEST A CREDIT LINE INCREASE OF XX...   \n",
              "3  I have asked One Main Financial not to call my...   \n",
              "4  I have received several calls from a XXXX XXXX...   \n",
              "\n",
              "                             Company public response  \\\n",
              "0                                                NaN   \n",
              "1  Company believes it acted appropriately as aut...   \n",
              "2                                                NaN   \n",
              "3                                                NaN   \n",
              "4  Company has responded to the consumer and the ...   \n",
              "\n",
              "                            Company State ZIP code           Tags  \\\n",
              "0                          Citibank    TX    75241            NaN   \n",
              "1  Larsen MacColl Partners II, L.P.    TX    76548  Servicemember   \n",
              "2                       Capital One    NC    271XX            NaN   \n",
              "3   OneMain Financial Holdings, LLC    MO    634XX            NaN   \n",
              "4              GMA Investments, LLC    SC    296XX            NaN   \n",
              "\n",
              "  Consumer consent provided? Submitted via Date sent to company  \\\n",
              "0                        NaN           Web           2015-01-17   \n",
              "1                        NaN         Phone           2016-06-22   \n",
              "2           Consent provided           Web           2015-09-04   \n",
              "3           Consent provided           Web           2016-05-20   \n",
              "4           Consent provided           Web           2016-07-07   \n",
              "\n",
              "      Company response to consumer Timely response?  Complaint ID  \n",
              "0      Closed with monetary relief              Yes       1198133  \n",
              "1          Closed with explanation              Yes       1979989  \n",
              "2          Closed with explanation              Yes       1552090  \n",
              "3  Closed with non-monetary relief              Yes       1929306  \n",
              "4          Closed with explanation              Yes       2001667  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e95a950f-f9f9-4b6d-8a64-c4ee456b46cf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date received</th>\n",
              "      <th>Product</th>\n",
              "      <th>Sub-product</th>\n",
              "      <th>Issue</th>\n",
              "      <th>Sub-issue</th>\n",
              "      <th>Consumer complaint narrative</th>\n",
              "      <th>Company public response</th>\n",
              "      <th>Company</th>\n",
              "      <th>State</th>\n",
              "      <th>ZIP code</th>\n",
              "      <th>Tags</th>\n",
              "      <th>Consumer consent provided?</th>\n",
              "      <th>Submitted via</th>\n",
              "      <th>Date sent to company</th>\n",
              "      <th>Company response to consumer</th>\n",
              "      <th>Timely response?</th>\n",
              "      <th>Complaint ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-17</td>\n",
              "      <td>Credit card</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Customer service / Customer relations</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Citibank</td>\n",
              "      <td>TX</td>\n",
              "      <td>75241</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Web</td>\n",
              "      <td>2015-01-17</td>\n",
              "      <td>Closed with monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1198133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-06-22</td>\n",
              "      <td>Consumer Loan</td>\n",
              "      <td>Title loan</td>\n",
              "      <td>Payment to acct not credited</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Company believes it acted appropriately as aut...</td>\n",
              "      <td>Larsen MacColl Partners II, L.P.</td>\n",
              "      <td>TX</td>\n",
              "      <td>76548</td>\n",
              "      <td>Servicemember</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Phone</td>\n",
              "      <td>2016-06-22</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1979989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-09-04</td>\n",
              "      <td>Credit card</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Credit line increase/decrease</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I WANT TO REQUEST A CREDIT LINE INCREASE OF XX...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Capital One</td>\n",
              "      <td>NC</td>\n",
              "      <td>271XX</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Consent provided</td>\n",
              "      <td>Web</td>\n",
              "      <td>2015-09-04</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1552090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-05-17</td>\n",
              "      <td>Consumer Loan</td>\n",
              "      <td>Installment loan</td>\n",
              "      <td>Problems when you are unable to pay</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I have asked One Main Financial not to call my...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>OneMain Financial Holdings, LLC</td>\n",
              "      <td>MO</td>\n",
              "      <td>634XX</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Consent provided</td>\n",
              "      <td>Web</td>\n",
              "      <td>2016-05-20</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1929306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-07-07</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Other (i.e. phone, health club, etc.)</td>\n",
              "      <td>Improper contact or sharing of info</td>\n",
              "      <td>Contacted employer after asked not to</td>\n",
              "      <td>I have received several calls from a XXXX XXXX...</td>\n",
              "      <td>Company has responded to the consumer and the ...</td>\n",
              "      <td>GMA Investments, LLC</td>\n",
              "      <td>SC</td>\n",
              "      <td>296XX</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Consent provided</td>\n",
              "      <td>Web</td>\n",
              "      <td>2016-07-07</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2001667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e95a950f-f9f9-4b6d-8a64-c4ee456b46cf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e95a950f-f9f9-4b6d-8a64-c4ee456b46cf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e95a950f-f9f9-4b6d-8a64-c4ee456b46cf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aOsQE8-X6xPW",
        "outputId": "e9f14f03-c4b7-4180-d782-eb5ae5dce67f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 358810 entries, 0 to 358809\n",
            "Data columns (total 18 columns):\n",
            " #   Column                        Non-Null Count   Dtype \n",
            "---  ------                        --------------   ----- \n",
            " 0   Date received                 358810 non-null  object\n",
            " 1   Product                       358810 non-null  object\n",
            " 2   Sub-product                   255024 non-null  object\n",
            " 3   Issue                         358810 non-null  object\n",
            " 4   Sub-issue                     139436 non-null  object\n",
            " 5   Consumer complaint narrative  56180 non-null   object\n",
            " 6   Company public response       67931 non-null   object\n",
            " 7   Company                       358810 non-null  object\n",
            " 8   State                         355907 non-null  object\n",
            " 9   ZIP code                      355899 non-null  object\n",
            " 10  Tags                          50226 non-null   object\n",
            " 11  Consumer consent provided?    101580 non-null  object\n",
            " 12  Submitted via                 358810 non-null  object\n",
            " 13  Date sent to company          358810 non-null  object\n",
            " 14  Company response to consumer  358810 non-null  object\n",
            " 15  Timely response?              358810 non-null  object\n",
            " 16  Consumer disputed?            358810 non-null  object\n",
            " 17  Complaint ID                  358810 non-null  int64 \n",
            "dtypes: int64(1), object(17)\n",
            "memory usage: 49.3+ MB\n"
          ]
        }
      ],
      "source": [
        "df1.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JsUfd8DL6zPZ",
        "outputId": "30c88e3c-ff53-4248-db45-7bc1e4b96918"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 119606 entries, 0 to 119605\n",
            "Data columns (total 17 columns):\n",
            " #   Column                        Non-Null Count   Dtype \n",
            "---  ------                        --------------   ----- \n",
            " 0   Date received                 119606 non-null  object\n",
            " 1   Product                       119606 non-null  object\n",
            " 2   Sub-product                   84923 non-null   object\n",
            " 3   Issue                         119606 non-null  object\n",
            " 4   Sub-issue                     46356 non-null   object\n",
            " 5   Consumer complaint narrative  18914 non-null   object\n",
            " 6   Company public response       22460 non-null   object\n",
            " 7   Company                       119606 non-null  object\n",
            " 8   State                         118670 non-null  object\n",
            " 9   ZIP code                      118669 non-null  object\n",
            " 10  Tags                          16977 non-null   object\n",
            " 11  Consumer consent provided?    33907 non-null   object\n",
            " 12  Submitted via                 119606 non-null  object\n",
            " 13  Date sent to company          119606 non-null  object\n",
            " 14  Company response to consumer  119606 non-null  object\n",
            " 15  Timely response?              119606 non-null  object\n",
            " 16  Complaint ID                  119606 non-null  int64 \n",
            "dtypes: int64(1), object(16)\n",
            "memory usage: 15.5+ MB\n"
          ]
        }
      ],
      "source": [
        "df2.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XOdSX4Gi1awV"
      },
      "outputs": [],
      "source": [
        " df1_types=pd.DataFrame(df1.dtypes, columns = ['Train'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SB3JIqy03RzC"
      },
      "outputs": [],
      "source": [
        " df2_types=pd.DataFrame(df2.dtypes, columns = ['Test'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "WijucBK83QcX",
        "outputId": "4ead3572-87a4-4daa-f0be-bcfa1643e56b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Train    Test\n",
              "Date received                 object  object\n",
              "Product                       object  object\n",
              "Sub-product                   object  object\n",
              "Issue                         object  object\n",
              "Sub-issue                     object  object\n",
              "Consumer complaint narrative  object  object\n",
              "Company public response       object  object\n",
              "Company                       object  object\n",
              "State                         object  object\n",
              "ZIP code                      object  object\n",
              "Tags                          object  object\n",
              "Consumer consent provided?    object  object\n",
              "Submitted via                 object  object\n",
              "Date sent to company          object  object\n",
              "Company response to consumer  object  object\n",
              "Timely response?              object  object\n",
              "Consumer disputed?            object     NaN\n",
              "Complaint ID                   int64   int64"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7abeb95b-3dc5-4237-8f60-97e0e0c83667\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Train</th>\n",
              "      <th>Test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Date received</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-product</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Issue</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-issue</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer complaint narrative</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company public response</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>State</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZIP code</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tags</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer consent provided?</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Submitted via</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date sent to company</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company response to consumer</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Timely response?</th>\n",
              "      <td>object</td>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer disputed?</th>\n",
              "      <td>object</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Complaint ID</th>\n",
              "      <td>int64</td>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7abeb95b-3dc5-4237-8f60-97e0e0c83667')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7abeb95b-3dc5-4237-8f60-97e0e0c83667 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7abeb95b-3dc5-4237-8f60-97e0e0c83667');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "data_types = pd.concat([df1_types, df2_types], axis = 1)\n",
        "data_types"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ul7GwCBB4Ncg",
        "outputId": "323554ba-f804-48e2-b76f-5b21a2e9e7e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((358810, 18), (119606, 17))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df1.shape, df2.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvsFaQed4-pL",
        "outputId": "c840ac9b-b70f-47bd-8092-14303bad6587"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date received', 'Product', 'Sub-product', 'Issue', 'Sub-issue',\n",
              "       'Consumer complaint narrative', 'Company public response', 'Company',\n",
              "       'State', 'ZIP code', 'Tags', 'Consumer consent provided?',\n",
              "       'Submitted via', 'Date sent to company', 'Company response to consumer',\n",
              "       'Timely response?', 'Consumer disputed?', 'Complaint ID'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df1.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yDqNO4bV5EAj",
        "outputId": "b2ce95bb-e997-46b7-a4e8-b9a4e5d065d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date received', 'Product', 'Sub-product', 'Issue', 'Sub-issue',\n",
              "       'Consumer complaint narrative', 'Company public response', 'Company',\n",
              "       'State', 'ZIP code', 'Tags', 'Consumer consent provided?',\n",
              "       'Submitted via', 'Date sent to company', 'Company response to consumer',\n",
              "       'Timely response?', 'Complaint ID'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df2.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "2FU9XabS5I0t",
        "outputId": "cb80226c-3487-4747-e496-ce59cc8c91aa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              percent_missing\n",
              "Date received                        0.000000\n",
              "Product                              0.000000\n",
              "Sub-product                         28.925058\n",
              "Issue                                0.000000\n",
              "Sub-issue                           61.139322\n",
              "Consumer complaint narrative        84.342688\n",
              "Company public response             81.067696\n",
              "Company                              0.000000\n",
              "State                                0.809063\n",
              "ZIP code                             0.811293\n",
              "Tags                                86.002062\n",
              "Consumer consent provided?          71.689752\n",
              "Submitted via                        0.000000\n",
              "Date sent to company                 0.000000\n",
              "Company response to consumer         0.000000\n",
              "Timely response?                     0.000000\n",
              "Consumer disputed?                   0.000000\n",
              "Complaint ID                         0.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-45407fa4-f4b1-412a-8d2d-e340ddc0c119\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>percent_missing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Date received</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-product</th>\n",
              "      <td>28.925058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Issue</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-issue</th>\n",
              "      <td>61.139322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer complaint narrative</th>\n",
              "      <td>84.342688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company public response</th>\n",
              "      <td>81.067696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>State</th>\n",
              "      <td>0.809063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZIP code</th>\n",
              "      <td>0.811293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tags</th>\n",
              "      <td>86.002062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer consent provided?</th>\n",
              "      <td>71.689752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Submitted via</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date sent to company</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company response to consumer</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Timely response?</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer disputed?</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Complaint ID</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45407fa4-f4b1-412a-8d2d-e340ddc0c119')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-45407fa4-f4b1-412a-8d2d-e340ddc0c119 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-45407fa4-f4b1-412a-8d2d-e340ddc0c119');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "percent_missing = df1.isnull().sum() * 100 / len(df1)\n",
        "missing_value_df1 = pd.DataFrame({\n",
        "                                 'percent_missing': percent_missing})\n",
        "missing_value_df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "V7067M-n771p",
        "outputId": "5508d48e-2ebb-4701-b463-01e6597a1cbe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              percent_missing\n",
              "Date received                        0.000000\n",
              "Product                              0.000000\n",
              "Sub-product                         28.997709\n",
              "Issue                                0.000000\n",
              "Sub-issue                           61.242747\n",
              "Consumer complaint narrative        84.186412\n",
              "Company public response             81.221678\n",
              "Company                              0.000000\n",
              "State                                0.782569\n",
              "ZIP code                             0.783406\n",
              "Tags                                85.805896\n",
              "Consumer consent provided?          71.651088\n",
              "Submitted via                        0.000000\n",
              "Date sent to company                 0.000000\n",
              "Company response to consumer         0.000000\n",
              "Timely response?                     0.000000\n",
              "Complaint ID                         0.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1c4fd3b7-4276-4090-bf2e-272b6e868a4b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>percent_missing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Date received</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-product</th>\n",
              "      <td>28.997709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Issue</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-issue</th>\n",
              "      <td>61.242747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer complaint narrative</th>\n",
              "      <td>84.186412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company public response</th>\n",
              "      <td>81.221678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>State</th>\n",
              "      <td>0.782569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZIP code</th>\n",
              "      <td>0.783406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tags</th>\n",
              "      <td>85.805896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer consent provided?</th>\n",
              "      <td>71.651088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Submitted via</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date sent to company</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company response to consumer</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Timely response?</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Complaint ID</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1c4fd3b7-4276-4090-bf2e-272b6e868a4b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1c4fd3b7-4276-4090-bf2e-272b6e868a4b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1c4fd3b7-4276-4090-bf2e-272b6e868a4b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "percent_missing = df2.isnull().sum() * 100 / len(df2)\n",
        "missing_value_df2 = pd.DataFrame({\n",
        "                                 'percent_missing': percent_missing})\n",
        "missing_value_df2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "qh8mYgAz8Oih",
        "outputId": "0d17b813-b12c-4688-efb2-2758d579fca8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   Test       Test\n",
              "Date received                  0.000000   0.000000\n",
              "Product                        0.000000   0.000000\n",
              "Sub-product                   28.925058  28.997709\n",
              "Issue                          0.000000   0.000000\n",
              "Sub-issue                     61.139322  61.242747\n",
              "Consumer complaint narrative  84.342688  84.186412\n",
              "Company public response       81.067696  81.221678\n",
              "Company                        0.000000   0.000000\n",
              "State                          0.809063   0.782569\n",
              "ZIP code                       0.811293   0.783406\n",
              "Tags                          86.002062  85.805896\n",
              "Consumer consent provided?    71.689752  71.651088\n",
              "Submitted via                  0.000000   0.000000\n",
              "Date sent to company           0.000000   0.000000\n",
              "Company response to consumer   0.000000   0.000000\n",
              "Timely response?               0.000000   0.000000\n",
              "Consumer disputed?             0.000000        NaN\n",
              "Complaint ID                   0.000000   0.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a72f73f6-e72b-403a-b7ad-bf3835870aaf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Test</th>\n",
              "      <th>Test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Date received</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-product</th>\n",
              "      <td>28.925058</td>\n",
              "      <td>28.997709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Issue</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sub-issue</th>\n",
              "      <td>61.139322</td>\n",
              "      <td>61.242747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer complaint narrative</th>\n",
              "      <td>84.342688</td>\n",
              "      <td>84.186412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company public response</th>\n",
              "      <td>81.067696</td>\n",
              "      <td>81.221678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>State</th>\n",
              "      <td>0.809063</td>\n",
              "      <td>0.782569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZIP code</th>\n",
              "      <td>0.811293</td>\n",
              "      <td>0.783406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tags</th>\n",
              "      <td>86.002062</td>\n",
              "      <td>85.805896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer consent provided?</th>\n",
              "      <td>71.689752</td>\n",
              "      <td>71.651088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Submitted via</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date sent to company</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Company response to consumer</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Timely response?</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer disputed?</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Complaint ID</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a72f73f6-e72b-403a-b7ad-bf3835870aaf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a72f73f6-e72b-403a-b7ad-bf3835870aaf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a72f73f6-e72b-403a-b7ad-bf3835870aaf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "Missing_values=pd.concat([missing_value_df1,missing_value_df2],axis=1)\n",
        "Missing_values.rename(columns={'percent_missing':'Train','percent_missing':'Test'},inplace=True)\n",
        "Missing_values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cg9bEZ-GAWCF"
      },
      "source": [
        "Drop columns where more than 25% of data are missing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vr3XKQIIFhPw"
      },
      "outputs": [],
      "source": [
        "missing_values = ['Sub-product', 'Sub-issue', 'Consumer complaint narrative', 'Company public response', 'Tags', 'Consumer consent provided?']\n",
        "df1= df1.drop(missing_values, axis = 1)\n",
        "df2= df2.drop(missing_values, axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "km_erOgoGBuz"
      },
      "source": [
        "Extract Date, Month, and Year from the \"Date Received\" Column and create new fields for year, month, and day."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "id": "kKM2_6VlHYZF",
        "outputId": "993bd46f-f0b9-46d4-a689-7b5706ab92c4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Date received                  Product  \\\n",
              "0         2015-10-14         Credit reporting   \n",
              "1         2015-04-26  Bank account or service   \n",
              "2         2013-12-20              Credit card   \n",
              "3         2016-03-03          Debt collection   \n",
              "4         2015-01-30          Debt collection   \n",
              "...              ...                      ...   \n",
              "358805    2014-04-14         Credit reporting   \n",
              "358806    2013-03-14                 Mortgage   \n",
              "358807    2013-02-01              Credit card   \n",
              "358808    2015-06-25          Debt collection   \n",
              "358809    2015-05-12         Credit reporting   \n",
              "\n",
              "                                           Issue  \\\n",
              "0         Incorrect information on credit report   \n",
              "1                       Deposits and withdrawals   \n",
              "2                                          Other   \n",
              "3                Disclosure verification of debt   \n",
              "4                Disclosure verification of debt   \n",
              "...                                          ...   \n",
              "358805    Incorrect information on credit report   \n",
              "358806  Loan modification,collection,foreclosure   \n",
              "358807                            Payoff process   \n",
              "358808     Cont'd attempts collect debt not owed   \n",
              "358809    Incorrect information on credit report   \n",
              "\n",
              "                                          Company State ZIP code  \\\n",
              "0                                         Equifax    GA    30134   \n",
              "1                           Wells Fargo & Company    GA    319XX   \n",
              "2                                        Citibank    SC    29203   \n",
              "3            FAIR COLLECTIONS & OUTSOURCING, INC.    OH    43082   \n",
              "4       HCFS Health Care Financial Services, Inc.    CA    90036   \n",
              "...                                           ...   ...      ...   \n",
              "358805                                   Experian    OR    97306   \n",
              "358806                                   Citibank    OH    43026   \n",
              "358807                                Capital One    TX    76021   \n",
              "358808                       Encore Capital Group    FL    33256   \n",
              "358809     TransUnion Intermediate Holdings, Inc.    FL    32583   \n",
              "\n",
              "       Submitted via Date sent to company     Company response to consumer  \\\n",
              "0                Web           2015-10-14          Closed with explanation   \n",
              "1                Web           2015-04-26          Closed with explanation   \n",
              "2              Phone           2014-01-03  Closed with non-monetary relief   \n",
              "3           Referral           2016-03-04          Closed with explanation   \n",
              "4                Web           2015-01-30          Closed with explanation   \n",
              "...              ...                  ...                              ...   \n",
              "358805           Web           2014-04-13  Closed with non-monetary relief   \n",
              "358806      Referral           2013-03-18  Closed with non-monetary relief   \n",
              "358807           Web           2013-02-01                           Closed   \n",
              "358808         Phone           2015-06-29  Closed with non-monetary relief   \n",
              "358809      Referral           2015-06-03          Closed with explanation   \n",
              "\n",
              "       Timely response? Consumer disputed?  Complaint ID  Year Month Day  \n",
              "0                   Yes                 No       1605653  2015    10  14  \n",
              "1                   Yes                Yes       1347613  2015    04  26  \n",
              "2                   Yes                 No        640394  2013    12  20  \n",
              "3                   Yes                 No       1815134  2016    03  03  \n",
              "4                   Yes                Yes       1218613  2015    01  30  \n",
              "...                 ...                ...           ...   ...   ...  ..  \n",
              "358805              Yes                 No        806868  2014    04  14  \n",
              "358806              Yes                 No        355254  2013    03  14  \n",
              "358807              Yes                Yes        283994  2013    02  01  \n",
              "358808              Yes                 No       1438670  2015    06  25  \n",
              "358809              Yes                 No       1372035  2015    05  12  \n",
              "\n",
              "[358810 rows x 15 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-06f25a49-857e-4a11-a28a-52a19526e717\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date received</th>\n",
              "      <th>Product</th>\n",
              "      <th>Issue</th>\n",
              "      <th>Company</th>\n",
              "      <th>State</th>\n",
              "      <th>ZIP code</th>\n",
              "      <th>Submitted via</th>\n",
              "      <th>Date sent to company</th>\n",
              "      <th>Company response to consumer</th>\n",
              "      <th>Timely response?</th>\n",
              "      <th>Consumer disputed?</th>\n",
              "      <th>Complaint ID</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-10-14</td>\n",
              "      <td>Credit reporting</td>\n",
              "      <td>Incorrect information on credit report</td>\n",
              "      <td>Equifax</td>\n",
              "      <td>GA</td>\n",
              "      <td>30134</td>\n",
              "      <td>Web</td>\n",
              "      <td>2015-10-14</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1605653</td>\n",
              "      <td>2015</td>\n",
              "      <td>10</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015-04-26</td>\n",
              "      <td>Bank account or service</td>\n",
              "      <td>Deposits and withdrawals</td>\n",
              "      <td>Wells Fargo &amp; Company</td>\n",
              "      <td>GA</td>\n",
              "      <td>319XX</td>\n",
              "      <td>Web</td>\n",
              "      <td>2015-04-26</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1347613</td>\n",
              "      <td>2015</td>\n",
              "      <td>04</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013-12-20</td>\n",
              "      <td>Credit card</td>\n",
              "      <td>Other</td>\n",
              "      <td>Citibank</td>\n",
              "      <td>SC</td>\n",
              "      <td>29203</td>\n",
              "      <td>Phone</td>\n",
              "      <td>2014-01-03</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>640394</td>\n",
              "      <td>2013</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-03-03</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Disclosure verification of debt</td>\n",
              "      <td>FAIR COLLECTIONS &amp; OUTSOURCING, INC.</td>\n",
              "      <td>OH</td>\n",
              "      <td>43082</td>\n",
              "      <td>Referral</td>\n",
              "      <td>2016-03-04</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1815134</td>\n",
              "      <td>2016</td>\n",
              "      <td>03</td>\n",
              "      <td>03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-30</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Disclosure verification of debt</td>\n",
              "      <td>HCFS Health Care Financial Services, Inc.</td>\n",
              "      <td>CA</td>\n",
              "      <td>90036</td>\n",
              "      <td>Web</td>\n",
              "      <td>2015-01-30</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1218613</td>\n",
              "      <td>2015</td>\n",
              "      <td>01</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358805</th>\n",
              "      <td>2014-04-14</td>\n",
              "      <td>Credit reporting</td>\n",
              "      <td>Incorrect information on credit report</td>\n",
              "      <td>Experian</td>\n",
              "      <td>OR</td>\n",
              "      <td>97306</td>\n",
              "      <td>Web</td>\n",
              "      <td>2014-04-13</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>806868</td>\n",
              "      <td>2014</td>\n",
              "      <td>04</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358806</th>\n",
              "      <td>2013-03-14</td>\n",
              "      <td>Mortgage</td>\n",
              "      <td>Loan modification,collection,foreclosure</td>\n",
              "      <td>Citibank</td>\n",
              "      <td>OH</td>\n",
              "      <td>43026</td>\n",
              "      <td>Referral</td>\n",
              "      <td>2013-03-18</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>355254</td>\n",
              "      <td>2013</td>\n",
              "      <td>03</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358807</th>\n",
              "      <td>2013-02-01</td>\n",
              "      <td>Credit card</td>\n",
              "      <td>Payoff process</td>\n",
              "      <td>Capital One</td>\n",
              "      <td>TX</td>\n",
              "      <td>76021</td>\n",
              "      <td>Web</td>\n",
              "      <td>2013-02-01</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>283994</td>\n",
              "      <td>2013</td>\n",
              "      <td>02</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358808</th>\n",
              "      <td>2015-06-25</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Cont'd attempts collect debt not owed</td>\n",
              "      <td>Encore Capital Group</td>\n",
              "      <td>FL</td>\n",
              "      <td>33256</td>\n",
              "      <td>Phone</td>\n",
              "      <td>2015-06-29</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1438670</td>\n",
              "      <td>2015</td>\n",
              "      <td>06</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358809</th>\n",
              "      <td>2015-05-12</td>\n",
              "      <td>Credit reporting</td>\n",
              "      <td>Incorrect information on credit report</td>\n",
              "      <td>TransUnion Intermediate Holdings, Inc.</td>\n",
              "      <td>FL</td>\n",
              "      <td>32583</td>\n",
              "      <td>Referral</td>\n",
              "      <td>2015-06-03</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1372035</td>\n",
              "      <td>2015</td>\n",
              "      <td>05</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>358810 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-06f25a49-857e-4a11-a28a-52a19526e717')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-06f25a49-857e-4a11-a28a-52a19526e717 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-06f25a49-857e-4a11-a28a-52a19526e717');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df1['Date received'] = pd.to_datetime(df1['Date received'], errors='coerce')\n",
        "df1['Year'] = df1['Date received'].dt.strftime('%Y')\n",
        "df1['Month'] = df1['Date received'].dt.strftime('%m')\n",
        "df1['Day'] = df1['Date received'].dt.strftime('%d')\n",
        "df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938
        },
        "id": "Kv9n2bG1JQxX",
        "outputId": "220b0133-0df4-4d60-9234-f54ff8649b90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Date received           Product  \\\n",
              "0         2015-01-17       Credit card   \n",
              "1         2016-06-22     Consumer Loan   \n",
              "2         2015-09-04       Credit card   \n",
              "3         2016-05-17     Consumer Loan   \n",
              "4         2016-07-07   Debt collection   \n",
              "...              ...               ...   \n",
              "119601    2015-02-09       Credit card   \n",
              "119602    2012-11-13       Credit card   \n",
              "119603    2012-05-15          Mortgage   \n",
              "119604    2015-06-07  Credit reporting   \n",
              "119605    2015-03-20   Debt collection   \n",
              "\n",
              "                                           Issue  \\\n",
              "0          Customer service / Customer relations   \n",
              "1                   Payment to acct not credited   \n",
              "2                  Credit line increase/decrease   \n",
              "3            Problems when you are unable to pay   \n",
              "4            Improper contact or sharing of info   \n",
              "...                                          ...   \n",
              "119601                      Credit determination   \n",
              "119602                          Balance transfer   \n",
              "119603  Loan modification,collection,foreclosure   \n",
              "119604    Incorrect information on credit report   \n",
              "119605                     Communication tactics   \n",
              "\n",
              "                                 Company State ZIP code Submitted via  \\\n",
              "0                               Citibank    TX    75241           Web   \n",
              "1       Larsen MacColl Partners II, L.P.    TX    76548         Phone   \n",
              "2                            Capital One    NC    271XX           Web   \n",
              "3        OneMain Financial Holdings, LLC    MO    634XX           Web   \n",
              "4                   GMA Investments, LLC    SC    296XX           Web   \n",
              "...                                  ...   ...      ...           ...   \n",
              "119601                          Citibank    IL    60803           Web   \n",
              "119602                       Capital One    KS    67114           Web   \n",
              "119603                   Bank of America    CA    95691      Referral   \n",
              "119604                          Experian    OH    436XX           Web   \n",
              "119605             Northland Group, Inc.    FL    322XX           Web   \n",
              "\n",
              "       Date sent to company     Company response to consumer Timely response?  \\\n",
              "0                2015-01-17      Closed with monetary relief              Yes   \n",
              "1                2016-06-22          Closed with explanation              Yes   \n",
              "2                2015-09-04          Closed with explanation              Yes   \n",
              "3                2016-05-20  Closed with non-monetary relief              Yes   \n",
              "4                2016-07-07          Closed with explanation              Yes   \n",
              "...                     ...                              ...              ...   \n",
              "119601           2015-02-09          Closed with explanation              Yes   \n",
              "119602           2012-11-14          Closed with explanation              Yes   \n",
              "119603           2012-05-15            Closed without relief              Yes   \n",
              "119604           2015-06-07          Closed with explanation              Yes   \n",
              "119605           2015-03-20          Closed with explanation              Yes   \n",
              "\n",
              "        Complaint ID  Year Month Day  \n",
              "0            1198133  2015    01  17  \n",
              "1            1979989  2016    06  22  \n",
              "2            1552090  2015    09  04  \n",
              "3            1929306  2016    05  17  \n",
              "4            2001667  2016    07  07  \n",
              "...              ...   ...   ...  ..  \n",
              "119601       1230729  2015    02  09  \n",
              "119602        189749  2012    11  13  \n",
              "119603         78574  2012    05  15  \n",
              "119604       1409765  2015    06  07  \n",
              "119605       1294040  2015    03  20  \n",
              "\n",
              "[119606 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7c2700fe-2705-415f-9b20-84ccb47933ac\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date received</th>\n",
              "      <th>Product</th>\n",
              "      <th>Issue</th>\n",
              "      <th>Company</th>\n",
              "      <th>State</th>\n",
              "      <th>ZIP code</th>\n",
              "      <th>Submitted via</th>\n",
              "      <th>Date sent to company</th>\n",
              "      <th>Company response to consumer</th>\n",
              "      <th>Timely response?</th>\n",
              "      <th>Complaint ID</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-01-17</td>\n",
              "      <td>Credit card</td>\n",
              "      <td>Customer service / Customer relations</td>\n",
              "      <td>Citibank</td>\n",
              "      <td>TX</td>\n",
              "      <td>75241</td>\n",
              "      <td>Web</td>\n",
              "      <td>2015-01-17</td>\n",
              "      <td>Closed with monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1198133</td>\n",
              "      <td>2015</td>\n",
              "      <td>01</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-06-22</td>\n",
              "      <td>Consumer Loan</td>\n",
              "      <td>Payment to acct not credited</td>\n",
              "      <td>Larsen MacColl Partners II, L.P.</td>\n",
              "      <td>TX</td>\n",
              "      <td>76548</td>\n",
              "      <td>Phone</td>\n",
              "      <td>2016-06-22</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1979989</td>\n",
              "      <td>2016</td>\n",
              "      <td>06</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-09-04</td>\n",
              "      <td>Credit card</td>\n",
              "      <td>Credit line increase/decrease</td>\n",
              "      <td>Capital One</td>\n",
              "      <td>NC</td>\n",
              "      <td>271XX</td>\n",
              "      <td>Web</td>\n",
              "      <td>2015-09-04</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1552090</td>\n",
              "      <td>2015</td>\n",
              "      <td>09</td>\n",
              "      <td>04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-05-17</td>\n",
              "      <td>Consumer Loan</td>\n",
              "      <td>Problems when you are unable to pay</td>\n",
              "      <td>OneMain Financial Holdings, LLC</td>\n",
              "      <td>MO</td>\n",
              "      <td>634XX</td>\n",
              "      <td>Web</td>\n",
              "      <td>2016-05-20</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1929306</td>\n",
              "      <td>2016</td>\n",
              "      <td>05</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-07-07</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Improper contact or sharing of info</td>\n",
              "      <td>GMA Investments, LLC</td>\n",
              "      <td>SC</td>\n",
              "      <td>296XX</td>\n",
              "      <td>Web</td>\n",
              "      <td>2016-07-07</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2001667</td>\n",
              "      <td>2016</td>\n",
              "      <td>07</td>\n",
              "      <td>07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119601</th>\n",
              "      <td>2015-02-09</td>\n",
              "      <td>Credit card</td>\n",
              "      <td>Credit determination</td>\n",
              "      <td>Citibank</td>\n",
              "      <td>IL</td>\n",
              "      <td>60803</td>\n",
              "      <td>Web</td>\n",
              "      <td>2015-02-09</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1230729</td>\n",
              "      <td>2015</td>\n",
              "      <td>02</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119602</th>\n",
              "      <td>2012-11-13</td>\n",
              "      <td>Credit card</td>\n",
              "      <td>Balance transfer</td>\n",
              "      <td>Capital One</td>\n",
              "      <td>KS</td>\n",
              "      <td>67114</td>\n",
              "      <td>Web</td>\n",
              "      <td>2012-11-14</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>189749</td>\n",
              "      <td>2012</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119603</th>\n",
              "      <td>2012-05-15</td>\n",
              "      <td>Mortgage</td>\n",
              "      <td>Loan modification,collection,foreclosure</td>\n",
              "      <td>Bank of America</td>\n",
              "      <td>CA</td>\n",
              "      <td>95691</td>\n",
              "      <td>Referral</td>\n",
              "      <td>2012-05-15</td>\n",
              "      <td>Closed without relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>78574</td>\n",
              "      <td>2012</td>\n",
              "      <td>05</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119604</th>\n",
              "      <td>2015-06-07</td>\n",
              "      <td>Credit reporting</td>\n",
              "      <td>Incorrect information on credit report</td>\n",
              "      <td>Experian</td>\n",
              "      <td>OH</td>\n",
              "      <td>436XX</td>\n",
              "      <td>Web</td>\n",
              "      <td>2015-06-07</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1409765</td>\n",
              "      <td>2015</td>\n",
              "      <td>06</td>\n",
              "      <td>07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119605</th>\n",
              "      <td>2015-03-20</td>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Communication tactics</td>\n",
              "      <td>Northland Group, Inc.</td>\n",
              "      <td>FL</td>\n",
              "      <td>322XX</td>\n",
              "      <td>Web</td>\n",
              "      <td>2015-03-20</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1294040</td>\n",
              "      <td>2015</td>\n",
              "      <td>03</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119606 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c2700fe-2705-415f-9b20-84ccb47933ac')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7c2700fe-2705-415f-9b20-84ccb47933ac button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7c2700fe-2705-415f-9b20-84ccb47933ac');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df2['Date received'] = pd.to_datetime(df2['Date received'], errors='coerce')\n",
        "df2['Year'] = df2['Date received'].dt.strftime('%Y')\n",
        "df2['Month'] = df2['Date received'].dt.strftime('%m')\n",
        "df2['Day'] = df2['Date received'].dt.strftime('%d')\n",
        "df2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VM48dA0VKGFV"
      },
      "source": [
        "Calculate the Number of Days the Complaint was with the Company and create a new\n",
        "field as “Days held”\n",
        "\n",
        "ie Days held = Date sent to company - Date received"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0P0GFtViJob1"
      },
      "outputs": [],
      "source": [
        "df1['Date sent to company'] = pd.to_datetime(df1['Date sent to company'])\n",
        "df2['Date sent to company'] = pd.to_datetime(df2['Date sent to company'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sMugzjC8Kgwz"
      },
      "outputs": [],
      "source": [
        "df1['Days Held']=df1['Date sent to company']-df1['Date received']\n",
        "df2['Days Held']=df2['Date sent to company']-df2['Date received']\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V05ysif2Wbm7"
      },
      "source": [
        "Drop \"Date Received\",\"Date Sent to Company\",\"ZIP Code\", \"Complaint ID\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yWmwIHUizKzL"
      },
      "outputs": [],
      "source": [
        "df1 = df1.drop(['Date received', 'Date sent to company','ZIP code', 'Complaint ID'], axis = 1)\n",
        "df2 = df2.drop(['Date received', 'Date sent to company','ZIP code', 'Complaint ID'], axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rg8AyGEEXlG9"
      },
      "source": [
        "Imputing Null value in “State” by Mode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYAoj54PY47i",
        "outputId": "81423c25-37aa-4b9c-8b70-091ffe905afd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0    CA\n",
              " Name: State, dtype: object,\n",
              " 0    CA\n",
              " Name: State, dtype: object)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "\n",
        "df1['State'].mode(),df2['State'].mode()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PdY8w4wSiww6"
      },
      "outputs": [],
      "source": [
        "df1['State'] = df1['State'].fillna(value='CA')\n",
        "df2['State'] = df2['State'].fillna(value='CA')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hmLLHvzTjnxw"
      },
      "source": [
        "create a new field 'Week_Received' where\n",
        "we calculate the week based on the day of receiving.\n",
        "\n",
        "Using binning and apply cut() function to the column day"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9aT6FVnpY6fD"
      },
      "outputs": [],
      "source": [
        "# define the bins for week grouping\n",
        "bins = np.array([0, 8, 16, 22, np.inf])#np.inf for infinity\n",
        "# define the labels for each bin\n",
        "labels = [1, 2, 3, 4]\n",
        "\n",
        "df1['Day'] =df1['Day'].astype(float)\n",
        "df2['Day'] =df2['Day'].astype(float)\n",
        "\n",
        "\n",
        "# apply the cut function to the Day_Received column of the train and test dataframes\n",
        "df1['Week_Received'] = pd.cut(df1['Day'], bins=bins, labels=labels, include_lowest=True)\n",
        "df2['Week_Received'] = pd.cut(df2['Day'], bins=bins, labels=labels, include_lowest=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "id": "YgFQYpD5o2Pr",
        "outputId": "29f0d2c4-dcd0-45c1-e3d7-ab9dee4f0a57"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Product                                     Issue  \\\n",
              "0              Credit reporting    Incorrect information on credit report   \n",
              "1       Bank account or service                  Deposits and withdrawals   \n",
              "2                   Credit card                                     Other   \n",
              "3               Debt collection           Disclosure verification of debt   \n",
              "4               Debt collection           Disclosure verification of debt   \n",
              "...                         ...                                       ...   \n",
              "358805         Credit reporting    Incorrect information on credit report   \n",
              "358806                 Mortgage  Loan modification,collection,foreclosure   \n",
              "358807              Credit card                            Payoff process   \n",
              "358808          Debt collection     Cont'd attempts collect debt not owed   \n",
              "358809         Credit reporting    Incorrect information on credit report   \n",
              "\n",
              "                                          Company State Submitted via  \\\n",
              "0                                         Equifax    GA           Web   \n",
              "1                           Wells Fargo & Company    GA           Web   \n",
              "2                                        Citibank    SC         Phone   \n",
              "3            FAIR COLLECTIONS & OUTSOURCING, INC.    OH      Referral   \n",
              "4       HCFS Health Care Financial Services, Inc.    CA           Web   \n",
              "...                                           ...   ...           ...   \n",
              "358805                                   Experian    OR           Web   \n",
              "358806                                   Citibank    OH      Referral   \n",
              "358807                                Capital One    TX           Web   \n",
              "358808                       Encore Capital Group    FL         Phone   \n",
              "358809     TransUnion Intermediate Holdings, Inc.    FL      Referral   \n",
              "\n",
              "           Company response to consumer Timely response? Consumer disputed?  \\\n",
              "0               Closed with explanation              Yes                 No   \n",
              "1               Closed with explanation              Yes                Yes   \n",
              "2       Closed with non-monetary relief              Yes                 No   \n",
              "3               Closed with explanation              Yes                 No   \n",
              "4               Closed with explanation              Yes                Yes   \n",
              "...                                 ...              ...                ...   \n",
              "358805  Closed with non-monetary relief              Yes                 No   \n",
              "358806  Closed with non-monetary relief              Yes                 No   \n",
              "358807                           Closed              Yes                Yes   \n",
              "358808  Closed with non-monetary relief              Yes                 No   \n",
              "358809          Closed with explanation              Yes                 No   \n",
              "\n",
              "        Year Month   Day Days Held Week_Received  \n",
              "0       2015    10  14.0    0 days             2  \n",
              "1       2015    04  26.0    0 days             4  \n",
              "2       2013    12  20.0   14 days             3  \n",
              "3       2016    03   3.0    1 days             1  \n",
              "4       2015    01  30.0    0 days             4  \n",
              "...      ...   ...   ...       ...           ...  \n",
              "358805  2014    04  14.0   -1 days             2  \n",
              "358806  2013    03  14.0    4 days             2  \n",
              "358807  2013    02   1.0    0 days             1  \n",
              "358808  2015    06  25.0    4 days             4  \n",
              "358809  2015    05  12.0   22 days             2  \n",
              "\n",
              "[358810 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-05a6d6c6-0d71-471e-b105-2c717d5f4ef8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Product</th>\n",
              "      <th>Issue</th>\n",
              "      <th>Company</th>\n",
              "      <th>State</th>\n",
              "      <th>Submitted via</th>\n",
              "      <th>Company response to consumer</th>\n",
              "      <th>Timely response?</th>\n",
              "      <th>Consumer disputed?</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Days Held</th>\n",
              "      <th>Week_Received</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Credit reporting</td>\n",
              "      <td>Incorrect information on credit report</td>\n",
              "      <td>Equifax</td>\n",
              "      <td>GA</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>2015</td>\n",
              "      <td>10</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bank account or service</td>\n",
              "      <td>Deposits and withdrawals</td>\n",
              "      <td>Wells Fargo &amp; Company</td>\n",
              "      <td>GA</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2015</td>\n",
              "      <td>04</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Credit card</td>\n",
              "      <td>Other</td>\n",
              "      <td>Citibank</td>\n",
              "      <td>SC</td>\n",
              "      <td>Phone</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>2013</td>\n",
              "      <td>12</td>\n",
              "      <td>20.0</td>\n",
              "      <td>14 days</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Disclosure verification of debt</td>\n",
              "      <td>FAIR COLLECTIONS &amp; OUTSOURCING, INC.</td>\n",
              "      <td>OH</td>\n",
              "      <td>Referral</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>2016</td>\n",
              "      <td>03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Disclosure verification of debt</td>\n",
              "      <td>HCFS Health Care Financial Services, Inc.</td>\n",
              "      <td>CA</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2015</td>\n",
              "      <td>01</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358805</th>\n",
              "      <td>Credit reporting</td>\n",
              "      <td>Incorrect information on credit report</td>\n",
              "      <td>Experian</td>\n",
              "      <td>OR</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>2014</td>\n",
              "      <td>04</td>\n",
              "      <td>14.0</td>\n",
              "      <td>-1 days</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358806</th>\n",
              "      <td>Mortgage</td>\n",
              "      <td>Loan modification,collection,foreclosure</td>\n",
              "      <td>Citibank</td>\n",
              "      <td>OH</td>\n",
              "      <td>Referral</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>2013</td>\n",
              "      <td>03</td>\n",
              "      <td>14.0</td>\n",
              "      <td>4 days</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358807</th>\n",
              "      <td>Credit card</td>\n",
              "      <td>Payoff process</td>\n",
              "      <td>Capital One</td>\n",
              "      <td>TX</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2013</td>\n",
              "      <td>02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358808</th>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Cont'd attempts collect debt not owed</td>\n",
              "      <td>Encore Capital Group</td>\n",
              "      <td>FL</td>\n",
              "      <td>Phone</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>2015</td>\n",
              "      <td>06</td>\n",
              "      <td>25.0</td>\n",
              "      <td>4 days</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358809</th>\n",
              "      <td>Credit reporting</td>\n",
              "      <td>Incorrect information on credit report</td>\n",
              "      <td>TransUnion Intermediate Holdings, Inc.</td>\n",
              "      <td>FL</td>\n",
              "      <td>Referral</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>2015</td>\n",
              "      <td>05</td>\n",
              "      <td>12.0</td>\n",
              "      <td>22 days</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>358810 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-05a6d6c6-0d71-471e-b105-2c717d5f4ef8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-05a6d6c6-0d71-471e-b105-2c717d5f4ef8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-05a6d6c6-0d71-471e-b105-2c717d5f4ef8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938
        },
        "id": "twp4dFcrphoX",
        "outputId": "19cb0e01-24bb-4407-dddf-813e1143b525"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Product                                     Issue  \\\n",
              "0            Credit card     Customer service / Customer relations   \n",
              "1          Consumer Loan              Payment to acct not credited   \n",
              "2            Credit card             Credit line increase/decrease   \n",
              "3          Consumer Loan       Problems when you are unable to pay   \n",
              "4        Debt collection       Improper contact or sharing of info   \n",
              "...                  ...                                       ...   \n",
              "119601       Credit card                      Credit determination   \n",
              "119602       Credit card                          Balance transfer   \n",
              "119603          Mortgage  Loan modification,collection,foreclosure   \n",
              "119604  Credit reporting    Incorrect information on credit report   \n",
              "119605   Debt collection                     Communication tactics   \n",
              "\n",
              "                                 Company State Submitted via  \\\n",
              "0                               Citibank    TX           Web   \n",
              "1       Larsen MacColl Partners II, L.P.    TX         Phone   \n",
              "2                            Capital One    NC           Web   \n",
              "3        OneMain Financial Holdings, LLC    MO           Web   \n",
              "4                   GMA Investments, LLC    SC           Web   \n",
              "...                                  ...   ...           ...   \n",
              "119601                          Citibank    IL           Web   \n",
              "119602                       Capital One    KS           Web   \n",
              "119603                   Bank of America    CA      Referral   \n",
              "119604                          Experian    OH           Web   \n",
              "119605             Northland Group, Inc.    FL           Web   \n",
              "\n",
              "           Company response to consumer Timely response?  Year Month   Day  \\\n",
              "0           Closed with monetary relief              Yes  2015    01  17.0   \n",
              "1               Closed with explanation              Yes  2016    06  22.0   \n",
              "2               Closed with explanation              Yes  2015    09   4.0   \n",
              "3       Closed with non-monetary relief              Yes  2016    05  17.0   \n",
              "4               Closed with explanation              Yes  2016    07   7.0   \n",
              "...                                 ...              ...   ...   ...   ...   \n",
              "119601          Closed with explanation              Yes  2015    02   9.0   \n",
              "119602          Closed with explanation              Yes  2012    11  13.0   \n",
              "119603            Closed without relief              Yes  2012    05  15.0   \n",
              "119604          Closed with explanation              Yes  2015    06   7.0   \n",
              "119605          Closed with explanation              Yes  2015    03  20.0   \n",
              "\n",
              "       Days Held Week_Received  \n",
              "0         0 days             3  \n",
              "1         0 days             3  \n",
              "2         0 days             1  \n",
              "3         3 days             3  \n",
              "4         0 days             1  \n",
              "...          ...           ...  \n",
              "119601    0 days             2  \n",
              "119602    1 days             2  \n",
              "119603    0 days             2  \n",
              "119604    0 days             1  \n",
              "119605    0 days             3  \n",
              "\n",
              "[119606 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae76c44f-53a0-4813-9c0e-14ddc5594b36\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Product</th>\n",
              "      <th>Issue</th>\n",
              "      <th>Company</th>\n",
              "      <th>State</th>\n",
              "      <th>Submitted via</th>\n",
              "      <th>Company response to consumer</th>\n",
              "      <th>Timely response?</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Days Held</th>\n",
              "      <th>Week_Received</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Credit card</td>\n",
              "      <td>Customer service / Customer relations</td>\n",
              "      <td>Citibank</td>\n",
              "      <td>TX</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2015</td>\n",
              "      <td>01</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Consumer Loan</td>\n",
              "      <td>Payment to acct not credited</td>\n",
              "      <td>Larsen MacColl Partners II, L.P.</td>\n",
              "      <td>TX</td>\n",
              "      <td>Phone</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2016</td>\n",
              "      <td>06</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Credit card</td>\n",
              "      <td>Credit line increase/decrease</td>\n",
              "      <td>Capital One</td>\n",
              "      <td>NC</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2015</td>\n",
              "      <td>09</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Consumer Loan</td>\n",
              "      <td>Problems when you are unable to pay</td>\n",
              "      <td>OneMain Financial Holdings, LLC</td>\n",
              "      <td>MO</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2016</td>\n",
              "      <td>05</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3 days</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Improper contact or sharing of info</td>\n",
              "      <td>GMA Investments, LLC</td>\n",
              "      <td>SC</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2016</td>\n",
              "      <td>07</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119601</th>\n",
              "      <td>Credit card</td>\n",
              "      <td>Credit determination</td>\n",
              "      <td>Citibank</td>\n",
              "      <td>IL</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2015</td>\n",
              "      <td>02</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119602</th>\n",
              "      <td>Credit card</td>\n",
              "      <td>Balance transfer</td>\n",
              "      <td>Capital One</td>\n",
              "      <td>KS</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2012</td>\n",
              "      <td>11</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1 days</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119603</th>\n",
              "      <td>Mortgage</td>\n",
              "      <td>Loan modification,collection,foreclosure</td>\n",
              "      <td>Bank of America</td>\n",
              "      <td>CA</td>\n",
              "      <td>Referral</td>\n",
              "      <td>Closed without relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2012</td>\n",
              "      <td>05</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119604</th>\n",
              "      <td>Credit reporting</td>\n",
              "      <td>Incorrect information on credit report</td>\n",
              "      <td>Experian</td>\n",
              "      <td>OH</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2015</td>\n",
              "      <td>06</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119605</th>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Communication tactics</td>\n",
              "      <td>Northland Group, Inc.</td>\n",
              "      <td>FL</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2015</td>\n",
              "      <td>03</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119606 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae76c44f-53a0-4813-9c0e-14ddc5594b36')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae76c44f-53a0-4813-9c0e-14ddc5594b36 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ae76c44f-53a0-4813-9c0e-14ddc5594b36');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2lPxB1kBpkCg"
      },
      "source": [
        "Store data of disputed people into the “disputed_cons” variable for future tasks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 973
        },
        "id": "qno1opeypjiN",
        "outputId": "71e68af9-5d3e-4435-9bcf-4906dd4fcaf3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Product                                     Issue  \\\n",
              "1       Bank account or service                  Deposits and withdrawals   \n",
              "4               Debt collection           Disclosure verification of debt   \n",
              "5                   Credit card                      APR or interest rate   \n",
              "7                   Credit card                            Payoff process   \n",
              "8       Bank account or service                  Deposits and withdrawals   \n",
              "...                         ...                                       ...   \n",
              "358787                 Mortgage            Credit decision / Underwriting   \n",
              "358791                 Mortgage  Loan modification,collection,foreclosure   \n",
              "358797          Debt collection     Cont'd attempts collect debt not owed   \n",
              "358798         Credit reporting    Incorrect information on credit report   \n",
              "358807              Credit card                            Payoff process   \n",
              "\n",
              "                                          Company State Submitted via  \\\n",
              "1                           Wells Fargo & Company    GA           Web   \n",
              "4       HCFS Health Care Financial Services, Inc.    CA           Web   \n",
              "5                      TD Bank US Holding Company    FL           Web   \n",
              "7                                     Capital One    IL           Web   \n",
              "8                  Citizens Financial Group, Inc.    PA           Web   \n",
              "...                                           ...   ...           ...   \n",
              "358787                       JPMorgan Chase & Co.    CA   Postal mail   \n",
              "358791                            Bank of America    TX      Referral   \n",
              "358797                 Convergent Resources, Inc.    IN           Web   \n",
              "358798                                    Equifax    IL           Web   \n",
              "358807                                Capital One    TX           Web   \n",
              "\n",
              "           Company response to consumer Timely response? Consumer disputed?  \\\n",
              "1               Closed with explanation              Yes                Yes   \n",
              "4               Closed with explanation              Yes                Yes   \n",
              "5               Closed with explanation              Yes                Yes   \n",
              "7               Closed with explanation              Yes                Yes   \n",
              "8                    Closed with relief              Yes                Yes   \n",
              "...                                 ...              ...                ...   \n",
              "358787          Closed with explanation              Yes                Yes   \n",
              "358791          Closed with explanation              Yes                Yes   \n",
              "358797          Closed with explanation              Yes                Yes   \n",
              "358798  Closed with non-monetary relief              Yes                Yes   \n",
              "358807                           Closed              Yes                Yes   \n",
              "\n",
              "        Year Month   Day Days Held Week_Received  \n",
              "1       2015    04  26.0    0 days             4  \n",
              "4       2015    01  30.0    0 days             4  \n",
              "5       2014    01  10.0    5 days             2  \n",
              "7       2015    12   7.0    0 days             1  \n",
              "8       2012    04   5.0    0 days             1  \n",
              "...      ...   ...   ...       ...           ...  \n",
              "358787  2014    05  30.0    5 days             4  \n",
              "358791  2013    08  12.0    1 days             2  \n",
              "358797  2016    06  20.0    0 days             3  \n",
              "358798  2013    02  26.0    0 days             4  \n",
              "358807  2013    02   1.0    0 days             1  \n",
              "\n",
              "[76172 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-141768ef-900d-4ed1-9ef1-b3e206994ff4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Product</th>\n",
              "      <th>Issue</th>\n",
              "      <th>Company</th>\n",
              "      <th>State</th>\n",
              "      <th>Submitted via</th>\n",
              "      <th>Company response to consumer</th>\n",
              "      <th>Timely response?</th>\n",
              "      <th>Consumer disputed?</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Days Held</th>\n",
              "      <th>Week_Received</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bank account or service</td>\n",
              "      <td>Deposits and withdrawals</td>\n",
              "      <td>Wells Fargo &amp; Company</td>\n",
              "      <td>GA</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2015</td>\n",
              "      <td>04</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Disclosure verification of debt</td>\n",
              "      <td>HCFS Health Care Financial Services, Inc.</td>\n",
              "      <td>CA</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2015</td>\n",
              "      <td>01</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Credit card</td>\n",
              "      <td>APR or interest rate</td>\n",
              "      <td>TD Bank US Holding Company</td>\n",
              "      <td>FL</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2014</td>\n",
              "      <td>01</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5 days</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Credit card</td>\n",
              "      <td>Payoff process</td>\n",
              "      <td>Capital One</td>\n",
              "      <td>IL</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2015</td>\n",
              "      <td>12</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Bank account or service</td>\n",
              "      <td>Deposits and withdrawals</td>\n",
              "      <td>Citizens Financial Group, Inc.</td>\n",
              "      <td>PA</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2012</td>\n",
              "      <td>04</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358787</th>\n",
              "      <td>Mortgage</td>\n",
              "      <td>Credit decision / Underwriting</td>\n",
              "      <td>JPMorgan Chase &amp; Co.</td>\n",
              "      <td>CA</td>\n",
              "      <td>Postal mail</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2014</td>\n",
              "      <td>05</td>\n",
              "      <td>30.0</td>\n",
              "      <td>5 days</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358791</th>\n",
              "      <td>Mortgage</td>\n",
              "      <td>Loan modification,collection,foreclosure</td>\n",
              "      <td>Bank of America</td>\n",
              "      <td>TX</td>\n",
              "      <td>Referral</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2013</td>\n",
              "      <td>08</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1 days</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358797</th>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Cont'd attempts collect debt not owed</td>\n",
              "      <td>Convergent Resources, Inc.</td>\n",
              "      <td>IN</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2016</td>\n",
              "      <td>06</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358798</th>\n",
              "      <td>Credit reporting</td>\n",
              "      <td>Incorrect information on credit report</td>\n",
              "      <td>Equifax</td>\n",
              "      <td>IL</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2013</td>\n",
              "      <td>02</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358807</th>\n",
              "      <td>Credit card</td>\n",
              "      <td>Payoff process</td>\n",
              "      <td>Capital One</td>\n",
              "      <td>TX</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2013</td>\n",
              "      <td>02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>76172 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-141768ef-900d-4ed1-9ef1-b3e206994ff4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-141768ef-900d-4ed1-9ef1-b3e206994ff4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-141768ef-900d-4ed1-9ef1-b3e206994ff4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "disputed_cons = df1[df1['Consumer disputed?'] == 'Yes']\n",
        "disputed_cons"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guCwuq0kqeYw"
      },
      "source": [
        "Plot bar graph for the total no of disputes with the help of seaborn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "HodZzTyKq_tg",
        "outputId": "0896a1a7-1528-4217-dc97-60ed89944c34"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(x=\"Consumer disputed?\",data=df1,palette=['green','blue'])\n",
        "plt.xlabel('Consumer disputed?')\n",
        "plt.ylabel('Count')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HX03KpvVr9-x"
      },
      "source": [
        "*Here we creates a bar chart using the Seaborn library that shows the number of occurrences of each unique value in the \"Consumer disputed?\" column of the train  DataFrame.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMWJZDXcs7rd"
      },
      "source": [
        "Plot bar graph for the total no of disputes products-wise with help of seaborn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "Y4P_c9Kos6h1",
        "outputId": "a0c4bcb2-8573-46d8-aca9-2d53166a6810"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbf44bd3850>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(x=\"Product\",hue=\"Consumer disputed?\",data=df1,palette=['green','blue'])#hue function to seperate values in consumer disputed column\n",
        "plt.xlabel('Product')\n",
        "plt.xticks(rotation=90)\n",
        "plt.ylabel('Count')\n",
        "plt.legend(title =\"Consumeres Disputed\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a4iJesKU0DEq"
      },
      "source": [
        "*It showsthe frequency of disputed and not disputed consumers based on the category of products.italicized text*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5dBh-Hz-ymqB"
      },
      "source": [
        "Plot bar graph for the total no of disputes with Top Issues by Highest Disputes , with help of seaborn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 786
        },
        "id": "WsuKwH5ayiML",
        "outputId": "c1643d44-6d03-4a0e-e3b2-e303b2ab7997"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbf44bd0940>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "top_issues_disputes = disputed_cons['Issue'].value_counts().sort_values(ascending = False).head(10)\n",
        "sns.barplot(x = top_issues_disputes.index, y = top_issues_disputes.values)\n",
        "plt.xlabel('Issues')\n",
        "plt.xticks(rotation=90)\n",
        "plt.ylabel('Count')\n",
        "plt.legend(title =\"Top Issues with Highest Disputes\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WB646Cb_0exO"
      },
      "source": [
        "*Here we demonstrates the top 10 most common consumer issues that were disputed*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ADf_SneIzwjl"
      },
      "source": [
        "Plot bar graph of the total no of disputes by State with Maximum Disputes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "gqCSdPem09UW",
        "outputId": "47403aaa-c61e-4e83-aa38-3e4166392377"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbf3d8add50>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from matplotlib.font_manager import FontProperties\n",
        "max_dispute_state=disputed_cons['State'].value_counts().sort_values(ascending = False).head(10)\n",
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "sns.barplot(x = max_dispute_state.index,y=max_dispute_state.values)\n",
        "plt.xticks(rotation = 90, fontsize = 15)\n",
        "plt.yticks(fontsize = 10)\n",
        "plt.xlabel('State', fontsize = 15)\n",
        "plt.ylabel('Count', fontsize = 15)\n",
        "plt.legend(title='State with maximum number of disputes',title_fontsize = 20,loc='upper center')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9k9wtDvU_4wd"
      },
      "source": [
        "*The above graph shows the states that reported maximum number of disputes*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CAXReObk_N9t"
      },
      "source": [
        "Plot bar graph of the total no of disputes Submitted Via different source"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "iwYr--nJ_tkk",
        "outputId": "b3e1006d-ab73-474f-be1a-b93f22372a15"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(x = 'Submitted via', data = disputed_cons)\n",
        "plt.xticks(rotation = 90, fontsize = 10)\n",
        "plt.yticks(fontsize = 10)\n",
        "plt.xlabel('Source', fontsize = 15)\n",
        "plt.ylabel('Count', fontsize = 15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HveMknVuAOX1"
      },
      "source": [
        "*The above plot shows the number of disputed consumers from each source*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmhl0RGbAoGr"
      },
      "source": [
        "Plot bar graph for the total no of disputes where Company's Response to the Complaints"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "id": "gkgY17RhA01w",
        "outputId": "120ef4d0-c9e4-4eac-d7cc-e5d731b4bf26"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(x = 'Company response to consumer', data = df1)\n",
        "plt.xticks(rotation = 90, fontsize = 10)\n",
        "plt.yticks(fontsize = 10)\n",
        "plt.xlabel('Response of Company', fontsize = 15)\n",
        "plt.ylabel('Count', fontsize = 15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RnbpsTC8BoDS"
      },
      "source": [
        "*82% disputes are closed with explanation at the initial stage*\n",
        "*89% disputes are either closed with explanation or non-monetary relief in the earlier stage*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYOaC-N2B7wk"
      },
      "source": [
        "Plot bar graph of the total no of disputes where the Company's Response Leads to\n",
        "Disputes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "id": "iVf4gszuCIhl",
        "outputId": "15dd70e1-6000-4390-e20a-8c6ee5dcf0fa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(x = 'Company response to consumer', data = disputed_cons)\n",
        "plt.xticks(rotation = 90, fontsize = 10)\n",
        "plt.yticks(fontsize = 10)\n",
        "plt.xlabel('Response of company', fontsize = 15)\n",
        "plt.ylabel('Count', fontsize = 15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vsfkb7eqEJRH"
      },
      "source": [
        "Plot bar graph for the total no of disputes Whether there are Disputes Instead of Timely Response"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "_wDPGAE-EVl6",
        "outputId": "99e847bd-b519-447e-e81f-068642644860"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(x = 'Timely response?', data = disputed_cons)\n",
        "plt.xticks(rotation = 90, fontsize = 10)\n",
        "plt.yticks(fontsize = 10)\n",
        "plt.xlabel('Timely response', fontsize = 15)\n",
        "plt.ylabel('Count', fontsize = 15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H6KNSNcxEz-k"
      },
      "source": [
        "*It shows that usualy the company respond to issues timely ,there is only 2% disputes based on not geting response*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LI8UHrc4Hx0U"
      },
      "source": [
        "Plot bar graph of the total no of disputes over Year Wise Complaints"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "r4kUfPPBH8OO",
        "outputId": "9ee9f09e-39c9-4844-a7db-38c969442145"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(x = 'Year', data = df1)\n",
        "plt.xticks(fontsize = 10, ha = \"right\")\n",
        "plt.yticks(fontsize = 10)\n",
        "plt.xlabel('Year', fontsize = 15)\n",
        "plt.ylabel('Count', fontsize = 15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KnUMusb_IXf4"
      },
      "source": [
        "Plot bar graph of the total no of disputes over Year Wise Disputes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "fMCpdU1GI09R",
        "outputId": "265c623d-7c22-4420-bda3-2ff51e36bbf5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(x = 'Year', data = disputed_cons)\n",
        "plt.xticks(fontsize = 10, ha = \"right\")\n",
        "plt.yticks(fontsize = 10)\n",
        "plt.xlabel('Year', fontsize = 15)\n",
        "plt.ylabel('Count', fontsize = 15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h8lIn2FNJLqK"
      },
      "source": [
        "Plot bar graph for the top companies with Highest Complaints"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 727
        },
        "id": "7wUg8r6UJab7",
        "outputId": "b98cff8f-f406-4aae-af0f-c410921045fa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "company_most_complaints = df1['Company'].value_counts().sort_values(ascending = False).head(10)\n",
        "sns.barplot(x = company_most_complaints.index, y = company_most_complaints.values)\n",
        "plt.xticks(rotation = 90, fontsize = 10, ha = \"right\")\n",
        "plt.yticks(fontsize = 10)\n",
        "plt.xlabel('Company', fontsize = 15)\n",
        "plt.ylabel('Count', fontsize = 15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Eb_s6DTJ1Sm"
      },
      "source": [
        "Convert negative values in Days held column to zero"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9JKuT0NzXY7",
        "outputId": "f66787e0-f5ce-477e-d29e-d0e1f3dba8da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                        358810\n",
              "mean      4 days 10:57:07.690421114\n",
              "std      16 days 17:48:00.827311545\n",
              "min               -1 days +00:00:00\n",
              "25%                 0 days 00:00:00\n",
              "50%                 1 days 00:00:00\n",
              "75%                 4 days 00:00:00\n",
              "max               993 days 00:00:00\n",
              "Name: Days Held, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "df1['Days Held'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KSqFbdACz8Bn",
        "outputId": "6827f91e-c202-40ca-b6f3-d816baa9d526"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                        119606\n",
              "mean      4 days 10:38:47.602294199\n",
              "std      16 days 05:36:03.796515735\n",
              "min               -1 days +00:00:00\n",
              "25%                 0 days 00:00:00\n",
              "50%                 1 days 00:00:00\n",
              "75%                 4 days 00:00:00\n",
              "max               832 days 00:00:00\n",
              "Name: Days Held, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "df2['Days Held'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z0FW6-glJxUi"
      },
      "outputs": [],
      "source": [
        "df1['Days Held'] =df1['Days Held'].astype(int)\n",
        "df2['Days Held'] =df2['Days Held'].astype(int)\n",
        "def non_negative_days(x):\n",
        "    if x < 0:\n",
        "        return 0\n",
        "    else:\n",
        "        return x\n",
        "\n",
        "df1['Days_Held_Train'] = df1['Days Held'].apply(non_negative_days)\n",
        "df2['Days_Held_Test'] = df2['Days Held'].apply(non_negative_days)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TN6LbJ390tPR",
        "outputId": "8a4c49e6-1b52-4aac-9975-4b3378ccf75d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    3.588100e+05\n",
              "mean     3.860234e+14\n",
              "std      1.446186e+15\n",
              "min      0.000000e+00\n",
              "25%      0.000000e+00\n",
              "50%      8.640000e+13\n",
              "75%      3.456000e+14\n",
              "max      8.579520e+16\n",
              "Name: Days_Held_Train, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "df1['Days_Held_Train'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AVSo80wj1s9U",
        "outputId": "3e1885f3-ae8e-4eaf-fc6a-7e191bae112d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1.196060e+05\n",
              "mean     3.849274e+14\n",
              "std      1.402259e+15\n",
              "min      0.000000e+00\n",
              "25%      0.000000e+00\n",
              "50%      8.640000e+13\n",
              "75%      3.456000e+14\n",
              "max      7.188480e+16\n",
              "Name: Days_Held_Test, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "df2['Days_Held_Test'].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UjFe34BOwoUR"
      },
      "source": [
        "**TEXT PREPROCESSING**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b4ZS_abN2BnV"
      },
      "outputs": [],
      "source": [
        "\n",
        "stop_words = set(stopwords.words('english'))\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "stemmer = PorterStemmer()\n",
        "\n",
        "def preprocess_text(text):\n",
        "    # Tokenize the text\n",
        "    tokens = wordpunct_tokenize(text.lower())\n",
        "\n",
        "    # Remove punctuation and stop words\n",
        "    filtered_tokens = [token for token in tokens if token not in string.punctuation and token not in stop_words]\n",
        "\n",
        "    # Lemmatize the tokens\n",
        "    lemmatized_tokens = [lemmatizer.lemmatize(token, pos='v') for token in filtered_tokens]\n",
        "\n",
        "    # Stem the tokens\n",
        "    stemmed_tokens = [stemmer.stem(token) for token in lemmatized_tokens]\n",
        "\n",
        "    # Join the tokens back into a sentence\n",
        "    preprocessed_text = ' '.join(stemmed_tokens)\n",
        "\n",
        "    return preprocessed_text\n",
        "\n",
        "# Apply the preprocessing function to the \"Issue\" column of the training and test dataframes\n",
        "clean_data_train = df1['Issue'].apply(preprocess_text)\n",
        "clean_data_test = df2['Issue'].apply(preprocess_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z_XsQMrvwSyx"
      },
      "outputs": [],
      "source": [
        "df1['Issues_cleaned'] = clean_data_train\n",
        "df2['Issues_cleaned'] = clean_data_test\n",
        "df1= df1.drop('Issue', axis = 1)\n",
        "df2= df2.drop('Issue', axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3eKvGqJ1vZo"
      },
      "source": [
        "Drop Unnecessary Columns for the Model Building\n",
        "like:'Company', 'State', 'Year_Received', 'Days_held'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gAxDSvBsw2B5"
      },
      "outputs": [],
      "source": [
        "drop_cols = ['Company', 'State', 'Year', 'Days Held']\n",
        "df1= df1.drop(drop_cols, axis = 1)\n",
        "df2= df2.drop(drop_cols, axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jC2k5NUeMBgD"
      },
      "source": [
        "Change Consumer Disputed Column to 0 and 1(yes to 1, and no to 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d9esItKjMEdk"
      },
      "outputs": [],
      "source": [
        "df1['Consumer disputed?'] = np.where(df1['Consumer disputed?'] == \"Yes\", 1, 0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFFDFAn_OCMh"
      },
      "source": [
        "**Create Dummy Variables for catagorical features**\n",
        "Create Dummy Variables for categorical features and concat with the original data frame\n",
        "like: 'Product,’ 'Submitted via,’ 'Company response to consumer,’ 'Timely response?'?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x-Tla9WCOjzv"
      },
      "outputs": [],
      "source": [
        "dummy_cols = ['Product', 'Submitted via', 'Company response to consumer', 'Timely response?']\n",
        "df_train_dummies = pd.get_dummies(df1[dummy_cols], prefix_sep = '_', drop_first = True)\n",
        "df_test_dummies = pd.get_dummies(df2[dummy_cols], prefix_sep = '_', drop_first = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 956
        },
        "id": "l9rxz75Dk5E2",
        "outputId": "19612571-fed5-49b6-fcb8-051cd5e9c5d2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Product Submitted via  \\\n",
              "0              Credit reporting           Web   \n",
              "1       Bank account or service           Web   \n",
              "2                   Credit card         Phone   \n",
              "3               Debt collection      Referral   \n",
              "4               Debt collection           Web   \n",
              "...                         ...           ...   \n",
              "358805         Credit reporting           Web   \n",
              "358806                 Mortgage      Referral   \n",
              "358807              Credit card           Web   \n",
              "358808          Debt collection         Phone   \n",
              "358809         Credit reporting      Referral   \n",
              "\n",
              "           Company response to consumer Timely response?  Consumer disputed?  \\\n",
              "0               Closed with explanation              Yes                   0   \n",
              "1               Closed with explanation              Yes                   1   \n",
              "2       Closed with non-monetary relief              Yes                   0   \n",
              "3               Closed with explanation              Yes                   0   \n",
              "4               Closed with explanation              Yes                   1   \n",
              "...                                 ...              ...                 ...   \n",
              "358805  Closed with non-monetary relief              Yes                   0   \n",
              "358806  Closed with non-monetary relief              Yes                   0   \n",
              "358807                           Closed              Yes                   1   \n",
              "358808  Closed with non-monetary relief              Yes                   0   \n",
              "358809          Closed with explanation              Yes                   0   \n",
              "\n",
              "       Month   Day Week_Received   Days_Held_Train  \\\n",
              "0         10  14.0             2                 0   \n",
              "1         04  26.0             4                 0   \n",
              "2         12  20.0             3  1209600000000000   \n",
              "3         03   3.0             1    86400000000000   \n",
              "4         01  30.0             4                 0   \n",
              "...      ...   ...           ...               ...   \n",
              "358805    04  14.0             2                 0   \n",
              "358806    03  14.0             2   345600000000000   \n",
              "358807    02   1.0             1                 0   \n",
              "358808    06  25.0             4   345600000000000   \n",
              "358809    05  12.0             2  1900800000000000   \n",
              "\n",
              "                        Issues_cleaned  \n",
              "0       incorrect inform credit report  \n",
              "1                     deposit withdraw  \n",
              "2                                       \n",
              "3                 disclosur verif debt  \n",
              "4                 disclosur verif debt  \n",
              "...                                ...  \n",
              "358805  incorrect inform credit report  \n",
              "358806   loan modif collect foreclosur  \n",
              "358807                  payoff process  \n",
              "358808   cont attempt collect debt owe  \n",
              "358809  incorrect inform credit report  \n",
              "\n",
              "[358810 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cc6c36ab-72d1-4bf0-a1ca-a4eebb63b1e2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Product</th>\n",
              "      <th>Submitted via</th>\n",
              "      <th>Company response to consumer</th>\n",
              "      <th>Timely response?</th>\n",
              "      <th>Consumer disputed?</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Week_Received</th>\n",
              "      <th>Days_Held_Train</th>\n",
              "      <th>Issues_cleaned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Credit reporting</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>incorrect inform credit report</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bank account or service</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>04</td>\n",
              "      <td>26.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>deposit withdraw</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Credit card</td>\n",
              "      <td>Phone</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1209600000000000</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Referral</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>86400000000000</td>\n",
              "      <td>disclosur verif debt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>01</td>\n",
              "      <td>30.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>disclosur verif debt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358805</th>\n",
              "      <td>Credit reporting</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>04</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>incorrect inform credit report</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358806</th>\n",
              "      <td>Mortgage</td>\n",
              "      <td>Referral</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>03</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2</td>\n",
              "      <td>345600000000000</td>\n",
              "      <td>loan modif collect foreclosur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358807</th>\n",
              "      <td>Credit card</td>\n",
              "      <td>Web</td>\n",
              "      <td>Closed</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>payoff process</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358808</th>\n",
              "      <td>Debt collection</td>\n",
              "      <td>Phone</td>\n",
              "      <td>Closed with non-monetary relief</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>06</td>\n",
              "      <td>25.0</td>\n",
              "      <td>4</td>\n",
              "      <td>345600000000000</td>\n",
              "      <td>cont attempt collect debt owe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358809</th>\n",
              "      <td>Credit reporting</td>\n",
              "      <td>Referral</td>\n",
              "      <td>Closed with explanation</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>05</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1900800000000000</td>\n",
              "      <td>incorrect inform credit report</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>358810 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cc6c36ab-72d1-4bf0-a1ca-a4eebb63b1e2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cc6c36ab-72d1-4bf0-a1ca-a4eebb63b1e2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cc6c36ab-72d1-4bf0-a1ca-a4eebb63b1e2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8Ut7cwW4PS9G"
      },
      "outputs": [],
      "source": [
        "df1= df1.drop(dummy_cols, axis = 1)\n",
        "df2= df2.drop(dummy_cols, axis = 1)\n",
        "df1= pd.concat([df1, df_train_dummies], axis = 1)\n",
        "df2 = pd.concat([df2, df_test_dummies], axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        },
        "id": "njO87fzJvhhi",
        "outputId": "99ddd09a-2310-4484-a4e8-c628c1799e01"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Consumer disputed? Month   Day Week_Received   Days_Held_Train  \\\n",
              "0                        0    10  14.0             2                 0   \n",
              "1                        1    04  26.0             4                 0   \n",
              "2                        0    12  20.0             3  1209600000000000   \n",
              "3                        0    03   3.0             1    86400000000000   \n",
              "4                        1    01  30.0             4                 0   \n",
              "...                    ...   ...   ...           ...               ...   \n",
              "358805                   0    04  14.0             2                 0   \n",
              "358806                   0    03  14.0             2   345600000000000   \n",
              "358807                   1    02   1.0             1                 0   \n",
              "358808                   0    06  25.0             4   345600000000000   \n",
              "358809                   0    05  12.0             2  1900800000000000   \n",
              "\n",
              "                        Issues_cleaned  Product_Consumer Loan  \\\n",
              "0       incorrect inform credit report                      0   \n",
              "1                     deposit withdraw                      0   \n",
              "2                                                           0   \n",
              "3                 disclosur verif debt                      0   \n",
              "4                 disclosur verif debt                      0   \n",
              "...                                ...                    ...   \n",
              "358805  incorrect inform credit report                      0   \n",
              "358806   loan modif collect foreclosur                      0   \n",
              "358807                  payoff process                      0   \n",
              "358808   cont attempt collect debt owe                      0   \n",
              "358809  incorrect inform credit report                      0   \n",
              "\n",
              "        Product_Credit card  Product_Credit reporting  \\\n",
              "0                         0                         1   \n",
              "1                         0                         0   \n",
              "2                         1                         0   \n",
              "3                         0                         0   \n",
              "4                         0                         0   \n",
              "...                     ...                       ...   \n",
              "358805                    0                         1   \n",
              "358806                    0                         0   \n",
              "358807                    1                         0   \n",
              "358808                    0                         0   \n",
              "358809                    0                         1   \n",
              "\n",
              "        Product_Debt collection  ...  Submitted via_Phone  \\\n",
              "0                             0  ...                    0   \n",
              "1                             0  ...                    0   \n",
              "2                             0  ...                    1   \n",
              "3                             1  ...                    0   \n",
              "4                             1  ...                    0   \n",
              "...                         ...  ...                  ...   \n",
              "358805                        0  ...                    0   \n",
              "358806                        0  ...                    0   \n",
              "358807                        0  ...                    0   \n",
              "358808                        1  ...                    1   \n",
              "358809                        0  ...                    0   \n",
              "\n",
              "        Submitted via_Postal mail  Submitted via_Referral  Submitted via_Web  \\\n",
              "0                               0                       0                  1   \n",
              "1                               0                       0                  1   \n",
              "2                               0                       0                  0   \n",
              "3                               0                       1                  0   \n",
              "4                               0                       0                  1   \n",
              "...                           ...                     ...                ...   \n",
              "358805                          0                       0                  1   \n",
              "358806                          0                       1                  0   \n",
              "358807                          0                       0                  1   \n",
              "358808                          0                       0                  0   \n",
              "358809                          0                       1                  0   \n",
              "\n",
              "        Company response to consumer_Closed with explanation  \\\n",
              "0                                                       1      \n",
              "1                                                       1      \n",
              "2                                                       0      \n",
              "3                                                       1      \n",
              "4                                                       1      \n",
              "...                                                   ...      \n",
              "358805                                                  0      \n",
              "358806                                                  0      \n",
              "358807                                                  0      \n",
              "358808                                                  0      \n",
              "358809                                                  1      \n",
              "\n",
              "        Company response to consumer_Closed with monetary relief  \\\n",
              "0                                                       0          \n",
              "1                                                       0          \n",
              "2                                                       0          \n",
              "3                                                       0          \n",
              "4                                                       0          \n",
              "...                                                   ...          \n",
              "358805                                                  0          \n",
              "358806                                                  0          \n",
              "358807                                                  0          \n",
              "358808                                                  0          \n",
              "358809                                                  0          \n",
              "\n",
              "        Company response to consumer_Closed with non-monetary relief  \\\n",
              "0                                                       0              \n",
              "1                                                       0              \n",
              "2                                                       1              \n",
              "3                                                       0              \n",
              "4                                                       0              \n",
              "...                                                   ...              \n",
              "358805                                                  1              \n",
              "358806                                                  1              \n",
              "358807                                                  0              \n",
              "358808                                                  1              \n",
              "358809                                                  0              \n",
              "\n",
              "        Company response to consumer_Closed with relief  \\\n",
              "0                                                     0   \n",
              "1                                                     0   \n",
              "2                                                     0   \n",
              "3                                                     0   \n",
              "4                                                     0   \n",
              "...                                                 ...   \n",
              "358805                                                0   \n",
              "358806                                                0   \n",
              "358807                                                0   \n",
              "358808                                                0   \n",
              "358809                                                0   \n",
              "\n",
              "        Company response to consumer_Closed without relief  \\\n",
              "0                                                       0    \n",
              "1                                                       0    \n",
              "2                                                       0    \n",
              "3                                                       0    \n",
              "4                                                       0    \n",
              "...                                                   ...    \n",
              "358805                                                  0    \n",
              "358806                                                  0    \n",
              "358807                                                  0    \n",
              "358808                                                  0    \n",
              "358809                                                  0    \n",
              "\n",
              "        Timely response?_Yes  \n",
              "0                          1  \n",
              "1                          1  \n",
              "2                          1  \n",
              "3                          1  \n",
              "4                          1  \n",
              "...                      ...  \n",
              "358805                     1  \n",
              "358806                     1  \n",
              "358807                     1  \n",
              "358808                     1  \n",
              "358809                     1  \n",
              "\n",
              "[358810 rows x 28 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6f5b9a9e-70f2-4388-b2d4-066c3179c36d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Consumer disputed?</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Week_Received</th>\n",
              "      <th>Days_Held_Train</th>\n",
              "      <th>Issues_cleaned</th>\n",
              "      <th>Product_Consumer Loan</th>\n",
              "      <th>Product_Credit card</th>\n",
              "      <th>Product_Credit reporting</th>\n",
              "      <th>Product_Debt collection</th>\n",
              "      <th>...</th>\n",
              "      <th>Submitted via_Phone</th>\n",
              "      <th>Submitted via_Postal mail</th>\n",
              "      <th>Submitted via_Referral</th>\n",
              "      <th>Submitted via_Web</th>\n",
              "      <th>Company response to consumer_Closed with explanation</th>\n",
              "      <th>Company response to consumer_Closed with monetary relief</th>\n",
              "      <th>Company response to consumer_Closed with non-monetary relief</th>\n",
              "      <th>Company response to consumer_Closed with relief</th>\n",
              "      <th>Company response to consumer_Closed without relief</th>\n",
              "      <th>Timely response?_Yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>incorrect inform credit report</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>04</td>\n",
              "      <td>26.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>deposit withdraw</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1209600000000000</td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>86400000000000</td>\n",
              "      <td>disclosur verif debt</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>01</td>\n",
              "      <td>30.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>disclosur verif debt</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358805</th>\n",
              "      <td>0</td>\n",
              "      <td>04</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>incorrect inform credit report</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358806</th>\n",
              "      <td>0</td>\n",
              "      <td>03</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2</td>\n",
              "      <td>345600000000000</td>\n",
              "      <td>loan modif collect foreclosur</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358807</th>\n",
              "      <td>1</td>\n",
              "      <td>02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>payoff process</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358808</th>\n",
              "      <td>0</td>\n",
              "      <td>06</td>\n",
              "      <td>25.0</td>\n",
              "      <td>4</td>\n",
              "      <td>345600000000000</td>\n",
              "      <td>cont attempt collect debt owe</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358809</th>\n",
              "      <td>0</td>\n",
              "      <td>05</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1900800000000000</td>\n",
              "      <td>incorrect inform credit report</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>358810 rows × 28 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f5b9a9e-70f2-4388-b2d4-066c3179c36d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6f5b9a9e-70f2-4388-b2d4-066c3179c36d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6f5b9a9e-70f2-4388-b2d4-066c3179c36d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sy9WdixMVm0j"
      },
      "source": [
        "**Calculating TF-IDF**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XnwuzzbqVncl",
        "outputId": "9b332569-0a5c-469f-8a00-26f332ea0854"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Idf_weights\n",
              "exchang      11.998819\n",
              "damag        11.593354\n",
              "destroy      11.593354\n",
              "save         11.305672\n",
              "overdraft    11.305672"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-72397fe9-f639-44f4-9954-7a56ae291e02\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Idf_weights</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>exchang</th>\n",
              "      <td>11.998819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>damag</th>\n",
              "      <td>11.593354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>destroy</th>\n",
              "      <td>11.593354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>save</th>\n",
              "      <td>11.305672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>overdraft</th>\n",
              "      <td>11.305672</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72397fe9-f639-44f4-9954-7a56ae291e02')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-72397fe9-f639-44f4-9954-7a56ae291e02 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-72397fe9-f639-44f4-9954-7a56ae291e02');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "tf = TfidfVectorizer()\n",
        "issues_cleaned_train = tf.fit_transform(df1['Issues_cleaned']).toarray()\n",
        "issues_cleaned_test = tf.fit_transform(df2['Issues_cleaned']).toarray()\n",
        "tf_columns_train = []\n",
        "tf_columns_test = []\n",
        "for i in range(issues_cleaned_train.shape[1]):\n",
        "    tf_columns_train.append('Feature' + str(i+1))\n",
        "for i in range(issues_cleaned_test.shape[1]):\n",
        "    tf_columns_test.append('Feature' + str(i+1))\n",
        "issues_train = pd.DataFrame(issues_cleaned_train, columns = tf_columns_train)\n",
        "issues_test = pd.DataFrame(issues_cleaned_test, columns = tf_columns_test)\n",
        "weights = pd.DataFrame(tf.idf_, index = tf.get_feature_names_out(), columns = ['Idf_weights']).sort_values(by = 'Idf_weights', ascending = False)\n",
        "weights.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        },
        "id": "7ixlqJ2mwp6V",
        "outputId": "28b83111-be98-4de8-bfc5-15e86085b1f7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Consumer disputed? Month   Day Week_Received   Days_Held_Train  \\\n",
              "0                        0    10  14.0             2                 0   \n",
              "1                        1    04  26.0             4                 0   \n",
              "2                        0    12  20.0             3  1209600000000000   \n",
              "3                        0    03   3.0             1    86400000000000   \n",
              "4                        1    01  30.0             4                 0   \n",
              "...                    ...   ...   ...           ...               ...   \n",
              "358805                   0    04  14.0             2                 0   \n",
              "358806                   0    03  14.0             2   345600000000000   \n",
              "358807                   1    02   1.0             1                 0   \n",
              "358808                   0    06  25.0             4   345600000000000   \n",
              "358809                   0    05  12.0             2  1900800000000000   \n",
              "\n",
              "                        Issues_cleaned  Product_Consumer Loan  \\\n",
              "0       incorrect inform credit report                      0   \n",
              "1                     deposit withdraw                      0   \n",
              "2                                                           0   \n",
              "3                 disclosur verif debt                      0   \n",
              "4                 disclosur verif debt                      0   \n",
              "...                                ...                    ...   \n",
              "358805  incorrect inform credit report                      0   \n",
              "358806   loan modif collect foreclosur                      0   \n",
              "358807                  payoff process                      0   \n",
              "358808   cont attempt collect debt owe                      0   \n",
              "358809  incorrect inform credit report                      0   \n",
              "\n",
              "        Product_Credit card  Product_Credit reporting  \\\n",
              "0                         0                         1   \n",
              "1                         0                         0   \n",
              "2                         1                         0   \n",
              "3                         0                         0   \n",
              "4                         0                         0   \n",
              "...                     ...                       ...   \n",
              "358805                    0                         1   \n",
              "358806                    0                         0   \n",
              "358807                    1                         0   \n",
              "358808                    0                         0   \n",
              "358809                    0                         1   \n",
              "\n",
              "        Product_Debt collection  ...  Submitted via_Phone  \\\n",
              "0                             0  ...                    0   \n",
              "1                             0  ...                    0   \n",
              "2                             0  ...                    1   \n",
              "3                             1  ...                    0   \n",
              "4                             1  ...                    0   \n",
              "...                         ...  ...                  ...   \n",
              "358805                        0  ...                    0   \n",
              "358806                        0  ...                    0   \n",
              "358807                        0  ...                    0   \n",
              "358808                        1  ...                    1   \n",
              "358809                        0  ...                    0   \n",
              "\n",
              "        Submitted via_Postal mail  Submitted via_Referral  Submitted via_Web  \\\n",
              "0                               0                       0                  1   \n",
              "1                               0                       0                  1   \n",
              "2                               0                       0                  0   \n",
              "3                               0                       1                  0   \n",
              "4                               0                       0                  1   \n",
              "...                           ...                     ...                ...   \n",
              "358805                          0                       0                  1   \n",
              "358806                          0                       1                  0   \n",
              "358807                          0                       0                  1   \n",
              "358808                          0                       0                  0   \n",
              "358809                          0                       1                  0   \n",
              "\n",
              "        Company response to consumer_Closed with explanation  \\\n",
              "0                                                       1      \n",
              "1                                                       1      \n",
              "2                                                       0      \n",
              "3                                                       1      \n",
              "4                                                       1      \n",
              "...                                                   ...      \n",
              "358805                                                  0      \n",
              "358806                                                  0      \n",
              "358807                                                  0      \n",
              "358808                                                  0      \n",
              "358809                                                  1      \n",
              "\n",
              "        Company response to consumer_Closed with monetary relief  \\\n",
              "0                                                       0          \n",
              "1                                                       0          \n",
              "2                                                       0          \n",
              "3                                                       0          \n",
              "4                                                       0          \n",
              "...                                                   ...          \n",
              "358805                                                  0          \n",
              "358806                                                  0          \n",
              "358807                                                  0          \n",
              "358808                                                  0          \n",
              "358809                                                  0          \n",
              "\n",
              "        Company response to consumer_Closed with non-monetary relief  \\\n",
              "0                                                       0              \n",
              "1                                                       0              \n",
              "2                                                       1              \n",
              "3                                                       0              \n",
              "4                                                       0              \n",
              "...                                                   ...              \n",
              "358805                                                  1              \n",
              "358806                                                  1              \n",
              "358807                                                  0              \n",
              "358808                                                  1              \n",
              "358809                                                  0              \n",
              "\n",
              "        Company response to consumer_Closed with relief  \\\n",
              "0                                                     0   \n",
              "1                                                     0   \n",
              "2                                                     0   \n",
              "3                                                     0   \n",
              "4                                                     0   \n",
              "...                                                 ...   \n",
              "358805                                                0   \n",
              "358806                                                0   \n",
              "358807                                                0   \n",
              "358808                                                0   \n",
              "358809                                                0   \n",
              "\n",
              "        Company response to consumer_Closed without relief  \\\n",
              "0                                                       0    \n",
              "1                                                       0    \n",
              "2                                                       0    \n",
              "3                                                       0    \n",
              "4                                                       0    \n",
              "...                                                   ...    \n",
              "358805                                                  0    \n",
              "358806                                                  0    \n",
              "358807                                                  0    \n",
              "358808                                                  0    \n",
              "358809                                                  0    \n",
              "\n",
              "        Timely response?_Yes  \n",
              "0                          1  \n",
              "1                          1  \n",
              "2                          1  \n",
              "3                          1  \n",
              "4                          1  \n",
              "...                      ...  \n",
              "358805                     1  \n",
              "358806                     1  \n",
              "358807                     1  \n",
              "358808                     1  \n",
              "358809                     1  \n",
              "\n",
              "[358810 rows x 28 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5dcd4de1-824f-47c6-9f83-6eae1ebfb6db\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Consumer disputed?</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Week_Received</th>\n",
              "      <th>Days_Held_Train</th>\n",
              "      <th>Issues_cleaned</th>\n",
              "      <th>Product_Consumer Loan</th>\n",
              "      <th>Product_Credit card</th>\n",
              "      <th>Product_Credit reporting</th>\n",
              "      <th>Product_Debt collection</th>\n",
              "      <th>...</th>\n",
              "      <th>Submitted via_Phone</th>\n",
              "      <th>Submitted via_Postal mail</th>\n",
              "      <th>Submitted via_Referral</th>\n",
              "      <th>Submitted via_Web</th>\n",
              "      <th>Company response to consumer_Closed with explanation</th>\n",
              "      <th>Company response to consumer_Closed with monetary relief</th>\n",
              "      <th>Company response to consumer_Closed with non-monetary relief</th>\n",
              "      <th>Company response to consumer_Closed with relief</th>\n",
              "      <th>Company response to consumer_Closed without relief</th>\n",
              "      <th>Timely response?_Yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>incorrect inform credit report</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>04</td>\n",
              "      <td>26.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>deposit withdraw</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1209600000000000</td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>86400000000000</td>\n",
              "      <td>disclosur verif debt</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>01</td>\n",
              "      <td>30.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>disclosur verif debt</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358805</th>\n",
              "      <td>0</td>\n",
              "      <td>04</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>incorrect inform credit report</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358806</th>\n",
              "      <td>0</td>\n",
              "      <td>03</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2</td>\n",
              "      <td>345600000000000</td>\n",
              "      <td>loan modif collect foreclosur</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358807</th>\n",
              "      <td>1</td>\n",
              "      <td>02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>payoff process</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358808</th>\n",
              "      <td>0</td>\n",
              "      <td>06</td>\n",
              "      <td>25.0</td>\n",
              "      <td>4</td>\n",
              "      <td>345600000000000</td>\n",
              "      <td>cont attempt collect debt owe</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358809</th>\n",
              "      <td>0</td>\n",
              "      <td>05</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1900800000000000</td>\n",
              "      <td>incorrect inform credit report</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>358810 rows × 28 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5dcd4de1-824f-47c6-9f83-6eae1ebfb6db')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5dcd4de1-824f-47c6-9f83-6eae1ebfb6db button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5dcd4de1-824f-47c6-9f83-6eae1ebfb6db');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "df1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ibbgQTHKitwO"
      },
      "source": [
        "Replacing Issues_cleaned by Vectorized Issues"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t5kTvQKXhLI4"
      },
      "outputs": [],
      "source": [
        "df1 = df1.drop('Issues_cleaned', axis = 1)\n",
        "df2= df2.drop('Issues_cleaned', axis = 1)\n",
        "df1= pd.concat([df1, issues_train], axis = 1)\n",
        "df2= pd.concat([df2, issues_test], axis = 1)\n",
        "Feature168 = [0] * 119606\n",
        "df2['Feature168'] = Feature168"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ofx4Br8HimC8"
      },
      "source": [
        "observe train and test data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "bc2ugizbi3rW",
        "outputId": "bcf12b3a-6136-4fea-8156-310051e53b8b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Consumer disputed? Month   Day Week_Received   Days_Held_Train  \\\n",
              "0                   0    10  14.0             2                 0   \n",
              "1                   1    04  26.0             4                 0   \n",
              "2                   0    12  20.0             3  1209600000000000   \n",
              "3                   0    03   3.0             1    86400000000000   \n",
              "4                   1    01  30.0             4                 0   \n",
              "\n",
              "   Product_Consumer Loan  Product_Credit card  Product_Credit reporting  \\\n",
              "0                      0                    0                         1   \n",
              "1                      0                    0                         0   \n",
              "2                      0                    1                         0   \n",
              "3                      0                    0                         0   \n",
              "4                      0                    0                         0   \n",
              "\n",
              "   Product_Debt collection  Product_Money transfers  ...  Feature138  \\\n",
              "0                        0                        0  ...         0.0   \n",
              "1                        0                        0  ...         0.0   \n",
              "2                        0                        0  ...         0.0   \n",
              "3                        1                        0  ...         0.0   \n",
              "4                        1                        0  ...         0.0   \n",
              "\n",
              "   Feature139  Feature140  Feature141  Feature142  Feature143  Feature144  \\\n",
              "0         0.0         0.0         0.0         0.0         0.0     0.00000   \n",
              "1         0.0         0.0         0.0         0.0         0.0     0.00000   \n",
              "2         0.0         0.0         0.0         0.0         0.0     0.00000   \n",
              "3         0.0         0.0         0.0         0.0         0.0     0.63037   \n",
              "4         0.0         0.0         0.0         0.0         0.0     0.63037   \n",
              "\n",
              "   Feature145  Feature146  Feature147  \n",
              "0    0.000000         0.0         0.0  \n",
              "1    0.707107         0.0         0.0  \n",
              "2    0.000000         0.0         0.0  \n",
              "3    0.000000         0.0         0.0  \n",
              "4    0.000000         0.0         0.0  \n",
              "\n",
              "[5 rows x 174 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-85f3598d-69f4-48e9-bf3d-76cc6c8466e5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Consumer disputed?</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Week_Received</th>\n",
              "      <th>Days_Held_Train</th>\n",
              "      <th>Product_Consumer Loan</th>\n",
              "      <th>Product_Credit card</th>\n",
              "      <th>Product_Credit reporting</th>\n",
              "      <th>Product_Debt collection</th>\n",
              "      <th>Product_Money transfers</th>\n",
              "      <th>...</th>\n",
              "      <th>Feature138</th>\n",
              "      <th>Feature139</th>\n",
              "      <th>Feature140</th>\n",
              "      <th>Feature141</th>\n",
              "      <th>Feature142</th>\n",
              "      <th>Feature143</th>\n",
              "      <th>Feature144</th>\n",
              "      <th>Feature145</th>\n",
              "      <th>Feature146</th>\n",
              "      <th>Feature147</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>14.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>04</td>\n",
              "      <td>26.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</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>0.00000</td>\n",
              "      <td>0.707107</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1209600000000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>03</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>86400000000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.63037</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>01</td>\n",
              "      <td>30.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.63037</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 174 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85f3598d-69f4-48e9-bf3d-76cc6c8466e5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-85f3598d-69f4-48e9-bf3d-76cc6c8466e5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85f3598d-69f4-48e9-bf3d-76cc6c8466e5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "df1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "Q6o9F13rjAbb",
        "outputId": "957c2ebe-1497-4e2c-b2aa-be9bf7940746"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Month   Day Week_Received   Days_Held_Test  Product_Consumer Loan  \\\n",
              "0    01  17.0             3                0                      0   \n",
              "1    06  22.0             3                0                      1   \n",
              "2    09   4.0             1                0                      0   \n",
              "3    05  17.0             3  259200000000000                      1   \n",
              "4    07   7.0             1                0                      0   \n",
              "\n",
              "   Product_Credit card  Product_Credit reporting  Product_Debt collection  \\\n",
              "0                    1                         0                        0   \n",
              "1                    0                         0                        0   \n",
              "2                    1                         0                        0   \n",
              "3                    0                         0                        0   \n",
              "4                    0                         0                        1   \n",
              "\n",
              "   Product_Money transfers  Product_Mortgage  ...  Feature138  Feature139  \\\n",
              "0                        0                 0  ...         0.0         0.0   \n",
              "1                        0                 0  ...         0.0         0.0   \n",
              "2                        0                 0  ...         0.0         0.0   \n",
              "3                        0                 0  ...         0.0         0.0   \n",
              "4                        0                 0  ...         0.0         0.0   \n",
              "\n",
              "   Feature140  Feature141  Feature142  Feature143  Feature144  Feature145  \\\n",
              "0         0.0         0.0         0.0         0.0         0.0         0.0   \n",
              "1         0.0         0.0         0.0         0.0         0.0         0.0   \n",
              "2         0.0         0.0         0.0         0.0         0.0         0.0   \n",
              "3         0.0         0.0         0.0         0.0         0.0         0.0   \n",
              "4         0.0         0.0         0.0         0.0         0.0         0.0   \n",
              "\n",
              "   Feature146  Feature168  \n",
              "0         0.0           0  \n",
              "1         0.0           0  \n",
              "2         0.0           0  \n",
              "3         0.0           0  \n",
              "4         0.0           0  \n",
              "\n",
              "[5 rows x 173 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c25b23c3-2040-4cd9-bb27-970053292773\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Week_Received</th>\n",
              "      <th>Days_Held_Test</th>\n",
              "      <th>Product_Consumer Loan</th>\n",
              "      <th>Product_Credit card</th>\n",
              "      <th>Product_Credit reporting</th>\n",
              "      <th>Product_Debt collection</th>\n",
              "      <th>Product_Money transfers</th>\n",
              "      <th>Product_Mortgage</th>\n",
              "      <th>...</th>\n",
              "      <th>Feature138</th>\n",
              "      <th>Feature139</th>\n",
              "      <th>Feature140</th>\n",
              "      <th>Feature141</th>\n",
              "      <th>Feature142</th>\n",
              "      <th>Feature143</th>\n",
              "      <th>Feature144</th>\n",
              "      <th>Feature145</th>\n",
              "      <th>Feature146</th>\n",
              "      <th>Feature168</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>06</td>\n",
              "      <td>22.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>09</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>05</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3</td>\n",
              "      <td>259200000000000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>07</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 173 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c25b23c3-2040-4cd9-bb27-970053292773')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c25b23c3-2040-4cd9-bb27-970053292773 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c25b23c3-2040-4cd9-bb27-970053292773');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "df2.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gg-Uk61UjKSP"
      },
      "source": [
        "Observe Shape of new Train and Test Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jwdBVSwLjJw2",
        "outputId": "9a2d95cc-e2f2-4c96-d9f6-7351a042e20d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((358810, 174), (119606, 173))"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "df1.shape, df2.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tVaPy1spjY2t"
      },
      "source": [
        "Scaling the Data Sets (note: Discard dependent variable before doing standardization)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qnf0fHb4jcdG"
      },
      "outputs": [],
      "source": [
        "df_train_scaled = pd.DataFrame(StandardScaler().fit_transform(df1.drop('Consumer disputed?', axis = 1)), columns = df2.columns)\n",
        "df_test_scaled = pd.DataFrame(StandardScaler().fit_transform(df2), columns = df2.columns)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UwmrMtcD3hYh"
      },
      "source": [
        "Do feature selection with help of PCA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PXQqgYkf3kpB",
        "outputId": "6ce78e9d-2939-44ca-fae4-6952c7007eac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Explained Info  Importance\n",
              "0         0.035774    0.035774\n",
              "1         0.030918    0.066692\n",
              "2         0.026115    0.092806\n",
              "3         0.024114    0.116920\n",
              "4         0.023031    0.139951\n",
              "5         0.022458    0.162409\n",
              "6         0.021937    0.184346\n",
              "7         0.021441    0.205787\n",
              "8         0.021107    0.226894\n",
              "9         0.020782    0.247676\n",
              "10        0.019425    0.267100\n",
              "11        0.018808    0.285909\n",
              "12        0.018038    0.303946\n",
              "13        0.017709    0.321655\n",
              "14        0.017351    0.339006\n",
              "15        0.017296    0.356303\n",
              "16        0.017172    0.373474\n",
              "17        0.016889    0.390364\n",
              "18        0.016753    0.407116\n",
              "19        0.016601    0.423717\n",
              "20        0.016517    0.440233\n",
              "21        0.015473    0.455706\n",
              "22        0.015301    0.471007\n",
              "23        0.014240    0.485247\n",
              "24        0.014030    0.499277\n",
              "25        0.013831    0.513108\n",
              "26        0.013174    0.526281\n",
              "27        0.012991    0.539272\n",
              "28        0.012737    0.552009\n",
              "29        0.012627    0.564636\n",
              "30        0.012146    0.576782\n",
              "31        0.012039    0.588821\n",
              "32        0.011940    0.600761\n",
              "33        0.011909    0.612670\n",
              "34        0.011802    0.624472\n",
              "35        0.011635    0.636107\n",
              "36        0.011583    0.647689\n",
              "37        0.011526    0.659216\n",
              "38        0.011433    0.670649\n",
              "39        0.011361    0.682010\n",
              "40        0.011019    0.693029\n",
              "41        0.010984    0.704013\n",
              "42        0.010332    0.714344\n",
              "43        0.009919    0.724263\n",
              "44        0.009496    0.733759\n",
              "45        0.009254    0.743013\n",
              "46        0.008967    0.751980\n",
              "47        0.008812    0.760792\n",
              "48        0.008686    0.769478\n",
              "49        0.008290    0.777768\n",
              "50        0.007808    0.785576\n",
              "51        0.007630    0.793206\n",
              "52        0.007190    0.800395\n",
              "53        0.006880    0.807275\n",
              "54        0.006603    0.813878\n",
              "55        0.006536    0.820415\n",
              "56        0.006234    0.826648\n",
              "57        0.006206    0.832854\n",
              "58        0.006095    0.838949\n",
              "59        0.006047    0.844996"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-819636cc-0ce7-437d-840b-b99a8673d828\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Explained Info</th>\n",
              "      <th>Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.035774</td>\n",
              "      <td>0.035774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.030918</td>\n",
              "      <td>0.066692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.026115</td>\n",
              "      <td>0.092806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.024114</td>\n",
              "      <td>0.116920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.023031</td>\n",
              "      <td>0.139951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.022458</td>\n",
              "      <td>0.162409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.021937</td>\n",
              "      <td>0.184346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.021441</td>\n",
              "      <td>0.205787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.021107</td>\n",
              "      <td>0.226894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.020782</td>\n",
              "      <td>0.247676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.019425</td>\n",
              "      <td>0.267100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.018808</td>\n",
              "      <td>0.285909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.018038</td>\n",
              "      <td>0.303946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.017709</td>\n",
              "      <td>0.321655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.017351</td>\n",
              "      <td>0.339006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.017296</td>\n",
              "      <td>0.356303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.017172</td>\n",
              "      <td>0.373474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.016889</td>\n",
              "      <td>0.390364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.016753</td>\n",
              "      <td>0.407116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.016601</td>\n",
              "      <td>0.423717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.016517</td>\n",
              "      <td>0.440233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.015473</td>\n",
              "      <td>0.455706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.015301</td>\n",
              "      <td>0.471007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.014240</td>\n",
              "      <td>0.485247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.014030</td>\n",
              "      <td>0.499277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.013831</td>\n",
              "      <td>0.513108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.013174</td>\n",
              "      <td>0.526281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.012991</td>\n",
              "      <td>0.539272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.012737</td>\n",
              "      <td>0.552009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.012627</td>\n",
              "      <td>0.564636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.012146</td>\n",
              "      <td>0.576782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>0.012039</td>\n",
              "      <td>0.588821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>0.011940</td>\n",
              "      <td>0.600761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0.011909</td>\n",
              "      <td>0.612670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>0.011802</td>\n",
              "      <td>0.624472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>0.011635</td>\n",
              "      <td>0.636107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>0.011583</td>\n",
              "      <td>0.647689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>0.011526</td>\n",
              "      <td>0.659216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>0.011433</td>\n",
              "      <td>0.670649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>0.011361</td>\n",
              "      <td>0.682010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>0.011019</td>\n",
              "      <td>0.693029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>0.010984</td>\n",
              "      <td>0.704013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>0.010332</td>\n",
              "      <td>0.714344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>0.009919</td>\n",
              "      <td>0.724263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>0.009496</td>\n",
              "      <td>0.733759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0.009254</td>\n",
              "      <td>0.743013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>0.008967</td>\n",
              "      <td>0.751980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.008812</td>\n",
              "      <td>0.760792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>0.008686</td>\n",
              "      <td>0.769478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.008290</td>\n",
              "      <td>0.777768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>0.007808</td>\n",
              "      <td>0.785576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>0.007630</td>\n",
              "      <td>0.793206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>0.007190</td>\n",
              "      <td>0.800395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>0.006880</td>\n",
              "      <td>0.807275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>0.006603</td>\n",
              "      <td>0.813878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>0.006536</td>\n",
              "      <td>0.820415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>0.006234</td>\n",
              "      <td>0.826648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>0.006206</td>\n",
              "      <td>0.832854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>0.006095</td>\n",
              "      <td>0.838949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>0.006047</td>\n",
              "      <td>0.844996</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-819636cc-0ce7-437d-840b-b99a8673d828')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-819636cc-0ce7-437d-840b-b99a8673d828 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-819636cc-0ce7-437d-840b-b99a8673d828');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# Create an empty list to store the column names for the PCA components\n",
        "pca_columns = ['PC' + str(i+1) for i in range(df_train_scaled.shape[1])]\n",
        "\n",
        "# Initialize and fit the PCA model\n",
        "pca_model = PCA()\n",
        "pca_model.fit(df_train_scaled)\n",
        "\n",
        "# Transform the scaled training data using the PCA model and create a DataFrame\n",
        "df_pca_train = pd.DataFrame(pca_model.transform(df_train_scaled), columns=pca_columns)\n",
        "\n",
        "# Get the explained variance ratio for each PCA component and sort in descending order\n",
        "explained_info_train = pd.DataFrame(pca_model.explained_variance_ratio_, columns=['Explained Info']).sort_values(by='Explained Info', ascending=False)\n",
        "\n",
        "# Calculate the cumulative sum of explained variance ratios to determine importance\n",
        "explained_info_train['Importance'] = explained_info_train['Explained Info'].cumsum()\n",
        "\n",
        "# Select the first 60 components and their corresponding importance values\n",
        "explained_info_train = explained_info_train.head(60)\n",
        "\n",
        "# Print the DataFrame showing the variable names and their importance\n",
        "explained_info_train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6C9caag-wNX"
      },
      "source": [
        "Select top features which are covering 80% of the information (n=53)\n",
        "store this data into new dataframe,"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UGCD-VhH5ZmC"
      },
      "outputs": [],
      "source": [
        "pca_columns = []\n",
        "for i in range(53):\n",
        "    pca_columns.append('PC' + str(i+1))\n",
        "pca_model = PCA(n_components = 53)\n",
        "pca_model.fit(df_train_scaled)\n",
        "df_pca_train = pd.DataFrame(pca_model.transform(df_train_scaled), columns = pca_columns)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "tlN5VIAB_Bl8",
        "outputId": "1b634f75-1bab-4c5e-e8af-22e1aaa21b8f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
              "0  4.603277 -2.096129 -1.936024 -0.176654 -0.584832  0.178673  0.018639   \n",
              "1  0.248003  0.253227  0.890039  0.139572  0.303973 -0.059951  0.017968   \n",
              "2  0.521838  0.141570  1.201655  0.016326  0.737986  0.009222 -0.605135   \n",
              "3  0.118162  3.445979 -0.136791 -0.352647 -0.668877 -0.175634  0.090097   \n",
              "4  0.548774  3.786464 -0.123043 -0.283320 -1.086972 -0.343409 -0.019762   \n",
              "\n",
              "        PC8       PC9      PC10  ...      PC44      PC45      PC46      PC47  \\\n",
              "0 -0.091520  0.043442  0.340097  ... -0.676768 -0.341800 -0.088813 -0.327961   \n",
              "1  0.130492 -0.052920 -0.081702  ... -0.412707 -0.103871 -0.342106 -0.271240   \n",
              "2 -0.342750 -0.164352  0.019588  ...  1.144004  0.403189  0.657543  0.687267   \n",
              "3 -0.867944  0.153412  0.209588  ... -0.133464 -0.170066 -0.167979 -0.141928   \n",
              "4 -0.703638  0.262340  0.216510  ...  0.130030  0.002576  0.036235 -0.102362   \n",
              "\n",
              "       PC48      PC49      PC50      PC51      PC52      PC53  \n",
              "0  0.591833  0.821398  0.076757 -0.149808  0.342707  0.108567  \n",
              "1  0.899859  2.600221 -0.315878 -0.215445  0.532730  0.461378  \n",
              "2 -1.277475 -1.420779 -0.249913 -0.095743 -0.628890 -1.222167  \n",
              "3 -0.512002 -1.175993  0.194785  0.263774 -0.287193  0.104359  \n",
              "4  0.267651  0.918886 -0.087786 -0.050807  0.049427  0.413730  \n",
              "\n",
              "[5 rows x 53 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8e0f2a5f-970f-42cc-80c8-f10b2b444dee\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PC1</th>\n",
              "      <th>PC2</th>\n",
              "      <th>PC3</th>\n",
              "      <th>PC4</th>\n",
              "      <th>PC5</th>\n",
              "      <th>PC6</th>\n",
              "      <th>PC7</th>\n",
              "      <th>PC8</th>\n",
              "      <th>PC9</th>\n",
              "      <th>PC10</th>\n",
              "      <th>...</th>\n",
              "      <th>PC44</th>\n",
              "      <th>PC45</th>\n",
              "      <th>PC46</th>\n",
              "      <th>PC47</th>\n",
              "      <th>PC48</th>\n",
              "      <th>PC49</th>\n",
              "      <th>PC50</th>\n",
              "      <th>PC51</th>\n",
              "      <th>PC52</th>\n",
              "      <th>PC53</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.603277</td>\n",
              "      <td>-2.096129</td>\n",
              "      <td>-1.936024</td>\n",
              "      <td>-0.176654</td>\n",
              "      <td>-0.584832</td>\n",
              "      <td>0.178673</td>\n",
              "      <td>0.018639</td>\n",
              "      <td>-0.091520</td>\n",
              "      <td>0.043442</td>\n",
              "      <td>0.340097</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.676768</td>\n",
              "      <td>-0.341800</td>\n",
              "      <td>-0.088813</td>\n",
              "      <td>-0.327961</td>\n",
              "      <td>0.591833</td>\n",
              "      <td>0.821398</td>\n",
              "      <td>0.076757</td>\n",
              "      <td>-0.149808</td>\n",
              "      <td>0.342707</td>\n",
              "      <td>0.108567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.248003</td>\n",
              "      <td>0.253227</td>\n",
              "      <td>0.890039</td>\n",
              "      <td>0.139572</td>\n",
              "      <td>0.303973</td>\n",
              "      <td>-0.059951</td>\n",
              "      <td>0.017968</td>\n",
              "      <td>0.130492</td>\n",
              "      <td>-0.052920</td>\n",
              "      <td>-0.081702</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.412707</td>\n",
              "      <td>-0.103871</td>\n",
              "      <td>-0.342106</td>\n",
              "      <td>-0.271240</td>\n",
              "      <td>0.899859</td>\n",
              "      <td>2.600221</td>\n",
              "      <td>-0.315878</td>\n",
              "      <td>-0.215445</td>\n",
              "      <td>0.532730</td>\n",
              "      <td>0.461378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.521838</td>\n",
              "      <td>0.141570</td>\n",
              "      <td>1.201655</td>\n",
              "      <td>0.016326</td>\n",
              "      <td>0.737986</td>\n",
              "      <td>0.009222</td>\n",
              "      <td>-0.605135</td>\n",
              "      <td>-0.342750</td>\n",
              "      <td>-0.164352</td>\n",
              "      <td>0.019588</td>\n",
              "      <td>...</td>\n",
              "      <td>1.144004</td>\n",
              "      <td>0.403189</td>\n",
              "      <td>0.657543</td>\n",
              "      <td>0.687267</td>\n",
              "      <td>-1.277475</td>\n",
              "      <td>-1.420779</td>\n",
              "      <td>-0.249913</td>\n",
              "      <td>-0.095743</td>\n",
              "      <td>-0.628890</td>\n",
              "      <td>-1.222167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.118162</td>\n",
              "      <td>3.445979</td>\n",
              "      <td>-0.136791</td>\n",
              "      <td>-0.352647</td>\n",
              "      <td>-0.668877</td>\n",
              "      <td>-0.175634</td>\n",
              "      <td>0.090097</td>\n",
              "      <td>-0.867944</td>\n",
              "      <td>0.153412</td>\n",
              "      <td>0.209588</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.133464</td>\n",
              "      <td>-0.170066</td>\n",
              "      <td>-0.167979</td>\n",
              "      <td>-0.141928</td>\n",
              "      <td>-0.512002</td>\n",
              "      <td>-1.175993</td>\n",
              "      <td>0.194785</td>\n",
              "      <td>0.263774</td>\n",
              "      <td>-0.287193</td>\n",
              "      <td>0.104359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.548774</td>\n",
              "      <td>3.786464</td>\n",
              "      <td>-0.123043</td>\n",
              "      <td>-0.283320</td>\n",
              "      <td>-1.086972</td>\n",
              "      <td>-0.343409</td>\n",
              "      <td>-0.019762</td>\n",
              "      <td>-0.703638</td>\n",
              "      <td>0.262340</td>\n",
              "      <td>0.216510</td>\n",
              "      <td>...</td>\n",
              "      <td>0.130030</td>\n",
              "      <td>0.002576</td>\n",
              "      <td>0.036235</td>\n",
              "      <td>-0.102362</td>\n",
              "      <td>0.267651</td>\n",
              "      <td>0.918886</td>\n",
              "      <td>-0.087786</td>\n",
              "      <td>-0.050807</td>\n",
              "      <td>0.049427</td>\n",
              "      <td>0.413730</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 53 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e0f2a5f-970f-42cc-80c8-f10b2b444dee')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8e0f2a5f-970f-42cc-80c8-f10b2b444dee button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8e0f2a5f-970f-42cc-80c8-f10b2b444dee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "df_pca_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yEJvwXh7_Uuj"
      },
      "outputs": [],
      "source": [
        "#same for test data also\n",
        "pca_model = PCA(n_components = 53)\n",
        "pca_model.fit(df_test_scaled)\n",
        "df_pca_test = pd.DataFrame(pca_model.transform(df_test_scaled), columns = pca_columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "9F0617Un_pxo",
        "outputId": "6d59dd58-fda0-4dad-853c-27ee14c87e13"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             PC1       PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
              "0       0.230365 -0.039164  2.828339 -0.095061  0.803385 -0.440456  1.319092   \n",
              "1       0.175584 -0.539215  1.630218 -0.634653 -0.333634  1.138484  0.109410   \n",
              "2       1.309980 -0.144338  2.142646  0.200869  1.501726 -0.251144  1.851610   \n",
              "3       1.465764 -0.013034  2.434475  0.125885  2.814036 -1.512070 -5.079684   \n",
              "4       1.250681  3.164659  1.317205 -0.260655 -2.761790 -1.699545  0.534516   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "119601  1.222385 -0.228442  0.831040  0.106301  0.543035 -0.027278  0.916526   \n",
              "119602  0.488166  0.329996  2.090604  0.262651  1.110347 -0.099820  1.443329   \n",
              "119603 -3.302682 -1.001246 -2.496388  0.474789  2.639064  1.061252  0.083605   \n",
              "119604  4.586100 -2.114800 -1.933135 -0.175142 -0.551000  0.194602 -0.039173   \n",
              "119605  0.496893  1.913491  0.525321 -0.075487 -0.633012 -0.391848 -0.017296   \n",
              "\n",
              "             PC8       PC9      PC10  ...      PC44      PC45      PC46  \\\n",
              "0      -0.653719 -0.022778  0.046561  ...  1.699402 -0.165027  0.461826   \n",
              "1       1.926613  3.516773  4.766549  ...  2.410740  3.331222  1.361506   \n",
              "2      -0.153607 -0.350019  0.309958  ... -0.822839  0.450871  0.453904   \n",
              "3      -0.329645  0.219181  0.312666  ... -1.573823  3.863751 -5.228339   \n",
              "4       7.508294  7.251443 -9.873107  ... -0.407273  0.247933  0.229595   \n",
              "...          ...       ...       ...  ...       ...       ...       ...   \n",
              "119601 -0.059335 -0.098457  0.098718  ... -1.062042 -0.119056  0.031839   \n",
              "119602 -0.054452 -0.022563  0.163044  ... -1.881287 -0.361314  3.025443   \n",
              "119603  0.753022 -0.126885 -0.325026  ...  1.415885 -0.138075 -1.225466   \n",
              "119604 -0.150405 -0.105285  0.321468  ... -0.725107  0.277167  0.217175   \n",
              "119605  0.511484 -0.014538 -0.187850  ... -0.544485  0.136045  0.362423   \n",
              "\n",
              "            PC47      PC48      PC49      PC50      PC51      PC52      PC53  \n",
              "0       1.083007 -2.364445 -2.467626 -0.201802 -0.900114 -1.132631 -0.326147  \n",
              "1      -5.531416  0.532985  0.021960 -9.820618 -0.127712 -1.555762  1.307369  \n",
              "2       0.239510  0.676759 -1.135432  0.043048 -0.580447  0.523230 -0.660569  \n",
              "3       3.866490 -1.216626 -0.435858 -1.123660 -1.421029  0.412346 -0.305645  \n",
              "4      -0.237790  0.135462  0.157180  0.319891  0.178398 -0.078394 -0.109889  \n",
              "...          ...       ...       ...       ...       ...       ...       ...  \n",
              "119601 -2.034571  0.252285  0.171486  0.238108  2.309811 -0.272105  1.447378  \n",
              "119602  1.717258 -0.883142 -0.032578 -1.084436 -0.171611  0.742135 -0.491810  \n",
              "119603  1.329841 -1.139405  0.611619 -0.793561  0.545500 -0.273238  1.209061  \n",
              "119604 -0.422135  0.492344 -0.704655  0.218900 -0.336277  0.048142  0.066908  \n",
              "119605 -0.363992  0.186255 -0.096347  0.092663 -0.048421  0.030167  0.062563  \n",
              "\n",
              "[119606 rows x 53 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3b5c6d52-30a8-4fa3-b80e-0891ed9fd4f6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PC1</th>\n",
              "      <th>PC2</th>\n",
              "      <th>PC3</th>\n",
              "      <th>PC4</th>\n",
              "      <th>PC5</th>\n",
              "      <th>PC6</th>\n",
              "      <th>PC7</th>\n",
              "      <th>PC8</th>\n",
              "      <th>PC9</th>\n",
              "      <th>PC10</th>\n",
              "      <th>...</th>\n",
              "      <th>PC44</th>\n",
              "      <th>PC45</th>\n",
              "      <th>PC46</th>\n",
              "      <th>PC47</th>\n",
              "      <th>PC48</th>\n",
              "      <th>PC49</th>\n",
              "      <th>PC50</th>\n",
              "      <th>PC51</th>\n",
              "      <th>PC52</th>\n",
              "      <th>PC53</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.230365</td>\n",
              "      <td>-0.039164</td>\n",
              "      <td>2.828339</td>\n",
              "      <td>-0.095061</td>\n",
              "      <td>0.803385</td>\n",
              "      <td>-0.440456</td>\n",
              "      <td>1.319092</td>\n",
              "      <td>-0.653719</td>\n",
              "      <td>-0.022778</td>\n",
              "      <td>0.046561</td>\n",
              "      <td>...</td>\n",
              "      <td>1.699402</td>\n",
              "      <td>-0.165027</td>\n",
              "      <td>0.461826</td>\n",
              "      <td>1.083007</td>\n",
              "      <td>-2.364445</td>\n",
              "      <td>-2.467626</td>\n",
              "      <td>-0.201802</td>\n",
              "      <td>-0.900114</td>\n",
              "      <td>-1.132631</td>\n",
              "      <td>-0.326147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.175584</td>\n",
              "      <td>-0.539215</td>\n",
              "      <td>1.630218</td>\n",
              "      <td>-0.634653</td>\n",
              "      <td>-0.333634</td>\n",
              "      <td>1.138484</td>\n",
              "      <td>0.109410</td>\n",
              "      <td>1.926613</td>\n",
              "      <td>3.516773</td>\n",
              "      <td>4.766549</td>\n",
              "      <td>...</td>\n",
              "      <td>2.410740</td>\n",
              "      <td>3.331222</td>\n",
              "      <td>1.361506</td>\n",
              "      <td>-5.531416</td>\n",
              "      <td>0.532985</td>\n",
              "      <td>0.021960</td>\n",
              "      <td>-9.820618</td>\n",
              "      <td>-0.127712</td>\n",
              "      <td>-1.555762</td>\n",
              "      <td>1.307369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.309980</td>\n",
              "      <td>-0.144338</td>\n",
              "      <td>2.142646</td>\n",
              "      <td>0.200869</td>\n",
              "      <td>1.501726</td>\n",
              "      <td>-0.251144</td>\n",
              "      <td>1.851610</td>\n",
              "      <td>-0.153607</td>\n",
              "      <td>-0.350019</td>\n",
              "      <td>0.309958</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.822839</td>\n",
              "      <td>0.450871</td>\n",
              "      <td>0.453904</td>\n",
              "      <td>0.239510</td>\n",
              "      <td>0.676759</td>\n",
              "      <td>-1.135432</td>\n",
              "      <td>0.043048</td>\n",
              "      <td>-0.580447</td>\n",
              "      <td>0.523230</td>\n",
              "      <td>-0.660569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.465764</td>\n",
              "      <td>-0.013034</td>\n",
              "      <td>2.434475</td>\n",
              "      <td>0.125885</td>\n",
              "      <td>2.814036</td>\n",
              "      <td>-1.512070</td>\n",
              "      <td>-5.079684</td>\n",
              "      <td>-0.329645</td>\n",
              "      <td>0.219181</td>\n",
              "      <td>0.312666</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.573823</td>\n",
              "      <td>3.863751</td>\n",
              "      <td>-5.228339</td>\n",
              "      <td>3.866490</td>\n",
              "      <td>-1.216626</td>\n",
              "      <td>-0.435858</td>\n",
              "      <td>-1.123660</td>\n",
              "      <td>-1.421029</td>\n",
              "      <td>0.412346</td>\n",
              "      <td>-0.305645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.250681</td>\n",
              "      <td>3.164659</td>\n",
              "      <td>1.317205</td>\n",
              "      <td>-0.260655</td>\n",
              "      <td>-2.761790</td>\n",
              "      <td>-1.699545</td>\n",
              "      <td>0.534516</td>\n",
              "      <td>7.508294</td>\n",
              "      <td>7.251443</td>\n",
              "      <td>-9.873107</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.407273</td>\n",
              "      <td>0.247933</td>\n",
              "      <td>0.229595</td>\n",
              "      <td>-0.237790</td>\n",
              "      <td>0.135462</td>\n",
              "      <td>0.157180</td>\n",
              "      <td>0.319891</td>\n",
              "      <td>0.178398</td>\n",
              "      <td>-0.078394</td>\n",
              "      <td>-0.109889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119601</th>\n",
              "      <td>1.222385</td>\n",
              "      <td>-0.228442</td>\n",
              "      <td>0.831040</td>\n",
              "      <td>0.106301</td>\n",
              "      <td>0.543035</td>\n",
              "      <td>-0.027278</td>\n",
              "      <td>0.916526</td>\n",
              "      <td>-0.059335</td>\n",
              "      <td>-0.098457</td>\n",
              "      <td>0.098718</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.062042</td>\n",
              "      <td>-0.119056</td>\n",
              "      <td>0.031839</td>\n",
              "      <td>-2.034571</td>\n",
              "      <td>0.252285</td>\n",
              "      <td>0.171486</td>\n",
              "      <td>0.238108</td>\n",
              "      <td>2.309811</td>\n",
              "      <td>-0.272105</td>\n",
              "      <td>1.447378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119602</th>\n",
              "      <td>0.488166</td>\n",
              "      <td>0.329996</td>\n",
              "      <td>2.090604</td>\n",
              "      <td>0.262651</td>\n",
              "      <td>1.110347</td>\n",
              "      <td>-0.099820</td>\n",
              "      <td>1.443329</td>\n",
              "      <td>-0.054452</td>\n",
              "      <td>-0.022563</td>\n",
              "      <td>0.163044</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.881287</td>\n",
              "      <td>-0.361314</td>\n",
              "      <td>3.025443</td>\n",
              "      <td>1.717258</td>\n",
              "      <td>-0.883142</td>\n",
              "      <td>-0.032578</td>\n",
              "      <td>-1.084436</td>\n",
              "      <td>-0.171611</td>\n",
              "      <td>0.742135</td>\n",
              "      <td>-0.491810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119603</th>\n",
              "      <td>-3.302682</td>\n",
              "      <td>-1.001246</td>\n",
              "      <td>-2.496388</td>\n",
              "      <td>0.474789</td>\n",
              "      <td>2.639064</td>\n",
              "      <td>1.061252</td>\n",
              "      <td>0.083605</td>\n",
              "      <td>0.753022</td>\n",
              "      <td>-0.126885</td>\n",
              "      <td>-0.325026</td>\n",
              "      <td>...</td>\n",
              "      <td>1.415885</td>\n",
              "      <td>-0.138075</td>\n",
              "      <td>-1.225466</td>\n",
              "      <td>1.329841</td>\n",
              "      <td>-1.139405</td>\n",
              "      <td>0.611619</td>\n",
              "      <td>-0.793561</td>\n",
              "      <td>0.545500</td>\n",
              "      <td>-0.273238</td>\n",
              "      <td>1.209061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119604</th>\n",
              "      <td>4.586100</td>\n",
              "      <td>-2.114800</td>\n",
              "      <td>-1.933135</td>\n",
              "      <td>-0.175142</td>\n",
              "      <td>-0.551000</td>\n",
              "      <td>0.194602</td>\n",
              "      <td>-0.039173</td>\n",
              "      <td>-0.150405</td>\n",
              "      <td>-0.105285</td>\n",
              "      <td>0.321468</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.725107</td>\n",
              "      <td>0.277167</td>\n",
              "      <td>0.217175</td>\n",
              "      <td>-0.422135</td>\n",
              "      <td>0.492344</td>\n",
              "      <td>-0.704655</td>\n",
              "      <td>0.218900</td>\n",
              "      <td>-0.336277</td>\n",
              "      <td>0.048142</td>\n",
              "      <td>0.066908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119605</th>\n",
              "      <td>0.496893</td>\n",
              "      <td>1.913491</td>\n",
              "      <td>0.525321</td>\n",
              "      <td>-0.075487</td>\n",
              "      <td>-0.633012</td>\n",
              "      <td>-0.391848</td>\n",
              "      <td>-0.017296</td>\n",
              "      <td>0.511484</td>\n",
              "      <td>-0.014538</td>\n",
              "      <td>-0.187850</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.544485</td>\n",
              "      <td>0.136045</td>\n",
              "      <td>0.362423</td>\n",
              "      <td>-0.363992</td>\n",
              "      <td>0.186255</td>\n",
              "      <td>-0.096347</td>\n",
              "      <td>0.092663</td>\n",
              "      <td>-0.048421</td>\n",
              "      <td>0.030167</td>\n",
              "      <td>0.062563</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119606 rows × 53 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b5c6d52-30a8-4fa3-b80e-0891ed9fd4f6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3b5c6d52-30a8-4fa3-b80e-0891ed9fd4f6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3b5c6d52-30a8-4fa3-b80e-0891ed9fd4f6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "df_pca_test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XNTfb9ev_6VN"
      },
      "source": [
        "Split the Data Sets Into X and Y by dependent and independent variables (data selected by PCA)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rj6MR9RZLGD7"
      },
      "outputs": [],
      "source": [
        "X = df_pca_train\n",
        "y = df1['Consumer disputed?']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kpzeSRoUMHCt"
      },
      "source": [
        "Split dataset into Train, and Test datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R-ol1VPpMNxv"
      },
      "outputs": [],
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.3, random_state = 17)\n",
        "X_test = df_pca_test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WdHP_taz8jZp"
      },
      "source": [
        "Shapes of the datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFJoqCGv8n9Q",
        "outputId": "8976fc53-ea03-42d7-9bb2-65dae2bdb114"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((251167, 53), (107643, 53), (251167,), (107643,), (119606, 53))"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ],
      "source": [
        "X_train.shape, X_val.shape, y_train.shape, y_val.shape, X_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1pG7qUZS9Gca"
      },
      "source": [
        "**Model building**\n",
        "Build given models and measure their test and validation accuracy build given models:\n",
        "\n",
        "LogisticRegression\n",
        "\n",
        "DecisionTreeClassifier\n",
        "\n",
        "RandomForestClassifier\n",
        "\n",
        "AdaBoostClassifier\n",
        "\n",
        "GradientBoostingClassifier\n",
        "\n",
        "KNeighborsClassifier\n",
        "\n",
        "XGBClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "DMh1dyQu9Toq",
        "outputId": "ee0795bf-13ea-4b6c-ec43-bd541c4a9077"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Modelling Algorithm  Train Accuracy  Validation Accuracy  Difference\n",
              "0          LogisticRegression        0.787846             0.787390    0.057947\n",
              "3          AdaBoostClassifier        0.787846             0.787390    0.057947\n",
              "4  GradientBoostingClassifier        0.787934             0.787343    0.074953\n",
              "6               XGBClassifier        0.789323             0.787093    0.282637\n",
              "5        KNeighborsClassifier        0.799205             0.750044    6.151258\n",
              "2      RandomForestClassifier        0.878981             0.748641   14.828487\n",
              "1      DecisionTreeClassifier        0.879009             0.726448   17.356047"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2dea3539-e06d-46d6-bbee-b3ccfa0a27e8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Modelling Algorithm</th>\n",
              "      <th>Train Accuracy</th>\n",
              "      <th>Validation Accuracy</th>\n",
              "      <th>Difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0.787846</td>\n",
              "      <td>0.787390</td>\n",
              "      <td>0.057947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AdaBoostClassifier</td>\n",
              "      <td>0.787846</td>\n",
              "      <td>0.787390</td>\n",
              "      <td>0.057947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GradientBoostingClassifier</td>\n",
              "      <td>0.787934</td>\n",
              "      <td>0.787343</td>\n",
              "      <td>0.074953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XGBClassifier</td>\n",
              "      <td>0.789323</td>\n",
              "      <td>0.787093</td>\n",
              "      <td>0.282637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>KNeighborsClassifier</td>\n",
              "      <td>0.799205</td>\n",
              "      <td>0.750044</td>\n",
              "      <td>6.151258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>0.878981</td>\n",
              "      <td>0.748641</td>\n",
              "      <td>14.828487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DecisionTreeClassifier</td>\n",
              "      <td>0.879009</td>\n",
              "      <td>0.726448</td>\n",
              "      <td>17.356047</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2dea3539-e06d-46d6-bbee-b3ccfa0a27e8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2dea3539-e06d-46d6-bbee-b3ccfa0a27e8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2dea3539-e06d-46d6-bbee-b3ccfa0a27e8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "models = [LogisticRegression(), DecisionTreeClassifier(), RandomForestClassifier(), AdaBoostClassifier(), GradientBoostingClassifier(), KNeighborsClassifier(), XGBClassifier()]\n",
        "model_names = ['LogisticRegression', 'DecisionTreeClassifier', 'RandomForestClassifier', 'AdaBoostClassifier', 'GradientBoostingClassifier', 'KNeighborsClassifier', 'XGBClassifier']\n",
        "accuracy_train = []\n",
        "accuracy_val = []\n",
        "for model in models:\n",
        "    mod = model\n",
        "    mod.fit(X_train, y_train)\n",
        "    y_pred_train = mod.predict(X_train)\n",
        "    y_pred_val = mod.predict(X_val)\n",
        "    accuracy_train.append(accuracy_score(y_train, y_pred_train))\n",
        "    accuracy_val.append(accuracy_score(y_val, y_pred_val))\n",
        "data = {'Modelling Algorithm' : model_names, 'Train Accuracy' : accuracy_train, 'Validation Accuracy' : accuracy_val}\n",
        "data = pd.DataFrame(data)\n",
        "data['Difference'] = ((np.abs(data['Train Accuracy'] - data['Validation Accuracy'])) * 100)/(data['Train Accuracy'])\n",
        "data.sort_values(by = 'Validation Accuracy', ascending = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08ToFAsZXrPI"
      },
      "source": [
        "\n",
        "\n",
        "Final Model and Prediction for test data file\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "krev4F8yX1gr",
        "outputId": "5e0a31c5-1ff6-4219-e98d-68d04dae0f00"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Prediction\n",
              "0           0\n",
              "1           0\n",
              "2           0\n",
              "3           0\n",
              "4           0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-878247e4-afe8-4dd3-874c-9611508756a4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Prediction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-878247e4-afe8-4dd3-874c-9611508756a4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-878247e4-afe8-4dd3-874c-9611508756a4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-878247e4-afe8-4dd3-874c-9611508756a4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "lr = LogisticRegression()\n",
        "lr.fit(X_train, y_train)\n",
        "y_pred_test = lr.predict(X_test)\n",
        "y_pred_test = pd.DataFrame(y_pred_test, columns = ['Prediction'])\n",
        "y_pred_test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Export predictions to CSV"
      ],
      "metadata": {
        "id": "x90JSZc-apTZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_test.to_csv('Prediction.csv')"
      ],
      "metadata": {
        "id": "IoVvB40cbEMU"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}