data_cleaning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SimonSaysGiveMeSmile/Caffeinated-Treasury-Bot-ORIE-4741-/blob/main/data_cleaning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3ee1e64b",
      "metadata": {
        "id": "3ee1e64b"
      },
      "source": [
        "# **ORIE 4741 / 5741 Term Project**\n",
        "## *- Caffinated Treasury Bot - Data Cleaning*\n",
        "\n",
        "### Authors:\n",
        "Simon (Jiahe) Tian - jt886@cornell.edu <br>\n",
        "Cecilia Yang - xy96@cornell.edu <br>\n",
        "Kayla Yang - <br>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "482120cb",
      "metadata": {
        "id": "482120cb"
      },
      "source": [
        "## Project Objective\n",
        "\n",
        "The purpose of this project is to empower user without sufficient financial knowledge to understand or improve their credit scores.\n",
        "\n",
        "By providing the users with comprehensive insights and recommendations on how to improve their credit score, the users will be able to make informed decisions that will benifit their financial status in both short-term and long-term."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "511a86c1",
      "metadata": {
        "id": "511a86c1"
      },
      "source": [
        "## Executive Summary"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d2d94c65",
      "metadata": {
        "id": "d2d94c65"
      },
      "source": [
        "## Project Outline"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "46cd90df",
      "metadata": {
        "id": "46cd90df"
      },
      "source": [
        "## Dataset Research and Initial Analysis\n",
        "\n",
        "### **Dataset #1: Credit Score Classification**\n",
        "\n",
        "URL: https://www.kaggle.com/datasets/parisrohan/credit-score-classification/data<br>\n",
        "Number of Columns: 27<br>\n",
        "Number of Rows: 50000<br>\n",
        "Missing Headings? NO<br>\n",
        "Mising Values? YES<br>\n",
        "Pseudodata? MAYBE<br>\n",
        "\n",
        "**Commment:** <br>\n",
        "Included in the analysis as the dataset is large and contains many features.\n",
        "\n",
        "\n",
        "### **Dataset #2: Credit Card Approval Prediction**\n",
        "\n",
        "URL: https://www.kaggle.com/datasets/rikdifos/credit-card-approval-prediction/data<br>\n",
        "Number of Columns: 18<br>\n",
        "Number of Rows: 50000<br>\n",
        "Missing Headings? NO<br>\n",
        "Mising Values? YES<br>\n",
        "Pseudodata? MAYBE<br>\n",
        "\n",
        "**Commment:** <br>\n",
        "Included in the analysis as the dataset is large and contains many features."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "36fc9776",
      "metadata": {
        "id": "36fc9776"
      },
      "source": [
        "## Importing Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "67301d6d",
      "metadata": {
        "id": "67301d6d"
      },
      "outputs": [],
      "source": [
        "# Import data manipulation libraries\n",
        "#import necessary packages\n",
        "import re\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy import stats\n",
        "\n",
        "import plotly.express as px\n",
        "px.defaults.width, px.defaults.height = 1000, 600\n",
        "\n",
        "# Set precision\n",
        "pd.set_option('display.precision', 4)\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# This sets the aesthetics for seaborn plots\n",
        "sns.set()\n",
        "\n",
        "from scipy.stats import percentileofscore"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4f83a241",
      "metadata": {
        "id": "4f83a241"
      },
      "source": [
        "## Credit Score Prediction Dataset (Dataset #1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9261bcf2",
      "metadata": {
        "id": "9261bcf2"
      },
      "source": [
        "## Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "9e7551a0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9e7551a0",
        "outputId": "fd750513-5289-4abd-a7e5-b19375db028f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-1cfd7955d060>:2: DtypeWarning: Columns (26) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df_train=pd.read_csv('train.csv')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of the training set:  (100000, 28)\n",
            "Size of the testing set:  (50000, 27)\n"
          ]
        }
      ],
      "source": [
        "#Import CSV Files for Dataset #1\n",
        "df_train=pd.read_csv('train.csv')\n",
        "df_test=pd.read_csv('test.csv')\n",
        "\n",
        "print(\"Size of the training set: \", df_train.shape)\n",
        "print(\"Size of the testing set: \", df_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "bf896d73",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bf896d73",
        "outputId": "eab847f7-7131-46b7-f12e-51a146f650b8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of the training set after dpuplicate removal:  (100000, 28)\n",
            "Size of the testing set after duplicate removal:  (50000, 27)\n"
          ]
        }
      ],
      "source": [
        "#remove duplicated rows\n",
        "df_train_unique = df_train.drop_duplicates(keep='first')\n",
        "df_test_unique = df_test.drop_duplicates(keep='first')\n",
        "\n",
        "print(\"Size of the training set after dpuplicate removal: \", df_train_unique.shape)\n",
        "print(\"Size of the testing set after duplicate removal: \", df_test_unique.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since the shapes of the two dataframes remain unchanged after dropping duplicate rows, we try another method."
      ],
      "metadata": {
        "id": "504wcZQ8GKYQ"
      },
      "id": "504wcZQ8GKYQ"
    },
    {
      "cell_type": "markdown",
      "id": "c5bd5d4f",
      "metadata": {
        "id": "c5bd5d4f"
      },
      "source": [
        "## Training Dataset Cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We first check if there are any non-numerical values within columns that are supposed to be numerical."
      ],
      "metadata": {
        "id": "z3uNvll4V_8n"
      },
      "id": "z3uNvll4V_8n"
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to check if there are non-numerical variables within each column\n",
        "\n",
        "def check_non_numeric_values(df, column_name):\n",
        "\n",
        "    numeric_series = pd.to_numeric(df[column_name], errors='coerce')\n",
        "\n",
        "    # Find which entries are NaN in the numeric series but not in the original series\n",
        "    non_numeric = numeric_series.isna() & df[column_name].notna()\n",
        "\n",
        "    non_numeric_count = non_numeric.sum()\n",
        "    print(\"There are\", non_numeric_count, \"non-numerical values in the\", column_name, \"column.\")\n",
        "    return\n",
        "\n",
        "before_num_convert = check_non_numeric_values(df_train_unique, 'Age')\n",
        "before_num_convert = check_non_numeric_values(df_train_unique, 'Annual_Income')\n",
        "before_num_convert = check_non_numeric_values(df_train_unique, 'Outstanding_Debt')\n",
        "before_num_convert = check_non_numeric_values(df_train_unique, 'Amount_invested_monthly')\n",
        "before_num_convert = check_non_numeric_values(df_train_unique, 'Monthly_Balance')\n",
        "before_num_convert = check_non_numeric_values(df_train_unique, 'Changed_Credit_Limit')\n",
        "before_num_convert = check_non_numeric_values(df_train_unique, 'Num_of_Loan')\n",
        "before_num_convert = check_non_numeric_values(df_train_unique, 'Num_of_Delayed_Payment')\n",
        "before_num_convert = check_non_numeric_values(df_train_unique, 'Num_Credit_Inquiries')\n",
        "before_num_convert = check_non_numeric_values(df_train_unique, 'Num_Bank_Accounts')\n",
        "before_num_convert = check_non_numeric_values(df_train_unique, 'Delay_from_due_date')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xvcqU-_aUGfT",
        "outputId": "517820a1-81c0-4215-e03a-6e09ea623c15"
      },
      "id": "xvcqU-_aUGfT",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 4939 non-numerical values in the Age column.\n",
            "There are 6980 non-numerical values in the Annual_Income column.\n",
            "There are 1009 non-numerical values in the Outstanding_Debt column.\n",
            "There are 4305 non-numerical values in the Amount_invested_monthly column.\n",
            "There are 9 non-numerical values in the Monthly_Balance column.\n",
            "There are 2091 non-numerical values in the Changed_Credit_Limit column.\n",
            "There are 4785 non-numerical values in the Num_of_Loan column.\n",
            "There are 2744 non-numerical values in the Num_of_Delayed_Payment column.\n",
            "There are 0 non-numerical values in the Num_Credit_Inquiries column.\n",
            "There are 0 non-numerical values in the Num_Bank_Accounts column.\n",
            "There are 0 non-numerical values in the Delay_from_due_date column.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "a5dfb755",
      "metadata": {
        "id": "a5dfb755"
      },
      "outputs": [],
      "source": [
        "# Convert the following columns to numerical columns\n",
        "df_train_unique['Age'] = pd.to_numeric(df_train_unique['Age'], errors='coerce')\n",
        "df_train_unique['Annual_Income'] = pd.to_numeric(df_train_unique['Annual_Income'], errors='coerce')\n",
        "df_train_unique['Outstanding_Debt'] = pd.to_numeric(df_train_unique['Outstanding_Debt'], errors='coerce')\n",
        "df_train_unique['Amount_invested_monthly'] = pd.to_numeric(df_train_unique['Amount_invested_monthly'], errors='coerce')\n",
        "df_train_unique['Monthly_Balance'] = pd.to_numeric(df_train_unique['Monthly_Balance'], errors='coerce')\n",
        "df_train_unique['Changed_Credit_Limit'] = pd.to_numeric(df_train_unique['Changed_Credit_Limit'], errors='coerce')\n",
        "df_train_unique['Monthly_Balance'] = pd.to_numeric(df_train_unique['Monthly_Balance'], errors='coerce')\n",
        "df_train_unique['Num_of_Loan'] = pd.to_numeric(df_train_unique['Num_of_Loan'], errors='coerce')\n",
        "df_train_unique['Num_of_Delayed_Payment'] = pd.to_numeric(df_train_unique['Num_of_Delayed_Payment'], errors='coerce')\n",
        "\n",
        "# df_train_unique"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "after_num_convert = check_non_numeric_values(df_train_unique, 'Age')\n",
        "after_num_convert = check_non_numeric_values(df_train_unique, 'Annual_Income')\n",
        "after_num_convert = check_non_numeric_values(df_train_unique, 'Outstanding_Debt')\n",
        "after_num_convert = check_non_numeric_values(df_train_unique, 'Amount_invested_monthly')\n",
        "after_num_convert = check_non_numeric_values(df_train_unique, 'Monthly_Balance')\n",
        "after_num_convert = check_non_numeric_values(df_train_unique, 'Changed_Credit_Limit')\n",
        "after_num_convert = check_non_numeric_values(df_train_unique, 'Num_of_Loan')\n",
        "after_num_convert = check_non_numeric_values(df_train_unique, 'Num_of_Delayed_Payment')\n",
        "after_num_convert = check_non_numeric_values(df_train_unique, 'Num_Credit_Inquiries')\n",
        "after_num_convert = check_non_numeric_values(df_train_unique, 'Num_Bank_Accounts')\n",
        "after_num_convert = check_non_numeric_values(df_train_unique, 'Delay_from_due_date')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ChzifcwxUrH8",
        "outputId": "471c95ce-a7ef-4047-cae9-cfb7bc4b91a5"
      },
      "id": "ChzifcwxUrH8",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 0 non-numerical values in the Age column.\n",
            "There are 0 non-numerical values in the Annual_Income column.\n",
            "There are 0 non-numerical values in the Outstanding_Debt column.\n",
            "There are 0 non-numerical values in the Amount_invested_monthly column.\n",
            "There are 0 non-numerical values in the Monthly_Balance column.\n",
            "There are 0 non-numerical values in the Changed_Credit_Limit column.\n",
            "There are 0 non-numerical values in the Num_of_Loan column.\n",
            "There are 0 non-numerical values in the Num_of_Delayed_Payment column.\n",
            "There are 0 non-numerical values in the Num_Credit_Inquiries column.\n",
            "There are 0 non-numerical values in the Num_Bank_Accounts column.\n",
            "There are 0 non-numerical values in the Delay_from_due_date column.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "745b90a3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "745b90a3",
        "outputId": "b2a3e87c-421f-417e-8be9-4dfbdf314284"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Age  Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  \\\n",
              "count  95061.0000     9.3020e+04             84998.0000        100000.0000   \n",
              "mean     110.9345     1.7858e+05              4194.1708            17.0913   \n",
              "std      689.4079     1.4429e+06              3183.6862           117.4048   \n",
              "min     -500.0000     7.0059e+03               303.6454            -1.0000   \n",
              "25%       24.0000     1.9436e+04              1625.5682             3.0000   \n",
              "50%       33.0000     3.7551e+04              3093.7450             6.0000   \n",
              "75%       42.0000     7.2843e+04              5957.4483             7.0000   \n",
              "max     8698.0000     2.4198e+07             15204.6333          1798.0000   \n",
              "\n",
              "       Num_Credit_Card  Interest_Rate  Num_of_Loan  Delay_from_due_date  \\\n",
              "count      100000.0000    100000.0000   95215.0000          100000.0000   \n",
              "mean           22.4744        72.4660       2.7803              21.0688   \n",
              "std           129.0574       466.4226      62.5009              14.8601   \n",
              "min             0.0000         1.0000    -100.0000              -5.0000   \n",
              "25%             4.0000         8.0000       1.0000              10.0000   \n",
              "50%             5.0000        13.0000       3.0000              18.0000   \n",
              "75%             7.0000        20.0000       5.0000              28.0000   \n",
              "max          1499.0000      5797.0000    1496.0000              67.0000   \n",
              "\n",
              "       Num_of_Delayed_Payment  Changed_Credit_Limit  Num_Credit_Inquiries  \\\n",
              "count              90254.0000            97909.0000            98035.0000   \n",
              "mean                  31.0331               10.3890               27.7543   \n",
              "std                  226.9558                6.7895              193.1773   \n",
              "min                   -3.0000               -6.4900                0.0000   \n",
              "25%                    9.0000                5.3200                3.0000   \n",
              "50%                   14.0000                9.4000                6.0000   \n",
              "75%                   18.0000               14.8700                9.0000   \n",
              "max                 4397.0000               36.9700             2597.0000   \n",
              "\n",
              "       Outstanding_Debt  Credit_Utilization_Ratio  Total_EMI_per_month  \\\n",
              "count        98991.0000               100000.0000          100000.0000   \n",
              "mean          1426.5037                   32.2852            1403.1182   \n",
              "std           1155.0458                    5.1169            8306.0413   \n",
              "min              0.2300                   20.0000               0.0000   \n",
              "25%            566.0800                   28.0526              30.3067   \n",
              "50%           1166.3700                   32.3058              69.2495   \n",
              "75%           1948.2000                   36.4967             161.2242   \n",
              "max           4998.0700                   50.0000           82331.0000   \n",
              "\n",
              "       Amount_invested_monthly  Monthly_Balance  \n",
              "count               91216.0000       98791.0000  \n",
              "mean                  195.5395         402.5513  \n",
              "std                   199.5645         213.9255  \n",
              "min                     0.0000           0.0078  \n",
              "25%                    72.2367         270.1066  \n",
              "50%                   128.9545         336.7312  \n",
              "75%                   236.8158         470.2629  \n",
              "max                  1977.3261        1602.0405  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-efa596ad-46a7-42c2-b080-f1d044cba88b\" 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>Age</th>\n",
              "      <th>Annual_Income</th>\n",
              "      <th>Monthly_Inhand_Salary</th>\n",
              "      <th>Num_Bank_Accounts</th>\n",
              "      <th>Num_Credit_Card</th>\n",
              "      <th>Interest_Rate</th>\n",
              "      <th>Num_of_Loan</th>\n",
              "      <th>Delay_from_due_date</th>\n",
              "      <th>Num_of_Delayed_Payment</th>\n",
              "      <th>Changed_Credit_Limit</th>\n",
              "      <th>Num_Credit_Inquiries</th>\n",
              "      <th>Outstanding_Debt</th>\n",
              "      <th>Credit_Utilization_Ratio</th>\n",
              "      <th>Total_EMI_per_month</th>\n",
              "      <th>Amount_invested_monthly</th>\n",
              "      <th>Monthly_Balance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>95061.0000</td>\n",
              "      <td>9.3020e+04</td>\n",
              "      <td>84998.0000</td>\n",
              "      <td>100000.0000</td>\n",
              "      <td>100000.0000</td>\n",
              "      <td>100000.0000</td>\n",
              "      <td>95215.0000</td>\n",
              "      <td>100000.0000</td>\n",
              "      <td>90254.0000</td>\n",
              "      <td>97909.0000</td>\n",
              "      <td>98035.0000</td>\n",
              "      <td>98991.0000</td>\n",
              "      <td>100000.0000</td>\n",
              "      <td>100000.0000</td>\n",
              "      <td>91216.0000</td>\n",
              "      <td>98791.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>110.9345</td>\n",
              "      <td>1.7858e+05</td>\n",
              "      <td>4194.1708</td>\n",
              "      <td>17.0913</td>\n",
              "      <td>22.4744</td>\n",
              "      <td>72.4660</td>\n",
              "      <td>2.7803</td>\n",
              "      <td>21.0688</td>\n",
              "      <td>31.0331</td>\n",
              "      <td>10.3890</td>\n",
              "      <td>27.7543</td>\n",
              "      <td>1426.5037</td>\n",
              "      <td>32.2852</td>\n",
              "      <td>1403.1182</td>\n",
              "      <td>195.5395</td>\n",
              "      <td>402.5513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>689.4079</td>\n",
              "      <td>1.4429e+06</td>\n",
              "      <td>3183.6862</td>\n",
              "      <td>117.4048</td>\n",
              "      <td>129.0574</td>\n",
              "      <td>466.4226</td>\n",
              "      <td>62.5009</td>\n",
              "      <td>14.8601</td>\n",
              "      <td>226.9558</td>\n",
              "      <td>6.7895</td>\n",
              "      <td>193.1773</td>\n",
              "      <td>1155.0458</td>\n",
              "      <td>5.1169</td>\n",
              "      <td>8306.0413</td>\n",
              "      <td>199.5645</td>\n",
              "      <td>213.9255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-500.0000</td>\n",
              "      <td>7.0059e+03</td>\n",
              "      <td>303.6454</td>\n",
              "      <td>-1.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>-100.0000</td>\n",
              "      <td>-5.0000</td>\n",
              "      <td>-3.0000</td>\n",
              "      <td>-6.4900</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2300</td>\n",
              "      <td>20.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>24.0000</td>\n",
              "      <td>1.9436e+04</td>\n",
              "      <td>1625.5682</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>4.0000</td>\n",
              "      <td>8.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>10.0000</td>\n",
              "      <td>9.0000</td>\n",
              "      <td>5.3200</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>566.0800</td>\n",
              "      <td>28.0526</td>\n",
              "      <td>30.3067</td>\n",
              "      <td>72.2367</td>\n",
              "      <td>270.1066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>33.0000</td>\n",
              "      <td>3.7551e+04</td>\n",
              "      <td>3093.7450</td>\n",
              "      <td>6.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>18.0000</td>\n",
              "      <td>14.0000</td>\n",
              "      <td>9.4000</td>\n",
              "      <td>6.0000</td>\n",
              "      <td>1166.3700</td>\n",
              "      <td>32.3058</td>\n",
              "      <td>69.2495</td>\n",
              "      <td>128.9545</td>\n",
              "      <td>336.7312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>42.0000</td>\n",
              "      <td>7.2843e+04</td>\n",
              "      <td>5957.4483</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>20.0000</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>28.0000</td>\n",
              "      <td>18.0000</td>\n",
              "      <td>14.8700</td>\n",
              "      <td>9.0000</td>\n",
              "      <td>1948.2000</td>\n",
              "      <td>36.4967</td>\n",
              "      <td>161.2242</td>\n",
              "      <td>236.8158</td>\n",
              "      <td>470.2629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8698.0000</td>\n",
              "      <td>2.4198e+07</td>\n",
              "      <td>15204.6333</td>\n",
              "      <td>1798.0000</td>\n",
              "      <td>1499.0000</td>\n",
              "      <td>5797.0000</td>\n",
              "      <td>1496.0000</td>\n",
              "      <td>67.0000</td>\n",
              "      <td>4397.0000</td>\n",
              "      <td>36.9700</td>\n",
              "      <td>2597.0000</td>\n",
              "      <td>4998.0700</td>\n",
              "      <td>50.0000</td>\n",
              "      <td>82331.0000</td>\n",
              "      <td>1977.3261</td>\n",
              "      <td>1602.0405</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_train_unique\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33288.50060568091,\n        \"min\": -500.0,\n        \"max\": 95061.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          110.93450521244253,\n          33.0,\n          95061.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual_Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8475633.53344478,\n        \"min\": 7005.93,\n        \"max\": 24198062.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          178578.96419941948,\n          37550.74,\n          93020.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monthly_Inhand_Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28721.02050010101,\n        \"min\": 303.6454166666666,\n        \"max\": 84998.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4194.170849600523,\n          3093.745000000001,\n          84998.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_Bank_Accounts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35262.45571123334,\n        \"min\": -1.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          17.09128,\n          6.0,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_Credit_Card\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35274.94819246088,\n        \"min\": 0.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          22.47443,\n          5.0,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Interest_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35090.285071687635,\n        \"min\": 1.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          72.46604,\n          13.0,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_of_Loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33593.45756475518,\n        \"min\": -100.0,\n        \"max\": 95215.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.780339232263824,\n          3.0,\n          95215.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Delay_from_due_date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35347.57054259866,\n        \"min\": -5.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          21.06878,\n          18.0,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_of_Delayed_Payment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31709.180445057897,\n        \"min\": -3.0,\n        \"max\": 90254.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.033051166707292,\n          14.0,\n          90254.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Changed_Credit_Limit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34612.159443300574,\n        \"min\": -6.49,\n        \"max\": 97909.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.389025115157953,\n          9.4,\n          97909.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_Credit_Inquiries\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34529.0265942048,\n        \"min\": 0.0,\n        \"max\": 98035.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          27.75425103279441,\n          6.0,\n          98035.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outstanding_Debt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34462.44339983299,\n        \"min\": 0.23,\n        \"max\": 98991.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1426.5037001343558,\n          1166.37,\n          98991.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_Utilization_Ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35345.024886297695,\n        \"min\": 5.116875070150906,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.2851725189436,\n          32.30578367171092,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total_EMI_per_month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 41793.58784756127,\n        \"min\": 0.0,\n        \"max\": 100000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1403.1182166159933,\n          69.24947329972044,\n          100000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount_invested_monthly\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32114.311685903533,\n        \"min\": 0.0,\n        \"max\": 91216.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          195.5394560265526,\n          128.95453805190283,\n          91216.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monthly_Balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34764.78847209447,\n        \"min\": 0.0077596647753352,\n        \"max\": 98791.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          402.5512581105154,\n          336.73122455696387,\n          98791.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df_train_unique.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "022da16c",
      "metadata": {
        "id": "022da16c"
      },
      "source": [
        "As shown in above, there exists unrealistic values within each columns.\n",
        "\n",
        "For instance, the max age is shown to be \"8698.00\", or the maximum interest rate is \"5797.00\".\n",
        "\n",
        "These outliers and unrealistic values will hinder the accuracy and generalization of the algorithms learned from this dataset.\n",
        "\n",
        "Thus, we want to understand the dataset better and remove any outliers or NaN values that will interfer with the model training later.\n",
        "\n",
        "The outlier_threshold function analyzes and visualizes the distribution of values in a DataFrame column up to a specified range (max_range).\n",
        "\n",
        "It calculates the percentile of each value within this range and plots the differences between these percentiles to help identify potential outliers and distribution patterns.\n",
        "\n",
        "The function also generates a histogram of the column values to visually assess the overall data spread.\n",
        "\n",
        "**Key steps in the function include:**\n",
        "\n",
        "1. Creating a sequence of values from 0 to max_range and calculating their percentile ranks.\n",
        "2. Creating a DataFrame from these values and their percentiles, and computing differences between successive percentiles.\n",
        "3. Plotting these percentile differences and the column's histogram to visualize the data distribution and any abrupt changes.\n",
        "4. Determining outliers by calculating a threshold based on the quantile of the column that corresponds to a calculated max_percentile."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def outlier_threshold(df, col_name, max_range):\n",
        "    print(\"\\n\", col_name, \"\\n\")\n",
        "\n",
        "    # Convert column to numeric, coercing errors to NaN\n",
        "    df[col_name] = pd.to_numeric(df[col_name], errors='coerce')\n",
        "\n",
        "    # Generate the range of numbers from 0 to the max_range\n",
        "    num_cards = range(0, int(max_range) + 1)\n",
        "    percentiles = {card: percentileofscore(df[col_name].dropna(), card, kind='rank') for card in num_cards}\n",
        "\n",
        "    # Convert dictionary to DataFrame\n",
        "    percentile_df = pd.DataFrame(list(percentiles.items()), columns=[col_name, 'Percentile'])\n",
        "    percentile_df['Percentile'] /= 100  # Convert percentile to a fraction\n",
        "    percentile_df['Percentile_Difference'] = percentile_df['Percentile'].diff()\n",
        "\n",
        "    # Plotting the percentile differences\n",
        "    plt.figure(figsize = (15, 3))\n",
        "    plt.plot(percentile_df[col_name], percentile_df[\"Percentile_Difference\"])\n",
        "    plt.title('Percentile Difference Between Different ' + col_name)\n",
        "    plt.xlabel(col_name)\n",
        "    plt.ylabel('Percentile Difference')\n",
        "    plt.show()\n",
        "\n",
        "    # Plotting the distribution of the number of credit cards\n",
        "    plt.figure(figsize=(15, 3))\n",
        "    sns.histplot(df[col_name], kde=False, bins=int(df[col_name].max()))\n",
        "    plt.xlim(0, max_range)\n",
        "    plt.title('Distribution of ' + col_name)\n",
        "    plt.xlabel(col_name)\n",
        "    plt.ylabel('Frequency')\n",
        "    plt.show()\n",
        "\n",
        "    # Calculate and count outliers\n",
        "    max_percentile = percentileofscore(df[col_name].dropna(), max_range, kind='rank') / 100\n",
        "    threshold = df[col_name].quantile(max_percentile)\n",
        "    outliers = df[df[col_name] >= threshold]\n",
        "    print(\"Number of outliers:\", len(outliers))"
      ],
      "metadata": {
        "id": "kGbtxx2-c1vV"
      },
      "id": "kGbtxx2-c1vV",
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop all rows with NaN values for initial analysis\n",
        "df_train_unique_drop = df_train_unique.dropna()\n",
        "df_test_unique_drop = df_test_unique.dropna()\n",
        "\n",
        "print(\"Size of the training set after dropping NaN: \", df_train_unique_drop.shape)\n",
        "print(\"Size of the testing set after dropping NaN: \", df_test_unique_drop.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_j15Xc8SMBfh",
        "outputId": "224c7d30-b5fe-4796-b680-8de7bd3acb2a"
      },
      "id": "_j15Xc8SMBfh",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of the training set after dropping NaN:  (40147, 28)\n",
            "Size of the testing set after dropping NaN:  (26459, 27)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Function calls to plot out the percentile difference as well as data distribution\n",
        "outlier_threshold(df_test_unique_drop, 'Monthly_Inhand_Salary', 15204.6333)\n",
        "outlier_threshold(df_test_unique_drop, 'Num_Bank_Accounts', 12)\n",
        "outlier_threshold(df_test_unique_drop, 'Num_Credit_Card', 12)\n",
        "outlier_threshold(df_test_unique_drop, 'Interest_Rate', 40)\n",
        "outlier_threshold(df_test_unique_drop, 'Delay_from_due_date', 65)\n",
        "outlier_threshold(df_test_unique_drop, 'Num_Credit_Inquiries', 20)\n",
        "outlier_threshold(df_test_unique_drop, 'Credit_Utilization_Ratio', 50)\n",
        "outlier_threshold(df_test_unique_drop, 'Total_EMI_per_month', 600)\n",
        "outlier_threshold(df_test_unique_drop, 'Num_of_Delayed_Payment', 30)\n",
        "outlier_threshold(df_test_unique_drop, 'Num_of_Loan', 16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GKPrKqMSKLIC",
        "outputId": "acf7e74c-0d22-47a0-caeb-42ea6231e2c9"
      },
      "id": "GKPrKqMSKLIC",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Monthly_Inhand_Salary \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-8836c912d7c9>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[col_name] = pd.to_numeric(df[col_name], errors='coerce')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers: 2\n",
            "\n",
            " Num_Bank_Accounts \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-8836c912d7c9>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[col_name] = pd.to_numeric(df[col_name], errors='coerce')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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RhKZzvSGfv/nmG6Xl7777rhAeHi4UFBQoLX/jjTeEoKAgobKyUhAEQThx4oTg4+MjDB8+XLFMENru9QkMDBTeeeedZl+L5p5bXV2dMGrUKOGxxx5TxNPU9Tl06NAmj3//69nwvMeNGyfU1NQolr/55puCr6+v8NxzzyntP23aNGHo0KEqxd2gIb77/wUHBwuHDx9W2raurk7pHigIglBcXCwMGDBA6XVteE1CQkKEoqIixfL9+/cLPj4+wsGDBxXL7s2706dPC3379hXmzJnT6DyquHDhguDj4yPEx8cLglCfHxEREcLixYuVtlPlPqTKe1VcXJzg4+MjfPXVV0rrX3nlFcHX11fIyMgQBKHp+3mD++8rmsjToKCgFt9viIhIPewqS0RE7cKzzz6LsLAwDB48GG+88QbMzc2xfPly2NvbIy4uDnK5HFFRUSgoKFD8s7W1hZubm6LraQMjIyNMmjRJadm+fftgbW3d5AD1DQO6x8bGolOnTggPD1c6T8+ePWFmZtboPN7e3ggODlY8trGxgYeHB7KysjTymhQVFeHEiROIiopCWVmZIp7CwkIMHDgQ6enpyMvLe6hzNIwn19puYc05duwYSkpKMHbsWKXXUCwWo3fv3o1eQwB48sknlR63xe9h37596N69e5MtFO/9/Xt5ecHT01PpvA3dtpuK/X7Gxsb47rvv8N1332HNmjX44IMPYGZmhjlz5ih19YyNjUVQUBCkUqnSuQYMGACZTIZTp0498FxBQUE4ffo0ACgmHZk2bRqsra0Vre5Onz4NqVQKHx8fAFD5Wrpy5QrS09Mxfvx4FBYWKrarqKhAWFgYTp06pejS22D69OlKj4ODg1FUVKToVqouTeeoIAjYt28fhg0bBkEQlF6HgQMHorS0tFHXvokTJyq1wGpPrw/wV6u7q1evYv/+/Q99vAYTJkxQaqEWEBAAQRAwefJkpe0CAgJw8+ZN1NXVtfocy5Ytw3fffYdvv/0WH330Edzd3fHqq6/i7Nmzim0kEoliXEO5XI6ioiLU1dXB398fly9fbnTMMWPGwNLSUvG44f7Q1L35xIkTeO655xAWFoZly5apNfbojh07YGtrq5hARiQSYcyYMdi9ezdkMpliO1XuQ6q8Vx0+fBgSiaTRUAuzZs2CIAg4fPhwq59Dg4fJU6lUiqSkpId+XyIiImXsKktERO3CggUL4OHhAYlEAltbW3h4eCgGf09PT4cgCM12Abt3BlQAsLe3b/ThKzMzEx4eHo22vVdGRgZKS0sV44rdr2EyhgYODg6NtrG0tGw0Dpu6MjMzIQgCvvjiC3zxxRfNxtTamUrvVVFRAQCKLoQPKz09HQAQHR3d5Pr7u+MaGBiga9euSsva4veQmZn5wC6EGRkZSElJUfm8TZFIJEoz7wLA4MGDMWrUKPzvf/9TjCmVkZGBa9euNXuue8e7ak5wcDA2btyIjIwMZGZmQiQSITAwEMHBwTh9+jSmTp2K06dPo2/fvq2+lhp+jw3jGzaltLRUqTji6OiotL6hy21xcXGT3bBVpekcLSgoQElJCTZt2oRNmzY1u8297p8Ruj29Pg3Gjx+Pr776Cl9++aXGZvu8P+aGiWjuv+Y6deoEuVyO0tJSWFtbt+ocwcHBSpNTjB49GqNHj8bixYuVxmbbunUrvv32W6SlpaG2tlax/P7fTVPxNfweSkpKlJZXV1dj7ty56NmzJz7//PMW3x+aI5PJsGvXLoSGhiqNyRoQEIBvv/0Wx48fx8CBAwGodh9S5b0qJycHXbp0aZQ3DcMw5OTktPp5NHiYPH377bcRExODIUOGoGfPnhg8eDAmTpwIFxcXteMhIiIW7oiIqJ0ICAhQzCp7P7lcDpFIhNWrVzca4B9Ao1lIWzs20b3n6dy5Mz799NMm19/74RJoPNmApjW02Jk1axYGDRrU5DaqjIHVkuvXr0MikTT54Vcdwp8Doy9ZsgR2dnaN1t//mhkZGTWanVNbvwe5XA4fHx/Mnz+/yfX3FxhV1bVrV3h4eCi1opPL5QgPD8dzzz3X5D7u7u4PPG5QUBAA4NSpU8jKykKPHj1gZmaG4OBg/PDDDygvL8eVK1eUJqZQ9Vpq+D3+4x//aHZmz/uvu+ZmWRUeMGHCgyQnJwN4+Fxv0HBdPfbYY3j88ceb3KZhvMYG999T2tPr06Ch1V1MTEyrJ6+RyWRN5kNzMbflczE3N0dAQAAOHDigmGV6+/btiImJwYgRIzB79mx07twZEokEq1atarIVXXP3hPvjMzIyQkREBA4ePIgjR44ojUOpqhMnTuDOnTvYtWsXdu3a1Wj9jh07FIW7R62hhd797m0FeL+H+d2OGTMGwcHBiIuLQ3x8PNasWYPVq1dj2bJlipnLiYio9Vi4IyKidq9hMG5nZ+cmB6dX9RhJSUmora1tdnByV1dXHD9+HH379lW7+He/5j44qaKhlYKhoWGjVlyakJubi1OnTiEwMFAjLX6Av2Lu3Lmz2jG3xe/B1dW10ay2TW1z9epVhIWFPdTvrSkymUzRcqzhXBUVFQ98jVqKw9HREY6Ojjhz5gyysrIU3QGDg4Px0UcfITY2FjKZDP369VM6ryrXUsPv0cLCok1yrzV+++03iEQihIeHa+R4NjY2MDc3h1wuV/u5tafX516PPfYYVqxYgeXLlyvNoNvA0tKyUaszoP5e0J5aRTUUlhoKd3v37oWLiwuWL1+udE0sXbr0oc4jEonw6aef4qWXXsJrr72G1atXK7q7qmrHjh3o3LkzFixY0GhdXFwc4uLisGjRIpiYmKh8H3rQe5WTkxOOHz/eaFKh1NRUxXqg+ZaGubm5qj/BVurSpQtmzJiBGTNmID8/H48//jhWrlzJwh0R0UPgGHdERNTujRo1ChKJBMuXL2/0rb8gCCgsLFTpGIWFhVi/fn2jdQ3HjIqKgkwma3L20Lq6uiY/8D6IqakpgPpuc63VuXNnhISEYNOmTbh9+3aj9ap0p2xOUVER3nzzTchkMrzwwgtqH+d+gwYNgoWFBVatWqXUna2BKjG3xe9h1KhRuHr1KuLi4hqtu/f3n5eXh59//rnRNlVVVUqFt9ZIS0tDWlqaYvbXhnMlJibiyJEjjbYvKSlRjBXWkD/NPeegoCCcOHEC58+fV7TA8/Pzg7m5uWJG0Htnc1T1WvL394erqyu+/fbbJseWe5jca42vv/4aR48exZgxY1RqhagKiUSC0aNHY+/evYrWfPdS5bm11etjZmamVn43aGh1d+XKFRw8eLDRehcXFyQlJaGmpkax7NChQ7h586ba59S0oqIiJCYmws7ODp07dwbwVwu6e3M2KSkJ586de+jzGRkZYfny5ejVqxdeeOEFnD9/XuV9q6qqsG/fPgwZMgSRkZGN/s2YMQPl5eWK34Uq9yFV3qsiIiIgk8kabfP9999DJBIhIiICQH1h2draWjEWZoOffvpJ5efYlKbyVCaTNXqf69y5M7p06aKUb0RE1HpscUdERO2eq6srXn/9dfz3v/9FTk4ORowYAXNzc2RnZ2P//v2YOnUqZs+e3eIxJk6ciG3btuGjjz5SFDkqKytx/PhxPPnkkxgxYgRCQkIwbdo0rFq1CleuXEF4eDgMDQ2Rnp6O2NhYvPfee4iMjGxV7H5+fpBIJFi9ejVKS0thZGSE/v37Kz6QPsj777+Pp556CuPHj8fUqVPh4uKCu3fv4ty5c7h16xZ+++23Bx4jPT0d27dvhyAIKC8vx9WrVxEbG4uKigrExMQoPuRpgoWFBRYuXIh//OMfmDRpEsaMGQMbGxvk5ubijz/+QN++fZtsmXKvtvg9zJ49G3v37sVrr72GyZMno2fPniguLsbBgwexaNEidO/eHRMmTMCePXvw/vvvIyEhAX379oVMJkNqaipiY2PxzTffNNudu0FdXR22b98OoP5DdnZ2NjZu3Ai5XI558+YpxXPw4EG88MILePzxx9GzZ09UVlYiOTkZe/fuxYEDB2BjYwMTExN4e3tjz549cHd3h5WVFbp166aYbCI4OBg7duyASCRSFO4kEgn69OmDo0ePIiQkRGm8R1WvJbFYjMWLF+P555/HuHHjMGnSJNjb2yMvLw8JCQmwsLDAypUrW/U7UPV1q6mpQU5ODg4ePIhr164hNDQUH3zwgcbOBQBvvfUWEhISMHXqVDzxxBPw9vZGcXExLl26hOPHj+PkyZMt7t9Wr0/Pnj1x/PhxfPfdd+jSpQucnZ3Ru3fvVh2jYay7K1euNFr3xBNPYO/evXjuuecQFRWFzMxM7NixQ2PdkNWxd+9emJmZQRAE3L59G1u2bEFxcTEWLVqkaF03ZMgQ7Nu3D/PmzcOQIUMU15W3t7faBfV7mZiYYNWqVXjmmWfw/PPPY926dYprrCUHDx5EeXl5k60bASAwMBA2Njb47bffMGbMGJXuQ6q8Vw0bNgyhoaH47LPPkJOTA19fX8THx+PAgQOIjo5W+n0+8cQT+Prrr/Hee+/B398fp0+fVpooRx1N5amHhwcGDx6M0aNHo3v37jAzM8OxY8dw4cIFxMTEPNT5iIj0HQt3RETUIcyZMwfu7u74/vvv8eWXXwKoHzssPDy82Q9N92oonq1YsQI7d+7Evn37YGVlhb59+yqNZ/XBBx/A398fGzduxGeffQaJRAInJyc89thj6Nu3b6vjtrOzw6JFi7Bq1Sq89957kMlk+OGHH1Qu3Hl7e2PLli1Yvnw5tm7diqKiItjY2KBHjx5KhaCWxMfHIz4+HmKxGBYWFnB2dsbEiRMxbdo0eHt7t/o5Pcj48ePRpUsXfP3111izZg1qampgb2+P4ODgRrP9NkfTvwdzc3OsX78ey5YtQ1xcHLZu3YrOnTsjLCxMMbmHWCzGl19+ie+//x7bt29HXFwcTE1N4ezsjJkzZ6rUTbumpgb/+Mc/FI8tLCzQq1cvfPLJJ0oTUZiammLdunVYtWoVYmNjsW3bNlhYWMDd3R2vvPKKYhIAAFi8eDE+/PBDfPTRR6itrcXLL7+sVLgDAE9PT6VJAYKDg3H06FGl2XYbqHothYaGYtOmTfjqq6/w448/oqKiAnZ2dggICMC0adNUet1Vde/rZmpqChsbG/j7+2PevHkYOXJks+NuqcvW1habN2/Gl19+ibi4OGzYsAFWVlbw9vbG22+/rdIx2uL1iYmJwYIFC/D555+jqqoKjz/+eKsLdwYGBnjxxRebHKtx0KBBiImJwXfffYd///vf8Pf3x8qVK/Gf//xHrXg1YeHChYqfzczM4Ovri9dffx1RUVGK5ZMmTcLdu3exadMmHD16FN7e3vjkk08QGxv7wCKrqiwsLLBmzRo8/fTTmDVrFtavXw83N7cW9/ntt99gbGzcbDdusViMIUOGYMeOHSgsLIS1tfUD70OqvFeJxWKsWLECS5cuxe7du/Hrr7/CyckJ//jHPzBr1iylGObNm4eCggLs3bsXe/bsQUREBL755ptmJ8ZRRVN5+sEHH+DJJ59EfHw89u3bB0EQ4OrqqvjyiYiI1CcSNDUiLhEREREREREREWkMx7gjIiIiIiIiIiJqh9hVloiIiIiINKK0tBRVVVUtbmNnZ/eIolFfeXn5A8fPs7GxUUycQURE1FZYuCMiIiIiIo3417/+ha1bt7a4zbVr1x5RNOr79ttvsXz58ha3OXDgAJydnR9RREREpK84xh0REREREWnEjRs3cPv27Ra3GTBgwCOKRn1ZWVnIyspqcZugoCAYGxs/ooiIiEhfsXBHRERERERERETUDnFyCiIiIiIiIiIionaIhTsiIiIiIiIiIqJ2iJNTPCKCIEAuZ69k0hyxWMScIr3F/Cd9xvwnfcb8J33Ha4D0ma7lv1gsgkgkeuB2LNw9InK5gIKCcm2HQTrCwEAMa2tzlJRUoK5Oru1wiB4p5j/pM+Y/6TPmP+k7XgOkz3Qx/21szCGRPLhwx66yRERERERERERE7RALd0RERERERERERO0QC3dERERERERERETtEAt3RERERERERERE7RALd0RERERERERERO0QC3dERERERERERETtEAt3RERERERERERE7ZCBtgMgIiIiImqvauvkuFNUiduFlbhdWIHCsmpIxGIYGvz5T3LPzwb3L5c0u51ELIJIJNL20yMiIqJ2joU7IiIiItJrNbUyRXEu788CXf3/lSgoqYLQBucUidBEQU/SuBDYysKggYEYRk3uL1F6LBazaEhERNQRsHBHRERERDqvukaG20X1Rbn6Al2FolBXWFrd4r4mRhJ0sTZFF2szdJYaQy4HamVy1NbJUFsnr/8nk6Puz59rGpb9ubzh5zqZXHFMQQBqauWoqZW3cOa2IxGLYPBnIc/I8L4C4T3FPoP7iodNFQUNFI8ljYqLjY5tIIaBRMzWhkRERCpi4Y6IiIiIdEJldV19l9Yi5VZzeYUVKC6raXFfU2MD2FubKgp09tamsLc2QxdrU3QyM9RIoUkuCJDdU8hTKvLdt7xWVl8UrLtvvVJRUKlgKGt0nJr7tpHJ/2o7KJMLkNXIUA0ZUPnQT63VDJooFN5bFLy/YGhoIIGxoRidrcwQ1M0W1p2MH33QREREWsDCHRERERF1GBVVdbhdVIG8gntaz/3ZzbWkvOXinLmJAext6otxXaz+Ksx1sTaFhalminMtEYtEEBtIYGggadPzNEcml6OuTrinuCdrsih4f7FQse39xcVmi46NWyPW1sqVuhzXyepbIFa23NixSZsPXEdkqCvGhLnB2FA7ryUREdGjwsIdEREREbUrZZW1iskg7u/WWlZZ2+K+ncwM/yzMmcHepr4oZ29tBjur+uKcPpOIxZAYAcZ49MUuQRAgkwstFPweXBiUyQVk5JXhQspd7DiWjmMXb2LasG4I8rVj11siItJZLNwRERER0SMlCALKKmsVE0HcLlSeGKK8qq7F/S3NjRQt5e7t1mpnZQozE/552x6JRCIYSEQwkIhhqmYvVwMDMayszBB3PA0/xSUjv6QaX227CD83azw10gdOtuaaDZqIiKgd4F82RERERKRxgiCgpKK2yVZztwsrUVndcnHOysJIUZTrcs94c3ZWpjA15p+w+kokEqGfnz16uNtgz4kM7D6RiSsZhXh/zUkMD3LGhIEeLN4SEZFO4bsaEREREalFEAQUldX81WquqBJ5BX+NO1ddI2txfxupMbpYmd5ToKv/387KFMZGHLuMmmdsKMHEQZ4I7+WAjQeuI/H6XcSdzkLC5VuYPMQL4b0cIGb3WSIi0gEs3BERERFRs+SCgKLS6qa7tRZVoKZW3uy+IgA2UpM/x5oz+3NCCFNFyzkjTixAD8nOyhSvTA7AxbR8/BR3HbcKKvDd7qv441wuZoz0gYeDVNshEhERPRQW7oiIiIj0nFwuoKC0SmmcuYYC3e2iStTWtVCcEwG2lib1hbl7urTaW5vC1tIUhgbiR/hMSF/5e3TGB7Otsf90NrbHpyE1twSL157GwAAHTB7sBam5kbZDJCIiUku7K9ylpKRg8eLFSExMhLm5OSZMmIDXX38dRkYtv9muX78ehw8fRlJSEgoLC/HFF18gMjJSaZtjx45h8+bNSEpKQn5+PpycnDBp0iRER0fD0PCvWcZiYmKwdevWRudYvXo1IiIiNPNEiYiIiB4hmVyO/JJq5VZzBRW4XVSJO0WVqJMJze4rEYuUinMNhTl7azN0tjSBgYTFOdI+A4kYkaGu6N/THpsPpeD4pVs4cv4mTl+7g4mDPDCsrxMkYuYqERF1LO2qcFdcXIzo6Gi4u7tj2bJlyMvLw8cff4yqqiosWLCgxX23b98OABg8eDC2bdvW5DYbN25EVVUVXn31VTg4OCApKQnLli1DSkoKPvroI6VtXVxc8Omnnyot8/LyUv/JEREREbWxOpkc+SVVyCuoVB53rrASd4sqIZO3XJyzU3Rl/avVXBdrU3S2NGHBgzoMKwtjPD++B4b0ccT6uGRk5pVhw/7rOJyUixkjfNDdzVrbIRIREamsXRXuNm7ciPLycixfvhxWVlYAAJlMhkWLFmHu3Lmwt7dvcV+xWIzs7OxmC3cLFy6EjY2N4nFoaCjkcjk+//xz/P3vf1daZ2JigsDAQE08LSIiIiKNqa2T42Z+OXLvlDfq1nq3uApyofninIFEXN9izurPVnM2fxborExhIzWBWMzB/El3dHO2woLofjiclItfD6ci5045lmxIRL/uXTBtmDdspCbaDpGIiOiB2lXh7vDhwwgLC1MU7QAgKioK77//PuLj4zFp0qRm9xWr8C3wvYW5Bn5+fhAEAXfu3GlyPREREdGjJggCistrcCu/ArcK6v/dzK9AXkEF7hZXooWGczAy+LM4d2+3Vqv6Ip1VJ2POtEl6RSwWYUgfJwR374KtR1Lxe2IOTl29jaSUuxgb5o7IEBcYGnCSFCIiar/aVeEuNTUVkydPVlomlUphZ2eH1NTUNjnn2bNnYWRkBGdnZ6XlGRkZCAoKQnV1NXx8fPDSSy9hxIgRbRIDERER6afaOhnyCir/LMyVK4p0twoqUFkta3Y/Y0PJPV1ZzZR+trQwYnGO6D4WpoaYOcoXg3s74qe4ZCRnF2Pr4VQcPZ+LJ4f7oLd3Z4h43RARUTvUrgp3JSUlkEobT9luaWmJ4uJijZ8vPT0dP/zwA6ZPnw5zc3PFcj8/P/Tq1Qve3t4oLS3Fhg0bMG/evCYnvGgNA86qRhoi+XMQcAkHAyc9xPynjkYQBBSV1eDm3XLcbCjQ5Vcg92458our0FzjOZEIsLM0hYOtGbp2NoeDjRmculjAx70zJIIc8paa3RHpIE3c/z2dLPFedDCOX8rDpgPXcaeoCku3nEeAV2fMGOUDh87mDz4IkZbwbyDSZ/qc/+2qcPcolZWV4ZVXXoGzszPeeOMNpXXR0dFKj4cNG4bp06dj6dKlahfuxGIRrK35hwBpllRqqu0QiLSG+U/tTVVNHW7eLUf27TLk3ClDdl4Zcu6UIudOWYut58xNDeFsZwGnLhZwsrOAc5f6nx1tzdmFj6gJmrj/jx1kgWEhbtgUdw3bD6fgfEo+Ln99AhMivDB1hA/MTAw1EClR2+DfQKTP9DH/21XhTiqVorS0tNHy4uJiWFpaauw8NTU1mDdvHoqLi7Fp0yaYmZm1uL1YLMaoUaPwySefoKqqCiYmrR/IVi4XUFJSoW7IREokEjGkUlOUlFRCJpNrOxyiR4r5T9okCAIKS6txM7++5VzD/7fyK3C3uKrZ/UQioIuVKbp2NoejrRm62pjDobMZHDqbQWpu1GQXvbLSxsdj/pM+a4v8nxDujpDudli/LxnnU/Kx5dANHDydhWnDvBHm35XdZ6ld4XsA6TNdzH+p1FSlFoTtqnDn6enZaCy70tJS3LlzB56enho5h1wux9tvv41Lly5h/fr1cHBw0MhxVVFXpxvJRe2HTCZnXpHeYv5TW6qukSmNN9cwBl1eQSWqa1toPWdigK6dzdDVpuFffYGui7UpDJr5w0wmE4BmO8w2jflP+kzT+W9naYrXpgQg6UY+NhxIxp2iKqzcfgkHzmRjxkgfuNp30ti5iDSB7wGkz/Qx/9tV4S4iIgIrV65UGusuNjYWYrEY4eHhGjnHokWLcOjQIaxZswa+vr4q7SOXyxEbG4tu3bqp1dqOiIiI2h+5IKCwpFqpMNfwc0FJdbP7iUUi2FmbwqGhONf5r/87mRqyhQ5RByQSiRDYzRY9PawRezILu46n43p2MRZ9fwpDAp3weIQnLEzZfZaIiB69dlW4mz59OtatW4d58+Zh7ty5yMvLw5IlSzB9+nTY29srtouOjkZubi7i4uIUyy5cuICcnBwUFBQAAJKSkgAANjY2CAkJAQCsXLkSGzduxOzZs2FkZIRz584p9vf29oaFhQVycnIQExODsWPHws3NDcXFxdiwYQMuXryIZcuWPYJXgYiIiDSpqqYOeQWVyrO25lfgVmEFamqb/8bWwtRQUZC7t0hnZ9V86zki6tgMDSQYP8Ad4f5d8fOhGzh55TYOJebg5JU8TBrshcG9HSEWszhPRESPjkgQBLWnJDt37hwSEhKQn5+Pp556Cu7u7qisrERqairc3d2VZmpVVUpKCj788EMkJibC3NwcEyZMwBtvvAEjIyPFNjNnzkROTg4OHjyoWBYTE4OtW7c2Ol5ISAjWrVun2O/kyZNNnveHH35AaGgoioqKMH/+fFy+fBn5+fkwNDSEv78/5syZg0GDBrX6+TSQyeQoKChXe3+iexkYiGFtbY7CwnK9ayZMxPynpsgFAQXFVfUt5+4tzhVUoLC0+dZzErEIdlamcLine6tDZ3N07WzWLlvXMP9Jn2kj/69mFGL9/mTk3Kn/O97V3gIzRvqgm7PVIzk/0b34HkD6TBfz38bGXKUx7tQq3NXU1ODNN9/EgQMHIAgCRCIRvv32W4SFhaG6uhoRERF49tln8eKLL6oVvC5i4Y40SRdvWkSqYv7rt8rqunu6tv5VoLtdWIGaFvKhk5nhX+POdTaDg019cc7W0qRDtZ5j/pM+01b+y+RyHDqbg21H0lBRXQcACOtpjyeGesPKwviRxUHE9wDSZ7qY/6oW7tTqKvvFF1/g999/x8KFCxEaGorIyEjFOmNjY0RGRuLAgQMs3BEREVGryeUC8kuq7ivOleNmQQWKy2qa3U8iFqGLtelfrebuGX+uPbaeI6KOQSIWY0SwC0J62OPXP1JwJOkmjl/Kw9nrd/FYuDtGBrt0qC8AiIioY1GrcLdr1y5Mnz4d06ZNQ2FhYaP1Xl5eiI2NfejgiIiISHdVVDW0nitXtJy7WVCBvIJK1Mma/yZVamaIrg2FuYYWdH+2npOI+eGZiNqG1MwIz0b5YXCgE9bHJSM1twSbD9UX8p4a0Q3+np21HSIREekgtQp3+fn5Lc7IKpFIUFVVpXZQREREpBvkcgF3iysbdW29VVCB4vLmW88ZSESwt248a6uDjRnMTNh6joi0x8NBindnBuHYhVv45fcbuFVQgf/9nIQ+3WwxbXg3dLEy1XaIRESkQ9Qq3Dk4OCA1NbXZ9WfPnoWrq6vaQREREVHHUlFVWz8pxD3FuZsF9WPP1cmaH07X0tzoz66tykU6W0tTztxIRO2WWCTCwAAH9PWxw2/xadh/OhuJ1+/iQmoBokJdMSbMDcaGEm2HSUREOkCtwt24cePw3XffYdSoUXB3dwcAiET1f1z//PPP2LNnD9566y2NBUlERESPlkwuR02tHLV1ctTUyVBb1/CzHGWVtfcU6Oq7uZZU1DZ7LAOJGF1tTJUKcw6dzWFvbQYzE7X+FCEiahfMTAwwfXg3DOrtiJ/iknEloxA7jqXj2MWbmDasG4J87RSfk4iIiNSh9qyyL774Ik6cOAFPT0/cuHEDPj4+KC4uxq1btzB48GB89dVXkEj4LVMDzipLmqSLM+oQqUrf8l8QBMjkgqJoVlsrq/+/7q+iWk2dHHX3/Fxb27jYVlt33361snvW/bW+YX956/88gJWF0Z/FOXM43FOk6yw1Yes5DdG3/Ce6V3vPf0EQcObaHWw6eB35JdUAAD83azw1ohuc7Cy0HB3pgvZ+DRC1JV3Mf1VnlVWrcAfUvzH99ttv2Lt3LzIyMiCXy+Hq6oqoqChMmDCB3yzdh4U70iRdvGkRqUqb+d9QRKtvifZXoUulolmtHLWylotqim0atv/zuOq9U2uOoYEYhhIxDA3FMDIQw9TIAF1szJSKc11tzGBqzNZzbY33f9JnHSX/q2tl2HMiA7tPZKJOJodYJMLwIGdMGOjBVsb0UDrKNUDUFnQx/9u8cEetw8IdaZIu3rSIVNWQ/wUFZaiqrlMUv5prjaZUIKttptjW0AJNUVhrooXan8fS9rumoUF98az+fwkMDeuLakYGYhgaShTrFOsNxDAyFMPQQFK/neFf65S2NZTcc+y/fjYwEEPML+PaDd7/SZ91tPy/U1SJjQeuI/H6XQD1M2JPHuKF8F4OvK+SWjraNUCkSbqY/6oW7tT6yqeoqAi3bt1C9+7dm1x/7do1dO3aFZaWluocnoiIqEl3iyux6eANXEwrQE2NDNr+5snovqLXvYWvRgU0w8ZFMcW2hve2aJMoFeca1hsZiGEgEbNFOxFRB2FnZYpXJgfgYlo+foq7jlsFFfhu91X8npiLGSN94Oko1XaIRETUAajV4u6dd95BWloafv755ybXT58+HZ6envj3v//90AHqCra4I03SxW8biFoik8sRdyob246moqa2cc6LAKWiV6MCWkOrNMN7i2YSGNxbQGuptZrkr/UNxzWQiFhEo0eO93/SZx05/+tkcuw/nY3t8WmorpEBAAYGOGDKYC9IzY20HB11FB35GiB6WLqY/23a4u7EiRN48sknm10/dOhQbNy4UZ1DExERKUm7WYK1sVeRmVcGAPB1tcLcSQEwgAAxRDA0ELOIRkRE7ZqBRIzIUFf072mPzYdScPzSLRw9fxNnrt3BxIEeGBbkBIn4wR/eiIhI/6hVuCsoKIC1tXWz662srJCfn692UERERJXVddh6JBUHzmRDEABzEwM8MdQbQ/o6obONhU5920ZERPrBysIYz4/vgaF9nPBj3DVk5pVhw4HrOJyUi6dG+sDPrfnPWEREpJ/UKtzZ2dnh8uXLza6/dOkSbGxs1A6KiIj0W2LyHfwYl4zC0moAQP+e9pg+rBuk5kYc0JuIiDo8b2dLLIjuh8NJufj1cCpy7pbjkw2JCO7eBdOGeqOzpYm2QyQionZCrcLdiBEj8NNPPyEiIgLDhw9XWrd//378+uuvmD59ukYCJCIi/VFYWo31cck4m3wHAGBnZYKZo33h79FZy5ERERFpllgswpA+Tgju3gXbjqTiUGIOTl+9jfM37mJsmBsiQ11haCDRdphERKRlak1OUVpaiqeeego3btxA9+7d0a1bNwDA9evXcfXqVXh5eeGnn36CVMqZkhpwcgrSJF0cmJP0m1wu4ODZbPx6OBVVNTJIxCKMDnHF+HB3GBsqf2hh/pM+Y/6TPtP1/M/MK8VPcclIzi4GUP/l1fTh3RDobctxXAmA7l8DRC3RxfxXdXIKtQp3AFBRUYFvvvkGcXFxyMzMBAC4urpi1KhRmD17NszMzNQ5rM5i4Y40SRdvWqS/MvNKsTb2GtJulgAAvJykiB7dHc5dLJrcnvlP+oz5T/pMH/JfEAQkXMnDzwdvoKisBgDg72mDp0b4oKsNP1/pO324Boiao4v53+aFO2odFu5Ik3TxpkX6p7pGhu3xadh3MgtyQYCpsQRTBnthcB+nFsexY/6TPmP+kz7Tp/yvqqnDzmMZ2HsyEzK5AIlYhFH9XDBugDtMjdUa7Yh0gD5dA0T308X8V7Vwx7s+ERE9cudT8vHjvmu4W1wFAAju3gVPDu8G607GWo6MiIhI+0yMDDBliBcGBThgw4HrOJ+Sjz0JmTh26RamDvVG/x727D5LRKQn1C7cpaSkYMuWLcjOzkZxcTHub7gnEomwdu3ahw6QiIh0R3FZNTYcuI6TV24DADpLjTFjlC8CvW21HBkREVH7Y29jhtef6I1zN+5i4/7ruF1UidU7LuP3xBzMGOkDV/tO2g6RiIjamFqFu23btuHdd9+FgYEBPDw8mpyEgj1wiYiogVwQcDgpF78cSkFFdR1EImBksAsmDvKAiREbfxMREbUk0NsWPd2tsfdkFnYeT8f17GIs+v4UhgQ64fEIT1iYGmo7RCIiaiNqfVpavnw5/Pz8sHr1atjY2Gg6JiIi0iE5d8qwdu813Phzljy3rp3wbGR3uHVlKwEiIiJVGRpIMG6AOwb4d8XPh27g5JXbOJSYg5NX8jBpsBcG93aEWMzus0REukatwt3t27cxa9YsFu2IiKhZNbUy7Dyejj0n6gfWNjaSYNIgTwwPcuYHCyIiIjXZSE3wwgR/DAksxE/7k5F9pxzr9l7DH+fqu892c7bSdohERKRBahXufH19cfv2bU3HQkREOuJyegF+2HsNtwsrAdR38Xl6lA9spCZajoyIiEg3dHezxvt/64dDZ3Ow7UgaMvPK8NGPZxHW0x5PDPWGlQUnfCIi0gVqFe5iYmLw2muvISIiAn379tV0TERE1EGVVNRg04EbOH7pFgDAysIIM0b6oq+PLWe/IyIi0jCJWIwRwS4I6WGPX/9IwZGkmzh+KQ9nr9/FY+HuGBnsAgOJWNthEhHRQ1CrcLd69Wp06tQJM2bMgLe3NxwcHCAWK78hiEQirFixQiNBEhFR+yYIAuIv3MLPh26grLIWIgDD+jpj0mBPmBpz8gkiIqK2JDUzwrNRfhgc6IT1cclIzS3B5kMpOJx0E0+N6IZenp21HSIREalJrU9TycnJAAAHBweUl5fjxo0bjbZhywoiIv1wM79+bJ2rmUUAAGc7C0RH+cLL0VK7gREREekZDwcp3p0ZhGMXbuGX328gr6ACn/2chEBvW0wf0Q1drEy1HSIREbWSWoW7gwcPajoOhZSUFCxevBiJiYkwNzfHhAkT8Prrr8PIyKjF/davX4/Dhw8jKSkJhYWF+OKLLxAZGdlou7y8PCxevBhHjx6FoaEhRo4cifnz58PCwkJpu4MHD+Lzzz9HWloaHB0dMWfOHEyePFmjz5WIqCOrrZNjz4kM7DyejjqZACMDMSYM8mC3HCIiIi0Si0QYGOCAvj52+C0+DQfOZOPcjbu4mFaAyFBXjA1zg7GhRNthEhGRitrVJ6vi4mJER0ejtrYWy5YtwxtvvIGff/4ZH3/88QP33b59OwoLCzF48OBmt6mtrcVzzz2H9PR0/Pe//8XChQtx9OhRvPXWW0rbnT59Gi+//DICAwOxevVqREVF4b333kNsbOxDP0ciIl2QnFWEhd+dxLajaaiTCfD3tMGHz4UiKtSNRTsiIqJ2wMzEANOHd8PCWSHwc7NGnUyOncfS8d7qEzh19TYEQdB2iEREpAK1Bx6SyWSIjY1FQkIC8vPz8eqrr8LX1xelpaU4fvw4+vbtC1tb21Ydc+PGjSgvL8fy5cthZWWlOM+iRYswd+5c2Nvbt7ivWCxGdnY2tm3b1uQ2e/fuxfXr17F79254enoCAKRSKWbPno3z588jICAAALBixQoEBATggw8+AAD0798fWVlZWLp0aZOt+IiI9EVZZS02H7qBI+dvAgCk5kZ4cng3hPh14RAJRERE7ZCTrTnenh6Is8l3sPHADeSXVGHFtovwc7PGUyO6wcnO4sEHISIirVGrWURJSQmefPJJvPXWW9i5cycOHjyIgoICAICZmRkWL16MH374odXHPXz4MMLCwhRFOwCIioqCXC5HfHx8i/vePzlGc8f39fVVFO0AIDw8HFZWVvjjjz8AADU1NUhISGhUoBszZgxSUlKQnZ3dimdERKQbBEHAiUu38M/VJxRFu8GBjvjX86EI7WHPoh0REVE7JhKJEOTbBYufD8Vj4e4wNBDjSkYh3v/2FH7an4yKqlpth0hERM1Qq3D36aef4vr161izZg3279+v1MxaIpFg9OjRikJYa6SmpioV1YD6FnF2dnZITU1VJ9QHHl8kEsHDw0Nx/MzMTNTW1jbazsvLS3EMIiJ9cruwAv/7OQlf77iMkopaONqaI2ZGX0RHdoe5iaG2wyMiIiIVGRtKMHGQJ/71XCj6+thBLgjYfzob7359AkeSciFn91kionZHra6yBw4cwMyZMxEeHo7CwsJG693d3bF169ZWH7ekpARSqbTRcktLSxQXF6sTaqPjd+rUqcXjN/x/fxwNjx8mDgMDjvtEmiH5cwwxCccSozZUJ5Njz4lMbDuSito6OQwl9ZNPjAnT7jh2zH/SZ8x/0mfMf83pamuO16f2xsXUfKzbew038yvw3Z6r+CMpFzNH+8LLiTPDt0e8Bkif6XP+q1W4Ky0thbOzc7Pr6+rqIJPJ1A5KF4nFIlhbm2s7DNIxUqmptkMgHXU1owBfbk5C+s0SAECAty3mTekNx3Y0Dg7zn/QZ85/0GfNfcwYFmaN/b2fsPJqKDfuuITW3BIu+O4WRIa54ZkwPWHUy1naI1AReA6TP9DH/1Srcubq64tKlS82uj4+PV3QtbQ2pVIrS0tJGy4uLi2Fp+fDf+kilUpSVlTV5fAcHBwBQnOf+OEpKSpTWt5ZcLqCkpEKtfYnuJ5GIIZWaoqSkEjKZXNvhkA6pqKrDz4du4NCZbAgALEwN8dTIbgjv5QCRSITCwnJth8j8J73G/Cd9xvxvO0N6OyDQ0wabDt5A/IWbiDuZifikXDw+2BPDg5w5Y3w7wWuA9Jku5r9UaqpSC0K1CndTpkzBp59+itDQUPTv3x9A/VhxNTU1+PLLL3HkyBHFjKyt4enp2WgMudLSUty5c6fRmHPq8PT0RHJystIyQRCQlpaG8PBwAPVFSUNDQ6SmpmLQoEGK7Rriepg46up0I7mo/ZDJ5Mwr0ghBEHDm2h2s35+M4rIaAEB4r66YOtQbncyMIJMJANrXuDfMf9JnzH/SZ8z/tmFhaojZY/0wuLcj1sclIyOvFOv3JeP3szl4aqQP/NystR0i/YnXAOkzfcx/tQp30dHRuHHjBt58803F2G9vv/02ioqKUFdXh2nTpuGJJ55o9XEjIiKwcuVKpbHuYmNjIRaLFYW1hxEREYHffvsN6enpcHd3BwAcP34cRUVFGDx4MADAyMgIoaGh2Lt3L6KjoxX77t69G15eXi12ESYi6ojyi6vw475rSErJBwDYW5vimcju/AOdiIhID3k7W+L/ooNx+Hwufv0jFTl3y/HJhkQEd++CaUO90dnSRNshEhHpFZEgqD910OnTp7F3715kZGRALpfD1dUVUVFR6Nevn1rHKy4uxtixY+Hh4YG5c+ciLy8PH3/8McaPH48FCxYotouOjkZubi7i4uIUyy5cuICcnBwUFBRg0aJFmDVrFnr37g0bGxuEhIQAAGprazFp0iQAwJtvvonKykosWbIEvr6+WLVqldLzeuaZZzB16lRERUUhISEBX331FT777DNERUWp9dxkMjkKCrTfxYx0g4GBGNbW5igsLNe7bxtIc2RyOfafzsa2I2morpVBIhZhTH83jBvgBkMDibbDaxbzn/QZ85/0GfP/0SurrMW2I6k4lJgDQQCMDMQYO8AdkSGuMOTEe48crwHSZ7qY/zY25ip1lW114a6yshJ///vfMWrUKDz22GNqB9iclJQUfPjhh0hMTIS5uTkmTJiAN954A0ZGRoptZs6ciZycHBw8eFCxLCYmpsmZbENCQrBu3TrF47y8PCxevBhHjx6FgYEBRo4ciXfffRcWFsoDrh84cACff/450tLS4OjoiDlz5mDKlClqPy8W7kiTdPGmRY9W+q0SrN1zDRl59eN5+jhb4pnI7nC0bf+T6DD/SZ8x/0mfMf+1J+t2GdbHJSM5qwgAYG9jhqdH+qCnh412A9MzvAZIn+li/rdZ4Q4A+vbti5iYGEydOlWt4PQRC3ekSbp406JHo7K6DtuOpGH/mSwIAmBmbICpw7wxMMABYpFI2+GphPlP+oz5T/qM+a9dgiAg4XIeNh28geLy+vFwg7t3wfRh3rCRsvvso8BrgPSZLua/qoU7tca4CwoKQmJiIgt3REQdSOL1O1gfl4yCkmoAQP8e9pg2vBsszY0esCcRERHpO5FIhP49uyLAyxbbj6bhwJlsnL56GxdS8vHYQHeMDHbh7LNERG1ArRZ3WVlZmD17NqKiovDkk0+ia9eubRGbTmGLO9IkXfy2gdpOYWk1fopLxpnkOwAAW0sTPDPaF/6enbUcmXqY/6TPmP+kz5j/7UtmXil+jEvGjexiAIBDZzM8PcqXk1u1IV4DpM90Mf/btKtsnz59IJPJUFtbCwCQSCRKY9AB9d/InDlzprWH1lks3JEm6eJNizRPLhdwKDEHW/5IQVWNDGKRCKNDXfBYuAeMDdvv5BMPwvwnfcb8J33G/G9/5IKAYxduYfPvN1BaUf/ZMLSHPaYN84aVhbGWo9M9vAZIn+li/rdpV9nRo0dD1EHGQiIi0keZeaX4Ye81pOaWAAA8HaWIjuwOly4WD9iTiIiISDVikQgDAxzQx8cWvx5Oxe9nc5BwOQ9JN+5i4iBPDA9ygkTM7rNERA9DrRZ31HpscUeapIvfNpBmVNfK8NvRNOw9mQW5IMDUWILJg70wJNAJYrFufOHC/Cd9xvwnfcb8b//Sb5Vg3d5kpN2s/+LQ2c4cT4/yhY+LlXYD0xG8Bkif6WL+t2mLOyIian8upOZj3d5ruFtcBQAI8rXDUyN8YN2JXVWIiIio7bl3leK9Z4JwJCkXv/yeguw75fh4/VmE+3fFlKHenBCLiEgNahfucnNzsXLlSiQkJKCgoABfffUV+vXrp/h50qRJ6NGjhyZjJSKiJhSXVWPDges4eeU2AMBGaoynR/oisJutliMjIiIifSMWiTA40AlBvl3wy+8pOJKUi/iLt3D2+l1MivDE0D660wuAiOhRUKtwd+PGDcyYMQNyuRwBAQHIzMxEXV0dAMDGxgZnzpxBRUUF/v3vf2s0WCIi+otcEHA4KRe/HEpBRXUdRCJgZLALJg7ygIkRG1QTERGR9liYGuLZqO4Y1NsBP+5NRkZeKdbHJePI+VzMHOULLydLbYdIRNQhqPXJ7pNPPkGnTp3w888/AwAGDBigtH7w4MHYs2fPw0dHRERNyrlbjh9ir+J6djEAwM2+E6KjfOHeVarlyIiIiIj+4uVoif+LDsbv53Lw6x+pyMwrw7/WnUFEbwdMHuyFTmbsPktE1BK1CnenTp3CvHnzYGNjg8LCwkbrHR0dkZeX99DBERGRsto6GXYcy8CeExmQyQUYG0rweARnbSMiIqL2SywWYVhfZwT7dsHm328g/sItHE66iTPX7mDyEC9E9HaEWMTus0RETVGrcCcIAkxMTJpdX1BQACMjfnNCRKRJV9IL8MPea8grrAQABHrbYsZIH3S2bP5+TERERNReSM2NMHtsDwwKcMSP+5KRfacMP8Rew5Gkm5g52oc9B4iImqBW84wePXrgjz/+aHJdXV0ddu3ahd69ez9UYEREVK+0ogbf7LyMTzaeQ15hJSwtjPDSRH+8MrkXi3ZERETU4fi4WOH9vwXjyeHdYGIkQdrNEnz4/Wms23sN5VW12g6PiKhdUavF3Zw5c/DCCy/g/fffx9ixYwEA+fn5OHbsGFauXInU1FQsWLBAo4ESEekbQRBw7OItbDp4A2WVtRABGNrXCZMivGBmwskniIiIqOOSiMUY2c8F/fy64OeDN3Dich4OJebg9LXbeGKINwb06srus0REAESCIAjq7Lht2zb8+9//RmlpKQRBgEgkgiAIsLCwwMKFCzFu3DhNx9qhyWRyFBSUazsM0hEGBmJYW5ujsLAcdXVybYdDbeBWQQV+iL2Kq5lFAABnO3NER3bnDGxg/pN+Y/6TPmP+67arGYX4MS4ZuXfrPzN5O1vi6ZE+cLXvpOXI2g9eA6TPdDH/bWzMIZE8uCOs2oU7AKioqMCxY8eQnp4OuVwOV1dXDBw4EBYWFuoeUmexcEeapIs3LapXWyfHnoQM7DyWgTqZHEYGYkwY6IGR/VxgoMJNXR8w/0mfMf9JnzH/dV+dTI79p7Ox/WgaqmtlEImA4UHOmDjQk70NwGuA9Jsu5r+qhTuV7n4hISH44IMPEBkZCQBYvnw5Ro0aBR8fH4wYMeLhIiUiIgBAclYR1sZexc38CgCAv4cNnh7tiy5WplqOjIiIiKjtGUjEiAx1RYhfF2w6eAOnrt7G/tPZOHXlNqYO80b/HvYQsfssEekZlQp3FRUVqKqqUjxevnw53Nzc4OPj02aBERHpi/KqWmw+dAOHk24CAKRmhpg+ohtC/fjHKREREekfG6kJXpzoj4i0AvwYl4y8ggqs3nEZh8/l4ulRPnCyYw8vItIfKhXuXFxcEBsbi6CgIEU32MrKShQVFbW4n5WV1cPGR0SkswRBQMKVPGzcfx0lFfUzqEX0dsSUIV6wMDXUcnRERERE2tXTwwYfzArB3pOZ2HksHdeyirDwu1MYGeyC8eHuMDVm91ki0n0qjXG3bds2vPvuu2jtcHhXrlxROzBdwzHuSJN0sX+/vrldVIkf917DxbQCAIBDZzNER3aHj4uVdgPrAJj/pM+Y/6TPmP/67W5xJTbsv47E63cBANadjDFtmDf6de+iNz0UeA2QPtPF/NfoGHcTJ05EQEAATp48ibt372L58uUYOXIkfH19HzpQIiJ9UieTY9+pLPx2NA01dXIYSMQYN8ANUaFuMDTg5BNERERETbG1NMUrkwNwPuUufoq7jttFlVi5/RIOJ+VixkgfOHQ213aIRERtQqXCXVlZGdzc3ODp6QkA+PXXXzFx4kQMHz68TYMjItIlKTnFWBt7Fdl36lvfdne1wjOR3dHVxkzLkRERERF1DAFetvBzs8buE5nYdTwDl9MLsWDNSUSGumJcmDuMjSTaDpGISKNUat7Rr18/7N69W/E4NDQUtra2bRYUEZEuqaiqw7p91/DvdWeQfaccFqaGmD3WD39/sg+LdkREREStZGggwYSBHlj8XAgCvDpDJhew63gG/vnNCZxNvtPqIZ6IiNozlVrcGRoaoqamRvF427ZtGDBgAHr37t1mgRERdXSCIODMtTv4aX8yisrq76Hh/l0xdZg3OpkZaTk6IiIioo6ti7UZXpsSgHPX7+Kn/deRX1KF5b9eQC/Pzpgxshu6WPMLUiLq+FQq3Hl6emLz5s1wcnJCp06dIAgCcnJycOnSpRb369mzp0aCJCLqaPKLq7A+LhnnbtQPoNzF2hTRo33h526j5ciIiIiIdIdIJEIfHzv08LDBruPpiE3IxIXUfPzzm0KM6e+KMf3dYGTI7rNE1HGpNKvs4cOH8cYbb6CiokKlgwqCAJFIxFll78FZZUmTdHFGHV0hk8tx4HQ2th5JQ3WtDBKxCFH93TB+gBsMDfhHoyYw/0mfMf9JnzH/SRW3Ciqwft81XEovBADYWZngqRE+6O3d8Yd64jVA+kwX81+js8pGRETgwIEDuHDhAvLz8xETE4OpU6eiT58+Dx0oEZGuSL9VgrV7riEjrxQA4O1siejI7nCy5SxnRERERI9CVxszvDktEGeu3cGGA9dxp6gKX/xyHn262eLJ4d1ga2Wq7RCJiFpFpcIdAFhZWWHQoEEAgC1btiAqKgphYWEaDyglJQWLFy9GYmIizM3NMWHCBLz++uswMmp5PChBELB69Wr89NNPKCgogJ+fH+bPn4/AwEDFNjExMdi6dWuT+7/11luYM2dOi9utXr0aERER6j85ItJJVTV12Ho4DfvPZEEQADNjAzwx1AuDejtCLBJpOzwiIiIivSISiRDcvQv8PW2wIz4d+05lIfH6XVxKK8DYAe6IDHGFoYFK8zQSEWmdyoW7e61bt07TcQAAiouLER0dDXd3dyxbtgx5eXn4+OOPUVVVhQULFrS47+rVq7F06VK8/fbb8PX1xfr16zFr1ixs374dLi4uAICXXnoJ06dPV9pv9+7dWLt2baOCnIuLCz799FOlZV5eXhp4lkSkS85dv4sf466hoKQaABDi1wVPDu8GSwtjLUdGREREpN9MjAzwxFBvDOjlgPX7ruFqZhG2Hk7FsQs3MWOUD/w9Oms7RCKiB1KpcLdt2zYAwIQJEyASiRSPH2TixImtCmbjxo0oLy/H8uXLYWVlBQCQyWRYtGgR5s6dC3t7+yb3q66uxqpVqzBr1iw8++yzAICgoCBERkZizZo1WLhwIQDA1dUVrq6uSvv+97//hbe3N7p376603MTERKm1HhHRvQpLq/HT/mScuXYHAGBraYKZo33Ry5N/ABIRERG1J0625vj7k32QcCUPmw7eQF5hJf63KQnBvnaYPrwbbKQm2g6RiKhZKhXuYmJiIBKJMGbMGBgZGSEmJuaB+4hEolYX7g4fPoywsDBF0Q4AoqKi8P777yM+Ph6TJk1qcr+zZ8+irKwMUVFRimVGRkYYOXIk4uLimj1fXl4eTp8+jddee61VcRKR/pLJ5Th0Nge/Hk5FVY0MYpEIo0Nc8Fi4B4yNOPkEERERUXskEonQv0dX9PayxbYjaThwJhunr93BhdQCPBbujpH9XGCgwiDxRESPmkqFuwMHDgCAYpy5hsealpqaismTJystk0qlsLOzQ2pqaov7AYCnp6fSci8vL6xduxZVVVUwMWn8LcrOnTshl8sxduzYRusyMjIQFBSE6upq+Pj44KWXXsKIESPUeVpEpCMupuZj48EbyL1bP0O0h4MU0ZG+cLXvpOXIiIiIiEgVpsYGeHJENwwMcMCP+67henYxNv+egqMXbuLpUb7wc7PWdohEREpUKtw5OTm1+FhTSkpKIJVKGy23tLREcXFxi/sZGRnB2Fh5TCmpVApBEFBcXNxs4a5Pnz6KMfAa+Pn5oVevXvD29kZpaSk2bNiAefPm4YsvvkBkZKSaz65++mIiTWiYMlqVqaPp4eXeLceG/clIupEPALAwNcTkIV4Y2scJYjEnn3jUmP+kz5j/pM+Y/6RJHo5S/DM6GEfP38TGA9dxM78Cn2xIRP+e9nhqhA+sOrW/8Yp5DZA+0+f8V2tyCgAoLy9HTk4OysvLYW5uDmdnZ5iZmWkytjaVkpKCy5cv4//+7/8arYuOjlZ6PGzYMEyfPh1Lly5Vu3AnFotgbW2u1r5EzZFKOZ19WyqtqMHGfdewKz4NMrkAiViE8YM8MW2kLyxMDbUdnt5j/pM+Y/6TPmP+kyY9NqQbhoW648c9V7DnWBpOXMpD0o18zIjsjnHhHu2ySMBrgPSZPuZ/qwt3hw8fxsqVK5GUlAS5XK5YLpFI0KdPH7zwwgsIDw9XKxipVIrS0tJGy4uLi2FpadnifjU1NaiurlZqdVdSUgKRSNTkvjt27ICBgQHGjBnzwLjEYjFGjRqFTz75pNlutw8ilwsoKalo9X5ETZFIxJBKTVFSUgmZTP7gHahVGsax2/JHKsorawEAfbrZYvqIbnDobI7aqhoUVtVoOUr9xfwnfcb8J33G/Ke2NG2oF0K722Ft7FWk5JTgm+0Xsfd4Op6J7A5fVytthweA1wDpN13Mf6nUVKUvB1pVuPv+++/xn//8BxKJBCEhIejWrRvMzMxQUVGB5ORknD59Gs8//zzmz5+PmTNntjpoT0/PRmPZlZaW4s6dO43Gr7t/PwBIS0tTmh02NTUVjo6OTRbadu3ahbCwMNjY2LQ6TnXV1elGclH7IZPJmVcadv84dk625pg+vBt6etTfK/h6tx/Mf9JnzH/SZ8x/aivOdhaY/3QQjp6/iV9+T0HW7TL864fTGODfFU8M9YaluZG2QwTAa4D0mz7mv8qFu5SUFHz66afo3bs3PvvsMzg4ODTaJjc3F2+++Sb+85//YMCAAfDy8mpVMBEREVi5cqXSWHexsbEQi8UttuLr27cvLCwssGfPHkXhrra2Fvv27UNERESj7ZOSkpCZmYl58+apFJdcLkdsbCy6deumVms7Imr/buaXY9PBGzif8tc4do8P8kBEoCMk4vbXRYKIiIiINE8sEiGityP6+thhyx8pOHwuF8cu3kLi9buYFOHJMY6J6JFTuXC3ceNGmJmZYdWqVc12W3V0dMTKlSsxatQobN68GTExMa0KZvr06Vi3bh3mzZuHuXPnIi8vD0uWLMH06dNhb2+v2C46Ohq5ubmIi4sDABgbG2Pu3LlYtmwZbGxs4OPjgw0bNqCoqAizZ89udJ4dO3bAxMQEI0eObLQuJycHMTExGDt2LNzc3FBcXIwNGzbg4sWLWLZsWaueDxG1f2WVtfjtaBoOJeYoxrEbHuSMx8LdYWbCceyIiIiI9JGFqSGiI7tjUIAj1u27hoxbpVgfl4wj53Mxc5QvvJyaH8qJiEiTVC7cnTlzBpGRkS2ONQcAVlZWiIyMxMmTJ1sdjKWlJdauXYsPP/wQ8+bNg7m5OaZMmYI33nhDaTu5XA6ZTKa07Pnnn4cgCPj2229RUFAAPz8/rFmzptGMsTKZDLGxsRg6dCjMzRtPFmFubg4LCwusWLEC+fn5MDQ0hL+/P1avXo1Bgwa1+jkRUftUJ5Pjj3O52HYkFeVVdQCAQG9bTB3mja42HWeiHSIiIiJqO56OUvzfM8H441z9+MeZeWX417oziOjtgMmDvdDJrH10nyUi3SUSBEFQZcPQ0FC8+uqrmDFjxgO3Xb9+PZYuXYqEhISHDlBXyGRyFBSUazsM0hEGBmJYW5ujsLBc7/r3a8LF1HxsOHAdN/PrJ4y5fxw7at+Y/6TPmP+kz5j/pG0l5TXY/PsNxF+4BQAwNzHA5CFeiOjtCLGo7bvP8hogfaaL+W9jY67ZySnKysrQqVMnlba1sLBAWVmZqocmInokOI4dEREREalLam6E2WN7IKK3I9btTUb2nTL8EHsNR5JyMXO0L9y7SrUdIhHpIJULdzKZDCIVv0UQiUSQy3WjAkpEHR/HsSMiIiIiTenmbIX3/xaMg2dzsO1IKtJuluLD709jSB8nTBrsCXP+fUlEGqRy4Q4Atm3bhqSkpAdul5aWpnZARESawnHsiIiIiKgtSMRijAx2Qb/uXbD50A0cv5SHQ4k5OHX1Np4Y6oXwXg6PpPssEem+VhXu4uPjER8fr9K2qrbOIyJqC43GsbMzx/RhHMeOiIiIiDTHysIYz4/viYjejvhxXzJy7pbju91XcSTpJp4e5QNXe9WGmyIiao7Kk1PQw+HkFKRJujgwp6Y0OY5dhCciejtwHDsdwfwnfcb8J33G/Kf2rk4mx/7T2dh+NA3VtTKIRMDwvs6YOMgTZiatajPTJF4DpM90Mf81PjkFEVF7xnHsiIiIiEibDCRiRIa6IsSvCzYdvIFTV29j/5lsnLp6G1OHeaN/D3v2TCOiVmPhjog6tDqZHL8n5mD70TSOY0dEREREWmcjNcGLE/0RkV6A9fuScaugAqt3XMbhc7l4epQPnOwstB0iEXUgLNwRUYd1ITUfGzmOHRERERG1Qz3dbbBoVgj2ncrEjvh0XMsqwsLvTmFEsDMeC/eAqTE/jhPRg/FOQUQdzs38cmw8cAMXUjmOHRERERG1X4YGYowNc0f/Hl2x4cB1nE2+g70ns5BwOQ/Th3dDv+5d2H2WiFrEwh0RdRgN49gdPJsDuVA/jt2IYGeMH8Bx7IiIiIio/epsaYKXJ/XC+ZR8/BSXjNtFlVi5/RIOJ+VixkgfOHQ213aIRNROsXBHRO1ec+PYTRvmDXuOY0dEREREHUSAV2f4uYVgz4lM7DqRgcvphViw5iQiQ10xLswdxkYSbYdIRO3MQxfubt++jYKCAri6usLMjB+giUizmhzHbng39HTnOHZERERE1PEYGkjw2EAP9Pfvip/iknE+JR+7jmfgxKVbmD7cB319bNl9logU1B4Mav/+/YiMjMTgwYPx+OOPIykpCQBQUFCAiRMnYv/+/RoLkoj0z838cnz2cxI++zkJN/MrYGFqiJmjfbHwb/1YtCMiIiKiDq+LlSlef6I3XpncC52lJsgvqcaXWy/g883nkVdYoe3wiKidUKtwd/DgQbzyyiuwtrbGvHnzIAiCYp2NjQ3s7e2xZcsWjQVJRPqjrLIWP8Ul4/++OYkLqfmQiEUYHeKCj+f2x9A+Tpx8goiIiIh0Sp9udlj8fCjGDXCHgUSEC6n5+L9vTmLbkVTU1Mq0HR4RaZlaXWW//PJLBAcHY926dSgsLMTy5cuV1gcGBmLTpk0aCZCI9APHsSMiIiIifWVsKMGkCE8M8O+K9XHJuJRWgN/i03Hs4i08NdIHgd622g6RiLRErcLd9evXERMT0+x6W1tb5Ofnqx0UEekXjmNHRERERAR0tTHDm1N748y1O9hw4DruFldh6S/nEehti5mjfWFtzdlnifSNWoU7U1NTVFZWNrs+KysLVlZW6sZERHoi9245Nh28gQup9YV+C1NDPB7hiYjeDuwSS0RERER6SSQSIbh7F/h72mDHsXTsO5mFczfu4lJ6ASZEeGFIbweYGT/0PJNE1EGodbWHhoZi27ZtiI6ObrTuzp07+PnnnzF06NCHDo6IdFNZZS22H03DobM5kAsCJGIRRgQ7Y/wAd5iZGGo7PCIiIiIirTMxMsATQ7wR7u+AH/ddw9XMIvxy8Dp+O5KCIYFOiAx1hZWFsbbDJKI2JhLunVlCRampqZg2bRqcnJwQGRmJL774ArNmzYKBgQE2bdoEQRCwZcsWODs7t0XMHZJMJkdBQbm2wyAdYWAghrW1OQoLy1FXJ9d2OCprahy7Pt1sMXUox7Ej1XXU/CfSBOY/6TPmP+kzQRBwIa0AO49n4EZWEQDAQCJGRG8HjOnvBhupiXYDJGpjuvgeYGNjDonkwT3N1CrcAfXj3P3rX/9CQkKC0qyyISEheP/99+Hl5aXOYXUWC3ekSR3xpnU+JR+bDiqPY/fk8G7owXHsqJU6Yv4TaQrzn/QZ85/0nYGBGFZWZjh8JhPbDqfhRk4xAEAiFiG8lwPGhLmhi5WplqMkahu6+B7Q5oW7BsXFxcjIyIAgCHBxcYGNDT+EN4WFO9KkjnTTamocu0kRnhjEcexITR0p/4k0jflP+oz5T/ru3mugtlaGq5lF2BGfhquZRQAAsUiEsJ72GBPmBofOnMSCdIsuvgeoWrh76BEtLS0tERAQ8LCHISIdw3HsiIiIiIjahkgkgp+bNfzcrHE9uwg74tNxMa0A8Rdv4dilW+jXvQvGDXCHs52FtkMlooekUuFu27Ztah184sSJau1HRB0Xx7EjIiIiInp0ujlb4c1pgUjNLcHOY+k4d+MuTl65jZNXbiPIxw7jBrjDrWsnbYdJRGpSqats9+7dW39gkQhXrlxRKyhdxK6ypEnttZkwx7GjR6G95j/Ro8D8J33G/Cd9p+o1kJlXip3H0nHm2h00fNjv7dUZ48Ld4eVo+WiCJdIwXXwP0GhX2QMHDjx0QESkuziOHRERERFR++Bq3wkvPd4LOXfLsetYOhKu5CEpJR9JKfno6W6N8eEe8HGx0naYRKQilQp3Tk5ObR0HEXVAHMeOiIiIiKh9crI1x5zHemLCQA/sOp6B45du4VJ6IS6lF8LXxQrjw93h52YNkUik7VCJqAUPPTmFpqWkpGDx4sVITEyEubk5JkyYgNdffx1GRkYt7icIAlavXo2ffvoJBQUF8PPzw/z58xEYGKjYJiEhAc8880yjfceMGYPPPvtMadnBgwfx+eefIy0tDY6OjpgzZw4mT56skedI1NHVyeQ4lJiD3ziOHRERERFRu2ZvY4ZZY/3wWLg7dp/IwJHzN3EtqwjXNp6Dl5MU4we4o5dnZxbwiNoplQp3M2fOhFgsxpo1a2BgYNBk8et+IpEIa9eubVUwxcXFiI6Ohru7O5YtW4a8vDx8/PHHqKqqwoIFC1rcd/Xq1Vi6dCnefvtt+Pr6Yv369Zg1axa2b98OFxcXpW0/+ugjeHp6Kh5bW1srrT99+jRefvllTJkyBe+++y5OnDiB9957D+bm5oiMjGzVcyLSNfePY+dsZ47pHMeOiIiIiKhds7UyxTOR3TFugDv2JGTicFIuUnJK8Pnm83Dr2gnjB7gjsJstxCzgEbUrKre4k8v/GvxPhfksVNrmfhs3bkR5eTmWL18OKysrAIBMJsOiRYswd+5c2NvbN7lfdXU1Vq1ahVmzZuHZZ58FAAQFBSEyMhJr1qzBwoULlbbv1q0bevXq1WwcK1asQEBAAD744AMAQP/+/ZGVlYWlS5eycEd6K+duOTYdvI6LqQUA/hrHLqK3I8RivrkTEREREXUENlITzBjpg3Fhbth7MgsHE7ORcasUy3+9AGc7c4wb4I5g3y78G5+onVCpcLdu3boWH2vK4cOHERYWpijaAUBUVBTef/99xMfHY9KkSU3ud/bsWZSVlSEqKkqxzMjICCNHjkRcXFyrYqipqUFCQgLefvttpeVjxozBzp07kZ2dDWdn51Ydk6gja2ocu5HBLhg3wB1mJu2utz0REREREanA0sIYU4d5I6q/K/adysKBM9nIvlOOldsvwaFzGsaGuSG0hz0nmyPSMrWuwFOnTqGgoKDZ9QUFBTh16lSrj5uamqrUhRUApFIp7OzskJqa2uJ+ABrt6+XlhdzcXFRVVSktnzNnDvz8/BAREYH//Oc/SuszMzNRW1vb5LHuPReRrquTyRF3OgvzVx3HgTPZkAsC+nSzxeLnQzF1mDeLdkREREREOqCTmREmD/bCJy8NwISBHjAzNsDN/Ap8s/MK3vs6AYeTclEnkz/4QETUJtT65P3MM89gyZIlGD9+fJPrT5w4gbfeegtXrlxp1XFLSkoglUobLbe0tERxcXGL+xkZGcHY2FhpuVQqhSAIKC4uhomJCTp16oTnnnsO/fr1g7GxMU6cOIFvv/0WqampWLVqFQAoznN/HA2PW4rjQQwM+E0FaYZEIlb6X9OSbtzFT3HJinHsXLpY4KmRPujpwXHsSPvaOv+J2jPmP+kz5j/pu7a+BiwtjDF5iBfGhLlh/+lsxCZk4HZRJb7fcxU74tMxLtwdg3o7wMhA0ibnJ2qJPr8HqFW4e9D4dTU1NZBI2t/F3KNHD/To0UPxOCwsDF26dMEHH3yA8+fPIyAgoM3OLRaLYG1t3mbHJ/0klZpq9HiZt0qwZsclnL16u/745kZ4OsoPo0LdIOEYF9TOaDr/iToS5j/pM+Y/6bu2vgasATwzzhJTR/oi9kQ6fj10A/klVVj7ZwFv0lBvjO7vBhMj9sChR08f3wNUvtJyc3ORk5OjeJyamtpkd9iSkhJs3LgRjo6OrQ5GKpWitLS00fLi4mJYWlq2uF9NTQ2qq6uVWt2VlJRAJBK1uG9UVBQ++OADXLx4EQEBAYpt74+jpKQEAFo8VkvkcgElJRVq7Ut0P4lEDKnUFCUllZBpoNl6aUUNth1Jw4HT2Ypx7EaFuNY3lTcxQEkxc5faD03nP1FHwvwnfcb8J32njWtgcIADwnp0weFzudh5LB0FJVX4ZvtF/Lw/GVGhrhgW5AxTYxbwqO3p4nuAVGqqUgtCla+wX3/9FcuXL4dIJIJIJMLKlSuxcuXKRtsJggCJRIJFixa1LmLUj1F3/xhypaWluHPnTqMx5+7fDwDS0tLQvXt3xfLU1FQ4OjrCxMRE5RhcXV1haGiI1NRUDBo0SOlY955LHXV1upFc1H7IZPKHyqs6mRyHEnPw29E0lFfVAQD6dLPF1GHesLc2q9+GeUvt1MPmP1FHxvwnfcb8J333qK8BMUQYEuiEgb0cEH/hJnYdz8Dd4ipsOngDO4+lY2Q/F4wIcoaZieEji4n0lz6+B6hcuIuKikK3bt0gCAJef/11zJw5E8HBwUrbiEQimJqaws/PD7a2tq0OJiIiAitXrlQa6y42NhZisRjh4eHN7te3b19YWFhgz549isJdbW0t9u3bh4iIiBbPuWvXLgBAr169ANTPRhsaGoq9e/ciOjpasd3u3bvh5eXFGWVJZ5xPycemg9cV49g525lj+vBu6OHOceyIiIiIiEiZgUSMwYFOCO/lgITLedh5PAN5BRXYdiQNe09mYniQC0b1c4GFKQt4RJqkcuHOy8tLMbPqRx99hODgYLi4uGg0mOnTp2PdunWYN28e5s6di7y8PCxZsgTTp0+Hvb29Yrvo6Gjk5uYiLi4OAGBsbIy5c+di2bJlsLGxgY+PDzZs2ICioiLMnj1bsd/bb78NNzc39OjRQzE5xffff48RI0YoCncA8OKLL+KZZ57BwoULERUVhYSEBOzcuROfffaZRp8vkTbk3C3HpoPXcTG1fmboTmaGeDzCExEBjhBzHDsiIiIiImqBgUSM8F4OCOvZFaeu3sbOY+nIuVuOncfSEXc6C8P6OGFUiCsszY20HSqRThAJD5pp4hFLSUnBhx9+iMTERJibm2PChAl44403YGT010U/c+ZM5OTk4ODBg4plgiDg66+/xk8//YSCggL4+flh/vz56NOnj2KbVatWYceOHcjJyUFtbS2cnJwwfvx4zJkzR+n4AHDgwAF8/vnnSEtLg6OjI+bMmYMpU6ao/bxkMjkKCsrV3p/oXgYGYlhbm6OwsFzlZsJllbXYfiQNhxJzFOPYjQx2wbgB7jAz4bgU1HGok/9EuoL5T/qM+U/6rr1eA3JBQGLyHew4lo7MvDIAgJGBGBGBjogKdYN1J+MHHIHowdpr/j8MGxtzlca4U7twl5KSgi1btiA7OxvFxcWNZpoViURYu3atOofWSSzckSa15qalyjh2RB2JLr5pE6mK+U/6jPlP+q69XwOCIOB8Sj52HEtHam795I4GEhEGBTgiqr8rbC31bzZQ0pz2nv/qULVwp1Yzm23btuHdd9+FgYEBPDw8FOPR3audNeQj0kvnU+5i44EbuFXQMI6dBZ4c7g0/jmNHREREREQaJBKJ0NvbFgFenXE5vRA74tOQnF2MQ4k5OJyUiwH+XTEmzI2NB4haSa3C3fLly+Hn54fVq1fDxoYFAKL2huPYERERERGRNohEIvT0sEFPDxtcyyzEb/HpuJJRiCPnb+LohZvo38Me4wa4w6GzubZDJeoQ1Crc3b59G7NmzWLRjqid4Th2RERERETUXvi6WuPvrta4kVOMncfScT4lH8cv5eHEpTwEd++C8QPc4dzFQtthErVran2S9/X1xe3btzUdCxGpiePYERERERFRe+XtZInXn+iN9Fsl2BGfjsTrd3Hq6m2cunobfbrZYny4O9y7Nh6Ci4jULNzFxMTgtddeQ0REBPr27avpmIioFTiOHRERERERdQTuXaV4ZXIAsm6XYeexdJy+ehuJ1+8i8fpd9PLsjPHh7vB2stR2mETtilqFu9WrV6NTp06YMWMGvL294eDgALFYeSYMkUiEFStWaCRIImos81YJVv16HudT8gFwHDsiIiIiIuoYXLpY4MWJ/riZX46dxzKQcDkPF1LzcSE1H35u1ngs3B2+rtbaDpOoXVCrcJecnAwAcHBwQHl5OW7cuNFoG5GIhQPSDkEQIBcEyOX483/hnv9R/3/Dsobl965rarnS4/pjC4IAmdKxBQgC6pc1PJYLkN1zbuHP5TLF+r+OLftz+4bjy+TCPc9FOb46mYCrmYWQy/8cx66fC8aFcRw7IiIiIiLqOBw6m+P58T0wYaA7dh3PwLGLt3AloxBXMgrh42yJceHu6Oluw/oC6TWRIAiCtoPQBzKZHAUF5doOQ2PO3biLG9nFzRfGVCpa1S9Xftxc0aqJ4to9299bWNOnhA7ytcOUIV4cx470ioGBGNbW5igsLEddnVzb4RA9Usx/0mfMf9J3+nAN3C2uxJ4TmThyPhd1svpPdp6OUowb4I7eXp1ZwNNjupj/NjbmkEjED9yOzXOo1WRyOVZuu4iaDnqxiEUiiMWAWCyq/1kkqv9ZLIJY1MJypcf3bCu+Z1tRE8dR/F9/DJFYBMk9y0ViQHLPOUVi0T2PAdGfP0v+PJdIBBgaiOHrYQu7TkY6c9MiIiIiIiL9ZmtpipmjfTFugDtiEzLxx7kcpOaWYOkv5+Fqb4HxA9zRx8cOYhbwSI+o3eJOJpMhNjYWCQkJyM/Px6uvvgpfX1+Ulpbi+PHj6Nu3L2xtbTUdb4elay3uEi7nISW3uMWClUQsguie5RKxSFGEarl49leRq9nl4vvXNbH8/u3/XKYLdPHbBiJVMf9JnzH/SZ8x/0nf6eM1UFJeg72nMnHwbA6qa2QAACdbc4wd4IaQ7vYc21uP6GL+q9riTq3CXUlJCZ577jmcP38eZmZmqKysxLfffouwsDDIZDIMHToUEydOxJtvvqlW8LpI1wp3pF26eNMiUhXzn/QZ85/0GfOf9J0+XwNllbWIO5WF/WeyUVldBwCwtzHDuDA3hPawh4EKxQ/q2HQx/1Ut3KmV3Z9++imuX7+ONWvWYP/+/bi39ieRSDB69Gj88ccf6hyaiIiIiIiIiEjBwtQQj0d44pMXw/D4IA+Ymxggr6ACa3Zdwbtfn8Af53JQJ9ONYg7R/dQq3B04cAAzZ85EeHh4k4NDuru7Iycn56GDIyIiIiIiIiICADMTQ4wP98CSFwfgiaFekJoZ4m5xFdbGXsM7K4/jwJls1NbJtB0mkUapNTlFaWkpnJ2dm11fV1cHmYwXCxERERERERFplqmxAaJC3TCsrzMOn8vFnoQMFJZWY31cMnYeS0dkqCuGBDrB2Eii7VCJHppahTtXV1dcunSp2fXx8fHw8vJSOygiIiIiIiIiopYYG0owsp8LhvRxxNHzN7H7RAbyS6qx6eAN7DqegdEhLhjW1xmmxmqVPojaBbW6yk6ZMgVbtmzB7t27FePbiUQi1NTU4LPPPsORI0cwbdo0jQZKRERERERERHQ/QwMJhvZ1xkdzw/C3qO7oYmWKsspabPkjFf9YcQzbj6ahvKpW22ESqUWtWWUFQcD//d//4ZdffoFUKkVJSQlsbW1RVFSEuro6TJs2DYsWLWqLeDsszipLmqSLM+oQqYr5T/qM+U/6jPlP+o7XgOpkcjlOXr6NncfTcTO/AgBgYiTB8CBnjOrngk5mRlqOkFpLF/Nf1Vll1SrcNTh9+jT27t2LjIwMyOVyuLq6IioqCv369VP3kDqLhTvSJF28aRGpivlP+oz5T/qM+U/6jtdA68nlAk5fu42dx9KRfaf+87iRoRhD+zghMsQVlhbGWo6QVKWL+f9ICnekOhbuSJN08aZFpCrmP+kz5j/pM+Y/6TteA+qTCwKSrt/Fb8fSkXGrFABgaCBGRG9HRIW6wkZqouUI6UF0Mf9VLdypNcZdVlYWDh482Oz6gwcPIjs7W51DExERERERERFpjFgkQh8fOyyIDsbrT/SGl5MUtXVyHDiTjXdWHsfa2Ku4U1Sp7TCJmqTW1CpLlixBWVkZhg0b1uT69evXQyqV4rPPPnuo4IiIiIiIiIiINEEkEiHAqzN6edrgSkYhdsSn41pWEf44l4sjSTcR5m+PcWHusLcx03aoRApqFe4SExMRHR3d7PqwsDCsXbtW7aCIiIiIiIiIiNqCSCRCD3cb9HC3QXJWEXYcS8eltALEX7iFYxdvIdTPHmMHuMPJ1lzboRKpV7grKSmBuXnzCWxmZoaioiJ1YyIiIiIiIiIianM+LlZ4a1ogUnKLsTM+HUkp+ThxOQ8Jl/PQ19cO4we4w9W+k7bDJD2m1hh3Dg4OOHv2bLPrz5w5g65du6odFBERERERERHRo+LlaInXnuiN95/thyAfOwgAzly7g4XfncLSX84j7WaJtkMkPaVW4W7cuHHYtWsXfvjhB8jlf83mIZPJsHbtWuzevRvjxo3TWJBERERERERERG3NrWsnzJvUCx/ODkFoD3uIRMC5G3fx4drT+O/GRFzNKIQgCNoOk/SISFAj42pqajBnzhycOHECNjY28PDwAACkpaWhoKAAISEh+Oabb2BkZKTxgDsqmUyOgoJybYdBOkIXp8ImUhXzn/QZ85/0GfOf9B2vAe24VVCBXcfTcfxiHuR/lk+8HKUYE+aG3t62EItEWo5QP+hi/tvYmEMieXB7OrUKdwAgl8uxdetWxMXFITMzEwDg6uqKUaNGYeLEiRCL1WrMh5SUFCxevBiJiYkwNzfHhAkT8Prrrz+wCCgIAlavXo2ffvoJBQUF8PPzw/z58xEYGKjY5tixY9i8eTOSkpKQn58PJycnTJo0CdHR0TA0NFRsFxMTg61btzY6x+rVqxEREaHW82LhjjRJF29aRKpi/pM+Y/6TPmP+k77jNaBdd4sqsedkJo6ev4naP19/J1tzjOnvhpAeXSBRswZCqtHF/Fe1cNfqySmqqqrw2WefITQ0FJMnT8bkyZPVCrApxcXFiI6Ohru7O5YtW4a8vDx8/PHHqKqqwoIFC1rcd/Xq1Vi6dCnefvtt+Pr6Yv369Zg1axa2b98OFxcXAMDGjRtRVVWFV199FQ4ODkhKSsKyZcuQkpKCjz76SOl4Li4u+PTTT5WWeXl5aey5EhEREREREVHHYGtlipmjfPFYuAfiTmXhUGI2cu6WY/XOy9h6JBWRoa4Y2MsBRoYSbYdKOqbVhTsTExNs2rQJ3t7eGg9m48aNKC8vx/Lly2FlZQWgfty8RYsWYe7cubC3t29yv+rqaqxatQqzZs3Cs88+CwAICgpCZGQk1qxZg4ULFwIAFi5cCBsbG8V+oaGhkMvl+Pzzz/H3v/9daZ2JiYlSaz0iIiIiIiIi0m+W5kaYMsQLY/q74VBiNuJOZeFucRV+3JeM346mYWQ/Fwzt4wwzk1aXW4iapFZbzp49eyI5OVnTseDw4cMICwtTFO0AICoqCnK5HPHx8c3ud/bsWZSVlSEqKkqxzMjICCNHjsThw4cVy+4tzDXw8/ODIAi4c+eOZp4EEREREREREek0MxMDjA1zx5IXB2DGSB90lpqgpKIWW/5Ixd9XxGPLHykoKa/RdpikA9Qq3L377rvYvXs3Nm/ejLq6Oo0Fk5qaCk9PT6VlUqkUdnZ2SE1NbXE/AI329fLyQm5uLqqqqprd9+zZszAyMoKzs7PS8oyMDAQFBcHf3x+TJk3C/v37W/t0iIiIiIiIiEiHGRlKMDzIGR/N7Y/nxvnB0dYcldUy7Dqegb+vOIYf913D3aJKbYdJHZhabTdjYmIgEomwYMECLF68GPb29jA2NlbaRiQS4bfffmvVcUtKSiCVShstt7S0RHFxcYv7GRkZNYpBKpVCEAQUFxfDxMSk0X7p6en44YcfMH36dJibmyuW+/n5oVevXvD29kZpaSk2bNiAefPm4YsvvkBkZGSrntO9DAw4WCVpRsMAlqoMZEmka5j/pM+Y/6TPmP+k73gNtG8GBmJEBDphYG9HJCbfwc5j6UjJKcHBszn4PTEXYf72GDfAHU52FtoOtUPS5/xXq3BnZWUFKysreHh4aDqeR6asrAyvvPIKnJ2d8cYbbyiti46OVno8bNgwTJ8+HUuXLlW7cCcWi2Btbf7gDYlaQSo11XYIRFrD/Cd9xvwnfcb8J33Ha6D9G9HfAsND3XEh5S42H7iOc8l3EH/hFuIv3EJoz654Yng3+Lo1HsqLHkwf81+twt26des0HQeA+hZypaWljZYXFxfD0tKyxf1qampQXV2t1OqupKQEIpGo0b41NTWYN28eiouLsWnTJpiZmbUYl1gsxqhRo/DJJ5+gqqqqydZ7DyKXCygpqWj1fkRNkUjEkEpNUVJSCZlMN6bCJlIV85/0GfOf9Bnzn/Qdr4GOx6WzGd6c2hupuSXYdSwdp6/eRsKlW0i4dAt+btYYH+6Onh42EIlE2g613dPF/JdKTVVqQdiupjnx9PRsNJZdaWkp7ty502j8uvv3A4C0tDR0795dsTw1NRWOjo5KhTa5XI63334bly5dwvr16+Hg4KDhZ9G8ujrdSC5qP2QyOfOK9Bbzn/QZ85/0GfOf9B2vgY7HtYsFXpzoj5v55dhzIhPHL93ClYxCXMkohHvXThjT3w19fe0gZgHvgfQx/9XuHFxWVoavv/4as2fPxsSJE3H+/HkAQFFREb777jtkZGS0+pgRERE4duwYSkpKFMtiY2MhFosRHh7e7H59+/aFhYUF9uzZo1hWW1uLffv2ISIiQmnbRYsW4dChQ/jqq6/g6+urUlxyuRyxsbHo1q2bWq3tiIiIiIiIiEi/OXQ2x6yxfvh4bhhGBDvDyECM9Ful+GrbRfxzdQKOnM9FnY60JiPNUavF3a1bt/D000/j1q1bcHNzQ2pqKsrLywHUj3+3ceNG5OTk4J///Gerjjt9+nSsW7cO8+bNw9y5c5GXl4clS5Zg+vTpsLe3V2wXHR2N3NxcxMXFAQCMjY0xd+5cLFu2DDY2NvDx8cGGDRtQVFSE2bNnK/ZbuXIlNm7ciNmzZ8PIyAjnzp1TrPP29oaFhQVycnIQExODsWPHws3NDcXFxdiwYQMuXryIZcuWqfNyEREREREREREBADpbmuCpET4YN8Ad+09n4+CZbNwqqMB3u69i+9E0jA5xRURvRxgbSrQdKrUDahXulixZgvLycmzbtg02NjYYMGCA0voRI0bg999/b/VxLS0tsXbtWnz44YeYN28ezM3NMWXKlEaTR8jlcshkMqVlzz//PARBwLfffouCggL4+flhzZo1cHFxUWwTHx8PAFizZg3WrFmjtP8PP/yA0NBQmJubw8LCAitWrEB+fj4MDQ3h7++P1atXY9CgQa1+TkRERERERERE95OaGWFShCeiQl3x+7kc7DuZhYKSamzYfx074tMxMtgZw4KcYW5iqO1QSYtEgiAIrd0pNDQU0dHReOmll1BYWIiwsDB89913CAsLAwBs3LgRS5YswdmzZzUecEclk8lRUFCu7TBIRxgYiGFtbY7CwnK9699PxPwnfcb8J33G/Cd9x2tA99XWyRB/4Rb2JGTgTlEVAMDYSIKhfZwwqp8LrCyMH3AE3aWL+W9jY952k1NUVVXBxqb5qYsbus0SEREREREREdGDGRpIMKSPEwb1dsCpq7ex+3gmsu+UITYhE/tPZ2Ngr66IDHVFF2szbYdKj5Bak1N4eXnh1KlTza7fv38/evTooXZQRERERERERET6SCIWo3+Prlg0qx9emxIAb2dL1Mnk+P1cLuZ/fQKrfruErNtl2g6THhG1WtxFR0cjJiYGvr6+iIqKAgAIgoCMjAwsX74c586d40QORERERERERERqEolE6O1ti97etkjOKsKu4xm4kJqPhMt5SLichwCvzhgb5oZuzlbaDpXakFpj3AHAihUrsHz5cgiCALlcDrFYDEEQIBaL8dprr2HOnDmajrVD4xh3pEm62L+fSFXMf9JnzH/SZ8x/0ne8BggAMvNKsftEBk5dvY2Gao6PsyXGhLmjl6cNRCKRdgNsI7qY/6qOcad24Q4AcnNzsW/fPmRkZEAul8PV1RWjRo1SmsmV6rFwR5qkizctIlUx/0mfMf9JnzH/Sd/xGqB75RVUYE9CJo5dvIk6WX1Zx7WLBcaEuSHYtwvEYt0q4Oli/rdJ4a66uhoHDhxAdnY2rKysMGTIEHTp0uWhAtUXLNyRJuniTYtIVcx/0mfMf9JnzH/Sd7wGqCmFpdXYdyoTvyfmorpWBgDoYm2KqFBXDPB3gKGBWlMbtDu6mP8an1U2Pz8f06dPR3Z2Nhpqfaampvjyyy8xYMAA9SMlIiIiIiIiIqJWs+5kjGnDumFsmDsOnMnG/tNZuF1YibWx17D9aBpGh7hicKAjTIzUmuKA2gGVS69fffUVcnJy8Oyzz2LVqlV49913YWxsjAULFrRlfERERERERERE1AILU0NMGOiBT14agOnDvGHdyRhFZTXYdPAG/v7VMWw7koqyylpth0lqULnkevToUUyYMAHvvPOOYpmtrS3eeustpKamwtPTs00CJCIiIiIiIiKiBzMxMsCoEFcM7euM45duYc+JDOQVVuK3+HTsPZmFwYGOGNXPBTZSE22HSipSuXB38+ZNBAUFKS0LCgqCIAjIz89n4Y6IiIiIiIiIqB0wNBAjorcjBvZywJnkO9h1PB2ZeWXYdyoLB85kY4B/V0T1d0NXGzNth0oPoHLhrqamBsbGxkrLjIyMAAB1dXWajYqIiIiIiIiIiB6KWCxCv+5dEOxrh0tpBdh1PAPXsopw5PxNHD1/E0Hdu2Bsfze4de2k7VCpGa0anTAnJweXLl1SPC4tLQUAZGRkQCqVNtq+Z8+eDxkeERERERERERE9DJFIBH/PzvD37IwbOcXYfTwD527cxemrt3H66m34e9hgbJgbfFysIBKJtB0u3UMkNEwR+wDdu3dv8pcnCEKj5Q3Lrly5opkodYBMJkdBQbm2wyAdoYtTYROpivlP+oz5T/qM+U/6jtcAaVr27TLsTsjAycu3If+zNOTlJMXY/u4I8O4McTsq4Oli/tvYmEMiefCcsSq3uPvoo48eKiAiIiIiIiIiImofnLtYYM74npg4yBN7EzJx5PxNpOSUYOmW83CyM8eY/m4I8esCifjBxSVqOyq3uKOHwxZ3pEm6+G0DkaqY/6TPmP+kz5j/pO94DVBbKy6rxr5TWTiUmIOqGhkAwNbSBFGhrhgY4ABDA4nWYtPF/Nd4izsiIiIiIiIiItJNlhbGeGKoN8aGueHA2RzEncrC3eIqrNuXjO3x6RjVzwVD+zjB1JilpEeJrzYREREREREREQEAzEwMMX6AO0b1c8GRpFzsPZmJ/JJq/PJ7CnYdz8DwICeMCHaB1MxI26HqBRbuiIiIiIiIiIhIibGhBCOCXTCkjxMSLudh94kM3MyvwM5jGdh3MguDejtidIgLbC1NtR2qTmPhjoiIiIiIiIiImmQgESO8lwPC/LsiMfkudp9IR9rNUhw4k43fE3PQv4c9ovq7wdHWXNuh6iQW7oiIiIiIiIiIqEVikQhBvnbo62OLKxmF2HU8A1cyChF/8RaOXbyFPj52GBvmBg8HqbZD1Sks3BERERERERERkUpEIhF6uNugh7sNUnNLsPtEBs4m31H883OzxtgwN/i5WUMkEmk73A6PhTsiIiIiIiIiImo1T0cpXp7UC7l3y7HnRAZOXM7DlYxCXMkohIdDJ4zp744+PrYQs4CnNhbuiIiIiIiIiIhIbY625pg9rgcmDPLA3pNZOJKUi7Sbpfhy6wU4dDbDmP5uCO1hDwOJWNuhdjh8xYiIiIiIiIiI6KHZWppixkgfLHlxAMYNcIOpsQFu5ldgza4rmL/qOPafzkJ1rUzbYXYobHFHREREREREREQaIzU3wqQIL0SFuuFQYg72ncpCfkk1ftp/HTuOpWNEsAuG93WCmYmhtkNt91i4IyIiIiIiIiIijTM1NsCY/m4YEeSM+As3sSchE3eLq7D1cCr2nMjA0D5OGNXPBZYWxtoOtd1qd11lU1JS8Le//Q2BgYEIDw/HkiVLUFNT88D9BEHA119/jSFDhiAgIADTpk3DuXPnGm2Xl5eHV155BX369EFISAjee+89lJWVNdru4MGDeOyxx9CrVy+MHj0aW7Zs0cTTIyIiIiIiIiLSK0aGEgzt64yP5vbH8+N7wMnOHFU1MuxJyMTfVxzHur3XcKeoUtthtkvtqnBXXFyM6Oho1NbWYtmyZXjjjTfw888/4+OPP37gvqtXr8bSpUvx7LPPYtWqVbCzs8OsWbOQlZWl2Ka2thbPPfcc0tPT8d///hcLFy7E0aNH8dZbbykd6/Tp03j55ZcRGBiI1atXIyoqCu+99x5iY2M1/pyJiIiIiIiIiPSBRCxGWM+uWDQrBK9ODoCXkxR1MjkOJeZg/qoT+HrHJWTfady4Sp+1q66yGzduRHl5OZYvXw4rKysAgEwmw6JFizB37lzY29s3uV91dTVWrVqFWbNm4dlnnwUABAUFITIyEmvWrMHChQsBAHv37sX169exe/dueHp6AgCkUilmz56N8+fPIyAgAACwYsUKBAQE4IMPPgAA9O/fH1lZWVi6dCkiIyPb7gUgIiIiIiIiItJxYpEIgd1s0du7M5KzirDreAYuphXgxKU8nLiUh0BvW4wJc4O3k6W2Q9W6dtXi7vDhwwgLC1MU7QAgKioKcrkc8fHxze539uxZlJWVISoqSrHMyMgII0eOxOHDh5WO7+vrqyjaAUB4eDisrKzwxx9/AABqamqQkJDQqEA3ZswYpKSkIDs7+2GfJhERERERERGR3hOJRPB1tcab0wLx/rP9ENy9C0QAzt24i3+vO4P/rD+Li6n5EARB26FqTbsq3KWmpioV1YD6FnF2dnZITU1tcT8Ajfb18vJCbm4uqqqqmj2+SCSCh4eH4hiZmZmora1t8lj3nouIiIiIiIiIiDTDrWsnvDTRH/+a0x+DAhwgEYtwLasI//s5CQvWnMS1jAJth6gV7aqrbElJCaRSaaPllpaWKC4ubnE/IyMjGBsrz0IilUohCAKKi4thYmKCkpISdOrUqcXjN/x/fxwNj1uK40EMDNpVnZQ6MIlErPQ/kT5h/pM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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers: 341\n",
            "\n",
            " Num_Credit_Card \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-8836c912d7c9>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[col_name] = pd.to_numeric(df[col_name], errors='coerce')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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PPy8EBQUJFy9eFARBEPbu3dvi/dX3WtJqtcLYsWOFuXPn6s6PIAhCbW2tMGrUKOGpp57SLWvOg9dee63FMRctWiTExMTc9pw1n7eBAwfecn1SUpIQFBQk/Otf/9Itu/naEYS2c705n9esWdNi+euvvy4MGzZMKC0tbbH8pZdeEiIjI4Xa2lpBEAThxIkTQlBQkPDAAw/olglC552fgQMHCq+++uotz8WtXptarRbGjh0rTJ48WRdPW9fn/fff3+bxbz6fza974sSJQkNDg275yy+/LCiVSmH+/Pkt9p8xY4Zw//336xW3IAhCcnKyEBQUJCxfvrxdrzUiIqLF6xEE/e+R//znP4WgoCDh7NmzumUlJSVCZGSkEBQUJFy+fFm3/Obzce7cuVb3RX1FR0cLkydP1nv7G/Os2T/+8Q9hwIABLV7jrT7r9u3bJwQFBQmrV6/WLVOr1cJjjz1m8GsgIqKOwe6rRETUbT355JMYMmQIRo4ciZdeegkymQwrV66Eu7s79u3bB61Wi9jYWJSWlur+uLi4oG/fvrruoM0sLS113Z6a7d27F05OTm0O9t48OPru3bthb2+PYcOGtXiesLAw2NratnqegIAAREVF6R4rFAr4+vri8uXLHXJOysvLceLECcTGxqKqqkoXT1lZGe69915cunQJBQUFd/UczeO3VVdXd0TIOHbsGFQqFSZMmNDiHIrFYgwYMKDVOQSARx99tMXjzngf9u7di+Dg4DZbFt74/vv7+8PPz6/F8zZ3pW4r9ptZWVnhyy+/xJdffom1a9firbfegq2tLRYsWNCi++Xu3bsRGRkJuVze4rmGDh0KjUaD+Pj4Oz5XZGQkTp06BQC6CTxmzJgBJycnXWu5U6dOQS6XIygoCAD0vpaSk5Nx6dIlTJo0CWVlZbrtampqMGTIEMTHx7fq9jdz5swWj6OiolBeXq7r6mmojs5RQRCwd+9ejBo1CoIgtDgP9957LyorK5GYmNhin6lTp7aYXbg7nR/gemu5lJQU7N+//66P12zKlCktWqWGh4dDEAQ8/PDDLbYLDw9Hfn4+1Gq1Xsdtfs3NLRf1NXbs2BYzybbnHnno0CEMHDiwRQtGhUKBSZMmtSuG9qqqqmrX67wxz5pfU1RUFGpra5GZmdli27Y+6w4fPgypVNriviqRSHrERCdERKaO3VeJiKjbWrJkCXx9fSGRSODi4gJfX1/dQOqXLl2CIAi37JZ140yhAODu7t5qoO6cnBz4+vq22vZG2dnZqKys1I3jdbPmiQ2a9e7du9U2Dg4OrcY9M1ROTg4EQcCHH36IDz/88JYxtXdGzxvV1NQAaP+X41u5dOkSAGDOnDltrr+5i6xUKkWvXr1aLOuM9yEnJ+eO3fqys7ORkZGh9/O2RSKRtJihFgBGjhyJsWPH4j//+Y9u3L3s7Gykpqbe8rluHOfuVqKiorBx40ZkZ2cjJycHIpEIAwcORFRUFE6dOoVHHnkEp06dQkRERLuvpeb38XZd3iorK+Hg4KB7fHM3xOZusBUVFW12jdZXR+doaWkpVCoVfvjhB/zwww+33OZGN8+c3J3OT7NJkybh008/xSeffNLu2Vtv5eaYmyd1ufmas7e3h1arRWVlJZycnO543ObX295C683vQ3vukVeuXMGAAQNarW+rS3RHsrOza9frTE9Px3//+1+cOHGiVcH25nHh2vqsy8vLg6ura6vrpbNfJxER3RmLckRE1G2Fh4frZl+9mVarhUgkwurVq1sNlg+g1WydN7Y0aA+tVgtnZ2e8//77ba6/sYUG0Hrg/o7W3NJm7ty5GD58eJvb6DPm1O2kp6dDIpG0+rJrKOHaQPMrVqyAq6trq/U3nzNLS8tWs1ga633QarUICgrCa6+91ub6m4uH+urVqxd8fX1btH7TarUYNmwY5s+f3+Y+Pj4+dzxu89hW8fHxuHz5MkJDQ2Fra4uoqCh8/fXXqK6uRnJycotJHvS9lprfx7/97W8ICQlp8/lvvu5uNRupcIfJB+4kLS0NwN3nerPm62ry5Ml46KGH2tymeXzEZjffU7rT+WnW3Fpu8eLF7Z4IRqPRtJkPt4r5bl9L3759IZVKde+tvm5+H7riHnm3/Pz8kJycjIaGhjvO6qpSqTBr1izY2dnh+eefh7e3N6ysrJCYmIj333+/VetLQz/riIjIOFiUIyKiHsnb2xuCIMDT09PgX/u9vb1x9uxZNDY2tuiOdfM2x48fR0RERId92WnuGmkILy8vAICFhUWr1lcd4cqVK4iPj8fAgQM7pKUOcD1mZ2dng2PujPfB29u71eyvbW2TkpKCIUOG3NX71haNRqNr8dX8XDU1NXc8R7eLw8PDAx4eHkhISMDly5d1XXijoqLwzjvvYPfu3dBoNIiOjm7xvPpcS83vo52dXafkXnts27YNIpEIw4YN65DjKRQKyGQyaLVag19bdzo/N5o8eTI+++wzrFy5ssVMs80cHBygUqlaLb9y5YruNXUFGxsb3HPPPThx4gTy8/PbbO2qj/bcIz08PJCdnd1q+Y3dym/lbu4H999/P06fPo29e/di4sSJt902Li4O5eXlWLlyZYvr9sZZyO+kT58+OHHiBKqrq1u0ltPndRIRUefimHJERNQjjR07FhKJBCtXrmzVEkMQBJSVlel1jLKyMnz33Xet1jUfMzY2FhqNps1ZNtVqdZtfZu/ExsYGQOtuR/pwdnZGTEwMfvjhBxQWFrZar08Xx1spLy/Hyy+/DI1Gg2eeecbg49xs+PDhsLOzw6pVq9DY2NhqvT4xd8b7MHbsWKSkpGDfvn2t1t34/hcUFODHH39stU1dXV2Lolp7ZGVlISsrSzdLavNznT59Gr///nur7VUqlW5srub8udVrjoyMxIkTJ3Du3Dldy7mQkBDIZDLdzJlhYWG67fW9lvr16wdvb2+sW7euza53d5N77fHFF1/gyJEjePDBB/VqPagPiUSCcePGYc+ePW221NLntXXW+bG1tTUov5s1t5ZLTk7GwYMHW6338vLC2bNn0dDQoFv266+/Ij8/3+DnNNSiRYsgCAL+9re/tXkOL1y4gM2bN9/2GO25R44cORJnzpzBuXPnWqz/5Zdf7hjrna7D25k5cyZcXV3x7rvvtlkYKykp0d3rmlsg3nhtNjQ04Pvvv9f7+UaMGAG1Wo0NGzbolmk0Gnz77bftjp2IiDoWW8oREVGP5O3tjRdffBH//ve/kZeXh9GjR0MmkyE3Nxf79+/HI488gnnz5t32GFOnTsWWLVvwzjvv6AoYtbW1OH78OB599FGMHj0aMTExmDFjBlatWoXk5GQMGzYMFhYWuHTpEnbv3o033ngD48ePb1fsISEhkEgkWL16NSorK2FpaYl77rkHzs7Oeu2/dOlSPPbYY5g0aRIeeeQReHl5obi4GGfOnMHVq1exbdu2Ox7j0qVL2Lp1KwRBQHV1NVJSUrB7927U1NRg8eLFGDFiRLte0+3Y2dnhzTffxN/+9jdMmzYNDz74IBQKBa5cuYJDhw4hIiICS5Ysue0xOuN9mDdvHvbs2YMXXngBDz/8MMLCwlBRUYGDBw9i2bJlCA4OxpQpU7Br1y4sXboUJ0+eREREBDQaDTIzM7F7926sWbPmll2sm6nVamzduhVA0xfr3NxcbNy4EVqtFosWLWoRz8GDB/HMM8/goYceQlhYGGpra5GWloY9e/bgwIEDUCgUsLa2RkBAAHbt2gUfHx84OjoiMDBQN3FDVFQUfvnlF4hEIl1RTiKRYNCgQThy5AhiYmJadJnT91oSi8VYvnw5nn76aUycOBHTpk2Du7s7CgoKcPLkSdjZ2eHzzz9v13ug73lraGhAXl4eDh48iNTUVAwePBhvvfVWhz0XAPz1r3/FyZMn8cgjj+BPf/oTAgICUFFRgcTERBw/fhxxcXG33b+zzk9YWBiOHz+OL7/8Em5ubvD09GxzHLTbaR5bLjk5udW6P/3pT9izZw/mz5+P2NhY5OTk4JdffjFKF8/me8GyZcsQGxuLKVOmoG/fvqiurkZcXBwOHjzYouv1reh7j5w/fz62bt2K+fPn44knnoCNjQ1+/PFHeHh4IDU19bbP4e3tDblcjo0bN0Imk8HW1hbh4eF6tS50cHDAJ598ggULFmDq1KmYPHmyrlCelJSE7du3Y9CgQQCAQYMGwcHBAYsXL8bs2bMhEol09259jRo1ChEREbprPCAgAHv37jXohyEiIupYLMoREVGPtWDBAvj4+OCrr77CJ598AqBprK5hw4a12U3rZs2Fsc8++wzbt2/H3r174ejoiIiIiBbjR7311lvo168fNm7ciA8++AASiQR9+vTB5MmTERER0e64XV1dsWzZMqxatQpvvPEGNBoNvv76a72LcgEBAdi0aRNWrlyJzZs3o7y8HAqFAqGhoS2KPLdz9OhRHD16FGKxGHZ2dvD09MTUqVMxY8YMBAQEtPs13cmkSZPg5uaGL774AmvXrkVDQwPc3d0RFRXVaqbAW+no90Emk+G7777Dxx9/jH379mHz5s1wdnbGkCFDdBNliMVifPLJJ/jqq6+wdetW7Nu3DzY2NvD09MTs2bP16jrd0NCAv/3tb7rHdnZ26N+/P/7v//6vxaQONjY2+Oabb7Bq1Srs3r0bW7ZsgZ2dHXx8fPCXv/xFN6A+ACxfvhxvv/023nnnHTQ2NuK5555rUZQDmsatunGA/aioKBw5cqTFrLTN9L2WBg8ejB9++AGffvopvv32W9TU1MDV1RXh4eGYMWOGXuddXzeeNxsbGygUCvTr1w+LFi3CmDFjbjmGmaFcXFzw008/4ZNPPsG+ffuwYcMGODo6IiAgAK+88opex+iM87N48WIsWbIE//3vf1FXV4eHHnqo3UU5qVSKZ599ts2xEYcPH47Fixfjyy+/xL/+9S/069cPn3/+Od577z2D4r1bM2fORP/+/bFu3Tps2bIFZWVlsLW1RWhoKN555x1Mnjz5jsfQ9x7p5uaGr7/+GsuXL8cXX3wBR0dHzJw5E25ubnjjjTdu+xwWFhZ499138Z///Advvvkm1Go13nnnHb27/A4YMAC//PIL1q5di99++w1bt26FWCyGn58fFixYoJsZ1cnJSfd+/Pe//4VcLsfkyZMxZMiQO/7w1EwsFuOzzz7Dv/71L13X71GjRmHx4sWYOnWqXscgIqLOIRI6aiRZIiIiIiIiIiIi0gvHlCMiIiIiIiIiIupi7L5KRERERESdprKyEnV1dbfdxtXVtYui6Tx1dXV3HKfNwcGhxZiORERk3liUIyIiIiKiTvPPf/7zjrOm3mlihZ5g586dbY7bd6Ovv/4agwcP7qKIiIiou+OYckRERERE1GkuXryIwsLC224zdOjQLoqm8xQWFuLixYu33SYsLAwODg5dFBEREXV3LMoRERERERERERF1MU70QERERERERERE1MVYlCMiIiIiIiIiIupinOihAwiCAK2WvYCpY4jFIuYTmTVeA2TOmP9kzpj/ZM6Y/2TOTC3/xWIRRCKRXtuyKNcBtFoBpaXVxg6DTIBUKoaTkwwqVQ3Uaq2xwyHqcrwGyJwx/8mcMf/JnDH/yZyZYv4rFDJIJPoV5dh9lYiIiIiIiIiIqIuxKEdERERERERERNTFWJQjIiIiIiIiIiLqYizKERERERERERERdTEW5YiIiIiIiIiIiLoYi3JERERERERERERdjEU5IiIiIiIiIiKiLiY1dgBERERE5kqj1eJqSQ0uF1Yhv7QGcntrWEtEsLe1gIPMCo72VrC3tYBYJDJ2qERERETUwViUIyIiIuoCNXWNuFxYhZzCKlwurMLlgirkFVdDrdHedj+xSAS5zAKOdlZwtLOCg53l9b9l1x/LZRaQiNkJgoiIiKinYFGOiIiIqANpBQHF5bVNhbfCKuQUNP1doqprc3srSwm83Ozg7WYHiVSCwtJqlFXWo7yqAZXVDdAKAsqrGlBe1QCg8pbPKwJgL7OEY3PRTmZ5rZBnCYdrRTwnOyvIZZaQSli8IyIiIjI2FuWIiIiIDFTfqEFeUTUuF1bqWsDlFlahrkHT5vbOcmt4udk1FeHcm/52cbSBWCSCVCqGk5MMZWXVUKubWs9ptFqoqhtRXlWPiqoGlFdf+7vq+t/lVfVQVTdCKwhQVTdAVd2AnIKq28ZtZ2NxQ8HO8norPJklHO2t4ChrWm4hlXT4OSMiIiKiJizKEREREd2BcK21WlPrt0pdK7irpTUQhNbbSyVi9HGRweta4c3bzQ6ebnaQWVu063klYjGc7K3gZG912+20WgGVtY0or6xHRXX9tZZ1NxTwqq8/1mgFVNU2oqq2EblFt39+mbVU11W2aYy7ll1mm1vhWVmweEdERETUXizKEREREd1ArdEiv6RGV3xr7n5aVdvY5vZyWwt4udtfbwHnZodezrZdOr6bWCyCg8wSDjJLAPa33E4rCKiubUR5VQMqqm4q3lXX31DIa4Bao0V1nRrVdWrkFVff9vltrCQ3dZm9Vsi71mXW4do6Gyv+15OIiIioGf9nRERERGarqrZR1+rtckFTEe5KSTXUmtbN38QiEXo527Yovnm52cHB7vat2LoTsUgEe1tL2NtawsvN7pbbCYKAmno1yivrUV7dRgHvhkJeQ6MWtfUa1NbXIL+k5rbPb2Uh0bWuc7xpwoobl9tYSSHijLNERERk4liUIyIiIpOnFQQUldVem/20EpcLqnC5qAqlqvo2t7exksDL1a5FC7g+LjJYmkk3TZFIBJm1BWTWFujjeuvtBEFAbb1G12W2RfGuuqFFUa+uQYP6Rg0KympRUFZ72+e3lIqvtbRrGt/uxllnb/y3zJrFOyIiIuq5WJQjIiIik1LfoEFuUZVu4oXLhZXILaxGfWPbky+4OlrDy61l91NnB2sWe/QgEolgay2FrbUUvZ1lt922rkF9w+QU1wp4bbTCq6lXo0GtRVF5HYrK256xtplUImoa6+6GVna6Qp799e60drYWEPP9JCIiom6GRTkiIiLqkQRBQFll/fXi27Xup4VltWhj7gVYSMXwdJVdK75dL8JxnLOuYW0phbVCCneF7W23a2jU3KbLbHMhrwFVtY1QawSUqOpQorp98U4iFkEus2wq2smsWsww29z6ztHOEnKZJYuxRERE1GX4v1AiIiLq9tQaLa4UV98w8UJTAa66Tt3m9g52ltdavV0vvrkrbLp08gUyjKWFBG6ONnBztLntdo1qLSqq61u2vquuR3ll01h3zcsraxqh0TYVcMsq6wFU3vKYAX0cMHdCCHrdoXBIRERE1BFYlCMiIqJupbKmocWsp5cLq5BfUg2NtnX7N4m4afIF75tav8lllkaInLqShVQMFwcbuDjcvnin1mihqm5oo8tsczfapuKdqqYBF/Mq8Oa6OPzp/gDcH9GHXV6JiIioU7EoR0REREah1QooKKu5PvtpYRVyCipRXtXQ5vYyaym83OzgeUMLOA8XGSykbP1GtyaViKGQW0Mht77tdqWqOqzdkYzk7DJ8ty8NZ9KL8NSDIXfcj4iIiMhQLMoRERFRp6utVyO3qKpFC7i8oio0qLVtbu/mZKObdKG5BZxCbsXxvqjTKOTW+OvMgfj1jzz89OtFJF4qwz/WxmHWmCDcE+bO3CMiIqIOx6IcERERdRhBaBp4v2nihest4ArLa9vc3tJCDC9XO123Uy83e/RxlXHyBTIKsUiEByI9EearwJrtSci8osLq7Un4I70Is8cpIbdlt2giIiLqOPwfLxERERmkUa3BleIa5Fyb9bT5T01925MvONlb6Ypv3u5Nrd/cHG0gFrMFEnUvvRS2eG1WBHaeyMG2I1lISC1C+uVyzIkNxqBAV2OHR0RERCaCRTkiIiK6o4rqBt2Mp80t4PJLaqAV2p58wcNFdkP3Uzt4udvDzsbCCJETGUYiFmPSUB+E+zljzfYk5BVX4+NN53FveG88+kAgW3MSERHRXeP/JoiIiEhHo9XiamltUwHuhu6nFdVtT75gZ2NxQ9fTphZwvZ1tIZVw8gUyDX172WPJk1HY/HsW9pzMwZFz+Ui+VIZ5E0IQ3NfJ2OERERFRD8aiHBERkZkrLKtBXHIhTqcXI7eoCo1tTL4gAuCusL2h+NY0/pujnSUHwCeTZyGV4JH7AzAwwAVrtiehuKIOKzacxthoL0wb4QdLC4mxQyQiIqIeiEU5IiIiM1RcUYv4lELEJRci+2pli3VWlpLrky+4N/3t6WIHK0sWHsi8BXk5YtncGPz460UcOnMFe+Mv43xmCZ6eFAqfXnJjh0dEREQ9DItyREREZqJUVYdTKYWISylE5hWVbrlYJEJIX0dEh7hD6e0IV0cbiNn6jahNNlZSzBkfjEGBLvhyZwryS2rwz68TMGmoDx4c0pddt4mIiEhvLMoRERGZsIqqepxKLUJccgHScyt0y0UAlN5NhbhIpSvktpbGC5KoBwr3d8Hb8wfjmz2piE8pxJYjWThzsRhPTwpFb2eZscMjIiKiHuCuinJnzpzByZMnUVJSgsceeww+Pj6ora1FZmYmfHx8IJPxPyRERERdrbKmAQnXCnGpl8tx4wSpgZ4OiAlxR5TSFQ52VsYLksgE2NlY4Nmp/RCRVIBv96bi0tVKvPllPKaP9McDUZ5scUpERES3ZVBRrqGhAS+//DIOHDgAQRAgEolw//33w8fHB2KxGHPnzsWTTz6JZ599tqPjJSIiojZU1zXij9QixKUUIvlSGbQ3VOL8POSICXZDVLAbFHJrI0ZJZJoGh7ojyMsRX+5MxoWsUmw4kI7T6UWYOyEELg42xg6PiIiIuimDinIffvghfvvtN7z55psYPHgwxo8fr1tnZWWF8ePH48CBAwYV5TIyMrB8+XKcPn0aMpkMU6ZMwYsvvghLy9t3q/nuu+9w+PBhnD17FmVlZfjwww9bxAUAJ0+exBNPPNFq3wcffBAffPBBu2MlIiIyppo6NU6nFyE+pRCJWaXQaK8X4vq62yMmxA3RwW5wcWRRgKizOdlb4aVHBuDQmSvYeDAdKTnlWLI2Do+NDsKw/r04SzERERG1YlBRbseOHZg5cyZmzJiBsrKyVuv9/f2xe/fudh+3oqICc+bMgY+PDz7++GMUFBTg3XffRV1dHZYsWXLbfbdu3QoAGDlyJLZs2XLbbd955x34+fnpHjs5ObU7ViIiImOoa1DjzMVixCcX4nxmKdQarW6dp6sdokPcEBPiBncnWyNGSWSeRCIR7hvUByE+Tli7PRkX8yqwbmcyTqcXYc74YMhlHLuRiIiIrjOoKFdSUgKlUnnL9RKJBHV1de0+7saNG1FdXY2VK1fC0dERAKDRaLBs2TIsXLgQ7u7ut91XLBYjNzf3jkW5wMBA9O/fv93xERERGUN9owbnM0oQl1yAcxklaFBfL8T1drZFTIg7ooPd4OHCsVyJugN3J1ssfjwCe+JysPn3TJxOL0Z67knMGR+MSKWrscMjIiKibsKgolzv3r2RmZl5y/V//PEHvL29233cw4cPY8iQIbqCHADExsZi6dKlOHr0KKZNm3bLfcViTj9PRESmo1GtwfnMUsSnFOJMejHqGzW6dW5ONogJcUNMsDv6uMrYLY6oGxKLRYi9py/6+Tlj9S9JyC2qwiebz2Nov154bHQgbK0tjB0iERERGZlBRbmJEyfiyy+/xNixY+Hj4wMAui8EP/74I3bt2oW//vWv7T5uZmYmHn744RbL5HI5XF1db1sEbK8FCxagvLwcrq6umDBhAl544QVYW3PgayIiMi61RovErFLEJRfizMUi1NZfL8S5OFgjOtgNMSHu8Ha3YyGOqIfwcrPDkiejsPVIFnaeyMaxC1eRnF2GuRNCEOajMHZ4REREZEQGFeWeeeYZnD17FrNmzYKfnx9EIhHeeecdVFRU4OrVqxg5ciSefPLJdh9XpVJBLpe3Wu7g4ICKigpDQm3B3t4e8+fPR3R0NKysrHDixAmsW7cOmZmZWLVq1V0dWyplSz26exKJuMXfRObGHK8BjVaLpKwynEwqQEJqIarr1Lp1TvZWiAl1xz2h7vDzkLMQZ+LMMf/NhVQqxowHAhGhdMUXWxNRUFaLf288gzHRXnhkVACsLCTGDtHomP9kzpj/ZM7MPf8NKspZWlpizZo12LZtG/bs2QOtVouGhgYolUq8+OKLmDJlSrf84hAaGorQ0FDd4yFDhsDNzQ1vvfUWzp07h/DwcIOOKxaL4OTEcXyo48jlnCmRzJupXwMarYDEzGL8fuYKjp27AlV1g26dk70Vhg3wwPCBfRDcVwGxuPt9nlLnMvX8N2cxTjKEB7njy+2J2HnsEvbFX0bSpVK89GgElH3Zag5g/pN5Y/6TOTPX/DeoKAc0dVedMmUKpkyZ0mHByOVyVFZWtlpeUVEBBweHDnueG8XGxuKtt97ChQsXDC7KabUCVKqaDo6MzJFEIoZcbgOVqhaaG2ZUJDIXpnwNaAUB6ZfLcTKpAPHJhai4oRBnb2uB6GA3DA51h9LbSVeIq6jgZ4s5MeX8p5ZmjgpAaF8nrN2ehLyiavy/j3/HpGG+mDrcF1IzbSnA/Cdzxvwnc2aK+S+X2+jd8s+golx5eTmuXr2K4ODgNtenpqaiV69e7S6k+fn5tRo7rrKyEkVFRfDz8zMk1C6jVptG8lD3oNFomVNk1kzlGhAEAZlXVIhLLsSp1EKUVdbr1smspYgIckVMiDuC+zpCcm3CIq1WgFYrGCtk6gZMJf/p9kL7OuGteTH4bl8aTiQWYNuRLJxJL8L8iaHwdLUzdnhGw/wnc8b8J3NmrvlvUFHunXfeQVZWFn788cc21y9duhR+fn7417/+1a7jjhgxAp9//nmLseV2794NsViMYcOGGRLqHe3YsQMA0L9//045PhERmRdBEJBdUIm45ELEJxeiRFWnW2djJcGgQFfEhLgh1Edhti1iiKiJzNoCCyaFYVCgK77Zk4qcgiq89VU8po3wx9hoL3ZfJyIiMnEGFeVOnDiBRx999Jbr77//fmzcuLHdx505cya++eYbLFq0CAsXLkRBQQFWrFiBmTNnwt3dXbfdnDlzcOXKFezbt0+37Pz588jLy0NpaSkA4OzZswAAhUKBmJgYAMArr7yCvn37IjQ0VDfRw1dffYXRo0ezKEdERAYTBAG5RdWIS27qmlpYXqtbZ2UpwaAAF0SHuKGfrzMsODEQEd0kOtgNQZ4O+GpXCs5mlODHXy/iTHoR5k4MhZujeY6xQ0REZA4MKsqVlpbCycnplusdHR1RUlLS7uM6ODhg/fr1ePvtt7Fo0SLIZDJMnz4dL730UovttFotNBpNi2XfffcdNm/erHu8bt06AEBMTAy++eYbAEBgYCB++eUXrFu3Do2NjejTpw+eeeYZLFiwoN2xEhER5RVXIz65APEphcgvuT7+m6VUjPAAF8QEuyHc3xmWnFmRiO7Awc4Kz08Px+/n8rHhQDrSciuwdG0cZj4QgBEDPLrlJGpERER0d0SCILR74JpRo0Zh+PDhWLZsWZvrlyxZgsOHD+O333672/h6BI1Gi9LSamOHQSZAKhXDyUmGsrJqs+xPT9QTroGC0hrEJRcgLqUQeUXX7/1SiRj9/RSICXHHgABnWFsaPJcSmamekP/UNYrKa7F2RzLSLpcDAML9nfFkbDAc7ayMG1gnYv6TOWP+kzkzxfxXKGSdO9HD6NGj8f3332PEiBF44IEHWqzbv38/fv75Z8ycOdOQQxMREXU7ReW1iE8pRFxyAXIKqnTLJWIRwnwViAlxw6BAV9hYsRBHRHfP1dEGf3tsEPbFX8amQ5k4l1GCf6w5idnjlIgJcb/zAYiIiKhHMKilXGVlJR577DFcvHgRwcHBCAwMBACkp6cjJSUF/v7++P7773WTNZg6tpSjjmKKvxIQtUd3ugZKVXXXCnGFyMpX6ZaLRSKE+jghOsQNEUGukFlbGDFKMiXdKf+p+8grqsKa7cnILqgEAAwOdcfjY4JgZ2Na9x7mP5kz5j+ZM1PM//a0lDOoKAcANTU1WLNmDfbt24ecnBwAgLe3N8aOHYt58+bB1tbWkMP2SCzKUUcxxRsSUXsY+xoor6pHfErTrKkX8yp0y0UiINi7qRAXGeQKe1vLLo+NTJ+x85+6L7VGi+3HLmH7sWxoBQGOdpaY+2AI+vk5Gzu0DsP8J3PG/CdzZor53yVFObqORTnqKKZ4QyJqD2NcA6rqBiSkNrWIS7tcjuYPRRGAQE8HRIe4I0rpCgcTHsuJugd+BtCdZF5RYc32JFwtbZpY5r5BffDI/f4mMYYl85/MGfOfzJkp5n+njylHRETUk1XVNuKPtCLEJRcgObsMN/485e8hR3SIO6KD3eBkz0IcEXUffh5yLH0qGpsOZWD/qVz8djoPSVmlmDcxBIGejsYOj4iIiNrJ4KJcRkYGNm3ahNzcXFRUVODmBncikQjr16+/6wCJiIg6Qk2dGqfTixCXXIikS6XQaK9/bvn0skdMiDuigl3h4mBjxCiJiG7PykKCx0YHYVCAC9buTEZheS3e/e4PjB/sjan3+sFCqt8v80RERGR8BhXltmzZgtdffx1SqRS+vr5tTujAXrFERGRstfVqnLlYjPjkQlzIKoFac/2zycvNDjEhbogOdoObk/mMg0pEpiHER4G35g7Ghv1pOHrhKnadyMH5jBLMnxgKb3d7Y4dHREREejCoKLdy5UqEhIRg9erVUCgUHR0TERGRweobNDib0VSIO5dZgsYbxqbwcJEhJtgN0SFu6O0sM2KURER3z9ZainkTQzEoyBXrd6cgt6gab68/hanDfTF+sDckYraaIyIi6s4MKsoVFhZi7ty5LMgREVG30KjW4FxGKeJTCnDmYjEaGq8X4tydbBAd4o6YEDd4utoZMUoios4REeSKgD4OWL87BafTi7HpUCbOXCzG/AmhcFewJTAREVF3ZVBRTqlUorCwsKNjISIi0lujWovErKZC3On0YtQ1aHTrXBysEXNtsgZvdzuIRCIjRkpE1PnkMks8N60/jl24iu/3pyEjT4WlX8Zhxv0BuG9QH94HiYiIuiGDinKLFy/GCy+8gBEjRiAiIqKjYyIiImqTWqNFcnYZ4pIL8EdaMWrr1bp1CrkVooPdEBPiDp9e9vwCSkRmRyQSYVj/3gj2dsK6nclIzi7DN3vTcDq9GE89GMIZpYmIiLoZg4pyq1evhr29PR5//HEEBASgd+/eEN80ZoVIJMJnn33WIUESEZH50mi1SM0pR1xyIf5IK0JVbaNunYOdJaKVTYU4vz5yiFmIIyKCs4M1/jpzIA4m5OKn3zJwIasU/1hzErPGBmFwqDt/tCAiIuomDCrKpaWlAQB69+6N6upqXLx4sdU2/LAnIiJDaQUBqTlliEspREJKIVQ11wtxclsLRAa7ISbYDYGejhCL+XlDRHQzsUiE0VFeCPNVYM32JGTlV+KLX5LwR3oxZo8Ngr2tpbFDJCIiMnsiQRAEYwfR02k0WpSWVhs7DDIBUqkYTk4ylJVVQ33DjJFE5kAQBGQXVOFMRgl+P5OHssp63TqZtRSRSjfEhLhB6e3IGQXJJPEzgDqLRqvFjuPZ+OXoJWi0AhxklngyNhgDAlyMHZoO85/MGfOfzJkp5r9CIYNEot/3FYNayhEREXWkmjo11u9OQXzK9UmEbKykiAhyQUyIO0L6OkGq5wcbERG1JBGLMXmYL8L9nbFmezKuFFfjw/+dw4gBvTFjVCBsrPiVgIiIyBgM/gTWaDTYvXs3Tp48iZKSEjz//PNQKpWorKzE8ePHERERAReX7vPrGxERdU8ZVyqwamsiiivqIBGLMHxgHwwKcEawtxMspCzEERF1FJ9ecix9Mgo/H87E3rjLOHw2H0mXyjBvQgiU3k7GDo+IiMjsGFSUU6lUmD9/Ps6dOwdbW1vU1tZi1qxZAABbW1ssX74cU6dOxcsvv9yhwRIRkenQCgJ2n8zB5sOZ0GgFuDhYY9G0/ojq52FSzdeJiLoTC6kEM0YFYmCAC9buSEZxRR1WfH8aY2O8MG2EHyykEmOHSEREZDYMaoLw/vvvIz09HWvXrsX+/ftx47B0EokE48aNw6FDhzosSCIiMi0VVfX44Icz+N9vGdBoBUQHu+HNp2Lg38fB2KEREZkFpbcTls2NwYgBvSEA2BN3Gcu+OoXsq5XGDo2IiMhsGFSUO3DgAGbPno1hw4a1Ocuqj48P8vLy7jo4IiIyPReySrB0XRwSL5XBUirGk7HBeGZKGGytOaYREVFXsrGS4snYEDw/PRxymSWuFFdj+densO1oFjRatlYmIiLqbAZ9A6qsrISnp+ct16vVamg0GoODIiIi06PWaLH5cCZ2ncwBAPRxleGZKf3Qx0Vm5MiIiMzbwAAXvD0vBt/sScWp1CJs+T0LZy+WYP7EEPR25j2aiIiosxjUUs7b2xuJiYm3XH/06FH4+/sbHBQREZmWwvJavPPtH7qC3P2D+uAfT0SxIEdE1E3Y21ri2an9sGBSKGytpMjKV+HNL+Ox79RlaG8YqoaIiIg6jkFFuenTp2PTpk3YuXOnbjw5kUiEhoYGfPDBB/j9998xY8aMDg2UiIh6prjkAiz7Mg5Z+SrYWknx56n9MHucEpYWHEyciKg7EYlEuCesF96aF4MwHyc0qrXYsD8d/954BiUVdcYOj4iIyOSIBKH9P30JgoB//OMf+N///ge5XA6VSgUXFxeUl5dDrVZjxowZWLZsWWfE2y1pNFqUllYbOwwyAVKpGE5OMs48SSahvlGDDfvTcPhsPgAgoI8DFkwOhYuDzS334TVA5oz5T92JIAj47XQefvj1IhoatbCxkuCx0UEY2q9Xm2NK3y3mP5kz5j+ZM1PMf4VCBolEvzZwBhXlmp06dQp79uxBdnY2tFotvL29ERsbi+joaEMP2SOxKEcdxRRvSGSecgur8NnWC8gvqYEIwIShfTHlXl9IxLf/cOI1QOaM+U/dUUFpDdbsSEJGngoAMCjQBXPGB0Mus+zQ52H+kzlj/pM5M8X8b09Rrt0TPdTW1uL//b//h7Fjx2Ly5MmIiopqd4BERGSamltWbDhwEWqNFg52llgwMRQhPgpjh0ZERAZwV9jitccjsetkNrb8noXT6cW4mHcSc8YHIyLI1djhERER9WjtHlPOxsYGx44dQ10dx5UgIqLrqusa8enmC/hmbxrUGi3C/Z2xbG4MC3JERD2cWCzChCE++MecKHi6ylBZ04iVP5/H2u1JqKlTGzs8IiKiHsugiR4iIyNx+vTpjo6FiIh6qPTccry5Lg4JaUWQiEWYOSoAz08Ph9y2Y7s3ERGR8Xi72+Mfc6IRe483RCLg6IWrWLLuJJIvlRo7NCIioh7JoKLckiVLkJCQgA8++ABXr17t6JiIiKiH0GoF/HLsEt777jRKVPVwc7TB67MjMTbGG+JOGAiciIiMy0Iqxp/uC8DixyPg5miDUlU9/m/jGXy/Lw31jRpjh0dERNSjGDTRw6BBg6DRaNDY2AgAkEgksLRs2RpCJBIhISGhY6Ls5jjRA3UUUxzkkkxXWWU9Vv+SiJSccgDAPWHumD1WCRurdg9XqsNrgMwZ8596mroGNX78NQO/nc4DAPRS2GL+xFD4ecjbfSzmP5kz5j+ZM1PM/06d6AEAxo0b1ylToRMRUc9w9mIx1u5IRlVtIywtxJg9Vomh/Xrxs4GIyIxYW0rxxDglBgW64MudybhaWoN/fZOACUP6YtIwH0j1/EJCRERkrgxqKUctsaUcdRRT/JWATEujWotNhzKwN/4yAMDbzQ4Lp4Sht7OsQ47Pa4DMGfOferKq2kZ8ty8NJ5MKAAB93e0xf2II+rja6bU/85/MGfOfzJkp5n+nt5QjIiLzU1BWg8+3JiL7aiUAYHSkJ/50vz8spBIjR0ZERMZmZ2OBhZPDMCjQBd/sSUV2QSWWfXUK00b4YWy0F8RitqQmIiK6mcFtyq9cuYIlS5Zg3LhxiI6ORnx8PACgtLQUy5cvR1JSUocFSURExnX8wlW8+WU8sq9WQmYtxV8e7o/HxgSxIEdERC3EhLjj7fmDEe7vDLVGix9/vYgVG06jqLzW2KERERF1OwYV5S5evIiHHnoIu3btgqenJ6qqqqBWqwEACoUCCQkJ+Pbbbzs0UCIi6np1DWqs3Z6E1duTUN+gQZCXI5bNjcGgQFdjh0ZERN2Uo50VXpgejjnjlbCykCDtcjmWrIvD4bNXwJFziIiIrjOo++r//d//wd7eHj/++CMAYOjQoS3Wjxw5Ert27br76IiIyGiyr1bi822JKCitgUgETB7mi0lDfdgFiYiI7kgkEmHkwD4I8VFg7fYkpOdW4KtdKfgjrQhPxQbDwc7K2CESEREZnUEt5eLj4/Hoo49CoVC0OdOeh4cHCgoK7jo4IiLqeoIgYN+py/jnN6dQUFoDJ3sr/O3RQZhyry8LckRE1C5ujjZ49bEIPHJ/AKQSEc5llOAfa+NwKqXQ2KEREREZnUEt5QRBgLW19S3Xl5aWwtLS0uCgiIjIOCprGvDlzhScuVgMABgY4IK5E0JgZ2Nh5MiIiKinEotFGD/YG/38FFjzSxJyCqvw6ZYLuCfMHY+PCYLMmp8xRERkngxqKRcaGopDhw61uU6tVmPHjh0YMGDAXQVGRERdKzWnDG9+GY8zF4shlYjw2OhA/OXh/izIERFRh/B0tcPf50Rh4lAfiETAicQCLFkbhwtZJcYOjYiIyCgMKsotWLAAv//+O5YuXYr09HQAQElJCY4dO4a5c+ciMzMTCxYs6NBAiYioc2i0Wmz5PRMrNpxGWWU9eils8fcnojA6yqvNIQqIiIgMJZWIMW2EH16fFQl3JxuUVdbjPz+cxfpdKairVxs7PCIioi4lEgycAmnLli3417/+hcrKSgiCAJFIBEEQYGdnhzfffBMTJ07s6Fi7LY1Gi9LSamOHQSZAKhXDyUmGsrJqqNVaY4dDZqBUVYcvtiUiLbcCADCsfy88PiYI1pYGjW5w13gNkDlj/pO5qW/U4H+/ZuDAH7kAAA8XGRZOCYOXq52RIyPqWrz/kzkzxfxXKGSQSPRrA2dwUQ4AampqcOzYMVy6dAlarRbe3t649957YWdnXh+kLMpRRzHFGxJ1X6fTi7BuRzKq69SwspTgiXFKDAnrZdSYeA2QOWP+k7lKvFSKL3cko7SyHhKxCA+P9MfYGC+I2VqbzATv/2TOTDH/O7woFxMTg7feegvjx48HAKxcuRJjx45FUFDQ3UVqIliUo45iijck6n4a1Rr8+GsGDiQ0tUzo28sez0wJg7uTrZEj4zVA5o35T+asrlGDb/am4fj5fABAmK8C8yeEwMHOysiREXU+3v/JnJli/renKKfXVjU1Nairq9M9XrlyJVJTUw2LjoiIjCa/pBrLv07QFeTGxXjhjdmR3aIgR0RE5svOxgKvzYnGUw8Gw1IqRmJWKZasi8O5jGJjh0ZERNRp9Bo0yMvLC7t370ZkZKSua2ptbS3Ky8tvu5+jo+PdxkdERB1AEAQcPX8V3+5LRUOjFnY2Fpg/MQTh/i7GDo2IiAgAIBKJcH+EJ/w8HLBqayJyi6rw35/OYXSUJ/50XwAspAbNUUdERNRt6dV9dcuWLXj99dfR3uHnkpOTDQ6sJ2H3Veoopth0l4yvtl6Nb/ak4kRSAQAgpK8T5k8MhZN99+sSxGuAzBnzn8zZzfnfqNbgp18zsP9ay24vNzssnBwGDxeZkSMl6ni8/5M5M8X8b0/3Vb1ayk2dOhXh4eGIi4tDcXExVq5ciTFjxkCpVN5VoERE1Lmy8lVYtTURheW1EItEmDrcFw/e0xdiMQfPJiKi7stCKsFjY4IQ5qvA2h3JuFxYhbe+isejowMxYoAHRJwEgoiITIBeRbmqqir07dsXfn5+AICff/4ZU6dOxQMPPNCpwRERkWG0goB98Zfxv98yoNEKcJZbYeHkfgjwdDB2aERERHobEOCCt+bFYO32JCReKsP63am4kFWKJ2ODIbO2MHZ4REREd0Wv9nTR0dHYuXOn7vHgwYPh4sJxiIiIuiNVdQM+/Okcfjh4ERqtgEilK96cG8OCHBER9UiOdlZ4acZAPHJ/ACRiERJSi7B0XRzSLpcbOzQiIqK7oldRzsLCAg0NDbrHW7ZsQU5OTqcElJGRgaeeegoDBw7EsGHDsGLFihbPfSvfffcdFi5ciHvuuQdKpRK7d+9uc7uCggL85S9/waBBgxATE4M33ngDVVVVHf0yiIiMIulSKZaui8P5zBJIJWLMHqfEn6f2Y2sCIiLq0cQiEcYP9sbrsyPh5mSDUlU93vv+D2w+nAmN1jTGICIiIvOjV/dVPz8//PTTT+jTpw/s7e0hCALy8vKQmJh42/3CwsLaFUxFRQXmzJkDHx8ffPzxxygoKMC7776Luro6LFmy5Lb7bt26FQAwcuRIbNmypc1tGhsbMX/+fADAv//9b9TV1eG9997DX//6V6xatapdsRIRdScarRZbfs/CzuPZEAD0drbFs1P6wdPNztihERERdRjf3nIsfTIa3+9Pw9HzV/HLsUtIzi7DgkmhcHG0MXZ4RERE7aJXUe7ll1/GSy+9hKeeegpA03TlH374IT788MM2txcEASKRqN2zr27cuBHV1dVYuXIlHB0dAQAajQbLli3DwoUL4e7uftt9xWIxcnNzb1mU27NnD9LT07Fz507d+HhyuRzz5s3DuXPnEB4e3q54iYi6g+KKWnyxLQkX8yoAACMGeODR0YGwspAYOTIiIqKOZ2MlxbwJoejn64yv96TgYl4Fln4ZjznjlYgJufX3BSIiou5Gr6LciBEjcODAAZw/fx4lJSVYvHgxHnnkEQwaNKhDgzl8+DCGDBmiK8gBQGxsLJYuXYqjR49i2rRpt9xXLL5zT9zDhw9DqVTqCnIAMGzYMDg6OuLQoUMsyhFRj3MqpRBf7UpBTb0aNlYSzBkfzC8kRERkFgaHusPPQ44vfklERp4Kn29NxIXMUjw2JhDWlnp9zSEiIjIqvT+tHB0dMXz4cADApk2bEBsbiyFDhnRoMJmZmXj44YdbLJPL5XB1dUVmZmaHHP/GghzQ1OrP19f3ro8vleo1PB/RbUkk4hZ/E91KQ6MG3+9Lw8E/8gAA/n3k+PPU/nB16tldd3gNkDlj/pM5MzT/e7vI8Pc5Udh8OAu/HMnCkfP5uJhXgT8/1A8+veWdESpRh+P9n8yZuee/QT8hffPNNx0dBwBApVJBLm/94eng4ICKiooOOb69vX2HH18sFsHJSXY3oRG1IJf37MIKda7sqyr83zenkH21EgDw8P0BmBUbAqkJfZDxGiBzxvwnc2Zo/j/9UDjuCffAf75LwNXSGrz1VTyeeDAUU0b4QywWdXCURJ2D938yZ+aa/3oV5ZrHaJsyZQpEItEtx2y72dSpUw0Mq2fRagWoVDXGDoNMgEQihlxuA5WqFhoNZxKjlgRBwG+nr+C7valoUGshl1li4eQw9Pd3RqWq1tjhdQheA2TOmP9kzjoi/z0VNnhr/mCs3Z6EhNQirPslEXGJ+VgwOQyOdlYdHDFRx+H9n8yZKea/XG6jd8s/vYpyixcvhkgkwoMPPghLS0ssXrz4jvuIRKJ2F+XkcjkqKytbLa+oqICDg0O7jnWr41dVVbV5/N69e9/VsdVq00ge6h40Gi1zilqoqVNj/e4UxKcUAgDCfBWYPzEUDjJLk8wVXgNkzpj/ZM7uNv+tLST489R+OHTmCjYeSMeFzFK88cUJzJsQgnB/lw6MlKjj8f5P5sxc81+votyBAwcAAJaWli0edzQ/P79WY7tVVlaiqKio1Vhwhh4/LS2txTJBEJCVlYVhw4bd9fGJiDpDRl4FVm1LRHFFHSRiEaaN8MO4wd4Qi9gdh4iI6GYikQj3DeqDQC9HrNqaiNyiKvz3p3MYHeWJP90XAAuOBU1ERN2EXkW5Pn363PZxRxkxYgQ+//zzFmPL7d69G2KxuEOKZiNGjMC2bdtw6dIl+Pj4AACOHz+O8vJyjBw58q6PT0TUkbSCgN0nc7D5cCY0WgEuDtZYOCUM/h5333KYiIjI1PVxkeEfcyLx068Z2J+Qi/2ncpGaU46Fk8Pg4cLxoImIyPgMniu8uroaeXl5qK6uhkwmg6enJ2xtbe8qmJkzZ+Kbb77BokWLsHDhQhQUFGDFihWYOXMm3N3dddvNmTMHV65cwb59+3TLzp8/j7y8PJSWlgIAzp49CwBQKBSIiYkBAIwbNw6rVq3CX/7yF7z88suora3FihUrcN999yE8PPyuYici6kgVVfVYsz0JiZfKAAAxIW54YlwwbK0Nvm0TERGZHQupBI+NCUKYrwJrdyTjcmEV3voqHo+ODsSIAR4QsdU5EREZkUgQBKE9Oxw+fBiff/45zp49C632en9fiUSCQYMG4ZlnnrmrVm0ZGRl4++23cfr0achkMkyZMgUvvfSSrussAMyePRt5eXk4ePCgbtnixYuxefPmVseLiYlpMVtsQUEBli9fjiNHjkAqlWLMmDF4/fXXYWdnZ3DMGo0WpaXVBu9P1EwqFcPJSYaysmqz7E9PTS5klWDNL0lQ1TTCUirGY2OCMDy8t1l8ceA1QOaM+U/mrCvyv/zaD15J137wilK6Yk5sMGTWFp3yfET64v2fzJkp5r9CIdN7ood2FeW++uorvPfee5BIJIiOjkZgYCBsbW1RU1ODtLQ0nDp1ClqtFq+99hpmz55t8AvoaViUo45iijck0p9ao8XPhzOx+2QOAMDTVYaFU/qhjxl1seE1QOaM+U/mrKvyXysI2BOXg58PNQ0NoZBbYcGkMAR5OXbacxLdCe//ZM5MMf/bU5TTux9URkYG3n//fQwYMAAffPBBm7OVXrlyBS+//DLee+89DB06FP7+/vpHTURkxgrLa7FqayKy8lUAgPsH9cGMUQGwtJAYOTIiIiLTIRaJEDu4L4K9nbBqWyIKy2rx3vd/YNJQH0wa5gOJmJNAEBFR19H7U2fjxo2wtbXFqlWr2izIAYCHhwc+//xz2Nra4qeffuqwIImITFlccgGWfRmHrHwVbK2kWPRQP8wep2RBjoiIqJP49pZj6ZPRGNavFwQB2Hb0Et777jSKy2uNHRoREZkRvYtyCQkJGD9+PBwcbj/rn6OjI8aPH4+4uLi7Do6IyJTVN2jw5c5kfL41EbX1GgR4OuDNudGIVLoZOzQiIiKTZ2MlxbyJoVgwORQ2VhJczKvA0i/jEZdcYOzQiIjITOhdlMvLy4NSqdRrW6VSiby8PIODIiIydbmFVXhrfTx+P5cPEYCJQ33w6mOD4OJgY+zQiIiIzMo9ob3w5lMx8PeQo7Zejc+3JmLdjmTUNaiNHRoREZk4vceUq6qqgr29vV7b2tnZoaqqyuCgiIhMlSAI+O10HjYcuAi1RgsHO0ssmBiKEB+FsUMjIiIyW66ONnj18QhsO3oJO45dwpHz+UjPq8Azk8PQt5d+34GIiIjaS++inEajgUgk0mtbkUgErdY0Zs0gIuooVbWN+GpXCv5IKwIAhPs7Y+6EEMhtLY0cGREREUklYkwb4YcwHyd88UsSCkprsPzrU3h4pD/GxnhBrOd3ISIiIn3pXZQDgC1btuDs2bN33C4rK8vggIiITFF6bjlWbUtEqaoeErEIf7rPH2OivfT+sYOIiIi6htLbCcvmxuh+SPvx14tIvFSK+RNC4GBnZezwiIjIhLSrKHf06FEcPXpUr235RZOICNBqBew4fglbj1yCVhDg5mSDZ6aEwaeX3NihERER0S3Y2Vhg0UP9cOjMFWw8kI7ErFIsXReHuRNCEe7vbOzwiIjIROhdlEtJSenMOIiITE5ZZT1W/5KIlJxyAMCQMHfMGquEjVW7fg8hIiIiIxCJRLhvUB8Eejli1dYLyC2qxn9/OosxUV6Yfp8/LKR6z5lHRETUJn4zJCLqBGcvFmPtjmRU1TbCykKCWWODMKx/b2OHRURERO3Ux0WGf8yJwk+/ZmB/Qi72nbqMlJwyLJwcBg8XmbHDIyKiHoxFOSKiDtSo1mLToQzsjb8MAPB2s8PCKWHo7cz/tBMREfVUFlIJHhsThFBfBdbtSMblwiq89VU8Hh0diBEDPDh0DxERGYRtromIOkhBaQ3+9U2CriA3OtITbzwRxYIcERGRiRgY4IK35sUg1McJDWot1u9OxWdbLqC6rtHYoRERUQ/ElnJERB3g+IWr+HpvKuobNJBZSzF3QggGBboaOywiIiLqYI52Vnh5xkDsicvBz4cycSq1CJn5KiyYFIYgL0djh0dERD0Ii3JERHehrkGNb/em4diFqwCAIC9HLJgUCoXc2siRERERUWcRi0SIHdwXwd5OWLUtEYVltXjv+z8waagPJg3zgUTMDklERHRnLMoRERko+2olPt+WiILSGohEwJRhvpg41AdiMceVISIiMge+veVY+mQ0vt+XhqMXrmLb0UtIyi7DgkmhcHGwMXZ4RETUzd31TziFhYVISUlBTU1NR8RDRNTtCYKAffGX8c9vTqGgtAZO9lb426ODMPleXxbkiIiIzIyNlRTzJoZiweRQ2FhJcDG3AkvXxSMuucDYoRERUTdncFFu//79GD9+PEaOHImHHnoIZ8+eBQCUlpZi6tSp2L9/f4cFSUTUXVTWNODjTeex4UA61BoBAwNcsGxuDJTeTsYOjYiIiIzontBeePOpGPh7yFFbr8bnWxOxbmcy6hrUxg6NiIi6KYOKcgcPHsRf/vIXODk5YdGiRRAEQbdOoVDA3d0dmzZt6rAgiYi6g9ScMrz5ZTzOXCyGVCLC42OC8JeH+8POxsLYoREREVE34Opog1cfj8DEoX0hAnDkXD6WfXUK2VcrjR0aERF1QwYV5T755BNERUVhw4YNePzxx1utHzhwIJKTk+86OCKi7kCj1WLL75lYseE0yirr0Uthi78/EYUHIj0hErG7KhEREV0nlYgxbYQ//t+jg+Bkb4WC0hos//oU9sTlQHtDYwYiIiKDinLp6emIjY295XoXFxeUlJQYHBQRUXdRqqrD/31/GtuOXoIgAMP698KSJ6Pg7W5v7NCIiIioGwvu64Rlc2MwKNAFGq2AHw5exH9/PIuKqnpjh0ZERN2EQUU5Gxsb1NbW3nL95cuX4ejoaGhMRETdwum0IixdF4e03ApYW0qwYFIo5k0IhbUlJ64mIiKiO7OzscBz0/rjiXFKWEjFuJBViqXr4nAugw0YiIjIwKLc4MGDsWXLFqjVrQctLSoqwo8//oh77733roMjIjKGRrUG3+1Nw8c/n0d1nRp9e9lj6VPRuCesl7FDIyIioh5GJBLhvkF9sOTJaHi6yqCqacR/fzqLDfvT0ajWGjs8IiIyIoOae7z44ouYMWMGpk+fjvHjx0MkEuHIkSM4ceIEfvjhBwiCgEWLFnV0rEREnS6/pBqfb03E5cIqAMC4GC88PNIfUonBk1UTERERoY+LDP+YE4Uff83AgYRc7Dt1Gak5ZVg4JQy9nWXGDo+IiIxAJAiGjTaanp6Of/7znzh58mSL2VdjYmKwdOlS+Pv7d1iQ3Z1Go0VpabWxwyATIJWK4eQkQ1lZNdT85bRLCYKAw2evYMOBdDQ0amFva4F5E0IR7u9s7NDMCq8BMmfMfzJn5pb/Zy4WY92OZFTVNsLSQozHRgdheHhvTiBlpswt/4luZIr5r1DIINGzUYfBRblmFRUVyM7OhiAI8PLygkKhuJvD9UgsylFHMcUbUk9QUVWPr3al4Oy18V1C+jrh6UmhcLSzMnJk5ofXAJkz5j+ZM3PM/7LKeqzdkYSkS2UAgCilK+bEBkNmbWHkyKirmWP+EzUzxfxvT1Hurkcrd3BwQHh4+N0ehojIKBJSC7F+dyqqahshlYgwbYQ/xkZ7QSzmL9VERETUeZzsrfDyjIHYczIHPx/OxKnUImTmq7BgUhiCvByNHR4REXUBvYpyW7ZsMejgU6dONWg/IqLOVlOnxvf703DswlUAgJebHZ6eGApPNzsjR0ZERETmQiwSIfaevgju64RV2xJRWFaL977/A5OG+mDSMB9IxBzTlojIlOnVfTU4OLj9BxaJkJycbFBQPQ27r1JHMcWmu91RSnYZ1u5IQomqHiIREDu4L6bc6wsLKf/ja2y8BsicMf/JnDH/gdp6Nb7fl4aj134wDPB0wIJJoXBxsDFyZNTZmP9kzkwx/zu8++qBAwfuKiAiou6gUa3BpkOZ2Bt/GQDg6miN+RNDEejpaNzAiIiIyOzZWEkxb2IownwV+GZvKi7mVmDpunjMGa9ETIi7scMjIqJOoFdRrk+fPp0dBxFRp8q+WonV25NwpbipVeuIAR6Y+UAArC3vemhNIiIiog5zT1gv+PdxwBfbEpFxRYXPtybiQlYpHh8dBCtLibHDIyKiDsRvo0Rk0jRaLXaeyMG2I1nQaAXIZZZ4MjYYAwNcjB0aERERUZtcHW3w6uMR2HY0CzuOZePIuXyk51bgmclh6NvL3tjhERFRB9GrKDd79myIxWKsXbsWUqkUTzzxxB33EYlEWL9+/V0HSERkqIKyGqzZnoSMPBUAIDLIFbPHKyG3tTRyZERERES3J5WIMW2EP0L7KrB6exIKSmuw/OtTmH6fP8ZEe0Es4kzxREQ9nd4t5bTa6wPu6TE3hF7bEBF1BkEQ8NuZK/jhYDoaGrWwsZLgsdFBGNqvF0T8DywRERH1IMF9nbBsbgy+3JmM0+nF+OHgRSRmlWLexFA4yPhDIxFRT6bX7Kt0e5x9lTqKKc4809XKq+rx5c4UnM8sAQAEezti3oRQODtYGzky0gevATJnzH8yZ8z/O2v+0XHjgXQ0qrWQ21pg3sRQ9PdzNnZodJeY/2TOTDH/2zP7qn5b3SQ+Ph6lpaW3XF9aWor4+HhDDk1EZLD4lEL8Y81JnM8sgVQixsxRAXjl0UEsyBEREVGPJxKJcP+gPlgyJwqerjKoahrxwY9nsWF/U5GOiIh6HoOKck888QSOHj16y/UnTpzQa9w5IqKOUFPXiC9+ScRnWy6guk4Nb3c7LH0yCmNjvDneChEREZmUPq52+MecKDwQ6QkA2HfqMv759Snkl7DnDhFRT2PQ7Kt36vHa0NAAiYTTdRNR50u6VIq1O5JRVlkPkQiYMKQvJg/zhVTP5sJEREREPY2FVILHxwQhzFeBdTuSkVNYhWVfxeOx0UEYHt6bY+gSEfUQehflrly5gry8PN3jzMzMNruoqlQqbNy4ER4eHh0TIRFRGxoaNfjfbxnYn5ALAHBzssH8iaEI6ONg5MiIiIiIusbAABcsmxuDNduTkJxdhq92peBCZgnmxAZDZm1h7PCIiOgO9C7K/fzzz1i5ciVEIhFEIhE+//xzfP755622EwQBEokEy5Yt69BAiYiaZeWrsGZ7EvJLagAA9w3qgxn3B8DKki10iYiIyLw42VvhrzMHYs/JHPx8OBOnUouQma/CgklhCPJyNHZ4RER0G3oX5WJjYxEYGAhBEPDiiy9i9uzZiIqKarGNSCSCjY0NQkJC4OLi0uHBEpF502i12HEsG78cuwSNVoCDnSWeig1BuD9nHSMiIiLzJRaJEHtPXwT3dcKqrYkoLK/Fe9//gUlDfTBpmA8kYg7rQUTUHYmEOw0Q14bNmzcjKioKXl5enRFTj6PRaFFayoFV6e6Z4nTQHeVqaQ1W/5KErHwVACAq2A1PjFPCzoZdM0wJrwEyZ8x/MmfM/45TW6/Gd/vScOzCVQBAgKcDFkwKhYuDjZEjo1th/pM5M8X8VyhkkOg5xrlBEz089NBDhuxGRNRugiDg4B95+OnXi2hQa2FjJcWssUG4J9SdgxgTERER3cTGSor5E0PRz1eBr/ek4mJuBZaui8ec8UrEhLgbOzwiIrqBQUU5AMjIyMCmTZuQm5uLioqKVjOyikQirF+//q4DJCLzVVZZj3U7k5GYVQoACOnrhHkTQqCQWxs5MiIiIqLu7Z6wXvDr44AvtiUi84oKn29NxIWsUjw+Oojj8BIRdRMGFeW2bNmC119/HVKpFL6+vpDL5a22MaBXLBGRzsmkAny7NxXVdWpYSMWYfp8/Hoj0hJit44iIiIj04uZog8WPR2Db0SzsOJaNI+fykZ5bgWcmh6FvL3tjh0dEZPYMKsqtXLkSISEhWL16NRQKRUfHRERmrKq2Ed/uTUVcciEAoG8veyyYFIrezjIjR0ZERETU80glYkwb4Y+Qvgqs2Z6EgtIaLP/6FKbf548x0V78wZOIyIgMmoansLAQDz/8MAtyRNShLmSVYMnak4hLLoRYJMLkYT54Y3YkC3JEREREdymkrxOWzY3BoEAXaLQCfjh4Ef/98SwqqhuMHRoRkdkyqCinVCpRWFjY0bEQkZmqb9Tg272p+M8PZ1Fe1QB3hS1enx2JqcP9INVz1hoiIiIiuj07Gws8N60/Zo9TwkIqxoWsUixdexLnM0uMHRoRkVkyqPvq4sWL8cILL2DEiBGIiIjo6JiIyIxkXlFh9bWuFADwQIQnpt/vDysLDkBMRERE1NFEIhHuH9QHQZ4OWLUtEblF1fjgx7MI9naEpYUEYpEIIhEgFosgFomu/Y2m5W0sa95OJL72uM39m7bXf38RxGK02l8sbl7XfJwb9mkV5037X1vW1v4iduElIiMxqCi3evVq2Nvb4/HHH0dAQAB69+4NsbhlaxaRSITPPvus3cfOyMjA8uXLcfr0achkMkyZMgUvvvgiLC0tb7ufIAhYvXo1vv/+e5SWliIkJASvvfYaBg4cqNvm5MmTeOKJJ1rt++CDD+KDDz5od6xEZDi1Rovtxy5h+7FsaAUBjnaWmDshBP18nY0dGhEREZHJ6+Nqh3/MicKPBzNw4I9cpOSUGzskoxGhueB3U/GxjeKkqI1C382FxFvvf4uCp1gMr15yhPZ1QB9nGYuERGbEoKJcWloaAKB3796orq7GxYsXW21jyI2koqICc+bMgY+PDz7++GMUFBTg3XffRV1dHZYsWXLbfVevXo2PPvoIr7zyCpRKJb777jvMnTsXW7duhZeXV4tt33nnHfj5+ekeOzk5tTtWIjJcfkk1Vv+ShEtXKwEAMSFumDVWCTsbCyNHRkRERGQ+LKQSPD42CEP798KV4mpotQK0ggBBALSCcO0xoNUKEASh1TLttWWC9sbtm9YLNz7W4g77t17Wcp+b929apu/+gnD78yAA0GiFGx4Zwfl8AICrozUig9wQqXSFr4ecE3EQmTiDinIHDx7s6DgAABs3bkR1dTVWrlwJR0dHAIBGo8GyZcuwcOFCuLu7t7lffX09Vq1ahblz5+LJJ58EAERGRmL8+PFYu3Yt3nzzzRbbBwYGon///p3yGojo1rSCgAMJufjfbxloVGthayXF7HFKDA5t+9omIiIios7n21sO395yY4fRaYRWhUZ9inoti4ut97+2j27/G47ZVsGyOYab99cKgAi4XFSNhOQCFJXXYXdcDnbH5cDJ3goRQa6IUroi0NMRYjELdESmxqCiXGc5fPgwhgwZoivIAUBsbCyWLl2Ko0ePYtq0aW3u98cff6CqqgqxsbG6ZZaWlhgzZgz27dvX2WETkR5KVXVYtzMZSZfKAABhvgrMfTAETvZWRo6MiIiIiEyZqLnbKERANxy2WCoVw8lJhqsFKpxOK8Kp1EKczShBWWU9DiTk4kBCLuxtLTAosKlAF9zXiZOhEZkIg4tyGo0Gu3fvxsmTJ1FSUoLnn38eSqUSlZWVOH78OCIiIuDi4tKuY2ZmZuLhhx9usUwul8PV1RWZmZm33Q9Aiy6pAODv74/169ejrq4O1tbWuuULFixAeXk5XF1dMWHCBLzwwgst1htCKuVNke6e5NqHq8SEPmQFQcDxC1fx9e5U1NSrYSkVY+boQDwQ6cnxMqgVU7wGiPTF/Cdzxvwnc9ac97Y2FrinXy/c068XGtQaJGaV4lRyIf5IK0JlTSMOn72Cw2evwNZaikGBrogOcUM/PwUspd2w0kikJ3O//xtUlFOpVJg/fz7OnTsHW1tb1NbWYtasWQAAW1tbLF++HFOnTsXLL7/c7uPK5a2bTTs4OKCiouK2+1laWsLKqmWLG7lcDkEQUFFRAWtra9jb22P+/PmIjo6GlZUVTpw4gXXr1iEzMxOrVq1qV6w3EotFcHKSGbw/0c3kchtjh9AhVNUN+HTTWRw9ewUAEOTtiJcejYCnm72RI6PuzlSuASJDMP/JnDH/yZzdnP/urnKMivGBWqPF+YvFOHY+HyfO56O8qh5Hz+fj6Pl82FhJEBXSC0PDeyMy2B02Vt2qMxyR3sz1/m/QFfv+++8jPT0da9euRUhICIYOHapbJ5FIMG7cOBw6dKjdRbnOFhoaitDQUN3jIUOGwM3NDW+99RbOnTuH8PBwg46r1QpQqWo6KkwyYxKJGHK5DVSqWmg0WmOHc1fOZRRjzS9JKK9qgEQswpThvpg0zAcSsRhlZdXGDo+6KVO6Bojai/lP5oz5T+ZMn/z3cZPB54EAzLzfH2mXy3EqpRCnUgpRWlmP38/k4fczebCQihHu74yoYDcMDHSBzJqTqFH3Z4r3f7ncRu+WfwYV5Q4cOIDZs2dj2LBhKCsra7Xex8cHmzdvbvdx5XI5KisrWy2vqKiAg4PDbfdraGhAfX19i9ZyKpUKIpHotvvGxsbirbfewoULFwwuygGAWm0ayUPdg0aj7bE5Vd+gwQ+/XsRvp/MAAL2dbTF/Yih8e8shaAG1tme+LupaPfkaILpbzH8yZ8x/Mmf65n9AHwcE9HHAI6MCkJWvQkJqERJSC1FUXnft30WQiEUI9VEgUumKQYEusLe17IJXQGQ4c73/G1SUq6yshKen5y3Xq9VqaDSadh/Xz8+v1dhxlZWVKCoqajVe3M37AUBWVhaCg4N1yzMzM+Hh4XHX48URkX4y8iqwensSCstqAQCjIz0x/T5/WFpwnAsiIiIioo4kFong7+EAfw8H/Ok+f1wurMKpawW6/JIanM8swfnMEqzfDSi9HBGpdENEkCsnWiPqRgwqynl7eyMxMfGW648ePQp/f/92H3fEiBH4/PPPW4wtt3v3bojFYgwbNuyW+0VERMDOzg67du3SFeUaGxuxd+9ejBgx4rbPuWPHDgBA//792x0vETVRa7TYdjQLO45nQxAAJ3srzJ0QgjAfhbFDIyIiIiIyeSKRCN7u9vB2t8e0EX64UlyNhLSmAl1OQRVScsqRklOO7/elwb+PAyKVrohUusLFwTzH8SLqLgwqyk2fPh3vv/8+Bg8ejHvuuQdA002goaEBn3zyCX7//Xe89dZb7T7uzJkz8c0332DRokVYuHAhCgoKsGLFCsycORPu7u667ebMmYMrV65g3759AAArKyssXLgQH3/8MRQKBYKCgrBhwwaUl5dj3rx5uv1eeeUV9O3bF6GhobqJHr766iuMHj2aRTkiA+UVV2PNL0nILmjqej4kzB2PjwmCLcewICIiIiIyCg8XGTxcZJg01AeF5bX441oLuowrKlzMq8DFvAr8cPAi+vayR5TSFZFKN/RS2Bo7bCKzY1BRbs6cObh48SJefvllXYu2V155BeXl5VCr1ZgxYwb+9Kc/tfu4Dg4OWL9+Pd5++20sWrQIMpkM06dPx0svvdRiO61W26p77NNPPw1BELBu3TqUlpYiJCQEa9euhZeXl26bwMBA/PLLL1i3bh0aGxvRp08fPPPMM1iwYIEBZ4HIvGkFAfvjL+N/hzKh1mghs5ZizvhgRAW7GTs0IiIiIiK6xs3RBuMHe2P8YG+UqurwR1rTuHNpueXIvlqJ7KuV2HQoE31cZYgMckWU0g19XGUQiUTGDp3I5IkEQRAM3fnUqVPYs2cPsrOzodVq4e3tjdjYWERHR3dkjN2eRqNFaSlnk6S7J5WK4eQkQ1lZdbce5LKkog5rdyQhJaccANDfzxlPPRgMRzuOT0F3p6dcA0SdgflP5oz5T+bMWPmvqm7AH+lNBbqU7DJotNdLA+5ONohUuiFS6QqfXvYs0FGnMcX7v0Ih03v21bsqylETFuWoo3T3G5IgCDh24Sq+35+G2noNLC3EmDEqEPcN9OAHNXWI7n4NEHUm5j+ZM+Y/mbPukP/VdY04k16MhNQiXMgqhVpzPQ5nuRUigpoKdAGeDhDz//3UgbpD/ne09hTlDOq+evnyZaSnp2PUqFFtrj948CCCgoJuO0MrEfUslTUN+Hp3KhLSigAA/h5yzJ8YCneOPUFERERE1KPJrC0wrH9vDOvfG7X1apzPLMGp1CKczyhBiaoe+05dxr5Tl+Egs0REUNMkEUpvR0jE+hUeiKhtBhXlVqxYgaqqqlsW5b777jvI5XJ88MEHdxUcEXUPZy8W48tdKVBVN0AiFmHKvb6IvcebH8JERERERCbGxkqKmBB3xIS4o6FRgwtZpUhILcSZiyWoqG7Ar6fz8OvpPNjZWGBgoAuilK4I6auAhZTfDYjay6Ci3OnTpzFnzpxbrh8yZAjWr19vcFBE1D3UNaix8cBFHD57BUDTLE5PTwxF3172Ro6MiIiIiIg6m6WFBBFBrogIcoVao0XSpTIkpBbidHoxqmobceRcPo6cy4eNlQQDAlwQGeSGfn4KWFlIjB06UY9gUFFOpVJBJpPdcr2trS3Ky8sNjYmIuoH03HKs2Z6EovI6AMDYaC88PNIPFlJ+wBIRERERmRupRIxwf2eE+zvjCa0WaTnlSEgrQkJaESqqGnAisQAnEgtgaSFGuJ8zIpSuGODvAhsrg8oORGbBoKujd+/e+OOPP/DYY4+1uT4hIQG9evW6q8CIyDjUGi22HsnCzhPZEISmgV3nTghFSF8nY4dGRERERETdgEQsRoiPAiE+Cjw2JgiZeSqcSi1EQmoRSlR1OJVahFOpRZBKRAjzUSBS6YaBgS6ws7EwduhE3YpBRbmJEyfi008/RXh4OGbNmgXxtXGlNBoNvv32W+zcuRPPPPNMhwZKRJ0vt6gKq39JwuXCKgDAsH698OjoINha89ctIiIiIiJqTSwSIcDTAQGeDpgxKgDZBZVIuFaUKyitwdmMEpzNKIFELEKwtyMilW4YFOQKB5mlsUMnMjqRIAhCe3dqaGjAggULcOLECSgUCvj6+gIAsrKyUFpaipiYGKxZswaWluZxkWk0WpSWVhs7DDIBxpoOWqsVsDf+Mn4+nAG1RoCdjQXmjFciUunWZTEQAaY5JTqRvpj/ZM6Y/2TOTDX/BUFAXnE1ElKLkJBaiNyi69+ZRQACvRwRqXRFZJArFHJr4wVKRmWK+a9QyCCR6DfxiUFFOQDQarXYvHkz9u3bh5ycHACAt7c3xo4di6lTp+paz5kDFuWooxjjhlRcXos1O5KRdrkcABDu74ynYoPhYGfVJc9PdCNT/FAm0hfzn8wZ85/Mmbnkf0Fpja6L66WrlS3W+XnIdQU6NydbI0VIxmCK+d+pRbm6ujp88MEHGDx4MEaNGmVQgKaGRTnqKF15QxIEAUfPX8X3+9NQ16CBlYUEMx8IwIgBHhCJRJ363ES3YoofykT6Yv6TOWP+kzkzx/wvqahrmiQitRAXcytwY1HCy82uqUCndEMfl1tPMEmmwRTzvz1FuXYPFGVtbY0ffvgBAQEB7Q6MiLoHVXUD1u9Owen0YgBAgKcD5k8I4a9SRERERETU6ZwdrDE22gtjo71QXlWP02lNY9Cl5pTjcmEVLhdWYcvvWejtbHutBZ0bvN3t2HiATI5Bo7eHhYUhLS2to2Mhoi5wOr0I63elQFXTCIlYhKnDfRE7uC/EYn7AERERERFR13K0s8L9EZ64P8ITlTUNOJNejIS0IiRmlSK/pAbbj2Vj+7FsuDpaIzLIDZFKV/h6yCFmgY5MgEFFuddffx0LFixAUFAQHnroIUilnJmRqLurrVdjw4F0HDmXDwDo4yrD0xND4e1ub+TIiIiIiIiIAHtbSwwf4IHhAzxQU6fG2YxiJKQW4UJmCYrK67A7Lge743LgZG+FiCBXRCldEejpyAYG1GMZNNHDpEmTUFZWhpKSElhaWsLd3R1WVi0HhReJRNi2bVuHBdqdcUw56iid1Z8+7XI51mxPQnFFHUQAxg32xkPD/WAhNZ8JWahnMMUxJYj0xfwnc8b8J3PG/L+z+gYNzmeW4FRqIc5mlKC+QaNbZ29rgUGBTQW64L5OkOo5lhd1D6aY/506phwAODo6wtHREb6+vobsTkRdpFGtxZbfM7H7ZA4EAM5ya8yfGAKlt5OxQyMiIiIiItKLlaUEUcFuiAp2Q6Nag8RLZUhILcSZ9GJU1jTi8NkrOHz2CmytpBgY6IJIpSv6+SpgIZUYO3Si2zKoKPfNN990dBxE1MEuF1Zh9S+JyC1qasV5b//eeHR0IGys2N2ciIiIiIh6JgupBAMDXDAwwAVqjRapOeVISC3EH2lFUNU04tiFqzh24SqsLCUY4O+MSKUb+vspYG3J70HU/TAriUyMVitgd1wONh/OhEYrwN7WAk+OD8agIFdjh0ZERERERNRhpBIxwnwVCPNVYNZYJdJzy5GQWoSEtCKUVdYjLrkQccmFsJCK0c9XgSilGwYEOMPW2sLYoRMBuIuiXFVVFb7//nucPHkSJSUleOuttxAeHo7y8nJs3rwZo0aNQt++fTsyViK6g6LyWqzZnoT03AoAwKBAF8wZHwy5zNLIkREREREREXUesVgEpbcTlN5OmDk6EFn5qqYCXWohisrrcDq9GKfTiyERixDqo0Ck0hWDAl1gb8vvSmQ8BhXlrl69ilmzZuHq1avo27cvMjMzUV3d1EXO0dERGzduRF5eHv7+9793aLBE1DZBEPD7uXxsOJCO+gYNrCwleGx0IO7t3xsiThVORERERERmRCwSwd/DAf4eDvjTff64XFiFU9cKdPklNTifWYLzmSVYvxtQejk2jVendGNjBupyBhXlVqxYgerqamzZsgUKhQJDhw5tsX706NH47bffOiI+IrqDiuoGrN+VgjMXiwEAQZ4OmDcxFK6ONkaOjIiIiIiIyLhEIhG83e3h7W6PaSP8cKW4GglpTQW6nIIqpOSUIyWnHN/tS0NoXydEh7gjUukKGbu4UhcwqCh39OhRzJkzBwEBASgrK2u13svLC/n5+XcdHBHdXkJqEdbvTkFVbSOkEhGmjfDH2GgviMVsHUdERERERHQzDxcZPFxkmDTUB4XltfgjtQjxKQXIyq9E4qUyJF4qwzd7UtHPV4GYUHcMDHDhZHnUaQzKrLq6OigUiluub+7KSkSdo7Zeje/3p+Ho+asAAC83Ozw9MRSebnZGjoyIiIiIiKhncHO0wfjB3hg/2BuFZTXXJoYoQG5RNc5mlOBsRgkspGKE+ztjcIg7wv2dYWkhMXbYZEIMKsr5+/sjPj4eM2fObHP9/v37ERoaeleBEVHbUrLLsHZHMkpUdRABGH+PN6be6wcLqdjYoREREREREfVIbk62mDjUBxOH+iCvuBrxyQU4mVyIgtKaaxNGFMHKUoJBgS6ICXZHmK+C38HorhlUlJszZw4WL14MpVKJ2NhYAE0DzWdnZ2PlypU4c+YMPv744w4NlMjcNao12HQoE/viL0MA4OJgjfkTQxHk5Wjs0IiIiIiIiExGHxcZ+gz3w5R7fZFTUIW4lALEJRWiRFWHE4kFOJFYAFsrKSKUrogJcUNIXydIxCzQUfuJBEEQDNnxs88+w8qVKyEIArRaLcRiMQRBgFgsxgsvvIAFCxZ0dKzdlkajRWkpu+zS3ZNKxXBykqGsrBpqtVa3PPtqJdZsT0JecVOejRjggRmjAji2AZmcW10DROaA+U/mjPlP5oz53zMIgoDMKyqcTC5AfEohKqoadOvsbS0QpXRDTIgbAr0cIRZxjG99mWL+KxQySCT6FWkNLsoBwJUrV7B3715kZ2dDq9XC29sbY8eOhZeXl6GH7JFYlKOOcvMNSasVsOtkNrb8ngWNVoBcZoknY4MxMMDF2KESdQpT/FAm0hfzn8wZ85/MGfO/59FqBaTnluNkciFOpRSiqrZRt87J3qqpQBfqBr/ecohYoLstU8z/TivK1dfX48CBA8jNzYWjoyPuu+8+uLm5GRyoqWBRjjrKjTekvKIqrN2ejIt5FQCAiCBXPDFeCbmtpZGjJOo8pvihTKQv5j+ZM+Y/mTPmf8+m1miRkl2GuORCJKQVobZerVvn4mCNmBB3xIS4wcvNjgW6Nphi/renKKd337eSkhLMnDkTubm5aK7j2djY4JNPPsHQoUMNi5SIWhEEAQcTcrFhfzrqGzWwsZLgsdFBGNqvF2/iRERERERE3YhUIkY/P2f083PG7HFKXMgqQVxyIc6kF6O4og47T2Rj54ls9FLYIibEDYND3dHbWWbssKmb0Lul3Ntvv40NGzZgzpw5uOeee5CdnY1PP/0UdnZ22L9/f2fH2a2xpRzdDUEQUFXbiBJVHcqqGnA8sQCnkgsAAMHejpg7IQQuDjZGjpKoa5jiL2VE+mL+kzlj/pM5Y/6bpvpGDc5llCAuqQBnM0qg1lx/b73c7BAT4oboEHe4OZr3dz1TzP9OaSl35MgRTJkyBa+++qpumYuLC/76178iMzMTfn5+7Y+UyAxoBQEVVQ0oUdWhpKIOJao6FFdc/3dJRR3qGzUt9rGQiPHwSD+MjvbiIKFEREREREQ9jJWFBNHBbogOdkNtvRpn0otxMrkAiVmluFxYhcuFVdh0KBO+veUYHOKGqGA3KOTWxg6bupjeRbn8/HxERka2WBYZGQlBEFBSUsKiHJktjVaLMlX99WLbTUW3UlUd1Jo7N0iVyyzh6mgN715yjInyRC8n2y6InoiIiIiIiDqTjZUUQ/r1wpB+vVBV24g/0ooQl1yA5OwyZOWrkJWvwsaDFxHk6YCYUHdEKd0gl3EscXOgd1GuoaEBVlZWLZZZWjYliVqtbmsXIpPQqNagRFV/Uyu3Wt3jssoGaO/QC1wkapqFx0VuDWeHa3/k1nBxsLn2bytYSCUm2XSXiIiIiIiImtjZWGDEAA+MGOCBiuoGnEopRFxyAdJzK5B27c93+9IQ2tcJ0SHuiFS6QmZtYeywqZPoXZQDgLy8PCQmJuoeV1ZWAgCys7Mhl8tbbR8WFnaX4RF1vroGNUoqrrdyu7mLaUV1wx2PIRGL4HxDwa25+OZyrfjmaG8FqZ59yomIiIiIiMj0Ocgs8UCkJx6I9ESpqg7x1wp0WfmVSLxUhsRLZfhmTyr6+SoQE+qOgQEusLFqVxmHujm9J3oIDg5uc+ZHQRBaLW9elpyc3DFRdnOc6KH7EgQB1XW3Kro1tXarrrtzS09LC7Gu6ObiYANnudW14ltTSzcHO8sOGfuNLeXI3PEaIHPG/Cdzxvwnc8b8p5sVltUgPqUQJ5MKkVtUpVtuIRUj3N8Zg0PcEe7vDEsLiRGj7BimmP+dMtHDO++8Y3BARJ1FEASoqhtQ3Fxsq6hr9e/6Bs0dj2NrJb2hS+n17qXNrd3sbCzaLEoTERERERERdSQ3J1tMGOKDCUN8kFdcjfjkApxMLkRBaQ0SUouQkFoEK0sJBgW6ICbYHWG+ClhI2TOrJ9K7pRzdGlvKdR6NVovyyoYWLduaW7s1tXyrbzG19K3IbS3aGMftevHN1rp7NAE2xV8JiNqD1wCZM+Y/mTPmP5kz5j/pQxAEXC6swsnkAsQlFaJEVadbZ2slRYTSFTEhbgjp6wSJuOcU6Ewx/zulpRxRZ2hUa1FaeUOR7aaiW1ll/Z0nUQDgaG/VYiy3G/+tkFvDygSa9RIREREREZF5EolE8Ha3h7e7PaaP9EdmvgpxSYWISylARVUDjpzLx5Fz+bC3tUCU0g0xIW4I9HLskGGWqPOwKEedqr5Bc0N30trr/742kYKqqgF3aqopEYugkFu1aNmma+3mYA0FJ1EgIiIiIiIiMyESieDv4QB/DwfMGBWA9NxynEwuxKmUQlTWNOLX03n49XQeHO0sER3sjphQN/j1lnNIpm6IRTkymCAIqKlX33Ist5KKOlTVNt7xOBZSsW6W0rbGdXO0s4JYzJsHERERERER0Y3EYhGU3k5Qejvh8TGBSM4uQ1xSIRLSilBe1YB9py5j36nLcHGwRkyIO2JC3ODlZscCXTfBohzdkiAIUNU03tSl9IZx3VR1qK2/8yQKNlYSOMttWhTebiy62dtyEgUiIiIiIiKiuyERi9HP1xn9fJ0xe5wSiVmliEsuwOn0YhRX1GHniWzsPJGNXgpbxIS4ISbEHR4uMmOHbdZYlKMWMvIqsO3oJRSV16JUVYcGPQZatLOxuF5ou7Hodq3Fm621RRdETkRERERERERAU4+0gYEuGBjogvpGDc5llCAuuQBnL5bgamkNth29hG1HL8HT1Q6DQ90QHeION0cbY4dtdliUoxaOXbiK85klusciAA52ltcKbTatu5jKrWFlyUkUiIiIiIiIiLojKwsJooPdEB3shtp6Nc6kF+NkcgESs0qRW1SF3ENV2HQoE7695Rgc4oaoYDco5NbGDtsssChHLTw0wg9Kb0fY2VjAxcEaTvbWsJByEgUiIiIiIiKins7GSooh/XphSL9eqKptxB9pRYhLLkBydhmy8lXIyldh48GLCPJ0QEyoO6KUbpDLLI0dtskSCYJwp8kv6Q40Gi1KS6uNHQaZAKlUDCcnGcrKqqHWo+swkanhNUDmjPlP5oz5T+aM+U/dQUV1A06lFCI+uQBpuRW65SIRENLXCTEh7ohUukLWwcNTmWL+KxQySCT6NW5iSzkiIiIiIiIiIjPmILPEA5GeeCDSE6WqOsSnFCIuuRBZ+SokXSpD0qUyfLMnFf18FYgJdcfAABfYWLGkdLd4BomIiIiIiIiICACgkFtjXIw3xsV4o7C8FvHJBTiZVIjcoiqczSjB2YwSWEjFCPd3xuAQd/T3d4aVBceaNwSLckRERERERERE1Iqbow0mDPHBhCE+uFJcjbjkApxMLkRBaQ0SUouQkFoEK0sJBgW4ICbEHWG+Co5L3w7driiXkZGB5cuX4/Tp05DJZJgyZQpefPFFWFrefmBBQRCwevVqfP/99ygtLUVISAhee+01DBw4sMV2BQUFWL58OY4cOQILCwuMGTMGr732Guzs7DrxVRERERERERER9VweLjJMHe6HKff64nJhFU4mFyA+uRDFFXU4kVSAE0kFsLWSIkLpipgQN4T0dYJEzALd7XSriR4qKiowYcIE+Pj4YOHChSgoKMC7776LyZMnY8mSJbfd94svvsBHH32EV155BUqlEt999x2OHTuGrVu3wsvLCwDQ2NiIadOmAQBeeukl1NXV4b333kNwcDBWrVplcNyc6IE6iikOcknUHrwGyJwx/8mcMf/JnDH/qScTBAGZ+SrEJRUiLqUAFVUNunX2thaIUrohJsQNgV6OEItErfY3xfzvsRM9bNy4EdXV1Vi5ciUcHR0BABqNBsuWLcPChQvh7u7e5n719fVYtWoV5s6diyeffBIAEBkZifHjx2Pt2rV48803AQB79uxBeno6du7cCT8/PwCAXC7HvHnzcO7cOYSHh3f2SyQiIiIiIiIiMgkikQj+Hg7w93DAjFEBSM8tR1xyIeJTClFZ04hfT+fh19N5cLSzRHSwO2JC3eDXWw5RGwU6c9St2hEePnwYQ4YM0RXkACA2NhZarRZHjx695X5//PEHqqqqEBsbq1tmaWmJMWPG4PDhwy2Or1QqdQU5ABg2bBgcHR1x6NChjn0xRERERERERERmQiwWQenthNnjlPjgL8Pw8owBuLd/b9hYSVFe1YB9py7jn18n4NXPj+N/v2Ugp6AS3ajzplF0q5ZymZmZePjhh1ssk8vlcHV1RWZm5m33A9Ci2AYA/v7+WL9+Perq6mBtbY3MzMxW24hEIvj6+t72+EREREREREREpB+JWIx+vs7o5+uM2eOUSMwqRVxyAU6nF6O4og47T2Rj54ls9Ha2xcR7/XBvv7Z7Rpq6blWUU6lUkMvlrZY7ODigoqLitvtZWlrCysqqxXK5XA5BEFBRUQFra2uoVCrY29u3+/j6kHJ2EeoAzf3O9e1/TmRqeA2QOWP+kzlj/pM5Y/6TqZNKxYgKcUNUiBvqGzU4e7EYJxMLcCa9GPklNVi99QL6+yngKLv9BJ+mqFsV5XoqsVgEJyeZscMgEyKX2xg7BCKj4jVA5oz5T+aM+U/mjPlP5qKXmxzjhvqhpq4RcYlXAZEIvp5Oxg7LKLpVUU4ul6OysrLV8oqKCjg4ONx2v4aGBtTX17doLadSqSASiXT7yuVyVFVVtXn83r17Gxy3VitApaoxeH+iZhKJGHK5DVSqWmg0pjHzDFF78Bogc8b8J3PG/CdzxvwncxYR6GJy+S+X2/TM2Vf9/Pxaje1WWVmJoqKiVmPB3bwfAGRlZSE4OFi3PDMzEx4eHrC2ttZtl5aW1mJfQRCQlZWFYcOG3VXspjJ1L3UPGo2WOUVmjdcAmTPmP5kz5j+ZM+Y/mTNzzf9u1Wl9xIgROHbsGFQqlW7Z7t27IRaLb1s0i4iIgJ2dHXbt2qVb1tjYiL1792LEiBEtjp+SkoJLly7plh0/fhzl5eUYOXJkx74YIiIiIiIiIiKiW+hWRbmZM2dCJpNh0aJFOHLkCDZt2oQVK1Zg5syZcHe/PhPHnDlzMGbMGN1jKysrLFy4EOvWrcP69etx/Phx/PWvf0V5eTnmzZun227cuHEIDAzEX/7yF/z666/YuXMnXn/9ddx3330IDw/v0tdKRERERERERETmq1t1X3VwcMD69evx9ttvY9GiRZDJZJg+fTpeeumlFttptVpoNJoWy55++mkIgoB169ahtLQUISEhWLt2Lby8vHTbWFhYYM2aNVi+fDlefvllSKVSjBkzBq+//nqXvD4iIiIiIiIiIiIAEAmCIBg7iJ5Oo9GitLTa2GGQCZBKxXBykqGsrNos+9MT8Rogc8b8J3PG/Cdzxvwnc2aK+a9QyPSe6KFbdV8lIiIiIiIiIiIyByzKERERERERERERdTF2X+0AgiBAq+VppI4hkYih0ZhGs10iQ/AaIHPG/Cdzxvwnc8b8J3NmavkvFosgEon02pZFOSIiIiIiIiIioi7G7qtERERERERERERdjEU5IiIiIiIiIiKiLsaiHBERERERERERURdjUY6IiIiIiIiIiKiLsShHRERERERERETUxViUIyIiIiIiIiIi6mIsyhEREREREREREXUxFuWIiIiIiIiIiIi6GItyREREREREREREXYxFOSIiIiIiIiIioi7GohwREREREREREVEXY1GOiIiIiIiIiIioi7EoR0RERERERERE1MVYlCPqBnbt2oVnn30WI0aMwMCBAzFlyhT873//gyAIxg6NqMtVV1djxIgR/7+9ew+q6rrbOP5FoxS5CBgvVYSiJATkICoKqJGItEaDKEQjNVO0lqDEpARDDcaCQY2mVlsdVEaoJDXxhgHFW3SUOkySBq2mxNhQI3gpl7Ra0XDTSIT3jwznLcELKpxj5PnMOONZa521n31mMx5/rL027u7ufP755+aOI2ISO3bsYNKkSRgMBvz8/IiKiuLatWvmjiXS5nJzc5kyZQqDBg1i5MiRxMbGUlJSYu5YIq3u/PnzJCUlMXHiRDw9PQkJCbnpuO3btzN27FgMBgOhoaEcPnzYxElFWt+drv/q6mpSUlKYPHkyvr6+DB8+nNmzZ3Pq1CkzJTYdFeVEHgDvvPMOVlZWJCQkkJqayqhRo0hMTGTt2rXmjiZicuvWrePGjRvmjiFiMqmpqSxevJjx48ezYcMGFi1ahJOTk34O5KF35MgRXnrpJdzc3Fi7di2vv/46//znP5k5c6aK0vLQOX36NHl5ebi4uNC/f/+bjtm7dy+JiYmMGzeO9PR0fHx8eOmllygoKDBtWJFWdqfrv7y8nG3btjFixAhWrVrF4sWLqaqqYurUqRQXF5shselYNGgpjojZVVRU4Ojo2KQtMTGRffv28be//Y0OHVQ/l/ahuLiYyZMn89prr7Fw4ULef/99DAaDuWOJtJkzZ84wYcIE1q1bR2BgoLnjiJhUUlISH3/8MYcOHcLCwgKA/Px8pk+fzqZNm/D19TVzQpHWU19fb/xOn5CQwMmTJ9mzZ0+TMWPHjsXLy4uVK1ca2yIiIrC1tSU9Pd2keUVa052u/9raWiwsLLCysjK21dTUEBQUREhICImJiSbPbCr6n77IA+D7BTkADw8Pqqurqa2tNUMiEfNYsmQJERERuLq6mjuKiElkZ2fj5OSkgpy0S99++y3W1tbGghyAra0tgLbwkIfOnX7JXlJSwrlz5xg3blyT9vHjx/PJJ59w/fr1town0qbudP136dKlSUEOwNraGmdnZy5cuNCW0cxORTmRB9Tx48fp2bMnNjY25o4iYhL79+/nyy+/ZM6cOeaOImIyn332GY8//jjr1q0jICAALy8vIiIi+Oyzz8wdTaTNhYeHU1xczKZNm6iqqqKkpIQ//OEPeHp6MnjwYHPHEzGpM2fOADT7xWT//v2pq6vTXovS7lRWVnL69Gn69etn7ihtSkU5kQfQsWPH2LdvHzNnzjR3FBGTuHr1Km+99RZxcXEqREu7cvHiRT766CNycnJYuHAha9euxcLCgpkzZ3Lp0iVzxxNpU76+vqxZs4aVK1fi6+tLcHAwly5dIj09nY4dO5o7nohJff311wDY2dk1aW983dgv0l78/ve/x8LCgp///OfmjtKmVJQTecD8+9//Ji4uDj8/PyIjI80dR8QkUlNT6datG88++6y5o4iYVENDA7W1taxevZqnn36awMBAUlNTaWho4L333jN3PJE29emnnzJv3jyee+45/vznP7N69Wrq6+uJjo7Wgx5ERNqxrKwsMjMzSUpKolevXuaO06YeMXcAEfl/lZWVvPDCC9jb25OSkqIHPEi7UFZWRkZGBmvXrqWqqgrAuJdibW0tNTU1WFtbmzOiSJuxs7PD3t6eJ554wthmb2+Pp6cnRUVFZkwm0vaWLFmCv78/CQkJxjYfHx+eeuopcnJymDp1qhnTiZhW165dAaiqqqJ79+7G9srKyib9Ig+7vLw8kpKSePHFFwkLCzN3nDanopzIA+LatWvMmjWLqqoqtm3bZtzoWORhV1paSl1dHdHR0c36IiMjGThwIJmZmWZIJtL23Nzc+Ne//nXTvm+++cbEaURMq7i4mDFjxjRp69WrFw4ODrf8uRB5WDXum3XmzJkme2idOXOGTp060bdvX3NFEzGZgoICYmNjmTRpErGxseaOYxIqyok8AL799lteeeUVzpw5w6ZNm+jZs6e5I4mYjIeHBxs3bmzSVlhYyLJly0hOTsZgMJgpmUjbGz16NNnZ2RQWFuLh4QHA5cuX+cc//sGMGTPMG06kjfXu3ZsvvviiSVtZWRmXL1+mT58+ZkolYh59+/blJz/5Cfv37yc4ONjYvm/fPgICAujcubMZ04m0vaKiImbNmoW/vz/JycnmjmMyKsqJPACSk5M5fPgwCQkJVFdXU1BQYOzz9PTUP8LyULOzs8PPz++mfQMGDGDAgAEmTiRiOsHBwRgMBn79618TFxeHpaUlaWlpdO7cmWnTppk7nkibioiIYOnSpSxZsoSgoCCuXLli3GN03Lhx5o4n0qquXr1KXl4e8F3xubq6mv379wMwbNgwHB0defnll4mPj8fZ2Rk/Pz/27dvHiRMntMeo/ODd6fpvaGjgV7/6FZaWlkyfPp2TJ08a32tjY4Obm5tZcpuCRUNDQ4O5Q4i0d0FBQZSVld20Lzc3FycnJxMnEjGvI0eOEBkZyfvvv6+VcvLQq6ioYNmyZRw+fJi6ujp8fX2ZP3/+Q/0FVAS+e9DJ1q1b2bJlCyUlJVhbW+Pj40NcXBz9+/c3dzyRVlVaWtrsdu1GGzduNP6Ccvv27aSnp1NeXo6rqytz585l9OjRpowq0urudP0Dt3zI4bBhw3j33XfbLJu5qSgnIiIiIiIiIiJiYnq0o4iIiIiIiIiIiImpKCciIiIiIiIiImJiKsqJiIiIiIiIiIiYmIpyIiIiIiIiIiIiJqainIiIiIiIiIiIiImpKCciIiIiIiIiImJiKsqJiIiIiIiIiIiYmIpyIiIiImJSCQkJBAUFNWlzd3cnJSXFTInaRnZ2Nu7u7pSWlpo7ioiIiDyAVJQTERERuQuNhRaDwcB//vOfZv2/+MUvCAkJMUOyljt48CBRUVH4+fnh5eXFyJEjiY2N5ZNPPjF3NKNPP/2UlJQUKisr73mOwsJC4uPjCQwMxMvLi2HDhjFjxgyysrK4ceNGK6YVERERuXuPmDuAiIiIyA/R9evXSUtLIzEx0dxRWqyhoYHXX3+d7OxsPD09+eUvf8mjjz7KxYsXOXjwIDNmzGDLli0MHjzY5NlOnDhBx44dja///ve/s2bNGsLCwrCzs7vr+bZv387ChQvp1q0bEydOxMXFhZqaGvLz81mwYAEXL15k9uzZrXkKIiIiIndFRTkRERGRe+Dh4UFmZibR0dH07NnT3HFaJCMjg+zsbKZPn878+fOxsLAw9sXExLBz504eeeTWXw9ra2vp0qVLm2SztLRstbkKCgpYuHAhPj4+pKWlYWNjY+ybMWMGn3/+OadPn77v49TX11NXV9eq2UVERKT90O2rIiIiIvdg1qxZ1NfXk56efssxpaWluLu7k52d3azv+3uopaSk4O7uztmzZ4mPj2fIkCH4+/uzatUqGhoa+Oqrr4iJiWHw4MGMGDGCjIyMu8p77do10tLS6NevH6+99lqTglyjSZMm4e3tDfz/bbpHjx7ljTfeICAggMDAQOPYvLw8pk2bho+PD4MGDSI6Ovqmha5Dhw4REhKCwWAgJCSEgwcP3jTf/34eKSkpLF++HIAxY8bg7u5+V3uzrVmzBgsLC1asWNGkINfIYDAQHh5ufL1hwwYiIiLw8/PD29ub8PBw9u/ff9OMixYtYteuXTzzzDMYDAY+/PBDAE6fPk1kZCTe3t6MGjWKdevWUV9f36K8IiIi0j5ppZyIiIjIPXBycmLixIlkZmbywgsvtNpqubi4OPr378+rr75KXl4eqamp2Nvbs3XrVvz9/YmPj2f37t387ne/w2AwMHTo0BbNe/z4ca5cuUJkZGST20TvJDk5GUdHR+bMmUNtbS0AO3fuJCEhgZEjRxIfH8/Vq1fZsmUL06ZNY8eOHTg5OQHw0Ucf8fLLL+Pm5sarr77K5cuXmT9/Pr169brtMX/6059y7tw59uzZw/z583FwcADA0dHxjnmvXr1Kfn4+vr6+9O7du0XnuHHjRoKCgpgwYQJ1dXXs3buX2NhY1q9fz1NPPdVkbH5+Ph988AHPP/88Dg4O9OnTh4sXLxIZGcmNGzeIjo7GysqKzMxMraATERGR21JRTkREROQexcTEkJOTQ3p6Or/97W9bZU5vb28WLVoEwNSpUwkKCuKtt95i7ty5REdHAxASEsKTTz5JVlZWi4tyxcXFwHerve5G165deeedd4yFvJqaGt58802mTJnC4sWLjePCwsJ4+umnWb9+vbF9xYoVdOvWjc2bN2NrawvAsGHDmDlzJn369LnlMZ944gk8PT3Zs2cPwcHBxiJfS5w/f566ujoef/zxFr/nwIED/OhHPzK+fv755wkPD+ftt99uVpQ7e/Ysu3fvxs3Nzdi2dOlSKioq2L59u3GlYVhYGD/72c9anEFERETaH92+KiIiInKP+vbtS2hoKJmZmVy4cKFV5pw8ebLx7x07dsTLy4uGhoYm7XZ2dri6ulJSUtLieaurqwGwtra+qzzPPfdck5V1f/3rX6msrOSZZ56hoqLC+KdDhw4MHDiQI0eOAHDhwgUKCwsJCwszFuQARowY0aSg1dru5Tz/tyD39ddfU1VVxZAhQ/jiiy+ajR06dGiz/Hl5efj4+BgLcvDdqr4JEybcbXwRERFpR7RSTkREROQ+vPjii+zatYu0tLRWWS33/VsubW1tsbS0bHbrpq2tLVeuXGnxvI17q9XU1NxVnu+vUjt37hwA06dPv+1xysvLAXBxcWk2xtXV9aYFr9ZwL+d5+PBhUlNTKSws5Pr168b2m+27d7NVe+Xl5QwcOLBZu6ura4sziIiISPujopyIiIjIffjf1XKNt5c2ullRB+DGjRu3nK9Dh+Y3MtxqD7iGhoYW5+zXrx8Ap06dIjg4uMXv+/6+aI3HXL58Od27d282/m72q2sLLi4uPPLII3z55ZctGn/s2DFiYmIYOnQoCxcupHv37nTq1ImsrCz27NnTbPz/rqoTERERuR8qyomIiIjcp5iYGHbt2tXsSaxdu3YFoLKyskl74yoyUxoyZAhdu3Zl7969zJ49+56LZ3379gWgW7duDB8+/JbjGlf8nT9/vlnf2bNn73icWxU078TKygp/f3/y8/P56quv+PGPf3zb8QcOHMDS0pINGzbQuXNnY3tWVlaLj9m7d+97Pk8RERFpv7SnnIiIiMh9cnZ2JjQ0lG3btnHx4kVju42NDQ4ODhw7dqzJ+M2bN5s6IlZWVkRFRVFcXMyKFStuusouJyeHEydO3HaeJ598EhsbG9avX09dXV2z/oqKCgB69OiBh4cHO3bsoKqqytj/8ccfU1RU1KK8QJP3ttScOXNoaGhg3rx5N72N9eTJk+zYsQP4bmWfhYVFk9WLpaWl5Obmtvh4gYGBFBQUNPnsKioq2L17911nFxERkfZDK+VEREREWsHs2bPJycnh7NmzPPbYY8b2KVOmkJaWxoIFC/Dy8uLYsWNmW0EVFRVFUVERGRkZHDlyhLFjx/Loo4/y3//+l0OHDnHixAm2bt162zlsbGx44403mDdvHuHh4YwfPx5HR0fKy8vJy8tj8ODBJCUlATB37lxmzZrFtGnTePbZZ7ly5Qrvvfcejz32GLW1tbc9zoABAwD44x//yPjx4+nUqROjR4+mS5cudzzPxgzJycmMGzeOiRMn4uLiQk1NDUePHuUvf/kLr7zyCvBdQe3tt98mKiqKkJAQLl26xObNm3F2dubUqVMt+FS/+1xzcnKIiooiMjISKysrMjMz6d27d4vnEBERkfZHRTkRERGRVuDi4kJoaKhxBVajOXPmUFFRwYEDB/jggw8YNWoUf/rTnwgICDB5xg4dOrB8+XLGjBlDZmYmGRkZVFdX4+DgwNChQ/nNb37DoEGD7jjPhAkT6NGjB2lpaWzYsIHr16/Ts2dPfH19CQ8PN44bNWoUq1evZtWqVaxcuRJnZ2eWLVtGbm4uR48eve0xvL29iY2NZevWrXz44YfU19eTm5vboqIcQEREBAaDgYyMDHbu3Mnly5fp0qULnp6eLFu2jNDQUAACAgJ48803SU9PZ+nSpTg5OREfH09ZWVmLC2o9evRg48aNLFmyhLS0NOzt7YmIiKBHjx4sWLCgRXOIiIhI+2PRcDc7BIuIiIiIiIiIiMh9055yIiIiIiIiIiIiJqbbV0VERER+4CoqKpo8qOD7OnXqhL29vekCtZGqqiquXbt22zHdu3c3URoRERGR+6PbV0VERER+4IKCgigrK7tl/7Bhw3j33XdNmKhtJCQkNNuz7/v0YAURERH5oVBRTkREROQH7vjx43zzzTe37Lezs8PLy8uEidpGUVERFy5cuO2Y4cOHmyiNiIiIyP1RUU5ERERERERERMTE9KAHERERERERERERE1NRTkRERERERERExMRUlBMRERERERERETExFeVERERERERERERMTEU5ERERERERERERE1NRTkRERERERERExMRUlBMRERERERERETExFeVERERERERERERM7P8AbqO896MRSIAAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers: 597\n",
            "\n",
            " Interest_Rate \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-8836c912d7c9>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[col_name] = pd.to_numeric(df[col_name], errors='coerce')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers: 516\n",
            "\n",
            " Delay_from_due_date \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-8836c912d7c9>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[col_name] = pd.to_numeric(df[col_name], errors='coerce')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers: 29\n",
            "\n",
            " Num_Credit_Inquiries \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-8836c912d7c9>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[col_name] = pd.to_numeric(df[col_name], errors='coerce')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers: 479\n",
            "\n",
            " Credit_Utilization_Ratio \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-8836c912d7c9>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[col_name] = pd.to_numeric(df[col_name], errors='coerce')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers: 1\n",
            "\n",
            " Total_EMI_per_month \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-8836c912d7c9>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[col_name] = pd.to_numeric(df[col_name], errors='coerce')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers: 1381\n",
            "\n",
            " Num_of_Delayed_Payment \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-8836c912d7c9>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[col_name] = pd.to_numeric(df[col_name], errors='coerce')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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JCQlyc3NzKDJL/5zH3GjevLm6desmi8Wi4sWLq2rVqvLy8rJv/+OPPzRlyhR9+eWXmdYSPHfunP3PERERKlOmjFavXq3w8HDZbDZ99tlnat68eaYxV65cOXshM0PJkiVv+F5LTExUcnKyFi9erMWLF2f5ehITEx0e33PPPQ6PM/4BIyfvXwAAzIpiHgAAt5njx48rJSUl0xf/axUtWlQff/yxtm/frq+//lrffPONYmNjtXjxYs2bN09Wq/WGx8nJOnd5lZ6eflOZnCG74xj/ullGQenXr59Wr16t2bNnq0WLFpm2/7tYkiE9PT3L9uxer9nPw83av3+/pH+KyTabTXfccYfefPPNLPtfb/3CVatWafjw4WrRooV69OihO+64Q1arVTNnzsw046tt27YaP3681qxZo759+2r16tUKCQlxKMDZbDYFBgba12z7t38XuZypXLlyatCgQZbb0tPT9fTTTyspKUk9e/ZUQECAvLy8dOLECQ0fPtxhNq/ValX79u21ZMkSjR07Vjt37tTJkyft6wJeK7fvtYzjdejQQQ899FCWff9905Ts/lHC1d6/AABkhWIeAAC3mVWrVkmSIiMjr9vPzc3NfpndiBEjNGPGDE2aNEnbt29XgwYNsi0K5da1d5KUrn6pPnLkiMOX8OxmwP3xxx+qUKGC/XFOsvn5+Wnr1q06d+6cw0yh+Ph4+3Zn8PPz0759+2Sz2RwKCRnH+fdModyqWLGiOnTooMWLF2c5683b2zvbc3ir3XPPPTp06FCmdmefk+ycP39eGzdu1N13322fIVmxYkVt3bpVtWvXznFBet26dapQoYKmTZvm8B7MuCHEtUqVKqUmTZpozZo1at++vXbu3JnpZisVK1bU//73P4WHh1/3Pe3n5yebzabff//doRiYcR6d7bffftPhw4f1+uuvq2PHjvb2LVu2ZNk/Ojpa8+bN05dffqm4uDj5+vre8OdPTvj6+qp48eKy2WzZFiBzw9k/4wAAuFVYMw8AgNvI1q1bNX36dJUvXz7LmTEZzp49m6ktODhYknT58mVJUrFixSQ577K0Tz/91OHyvLVr1+rUqVNq1KiRva1ChQr6+eef7Rkk6auvvtKff/7psK+cZGvUqJHS09P18ccfO7TPnz9fFovF4fh50ahRI506dUqxsbH2trS0NC1YsEBeXl564IEHnHIc6ersvLS0NM2ZMyfTtooVKyolJUX/+9//7G0nT57Uhg0bnHb8m9W4cWPt3r1bu3btsrelpqZqyZIl8vPzU9WqVfPt2BcvXtQLL7ygs2fPqm/fvvbCTZs2bZSenu5wp9gMaWlp131PZcwgu3Z2188//6yffvopy/7R0dE6cOCAJk6cKKvV6rBeXEaWEydOaMmSJVnmT01NlST7e3TBggUOfT744INss+ZFRjH62tdpGEa2dzKuVq2agoKCtGzZMq1fv15t27aVu7vz5gxYrVa1bt1a69at02+//ZZp+78vsb1ZxYoVU0pKSl7jAQBwyzEzDwAAFxUXF6f4+Hilp6frr7/+0vbt27Vlyxbdc889eu+991SkSJFsn/vuu+/qhx9+UOPGjeXn56fTp09r4cKFKleunOrUqSPpalHI29tbixYtUvHixeXl5aUaNWo4zJDLCR8fHz3++OPq1KmTTp8+rQ8++ECVKlVSly5d7H06d+6sdevWqWfPnmrTpo1+//13rVmzJtMlwznJ1qxZM9WrV0+TJk1SQkKCgoKCtGXLFm3atEndu3e/7uXIOdG1a1ctXrxYw4cP1y+//CI/Pz+tW7fOPiPr3+uH5UXG7LyVK1dm2hYVFaU333xTAwYMUExMjC5evKhPPvlE/v7+mW4SkN969+6tzz//XL169VJMTIx8fHz06aef6tixY5o6depNr894IydOnLDPSE1NTdXBgwftxeJnnnlGjz76qL1v3bp11bVrV82cOVN79+5VRESEPDw8dPjwYa1du1YvvviiHnzwwSyP06RJE61fv17PPvusmjRpomPHjmnRokWqWrWqvfB2rcaNG6tUqVJau3atGjVqlGkdy+joaH3xxRf673//q+3bt6t27dpKT09XfHy81q5dqzlz5ig0NFTBwcFq166dFi5cqJSUFIWFhWnbtm2ZZrs6S0BAgCpWrKjXX39dJ06cUIkSJbRu3brrFjo7duyo119/XZKu+w8JufX8889r+/bt6tKlizp37qyqVasqKSlJv/zyi7Zu3aodO3bkeJ/33XefYmNjNX78eIWGhsrLy0vNmjVzenYAAJyNYh4AAC4q49I+Dw8PlSpVSoGBgRo5cqQ6dep0w8JRs2bNlJCQoOXLl+vMmTMqXbq06tatq4EDB9oXn/fw8NCECRP09ttva+zYsUpLS9P48eNzXczr27ev9u3bp1mzZun8+fMKDw/Xf//7X/ssO0lq2LChhg8frvfff1/jxo1TSEiIZsyYYS8SZMhJNjc3N7333nuaMmWKYmNjtWLFCvn5+emFF17QM888k6vXkpWiRYtqwYIFevPNN7Vy5UqdO3dO/v7+Gj9+fI7u+nuzMtbO+/daeKVLl9a0adM0YcIEvfHGGypfvryee+45HTly5JYX8+68804tWrRIb7zxhj766CNdunRJQUFBmjFjhpo0aeK04+zdu1cvvPCC/WYOd999t5o2barOnTvb78J7rZdfflkhISFatGiRJk2aJKvVKj8/P3Xo0EG1a9fO9jidOnXSX3/9pcWLF+vbb79V1apV9cYbb2jt2rVZFpM8PT0VFRWlhQsXOtz4IoObm5veffddzZ8/X6tWrdKGDRtUrFgxlS9fXjExMfL397f3HTdunEqXLq01a9Zo06ZNqlevnmbNmqXGjRvn8qxlz8PDQzNmzNCrr76qmTNnqkiRImrZsqWeeOKJLF+HJLVv315vvvmmKlSokOU5z6s777xTS5cu1bvvvqsNGzbok08+UalSpVS1alUNHTo0V/t8/PHHtXfvXq1YsULz58+Xn58fxTwAgEuwGKwCCwAAAOSLcePGadmyZdqyZYtD4fp2k5iYqIYNG6p///569tlnCzoOAAC3NdbMAwAAAPLBpUuXtHr1arVu3fq2LuRJ0sqVK5Wenp7tzD0AAOA8XGYLAACAHLl48eINbxzg4+MjT0/PXB/j1KlT191etGhR+yXhZnP69Gl99913Wrdunc6ePasnn3wy34+Znp5+wxtBeHl5qXjx4k497tatW3Xw4EHNmDFDLVq0UPny5Z26fwAAkBnFPAAAAORIbGysRowYcd0+H374oerVq5frY0RGRl53+0MPPaQJEybkev/56cCBAxo6dKjuuOMOjRo1yn6n6Pz0559/qnnz5tftM2DAAA0cONCpx50+fbp27dqlsLAwjR492qn7BgAAWWPNPAAAAOTIyZMndeDAgev2ue++++Tj45PrY3z33XfX3X7XXXepatWqud7/7ebSpUv68ccfr9unQoUKub6BDQAAMA+KeQAAAAAAAICL4AYYAAAAAAAAgIugmAcAAAAAAAC4CG6AkQ8Mw5DNxtXLKNzc3CyMAxR6jAOAcQAwBgDGAeDmZpHFYnHa/ijm5QOLxaLk5FSlpdkKOgpQINzd3VS6dHHGAQo1xgHAOAAYAwDjAJAkX9/islqdV8zjMlsAAAAAAADARVDMAwAAAAAAAFwExTwAAAAAAADARVDMAwAAAAAAAFwExTwAAAAAAADARVDMAwAAAAAAAFwExTwAAAAAAADARVDMAwAAAAAAAFwExTwAAAAAAADARbgXdAAAAAAAruGtRbtkM4yCjpGJm8Wi5x8NK+gYAADcEhTzAAAAANwUm2HIhLU82WTCUAAA5BMuswUAAAAAAABcBMU8AAAAAAAAwEVQzAMAAAAAAABchKmKeUeOHNGYMWMUHR2t6tWrq127dpn6xMTEKCgoKNN/Bw8edOiXkpKikSNHqm7dugoLC9OgQYN08uTJTPvbuXOnunbtqho1aqhp06aaNWuWDDMuBAIAAAAAAIBCz1Q3wNi/f782b96smjVrymazZVtUq127toYNG+bQVr58eYfHgwcP1oEDBzR27FgVKVJE77zzjnr16qXly5fL3f3qyz5y5Ih69OihiIgIDR48WPv27dObb74pq9WqHj165M+LBAAAKERGz/hOl6+kme4fS7n7KQAAcFWmKuY1a9ZMLVq0kCQNHz5ce/bsybKft7e3atWqle1+du3apW+//VZz585VZGSkJMnf319RUVFav369oqKiJElz585V6dKl9fbbb8vT01Ph4eFKTEzUjBkzFBMTI09PT+e+QAAAgEIm4+6nJqvlcfdTAADgskx1ma2bm3PixMXFydvbWxEREfa2gIAABQcHKy4uzqFf8+bNHYp2UVFRSk5O1q5du5ySBQAAAAAAAHAWUxXzbtaOHTtUq1YthYaGqlu3bvr+++8dtsfHx8vf318Wi8WhPSAgQPHx8ZKk1NRU/fnnnwoICMjUx2Kx2PsBAAAAAAAAZmGqy2xvxgMPPKDo6GhVrlxZJ0+e1Ny5c/X0009rwYIFCgu7uu5JcnKySpYsmem5Pj4+9kt3U1JSJF29ZPdanp6eKlasmJKSkvKU02p1yTop4BQZ73/GAQozxgFwzfvfIllkuX7nW8xikdzdGZ859e9/LDcLs/598lkAMA4A6ernlDO5XDFv0KBBDo+bNGmidu3aafr06Zo9e3YBpcrM27tYQUcAChzjAGAcAJLkbrUWdIRMLBapdOniBR3D5bi7W023/qFk/r9PPgsAxgHgTC5XzPs3Ly8vNW7cWOvWrbO3eXt76/jx45n6JiUlycfHR5LsM/cyZuhluHz5si5cuGDvl1vJyReUnm7L0z4AV2W1usnbuxjjAIUa4wD4ZxZGWnq6zHa/CYtFOnPmfEHHcDlpaemmLeaZ8e+TzwKAcQBIko9PMafdJ0K6DYp5WQkICNDWrVtlGIbDpQCHDh1SYGCgpKtFwLvvvjvT2niHDh2SYRiZ1tLLqfR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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers: 214\n",
            "\n",
            " Num_of_Loan \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-8836c912d7c9>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[col_name] = pd.to_numeric(df[col_name], errors='coerce')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers: 134\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "7cd5c7d4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7cd5c7d4",
        "outputId": "429b9b09-18c4-4c2d-f9a3-3b6f0fa3cbd2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The percentage of data remaining after outlier removal of Monthly_Inhand_Salary is: 99.99501830771914\n",
            "The percentage of data remaining after outlier removal of Num_Bank_Accounts is: 98.66490646872744\n",
            "The percentage of data remaining after outlier removal of Num_Credit_Card is: 97.62373278202605\n",
            "The percentage of data remaining after outlier removal of Interest_Rate is: 97.84292724238423\n",
            "The percentage of data remaining after outlier removal of Delay_from_due_date is: 99.40468777243629\n",
            "The percentage of data remaining after outlier removal of Num_Credit_Inquiries is: 98.37845916257753\n",
            "The percentage of data remaining after outlier removal of Credit_Utilization_Ratio is: 99.81816823174833\n",
            "The percentage of data remaining after outlier removal of Total_EMI_per_month is: 95.65596433108327\n",
            "The percentage of data remaining after outlier removal of Num_of_Delayed_Payment is: 98.59267193065484\n",
            "The percentage of data remaining after outlier removal of Num_of_Loan is: 95.41933394774205\n"
          ]
        }
      ],
      "source": [
        "# Calculate how much data remains after outlier removal\n",
        "\n",
        "def percent_between_values(df, col_name, lower_bound, upper_bound):\n",
        "\n",
        "    # Count the total number of data points within the specified range\n",
        "    count_in_range = df[(df[col_name] >= lower_bound) & (df[col_name] <= upper_bound)].shape[0]\n",
        "\n",
        "    # Calculate the total number of data points in the column\n",
        "    total_count = df.shape[0]\n",
        "\n",
        "    # Calculate the percentage of data within the range\n",
        "    percent = (count_in_range / total_count) * 100\n",
        "\n",
        "    print(\"The percentage of data remaining after outlier removal of\", col_name, \"is:\", percent)\n",
        "\n",
        "    return\n",
        "\n",
        "percent_between_values(df_train_unique_drop, 'Monthly_Inhand_Salary', 0, 15204.63)\n",
        "percent_between_values(df_train_unique_drop, 'Num_Bank_Accounts', 0, 12)\n",
        "percent_between_values(df_train_unique_drop, 'Num_Credit_Card', 0, 12)\n",
        "percent_between_values(df_train_unique_drop, 'Interest_Rate', 0, 35)\n",
        "percent_between_values(df_train_unique_drop, 'Delay_from_due_date', 0, 65)\n",
        "percent_between_values(df_train_unique_drop, 'Num_Credit_Inquiries', 0, 18)\n",
        "percent_between_values(df_train_unique_drop, 'Credit_Utilization_Ratio', 22, 48)\n",
        "percent_between_values(df_train_unique_drop, 'Total_EMI_per_month', 0, 500)\n",
        "percent_between_values(df_train_unique_drop, 'Num_of_Delayed_Payment',0,30)\n",
        "percent_between_values(df_train_unique_drop, 'Num_of_Loan',0,12)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8d2fd605",
      "metadata": {
        "id": "8d2fd605"
      },
      "source": [
        "Therefore, we can conclude the following regarding the outliers of each columns:\n",
        "\n",
        "---\n",
        "\n",
        "#### **1. Monthly_Inhand_Salary**\n",
        "\n",
        "- Min Cutoff Threshold: N/A\n",
        "- Max Cutoff Threshold: N/A\n",
        "- Percentage Covered: 99.99622990065788\n",
        "\n",
        "**Explanation:** The Monthly_Inhand_Salary is evenly distributed across the entire range of the dataset.\n",
        "\n",
        "---\n",
        "\n",
        "#### **2. Num_Bank_Accounts**\n",
        "\n",
        "- Min Cutoff Threshold: 0\n",
        "- Max Cutoff Threshold: 12\n",
        "- Percentage Covered: 98.7219363230221  \n",
        "\n",
        "---\n",
        "\n",
        "#### **3. Num_Credit_Card**\n",
        "\n",
        "- Min Cutoff Threshold: 0\n",
        "- Max Cutoff Threshold: 12\n",
        "- Percentage Covered: 97.70023940130822\n",
        "\n",
        "---\n",
        "\n",
        "#### **4. Interest_Rate**\n",
        "\n",
        "- Min Cutoff Threshold: 0\n",
        "- Max Cutoff Threshold: 35\n",
        "- Percentage Covered: 97.85858357367717\n",
        "\n",
        "---\n",
        "\n",
        "#### **5. Delay_from_due_date**\n",
        "\n",
        "- Min Cutoff Threshold: 0\n",
        "- Max Cutoff Threshold: 65\n",
        "- Percentage Covered: 99.42694489999812\n",
        "\n",
        "---\n",
        "\n",
        "#### **6. Num_Credit_Inquiries**\n",
        "\n",
        "- Min Cutoff Threshold: 0\n",
        "- Max Cutoff Threshold: 18\n",
        "- Percentage Covered: 98.37131708420517\n",
        "\n",
        "---\n",
        "\n",
        "#### **7. Credit_Utilization_Ratio**\n",
        "\n",
        "- Min Cutoff Threshold: 22\n",
        "- Max Cutoff Threshold: 48\n",
        "- Percentage Covered: 99.82092028124941\n",
        "\n",
        "---\n",
        "\n",
        "#### **8. Total_EMI_per_month**\n",
        "\n",
        "- Min Cutoff Threshold: 0\n",
        "- Max Cutoff Threshold: 500\n",
        "- Percentage Covered: 95.6549605082094\n",
        "\n",
        "---\n",
        "\n",
        "#### **9. Num_of_Delayed_Payment**\n",
        "\n",
        "- Min Cutoff Threshold: 0\n",
        "- Max Cutoff Threshold: 30\n",
        "- Percentage Covered: 99.20245132127955\n",
        "\n",
        "---\n",
        "\n",
        "#### **10. Num_of_Loan**\n",
        "\n",
        "- Min Cutoff Threshold: 0\n",
        "- Max Cutoff Threshold: 12\n",
        "- Percentage Covered: 99.4980006954103"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We now want to ensure that all numerical columns do not contain non-numerical values before outlier removal"
      ],
      "metadata": {
        "id": "UuYzYK96R42F"
      },
      "id": "UuYzYK96R42F"
    },
    {
      "cell_type": "code",
      "source": [
        "# Count total NaN values\n",
        "\n",
        "nan_counts = df_train_unique.isna().sum()\n",
        "nan_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zogBDW6KOIX2",
        "outputId": "d7bf769f-0502-4230-9f24-a28813edb68c"
      },
      "id": "zogBDW6KOIX2",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                              0\n",
              "Customer_ID                     0\n",
              "Month                           0\n",
              "Name                         9985\n",
              "Age                          4939\n",
              "SSN                             0\n",
              "Occupation                      0\n",
              "Annual_Income                6980\n",
              "Monthly_Inhand_Salary       15002\n",
              "Num_Bank_Accounts               0\n",
              "Num_Credit_Card                 0\n",
              "Interest_Rate                   0\n",
              "Num_of_Loan                  4785\n",
              "Type_of_Loan                11408\n",
              "Delay_from_due_date             0\n",
              "Num_of_Delayed_Payment       9746\n",
              "Changed_Credit_Limit         2091\n",
              "Num_Credit_Inquiries         1965\n",
              "Credit_Mix                      0\n",
              "Outstanding_Debt             1009\n",
              "Credit_Utilization_Ratio        0\n",
              "Credit_History_Age           9030\n",
              "Payment_of_Min_Amount           0\n",
              "Total_EMI_per_month             0\n",
              "Amount_invested_monthly      8784\n",
              "Payment_Behaviour               0\n",
              "Monthly_Balance              1209\n",
              "Credit_Score                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "0c88e14d",
      "metadata": {
        "id": "0c88e14d"
      },
      "outputs": [],
      "source": [
        "# We are removing outliers as per the previous analysis and visualizations.\n",
        "\n",
        "df=df_train_unique\n",
        "\n",
        "#Age\n",
        "df.drop(df[df['Age'] < 0].index, inplace=True)\n",
        "df.drop(df[df['Age'] >100].index, inplace=True)\n",
        "# Annual_Income\n",
        "# Monthly_Inhand_Salary\n",
        "\n",
        "# Num_Bank_Accounts\n",
        "df.drop(df[df['Num_Bank_Accounts'] < 0].index, inplace=True)\n",
        "df.drop(df[df['Num_Bank_Accounts'] > 12].index, inplace=True)\n",
        "\n",
        "# Num_Credit_Card\n",
        "df.drop(df[df['Num_Credit_Card'] < 0].index, inplace=True)\n",
        "df.drop(df[df['Num_Credit_Card'] > 12].index, inplace=True)\n",
        "\n",
        "# Interest_Rate\n",
        "df.drop(df[df['Interest_Rate'] > 35].index, inplace=True)\n",
        "\n",
        "# Num_of_Loan\n",
        "df.drop(df[df['Num_of_Loan'] < 0].index, inplace=True)\n",
        "df.drop(df[df['Num_of_Loan'] > 16].index, inplace=True)\n",
        "\n",
        "# Type_of_Loan\n",
        "# Delay_from_due_date\n",
        "df.drop(df[df['Delay_from_due_date'] < 0].index, inplace=True)\n",
        "\n",
        "# Num_of_Delayed_Payment\n",
        "df.drop(df[df['Num_of_Delayed_Payment'] < 0].index, inplace=True)\n",
        "df.drop(df[df['Num_of_Delayed_Payment'] > 30].index, inplace=True)\n",
        "df['Num_of_Delayed_Payment'].fillna(method='ffill', inplace=True)\n",
        "\n",
        "# Changed_Credit_Limit\n",
        "# Num_Credit_Inquiries\n",
        "df.drop(df[df['Num_Credit_Inquiries'] < 0].index, inplace=True)\n",
        "df.drop(df[df['Num_Credit_Inquiries'] > 18].index, inplace=True)\n",
        "df['Num_Credit_Inquiries'].fillna(method='ffill', inplace=True)\n",
        "\n",
        "# Credit_Mix\n",
        "df['Credit_Mix'].fillna('Unknown', inplace=True)\n",
        "df['Credit_Mix'].astype('object')\n",
        "\n",
        "# Outstanding_Debt\n",
        "# Credit_Utilization_Ratio\n",
        "# Payment_of_Min_Amount\n",
        "# Total_EMI_per_month\n",
        "df.drop(df[df['Total_EMI_per_month'] > 500].index, inplace=True)\n",
        "\n",
        "# Amount_invested_monthly\n",
        "df['Amount_invested_monthly'].fillna(method='ffill', inplace=True)\n",
        "\n",
        "# Monthly_Balance\n",
        "df.drop(df[df['Monthly_Balance'] < 0].index, inplace = True)\n",
        "df['Monthly_Balance'].fillna(method='ffill', inplace=True)\n",
        "\n",
        "# Credit_Score\n",
        "# Payment_Behaviour\n",
        "df['Payment_Behaviour'].replace('!@9#%8', pd.NA, inplace=True)\n",
        "df['Payment_Behaviour'].fillna(df['Payment_Behaviour'].mode()[0], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the size of remining dataset\n",
        "\n",
        "print(\"Size of the training set after removing outliers: \", df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aptSpw7ARVcz",
        "outputId": "1763efae-9ad1-4dcf-8a60-e7c00924df29"
      },
      "id": "aptSpw7ARVcz",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of the training set after removing outliers:  (81032, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "fb6f9edc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fb6f9edc",
        "outputId": "3f94bbbd-d553-47cd-d975-357390c2d34d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of the training set after replacing NaN:  (81032, 28)\n"
          ]
        }
      ],
      "source": [
        "# Credit_History_Age\n",
        "import re\n",
        "#change the format of credit_history_age\n",
        "def convert_age_to_years(age_str):\n",
        "    if pd.isna(age_str):\n",
        "        return None\n",
        "    # Extract years and months\n",
        "    age_str = str(age_str)\n",
        "    pattern = r'(\\d+)\\s+Years\\s+and\\s+(\\d+)\\s+Months'\n",
        "    match = re.match(pattern, age_str)\n",
        "    if match:\n",
        "        years = int(match.group(1))\n",
        "        months = int(match.group(2))\n",
        "        return years + months / 12\n",
        "    else:\n",
        "        return None\n",
        "\n",
        "df['Credit_History_Age'] = df['Credit_History_Age'].apply(convert_age_to_years)\n",
        "df['Credit_History_Age'].fillna(method='ffill', inplace=True)\n",
        "\n",
        "\n",
        "print(\"Size of the training set after replacing NaN: \", df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "33f9bb03",
      "metadata": {
        "id": "33f9bb03"
      },
      "outputs": [],
      "source": [
        "df_train_final=df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count total NaN values\n",
        "\n",
        "nan_counts = df_train_final.isna().sum()\n",
        "nan_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TWlB1D21HU6q",
        "outputId": "9daf864c-1cfc-4828-c5b0-4f4f7355c3ce"
      },
      "id": "TWlB1D21HU6q",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                          0\n",
              "Customer_ID                 0\n",
              "Month                       0\n",
              "Name                        0\n",
              "Age                         0\n",
              "SSN                         0\n",
              "Occupation                  0\n",
              "Annual_Income               0\n",
              "Monthly_Inhand_Salary       0\n",
              "Num_Bank_Accounts           0\n",
              "Num_Credit_Card             0\n",
              "Interest_Rate               0\n",
              "Num_of_Loan                 0\n",
              "Type_of_Loan                0\n",
              "Delay_from_due_date         0\n",
              "Num_of_Delayed_Payment      0\n",
              "Changed_Credit_Limit        0\n",
              "Num_Credit_Inquiries        0\n",
              "Credit_Mix                  0\n",
              "Outstanding_Debt            0\n",
              "Credit_Utilization_Ratio    0\n",
              "Credit_History_Age          0\n",
              "Payment_of_Min_Amount       0\n",
              "Total_EMI_per_month         0\n",
              "Amount_invested_monthly     0\n",
              "Payment_Behaviour           0\n",
              "Monthly_Balance             0\n",
              "Credit_Score                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "96add75f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96add75f",
        "outputId": "d98b3ac8-5f0c-4671-a4b7-cc06d0af07f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of the training set after dropping all remaining NaN values:  (44681, 28)\n"
          ]
        }
      ],
      "source": [
        "print(\"Size of the training set after dropping all remaining NaN values: \", df_train_final.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "41d57a67",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "41d57a67",
        "outputId": "79b62c97-09ae-4f2e-8ade-6e0661164d47"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Age  Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  \\\n",
              "count  44681.0000     4.4681e+04             44681.0000         44681.0000   \n",
              "mean      32.8546     1.7716e+05              3996.5620             5.5762   \n",
              "std       10.6227     1.4522e+06              3066.5046             2.5571   \n",
              "min       14.0000     7.0059e+03               303.6454             0.0000   \n",
              "25%       24.0000     1.8755e+04              1567.7867             4.0000   \n",
              "50%       32.0000     3.5854e+04              2975.7250             6.0000   \n",
              "75%       41.0000     6.9342e+04              5685.0567             8.0000   \n",
              "max       95.0000     2.4198e+07             15204.6333            11.0000   \n",
              "\n",
              "       Num_Credit_Card  Interest_Rate  Num_of_Loan  Delay_from_due_date  \\\n",
              "count       44681.0000     44681.0000   44681.0000           44681.0000   \n",
              "mean            5.6755        15.3511       4.0071              22.3009   \n",
              "std             2.0818         8.8582       2.2245              15.2076   \n",
              "min             0.0000         1.0000       1.0000               0.0000   \n",
              "25%             4.0000         8.0000       2.0000              11.0000   \n",
              "50%             6.0000        15.0000       4.0000              19.0000   \n",
              "75%             7.0000        21.0000       6.0000              29.0000   \n",
              "max            11.0000        34.0000       9.0000              67.0000   \n",
              "\n",
              "       Num_of_Delayed_Payment  Changed_Credit_Limit  Num_Credit_Inquiries  \\\n",
              "count              44681.0000            44681.0000            44681.0000   \n",
              "mean                  13.8652               10.7977                6.1404   \n",
              "std                    6.1728                6.9340                3.9048   \n",
              "min                    0.0000               -6.4500                0.0000   \n",
              "25%                    9.0000                5.6600                3.0000   \n",
              "50%                   14.0000                9.7300                6.0000   \n",
              "75%                   19.0000               15.5200                9.0000   \n",
              "max                   28.0000               36.4900               17.0000   \n",
              "\n",
              "       Outstanding_Debt  Credit_Utilization_Ratio  Credit_History_Age  \\\n",
              "count        44681.0000                44681.0000          44681.0000   \n",
              "mean          1524.4134                   32.1572             17.6738   \n",
              "std           1196.6700                    5.0647              8.3340   \n",
              "min              0.2300                   20.0000              0.1667   \n",
              "25%            614.6000                   27.9693             11.0833   \n",
              "50%           1255.5700                   32.1649             17.5000   \n",
              "75%           2164.8400                   36.3697             24.2500   \n",
              "max           4998.0700                   49.5645             33.6667   \n",
              "\n",
              "       Total_EMI_per_month  Amount_invested_monthly  Monthly_Balance  \n",
              "count           44681.0000               44681.0000       44681.0000  \n",
              "mean              109.5443                 188.2025         380.4220  \n",
              "std                94.3603                 192.7834         193.8858  \n",
              "min                 4.4628                   0.0000           0.0078  \n",
              "25%                40.1775                  69.8446         264.4731  \n",
              "50%                75.0023                 124.1070         324.0091  \n",
              "75%               155.9091                 226.9512         441.2153  \n",
              "max               499.9292                1977.3261        1552.9461  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d4e5ec39-7eef-4eee-9355-159e10b317c2\" 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>Age</th>\n",
              "      <th>Annual_Income</th>\n",
              "      <th>Monthly_Inhand_Salary</th>\n",
              "      <th>Num_Bank_Accounts</th>\n",
              "      <th>Num_Credit_Card</th>\n",
              "      <th>Interest_Rate</th>\n",
              "      <th>Num_of_Loan</th>\n",
              "      <th>Delay_from_due_date</th>\n",
              "      <th>Num_of_Delayed_Payment</th>\n",
              "      <th>Changed_Credit_Limit</th>\n",
              "      <th>Num_Credit_Inquiries</th>\n",
              "      <th>Outstanding_Debt</th>\n",
              "      <th>Credit_Utilization_Ratio</th>\n",
              "      <th>Credit_History_Age</th>\n",
              "      <th>Total_EMI_per_month</th>\n",
              "      <th>Amount_invested_monthly</th>\n",
              "      <th>Monthly_Balance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>44681.0000</td>\n",
              "      <td>4.4681e+04</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "      <td>44681.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>32.8546</td>\n",
              "      <td>1.7716e+05</td>\n",
              "      <td>3996.5620</td>\n",
              "      <td>5.5762</td>\n",
              "      <td>5.6755</td>\n",
              "      <td>15.3511</td>\n",
              "      <td>4.0071</td>\n",
              "      <td>22.3009</td>\n",
              "      <td>13.8652</td>\n",
              "      <td>10.7977</td>\n",
              "      <td>6.1404</td>\n",
              "      <td>1524.4134</td>\n",
              "      <td>32.1572</td>\n",
              "      <td>17.6738</td>\n",
              "      <td>109.5443</td>\n",
              "      <td>188.2025</td>\n",
              "      <td>380.4220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.6227</td>\n",
              "      <td>1.4522e+06</td>\n",
              "      <td>3066.5046</td>\n",
              "      <td>2.5571</td>\n",
              "      <td>2.0818</td>\n",
              "      <td>8.8582</td>\n",
              "      <td>2.2245</td>\n",
              "      <td>15.2076</td>\n",
              "      <td>6.1728</td>\n",
              "      <td>6.9340</td>\n",
              "      <td>3.9048</td>\n",
              "      <td>1196.6700</td>\n",
              "      <td>5.0647</td>\n",
              "      <td>8.3340</td>\n",
              "      <td>94.3603</td>\n",
              "      <td>192.7834</td>\n",
              "      <td>193.8858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>14.0000</td>\n",
              "      <td>7.0059e+03</td>\n",
              "      <td>303.6454</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-6.4500</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2300</td>\n",
              "      <td>20.0000</td>\n",
              "      <td>0.1667</td>\n",
              "      <td>4.4628</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>24.0000</td>\n",
              "      <td>1.8755e+04</td>\n",
              "      <td>1567.7867</td>\n",
              "      <td>4.0000</td>\n",
              "      <td>4.0000</td>\n",
              "      <td>8.0000</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>11.0000</td>\n",
              "      <td>9.0000</td>\n",
              "      <td>5.6600</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>614.6000</td>\n",
              "      <td>27.9693</td>\n",
              "      <td>11.0833</td>\n",
              "      <td>40.1775</td>\n",
              "      <td>69.8446</td>\n",
              "      <td>264.4731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>32.0000</td>\n",
              "      <td>3.5854e+04</td>\n",
              "      <td>2975.7250</td>\n",
              "      <td>6.0000</td>\n",
              "      <td>6.0000</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>4.0000</td>\n",
              "      <td>19.0000</td>\n",
              "      <td>14.0000</td>\n",
              "      <td>9.7300</td>\n",
              "      <td>6.0000</td>\n",
              "      <td>1255.5700</td>\n",
              "      <td>32.1649</td>\n",
              "      <td>17.5000</td>\n",
              "      <td>75.0023</td>\n",
              "      <td>124.1070</td>\n",
              "      <td>324.0091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>41.0000</td>\n",
              "      <td>6.9342e+04</td>\n",
              "      <td>5685.0567</td>\n",
              "      <td>8.0000</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>21.0000</td>\n",
              "      <td>6.0000</td>\n",
              "      <td>29.0000</td>\n",
              "      <td>19.0000</td>\n",
              "      <td>15.5200</td>\n",
              "      <td>9.0000</td>\n",
              "      <td>2164.8400</td>\n",
              "      <td>36.3697</td>\n",
              "      <td>24.2500</td>\n",
              "      <td>155.9091</td>\n",
              "      <td>226.9512</td>\n",
              "      <td>441.2153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>95.0000</td>\n",
              "      <td>2.4198e+07</td>\n",
              "      <td>15204.6333</td>\n",
              "      <td>11.0000</td>\n",
              "      <td>11.0000</td>\n",
              "      <td>34.0000</td>\n",
              "      <td>9.0000</td>\n",
              "      <td>67.0000</td>\n",
              "      <td>28.0000</td>\n",
              "      <td>36.4900</td>\n",
              "      <td>17.0000</td>\n",
              "      <td>4998.0700</td>\n",
              "      <td>49.5645</td>\n",
              "      <td>33.6667</td>\n",
              "      <td>499.9292</td>\n",
              "      <td>1977.3261</td>\n",
              "      <td>1552.9461</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  .colab-df-quickchart:hover {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_train_final\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15784.540275706522,\n        \"min\": 10.622682473804971,\n        \"max\": 44681.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.854569056198386,\n          32.0,\n          44681.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual_Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8478334.989168225,\n        \"min\": 7005.93,\n        \"max\": 24198062.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          177155.35728430428,\n          35853.56,\n          44681.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monthly_Inhand_Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14862.522666680958,\n        \"min\": 303.6454166666666,\n        \"max\": 44681.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3996.561985434476,\n          2975.725,\n          44681.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_Bank_Accounts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15795.243885108393,\n        \"min\": 0.0,\n        \"max\": 44681.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.576173317517513,\n          6.0,\n          44681.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_Credit_Card\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15795.31337187134,\n        \"min\": 0.0,\n        \"max\": 44681.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.675454891340839,\n          6.0,\n          44681.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Interest_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15791.909261734314,\n        \"min\": 1.0,\n        \"max\": 44681.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15.351066448826122,\n          15.0,\n          44681.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_of_Loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15795.693337670737,\n        \"min\": 1.0,\n        \"max\": 44681.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.007072357377856,\n          4.0,\n          44681.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Delay_from_due_date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15788.872927464423,\n        \"min\": 0.0,\n        \"max\": 44681.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          22.300910901725565,\n          19.0,\n          44681.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_of_Delayed_Payment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15792.573675799176,\n        \"min\": 0.0,\n        \"max\": 44681.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13.865222354020725,\n          14.0,\n          44681.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Changed_Credit_Limit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 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},\n    {\n      \"column\": \"Total_EMI_per_month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15748.4026804846,\n        \"min\": 4.4628374669131645,\n        \"max\": 44681.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          109.54431507812919,\n          75.0023106523225,\n          44681.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount_invested_monthly\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15670.203551715364,\n        \"min\": 0.0,\n        \"max\": 44681.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          188.20246107315666,\n          124.10697181558012,\n          44681.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monthly_Balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15644.700256032917,\n        \"min\": 0.0077596647753352,\n        \"max\": 44681.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          380.4219744670177,\n          324.0091165501712,\n          44681.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "df_train_final.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9323fad3",
      "metadata": {
        "id": "9323fad3"
      },
      "source": [
        "#### clean the test data with the same procedure"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "be52d3ca",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "be52d3ca",
        "outputId": "d0c69d03-3c8f-477e-abe8-80a42d23ecb8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Age  Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  \\\n",
              "count  21938.000     2.1938e+04             21938.0000         21938.0000   \n",
              "mean      33.376     1.5839e+05              3936.8754             5.5636   \n",
              "std       10.605     1.3156e+06              3040.6639             2.5767   \n",
              "min       14.000     7.0059e+03               303.6454             0.0000   \n",
              "25%       25.000     1.8668e+04              1548.3483             4.0000   \n",
              "50%       33.000     3.5609e+04              2944.6133             6.0000   \n",
              "75%       42.000     6.7686e+04              5532.5033             8.0000   \n",
              "max       56.000     2.4017e+07             15204.6333            11.0000   \n",
              "\n",
              "       Num_Credit_Card  Interest_Rate  Num_of_Loan  Delay_from_due_date  \\\n",
              "count       21938.0000     21938.0000   21938.0000           21938.0000   \n",
              "mean            5.6742        15.3515       3.9973              22.3138   \n",
              "std             2.0970         8.8810       2.2285              15.2552   \n",
              "min             0.0000         1.0000       1.0000               0.0000   \n",
              "25%             4.0000         8.0000       2.0000              11.0000   \n",
              "50%             6.0000        15.0000       4.0000              19.0000   \n",
              "75%             7.0000        21.0000       6.0000              29.0000   \n",
              "max            11.0000        34.0000       9.0000              67.0000   \n",
              "\n",
              "       Num_of_Delayed_Payment  Changed_Credit_Limit  Num_Credit_Inquiries  \\\n",
              "count              21938.0000            21938.0000            21938.0000   \n",
              "mean                  13.8447               10.7380                7.6749   \n",
              "std                    6.1866                6.9611                3.9881   \n",
              "min                    0.0000               -6.4500                0.0000   \n",
              "25%                    9.0000                5.5900                5.0000   \n",
              "50%                   14.0000                9.6800                7.0000   \n",
              "75%                   19.0000               15.4700               11.0000   \n",
              "max                   28.0000               36.6500               17.0000   \n",
              "\n",
              "       Outstanding_Debt  Credit_Utilization_Ratio  Credit_History_Age  \\\n",
              "count        21938.0000                21938.0000          21938.0000   \n",
              "mean          1533.9416                   32.1243             18.1718   \n",
              "std           1193.9589                    5.0464              8.3338   \n",
              "min              0.2300                   20.5097              0.8333   \n",
              "25%            628.1100                   27.9341             11.5833   \n",
              "50%           1264.6100                   32.1695             18.0000   \n",
              "75%           2186.2000                   36.2740             24.6667   \n",
              "max           4997.1000                   48.0965             34.0000   \n",
              "\n",
              "       Total_EMI_per_month  Amount_invested_monthly  Monthly_Balance  \n",
              "count           21938.0000               21938.0000       21938.0000  \n",
              "mean              113.7971                 185.1059         378.4843  \n",
              "std                98.0876                 187.3826         191.7136  \n",
              "min                 4.4628                   0.0000           0.1034  \n",
              "25%                41.3204                  70.0223         263.9701  \n",
              "50%                78.2712                 123.5700         322.7969  \n",
              "75%               164.0646                 222.2886         438.6903  \n",
              "max               499.9292                1801.3582        1496.4089  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-25ed36ff-9bfd-4bbb-817e-dd6504af413a\" 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>Age</th>\n",
              "      <th>Annual_Income</th>\n",
              "      <th>Monthly_Inhand_Salary</th>\n",
              "      <th>Num_Bank_Accounts</th>\n",
              "      <th>Num_Credit_Card</th>\n",
              "      <th>Interest_Rate</th>\n",
              "      <th>Num_of_Loan</th>\n",
              "      <th>Delay_from_due_date</th>\n",
              "      <th>Num_of_Delayed_Payment</th>\n",
              "      <th>Changed_Credit_Limit</th>\n",
              "      <th>Num_Credit_Inquiries</th>\n",
              "      <th>Outstanding_Debt</th>\n",
              "      <th>Credit_Utilization_Ratio</th>\n",
              "      <th>Credit_History_Age</th>\n",
              "      <th>Total_EMI_per_month</th>\n",
              "      <th>Amount_invested_monthly</th>\n",
              "      <th>Monthly_Balance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>21938.000</td>\n",
              "      <td>2.1938e+04</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "      <td>21938.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>33.376</td>\n",
              "      <td>1.5839e+05</td>\n",
              "      <td>3936.8754</td>\n",
              "      <td>5.5636</td>\n",
              "      <td>5.6742</td>\n",
              "      <td>15.3515</td>\n",
              "      <td>3.9973</td>\n",
              "      <td>22.3138</td>\n",
              "      <td>13.8447</td>\n",
              "      <td>10.7380</td>\n",
              "      <td>7.6749</td>\n",
              "      <td>1533.9416</td>\n",
              "      <td>32.1243</td>\n",
              "      <td>18.1718</td>\n",
              "      <td>113.7971</td>\n",
              "      <td>185.1059</td>\n",
              "      <td>378.4843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.605</td>\n",
              "      <td>1.3156e+06</td>\n",
              "      <td>3040.6639</td>\n",
              "      <td>2.5767</td>\n",
              "      <td>2.0970</td>\n",
              "      <td>8.8810</td>\n",
              "      <td>2.2285</td>\n",
              "      <td>15.2552</td>\n",
              "      <td>6.1866</td>\n",
              "      <td>6.9611</td>\n",
              "      <td>3.9881</td>\n",
              "      <td>1193.9589</td>\n",
              "      <td>5.0464</td>\n",
              "      <td>8.3338</td>\n",
              "      <td>98.0876</td>\n",
              "      <td>187.3826</td>\n",
              "      <td>191.7136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>14.000</td>\n",
              "      <td>7.0059e+03</td>\n",
              "      <td>303.6454</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-6.4500</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2300</td>\n",
              "      <td>20.5097</td>\n",
              "      <td>0.8333</td>\n",
              "      <td>4.4628</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.1034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>25.000</td>\n",
              "      <td>1.8668e+04</td>\n",
              "      <td>1548.3483</td>\n",
              "      <td>4.0000</td>\n",
              "      <td>4.0000</td>\n",
              "      <td>8.0000</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>11.0000</td>\n",
              "      <td>9.0000</td>\n",
              "      <td>5.5900</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>628.1100</td>\n",
              "      <td>27.9341</td>\n",
              "      <td>11.5833</td>\n",
              "      <td>41.3204</td>\n",
              "      <td>70.0223</td>\n",
              "      <td>263.9701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>33.000</td>\n",
              "      <td>3.5609e+04</td>\n",
              "      <td>2944.6133</td>\n",
              "      <td>6.0000</td>\n",
              "      <td>6.0000</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>4.0000</td>\n",
              "      <td>19.0000</td>\n",
              "      <td>14.0000</td>\n",
              "      <td>9.6800</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>1264.6100</td>\n",
              "      <td>32.1695</td>\n",
              "      <td>18.0000</td>\n",
              "      <td>78.2712</td>\n",
              "      <td>123.5700</td>\n",
              "      <td>322.7969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>42.000</td>\n",
              "      <td>6.7686e+04</td>\n",
              "      <td>5532.5033</td>\n",
              "      <td>8.0000</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>21.0000</td>\n",
              "      <td>6.0000</td>\n",
              "      <td>29.0000</td>\n",
              "      <td>19.0000</td>\n",
              "      <td>15.4700</td>\n",
              "      <td>11.0000</td>\n",
              "      <td>2186.2000</td>\n",
              "      <td>36.2740</td>\n",
              "      <td>24.6667</td>\n",
              "      <td>164.0646</td>\n",
              "      <td>222.2886</td>\n",
              "      <td>438.6903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>56.000</td>\n",
              "      <td>2.4017e+07</td>\n",
              "      <td>15204.6333</td>\n",
              "      <td>11.0000</td>\n",
              "      <td>11.0000</td>\n",
              "      <td>34.0000</td>\n",
              "      <td>9.0000</td>\n",
              "      <td>67.0000</td>\n",
              "      <td>28.0000</td>\n",
              "      <td>36.6500</td>\n",
              "      <td>17.0000</td>\n",
              "      <td>4997.1000</td>\n",
              "      <td>48.0965</td>\n",
              "      <td>34.0000</td>\n",
              "      <td>499.9292</td>\n",
              "      <td>1801.3582</td>\n",
              "      <td>1496.4089</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25ed36ff-9bfd-4bbb-817e-dd6504af413a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-598edf73-f202-45f7-b4e7-7f027749c758 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_test_final\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7745.460399178855,\n        \"min\": 10.60500014670308,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33.376014221898075,\n          33.0,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual_Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8421091.639799548,\n        \"min\": 7005.93,\n        \"max\": 24017307.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          158388.20235550188,\n          35608.525,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monthly_Inhand_Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7638.059910600193,\n        \"min\": 303.6454166666666,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3936.8754268786665,\n          2944.613333333333,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_Bank_Accounts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7754.379127576163,\n        \"min\": 0.0,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.563588294283891,\n          6.0,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_Credit_Card\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7754.448261295726,\n        \"min\": 0.0,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.674218251435865,\n          6.0,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Interest_Rate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7751.046511439864,\n        \"min\": 1.0,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15.351490564317622,\n          15.0,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_of_Loan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7754.829082092228,\n        \"min\": 1.0,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.9972650196006927,\n          4.0,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Delay_from_due_date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7748.017876579728,\n        \"min\": 0.0,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          22.313839000820494,\n          19.0,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_of_Delayed_Payment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7751.711566009345,\n        \"min\": 0.0,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          13.844744279332664,\n          14.0,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Changed_Credit_Limit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7752.291891122284,\n        \"min\": -6.45,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.738037651563499,\n          9.68,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num_Credit_Inquiries\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7753.646539512567,\n        \"min\": 0.0,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.674947579542347,\n          7.0,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outstanding_Debt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7314.193715989234,\n        \"min\": 0.23,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1533.9416250341874,\n          1264.61,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_Utilization_Ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7746.053941397695,\n        \"min\": 5.0463608899659524,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.12430654949764,\n          32.16952113939762,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History_Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7750.422742918458,\n        \"min\": 0.8333333333333334,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          18.171817546418698,\n          18.0,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total_EMI_per_month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7707.274111608533,\n        \"min\": 4.4628374669131645,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          113.7970782923071,\n          78.27115779829765,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount_invested_monthly\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7648.141877662155,\n        \"min\": 0.0,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          185.1058583518729,\n          123.57000553020089,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Monthly_Balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7613.4069279656005,\n        \"min\": 0.103402231183395,\n        \"max\": 21938.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          378.48429859936795,\n          322.7968506659694,\n          21938.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "#convert below columns to numerical columns\n",
        "df_test_unique['Age'] = pd.to_numeric(df_test_unique['Age'], errors='coerce')\n",
        "df_test_unique['Annual_Income'] = pd.to_numeric(df_test_unique['Annual_Income'], errors='coerce')\n",
        "df_test_unique['Outstanding_Debt'] = pd.to_numeric(df_test_unique['Outstanding_Debt'], errors='coerce')\n",
        "df_test_unique['Amount_invested_monthly'] = pd.to_numeric(df_test_unique['Amount_invested_monthly'], errors='coerce')\n",
        "df_test_unique['Monthly_Balance'] = pd.to_numeric(df_test_unique['Monthly_Balance'], errors='coerce')\n",
        "df_test_unique['Changed_Credit_Limit'] = pd.to_numeric(df_test_unique['Changed_Credit_Limit'], errors='coerce')\n",
        "df_test_unique['Monthly_Balance'] = pd.to_numeric(df_test_unique['Monthly_Balance'], errors='coerce')\n",
        "df_test_unique['Num_of_Loan'] = pd.to_numeric(df_test_unique['Num_of_Loan'], errors='coerce')\n",
        "df_test_unique['Num_of_Delayed_Payment'] = pd.to_numeric(df_test_unique['Num_of_Delayed_Payment'], errors='coerce')\n",
        "\n",
        "df=df_test_unique\n",
        "\n",
        "#Age\n",
        "df.drop(df[df['Age'] < 0].index, inplace=True)\n",
        "df.drop(df[df['Age'] >100].index, inplace=True)\n",
        "# Annual_Income\n",
        "# Monthly_Inhand_Salary\n",
        "# Num_Bank_Accounts\n",
        "df.drop(df[df['Num_Bank_Accounts'] < 0].index, inplace=True)\n",
        "df.drop(df[df['Num_Bank_Accounts'] > 12].index, inplace=True)\n",
        "# Num_Credit_Card\n",
        "df.drop(df[df['Num_Credit_Card'] < 0].index, inplace=True)\n",
        "df.drop(df[df['Num_Credit_Card'] > 12].index, inplace=True)\n",
        "# Interest_Rate\n",
        "df.drop(df[df['Interest_Rate'] > 35].index, inplace=True)\n",
        "# Num_of_Loan\n",
        "df.drop(df[df['Num_of_Loan'] < 0].index, inplace=True)\n",
        "df.drop(df[df['Num_of_Loan'] > 16].index, inplace=True)\n",
        "# Type_of_Loan\n",
        "# Delay_from_due_date\n",
        "df.drop(df[df['Delay_from_due_date'] < 0].index, inplace=True)\n",
        "# Num_of_Delayed_Payment\n",
        "df.drop(df[df['Num_of_Delayed_Payment'] < 0].index, inplace=True)\n",
        "df.drop(df[df['Num_of_Delayed_Payment'] > 30].index, inplace=True)\n",
        "df['Num_of_Delayed_Payment'].fillna(method='ffill', inplace=True)\n",
        "# Changed_Credit_Limit\n",
        "# Num_Credit_Inquiries\n",
        "df.drop(df[df['Num_Credit_Inquiries'] < 0].index, inplace=True)\n",
        "df.drop(df[df['Num_Credit_Inquiries'] > 18].index, inplace=True)\n",
        "df['Num_Credit_Inquiries'].fillna(method='ffill', inplace=True)\n",
        "# Credit_Mix\n",
        "df['Credit_Mix'].fillna('Unknown', inplace=True)\n",
        "df['Credit_Mix'].astype('object')\n",
        "# Outstanding_Debt\n",
        "# Credit_Utilization_Ratio\n",
        "# Payment_of_Min_Amount\n",
        "# Total_EMI_per_month\n",
        "df.drop(df[df['Total_EMI_per_month'] > 500].index, inplace=True)\n",
        "# Amount_invested_monthly\n",
        "df['Amount_invested_monthly'].fillna(method='ffill', inplace=True)\n",
        "# Monthly_Balance\n",
        "df.drop(df[df['Monthly_Balance'] < 0].index, inplace = True)\n",
        "df['Monthly_Balance'].fillna(method='ffill', inplace=True)\n",
        "# Credit_Score\n",
        "# Payment_Behaviour\n",
        "df['Payment_Behaviour'].replace('!@9#%8', pd.NA, inplace=True)\n",
        "df['Payment_Behaviour'].fillna(df['Payment_Behaviour'].mode()[0], inplace=True)\n",
        "#Credit History Age\n",
        "df['Credit_History_Age'] = df['Credit_History_Age'].apply(convert_age_to_years)\n",
        "df['Credit_History_Age'].fillna(method='ffill', inplace=True)\n",
        "\n",
        "df_test_final=df.dropna()\n",
        "df_test_final.describe()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count total NaN values\n",
        "\n",
        "nan_counts = df_test_final.isna().sum()\n",
        "nan_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FMMtS9NPHtcT",
        "outputId": "3fc16682-fa8f-498b-de5f-478b89841626"
      },
      "id": "FMMtS9NPHtcT",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                          0\n",
              "Customer_ID                 0\n",
              "Month                       0\n",
              "Name                        0\n",
              "Age                         0\n",
              "SSN                         0\n",
              "Occupation                  0\n",
              "Annual_Income               0\n",
              "Monthly_Inhand_Salary       0\n",
              "Num_Bank_Accounts           0\n",
              "Num_Credit_Card             0\n",
              "Interest_Rate               0\n",
              "Num_of_Loan                 0\n",
              "Type_of_Loan                0\n",
              "Delay_from_due_date         0\n",
              "Num_of_Delayed_Payment      0\n",
              "Changed_Credit_Limit        0\n",
              "Num_Credit_Inquiries        0\n",
              "Credit_Mix                  0\n",
              "Outstanding_Debt            0\n",
              "Credit_Utilization_Ratio    0\n",
              "Credit_History_Age          0\n",
              "Payment_of_Min_Amount       0\n",
              "Total_EMI_per_month         0\n",
              "Amount_invested_monthly     0\n",
              "Payment_Behaviour           0\n",
              "Monthly_Balance             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "99f1c9cd",
      "metadata": {
        "id": "99f1c9cd"
      },
      "source": [
        "### Exporting cleaned data files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "d153174b",
      "metadata": {
        "id": "d153174b"
      },
      "outputs": [],
      "source": [
        "df_train_final.to_csv('train_cleaned.csv', index=False)\n",
        "df_test_final.to_csv('test_cleaned.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DRAFTS"
      ],
      "metadata": {
        "id": "mkay4TtMQEWJ"
      },
      "id": "mkay4TtMQEWJ"
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting to Numerical values:\n",
        "\n",
        "def clear_non_numeric_values(df, column_name):\n",
        "    \"\"\"\n",
        "    Cleans a column in the dataframe by converting non-numeric values to NaN\n",
        "    \"\"\"\n",
        "    # Convert column to numeric, coercing errors to NaN\n",
        "    df[column_name] = pd.to_numeric(df[column_name], errors='coerce')\n",
        "\n",
        "    return df"
      ],
      "metadata": {
        "id": "1K8ARuamP3lX"
      },
      "id": "1K8ARuamP3lX",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#clear the non_numeric values of the numeric columns\n",
        "df_train_unique=clear_non_numeric_values(df_train_unique, 'Age')\n",
        "df_train_unique=clear_non_numeric_values(df_train_unique, 'Annual_Income')\n",
        "df_train_unique=clear_non_numeric_values(df_train_unique, 'Outstanding_Debt')\n",
        "df_train_unique=clear_non_numeric_values(df_train_unique, 'Amount_invested_monthly')\n",
        "df_train_unique=clear_non_numeric_values(df_train_unique, 'Monthly_Balance')\n",
        "df_train_unique=clear_non_numeric_values(df_train_unique, 'Changed_Credit_Limit')\n",
        "df_train_unique=clear_non_numeric_values(df_train_unique, 'Monthly_Balance')\n",
        "df_train_unique=clear_non_numeric_values(df_train_unique, 'Num_of_Loan')\n",
        "df_train_unique=clear_non_numeric_values(df_train_unique, 'Num_of_Delayed_Payment')\n",
        "df_train_unique=clear_non_numeric_values(df_train_unique, 'Num_Credit_Inquiries')\n",
        "df_train_unique=clear_non_numeric_values(df_train_unique, 'Num_Bank_Accounts')\n",
        "df_train_unique=clear_non_numeric_values(df_train_unique, 'Delay_from_due_date')\n",
        "df_train_unique=clear_non_numeric_values(df_train_unique, 'Num_of_Delayed_Payment')\n",
        "# df_train_unique\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#clear the non_numeric values of the numeric columns\n",
        "df_test_unique=clear_non_numeric_values(df_test_unique, 'Age')\n",
        "df_test_unique=clear_non_numeric_values(df_test_unique, 'Annual_Income')\n",
        "df_test_unique=clear_non_numeric_values(df_test_unique, 'Outstanding_Debt')\n",
        "df_test_unique=clear_non_numeric_values(df_test_unique, 'Amount_invested_monthly')\n",
        "df_test_unique=clear_non_numeric_values(df_test_unique, 'Monthly_Balance')\n",
        "df_test_unique=clear_non_numeric_values(df_test_unique, 'Changed_Credit_Limit')\n",
        "df_test_unique=clear_non_numeric_values(df_test_unique, 'Monthly_Balance')\n",
        "df_test_unique=clear_non_numeric_values(df_test_unique, 'Num_of_Loan')\n",
        "df_test_unique=clear_non_numeric_values(df_test_unique, 'Num_of_Delayed_Payment')\n",
        "df_test_unique=clear_non_numeric_values(df_test_unique, 'Num_Credit_Inquiries')\n",
        "df_test_unique=clear_non_numeric_values(df_test_unique, 'Num_Bank_Accounts')\n",
        "df_test_unique=clear_non_numeric_values(df_test_unique, 'Delay_from_due_date')\n",
        "df_test_unique=clear_non_numeric_values(df_test_unique, 'Num_of_Delayed_Payment')\n",
        "\n"
      ],
      "metadata": {
        "id": "vkuFZ_q-QDh4"
      },
      "id": "vkuFZ_q-QDh4",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}