BT2101_disrudy_colab_old.ipynb

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  "metadata": {
    "colab": {
      "name": "BT2101 disrudy ",
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      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/reonho/bt2101disrudy/blob/master/BT2101_disrudy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4Rm0wjQMUHi",
        "colab_type": "text"
      },
      "source": [
        "# BT2101 DISRUDY GROUP PROJECT\n",
        "\n",
        "## Table of Contents\n",
        "1. Problem Description (Brief Write Up)\n",
        "2. Exploratory Data Analysis (EDA)\n",
        "3. Data Pre-processing\n",
        "4. Model Selection\n",
        "5. Evaluation\n",
        "6. Discussion and Possible Improvements\n",
        "\n",
        "## 1. Problem Description\n",
        "\n",
        "The data set we will be working on contains payment information of 30,000 credit card holders obtained from a bank in Taiwan. \n",
        "\n",
        "Each data sample is described by 23 feature attributes and a binary target feature (default or not) valued 0 (= not default) or 1 (= default). \n",
        "\n",
        "The 23 explanatory attributes are:\n",
        "\n",
        "### X1 - X5: Indivual attributes of customer\n",
        "\n",
        "X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. \n",
        "\n",
        "X2: Gender (1 = male; 2 = female). \n",
        "\n",
        "X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). \n",
        "\n",
        "X4: Marital status (1 = married; 2 = single; 3 = others). \n",
        "\n",
        "X5: Age (year). \n",
        "\n",
        "### X6 - X11: Repayment history from April to Septemeber 2005\n",
        "The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months, . . . 8 = payment delay for eight months; 9 = payment delay for nine months and above.\n",
        "\n",
        "\n",
        "X6 = the repayment status in September, 2005\n",
        "\n",
        "X7 = the repayment status in August, 2005\n",
        "\n",
        "X8 = the repayment status in July, 2005\n",
        "\n",
        "X9 = the repayment status in June, 2005\n",
        "\n",
        "X10 = the repayment status in May, 2005\n",
        "\n",
        "X11 = the repayment status in April, 2005. \n",
        "\n",
        "### X12 - X17: Amount of bill statement (NT dollar) from April to September 2005\n",
        "\n",
        "X12 = amount of bill statement in September, 2005; \n",
        "\n",
        "X13 = amount of bill statement in August, 2005\n",
        "\n",
        ". . .\n",
        "\n",
        "X17 = amount of bill statement in April, 2005. \n",
        "\n",
        "### X18 - X23: Amount of previous payment (NT dollar)\n",
        "X18 = amount paid in September, 2005\n",
        "\n",
        "X19 = amount paid in August, 2005\n",
        "\n",
        ". . .\n",
        "\n",
        "X23 = amount paid in April, 2005. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aM_aIU6UPHe4",
        "colab_type": "text"
      },
      "source": [
        "## EDA\n",
        "\n",
        "In this section we will explore the data set, its shape and its features to get an idea of the data.\n",
        "\n",
        "### Importing packages and the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Is0wEkk3LJCt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_Z7u_9vRC5m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sklearn as sk\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhmX9KWWyrUW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/reonho/bt2101disrudy/master/card.csv'\n",
        "df = pd.read_csv(url,  header = 1, index_col = 0)\n",
        "# Dataset is now stored in a Pandas Dataframe\n",
        "df0 = df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FhJ2eAxVQhBm",
        "colab_type": "code",
        "outputId": "bb72bf03-8c34-4298-9d7c-8c76f803d302",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "#rename the target variable to \"Y\" for convenience\n",
        "df[\"Y\"] = df[\"default payment next month\"] \n",
        "df = df.drop(\"default payment next month\", axis = 1)\n",
        "df.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LIMIT_BAL</th>\n",
              "      <th>SEX</th>\n",
              "      <th>EDUCATION</th>\n",
              "      <th>MARRIAGE</th>\n",
              "      <th>AGE</th>\n",
              "      <th>PAY_0</th>\n",
              "      <th>PAY_2</th>\n",
              "      <th>PAY_3</th>\n",
              "      <th>PAY_4</th>\n",
              "      <th>PAY_5</th>\n",
              "      <th>PAY_6</th>\n",
              "      <th>BILL_AMT1</th>\n",
              "      <th>BILL_AMT2</th>\n",
              "      <th>BILL_AMT3</th>\n",
              "      <th>BILL_AMT4</th>\n",
              "      <th>BILL_AMT5</th>\n",
              "      <th>BILL_AMT6</th>\n",
              "      <th>PAY_AMT1</th>\n",
              "      <th>PAY_AMT2</th>\n",
              "      <th>PAY_AMT3</th>\n",
              "      <th>PAY_AMT4</th>\n",
              "      <th>PAY_AMT5</th>\n",
              "      <th>PAY_AMT6</th>\n",
              "      <th>Y</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-2</td>\n",
              "      <td>-2</td>\n",
              "      <td>3913</td>\n",
              "      <td>3102</td>\n",
              "      <td>689</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>689</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>120000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>-1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2682</td>\n",
              "      <td>1725</td>\n",
              "      <td>2682</td>\n",
              "      <td>3272</td>\n",
              "      <td>3455</td>\n",
              "      <td>3261</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "      <td>2000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>90000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>29239</td>\n",
              "      <td>14027</td>\n",
              "      <td>13559</td>\n",
              "      <td>14331</td>\n",
              "      <td>14948</td>\n",
              "      <td>15549</td>\n",
              "      <td>1518</td>\n",
              "      <td>1500</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>5000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>46990</td>\n",
              "      <td>48233</td>\n",
              "      <td>49291</td>\n",
              "      <td>28314</td>\n",
              "      <td>28959</td>\n",
              "      <td>29547</td>\n",
              "      <td>2000</td>\n",
              "      <td>2019</td>\n",
              "      <td>1200</td>\n",
              "      <td>1100</td>\n",
              "      <td>1069</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>50000</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>57</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8617</td>\n",
              "      <td>5670</td>\n",
              "      <td>35835</td>\n",
              "      <td>20940</td>\n",
              "      <td>19146</td>\n",
              "      <td>19131</td>\n",
              "      <td>2000</td>\n",
              "      <td>36681</td>\n",
              "      <td>10000</td>\n",
              "      <td>9000</td>\n",
              "      <td>689</td>\n",
              "      <td>679</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    LIMIT_BAL  SEX  EDUCATION  MARRIAGE  ...  PAY_AMT4  PAY_AMT5  PAY_AMT6  Y\n",
              "ID                                       ...                                 \n",
              "1       20000    2          2         1  ...         0         0         0  1\n",
              "2      120000    2          2         2  ...      1000         0      2000  1\n",
              "3       90000    2          2         2  ...      1000      1000      5000  0\n",
              "4       50000    2          2         1  ...      1100      1069      1000  0\n",
              "5       50000    1          2         1  ...      9000       689       679  0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zcuPyfM86AKj",
        "colab_type": "code",
        "outputId": "3a9a367a-0237-463b-bf23-fd44039c3611",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "size = df.shape\n",
        "\n",
        "print(\"Data has {} Columns and {} Rows\".format(size[1], size[0]))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data has 24 Columns and 30000 Rows\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVaSnvJP3VbO",
        "colab_type": "code",
        "outputId": "80e6f498-1b7f-45a0-96ad-45393597bc1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "#check for null values\n",
        "df.isnull().sum()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LIMIT_BAL    0\n",
              "SEX          0\n",
              "EDUCATION    0\n",
              "MARRIAGE     0\n",
              "AGE          0\n",
              "PAY_0        0\n",
              "PAY_2        0\n",
              "PAY_3        0\n",
              "PAY_4        0\n",
              "PAY_5        0\n",
              "PAY_6        0\n",
              "BILL_AMT1    0\n",
              "BILL_AMT2    0\n",
              "BILL_AMT3    0\n",
              "BILL_AMT4    0\n",
              "BILL_AMT5    0\n",
              "BILL_AMT6    0\n",
              "PAY_AMT1     0\n",
              "PAY_AMT2     0\n",
              "PAY_AMT3     0\n",
              "PAY_AMT4     0\n",
              "PAY_AMT5     0\n",
              "PAY_AMT6     0\n",
              "Y            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgdgYfpR6hUM",
        "colab_type": "code",
        "outputId": "35ce1125-e857-49cc-8585-33b90768a8b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LIMIT_BAL</th>\n",
              "      <th>SEX</th>\n",
              "      <th>EDUCATION</th>\n",
              "      <th>MARRIAGE</th>\n",
              "      <th>AGE</th>\n",
              "      <th>PAY_0</th>\n",
              "      <th>PAY_2</th>\n",
              "      <th>PAY_3</th>\n",
              "      <th>PAY_4</th>\n",
              "      <th>PAY_5</th>\n",
              "      <th>PAY_6</th>\n",
              "      <th>BILL_AMT1</th>\n",
              "      <th>BILL_AMT2</th>\n",
              "      <th>BILL_AMT3</th>\n",
              "      <th>BILL_AMT4</th>\n",
              "      <th>BILL_AMT5</th>\n",
              "      <th>BILL_AMT6</th>\n",
              "      <th>PAY_AMT1</th>\n",
              "      <th>PAY_AMT2</th>\n",
              "      <th>PAY_AMT3</th>\n",
              "      <th>PAY_AMT4</th>\n",
              "      <th>PAY_AMT5</th>\n",
              "      <th>PAY_AMT6</th>\n",
              "      <th>Y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>3.000000e+04</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>3.000000e+04</td>\n",
              "      <td>30000.00000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>167484.322667</td>\n",
              "      <td>1.603733</td>\n",
              "      <td>1.853133</td>\n",
              "      <td>1.551867</td>\n",
              "      <td>35.485500</td>\n",
              "      <td>-0.016700</td>\n",
              "      <td>-0.133767</td>\n",
              "      <td>-0.166200</td>\n",
              "      <td>-0.220667</td>\n",
              "      <td>-0.266200</td>\n",
              "      <td>-0.291100</td>\n",
              "      <td>51223.330900</td>\n",
              "      <td>49179.075167</td>\n",
              "      <td>4.701315e+04</td>\n",
              "      <td>43262.948967</td>\n",
              "      <td>40311.400967</td>\n",
              "      <td>38871.760400</td>\n",
              "      <td>5663.580500</td>\n",
              "      <td>5.921163e+03</td>\n",
              "      <td>5225.68150</td>\n",
              "      <td>4826.076867</td>\n",
              "      <td>4799.387633</td>\n",
              "      <td>5215.502567</td>\n",
              "      <td>0.221200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>129747.661567</td>\n",
              "      <td>0.489129</td>\n",
              "      <td>0.790349</td>\n",
              "      <td>0.521970</td>\n",
              "      <td>9.217904</td>\n",
              "      <td>1.123802</td>\n",
              "      <td>1.197186</td>\n",
              "      <td>1.196868</td>\n",
              "      <td>1.169139</td>\n",
              "      <td>1.133187</td>\n",
              "      <td>1.149988</td>\n",
              "      <td>73635.860576</td>\n",
              "      <td>71173.768783</td>\n",
              "      <td>6.934939e+04</td>\n",
              "      <td>64332.856134</td>\n",
              "      <td>60797.155770</td>\n",
              "      <td>59554.107537</td>\n",
              "      <td>16563.280354</td>\n",
              "      <td>2.304087e+04</td>\n",
              "      <td>17606.96147</td>\n",
              "      <td>15666.159744</td>\n",
              "      <td>15278.305679</td>\n",
              "      <td>17777.465775</td>\n",
              "      <td>0.415062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-165580.000000</td>\n",
              "      <td>-69777.000000</td>\n",
              "      <td>-1.572640e+05</td>\n",
              "      <td>-170000.000000</td>\n",
              "      <td>-81334.000000</td>\n",
              "      <td>-339603.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>3558.750000</td>\n",
              "      <td>2984.750000</td>\n",
              "      <td>2.666250e+03</td>\n",
              "      <td>2326.750000</td>\n",
              "      <td>1763.000000</td>\n",
              "      <td>1256.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>8.330000e+02</td>\n",
              "      <td>390.00000</td>\n",
              "      <td>296.000000</td>\n",
              "      <td>252.500000</td>\n",
              "      <td>117.750000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>140000.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22381.500000</td>\n",
              "      <td>21200.000000</td>\n",
              "      <td>2.008850e+04</td>\n",
              "      <td>19052.000000</td>\n",
              "      <td>18104.500000</td>\n",
              "      <td>17071.000000</td>\n",
              "      <td>2100.000000</td>\n",
              "      <td>2.009000e+03</td>\n",
              "      <td>1800.00000</td>\n",
              "      <td>1500.000000</td>\n",
              "      <td>1500.000000</td>\n",
              "      <td>1500.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>240000.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>67091.000000</td>\n",
              "      <td>64006.250000</td>\n",
              "      <td>6.016475e+04</td>\n",
              "      <td>54506.000000</td>\n",
              "      <td>50190.500000</td>\n",
              "      <td>49198.250000</td>\n",
              "      <td>5006.000000</td>\n",
              "      <td>5.000000e+03</td>\n",
              "      <td>4505.00000</td>\n",
              "      <td>4013.250000</td>\n",
              "      <td>4031.500000</td>\n",
              "      <td>4000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>964511.000000</td>\n",
              "      <td>983931.000000</td>\n",
              "      <td>1.664089e+06</td>\n",
              "      <td>891586.000000</td>\n",
              "      <td>927171.000000</td>\n",
              "      <td>961664.000000</td>\n",
              "      <td>873552.000000</td>\n",
              "      <td>1.684259e+06</td>\n",
              "      <td>896040.00000</td>\n",
              "      <td>621000.000000</td>\n",
              "      <td>426529.000000</td>\n",
              "      <td>528666.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            LIMIT_BAL           SEX  ...       PAY_AMT6             Y\n",
              "count    30000.000000  30000.000000  ...   30000.000000  30000.000000\n",
              "mean    167484.322667      1.603733  ...    5215.502567      0.221200\n",
              "std     129747.661567      0.489129  ...   17777.465775      0.415062\n",
              "min      10000.000000      1.000000  ...       0.000000      0.000000\n",
              "25%      50000.000000      1.000000  ...     117.750000      0.000000\n",
              "50%     140000.000000      2.000000  ...    1500.000000      0.000000\n",
              "75%     240000.000000      2.000000  ...    4000.000000      0.000000\n",
              "max    1000000.000000      2.000000  ...  528666.000000      1.000000\n",
              "\n",
              "[8 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6hhPNl1Slau",
        "colab_type": "text"
      },
      "source": [
        "### Exploring the features\n",
        "We generate summary statistics for each of the attributes\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Sp2F3gzXX2F",
        "colab_type": "text"
      },
      "source": [
        "**Exploring target attribute:**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCSEICWwXWgX",
        "colab_type": "code",
        "outputId": "31809717-a93d-4d53-e4a3-ae2d0eca1816",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "All = df.shape[0]\n",
        "default = df[df['Y'] == 1]\n",
        "nondefault = df[df['Y'] == 0]\n",
        "\n",
        "x = len(default)/All\n",
        "y = len(nondefault)/All\n",
        "\n",
        "print('defaults :',x*100,'%')\n",
        "print('non defaults :',y*100,'%')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "defaults : 22.12 %\n",
            "non defaults : 77.88000000000001 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W4TWo-gkYTql",
        "colab_type": "code",
        "outputId": "8f317a10-f355-418c-8ee2-c2ded888f102",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "# plotting target attribute against frequency\n",
        "labels = ['non default','default']\n",
        "classes = pd.value_counts(df['Y'], sort = True)\n",
        "classes.plot(kind = 'bar', rot=0)\n",
        "plt.title(\"Target attribute distribution\")\n",
        "plt.xticks(range(2), labels)\n",
        "plt.xlabel(\"Class\")\n",
        "plt.ylabel(\"Frequency\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Frequency')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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A8aMqSpI0Mw17on4/4NnArXD/A7sePaqiJEkz07Ch8oequmtipD3G18uLJUl/ZNhQ+UGS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\nhJtHbfUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7Mza2jGiL_9",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxsZ8GTGarMC",
        "colab_type": "text"
      },
      "source": [
        "**Checking of categorical attributes:**\n",
        "\n",
        "since all categorical attributes are in numerical format, there is no need of converying categorical data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSiH-BxjyJ_e",
        "colab_type": "text"
      },
      "source": [
        "**1) Analysis of categorical data groups**\n",
        "\n",
        "- Sex\n",
        "- Education\n",
        "- Marriage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s61SSRII00UB",
        "colab_type": "code",
        "outputId": "5e3544bf-f653-4931-8c64-807c3223e2a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "df[\"SEX\"].value_counts()\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    18112\n",
              "1    11888\n",
              "Name: SEX, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z92LGXPKetjL",
        "colab_type": "text"
      },
      "source": [
        "**2) Analysis of categorical data to default paymment(target attribute)**\n",
        "\n",
        "- Sex\n",
        "- Education\n",
        "- Marriage\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3IJzhwwe5KK",
        "colab_type": "code",
        "outputId": "1b12b290-decf-4b25-8209-1d18cb36c2a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "#proportion of target attribute (for reference)\n",
        "print('Total target attributes:')\n",
        "print('non defaults :',y*100,'%')\n",
        "print('defaults :',x*100,'%')\n",
        "print(\"--------------------------------------------------------\")\n",
        "#analysing default payment with Sex\n",
        "sex_target = pd.crosstab(df[\"Y\"], df[\"SEX\"]).apply(lambda r: r/r.sum()*100).rename(columns = {1: \"Male\", 2: \"Female\"}, index = {0: \"non defaults\", 1: \"defaults\"})\n",
        "print(sex_target)\n",
        "print(\"--------------------------------------------------------\")\n",
        "#analysing default payment with education\n",
        "education_target = pd.crosstab(df[\"Y\"], df[\"EDUCATION\"]).apply(lambda r: r/r.sum()*100).rename(index = {0: \"non defaults\", 1: \"defaults\"})\n",
        "print(education_target)\n",
        "print(\"--------------------------------------------------------\")\n",
        "#analysing default payment with marriage\n",
        "marriage_target = pd.crosstab(df[\"Y\"], df[\"MARRIAGE\"]).apply(lambda r: r/r.sum()*100).rename(columns = {0: \"unknown\",1: \"married\", 2: \"single\", 3: \"others\"},index = {0: \"non defaults\", 1: \"defaults\"})\n",
        "print(marriage_target)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total target attributes:\n",
            "non defaults : 77.88000000000001 %\n",
            "defaults : 22.12 %\n",
            "--------------------------------------------------------\n",
            "SEX                Male     Female\n",
            "Y                                 \n",
            "non defaults  75.832773  79.223719\n",
            "defaults      24.167227  20.776281\n",
            "--------------------------------------------------------\n",
            "EDUCATION         0          1          2  ...          4          5          6\n",
            "Y                                          ...                                 \n",
            "non defaults  100.0  80.765234  76.265146  ...  94.308943  93.571429  84.313725\n",
            "defaults        0.0  19.234766  23.734854  ...   5.691057   6.428571  15.686275\n",
            "\n",
            "[2 rows x 7 columns]\n",
            "--------------------------------------------------------\n",
            "MARRIAGE        unknown    married     single     others\n",
            "Y                                                       \n",
            "non defaults  90.740741  76.528296  79.071661  73.993808\n",
            "defaults       9.259259  23.471704  20.928339  26.006192\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kOriUQ0wxbhD",
        "colab_type": "text"
      },
      "source": [
        "**Conclusion**\n",
        "\n",
        "categorical attributes SEX and MARRIAGE have approximately same distribution throughout groups of categories"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVUV0z5p0OEf",
        "colab_type": "text"
      },
      "source": [
        "**Meaning of attributes X6-X11**\n",
        "\n",
        "The numeric value in these attributes showedthe past history of a credit card holder, example -2 means:No consumption of credit card, -1 means that holder paid thefull balance, 0 means the use of revolving credit; 1= paymentdelay of one month; 2= payment delay of two months andso on."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MewzQ1N6e6sf",
        "colab_type": "text"
      },
      "source": [
        "To find out the relationships between the features, we calculate the correlation coefficients with the target for all attributes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awXnqvLOS-wB",
        "colab_type": "code",
        "outputId": "0e3394d2-463b-4422-b4a9-ca122600f1d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "df.drop(\"Y\", axis=1).apply(lambda x: x.corr(df.Y) if x.corr(df.Y) > 0 else -x.corr(df.Y)).sort_values(ascending = False)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PAY_0        0.324794\n",
              "PAY_2        0.263551\n",
              "PAY_3        0.235253\n",
              "PAY_4        0.216614\n",
              "PAY_5        0.204149\n",
              "PAY_6        0.186866\n",
              "LIMIT_BAL    0.153520\n",
              "PAY_AMT1     0.072929\n",
              "PAY_AMT2     0.058579\n",
              "PAY_AMT4     0.056827\n",
              "PAY_AMT3     0.056250\n",
              "PAY_AMT5     0.055124\n",
              "PAY_AMT6     0.053183\n",
              "SEX          0.039961\n",
              "EDUCATION    0.028006\n",
              "MARRIAGE     0.024339\n",
              "BILL_AMT1    0.019644\n",
              "BILL_AMT2    0.014193\n",
              "BILL_AMT3    0.014076\n",
              "AGE          0.013890\n",
              "BILL_AMT4    0.010156\n",
              "BILL_AMT5    0.006760\n",
              "BILL_AMT6    0.005372\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C6c_Gz6wUrJ8",
        "colab_type": "text"
      },
      "source": [
        "We observe that the \"repayment status\" attributes are the most highly correlated with the target variable and we would expect them to be more significant in predicting credit default.\n",
        "\n",
        "The least correlated attributes are the bill amounts.\n",
        "\n",
        "We can also generate a correlation matrix (heatmap) to see which features are the most correlated with each other."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2Mg6Zi_Q_rf",
        "colab_type": "code",
        "outputId": "c62745cc-14a2-4161-b7f0-605bf45f246e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 964
        }
      },
      "source": [
        "f = plt.figure(figsize=(19, 15))\n",
        "plt.matshow(df.corr(), fignum=f.number)\n",
        "plt.xticks(range(df.shape[1]), df.columns, fontsize=14, rotation=45)\n",
        "plt.yticks(range(df.shape[1]), df.columns, fontsize=14)\n",
        "cb = plt.colorbar()\n",
        "cb.ax.tick_params(labelsize=14)\n",
        "plt.title('Correlation Matrix', fontsize=16)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Correlation Matrix')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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z9Zcp8rctLdLSilqUlMpKpQtQavAFnbqx/jPCYns0ucYxYdF9GNizbL0FlcGF\nwCnp8x7ATwir9LnALjXrFhoQNB1jEDGTwD3pRW1EbtlgIohTO7kAfHSOHS5cX6ulKj2Iq3J9ytZR\n9vFrtMxftoaclqUK3PduRK/2fsAmdZYPIdyO7ydmfRqe8otooEqLtLSkltwxs1hXw4DtiU6Gcbnl\n+xEzp32+q22lRVoGuhYlpTJT6QKUCrioUxs2ziWiuR9IZ4/HX4G76WcBgogpUSG8NK4FZswtu5+I\nX/EG4TVxHDBbM68JEW3/fqY1bGyU8tYpuwyrmIjZHtamJu4JJTSkgcWAFYDVS9axMjFMaacydaTj\nfo3oUR1agXtlH2Lqx9IDHxMBm58Dtixo/5cRM6CcSXjjXUQMLxyaW2co0Ti8h3A1nkFapEVaphxj\nPmD29DkfSHymVK+9CFyVyx9eu660SMtA16KkVJVUugClPl7ALqysTG3YOI8YirIfETD0aToNAC1t\n2CCmRZy5Jm84YcA4In3/P+AVIgr0IoQ73j+BhQrUNSuwKDAXndNm1TVsAEun7ycVpadVEzFV5Svp\nemZTXm4EzJSWN+0BTTRSHyJ6QV4i1xPS5DLZjWgsP0rM3nNVSTos3eMdKX03Xx81++UJ2Dvp2Kae\n1iZr2ZmYgvQb1DGw9EUPnV5caxHTs65MRLX/W6rbHiGGtS2d1htKNBovaHQ5SIu0tLCWrYBX03G/\nTnqm5JZnvd7jmbpx2PB3JmmRllbVoqRUpVS6AKXpvHARGXyOz1gnb9j4NfHC/xCdBo2WjqGRXobG\npYr92/lzJhqglwL/SJX/yrntBlOglwawLHAn8CTwFhGMdO60bBCwLmHYeJjOccKnErOeDEOW9Kwc\n10zltxWweEq3EfFg9qJmTHbBWnYAPiSC6q4J7EgYFnZocpnsmHRsSxjovkoYWQqLCdMDTZcCPySm\nkT6Gmh7VZtzPhHdWO7B5+j4LYVBcooTymJ0IbLh/+r4AYYjbD1iIBvWYpf3eAxyUy/tcqucfI6aG\nPpwwPBmdLspF9BpKi7S0jBbiOXwK8G+iTn+dmGnlVMKQksX3GEH0ev8HuLXR5SAt0tLKWpSUqpZK\nF6A0HRctZi55H1ilB+vmDRsHkQwZtL5BYwViTO5ZwGZM25BahejhfwUYncsvdPwgMaXu+8DZREPm\n9+n7xnkNdHpsPEA0wIbndSo5RE/DoyQXy1z+H4CngO2apGMJYrjWvrm82YC7aKJ3Tbrnnwa+kctb\nHLg1vdwcBIxqoh4jxr7fSczWszthWDgs5X8XmLUJOtYgGkNnp++LEYbOpwiDz19pYpwNosH2AmF0\nXTy9fN5FBGt7mhj6NnuDjrXT+ihsAAAgAElEQVRzemmdM31/mJiub2OiUdgBXJBbv8iYBNIiLS2j\nhZgR7iXCg3NBYP/0O70f+AGwTFrPgF2I4ItFBXmUFmlpSS1KSlVKpQtQ6uUFizHjnwJb9WKbtprv\npY9972MZLJRehs6oybea70cS8UTmbpKu5YiGy0m5vM8Bk0kNrvw1ITw2xgO3lF2mVUyE8e5lOoea\nzJRbdi3RWCzcW4MYHnQRuTgaKf/HwO/S5yFN0DGUmPIwH1D2r0RPzW3pN/EsBQanrNGT9Z7+ENg+\nfd6ZMGy8SRgWmnF92ohpmW8nGkMvAb8ghulsTRg3/tWMa5T0jCJmXVoVuCKVz6hcWT0CbJu+99Vb\nYzQRQ2SntN87yHnwpRfeprgcS4u0tIoWOj06TwPOzOUPIowpTwETgR8xbXDxRhtXpEVaWlKLklLV\n0iBEy2Bm2wG/JALP/Sk/z/tn0JHbR5u7TypEYPPYhGi4/ChfBu4erYSYsxvgZsJVf/2iBaVjHkF4\nXPwut2h7otE13MwONrMVzWxRd29391uB7YgebjEtlxNDhc4EcPePzWyGtGxrwsPlgCboeBk4zd3v\nBsjdc5704e6T07IZixBgZoPS7/Zsd3825X2baECvDXzJ3RdNqx9RhIZa3D2rV94nvKVw94uI8pod\neMLdPypSg5kNdvd2ImjqR8AZhCfPoe5+gbtfSQQgXAw4uEgtGe7+KvGbP424P65Kebj7EcRwuN3T\nd+/jsV4ihkFdSAxf+7K7vw1T7pkX3b3dzNr6chxpkZb+pCXVGRDGxz3MbI70/V+EcWUtwqi+GrBz\nzXtGBw1EWqSlVbUIUTWyoE2i4pjZ3sTc7QBHufvpPdzOco39/YF1gB1buXIzs8uAudx9vTrLzN3d\nzIa4+2Qzu4lwwdsw9zAoStdIomd2fuCLRFyP7wM/Bd4BNiBc02cjetYvc/e/FKmpVcldx72BY4Hz\n3P2EtGww0Vi8HviHu5/UTF1EvdlhZucQ9+HX0rJ/Ave5e1Maz2a2HPCqu7+Zy/sz8KK7H9SE42fX\naFfgi+6+p5k9RAR0vYZo1P8E+F6R9U0y1GaNnxOA69z9jtzy2YnhQ+e5+5lF6UjHGpTujTUI48qC\nhMHp+tyyo4CVsvumAceaLx3rr+5+Rt/PQlqkZUBpGUd0/KxOxHDa2t3fSMvmA/6X6rkp71LSIi3S\nIkT1kKdGC5AaducA2xCRjk8ys89syNUYNPYhAgld3uIGjTbgE8DNbHBq4E4hV3GfmcrtYGCfog0a\n6dhvAVsS08Y+D3yPeMgc4+4/cvdNidkQTiWCPD5ZtKaeUluOZZO7jlcTwWB3M7Pspbfd3ScSM99M\nbLau3O9nAjAJwMz+Dowkhj80S8tjNQaNWQnvlaeadPzsGt0JLG1mrxKeUdu7+4+I4V9rEh4tRepo\nzwwb7n503qCRmIEYDvNCkTqSluzeeJiYhvIt4LtmthAwNNVf6xN1RKOO9TZxbtMYeZuFtEhLq2nJ\n9WDfRjyXnwQ2dfc3smXu/kpqFA4qslEoLdLSqlqEqBLy1Kg4ZrY6Mf3o1u5+lcUwh12J2UxOc/dj\nu9iu1qBxBrC7u1/RHOWNw8wWIIKi/jl9P55oMH3B3R/Oen1y648keogL9YQwszmBhQkjy0vu/m7K\nn51oiK8JrOXuT+d6tafyJClKW28ws1WAo4kAlB+UraeW1OOwE3AUESPlecIbZg5gBXf/tCRdpxDR\n+Wcggngu4+EdNLiZmlJDeWYi7sdcwJrNMOLljr8QEYjsOeBAd38tt2yq+75ZmtKx24gI8L8nhsOs\n0+RymZkY+vJTYmjMa4TX2AiiPuvzPZIr32WAx4nguaXU8dIiLS2s5QHgYXffrYzjS4u09ActQpSN\nPDUqjJkt4zGOfzF3vwqih8PdzycChn7PzE6st23OoLE3JRs0zGwpM1s3fe5pHJBs2yGEgeJYM8vc\ntX9BNG6vMrOF63ieHExMq/pQ35R3q2s5YrrYS4AbgR+b2fwA7v4OMfTkKeBaM1s+ux65hl0pDfFa\nzGxFwtr/Yt6g0dvr1Ifjj/ysddz9FaJhuCYx3d+7hKFvBXf/1Bo05ronWmqYlYiZMpoGGjR6oyP9\nPrYkgnLOTQwDadg49B5enxeIqeP2yhs00rKGGTSmo1y2I2ZTGAWMLaFcPnD3y4FlgAuIYKZXkgwa\njfCOynriiFmevkfcB6UgLdJSJS09+Y3m6oOfAsub2cqN1CAt0tLqWoRoKbwC0UqVpk3A8oRL2Vl0\nRjseXLPO7sTMGid2sY/diJkIti75XK4Crkifex3tP5XFtYTxIJs1YDNi5pCXgL2AlYGtiLgj7wEr\nFng+KxKznJxF9NCfSQxFODgtz2aFmI0I3PQk8Pmy76kuzmMCcHJJx9+SiH/Ql300ZGri6dFCNOSv\noIHTJPdWBxGYdgPC06ah0zU34vqUda8Aw4gplU8ss1zoIto8Bc4wQYVmt5IWaSlDy3TUF0sTsQn2\nK+CcpEVaWlKLklKrJXlqVJeXiIb8msBpFmPGp+qV9k6Pje+Y2U/yG5vZMMLleiuPGQDK5Dki/gHu\n3qse2zS05FEiVkE7cICZbeHu1xDzbz9KzAhzHzE/96LEkI+HG6g/r2cZwrPhV+7+bXd/hBg778Aq\n0Dle2GM4yqZp2W/MbGgRmqYHM1ueiIXwQ3c/Opd/rJmd1SwZwCJmNmNPetAtGJT/7I0b5tErLYnr\nCSPbp2Y2tEFaeqXDY3aRW9z95KRjSMllUhS9LZcJwM3ufmyZ5ZLVBbn7dlDyXJlmGIyZrW9mC/Za\nTOdsT5jZV4nZcPqEtEhLq2rJdkfv6ot/E/HKzktaGumpKC3S0qpahGgpZNSoGGa2kpktkhrEJwK3\nEEG0MsPGVC7UybBxBLByvjJLL/XnuPvVzT2DutwKLGNms/ew8Tql8e8RIX2Quz8BHEp4phxmZpu4\n++3uvgmwKjGry/pEI/OxYk4DiKEtMwP3Wuf0nQcAMwEjzOxUM/uamS2R9L+X9G3jFZlK18xmAn4O\ndLj7cbn87wKHEG7yzeANYmjAHPUaefXIGYzcGxvwtlda0j35Tmaka+C1nR4dU9bzxsZp6a2WIl+m\npqdcphgxyi6X3H3bUc+wa2GE/h0xpG50T4Xk921m+xKxTfp0rtIiLa2qJcf01BeXZR1Hve18kRZp\n6adahGgtinIBUep9ItzOPiaGUCyW8mYlpkb8FxEbIxuK0lazreX/l3we8wDz575/jgiUt3heez2t\nRHyCB4AxNfnZkI5lgJuBvwHLlnR+lxOxHTYDjiEiuB8DbAFcSvTgTyCMOQeWfT3q6B8CbAy8DPw5\n5R1OzNSwUROOn92rsxIB4tbq6n6o3SZ9/gZwREW07NIILf2sTKRlOrQQXmZPEh5Uo3ugI7/vfYhp\no7dp0LlKi7S0nJbp/I0Oyn1uSH0uLdLSylqUlFo1lS5AKV0IGAz8lhgbdz0R/Cdv2DiVTsNG3THi\n3VV+TTyPWYH/EvEuniaCf52Vvu8JjPiM7UcQRo1ngJVqlmWGjeUIQ0JTDQb58ibihLwOvA9sXrPe\nUCJI4S+Bpcu+JvXukXS/rUf0CvwnncvY2vuIiNuyaoOOvS5hlFowfW8D7gaO74nm9HlfIp7Jpv1B\nS1V0SEs1tBCzKT1NBMJdsM7yUUQMn9G5vH2IOEINaaBKi7S0mpaBWl9Ii7Q0UouSUqun0gUo5S5G\neDQ8S0Tsvws4G1g0LcsMG/cCp9OgoHcN1p8ZW1ZM6dvADwnPio8JA8B/gb+k/F2ImCFzp+0yo8XM\naZvx5AJsEmMNs3X+BJzfhHOq9YgZmvt8IfACMZ5xpkxjbXlUIREvnAcRU83OlssfS8QluS9fzun/\nCYSRbakGHH9poifvHWJqy5vpnDnmvPz1r9mutkfvXfr+AlwJLVXRIS3laaGOIZqpG4cL5fLnIby/\nXqXT222/RpyntEhLq2op+jcqLdIyELQoKfWHVLoApXQhoud8RsJD4+CUHmNaw8bJRGP/gLI11+hf\njhg2M2udZXMRlufziBlKTiKMM88AzwOjsjLIbTOMiCcynmkNG4MJw8iRBZ/T8sTsJVuRG+rC1C5/\nf0rXaRc6DRt1Zzwo8dosTxguzgZ+ULPMCMPG68BfcvknEUNoVmmQhkHp/l4C+CJh8Pod8AgRALbb\nBzKwNw3qXayKlqrokJbmawHmzH3+rMbhAkQdehvhljwkrbMIEYR52z6eo7RIS0tqSfvq9/WFtEhL\n0VqUlPpDKl3AQE6pIlu4Jm8/YjjATIRL2cNMbdiYjbDMFjYl4HScx4rAp8AxubxBRIM56/U/nTrT\nVBEN7l2JOBV/I4KerpKWzUT07rxITM84W3oAnEh4SCxe8HldQHgq3ETMRPNzYH5ghpr1rgIeIqaW\nnbHs61GjbRmiF+AUckN/CCPM1+j0fBlL9BRcBhxPeNY0xKCR3Q/5/7n82QkPpHZg+5RnNevsDUyk\nQVMTV0VLVXRIS3O1AF8G/gGsm8vrqnH4FDHs8F6mbhRmXnHz9PH8pEVaWlJL7lj9ur6QFmlphhYl\npf6QShcwUBOwPdET/gTRGF46t+wvwHfT5+8SMSZ+DCxZs4/SDRvASkQQ0FNq8oem/5lR41DC6yJ7\nsTHCu+Mh4BrC7e7yVInfD+ycnWNa/jrhpndd2s9KTTi3scBPiDGPY9O1uo6IE7I8adhMWvcGwhtl\nlrKvSU7TzISh6Lc1+UcTxpqJ2QMz5a9LuDF2ACs34PirE0aVITX5lr930wP8DOATYNeadechhsz0\n6cFdFS1V0SEt5WkBViYMtVcBX8zvv866CxPebM+RaxTWW3c6z1VapKXltBT9G5UWaRkIWpSU+lsq\nXcBAS3QOn/gdYQy4i4ij8Qdi5oxRRK/6JbltDidmqji0bP0157IsYZg5piZ/L8IToI1Oo8YyxMtN\n5nGyIuE2dwZTBw7bgOjdeRTYMpe/I3AYMYPAQk06v/mJoSX75/LWJLwYxqdr9o38+mVfkxr9owlD\nTL4cNyRim4wlDGUfETFBBqW0FrBIA469NmEcuZgwWC1LzgiU1skP45kVOBe4vc6+5ugPWqqiQ1rK\n0UK44c+QPq8I/JswYH+xi33PRoy5HkVnHIKGxOmRFmlpYS0Dor6QFmkpUouSUn9MpQsYaCn3YjAr\n8H9ETIYfEI3Na4kgodekim+X3HY7UgHPjJyekYQx5kFSLImUfyRhWV6zZv1ZCM+ALVJF/j5wYlrW\nVvN/XWJ4yZXUidFR4DktCuxUk7cTYdhYJH2/kIgFcggxHKUjXbdKDTtJWtchDDAr5fIWYmqvoJ+m\nc1itwcdelxiStB0xfOp+wmvkG0w9DCbfMzGcqQNgNap3sRJaqqJDWpqvhTDWvpL+Z15sK1GncZiW\nzUN4fp1T75h9PEdpkZaW1JL21e/rC2mRlqK1KCn1x1S6gIGUgM2A7wHzpe+zExbbO4G9Ut5GwPeB\nl4Bl6uyjdMMGneMAzyLGy56Qvh8KvAlsVLO+EdOc/hVYkvDEeI2pg2/WjhfcK1X+K9Tuq6BzGkVM\nb/oasF8ufxliDPGawPnA/5jaSLAssETZ16SLc1qZMFhMMyYzdw1XT9dw+QYdM3+Ms4HT0ueNgN0I\nY9YNRMDbodTpvWvgC0QltFRFh7SUq4XwPnuK8JKqbRxeQ2ocAnMQsYSeocZFuVFJWqSl1bQMtPpC\nWqSlCC1FJOK9fo2q61Tq/6l0AQMpEfEZ3iCGUWQzfswGXERYbPelM4jWbOl/1WbSWDjpXzl9/yFh\npLiRCEi5VsrPV+K7EO6rKwGrAqsQL0h/BL6QW2/KmEJietsPgS836bzWSA+WfxJGjENzyzKPjJeA\nz+XyK3Vt6ukivEheIQVVZdpYJz8k4oTMXoCOPQmD3cy5vBeJGWWeSemP1ATLLahMKqGlKjqkpTla\niKGG+f3cSwSCHsu0jcOrgc2JRuGT1ARWbMB5SYu0tKSWOtr6ZX0hLdLSTC0NPKdrgT+kzzJqKJWW\nShcw0BIxC8gLxCwftYaNe4lhDZlho1KNZiI4ZlbhblVzTq+lcxie105MDfpReuH5GDg85a9FxNi4\nnNzQBzob29sS8SBGF31euWOfm455HhHr5JCUP3e6NkeVfQ260f454GfEOObhufyt6QzWljfIzJuu\n27vAcg04/nrACYSR5Ku5/MeBY9Pnh4mX3rmJ+B0/SGXeUO+jqmipig5pKUcLMRX0JcDtwPdy+XdT\nv3H4GGE8fZjGN1ClRVpaVcuAqC+kRVqK1FJkIrxPrihbh5JS6QL6c2Jqb4X8GLkfUt+wcSHhKfC9\nqlVoRGP5beC0THPN8pNT5XwSKYBR+vwxEYjyI+DUfLkAqxGN7T8ytWGjDTiHeKkaUeR5peNlcU7W\nIYYDbQb8FrgPOCAtuwj4e9nXoQv9g9N900GnUWb33PJtid60dmLmlusJz5rxwOcbcPw9CaPWbURA\n24+AA9OyHQj35BeJF+S60/g16n6vipaq6JCWcrQQ026/R3h5/Sb9NvfKLb87/f7G0tk4/DzxDMiM\n2o1qFEqLtLSqlgFRX0iLtBSppdGJznf4rPNyS8L7euaqalYaGKl0Af05AXPWfO+JYeOvRKO0Mi5c\nwIyEgeHnNfkzAUsAKxBDR44GHiGMMmcRBo3tyRk0ctt+lQg2ujo1hg3g+PQwmCamSAPPaTSwbU3e\nSGKK2e8SgU1/SwwL2hFYkIjxsUfZ16OL8zk43UvrAd8hPDCuTHkzEtPnHkYEdruaiH/SiFlO9iQC\nw25LGFeWTffKC6mMl04P9EdqtssPkWnUuNVKaKmKDmkpR0va9yTSrEPEGOmriDhB8+bWu5nwolqX\nZFjNLWtko1BapKVVtfT7+kJapKVILUUkkjEz9/2LwAfAgmVrUxrYqXQB/TURvf63A2Nr8usZNg6n\n07Axgk7rZyUqtVQp3wF8K5f3FcKb4kNiWtc7CSvt0UQvzwdpndeBy2r2dzQR52HF9D0binIhMff2\nx6SYHQWdz2gitkk2c8nOwFJp2UZEjJCFgUUIw8bdhEvgz4DFyr4eXZzT2oQnzerp+5yEoaODGDrz\nfdJ0ug085ti0/+Pz9yvwzVS+i6Xv+xOeIosXeP6V0FIVHdJSjpbcvo+ryX+YMJC+k+qTrVL+bYTR\nt88eU9IiLf1Qy/Hpe7+sL6RFWorUUtD5LUq87/8aOJaY4vnzwE3ABmmdQmLpKCl9VhqEKIrXiIrt\nSDNbO8t093Yza0ufjwAuJQKE7mdmc7r7h+7eYWaD3N1LUT4tw4io5sub2bJmdhQxhm4UMYXr/sDi\nwFnufjJwBjCGCBg2HhiRlYGZHUnEDdnd3R82szZ3v5Oo8Dcj3PLWdPcHCjyfQUQv0z1E4/+LwI1m\ndhAxI814YBV3f54YbvMiEd35dHd/rkBd0427306Mw/y2mc3k7m8SwU+fJow0Y4Fnzex0M2szM2vA\nYV8mjF1jzGxs7n6dheilmJS+35++r9mAY1ZdS1V0SEs5WrJ9r2ZmqwGY2RXEtHw/IOq34cAPzGwO\nd18H+D3h4dZopEVaWl1Lf68vpEVaitRSBIOImVpmJ2K2XQrcQrxj7gDg7p9m7RwhmkrZVpX+nIiG\n/nVEBbB2Lt+Y2s3sXsJDoRKeGV2cy/rAZMKz5H1i3G02o8ZgYtjMdd2UQTas5nVg4zrrjQTmp0lW\na2LYzJXEuMYtgE2BvyedWcCzLNbGouRcb6uaiCE9dwAzEGOhXyVNmwvMQ3ikfK7Bx8zf40ul++Rj\nph3acyPwp4LPvxJaqqJDWsrRktv3den3eD+5SPZEr1YHsE3Ndg0fiywt0tIPtPTr+kJapKVILUUn\n4r19VaKD83Vyw9SLqBeUlLpLpQvo74loPE9j2EjL5ieGP5yT/fiptmFjNDEda22skEHELCZnEUE+\nrWb5EsQ0qRPonP3E6HTLO4UYsjJLk89nKeBv6cGyNGEMWBm4Ati56teji3O6Jb10vgys0KRjLpHK\n8SGiF2LHlN9GZ+C4eZvxgKuKlqrokJZytKR930DEtvlayhuU6r2Viej300x/XeB5Sou0tKqWfl9f\nSIu0tFqi8/09+z+C8Dp/B/hJ2fqUBmYqXcBASNQxbBA957cRsSSyKdBarnIjAomdDPwPWKKb9RYj\nPCH+Aaybyz+RMHaMKfHa/IMwbKxdhoYGnUf2YPkSMeRku3x+E46/eCrDx0lxPeodv0kvE5XQUhUd\n0lKOllTnXVenzvsLEWixaVN2S4u0tLCWAVFfSIu0tHoihtjsSXSsnVG2HqWBl7KGkCgYM1uCCDQ5\niPDMOABYgAiWOdnMBrv7p2Vq7C1mtjMRO2N7YBN3f/Az1s/KYDAxw8j6hFHji+5+f8Fye6rrRHe/\nrSwtfcXM5iEC1F7p7kc2+diLE9MAtgEnuPsdzTx+FbVURYe0lKMlV7cY4ZF2CDET0XKp3h/k7h2N\nOp60SEs/1TIg6gtpkZZWx8xmBbYC7nL3p8rWIwYYZVtVBlIivAKuJayYT9DpodFykYKJoRs3EcNO\nejz1aiqDa4go0JOIgJxVOJ9M17+ANcrW08dz2YkYv7l6CcdegohL8iBpdpuSr2npWqqiQ1rK0ZLb\n96dEtPvS6n1pkZZ+oKVf1xfSIi2tnmixYdtK/SfJU6PJmNnSxGwhh3lECG45D40MM5sb+MTd3+vl\ndksRM6Qc5e6PFyJuOkjX5mTi2rxYtp7pxczmBy4CvuHuL5Vw/GWAvYj4KU3pyau6lqrokJZytFSp\n3pcWaWlhLQOivpAWaRFC9B4ZNUqklQ0afcXMhrj75LJ11GJmQ9190mevWW3MbEZ3n1gBHW3u3l62\nDqiOlqroAGnpiiK1VKnel5b6SEt9KqZlQNQXvUVa6iMtQvR/ZNQQQgghhBBCCCFESzKobAFCCCGE\nEEIIIYQQ04OMGkIIIYQQQgghhGhJZNQQQgghhBBCCCFESyKjRj/CzPYuW0OGtNSnKlqqogOkpSuk\npT7SUp+qaKmKDpCWrpCW+khLfaqipSo6QFq6okpaRDGY2TpmdrWZvWxmbma79mCb5c3sVjP7OG13\nrJlZo7XJqNG/qFJlIi31qYqWqugAaekKaamPtNSnKlqqogOkpSukpT7SUp+qaKmKDpCWrqiSFlEM\nI4DHgEOAjz9rZTObBbgeeA1YNW13BHBYo4UNbvQOhRBCCCGEEEII0X9w92uBawHMbFwPNtkJGAZ8\n090/Bh4zs6WBw8zsR97AaVg1pWtFmHOONl949JA+7eONt9qZa2Rbn7U8+fJcfd7HpxM/YvCMw/u8\nn8ET+j6V96RPJzB08LA+76djSN8dmyZP+oghQ/teLp8O65vXVvuEj2gb1ncdQ9+Z3Od9TGqfwNC2\nvl8fGlCXTWr/mKFtM/VdC333qmtYuTTAwa9h5dIAb8NG/Z4bQaOu0cQ5+96/0P7RR7QN7/tvur/o\ngAZr6eOt2/7hR7SNqEi5NFJLH6vdRl0j6/urQsOei42gYfduA+r/hmnp673SyOvT199zP63nhr7f\n0aftJ03+iKFD+q5l4sR3mTT5o4YPTyiTL6033N96uwEVVYHc/8gnjwMTc1nnuvu5Xa1vZh8CB7r7\nuG7W+R0w0t03zeWtCtwLLOruz/dZeEKeGhVh4dFDuPfvo8uWAcBqR+1XtoQpjHzw3bIlTGHiqGo8\nwADeXHFo2RIAWPDyV8qW0MnkT8tW0ElbdUb2+eC+GzobRluFtFSoXJ7effayJXRSpX6OCr3SenV+\n0tWiIvfL0Herc4EaYWBpFB3VeFUAwPrWXm4o3laRG7dijL5+4mev1AT+9cA5ZUtoOG+93c69f1+w\nbBnd0jbvMxPdfUyDdzsK+G9N3mu5ZQ0zalTnKSCEEEIIIYQQQgjRC2TUEEIIIYQQQgghRCN5FZin\nJm+e3LKGoeEnQgghhBBCCCFEATjQQYXGYDWPu4AfmNmM7p6Nb9oIeAUY38gDyVNDCCGEEEIIIYQQ\nXWJmI8xsJTNbibAjLJi+L5iWn2ZmN+Y2uRiYAIwzs+XMbGvgSKChM5+AjBpCCCGEEEIIIYTonjHA\ngynNBJyQPp+Yls8LLJat7O7vEZ4Z8wH3AecAZwE/arQwDT8RQgghhBBCCCEKwWn31h9+4u630M3c\nZO6+a528R4F1ilMVyFNDCCGEEEIIIYQQLYmMGkIIIYQQQgghhGhJZNQQQgghhBBCCCFES6KYGkII\nIYQQQgghRAHElK4NnexD1CBPDSGEEEIIIYQQQrQkMmoIIYQQQgghhBCiJSnEqGFm48zsmi6WjTez\nw3PfbzEzN7Nj6qx7aVr283r7TvvybtItPdCa30e7mb1sZr8ys5m7WP9hM/vUzJbszXkLIYQQQggh\nhBh4dFT8r9WpiqfGS8CuZjZl3lszGwl8NS3rilWBeVP6cspbLZe3dQ+Pf2Jaf0Hgm8AmwBm1K5nZ\nasDcwO+APXq4byGEEEIIIYQQQhRAVYwafwNGAGNzeTsD9wD/6Wojd3/D3V9191eBt1L2lDx3f7uH\nx/8grf+yu98AXAasXGe9PYCLgQuAb5iZAq0KIYQQQgghhBAlURWjxmTC+2H3XN7uwG+bLcTMFgS+\nRBhU8vnDga8DFwF3AB8DmzVbnxBCCCGEEEKI1sBx2r3aqdWpilED4HxgazObxczGAAsDlzfp2KeY\n2Ydm9jHwAvAOcFTNOtsBL7n7g+7uhHFjz74c1Mz2NrP7zOy+N95q78uuhBBCCCGEEEKIAUdljBru\n/iTwMLADMczjEnef0KTD/whYCVgB2AAYCvzVzPLlsydwYe77hcCXzWy+6T2ou5/r7mPcfcxcI9um\ndzdCCCGEEEIIIcSApGoxIc4H9gcWJYaANIu33P3Z9PkZMzsUuAtYD7jRzJYG1gLWMLNTctu1AbsB\npyCEEEIIIYQQQtTQQesP8agylfHUSFwKLAn8193v+ayVCyQbCzIs/d+DiLGxIuHRkaXjgd3zs7YI\nIYQQQgghhBCiORTpqVtNBcsAACAASURBVDGLma1Uk/dudxu4+wdmNj+dRoVmMbOZjQIMGE1M5/oG\n8E8zGwJ8AzjF3R/Lb2RmbwHHEh4dN6Xsuuft7uML1C+EEEIIIYQQQgw4ijRqrA08WJN3xWdt5O7v\nFSOnW45NCcKY8S9gY3d/y8y2BuaijnZ3/5+Z3UnE28iMGl2d97ZFCBdCCCGEEEIIIQYqhRg13H1X\nYNcerju2N8vTvuutdx/hadEr3H3hz1h+Jd0M03H3dWq01dUnhBBCCCGEEGJg4UC7YmoUStViaggh\nhBBCCCGEEEL0iH5t1DCznczswy7S42XrE0IIIYQQQgghxPRTtSldG83VxKwl9ZjcTCFCCCGEEEII\nIQYemtK1WPq1UcPdPwA+KFuHEEIIIYQQQgghGk+/Hn4ihBBCCCGEEEKI/ku/9tQQQgghhBBCCCHK\nwoF21/CTIpGnhhBCCCGEEEIIIVoSGTWEEEIIIYQQQgjRkmj4iRBCCCGEEEIIURAdZQvo58hTQwgh\nhBBCCCGEEC2JPDUqwpMvz8VqR+1XtgwA7j31l2VLmMIXjqxGmQAMnlidAD8jH5tctgQA3l9xnrIl\nTME6qnN9sLIFdOJWITEVkuIVMunPc3eV7t3qXCSvjpRK3btiWjra1AdaD6tQ1VKp37Ooy8fzDC1b\nAgAdQyr0gBYtg+4aIYQQQgghhBBCtCTy1BBCCCGEEEIIIQrAcdqpkOtUP0SeGkIIIYQQQgghhGhJ\nZNQQQgghhBBCCCFES6LhJ0IIIYQQQgghRBE4tGv0SaHIU0MIIYQQQgghhBAtiYwaQgghhBBCCCGE\naEk0/EQIIYQQQgghhCgABzrKFtHPkaeGEEIIIYQQQgghWhIZNYQQQgghhBBCCNGSyKghhBBCCCGE\nEEKIlkQxNYQQQgghhBBCiEIw2rGyRfRr5KkhhBBCCCGEEEKIlkRGDcDM5jKzX5jZeDP7xMxeM7Mb\nzWyjtPwWM/M66ZK0fDkzm2hm29Tsd0Mzm2xma5VxXkIIIYQQQgghRH9Gw0+CK4BhwB7As8DcwLrA\nyNw6FwBH1Wz3MYC7P2ZmxwG/MrM73P01M5s1bXOWu99Z9AkIIYQQQgghhKgWDnR42Sr6NwPeqGFm\nswFrAxu5+40p+wXgXzWrTnD3V7vZ1Q+BLYBzga8CPwPeAY5trGIhhBBCCCGEEEKAhp8AfJjSFmY2\n4/TuxN07gG8CG5rZ74GvA99w90mNkSmEEEIIIYQQQog8A96o4e6fArsCOwPvmtldZnammX2hZtW9\nzezDmrR/zb6eJTw2dgROd/eHuju2me1tZveZ2X2fTvyocSclhBBCCCGEEKIStKcZUKqaWp0Bb9QA\ncPcrgPmAzYG/AWsCd5tZPobGpcBKNen3+f2Y2UzADsAE4Itm1u0d4u7nuvsYdx8zeMbhjTodIYQQ\nQgghhBBiQCCjRsLdJ7r79e5+oruvCfwWON7MhqZV3nP3Z2vSezW7+QERp2Q1YAxwYPPOQAghhBBC\nCCGEGFgM+ECh3fAEUT49irNhZusB+wPrufvjZvZt4Cdmdq27P1egTiGEEEIIIYQQFcShXwzxqDID\n3qhhZiOBPwLnA48AHxBeFt8BbnT399MokmFmNqpm80nu/raZzUxM33q2u98O4O7nmdk2wAVmNjYF\nEhVCCCGEEEIIIUSD0PCTmPnkbuAQ4FbgceBU4GJg+9x6uwH/q0lXp2U/IeJofL9m33sAy6d9CyGE\nEEIIIYQQooEMeE8Nd/8EOCqlrtYZ+xn72KOL/JeB2fuiTwghhBBCCCGEEPUZ8EYNIYQQQgghhBCi\nKDpcMTWKRMNPhBBCCCGEEEII0ZLIqCGEEEIIIYQQQoiWRMNPhBBCCCGEEEKIAtCUrsUjTw0hhBBC\nCCGEEEK0JDJqCCGEEEIIIYQQoiXR8BMhhBBCCCGEEKIAHKNdvgSFotIVQgghhBBCCCFESyKjhhBC\nCCGEEEIIIVoSDT8RQgghhBBCCCEKosM1+0mRyKhREQZPaGfkg++WLQOALxy5X9kSpnDP6b8sW8IU\nNtxx97IlTOF/a85YtgQABn9UtoIcVfI787IF5KiQFusoW0En1l62gk6GTKjQRaqQFKuSlvbqiKlS\nuVSFtkkVKhSrTsPFOqpTLlVqz1XpN1Slcvlk5mq8SHk1ZIgWQ7eNEEIIIYQQQgghWhIZNYQQQggh\nhBBCCNGSaPiJEEIIIYQQQghRAA60U6GxRv0QeWoIIYQQQgghhBCiJZFRQwghhBBCCCGEEC2Jhp8I\nIYQQQgghhBCFYLRrWpdCUekKIYQQQgghhBCiJZFRQwghhBBCCCGEEC2Jhp8IIYQQQgghhBAF4ECH\nfAkKRaUrhBBCCCGEEEKIlkRGDSGEEEIIIYQQQrQkMmoIIYQQQgghhBCiJVFMDSGEEEIIIYQQoiDa\nsbIl9Gt65KlhZuPMzOuku9Py8bm8iWb2kpn9ycw2r9nPwmmdMXWOcYuZ/bwmbyUzu9TMXjWzT8zs\n2aRl+TrbX21m7Wa2UU3+f7vQnqUbcusdWrPt58zsEjN73cwmmdl/zOxMM5utZr070r52qMnf08ze\n7UkZCyGEEEIIIYQQonf0ZvjJDcC8NWmT3PITU96SwNeB8cCfag0VPcXMNgPuAUYAuwBLp/3+Dzi9\nZt15gQ2AHwN71uzq8zm9m6a8lXN523Vx/NWBe4GZgC2AxYFDgM2AO81s1ppNJgInm9nQXp6qEEII\nIYQQQgghpoPeDD/5xN1f7Wb5B7nlLxIN/yeBX5vZFe5+c08PZGbDgAuAv7v7FrlFzwP31XpKALsC\n1wE/A/5tZiPd/S0Ad38jt9+308c3ujsXMxuUjv8EsJW7d2TnZWYPAU8TRpxDcpv9Adgc2Bf4aU/P\nVQghhBBCCCFE/8TdaHeFsiySokv3t8A7wDa93O5LwJzUeGRkuPuUIR1mZsDuwEXu/gLh3bHLdKnt\nZBXCM+TMnEEjO/ZLwCXADjXbvA+cBBxjZrP05CBmtreZ3Wdm9036dEIfJQshhBBCCCGEEAOL3hg1\nvmxmH9akH3S3gbu3E14Ni/ZS1xLp/5M9WHddYA7gr+n774A9enm8Wpb8jOM/AcxlZnPU5P+KMG58\npycHcfdz3X2Mu48ZOnjY9CkVQgghhBBCCCEGKL0ZfnIbsHdNXk+CYBrgvThOtk1P2RO4zN0npe+X\nAz83sy+4+z29PG6fcPdJZnY08BszO6eZxxZC/D979x5mWVXe+/77o+lWhAgKBpt4IZoYL4eIpo8E\nNVE3ojzEqDuYEEGx5dKJHEncxpOYbDF5QkzcW5QoShC3BEFQtDEJeIuBYDhRxLQSlUQiyF1AQRTo\n+6Xe88ecXSwXq6qru2qx5lr9/TzPeKrWGGOO+a7Z1auLl3GRJEmSumfK00+Gantmaqytquv7yt2z\nXZBkEc2shxvaqvvar/2bbALsBdzbfv+d9uvTtjH+XjRLW1Yk2ZxkM3APzeae/RuGbo+t93/6DO1P\nB+6mWVrT7+PAtcCfzeP+kiRJkiRpG4a9p8bxNMmKlQBVdQ9NMuCXeju1e1D8HPBfbdUX2n5vHTRo\nz0ahRwN3Ac8EDuwpK4Ajk+y+g3F/jSax8QftpqG99348zSksF1TVg2agtHV/SLME5qk7eH9JkiRJ\nkrQN27P85GFJHttXt6XndJGfatsXA4+nOSr1JOD9VfUvPde8B3hrktuBK4G9gZNpkhOfBKiqNUmO\nBz6Z5DPAXwPX0eyd8d9pjmT9NZrEwcqquqY3qCTfAd4FHAmcvR3vkfb+U0mOpUmu/F2SvwK+R5Mw\neRfNzJO3z3L9ZUkuBf4fYMP23l+SJEmSNP4K2DL0uQQ7t+15ui8G7ugrV/e0v72tux74BPCzwG9U\n1Ul94/xv4E9pZjN8A/gUsAZ4UVWt29qpqv4BOBhYC3yUZhbHJ2kSJn+Y5NnAs2hngfRq99e4mHks\nQamqLwEH0SQlLgG+S3NU66eB51XVvbNcDvBHwJIdvb8kSZIkSZrdnGZqVNVyYPks7fvP9YbtiSin\nt2Vbfb9GM+NjJjPuuFJVxwyo+8pM11TV4wbUXQP81hzifP6Aum8Ai7Z1rSRJkiRJ2jHOg5EkSZIk\nSWNpe/bUkCRJkiRJcxa2lHMJhsmnK0mSJEmSxpJJDUmSJEmSNJZcfiJJkiRJ0hAUMOVcgqHy6UqS\nJEmSpLFkUkOSJEmSJI0ll59IkiRJkjQkWyqjDmGiOVNDkiRJkiSNJZMakiRJkiRpLLn8pCOmFu/C\n+sfuPuowANh1fY06hGkvPurYUYcw7dILzh51CNNeut+Bow4BgB+97uBRh/CADs3qc4bhDHwuA+26\nrjufuZ36M+rQY9EMOvLzMrW4I4F0TKU7zyVTo47gAdWh/6WbDn3OLV7bjWC69LOyUIqwxbkEQ+XT\nlSRJkiRJY8mkhiRJkiRJGksmNSRJkiRJ0lhyTw1JkiRJkoZkqkubuUwgn64kSZIkSRpLJjUkSZIk\nSdJYcvmJJEmSJElDUOCRrkPm05UkSZIkSWPJpIYkSZIkSRpLJjUkSZIkSRqCImypbpe5SnJikhuT\nrE/ytSS/so3+RyX59yRrk9yZ5KNJHjvvh9rHpIYkSZIkSZpRkiOB9wJ/CTwL+DLwuSRPmKH/84Dz\ngI8AzwBeCTwdOH+hYzOpIUmSJEmSZvNm4Jyq+lBVfbuqTgLuAN4wQ/+Dgduq6rSqurGqvgKcDhy0\n0IGZ1JAkSZIkSQMlWQL8EvCFvqYvAM+d4bIvAUuT/Hoa+wC/DXx2oeNbkKRGknOSVJIPD2j7X23b\np/vq923X4tyS5EFxJLmpva6SrEtybZL/N0l6+uzf06eS3JvkK0l+vW+s5UlWzxD7N5JsTvKUGdr3\nTXJakuvaeH+Q5MtJTkqyxwzx9pZ3bvsJSpIkSZIm0RS7dLoA+yRZ1VNW9L2FfYBFwPf76r8PDNwj\no6qupElinA9sBO4CArxuAR8tsLAzNW4FfivJ7lsrkuwKHAPcMqD/64BLgPXAS2cY88+BpcDTgFNp\n1u/0P2CAw9p+BwFfBS5K8n9tK+AkzwF+GjgXOG5A+/7A19vxTwae3d7jL4FDgJfPEG9v+YttxSFJ\nkiRJ0ojcXVXLespZ8x0wydNplpucQjPL4zCaBMgH5zt2v4VManwTuA74rZ66X6NJWnxxQP9jaZIJ\n5zEgodC6v6rurKqbqur/tPd4yYB+P2z7XQv8T2Ax8KI5xHwccAHwt8AxbRKm198AU8Cyqvp4Vf1n\nux7o01X1SuBjM8TbWwbOEJEkSZIkaQzcDWwB9u2r3xe4c4Zr/hj4alW9q6q+WVX/CJwIvDbJ4xYy\nuIXeU+PDNMmKrY6lSRhUb6f26Je9gc8DHwVeluQxMw3arsF5Ic2MjU2z9FsMnNC+nLFf23d3mukw\nHwX+FVgHvKynfW+aGSQfqKo1g8aoqhpUP1dJVmyd4rNp48BbSJIkSZLGVBVsqV06Xbb9Hmoj8DXg\n0L6mQ2lOQRnkETSJkF5bXy9oHmKhkxoXAMuS/Hx7/uxhwDkD+h0HXFhVm6rqRpolI4PW1ryj3Qtj\nA3A5zRqc9w3od0Xbbz3wbuBG4BPbiPU3gVur6uo2OfFR4Pie9p9r7/dfvRcluS3J6racOSjevvIy\nZlBVZ22d4rN4ye4zdZMkSZIkaZTeAyxPcnySpyV5L7AfcCZAknOTnNvT/xLgFUnekORJ7RGv7wO+\nXlWDtqfYYf3LLealqn6U5O9oZmj8GPhiVd3Ss7cnSR5Jk1D4bz2XnkdzRMypfUO+h2b2x2OAdwBf\nqKpBmaCjgP8AngKcBqyoqnu2Ee7x7X17Y/iTJPtV1e2zXPcrNJuknAU8fIZ4e92xjTgkSZIkSeqs\nqrqwXc3wNpq9I68BDq+qm9suT+jrf06SnwLeSDPx4F7gn4E/WujYFjSp0Tob+AiwGnj7gPajaKai\nfKk32QEsSvK8qvpST90Pq+p64PokRwDXJbmqqi7vG/O2qrqubV8NfDLJ06vq7kEBJnkq8Dzg4CTv\n6I0BeD1NAuV6mmUzT+29tp1ZQpK1A4beGq8kSZIkaacXpsi2u42BqjoDOGOGthcOqDudZrPQoVro\n5ScAl9Ec2bIP8PcD2o8D3g8c2Fc+w8wbhlJVP2qvOy192ZC+fv8C/CeDEyq9MVwFPLMvhj8Djk2S\nqvohzbm7b+w9ulWSJEmSJHXDgic12v0pfhH42ara0NuW5BeBZcCHquqa3kKz/OO32ikqMzkD+AWa\n5SuzeTewIsnj+xvazUSPAS4YEMNZwP48cHLKiTTP6GtJXp3k6UmekuTVNAmR/o1PfirJY/vKntuI\nVZIkSZIk7YBhzNSgqu6vqvsGNB0PXFdV3xzQ9uk2nlfPMu4PaJIff5Zkttg/DdwEnDyg7ddp9ui4\naMD4dwBfauOkqm4AnkVzSsspwNXA12n2/zgDeFPfEG+n2UOjt3xgljglSZIkSROqGP/TT7puQfbU\nqKrl82lv+6yh2Wtj6+v9Z+i3ouflTfDgBUrtbJGn9rw+h/YUlqr6FLMkc6rqV/te3wn8fltmi39g\nvJIkSZIkaTjGPy0jSZIkSZJ2SiY1JEmSJEnSWBrGka6SJEmSJAnY4lyCofLpSpIkSZKksWRSQ5Ik\nSZIkjSWXn0iSJEmSNARFmKoHHdipBeRMDUmSJEmSNJZMakiSJEmSpLHk8hNJkiRJkobE00+Gy6RG\nR2x+RLj7mUtGHQYAe1+zadQhTLvjuQ8fdQjTXrrfgaMOYdo/3v7vow4BgOf/3kGjDmFapkYdgcZJ\nl5a2rtu7O7/opEYdgbbJP6MH2WXzqCPo0aE/n+rOR0u3/o3u0Od/l35e0pH/KuzSz63Ghz82kiRJ\nkiRpLHUkJydJkiRJ0mQpYMopKEPl05UkSZIkSWPJpIYkSZIkSRpLJjUkSZIkSdJYck8NSZIkSZKG\nImzp1LE7k8eZGpIkSZIkaSyZ1JAkSZIkSWPJ5SeSJEmSJA2BR7oOn09XkiRJkiSNJZMakiRJkiRp\nLLn8RJIkSZKkIfH0k+FypoYkSZIkSRpLJjWAJM9OsiXJl2Zof0GSi5PclWRDkhuSrExySE+fFyap\nGcpTH7p3I0mSJEnSzsHlJ43jgTOAY5I8raq+vbUhye+0becDRwI3AHsALwDeBzyjb6xnAPf01d01\npLglSZIkSR1VFU8/GbKdPqmRZDfgKOBXgEcAxwFvadseT5O4eG9Vvbnv0muSnDFgyB9U1d1DDFmS\nJEmSJOHyE4BXATdX1beA82hmayzuaVsC/O9BF1ZVPTQhSpIkSZKkfiY1mpkZ57Xf/wuwFnhF+/op\nwH1VdefWzklelmR1T/mVvvFu6mu/baYbJ1mRZFWSVVvWrlnAtyRJkiRJ0uTbqZefJPk54Pk0y0+o\nqkpyPk2iY+UMl10OHAg8CvgqsKiv/UXAj3peb5np/lV1FnAWwG5LH++sD0mSJEmaMFvcU2Ooduqk\nBs0GoYuAW5Lps4MD0/tpfAd4ZJKlVXUHQFWtAa5Pss8MY97onhqSJEmSJA3fTpsySrIr8Drgj2lm\nXmwtzwS+CbyeZrbGpraPJEmSJEnqkJ15psavAfsAH6qqH/Y2JPk48LvAKcCbgPcneTRwNs2Rro8C\nfrPt3r+85KfbhEmve6pq4wLHL0mSJEnqsAKmyDb7acfttDM1aPbNuLw/odH6JLA/cGhVnQEcAuwF\nfAK4Dvg88Azg5VX1//Vd+x/AHX3lV4fxBiRJkiRJ2pnttDM1qurls7TdAA+k06rqcpoNQmcb74u9\n10iSJEmSpOHaaZMakiRJkiQNVzz9ZMh8upIkSZIkaSyZ1JAkSZIkSWPJpIYkSZIkSRpL7qkhSZIk\nSdIQFDBVnicxTM7UkCRJkiRJY8mkhiRJkiRJGksuP5EkSZIkaUi2OJdgqHy6kiRJkiRpLJnUkCRJ\nkiRJY8nlJx2x5EebeMLK20cdBgD3PXPfUYcwbdc1o47gAT963cGjDmHa83/voFGHAMC/vu+Dow5h\n2onf++VRhzDtuvseM+oQpq3dtHjUIUxbv6k7/+Ss39id51JX7znqEB7Qpc3Zp0YdgMbFY76xedQh\nTKtFo46gozr02VLpUDAd8r1DRh1BY/Plo45g4RXx9JMhc6aGJEmSJEkaSyY1JEmSJEnSWOrOXGBJ\nkiRJkibMlHMJhsqnK0mSJEmSxpJJDUmSJEmSNJZMakiSJEmSpLHknhqSJEmSJA1BFWzxSNehcqaG\nJEmSJEkaSyY1JEmSJEnSWHL5iSRJkiRJQzLl8pOhcqaGJEmSJEkaSyY1JEmSJEnSWHL5iSRJkiRJ\nQ1CEqXIuwTBNzNNNck6SasumJDckOTXJ7n39Lk6yJcmhffXvTHJrkj376g9IsiHJEXOIYe8k5ye5\nL8mPk3ykfzxJkiRJkrQwJiap0boUWAo8CXgbcCJw6tbGJEuBQ4DTgOP7rn07cA/wvp7+i4GPABdW\n1UVzuP/HgQOAlwC/BhwEnLNjb0WSJEmSJM1m0pafbKiqO9vvL0jyIuCVwBvauuXA54HTgWuT7F1V\nPwSoqo1JXgOsSvKpqvoHmsTIPsBJ27pxkgOAFwO/XFVXtXW/C1ye5MlV9d0Fe5eSJEmSpLGwBU8/\nGaZJm6nRbx2wGCBJgGOBj1bVzcBVwGt7O1fVt4CTgQ8meSnwVuD1VXXvHO51MHDv1oRG6wpgPfDc\n+b4RSZIkSZL0kyY2qZHkOcBRwGVt1QuARwOfaV+fCxw34NJTgeuAzwJnVtVlA/oM8ljgB70VVTUF\n3NW2DYpxRZJVSVZt3LJ2jreRJEmSJEkweUmNw5KsTrIeuJJmpsTWpSPHA5+oqo3t65XAk5Mc1DtA\nm4h4B82zOWWYwVbVWVW1rKqWLVn0iGHeSpIkSZKkiTNpe2pcAawANgG3V9UmgCR7AUcAS5Kc0NN/\nEU2y46q+cTb3fZ2LO4Gf7q1IsgvwmLZNkiRJkrQTKWCq3FNjmCYtqbG2qq4fUH80zTKQw/vqDwbe\nneRNVbVmnve+EtgzyXOq6qtt3fOBhwNfnufYkiRJkiSpz6QtP5nJccDKqrqmt9Ac1zoFHDnfG7Sb\njF4KfCjJQUmeC5wJ/L0nn0iSJEmStPAmbabGgyR5NvAs4I39be0xrhfTLEE5ewFu99s0x8VeSpMs\n+Xvg9xZgXEmSJEnS2AlTtbPMJRiNiUlqVNXyGeq/DjMfDFxVxwyou3S2a2YZ64c0J65IkiRJkqQh\nM2UkSZIkSZLG0sTM1Bi2JCcDfzRD8+VV9esPZTySJEmSpO6b2v5FANoOJjXm7gPAx2ZoW/tQBiJJ\nkiRJkkxqzFlV3QPcM+o4JEmSJElSwz01JEmSJEnSWHKmhiRJkiRJQ1AFW8o9NYbJmRqSJEmSJGks\nmdSQJEmSJEljyeUnkiRJkiQNyVQ5l2CYfLqSJEmSJGksOVOjK6pg0+ZRRwFApmrUITygS2m3Du3v\nk6lRR9A48Xu/POoQpp3xM18ZdQjTTn/EE0cdwrRvrn7cqEOYdu+mh486hGn3btht1CFMu2m3R446\nhG7qyOecZpYO/brQFbVLh35Z6JAu/U/qLsXSJYvWdOTBbBl1ABpHJjUkSZIkSRqCIkx5+slQdSQl\nJ0mSJEmStH1MakiSJEmSpLHk8hNJkiRJkoZkqkub800gZ2pIkiRJkqSxZFJDkiRJkiSNJZMakiRJ\nkiRpLLmnhiRJkiRJQ1Dgka5D5kwNSZIkSZI0lkxqSJIkSZKkseTyE0mSJEmShmSqnEswTD5dSZIk\nSZI0lkxqSJIkSZKksTQRSY0k5ySptmxKckOSU5Ps3tfv4iRbkhzaV//OJLcm2bOv/oAkG5IcsY37\nPynJ2UluTLKuvf87kjx84d6lJEmSJGmsVJjqeBl3E5HUaF0KLAWeBLwNOBE4dWtjkqXAIcBpwPF9\n174duAd4X0//xcBHgAur6qJt3Ptp7dffAZ4B/B5wLPCeHXwvkiRJkiRpGyYpqbGhqu6sqlur6gLg\nfOCVPe3Lgc8DpwMvT7L31oaq2gi8BvjtJK9oq98G7AOctK0bV9VnqurYqvpCVd1QVZ8G/gqYdYaH\nJEmSJEnacZN8+sk6YDFAktDMnPjDqro5yVXAa4G/3tq5qr6V5GTgg0nWA28FDq+qe3fw/o8EfjSf\nNyBJkiRJGl8FTDH+Szy6bJJmakxL8hzgKOCytuoFwKOBz7SvzwWOG3DpqcB1wGeBM6vqsgF95nL/\nnwXeDJyxjX4rkqxKsmrjlnU7citJkiRJknZak5TUOCzJ6naWxZXAFTywdOR44BPtMhOAlcCTkxzU\nO0BVTQHvoHkup+xIEO3eHZ+nSaCcPlvfqjqrqpZV1bIli3bbkdtJkiRJkrTTmqTlJ1cAK4BNwO1V\ntQkgyV40e1ssSXJCT/9FNMmOq/rG2dz3dc6S7AdcDlwNLK+q2t4xJEmSJEnS3ExSUmNtVV0/oP5o\n4C7g8L76g4F3J3lTVa2Z782T/AxNQuPfgddU1Zb5jilJkiRJGm+TcGxql01SUmMmxwErq+qa3sok\n3wHeBRwJnD2fGyR5HPBF4GaavTT2afYmBeAH7bIWSZIkSZK0gCZpT40HSfJs4Fk0e2j8hHZ/jYtp\nlqDM12HAk4H/BtwK3NFT9luA8SVJkiRJUp+JSGpU1fKqetmA+q9XVarqyzNcd0xVPbev7tL2mh9v\nx/3/T3vNoHLb9r8jSZIkSdK4K5rlJ10uc5XkxCQ3Jlmf5GtJfmUb/Zck+fP2mg1Jbknye/N9pv12\nhuUnkiRJkiRpByU5EngvcCLwr+3XzyV5elXdMsNlHwceR3Ogx3XAvsCCH/s5ETM1hi3Jye1xsYPK\nJaOOT5IkSZKkIXozcE5Vfaiqvl1VJ9Fst/CGQZ2TvAQ4BDi8qv6pqm6qqquq6osLHZgzNebmA8DH\nZmhb+1AGIkmSJEkaH+N++kmSJcAvAaf2NX0BeO6DrwDglcC/AW9OcgywDvgc8CdVtXoh4zOpMQdV\ndQ9wz6jjkCRJVOejgQAAIABJREFUkiRpge2TZFXP67Oq6qzedmAR8P2+674PvHiGMZ8EPB/YABwB\n7AWcTnOQxqsWIuitTGpIkiRJkrTzuruqli3wmLvQ7JN6VFXdC5DkjcA/Jtm3qvoTJDvMpIYkSZIk\nSUNQbN8JIx11N7CFZqPPXvsCd85wzR3A97YmNFrfbr8+gQfP+thhbhQqSZIkSZIGqqqNwNeAQ/ua\nDgW+PMNlXwL2S7JHT91T2q83L2R8JjUkSZIkSdJs3gMsT3J8kqcleS/N/hhnAiQ5N8m5Pf0vAH4I\n/G2SZyR5Hs2RsCur6gcLGZjLTyRJkiRJ0oyq6sIkewNvA5YC19Ac17p11sUT+vqvTvJims1B/w34\nEfD3wFsXOjaTGpIkSZIkDckUY7+nBgBVdQZwxgxtLxxQ91/AS4YclkmN7ggs6shqoC79natRB/CA\n8d/fZ+Fdd99jRh3CtNMf8cRRhzDtpEct6DLBeTkzm0cdwrRr1y0ddQjTds3UqEOYdsum7ny4TC3u\n0Idudx5Lt3Toj6gr/y5u2Ksjvz8BS+7vzmdLLerIH5DGwi4d+XXBn1rtiO78KyBJkiRJkrQdnKkh\nSZIkSdIwFJNwpGunOVNDkiRJkiSNJZMakiRJkiRpLLn8RJIkSZKkIShcfjJsztSQJEmSJEljyaSG\nJEmSJEkaSyY1JEmSJEnSWHJPDUmSJEmShsQ9NYbLmRqSJEmSJGksmdSQJEmSJEljyeUnkiRJkiQN\nQRGXnwyZMzUkSZIkSdJYmoikRpJzklRbNiW5IcmpSXbv63dxki1JDu2rf2eSW5Ps2Vd/QJINSY7Y\nxv13TXJJkluSrE9ye5Jzk+y3cO9SkiRJkiT1moikRutSYCnwJOBtwInAqVsbkywFDgFOA47vu/bt\nwD3A+3r6LwY+AlxYVRfN8f6/CfxCz9eVO/heJEmSJEkToCqdLuNukvbU2FBVd7bfX5DkRcArgTe0\ndcuBzwOnA9cm2buqfghQVRuTvAZYleRTVfUPNImRfYCTtnXjqtoMvLen6uYk/wtYmWRxVW1agPcn\nSZIkSZJ6TNJMjX7rgMUASQIcC3y0qm4GrgJe29u5qr4FnAx8MMlLgbcCr6+qe7f3xkn2Bo4CvmxC\nQ5IkSZKk4ZjIpEaS59AkFS5rq14APBr4TPv6XOC4AZeeClwHfBY4s6ouG9Bntvu+O8ka4G6apTCv\n2Eb/FUlWJVm1ccva7bmVJEmSJGkMTJFOl3E3SUmNw5KsTrIeuBK4ggeWjhwPfKKqNravVwJPTnJQ\n7wBVNQW8g+a5nLIDMfwV8CzgsHaMj7SzRAaqqrOqallVLVuy6BE7cDtJkiRJknZek7SnxhXACmAT\ncPvWZR9J9gKOAJYkOaGn/yKaZMdVfeNs7vs6Z1V1N80sje8k+S/gRuCXaZIskiRJkiRpAU1SUmNt\nVV0/oP5o4C7g8L76g4F3J3lTVa0ZQjxbZ8E8bAhjS5IkSZK005ukpMZMjgNWVtU1vZVJvgO8CzgS\nOHs+N0jyPOAXgS8DPwZ+DvgL4Ls4S0OSJEmSdkpVMDUBx6Z22STtqfEgSZ5Ns8fFyv62dn+Ni2mW\noMzXOprkyD8D/wV8CLga+NWq2rAA40uSJEmSpD4TMVOjqpbPUP91mHk716o6ZkDdpbNdM8t9Xrg9\n10iSJEmSpPmZiKSGJEmSJEldVC4/GaqJXn6yUJKc3B4XO6hcMur4JEmSJEnaGTlTY24+AHxshra1\nD2UgkiRJkiSpYVJjDqrqHuCeUcchSZIkSRon8fSTIXP5iSRJkiRJGksmNSRJkiRJ0lhy+YkkSZIk\nSUPi6SfD5UwNSZIkSZI0lkxqSJIkSZKksWRSQ5IkSZIkjSX31OiKQO26aNRRAFDp0JqvGnUAms3a\nTYtHHcK0b65+3KhDmHZmNo86hGm/u9f3Rh3CtIt2vX/UIUy7etETRx3CtKu79Dk3NeoAHlD+hjJY\nl35eOiId+rnduEd3/n/hktXdeTC1uEO/W3ZI7dKd55Ito46gNYGfcQUe6Tpk3fnklSRJkiRJ2g4m\nNSRJkiRJ0lhycqckSZIkScNQUBO4rKZLnKkhSZIkSZLGkkkNSZIkSZI0llx+IkmSJEnSkEzh6SfD\n5EwNSZIkSZI0lkxqSJIkSZKksWRSQ5IkSZIkjSX31JAkSZIkaQgKqHJPjWFypoYkSZIkSRpLJjUk\nSZIkSdJYcvmJJEmSJElDEaZcfjJUEzFTI8k5Saotm5LckOTUJLv39bs4yZYkh/bVvzPJrUn27Ks/\nIMmGJEdsRyy7JbmmjeXA+b0zSZIkSZI0k4lIarQuBZYCTwLeBpwInLq1MclS4BDgNOD4vmvfDtwD\nvK+n/2LgI8CFVXXRdsRxGnDzDsQvSZIkSZK2wyQlNTZU1Z1VdWtVXQCcD7yyp3058HngdODlSfbe\n2lBVG4HXAL+d5BVt9duAfYCT5hpAO6PjucAfzeeNSJIkSZImQ1W3y7ibpKRGv3XAYoAkAY4FPlpV\nNwNXAa/t7VxV3wJOBj6Y5KXAW4HXV9W9c7lZkicC7weOBtYv1JuQJEmSJEmDTWRSI8lzgKOAy9qq\nFwCPBj7Tvj4XOG7ApacC1wGfBc6sqssG9Bl0v12BC4B3tsmRuca5IsmqJKs2blk318skSZIkSRKT\nldQ4LMnqJOuBK4EreGDpyPHAJ9plJgArgScnOah3gKqaAt5B81xO2Y57nwzcT8+eHHNRVWdV1bKq\nWrZk0W7bc6kkSZIkaQxUpdNl3E3Ska5XACuATcDtVbUJIMlewBHAkiQn9PRfRJPsuKpvnM19X+fi\nEOBgYFOz0mXaqiTnV9XrtmMsSZIkSZI0B5OU1FhbVdcPqD8auAs4vK/+YODdSd5UVWvmee/XAr3H\nxz6eZgnLkTw4aSJJkiRJkhbAJCU1ZnIcsLKqrumtTPId4F00iYez53ODqrqxb+ytG4V+t6pum8/Y\nkiRJkiRpsEnaU+NBkjwbeBbNHho/od1f42KaJSiSJEmSJC2o5tjU0e+b4Z4aHVdVy2eo/zow459S\nVR0zoO7S2a6ZYzzXz3cMSZIkSZI0u4meqSFJkiRJkibXRMzUGLYkJwN/NEPz5VX16w9lPJIkSZKk\n8TA1AUs8usykxtx8APjYDG1rH8pAJEmSJElSw6TGHFTVPcA9o45DkiRJkiQ9wKSGJEmSJElDUjXq\nCCabG4VKkiRJkqSxZFJDkiRJkiSNJZefSJIkSZI0JOXpJ0PlTA1JkiRJkjSWTGpIkiRJkqSx5PKT\nrkhg0aJRR9Ho0OyoTI06gh4dei5dsX5Tdz5C7t308FGHMO3adUtHHcK0i3a9f9QhTDtij/tGHcK0\nLdwy6hCmXdSlz5YuxdIhU935qCNd2kG/S7HoQTY8siO/VwJLVnfnF7rq0N/nLsmWjvwD4OeKdoB/\nrSVJkiRJGoIi7qkxZC4/kSRJkiRJY8mkhiRJkiRJGksuP5EkSZIkaUjcKmS4nKkhSZIkSZLGkkkN\nSZIkSZI0llx+IkmSJEnSMBSefjJkztSQJEmSJEljyaSGJEmSJEkaSy4/kSRJkiRpWDz+ZKicqSFJ\nkiRJksaSSQ1JkiRJkjSWTGpIkiRJkqSxNBFJjSTnJKm2bEpyQ5JTk+ze1+/iJFuSHNpX/84ktybZ\ns6/+gCQbkhwxhxhu64lha/mLhXmHkiRJkqRxVJVOl3E3EUmN1qXAUuBJwNuAE4FTtzYmWQocApwG\nHN937duBe4D39fRfDHwEuLCqLppjDG9vY9ha3rkjb0SSJEmSJG3bJCU1NlTVnVV1a1VdAJwPvLKn\nfTnweeB04OVJ9t7aUFUbgdcAv53kFW3124B9gJO2I4b72xi2ltXzeD+SJEmSJGkWk5TU6LcOWAyQ\nJMCxwEer6mbgKuC1vZ2r6lvAycAHk7wUeCvw+qq6dzvu+dYkP0zy70n+uJ3tIUmSJEnaSVV1u4y7\niUxqJHkOcBRwWVv1AuDRwGfa1+cCxw249FTgOuCzwJlVddmAPjM5DXg18CLgDOAtNLNCZotzRZJV\nSVZt3Lx2O24lSZIkSZImKalxWJLVSdYDVwJX8MDSkeOBT7TLTABWAk9OclDvAFU1BbyD5rmcsj03\nr6p3V9XlVfXNqjqrvfeK/s1H+645q6qWVdWyJbs+YntuJ0mSJEnSTm/XUQewgK4AVgCbgNurahNA\nkr2AI4AlSU7o6b+IJtlxVd84m/u+7qirgAA/B3xtnmNJkiRJksZMwUScMNJlk5TUWFtV1w+oPxq4\nCzi8r/5g4N1J3lRVa4YQz4Ht1zuHMLYkSZIkSTu9SUpqzOQ4YGVVXdNbmeQ7wLuAI4Gz53ODJM8D\n/m/gcuA+4CCaPTY+VVXfm8/YkiRJkiRpsEnaU+NBkjwbeBbNHho/od1f42KaJSjztZFmk9B/Af4T\n+FPgb2iOiZUkSZIkSUMwETM1qmr5DPVfp9nXYqbrjhlQd+ls18wwzr/RzM6QJEmSJKlRgHtqDNVE\nz9SQJEmSJEmTy6TGHCQ5uT0udlC5ZNTxSZIkSZK0M5qI5ScPgQ8AH5uhbe1DGYgkSZIkaXxUjTqC\nyWZSYw6q6h7gnlHHIUmSJEmSHuDyE0mSJEmSNJacqSFJkiRJ0rC4/GSonKkhSZIkSZLGkkkNSZIk\nSZI0llx+IkmSJEnSUISqjDqIieZMDUmSJEmSNJacqdEluy4adQQAVIdSXdky6gi6qSvJ3vUbF486\nhGn3btht1CFM2zVTow5h2tWLnjjqEKZt4ZZRhzDtt/a4d9QhTPvTUQfQVR3aVC0dimVq1+4E05Xn\n0pV/EzWzDY/szi+XS1Z3599of+eWFkaH/ipJkiRJkiTNnTM1JEmSJEkalo7MbJtUztSQJEmSJEmz\nSnJikhuTrE/ytSS/Msfrnp9kc5JrhhGXSQ1JkiRJkjSjJEcC7wX+EngW8GXgc0mesI3rHgWcC1w2\nrNhMakiSJEmSNAwFVel0maM3A+dU1Yeq6ttVdRJwB/CGbVz3YeAjwJU7/hBnZ1JDkiRJkqSd1z5J\nVvWUFb2NSZYAvwR8oe+6LwDPnWnQJCcC+wJ/sdAB93KjUEmSJEmSdl53V9WyWdr3ARYB3++r/z7w\n4kEXJDmA5uT6X66qLcnwzt82qSFJkiRJ0rDsZKefJHkYcCHwlqq6cdj3M6khSZIkSZJmcjewhWYp\nSa99gTsH9F8KPA342yR/29btAiTJZuDwqupfyrLD3FNDkiRJkiQNVFUbga8Bh/Y1HUpzCkq/7wEH\nAAf2lDOB69vvB12zw5ypIUmSJEnS0AxvP4mH0HuA85J8FfgS8LvAfjTJCpKcC1BVx1TVJuCa3ouT\n/ADYUFU/Ub8QTGpIkiRJkqQZVdWFSfYG3kazvOQammUkN7ddnjCq2ExqSJIkSZKkWVXVGcAZM7S9\ncBvX/hnwZwseFBOyp0aSc5JUWzYluSHJqUl27+t3cZItSQ7tq39nkluT7NlXf0CSDUmOmGMchyf5\nSpJ1SX6cZME2P5EkSZIkST9pIpIarUtppsE8iWZKzInAqVsbkywFDgFOA47vu/btwD3A+3r6LwY+\nAlxYVRdt6+ZJXgV8FDiHZvOTg9vvJUmSJEk7q+p4GXOTtPxkQ1VtPU7mgiQvAl4JvKGtWw58Hjgd\nuDbJ3lX1Q2h2c03yGmBVkk9V1T/QJEb2AU7a1o2T7Aq8F/iDqvrbnqZvL8D7kiRJkiRJA0zSTI1+\n64DF0ByGCxwLfLTdyOQq4LW9navqW8DJwAeTvBR4K/D6qrp3Dvd6Ds3Or5uTXJ3kjiSfT/LM2S5K\nsiLJqiSrNm5Zu73vT5IkSZKkndpEJjWSPAc4CrisrXoB8GjgM+3rc4HjBlx6KnAd8FngzKq6bECf\nQZ7Ufj2lLS8D7gT+Jcm+M11UVWdV1bKqWrZk0SPmeCtJkiRJ0tgY9fKSCV9+MklJjcOSrE6yHrgS\nuIIHlo4cD3yiqja2r1cCT05yUO8AVTUFvIPmuZyyHffe+hz/vKo+VVVfA04A1gCv2aF3I0mSJEmS\nZjVJe2pcAawANgG3V9UmgCR7AUcAS5Kc0NN/EU2y46q+cTb3fZ2LO9qv/7m1oqo2JbmeEZ7XK0mS\nJEnSJJukpMbaqrp+QP3RwF3A4X31BwPvTvKmqlozz3v/G00y5ReArwAkWUSzLOUf5jm2JEmSJGkc\nFVAZdRQTbZKSGjM5DlhZVdf0Vib5DvAu4Ejg7PncoKp+nOQs4JQkdwA3A78P7AGcP5+xJUmSJEnS\nYJO0p8aDJHk28CyaPTR+Qru/xsU0S1AWwpuBTwLn0czceBrwoqr6/gKNL0mSJEmSekzETI2qWj5D\n/deBGef6VNUxA+oune2aWcbaCPxBWyRJkiRJ0pBNRFJDkiRJkqQuqgk4NrXLJnr5yUJJcnJ7XOyg\ncsmo45MkSZIkaWfkTI25+QDwsRna1j6UgUiSJEmSpIZJjTmoqnuAe0YdhyRJkiRpzLj8ZKhcfiJJ\nkiRJksaSSQ1JkiRJkjSWXH4iSZIkSdKwVEYdwURzpoYkSZIkSRpLJjUkSZIkSdJYcvmJJEmSJElD\nEk8/GSqTGh2xfp9d+c6xjxp1GADs+5Xu/K1bvLY7sey6rjuxrNu7G5Os6uo9Rx3CtJt2e+SoQ5h2\ny6burJu8ujs/tlzUncfCn446gB7fXnHGqEOYdveWNaMOYdptm7vzK8qPp3YbdQjT7pt6+KhDmHZ/\nR57LO+88ctQhTFu0ftQRPGDz7qOOoJvWZNGoQ5hW3fh1DoC9rp8adQgA7Lph1BFoHHXor5IkSZIk\nSdLcmdSQJEmSJEljqTtzOyVJkiRJmiTVFg2NMzUkSZIkSdJYMqkhSZIkSZLGkstPJEmSJEkaikB1\n6Ai4CeRMDUmSJEmSNJZMakiSJEmSpLHk8hNJkiRJkobF00+GypkakiRJkiRpLJnUkCRJkiRJY8nl\nJ5IkSZIkDYvLT4bKmRqSJEmSJGkszTupkeScJNVT7k7y6SRP7elTSV7V8/qmJG+ZYbz92/7L5hHT\nkiR3JVmdZM8B7V9s73HygLYL27b398Q+Wzmn7fc/k3wpyZok5uIkSZIkSRqyhZqpcSmwtC0vAXYD\n/m6Bxt4RrwRuBK4Ejpqhz63A8iTZWpFkb+AVbdtWS3vKCQPqfr+texjwKeCvF+YtSJIkSZKk2SzU\nnhobqurO9vs7k5wGXJJkt6pat0D32B7HAecBP6ZJOvzNgD6fo0l+vBC4vK17DXAVPaueet4XSX7c\nX9fT7+1tn1f1t0mSJEmSdlLO4x+qBd9TI8lPAUcC3xpFQiPJE2kSFR+nmTnx1CTPHNB1E3AucGxP\n3bHAh4cd41ZJViRZlWTVljVrHqrbSpIkSZI0ERYqqXFYu3/FauA+4AXMvOxj2F4P/FNV3VVVa2gS\nGyfM0Pds4DeSPLLdw2N/YOVDEyZU1VlVtayqli3affeH6raSJEmSJE2EhUpqXAEc2JbnAJcBX0jy\n+AUaf06S7EKT1Divp/o84OgkD+/vX1XfBr4BvJpmycrHq2rtQxGrJEmSJGnCFVDpdhlzC5XUWFtV\n17fl34DjgUcCKxZo/Ll6CfAE4Pwkm5Nsptk7Yy/giBmuORv4HZrExtkPSZSSJEmSJGneFnxPjVYB\nU8AjhjT+TI6jWW5yYF/5UNs2yIXAU4DbquqqhyJISZIkSZI0fwt1+snDkjy2/f5RwBuBPYBLZrlm\nvyQH9tXd1vP9U9qZFr2urar1gwZL8hjg5cBvVtU1fW0fBq5M8uSq+m5vW1Xdn+RngC2zxLpNSZ4A\nPJpmXw563tv1VbV6PmNLkiRJksZTPP1kqBYqqfFi4I72+/uBa2mSC1+c5Zr/0ZZeJwGfbr8/f8A1\nBwDXDKgHeC2wAfjHAW1fBW6lma3xJ/2NVXXvLHHO1Z8Dr+t5fXX79UXAFxdgfEmSJEmS1GPeSY2q\nWg4s30af9L3efxvDbvduJVX1HuA9M7QV8MSe1y/cxlgD26tq5UyxzeU5SJIkSZKkhbNQMzUkSZIk\nSVI/l58M1bA2Ch2KJP+RZPUM5ehRxydJkiRJkh464zZT43Bg8Qxt338oA5EkSZIkSaM1VkmNqrp5\n1DFIkiRJkqRuGKvlJ5IkSZIkSVuZ1JAkSZIkSWPJpIYkSZIkSRpLY7WnhiRJkiRJ4yQe6TpUztSQ\nJEmSJEljyZkaXdKVDF4y6gge0JVnAtChx9KZbG+HnkmXTC3uyh8QMDXqAHr48zLQ3VvWjDqEafss\n2n3UIUxbW6tHHYLGxLp9u/NBt8ctHfr/hR36p6gzv7dAt/6XboeeS3Xk3+gOPRKNEZMakiRJkiQN\nS1eyRhOqS7lKSZIkSZKkOTOpIUmSJEmSxpJJDUmSJEmSNJbcU0OSJEmSpGEo3AF1yJypIUmSJEmS\nxpJJDUmSJEmSNJZcfiJJkiRJ0rC4/GSonKkhSZIkSZLGkkkNSZIkSZI0llx+IkmSJEnSkMTlJ0Pl\nTA1JkiRJkjSWTGpIkiRJkqSx5PITSZIkSZKGxeUnQzXvmRpJzklSPeXuJJ9O8tSePpXkVT2vb0ry\nlhnG27/tv2weMS1JcleS1Un2HND+xfYeJw9ou7Bte39P7LOVc9qYP5zkhiTr2q9/lWS3HX0PkiRJ\nkiRpdgu1/ORSYGlbXgLsBvzdAo29I14J3AhcCRw1Q59bgeVJsrUiyd7AK9q2rZb2lBMG1P0+8FRg\nEfAG4BnAScAxwHsX5u1IkiRJkqR+C7X8ZENV3dl+f2eS04BLkuxWVesW6B7b4zjgPODHNEmHvxnQ\n53M0yY8XApe3da8BrqJnglDP+yLJj/vrWp9vy1Y3JHkHcAqwYh7vQ5IkSZIkzWDBNwpN8lPAkcC3\nRpHQSPJEmkTFx4FPAU9N8swBXTcB5wLH9tQdC3x4gUJ5JPCj2TokWZFkVZJVW9asWaDbSpIkSZI6\nozpextxCJTUOa/evWA3cB7yAmZd9DNvrgX+qqruqag1NYuOEGfqeDfxGkke2e3jsD6ycbwBtYuUt\nwBmz9auqs6pqWVUtW7T77vO9rSRJkiRJO5WFSmpcARzYlucAlwFfSPL4BRp/TpLsQpPUOK+n+jzg\n6CQP7+9fVd8GvgG8mmbJyserau08Y9iXZinKPwGnzWcsSZIkSZI0s4XaU2NtVV2/9UWS44F7afaT\neNAJI0P0EuAJwPlJzu+pXwQcAZw/4JqzgROBJwEvnc/NkzwW+GfgGuC1VTUBk3kkSZIkSTsi1RQN\nz4LvqdEqYAp4xJDGn8lxNMtNDuwrH2rbBrkQeApwW1VdtaM3TrIU+CLwbeDVVbV5R8eSJEmSJEnb\ntlAzNR7WzlIAeBTwRmAP4JJZrtkvyYF9dbf1fP+UJP2JgWurav2gwZI8Bng58JtVdU1f24eBK5M8\nuaq+29tWVfcn+RlgyyyxzirJfjQJjduBNwH79JwUe1dV7fDYkiRJkiRpsIVKarwYuKP9/n7gWprk\nwhdnueZ/tKXXScCn2+8HLRU5gGZpxyCvBTYA/zig7avArTSzNf6kv7Gq7p0lzrl4CfDzbbmlr+1n\ngZvmOb4kSZIkaRxVtt1HO2zeSY2qWg4s30af9L3efxvDbvefelW9B3jPDG0FPLHn9Qu3MdbA9qpa\nOSi2qjoHOGeusUqSJEmSpPkb1p4akiRJkiRJQzVWSY0k/5Fk9Qzl6FHHJ0mSJEnST6iOlzG3UHtq\nPFQOBxbP0Pb9hzIQSZIkSZI0WmOV1Kiqm0cdgyRJkiRJ6oaxWn4iSZIkSZK01VjN1JAkSZIkaZxk\nAvat6DJnakiSJEmSpLFkUkOSJEmSJI0ll59IkiRJkjQsLj8ZKpMaXZJRB9CojsQBHVt/1qVYumJq\n1AH06FIsHfo7VH7KD9ahv8+3be7OH9LaWj3qEKY9Ydc9Rh3CtC0dei4bOzTJdktHPux22dSNOADu\n+/ktow5h2h43LRp1CNOqOz+2THXnsUhaIB36iJEkSZIkSZq77vzvIUmSJEmSJkl1bPb5BHKmhiRJ\nkiRJGksmNSRJkiRJ0lgyqSFJkiRJksaSe2pIkiRJkjQs7qkxVM7UkCRJkiRJY8mkhiRJkiRJGksu\nP5EkSZIkaVhcfjJUztT4/9m796i7q/re9+8P4WoQUVBuoqhV2T31CJitpdoDrXc4IkMoiAjmJlYE\nL3vYfTxsaMf2UmmL4BUVhYHcBMVCQVQQdpWxLRsa3VCxUEQQ49EgGEGSQCDJ9/yxfk+yWKz15Lms\nlbVWeL/GmCPPb875+83v77nk8s28SJIkSZKksWRSQ5IkSZIkjSWXn0iSJEmSNCBx+clAOVNDkiRJ\nkiSNpVknNZKcm6Tayv1Jvplk77Y+leTwtuufJ/lgj+ft1fSfN4uYtk5yX5IVSZ7Wpf17zRindGm7\npGn7bFvsk5Vzk2yR5Iokv0jySJJfJ7kgyR4zfQdJkiRJkjS5fs3UuBbYrSmvA7YDLuvTs2fiUOBu\n4AbgbT36LAXmJ8lERZKdgDc3bRN2ayvv7FL3vqbufwBHAC8GDgOez3A/B5IkSZIkbdb6tafG6qpa\n1ny8LMkZwJVJtquqh/s0xnQsAs4HHqCVdPh8lz7fppX8OBD456bu7cCNtB260/ZeJHmgs67NJ9s+\nvifJqcA/Jdm2qh6Z8ZtIkiRJkqSu+r6nRpKnAkcCPx5GQiPJc2klKi4G/hHYO8lLu3R9DDgPWNhW\ntxA4uw8xPAM4GrjRhIYkSZIkadwlOT7J3c2WCz9M8qeT9H1LkmuabSEeSnJjkkMGEVe/khpvaPav\nWAH8HjiA3ss+Bm0B8N2quq+qVtJKbLyzR99zgLck2aHZw2Mv4NKZDpzk75KsBH4LPAf4vzfS/7gk\nS5IsWbty5UyHlSRJkiRpYJIcCXwK+FtgX+BfgG8neU6PWw6gtUXDwU3/bwGXTZYImal+JTW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fOBfYH9gbP78zqSJEmSpHGUqpEu465fe2qsrqplzcfLkpwBXJlku6p6uE9jTMciWsmFB2glHT7f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\nUQMAAAAYJUUNAAAAYJT+B866qpVlCydpAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1368x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXQQgn7kU0Uc",
        "colab_type": "text"
      },
      "source": [
        "The heatmap reveals that the payment and billed amounts across the 5 months are highly correlated with each other"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "usBpD2aFLDDz",
        "colab_type": "text"
      },
      "source": [
        "- Linear regression\n",
        "- Scatterplot matrix\n",
        "- Boxplot matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AQBksEyEf4Sf",
        "colab_type": "text"
      },
      "source": [
        "## Data Preprocessing\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLGx_cqChhbQ",
        "colab_type": "code",
        "outputId": "7388cdfc-234b-45bc-e103-413f2a133c56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#check for null values:\n",
        "df.isnull().values.any()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZNrGBJkf8zC",
        "colab_type": "code",
        "outputId": "b6c4ea3f-2579-4588-ee70-c7c209c573f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "import sklearn.preprocessing as skp\n",
        "df1 = pd.DataFrame(skp.normalize(df))\n",
        "df1.columns = df.columns\n",
        "df1.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LIMIT_BAL</th>\n",
              "      <th>SEX</th>\n",
              "      <th>EDUCATION</th>\n",
              "      <th>MARRIAGE</th>\n",
              "      <th>AGE</th>\n",
              "      <th>PAY_0</th>\n",
              "      <th>PAY_2</th>\n",
              "      <th>PAY_3</th>\n",
              "      <th>PAY_4</th>\n",
              "      <th>PAY_5</th>\n",
              "      <th>PAY_6</th>\n",
              "      <th>BILL_AMT1</th>\n",
              "      <th>BILL_AMT2</th>\n",
              "      <th>BILL_AMT3</th>\n",
              "      <th>BILL_AMT4</th>\n",
              "      <th>BILL_AMT5</th>\n",
              "      <th>BILL_AMT6</th>\n",
              "      <th>PAY_AMT1</th>\n",
              "      <th>PAY_AMT2</th>\n",
              "      <th>PAY_AMT3</th>\n",
              "      <th>PAY_AMT4</th>\n",
              "      <th>PAY_AMT5</th>\n",
              "      <th>PAY_AMT6</th>\n",
              "      <th>Y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.969135</td>\n",
              "      <td>0.000097</td>\n",
              "      <td>0.000097</td>\n",
              "      <td>0.000048</td>\n",
              "      <td>0.001163</td>\n",
              "      <td>0.000097</td>\n",
              "      <td>0.000097</td>\n",
              "      <td>-0.000048</td>\n",
              "      <td>-0.000048</td>\n",
              "      <td>-0.000097</td>\n",
              "      <td>-0.000097</td>\n",
              "      <td>0.189611</td>\n",
              "      <td>0.150313</td>\n",
              "      <td>0.033387</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.033387</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.998005</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>0.000216</td>\n",
              "      <td>-0.000008</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000017</td>\n",
              "      <td>0.022305</td>\n",
              "      <td>0.014346</td>\n",
              "      <td>0.022305</td>\n",
              "      <td>0.027212</td>\n",
              "      <td>0.028734</td>\n",
              "      <td>0.027121</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008317</td>\n",
              "      <td>0.008317</td>\n",
              "      <td>0.008317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.016633</td>\n",
              "      <td>0.000008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.898265</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.000339</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.291826</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.135329</td>\n",
              "      <td>0.143034</td>\n",
              "      <td>0.149192</td>\n",
              "      <td>0.155190</td>\n",
              "      <td>0.015151</td>\n",
              "      <td>0.014971</td>\n",
              "      <td>0.009981</td>\n",
              "      <td>0.009981</td>\n",
              "      <td>0.009981</td>\n",
              "      <td>0.049904</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.456620</td>\n",
              "      <td>0.000018</td>\n",
              "      <td>0.000018</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000338</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.429132</td>\n",
              "      <td>0.440483</td>\n",
              "      <td>0.450145</td>\n",
              "      <td>0.258575</td>\n",
              "      <td>0.264465</td>\n",
              "      <td>0.269835</td>\n",
              "      <td>0.018265</td>\n",
              "      <td>0.018438</td>\n",
              "      <td>0.010959</td>\n",
              "      <td>0.010046</td>\n",
              "      <td>0.009763</td>\n",
              "      <td>0.009132</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.615783</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.000025</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>0.000702</td>\n",
              "      <td>-0.000012</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.000012</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106124</td>\n",
              "      <td>0.069830</td>\n",
              "      <td>0.441331</td>\n",
              "      <td>0.257890</td>\n",
              "      <td>0.235795</td>\n",
              "      <td>0.235611</td>\n",
              "      <td>0.024631</td>\n",
              "      <td>0.451750</td>\n",
              "      <td>0.123157</td>\n",
              "      <td>0.110841</td>\n",
              "      <td>0.008485</td>\n",
              "      <td>0.008362</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   LIMIT_BAL       SEX  EDUCATION  ...  PAY_AMT5  PAY_AMT6         Y\n",
              "0   0.969135  0.000097   0.000097  ...  0.000000  0.000000  0.000048\n",
              "1   0.998005  0.000017   0.000017  ...  0.000000  0.016633  0.000008\n",
              "2   0.898265  0.000020   0.000020  ...  0.009981  0.049904  0.000000\n",
              "3   0.456620  0.000018   0.000018  ...  0.009763  0.009132  0.000000\n",
              "4   0.615783  0.000012   0.000025  ...  0.008485  0.008362  0.000000\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    }
  ]
}