eda_credit_default.ipynb

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      "name": "eda_credit_default.ipynb",
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        "<a href=\"https://colab.research.google.com/github/martin-fabbri/colab-notebooks/blob/master/eda_credit_default.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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      },
      "source": [
        "# EDA of Credit Default "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m0mI3VtbxtvJ"
      },
      "source": [
        "## Goal\n",
        "\n",
        "To predict whether the customer will default on their credit card payment next month.\n",
        "\n",
        "This exploratory data analysis should assits to:\n",
        "1. Understand variable relations within the underlyind data.\n",
        "2. Identity data transformations required for modeling\n",
        "3. Identify any required data cleaning\n",
        "4. Generate research questions "
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "## Dataset\n",
        "\n",
        "This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005.\n",
        "\n",
        "> Yeh, I. C., & Lien, C. H. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473-2480.\n",
        "https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients"
      ]
    },
    {
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      "source": [
        "#@title ##Setup\n",
        "#@markdown Import data manipulation and plotting dependencies.\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mSJ78R0M2Q27"
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      "source": [
        "\n",
        "### Dataset columns:\n",
        "\n",
        "Total 25 columns, with 1 target variable ```default.payment.next.month```, and 23 explanatory variables (ID excluded).\n",
        "\n",
        "- **LIMIT_BAL:** Amount of given credit\n",
        "- **SEX, EDUCATION**, MARRIAGE, AGE: client demographic features\n",
        "- **PAY[1-6]:** repayment status\n",
        "- **BILL_AMT[1-6]:** amount on bill statement\n",
        "- **PAY_AMT[1-6]:** payment amount\n",
        "- **default payment next month:** target, whether the client defaulted the following month"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# Load the data\n",
        "data_url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/00350/default%20of%20credit%20card%20clients.xls\"\n",
        "df = pd.read_excel(io=data_url, header=1)\n",
        "df = df.drop(columns=['ID'])\n",
        "df = df.rename(columns={'default payment next month': 'DEFAULT', 'PAY_0':'PAY_1'})\n",
        "fill = (df.EDUCATION == 0) | (df.EDUCATION == 5) | (df.EDUCATION == 6)\n",
        "df.loc[fill, 'EDUCATION'] = 4\n",
        "df.sample(5)"
      ],
      "execution_count": 13,
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LIMIT_BAL</th>\n",
              "      <th>SEX</th>\n",
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              "      <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",
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            "text/plain": [
              "       LIMIT_BAL  SEX  EDUCATION  ...  PAY_AMT5  PAY_AMT6  DEFAULT\n",
              "10341     240000    1          1  ...    167747      4810        0\n",
              "8446       80000    1          1  ...         0         0        1\n",
              "16594      10000    1          2  ...       200         0        1\n",
              "23276     300000    2          2  ...      5000      6000        0\n",
              "27435     210000    1          1  ...      4500     15000        0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
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        "outputId": "3210e97e-658e-4a8d-a9dd-62c857fd7823"
      },
      "source": [
        "df.describe().transpose()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LIMIT_BAL</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>167484.322667</td>\n",
              "      <td>129747.661567</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>50000.00</td>\n",
              "      <td>140000.0</td>\n",
              "      <td>240000.00</td>\n",
              "      <td>1000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SEX</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>1.603733</td>\n",
              "      <td>0.489129</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EDUCATION</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>1.853133</td>\n",
              "      <td>0.790349</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MARRIAGE</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>1.551867</td>\n",
              "      <td>0.521970</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AGE</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>35.485500</td>\n",
              "      <td>9.217904</td>\n",
              "      <td>21.0</td>\n",
              "      <td>28.00</td>\n",
              "      <td>34.0</td>\n",
              "      <td>41.00</td>\n",
              "      <td>79.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_1</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>-0.016700</td>\n",
              "      <td>1.123802</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_2</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>-0.133767</td>\n",
              "      <td>1.197186</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_3</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>-0.166200</td>\n",
              "      <td>1.196868</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_4</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>-0.220667</td>\n",
              "      <td>1.169139</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_5</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>-0.266200</td>\n",
              "      <td>1.133187</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_6</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>-0.291100</td>\n",
              "      <td>1.149988</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BILL_AMT1</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>51223.330900</td>\n",
              "      <td>73635.860576</td>\n",
              "      <td>-165580.0</td>\n",
              "      <td>3558.75</td>\n",
              "      <td>22381.5</td>\n",
              "      <td>67091.00</td>\n",
              "      <td>964511.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BILL_AMT2</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>49179.075167</td>\n",
              "      <td>71173.768783</td>\n",
              "      <td>-69777.0</td>\n",
              "      <td>2984.75</td>\n",
              "      <td>21200.0</td>\n",
              "      <td>64006.25</td>\n",
              "      <td>983931.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BILL_AMT3</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>47013.154800</td>\n",
              "      <td>69349.387427</td>\n",
              "      <td>-157264.0</td>\n",
              "      <td>2666.25</td>\n",
              "      <td>20088.5</td>\n",
              "      <td>60164.75</td>\n",
              "      <td>1664089.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BILL_AMT4</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>43262.948967</td>\n",
              "      <td>64332.856134</td>\n",
              "      <td>-170000.0</td>\n",
              "      <td>2326.75</td>\n",
              "      <td>19052.0</td>\n",
              "      <td>54506.00</td>\n",
              "      <td>891586.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BILL_AMT5</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>40311.400967</td>\n",
              "      <td>60797.155770</td>\n",
              "      <td>-81334.0</td>\n",
              "      <td>1763.00</td>\n",
              "      <td>18104.5</td>\n",
              "      <td>50190.50</td>\n",
              "      <td>927171.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BILL_AMT6</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>38871.760400</td>\n",
              "      <td>59554.107537</td>\n",
              "      <td>-339603.0</td>\n",
              "      <td>1256.00</td>\n",
              "      <td>17071.0</td>\n",
              "      <td>49198.25</td>\n",
              "      <td>961664.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_AMT1</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>5663.580500</td>\n",
              "      <td>16563.280354</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.00</td>\n",
              "      <td>2100.0</td>\n",
              "      <td>5006.00</td>\n",
              "      <td>873552.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_AMT2</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>5921.163500</td>\n",
              "      <td>23040.870402</td>\n",
              "      <td>0.0</td>\n",
              "      <td>833.00</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>5000.00</td>\n",
              "      <td>1684259.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_AMT3</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>5225.681500</td>\n",
              "      <td>17606.961470</td>\n",
              "      <td>0.0</td>\n",
              "      <td>390.00</td>\n",
              "      <td>1800.0</td>\n",
              "      <td>4505.00</td>\n",
              "      <td>896040.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_AMT4</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>4826.076867</td>\n",
              "      <td>15666.159744</td>\n",
              "      <td>0.0</td>\n",
              "      <td>296.00</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>4013.25</td>\n",
              "      <td>621000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_AMT5</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>4799.387633</td>\n",
              "      <td>15278.305679</td>\n",
              "      <td>0.0</td>\n",
              "      <td>252.50</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>4031.50</td>\n",
              "      <td>426529.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAY_AMT6</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>5215.502567</td>\n",
              "      <td>17777.465775</td>\n",
              "      <td>0.0</td>\n",
              "      <td>117.75</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>4000.00</td>\n",
              "      <td>528666.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEFAULT</th>\n",
              "      <td>30000.0</td>\n",
              "      <td>0.221200</td>\n",
              "      <td>0.415062</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count           mean  ...        75%        max\n",
              "LIMIT_BAL  30000.0  167484.322667  ...  240000.00  1000000.0\n",
              "SEX        30000.0       1.603733  ...       2.00        2.0\n",
              "EDUCATION  30000.0       1.853133  ...       2.00        6.0\n",
              "MARRIAGE   30000.0       1.551867  ...       2.00        3.0\n",
              "AGE        30000.0      35.485500  ...      41.00       79.0\n",
              "PAY_1      30000.0      -0.016700  ...       0.00        8.0\n",
              "PAY_2      30000.0      -0.133767  ...       0.00        8.0\n",
              "PAY_3      30000.0      -0.166200  ...       0.00        8.0\n",
              "PAY_4      30000.0      -0.220667  ...       0.00        8.0\n",
              "PAY_5      30000.0      -0.266200  ...       0.00        8.0\n",
              "PAY_6      30000.0      -0.291100  ...       0.00        8.0\n",
              "BILL_AMT1  30000.0   51223.330900  ...   67091.00   964511.0\n",
              "BILL_AMT2  30000.0   49179.075167  ...   64006.25   983931.0\n",
              "BILL_AMT3  30000.0   47013.154800  ...   60164.75  1664089.0\n",
              "BILL_AMT4  30000.0   43262.948967  ...   54506.00   891586.0\n",
              "BILL_AMT5  30000.0   40311.400967  ...   50190.50   927171.0\n",
              "BILL_AMT6  30000.0   38871.760400  ...   49198.25   961664.0\n",
              "PAY_AMT1   30000.0    5663.580500  ...    5006.00   873552.0\n",
              "PAY_AMT2   30000.0    5921.163500  ...    5000.00  1684259.0\n",
              "PAY_AMT3   30000.0    5225.681500  ...    4505.00   896040.0\n",
              "PAY_AMT4   30000.0    4826.076867  ...    4013.25   621000.0\n",
              "PAY_AMT5   30000.0    4799.387633  ...    4031.50   426529.0\n",
              "PAY_AMT6   30000.0    5215.502567  ...    4000.00   528666.0\n",
              "DEFAULT    30000.0       0.221200  ...       0.00        1.0\n",
              "\n",
              "[24 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EiVyLVP2-D_3"
      },
      "source": [
        "- Unbalanced dataset. Customer default(default payment next month) shows 22% samples labeled as default.\n",
        "\n",
        "- Repayment status ```PAY[1-6]``` shows that at least 75% of clients are paying their credit card bill on time."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6mgzQLVJxU7R"
      },
      "source": [
        "### Relation between ```default``` and other variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "xH11vF6V35Qf",
        "outputId": "4521d716-05c0-4a9d-8bfa-2fbc0d37c776"
      },
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "sns.heatmap(df.corr(), cmap='Reds');"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-S6Uvn4yUcF"
      },
      "source": [
        "### Education vs. ```default```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "-PBMTp4Yyku_",
        "outputId": "5706c0a1-1640-4480-c317-ce7f7faef3e9"
      },
      "source": [
        "sns.set(rc={'figure.figsize':(9,7)})\n",
        "edu = sns.countplot(x='EDUCATION', hue='DEFAULT', data=df)\n",
        "edu.set_xticklabels(['Graduate','College','High School','Other']);"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "SA-tcOOx2xaa",
        "outputId": "987523d0-0d61-49c7-9a5b-4f797dd10b0b"
      },
      "source": [
        "default0 = df.groupby(df['EDUCATION'][df['DEFAULT'] == 0]).size().reset_index(name='NOT_DEFAULT')\n",
        "default1 = df.groupby(df['EDUCATION'][df['DEFAULT'] == 1]).size().reset_index(name='DEFAULT')\n",
        "total = df.groupby('EDUCATION').size().reset_index(name='TOTAL')\n",
        "\n",
        "eduTable = default0.join(default1['DEFAULT']).join(total['TOTAL'])\n",
        "eduTable['EDUCATION'] = ['Graduate School','University','High School','Other']\n",
        "\n",
        "eduTable['NOT_DEFAULT'] = round((default0['NOT_DEFAULT']/total['TOTAL'])*100,2)\n",
        "eduTable['DEFAULT'] = round((default1['DEFAULT']/total['TOTAL'])*100,2)\n",
        "\n",
        "eduPct = eduTable.iloc[:,0:3]\n",
        "eduPct = eduPct.rename(columns={'NOT_DEFAULT': 'NOT_DEFAULT(%)', 'DEFAULT': 'DEFAULT(%)'})\n",
        "\n",
        "eduPct"
      ],
      "execution_count": 16,
      "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>EDUCATION</th>\n",
              "      <th>NOT_DEFAULT(%)</th>\n",
              "      <th>DEFAULT(%)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Graduate School</td>\n",
              "      <td>80.77</td>\n",
              "      <td>19.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>University</td>\n",
              "      <td>76.27</td>\n",
              "      <td>23.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>High School</td>\n",
              "      <td>74.84</td>\n",
              "      <td>25.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Other</td>\n",
              "      <td>92.95</td>\n",
              "      <td>7.05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         EDUCATION  NOT_DEFAULT(%)  DEFAULT(%)\n",
              "0  Graduate School           80.77       19.23\n",
              "1       University           76.27       23.73\n",
              "2      High School           74.84       25.16\n",
              "3            Other           92.95        7.05"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "ipNwNGP63mQ-",
        "outputId": "a27ce055-348e-472e-e482-088b4a029aed"
      },
      "source": [
        "sns.set(rc={'figure.figsize':(9,4)})\n",
        "sns.set_context(\"talk\", font_scale=0.8)\n",
        "\n",
        "ax = eduPct.plot(x='EDUCATION', kind='barh', stacked=True, title='Education Level vs. Default')\n",
        "ax.set_xlabel('PERCENT')\n",
        "ax.get_legend().set_bbox_to_anchor((1, 0.9))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_QCzaRFT39lF"
      },
      "source": [
        "There seems to be a correlation between lower levels of education and ```default```."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MJvmWYmS4T7R"
      },
      "source": [
        "### Marriage vs. ```default```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "qbOqbqCl4bD4",
        "outputId": "9600ae7a-c885-4cce-9b41-7c376ed14795"
      },
      "source": [
        "sns.set(rc={'figure.figsize':(9,7)})\n",
        "sns.set_context(\"talk\", font_scale=0.8)\n",
        "\n",
        "marri = sns.countplot(x=\"MARRIAGE\", hue='DEFAULT', data=df )\n",
        "marri.set_xticklabels(['Others','Married','Single','Divorce']);"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "E7_Dpvi34mCR",
        "outputId": "7f4ba3c1-1b09-4a2d-a143-665dae7318b3"
      },
      "source": [
        "default0 = df.groupby(df['MARRIAGE'][df['DEFAULT'] == 0]).size().reset_index(name='NOT_DEFAULT')\n",
        "default1 = df.groupby(df['MARRIAGE'][df['DEFAULT'] == 1]).size().reset_index(name='DEFAULT')\n",
        "total = df.groupby('MARRIAGE').size().reset_index(name='TOTAL')\n",
        "\n",
        "marriTable = default0.join(default1['DEFAULT']).join(total['TOTAL'])\n",
        "marriTable['MARRIAGE'] = ['Others','Married','Single','Divorce']\n",
        "\n",
        "marriTable['NOT_DEFAULT'] = round((default0['NOT_DEFAULT']/total['TOTAL'])*100,2)\n",
        "marriTable['DEFAULT'] = round((default1['DEFAULT']/total['TOTAL'])*100,2)\n",
        "\n",
        "marriPct = marriTable.iloc[:,0:3]\n",
        "marriPct = marriPct.rename(columns={'NOT_DEFAULT': 'NOT_DEFAULT(%)', 'DEFAULT': 'DEFAULT(%)'})\n",
        "\n",
        "sns.set(rc={'figure.figsize':(9,4)})\n",
        "sns.set_context(\"talk\", font_scale=0.8)\n",
        "\n",
        "ax = marriPct.plot(x='MARRIAGE', kind='barh', stacked=True, title='Marital Status vs. Default')\n",
        "ax.set_xlabel('PERCENT')\n",
        "ax.get_legend().set_bbox_to_anchor((1, 0.9))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XLlzAuXPnsH37dri6umLkyJHYtGlTuZy/oKCgXM5DRERERPS+EW2qT+XKleHv74+xY8fixx9/RFZWFoKDgxEcHAwAmD9/PkaOHKl0TEJCAmrVqoX4+HgAwM2bNzFixAh4eHigbdu2+OGHH5CXl6fo365dOyxbtgzDhw+Hu7s7tm/fjsLCQqxevRqdO3eGu7s72rZti82bNyuOiYuLg5+fn2LMxYsXo7CwsBwyQkRERESkOeU21ed1unbtitDQUFy4cEFpv6+vL3x8fJCYmAgbGxsAwM6dO9G4cWM4OjoiOzsbw4cPh6+vL8LCwvD06VOMGzcOBQUFmDFjhmKcbdu2Yfny5XBzc0Nubi6WLFmCAwcOYOHChahTpw7S0tLw4MEDAMCdO3cwbNgwfP/99/D29sbjx48RGBgIfX19BAQElF9SiIiIqMwkEsDMzFBjYxN9qEQv/F8U9RkZGUr7nZ2dUb9+fezatQvjx4+HXC5HZGQkpkyZAgA4cuQIAGDChAmQyWSwt7dHUFAQgoKCMH36dEj+/09mnz59UK9ePQCAgYEBNm3ahAULFsDV1RUAYGFhAQsLCwDAli1b4OXlhc6dOwMA7OzsMGbMGPz0008s/ImIiOiNBEFAcnIycnNzUVRUJHY4pGWkUikMDAxgZWWlqINfJnrhn5iYCAAwNzcv0da3b18sW7YM48aNw4kTJ5CTk4OOHTsqjrO1tYVMJlP0d3R0RG5uLlJTU2FpaQkAsLe3V7SnpaXh+fPncHJyemUs9+7dw+nTp3H48GHFvqKiIgiC8O4XSkREROVCEICMjByNjG1pafLKu/6CIODBgwfIzMyETFa8kAlReSoqKkJmZiZyc3Nhb2//yuJf9MI/JiYGhoaGqF+/PmJiYpTaOnXqhLlz5+LUqVPYtWsXunXrBn19fQDF3xQ8evQIRUVFij9cCQkJMDAwUNzBB6D0B69ixYowMjLC3bt34ezsXCKWSpUqoWfPnvj22281calERET0kUpOTkZmZiYqVKgIExMzscMhLZWdnYHMzDQkJyejUqVKJdpF+zj69OlTrFu3Dr/88gu+/PJLmJqaluhjZGQEHx8frFq1CocOHULfvn0VbW3atIEgCFi6dCny8/Px8OFDLFmyBL6+vq/9ekMikcDPzw8//vgjrl27BkEQkJqaikuXLgEABg4ciH379iE2Nhb5+fmQy+W4f/8+jh07ppkkEBER0UchNzcXMpkui34SlYmJGWQyXeTm5r6yvVzv+P/yyy9Yu3YtAMDMzAxubm5YtWoVmjZt+tpj+vTpg759+6JOnTqoVauWYr+JiQnWrl2LefPmoUWLFjAyMkLHjh0xceLEN8YwYcIEmJiYYOLEiXjy5AnMzc3h7+8PNzc3uLm5Yc2aNViyZAlCQkJQWFgIOzs7DBw4UD0JICIioo/Sv2cgEIlJKpW+9hkTicAJ7Bollxeh51e7xQ6DiEgrbZ3bBbInN5C4KUTsUKic2AyZDX2HOkhJydbI+JaWJpBKS84suH//PvLyClCpko1GzkukqqdPE6Gvr4uqVauWaBN9jj8RERHRx8rQUA8yWfl8EyCXFyEnJ79czkUfJhb+RERERBoik0mRV1iEuw8z3t75HTjZmUFfh1ON6M1Y+BMRERFp0N2HGZgW9odGz/F9QHPUrFpRo+egd7NnTzSOHj2CBQt+KtPxyclPMXLkUGzcuAVmZiWXwVcFC38iIiIiLRQQ4I8LF87hp59C4enZXGl/gwYN4e8/FgBw8OABbNu2Gbdv3wIAVK/+KQYMGIz27b0BAEFB4xEXdwFA8UPOeXl5MDT835uTp06djk6durwxjsuX46CnpweJRApTUxPUqVMXvXv3RcOGjRT95swJQWzsPujp6SodP27cF+jTpx/27PkN3303GwYGBkrtW7fuRJUqxc9ejBz5Ga5fv4aIiN2KfQDw6NEj9O7tg4iIPbC1tVU6vmnTBli+fCUaNmyEPXt+w+rVvyAqqngJ+osXz2PixM8VffPy8iCVSqGrWxxjlSo22Lp1J3Jzc7F8eSiWLVuh6BsVFYFVq4q3/f3HomfP3oq2RYsWwMTEBKNH/+8FslZWleDl5Y01a1Zh0qQvX5vPN2HhT0RERKSlzM3NERq6GE2aNFV6KeoL69atxq+/bsDkyV+hTZt2AICjRw/ju+/m4OHDh/jss2FYvHiZov+5c2cxbtxoHD5cum84hg4dofigkZz8FPv2xSAoaBwmTJiIvn0HKPp17NgZM2fOfu041tZVFEX5y27c+BtXr16BmZk5oqIiMHbsuFLF+Cr16zdQutaXPzS9sH//Xtjb28PZuToAIDU1FWFhy7Bu3SYIQhGGD/dDq1ZtYGFhgYsXz+PChXNYt+7XEufr0aM3hg0bjDFjAmBsbFLqeDkZjIiIiEhL+fj0wPPnzxAdHVmiLSkpEatXr8QXX0xC167dYGxsDGNjY3Tp4oMJEyZi5cqf8fhxktpjsrKqBD+/YRg6dASWL1+K7OwstYwbERGO2rVdMXDgYOzeHY3CwgK1jKuKI0cOwcPDU7GdmPgIDg4OsLW1hZ2dPRwcHJCY+Ai5uTmYN28upk8PgY6ObolxHB2rokIFM5w+fbpMcbDwJyIiItJS+vr6CAycgFWrwvDsmfISqKdO/QlBKELnzl1LHNeliw+Kiopw6tRJjcXWoUMn5Obm4vLlS+881rNn2ThwYD969OgFH5/uSE9Px9GjR949SBXduPE3qlWrrth2cHDEo0eP8PDhAyQkxCMxMREODo4IC1uGtm3bo2bNWq8dy9m5Oq5fv1qmODjVR8OkUgm+D2j+9o5ERKR2hnoywNoJNkNePzWAPi561k5ih/DB8fbuiB07tmLdujUYP/4Lxf60tDSYmZlDT0+vxDF6enowMzNHWlqqxuKqXNkaAJCZmanYd+DAPhw7dlip3+LFy+Dq6gYAePw4CV5erRRtjRt7YN68Bdi7dw8kEgm8vTvCyMgIzZo1R2TkTsVzCpqWlZUJE5P/Tc2pUKECgoNnYObMaQCA4OAZuHPnNs6dO4tVq9Zj6dKfcP36VVhaWmHixC9haWmpONbY2ASZmWVbJYqFfzlwcSzbk9f0bnR0iucqFhbKRY5EOzH/4mHuX6JrCJlNzXI7HfMvLomODHw3aelIJBIEBU3GuHFj0Lt3H8X+ihUrIiMjHfn5+SWK//z8fGRkpKNiRQuNxfXkyWMAgJmZmWJfhw5lm+MfGbkLXl7FRT8A9OjRC1OmBCE+/j4cHatCR6e4JC4sLFQ67sV0oBftZVWhghmys5W/UWnVqjVatWoNAMjNzcWwYYMxa9a3iI3dh5SUZISFrcauXeEIDf0Js2bNVRz37Fk2rK2tyxQHC/9ykJGRI3YIWsnMrHhFAeZfHMy/eJh7cTH/4nqR//eJk52Zxr/9d7Ize3unN3B1dUPr1m2xfPlSxT4PD09IJBLExu5Ft249lfrv378XEokUHh5N3+m8b/Lf/8bCwMAArq5132mcCxfO4c6d23j8+DFOnDgGAIoPhxEROxEUNBlWVlbQ19fHgwfxcHR0VBybkJAAALC3t3+nGGrWrIW7d28DaP/K9hUrlqF167aoWbM2fvstGvXruwMAGjZshJ07tyv1vXPnNrp27VamOFj4ExEREWmIXF4EfR1puayxL5cXvdPxgYGfo39/XxgYGKBBg4awsbHF8OGjsHjxIujo6KBVq7YAgGPHjmDJkkUYNWq00pKY6pKSkozY2P3YsGEtJkyYCBMT03caLyJiJ2rVqo0ff1xcYn94+DYEBIyHvr4+OnfuipUrw2BnZw9Hx6p4+vQJlixZCE/P5rC0tPrXkQLy8vKUxpLJZG/8VqBNm3bYvTsaI0eOLtEWF3cRf/11BuvXbwYAODg44NSpk+jWrQeOHz8Ke3sHRd/4+Hikp6ehSROPMmSChT8RERGRxuTk5IsdgsqqVLHBwIGDsX79GsW+UaPGwNGxKrZv34L58+cBAKpVc0Zw8HR4e3dU27k3bFiLLVt+hUQigalpBdSp44qffgpFw4aNlfrFxu7D4cMHlfb16zcQAQHjXzluamoqjhw5hJCQb18q3oEBAwZj69bNOHjwALp27YagoMlYt241Jk/+AqmpKTAzM0ezZs0xenSg0nFJSUlo3dpTaV+fPv0wZUrwa6+vQ4dOWLFiOW7fvqVY0hMonuLzn//MxcyZcxRr//fq5Yu4uIvo2LE9HB0dMXv2d4r+0dER6Nq1e5k/DEkEToTTKEEQkJyc/faOpHb8ul1czL94mHtxMf/i0nT+LS1NIJVKSuy/f/8+8vIKUKmS+u+A08ehvN7c+/RpIvT1dVG1atUSbbzjT0RERESkYT4+PeDj06PMx1tZVUJ09N53ioGFPxERERFpTFDQeMTFXXhl29atOzXynAC9Ggt/IiIiItKYxYuXiR0C/T++uZeIiIiISAuw8CciIiIi0gIs/ImIiIiItAALfyIiIiIiLcCHe4mIiIg0xNBQDzJZ+dxnlcuLPqgXhpWH8lo7/0PBwp+IiIhIQ2QyKaTyXOQ/vqvR8+hZOwEyg1IdExDgj8uX46CnpweJRApTUxPUqVMXvXv3RcOGjQAAc+aEIDZ2H/T0dJWOHTfuC/Tp0w979vyG776bDQMD5XP/e5nOkSM/w/Xr1xARsVtp6c5Hjx6hd28fRETsga2trdLxTZs2wPLlK9GwYSPs2fMbVq/+BVFRMQCAixfPY+LEzxV98/LyIJVKFW++rVLFBlu37kRubi6WLw/FsmUrFH2joiKwalXxtr//WPTs2VvRtmjRApiYmGD06ADFPiurSvDy8saaNaswadKXpcrv+4iFPxEREZEG5T++i8RNIRo9h82Q2dCxrVXq44YOHQF//7EAiu9u79sXg6CgcZgwYSL69h0AAOjYsTNmzpz92jGsrasoivKX3bjxN65evQIzM3NERUVg7NhxpY7xZfXrN8Dhw38otgMC/NGgQUPFdbywf/9e2Nvbw9m5OgAgNTUVYWHLsG7dJghCEYYP90OrVm1gYWGBixfP48KFc1i37tcS5+vRozeGDRuMMWMCYGxs8s7xi4lz/ImIiIgIVlaV4Oc3DEOHjsDy5UuRnZ31zmNGRISjdm1XDBw4GLt3R6OwsEANkarmyJFD8PDwVGwnJj6Cg4MDbG1tYWdnDwcHByQmPkJubg7mzZuL6dNDoKOjW2IcR8eqqFDBDKdPny632DWFhT8RERERKXTo0Am5ubm4fPnSO43z7Fk2DhzYjx49esHHpzvS09Nx9OgR9QSpghs3/ka1atUV2w4Ojnj06BEePnyAhIR4JCYmwsHBEWFhy9C2bXvUrPn6b0ycnavj+vWr5RG2RnGqDxEREREpVK5sDQDIzMwEABw4sA/Hjh1W6rN48TK4uroBAB4/ToKXVytFW+PGHpg3bwH27t0DiUQCb++OMDIyQrNmzREZuRPt23uXy3VkZWXCxOR/U3MqVKiA4OAZmDlzGgAgOHgG7ty5jXPnzmLVqvVYuvQnXL9+FZaWVpg48UtYWloqjjU2NkFmZka5xK1JLPyJiIiISOHJk8cAADMzMwBAhw5lm+MfGbkLXl7FRT8A9OjRC1OmBCE+/j4cHatCR6e4DC0sLFQ67sV0oBftZVWhghmys7OV9rVq1RqtWrUGAOTm5mLYsMGYNetbxMbuQ0pKMsLCVmPXrnCEhv6EWbPmKo579iwb1tbW7xTP+4BTfYiIiIhI4b//jYWBgQFcXeuWeYwLF87hzp3b+P33/6JLF2906eKN776bAwCIiNgJALCysoK+vj4ePIhXOjYhIQEAYG9vX+bzA0DNmrVw9+7t17avWLEMrVu3Rc2atfHPPzdQv747AKBhw0a4ceNvpb537txGrVq13yme9wELfyIiIiJCSkoytmzZhA0b1mL8+C9gYmJa5rEiInaiVq3a2LEjAr/+uhW//roVmzZtw8iRo7F3727FEpydO3fFypVhuH//HgRBwJMnj7FkyUJ4ejaHpaXVv0YUkJeXp/TPy98UvKxNm3Y4derkK9vi4i7ir7/OYNSoMQAABwcHnDp1EoWFBTh+/Cjs7R0UfePj45GenoYmTTzKnI/3Baf6EBEREWmQnrUTbIa8fqqMugVksAMAACAASURBVM5RVIbjNmxYiy1bfoVEIoGpaQXUqeOKn34KRcOGjRV9YmP34fDhg0rH9es3EAEB4185ZmpqKo4cOYSQkG9fKt6BAQMGY+vWzTh48AC6du2GoKDJWLduNSZP/gKpqSkwMzNHs2bNMXp0oNJxSUlJaN3aU2lfnz79MGVK8GuvrUOHTlixYjlu376lWNITKJ7i85//zMXMmXMUa//36uWLuLiL6NixPRwdHTF79neK/tHREejatfs7fRB6X0gEQRDEDuJjJggCkpOz396R1M7MzBAAkJGRI3Ik2on5Fw9zLy7mX1yazr+lpQmkUkmJ/ffv30deXgEqVbJR2s8394pLG9/c+/RpIvT1dVG1atUSbbzjT0RERKQhLMTF5ePTAz4+Pcp8vJVVJURH71VjROLiHH8iIiIiIi3Awp+IiIiISAtwqk85eDHfkMqXjo4MAPMvFuZfPMx9+Xoxf1suL360kvkXl46ODHx8kejVWPiXgxvx6WKHQEREGlLbyQIoyIH88V0AgFzkeLSdzNoJEj0jscMgei+x8NewoiIB08L+EDsMIiLSkK1zu0D25C4SN4WIHQoBsBkyG/oOdcr9vDo6Onj2LAcFBfnQ1dUr9/MTAUBBQT4KCwtgbPzqbxxZ+BMRERG9I3Nzczx79gxPnyZCR0cXEknJJT+JNEkQBBQWFkBXVwfm5q9eepSFPxEREdE7MjY2RrVq1ZCSkoL8/HwUFZXldVpEZSeVSqGnVwGWlpbQ0Xl1ic/Cn4iIiEgNdHR0YG1tLXYYRK/F5TyJiIiIiLQAC38iIiIiIi2gUuE/duxYZGdnK7bXrFmDrKwsxXZ6ejq6deum/uiIiIiIiEgtVCr8jx49iry8PMX28uXLkZ7+v7XpCwsLcevWLfVHR0REREREaqFS4f/yG/DEfCPeb7/9hq5du6p1zODgYAQHB6t1TCIiIiKi98l7uapPQkICFixYgHPnzuH58+eoUKECXF1d8dNPP6F79+7o3r272CESEREREX1Q3svC39/fH56enti3bx9MTU3x+PFjHD58WOywiIiIiIg+WCoX/hs3boShYfHrf+VyObZs2QIzMzMAwPPnz9UWUFpaGu7evYvFixejQoUKAIAqVapg4MCBAICIiAgsW7YMhw4dAlA8TaegoABmZmaIiYmBjo4OBgwYgM8//1wx5vHjx/HDDz/g4cOHcHV1hYeHByIiIhRjvCwzMxMLFy7EsWPH8Pz5c7i5uWHmzJlwcHBQ23USERGRZkgkgJmZocbGJvpQqVT429raYvfu3YptKysrxMbGKvWxsbFRS0AVK1bEp59+im+++QaDBg2Cq6srqlev/sZXXx84cADz58/H9OnTcfHiRXz22Wfw9PREo0aNkJCQgICAAMyePRs9evTAtWvXEBAQAH19/VeOJQgCxo0bhypVqiAyMhJGRkZYunQpxowZg+joaOjq6qrlOomIiIiIypNKhf/r7oxrysaNG7F+/Xps3LgRN2/ehKmpKYYMGYLAwMBX9m/cuDE6d+4MAGjYsCFcXFxw6dIlNGrUCHv27EGdOnXg6+sLAHBzc0OvXr2wd+/eV4517do1XLx4EWfOnFF8wzFx4kRs3rwZcXFxaNSokQaumIiIiNRFEICMjByNjG1pacK7/vTBei/n+FtYWGDSpEmYNGkScnJysG/fPnzzzTewtraGVFpyIaLKlSsrbRsbG+PZs2cAgKSkJNjZ2Sm1v7z9b/fv30dBQQFatmyptF8ulyMpKamsl0REREREJCqVCv/nz5/j8OHDimU0Z82apbSuv0wmw7Rp02BkZKT2AA0NDdG7d29s2rQJf//9N2rXrl2q46tUqYK///5bad+jR49e29/KygoGBgY4deoUdHTey89FRERERESlptI6/lFRUUpz/KOjo3Hv3j0kJSUhKSkJR48exbZt29QSUEZGBhYuXIh//vkHBQUFKCwsRGxsLP755x80bNiw1OP5+Pjg6tWriIqKQmFhIS5fvozIyMjX9m/YsCGqVauG2bNnIyUlRRFTbGwscnI087UhEREREZGmqXRLOyYmBsOHD1faN3/+fMUqN1FRUdi2bRtGjBjxzgHp6uoiJSUFn3/+OZ48eQIdHR3Y2dlhxowZ6Ny5MyIiIko1noODA5YvX4758+dj9uzZcHV1ha+vb4mHk1+QyWRYt24dQkND0bdvX6SlpcHMzAyNGjVC69at3/n6iIiIiIjEIBFUeA1vy5YtsWXLFkWh7+7ujt9++02xfefOHfTr1w9nz57VbLRq8sMPP+Cff/7BmjVrNH4uubwIPb/a/faORET0Qdo6twtkT24gcVOI2KEQAJshs6HvUAcpKdkaGd/S0gRSKZ/upQ+TSlN90tPT8e/PB8ePH4e9vb1iWyaTKc35f98cOnQIaWlpkMvlOHnyJMLDw9GtWzexwyIiIiIiKjcqTfWpVKkS7t69C0dHRwCAiYmJUvvdu3dhZWWl/ujU5Pz585g2bRpyc3NhbW2NwMBA9OjRQ+ywiIiIiIjKjUqFv4eHB9atW/faOe5r166Fh4eHWgNTpylTpmDKlClih0FEREREJBqVpvqMGjUKFy9eREBAAOLi4pCVlYXMzExcuHABo0ePRlxcHPz9/TUdKxERERERlZFKd/ydnZ0RGhqK4OBgDBgwQKnNwsICoaGhcHZ21kiARERERET07lR+Q1XLli3x+++/48SJE7h37x4A4JNPPkGLFi1gYGCgqfiIiIiIiEgNVFrO823Onj2LHTt2YP78+eqI6aMiCAKu3E4ROwwiItKQ2k4WQEEO8h/fFTsUAqBn7QSJnhGX8yR6BZXv+L8sMzMTkZGRCA8Px61bt1C7dm11xvVRcXE0FzsEraSjIwMAFBbKRY5EOzH/4mHuRaBrCJlNTQDMv9gkOjKo4Z4m0Uep1IX/i7v7+/fvR0FBAYYNG4Zly5bhk08+0UB4H4eMjByxQ9BKZmaGAJh/sTD/4mHuxcX8i+tF/omoJJVW9cnMzMT69evRtWtXBAYGwsjICBs2bIBUKoWvry+LfiIiIiKi95xKd/xbt26NJk2aYNy4cfDy8oKenp6m4yIiIiIiIjVS6Y6/rq4ukpOTkZaWhtzcXE3HREREREREaqZS4X/ixAn4+fkhJiYGLVq0wBdffIEjR45AIuFT7UREREREHwKVCn89PT307NkTW7Zswa5du1C5cmVMnToVhYWFWLt2Lc6fP6/pOImIiIiI6B2UeR3//Px87N27Fzt27MD58+dRpUoVHDlyRM3hffgEQUBysmbWEqY348oa4mL+xcPci4v5F5em8891/OlDVuZ1/F98C9CzZ0/cvHkTO3bsUGdcRERERESkRipN9XmbTz/9FNOnT1fHUEREREREpAEq3fEfMWLEW/tIJBKsWbPmnQMiIiIiIiL1U6nwt7a21nQcRERERESkQSoV/vPmzdN0HEREREREpEFqmeMvCAKioqLUMRQREREREWmAyoV/YWEhbt68iXv37intP3jwIHx8fDBjxgx1x0ZERERERGqi0lSf27dvY8yYMXj48CEAoGPHjpg1axYmTZqEixcvom/fvli9erVGAyUiIiIiorJTqfD/6aefYGNjg+nTpyM6Ohr79+/HzZs34eXlhSVLlsDU1FTTcRIRERER0TtQqfC/ePEiwsLCULduXTRo0AD79+/HoEGDMHjwYE3HR0REREREaqDSHP+UlBTY2NgAAMzMzGBoaAhPT0+NBkZEREREROqj8sO9Uun/ukokEujq6mokICIiIiIiUj+VpvoIgoB+/fpBJpMBAHJycjB06NASxX9sbKz6IyQiIiIionemUuE/fvx4TcdBREREREQaxMKfiIiIiEgLqOXNvc+fP8eyZcvUMRQREREREWmAyoV/amoqjhw5gj/++ANFRUUAit/mu379erRv3x6//vqrxoIkIiIiIqJ3o9JUn7i4OPj7+yMzMxMSiQT16tXD/PnzERgYiJycHAQEBKBPnz6ajpWIiIiIiMpIIgiC8LZOI0aMgKmpKcaNG4edO3di48aNcHBwwOjRo+Hr66u01CcpEwQBBQVyscPQSjo6xatQFRYy/2Jg/sXD3JedTFb895lcXlTmMZh/cenoyCAIAlJTn2lkfEtLE0ilEo2MTaRpKhX+np6e2LBhA2rUqIHnz5+jQYMGWLBgAbp161YeMX7QBEHAldspYodBREQqqO1kARTkIP/xXbFDoTLSs3aCRM8IKSnZGhmfhT99yFSa6pOWlgZLS0sAgJGREQwNDVG3bl2NBvaxKCoSMC3sD7HDICIiFWyd2wWyJ3eRuClE7FCojGyGzIa+Qx2xwyB6L6k0R0cikUAikShtv3iZFxERERERvf9UfnNvq1atFNuFhYXo3LlziX5XrlxRX2RERERERKQ2KhX+8+bN03QcRERERESkQSoV/r169dJ0HEREREREpEHvvA7nkydP8PPPP6Ndu3bqiIeIiIiIiDRApTv+r3L06FHs2LEDR48ehaWlJTp16qTOuIiIiIiISI1KVfg/efIEO3fuxM6dO5GVlYXs7GwsXLgQXbp00VR8RERERESkBipN9Tl69CgCAwPh5eWFCxcuYMqUKThx4gSkUilq1Kih6RjLbMWKFRg1atQ7jeHn54fQ0FA1RUREREREJA6VCv+AgAA4Ozvj4MGDWLVqFbp06QJ9ff1SncjPzw8uLi4IDw9X2p+VlQV3d3e4uLjgwYMHpRrzbcaOHYvVq1erdUwiIiIiog+RSoV/s2bNsHHjRsydOxdHjx6FIAhlOtmnn36KrVu3Ku2LioqCnZ1dmcYDit8p8Kp4CgoKyjwmEREREdHHRqXCf/Xq1di7dy+qVauGGTNmoGXLlvjhhx8AQOmNvm/Tvn17PHnyBJcvX1bs2759O/r376/Y/ueff/DZZ5/Bw8MDjRo1gp+fH65fv65oP336NFxcXBATEwNvb2/Ur18fz58/h4uLCzZs2IC+ffuiXr16OHHiBEJDQ+Hn56c4Ni8vD4sWLYKXlxcaN26MwYMH49q1a4r2wsJCzJ8/H82bN0fTpk3x448/lvlDDhERERHR+0Tl5Tzt7OwQFBSEI0eOICQkBLdu3YIgCAgKCsKKFStw7969t46ho6ODPn36KO76//XXX3j27BnatGmj1C8gIADHjh3DsWPH4OTkhHHjxpW4gx8bG4vw8HCcO3cORkZGAIo/RPznP//BxYsX4enpWeL8ISEhuHr1KjZv3oyTJ0+iS5cuGDlyJDIzMwEUf8CJjY3Fxo0bcezYMejr6+PChQuqpoiIiIjeAxIJYGZmqJF/SnG/k+i9U+p1/GUyGby9vbFq1SocOHAA7dq1w+bNm9G5c2eVju/fvz/279+PrKwsbNu2DX379lX61qBGjRrw9PSEvr4+jIyMMHnyZDx8+BDx8fFK40yZMgXm5ubQ19dXHD98+HA4OztDIpHAwMBAqX9aWhoiIyMREhICa2tr6OjoYPDgwTA3N8fhw4cBABERERg5ciScnZ2hp6eHcePGoWLFiqVNERERERHRe6fM6/gDgL29PSZOnIgJEyYoiue3sbGxgYeHB9asWYNDhw5h6tSpyM/PV7Q/ePAA8+fPR1xcHLKysiCVFn82SUlJgbOzs9K5XxXP67z44NC7d2+l/QUFBXj8+DEAICkpSWkMqVQKW1tbla6LiIiI3g+CAGRk5GhkbEtLE971pw+WSoW/q6urSoNduXJFpX4DBw7E6NGj0aFDB1SuXFlpNZ+ZM2eiYsWKiIyMhIWFBTIyMtCkSZMSc+1ffCB4274XrKysAAAxMTGwtrZ+ZZ8qVarg4cOHiu2ioiI8evRIpWsiIiIiInqfqVT4FxYWws7ODr1791bLHfCWLVti3bp1cHJyKtGWlZUFW1tbmJqaIisrCwsWLHjn8wHFzyh4eXlh9uzZmD59Ouzs7JCdnY1z586hVq1aqFy5Mnr27Ik1a9bAw8MD9vb2WLlyJdLS0tRyfiIiIiIiMalU+C9fvhzbt29HWFgYPD090b9/f7Rt2xYymaxMJ5VIJK98+BYApk+fjpCQEDRu3BiVK1dGUFBQibX/y2rhwoVYtWoVRowYgSdPnsDY2Bj169fHN998AwDw9/dHZmYmhgwZgqKiIvj6+sLd3V0t5yYiIiIiEpNEKMV6lYmJiQgPD8euXbsgl8vh6+uLPn36wMHBQZMxftDk8iL0/Gq32GEQEZEKts7tAtmTG0jcFCJ2KFRGNkNmQ9+hDlJSsjUyvqWlCaRSTvKnD1OpVvWxsbFRPMg7Z84cnD17Fh07dlQsh0lERERERO+nUi/nCQBnz55FTEwMLl++DHd3d+jr66s7LiIiIiIiUiOVl/NMTU1FREQEwsPDkZ6ejh49eiAyMlJpiU0iIiIiIno/qVT4v5jeU79+fYwfPx4dO3aEnp6epmMjIiIiIiI1UanwP3DgAGxtbaGrq4vIyEhERka+st/atWvVGhwREREREamHSoV/z549IeFr6oiIiIiIPlilWs6TSk8QBFy5nSJ2GEREpILaThZAQQ7yH98VOxQqIz1rJ0j0jLicJ9ErsPDXMEEQUFAgFzsMraSjU/yCucJC5l8MzL94mPuyk8mKF7uTy4vKPAbzLy4dHRkEQUBq6jONjM/Cnz5kKq/qQ2WXkZEjdghayczMEADzLxbmXzzMvbiYf3G9yD8RlVSmdfyJiIiIiOjDwsKfiIiIiEgLsPAnIiIiItICLPyJiIiIiLQAC38iIiIiIi3Awp+IiIiISAuw8CciIiIi0gIs/ImIiIiItAALfyIiIiIiLcDCn4iIiIhIC7DwJyIiIiLSAiz8iYiIiIi0AAt/IiIiIiItwMKfiIiIiEgLsPAnIiIiItICLPyJiIiIiLQAC38iIiIiIi3Awp+IiIiISAuw8CciIiIi0gIs/ImIiIiItAALfyIiIiIiLcDCn4iIiIhIC7DwJyIiIiLSAiz8iYiIiIi0AAt/IiIiIiItIBEEQRA7iI+ZIAgoKJCLHYZW0tGRAQAKC5l/MTD/4mHuxfUx5V8mK74/KJcXiRyJ6nR0ZBAEAampzzQyvqWlCaRSiUbGJtI0HbED0AY34tPFDoGIiKjUajtZAAU5kD++K3YoKpNZO0GiZyR2GETvJRb+GlZUJGBa2B9ih0FERFRqW+d2gezJXSRuChE7FJXZDJkNfYc6YodB9F7iHH8iIiIiIi3Awp+IiIiISAuw8CciIiIi0gIs/ImIiIiItAALfyIiIiIiLcDCn4iIiIhIC3zwhb+LiwtOnz4tdhhERERERO+196bwv3LlCsaOHYsmTZqgXr166Ny5M1asWIGCggIAwOnTp+Hi4iJylEREREREH6b3ovA/efIkBg0ahOrVqyMmJgZ//fUX5syZg8jISIwbNw5FReX3qvDCwkIIglBu5yMiIiIiKg/vReE/a9YsdOjQAVOmTEGlSpWgp6eHxo0b4+eff8Yff/yB3377Df7+/gAAd3d3uLu7Y+PGjYrjb926hQEDBsDd3R3dunXD+fPnlcaPiopC9+7d0bBhQ3Tt2hUxMTGKthffJMTExMDb2xv169fH8+fPsWnTJnh5ecHd3R3NmjVDcHBw+SSDiIiIiEgDdMQO4O7du7h37x5CQkq+DtzZ2Rl169bFiRMnsGrVKnz22We4cOFCiX47d+7EkiVLYGtri3nz5uGrr77CwYMHAQARERFYtmwZli5ditq1a+P8+fMYM2YMrK2t0ahRI8UYsbGxCA8Ph6GhIRITE7FgwQKEh4ejRo0aePbsGa5du6a5JBAREZHaSCSAmZmhxsYm+lCJfsc/NTUVAGBtbf3K9ipVqij6vM6IESPg6OgIHR0d9O/fHwkJCUhLSwMArFu3DgEBAXB1dYVUKkWjRo3g4+ODyMhIpTGmTJkCc3Nz6OvrQyaTQRAE3Lp1C9nZ2TA2Nkbjxo3VcLVEREREROIQ/Y6/hYUFAODx48dwdnYu0Z6UlAR7e/s3jlG5cmXFfxsZGQEAnj17hooVK+L+/fv4/vvv8cMPPyj6yOVypbv9AJTO4eDggEWLFmHr1q2YOXMmnJycMHz4cHTp0qX0F0hERETlShCAjIwcjYxtaWnCu/70wRK98HdyckLVqlURFRWFZs2aKbXduXMHly9fxpAhQyCVlu3LCSsrK0ycOBHdunV7Y7+Xx/fy8oKXlxcKCwtx4MABTJ48Ga6urnB0dCxTHEREREREYhJ9qg8AhISEYN++fVi0aBGSk5NRUFCAs2fPIjAwEE2bNkXnzp1hZWUFoPjDQGkMHToUy5Ytw+XLl1FUVIT8/HxcunQJV65cee0xd+7cwdGjR/Hs2TPo6OjA1NQUgiCU+cMHEREREZHYRL/jDwDNmzfH5s2b8fPPP6Nz587Iy8uDjY0NevTogVGjRkEmk8HJyQl+fn4YNGgQCgsLERQUhCFDhrx17KFDh6JixYoICQlBfHw8ZDIZatSogQkTJrz2mIKCAoSFheHmzZsQBAG2traYP3/+W6ccERERERG9ryQCF63XKLm8CD2/2i12GERERKW2dW4XyJ7cQOKmkivvva9shsyGvkMdpKRka2R8S0sTSKWc5E8fJs5dISIiIiLSAiz8iYiIiIi0AAt/IiIiIiItwMKfiIiIiEgLsPAnIiIiItICLPyJiIiIiLQAC38iIiIiIi3Adfw1TBAEXLmdInYYREREpVbbyQIoyEH+47tih6IyPWsnSPSMuI4/0Suw8NcwQRBQUCAXOwytpKMjAwAUFjL/YmD+xcPci+tjyr9MVjwxQC4vEjkS1enoyCAIAlJTn2lkfBb+9CHTETsAbZCRkSN2CFrJzMwQAPMvFuZfPMy9uJh/cb3IPxGVxDn+RERERERagIU/EREREZEWYOFPRERERKQFWPgTEREREWkBFv5ERERERFqAhT8RERERkRZg4U9EREREpAVY+BMRERERaQEW/kREREREWoCFPxERERGRFpAIgiCIHcTHTBAEMMPikEiK/838i4P5Fw9zLy7mX1yazr9EAkhenIToA8PCn4iIiIhIC3CqDxERERGRFmDhT0RERESkBVj4ExERERFpARb+RERERERagIU/EREREZEWYOFPRERERKQFWPgTEREREWkBFv5ERERERFqAhT8RERERkRZg4U9EREREpAVY+BMRERERaQEW/kREREREWoCFPxERERGRFmDhryFFRUVYtGgRmjVrBnd3d4wcORIPHz4UO6yP0oIFC9C1a1c0aNAALVq0wLRp05CWlqbU59q1axgwYADq1auHNm3aYOPGjSJF+3EbN24cXFxccPr0acW+P//8E927d0e9evXQsWNH7N27V8QIP05nzpzBoEGD4O7ujiZNmiAgIEDRxvxrVnJyMiZPngxPT080atQIAwYMwF9//aVoZ/7VJyYmBoMGDUKDBg3g4uJSov1tv+dzc3Mxc+ZMNGnSBA0aNEBQUBDS09PLK3yi94NAGvHLL78Ibdu2FW7fvi1kZ2cLM2bMEHx8fAS5XC52aB+dhQsXClevXhXy8/OF5ORkYfjw4cKYMWMU7VlZWYKnp6cQGhoq5ObmChcuXBAaN24s7Nu3T8SoPz6RkZHCiBEjhBo1aginTp0SBEEQEhISBDc3N2HHjh1CXl6ecOjQIcHNzU24ePGiyNF+PM6cOSM0aNBAiI6OFnJycoS8vDwhLi5OEATmvzyMHz9eGDJkiJCSkiIUFhYKa9asEerXry9kZGQw/2p27NgxYffu3UJ4eLhQo0YNpTZVfs9/8803Qq9evYSkpCQhPT1d8Pf3F0aPHl3el0EkKt7x15Bt27Zh1KhRqFatGoyNjfHll1/i7t27OHfunNihfXQmTZqE2rVrQ1dXF5aWlvDz88OZM2cU7QcOHIBUKkVgYCD09fVRv3599O3bF1u2bBEx6o9LUlISFi9ejG+//VZpf2RkJGrUqIG+fftCT08Pbdu2Rdu2bbFt2zaRIv34LFy4EP369UP37t1hYGAAPT09uLm5AWD+y8P9+/fRqVMnWFhYQCaToX///nj+/Dni4+OZfzVr2bIlfHx84ODgUKLtbb/nc3NzERUVhS+++ALW1tYwMzPD1KlTceTIETx69Ki8L4VINCz8NSArKwsPHz6Eq6urYl+FChVQtWpVXL9+XcTItMPJkydRs2ZNxfbff/+N2rVrQyr93//urq6u+Pvvv8UI76MjCAKmTZuGgIAA2NraKrX9/fffSn8OAOZenZ4/f464uDgAQO/eveHh4YH+/fvj5MmTAJj/8uDv748DBw7g6dOnKCgowObNm/HJJ5+gRo0azH85etvv+Xv37iEvLw9169ZVtDs7O8PQ0JB/L5NW0RE7gI9RdnY2gOJi/99MTU0VbaQZe/fuRXh4ODZt2qTYl52dDVNTU6V+FSpU4M9CTbZs2QJBENC/f/8SbdnZ2ahevbrSPuZefTIzM1FUVITdu3dj5cqV+PTTTxEZGYmxY8diz549zH85cHd3R1RUFFq0aAGZTAZzc3MsX74cenp6zH85etvv+Rf/frkP/14mbcM7/hpgYmICoPjO/79lZWUp2kj9YmJiEBISgrCwMNSpU0ex38TEpMQv9szMTP4s1CA+Ph5hYWGYO3fuK9tNTExK/Dlg7tXH2NgYAODr66uY7tavXz/Y29vj+PHjzL+GFRUVYdiwYahSpQrOnDmDS5cuYc6cOfD398eNGzeY/3L0tt/z/HuZqBgLfw0wNTWFnZ0drly5otiXlZWF+Ph41KpVS8TIPl7h4eGYPXs2VqxYgaZNmyq11axZE9euXUNRUZFi39WrV5WmA1HZnD17Funp6YppJh4eHgCAwMBAhISEoGbNmkp/DgDmXp1MTU1fOd9ZIpEAAPOvYRkZGUhI+L/27jWkqTeOA/jX5mWKojYtYcbsIguTZGgjSTMqxIJgRMultewGQS/KhFUoxmwitaIa2GXmG0OIeAdAHwAABpJJREFUcoqQUVCE1bq9CSKKscwihEjCDNlF2/Z/EY4O07xgjf/O9wPnxXPO83tug8PPnee4z9Dr9UhOTkZ0dDQ2btyIRYsWwW63c/3/oanu81lZWYiLixN8Hn19fXC73fw8SFSY+P8lOp0Ora2t6O/vh8vlgtlsRlZWFvLz88M9tIjT1taGs2fPorW1dcL1LS0thc/nw+XLlzE6OorXr1/j1q1b2LFjRxhGG1k2bdqE+/fvo7u7O3gAgMlkwtGjR6HRaOBwOGCz2TA2Nobe3l48fPgQOp0uzCOPHJWVlbDZbHA4HPD5fLDZbBgYGMDatWu5/n9Zamoqli5divb2doyMjMDv9+PBgwdwOp1YsWIF13+O+Xw+eL1ejI2NAQC8Xi+8Xi/8fv+U93mpVAqNRgOLxYKvX79ieHgYZrMZJSUlkMvl4ZwW0T8VFQgEAuEeRCTy+/04f/48Ojo64Ha7kZ+fD6PRiMzMzHAPLeIolUpER0cjNjZWcL6npyf4sunbt29hNBrx7t07pKamYt++fdDr9eEYbsRTKpVoa2sLfvv/9OlTNDU14dOnT8jIyMCRI0ewefPmMI8ycgQCATQ3N+PGjRtwuVzIzs5GTU0N1Go1AK7/3/bx40ecOXMGr169gtfrhVwuh16vh1arBcD1n0udnZ04ceJEyPnx+81U93mPx4PGxkbcvXsXPp8PxcXFMBqNSElJ+ZfTIAorJv5ERERERCLArT5ERERERCLAxJ+IiIiISASY+BMRERERiQATfyIiIiIiEWDiT0REREQkAkz8iYiIiIhEgIk/EREREZEIRId7AEREc+X48ePo6uoCAEgkEixcuBDFxcWorq5GamoqlErlhHGVlZWor68HAEEdqVQKuVwOrVaLPXv2CGKcTiesViueP3+OoaEhpKenIycnB7t27cLq1asBAOvXr8fAwEBIf+vWrcPVq1cFdaxWK0pKSoJ1uru7YTAY4HA4BPOazO8/mkZERDQRJv5EFFEKCgpw4cIF+Hw+vHnzBnV1dfjy5QusVisAoL6+HqWlpYKY+Ph4QXm8jsfjgd1ux6lTp5CQkIDy8nIAwOPHj3Ho0CGoVCqYTCYsXrwYLpcLjx49wsmTJ3Hv3r1gWwcOHMDu3bsF7cfFxYWUzWYzioqKIJFIQuZUW1uLmpqaYHnbtm3YsmWLoN3k5OSZLBMREYkQE38iiigxMTFIT08HAGRkZMDpdMJiscDj8QAAEhMTg9cn83sdnU6Hmzdv4smTJygvL4fb7caxY8egVqtx7do1Qdzy5cuDfxyMS0hImLI/rVaLzs5O2Gw2bN++PeR6UlISkpKSgmWJRDKtdomIiH7HPf5EFNGkUin8fj9+/vw549hAIIBnz56hr68PMTExAAC73Y5v377h4MGDE8bM5pt3mUyG/fv3w2KxwOVyzTieiIhoOpj4E1HEev/+Pdrb25GXl4fExEQAQF1dHVQqleC4c+eOIG68Tm5uLqqqqhAVFRXcVtPf3w8AWLZs2bTGcOnSpZD+WlpaQurt3bsXUVFRIU8RiIiI5gq3+hBRRHn58iVUKhV8Ph9GR0dRWFiIhoaG4PXq6mps2LBBEJOWliYoj9cZHBzEuXPnUFZWhry8PAC/ngLMRGVlJSoqKgTnUlJSQurFx8fj8OHDaGxshE6nm1EfRERE08HEn4giysqVK3H69GlIJBIsWLAAsbGxgusymQwKheKPbYzXUSgUaG5uRllZGXJycrBq1SosWbIEwK+nCQUFBVOOJzk5ecr+xm3duhVtbW24ePEi1Gr1tGKIiIimi1t9iCiiSKVSKBQKZGZmhiT9szF//nxUVFTAZDIhEAhgzZo1kMlkuHLlyoT1h4eHZ93XvHnzYDAY0NXVBafTOet2iIiIJsLEn4hEZWRkBIODg4Ljx48ff4zZuXMnPnz4gJ6eHsTHx6OpqQkvXrxAVVUVent78fnzZzgcDrS2tob8Vx6XyxXS39DQ0KR9FRUVobCwENevX5+T+RIREY3jVh8iEpWGhgbBnn9A+INaE0lLS4NGo4HFYkFZWRlKSkrQ0dEBq9WK2tpafP/+HWlpacjNzYXJZBLEtrS0hLzMm52djdu3b0/an8FggEajmcXsiIiIJhcVmOmbakRERERE9L/DrT5ERERERCLAxJ+IiIiISASY+BMRERERiQATfyIiIiIiEWDiT0REREQkAkz8iYiIiIhEgIk/EREREZEIMPEnIiIiIhIBJv5ERERERCLwH0RBwK4iY5sgAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lo98ZuMS5Ptd"
      },
      "source": [
        "Among those with Divorce status there's a higher prevalence to default."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uOLGwi5I6gNA"
      },
      "source": [
        "### Credit Behaivour vs. ```default```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "Efyz4-FM6glP",
        "outputId": "248b18ad-dc61-4c3b-f2b3-fe26aaccba26"
      },
      "source": [
        "default0 = df.groupby(df['PAY_1'][df['DEFAULT'] == 0]).size().reset_index(name='NOT_DEFAULT')\n",
        "default1 = df.groupby(df['PAY_1'][df['DEFAULT'] == 1]).size().reset_index(name='DEFAULT')\n",
        "total = df.groupby('PAY_1').size().reset_index(name='TOTAL')\n",
        "\n",
        "pay1Table = default0.join(default1['DEFAULT']).join(total['TOTAL'])\n",
        "pay1Table['PAY_1'] = ['No Consumption','Paid in Full','Use Revolving Credit','Delay 1 mth','Delay 2 mths'\n",
        "                     ,'Delay 3 mths','Delay 4 mths','Delay 5 mths','Delay 6 mths','Delay 7 mths','Delay 8 mths']\n",
        "\n",
        "pay1Table['NOT_DEFAULT'] = round((default0['NOT_DEFAULT']/total['TOTAL'])*100,2)\n",
        "pay1Table['DEFAULT'] = round((default1['DEFAULT']/total['TOTAL'])*100,2)\n",
        "\n",
        "pay1Pct = pay1Table.iloc[:,0:3]\n",
        "pay1Pct = pay1Pct.rename(columns={'NOT_DEFAULT': 'NOT_DEFAULT(%)', 'DEFAULT': 'DEFAULT(%)'})\n",
        "\n",
        "sns.set(rc={'figure.figsize':(9,5)})\n",
        "sns.set_context(\"talk\", font_scale=0.8)\n",
        "\n",
        "ax = pay1Pct.sort_index(ascending=False).plot(x='PAY_1', kind='barh', stacked=True, title='Credit Behaviour vs. Default')\n",
        "ax.set_xlabel('PERCENT')\n",
        "ax.get_legend().set_bbox_to_anchor((1, 0.9))\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e0rTHYZR7sYU"
      },
      "source": [
        "Those with delayed payments for more than 2 months have higher changes to default.\n",
        "\n",
        "```\n",
        "Contradicting information:\n",
        "- Those who paid in full are labeled as default. \n",
        "- There are instances where billed amount exceed the credit limit. Further data cleaning might be required.\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9AjiaLw89VMy"
      },
      "source": [
        "## Age vs. ```default```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "Xp6GTQ1e9U-F",
        "outputId": "10a7af2c-9b4b-4b78-b9c8-d6ace306eb1e"
      },
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1,2,figsize=(14,5))\n",
        "\n",
        "ax1.set_title('All Client', fontsize=14)\n",
        "ax2.set_title('Defaulted Client', fontsize=14)\n",
        "\n",
        "sns.distplot(df['AGE'], norm_hist=False, kde=False, ax=ax1);\n",
        "sns.distplot(df['AGE'][df['DEFAULT'] == 1], norm_hist=True, kde=False, ax=ax2);"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VH-pk9w-RA1"
      },
      "source": [
        "There is a slight spike in the number of defaults between the age group 26 t0 35, but the default distribution seems to be uniform in the group 21 to 40."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TaHksZDU_dA2"
      },
      "source": [
        "## Conclusions\n",
        "\n",
        "- Credit behaivour manifested in delay status seems the most important indicator for default.\n",
        "\n",
        "- Demographic data seems to have a lower correlation with ```default```. \n",
        "  - Those with divorse status show higher chance to default.\n",
        "  - Lower education levels show higher chance to defailt.\n",
        "  - There's a slight higher chance to default for the age group 26 to 35."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ujIqWR8L8zos"
      },
      "source": [
        "## Research questions\n",
        "\n",
        "National consumer protection agencies enforces equal credit opportunity laws in different countries, which generally prohibits credit discrimination on the basis of sex, marital status or age.\n",
        "\n",
        "- Is there a correlation between sex, age, mariages and the credit approval status? \n",
        "\n",
        "- Can this relationship be used to predict if a person is in risk of default(grant credit status)?\n",
        " \n",
        "- If yes, does the relationship indicate reasonable risk management strategies?\n"
      ]
    }
  ]
}