Neural_Nets_with_LendingClub_dataset.ipynb

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      "name": "Neural Nets with LendingClub dataset.ipynb",
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        "<a href=\"https://colab.research.google.com/github/goldenfingaz/Machine_Learning_with_Python/blob/main/Neural_Nets_with_LendingClub_dataset.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",
      "metadata": {
        "id": "lP_gPdF5_K2R"
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      "source": [
        "<a href=\"https://www.pieriandata.com\"><img src=\"../Pierian_Data_Logo.PNG\"></a>\n",
        "<strong><center>Copyright by Pierian Data Inc.</center></strong> \n",
        "<strong><center>Created by Jose Marcial Portilla.</center></strong>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Keras API Project Exercise\n",
        "\n",
        "## The Data\n",
        "\n",
        "We will be using a subset of the LendingClub DataSet obtained from Kaggle: https://www.kaggle.com/wordsforthewise/lending-club\n",
        "\n",
        "## NOTE: Do not download the full zip from the link! We provide a special version of this file that has some extra feature engineering for you to do. You won't be able to follow along with the original file!\n",
        "\n",
        "LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California.[3] It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission (SEC), and to offer loan trading on a secondary market. LendingClub is the world's largest peer-to-peer lending platform.\n",
        "\n",
        "### Our Goal\n",
        "\n",
        "Given historical data on loans given out with information on whether or not the borrower defaulted (charge-off), can we build a model thatcan predict wether or nor a borrower will pay back their loan? This way in the future when we get a new potential customer we can assess whether or not they are likely to pay back the loan. Keep in mind classification metrics when evaluating the performance of your model!\n",
        "\n",
        "The \"loan_status\" column contains our label.\n",
        "\n",
        "### Data Overview"
      ]
    },
    {
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        "id": "N8G-7kIR_K2Y"
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      "source": [
        "----\n",
        "-----\n",
        "There are many LendingClub data sets on Kaggle. Here is the information on this particular data set:\n",
        "\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>LoanStatNew</th>\n",
        "      <th>Description</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>loan_amnt</td>\n",
        "      <td>The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>term</td>\n",
        "      <td>The number of payments on the loan. Values are in months and can be either 36 or 60.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>int_rate</td>\n",
        "      <td>Interest Rate on the loan</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>installment</td>\n",
        "      <td>The monthly payment owed by the borrower if the loan originates.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>grade</td>\n",
        "      <td>LC assigned loan grade</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>sub_grade</td>\n",
        "      <td>LC assigned loan subgrade</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>emp_title</td>\n",
        "      <td>The job title supplied by the Borrower when applying for the loan.*</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>emp_length</td>\n",
        "      <td>Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>home_ownership</td>\n",
        "      <td>The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>annual_inc</td>\n",
        "      <td>The self-reported annual income provided by the borrower during registration.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>verification_status</td>\n",
        "      <td>Indicates if income was verified by LC, not verified, or if the income source was verified</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>issue_d</td>\n",
        "      <td>The month which the loan was funded</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>loan_status</td>\n",
        "      <td>Current status of the loan</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>purpose</td>\n",
        "      <td>A category provided by the borrower for the loan request.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>title</td>\n",
        "      <td>The loan title provided by the borrower</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>zip_code</td>\n",
        "      <td>The first 3 numbers of the zip code provided by the borrower in the loan application.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>addr_state</td>\n",
        "      <td>The state provided by the borrower in the loan application</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>dti</td>\n",
        "      <td>A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>earliest_cr_line</td>\n",
        "      <td>The month the borrower's earliest reported credit line was opened</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>open_acc</td>\n",
        "      <td>The number of open credit lines in the borrower's credit file.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>pub_rec</td>\n",
        "      <td>Number of derogatory public records</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>revol_bal</td>\n",
        "      <td>Total credit revolving balance</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>revol_util</td>\n",
        "      <td>Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>total_acc</td>\n",
        "      <td>The total number of credit lines currently in the borrower's credit file</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>initial_list_status</td>\n",
        "      <td>The initial listing status of the loan. Possible values are – W, F</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>application_type</td>\n",
        "      <td>Indicates whether the loan is an individual application or a joint application with two co-borrowers</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>mort_acc</td>\n",
        "      <td>Number of mortgage accounts.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>pub_rec_bankruptcies</td>\n",
        "      <td>Number of public record bankruptcies</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "\n",
        "---\n",
        "----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J5gaeVJv_K2f"
      },
      "source": [
        "## Loading the data and other imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjyupsh__K2f"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nW9PN0m6QKG6",
        "outputId": "273c9c71-167e-44be-8e46-259b2f84af86"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PgeFRSn2_K2f"
      },
      "source": [
        "df = pd.read_csv('/content/gdrive/My Drive/Datasets/lending_club_loan_two.csv')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iiKYk-SJ_K2f",
        "outputId": "28130fca-9799-4edc-85f9-2df2b718e365"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 396030 entries, 0 to 396029\n",
            "Data columns (total 27 columns):\n",
            " #   Column                Non-Null Count   Dtype  \n",
            "---  ------                --------------   -----  \n",
            " 0   loan_amnt             396030 non-null  float64\n",
            " 1   term                  396030 non-null  object \n",
            " 2   int_rate              396030 non-null  float64\n",
            " 3   installment           396030 non-null  float64\n",
            " 4   grade                 396030 non-null  object \n",
            " 5   sub_grade             396030 non-null  object \n",
            " 6   emp_title             373103 non-null  object \n",
            " 7   emp_length            377729 non-null  object \n",
            " 8   home_ownership        396030 non-null  object \n",
            " 9   annual_inc            396030 non-null  float64\n",
            " 10  verification_status   396030 non-null  object \n",
            " 11  issue_d               396030 non-null  object \n",
            " 12  loan_status           396030 non-null  object \n",
            " 13  purpose               396030 non-null  object \n",
            " 14  title                 394275 non-null  object \n",
            " 15  dti                   396030 non-null  float64\n",
            " 16  earliest_cr_line      396030 non-null  object \n",
            " 17  open_acc              396030 non-null  float64\n",
            " 18  pub_rec               396030 non-null  float64\n",
            " 19  revol_bal             396030 non-null  float64\n",
            " 20  revol_util            395754 non-null  float64\n",
            " 21  total_acc             396030 non-null  float64\n",
            " 22  initial_list_status   396030 non-null  object \n",
            " 23  application_type      396030 non-null  object \n",
            " 24  mort_acc              358235 non-null  float64\n",
            " 25  pub_rec_bankruptcies  395495 non-null  float64\n",
            " 26  address               396030 non-null  object \n",
            "dtypes: float64(12), object(15)\n",
            "memory usage: 81.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V2urNM8w_K2f"
      },
      "source": [
        "# Project Tasks\n",
        "\n",
        "**Complete the tasks below! Keep in mind is usually more than one way to complete the task! Enjoy**\n",
        "\n",
        "-----\n",
        "------\n",
        "\n",
        "# Section 1: Exploratory Data Analysis\n",
        "\n",
        "**OVERALL GOAL: Get an understanding for which variables are important, view summary statistics, and visualize the data**\n",
        "\n",
        "\n",
        "----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fRe3rcnL_K2g"
      },
      "source": [
        "**TASK: Since we will be attempting to predict loan_status, create a countplot as shown below.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "0ZnHl2u7_K2g",
        "outputId": "45f71563-c9eb-4dcb-da4b-05e4834bebc9"
      },
      "source": [
        "sns.countplot(data=df,x='loan_status')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3df536c490>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4W-AfDD-_K2g"
      },
      "source": [
        "**TASK: Create a histogram of the loan_amnt column.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "9dmqbN2m_K2h",
        "outputId": "9d69f12f-01d2-4602-e7af-afd010c281ad"
      },
      "source": [
        "plt.figure(figsize=[12,6])\n",
        "sns.histplot(data=df,x='loan_amnt',bins=40)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3df52a3550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AOut_s-D_K2h"
      },
      "source": [
        "**TASK: Let's explore correlation between the continuous feature variables. Calculate the correlation between all continuous numeric variables using .corr() method.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "QWlVpUEt_K2h",
        "outputId": "d30efe5a-c6fc-4b65-a8f5-99822059791d"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": 8,
      "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>loan_amnt</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>dti</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>revol_util</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>mort_acc</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>loan_amnt</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.168921</td>\n",
              "      <td>0.953929</td>\n",
              "      <td>0.336887</td>\n",
              "      <td>0.016636</td>\n",
              "      <td>0.198556</td>\n",
              "      <td>-0.077779</td>\n",
              "      <td>0.328320</td>\n",
              "      <td>0.099911</td>\n",
              "      <td>0.223886</td>\n",
              "      <td>0.222315</td>\n",
              "      <td>-0.106539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>int_rate</th>\n",
              "      <td>0.168921</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.162758</td>\n",
              "      <td>-0.056771</td>\n",
              "      <td>0.079038</td>\n",
              "      <td>0.011649</td>\n",
              "      <td>0.060986</td>\n",
              "      <td>-0.011280</td>\n",
              "      <td>0.293659</td>\n",
              "      <td>-0.036404</td>\n",
              "      <td>-0.082583</td>\n",
              "      <td>0.057450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>installment</th>\n",
              "      <td>0.953929</td>\n",
              "      <td>0.162758</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.330381</td>\n",
              "      <td>0.015786</td>\n",
              "      <td>0.188973</td>\n",
              "      <td>-0.067892</td>\n",
              "      <td>0.316455</td>\n",
              "      <td>0.123915</td>\n",
              "      <td>0.202430</td>\n",
              "      <td>0.193694</td>\n",
              "      <td>-0.098628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annual_inc</th>\n",
              "      <td>0.336887</td>\n",
              "      <td>-0.056771</td>\n",
              "      <td>0.330381</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.081685</td>\n",
              "      <td>0.136150</td>\n",
              "      <td>-0.013720</td>\n",
              "      <td>0.299773</td>\n",
              "      <td>0.027871</td>\n",
              "      <td>0.193023</td>\n",
              "      <td>0.236320</td>\n",
              "      <td>-0.050162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>0.016636</td>\n",
              "      <td>0.079038</td>\n",
              "      <td>0.015786</td>\n",
              "      <td>-0.081685</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.136181</td>\n",
              "      <td>-0.017639</td>\n",
              "      <td>0.063571</td>\n",
              "      <td>0.088375</td>\n",
              "      <td>0.102128</td>\n",
              "      <td>-0.025439</td>\n",
              "      <td>-0.014558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>open_acc</th>\n",
              "      <td>0.198556</td>\n",
              "      <td>0.011649</td>\n",
              "      <td>0.188973</td>\n",
              "      <td>0.136150</td>\n",
              "      <td>0.136181</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.018392</td>\n",
              "      <td>0.221192</td>\n",
              "      <td>-0.131420</td>\n",
              "      <td>0.680728</td>\n",
              "      <td>0.109205</td>\n",
              "      <td>-0.027732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub_rec</th>\n",
              "      <td>-0.077779</td>\n",
              "      <td>0.060986</td>\n",
              "      <td>-0.067892</td>\n",
              "      <td>-0.013720</td>\n",
              "      <td>-0.017639</td>\n",
              "      <td>-0.018392</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.101664</td>\n",
              "      <td>-0.075910</td>\n",
              "      <td>0.019723</td>\n",
              "      <td>0.011552</td>\n",
              "      <td>0.699408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol_bal</th>\n",
              "      <td>0.328320</td>\n",
              "      <td>-0.011280</td>\n",
              "      <td>0.316455</td>\n",
              "      <td>0.299773</td>\n",
              "      <td>0.063571</td>\n",
              "      <td>0.221192</td>\n",
              "      <td>-0.101664</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.226346</td>\n",
              "      <td>0.191616</td>\n",
              "      <td>0.194925</td>\n",
              "      <td>-0.124532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol_util</th>\n",
              "      <td>0.099911</td>\n",
              "      <td>0.293659</td>\n",
              "      <td>0.123915</td>\n",
              "      <td>0.027871</td>\n",
              "      <td>0.088375</td>\n",
              "      <td>-0.131420</td>\n",
              "      <td>-0.075910</td>\n",
              "      <td>0.226346</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.104273</td>\n",
              "      <td>0.007514</td>\n",
              "      <td>-0.086751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_acc</th>\n",
              "      <td>0.223886</td>\n",
              "      <td>-0.036404</td>\n",
              "      <td>0.202430</td>\n",
              "      <td>0.193023</td>\n",
              "      <td>0.102128</td>\n",
              "      <td>0.680728</td>\n",
              "      <td>0.019723</td>\n",
              "      <td>0.191616</td>\n",
              "      <td>-0.104273</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.381072</td>\n",
              "      <td>0.042035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mort_acc</th>\n",
              "      <td>0.222315</td>\n",
              "      <td>-0.082583</td>\n",
              "      <td>0.193694</td>\n",
              "      <td>0.236320</td>\n",
              "      <td>-0.025439</td>\n",
              "      <td>0.109205</td>\n",
              "      <td>0.011552</td>\n",
              "      <td>0.194925</td>\n",
              "      <td>0.007514</td>\n",
              "      <td>0.381072</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.027239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <td>-0.106539</td>\n",
              "      <td>0.057450</td>\n",
              "      <td>-0.098628</td>\n",
              "      <td>-0.050162</td>\n",
              "      <td>-0.014558</td>\n",
              "      <td>-0.027732</td>\n",
              "      <td>0.699408</td>\n",
              "      <td>-0.124532</td>\n",
              "      <td>-0.086751</td>\n",
              "      <td>0.042035</td>\n",
              "      <td>0.027239</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      loan_amnt  int_rate  ...  mort_acc  pub_rec_bankruptcies\n",
              "loan_amnt              1.000000  0.168921  ...  0.222315             -0.106539\n",
              "int_rate               0.168921  1.000000  ... -0.082583              0.057450\n",
              "installment            0.953929  0.162758  ...  0.193694             -0.098628\n",
              "annual_inc             0.336887 -0.056771  ...  0.236320             -0.050162\n",
              "dti                    0.016636  0.079038  ... -0.025439             -0.014558\n",
              "open_acc               0.198556  0.011649  ...  0.109205             -0.027732\n",
              "pub_rec               -0.077779  0.060986  ...  0.011552              0.699408\n",
              "revol_bal              0.328320 -0.011280  ...  0.194925             -0.124532\n",
              "revol_util             0.099911  0.293659  ...  0.007514             -0.086751\n",
              "total_acc              0.223886 -0.036404  ...  0.381072              0.042035\n",
              "mort_acc               0.222315 -0.082583  ...  1.000000              0.027239\n",
              "pub_rec_bankruptcies  -0.106539  0.057450  ...  0.027239              1.000000\n",
              "\n",
              "[12 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJiRLbv5_K2h"
      },
      "source": [
        "**TASK: Visualize this using a heatmap. Depending on your version of matplotlib, you may need to manually adjust the heatmap.**\n",
        "\n",
        "* [Heatmap info](https://seaborn.pydata.org/generated/seaborn.heatmap.html#seaborn.heatmap)\n",
        "* [Help with resizing](https://stackoverflow.com/questions/56942670/matplotlib-seaborn-first-and-last-row-cut-in-half-of-heatmap-plot)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "i0rI7KeK_K2i",
        "outputId": "9ed72cf1-e01f-4819-be11-73127e164314"
      },
      "source": [
        "plt.figure(figsize=[13,7])\n",
        "sns.heatmap(df.corr(),annot=True,cmap='coolwarm')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3df990f850>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LGHYXNBw_K2i"
      },
      "source": [
        "**TASK: You should have noticed almost perfect correlation with the \"installment\" feature. Explore this feature further. Print out their descriptions and perform a scatterplot between them. Does this relationship make sense to you? Do you think there is duplicate information here?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "_A4R90N7_K2j",
        "outputId": "ea26101b-e6b8-4178-df7f-fe3e116aae65"
      },
      "source": [
        "sns.scatterplot(data=df,x='installment',y='loan_amnt')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3deb84b590>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wAyGy6az_K2j"
      },
      "source": [
        "**TASK: Create a boxplot showing the relationship between the loan_status and the Loan Amount.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "RvhGGth4_K2j",
        "outputId": "bc403779-dd4e-41e5-d29f-1dfaabed1d1d"
      },
      "source": [
        "sns.boxplot(data=df, x='loan_status',y='loan_amnt')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3de9fceb10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VlYDgEo_K2j"
      },
      "source": [
        "**TASK: Calculate the summary statistics for the loan amount, grouped by the loan_status.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "N5RMw5Pg_K2j",
        "outputId": "89f0ec6e-be76-4d58-fe6b-81ef0079a015"
      },
      "source": [
        "df.groupby('loan_status')['loan_amnt'].describe().transpose()"
      ],
      "execution_count": 12,
      "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>loan_status</th>\n",
              "      <th>Charged Off</th>\n",
              "      <th>Fully Paid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>77673.000000</td>\n",
              "      <td>318357.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>15126.300967</td>\n",
              "      <td>13866.878771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8505.090557</td>\n",
              "      <td>8302.319699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8525.000000</td>\n",
              "      <td>7500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>14000.000000</td>\n",
              "      <td>12000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>20000.000000</td>\n",
              "      <td>19225.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>40000.000000</td>\n",
              "      <td>40000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "loan_status   Charged Off     Fully Paid\n",
              "count        77673.000000  318357.000000\n",
              "mean         15126.300967   13866.878771\n",
              "std           8505.090557    8302.319699\n",
              "min           1000.000000     500.000000\n",
              "25%           8525.000000    7500.000000\n",
              "50%          14000.000000   12000.000000\n",
              "75%          20000.000000   19225.000000\n",
              "max          40000.000000   40000.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "DTwJlHGUT6sm",
        "outputId": "c599945d-c702-495b-e82c-c3eec62457b5"
      },
      "source": [
        "pd.pivot_table(data=df,index='loan_status',values='loan_amnt',aggfunc=['mean','std','min','max'])"
      ],
      "execution_count": 13,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>loan_amnt</th>\n",
              "      <th>loan_amnt</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Charged Off</th>\n",
              "      <td>15126.300967</td>\n",
              "      <td>8505.090557</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>40000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fully Paid</th>\n",
              "      <td>13866.878771</td>\n",
              "      <td>8302.319699</td>\n",
              "      <td>500.0</td>\n",
              "      <td>40000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     mean          std       min       max\n",
              "                loan_amnt    loan_amnt loan_amnt loan_amnt\n",
              "loan_status                                               \n",
              "Charged Off  15126.300967  8505.090557    1000.0   40000.0\n",
              "Fully Paid   13866.878771  8302.319699     500.0   40000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "38XlmkJe_K2k"
      },
      "source": [
        "**TASK: Let's explore the Grade and SubGrade columns that LendingClub attributes to the loans. What are the unique possible grades and subgrades?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cN1m2mkZ_K2k",
        "outputId": "c0cca8a1-d831-48bc-95d1-ba190209d101"
      },
      "source": [
        "print(np.sort(df['grade'].unique()))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['A' 'B' 'C' 'D' 'E' 'F' 'G']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXtsgINO_K2m"
      },
      "source": [
        "subgrade = np.sort(df['sub_grade'].unique())"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ArGHj6jIYo3Y",
        "outputId": "cb68e9cf-684c-481d-808c-f0139d10ed49"
      },
      "source": [
        "print(subgrade)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['A1' 'A2' 'A3' 'A4' 'A5' 'B1' 'B2' 'B3' 'B4' 'B5' 'C1' 'C2' 'C3' 'C4'\n",
            " 'C5' 'D1' 'D2' 'D3' 'D4' 'D5' 'E1' 'E2' 'E3' 'E4' 'E5' 'F1' 'F2' 'F3'\n",
            " 'F4' 'F5' 'G1' 'G2' 'G3' 'G4' 'G5']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rQJ6pmcC_K2n"
      },
      "source": [
        "**TASK: Create a countplot per grade. Set the hue to the loan_status label.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "KA9j5GKu_K2n",
        "outputId": "272a60a6-3a60-4d8d-8faf-455575e2a8ce"
      },
      "source": [
        "sns.countplot(data=df,x='grade',hue='loan_status')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3de9f6de50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEGCAYAAACpXNjrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAfZUlEQVR4nO3de3hU5bn38e9NCARFQDEFJWKo260CgQhBqK1o0Rewrwq0tEpxC4hQK6j0atNa+1Ys1l0R6xG2BUWBXlaoWASrrVrBIh5DNMrBbYmANRQ5G8VKMXi/f8yTdMAJTGTNDBN+n+uaK2s986y17oljfqzTs8zdERERiVKTTBcgIiKNj8JFREQip3AREZHIKVxERCRyChcREYlc00wXcKg49thjvbCwMNNliIhklfLy8q3unr9vu8IlKCwsZPny5ZkuQ0Qkq5jZu4nadVhMREQip3AREZHIKVxERCRyOuciIlnh008/paqqil27dmW6lMNSXl4eBQUF5ObmJtVf4SIiWaGqqoqjjjqKwsJCzCzT5RxW3J1t27ZRVVVFp06dklpGh8VEJCvs2rWLtm3bKlgywMxo27Ztg/YaFS4ikjUULJnT0N+9wkVERCKXsnAxswfMbLOZrYxrO8bMnjGzNeHn0aHdzOxuM6s0szfNrEfcMiNC/zVmNiKuvaeZrQjL3G0hVuvbhoiIpE8qT+jPAqYCc+LargOedfdbzOy6MP8T4Hzg5PDqDdwL9DazY4CJQAngQLmZLXL3HaHPGOAV4ElgIPCn/WyjwXqWzjlwpySVT7kssnWJSDRatmzJzp07M7Lt5557jmbNmnHmmWdG0u9Qk7I9F3dfCmzfp3kQMDtMzwYGx7XP8ZiXgTZmdhwwAHjG3beHQHkGGBjea+XuL3vsUZpz9llXom2IiBwynnvuOV588cXI+h1q0n3OpZ27bwzT7wPtwnQH4L24flWhbX/tVQna97cNEZGE3J3S0lK6du1KUVER8+bNA2Dnzp2ce+659OjRg6KiIhYuXAjA+vXrOe200xgzZgxdunShf//+fPLJJ/Wu/+6776Zz585069aNSy65hPXr1/Ob3/yGO+64g+LiYp5//nkef/xxevfuzemnn855553Hpk2bEvYbOXIk8+fPr1t3y5YtAdi4cSN9+/aluLiYrl278vzzz6fwN3ZgGbvPxd3dzDyT2zCzscBYgI4dO6ayFBE5hP3hD3+goqKCN954g61bt9KrVy/69u1Lfn4+CxYsoFWrVmzdupU+ffpw0UUXAbBmzRoefvhh7rvvPr7zne/w6KOPcumllyZc/y233MK6deto3rw5H3zwAW3atOHKK6+kZcuW/OhHPwJgx44dvPzyy5gZ999/P7feeiu//vWvP9dv5syZCbfxu9/9jgEDBvCzn/2MPXv28M9//jMFv6nkpTtcNpnZce6+MRza2hzaNwAnxPUrCG0bgHP2aX8utBck6L+/bXyOu88AZgCUlJSkNOhE5NC1bNkyhg0bRk5ODu3atePss8+mrKyM888/n+uvv56lS5fSpEkTNmzYwKZNmwDo1KkTxcXFAPTs2ZP169fXu/5u3boxfPhwBg8ezODBiY/UV1VVcfHFF7Nx40Z2796d9M2KtXr16sXll1/Op59+yuDBg+tqy5R0HxZbBNRe8TUCWBjXflm4aqwPUB0ObT0F9Dezo8NVX/2Bp8J7H5pZn3CV2GX7rCvRNkREGuShhx5iy5YtlJeXU1FRQbt27epuJGzevHldv5ycHGpqaupdzxNPPMG4ceN47bXX6NWrV8K+V199NePHj2fFihVMnz693hsWmzZtymeffQbAZ599xu7duwHo27cvS5cupUOHDowcOZI5c6K7IOmLSOWlyA8DLwGnmFmVmY0GbgH+j5mtAc4L8xC72mstUAncB1wF4O7bgZuAsvCaFNoIfe4Py7xD7Eox9rMNEZGEzjrrLObNm8eePXvYsmULS5cu5YwzzqC6upovfelL5ObmsmTJEt59N+GjS/brs88+47333uPrX/86kydPprq6mp07d3LUUUfx0Ucf1fWrrq6mQ4fYqePZs2fXte/br7CwkPLycgAWLVrEp59+CsC7775Lu3btGDNmDFdccQWvvfbaF/pdRCVlh8XcfVg9b52boK8D4+pZzwPAAwnalwNdE7RvS7QNEZH6DBkyhJdeeonu3btjZtx66620b9+e4cOHc+GFF1JUVERJSQmnnnpqg9e9Z88eLr30Uqqrq3F3rrnmGtq0acOFF17I0KFDWbhwIffccw833ngj3/72tzn66KPp168f69atA/hcvzFjxjBo0CC6d+/OwIEDOfLII4HYVWVTpkwhNzeXli1bZnzPxWJ/16WkpMT3fRKl7nMROXS89dZbnHbaaZku47CW6L+BmZW7e8m+fTX8i4iIRE5D7ouIRGTcuHG88MILe7Vde+21jBo1KkMVZY7CRUQkItOmTct0CYcMHRYTEZHIKVxERCRyChcREYmczrmISFaK8lYBSO52gZycHIqKiurmH3vsMQoLCxP2nTVrFsuXL2fq1KnceOONe40PdiAjR47kr3/9K61bt6ZJkyZMmzaNr3zlK/X2P/PMMxOOnDxy5EguuOAChg4dmtR2o6RwERFJUosWLaioqEjLtqZMmcLQoUN5+umn+d73vsebb75Zb99DcUh+HRYTETkIhYWFbN26FYDly5dzzjnn1Nv3nXfeoUePugftsmbNmr3mE+nbty+VlZX1Dv8P/x52390ZP348p5xyCueddx6bN9c7bm/Kac9FRCRJn3zySd1ow506dWLBggUNWv6kk06idevWVFRUUFxczIMPPnjAe2Aef/xxioqKyMvLSzj8f3jCOwALFizg7bffZvXq1WzatInOnTtz+eWXN/yDRkDhIiKSpCgOi11xxRU8+OCD3H777cybN49XX301Yb/S0lJ++ctfkp+fz8yZM3H3hMP/t2/fvm6ZpUuX1j064Pjjj6dfv34HVevBULiIiByE+CHw6xsmP963vvUtfvGLX9CvXz969uxJ27ZtE/arPedSa9asWXXD/+fm5lJYWJjU9jJF51xERA5C/BD4jz766AH75+XlMWDAAL7//e83aFiYZIb/79u3b92jAzZu3MiSJUuS/yAR056LiGSlQ2Wk8YkTJzJ69Gh+/vOf7/dkfrzhw4ezYMEC+vfvn/R2khn+f8iQISxevJjOnTvTsWPH/V6+nGoacj/QkPsih7bGNOT+bbfdRnV1NTfddFOmS2mQhgy5rz0XEZE0GjJkCO+88w6LFy/OdCkppXAREUmjhl6+nK10Ql9ERCKncBERkcgpXEREJHIKFxERiZxO6ItIVvr7pKIDd2qAjjesOGCf999/nwkTJlBWVkabNm1o164dd955J//4xz+47bbb+OMf/xhpTQ1R3/D67s7NN9/M7NmzMTM6dOjA1KlT6dKlCwCPPPIIN9xwA+3bt2fJkiUMGzaMVatWMWrUKH7wgx984XoULiIiSXB3hgwZwogRI5g7dy4Ab7zxBps2bTroddfU1NC0aWr+HE+bNo0XX3yRN954gyOOOIKnn36aiy66iFWrVpGXl8fMmTO57777+NrXvsb7779PWVkZlZWVB71dHRYTEUnCkiVLyM3N5corr6xr6969O2eddRYAO3fuZOjQoZx66qkMHz6c2hvUJ02aRK9evejatStjx46taz/nnHOYMGECJSUl3HXXXZSVldGtWzeKi4spLS2la9euAOzZs4fS0lJ69epFt27dmD59OpD88PqTJ09m6tSpHHHEEQD079+fM888k4ceeohJkyaxbNkyRo8eTWlpKf3792fDhg0UFxfz/PPPH9TvS+EiIpKElStX0rNnz3rff/3117nzzjtZvXo1a9eu5YUXXgBg/PjxlJWVsXLlSj755JO9Dp3t3r2b5cuX88Mf/pBRo0Yxffp0KioqyMnJqeszc+ZMWrduTVlZGWVlZdx3332sW7dur+H158yZk/CBYR9++CEff/wxX/7yl/dqLykpYdWqVdxwww2UlJTw0EMPMWXKFBYtWsRJJ51ERUVFXWh+UQoXEZEInHHGGRQUFNCkSROKi4tZv349ENvj6d27N0VFRSxevJhVq1bVLXPxxRcD8MEHH/DRRx/VjQX23e9+t67P008/zZw5cyguLqZ3795s27aNNWvWHFLD6yeicBERSUKXLl3qRj9OpHnz5nXTOTk51NTUsGvXLq666irmz5/PihUrGDNmzF7D5B955JEH3K67c88991BRUUFFRQXr1q1LesDLVq1aceSRR7J27dq92svLy+tO6KeKTujLIUmDhsqhpl+/flx//fXMmDGDsWPHAvDmm29SXV1d7zK1QXLssceyc+dO5s+f/7mruQDatGnDUUcdxSuvvELv3r3rLhgAGDBgAPfeey/9+vUjNzeXv/3tb3To0IG+ffsyffp0RowYwebNm1myZMleezy1SktLueaaa3jkkUdo0aIFf/nLX1i2bFnduZtUUbiISFZK5tLhKJkZCxYsYMKECUyePJm8vDwKCwu588472bBhQ8Jl2rRpw5gxY+jatSvt27enV69e9a5/5syZjBkzhiZNmnD22WfTunVrIPbkyvXr19OjRw/cnfz8fB577LGkh9e/+uqr2bFjB0VFReTk5NC+fXsWLlxIixYtDv6Xsh8acj/QkPuHFv3uZV+Nacj9RHbu3EnLli0BuOWWW9i4cSN33XVXhqvam4bcFxHJMk888QS/+tWvqKmp4cQTT2TWrFmZLumgZOSEvpn9wMxWmdlKM3vYzPLMrJOZvWJmlWY2z8yahb7Nw3xleL8wbj0/De1vm9mAuPaBoa3SzK5L/ycUEWmYiy++mIqKClauXMkTTzxBfn5+pks6KGkPFzPrAFwDlLh7VyAHuASYDNzh7v8B7ABGh0VGAztC+x2hH2bWOSzXBRgI/I+Z5ZhZDjANOB/oDAwLfUUky+kwfuY09HefqUuRmwItzKwpcASwEegHzA/vzwYGh+lBYZ7w/rlmZqF9rrv/y93XAZXAGeFV6e5r3X03MDf0FZEslpeXx7Zt2xQwGeDubNu2jby8vKSXSfs5F3ffYGa3AX8HPgGeBsqBD9y9JnSrAjqE6Q7Ae2HZGjOrBtqG9pfjVh2/zHv7tPdOVIuZjQXGAnTs2PHgPpiIpFRBQQFVVVVs2bIl06UclvLy8igoKEi6f9rDxcyOJrYn0Qn4AHiE2GGttHP3GcAMiF0tlokaRCQ5ubm5dOrUKdNlSJIycVjsPGCdu29x90+BPwBfBdqEw2QABUDtheMbgBMAwvutgW3x7fssU1+7iIikSSbC5e9AHzM7Ipw7ORdYDSwBam9dHQEsDNOLwjzh/cUeO+i6CLgkXE3WCTgZeBUoA04OV581I3bSf1EaPpeIiASZOOfyipnNB14DaoDXiR2aegKYa2a/DG0zwyIzgd+aWSWwnVhY4O6rzOz3xIKpBhjn7nsAzGw88BSxK9EecPd/jxQnIiIpl5GbKN19IjBxn+a1xK702rfvLuDb9aznZuDmBO1PAk8efKUiIvJFaFRkERGJnMJFREQip3AREZHIKVxERCRyChcREYmcwkVERCKncBERkcgpXEREJHIKFxERiZzCRUREIqdwERGRyClcREQkcgoXERGJnMJFREQip3AREZHIKVxERCRyChcREYmcwkVERCKncBERkcgpXEREJHIKFxERiZzCRUREIqdwERGRyClcREQkcgoXERGJnMJFREQip3AREZHIKVxERCRyChcREYmcwkVERCKncBERkcgpXEREJHIZCRcza2Nm883sf83sLTP7ipkdY2bPmNma8PPo0NfM7G4zqzSzN82sR9x6RoT+a8xsRFx7TzNbEZa528wsE59TRORwlak9l7uAP7v7qUB34C3gOuBZdz8ZeDbMA5wPnBxeY4F7AczsGGAi0Bs4A5hYG0ihz5i45Qam4TOJiEiQ9nAxs9ZAX2AmgLvvdvcPgEHA7NBtNjA4TA8C5njMy0AbMzsOGAA84+7b3X0H8AwwMLzXyt1fdncH5sStS0RE0qBpBrbZCdgCPGhm3YFy4FqgnbtvDH3eB9qF6Q7Ae3HLV4W2/bVXJWj/HDMbS2xviI4dO37xT3QI6lk6J9L1lU+5LNL1iUjjlonDYk2BHsC97n468DH/PgQGQNjj8FQX4u4z3L3E3Uvy8/NTvTkRkcNGJsKlCqhy91fC/HxiYbMpHNIi/Nwc3t8AnBC3fEFo2197QYJ2ERFJk6TCxcyeTaYtGe7+PvCemZ0Sms4FVgOLgNorvkYAC8P0IuCycNVYH6A6HD57CuhvZkeHE/n9gafCex+aWZ9wldhlcesSEZE02O85FzPLA44Ajg1/wGsv6W1FPecxknQ18JCZNQPWAqOIBd3vzWw08C7wndD3SeAbQCXwz9AXd99uZjcBZaHfJHffHqavAmYBLYA/hZeIiKTJgU7ofw+YABxP7MR7bbh8CEz9oht19wqgJMFb5ybo68C4etbzAPBAgvblQNcvWp+IiByc/YaLu98F3GVmV7v7PWmqSUREslxSlyK7+z1mdiZQGL+Mu0d7vauIiDQKSYWLmf0WOAmoAPaE5tobFEVERPaS7E2UJUDncP5DRERkv5K9z2Ul0D6VhYiISOOR7J7LscBqM3sV+Fdto7tflJKqREQkqyUbLjemsggREWlckr1a7K+pLkRERBqPZK8W+4h/DyTZDMgFPnb3VqkqTEREsleyey5H1U6H8boGAX1SVZSIiGS3Bo+KHB7a9Rixh3WJiIh8TrKHxb4ZN9uE2H0vu1JSkYiIZL1krxa7MG66BlhP7NCYiIjI5yR7zmVUqgsREZHGI9mHhRWY2QIz2xxej5pZwYGXFBGRw1GyJ/QfJPZEyOPD6/HQJiIi8jnJhku+uz/o7jXhNQvIT2FdIiKSxZINl21mdqmZ5YTXpcC2VBYmIiLZK9lwuZzYM+3fBzYCQ4GRKapJRESyXLKXIk8CRrj7DgAzOwa4jVjoiIiI7CXZPZdutcEC4O7bgdNTU5KIiGS7ZMOliZkdXTsT9lyS3esREZHDTLIB8WvgJTN7JMx/G7g5NSWJiEi2S/YO/TlmthzoF5q+6e6rU1eWiIhks6QPbYUwUaCIiMgB6byJiHxOz9I5ka2rfMplka1LskeDn+ciIiJyIAoXERGJnMJFREQip3AREZHIKVxERCRyChcREYlcxsIlDN3/upn9Mcx3MrNXzKzSzOaZWbPQ3jzMV4b3C+PW8dPQ/raZDYhrHxjaKs3sunR/NhGRw10m91yuBd6Km58M3OHu/wHsAEaH9tHAjtB+R+iHmXUGLgG6AAOB/6l93gwwDTgf6AwMC31FRCRNMhIuZlYA/F/g/jBvxIaWmR+6zAYGh+lBYZ7w/rmh/yBgrrv/y93XAZXAGeFV6e5r3X03MDf0FRGRNMnUnsudwI+Bz8J8W+ADd68J81VAhzDdAXgPILxfHfrXte+zTH3tn2NmY81suZkt37Jly8F+JhERCdIeLmZ2AbDZ3cvTve19ufsMdy9x95L8/PxMlyMi0mhkYmyxrwIXmdk3gDygFXAX0MbMmoa9kwJgQ+i/ATgBqDKzpkBrYFtce634ZeprFxGRNEj7nou7/9TdC9y9kNgJ+cXuPhxYAgwN3UYAC8P0ojBPeH+xu3tovyRcTdYJOBl4FSgDTg5XnzUL21iUho8mIiLBoTQq8k+AuWb2S+B1YGZonwn81swqge3EwgJ3X2Vmvyf2GIAaYJy77wEws/HAU0AO8IC7r0rrJxEROcxlNFzc/TnguTC9ltiVXvv22UXsyZeJlr+ZBE/EdPcngScjLFVERBpAd+iLiEjkFC4iIhI5hYuIiERO4SIiIpFTuIiISOQULiIiEjmFi4iIRE7hIiIikVO4iIhI5BQuIiISOYWLiIhETuEiIiKRU7iIiEjkFC4iIhI5hYuIiERO4SIiIpFTuIiISOQULiIiEjmFi4iIRE7hIiIikVO4iIhI5BQuIiISOYWLiIhETuEiIiKRU7iIiEjkFC4iIhI5hYuIiERO4SIiIpFTuIiISOQULiIiErmmmS5AJNX+Pqko0vV1vGFFpOsTaYy05yIiIpFLe7iY2QlmtsTMVpvZKjO7NrQfY2bPmNma8PPo0G5mdreZVZrZm2bWI25dI0L/NWY2Iq69p5mtCMvcbWaW7s8pInI4y8SeSw3wQ3fvDPQBxplZZ+A64Fl3Pxl4NswDnA+cHF5jgXshFkbARKA3cAYwsTaQQp8xccsNTMPnEhGRIO3h4u4b3f21MP0R8BbQARgEzA7dZgODw/QgYI7HvAy0MbPjgAHAM+6+3d13AM8AA8N7rdz9ZXd3YE7cukREJA0yes7FzAqB04FXgHbuvjG89T7QLkx3AN6LW6wqtO2vvSpBe6LtjzWz5Wa2fMuWLQf1WURE5N8ydrWYmbUEHgUmuPuH8adF3N3NzFNdg7vPAGYAlJSUpHx7cvjoWTonsnWVT7kssnWJpEtG9lzMLJdYsDzk7n8IzZvCIS3Cz82hfQNwQtziBaFtf+0FCdpFRCRN0r7nEq7cmgm85e63x721CBgB3BJ+LoxrH29mc4mdvK92941m9hTw33En8fsDP3X37Wb2oZn1IXa47TLgnpR/MBFJKMr7jHSPUfbIxGGxrwL/Bawws4rQdj2xUPm9mY0G3gW+E957EvgGUAn8ExgFEELkJqAs9Jvk7tvD9FXALKAF8KfwEhGRNEl7uLj7MqC++07OTdDfgXH1rOsB4IEE7cuBrgdRpoiIHATdoS8iIpFTuIiISOQULiIiEjmFi4iIRE7hIiIikdPzXNJE1/qLyOFEey4iIhI5hYuIiERO4SIiIpFTuIiISOQULiIiEjldLSZyiNOVhpKNtOciIiKRU7iIiEjkFC4iIhI5hYuIiERO4SIiIpFTuIiISOQULiIiEjmFi4iIRE7hIiIikdMd+iLS6PQsnRPZusqnXBbZug4n2nMREZHIKVxERCRyChcREYmczrmIiOyHRqX+YrTnIiIikdOeiyRF/3oTkYbQnouIiERO4SIiIpFTuIiISOQULiIiErlGe0LfzAYCdwE5wP3ufkuGSxIRSUqUw9csOGpKZOtqyMU4jXLPxcxygGnA+UBnYJiZdc5sVSIih49GGS7AGUClu691993AXGBQhmsSETlsmLtnuobImdlQYKC7XxHm/wvo7e7j9+k3FhgbZk8B3k5hWccCW1O4/lTL5vqzuXZQ/Zmm+vfvRHfP37ex0Z5zSYa7zwBmpGNbZrbc3UvSsa1UyOb6s7l2UP2Zpvq/mMZ6WGwDcELcfEFoExGRNGis4VIGnGxmncysGXAJsCjDNYmIHDYa5WExd68xs/HAU8QuRX7A3VdluKy0HH5LoWyuP5trB9Wfaar/C2iUJ/RFRCSzGuthMRERySCFi4iIRE7hkkJmtsfMKszsDTN7zczOzHRNDWVmg83MzezUTNfSUGbW3szmmtk7ZlZuZk+a2X9muq5kxX1/al/XZbqmZMXVvip8/39oZln19ybB778w0zUly8zamdnvzGxt+O6/ZGZD0lqDzrmkjpntdPeWYXoAcL27n53hshrEzOYBxwOL3X1iputJlpkZ8CIw291/E9q6A63c/fmMFpek+O9Pttnnu/8l4HfAC1n2HcrK33893/0TgYvc/Z501ZFV/5LIcq2AHZkuoiHMrCXwNWA0scu5s8nXgU9r/+cCcPc3siVYGhN330xsJIzx4Q+fpFY/YPc+3/130xks0EgvRT6EtDCzCiAPOI7Yf/RsMgj4s7v/zcy2mVlPdy/PdFFJ6gpkS631qf3+1PqVu8/LWDUHwd3XhgFlvwRsynQ9SYr//a9z97QeVjoIXYDXMl2EwiW1PnH3YgAz+wowx8y6evYcixxG7LEFEBv8cxjZ/wc7m9R9fyQjGsXv38ymETsCsdvde6VruwqXNHH3l8zsWCAf2Jzpeg7EzI4htqdVZGZO7GZUN7PSLAnHVcDQTBchMWb2ZWAPWfDdbwRWAd+qnXH3ceFvz/J0FqFzLmkSrrbKAbZlupYkDQV+6+4nunuhu58ArAPOynBdyVoMNA8jXwNgZt3MLFvqbzTMLB/4DTA1S/5hku0WA3lm9v24tiPSXYT2XFIr/pitASPcfU8mC2qAYcDkfdoeDe1L019Ow7i7h0sv7zSznwC7gPXAhIwW1jD7nnP5s7tny+XItbXnAjXAb4HbM1vS4SF89wcDd5jZj4EtwMfAT9JZhy5FFhGRyOmwmIiIRE7hIiIikVO4iIhI5BQuIiISOYWLiIhETuEikqXMbH24OU7kkKNwETmEmJnuPZNGQV9kkTQys58DlxK7se09YmO1XQBUEBv/6WEz+xvw/4BmxEZ0GO7um8ysLfAw0AF4idiNubXrvRS4JizzCnBVFt2wK42Q9lxE0sTMehEb86k7cD5QEvd2M3cvcfdfA8uAPu5+OrEBQ38c+kwElrl7F2AB0DGs9zTgYuCrYaDFPcDwNHwkkXppz0Ukfb4KLHT3XcAuM3s87r34ofQLgHlmdhyxPZF1ob0v8E0Ad3/CzGqfD3Qu0BMoC49LaYEGiJQMU7iIHBo+jpu+B7jd3ReZ2TnAjQdY1og9dfCnKapNpMF0WEwkfV4ALjSzvPCUzwvq6dca2BCmR8S1LwW+C2Bm5wNHh/ZngaHhccKY2THhsbYiGaNwEUkTdy8DFgFvAn8CVgDVCbreCDxiZuXA1rj2XwB9zWwVscNjfw/rXU3sAoCnzexN4BliTz4VyRiNiiySRmbW0t13mtkRxPZExrp7xh9JKxI1nXMRSa8ZZtYZyCN2nkTBIo2S9lxERCRyOuciIiKRU7iIiEjkFC4iIhI5hYuIiERO4SIiIpH7/+eNN3O0CuwJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wu96SB1H_K2n"
      },
      "source": [
        "**TASK: Display a count plot per subgrade. You may need to resize for this plot and [reorder](https://seaborn.pydata.org/generated/seaborn.countplot.html#seaborn.countplot) the x axis. Feel free to edit the color palette. Explore both all loans made per subgrade as well being separated based on the loan_status. After creating this plot, go ahead and create a similar plot, but set hue=\"loan_status\"**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "2DWuY6cg_K2n",
        "outputId": "bb72bd2f-e2b8-4082-d5a7-aacb9ef71f86"
      },
      "source": [
        "plt.figure(figsize=[12,6])\n",
        "sns.countplot(data=df,x='sub_grade',order=subgrade,palette='summer')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3de9f67a10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "dwqGSWEo_K2o",
        "outputId": "e7e9d1a0-bb16-4c66-d216-96a6beed4556"
      },
      "source": [
        "plt.figure(figsize=[12,6])\n",
        "sns.countplot(data=df,x='sub_grade',order=subgrade,hue='loan_status',palette='Reds')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3de9eb0690>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7jKrvoCy_K2o"
      },
      "source": [
        "**TASK: It looks like F and G subgrades don't get paid back that often. Isloate those and recreate the countplot just for those subgrades.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "7kkzvOXQ_K2o",
        "outputId": "a1c01944-b902-47db-d50c-7555cf6b46b8"
      },
      "source": [
        "plt.figure(figsize=[12,6])\n",
        "sns.countplot(data=df,x='sub_grade',order=subgrade[25:],hue='loan_status',palette='Oranges')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3de9f450d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ejmlHgXa_K2o"
      },
      "source": [
        "**TASK: Create a new column called 'loan_repaid' which will contain a 1 if the loan status was \"Fully Paid\" and a 0 if it was \"Charged Off\".**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91wHPWE4_K2o"
      },
      "source": [
        "dictmap = {\"Fully Paid\":1, \"Charged Off\": 0}\n",
        "df['loan_repaid'] = df['loan_status'].map(dictmap)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "8kupzo12_K2p",
        "outputId": "bd36c04f-ca82-4c6f-e00d-4938b6aec103"
      },
      "source": [
        "df[['loan_status','loan_repaid']]"
      ],
      "execution_count": 22,
      "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>loan_status</th>\n",
              "      <th>loan_repaid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Charged Off</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396025</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396026</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396027</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396028</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396029</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>396030 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        loan_status  loan_repaid\n",
              "0        Fully Paid            1\n",
              "1        Fully Paid            1\n",
              "2        Fully Paid            1\n",
              "3        Fully Paid            1\n",
              "4       Charged Off            0\n",
              "...             ...          ...\n",
              "396025   Fully Paid            1\n",
              "396026   Fully Paid            1\n",
              "396027   Fully Paid            1\n",
              "396028   Fully Paid            1\n",
              "396029   Fully Paid            1\n",
              "\n",
              "[396030 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVjJxk2k_K2p"
      },
      "source": [
        "**CHALLENGE TASK: (Note this is hard, but can be done in one line!) Create a bar plot showing the correlation of the numeric features to the new loan_repaid column. [Helpful Link](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.bar.html)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "SgnK2co6_K2p",
        "outputId": "bc85957e-5d59-43ca-b3e0-9ee7900c1578"
      },
      "source": [
        "df.corr()['loan_repaid'][:-1].sort_values().plot(kind='bar')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3de9afc5d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1wDXA9sBH+yyPmZmNkyKmXivL0NBQLF26dNDFMDObUiRdGRFDw/f7zmIzs8o5EZiZVc6JwMysck4EZmaVm5KdxZJ+A/xqAk/dHvjfhotTa7zp/G9zPMebrvF2jYh1pviZkolgoiQt7dVj7niTO5bjOZ7jlY3npiEzs8o5EZiZVa62RLDA8aZkLMdzPMcrGK+qPgIzM1tXbTUCMzMbxonAzKxyTgQ2LpJ2W599DcXaZH32mU13kh4raYOu7Q0kbd7U60/7RCBpB0mnSDovb8+V9OYCca6RdHWPn2skXd1wrD3y72f0+mkyVg9n99h3Vo99Tbh0Pfc1QtJukjbt2t5M0uxS8XKMj0vaumt7G0lFpmOXdLSkLfPj90v6rxJ/L5K2He2n6XhdcVt9/yTt3/n/zNszJD2zULglQPcH/+bAhU29eL9rFk8FXwZOZc3Smr8AzgBOaTjOnzb8eqM5lrRs5z/2OBbAC5sOmJPPnsBWkl7edWgGsGnvZ0041o7AzsBmkp4OqCtWY9+CejgTeHbX9iN5374FYx4WEe/rbETEPZJeDLy/QKwPRMSZkg4grR/yKeDfgaY/vK4k/R2qx7EAnthwvI62379/B7oT6W977GvKphHx285GRPy2yRpBDYlg+4j4uqT3AkTEakmPNB0kIiYy5cVEY83PDw+LiAe7j3V/I2rY7qRktzXwZ137VwFvaTjWi4A3ALsA/zQs1vt6PaEhG0XEw52NiHg4r7BX0oaSNomIhyB9iwVKNX91/u5fAiyIiG+XqH1ERJGmwvXQ9vun6Bp2GRF/lFTqM/UBSc+IiKsAJO0D/L6pF68hETwgaTvSNxEk7Q/c13QQST+MiAMkrerE6hwCIiJmNB0TuIR1v3302te3iDgHOEfSsyKiWPNMjrUQWCjpyIjo1RRVym8kvTQiFgFIOpzy88ecDizJa3YDvBFYWCjWrZK+ABwMfDL3txRtHpa0DTCHrlpjRPygULi237+bJP0VqRYA8DbgpkKx3gWcKek20mfKjsArm3rxaX8fQW4D/SzwVNKayDOBoyPipwMtWB+6mk6+Cry269AM4PMRsUfB2DNJNYDZdH2RiIg3FYi1CXBkj1gnNh0rx3sS6YP58XnXLcDrI2JFiXhdcQ8lNdUALI6ICwrF2Rw4FLgmIpbnNcOfFhH/XSjeMcA7STW7ZcD+wKUR0XjTZY7X6vsn6XHAv5CaYoPUjv+uiLirULzHkGrmADdExB8ae+0KEsEmpCrx7qRMegOwQacqXiDeaRHxurH29RljHqnpZAi4ouvQKuDLEfGNpmL1iH0JcDGpHfjRJrYS39wlnU+qvQ2P1atvpMm4W+Q4vx3r3Ibi7QrMiYgL84f1hhGxqlCsA3KsU3NS3yIibi4U6xpS+/yPI2Lv3M/08Yh4+RhP7Tduq+9fGyS9HTg9Iu7N29sAr46IzzXx+jU0DV0aEc8AruvskHQVZTp0IHWoPiq3Ge7TcIztgXPzT3enXACl22c3j4jjCsfo2CUiDm0pFpI+Dvz9sIvtbyKiRMdtJ+ZbSB3/2wJPItX0Pg8cWCDWh0hfHnYnDaB4DKlW+ZymY2UPRsSDksj9ID+XtPvYT5uYtt+/NmvHwFsi4t+6YtyT/3YaSQTTdviopB1zh8pmkp7eNbzy+RQYeSLpvbl/YC9J9+efVcCdwDkNh9si/+wD/AWwE6k6/FbKJbiOc/OoljZcIulpLcWC1Pl+b2cjIu4BSv9b3076IL4/x1wOPK5QrJcBLwUeyLFuA7Yc9Rn9uSUPjf0msFjSOUxsHZH11fb7dw6wFWkY57e7fkrYUNKjo7AkbQg01hE+nWsErY48iYhPAJ+Q9ImIeG/Trz8s1kcAJP0AeEanGUHShyn3h9jxTuB9kh4C/kDZzvADgDdIuhl4qCvWXgViQbsjeDoeyqNbyDE3Yu3BBk16OCJCUmfgxGMLxQEgIl6WH35Y0kWkD83zC4Zs+/1rs3Z8PnBG7uwH+HMa/L+ctolggCNPzpP0vB7lKTFSYgfg4a7th/O+YiKi5DfI4Q5rMRa0O4Kn4/uS3kequR5MGnnyrUKxvp4/SLbOzQpvAr5YKBbw6GCNA0jJ7UfdwzsLaPv9O1fSiyPiOwVjdBxH+vD/i7y9GDi5qRef9p3FAJJeQmq77x7CVmrkSfdFvCmwH3BliZESkk4AXgF0OoePAM7ItZNiJO3Fuu2i/1UoVmudmzneYaxpny82gqcrnoBjgENINZ4LgJOj0IWZk82jsSJicYk4OdYHgaOBzt/GEcCZEVHkzukcs7X3Lzf9PpZUWy1dOy5q2icCSZ8n9Qm8gJRBjwIuj4jGp5kYIf4TgM9ExJGFXv8ZwHPz5g8i4icl4nTF+xKwF6nz/Y95dxQaPvpo52ZEPEXS40kfJKU6N1uV23mvKzncd5Ak3QD8Seemx9xUsywiinUYT1eS5gCfAOay9hfaRu7SnrZNQ12eHRF7Sbo6Ij4i6R+B81qMfwvwf0q9eL7T8KpSr9/D/hExt6VYLwOeTv73RcRt6prbpWlKNxt+lvR+bQxsCDxQ6hteRDwi6QZJsyLi1yViwMBudgS4jfSh1bn7fRPg1kKxWnv/JO2RR0D1HJjRufu3YacCHwI+TfpS+0YaHOxTQyLo/BH+Ln+jXEkaZVOEpM+y5mLbANibdj+oS7tU0tyIuL6FWK12bgL/CryKND/NEPB64CmFY24DXCfpcvJoHoCIeGlTASLigPy7lf6drmvgPtK/bXHePhi4vGDott6/1uf6AjaLiCWSFGk6mw9LuhL4YBMvXkMi+FYewvYp0gdyULaDbGnX49XAf0bEjwrGa9tXSMngDsqP5Gm9czMiVkjaMCIeAU6V9BOg5CiwDxR87bXkb8zXdY0y2xKYGxGXNRyqcw1cyZr+K4DvNRxnHW28f5Hn+oqIF4x2nqSDG+yDeUhpGurlkt5Bqllt0dBrT+8+gvwft39EXJK3NyHN4tf4XEPjKNPZpfoL2iBpBekb0TWs6SMoNuley52bPyBN9XAycAdwO/CGiPiTUjHXo0yXRsSzGnqtn5CGG3dqWBsAS/MNl61r+lqYbO+fpKua+r+VtC/wM9Kkj39Hmk7mUxHx40ZefzonAkh//BHx9EGXo2OylWe8mvxgGkfMGaw9QunuQnF2Jd0AuDHw16Rx75+LwnMNjVGmxv5eJC2LiL2H7bu64H0ZY5Wn0Wthsr1/bV7rkj4bEX850efX0DS0RNKRwH+VGpI3TpOhDP34iaT/II11f3S+phLDRyX9OfARUj/PH8nNUBSaz76rVvNgjju8PIOozTX599LmbJnro9FrYRK+f21e632NpKshEfw5qSljtaQHmcJjfSeJzUgJ4JCufcGaseJNejfw1IgoPRX0+iq1oEpb3kqaLfP9rJkts+m1JCazqf7+FTPtE8FYIyUk7RkR1412TsN6rdo0ZUTEG1sMdyPwuxbjjWUQtbkm/17mRMSr1npx6TnAbxqMMR5tXwttv3+/bDnehE37RLAeTqP8RG3d2pqbpAilFdDezLp3apeYcfG9pInnLmPtZqi/KhBrYJTWl9iP9EF1RUTc0XW4senLSWPsh/+t99rXlil5LWjtpVrX0WkmjcLTbQ/TV1J1ImjoW4nS3Ou9vnGsNbwyCi0C0qLTgJ+TJvU7kbQwzs8KxfoC8F2GjVAaoMa/wSot3vJB0r9TwGclnRgRXwKIiGsbiPEs0lq+MyUd23VoBummq0ZN4muhqffvz0Y5VqSZVNLREXHmKPv+ua/Xnxz9p4PT1BCvPGJhRKWGV7atMxKiM9pEadWkiyNi/1Kxmn7diZJ0SNMfXnkahmdHxMq8vR1wSZPTMEj6v8DzSX0En+86tAr4VqSprxszWa+FEu9fW3p9TjU5PNU1goZ0/3FL2oG0MhOkeY2KLF03IJ3l8e6V9FTSeO1S8+efJ2k+645QKjV89DnAh4FdSddG5xvsE3PcEh8iK0kfyB2r8r7GRMT3SbOcfjkifpWH40YUWgWt7WthkDUQFZ7QMk+i92JgZ0n/0nVoBumG1UY4Eaw9jXPfJL2CdBfz91hT1X9PRJzVZJwBWqC08tP7gUWkuxtL3R376vy7+87QYsNHgVNI48/XWhqzhK4mmhXAZUqLtgRwOHB1obAzJZ1LXoxG0n3AmyLiyhLBWrwW/rTh11svI01o2XCY20h3ar+U9HfZsYr0t9qIad80JGlJRBw41r4G4/0UOLjzzUdp6uQLB3l3apskzYu0FsSUI+myiHhmS7E+NNrxyIsPNRzzauDtEXFx3j6AdMNVkRvKpvu10NU82vm9BXBeRDx3zCePL86GwGkR8ZomX7fbtK0R5NEtmwPb52+wnY6iGaR1YUvZYFj1dyXTeEnQHt5Jn4uBrO+ojAIukvQpUmdfd1NU45MGlvigXw+PdJJALsMPJTXWvNBDq9eCWp49Fvh9/l10QstIs9Q+QdLGUWhhn2mbCEg3kr2LtJbvlaxJBPeTZiks5XxJFwD/mbdfCbSxgtFk0cTIjNZHZWSd2sDQsHglZpMEQGkJx3Wq5VFgISNSP8EXSH+bQfrb/J7ydMoFEl7b10Lbs8ee22NCy8ZWDRvmZuBHkhax9iy1/zTyU9ZfDU1DfxkRn2055stJy/NBGlHzjdHOn06aHMlQA0n7dG1uChwJrI6Ivy0Q66JRDkeJ5NPmtSBpaUQMdc+f1NbIs9ITWo7UlNhUzXLaJwIASc9m3aUVv1Io1rGk5SKLLcAxmTVx4Q0b676Opr4F9Yi7A/Bx4PERcZikucCzIuKUEvFGKcflEbFfmzFLaPtaaHv2UUmv77W/1GdLjrlFjvHbJl93OjcNASDpNOBJwDLWjAQJ0rz6JWwJ/Leku4EzSEsr3lko1mTUxNoLrSyg0sOXSStBnZC3f0F6D4slAknbdm1uQGrS2KpQrJ6LmDQ53HGYtq+F15H+D99BGlHzBKDk3b37dj3elLRW8lUU+GzJQ7VPA7bN2/8LvL6p6XGmfY1A0s9Ii2+0+g9VWuD9laSq/i0RcVCb8UvJVeAjWbeGVerDpDWSroiIfbtrNeoxdXPDMW9mTR/BatL8NCdGxA8LxPqbrs1NScMuf1ZoepDuuK1cC5LeGRH/PNa+UnJ/wdci4tACr30JcEJEXJS3nw98PCKe3cTrT/saAXAtsCOpmtimu0jV05WUu+FqEM4hLUF4JV0ja0poeV4jgAfynb2dhVv2J/1bS5pLmg76gBz3YtZe5a4xEbHW0oqS/gG4oESsYdq6Fuax7lQLb+ixr5QHgN0KvfZjO0kAICK+pwaXbq0hEWwPXK+0Jmz3kMDG1oTtJultwCuAmaTRC2+Jdtb3bcsuJb7xjKDNeY0gTVe+CHiSpB+R3sOjCsaDNNT2ftL00ACvIf27jy4cF9Lw6l1KvXhb14KkV5P+33bLo2o6ZgBF7kLPcb/F2uuTzwW+XijcTZI+QPrbAPh/NLiWRA2J4MMtx3sC8K6IWNZy3LZcIulpEXFNC7GeHBFHSzo8IhYqLYhz8ZjPmqCIuCrPy7M7aRjsDRHxhzGe1q+nRsTcru2LJBX54jBsKoYNSR/QJZv02roWLiHV+Ldn7QXlV1HuLm2Af+h6vBr4VUTcUijWm0iL7Zydty8GGpsSftr3EQxCvmNzTkScmu+m3CIibh50uZqQP6SeTBrXXHTx+s7omTwa5G2k5oXLO3P/FIi3Kes203w+Ih4sES/H/Crwr5HXnpX0TNLdvz1HpPQZq3syuNXAnRFR8oay1q+FNuY2GgRJQ6RBDLNZ8wW+setu2iYCST+MiAMkrWLtG3aKrlCWx/sOAbtHxFPyHYdnRkRfS8lNFhphZskoMKOk0hTNZwNPI43o2QL4QER8oelYOd7XSd8iv5p3vQbYOiKKNdPkwQy7A7/Ou2YBN5A+qJu70NM0BddFxB5NvN56xmz1WpB0NOlb+vdI1/lzgWLzfOV7JD5J6vcQBT9blGapfTepz/PRKdmbuu6mbSIYFEnLgKcDV3WNPBnYAuGlSHoca3fg/nqU0ycaY7fh3x577Wsw3vXDmml67ms4ZmtTNitNbPeXJd6rEeK1ei20PbeRpBXAn0VEyX6rTqwfRsQBY585MTX0EbTt4YgISZ2RJ6YrsZkAAAglSURBVI317E8Gkl5Kaod9PGk0yK6kDtw9C4Q7m3VXzzoL2KfHuU24StL+w5ppiozg6ShRkxrFNsB1eeBE9zQFRQZO0P610PY8X3e2kQSyD0k6mbTOdPegl0amW3EiaJAkkeYf+QKwtaS3kDp5vjjYkjXq74D9Sd+0ni7pBaQRDI2RtAcpsWyltSegm0FXLaSAfUid4b8mNSfuCtzQ6WSdBrW6UtOFr2NA18J5anduo6WSzgC+SYEP52HeCOwBPIY1TUONzbvlRNCg/O3naNIwxPtJbb8fjIjFgy1Zo/4QESslbSBpg4i4SNJnGo6xO+lmp61ZewK6VcBbGo7V7VDSt+bONMI/AO4tGK9tm0fEed07JL0V+H7TgQZ0LQRpedNOE8oC0peWUmYAvwMOGVaGEolg32hw1brh3EfQMEkLSaNArhh0WUqQdCFwBHASsB2peWjfpu5wHBbrWRFxadOvO0q8dwLHkC5kkf6dX4yWJy0sJd+d+v6I+G7e/lvgBRFxWKF4rV4L6r2c47Ton5N0KvCpUvckORE0TNLPScMrf8Xa7bBT/o8RHm3nfZD0Qfla0rw4p0dec7fhWH8PfJQ07/v5wF7AX0fEV0d94sTjXU2aZO6BvP1Y4NJp9N5tD5wLvIdU+9kDeHUUmuO+rWtB0l+Qhv0+Ebix69CWwI8iotGmy664M0k11NmsPd1K43e+59FlT6LQsG0ngoa1ObxyUNoaq92Z50fSy0hNRccCPyg4CuQaUu3mwby9KXBFRDytRLxByKO9LiRNEfKmKPgB0Na1IGkrUpPeJ4Djuw6tikLrW+e4l5DuNVlradOIOHvEJ008VtH/S/cRNGw6feD3onbXZH5M/v0S0vjz+1IfZDGnktYP7syZfwQFZx5tS497aTYmfXs+SlKxe2rauhYirQFwH2vWuG7L5hFxXBuBSv9fukZg49LmWG1JJ5E+jH8P7EfqPD43Cq4rrLRaV/dCKj8pFcumNkkfBS6JiCm/AqETgY2LpGu6m0okbQD8tFTzidJ8/fdFWrd1c2BGRNxRIlYNJO1MGhbb3ab9g8GVaOrKNa3Hktrs/0DhWQtKctOQjVfb69DuAcyW1P23WmwFqOlM0idJ79f1rL1IkxPBBETElvmLyhzK3t9SnGsENm6SjgQ688UUW4dWI6wuFxF/VSLedJfnq9krIoquI1GLPBfWO0lTeS8j3bNwSUQcONCCTYATgU1aGtDqctOVpPOAo6Ph9W5r1RllBvw4j27bg7RqWMnlMYtw05Ctlx4jTx49RLl20UGtLjdd/Q5YJmn4fDWuYU3MgxHxoCQkbRIRP5dU7O7fkpwIbL1ExCAWlG91dbkKLMo/1oxblNYp/iawWNI9pJvnphw3DdmkpbRa2DoiovG5ccz6kf9WtwLOL3WndklOBGaVkDSHdPftXNZeS6LIim82dZScq9tsQiT9MP9eJen+rp9Vku4fdPmmsFOBfyetfvYC0jDcIvM22dTiGoFZJSRdGRH7dN8U2Nk36LLZYLmz2KweD+U7wZdLegdwK2kdaKucawRmlZC0L2lZ0a1JK81tBfx9Z2lOq5cTgVllJM0g3fuxatBlscnBncVmlZA0lO+GvRq4RtJPJbl/wFwjMKtFXoHt7RFxcd4+APjcdFmBzSbONQKzejzSSQIAEfFD0lBSq5xrBGbTXF5sB+D1wGakKcSDNCX1gxFx7KDKZpODE4HZNCfpolEOR0S8sLXC2KTkRGBmAEiaFxELB10Oa58TgZkBIOmqiHjG2GfadOPOYjPr0KALYIPhRGBmHW4eqJQTgZl1uEZQKScCM+v40aALYIPhRGBWCUkfz0srdra3kfTRznZEvGMwJbNBcyIwq8dhEXFvZyMi7gFePMDy2CThRGBWjw0lbdLZkLQZsMko51slvDCNWT1OB5ZIOjVvvxHwDWTmG8rMaiLpUOCgvLk4Ii4YZHlscnCNwKwuPwNWR8SFkjaXtKUXqDH3EZhVQtJbgLOAL+RdOwPfHFyJbLJwIjCrx9uB5wD3A0TEcuBxAy2RTQpOBGb1eCgiHu5sSNoITythOBGY1eT7kt4HbCbpYOBM4FsDLpNNAh41ZFYJSQKOAQ4hzSt0AXBy+EOgek4EZhWQtCFwXUTsMeiy2OTjpiGzCkTEI8ANkmYNuiw2+fg+ArN6bANcJ+ly4IHOzoh46eCKZJOBE4FZPT4w6ALY5OQ+AjMDQNKlEfGsQZfD2uc+AjPr2HTQBbDBcCIwsw43D1TKicDMrHJOBGbW4cXrK+VRQ2YVkbQjsB+pGeiKiLij6/DrBlMqGzTXCMwqIekY4HLg5cBRwI8lvalzPCKuHVTZbLA8fNSsEpJuAJ4dESvz9nbAJRGx+2BLZoPmGoFZPVYC3auRrcr7rHLuIzCb5iQdmx+uAC6TdA6pj+Bw4OqBFcwmDScCs+lvy/z7xvzTcc4AymKTkPsIzMwq5xqBWSUkXUSPu4cj4oUDKI5NIk4EZvV4d9fjTYEjgdUDKotNIm4aMquYpMsjYr9Bl8MGyzUCs0pI2rZrcwNgCNhqQMWxScSJwKweV7Kmj2A18EvgzQMrjU0aTgRm9ZgLvA04gJQQLgaWDrRENim4j8CsEpK+DtwPnJ53vQbYOiKOHlypbDJwIjCrhKTrI2LuWPusPp5ryKweV0nav7Mh6Zm4achwjcCsGpJ+BuwO/DrvmgXcQOo4jojYa1Bls8FyIjCrhKRdRzseEb9qqyw2uTgRmJlVzn0EZmaVcyIwM6ucE4GZWeWcCMzMKudEYGZWuf8PfWnlaNwl/JUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ED1z73_G_K2p"
      },
      "source": [
        "---\n",
        "---\n",
        "# Section 2: Data PreProcessing\n",
        "\n",
        "**Section Goals: Remove or fill any missing data. Remove unnecessary or repetitive features. Convert categorical string features to dummy variables.**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "aW917iJO_K2q",
        "outputId": "b8ccbc51-1238-4cb3-e6dd-adbc556b15f0"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 24,
      "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>loan_amnt</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>verification_status</th>\n",
              "      <th>issue_d</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>purpose</th>\n",
              "      <th>title</th>\n",
              "      <th>dti</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>revol_util</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>initial_list_status</th>\n",
              "      <th>application_type</th>\n",
              "      <th>mort_acc</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>address</th>\n",
              "      <th>loan_repaid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.44</td>\n",
              "      <td>329.48</td>\n",
              "      <td>B</td>\n",
              "      <td>B4</td>\n",
              "      <td>Marketing</td>\n",
              "      <td>10+ years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>117000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>vacation</td>\n",
              "      <td>Vacation</td>\n",
              "      <td>26.24</td>\n",
              "      <td>Jun-1990</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36369.0</td>\n",
              "      <td>41.8</td>\n",
              "      <td>25.0</td>\n",
              "      <td>w</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0174 Michelle Gateway\\nMendozaberg, OK 22690</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8000.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>11.99</td>\n",
              "      <td>265.68</td>\n",
              "      <td>B</td>\n",
              "      <td>B5</td>\n",
              "      <td>Credit analyst</td>\n",
              "      <td>4 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>Debt consolidation</td>\n",
              "      <td>22.05</td>\n",
              "      <td>Jul-2004</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20131.0</td>\n",
              "      <td>53.3</td>\n",
              "      <td>27.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1076 Carney Fort Apt. 347\\nLoganmouth, SD 05113</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15600.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>10.49</td>\n",
              "      <td>506.97</td>\n",
              "      <td>B</td>\n",
              "      <td>B3</td>\n",
              "      <td>Statistician</td>\n",
              "      <td>&lt; 1 year</td>\n",
              "      <td>RENT</td>\n",
              "      <td>43057.0</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>12.79</td>\n",
              "      <td>Aug-2007</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11987.0</td>\n",
              "      <td>92.2</td>\n",
              "      <td>26.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>87025 Mark Dale Apt. 269\\nNew Sabrina, WV 05113</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7200.0</td>\n",
              "      <td>36 months</td>\n",
              "      <td>6.49</td>\n",
              "      <td>220.65</td>\n",
              "      <td>A</td>\n",
              "      <td>A2</td>\n",
              "      <td>Client Advocate</td>\n",
              "      <td>6 years</td>\n",
              "      <td>RENT</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>Nov-2014</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit card refinancing</td>\n",
              "      <td>2.60</td>\n",
              "      <td>Sep-2006</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5472.0</td>\n",
              "      <td>21.5</td>\n",
              "      <td>13.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>823 Reid Ford\\nDelacruzside, MA 00813</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24375.0</td>\n",
              "      <td>60 months</td>\n",
              "      <td>17.27</td>\n",
              "      <td>609.33</td>\n",
              "      <td>C</td>\n",
              "      <td>C5</td>\n",
              "      <td>Destiny Management Inc.</td>\n",
              "      <td>9 years</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>Verified</td>\n",
              "      <td>Apr-2013</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>Credit Card Refinance</td>\n",
              "      <td>33.95</td>\n",
              "      <td>Mar-1999</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24584.0</td>\n",
              "      <td>69.8</td>\n",
              "      <td>43.0</td>\n",
              "      <td>f</td>\n",
              "      <td>INDIVIDUAL</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>679 Luna Roads\\nGreggshire, VA 11650</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   loan_amnt  ... loan_repaid\n",
              "0    10000.0  ...           1\n",
              "1     8000.0  ...           1\n",
              "2    15600.0  ...           1\n",
              "3     7200.0  ...           1\n",
              "4    24375.0  ...           0\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mXtTPVpT_K2q"
      },
      "source": [
        "# Missing Data\n",
        "\n",
        "**Let's explore this missing data columns. We use a variety of factors to decide whether or not they would be useful, to see if we should keep, discard, or fill in the missing data.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2HysRQJF_K2q"
      },
      "source": [
        "**TASK: What is the length of the dataframe?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EHjLKeLK_K2q",
        "outputId": "dfaa9844-07d8-46db-e5e4-0917f7ac458b"
      },
      "source": [
        "df.shape[0]"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "396030"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QyHD-UDkgP1P",
        "outputId": "3f88bc7d-484f-495c-d8af-a41bb6f6bae6"
      },
      "source": [
        "len(df)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "396030"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "euTXS0Nd_K2q"
      },
      "source": [
        "**TASK: Create a Series that displays the total count of missing values per column.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZrTOuxo_K2r",
        "outputId": "4bf68f71-debb-4c7c-f1a4-16cd1f3e99bc"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt                   0\n",
              "term                        0\n",
              "int_rate                    0\n",
              "installment                 0\n",
              "grade                       0\n",
              "sub_grade                   0\n",
              "emp_title               22927\n",
              "emp_length              18301\n",
              "home_ownership              0\n",
              "annual_inc                  0\n",
              "verification_status         0\n",
              "issue_d                     0\n",
              "loan_status                 0\n",
              "purpose                     0\n",
              "title                    1755\n",
              "dti                         0\n",
              "earliest_cr_line            0\n",
              "open_acc                    0\n",
              "pub_rec                     0\n",
              "revol_bal                   0\n",
              "revol_util                276\n",
              "total_acc                   0\n",
              "initial_list_status         0\n",
              "application_type            0\n",
              "mort_acc                37795\n",
              "pub_rec_bankruptcies      535\n",
              "address                     0\n",
              "loan_repaid                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MKQkbKhx_K2r"
      },
      "source": [
        "**TASK: Convert this Series to be in term of percentage of the total DataFrame**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRBIBOUv_K2r",
        "outputId": "8fc94a09-6365-44e7-f095-34e44fe73050"
      },
      "source": [
        "df.isnull().sum()/len(df)*100"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt               0.000000\n",
              "term                    0.000000\n",
              "int_rate                0.000000\n",
              "installment             0.000000\n",
              "grade                   0.000000\n",
              "sub_grade               0.000000\n",
              "emp_title               5.789208\n",
              "emp_length              4.621115\n",
              "home_ownership          0.000000\n",
              "annual_inc              0.000000\n",
              "verification_status     0.000000\n",
              "issue_d                 0.000000\n",
              "loan_status             0.000000\n",
              "purpose                 0.000000\n",
              "title                   0.443148\n",
              "dti                     0.000000\n",
              "earliest_cr_line        0.000000\n",
              "open_acc                0.000000\n",
              "pub_rec                 0.000000\n",
              "revol_bal               0.000000\n",
              "revol_util              0.069692\n",
              "total_acc               0.000000\n",
              "initial_list_status     0.000000\n",
              "application_type        0.000000\n",
              "mort_acc                9.543469\n",
              "pub_rec_bankruptcies    0.135091\n",
              "address                 0.000000\n",
              "loan_repaid             0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kh5lMYkt_K2r"
      },
      "source": [
        "**TASK: Let's examine emp_title and emp_length to see whether it will be okay to drop them. Print out their feature information using the feat_info() function from the top of this notebook.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2kquMpO1_K2s"
      },
      "source": [
        "**TASK: How many unique employment job titles are there?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "shFAYMAy_K2s",
        "outputId": "f9a76b1d-1723-41f6-908b-514a26d3bb11"
      },
      "source": [
        "df['emp_title'].nunique()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "173105"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sgezoSnK_K2s",
        "outputId": "b77432b6-c24d-4931-a7f6-18b3a5e62b3f"
      },
      "source": [
        "df['emp_title'].value_counts()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Teacher                                     4389\n",
              "Manager                                     4250\n",
              "Registered Nurse                            1856\n",
              "RN                                          1846\n",
              "Supervisor                                  1830\n",
              "                                            ... \n",
              "CTE Express                                    1\n",
              "Office of the Deputy Mayor for Education       1\n",
              "GroupM ESP                                     1\n",
              "Frac Helper                                    1\n",
              "Sheriff's Deputy II                            1\n",
              "Name: emp_title, Length: 173105, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "poIa2GnT_K2s"
      },
      "source": [
        "**TASK: Realistically there are too many unique job titles to try to convert this to a dummy variable feature. Let's remove that emp_title column.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDuIaxqu_K2s"
      },
      "source": [
        "df = df.drop('emp_title',axis=1)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RW4EG817_K2t"
      },
      "source": [
        "**TASK: Create a count plot of the emp_length feature column. Challenge: Sort the order of the values.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9ac0skI_K2t",
        "outputId": "f16aa25c-8774-4275-a4f8-03694ec5f8fa"
      },
      "source": [
        "sorted(df['emp_length'].dropna().unique())"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1 year',\n",
              " '10+ years',\n",
              " '2 years',\n",
              " '3 years',\n",
              " '4 years',\n",
              " '5 years',\n",
              " '6 years',\n",
              " '7 years',\n",
              " '8 years',\n",
              " '9 years',\n",
              " '< 1 year']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7HTFtciZ_K2t"
      },
      "source": [
        "emp_length_order = [\n",
        " '< 1 year',\n",
        " '1 year',\n",
        " '2 years',\n",
        " '3 years',\n",
        " '4 years',\n",
        " '5 years',\n",
        " '6 years',\n",
        " '7 years',\n",
        " '8 years',\n",
        " '9 years',\n",
        " '10+ years']"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "L91gZRN1_K2t",
        "outputId": "f48a006f-7ebe-480c-bbf4-4154097f3db5"
      },
      "source": [
        "plt.figure(figsize=[12,6])\n",
        "sns.countplot(data=df,x='emp_length',order=emp_length_order)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3de9aaf350>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mOiHqqSh_K2t"
      },
      "source": [
        "**TASK: Plot out the countplot with a hue separating Fully Paid vs Charged Off**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "sXU8yozy_K2t",
        "outputId": "1d7095ff-2ebb-4166-d5e1-0c5c0c089601"
      },
      "source": [
        "plt.figure(figsize=[12,6])\n",
        "sns.countplot(data=df,x='emp_length',hue='loan_status',order=emp_length_order)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3de9b9bad0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AOdkc0Gm_K2u"
      },
      "source": [
        ""
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ysOaYXdc_K2u"
      },
      "source": [
        "**CHALLENGE TASK: This still doesn't really inform us if there is a strong relationship between employment length and being charged off, what we want is the percentage of charge offs per category. Essentially informing us what percent of people per employment category didn't pay back their loan. There are a multitude of ways to create this Series. Once you've created it, see if visualize it with a [bar plot](https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.DataFrame.plot.html). This may be tricky, refer to solutions if you get stuck on creating this Series.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HN4gpBw7_K2u"
      },
      "source": [
        "emp_co = df[df['loan_status']=='Charged Off'].groupby('emp_length').count()['loan_status']\n"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Y36YgJc_K2u"
      },
      "source": [
        "emp_fp = df[df['loan_status']=='Fully Paid'].groupby('emp_length').count()['loan_status']"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vE0l4mkj_K2u",
        "outputId": "f9b1a32c-796d-4a83-f381-b36bf2ff7077"
      },
      "source": [
        "emp_co/emp_fp"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "emp_length\n",
              "1 year       0.248649\n",
              "10+ years    0.225770\n",
              "2 years      0.239560\n",
              "3 years      0.242593\n",
              "4 years      0.238213\n",
              "5 years      0.237911\n",
              "6 years      0.233341\n",
              "7 years      0.241887\n",
              "8 years      0.249625\n",
              "9 years      0.250735\n",
              "< 1 year     0.260830\n",
              "Name: loan_status, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZrKZxcY_K2u"
      },
      "source": [
        "emp_length = emp_co/(emp_co+emp_fp)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "El13ztlP_K2u",
        "outputId": "a2899118-3b46-4389-d845-ef86921266ee"
      },
      "source": [
        "emp_length.plot(kind='bar')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3de9924b90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wnNwdzDe_K2v"
      },
      "source": [
        "**TASK: Charge off rates are extremely similar across all employment lengths. Go ahead and drop the emp_length column.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DE2DnyHB_K2v"
      },
      "source": [
        "df = df.drop('emp_length',axis=1)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQ02dX7H_K2v"
      },
      "source": [
        "**TASK: Revisit the DataFrame to see what feature columns still have missing data.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9mwjdrZE_K2v",
        "outputId": "57e9e4a6-f1d4-4757-f77f-25fe65857204"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt                   0\n",
              "term                        0\n",
              "int_rate                    0\n",
              "installment                 0\n",
              "grade                       0\n",
              "sub_grade                   0\n",
              "home_ownership              0\n",
              "annual_inc                  0\n",
              "verification_status         0\n",
              "issue_d                     0\n",
              "loan_status                 0\n",
              "purpose                     0\n",
              "title                    1755\n",
              "dti                         0\n",
              "earliest_cr_line            0\n",
              "open_acc                    0\n",
              "pub_rec                     0\n",
              "revol_bal                   0\n",
              "revol_util                276\n",
              "total_acc                   0\n",
              "initial_list_status         0\n",
              "application_type            0\n",
              "mort_acc                37795\n",
              "pub_rec_bankruptcies      535\n",
              "address                     0\n",
              "loan_repaid                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LOmF9vFN_K2v"
      },
      "source": [
        "**TASK: Review the title column vs the purpose column. Is this repeated information?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pAytHuAF_K2w",
        "outputId": "8e48fcb8-0f13-4b2b-d11e-6c495b881d41"
      },
      "source": [
        "df['purpose'].head(10)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              vacation\n",
              "1    debt_consolidation\n",
              "2           credit_card\n",
              "3           credit_card\n",
              "4           credit_card\n",
              "5    debt_consolidation\n",
              "6      home_improvement\n",
              "7           credit_card\n",
              "8    debt_consolidation\n",
              "9    debt_consolidation\n",
              "Name: purpose, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DOf7pksw_K2w",
        "outputId": "0bbd7841-3567-4e6a-9074-25f0cdb9d4ad"
      },
      "source": [
        "df['title'].head(10)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                   Vacation\n",
              "1         Debt consolidation\n",
              "2    Credit card refinancing\n",
              "3    Credit card refinancing\n",
              "4      Credit Card Refinance\n",
              "5         Debt consolidation\n",
              "6           Home improvement\n",
              "7       No More Credit Cards\n",
              "8         Debt consolidation\n",
              "9         Debt Consolidation\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yo46vGLS_K2w"
      },
      "source": [
        "**TASK: The title column is simply a string subcategory/description of the purpose column. Go ahead and drop the title column.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NweL2O9z_K2w"
      },
      "source": [
        "df = df.drop('title',axis=1)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEA9m_1e_K2x"
      },
      "source": [
        "---\n",
        "**NOTE: This is one of the hardest parts of the project! Refer to the solutions video if you need guidance, feel free to fill or drop the missing values of the mort_acc however you see fit! Here we're going with a very specific approach.**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twvEeEVe_K2x"
      },
      "source": [
        "**TASK: Create a value_counts of the mort_acc column.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1oFTrTQ2_K2x",
        "outputId": "08a0d12a-0ebc-4c7d-e1ae-25ba3d74b430"
      },
      "source": [
        "df['mort_acc'].value_counts()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0     139777\n",
              "1.0      60416\n",
              "2.0      49948\n",
              "3.0      38049\n",
              "4.0      27887\n",
              "5.0      18194\n",
              "6.0      11069\n",
              "7.0       6052\n",
              "8.0       3121\n",
              "9.0       1656\n",
              "10.0       865\n",
              "11.0       479\n",
              "12.0       264\n",
              "13.0       146\n",
              "14.0       107\n",
              "15.0        61\n",
              "16.0        37\n",
              "17.0        22\n",
              "18.0        18\n",
              "19.0        15\n",
              "20.0        13\n",
              "24.0        10\n",
              "22.0         7\n",
              "21.0         4\n",
              "25.0         4\n",
              "27.0         3\n",
              "23.0         2\n",
              "32.0         2\n",
              "26.0         2\n",
              "31.0         2\n",
              "30.0         1\n",
              "28.0         1\n",
              "34.0         1\n",
              "Name: mort_acc, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D0rXvsEP_K2y"
      },
      "source": [
        "**TASK: There are many ways we could deal with this missing data. We could attempt to build a simple model to fill it in, such as a linear model, we could just fill it in based on the mean of the other columns, or you could even bin the columns into categories and then set NaN as its own category. There is no 100% correct approach! Let's review the other columsn to see which most highly correlates to mort_acc**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfmQKkPX_K2y",
        "outputId": "08663547-f185-4a59-f137-2ce45963b3d2"
      },
      "source": [
        "df.corr()['mort_acc']"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt               0.222315\n",
              "int_rate               -0.082583\n",
              "installment             0.193694\n",
              "annual_inc              0.236320\n",
              "dti                    -0.025439\n",
              "open_acc                0.109205\n",
              "pub_rec                 0.011552\n",
              "revol_bal               0.194925\n",
              "revol_util              0.007514\n",
              "total_acc               0.381072\n",
              "mort_acc                1.000000\n",
              "pub_rec_bankruptcies    0.027239\n",
              "loan_repaid             0.073111\n",
              "Name: mort_acc, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AW-QaKtK_K2y"
      },
      "source": [
        ""
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HXDk7T2x_K2y"
      },
      "source": [
        "**TASK: Looks like the total_acc feature correlates with the mort_acc , this makes sense! Let's try this fillna() approach. We will group the dataframe by the total_acc and calculate the mean value for the mort_acc per total_acc entry. To get the result below:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xOC6z3E0_K2y"
      },
      "source": [
        "total_acc_avg = df.groupby('total_acc')['mort_acc'].mean()"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONx6TGl3_K2y",
        "outputId": "117028ac-c9e3-4295-964c-a71967805a84"
      },
      "source": [
        "total_acc_avg"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "total_acc\n",
              "2.0      0.000000\n",
              "3.0      0.052023\n",
              "4.0      0.066743\n",
              "5.0      0.103289\n",
              "6.0      0.151293\n",
              "           ...   \n",
              "124.0    1.000000\n",
              "129.0    1.000000\n",
              "135.0    3.000000\n",
              "150.0    2.000000\n",
              "151.0    0.000000\n",
              "Name: mort_acc, Length: 118, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0n39Wy0y_K2z"
      },
      "source": [
        "**CHALLENGE TASK: Let's fill in the missing mort_acc values based on their total_acc value. If the mort_acc is missing, then we will fill in that missing value with the mean value corresponding to its total_acc value from the Series we created above. This involves using an .apply() method with two columns. Check out the link below for more info, or review the solutions video/notebook.**\n",
        "\n",
        "[Helpful Link](https://stackoverflow.com/questions/13331698/how-to-apply-a-function-to-two-columns-of-pandas-dataframe) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rd3h84HO_K2z"
      },
      "source": [
        "def fill_mort_acc(total_acc,mort_acc):\n",
        "  if np.isnan(mort_acc):\n",
        "    return total_acc_avg[total_acc]\n",
        "  else:\n",
        "    return mort_acc"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jDFIFcK_K2z"
      },
      "source": [
        "df['mort_acc'] = df.apply(lambda x: fill_mort_acc(x['total_acc'],x['mort_acc']),axis=1)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "51elxJat_K2z",
        "outputId": "53989889-f7bb-456d-f908-32b5d37f4366"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt                 0\n",
              "term                      0\n",
              "int_rate                  0\n",
              "installment               0\n",
              "grade                     0\n",
              "sub_grade                 0\n",
              "home_ownership            0\n",
              "annual_inc                0\n",
              "verification_status       0\n",
              "issue_d                   0\n",
              "loan_status               0\n",
              "purpose                   0\n",
              "dti                       0\n",
              "earliest_cr_line          0\n",
              "open_acc                  0\n",
              "pub_rec                   0\n",
              "revol_bal                 0\n",
              "revol_util              276\n",
              "total_acc                 0\n",
              "initial_list_status       0\n",
              "application_type          0\n",
              "mort_acc                  0\n",
              "pub_rec_bankruptcies    535\n",
              "address                   0\n",
              "loan_repaid               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0m6gsSt-_K2z"
      },
      "source": [
        "**TASK: revol_util and the pub_rec_bankruptcies have missing data points, but they account for less than 0.5% of the total data. Go ahead and remove the rows that are missing those values in those columns with dropna().**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "015J7iJY_K20"
      },
      "source": [
        "df = df.dropna()"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mlrz5fXk_K20",
        "outputId": "50bc619c-6abc-4241-8bf5-e4a33b9f3bf6"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt               0\n",
              "term                    0\n",
              "int_rate                0\n",
              "installment             0\n",
              "grade                   0\n",
              "sub_grade               0\n",
              "home_ownership          0\n",
              "annual_inc              0\n",
              "verification_status     0\n",
              "issue_d                 0\n",
              "loan_status             0\n",
              "purpose                 0\n",
              "dti                     0\n",
              "earliest_cr_line        0\n",
              "open_acc                0\n",
              "pub_rec                 0\n",
              "revol_bal               0\n",
              "revol_util              0\n",
              "total_acc               0\n",
              "initial_list_status     0\n",
              "application_type        0\n",
              "mort_acc                0\n",
              "pub_rec_bankruptcies    0\n",
              "address                 0\n",
              "loan_repaid             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vdpl8SvC_K20"
      },
      "source": [
        "## Categorical Variables and Dummy Variables\n",
        "\n",
        "**We're done working with the missing data! Now we just need to deal with the string values due to the categorical columns.**\n",
        "\n",
        "**TASK: List all the columns that are currently non-numeric. [Helpful Link](https://stackoverflow.com/questions/22470690/get-list-of-pandas-dataframe-columns-based-on-data-type)**\n",
        "\n",
        "[Another very useful method call](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.select_dtypes.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4zVEYVS_K20",
        "outputId": "f3c05dea-22ad-41d6-b2cb-0b261826f653"
      },
      "source": [
        "df.select_dtypes(exclude=np.number).columns"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['term', 'grade', 'sub_grade', 'home_ownership', 'verification_status',\n",
              "       'issue_d', 'loan_status', 'purpose', 'earliest_cr_line',\n",
              "       'initial_list_status', 'application_type', 'address'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNX_97bD4dBM",
        "outputId": "5bdff5b2-03be-497c-9455-87cebb0d2f2b"
      },
      "source": [
        "df.select_dtypes(['object']).columns"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['term', 'grade', 'sub_grade', 'home_ownership', 'verification_status',\n",
              "       'issue_d', 'loan_status', 'purpose', 'earliest_cr_line',\n",
              "       'initial_list_status', 'application_type', 'address'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGKuIZWu_K20"
      },
      "source": [
        "---\n",
        "**Let's now go through all the string features to see what we should do with them.**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### term feature\n",
        "\n",
        "**TASK: Convert the term feature into either a 36 or 60 integer numeric data type using .apply() or .map().**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Geto1xWE_K21",
        "outputId": "a2ac9cd9-b779-4a46-a571-6194d62b2fcd"
      },
      "source": [
        "df['term'].value_counts()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 36 months    301247\n",
              " 60 months     93972\n",
              "Name: term, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHT4urla_K21"
      },
      "source": [
        "df['term'] = df['term'].apply(lambda term: int(term[:3]))"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RA2qaUNt_K21",
        "outputId": "b59994b7-5950-4666-d7e3-97642fc3e1a3"
      },
      "source": [
        "df['term']"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         36\n",
              "1         36\n",
              "2         36\n",
              "3         36\n",
              "4         60\n",
              "          ..\n",
              "396025    60\n",
              "396026    36\n",
              "396027    36\n",
              "396028    60\n",
              "396029    36\n",
              "Name: term, Length: 395219, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q5A1ii4L_K21"
      },
      "source": [
        "### grade feature\n",
        "\n",
        "**TASK: We already know grade is part of sub_grade, so just drop the grade feature.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsnezGPg_K21"
      },
      "source": [
        "df = df.drop('grade',axis=1)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "btySwfbv_K21"
      },
      "source": [
        "**TASK: Convert the subgrade into dummy variables. Then concatenate these new columns to the original dataframe. Remember to drop the original subgrade column and to add drop_first=True to your get_dummies call.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CwGhJ4HW_K22"
      },
      "source": [
        "dummies = pd.get_dummies(df['sub_grade'],drop_first=True)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-fRioAB_K22"
      },
      "source": [
        "df = pd.concat([df.drop('sub_grade',axis=1),dummies],axis=1)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ytA9Xrb_K22",
        "outputId": "5f4cbc9c-c0ae-4d26-ec61-99a3b2e7a594"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['loan_amnt', 'term', 'int_rate', 'installment', 'home_ownership',\n",
              "       'annual_inc', 'verification_status', 'issue_d', 'loan_status',\n",
              "       'purpose', 'dti', 'earliest_cr_line', 'open_acc', 'pub_rec',\n",
              "       'revol_bal', 'revol_util', 'total_acc', 'initial_list_status',\n",
              "       'application_type', 'mort_acc', 'pub_rec_bankruptcies', 'address',\n",
              "       'loan_repaid', 'A2', 'A3', 'A4', 'A5', 'B1', 'B2', 'B3', 'B4', 'B5',\n",
              "       'C1', 'C2', 'C3', 'C4', 'C5', 'D1', 'D2', 'D3', 'D4', 'D5', 'E1', 'E2',\n",
              "       'E3', 'E4', 'E5', 'F1', 'F2', 'F3', 'F4', 'F5', 'G1', 'G2', 'G3', 'G4',\n",
              "       'G5'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VyHieO35_K22"
      },
      "source": [
        "### verification_status, application_type,initial_list_status,purpose \n",
        "**TASK: Convert these columns: ['verification_status', 'application_type','initial_list_status','purpose'] into dummy variables and concatenate them with the original dataframe. Remember to set drop_first=True and to drop the original columns.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZW8zdxA_K22"
      },
      "source": [
        "dummies = pd.get_dummies(df[['verification_status', 'application_type','initial_list_status','purpose']],drop_first=True)"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W6oXOl_t_K23"
      },
      "source": [
        "df = pd.concat([df.drop(['verification_status', 'application_type','initial_list_status','purpose'],axis=1),dummies],axis=1)"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "HNUal13B8Grr",
        "outputId": "4ce1a4b8-086a-4eaa-e122-8c1c2e1f3002"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 66,
      "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>loan_amnt</th>\n",
              "      <th>term</th>\n",
              "      <th>int_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>home_ownership</th>\n",
              "      <th>annual_inc</th>\n",
              "      <th>issue_d</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>dti</th>\n",
              "      <th>earliest_cr_line</th>\n",
              "      <th>open_acc</th>\n",
              "      <th>pub_rec</th>\n",
              "      <th>revol_bal</th>\n",
              "      <th>revol_util</th>\n",
              "      <th>total_acc</th>\n",
              "      <th>mort_acc</th>\n",
              "      <th>pub_rec_bankruptcies</th>\n",
              "      <th>address</th>\n",
              "      <th>loan_repaid</th>\n",
              "      <th>A2</th>\n",
              "      <th>A3</th>\n",
              "      <th>A4</th>\n",
              "      <th>A5</th>\n",
              "      <th>B1</th>\n",
              "      <th>B2</th>\n",
              "      <th>B3</th>\n",
              "      <th>B4</th>\n",
              "      <th>B5</th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "      <th>C3</th>\n",
              "      <th>C4</th>\n",
              "      <th>C5</th>\n",
              "      <th>D1</th>\n",
              "      <th>D2</th>\n",
              "      <th>D3</th>\n",
              "      <th>D4</th>\n",
              "      <th>D5</th>\n",
              "      <th>E1</th>\n",
              "      <th>E2</th>\n",
              "      <th>E3</th>\n",
              "      <th>E4</th>\n",
              "      <th>E5</th>\n",
              "      <th>F1</th>\n",
              "      <th>F2</th>\n",
              "      <th>F3</th>\n",
              "      <th>F4</th>\n",
              "      <th>F5</th>\n",
              "      <th>G1</th>\n",
              "      <th>G2</th>\n",
              "      <th>G3</th>\n",
              "      <th>G4</th>\n",
              "      <th>G5</th>\n",
              "      <th>verification_status_Source Verified</th>\n",
              "      <th>verification_status_Verified</th>\n",
              "      <th>application_type_INDIVIDUAL</th>\n",
              "      <th>application_type_JOINT</th>\n",
              "      <th>initial_list_status_w</th>\n",
              "      <th>purpose_credit_card</th>\n",
              "      <th>purpose_debt_consolidation</th>\n",
              "      <th>purpose_educational</th>\n",
              "      <th>purpose_home_improvement</th>\n",
              "      <th>purpose_house</th>\n",
              "      <th>purpose_major_purchase</th>\n",
              "      <th>purpose_medical</th>\n",
              "      <th>purpose_moving</th>\n",
              "      <th>purpose_other</th>\n",
              "      <th>purpose_renewable_energy</th>\n",
              "      <th>purpose_small_business</th>\n",
              "      <th>purpose_vacation</th>\n",
              "      <th>purpose_wedding</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000.0</td>\n",
              "      <td>36</td>\n",
              "      <td>11.44</td>\n",
              "      <td>329.48</td>\n",
              "      <td>RENT</td>\n",
              "      <td>117000.0</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>26.24</td>\n",
              "      <td>Jun-1990</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>36369.0</td>\n",
              "      <td>41.8</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0174 Michelle Gateway\\nMendozaberg, OK 22690</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</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>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8000.0</td>\n",
              "      <td>36</td>\n",
              "      <td>11.99</td>\n",
              "      <td>265.68</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>65000.0</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>22.05</td>\n",
              "      <td>Jul-2004</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20131.0</td>\n",
              "      <td>53.3</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1076 Carney Fort Apt. 347\\nLoganmouth, SD 05113</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</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>0</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15600.0</td>\n",
              "      <td>36</td>\n",
              "      <td>10.49</td>\n",
              "      <td>506.97</td>\n",
              "      <td>RENT</td>\n",
              "      <td>43057.0</td>\n",
              "      <td>Jan-2015</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>12.79</td>\n",
              "      <td>Aug-2007</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11987.0</td>\n",
              "      <td>92.2</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>87025 Mark Dale Apt. 269\\nNew Sabrina, WV 05113</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</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>0</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7200.0</td>\n",
              "      <td>36</td>\n",
              "      <td>6.49</td>\n",
              "      <td>220.65</td>\n",
              "      <td>RENT</td>\n",
              "      <td>54000.0</td>\n",
              "      <td>Nov-2014</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>2.60</td>\n",
              "      <td>Sep-2006</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5472.0</td>\n",
              "      <td>21.5</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>823 Reid Ford\\nDelacruzside, MA 00813</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</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>0</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>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24375.0</td>\n",
              "      <td>60</td>\n",
              "      <td>17.27</td>\n",
              "      <td>609.33</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>55000.0</td>\n",
              "      <td>Apr-2013</td>\n",
              "      <td>Charged Off</td>\n",
              "      <td>33.95</td>\n",
              "      <td>Mar-1999</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>24584.0</td>\n",
              "      <td>69.8</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>679 Luna Roads\\nGreggshire, VA 11650</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>0</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   loan_amnt  term  ...  purpose_vacation  purpose_wedding\n",
              "0    10000.0    36  ...                 1                0\n",
              "1     8000.0    36  ...                 0                0\n",
              "2    15600.0    36  ...                 0                0\n",
              "3     7200.0    36  ...                 0                0\n",
              "4    24375.0    60  ...                 0                0\n",
              "\n",
              "[5 rows x 71 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NKxM3H0T_K23"
      },
      "source": [
        "### home_ownership\n",
        "**TASK:Review the value_counts for the home_ownership column.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zeF52gqn_K23",
        "outputId": "83e83d81-7bb9-4191-f118-0c0d2899e2b5"
      },
      "source": [
        "df['home_ownership'].value_counts()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MORTGAGE    198022\n",
              "RENT        159395\n",
              "OWN          37660\n",
              "OTHER          110\n",
              "NONE            29\n",
              "ANY              3\n",
              "Name: home_ownership, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTDW8BhU_K23"
      },
      "source": [
        "df['home_ownership'] = df['home_ownership'].replace(['NONE','ANY'],'OTHER')"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UHU5dEzk85AH",
        "outputId": "88de0287-71b9-4f2e-cb07-45111c64b8f9"
      },
      "source": [
        "df['home_ownership'].value_counts()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MORTGAGE    198022\n",
              "RENT        159395\n",
              "OWN          37660\n",
              "OTHER          142\n",
              "Name: home_ownership, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wsoyVMjN_K23"
      },
      "source": [
        "**TASK: Convert these to dummy variables, but [replace](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.replace.html) NONE and ANY with OTHER, so that we end up with just 4 categories, MORTGAGE, RENT, OWN, OTHER. Then concatenate them with the original dataframe. Remember to set drop_first=True and to drop the original columns.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTIWFaRU9DiH"
      },
      "source": [
        "dummies = pd.get_dummies(df['home_ownership'],drop_first=True)"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "05I3ngDx9D6V"
      },
      "source": [
        "df = pd.concat([df.drop('home_ownership',axis=1),dummies],axis=1)"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofbR29ry_K24"
      },
      "source": [
        "### address\n",
        "**TASK: Let's feature engineer a zip code column from the address in the data set. Create a column called 'zip_code' that extracts the zip code from the address column.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HpXG9apu94Dl",
        "outputId": "4332718b-34fc-47f4-c469-0eabf22190f3"
      },
      "source": [
        "df['address']"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              0174 Michelle Gateway\\nMendozaberg, OK 22690\n",
              "1           1076 Carney Fort Apt. 347\\nLoganmouth, SD 05113\n",
              "2           87025 Mark Dale Apt. 269\\nNew Sabrina, WV 05113\n",
              "3                     823 Reid Ford\\nDelacruzside, MA 00813\n",
              "4                      679 Luna Roads\\nGreggshire, VA 11650\n",
              "                                ...                        \n",
              "396025       12951 Williams Crossing\\nJohnnyville, DC 30723\n",
              "396026    0114 Fowler Field Suite 028\\nRachelborough, LA...\n",
              "396027     953 Matthew Points Suite 414\\nReedfort, NY 70466\n",
              "396028    7843 Blake Freeway Apt. 229\\nNew Michael, FL 2...\n",
              "396029          787 Michelle Causeway\\nBriannaton, AR 48052\n",
              "Name: address, Length: 395219, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmnjePpZ_K24"
      },
      "source": [
        "df['zip_code'] = df['address'].apply(lambda address: address[-5:])"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4POdMAO_K24",
        "outputId": "c26c5c61-06c8-4584-ddec-ed5a0a330bc5"
      },
      "source": [
        "df['zip_code']"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         22690\n",
              "1         05113\n",
              "2         05113\n",
              "3         00813\n",
              "4         11650\n",
              "          ...  \n",
              "396025    30723\n",
              "396026    05113\n",
              "396027    70466\n",
              "396028    29597\n",
              "396029    48052\n",
              "Name: zip_code, Length: 395219, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQ3S-pdUEHui"
      },
      "source": [
        "df = df.drop('address',axis=1)"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dvQ_2whG_K24"
      },
      "source": [
        "**TASK: Now make this zip_code column into dummy variables using pandas. Concatenate the result and drop the original zip_code column along with dropping the address column.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EARcGuIj_K24",
        "outputId": "61f6848b-31ca-48c3-f9bd-c1acbd33dd7b"
      },
      "source": [
        "df['zip_code'].value_counts()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "70466    56880\n",
              "22690    56413\n",
              "30723    56402\n",
              "48052    55811\n",
              "00813    45725\n",
              "29597    45393\n",
              "05113    45300\n",
              "11650    11210\n",
              "93700    11126\n",
              "86630    10959\n",
              "Name: zip_code, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_iEoOu8K_K24"
      },
      "source": [
        "dummies = pd.get_dummies(df['zip_code'],drop_first=True)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z01AV-xf_H28"
      },
      "source": [
        "df = pd.concat([df.drop('zip_code',axis=1),dummies],axis=1)"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0btkr0W_K24"
      },
      "source": [
        "### issue_d \n",
        "\n",
        "**TASK: This would be data leakage, we wouldn't know beforehand whether or not a loan would be issued when using our model, so in theory we wouldn't have an issue_date, drop this feature.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXQw_qIf_K25"
      },
      "source": [
        "df = df.drop('issue_d',axis=1)"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X4mTr1rI_K25"
      },
      "source": [
        "### earliest_cr_line\n",
        "**TASK: This appears to be a historical time stamp feature. Extract the year from this feature using a .apply function, then convert it to a numeric feature. Set this new data to a feature column called 'earliest_cr_year'.Then drop the earliest_cr_line feature.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2sMdoYKj_K25",
        "outputId": "12abaa89-7e31-4baa-f17c-aa6acb4767dc"
      },
      "source": [
        "df['earliest_cr_line']"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         Jun-1990\n",
              "1         Jul-2004\n",
              "2         Aug-2007\n",
              "3         Sep-2006\n",
              "4         Mar-1999\n",
              "            ...   \n",
              "396025    Nov-2004\n",
              "396026    Feb-2006\n",
              "396027    Mar-1997\n",
              "396028    Nov-1990\n",
              "396029    Sep-1998\n",
              "Name: earliest_cr_line, Length: 395219, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vErYmWUA_K25"
      },
      "source": [
        "df['earliest_cr_year'] = df['earliest_cr_line'].apply(lambda date: date[-4:])"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBVlw_TjAUBE"
      },
      "source": [
        "df = df.drop('earliest_cr_line',axis=1)"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DESeB6-u_K26",
        "outputId": "63ba938e-38f1-4b3e-ad35-3e9bcad7cc66"
      },
      "source": [
        "df['earliest_cr_year']"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         1990\n",
              "1         2004\n",
              "2         2007\n",
              "3         2006\n",
              "4         1999\n",
              "          ... \n",
              "396025    2004\n",
              "396026    2006\n",
              "396027    1997\n",
              "396028    1990\n",
              "396029    1998\n",
              "Name: earliest_cr_year, Length: 395219, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AINnymujAo1D"
      },
      "source": [
        "df['earliest_cr_year'] = pd.to_numeric(df['earliest_cr_year'])"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VnQAbTOnA479",
        "outputId": "50d6e707-e559-4b35-9020-d566ff28b931"
      },
      "source": [
        "df['earliest_cr_year']"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         1990\n",
              "1         2004\n",
              "2         2007\n",
              "3         2006\n",
              "4         1999\n",
              "          ... \n",
              "396025    2004\n",
              "396026    2006\n",
              "396027    1997\n",
              "396028    1990\n",
              "396029    1998\n",
              "Name: earliest_cr_year, Length: 395219, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DPLkZxlC_K26"
      },
      "source": [
        "## Train Test Split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TZF86G0W_K26"
      },
      "source": [
        "**TASK: Import train_test_split from sklearn.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-SQNhRr_K26"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AimeMTVl_K26"
      },
      "source": [
        "**TASK: drop the load_status column we created earlier, since its a duplicate of the loan_repaid column. We'll use the loan_repaid column since its already in 0s and 1s.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0j2ZWZB4_K27"
      },
      "source": [
        "df = df.drop('loan_status',axis=1)"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2GPCPoQ_K27"
      },
      "source": [
        "**TASK: Set X and y variables to the .values of the features and label.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkMqyofI_K27"
      },
      "source": [
        "X = df.drop('loan_repaid',axis=1).values"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8aIMLRvy_K27"
      },
      "source": [
        "y = df['loan_repaid'].values"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zUPkpejh_K28"
      },
      "source": [
        "**TASK: Perform a train/test split with test_size=0.2 and a random_state of 101.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8G_h-FE_K28"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vrcIlg9L_K28"
      },
      "source": [
        "## Normalizing the Data\n",
        "\n",
        "**TASK: Use a MinMaxScaler to normalize the feature data X_train and X_test. Recall we don't want data leakge from the test set so we only fit on the X_train data.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CwdHVLoy_K29"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0UtWQv9p_K29"
      },
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M9o0HErJ_K29"
      },
      "source": [
        "X_train = scaler.fit_transform(X_train)"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tyMAz0Ee_K29"
      },
      "source": [
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSisfXQC_K29"
      },
      "source": [
        "# Creating the Model\n",
        "\n",
        "**TASK: Run the cell below to import the necessary Keras functions.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4wGGfuw_K2-"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense,Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgj3G7nw_K2-"
      },
      "source": [
        "**TASK: Build a sequential model to will be trained on the data. You have unlimited options here, but here is what the solution uses: a model that goes 78 --> 39 --> 19--> 1 output neuron. OPTIONAL: Explore adding [Dropout layers](https://keras.io/layers/core/) [1](https://en.wikipedia.org/wiki/Dropout_(neural_networks)) [2](https://towardsdatascience.com/machine-learning-part-20-dropout-keras-layers-explained-8c9f6dc4c9ab)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnLnLijy8mzu",
        "outputId": "7dc8721a-c012-4136-fd09-b3d3fbabd752"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(316175, 78)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRiA8oUC_K2-"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Dense(78,activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Dense(39,activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Dense(19,activation='relu'))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "#Binary classification\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam')\n"
      ],
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uj-LfDKG91QV"
      },
      "source": [
        "early_stop = EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=25)"
      ],
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHKWGJ5x9tbB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7Yr01iD_K2_"
      },
      "source": [
        "**TASK: Fit the model to the training data for at least 25 epochs. Also add in the validation data for later plotting. Optional: add in a batch_size of 256.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XNvvE3Tx_K2_",
        "outputId": "ea1b3977-febc-4684-b12a-22993f6d0a0f"
      },
      "source": [
        "model.fit(x=X_train, y=y_train, epochs=50, validation_data=(X_test,y_test),callbacks=[early_stop])"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.3026 - val_loss: 0.2634\n",
            "Epoch 2/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2630 - val_loss: 0.2630\n",
            "Epoch 3/50\n",
            "9881/9881 [==============================] - 24s 2ms/step - loss: 0.2633 - val_loss: 0.2624\n",
            "Epoch 4/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2618 - val_loss: 0.2626\n",
            "Epoch 5/50\n",
            "9881/9881 [==============================] - 24s 2ms/step - loss: 0.2615 - val_loss: 0.2632\n",
            "Epoch 6/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2602 - val_loss: 0.2622\n",
            "Epoch 7/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2598 - val_loss: 0.2627\n",
            "Epoch 8/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2602 - val_loss: 0.2629\n",
            "Epoch 9/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2584 - val_loss: 0.2621\n",
            "Epoch 10/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2583 - val_loss: 0.2621\n",
            "Epoch 11/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2603 - val_loss: 0.2620\n",
            "Epoch 12/50\n",
            "9881/9881 [==============================] - 22s 2ms/step - loss: 0.2590 - val_loss: 0.2618\n",
            "Epoch 13/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2597 - val_loss: 0.2618\n",
            "Epoch 14/50\n",
            "9881/9881 [==============================] - 22s 2ms/step - loss: 0.2571 - val_loss: 0.2614\n",
            "Epoch 15/50\n",
            "9881/9881 [==============================] - 22s 2ms/step - loss: 0.2593 - val_loss: 0.2622\n",
            "Epoch 16/50\n",
            "9881/9881 [==============================] - 22s 2ms/step - loss: 0.2581 - val_loss: 0.2628\n",
            "Epoch 17/50\n",
            "9881/9881 [==============================] - 21s 2ms/step - loss: 0.2579 - val_loss: 0.2618\n",
            "Epoch 18/50\n",
            "9881/9881 [==============================] - 22s 2ms/step - loss: 0.2595 - val_loss: 0.2624\n",
            "Epoch 19/50\n",
            "9881/9881 [==============================] - 22s 2ms/step - loss: 0.2581 - val_loss: 0.2620\n",
            "Epoch 20/50\n",
            "9881/9881 [==============================] - 21s 2ms/step - loss: 0.2576 - val_loss: 0.2616\n",
            "Epoch 21/50\n",
            "9881/9881 [==============================] - 22s 2ms/step - loss: 0.2588 - val_loss: 0.2617\n",
            "Epoch 22/50\n",
            "9881/9881 [==============================] - 21s 2ms/step - loss: 0.2580 - val_loss: 0.2625\n",
            "Epoch 23/50\n",
            "9881/9881 [==============================] - 21s 2ms/step - loss: 0.2589 - val_loss: 0.2620\n",
            "Epoch 24/50\n",
            "9881/9881 [==============================] - 22s 2ms/step - loss: 0.2564 - val_loss: 0.2634\n",
            "Epoch 25/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2577 - val_loss: 0.2632\n",
            "Epoch 26/50\n",
            "9881/9881 [==============================] - 22s 2ms/step - loss: 0.2590 - val_loss: 0.2624\n",
            "Epoch 27/50\n",
            "9881/9881 [==============================] - 21s 2ms/step - loss: 0.2589 - val_loss: 0.2631\n",
            "Epoch 28/50\n",
            "9881/9881 [==============================] - 22s 2ms/step - loss: 0.2572 - val_loss: 0.2623\n",
            "Epoch 29/50\n",
            "9881/9881 [==============================] - 21s 2ms/step - loss: 0.2590 - val_loss: 0.2640\n",
            "Epoch 30/50\n",
            "9881/9881 [==============================] - 18s 2ms/step - loss: 0.2586 - val_loss: 0.2627\n",
            "Epoch 31/50\n",
            "9881/9881 [==============================] - 18s 2ms/step - loss: 0.2568 - val_loss: 0.2622\n",
            "Epoch 32/50\n",
            "9881/9881 [==============================] - 18s 2ms/step - loss: 0.2578 - val_loss: 0.2618\n",
            "Epoch 33/50\n",
            "9881/9881 [==============================] - 18s 2ms/step - loss: 0.2564 - val_loss: 0.2616\n",
            "Epoch 34/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2559 - val_loss: 0.2622\n",
            "Epoch 35/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2561 - val_loss: 0.2628\n",
            "Epoch 36/50\n",
            "9881/9881 [==============================] - 24s 2ms/step - loss: 0.2567 - val_loss: 0.2622\n",
            "Epoch 37/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2577 - val_loss: 0.2623\n",
            "Epoch 38/50\n",
            "9881/9881 [==============================] - 24s 2ms/step - loss: 0.2563 - val_loss: 0.2628\n",
            "Epoch 39/50\n",
            "9881/9881 [==============================] - 23s 2ms/step - loss: 0.2571 - val_loss: 0.2619\n",
            "Epoch 00039: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f3dd8b702d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64drPAwR_K2_"
      },
      "source": [
        "**TASK: OPTIONAL: Save your model.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WsYyQJik_K2_"
      },
      "source": [
        "model.save('/content/gdrive/My Drive/nn_model.h5')"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fd3KrzrE_K3A"
      },
      "source": [
        "# Section 3: Evaluating Model Performance.\n",
        "\n",
        "**TASK: Plot out the validation loss versus the training loss.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j_LoOvTf_K3A"
      },
      "source": [
        "model_loss = pd.DataFrame(model.history.history)"
      ],
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "kZyHLvNl_K3A",
        "outputId": "4b738568-5414-4f0f-dd87-2ace8c7a8440"
      },
      "source": [
        "model_loss.plot()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3dd7911d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fN1hi30B_K3A"
      },
      "source": [
        ""
      ],
      "execution_count": 281,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HUeLZMl8_K3A"
      },
      "source": [
        "**TASK: Create predictions from the X_test set and display a classification report and confusion matrix for the X_test set.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3FerILiM_K3B",
        "outputId": "bd900fa6-6a5d-49bf-96d9-a726bd4d0856"
      },
      "source": [
        "predictions = model.predict_classes(X_test)"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TTOPE0Z_K3B"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgMkgz90_K3B",
        "outputId": "2a20f77d-184b-464c-9b02-e911c1aa75c3"
      },
      "source": [
        "print(confusion_matrix(y_test, predictions))"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 6957  8701]\n",
            " [  187 63199]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VFcu2TcY_K3C",
        "outputId": "3a3c12e2-3940-4f54-b3e7-02d4c7e0f1e9"
      },
      "source": [
        "print(classification_report(y_test,predictions))"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.44      0.61     15658\n",
            "           1       0.88      1.00      0.93     63386\n",
            "\n",
            "    accuracy                           0.89     79044\n",
            "   macro avg       0.93      0.72      0.77     79044\n",
            "weighted avg       0.90      0.89      0.87     79044\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V47RZ1Un_K3C"
      },
      "source": [
        "**TASK: Given the customer below, would you offer this person a loan?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3fELOatM_K3C",
        "outputId": "f5c96804-57a3-4272-81b3-3925e841cf15"
      },
      "source": [
        "import random\n",
        "random.seed(101)\n",
        "random_ind = random.randint(0,len(df))\n",
        "\n",
        "new_customer = df.drop('loan_repaid',axis=1).iloc[random_ind]\n",
        "new_customer"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_amnt           25000.00\n",
              "term                   60.00\n",
              "int_rate               18.24\n",
              "installment           638.11\n",
              "annual_inc          61665.00\n",
              "                      ...   \n",
              "48052                   0.00\n",
              "70466                   0.00\n",
              "86630                   0.00\n",
              "93700                   0.00\n",
              "earliest_cr_year     1996.00\n",
              "Name: 305323, Length: 78, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9E0YtrN_K3D"
      },
      "source": [
        "new_cust = scaler.transform(new_customer.values.reshape(1,78))"
      ],
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J-FZnUwI0usk",
        "outputId": "f4e63fad-6370-4b64-ff12-e5fd29fac82e"
      },
      "source": [
        "new_cust"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.61538462, 1.        , 0.50331126, 0.40836493, 0.00708257,\n",
              "        0.0030113 , 0.2       , 0.        , 0.01593446, 0.07340581,\n",
              "        0.29530201, 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 1.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 1.        , 1.        ,\n",
              "        0.        , 0.        , 0.        , 1.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 1.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 1.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.75362319]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yo4sk-4x04hn",
        "outputId": "bab035be-b4e8-4ed6-bd74-e08345ef0bc1"
      },
      "source": [
        "model.predict_classes(new_cust)"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F2-0MqYG_K3D"
      },
      "source": [
        "**TASK: Now check, did this person actually end up paying back their loan?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1PGoH10_K3I",
        "outputId": "01d35b5b-77e1-42cf-cf1f-2e27809afef3"
      },
      "source": [
        "df.iloc[random_ind]['loan_repaid']"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WYO8cIfF_K3I"
      },
      "source": [
        "# GREAT JOB!"
      ]
    }
  ]
}