Final_Project-3.ipynb

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        "# CIS 545 Final Project: Introduction\n",
        "\n",
        "Hey! It's Arisht, Arnav and Salam, and this is our project!\n",
        "\n",
        "Our project aims to understand the behaviour of financial institutions in regards to **Loan Approvals or Denials and Loan Payment Difficulties**. Given a dataset that includes many features and attributes of about ~300,000 loan applicants, we proceeded by analyzing these applicants and applying several models in order to gain insight into the financial facet of loans. There seems to be many factors that affect the prospect of approving a loan, and our project dives more into understanding these underlying depths. At the end, we will have developed some models that are able to predict whether an applicant will end up having difficulties in paying back the loan or not based on a number of features. \n",
        "\n",
        "We hope you enjoy reading through our work!"
      ],
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      "cell_type": "markdown",
      "source": [
        "# Libraries and Setup\n",
        "\n",
        "Before we deal with the datasets, we want to run the following cell to load all the libraries that we will be using in our project. "
      ],
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      "source": [
        "# Importing relevant libraries\n",
        "import shutil\n",
        "import glob\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import sklearn.metrics as metrics\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn import datasets\n",
        "from sklearn.tree import DecisionTreeClassifier \n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import tree\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn import preprocessing\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from sklearn.tree import plot_tree\n",
        "\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the Datasets\n",
        "Our Kaggle datasets can be found at this [link](https://www.kaggle.com/datasets/gauravduttakiit/loan-defaulter)\n",
        "\n",
        "The application_data.csv file provides us with our main dataset that outlines the loan applicants along with multiple attributes and features. Our 'TARGET' variable is 1 when the client is dealing with payment difficulties, and is 0 otherwise. \n",
        "\n",
        "The columns_description.csv file provides us with a dataset that gives further description for the columns in the application dataset above."
      ],
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      "cell_type": "code",
      "source": [
        "# Retrieving dataset\n",
        "application_df = pd.read_csv('application_data.csv')\n",
        "columns_df = pd.read_csv('columns_description.csv')"
      ],
      "metadata": {
        "id": "_32CwjaUYTxa"
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      "execution_count": 2,
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      "source": [
        "#Take a look at the dataset\n",
        "application_df.head(5)"
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              "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
              "0      100002       1         Cash loans           M            N   \n",
              "1      100003       0         Cash loans           F            N   \n",
              "2      100004       0    Revolving loans           M            Y   \n",
              "3      100006       0         Cash loans           F            N   \n",
              "4      100007       0         Cash loans           M            N   \n",
              "\n",
              "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
              "0               Y             0          202500.0    406597.5      24700.5   \n",
              "1               N             0          270000.0   1293502.5      35698.5   \n",
              "2               Y             0           67500.0    135000.0       6750.0   \n",
              "3               Y             0          135000.0    312682.5      29686.5   \n",
              "4               Y             0          121500.0    513000.0      21865.5   \n",
              "\n",
              "   ...  FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
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        "#Take a look at the dataset\n",
        "#We are only interested in the descriptions for the columns in application_data\n",
        "columns_df = columns_df[columns_df.Table == 'application_data']\n",
        "columns_df.head(4)"
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              "   Unnamed: 0             Table                 Row  \\\n",
              "0           1  application_data          SK_ID_CURR   \n",
              "1           2  application_data              TARGET   \n",
              "2           5  application_data  NAME_CONTRACT_TYPE   \n",
              "3           6  application_data         CODE_GENDER   \n",
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    },
    {
      "cell_type": "code",
      "source": [
        "#Check datatypes and size\n",
        "print(application_df.info(verbose = True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aCSohI43aQXz",
        "outputId": "f34caca4-9e47-4118-dc66-1c8c9de7d3ad"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 244655 entries, 0 to 244654\n",
            "Data columns (total 122 columns):\n",
            " #    Column                        Dtype  \n",
            "---   ------                        -----  \n",
            " 0    SK_ID_CURR                    int64  \n",
            " 1    TARGET                        int64  \n",
            " 2    NAME_CONTRACT_TYPE            object \n",
            " 3    CODE_GENDER                   object \n",
            " 4    FLAG_OWN_CAR                  object \n",
            " 5    FLAG_OWN_REALTY               object \n",
            " 6    CNT_CHILDREN                  int64  \n",
            " 7    AMT_INCOME_TOTAL              float64\n",
            " 8    AMT_CREDIT                    float64\n",
            " 9    AMT_ANNUITY                   float64\n",
            " 10   AMT_GOODS_PRICE               float64\n",
            " 11   NAME_TYPE_SUITE               object \n",
            " 12   NAME_INCOME_TYPE              object \n",
            " 13   NAME_EDUCATION_TYPE           object \n",
            " 14   NAME_FAMILY_STATUS            object \n",
            " 15   NAME_HOUSING_TYPE             object \n",
            " 16   REGION_POPULATION_RELATIVE    float64\n",
            " 17   DAYS_BIRTH                    int64  \n",
            " 18   DAYS_EMPLOYED                 int64  \n",
            " 19   DAYS_REGISTRATION             float64\n",
            " 20   DAYS_ID_PUBLISH               int64  \n",
            " 21   OWN_CAR_AGE                   float64\n",
            " 22   FLAG_MOBIL                    int64  \n",
            " 23   FLAG_EMP_PHONE                int64  \n",
            " 24   FLAG_WORK_PHONE               int64  \n",
            " 25   FLAG_CONT_MOBILE              int64  \n",
            " 26   FLAG_PHONE                    int64  \n",
            " 27   FLAG_EMAIL                    int64  \n",
            " 28   OCCUPATION_TYPE               object \n",
            " 29   CNT_FAM_MEMBERS               float64\n",
            " 30   REGION_RATING_CLIENT          int64  \n",
            " 31   REGION_RATING_CLIENT_W_CITY   int64  \n",
            " 32   WEEKDAY_APPR_PROCESS_START    object \n",
            " 33   HOUR_APPR_PROCESS_START       int64  \n",
            " 34   REG_REGION_NOT_LIVE_REGION    int64  \n",
            " 35   REG_REGION_NOT_WORK_REGION    int64  \n",
            " 36   LIVE_REGION_NOT_WORK_REGION   int64  \n",
            " 37   REG_CITY_NOT_LIVE_CITY        int64  \n",
            " 38   REG_CITY_NOT_WORK_CITY        int64  \n",
            " 39   LIVE_CITY_NOT_WORK_CITY       int64  \n",
            " 40   ORGANIZATION_TYPE             object \n",
            " 41   EXT_SOURCE_1                  float64\n",
            " 42   EXT_SOURCE_2                  float64\n",
            " 43   EXT_SOURCE_3                  float64\n",
            " 44   APARTMENTS_AVG                float64\n",
            " 45   BASEMENTAREA_AVG              float64\n",
            " 46   YEARS_BEGINEXPLUATATION_AVG   float64\n",
            " 47   YEARS_BUILD_AVG               float64\n",
            " 48   COMMONAREA_AVG                float64\n",
            " 49   ELEVATORS_AVG                 float64\n",
            " 50   ENTRANCES_AVG                 float64\n",
            " 51   FLOORSMAX_AVG                 float64\n",
            " 52   FLOORSMIN_AVG                 float64\n",
            " 53   LANDAREA_AVG                  float64\n",
            " 54   LIVINGAPARTMENTS_AVG          float64\n",
            " 55   LIVINGAREA_AVG                float64\n",
            " 56   NONLIVINGAPARTMENTS_AVG       float64\n",
            " 57   NONLIVINGAREA_AVG             float64\n",
            " 58   APARTMENTS_MODE               float64\n",
            " 59   BASEMENTAREA_MODE             float64\n",
            " 60   YEARS_BEGINEXPLUATATION_MODE  float64\n",
            " 61   YEARS_BUILD_MODE              float64\n",
            " 62   COMMONAREA_MODE               float64\n",
            " 63   ELEVATORS_MODE                float64\n",
            " 64   ENTRANCES_MODE                float64\n",
            " 65   FLOORSMAX_MODE                float64\n",
            " 66   FLOORSMIN_MODE                float64\n",
            " 67   LANDAREA_MODE                 float64\n",
            " 68   LIVINGAPARTMENTS_MODE         float64\n",
            " 69   LIVINGAREA_MODE               float64\n",
            " 70   NONLIVINGAPARTMENTS_MODE      float64\n",
            " 71   NONLIVINGAREA_MODE            float64\n",
            " 72   APARTMENTS_MEDI               float64\n",
            " 73   BASEMENTAREA_MEDI             float64\n",
            " 74   YEARS_BEGINEXPLUATATION_MEDI  float64\n",
            " 75   YEARS_BUILD_MEDI              float64\n",
            " 76   COMMONAREA_MEDI               float64\n",
            " 77   ELEVATORS_MEDI                float64\n",
            " 78   ENTRANCES_MEDI                float64\n",
            " 79   FLOORSMAX_MEDI                float64\n",
            " 80   FLOORSMIN_MEDI                float64\n",
            " 81   LANDAREA_MEDI                 float64\n",
            " 82   LIVINGAPARTMENTS_MEDI         float64\n",
            " 83   LIVINGAREA_MEDI               float64\n",
            " 84   NONLIVINGAPARTMENTS_MEDI      float64\n",
            " 85   NONLIVINGAREA_MEDI            float64\n",
            " 86   FONDKAPREMONT_MODE            object \n",
            " 87   HOUSETYPE_MODE                object \n",
            " 88   TOTALAREA_MODE                float64\n",
            " 89   WALLSMATERIAL_MODE            object \n",
            " 90   EMERGENCYSTATE_MODE           object \n",
            " 91   OBS_30_CNT_SOCIAL_CIRCLE      float64\n",
            " 92   DEF_30_CNT_SOCIAL_CIRCLE      float64\n",
            " 93   OBS_60_CNT_SOCIAL_CIRCLE      float64\n",
            " 94   DEF_60_CNT_SOCIAL_CIRCLE      float64\n",
            " 95   DAYS_LAST_PHONE_CHANGE        float64\n",
            " 96   FLAG_DOCUMENT_2               float64\n",
            " 97   FLAG_DOCUMENT_3               float64\n",
            " 98   FLAG_DOCUMENT_4               float64\n",
            " 99   FLAG_DOCUMENT_5               float64\n",
            " 100  FLAG_DOCUMENT_6               float64\n",
            " 101  FLAG_DOCUMENT_7               float64\n",
            " 102  FLAG_DOCUMENT_8               float64\n",
            " 103  FLAG_DOCUMENT_9               float64\n",
            " 104  FLAG_DOCUMENT_10              float64\n",
            " 105  FLAG_DOCUMENT_11              float64\n",
            " 106  FLAG_DOCUMENT_12              float64\n",
            " 107  FLAG_DOCUMENT_13              float64\n",
            " 108  FLAG_DOCUMENT_14              float64\n",
            " 109  FLAG_DOCUMENT_15              float64\n",
            " 110  FLAG_DOCUMENT_16              float64\n",
            " 111  FLAG_DOCUMENT_17              float64\n",
            " 112  FLAG_DOCUMENT_18              float64\n",
            " 113  FLAG_DOCUMENT_19              float64\n",
            " 114  FLAG_DOCUMENT_20              float64\n",
            " 115  FLAG_DOCUMENT_21              float64\n",
            " 116  AMT_REQ_CREDIT_BUREAU_HOUR    float64\n",
            " 117  AMT_REQ_CREDIT_BUREAU_DAY     float64\n",
            " 118  AMT_REQ_CREDIT_BUREAU_WEEK    float64\n",
            " 119  AMT_REQ_CREDIT_BUREAU_MON     float64\n",
            " 120  AMT_REQ_CREDIT_BUREAU_QRT     float64\n",
            " 121  AMT_REQ_CREDIT_BUREAU_YEAR    float64\n",
            "dtypes: float64(85), int64(21), object(16)\n",
            "memory usage: 227.7+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can clearly see that most of the columns denote numerical variables, whereas the rest of the columns denote categorical variables that refer to gender, occupation type and so on. These differences in the data types of our variables will have to be accounted for when we get to the modeling. "
      ],
      "metadata": {
        "id": "qkx8xCNyaXmi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis (EDA)\n",
        "The first step in our analysis involves performing exploratory data analysis on the application dataset shown above. "
      ],
      "metadata": {
        "id": "Ob5-KxvAa7gc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dropping Some Columns\n",
        "Since our dataset contains 122 columns, we need to drop the columns that provide no valuable insight into the scope of our project. To identify these columns, we first chose to take a look at the columns that have a considerable number of null (empty) values (>50% in our case). After identifying these columns, we created a correlation matrix that displays the correlations between these columns and the TARGET variable in order to verify that none of these columns have a considerable correlation and can thus be dropped from our dataset. "
      ],
      "metadata": {
        "id": "BsEWB-I5eyvy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# percentage of null values in column\n",
        "# this will help us realize whether or not certain columns can be viable options as indicators\n",
        "percentagelist = (application_df.isnull().sum() / application_df.shape[0] * 100.00).tolist()\n",
        "columnlist = (application_df.columns).tolist()\n",
        "\n",
        "percentagetuplelist = []\n",
        "\n",
        "for p in range(len(percentagelist)):\n",
        "  percentagetuplelist.append((columnlist[p], percentagelist[p]))\n",
        "\n",
        "percentages_df = (pd.DataFrame(percentagetuplelist, columns = [\"column_name\", \"percentage_null\"])).sort_values(by = 'percentage_null', ascending = False)\n",
        "percentage50_df = percentages_df[percentages_df['percentage_null'] > 50.00]\n",
        "\n",
        "#Now we want to merge these column names with their description to understand what they refer to\n",
        "percentage50_df = pd.merge(percentage50_df, columns_df, how = 'inner', \n",
        "                           left_on = 'column_name', right_on = 'Row', copy = False)\n",
        "percentage50_df = percentage50_df[['column_name', 'Description', 'percentage_null']]\n",
        "percentage50_df.head(10)"
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        {
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b43c82dc-bd7a-4dfe-be4e-6f5ae4a2c6f9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b43c82dc-bd7a-4dfe-be4e-6f5ae4a2c6f9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that the majority of these columns correspond to information about the applicant's housing situation, which might not be relevant to the scope of our modeling since there are other attributes denoting living situations that are more complete (have relatively less null values)."
      ],
      "metadata": {
        "id": "LeXXeJ76c31d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlations between these columns and the target column\n",
        "corr_columns = percentage50_df['column_name'].tolist()\n",
        "corr_columns.append(\"TARGET\")\n",
        "corr_matrix = application_df[corr_columns].corr()\n",
        "\n",
        "# Visualize corr matrix only with target column\n",
        "ax = plt.subplots(figsize = (15, 9))\n",
        "ax = sns.heatmap(corr_matrix, cmap = 'RdBu', center = 0.00, vmin = -1.00, vmax = 1.00)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 689
        },
        "id": "BEJFWs2Wc_hy",
        "outputId": "19e614d0-3cac-4d6e-ed98-e6ec31633f0d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see from the correlation heatmap displayed above, none of these variables have a considerable correlation with the TARGET variable, suggesting that it would be valid for us to drop them from our dataset."
      ],
      "metadata": {
        "id": "EGUs0lIcdfCH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Displaying difference in number of columns after preliminary cleaning\n",
        "application_cleaned_df = application_df.drop(percentage50_df['column_name'].tolist(), axis = 1)\n",
        "print('We have reduced the total number of columns from ' + str(application_df.shape[1]) + ' to ' +\n",
        "      str(application_cleaned_df.shape[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEH-RxXgdt5M",
        "outputId": "8a2d4488-213e-412a-c00e-6fe787bf01de"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have reduced the total number of columns from 122 to 81\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we want to look at the FLAG_DOCUMENT columns in our datasets. These columns have values of 1 if the client provide that document, and 0 if the client did not provide that particular document. However, there is no additional information regarding the type of documents involved. Therefore, we want to look into these columns and check if they are essential to the scope of our project in any way. "
      ],
      "metadata": {
        "id": "Aye9ZjovFNoC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "columns = application_cleaned_df.columns.tolist() # converting to list\n",
        "\n",
        "# lists to append to\n",
        "to_remove_documents = []\n",
        "yes_list = []\n",
        "total_list = []\n",
        "total_sum = 0\n",
        "\n",
        "# iterate through columns of dataframe and add all flag_documents to be dropped\n",
        "for column in columns:\n",
        "  if column[0:13] == 'FLAG_DOCUMENT':\n",
        "    to_remove_documents.append(column)\n",
        "\n",
        "temp_graphing = to_remove_documents.copy()\n",
        "\n",
        "# iterates through and adds to yes_list and total_sum\n",
        "for column in temp_graphing:\n",
        "  total_agg = application_cleaned_df.groupby([column,'TARGET']).size()\n",
        "  if(column == \"FLAG_DOCUMENT_4\" or column == \"FLAG_DOCUMENT_10\" or column == \"FLAG_DOCUMENT_12\"):\n",
        "    total = total_agg[1][0]\n",
        "    total_list.append(total)\n",
        "    total_sum += total\n",
        "    yes_list.append(0)\n",
        "  else:\n",
        "    total = total_agg[1][1] + total_agg[1][0]\n",
        "    perc_yes_1 = (total_agg[1][1]/(total_agg[1][0] + total_agg[1][1])) * 100\n",
        "    total_list.append(total)\n",
        "    yes_list.append(perc_yes_1)\n",
        "    total_sum += total\n",
        "\n",
        "# processing for dataframe\n",
        "total_percent = (total_list/total_sum)*100\n",
        "temp_graphing = [temp[14:] for temp in temp_graphing]\n",
        "frame = {'Labels': temp_graphing,'Rate': yes_list, 'Total': total_list, '% Share': total_percent}\n",
        "\n",
        "# displaying findings\n",
        "df_documents = pd.DataFrame(frame)\n",
        "df_documents = df_documents.sort_values(by = \"Total\")\n",
        "display(df_documents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "iYQe01hIvYEg",
        "outputId": "0492c4cb-0b75-4026-b2b3-554f74ac585d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Labels       Rate   Total    % Share\n",
              "10     12   0.000000       2   0.000879\n",
              "8      10   0.000000       6   0.002637\n",
              "0       2  44.444444       9   0.003955\n",
              "2       4   0.000000      21   0.009228\n",
              "5       7   5.660377      53   0.023290\n",
              "15     17   3.278689      61   0.026805\n",
              "19     21  13.253012      83   0.036472\n",
              "18     20   7.627119     118   0.051852\n",
              "17     19   7.092199     141   0.061959\n",
              "13     15   3.103448     290   0.127434\n",
              "12     14   3.328710     721   0.316827\n",
              "11     13   3.403756     852   0.374392\n",
              "9      11   6.217071     949   0.417016\n",
              "7       9   6.471816     958   0.420971\n",
              "16     18   6.140351    1938   0.851610\n",
              "14     16   5.006258    2397   1.053307\n",
              "3       5   7.748380    3704   1.627638\n",
              "6       8   7.389534   19893   8.741525\n",
              "4       6   5.539832   21553   9.470974\n",
              "1       3   8.879876  173820  76.381229"
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              "                                                     [key], {});\n",
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Following this, we decided to display the distribution of FLAG_DOCUMENTs and their respective Target = 1 rates. "
      ],
      "metadata": {
        "id": "CRFS-NnM6ae7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting\n",
        "plt.figure(figsize = (12, 9))\n",
        "plt.bar(temp_graphing, yes_list)\n",
        "plt.title('FLAG_DOCUMENT Vs Target = 1 Rate')\n",
        "plt.xlabel('FLAG_DOCUMENT')\n",
        "plt.xticks(rotation = 90)\n",
        "plt.ylabel('Target = 1 Rate')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "PIzSfk6lGBjJ",
        "outputId": "21282935-3a14-447c-e6e5-435025122abb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The bar plot above demonstrates that the target rate varies for each flag document. However, the percentage values plotted are largely skewed given the uneven presence of each flag document type in the data chosen. For example, flag document 2 forms only 0.0006% of the total data whereas flag document 3 comprises of nearly 76% of the data. Our lack of understanding of the column along with the explained uncertainty justified the removal of the column.\n"
      ],
      "metadata": {
        "id": "cjX_fciFGNuz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# dropping columns\n",
        "num_columns = application_cleaned_df.shape[1]\n",
        "application_cleaned_df = application_cleaned_df.drop(columns = to_remove_documents)\n",
        "application_cleaned_df = application_cleaned_df.drop(columns = 'SK_ID_CURR')\n",
        "print('We have further reduced the total number of columns from ' + str(num_columns) + ' to ' +\n",
        "      str(application_cleaned_df.shape[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7cU8ayHAGewX",
        "outputId": "b17aa038-afad-41e1-f9db-d384ce79aada"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We have further reduced the total number of columns from 81 to 60\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Additional Data Cleaning\n",
        "We would like to look at some additional columns, and potentially drop the columns that are irrelevent in our case. Again, we will compute the percentage of missing values in each of the remaining columns. "
      ],
      "metadata": {
        "id": "auUm4GyvvAY4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "percentagelist = (application_cleaned_df.isnull().sum() / application_cleaned_df.shape[0] * 100.00).tolist()\n",
        "columnlist = (application_cleaned_df.columns).tolist()\n",
        "\n",
        "percentagetuplelist = []\n",
        "\n",
        "# add a tuple of column name and its percentage of null values to percentagetuplelist\n",
        "for p in range(len(percentagelist)):\n",
        "  percentagetuplelist.append((columnlist[p], percentagelist[p]))\n",
        "\n",
        "percentages_df = (pd.DataFrame(percentagetuplelist, columns = [\"column_name\", \"percentage_null\"])).sort_values(by = 'percentage_null', ascending = False)\n",
        "\n",
        "# Now we want to merge these column names with their description\n",
        "percentages_df = pd.merge(percentages_df, columns_df, how = 'inner', \n",
        "                           left_on = 'column_name', right_on = 'Row', copy = False)\n",
        "percentages_df = percentages_df[['column_name', 'Description', 'percentage_null']]\n",
        "\n",
        "percentages_df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "cExxYLOyHb-E",
        "outputId": "2f9fcaaf-45ab-42c5-a449-6b0cc1956bb9"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                    column_name  \\\n",
              "0                FLOORSMAX_MEDI   \n",
              "1                 FLOORSMAX_AVG   \n",
              "2                FLOORSMAX_MODE   \n",
              "3  YEARS_BEGINEXPLUATATION_MEDI   \n",
              "4   YEARS_BEGINEXPLUATATION_AVG   \n",
              "5  YEARS_BEGINEXPLUATATION_MODE   \n",
              "6                TOTALAREA_MODE   \n",
              "7           EMERGENCYSTATE_MODE   \n",
              "8               OCCUPATION_TYPE   \n",
              "9                  EXT_SOURCE_3   \n",
              "\n",
              "                                         Description  percentage_null  \n",
              "0  Normalized information about building where th...        49.838344  \n",
              "1  Normalized information about building where th...        49.837935  \n",
              "2  Normalized information about building where th...        49.837935  \n",
              "3  Normalized information about building where th...        48.845926  \n",
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              "5  Normalized information about building where th...        48.845517  \n",
              "6  Normalized information about building where th...        48.337864  \n",
              "7  Normalized information about building where th...        47.462345  \n",
              "8       What kind of occupation does the client have        31.369071  \n",
              "9         Normalized score from external data source        19.864707  "
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              "      <th>8</th>\n",
              "      <td>OCCUPATION_TYPE</td>\n",
              "      <td>What kind of occupation does the client have</td>\n",
              "      <td>31.369071</td>\n",
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              "      <td>EXT_SOURCE_3</td>\n",
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          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "While features with percentage_null as high as 47% should be dropped, we were uncertain whether one with 31% should be dropped as well. This is why we decided to plot the feature under consideration, occupation_type, against the rate at which the target variable = 1."
      ],
      "metadata": {
        "id": "8JE2J4bnnatf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# creating aggregate occupation dataframe to show all occupation types\n",
        "# we will use this in our EDA below\n",
        "agg_occupation = application_cleaned_df.groupby([\"OCCUPATION_TYPE\",\"TARGET\"]).size()\n",
        "agg_occupation_df = agg_occupation.to_frame().reset_index()\n",
        "agg_occupation_df = agg_occupation_df.rename(columns = {0: 'Total'})\n",
        "\n",
        "yes_rate = []\n",
        "occupation = list(set(agg_occupation_df[\"OCCUPATION_TYPE\"].to_list()))\n",
        "\n",
        "for i in range(0, len(agg_occupation), 2):\n",
        "  total = agg_occupation[i] + agg_occupation[i + 1]\n",
        "  amount = agg_occupation[i + 1]\n",
        "  percentage = (amount/total) * 100\n",
        "  yes_rate.append(percentage)\n",
        "\n",
        "# makes plot\n",
        "plt.figure(figsize = (12, 9))\n",
        "plt.bar(occupation, yes_rate)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.xlabel(\"Occupation\")\n",
        "plt.ylabel(\"Rate Target = 1\")\n",
        "plt.title(\"Occupation vs. Target Rate\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "8CsgyBT7IbcX",
        "outputId": "88a81465-229f-4c08-98d2-a5fe118390a9"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the bar plot above we see that the target = 1 rate varies largely across different occupation types justfying that it should remain in the data set, despite its relatively high percentage null value."
      ],
      "metadata": {
        "id": "HRg41fgdnzgb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# drops columns: All columns that have high percentage null except occupation type\n",
        "to_drop_null = percentages_df.head(n=8)[\"column_name\"].tolist()\n",
        "application_cleaned_df_rdoc_rnull = application_cleaned_df.drop(columns = to_drop_null)\n",
        "\n",
        "# drops null values\n",
        "application_model_df = application_cleaned_df_rdoc_rnull.dropna()"
      ],
      "metadata": {
        "id": "_6PJn9jjJEsL"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we will look into the FLAG columns to see if their presence in the dataset is essential as opposed to the FLAG_DOCUMENT columns we previously dropped. "
      ],
      "metadata": {
        "id": "oT1lpiTEjKVp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "4nhA6VMFajdO"
      },
      "outputs": [],
      "source": [
        "columns = application_model_df.columns.tolist()\n",
        "flag_columns_Y = []\n",
        "flag_columns_1 = []\n",
        "no_list = []\n",
        "yes_list = []\n",
        "\n",
        "for column in columns:\n",
        "  if column[0:4] == 'FLAG':\n",
        "    if(application_model_df[column][0] == \"N\" or \n",
        "       application_model_df[column][0] == \"Y\"):\n",
        "      flag_columns_Y.append(column)\n",
        "    else:\n",
        "      flag_columns_1.append(column)\n",
        "\n",
        "flag_columns_1.remove(\"FLAG_MOBIL\")\n",
        "\n",
        "for column in flag_columns_Y: \n",
        "  total_agg = application_model_df.groupby([column,'TARGET']).size()\n",
        "  perc_no_1 = total_agg[1]/(total_agg[0] + total_agg[1])\n",
        "  perc_yes_1 = total_agg[3]/(total_agg[2] + total_agg[3])\n",
        "  no_list.append(perc_no_1)\n",
        "  yes_list.append(perc_yes_1)\n",
        "\n",
        "for column in flag_columns_1:\n",
        "  total_agg = application_model_df.groupby([column,'TARGET']).size()\n",
        "  perc_no_1 = total_agg[0][1]/(total_agg[0][0] + total_agg[0][1])\n",
        "  perc_yes_1 = total_agg[1][1]/(total_agg[1][0] + total_agg[1][1])\n",
        "  no_list.append(perc_no_1)\n",
        "  yes_list.append(perc_yes_1)\n",
        "\n",
        "flag_columns = flag_columns_Y + flag_columns_1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "outputId": "8ea80c32-4e90-45b1-a3f8-d66a2d82c9db",
        "id": "hdMopcs5ajdP"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Citation: https://www.geeksforgeeks.org/plotting-multiple-bar-charts-using-matplotlib-in-python/\n",
        "#Owns Car, Owns Real Estate, Provided Information\n",
        "X_axis = np.arange(len(flag_columns))\n",
        "\n",
        "plt.subplots(figsize = (10, 8))  \n",
        "plt.bar(X_axis - 0.2, yes_list, 0.4, label = 'Yes')\n",
        "plt.bar(X_axis + 0.2, no_list, 0.4, label = 'No')\n",
        "\n",
        "plt.xticks(X_axis, flag_columns)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.xlabel(\"Groups\")\n",
        "plt.ylabel(\"Rate of Target = 1\")\n",
        "plt.title(\"Percentage of Issues with Default by FLAGS\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The bar plot above supports our exploration of the FLAG variables that indicate Yes and No for areas including whether a loan reciever owns a car, has provided a phone number, and so forth. Our observations were that the differences in rate of target = 1 was sufficient for the features to be explored in the modeling stage."
      ],
      "metadata": {
        "id": "UpVe1_EEoHQi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Distribution Visualizations\n",
        "Now that we have dropped some of the columns, we want to better understand the data by plotting the distributions of some of the variables in our dataset. These variable denote the gender of the applicants, the number of applicants that faced payment difficulties, the contract type of the loan, and so on. The remaining variables are outlined below. "
      ],
      "metadata": {
        "id": "SqqyE7gLfviQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# function to store values, with specific column from dataframe and rows to drop (ill-formatted)\n",
        "\n",
        "def create_plot_type(column, drops):\n",
        "  agg_column = application_cleaned_df.groupby([column,\"TARGET\"]).size()\n",
        "  agg_column_df = agg_column.to_frame().reset_index()\n",
        "  agg_column_df = agg_column_df.rename(columns = {0: 'Total'})\n",
        "  agg_column_df = agg_column_df.drop(labels = drops, axis = 0)\n",
        "\n",
        "  # list manipulation\n",
        "  yes_rate = []\n",
        "  occupation = list(set(agg_column_df[column].to_list()))\n",
        "  lists = agg_column.to_list()\n",
        "  for drop in drops:\n",
        "    del lists[drop]\n",
        "  agg_column = lists\n",
        "\n",
        "  # iterates through and appens to list\n",
        "  for i in range(0, len(agg_column), 2):\n",
        "    total = agg_column[i] + agg_column[i + 1]\n",
        "    amount = agg_column[i + 1]\n",
        "    percentage = (amount/total) * 100\n",
        "    yes_rate.append(percentage)\n",
        "\n",
        "  return (occupation, yes_rate)\n",
        "\n",
        "# saving values in list for plot\n",
        "x1, y1 = create_plot_type(\"NAME_TYPE_SUITE\", [])\n",
        "x2, y2 = create_plot_type(\"NAME_HOUSING_TYPE\", [])\n",
        "x3, y3 = create_plot_type(\"NAME_EDUCATION_TYPE\", [])\n",
        "x4, y4 = create_plot_type(\"OCCUPATION_TYPE\", [])\n",
        "x5, y5 = create_plot_type(\"WEEKDAY_APPR_PROCESS_START\", [])\n",
        "x6, y6 = create_plot_type(\"ORGANIZATION_TYPE\", [])\n",
        "x7, y7 = create_plot_type(\"NAME_INCOME_TYPE\", [0,9])\n",
        "x8, y8 = create_plot_type(\"NAME_FAMILY_STATUS\", [8])"
      ],
      "metadata": {
        "id": "-aoN248qIm7Z"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below, we will create plots for each of the 8 categorical variables and compare each with the Target Rate = 1 values. This will help with our analysis below on deciding which categorical variables to drop to improve our model."
      ],
      "metadata": {
        "id": "f3m6gpa31b6n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize = (12, 6))\n",
        "plt.bar(x1, y1)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.xlabel(\"NAME_TYPE_SUITE\")\n",
        "plt.ylabel(\"Rate Target = 1\")\n",
        "plt.title(\"NAME_TYPE_SUITE vs. Target Rate\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "Wcf-k4VptIBJ",
        "outputId": "3b963634-e629-4812-867a-a1ea5151beee"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize = (12, 6))\n",
        "plt.bar(x2, y2)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.xlabel(\"NAME_HOUSING_TYPE\")\n",
        "plt.ylabel(\"Rate Target = 1\")\n",
        "plt.title(\"NAME_HOUSING_TYPE vs. Target Rate\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "Sq3oCZlc1ycq",
        "outputId": "47d49162-0d36-4453-8921-25afe82ad9b1"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize = (12, 6))\n",
        "plt.bar(x3, y3)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.xlabel(\"NAME_EDUCATION_TYPE\")\n",
        "plt.ylabel(\"Rate Target = 1\")\n",
        "plt.title(\"NAME_EDUCATION_TYPE vs. Target Rate\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "_VJlR8DV1875",
        "outputId": "ff77aa12-9a9c-480c-aaf8-b8c5bad0bf68"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize = (12, 6))\n",
        "plt.bar(x4, y4)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.xlabel(\"OCCUPATION_TYPE\")\n",
        "plt.ylabel(\"Rate Target = 1\")\n",
        "plt.title(\"OCCUPATION_TYPE vs. Target Rate\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "s48EFn8r2Fm5",
        "outputId": "c2409e55-ea74-4ecd-d481-1ba361f95e3a"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize = (12, 6))\n",
        "plt.bar(x5, y5)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.xlabel(\"WEEKDAY_APPR_PROCESS_START\")\n",
        "plt.ylabel(\"Rate Target = 1\")\n",
        "plt.title(\"WEEKDAY_APPR_PROCESS_START vs. Target Rate\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "Zxt7hcAu2MsS",
        "outputId": "d546771b-0e41-4c8f-edf0-0052036b4bbc"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize = (20, 6))\n",
        "plt.bar(x6, y6)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.xlabel(\"ORGANIZATION_TYPE\")\n",
        "plt.ylabel(\"Rate Target = 1\")\n",
        "plt.title(\"ORGANIZATION_TYPE vs. Target Rate\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "mukAPT0F2dBa",
        "outputId": "6a4cb62e-3c30-4ab2-db43-38d84ae3f8bf"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize = (12, 6))\n",
        "plt.bar(x7, y7)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.xlabel(\"NAME_INCOME_TYPE\")\n",
        "plt.ylabel(\"Rate Target = 1\")\n",
        "plt.title(\"NAME_INCOME_TYPE vs. Target Rate\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "cQNdx85H2mlS",
        "outputId": "2f02237f-9872-438f-b0bf-dc646430d653"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize = (12, 6))\n",
        "plt.bar(x8, y8)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.xlabel(\"NAME_FAMILY_STATUS\")\n",
        "plt.ylabel(\"Rate Target = 1\")\n",
        "plt.title(\"NAME_FAMILY_STATUS vs. Target Rate\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "xb1Iv5Lx2vmj",
        "outputId": "349fb846-c402-473c-c1ad-fe1a47479df0"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The bar charts above signify that **OCCUPATION_TYPE, WEEKDAY_APPR_PROCESS_START, ORGANIZATION_TYPE, EXT_SOURCE_2** are categorical variables that we should consider dropping. Weekdays, have Target Rate = 1 values that are almost identical, implying they are insignificant. The other 3 were dropped to address overfitting. "
      ],
      "metadata": {
        "id": "jRwx87S0q1mt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# drops some columns from above - too many values so reduces overfitting, or they simply make no difference\n",
        "application_model_df_dropped = application_model_df.drop(columns = [\"OCCUPATION_TYPE\", \"WEEKDAY_APPR_PROCESS_START\", \"ORGANIZATION_TYPE\", \"EXT_SOURCE_2\"])"
      ],
      "metadata": {
        "id": "WHXsWXiPJO1p"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's look at the distribution of some other variables. "
      ],
      "metadata": {
        "id": "451D5XT1j_e8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# makes plot \n",
        "plt.figure(figsize = (12, 9))\n",
        "target_perc = (application_cleaned_df.groupby('TARGET').size() / \n",
        "               application_cleaned_df.groupby('TARGET').size().sum()) * 100\n",
        "ax = target_perc.plot(kind='bar')\n",
        "plt.title('Percentages for Target Category (Payment Difficulties)')\n",
        "plt.xlabel('Target')\n",
        "plt.ylabel('Percentage')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "VMiKN8YPjkKi",
        "outputId": "080d500a-8512-4328-e773-b225d9aa8b35"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see by the plot shown above, the vast majority of the clients in the dataset have not faced any payment difficulties as given by the 0 value of the TARGET variable. Only a small portion (~15% of the clients) have faced payment difficulties as given by the 1 value of the TARGET variable. These results clearly show a considerable imbalance in our data which is skewed more towards clients not having payment difficulties; this imbalance needs to be addressed in our modeling in order to account for any possible misinterpretations. "
      ],
      "metadata": {
        "id": "AlEt7wlkse4k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualize the percentage of each category to gain more insight \n",
        "#into the demographics of the applicants\n",
        "plt.figure(figsize = (12, 9))\n",
        "gender_perc = (application_cleaned_df.groupby('CODE_GENDER').size() / \n",
        "               application_cleaned_df.groupby('CODE_GENDER').size().sum()) * 100\n",
        "ax = gender_perc.plot(kind='bar', color = 'red')\n",
        "plt.title('Percentages for the Gender Category of all Clients')\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('Percentage')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "Gcn2OLqFgEt6",
        "outputId": "1cd8b21a-3c7c-4fb4-b500-a119f1573523"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see by the plot shown above, ~65% of the clients are female and ~35% of the clients are male. There is a tiny portion of clients that have chose not to identify their gender, labeled as XNA. "
      ],
      "metadata": {
        "id": "Es_amsKJqzHh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# makes plot\n",
        "plt.figure(figsize = (12, 9))\n",
        "contract_perc = (application_cleaned_df.groupby('NAME_CONTRACT_TYPE').size() / \n",
        "                 application_cleaned_df.groupby('NAME_CONTRACT_TYPE').size().sum())\n",
        "ax = contract_perc.plot(kind='bar', color = 'g')\n",
        "plt.title('Percentages for Contract Type')\n",
        "plt.xlabel('Contract Type')\n",
        "plt.ylabel('Percentage')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "id": "lzxIKtG9jq8r",
        "outputId": "27371d14-603e-44c5-9e65-9051926a4d82"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see by the plot shown above, the vast majority of the clients are have requested cash loans, and the remaining clients have requesting revolving loans. "
      ],
      "metadata": {
        "id": "scFVy4WKungg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Income and AMT Credit Distributions\n",
        "Now, we would like to take a look at the distributions of the Income and AMT Credit variables for the clients present in our dataset to see if there are any commonalities across the dataset. We chose to divide the Income and AMT Credit values into 'bins' that represent the bracket ranges (differing by $100k each). This allows us to gain a better understanding of which 'brackets' have the majority of clients."
      ],
      "metadata": {
        "id": "AMkrcH9GwG0m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Understand the distribution of income for applicants\n",
        "application_cleaned_df_dist = application_cleaned_df.copy()\n",
        "application_cleaned_df_dist['AMT_INCOME_RANGES'] = application_cleaned_df_dist['AMT_INCOME_TOTAL'] / 100000\n",
        "\n",
        "# We want to categorize the incomes into these ranges present\n",
        "range_numbers = [0,1,2,3,4,5,6,7,8,9,10,11]\n",
        "range_labels = ['0-100K','100K-200K', '200k-300k','300k-400k','400k-500k','500k-600k','600k-700k','700k-800k','800k-900k','900k-1M', '1M Above']\n",
        "\n",
        "# Shows distribution of income as percentage of all incomes included in the dataframe\n",
        "application_cleaned_df_dist['AMT_INCOME_RANGES'] = pd.cut(application_cleaned_df_dist['AMT_INCOME_RANGES'], range_numbers, labels = range_labels)\n",
        "income_ranges = application_cleaned_df_dist.groupby(['AMT_INCOME_RANGES']).count() / application_cleaned_df_dist.groupby(['AMT_INCOME_RANGES']).count().sum() * 100\n",
        "income_ranges = income_ranges.reset_index()\n",
        "application_cleaned_df_dist['AMT_INCOME_RANGES'].value_counts() / application_cleaned_df_dist['AMT_INCOME_RANGES'].value_counts().sum() * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mTmAU-PJvzix",
        "outputId": "b6e4c5f3-1929-46fd-e709-f6902268234f"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100K-200K    50.722373\n",
              "200k-300k    21.196701\n",
              "0-100K       20.755751\n",
              "300k-400k     4.782961\n",
              "400k-500k     1.742940\n",
              "500k-600k     0.348915\n",
              "600k-700k     0.283468\n",
              "800k-900k     0.099398\n",
              "700k-800k     0.053994\n",
              "900k-1M       0.008181\n",
              "1M Above      0.005318\n",
              "Name: AMT_INCOME_RANGES, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Graphing the ranges\n",
        "plt.figure(figsize=(12, 9))\n",
        "plt.bar(income_ranges['AMT_INCOME_RANGES'], income_ranges['TARGET'], color = 'b')\n",
        "plt.xticks(rotation = 90)\n",
        "plt.xlabel(\"Income Bracket\")\n",
        "plt.ylabel(\"Percentage Among All Income Shares\")\n",
        "plt.title(\"Distribution of Income for Loan Applicants\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "azH-XR2rv5Fg",
        "outputId": "374cc32f-d065-42bd-8651-f54e9f8655f1"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see from the plot shown above, more than 50% of loan applicants have incomes in the range of 100K-200K, constituting the vast majority across all other income brackets. Note that almost 92% of loan applicants have income less than 300K, which is consistent with the logical correlation that people with higher incomes are less likely to take out loans. "
      ],
      "metadata": {
        "id": "XWZXKBxQx6XI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Understand the distrubution of AMT Credit for applicants\n",
        "application_cleaned_df_dist['AMT_CREDIT_RANGES'] = application_cleaned_df_dist['AMT_CREDIT'] / 100000\n",
        "\n",
        "# We want to categorize the incomes into these ranges present\n",
        "range_numbers = [0,1,2,3,4,5,6,7,8,9,10,100]\n",
        "range_labels = ['0-100K','100K-200K', '200k-300k','300k-400k','400k-500k','500k-600k','600k-700k','700k-800k','800k-900k','900k-1M', '1M Above']\n",
        "\n",
        "# Shows distribution of credit brackets as percentage of all credit brackets included in the dataframe\n",
        "application_cleaned_df_dist['AMT_CREDIT_RANGES'] = pd.cut(application_cleaned_df_dist['AMT_CREDIT_RANGES'], range_numbers, labels = range_labels)\n",
        "credit_ranges = application_cleaned_df_dist.groupby(['AMT_CREDIT_RANGES']).count() / application_cleaned_df_dist.groupby(['AMT_CREDIT_RANGES']).count().sum() * 100\n",
        "credit_ranges = credit_ranges.reset_index()\n",
        "application_cleaned_df_dist['AMT_CREDIT_RANGES'].value_counts() / application_cleaned_df_dist['AMT_CREDIT_RANGES'].value_counts().sum() * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BlRffHLhv-VY",
        "outputId": "0593641c-d02c-4e34-ef0a-270c1adb5eb1"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "200k-300k    17.855756\n",
              "1M Above     16.244099\n",
              "500k-600k    11.093581\n",
              "400k-500k    10.435102\n",
              "100K-200K     9.794609\n",
              "300k-400k     8.593325\n",
              "600k-700k     7.783614\n",
              "800k-900k     7.103472\n",
              "700k-800k     6.221005\n",
              "900k-1M       2.913899\n",
              "0-100K        1.961538\n",
              "Name: AMT_CREDIT_RANGES, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Graphing the ranges\n",
        "plt.figure(figsize=(12, 9))\n",
        "plt.bar(credit_ranges['AMT_CREDIT_RANGES'], credit_ranges['TARGET'], color = 'orange')\n",
        "plt.xticks(rotation = 90)\n",
        "plt.xlabel(\"AMT Credit Bracket\")\n",
        "plt.ylabel(\"Percentage Among All AMT Credit Shares\")\n",
        "plt.title(\"Distrubtion of AMT Credit for Loan Applicants\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "e2-vCMkxwDo8",
        "outputId": "08e54c35-0f37-4388-b989-c04d7104450d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There seems to be a decent variation in terms of the distribution of the amount of loan requested by clients; however, about 16% of loan applicants have taken out a loan which amounts to more than 1M, which was quite surprising to us! Let's look into that for a moment."
      ],
      "metadata": {
        "id": "EUF7ZokQ1ER9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# distribution of payment difficulties within >1M class\n",
        "one_mil_loans = application_cleaned_df_dist[application_cleaned_df_dist['AMT_CREDIT_RANGES'] == '1M Above']\n",
        "one_mil_loans_perc = (one_mil_loans.groupby(['TARGET']).size() / one_mil_loans.groupby(['TARGET']).size().sum()) * 100\n",
        "one_mil_loans_perc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tvU7MuY01mGM",
        "outputId": "b4f4fd34-f2c8-4609-dc82-224aef3a7f73"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TARGET\n",
              "0    94.099441\n",
              "1     5.900559\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Among the applicants who have taken out a loan which amounts to more than 1M, the vast majority (~ 94%) have not had any payment difficulties, whereas only a tiny portion (~ 6%) have had some sort of difficulties in paying back the loan. Financial institutions that gives out a loan for an amount that exceeds 1M have definitely factored in other attributes of the applicant that gave them some level of security in having the loan paid back. That is, for a bank to give out a loan of such amount, it must have accounted for some other attributes that would push them to minimize the risk of such loan. These results motivate the need to do further modeling in order to identify these underlying attributes that affect loan approvals, which serves as a gateway to our modeling phase!"
      ],
      "metadata": {
        "id": "05vmwl0m3GKm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hypothesis given EDA"
      ],
      "metadata": {
        "id": "y7hGJ50x7Ql8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Following our EDA and the included bar plots we made the following overlying observations:\n",
        "\n",
        "1.   We see large variability within the target = 1 rate for different **education types** indicating that it could **potentially be a large contributor** in the classification process. Intuitively, this makes sense as higher education enables more financial stability and responsibility.\n",
        "2.   We see large variability within the target = 1 rate for different **income types** indicating that it could **potentially be a large contributor** in the classification process. Intuitively, this makes sense as different sources of income result in varying funding and allocation of finances.\n",
        "3.   While we decided to keep **flag** variables such as FLAG_MOBILE and FLAG_CAR given the differences we saw within EDA, we believe that they **shouldn't be a large** contributing factor given that it is hard to intuitively connect them with loan payments based on our preliminary knowledge of loan approvals. "
      ],
      "metadata": {
        "id": "UyZoa0-i7Y2T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Engineering\n",
        "We will now extract features from our raw data via data mining techniques in order to improve the performance of our following algorithms. Given that we are dealing with some categorical variables, we need to do some **one-hot encoding** in order to be able to use these variables in algorithms that deal with numerical variables. The following code blocks identify the categorical variables in our dataset, and turn them into their binary representations for each respective category using one-hot encoding."
      ],
      "metadata": {
        "id": "h3S9WaVQK6qG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# function to help with hot encoding\n",
        "def hotencode_df(df):\n",
        "  df_hotencode = df\n",
        "  columns = df_hotencode.columns.to_list()\n",
        "  df = df_hotencode.dtypes\n",
        "  df = df.reset_index().rename(columns={0: 'type'})\n",
        "\n",
        "  # keeps track of object types\n",
        "  obj_list = []\n",
        "  for index, row in df.iterrows():\n",
        "      if (row['type'] == object):\n",
        "        obj_list.append(row['index'])\n",
        "  \n",
        "  # makes yes = 1 and no = 0\n",
        "  for obj in obj_list: \n",
        "    if((set(df_hotencode[obj].to_list())) == set([\"N\",\"Y\"])):\n",
        "      df_hotencode[obj] = df_hotencode[obj].replace(['Y'],1)\n",
        "      df_hotencode[obj] = df_hotencode[obj].replace(['N'],0)\n",
        "      obj_list.remove(obj)\n",
        "    if((set(df_hotencode[obj].to_list())) == set([\"1\",\"0\"])):\n",
        "      df_hotencode[obj] = df_hotencode[obj].replace(['1'],1)\n",
        "      df_hotencode[obj] = df_hotencode[obj].replace(['0'],0)\n",
        "      obj_list.remove(obj)\n",
        "\n",
        "  # for loop to perform hot encoding\n",
        "  for col in obj_list:\n",
        "    #creating instance of one-hot-encoder\n",
        "    encoder = OneHotEncoder()\n",
        "    #perform one-hot encoding on 'team' column \n",
        "    encoder_df = pd.DataFrame(encoder.fit_transform(df_hotencode[[col]]).toarray())\n",
        "    values = list(set(df_hotencode[col].to_list()))\n",
        "    frame = {}\n",
        "    for i, value in enumerate(values):\n",
        "      frame[i] = col + \" \" + value\n",
        "    encoder_df = encoder_df.rename(frame, axis = 1)\n",
        "    for enc_col in encoder_df.columns.to_list():\n",
        "       df_hotencode[enc_col] = encoder_df[enc_col].to_list()\n",
        "    #merge one-hot encoded columns back with original DataFrame\n",
        "    df_hotencode = df_hotencode.drop(columns = col)\n",
        "  \n",
        "  return df_hotencode # returns hot encoded dataframe"
      ],
      "metadata": {
        "id": "nytPy1h-LVb4"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# gets features and labels of a dataframe\n",
        "def feature_label(df):\n",
        "  features = df.drop(columns = \"TARGET\") # drops TARGET variable\n",
        "  labels = df['TARGET']\n",
        "  labels = labels.astype(int)\n",
        "  return features, labels"
      ],
      "metadata": {
        "id": "OXRVrgrrL8vJ"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_df = application_model_df_dropped.copy()"
      ],
      "metadata": {
        "id": "C1bun2hBbhtH"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test-Train Split\n",
        "We will do a test-train split such that 80% of the data is used to train our models, whereas 20% of the data is used to test our models. Note that we use our TARGET variable, which represents having payment difficulties, as our label, and the remaining variables as the features. "
      ],
      "metadata": {
        "id": "TnphrQDrMKGq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# hot encodes dataframe and finds feature and labels\n",
        "df_hotencode = hotencode_df(model_df)\n",
        "features, labels = feature_label(df_hotencode)\n",
        "\n",
        "# test-train split\n",
        "x_train, x_test, y_train, y_test = train_test_split(features, labels, random_state = 42, test_size = 0.2)"
      ],
      "metadata": {
        "id": "7wa3IKRwMLUR"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SMOTE\n",
        "\n",
        "As we have noted earlier, our data is substantially imbalanced with regards to the TARGET variable. The challenge of working with imbalanced datasets is that most machine learning techniques will ignore, and in turn have poor performance on, the minority class, although typically it is performance on the minority class that is most important. Therefore, we will use **Synthetic Minority Oversampling Technique (SMOTE)** in order to balance the data by adding duplicating examples in the minority class that don’t necessarily add any new information to the model. "
      ],
      "metadata": {
        "id": "AV3d0rYlMnil"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pie chart to display inequal distribution before smoting\n",
        "labels = 'Target = 1', 'Target = 0'\n",
        "sizes = [sum(y_train == 1), sum(y_train == 0)]\n",
        "\n",
        "fig1, ax1 = plt.subplots(figsize = (10, 5))\n",
        "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax1.axis('equal')\n",
        "plt.title(\"Distribution of Target Results\")\n",
        "plt.show() # show pie chart"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "R3tFquJrSGE8",
        "outputId": "5e2ccc73-9ceb-488a-d01a-211ddf6d5b76"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As shown above, our dataset is heavily imbalanced. So, we perform SMOTE below."
      ],
      "metadata": {
        "id": "n8XkSKnVSr6J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# shows counts before\n",
        "print(\"Before OverSampling, counts of label '1': {}\".format(sum(y_train == 1)))\n",
        "print(\"Before OverSampling, counts of label '0': {} \\n\".format(sum(y_train == 0)))\n",
        "\n",
        "# smotes data\n",
        "sm = SMOTE()\n",
        "x_train_smote, y_train_smote = sm.fit_resample(x_train, y_train)\n",
        "\n",
        "# shows shape\n",
        "print('After OverSampling, the shape of train_X: {}'.format(x_train_smote.shape))\n",
        "print('After OverSampling, the shape of train_y: {} \\n'.format(y_train_smote.shape))\n",
        "  \n",
        "# shows counts after\n",
        "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_smote == 1)))\n",
        "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train_smote == 0)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ayKoKff3MtFQ",
        "outputId": "4eacfcf4-ff29-45f3-9a22-4352ea090231"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before OverSampling, counts of label '1': 9039\n",
            "Before OverSampling, counts of label '0': 97641 \n",
            "\n",
            "After OverSampling, the shape of train_X: (195282, 75)\n",
            "After OverSampling, the shape of train_y: (195282,) \n",
            "\n",
            "After OverSampling, counts of label '1': 97641\n",
            "After OverSampling, counts of label '0': 97641\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pie chart to display better distribution after smoting\n",
        "labels = 'Target = 1', 'Target = 0'\n",
        "sizes = [sum(y_train_smote == 1), sum(y_train_smote == 0)]\n",
        "\n",
        "fig1, ax1 = plt.subplots(figsize = (10, 5))\n",
        "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax1.axis('equal') \n",
        "plt.title(\"Distribution of Target Results After Smote\")\n",
        "plt.show() # show pie chart"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "JyVcdYxfhrR9",
        "outputId": "1aed9320-0ff9-4780-f50f-bba818b02544"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWTtWaOUkokd"
      },
      "source": [
        "# Modeling\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this section, models are created to predict the target variable. The baseline model is built using decision trees. We then take an alternative approach within supervised learning to explore logistical regression models. Findings in this section include the value of taking a trees approach for this dataset and approaches to bagging that help improve scores.\n",
        "\n",
        "For each model, we look into the training accuracy, testing accuracy, and the AUC of the ROC plot. Measuring training and testing accuracy helps recognize any underfitting or overfitting. In particular, the AUC score of the ROC curve will be used to assess whether the model is being \"improved\" and direct the following model development."
      ],
      "metadata": {
        "id": "ojaWU7iUlCQw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Decision Trees"
      ],
      "metadata": {
        "id": "S7IxXnHjBiAQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Baseline Model: Initial Model"
      ],
      "metadata": {
        "id": "K18sGZnc1Ink"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We know that the baseline model can't only predict the majority class because of the imbalance present in our data. While smoting balances the training set, it makes no impact on our testing data.\n",
        "\n",
        "This is why we decided to create a decision tree only using categorical variables as our baseline model. This was specifically given because of the large presence of features as a result of one hot encoding."
      ],
      "metadata": {
        "id": "9ozOFUJPm7D9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# hot encodes dataframe\n",
        "hotencode_model_df = hotencode_df(model_df)\n",
        "columns = hotencode_model_df.columns.to_list()\n",
        "\n",
        "# finds values of 0 or 1 only\n",
        "obj_list = []\n",
        "for column in columns: \n",
        "  if(set(hotencode_model_df[column].to_list()) == {0,1}):\n",
        "    obj_list.append(column)\n",
        "\n",
        "model_category_df = hotencode_model_df[obj_list]\n",
        "\n",
        "# re-smote\n",
        "features, labels = feature_label(model_category_df)\n",
        "x_train, x_test, y_train, y_test = train_test_split(features, labels, random_state = 42, test_size = 0.2)\n",
        "sm = SMOTE()\n",
        "x_train_smote, y_train_smote = sm.fit_resample(x_train, y_train)"
      ],
      "metadata": {
        "id": "HtDVCPH9uVeC"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creates decision tree classifier\n",
        "clf = DecisionTreeClassifier()\n",
        "clf = clf.fit(x_train_smote, y_train_smote)\n",
        "\n",
        "# sets variable values to predictions, accuracies, and scores\n",
        "y_pred1 = clf.predict(x_test)\n",
        "y_pred11 = clf.predict_proba(x_test)[:, 1]\n",
        "train_accuracy1 = accuracy_score(y_train_smote, clf.predict(x_train_smote))\n",
        "test_accuracy1 = accuracy_score(y_test, y_pred1)\n",
        "roc1 = roc_auc_score(y_test, clf.predict_proba(x_test)[: , 1])\n",
        "\n",
        "# prints metrics\n",
        "print(\"Training Accuracy: \" + str(train_accuracy1))\n",
        "print(\"Testing Accuracy: \" + str(test_accuracy1)) \n",
        "print(\"ROC Score: \" + str(roc1))\n",
        "\n",
        "fpr1, tpr1, _ = metrics.roc_curve(y_test, clf.predict_proba(x_test)[:,1]) # used for line chart"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YnkLziCViUn2",
        "outputId": "28da5421-d246-4e21-ccd7-93f0f32899ed"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.7229186509765365\n",
            "Testing Accuracy: 0.6183352080989877\n",
            "ROC Score: 0.5409691287487596\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the scores above, we observed that both our testing and training accuracies were relatively low, indicating underfitting. This will be adressed by increasing the complexity of the model and including a greater number of features. More specifically, while the baseline model included only categorical variables, we will now also include the continuous variables."
      ],
      "metadata": {
        "id": "WIMnjL3BrtA-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Baseline Model: Improvement by finding optimal max_depth"
      ],
      "metadata": {
        "id": "UdnWxwvi1NEy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we decided to incrementally change the model's max_depth in order to find the optimal depth that would maximize our testing accuracy."
      ],
      "metadata": {
        "id": "7IFEc6wJpupK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# re-smote\n",
        "features, labels = feature_label(hotencode_df(model_df))\n",
        "x_train, x_test, y_train, y_test = train_test_split(features, labels, random_state = 42, test_size = 0.2)\n",
        "sm = SMOTE()\n",
        "x_train_smote, y_train_smote = sm.fit_resample(x_train, y_train)"
      ],
      "metadata": {
        "id": "ko1peMS8sEca"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "-tci6MvIGISF"
      },
      "outputs": [],
      "source": [
        "# stores lists of accuracies for plot\n",
        "training_accuracy = []\n",
        "testing_accuracy = []\n",
        "roc_scores = []\n",
        "depths = []\n",
        "\n",
        "# iterates through max_depths and stores values in lists above\n",
        "for i in range(1,60,5):\n",
        "  clf = DecisionTreeClassifier(random_state = 16, max_depth = i)\n",
        "  clf = clf.fit(x_train_smote,y_train_smote)\n",
        "  training_accuracy.append(metrics.accuracy_score(y_train_smote, clf.predict(x_train_smote)))\n",
        "  testing_accuracy.append(metrics.accuracy_score(y_test, clf.predict(x_test)))\n",
        "  roc_scores.append(roc_auc_score(y_test, clf.predict_proba(x_test)[: , 1]))\n",
        "  depths.append(i)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below, we plot the training and testing accuracies from above."
      ],
      "metadata": {
        "id": "snh-Ktcb5Gkg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make plots\n",
        "plt.subplots(figsize = (10, 6))\n",
        "plt.plot(depths, training_accuracy)\n",
        "plt.xlabel(\"Depths\")\n",
        "plt.ylabel(\"Training Accuracy\")\n",
        "plt.title(\"Training Accuracy over Depth\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "dgZRKp1145O_",
        "outputId": "7114444c-b476-4beb-af41-30220be205d3"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make plots\n",
        "plt.subplots(figsize = (10, 6))\n",
        "plt.plot(depths, testing_accuracy)\n",
        "plt.xlabel(\"Depths\")\n",
        "plt.ylabel(\"Testing Accuracy\")\n",
        "plt.title(\"Testing Accuracy over Depth\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "aKKVjRAb4-hP",
        "outputId": "3ac225f4-f290-46d2-ddf6-752551281468"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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mgGNRUV7GFa+ewuKPz+MdZ03m9kfXMe9Li/nRIy/Q0tpW6PLMzKxIOJxZjyq1JoCjMXxgFZ+77BTu/vBrmTlmMP/yq5Vc8s3f84fnthS6NDMzKwIOZ9Zj2psA/r95J5RkE0BXnTR2CD95/9l85x2vYOe+Ft5+82N88PZl1G3bU+jSzMysgBzOrEc0tbRx7a+eZNKIAfy/86YVupyiIYmLTh3Lbz92Hh87fwaLVzfw+q88yJfvW82eppZCl2dmZgXgcGY94gcPP89zDbv7VBNAV1RXlvOh10/ntx87jwUnj+GbD6zhdV96kF8tX+9bb5iZ9TEOZ5a6g00Ax/W5JoCuGjesP9942+n89AOvYtTgKv7+zuX89Xcf4Yn6xkKXZmZmPcThzFL3+QNNALMKXUqv8copI/jVVa/hC28+lee37OZNN/6ef/rZCrbs2l/o0szMLGUOZ5aqh9ds4W43ARyV8jJx+ZmTWPQP83jvOVP5+R/rmf8fi7n5obU0tfjWG2ZmpcrhzFLjJoDuMaS6kk9fMot7rzmXM6YM54Z7nmLB1x9i0erNhS7NzMxS4HBmqbnVTQDd6viaQdz2njO59d1ziYD3/GAJ7/nB46xt2FXo0szMrBs5nFkqNjbu5RtuAkjF62Yex70fOZdPXTyTJS9s58KvPcS/3vMUO/c1F7o0MzPrBg5nlgo3AaSrqqKMK889ngc+fh5/efp4bv7dWuZ/aTF3Lamjrc233jAz680czqzb/f5ZNwH0lNGDq/niW2bzq6vOYdKIAfzjz1dw6Y0Ps2zdtkKXZmZmR8nhzLpVU0sb1y10E0BPO23CMH7+wVfztb+Zw+ad+/ir7zzCR+78E5sa9xW6NDMz6yKHM+tWbgIoHElcdvp4HvjYPK6afzz3PLmJ+V9azLceeJZ9za2FLs/MzPLkcGbdxk0AxWFgvwr+4cKZ/Oaa8zh3xii+dN8znP/VB/n1k5v8KCgzs17A4cy6jZsAisukkQP43t/O5Y73nUX/ynI+cPsy3nHLY6zetLPQpZmZ2WE4nFm3aG8CuGq+mwCKzTknjOKeD7+Wz77pZFZu2MFFX3+Ia3/1JJk9TYUuzczMOuFwZsesqaWNaxc+yeSRA7jyXDcBFKOK8jKuePUUFn98Hu84azK3P7qOeV9azI8eeYGWVj8KysysmDic2TG79eHnWduwm+ve6CaAYjd8YBWfu+wU7v7wa5k5ZjD/8quVXPLN3/OH57YUujQzM0ukGs4kLZC0WtIaSZ/oZPskSYsk/UnSCkkXJ+unSNoraXny9d0067SjtyGTbQI4f5abAHqTk8YO4SfvP5vvvOMV7NzXwttvfowP3r6Mum17Cl2amVmfV5HWG0sqB24EzgfqgSWSFkbEqpzdPg3cFRHfkTQLuAeYkmx7LiLmpFWfdY8bkiaAay9xE0BvI4mLTh3L/JmjufmhtXx78XP8euUmZk8YxvwTRzN/Zg2njBtKWZkKXaqZWZ+SWjgDzgTWRMRaAEl3ApcCueEsgCHJ66HAhhTrsW72+2e3cPcTG/no+TPcBNCLVVeW86HXT+ctcyfwX0vqWLS6ga/99hm++ptnGDWoivNmZIPaa0+oYeiAykKXa2ZW8pTWfY8kvQVYEBHvS5b/FjgrIq7O2WcscB8wHBgI/EVELJM0BVgJPAPsAD4dEb/r5DOuBK4EmDRp0hnr1q1L5Wexl2tqaWPB1x+itS249yPneq5Zidmyaz8PPdPAotUNPPRMA417mykvE2dMGs68mTXMP3E0M8cMRvKompnZ0ZC0LCLmdrYtzZGzfLwNuC0ivizpVcCPJJ0CbAQmRcRWSWcAv5R0ckTsyD04Im4CbgKYO3eu767Zg77/+2wTwK3vnutgVoJGDerHm18xgTe/YgItrW3U1mdY9HQDi1Zv5ou/Xs0Xf72aMUOqmT+zhnknjuacE0YxqF+hf52YmZWGNH+brgcm5ixPSNblei+wACAiHpFUDYyKiM3A/mT9MknPATOApSnWa3nakNnLNx9wE0BfUVFexhmTR3DG5BF8/MITeXHHPh5cnQ1q/1O7kZ88XkdluThz6gjmJZdAj68Z5FE1M7OjlGY4WwJMlzSVbCi7HHh7h33+DLweuE3SSUA10CCpBtgWEa2SpgHTgbUp1mpd4CaAvu24IdW89ZUTeesrJ9LU0sayddtZvHozi1Zv5oZ7nuKGe55iwvD+B5oKXjVtFP2rPLpqZpav1MJZRLRIuhq4FygHbo2IlZKuB5ZGxELgY8DNkq4h2xzw7ogISecC10tqBtqAD0TEtrRqtfy5CcByVVWU8arjR/Kq40fyyYtPon77HhavbmDx6s38bFk9P3p0XXafaSOZf2IN82eOZvLIgYUu28ysqKXWENDT5s6dG0uX+qpnmtwEYF2xr7mVx5/fxqLVm3lwdQNrt+wGYNqogcxLRtXOnDqCfhX+e2RmfU8xNwRYL9LeBPCDd7/SwcyOqLqynHNn1HDujBp4I7ywZXdy+bOB2x9bx60PP8+AqnJeffyoA40F44f1L3TZZmYF53Bmecl9EsD8maMLXY71QlNGDeTdo6by7nOmsreplUfWbmHR0w088PRmfvPUiwCceNzgA7fqOGPycCrL/YQ5M+t7fFnT8nLVHX/kN0+9yG8+ep7nmlm3igiea9h14FYdS17YRnNrMLhfBa+dMYp5J45m3owaRg+pLnSpZmbdxpc17Zi4CcDSJIkTRg/mhNGDef+509i5r5mH12w90AF6zxObADhl/BDmnziaeSeOZs7EYZT7sVJmVqI8cmaH5SYAK6SI4KmNO1m0ejOLV29m2brttAUMG1DJeTNqmHdiDedOr2HkoH6FLtXMrEs8cmZH7UATwHvcBGA9TxKzxg1h1rghXDX/BBr3NPO7NQ0serqBB5/ZzK+Wb0CCWWOHMH5Yf2oG92PUoH4v+V4zqB+jBlcxoMq/7sysd/BvKzuklzQBnOgmACu8oQMqueS0cVxy2jja2oInNzSy6OkGlrywjXVb97B03Xa27W7q9NiBVeWdhreD36sOvPb/iJhZITmc2SHdcPdTtIWfBGDFqaxMnDZhGKdNGPaS9c2tbWzb3UTDzv007NrPlgPfmw4sP7t5F4+s3UpmT3On7z24X8XLgtuhgl1VhTtKzax7OZxZp373bAN3P7GRj7kJwHqZyvIyjhtSzXF5dHc2tbSxdfd+GnbuZ8uu9u8Hg13Dzv08tWkHDTv3s3NfS6fvMbR/5SHDW03O8shBVb41iJnlxeHMXqappY3rFq5k8sgBvP/caYUuxyw1VRVljB3an7FDj3zz233NrWzZdTC8HQxzB1+v3JANcrv2dx7khg+ofEl4GzGwijKJtggisg0QAQeW2wKg/fXBdcHB/bPL2e28ZL/se8XLlpN96bBvZLcFyXvmvHd03A/oV1HGgKpy+leWU518P7BcWU7/quxydWWHbR33rSpnQGU5FQ6uZgc4nNnLuAnA7OWqK8uZMHwAE4YfeSR5b1M2yDXs6jgqd3B07k9/zrB9dxMBCJCyDRBlOd9BSGTXcXBbdl8okxDZ73RYbn8/AWVlB4/nwD45n9f+OWVQprID703Oe7W/t5L1+1ta2dfcyo59zexpamVfUyt7m7Nf+5rbunx+K8v1kiDXHvBestz+Olnfcd/c/fpXlTOgsoLqqrID+zoAWm/hcGYv0d4EcIGbAMyOWv+qciaOGNBnpwS0tQX7WlrZ29SaDW5JaNvb1Mqe5oNB7sC2jss56/Y2tdK4t/ngcrJuf8vRBUDfH8/ycXzNIO7+8GsL9vkOZ/YSn797FW0R/IubAMzsKJWViQFVFQyoqmBkSp/R2hadBrmO3zuGwVK5t6ela+SgqoJ+vsOZHfC7Zxu454lNbgIws6JXXiYG9qtgYD//M2alxxfgDcjOH7nuVyuZ4iYAMzOzgvL/chiQNAFscROAmZlZoXnkzNiQ2cs3f7vGTQBmZmZFwOHM+PzdqwjcBGBmZlYMHM76uPYmgKvmneAmADMzsyLgcNaHuQnAzMys+LghoA9rbwK4zU0AZmZmRcMjZ33U+pwmgHluAjAzMysaDmd91A1uAjAzMytKDmd90EPPZJsArp7vJgAzM7Ni43DWx+xvaeUzC90EYGZmVqzcENDH5DYB9KtwE4CZmVmx8chZH9LeBHDhyW4CMDMzK1YOZ33I5//XTQBmZmbFzuGsj3jomQb+78lsE8CE4W4CMDMzK1YOZ32AmwDMzMx6DzcE9AG3/M5NAGZmZr2FR876gDseXce5M2rcBGBmZtYLOJyVuM0797GhcR/nTh9V6FLMzMwsDw5nJW5FXSMAcyYOK3AlZmZmlg+HsxJXW5+hvEycPG5ooUsxMzOzPKQaziQtkLRa0hpJn+hk+yRJiyT9SdIKSRfnbPtkctxqSRemWWcpW16XYcZxg+lf5UYAMzOz3iC1cCapHLgRuAiYBbxNUse7n34auCsiTgcuB76dHDsrWT4ZWAB8O3k/64KIYEV9I3MmetTMzMyst0hz5OxMYE1ErI2IJuBO4NIO+wQwJHk9FNiQvL4UuDMi9kfE88Ca5P2sC9Zt3UPj3mZmT/B8MzMzs94izXA2HqjLWa5P1uX6DPBOSfXAPcCHunAskq6UtFTS0oaGhu6qu2TU1mcAmO1mADMzs16j0A0BbwNui4gJwMXAjyTlXVNE3BQRcyNibk1NTWpF9lbL6zJUV5YxffSgQpdiZmZmeUrzCQHrgYk5yxOSdbneS3ZOGRHxiKRqYFSex9oR1NZlOHX8UCrKC53BzczMLF9p/qu9BJguaaqkKrIT/Bd22OfPwOsBJJ0EVAMNyX6XS+onaSowHXg8xVpLTnNrGys37PB8MzMzs14mtZGziGiRdDVwL1AO3BoRKyVdDyyNiIXAx4CbJV1Dtjng3RERwEpJdwGrgBbgqohoTavWUrR60072t7R5vpmZmVkvk+qDzyPiHrIT/XPXXZvzehVwziGOvQG4Ic36StmBZgCPnJmZmfUqnoxUomrrMgwfUMnEEf0LXYqZmZl1gcNZiVpR38jsicOQVOhSzMzMrAsczkrQ7v0tPPPiTk7zJU0zM7Nex+GsBD25vpG2wI9tMjMz64UczkpQezOAR87MzMx6H4ezElRb38iE4f0ZNahfoUsxMzOzLnI4K0G1dRnfQsPMzKyXcjgrMVt37ad++15me76ZmZlZr+RwVmJW1DcCvvmsmZlZb+VwVmKW12UoE5wy3iNnZmZmvZHDWYmprc8wffRgBvZL9clcZmZmlhKHsxISEdlmAM83MzMz67UczkpI/fa9bN/TzOyJnm9mZmbWWzmclZDlddmbz7oZwMzMrPdyOCshtXUZ+lWUceKYwYUuxczMzI6Sw1kJqa3PcPK4IVSW+4/VzMyst/K/4iWipbWNJ9fv8HwzMzOzXs7hrEQ8u3kXe5tbmeNwZmZm1qsdMZxJ+rKkk3uiGDt6tUkzwGluBjAzM+vV8hk5ewq4SdJjkj4gyTfRKkK19RmGVFcwZeSAQpdiZmZmx+CI4SwibomIc4B3AVOAFZJ+LGl+2sVZ/mrrGpk9cRiSCl2KmZmZHYO85pxJKgdmJl9bgFrgo5LuTLE2y9PeplZWv7jT9zczMzMrAUd8AKOkrwKXAA8A/xoRjyeb/l3S6jSLs/ys3NBIa1u4U9PMzKwE5PN07BXApyNidyfbzuzmeuwoHHwygKcDmpmZ9Xb5XNbMkBPiJA2TdBlARDSmVZjlb0V9I+OGVjN6SHWhSzEzM7NjlE84uy43hEVEBrguvZKsq2rrM76FhpmZWYnIJ5x1tk8+l0OtB2zf3cS6rXs838zMzKxE5BPOlkr6iqTjk6+vAMvSLszyU1ufzDeb6PlmZmZmpSCfcPYhoAn4r+RrP3BVmkVZ/lbUNyLBqeMdzszMzErBES9PJl2an+iBWuwo1NZlOL5mEIOrKwtdipmZmXWDfO5zVgP8I3AycKAdMCJel2JdloeIoLY+w3kzRhe6FDMzM+sm+VzWvAN4GpgKfBZ4AViSYk2Wpw2N+9iyq4k5nm9mZmZWMvIJZyMj4vtAc0Q8GBF/B3jUrAjUtt981p2aZmZmJSOfW2I0J983SnoDsAEYkV5Jlq/augxV5WXMHDrGgcEAABxVSURBVDOk0KWYmZlZN8knnH1e0lDgY8A3gSHANalWZXlZXpfhpHFDqKrI6/n1ZmZm1gsc9l91SeXA9IhojIgnI2J+RJwREQvzeXNJCyStlrRG0ss6PiV9VdLy5OsZSZmcba052/L6vL6ktS14cn0jc/w8TTMzs5Jy2JGziGiV9Dbgq1194yTY3QicD9QDSyQtjIhVOe9/Tc7+HwJOz3mLvRExp6uf21c817CL3U2tnm9mZmZWYvK5HvawpG9Jeq2kV7R/5XHcmcCaiFgbEU3AncClh9n/bcBP8nhfI3tJE/AzNc3MzEpMPnPO2kevrs9ZFxy5Y3M8UJezXA+c1dmOkiaTvVXHAzmrqyUtBVqAL0TELzs57krgSoBJkyYdoZzSUluXYXC/CqaNGljoUszMzKwb5fOEgPk9UMflwM8iojVn3eSIWC9pGvCApCci4rkOtd0E3AQwd+7c6IE6i8aK+kZOmziUsjIVuhQzMzPrRvk8IeDaztZHxPWdrc+xHpiYszwhWdeZy+nwvM6IWJ98XytpMdn5aM+9/NC+Z19zK09t3MGV504rdClmZmbWzfKZc7Y756sVuAiYksdxS4DpkqZKqiIbwF7WdSlpJjAceCRn3XBJ/ZLXo4BzgFUdj+2rVm3cQUtbeL6ZmZlZCcrnsuaXc5clfQm4N4/jWiRdnexbDtwaESslXQ8szbkdx+XAnRGRe1nyJOB7ktrIBsgv5HZ59nXtTwaY405NMzOzkpNPQ0BHA8heojyiiLgHuKfDums7LH+mk+P+AJx6FLX1CSvqGzluSD/GDK0+8s5mZmbWq+Qz5+wJst2ZkB0Bq+GlnZvWw2rrMr6kaWZmVqLyGTm7JOd1C/BiRLSkVI8dQeOeZtZu2c1fnZHX4KWZmZn1Mvk0BIwFtkXEuqSDsr+kTu9XZulbsT4732y2R87MzMxKUj7h7DvArpzl3ck6K4AV9Y0AnOpnapqZmZWkfMKZcjspI6KNo2sksG6wvC7DtFEDGdq/stClmJmZWQryCWdrJX1YUmXy9ffA2rQLs5eLCJbXZfywczMzsxKWTzj7APBqsnf3b38+5pVpFmWd27RjHw079zPblzTNzMxKVj43od1M9kaxVmC1ddn5Zh45MzMzK11HHDmT9ENJw3KWh0u6Nd2yrDO19RkqysRJY4cUuhQzMzNLST6XNU+LiEz7QkRsJ/sQcuthtXUZTho7hOrK8kKXYmZmZinJJ5yVSRreviBpBO7W7HFtbcET9Y3Mnuj5ZmZmZqUsn5D1ZeARST8FBLwF+NdUq7KXWbtlNzv3t/jms2ZmZiUun4aA/5S0FHhdsurNEbEq3bKso9q65MkAbgYwMzMraXldnkzC2CpJxwNvl/TTiDg53dIsV219hoFV5RxfM6jQpZiZmVmK8unWHCfpGklLgJXJMb61Rg+rrW/k1AlDKS9ToUsxMzOzFB0ynEm6UtIiYDEwEngvsDEiPhsRT/RQfQbsb2nlqQ07fEnTzMysDzjcZc1vAY8Ab4+IpQCS4jD7W0qe3riTptY2NwOYmZn1AYcLZ2OBvwa+LGkMcBfgp20XQG29mwHMzMz6ikNe1oyIrRHx3Yg4D3g9kAFelPSUJN9KowfV1jUyalA/xg2tLnQpZmZmlrJ8bkJLRNRHxJcjYi5wKbAv3bIsV219hjkThyK5GcDMzKzU5RXOckXEMxFxfRrF2Mvt2NfMcw27OM3zzczMzPqELocz61lP1jcS4flmZmZmfYXDWZGrrW8EYPYEP1PTzMysLzjiEwIkvaKT1Y3Auoho6f6SLFdtXYbJIwcwbEBVoUsxMzOzHpDP45u+DbwCWEH2weenkH1SwFBJH4yI+1Ksr8+rrc/wyikjCl2GmZmZ9ZB8LmtuAE6PiLkRcQZwOrAWOB/4YprF9XWbd+xjY+M+zzczMzPrQ/IJZzMiYmX7QvIQ9JkRsTa9sgwOzjebM9HzzczMzPqKfC5rrpT0HeDOZPlvgFWS+gHNqVVm1NZlKC8Ts8Y6nJmZmfUV+YycvRtYA3wk+VqbrGsG5qdVmGXnm5143GD6V5UXuhQzMzPrIUccOYuIvcCXk6+OdnV7RQZARFBbl+ENp40rdClmZmbWg/K5lcY5wGeAybn7R8S09MqyF7buYce+Fs83MzMz62PymXP2feAaYBnQmm451q62LgPgxzaZmZn1MfmEs8aI+L/UK7GXWF6XoX9lOdNHDyp0KWZmZtaD8glniyT9B/DfwP72lRHxx9SqMmrrM5w6figV5X7ClpmZWV+STzg7K/k+N2ddAK/r/nIMoLm1jZUbdnDFqyYXuhQzMzPrYfl0ax717TIkLQC+DpQDt0TEFzps/yoHb8cxABgdEcOSbVcAn062fT4ifni0dfQ2qzftpKmlzfPNzMzM+qBDhjNJ74yI2yV9tLPtEfGVw72xpHLgRrKPeaoHlkhamDxhoP09rsnZ/0NkHw2FpBHAdWRH6wJYlhy7Pe+frBdbnjQDzPFjm8zMzPqcw01oGph8H9zJVz6z1M8E1kTE2ohoIvuEgUsPs//bgJ8kry8E7o+IbUkgux9YkMdnloQV9RlGDKxiwvD+hS7FzMzMetghR84i4nvJy99ExMO525J7nx3JeKAuZ7meg/PXXkLSZGAq8MBhjh3fyXFXAlcCTJo0KY+SeofaukZmTxiKpEKXYmZmZj0sn1bAb+a57lhcDvwsIrp0H7WIuCki5kbE3Jqamm4uqTB27W/hmc07Pd/MzMysjzrcnLNXAa8GajrMOxtCdoL/kawHJuYsT0jWdeZy4KoOx87rcOziPD6z13tyfSMRnm9mZmbWVx1u5KyK7NyyCl4632wH8JY83nsJMF3SVElVZAPYwo47SZoJDAceyVl9L3CBpOGShgMXJOtK3or69icD+LFNZmZmfdHh5pw9CDwo6baIWAcgqQwYFBE7jvTGEdEi6WqyoaocuDUiVkq6HlgaEe1B7XLgzoiInGO3Sfoc2YAHcH1EbDuaH7C3qa1rZMLw/owc1K/QpZiZmVkB5HMT2n+T9AGyz9VcAgyR9PWI+I8jHRgR9wD3dFh3bYflzxzi2FuBW/Oor6Qsr8swZ5IvaZqZmfVV+TQEzEpGyi4D/o9sV+XfplpVH7Vl137WZ/Yyx80AZmZmfVY+4axSUiXZcLYwIprJ3hjWuln7fLPZbgYwMzPrs/IJZ98DXiB7U9qHknuSHXHOmXXd8rpGygSnjB9S6FLMzMysQPJ5tuY3gG/krFon6aift2mHVluXYcZxgxlQlc9UQDMzMytFRxw5k3ScpO9L+r9keRZwReqV9TERQW19htmeb2ZmZtan5XNZ8zayt8MYlyw/A3wkrYL6qrpte8nsafZ8MzMzsz7ukOFMUvu1tVERcRfQBtn7l5G9rYZ1o+W++ayZmZlx+JGzx5PvuyWNJOnQlHQ20Jh2YX1NbV2GfhVlnDhmcKFLMTMzswI63MxzJd8/SvaxS8dLehioIb/HN1kX1NZlOGX8UCrL87nSbGZmZqXqcOEs94HnvyB7p38B+4G/AFakXFuf0dLaxpMbGnn7mZMLXYqZmZkV2OHCWTnZB5+rw/oB6ZXTNz3z4i72Nbcxe6Lnm5mZmfV1hwtnGyPi+h6rpA+rbX8ygG+jYWZm1ucdboJTxxEzS0ltXYah/SuZPNKDkmZmZn3d4cLZ63usij6utr6R2ROHITkPm5mZ9XWHDGcRsa0nC+mr9jS18MyLO5nt+5uZmZkZ+T0hwFK0csMOWtvC883MzMwMcDgruNq65MkA7tQ0MzMzHM4Krra+kfHD+jN6cHWhSzEzM7Mi4HBWYLV1GT9P08zMzA5wOCugbbub+PO2Pcye6PlmZmZmluVwVkArfPNZMzMz68DhrIBq6xqR4FRf1jQzM7OEw1kB1dZnOKFmEIP6He4pWmZmZtaXOJwVSERQW5fxfDMzMzN7CYezAlmf2cvW3U0OZ2ZmZvYSDmcFUlvXCODHNpmZmdlLOJwVSG19hqryMmaOGVLoUszMzKyIOJwVyPK6DLPGDaGqwn8EZmZmdpCTQQG0tgVPrm9kjuebmZmZWQcOZwWwZvMu9jS1+rFNZmZm9jIOZwVQW5c8GcAjZ2ZmZtaBw1kBLK/PMLi6gqkjBxa6FDMzMysyDmcFsKI+w+wJwygrU6FLMTMzsyLjcNbD9jW38vTGnZ5vZmZmZp1yOOthKzfsoKUtPN/MzMzMOpVqOJO0QNJqSWskfeIQ+7xV0ipJKyX9OGd9q6TlydfCNOvsSe3NAL6NhpmZmXWmIq03llQO3AicD9QDSyQtjIhVOftMBz4JnBMR2yWNznmLvRExJ636CmVFfYYxQ6o5bkh1oUsxMzOzIpTmyNmZwJqIWBsRTcCdwKUd9nk/cGNEbAeIiM0p1lMUausbPd/MzMzMDinNcDYeqMtZrk/W5ZoBzJD0sKRHJS3I2VYtaWmy/rLOPkDSlck+SxsaGrq3+hRk9jTx/Jbdnm9mZmZmh5TaZc0ufP50YB4wAXhI0qkRkQEmR8R6SdOAByQ9ERHP5R4cETcBNwHMnTs3erb0rltR3wh4vpmZmZkdWpojZ+uBiTnLE5J1ueqBhRHRHBHPA8+QDWtExPrk+1pgMXB6irX2iBX12WaAU31Z08zMzA4hzXC2BJguaaqkKuByoGPX5S/JjpohaRTZy5xrJQ2X1C9n/TnAKnq55XWNTKsZyJDqykKXYmZmZkUqtXAWES3A1cC9wFPAXRGxUtL1kt6U7HYvsFXSKmAR8A8RsRU4CVgqqTZZ/4XcLs/eKCKorc8wZ4IvaZqZmdmhpTrnLCLuAe7psO7anNcBfDT5yt3nD8CpadbW0zbt2EfDzv1uBjAzM7PD8hMCekj7zWd9Gw0zMzM7HIezHrK8rpHKcnHS2CGFLsXMzMyKmMNZD6mty3DS2CFUV5YXuhQzMzMrYg5nPaCtLXhifSOz3QxgZmZmR+Bw1gPWbtnFrv0tnm9mZmZmR+Rw1gOW1/nJAGZmZpYfh7MeUFuXYVC/CqbVDCp0KWZmZlbkHM56wIr6DKeOH0p5mQpdipmZmRU5h7OU7W9pZdXGHZw20fPNzMzM7MgczlL21MadNLeGH9tkZmZmeXE4S1n7kwH82CYzMzPLh8NZymrrM9QM7sfYodWFLsXMzMx6AYezlNXWZZg9YSiSmwHMzMzsyBzOUrRjXzPPNez2kwHMzMwsbw5nKXqiPnvzWc83MzMzs3w5nKWotj7bDODHNpmZmVm+HM5SVFuXYcrIAQwbUFXoUszMzKyXcDhLUW1doy9pmpmZWZc4nKXkxR372LRjn5sBzMzMrEsczlJy8Oaznm9mZmZm+XM4S0ltfYbyMnHyOIczMzMzy5/DWUpq6xqZOWYw1ZXlhS7FzMzMehGHsxS0tQUr6jNuBjAzM7MuczhLwQtbd7NjXwuzfX8zMzMz6yKHsxS033zWI2dmZmbWVQ5nKaita2RAVTnTRw8udClmZmbWyzicpaC2PsMp44dSXqZCl2JmZma9jMNZN2tqaWPlhh2eb2ZmZmZHxeGsm63etJOmljbPNzMzM7Oj4nDWzZa3NwP4sU1mZmZ2FBzOutmKugwjB1YxYXj/QpdiZmZmvZDDWTerrc9w2oShSG4GMDMzs65zOOtGu/a38OzmXZ5vZmZmZkfN4awbPVHfSIRvPmtmZmZHz+GsG61wM4CZmZkdo1TDmaQFklZLWiPpE4fY562SVklaKenHOeuvkPRs8nVFmnV2l9r6DBNH9GfEwKpCl2JmZma9VEVabyypHLgROB+oB5ZIWhgRq3L2mQ58EjgnIrZLGp2sHwFcB8wFAliWHLs9rXq7Q21dI6dP8qiZmZmZHb00R87OBNZExNqIaALuBC7tsM/7gRvbQ1dEbE7WXwjcHxHbkm33AwtSrPWYNezcz/rMXuZ4vpmZmZkdgzTD2XigLme5PlmXawYwQ9LDkh6VtKALxxaVA/PNHM7MzMzsGKR2WbMLnz8dmAdMAB6SdGq+B0u6ErgSYNKkSWnUl7faugxlgpPHDSloHWZmZta7pTlyth6YmLM8IVmXqx5YGBHNEfE88AzZsJbPsUTETRExNyLm1tTUdGvxXbW8vpEZxw1mQFWh866ZmZn1ZmmGsyXAdElTJVUBlwMLO+zzS7KjZkgaRfYy51rgXuACScMlDQcuSNYVpYhgRX3G883MzMzsmKU2zBMRLZKuJhuqyoFbI2KlpOuBpRGxkIMhbBXQCvxDRGwFkPQ5sgEP4PqI2JZWrcfqz9v2kNnTzGm+v5mZmZkdo1SvwUXEPcA9HdZdm/M6gI8mXx2PvRW4Nc36usvyuvZmgKEFrsTMzMx6Oz8hoBvU1jVSXVnGjOMGF7oUMzMz6+UczrrBivoMp4wbSmW5T6eZmZkdG6eJY9Tc2saTGxo938zMzMy6hcPZMXrmxZ3sa27zfDMzMzPrFg5nx6i2rhHAt9EwMzOzbuFwdoxW1GcYNqCSSSMGFLoUMzMzKwEOZ8doeV2G0yYMQ1KhSzEzM7MS4HB2DPY0tfDMizuZM8HzzczMzKx7OJwdgyfX76AtYLbnm5mZmVk3cTg7Bivqs08G8G00zMzMrLs4nB2D5XUZxg/rT83gfoUuxczMzEqEw9kxqK3P+P5mZmZm1q0czo7S1l37qdu2l9m+pGlmZmbdyOHsKK1Yn735rJsBzMzMrDs5nB2l2roMEpwy3pc1zczMrPs4nB2l2roM00cPYlC/ikKXYmZmZiXE4ewoRAS19Y2eb2ZmZmbdzuHsKNRv38u23U2eb2ZmZmbdzuHsKNQmN5/1yJmZmZl1N4ezo1Bbl6GqoowTxwwudClmZmZWYhzOjkJtXSMnjxtCVYVPn5mZmXUvp4suamlt44n1bgYwMzOzdDicddGahl3sbW71Y5vMzMwsFQ5nXVRb52YAMzMzS4/DWRctr2tkSHUFU0YOLHQpZmZmVoIczrpoRX2G0yYMo6xMhS7FzMzMSpDDWRfsa27l6U07Pd/MzMzMUuNw1gUrNzTS2haeb2ZmZmapcTjrgtq6RgDm+LFNZmZmlhKHsy6orc8wZkg1o4dUF7oUMzMzK1EOZ11QW5fxfDMzMzNLVUWhC+gtWlrbOGnsEF4zfVShSzEzM7MS5nCWp4ryMr7zzjMKXYaZmZmVOF/WNDMzMysiDmdmZmZmRSTVcCZpgaTVktZI+kQn298tqUHS8uTrfTnbWnPWL0yzTjMzM7NikdqcM0nlwI3A+UA9sETSwohY1WHX/4qIqzt5i70RMSet+szMzMyKUZojZ2cCayJibUQ0AXcCl6b4eWZmZma9XprhbDxQl7Ncn6zr6K8krZD0M0kTc9ZXS1oq6VFJl6VYp5mZmVnRKHRDwP8AUyLiNOB+4Ic52yZHxFzg7cDXJB3f8WBJVyYBbmlDQ0PPVGxmZmaWojTD2XogdyRsQrLugIjYGhH7k8VbgDNytq1Pvq8FFgOnd/yAiLgpIuZGxNyamprurd7MzMysANIMZ0uA6ZKmSqoCLgde0nUpaWzO4puAp5L1wyX1S16PAs4BOjYSmJmZmZWc1Lo1I6JF0tXAvUA5cGtErJR0PbA0IhYCH5b0JqAF2Aa8Ozn8JOB7ktrIBsgvdNLlaWZmZlZyFBGFrqFbzJ07N5YuXVroMszMzMyOSNKyZG79yxS6IcDMzMzMcjicmZmZmRWRkrmsKakBWHeMbzMK2NIN5Vh+fL57ls93z/L57nk+5z3L5/vYTI6ITm81UTLhrDtIWnqo67/W/Xy+e5bPd8/y+e55Puc9y+c7Pb6saWZmZlZEHM7MzMzMiojD2UvdVOgC+hif757l892zfL57ns95z/L5TonnnJmZmZkVEY+cmZmZmRURhzNA0gJJqyWtkfSJQtdTiiTdKmmzpCdz1o2QdL+kZ5PvwwtZY6mQNFHSIkmrJK2U9PfJep/vlEiqlvS4pNrknH82WT9V0mPJ75b/Sp4zbN1EUrmkP0n632TZ5zslkl6Q9ISk5ZKWJuv8OyUlfT6cSSoHbgQuAmYBb5M0q7BVlaTbgAUd1n0C+G1ETAd+myzbsWsBPhYRs4CzgauSv9M+3+nZD7wuImYDc4AFks4G/h34akScAGwH3lvAGkvR3wNP5Sz7fKdrfkTMybl9hn+npKTPhzPgTGBNRKyNiCbgTuDSAtdUciLiIbIPt891KfDD5PUPgct6tKgSFREbI+KPyeudZP/xGo/Pd2oia1eyWJl8BfA64GfJep/zbiRpAvAG4JZkWfh89zT/TkmJw1n2H626nOX6ZJ2l77iI2Ji83gQcV8hiSpGkKcDpwGP4fKcqucS2HNgM3A88B2QioiXZxb9butfXgH8E2pLlkfh8pymA+yQtk3Rlss6/U1JSUegCzCA78iDJrcPdSNIg4OfARyJiR3ZgIcvnu/tFRCswR9Iw4BfAzAKXVLIkXQJsjohlkuYVup4+4jURsV7SaOB+SU/nbvTvlO7lkTNYD0zMWZ6QrLP0vShpLEDyfXOB6ykZkirJBrM7IuK/k9U+3z0gIjLAIuBVwDBJ7f8T7N8t3ecc4E2SXiA7FeV1wNfx+U5NRKxPvm8m+z8fZ+LfKalxOIMlwPSky6cKuBxYWOCa+oqFwBXJ6yuAXxWwlpKRzL35PvBURHwlZ5PPd0ok1SQjZkjqD5xPdq7fIuAtyW4+590kIj4ZERMiYgrZ39kPRMQ78PlOhaSBkga3vwYuAJ7Ev1NS45vQApIuJjt/oRy4NSJuKHBJJUfST4B5wCjgReA64JfAXcAkYB3w1ojo2DRgXSTpNcDvgCc4OB/nU2Tnnfl8p0DSaWQnRJeT/Z/euyLieknTyI7sjAD+BLwzIvYXrtLSk1zW/HhEXOLznY7kvP4iWawAfhwRN0gaiX+npMLhzMzMzKyI+LKmmZmZWRFxODMzMzMrIg5nZmZmZkXE4czMzMysiDicmZmZmRURhzMzK2mSWiUtl7RSUq2kj0k66t99kj6V83qKpCe7p1IzsyyHMzMrdXsjYk5EnEz25rAXkb3P3tH61JF3MTM7eg5nZtZnJI+euRK4Wlnlkv5D0hJJKyT9P8je2FTSQ5LulrRa0ncllUn6AtA/GYm7I3nbckk3JyNz9yVPCEDShyWtSt73zsL8xGbWG/kmtGZW0iTtiohBHdZlgBOBS4HREfF5Sf2Ah4G/BiYDvwZmkb3z+a+B70XEz3LfT9IUYA0wNyKWS7oLWBgRt0vaAEyNiP2ShiXP3DQzOyKPnJlZX3YB8C5Jy8k+3mokMD3Z9nhErI2IVuAnwGsO8R7PR8Ty5PUyYEryegVwh6R3Ai1pFG9mpcnhzMz6lOQ5ga3AZkDAh5I5aXMiYmpE3Jfs2vGywqEuM+Q+u7GV7LMHAd4A3Ai8AlgiqaLjgWZmnXE4M7M+Q1IN8F3gW5Gd03Ev8EFJlcn2GZIGJrufKWlq0tn5N8Dvk/XN7fsf5nPKgIkRsQj4J2AoMOhwx5iZtfP/yZlZqeufXLasJHt58UfAV5Jtt5C9DPlHSQIagMuSbUuAbwEnAIuAXyTrbwJWSPoj8M+H+Mxy4HZJQ8mOzn3Dc87MLF9uCDAz60DSPODjEXFJoWsxs77HlzXNzMzMiohHzszMzMyKiEfOzMzMzIqIw5mZmZlZEXE4MzMzMysiDmdmZmZmRcThzMzMzKyIOJzZ/79RMApGwSgYBaNgFAwiAADKeLSVbngh+wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The plots above show the change in training and testing accuracy of our data as our max_depth value changes. We use these plots to determine the max_depth value at which the graph plateaus. Below, we find its precise index and use this hyperparameter value in our model."
      ],
      "metadata": {
        "id": "LQxi3mmwtBK2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "FLFgQwLQ7iyq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "389bb23f-dc14-4bb7-fd58-230432feddb6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.9086398131932283\n",
            "Testing Accuracy: 0.8949756280464942\n",
            "ROC Score: 0.6483767331935311\n"
          ]
        }
      ],
      "source": [
        "max_score_index = testing_accuracy.index(max(testing_accuracy)) # index of best max_depth\n",
        "\n",
        "max_Depth = depths[max_score_index] # finds max max_depth value from lists above\n",
        "\n",
        "# instantiates decision tree classifier\n",
        "clf = DecisionTreeClassifier(max_depth = max_Depth)\n",
        "clf = clf.fit(x_train_smote,y_train_smote)\n",
        "\n",
        "# sets variable values to predictions, accuracies, and scores\n",
        "y_pred2 = clf.predict(x_test)\n",
        "train_accuracy2 = accuracy_score(y_train_smote, clf.predict(x_train_smote))\n",
        "test_accuracy2 = accuracy_score(y_test, y_pred2)\n",
        "roc2 = roc_auc_score(y_test, clf.predict_proba(x_test)[: , 1])\n",
        "\n",
        "# prints metrics\n",
        "print(\"Training Accuracy: \" + str(train_accuracy2))\n",
        "print(\"Testing Accuracy: \" + str(test_accuracy2))\n",
        "print(\"ROC Score: \" + str(roc2))\n",
        "\n",
        "fpr2, tpr2, _ = metrics.roc_curve(y_test, clf.predict_proba(x_test)[:,1]) # used in line chart at end"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the scores listed above, our preliminary tuning through max_depth optimality and the inclusion of a greater number of parameters has improved the model's testing and training accuracies. We also observed that our ROC Score imporved significantly by around ~10% while our precision score remained the same.\n",
        "\n",
        "Here, we see that an alteration to decision trees greatly impacted our success metrics. Before further proceeding with a trees approach, it would be valuable to compare its effectiveness to other forms of supervised learning; we decided to look into logistic regression."
      ],
      "metadata": {
        "id": "FFbnvvUztVev"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Alternate Models"
      ],
      "metadata": {
        "id": "TTZonDlZBm3I"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y6HrFZs29BYd"
      },
      "source": [
        "### Modeling PCA Logistic Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We began by using PCA Logistic Regression given the simplicity and dimensionality reduction that the model allows."
      ],
      "metadata": {
        "id": "f7Uj-bb0FPnA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "yd_6yhnRS5N6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "outputId": "63205103-f677-480c-c321-7cbd53944b3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x792 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Scaling with standard scalar for PCA\n",
        "scaler = preprocessing.StandardScaler() \n",
        "x_train_sc = scaler.fit_transform(x_train_smote)\n",
        "x_test_sc = scaler.transform(x_test)\n",
        "\n",
        "# Instantiate and Fit PCA\n",
        "pca = PCA(n_components=len(x_train.columns))\n",
        "pca_x_train = pca.fit_transform(x_train_sc)\n",
        "pca_x_test = pca.transform(x_test_sc)\n",
        "\n",
        "# Saved the explained variance ratios into variable called \"explained_variance_ratios\"\n",
        "explained_variance_ratios = pca.explained_variance_ratio_\n",
        "# Saved the CUMULATIVE explained variance ratios into variable called \"cum_evr\"\n",
        "cum_evr = explained_variance_ratios.sum()\n",
        "\n",
        "plt.subplots(figsize = (20,11))\n",
        "plt.plot(np.arange(1, len(x_train.columns) + 1), np.cumsum(pca.explained_variance_ratio_))\n",
        "plt.plot(np.arange(1, len(x_train.columns) + 1), [1.0]*(len(x_train.columns)))\n",
        "plt.xlabel(\"Number of components\")\n",
        "plt.ylabel(\"Cumulative Explained Variance\")\n",
        "plt.title(\"Explained Variance over number of components\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Examining the cumulative variance over the number of components helped us visualize the plateau and decide to use 60 components."
      ],
      "metadata": {
        "id": "qyH9OmnWGfHL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "jWkH3wJlUnlM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b554cd9-095a-4f5a-8cec-0c65f3a00a6e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.7893712682172448\n",
            "Testing Accuracy: 0.7385451818522685\n",
            "ROC Score: 0.6251418254690486\n"
          ]
        }
      ],
      "source": [
        "#https://towardsdatascience.com/calculating-and-setting-thresholds-to-optimise-logistic-regression-performance-c77e6d112d7e#:~:text=Benchmarking\n",
        "\n",
        "# Instantiated, fit, predict, and score logistic regression\n",
        "pca = PCA(n_components=60)\n",
        "\n",
        "# train and test\n",
        "pca_x_train = pca.fit_transform(x_train_sc)\n",
        "pca_x_test = pca.transform(x_test_sc)\n",
        "\n",
        "# instantiated logistic regression\n",
        "pca_clf = LogisticRegression()\n",
        "pca_clf.fit(pca_x_train, y_train_smote)\n",
        "\n",
        "# sets variable values to predictions, accuracies, and scores\n",
        "y_pred4 = pca_clf.predict(pca_x_test)\n",
        "train_accuracy4 = accuracy_score(y_train_smote, pca_clf.predict(pca_x_train))\n",
        "test_accuracy4 = accuracy_score(y_test, y_pred4)\n",
        "roc4 = roc_auc_score(y_test, pca_clf.predict_proba(pca_x_test)[: , 1])\n",
        "\n",
        "# prints metrics\n",
        "print(\"Training Accuracy: \" + str(train_accuracy4))\n",
        "print(\"Testing Accuracy: \" + str(test_accuracy4))\n",
        "print(\"ROC Score: \" + str(roc4))\n",
        "\n",
        "fpr4, tpr4, _ = metrics.roc_curve(y_test, pca_clf.predict_proba(pca_x_test)[:,1]) # for use in line chart at end"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We found that all 4 measurements were lower than the measurements found by bagging decision trees. A potential reason could be that the large presence of categorical variables in our data set makes it better aligned to decision trees rather than logistic regression models. \n",
        "\n",
        "We know that PCA reduces dimensionality potentially underfitting the data. To look further into this, we decided to pursue non-pca logistic regression."
      ],
      "metadata": {
        "id": "3bFmyLQCGuH4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Non-PCA Initial Logistic Regression"
      ],
      "metadata": {
        "id": "DpgIJCX9-zvL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "rNBqMYY8xnb1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70d0b58d-e353-4154-dbbe-c920a5677a5a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.5796181931770465\n",
            "Testing Accuracy: 0.5883389576302962\n",
            "ROC Score: 0.6220744390693493\n"
          ]
        }
      ],
      "source": [
        "warnings.filterwarnings('ignore') # used here to surpress small warning - no impact on data\n",
        "\n",
        "# instantiates logistic regression\n",
        "clf = LogisticRegression()\n",
        "clf.fit(x_train_smote, y_train_smote)\n",
        "\n",
        "# sets variable values to predictions, accuracies, and scores\n",
        "y_pred5 = clf.predict(x_test)\n",
        "train_accuracy5 = accuracy_score(y_train_smote, clf.predict(x_train_smote))\n",
        "test_accuracy5 = accuracy_score(y_test, y_pred5)\n",
        "roc5 = roc_auc_score(y_test, clf.predict_proba(x_test)[: , 1])\n",
        "\n",
        "# prints metrics\n",
        "print(\"Training Accuracy: \" + str(train_accuracy5))\n",
        "print(\"Testing Accuracy: \" + str(test_accuracy5))\n",
        "print(\"ROC Score: \" + str(roc5))\n",
        "\n",
        "fpr5, tpr5, _ = metrics.roc_curve(y_test, clf.predict_proba(x_test)[:,1]) # for use in final line chart"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we notice that the training and testing accuracy further decrease. A possible explanation for this could be that PCA is able to consider the colinearity between features that non-PCA logistic regression is unable to. \n",
        "\n",
        "A potential reason for logistic regression peforming poorly could be the underfitting driven by a lack of complexity."
      ],
      "metadata": {
        "id": "TwUwHs_cFERI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### k-Nearest Neighbor"
      ],
      "metadata": {
        "id": "s7Rve1PW5P0i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We decided to adress potential underfitting by building a more complex model. We chose to use the k-Nearest Neighbor classification model."
      ],
      "metadata": {
        "id": "KCvl83U0MLYC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# hot encodes and finds features and labels\n",
        "df_hotencode = hotencode_df(model_df)\n",
        "features, labels = feature_label(df_hotencode)\n",
        "\n",
        "# test-train split \n",
        "x_train, x_test, y_train, y_test = train_test_split(features, labels, random_state = 42, test_size = 0.2)\n",
        "\n",
        "# re-smote\n",
        "sm = SMOTE()\n",
        "x_train_smote, y_train_smote = sm.fit_resample(x_train, y_train)\n",
        "\n",
        "# instantiates knn classifier\n",
        "neigh = KNeighborsClassifier(n_neighbors=2)\n",
        "neigh.fit(x_train_smote, y_train_smote)\n",
        "\n",
        "# sets variable values to predictions, accuracies, and scores\n",
        "y_pred6 = neigh.predict(x_test)\n",
        "y_pred_test_proba = neigh.predict_proba(x_test)[: , 1]\n",
        "test_accuracy6 = metrics.accuracy_score(y_test, y_pred6)\n",
        "roc6 = roc_auc_score(y_test, y_pred_test_proba)\n",
        "\n",
        "# prints metrics\n",
        "print('Testing Accuracy: ' + str(test_accuracy6))\n",
        "print(\"ROC Score: \" + str(roc6))\n",
        "\n",
        "fpr6, tpr6, _ = metrics.roc_curve(y_test, y_pred_test_proba) # for use in line chart"
      ],
      "metadata": {
        "id": "FKTMPfkE5yRC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9cb4e40-88d7-44e3-db08-adeb2799cc33"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing Accuracy: 0.8011623547056618\n",
            "ROC Score: 0.5268477104500315\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We find that we are able to significantly improve the testing accuracy however at the cost of the ROC.\n",
        "\n",
        "A large limitation of this method was that our machine does not have the computation power to compute the training accuracy, not allowing us to consider the bias-variance trade-off."
      ],
      "metadata": {
        "id": "4jXxBN1mHIos"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tree Based Models"
      ],
      "metadata": {
        "id": "uZ6U5kj9Bs7p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After seeing the minimal accuracy of logistic regression and kNN, we decided that it would be best to explore a tree based model. More specifically, we proceed by looking into combinations of trees through Random Forest and XG Boost."
      ],
      "metadata": {
        "id": "mJjmLSs1B3O4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random Forest"
      ],
      "metadata": {
        "id": "D90fQvz30K2J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Recognizing the impact that bagging had on our success metrics, we will choose to continue with random forests. This is because random forests are a method of bagging that generate and bag multiple decision trees."
      ],
      "metadata": {
        "id": "urFmg7RGApCz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "ocPXY_g8Q0ni",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "629fa592-2997-47cb-84c5-0a4e0c8dc03f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.9301932589793223\n",
            "Testing Accuracy: 0.9021372328458943\n",
            "ROC Score : 0.6589788395760926\n"
          ]
        }
      ],
      "source": [
        "# Instantiate RF with some chosen hyperparameters\n",
        "rf = RandomForestClassifier(n_estimators = 16, max_depth = 10)\n",
        "rf.fit(x_train_smote, y_train_smote)\n",
        "\n",
        "# sets variable values to predictions, accuracies, and scores\n",
        "y_pred7 = rf.predict(x_test)\n",
        "train_accuracy7 = rf.score(x_train_smote, y_train_smote)\n",
        "test_accuracy7 = rf.score(x_test, y_test)\n",
        "roc7 = roc_auc_score(y_test, rf.predict_proba(x_test)[:, 1])\n",
        "\n",
        "# prints metrics\n",
        "print(\"Training Accuracy: \" + str(train_accuracy7))\n",
        "print(\"Testing Accuracy: \" + str(test_accuracy7))\n",
        "print(\"ROC Score : \" + str(roc7))\n",
        "\n",
        "fpr7, tpr7, _ = metrics.roc_curve(y_test, rf.predict_proba(x_test)[:, 1]) # for use in line chart at end"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we see that random forests have slightly higher training and testing accuracies, however a lower ROC score. Theoretically, we would assume that with a data set such as ours that consists of a large number of features, a random forest approach would be effective. This is why we decide to look further into improving the random forest."
      ],
      "metadata": {
        "id": "H1tHc_2tBLYs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random Forest Grid Search CV"
      ],
      "metadata": {
        "id": "5lWYRPb06o7s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "One very effective way to tune the hyper parameters is to used GridSearch."
      ],
      "metadata": {
        "id": "iQsdHIAxgRMS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Choosing potential parameters to explore \n",
        "# Apply GridSearchCV to find the optimal features for RandomForestClassifier\n",
        "param_grid = { \n",
        "    'n_estimators': [200, 500],\n",
        "    'max_depth' : [5,10,15]\n",
        "}\n",
        "\n",
        "CV_rfc = GridSearchCV(estimator=rf, param_grid=param_grid, cv= 2, scoring = 'roc_auc')\n",
        "CV_rfc.fit(x_train_smote, y_train_smote)"
      ],
      "metadata": {
        "id": "1cpDQFyz7CzU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60479f21-1b5f-4804-ff9d-43e3ffcc68bf"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=2,\n",
              "             estimator=RandomForestClassifier(max_depth=10, n_estimators=16),\n",
              "             param_grid={'max_depth': [5, 10, 15], 'n_estimators': [200, 500]},\n",
              "             scoring='roc_auc')"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CV_rfc.best_params_ # finding best parameters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ewOksd8ImE54",
        "outputId": "531e582f-ed7b-4d22-8110-c7c47e21563a"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 15, 'n_estimators': 500}"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate RF with some chosen hyperparameters\n",
        "rf = RandomForestClassifier(n_estimators = 500, max_depth = 15)\n",
        "rf.fit(x_train_smote, y_train_smote)\n",
        "\n",
        "# sets variable values to predictions, accuracies, and scores\n",
        "y_pred8 = rf.predict(x_test)\n",
        "train_accuracy8 = rf.score(x_train_smote, y_train_smote)\n",
        "test_accuracy8 = rf.score(x_test, y_test)\n",
        "roc8 = roc_auc_score(y_test, rf.predict_proba(x_test)[:, 1])\n",
        "\n",
        "# prints metrics\n",
        "print(\"Training Accuracy: \" + str(train_accuracy8))\n",
        "print(\"Testing Accuracy: \" + str(test_accuracy8))\n",
        "print(\"ROC Score : \" + str(roc8))\n",
        "\n",
        "fpr8, tpr8, _ = metrics.roc_curve(y_test, rf.predict_proba(x_test)[:, 1]) # for use in line chart at end"
      ],
      "metadata": {
        "id": "3TvNlUg4LGBa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff2c9af9-d227-48b1-aab8-bd3cb0c01ed9"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.9477217562294528\n",
            "Testing Accuracy: 0.9094113235845519\n",
            "ROC Score : 0.6846272307457587\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Following this method, we found the optimum trees of 500 and max_depth 15. A limitation with this approach is that we were only able to test a specific number of n_estimators and max_depth given the computational time and power required for GridSearch."
      ],
      "metadata": {
        "id": "CMJoD-2KgqrR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### XG Boost"
      ],
      "metadata": {
        "id": "3feiyr9T0S_R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Recognizing the computational efficiency and tuning capabilities of XG Boosts, we decided to compare it to random forests. Furthermore, we were interested to see whether the gradient boosting approach XG Boosts utilizes would help generate more accurate trees and improve the final output."
      ],
      "metadata": {
        "id": "xFSvGCD5BaNb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgbcl = XGBClassifier() #XGBoost classifier\n",
        "\n",
        "# XGBoost fit and prediction\n",
        "xgbcl.fit(x_train_smote, y_train_smote)\n",
        "y_xbgcl = xgbcl.predict_proba(x_test)[:,1]\n",
        "\n",
        "# sets variable values to predictions, accuracies, and scores\n",
        "y_pred9 = xgbcl.predict(x_test)\n",
        "train_accuracy9 = xgbcl.score(x_train_smote, y_train_smote)\n",
        "test_accuracy9 = xgbcl.score(x_test, y_test)\n",
        "roc9 = roc_auc_score(y_test, y_xbgcl)\n",
        "\n",
        "# prints metrics\n",
        "print('Training Accuracy: ' + str(train_accuracy9))\n",
        "print('Testing Accuracy: ' + str(test_accuracy9))\n",
        "print(\"ROC Score: \" + str(roc9))\n",
        "\n",
        "fpr9, tpr9, _ = metrics.roc_curve(y_test, xgbcl.predict_proba(x_test)[:, 1]) # for use in line chart at end"
      ],
      "metadata": {
        "id": "REnfVOUjaO1r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2861414-c1ce-441d-e620-f5b5d84da4b4"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.9497291096977704\n",
            "Testing Accuracy: 0.9130483689538808\n",
            "ROC Score: 0.690865287361449\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, we see that XG Boost was able to produced the highest ROC score while maintaing a testing and training accuracy that compares to other models developed."
      ],
      "metadata": {
        "id": "Lc_gjlpXEVLg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analyzing our models\n",
        "\n",
        "After completing all the models above, we decided to plot graphs detailing the impact of each on our 4 metrics. We combined this to create the following 3 visualizations for ease:\n",
        "\n",
        "\n",
        "1.   An overarching line graph showing the ROC AUC value for each model\n",
        "2.   4 bar charts showing the change of all 3 metrics based on the model\n",
        "3.   Two confusion matrices to compare the baseline model to our final model\n",
        "4.   A final dataframe summarizing our findings"
      ],
      "metadata": {
        "id": "Bo6-HALSPwTr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Line graph of ROC AUC values"
      ],
      "metadata": {
        "id": "XOwkieo6oyTT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We plotted all the ROC AUC values onto one plot to help visualize the difference that changing and tuning our models had, collectively. "
      ],
      "metadata": {
        "id": "EqWQeMtIRXis"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plots all ROC AUC values\n",
        "ax = plt.subplots(figsize = (15, 8))\n",
        "ax = plt.plot(fpr1, tpr1, fpr2, tpr2, fpr4, tpr4, fpr5, tpr5, fpr6, tpr6, fpr7, tpr7, fpr8, tpr8, fpr9, tpr9)\n",
        "\n",
        "legend = [\"Baseline\", \"DT optimal max_depth\", \"PCA Logistic Regression\", \"Non-PCA Logistic Regression\", \"kNN\", \"Random Forest\", \"Random Forest Grid Search\", \"XGBoost\"]\n",
        "\n",
        "plt.legend(legend) # adds legend\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.title(\"ROC Curves for each model\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "A5A1nBR7PwET",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "1dd73fa4-75d6-48f2-8fa1-2a739a2d0aa3"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As evident from the graph above, every model we used improved from our baseline model, aside from the k-Nearest Neighbors classifier, which was very similar to the baseline. It is also clear that our tree based models were much better in regards to the ROC AUC values.  XG Boost provided us with the best metrics here (more on this below), however, the findings are very similar to that of Random Forests especially after tuning hyperparameters with GridSearch CV. "
      ],
      "metadata": {
        "id": "3lKMsS4jTN_5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bar charts of metrics used"
      ],
      "metadata": {
        "id": "M81m0IeRo8cd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we wanted to see the impact of each model on the metrics we used. First, we analyzed its impact on training accuracy. "
      ],
      "metadata": {
        "id": "VxduCkBJU8ww"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plots training accuracy for each model\n",
        "fig = plt.figure(figsize = (15, 8))\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "y_train_accuracy = legend.copy()\n",
        "y_train_accuracy.remove('kNN')\n",
        "train_accuracies = [train_accuracy1, train_accuracy2, train_accuracy4, train_accuracy5, train_accuracy7, train_accuracy8, train_accuracy9]\n",
        "train_accuracies = [float(i) for i in train_accuracies]\n",
        "ax.bar(y_train_accuracy, train_accuracies)\n",
        "plt.xticks(rotation=90)\n",
        "plt.title(\"Training Accuracy for each model\")\n",
        "plt.xlabel(\"Model\")\n",
        "plt.ylabel(\"Training Accuracy\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "HfXVnzAWfkLh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 786
        },
        "outputId": "6ff48734-deb8-4af4-8219-0bf1161e1763"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the graph, it is clear that the tree-based models had much higher training accuracies than the logistic regression models."
      ],
      "metadata": {
        "id": "70VpGhNjVKHu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plots testing accuracy for each model\n",
        "fig = plt.figure(figsize = (15, 8))\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "y_test_accuracy = legend.copy()\n",
        "test_accuracies = [test_accuracy1, test_accuracy2, test_accuracy4, test_accuracy5, test_accuracy6, test_accuracy7, test_accuracy8, test_accuracy9]\n",
        "test_accuracies = [float(i) for i in test_accuracies]\n",
        "ax.bar(y_test_accuracy, test_accuracies)\n",
        "plt.xticks(rotation=90)\n",
        "plt.title(\"Testing Accuracy for each model\")\n",
        "plt.xlabel(\"Model\")\n",
        "plt.ylabel(\"Testing Accuracy\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Ysn0GK4NjZwA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 786
        },
        "outputId": "e00f40de-9852-4fa6-bf67-cb1a511a165c"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This makes it clear that, once again, the testing accuracies of our tree based models were much higher than the classification based models. Barring non-PCA logistic regression, every model we implemented improved our testing accuracy."
      ],
      "metadata": {
        "id": "0EF6V29dWdP7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plots ROC Scores for each model\n",
        "fig = plt.figure(figsize = (15, 8))\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "y_roc = legend.copy()\n",
        "roc_accuracies = [roc1, roc2, roc4, roc5, roc6, roc7, roc8, roc9]\n",
        "roc_accuracies = [float(i) for i in roc_accuracies]\n",
        "ax.bar(y_roc, roc_accuracies)\n",
        "plt.xticks(rotation=90)\n",
        "plt.title(\"ROC Scores for each model\")\n",
        "plt.xlabel(\"Model\")\n",
        "plt.ylabel(\"ROC Score\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "E1S_qSchj6Kp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 786
        },
        "outputId": "a67b9f9d-91af-4b84-ae4f-647ee0bca8fb"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is evident from the graph above that all of our models, excluding kNN (which is very similar to the baseline), improve on the ROC scores - as we saw in the line chart above. This goes to show our models are working well to improve this metric. Furthermore, the tree based models are the best for measuring the ROC score."
      ],
      "metadata": {
        "id": "NYgxFC-3W3SN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Findings presented in a dataframe"
      ],
      "metadata": {
        "id": "iWYFE7wDpPb7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# presenting findings in dataframe\n",
        "train_accuracies_null = [train_accuracy1, train_accuracy2, train_accuracy4, train_accuracy5, \"NaN\", train_accuracy7, train_accuracy8, train_accuracy9]\n",
        "findings = {'Model': legend, 'ROC Score': roc_accuracies, 'Training Accuracy': train_accuracies_null, 'Testing Accuracy': test_accuracies}\n",
        "findings_df = pd.DataFrame(data = findings)"
      ],
      "metadata": {
        "id": "nEamqvKXkptZ"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(findings_df) # displays dataframe"
      ],
      "metadata": {
        "id": "8KdiiYNrmyy7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "ddfeb049-6caa-434e-f887-b8054ac1b585"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                         Model  ROC Score Training Accuracy  Testing Accuracy\n",
              "0                     Baseline   0.540969          0.722919          0.618335\n",
              "1         DT optimal max_depth   0.648377           0.90864          0.894976\n",
              "2      PCA Logistic Regression   0.625142          0.789371          0.738545\n",
              "3  Non-PCA Logistic Regression   0.622074          0.579618          0.588339\n",
              "4                          kNN   0.526848               NaN          0.801162\n",
              "5                Random Forest   0.658979          0.930193          0.902137\n",
              "6    Random Forest Grid Search   0.684627          0.947722          0.909411\n",
              "7                      XGBoost   0.690865          0.949729          0.913048"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2983307f-b248-4002-9d73-ffd904380818\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\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>Model</th>\n",
              "      <th>ROC Score</th>\n",
              "      <th>Training Accuracy</th>\n",
              "      <th>Testing Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Baseline</td>\n",
              "      <td>0.540969</td>\n",
              "      <td>0.722919</td>\n",
              "      <td>0.618335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DT optimal max_depth</td>\n",
              "      <td>0.648377</td>\n",
              "      <td>0.90864</td>\n",
              "      <td>0.894976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>PCA Logistic Regression</td>\n",
              "      <td>0.625142</td>\n",
              "      <td>0.789371</td>\n",
              "      <td>0.738545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Non-PCA Logistic Regression</td>\n",
              "      <td>0.622074</td>\n",
              "      <td>0.579618</td>\n",
              "      <td>0.588339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>kNN</td>\n",
              "      <td>0.526848</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.801162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.658979</td>\n",
              "      <td>0.930193</td>\n",
              "      <td>0.902137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Random Forest Grid Search</td>\n",
              "      <td>0.684627</td>\n",
              "      <td>0.947722</td>\n",
              "      <td>0.909411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.690865</td>\n",
              "      <td>0.949729</td>\n",
              "      <td>0.913048</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2983307f-b248-4002-9d73-ffd904380818')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2983307f-b248-4002-9d73-ffd904380818 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2983307f-b248-4002-9d73-ffd904380818');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we display all of our findings in a neatly presented dataframe. Note that kNN has an NaN value for its Training Accuracy; this is explained further in our limitations section."
      ],
      "metadata": {
        "id": "bKsvyW15nRYr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Final Model Selected: XG Boost"
      ],
      "metadata": {
        "id": "-bP9J6jcV1CJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Following the comparision of the models created, the highest ROC score was the primary reason for deciding upon XG Boost. It's tree based approach is well suited to the categorical variables and large number of features present in our data. Furthermore, it can be assumed that the computational efficiency, hyperparameter tuning, and gradient boost supported its success."
      ],
      "metadata": {
        "id": "I--p8J52hgVs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Confusion Matrices"
      ],
      "metadata": {
        "id": "V-z_M8hkpUcs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# confusion matrices for baseline and random forest\n",
        "cm1 = metrics.confusion_matrix(y_test, y_pred1)\n",
        "cm9 = metrics.confusion_matrix(y_test, y_pred9)"
      ],
      "metadata": {
        "id": "ROgUV5ddoa0L"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# displays first confusion matrix\n",
        "disp1 = ConfusionMatrixDisplay(confusion_matrix=cm1, display_labels=[0,1],)\n",
        "fig, ax = plt.subplots(figsize=(15,8))\n",
        "disp1.plot(ax=ax)\n",
        "plt.title(\"Confusion Matrix Baseline Model\")\n",
        "plt.show()\n",
        "\n",
        "# displays first confusion matrix\n",
        "disp2 = ConfusionMatrixDisplay(confusion_matrix=cm9, display_labels=[0,1],)\n",
        "fig, ax = plt.subplots(figsize=(15,8))\n",
        "disp2.plot(ax=ax)\n",
        "plt.title(\"Confusion Matrix XG Boost Model\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "14hpNyCdmMw1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b2362cca-780c-4c12-912b-869a967fa691"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we compare the confusion matrix from the baseline model to our best model generated using XG Boost. We see that even though XG Boost was trained on a balanced training set, it does a far better job of predicting on a test set that is highly skewed. Furthermore, we see that the false positive count decreased significantly from 8882 to 18. However, this comes at the cost of the False negative rate increasing from 1297 to 2301. Regardless, we see a large increase in total accuracy demonstrated from the correct predictions."
      ],
      "metadata": {
        "id": "-QZOAeBJN_Yx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importance of Features"
      ],
      "metadata": {
        "id": "Z2dMZrWzV59B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_labels = x_train_smote.columns\n",
        "feature_importance = (xgbcl.feature_importances_)\n",
        "\n",
        "#Finding the importance of each labels\n",
        "frame = {\"labels\": x_labels, \"importance\": feature_importance}\n",
        "\n",
        "df_importance = pd.DataFrame(frame)\n",
        "df_importance_sorted = df_importance.sort_values(by = \"importance\", ascending = False)\n",
        "display(df_importance_sorted)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "tuO3s99GWE8q",
        "outputId": "364e1fc2-0211-45ca-88ba-7ce47fb8b258"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                 labels  importance\n",
              "57       NAME_INCOME_TYPE State servant    0.115206\n",
              "62  NAME_EDUCATION_TYPE Academic degree    0.111465\n",
              "59  NAME_EDUCATION_TYPE Lower secondary    0.083139\n",
              "0                          FLAG_OWN_CAR    0.059071\n",
              "43                        CODE_GENDER F    0.058958\n",
              "..                                  ...         ...\n",
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    {
      "cell_type": "markdown",
      "source": [
        "Some observations we had included:\n",
        "\n",
        "*   **DAYS_EMPLOYED**: This feature was shown to have a high importance rating in the model produced. It is interesting to see how the length of time one works for has a strong relationship with their ability to pay loans.\n",
        "*   **AMT_INCOME_TOTAL**: This feature was shown to have no importance in the model produced. However, we would assume that a higher income would make an individual more likely to pay back loans.\n",
        "*   **Gender**: There seems to be a considerable difference between Males and Females in their ability to pay back loans without facing any difficulty. This is reflected in the model given this feature's high importance. \n",
        "\n",
        "Observations that aligned with our hypothesies included:\n",
        "\n",
        "1.   **Education Type**: As we predicted, several education types were very important in classifying loan repayment difficulties. Logically we can understand how no higher level education is the most important, given the large impact it must have on financial literacy and responsibility.\n",
        "2.   **Income Type**: As we predicted, several income types, such as commerical_associate, were very important in classifying loan repayment difficulties.\n",
        "3.   **Flag Variables**: Initially, we hypothesized that flag variables would not be very important when it comes to classifying. However, this wasn't true in the model given that features such as FLAG_CAR were particularly important."
      ],
      "metadata": {
        "id": "rHg8Dnk5WZg-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion and Wrap-up"
      ],
      "metadata": {
        "id": "Q02-W1w7UW1P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion\n",
        "Throughout this project, we analyzed a dataset consisting of loan applicants in order to understand the behind-the-scenes process of approving a loan request. Initially, we did some EDA to identify potential patterns within the data and compare them to our preliminary knowledge of the financial aspect of loan approvals. It turned out that there are various attributes and features that affect a client's ability to pay back a loan without facing any difficulties, many of which seemed interesting and *subtle* to us. This motivated the need to build multiple models that aim to predict whether a loan applicant would face payment difficulties in paying back the loan, especially by accounting for these underlying features. We built models that account for categorical features, as well as models that account for additional continuous variables, and we continued to improve these models based on the identified metrics. Finally, we conclude that the loan approval process is more versatile than a mere dichotomy; there are so many things that factor into the possibility of ending up with having payment difficulties in paying back a loan that stretches beyond salient factors like Income and AMT Credit Amounts as outlined in the observations above. Our project tackled models that have revealed unexpected depths in our data!"
      ],
      "metadata": {
        "id": "5dUWNpb8uSt_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Limitations\n",
        "\n",
        "\n",
        "1.   **Training accuracy for kNN**: The inability to get training accuracy for the kNN method led to an incomplete evaluation of the modeling technique. More specifically, we were unable to look into the bias-variance trade-off and decide upon whether the model was over or under fitting. Furthermore, the training accuracy could have prompted further research into models other than the random forest that was decided upon.\n",
        "2.   **Lack of focus on precision**: The project is based on predicting loans that are likely to cause problems. We know that allocating loans to risky clients is more costly than not allocating loans to clients that would be proftiable. The models we developed should have also taken this into account when measuring success, though the scope of our dataset did not signify whether a loan was accepted or not. \n"
      ],
      "metadata": {
        "id": "0ChJ0eMyh0mK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Potential Extentions\n",
        "\n",
        "1.   **Altering boundary conditions**: Specifically adressing limitation 2, it would be highly valuable to look into calculating features by setting boundaries other than 0.5 values. This would support the models in limiting false positives. A way to further explore this would to be placing a cap on false positives, and use the roc curve of the model as a way to measure the impact this would have on true positives. One potential way to implement this into the model would be to flip the TARGET variable values such that 1 corresponds to a client not having payment difficulties, and 0 corresponds to a client having payment difficulties. This valid transition would allow us to measure the ROC and cap the False Positive Ratio (FPR). \n",
        "2.   **Tuning Hyperparamters**: Further tuning of hyper parameters would be especially helpful in putting together more effective models. One great way to do this would be to use GridSearch CV. We were unable to do this for our project given the extensive runtime but it would be very valuable to do in a project with larger scale.\n",
        "3.   **Smoting Ratio**: Currently the project smotes the training set to make a 50/50 split. It would be valuable to see how a 2:1 ratio would have on the testing accuracies given how skewed the original data set is. It is important to ensure that this doesn't make the models predict the majority class. \n",
        "\n",
        "Thank you for taking the time to go through our project!"
      ],
      "metadata": {
        "id": "2zzSJlA1h256"
      }
    }
  ]
}