Draft_3_Credit_Scoring.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Draft_3_Credit_Scoring.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tio3kUZxHnXh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Imported Libraries\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.decomposition import PCA, TruncatedSVD\n",
        "import matplotlib.patches as mpatches\n",
        "import time\n",
        "\n",
        "# Classifier Libraries\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import collections\n",
        "\n",
        "\n",
        "# Other Libraries\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.under_sampling import NearMiss\n",
        "from imblearn.metrics import classification_report_imbalanced\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
        "from collections import Counter\n",
        "from sklearn.model_selection import KFold, StratifiedKFold\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "roMze5B4H_9-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "77604170-91ed-4216-a93b-805550b02085"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "relative_path = '/content/drive/My Drive/'"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-JFW25mIEkG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataset = pd.read_csv('/content/drive/My Drive/cs-training.csv')\n",
        "test_dataset = pd.read_csv('/content/drive/My Drive/cs-test.csv')"
      ],
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bG1-jIEMIRA3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataset = train_dataset.drop(columns={'Unnamed: 0'})\n",
        "test_dataset = test_dataset.drop(columns={'Unnamed: 0', 'SeriousDlqin2yrs'})"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUWJheHqIZde",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9ace3f4a-09bb-4e5f-8dc9-ec93c7ce6692"
      },
      "source": [
        "train_dataset.shape, test_dataset.shape"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((150000, 11), (101503, 10))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a98lA0N_IeQL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "0e5de815-f645-408c-d0d3-db32baad2c2c"
      },
      "source": [
        "train_dataset.head()"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SeriousDlqin2yrs</th>\n",
              "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
              "      <th>age</th>\n",
              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
              "      <th>DebtRatio</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
              "      <th>NumberOfTimes90DaysLate</th>\n",
              "      <th>NumberRealEstateLoansOrLines</th>\n",
              "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
              "      <th>NumberOfDependents</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.766127</td>\n",
              "      <td>45</td>\n",
              "      <td>2</td>\n",
              "      <td>0.802982</td>\n",
              "      <td>9120.0</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.957151</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0.121876</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.658180</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>0.085113</td>\n",
              "      <td>3042.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.233810</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>0.036050</td>\n",
              "      <td>3300.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0.907239</td>\n",
              "      <td>49</td>\n",
              "      <td>1</td>\n",
              "      <td>0.024926</td>\n",
              "      <td>63588.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   SeriousDlqin2yrs  ...  NumberOfDependents\n",
              "0                 1  ...                 2.0\n",
              "1                 0  ...                 1.0\n",
              "2                 0  ...                 0.0\n",
              "3                 0  ...                 0.0\n",
              "4                 0  ...                 0.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekQxLGhhIib1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "outputId": "58969eae-99d2-429c-fb23-b4837438a7d3"
      },
      "source": [
        "train_dataset.describe().T"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SeriousDlqin2yrs</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>0.066840</td>\n",
              "      <td>0.249746</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>6.048438</td>\n",
              "      <td>249.755371</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.029867</td>\n",
              "      <td>0.154181</td>\n",
              "      <td>0.559046</td>\n",
              "      <td>50708.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>52.295207</td>\n",
              "      <td>14.771866</td>\n",
              "      <td>0.0</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>109.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>0.421033</td>\n",
              "      <td>4.192781</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DebtRatio</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>353.005076</td>\n",
              "      <td>2037.818523</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.175074</td>\n",
              "      <td>0.366508</td>\n",
              "      <td>0.868254</td>\n",
              "      <td>329664.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <td>120269.0</td>\n",
              "      <td>6670.221237</td>\n",
              "      <td>14384.674215</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3400.000000</td>\n",
              "      <td>5400.000000</td>\n",
              "      <td>8249.000000</td>\n",
              "      <td>3008750.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>8.452760</td>\n",
              "      <td>5.145951</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>58.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumberOfTimes90DaysLate</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>0.265973</td>\n",
              "      <td>4.169304</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumberRealEstateLoansOrLines</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>1.018240</td>\n",
              "      <td>1.129771</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>54.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>0.240387</td>\n",
              "      <td>4.155179</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumberOfDependents</th>\n",
              "      <td>146076.0</td>\n",
              "      <td>0.757222</td>\n",
              "      <td>1.115086</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         count  ...        max\n",
              "SeriousDlqin2yrs                      150000.0  ...        1.0\n",
              "RevolvingUtilizationOfUnsecuredLines  150000.0  ...    50708.0\n",
              "age                                   150000.0  ...      109.0\n",
              "NumberOfTime30-59DaysPastDueNotWorse  150000.0  ...       98.0\n",
              "DebtRatio                             150000.0  ...   329664.0\n",
              "MonthlyIncome                         120269.0  ...  3008750.0\n",
              "NumberOfOpenCreditLinesAndLoans       150000.0  ...       58.0\n",
              "NumberOfTimes90DaysLate               150000.0  ...       98.0\n",
              "NumberRealEstateLoansOrLines          150000.0  ...       54.0\n",
              "NumberOfTime60-89DaysPastDueNotWorse  150000.0  ...       98.0\n",
              "NumberOfDependents                    146076.0  ...       20.0\n",
              "\n",
              "[11 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZFJnGUbIu1X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7309ae31-1303-45f3-ece0-3f75369df289"
      },
      "source": [
        "train_dataset.isnull().sum().max()\n",
        "test_dataset.isnull().sum().max()"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20103"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "880Sg2HNIuwC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#train_dataset.MonthlyIncome = train_dataset.MonthlyIncome.fillna(train_dataset.MonthlyIncome.median())\n",
        "#test_dataset.MonthlyIncome = test_dataset.MonthlyIncome.fillna(test_dataset.MonthlyIncome.median())"
      ],
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99lCG7hnIurQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "09900d83-ec4e-4cb9-aa6f-92df10646a02"
      },
      "source": [
        "train_dataset.columns, test_dataset.columns"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Index(['SeriousDlqin2yrs', 'RevolvingUtilizationOfUnsecuredLines', 'age',\n",
              "        'NumberOfTime30-59DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome',\n",
              "        'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate',\n",
              "        'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
              "        'NumberOfDependents'],\n",
              "       dtype='object'), Index(['RevolvingUtilizationOfUnsecuredLines', 'age',\n",
              "        'NumberOfTime30-59DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome',\n",
              "        'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate',\n",
              "        'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
              "        'NumberOfDependents'],\n",
              "       dtype='object'))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQsQeJzuIulo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "e49307a1-082c-40a1-9142-3a5444e6104f"
      },
      "source": [
        "# The classes are heavily skewed we need to solve this issue later.\n",
        "print('No Defaults', round(train_dataset['SeriousDlqin2yrs'].value_counts()[0]/len(train_dataset) * 100,2), '% of the dataset')\n",
        "print('Defaults', round(train_dataset['SeriousDlqin2yrs'].value_counts()[1]/len(train_dataset) * 100,2), '% of the dataset')"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No Defaults 93.32 % of the dataset\n",
            "Defaults 6.68 % of the dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nlkMKK0IugW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "outputId": "edc6c7f2-af5c-4b05-8f29-b43cf31496e7"
      },
      "source": [
        "colors = [\"#0101DF\", \"#DF0101\"]\n",
        "\n",
        "sns.countplot('SeriousDlqin2yrs', data=train_dataset, palette=colors)\n",
        "plt.title('Class Distributions \\n (0: No Default || 1: Default)', fontsize=14)"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Class Distributions \\n (0: No Default || 1: Default)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SO-ebdsuAzxs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "a63d19e0-7978-44df-e17a-7884d5e90ceb"
      },
      "source": [
        "#correlation matrix\n",
        "corr = train_dataset.corr()\n",
        "sns.heatmap(corr, cmap='coolwarm_r', annot_kws={'size':20})"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8acb8d4518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZA7-jpzIuX9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
        "\n",
        "# RobustScaler is less prone to outliers.\n",
        "std_scaler = StandardScaler()\n",
        "rob_scaler = RobustScaler()\n",
        "\n",
        "train_dataset['scaled_age'] = std_scaler.fit_transform(train_dataset['age'].values.reshape(-1,1))\n",
        "train_dataset['scaled_RevolvingUtilizationOfUnsecuredLines'] = std_scaler.fit_transform(train_dataset['RevolvingUtilizationOfUnsecuredLines'].values.reshape(-1,1))\n",
        "train_dataset['scaled_DebtRatio'] = std_scaler.fit_transform(train_dataset['DebtRatio'].values.reshape(-1,1))\n",
        "train_dataset['scaled_MonthlyIncome'] = std_scaler.fit_transform(train_dataset['MonthlyIncome'].values.reshape(-1,1))\n",
        "train_dataset['scaled_NumberOfOpenCreditLinesAndLoans'] = std_scaler.fit_transform(train_dataset['NumberOfOpenCreditLinesAndLoans'].values.reshape(-1,1))\n",
        "\n",
        "train_dataset.drop(['age','RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome', 'NumberOfOpenCreditLinesAndLoans'], axis=1, inplace=True)"
      ],
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LoRhbnOpIuPd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "88b19bb7-1d99-4de9-e22e-71d82a41298b"
      },
      "source": [
        "scaled_age = train_dataset['scaled_age']\n",
        "scaled_RevolvingUtilizationOfUnsecuredLines = train_dataset['scaled_RevolvingUtilizationOfUnsecuredLines']\n",
        "scaled_DebtRatio = train_dataset['scaled_DebtRatio']\n",
        "scaled_MonthlyIncome = train_dataset['scaled_MonthlyIncome']\n",
        "scaled_NumberOfOpenCreditLinesAndLoans = train_dataset['scaled_NumberOfOpenCreditLinesAndLoans']\n",
        "\n",
        "train_dataset.drop(['scaled_age', 'scaled_RevolvingUtilizationOfUnsecuredLines', 'scaled_DebtRatio', 'scaled_MonthlyIncome', 'scaled_NumberOfOpenCreditLinesAndLoans'], axis=1, inplace=True)\n",
        "\n",
        "train_dataset.insert(1, 'scaled_age', scaled_age)\n",
        "train_dataset.insert(2, 'scaled_RevolvingUtilizationOfUnsecuredLines', scaled_RevolvingUtilizationOfUnsecuredLines)\n",
        "train_dataset.insert(3, 'scaled_DebtRatio', scaled_DebtRatio)\n",
        "train_dataset.insert(4, 'scaled_MonthlyIncome', scaled_MonthlyIncome)\n",
        "train_dataset.insert(5, 'scaled_NumberOfOpenCreditLinesAndLoans', scaled_NumberOfOpenCreditLinesAndLoans)\n",
        "\n",
        "train_dataset.head()"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SeriousDlqin2yrs</th>\n",
              "      <th>scaled_age</th>\n",
              "      <th>scaled_RevolvingUtilizationOfUnsecuredLines</th>\n",
              "      <th>scaled_DebtRatio</th>\n",
              "      <th>scaled_MonthlyIncome</th>\n",
              "      <th>scaled_NumberOfOpenCreditLinesAndLoans</th>\n",
              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
              "      <th>NumberOfTimes90DaysLate</th>\n",
              "      <th>NumberRealEstateLoansOrLines</th>\n",
              "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
              "      <th>NumberOfDependents</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>-0.493860</td>\n",
              "      <td>-0.021150</td>\n",
              "      <td>-0.172833</td>\n",
              "      <td>0.170305</td>\n",
              "      <td>0.883657</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.832342</td>\n",
              "      <td>-0.020385</td>\n",
              "      <td>-0.173168</td>\n",
              "      <td>-0.282957</td>\n",
              "      <td>-0.865297</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.967735</td>\n",
              "      <td>-0.021582</td>\n",
              "      <td>-0.173186</td>\n",
              "      <td>-0.252229</td>\n",
              "      <td>-1.253953</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>-1.509307</td>\n",
              "      <td>-0.023281</td>\n",
              "      <td>-0.173210</td>\n",
              "      <td>-0.234293</td>\n",
              "      <td>-0.670969</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.223074</td>\n",
              "      <td>-0.020585</td>\n",
              "      <td>-0.173215</td>\n",
              "      <td>3.956851</td>\n",
              "      <td>-0.282312</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   SeriousDlqin2yrs  ...  NumberOfDependents\n",
              "0                 1  ...                 2.0\n",
              "1                 0  ...                 1.0\n",
              "2                 0  ...                 0.0\n",
              "3                 0  ...                 0.0\n",
              "4                 0  ...                 0.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WO1lrnstIuJO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_dataset['scaled_age'] = std_scaler.fit_transform(test_dataset['age'].values.reshape(-1,1))\n",
        "test_dataset['scaled_RevolvingUtilizationOfUnsecuredLines'] = std_scaler.fit_transform(test_dataset['RevolvingUtilizationOfUnsecuredLines'].values.reshape(-1,1))\n",
        "test_dataset['scaled_DebtRatio'] = std_scaler.fit_transform(test_dataset['DebtRatio'].values.reshape(-1,1))\n",
        "test_dataset['scaled_MonthlyIncome'] = std_scaler.fit_transform(test_dataset['MonthlyIncome'].values.reshape(-1,1))\n",
        "test_dataset['scaled_NumberOfOpenCreditLinesAndLoans'] = std_scaler.fit_transform(test_dataset['NumberOfOpenCreditLinesAndLoans'].values.reshape(-1,1))\n",
        "\n",
        "test_dataset.drop(['age','RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome', 'NumberOfOpenCreditLinesAndLoans'], axis=1, inplace=True)"
      ],
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKnjF-qHIuBQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "0f119c40-53e9-4054-c9e0-e5acc19a4a58"
      },
      "source": [
        "scaled_age = test_dataset['scaled_age']\n",
        "scaled_RevolvingUtilizationOfUnsecuredLines = test_dataset['scaled_RevolvingUtilizationOfUnsecuredLines']\n",
        "scaled_DebtRatio = test_dataset['scaled_DebtRatio']\n",
        "scaled_MonthlyIncome = test_dataset['scaled_MonthlyIncome']\n",
        "scaled_NumberOfOpenCreditLinesAndLoans = test_dataset['scaled_NumberOfOpenCreditLinesAndLoans']\n",
        "\n",
        "test_dataset.drop(['scaled_age', 'scaled_RevolvingUtilizationOfUnsecuredLines', 'scaled_DebtRatio', 'scaled_MonthlyIncome', 'scaled_NumberOfOpenCreditLinesAndLoans'], axis=1, inplace=True)\n",
        "\n",
        "test_dataset.insert(1, 'scaled_age', scaled_age)\n",
        "test_dataset.insert(2, 'scaled_RevolvingUtilizationOfUnsecuredLines', scaled_RevolvingUtilizationOfUnsecuredLines)\n",
        "test_dataset.insert(3, 'scaled_DebtRatio', scaled_DebtRatio)\n",
        "test_dataset.insert(4, 'scaled_MonthlyIncome', scaled_MonthlyIncome)\n",
        "test_dataset.insert(5, 'scaled_NumberOfOpenCreditLinesAndLoans', scaled_NumberOfOpenCreditLinesAndLoans)\n",
        "\n",
        "test_dataset.head()"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
              "      <th>scaled_age</th>\n",
              "      <th>scaled_RevolvingUtilizationOfUnsecuredLines</th>\n",
              "      <th>scaled_DebtRatio</th>\n",
              "      <th>scaled_MonthlyIncome</th>\n",
              "      <th>scaled_NumberOfOpenCreditLinesAndLoans</th>\n",
              "      <th>NumberOfTimes90DaysLate</th>\n",
              "      <th>NumberRealEstateLoansOrLines</th>\n",
              "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
              "      <th>NumberOfDependents</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.636376</td>\n",
              "      <td>-0.022556</td>\n",
              "      <td>-0.210891</td>\n",
              "      <td>-0.031638</td>\n",
              "      <td>-0.865756</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.310870</td>\n",
              "      <td>-0.024709</td>\n",
              "      <td>-0.210677</td>\n",
              "      <td>0.062615</td>\n",
              "      <td>1.272627</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.446191</td>\n",
              "      <td>-0.026850</td>\n",
              "      <td>-0.210578</td>\n",
              "      <td>-0.048538</td>\n",
              "      <td>0.689431</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>-0.974678</td>\n",
              "      <td>-0.025641</td>\n",
              "      <td>-0.210432</td>\n",
              "      <td>-0.100115</td>\n",
              "      <td>-0.282561</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>-1.718943</td>\n",
              "      <td>-0.021972</td>\n",
              "      <td>-0.210987</td>\n",
              "      <td>-0.081900</td>\n",
              "      <td>-0.865756</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   NumberOfTime30-59DaysPastDueNotWorse  ...  NumberOfDependents\n",
              "0                                     0  ...                 0.0\n",
              "1                                     0  ...                 2.0\n",
              "2                                     0  ...                 2.0\n",
              "3                                     1  ...                 0.0\n",
              "4                                     0  ...                 1.0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XRKpd4qrCHt-",
        "colab_type": "text"
      },
      "source": [
        "Splitting the Data (Original DataFrame)\n",
        "\n",
        "Before proceeding with the Random UnderSampling technique we have to separate the orginal dataframe for testing purposes. Although we are splitting the data when implementing Random UnderSampling or OverSampling techniques, we want to test our models on the original testing set not on the testing set created by either of these techniques. The main goal is to fit the model either with the dataframes that were undersample and oversample (in order for our models to detect the patterns), and test it on the original testing set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4r6ZP0hVIt70",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "69eaf90a-ac27-4cd7-c3df-84f2f05db23e"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "print('No Defaults', round(train_dataset['SeriousDlqin2yrs'].value_counts()[0]/len(train_dataset) * 100,2), '% of the dataset')\n",
        "print('Defaults', round(train_dataset['SeriousDlqin2yrs'].value_counts()[1]/len(train_dataset) * 100,2), '% of the dataset')\n",
        "\n",
        "X = train_dataset.drop('SeriousDlqin2yrs', axis=1)\n",
        "y = train_dataset['SeriousDlqin2yrs']\n",
        "\n",
        "X.scaled_MonthlyIncome = X.scaled_MonthlyIncome.fillna(X.scaled_MonthlyIncome.median())\n",
        "X.NumberOfDependents = X.NumberOfDependents.fillna(X.NumberOfDependents.median())\n",
        "\n",
        "sss = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n",
        "\n",
        "for train_index, test_index in sss.split(X, y):\n",
        "    print(\"Train:\", train_index, \"Test:\", test_index)\n",
        "    original_Xtrain, original_Xtest = X.iloc[train_index], X.iloc[test_index]\n",
        "    original_ytrain, original_ytest = y.iloc[train_index], y.iloc[test_index]\n",
        "\n",
        "# We already have X_train and y_train for undersample data thats why we're using original to distinguish and to not overwrite these variables.\n",
        "# original_Xtrain, original_Xtest, original_ytrain, original_ytest = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Check the Distribution of the labels\n",
        "\n",
        "# Turn into an array\n",
        "original_Xtrain = original_Xtrain.values\n",
        "original_Xtest = original_Xtest.values\n",
        "original_ytrain = original_ytrain.values\n",
        "original_ytest = original_ytest.values\n",
        "\n",
        "# See if both the train and test label distribution are similarly distributed\n",
        "train_unique_label, train_counts_label = np.unique(original_ytrain, return_counts=True)\n",
        "test_unique_label, test_counts_label = np.unique(original_ytest, return_counts=True)\n",
        "print('-' * 100)\n",
        "\n",
        "print('Label Distributions: \\n')\n",
        "print(train_counts_label/ len(original_ytrain))\n",
        "print(test_counts_label/ len(original_ytest))"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No Defaults 93.32 % of the dataset\n",
            "Defaults 6.68 % of the dataset\n",
            "Train: [ 29931  29932  29933 ... 149997 149998 149999] Test: [    0     1     2 ... 30887 30945 30986]\n",
            "Train: [     0      1      2 ... 149997 149998 149999] Test: [29931 29932 29933 ... 60044 60047 60053]\n",
            "Train: [     0      1      2 ... 149997 149998 149999] Test: [59995 59996 59997 ... 90908 90961 90962]\n",
            "Train: [     0      1      2 ... 149997 149998 149999] Test: [ 89945  89946  89947 ... 120571 120572 120576]\n",
            "Train: [     0      1      2 ... 120571 120572 120576] Test: [119958 119959 119960 ... 149997 149998 149999]\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Label Distributions: \n",
            "\n",
            "[0.93315833 0.06684167]\n",
            "[0.93316667 0.06683333]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhZdQBNtDElo",
        "colab_type": "text"
      },
      "source": [
        "Random Under-Sampling:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I1L2ufckD6iL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "93d4090a-d7fc-4656-bc56-d3795795008f"
      },
      "source": [
        "print(train_dataset['SeriousDlqin2yrs'].value_counts()[0])\n",
        "print(train_dataset['SeriousDlqin2yrs'].value_counts()[1])"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "139974\n",
            "10026\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tU2HrtmNPOs_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Separate train features and label \n",
        "train_dataset[\"SeriousDlqin2yrs\"] = train_dataset[\"SeriousDlqin2yrs\"].astype(int)\n",
        "y_train_dataset = train_dataset[\"SeriousDlqin2yrs\"]\n",
        "X_train_dataset = train_dataset.drop(labels = [\"SeriousDlqin2yrs\"],axis = 1)"
      ],
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hC2EQfy6It2T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "715328e9-a535-4483-81ac-4ef34f6fc7e7"
      },
      "source": [
        "# Since our classes are highly skewed we should make them equivalent in order to have a normal distribution of the classes.\n",
        "\n",
        "# Lets shuffle the data before creating the subsamples\n",
        "\n",
        "df = train_dataset.sample(frac=1, axis=1)\n",
        "\n",
        "# amount of fraud classes 492 rows.\n",
        "default_df = train_dataset.loc[train_dataset['SeriousDlqin2yrs'] == 1]\n",
        "non_default_df = train_dataset.loc[train_dataset['SeriousDlqin2yrs'] == 0][:10026]\n",
        "\n",
        "normal_distributed_df = pd.concat([default_df, non_default_df])\n",
        "\n",
        "# Shuffle dataframe rows\n",
        "new_df = normal_distributed_df.sample(frac=1, random_state=42)\n",
        "\n",
        "new_df.head()"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SeriousDlqin2yrs</th>\n",
              "      <th>scaled_age</th>\n",
              "      <th>scaled_RevolvingUtilizationOfUnsecuredLines</th>\n",
              "      <th>scaled_DebtRatio</th>\n",
              "      <th>scaled_MonthlyIncome</th>\n",
              "      <th>scaled_NumberOfOpenCreditLinesAndLoans</th>\n",
              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
              "      <th>NumberOfTimes90DaysLate</th>\n",
              "      <th>NumberRealEstateLoansOrLines</th>\n",
              "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
              "      <th>NumberOfDependents</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6705</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.358467</td>\n",
              "      <td>-0.021106</td>\n",
              "      <td>-0.173091</td>\n",
              "      <td>0.350359</td>\n",
              "      <td>0.106344</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74603</th>\n",
              "      <td>1</td>\n",
              "      <td>0.115409</td>\n",
              "      <td>-0.020133</td>\n",
              "      <td>-0.172982</td>\n",
              "      <td>-0.220390</td>\n",
              "      <td>0.495001</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6452</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.019984</td>\n",
              "      <td>-0.022813</td>\n",
              "      <td>-0.173119</td>\n",
              "      <td>0.161963</td>\n",
              "      <td>0.300672</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4365</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.561556</td>\n",
              "      <td>-0.023191</td>\n",
              "      <td>-0.172869</td>\n",
              "      <td>-0.102208</td>\n",
              "      <td>1.077985</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5823</th>\n",
              "      <td>0</td>\n",
              "      <td>1.672428</td>\n",
              "      <td>-0.024218</td>\n",
              "      <td>0.497099</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.865297</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       SeriousDlqin2yrs  ...  NumberOfDependents\n",
              "6705                  0  ...                 3.0\n",
              "74603                 1  ...                 0.0\n",
              "6452                  0  ...                 1.0\n",
              "4365                  0  ...                 1.0\n",
              "5823                  0  ...                 0.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1AFPZCWHEIYX",
        "colab_type": "text"
      },
      "source": [
        "Equally Distributing and Correlating:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4Q888cgItw4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "8ab4d2a2-fdc5-47c6-ecfe-d8ea0d000182"
      },
      "source": [
        "print('Distribution of the Classes in the subsample dataset')\n",
        "print(new_df['SeriousDlqin2yrs'].value_counts()/len(new_df))\n",
        "\n",
        "sns.countplot('SeriousDlqin2yrs', data=new_df, palette=colors)\n",
        "plt.title('Equally Distributed Classes', fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Distribution of the Classes in the subsample dataset\n",
            "1    0.5\n",
            "0    0.5\n",
            "Name: SeriousDlqin2yrs, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PLMnlsLEItrR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 842
        },
        "outputId": "71e16b0e-2f83-4f24-8b6a-308fdcb93066"
      },
      "source": [
        "# Make sure we use the subsample in our correlation\n",
        "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(14,10))\n",
        "\n",
        "# Entire DataFrame\n",
        "corr = df.corr()\n",
        "sns.heatmap(corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax1)\n",
        "ax1.set_title(\"Imbalanced Correlation Matrix \\n (don't use for reference)\", fontsize=14)\n",
        "\n",
        "\n",
        "sub_sample_corr = new_df.corr()\n",
        "sns.heatmap(sub_sample_corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax2)\n",
        "ax2.set_title('SubSample Correlation Matrix \\n (use for reference)', fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1008x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVhxvi3GFded",
        "colab_type": "text"
      },
      "source": [
        "Anomaly Detection:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIEzWUO4ItjL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Lets check for outliers in the dataset\n",
        "def detect_outliers(df,n,features):\n",
        "    outlier_indices = []\n",
        "    \n",
        "    # iterate over features(columns)\n",
        "    for col in features:\n",
        "        # 1st quartile (25%)\n",
        "        Q1 = np.percentile(df[col], 25)\n",
        "        # 3rd quartile (75%)\n",
        "        Q3 = np.percentile(df[col],75)\n",
        "        # Interquartile range (IQR)\n",
        "        IQR = Q3 - Q1\n",
        "        \n",
        "        # outlier step\n",
        "        outlier_step = 1.5 * IQR\n",
        "        \n",
        "        # Determine a list of indices of outliers for feature col\n",
        "        outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step )].index\n",
        "        \n",
        "        # append the found outlier indices for col to the list of outlier indices \n",
        "        outlier_indices.extend(outlier_list_col)\n",
        "        \n",
        "    # select observations containing more than 2 outliers\n",
        "    outlier_indices = Counter(outlier_indices)        \n",
        "    multiple_outliers = list( k for k, v in outlier_indices.items() if v > n )\n",
        "    \n",
        "    return multiple_outliers\n",
        "\n",
        "# These are the numerical features present in the dataset\n",
        "Outliers_to_drop = detect_outliers(new_df,2,[\"scaled_RevolvingUtilizationOfUnsecuredLines\",\n",
        "                                            \"scaled_age\",\n",
        "                                            \"NumberOfTime30-59DaysPastDueNotWorse\",\n",
        "                                            \"scaled_DebtRatio\",\n",
        "                                            \"scaled_MonthlyIncome\",\n",
        "                                            \"scaled_NumberOfOpenCreditLinesAndLoans\",\n",
        "                                            \"NumberOfTimes90DaysLate\",\n",
        "                                            \"NumberRealEstateLoansOrLines\",\n",
        "                                            \"NumberOfTime60-89DaysPastDueNotWorse\",\n",
        "                                            \"NumberOfDependents\"])"
      ],
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NwPMz0LWFuMX",
        "colab_type": "text"
      },
      "source": [
        "Dimensionality Reduction and Clustering:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2a8OO0WHKMl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "0a80baf3-c045-441d-eb45-ad7333d7f6e4"
      },
      "source": [
        "new_df.isna().sum()"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SeriousDlqin2yrs                                  0\n",
              "scaled_age                                        0\n",
              "scaled_RevolvingUtilizationOfUnsecuredLines       0\n",
              "scaled_DebtRatio                                  0\n",
              "scaled_MonthlyIncome                           3646\n",
              "scaled_NumberOfOpenCreditLinesAndLoans            0\n",
              "NumberOfTime30-59DaysPastDueNotWorse              0\n",
              "NumberOfTimes90DaysLate                           0\n",
              "NumberRealEstateLoansOrLines                      0\n",
              "NumberOfTime60-89DaysPastDueNotWorse              0\n",
              "NumberOfDependents                              463\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QeOu25oXHWC2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_df.scaled_MonthlyIncome = new_df.scaled_MonthlyIncome.fillna(new_df.scaled_MonthlyIncome.median())\n",
        "new_df.NumberOfDependents = new_df.NumberOfDependents.fillna(new_df.NumberOfDependents.median())\n",
        "test_dataset.scaled_MonthlyIncome = test_dataset.scaled_MonthlyIncome.fillna(test_dataset.scaled_MonthlyIncome.median())"
      ],
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUNBhfDQJd96",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "93ca82eb-b31c-4d7c-a858-3ed2f42f9107"
      },
      "source": [
        "new_df.isna().sum()"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SeriousDlqin2yrs                               0\n",
              "scaled_age                                     0\n",
              "scaled_RevolvingUtilizationOfUnsecuredLines    0\n",
              "scaled_DebtRatio                               0\n",
              "scaled_MonthlyIncome                           0\n",
              "scaled_NumberOfOpenCreditLinesAndLoans         0\n",
              "NumberOfTime30-59DaysPastDueNotWorse           0\n",
              "NumberOfTimes90DaysLate                        0\n",
              "NumberRealEstateLoansOrLines                   0\n",
              "NumberOfTime60-89DaysPastDueNotWorse           0\n",
              "NumberOfDependents                             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ph008KunjgCh",
        "colab_type": "text"
      },
      "source": [
        "t-SNE algorithm can pretty accurately cluster the cases that were default and non-default in our dataset. Although the subsample is pretty small, the t-SNE algorithm is able to detect clusters pretty accurately in every scenario (we shuffle the dataset before running t-SNE). This indicates that further predictive models will perform pretty well in separating default from non-default cases."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OvDoSG9VItek",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "a13abeaf-ff84-43c8-e4ca-d4013cb1c0ed"
      },
      "source": [
        "# New_df is from the random undersample data (fewer instances)\n",
        "X = new_df.drop('SeriousDlqin2yrs', axis = 1)\n",
        "y = new_df['SeriousDlqin2yrs']\n",
        "\n",
        "# T-SNE Implementation\n",
        "t0 = time.time()\n",
        "X_reduced_tsne = TSNE(n_components=2, random_state=42).fit_transform(X.values)\n",
        "t1 = time.time()\n",
        "print(\"T-SNE took {:.2} s\".format(t1 - t0))\n",
        "\n",
        "# PCA Implementation\n",
        "t0 = time.time()\n",
        "X_reduced_pca = PCA(n_components=2, random_state=42).fit_transform(X.values)\n",
        "t1 = time.time()\n",
        "print(\"PCA took {:.2} s\".format(t1 - t0))\n",
        "\n",
        "# TruncatedSVD\n",
        "t0 = time.time()\n",
        "X_reduced_svd = TruncatedSVD(n_components=2, algorithm='randomized', random_state=42).fit_transform(X.values)\n",
        "t1 = time.time()\n",
        "print(\"Truncated SVD took {:.2} s\".format(t1 - t0))"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "T-SNE took 2.3e+02 s\n",
            "PCA took 0.077 s\n",
            "Truncated SVD took 0.051 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2azy4XLVDfI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c53a7971-4703-4d9f-a7f4-6973311df9d7"
      },
      "source": [
        "X = new_df.drop('SeriousDlqin2yrs', axis = 1)\n",
        "X.shape"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20052, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CC3_7wj5Tjo5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "145cf0c0-4212-4c69-e995-ef8b91d4621e"
      },
      "source": [
        "c=(y==0)\n",
        "c1=(y==1)\n",
        "c.shape, c1.shape, y.shape, X_reduced_tsne.shape, X_reduced_pca.shape, X_reduced_svd.shape"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((20052,), (20052,), (20052,), (20052, 2), (20052, 2), (20052, 2))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5yvPjFx0GBp6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "59ec2651-3a59-4648-b3b6-c7ea1fec92e5"
      },
      "source": [
        "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24,6))\n",
        "# labels = ['No Default', 'Default']\n",
        "f.suptitle('Clusters using Dimensionality Reduction', fontsize=14)\n",
        "\n",
        "blue_patch = mpatches.Patch(color='#0A0AFF', label='No Default')\n",
        "red_patch = mpatches.Patch(color='#AF0000', label='Default')\n",
        "\n",
        "# t-SNE scatter plot\n",
        "ax1.scatter(X_reduced_tsne[:,0], X_reduced_tsne[:,1], c=(y == 0), cmap='coolwarm', label='No Default', linewidths=2)\n",
        "ax1.scatter(X_reduced_tsne[:,0], X_reduced_tsne[:,1], c=(y == 1), cmap='coolwarm', label='Default', linewidths=2)\n",
        "ax1.set_title('t-SNE', fontsize=14)\n",
        "\n",
        "ax1.grid(True)\n",
        "ax1.legend(handles=[blue_patch, red_patch])\n",
        "\n",
        "# PCA scatter plot\n",
        "ax2.scatter(X_reduced_pca[:,0], X_reduced_pca[:,1], c=(y == 0), cmap='coolwarm', label='No Default', linewidths=2)\n",
        "ax2.scatter(X_reduced_pca[:,0], X_reduced_pca[:,1], c=(y == 1), cmap='coolwarm', label='Default', linewidths=2)\n",
        "ax2.set_title('PCA', fontsize=14)\n",
        "\n",
        "ax2.grid(True)\n",
        "ax2.legend(handles=[blue_patch, red_patch])\n",
        "\n",
        "# TruncatedSVD scatter plot\n",
        "ax3.scatter(X_reduced_svd[:,0], X_reduced_svd[:,1], c=(y == 0), cmap='coolwarm', label='No Default', linewidths=2)\n",
        "ax3.scatter(X_reduced_svd[:,0], X_reduced_svd[:,1], c=(y == 1), cmap='coolwarm', label='Default', linewidths=2)\n",
        "ax3.set_title('Truncated SVD', fontsize=14)\n",
        "\n",
        "ax3.grid(True)\n",
        "ax3.legend(handles=[blue_patch, red_patch])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1728x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vQS5zJddX7--",
        "colab_type": "text"
      },
      "source": [
        "Classifiers (UnderSampling):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVDB5ImMItaZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Undersampling before cross validating (prone to overfit)\n",
        "X = new_df.drop('SeriousDlqin2yrs', axis=1)\n",
        "y = new_df['SeriousDlqin2yrs']"
      ],
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fw_itKe7Isns",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Our data is already scaled we should split our training and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# This is explicitly used for undersampling.\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-4r8hIXYar8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Turn the values into an array for feeding the classification algorithms.\n",
        "X_train = X_train.values\n",
        "X_test = X_test.values\n",
        "y_train = y_train.values\n",
        "y_test = y_test.values"
      ],
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKXZ6Up9Yf9Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Let's implement simple classifiers\n",
        "\n",
        "classifiers = {\n",
        "    \"LogisiticRegression\": LogisticRegression(),\n",
        "    \"KNearest\": KNeighborsClassifier(),\n",
        "    \"Support Vector Classifier\": SVC(),\n",
        "    \"DecisionTreeClassifier\": DecisionTreeClassifier()\n",
        "}"
      ],
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1GQWgJmXYkjk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "d7363d54-0d23-4c1b-a85f-a7f8e92b0995"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "\n",
        "for key, classifier in classifiers.items():\n",
        "    classifier.fit(X_train, y_train)\n",
        "    training_score = cross_val_score(classifier, X_train, y_train, cv=5)\n",
        "    print(\"Classifiers: \", classifier.__class__.__name__, \"Has a training score of\", round(training_score.mean(), 2) * 100, \"% accuracy score\")"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classifiers:  LogisticRegression Has a training score of 71.0 % accuracy score\n",
            "Classifiers:  KNeighborsClassifier Has a training score of 72.0 % accuracy score\n",
            "Classifiers:  SVC Has a training score of 75.0 % accuracy score\n",
            "Classifiers:  DecisionTreeClassifier Has a training score of 69.0 % accuracy score\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWS88aZYYr0c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Use GridSearchCV to find the best parameters.\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "# Logistic Regression \n",
        "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
        "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n",
        "grid_log_reg.fit(X_train, y_train)\n",
        "# log_reg best estimator\n",
        "log_reg = grid_log_reg.best_estimator_\n",
        "# kNN classifier\n",
        "knears_params = {\"n_neighbors\": list(range(2,5,1)), 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']}\n",
        "grid_knears = GridSearchCV(KNeighborsClassifier(), knears_params)\n",
        "grid_knears.fit(X_train, y_train)\n",
        "# KNears best estimator\n",
        "knears_neighbors = grid_knears.best_estimator_\n",
        "# Support Vector Classifier\n",
        "svc_params = {'C': [0.5, 0.7, 0.9, 1], 'kernel': ['rbf', 'poly', 'sigmoid', 'linear']}\n",
        "grid_svc = GridSearchCV(SVC(), svc_params)\n",
        "grid_svc.fit(X_train, y_train)\n",
        "# SVC best estimator\n",
        "svc = grid_svc.best_estimator_\n",
        "# DecisionTree Classifier\n",
        "tree_params = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": list(range(2,4,1)), \n",
        "              \"min_samples_leaf\": list(range(5,7,1))}\n",
        "grid_tree = GridSearchCV(DecisionTreeClassifier(), tree_params)\n",
        "grid_tree.fit(X_train, y_train)\n",
        "\n",
        "# tree best estimator\n",
        "tree_clf = grid_tree.best_estimator_"
      ],
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m1Nzf0vzZifE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "4ff6b281-2588-447c-b440-eb1dd8c63f9e"
      },
      "source": [
        "# Overfitting Case\n",
        "log_reg_score = cross_val_score(log_reg, X_train, y_train, cv=5)\n",
        "print('Logistic Regression Cross Validation Score: ', round(log_reg_score.mean() * 100, 2).astype(str) + '%')\n",
        "\n",
        "knears_score = cross_val_score(knears_neighbors, X_train, y_train, cv=5)\n",
        "print('Knears Neighbors Cross Validation Score', round(knears_score.mean() * 100, 2).astype(str) + '%')\n",
        "\n",
        "svc_score = cross_val_score(svc, X_train, y_train, cv=5)\n",
        "print('Support Vector Classifier Cross Validation Score', round(svc_score.mean() * 100, 2).astype(str) + '%')\n",
        "\n",
        "tree_score = cross_val_score(tree_clf, X_train, y_train, cv=5)\n",
        "print('DecisionTree Classifier Cross Validation Score', round(tree_score.mean() * 100, 2).astype(str) + '%')"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logistic Regression Cross Validation Score:  70.88%\n",
            "Knears Neighbors Cross Validation Score 71.1%\n",
            "Support Vector Classifier Cross Validation Score 74.55%\n",
            "DecisionTree Classifier Cross Validation Score 74.81%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a9RO-9-mc1A1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "d49b642c-87d4-4de5-bef6-cd15f9419836"
      },
      "source": [
        "# We will undersample during cross validating\n",
        "undersample_X = df.drop('SeriousDlqin2yrs', axis=1)\n",
        "undersample_y = df['SeriousDlqin2yrs']\n",
        "undersample_X.scaled_MonthlyIncome = undersample_X.scaled_MonthlyIncome.fillna(undersample_X.scaled_MonthlyIncome.median())\n",
        "undersample_X.NumberOfDependents = undersample_X.NumberOfDependents.fillna(undersample_X.NumberOfDependents.median())\n",
        "\n",
        "for train_index, test_index in sss.split(undersample_X, undersample_y):\n",
        "    print(\"Train:\", train_index, \"Test:\", test_index)\n",
        "    undersample_Xtrain, undersample_Xtest = undersample_X.iloc[train_index], undersample_X.iloc[test_index]\n",
        "    undersample_ytrain, undersample_ytest = undersample_y.iloc[train_index], undersample_y.iloc[test_index]\n",
        "    \n",
        "undersample_Xtrain = undersample_Xtrain.values\n",
        "undersample_Xtest = undersample_Xtest.values\n",
        "undersample_ytrain = undersample_ytrain.values\n",
        "undersample_ytest = undersample_ytest.values \n",
        "\n",
        "undersample_accuracy = []\n",
        "undersample_precision = []\n",
        "undersample_recall = []\n",
        "undersample_f1 = []\n",
        "undersample_auc = []\n",
        "\n",
        "# Implementing NearMiss Technique \n",
        "# Distribution of NearMiss (Just to see how it distributes the labels we won't use these variables)\n",
        "X_nearmiss, y_nearmiss = NearMiss().fit_sample(undersample_X.values, undersample_y.values)\n",
        "print('NearMiss Label Distribution: {}'.format(Counter(y_nearmiss)))\n",
        "# Cross Validating the right way\n",
        "for train, test in sss.split(undersample_Xtrain, undersample_ytrain):\n",
        "    undersample_pipeline = imbalanced_make_pipeline(NearMiss(sampling_strategy='majority'), log_reg) # SMOTE happens during Cross Validation not before..\n",
        "    undersample_model = undersample_pipeline.fit(undersample_Xtrain[train], undersample_ytrain[train])\n",
        "    undersample_prediction = undersample_model.predict(undersample_Xtrain[test])\n",
        "    \n",
        "    undersample_accuracy.append(undersample_pipeline.score(original_Xtrain[test], original_ytrain[test]))\n",
        "    undersample_precision.append(precision_score(original_ytrain[test], undersample_prediction))\n",
        "    undersample_recall.append(recall_score(original_ytrain[test], undersample_prediction))\n",
        "    undersample_f1.append(f1_score(original_ytrain[test], undersample_prediction))\n",
        "    undersample_auc.append(roc_auc_score(original_ytrain[test], undersample_prediction))"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train: [ 29931  29932  29933 ... 149997 149998 149999] Test: [    0     1     2 ... 30887 30945 30986]\n",
            "Train: [     0      1      2 ... 149997 149998 149999] Test: [29931 29932 29933 ... 60044 60047 60053]\n",
            "Train: [     0      1      2 ... 149997 149998 149999] Test: [59995 59996 59997 ... 90908 90961 90962]\n",
            "Train: [     0      1      2 ... 149997 149998 149999] Test: [ 89945  89946  89947 ... 120571 120572 120576]\n",
            "Train: [     0      1      2 ... 120571 120572 120576] Test: [119958 119959 119960 ... 149997 149998 149999]\n",
            "NearMiss Label Distribution: Counter({0: 10026, 1: 10026})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZ2VHdfYditv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Let's Plot LogisticRegression Learning Curve\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "def plot_learning_curve(estimator1, estimator2, estimator3, estimator4, X, y, ylim=None, cv=None,\n",
        "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
        "    f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(20,14), sharey=True)\n",
        "    if ylim is not None:\n",
        "        plt.ylim(*ylim)\n",
        "    # First Estimator\n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator1, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    ax1.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"#ff9124\")\n",
        "    ax1.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
        "    ax1.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
        "             label=\"Training score\")\n",
        "    ax1.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
        "             label=\"Cross-validation score\")\n",
        "    ax1.set_title(\"Logistic Regression Learning Curve\", fontsize=14)\n",
        "    ax1.set_xlabel('Training size (m)')\n",
        "    ax1.set_ylabel('Score')\n",
        "    ax1.grid(True)\n",
        "    ax1.legend(loc=\"best\")\n",
        "    \n",
        "    # Second Estimator \n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator2, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    ax2.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"#ff9124\")\n",
        "    ax2.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
        "    ax2.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
        "             label=\"Training score\")\n",
        "    ax2.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
        "             label=\"Cross-validation score\")\n",
        "    ax2.set_title(\"Knears Neighbors Learning Curve\", fontsize=14)\n",
        "    ax2.set_xlabel('Training size (m)')\n",
        "    ax2.set_ylabel('Score')\n",
        "    ax2.grid(True)\n",
        "    ax2.legend(loc=\"best\")\n",
        "    \n",
        "    # Third Estimator\n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator3, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    ax3.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"#ff9124\")\n",
        "    ax3.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
        "    ax3.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
        "             label=\"Training score\")\n",
        "    ax3.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
        "             label=\"Cross-validation score\")\n",
        "    ax3.set_title(\"Support Vector Classifier \\n Learning Curve\", fontsize=14)\n",
        "    ax3.set_xlabel('Training size (m)')\n",
        "    ax3.set_ylabel('Score')\n",
        "    ax3.grid(True)\n",
        "    ax3.legend(loc=\"best\")\n",
        "    \n",
        "    # Fourth Estimator\n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator4, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    ax4.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"#ff9124\")\n",
        "    ax4.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
        "    ax4.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
        "             label=\"Training score\")\n",
        "    ax4.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
        "             label=\"Cross-validation score\")\n",
        "    ax4.set_title(\"Decision Tree Classifier \\n Learning Curve\", fontsize=14)\n",
        "    ax4.set_xlabel('Training size (m)')\n",
        "    ax4.set_ylabel('Score')\n",
        "    ax4.grid(True)\n",
        "    ax4.legend(loc=\"best\")\n",
        "    return plt"
      ],
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlZ06j4ng6rm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=42)\n",
        "#plot_learning_curve(log_reg, knears_neighbors, svc, tree_clf, X_train, y_train, (0.87, 1.01), cv=cv, n_jobs=4)"
      ],
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nVLJFy-Hg6YF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "# Create a DataFrame with all the scores and the classifiers names.\n",
        "\n",
        "log_reg_pred = cross_val_predict(log_reg, X_train, y_train, cv=5,\n",
        "                             method=\"decision_function\")\n",
        "\n",
        "knears_pred = cross_val_predict(knears_neighbors, X_train, y_train, cv=5)\n",
        "\n",
        "svc_pred = cross_val_predict(svc, X_train, y_train, cv=5,\n",
        "                             method=\"decision_function\")\n",
        "\n",
        "tree_pred = cross_val_predict(tree_clf, X_train, y_train, cv=5)"
      ],
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o5FicaDhg6N5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "d767d7d5-8716-4f55-d324-bb5975acb6e8"
      },
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "print('Logistic Regression: ', roc_auc_score(y_train, log_reg_pred))\n",
        "print('KNears Neighbors: ', roc_auc_score(y_train, knears_pred))\n",
        "print('Support Vector Classifier: ', roc_auc_score(y_train, svc_pred))\n",
        "print('Decision Tree Classifier: ', roc_auc_score(y_train, tree_pred))"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logistic Regression:  0.7846077798065213\n",
            "KNears Neighbors:  0.7105890729716232\n",
            "Support Vector Classifier:  0.8192147123888337\n",
            "Decision Tree Classifier:  0.7485088543013448\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nP1BLrCyg6BJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "outputId": "c0fb88af-0e7d-4aa5-e64a-85d69df291d1"
      },
      "source": [
        "log_fpr, log_tpr, log_thresold = roc_curve(y_train, log_reg_pred)\n",
        "knear_fpr, knear_tpr, knear_threshold = roc_curve(y_train, knears_pred)\n",
        "svc_fpr, svc_tpr, svc_threshold = roc_curve(y_train, svc_pred)\n",
        "tree_fpr, tree_tpr, tree_threshold = roc_curve(y_train, tree_pred)\n",
        "\n",
        "\n",
        "def graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr):\n",
        "    plt.figure(figsize=(16,8))\n",
        "    plt.title('ROC Curve \\n Top 4 Classifiers', fontsize=18)\n",
        "    plt.plot(log_fpr, log_tpr, label='Logistic Regression Classifier Score: {:.4f}'.format(roc_auc_score(y_train, log_reg_pred)))\n",
        "    plt.plot(knear_fpr, knear_tpr, label='KNears Neighbors Classifier Score: {:.4f}'.format(roc_auc_score(y_train, knears_pred)))\n",
        "    plt.plot(svc_fpr, svc_tpr, label='Support Vector Classifier Score: {:.4f}'.format(roc_auc_score(y_train, svc_pred)))\n",
        "    plt.plot(tree_fpr, tree_tpr, label='Decision Tree Classifier Score: {:.4f}'.format(roc_auc_score(y_train, tree_pred)))\n",
        "    plt.plot([0, 1], [0, 1], 'k--')\n",
        "    plt.axis([-0.01, 1, 0, 1])\n",
        "    plt.xlabel('False Positive Rate', fontsize=16)\n",
        "    plt.ylabel('True Positive Rate', fontsize=16)\n",
        "    plt.annotate('Minimum ROC Score of 50% \\n (This is the minimum score to get)', xy=(0.5, 0.5), xytext=(0.6, 0.3),\n",
        "                arrowprops=dict(facecolor='#6E726D', shrink=0.05),\n",
        "                )\n",
        "    plt.legend()\n",
        "    \n",
        "graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr)\n",
        "plt.show()"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWzTVjvTg5mC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "precision, recall, threshold = precision_recall_curve(y_train, log_reg_pred)"
      ],
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCgczERtltkp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "109a99c8-bc07-4b9e-817e-9f1c57e7cf14"
      },
      "source": [
        "from sklearn.metrics import recall_score, precision_score, f1_score, accuracy_score\n",
        "y_pred = log_reg.predict(X_train)\n",
        "\n",
        "# Overfitting Case\n",
        "print('---' * 45)\n",
        "print('Overfitting: \\n')\n",
        "print('Recall Score: {:.2f}'.format(recall_score(y_train, y_pred)))\n",
        "print('Precision Score: {:.2f}'.format(precision_score(y_train, y_pred)))\n",
        "print('F1 Score: {:.2f}'.format(f1_score(y_train, y_pred)))\n",
        "print('Accuracy Score: {:.2f}'.format(accuracy_score(y_train, y_pred)))\n",
        "print('---' * 45)\n",
        "\n",
        "# How it should look like\n",
        "print('---' * 45)\n",
        "print('How it should be:\\n')\n",
        "print(\"Accuracy Score: {:.2f}\".format(np.mean(undersample_accuracy)))\n",
        "print(\"Precision Score: {:.2f}\".format(np.mean(undersample_precision)))\n",
        "print(\"Recall Score: {:.2f}\".format(np.mean(undersample_recall)))\n",
        "print(\"F1 Score: {:.2f}\".format(np.mean(undersample_f1)))\n",
        "print('---' * 45)"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "---------------------------------------------------------------------------------------------------------------------------------------\n",
            "Overfitting: \n",
            "\n",
            "Recall Score: 0.93\n",
            "Precision Score: 0.50\n",
            "F1 Score: 0.65\n",
            "Accuracy Score: 0.50\n",
            "---------------------------------------------------------------------------------------------------------------------------------------\n",
            "---------------------------------------------------------------------------------------------------------------------------------------\n",
            "How it should be:\n",
            "\n",
            "Accuracy Score: 0.12\n",
            "Precision Score: 0.07\n",
            "Recall Score: 0.73\n",
            "F1 Score: 0.13\n",
            "---------------------------------------------------------------------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z116rnLxiAcP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "undersample_y_score = log_reg.decision_function(original_Xtest)"
      ],
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCytVidHigLV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "081463e2-ebeb-4e12-bc47-ea87a3950d24"
      },
      "source": [
        "from sklearn.metrics import average_precision_score\n",
        "\n",
        "undersample_average_precision = average_precision_score(original_ytest, undersample_y_score)\n",
        "\n",
        "print('Average precision-recall score: {0:0.2f}'.format(\n",
        "      undersample_average_precision))"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average precision-recall score: 0.09\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z8YNPqHEilfA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "a04f7e20-acae-489e-e572-b07cea5f2794"
      },
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig = plt.figure(figsize=(12,6))\n",
        "\n",
        "precision, recall, _ = precision_recall_curve(original_ytest, undersample_y_score)\n",
        "\n",
        "plt.step(recall, precision, color='#004a93', alpha=0.2,\n",
        "         where='post')\n",
        "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
        "                 color='#48a6ff')\n",
        "\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.title('UnderSampling Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.2f}'.format(\n",
        "          undersample_average_precision), fontsize=16)"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'UnderSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.09')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aVzwkoQmi17j",
        "colab_type": "text"
      },
      "source": [
        "SMOTE Technique (Over-Sampling):"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mBmvVzMOi-Li",
        "colab_type": "text"
      },
      "source": [
        "SMOTE creates synthetic points from the minority class in order to reach an equal balance between the minority and majority class.\n",
        "\n",
        "It picks the distance between the closest neighbors of the minority class, in between these distances it creates synthetic points.\n",
        "\n",
        "Here, more information is retained since we didn't have to delete any rows unlike in random undersampling.\n",
        "\n",
        "Accuracy || Time Tradeoff: Although it is likely that SMOTE will be more accurate than random under-sampling, it will take more time to train since no rows are eliminated as previously stated."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNmtWSqRivpV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "71b36ad0-6811-4fe0-bf9a-683a1cb4060a"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
        "\n",
        "print('Length of X (train): {} | Length of y (train): {}'.format(len(original_Xtrain), len(original_ytrain)))\n",
        "print('Length of X (test): {} | Length of y (test): {}'.format(len(original_Xtest), len(original_ytest)))\n",
        "\n",
        "# List to append the score and then find the average\n",
        "accuracy_lst = []\n",
        "precision_lst = []\n",
        "recall_lst = []\n",
        "f1_lst = []\n",
        "auc_lst = []\n",
        "\n",
        "# Classifier with optimal parameters\n",
        "# log_reg_sm = grid_log_reg.best_estimator_\n",
        "log_reg_sm = LogisticRegression()\n",
        "\n",
        "rand_log_reg = RandomizedSearchCV(LogisticRegression(), log_reg_params, n_iter=4)\n",
        "# Implementing SMOTE Technique \n",
        "# Cross Validating the right way\n",
        "# Parameters\n",
        "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
        "for train, test in sss.split(original_Xtrain, original_ytrain):\n",
        "    pipeline = imbalanced_make_pipeline(SMOTE(sampling_strategy='minority'), rand_log_reg) # SMOTE happens during Cross Validation not before..\n",
        "    model = pipeline.fit(original_Xtrain[train], original_ytrain[train])\n",
        "    best_est = rand_log_reg.best_estimator_\n",
        "    prediction = best_est.predict(original_Xtrain[test])\n",
        "    \n",
        "    accuracy_lst.append(pipeline.score(original_Xtrain[test], original_ytrain[test]))\n",
        "    precision_lst.append(precision_score(original_ytrain[test], prediction))\n",
        "    recall_lst.append(recall_score(original_ytrain[test], prediction))\n",
        "    f1_lst.append(f1_score(original_ytrain[test], prediction))\n",
        "    auc_lst.append(roc_auc_score(original_ytrain[test], prediction))\n",
        "    \n",
        "print('---' * 45)\n",
        "print('')\n",
        "print(\"accuracy: {}\".format(np.mean(accuracy_lst)))\n",
        "print(\"precision: {}\".format(np.mean(precision_lst)))\n",
        "print(\"recall: {}\".format(np.mean(recall_lst)))\n",
        "print(\"f1: {}\".format(np.mean(f1_lst)))\n",
        "print('---' * 45)"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Length of X (train): 120000 | Length of y (train): 120000\n",
            "Length of X (test): 30000 | Length of y (test): 30000\n",
            "---------------------------------------------------------------------------------------------------------------------------------------\n",
            "\n",
            "accuracy: 0.7552916666666667\n",
            "precision: 0.16466056035926563\n",
            "recall: 0.6529105584947289\n",
            "f1: 0.2629682705868007\n",
            "---------------------------------------------------------------------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xff6dwPpw5jR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "f8449b09-4957-4089-cd2c-6379dc5ababe"
      },
      "source": [
        "labels = ['No Default', 'Default']\n",
        "smote_prediction = best_est.predict(original_Xtest)\n",
        "print(classification_report(original_ytest, smote_prediction, target_names=labels))"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "  No Default       0.97      0.75      0.85     27995\n",
            "     Default       0.16      0.67      0.26      2005\n",
            "\n",
            "    accuracy                           0.75     30000\n",
            "   macro avg       0.57      0.71      0.55     30000\n",
            "weighted avg       0.92      0.75      0.81     30000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRGi-TebxSfB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_score = best_est.decision_function(original_Xtest)"
      ],
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IB1hjXvmxZ_J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "28e13487-3884-41be-e126-1d9677819909"
      },
      "source": [
        "average_precision = average_precision_score(original_ytest, y_score)\n",
        "\n",
        "print('Average precision-recall score: {0:0.2f}'.format(\n",
        "      average_precision))"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average precision-recall score: 0.29\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jysiqz5dxia7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "96cc0b2c-3637-403b-b890-f40497d4fd98"
      },
      "source": [
        "fig = plt.figure(figsize=(12,6))\n",
        "\n",
        "precision, recall, _ = precision_recall_curve(original_ytest, y_score)\n",
        "\n",
        "plt.step(recall, precision, color='r', alpha=0.2,\n",
        "         where='post')\n",
        "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
        "                 color='#F59B00')\n",
        "\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.title('OverSampling Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.2f}'.format(\n",
        "          average_precision), fontsize=16)"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'OverSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.29')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 183
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZ0tKtWpxuTQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# SMOTE Technique (OverSampling) After splitting and Cross Validating\n",
        "sm = SMOTE(ratio='minority', random_state=42)\n",
        "# Xsm_train, ysm_train = sm.fit_sample(X_train, y_train)\n",
        "\n",
        "\n",
        "# This will be the data were we are going to \n",
        "Xsm_train, ysm_train = sm.fit_sample(original_Xtrain, original_ytrain)"
      ],
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TmsBvHxx4P6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b9f88d87-c98f-442d-f1c6-2aebc9097509"
      },
      "source": [
        "# We Improve the score by 2% points approximately \n",
        "# Implement GridSearchCV and the other models.\n",
        "\n",
        "# Logistic Regression\n",
        "t0 = time.time()\n",
        "log_reg_sm = grid_log_reg.best_estimator_\n",
        "log_reg_sm.fit(Xsm_train, ysm_train)\n",
        "t1 = time.time()\n",
        "print(\"Fitting oversample data took :{} sec\".format(t1 - t0))"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting oversample data took :0.8078110218048096 sec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hUJOPh0dyGGv",
        "colab_type": "text"
      },
      "source": [
        "Test Data with Logistic Regression:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-yXdM4cdAJlG",
        "colab_type": "text"
      },
      "source": [
        "We will evaluate the final performance of the classification models in the random undersampling subset (this is not the data from the original dataframe).The models that performed the best were logistic regression and support vector classifier (SVM)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aFeerXW-yAAV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "outputId": "4cef6ed1-0e44-4339-c5a9-cfa6583fc709"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "# Logistic Regression fitted using SMOTE technique\n",
        "y_pred_log_reg = log_reg_sm.predict(X_test)\n",
        "# Other models fitted with UnderSampling\n",
        "y_pred_knear = knears_neighbors.predict(X_test)\n",
        "y_pred_svc = svc.predict(X_test)\n",
        "y_pred_tree = tree_clf.predict(X_test)\n",
        "\n",
        "log_reg_cf = confusion_matrix(y_test, y_pred_log_reg)\n",
        "kneighbors_cf = confusion_matrix(y_test, y_pred_knear)\n",
        "svc_cf = confusion_matrix(y_test, y_pred_svc)\n",
        "tree_cf = confusion_matrix(y_test, y_pred_tree)\n",
        "\n",
        "fig, ax = plt.subplots(2, 2,figsize=(22,12))\n",
        "\n",
        "sns.heatmap(log_reg_cf, ax=ax[0][0], annot=True, cmap=plt.cm.copper)\n",
        "ax[0, 0].set_title(\"Logistic Regression \\n Confusion Matrix\", fontsize=14)\n",
        "ax[0, 0].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
        "ax[0, 0].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
        "\n",
        "sns.heatmap(kneighbors_cf, ax=ax[0][1], annot=True, cmap=plt.cm.copper)\n",
        "ax[0][1].set_title(\"KNearsNeighbors \\n Confusion Matrix\", fontsize=14)\n",
        "ax[0][1].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
        "ax[0][1].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
        "\n",
        "sns.heatmap(svc_cf, ax=ax[1][0], annot=True, cmap=plt.cm.copper)\n",
        "ax[1][0].set_title(\"Suppor Vector Classifier \\n Confusion Matrix\", fontsize=14)\n",
        "ax[1][0].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
        "ax[1][0].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
        "\n",
        "sns.heatmap(tree_cf, ax=ax[1][1], annot=True, cmap=plt.cm.copper)\n",
        "ax[1][1].set_title(\"DecisionTree Classifier \\n Confusion Matrix\", fontsize=14)\n",
        "ax[1][1].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
        "ax[1][1].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1584x864 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lai5wfDMAtGm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        },
        "outputId": "45d6a3d2-e302-47a5-8740-91dbfe54f037"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print('Logistic Regression:')\n",
        "print(classification_report(y_test, y_pred_log_reg))\n",
        "\n",
        "print('KNears Neighbors:')\n",
        "print(classification_report(y_test, y_pred_knear))\n",
        "\n",
        "print('Support Vector Classifier:')\n",
        "print(classification_report(y_test, y_pred_svc))\n",
        "\n",
        "print('Support Vector Classifier:')\n",
        "print(classification_report(y_test, y_pred_tree))"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logistic Regression:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.76      0.72      1979\n",
            "           1       0.74      0.65      0.69      2032\n",
            "\n",
            "    accuracy                           0.70      4011\n",
            "   macro avg       0.71      0.70      0.70      4011\n",
            "weighted avg       0.71      0.70      0.70      4011\n",
            "\n",
            "KNears Neighbors:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      0.84      0.74      1979\n",
            "           1       0.79      0.59      0.67      2032\n",
            "\n",
            "    accuracy                           0.71      4011\n",
            "   macro avg       0.73      0.71      0.71      4011\n",
            "weighted avg       0.73      0.71      0.71      4011\n",
            "\n",
            "Support Vector Classifier:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.87      0.78      1979\n",
            "           1       0.84      0.65      0.73      2032\n",
            "\n",
            "    accuracy                           0.76      4011\n",
            "   macro avg       0.77      0.76      0.76      4011\n",
            "weighted avg       0.77      0.76      0.76      4011\n",
            "\n",
            "Support Vector Classifier:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.61      0.70      1979\n",
            "           1       0.70      0.87      0.78      2032\n",
            "\n",
            "    accuracy                           0.74      4011\n",
            "   macro avg       0.76      0.74      0.74      4011\n",
            "weighted avg       0.76      0.74      0.74      4011\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v6QorB2wB0Mz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "da6fdc68-7582-423d-b772-cded2b3f3c4b"
      },
      "source": [
        "# Final Score in the test set of logistic regression\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Logistic Regression with Under-Sampling\n",
        "y_pred = log_reg.predict(X_test)\n",
        "undersample_score = accuracy_score(y_test, y_pred)\n",
        "\n",
        "# Logistic Regression with SMOTE Technique (Better accuracy with SMOTE t)\n",
        "y_pred_sm = best_est.predict(original_Xtest)\n",
        "oversample_score = accuracy_score(original_ytest, y_pred_sm)\n",
        "\n",
        "d = {'Technique': ['Random UnderSampling', 'Oversampling (SMOTE)'], 'Score': [undersample_score, oversample_score]}\n",
        "final_df = pd.DataFrame(data=d)\n",
        "\n",
        "# Move column\n",
        "score = final_df['Score']\n",
        "final_df.drop('Score', axis=1, inplace=True)\n",
        "final_df.insert(1, 'Score', score)\n",
        "\n",
        "final_df"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Technique</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random UnderSampling</td>\n",
              "      <td>0.704064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Oversampling (SMOTE)</td>\n",
              "      <td>0.745167</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Technique     Score\n",
              "0  Random UnderSampling  0.704064\n",
              "1  Oversampling (SMOTE)  0.745167"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4jBylhCuCrm5",
        "colab_type": "text"
      },
      "source": [
        "Neural Networks Testing Random UnderSampling Data vs OverSampling (SMOTE):"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_pXUvOgDC4b7",
        "colab_type": "text"
      },
      "source": [
        "In this phase of testing we will fit this model in both the random undersampled subset and oversampled dataset (SMOTE) in order to predict the final result using the original dataframe testing data. As stated previously, this will be a simple model composed of one input layer (where the number of nodes equals the number of features) plus bias node, one hidden layer with 32 nodes and one output node composed of two possible results 0 or 1 (No default or default). The learning rate will be 0.001, the optimizer we will use is the AdamOptimizer, the activation function that is used in this scenario is \"Relu\" and for the final outputs we will use sparse categorical cross entropy, which gives the probability whether an instance case is no fraud or fraud (The prediction will pick the highest probability between the two)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldiScZyKClSJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "ebd30e14-21c2-43d2-ed8e-1dd58b8a548a"
      },
      "source": [
        "import keras\n",
        "from keras import backend as K\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Activation\n",
        "from keras.layers.core import Dense\n",
        "from keras.optimizers import Adam\n",
        "from keras.metrics import categorical_crossentropy\n",
        "\n",
        "n_inputs = X_train.shape[1]\n",
        "\n",
        "undersample_model = Sequential([\n",
        "    Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),\n",
        "    Dense(32, activation='relu'),\n",
        "    Dense(2, activation='softmax')\n",
        "])\n",
        "\n",
        "undersample_model.summary()"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_4 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 32)                352       \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 2)                 66        \n",
            "=================================================================\n",
            "Total params: 528\n",
            "Trainable params: 528\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ceAUZEfyDsuv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "undersample_model.compile(Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 191,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPrcvRF0D8W1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "outputId": "d308d057-a502-40b8-d38a-b0c899d6a94b"
      },
      "source": [
        "undersample_model.fit(X_train, y_train, validation_split=0.2, batch_size=25, epochs=20, shuffle=True, verbose=2)"
      ],
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 12832 samples, validate on 3209 samples\n",
            "Epoch 1/20\n",
            " - 1s - loss: 0.6027 - accuracy: 0.7032 - val_loss: 0.5662 - val_accuracy: 0.7442\n",
            "Epoch 2/20\n",
            " - 1s - loss: 0.5507 - accuracy: 0.7435 - val_loss: 0.5368 - val_accuracy: 0.7566\n",
            "Epoch 3/20\n",
            " - 1s - loss: 0.5322 - accuracy: 0.7489 - val_loss: 0.5244 - val_accuracy: 0.7591\n",
            "Epoch 4/20\n",
            " - 1s - loss: 0.5269 - accuracy: 0.7517 - val_loss: 0.5225 - val_accuracy: 0.7622\n",
            "Epoch 5/20\n",
            " - 1s - loss: 0.5209 - accuracy: 0.7530 - val_loss: 0.5168 - val_accuracy: 0.7616\n",
            "Epoch 6/20\n",
            " - 1s - loss: 0.5186 - accuracy: 0.7547 - val_loss: 0.5135 - val_accuracy: 0.7653\n",
            "Epoch 7/20\n",
            " - 1s - loss: 0.5147 - accuracy: 0.7574 - val_loss: 0.5269 - val_accuracy: 0.7560\n",
            "Epoch 8/20\n",
            " - 1s - loss: 0.5143 - accuracy: 0.7563 - val_loss: 0.5158 - val_accuracy: 0.7622\n",
            "Epoch 9/20\n",
            " - 1s - loss: 0.5126 - accuracy: 0.7560 - val_loss: 0.5134 - val_accuracy: 0.7660\n",
            "Epoch 10/20\n",
            " - 1s - loss: 0.5140 - accuracy: 0.7576 - val_loss: 0.5109 - val_accuracy: 0.7650\n",
            "Epoch 11/20\n",
            " - 1s - loss: 0.5140 - accuracy: 0.7577 - val_loss: 0.5071 - val_accuracy: 0.7675\n",
            "Epoch 12/20\n",
            " - 1s - loss: 0.5104 - accuracy: 0.7587 - val_loss: 0.5057 - val_accuracy: 0.7697\n",
            "Epoch 13/20\n",
            " - 1s - loss: 0.5107 - accuracy: 0.7579 - val_loss: 0.5052 - val_accuracy: 0.7653\n",
            "Epoch 14/20\n",
            " - 1s - loss: 0.5084 - accuracy: 0.7569 - val_loss: 0.5073 - val_accuracy: 0.7622\n",
            "Epoch 15/20\n",
            " - 1s - loss: 0.5081 - accuracy: 0.7587 - val_loss: 0.5045 - val_accuracy: 0.7669\n",
            "Epoch 16/20\n",
            " - 1s - loss: 0.5073 - accuracy: 0.7593 - val_loss: 0.5059 - val_accuracy: 0.7616\n",
            "Epoch 17/20\n",
            " - 1s - loss: 0.5077 - accuracy: 0.7576 - val_loss: 0.5075 - val_accuracy: 0.7682\n",
            "Epoch 18/20\n",
            " - 1s - loss: 0.5079 - accuracy: 0.7562 - val_loss: 0.5067 - val_accuracy: 0.7600\n",
            "Epoch 19/20\n",
            " - 1s - loss: 0.5072 - accuracy: 0.7577 - val_loss: 0.5068 - val_accuracy: 0.7632\n",
            "Epoch 20/20\n",
            " - 1s - loss: 0.5051 - accuracy: 0.7603 - val_loss: 0.5072 - val_accuracy: 0.7653\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7f8accae7b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mI8yvsbeEA4v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "undersample_predictions = undersample_model.predict(original_Xtest, batch_size=200, verbose=0)"
      ],
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "47dgrnm4ELWg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "undersample_fraud_predictions = undersample_model.predict_classes(original_Xtest, batch_size=200, verbose=0)"
      ],
      "execution_count": 194,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qdWVID3zEPeL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import itertools\n",
        "\n",
        "# Create a confusion matrix\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title, fontsize=14)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "execution_count": 195,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CmiqZ_TWEpW1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "31b7d143-a3dc-413c-a75c-e0cf6ce3ac5f"
      },
      "source": [
        "undersample_cm = confusion_matrix(original_ytest, undersample_fraud_predictions)\n",
        "actual_cm = confusion_matrix(original_ytest, original_ytest)\n",
        "labels = ['No Default', 'Default']\n",
        "\n",
        "fig = plt.figure(figsize=(16,8))\n",
        "\n",
        "fig.add_subplot(221)\n",
        "plot_confusion_matrix(undersample_cm, labels, title=\"Random UnderSample \\n Confusion Matrix\", cmap=plt.cm.Reds)\n",
        "\n",
        "fig.add_subplot(222)\n",
        "plot_confusion_matrix(actual_cm, labels, title=\"Confusion Matrix \\n (with 100% accuracy)\", cmap=plt.cm.Greens)"
      ],
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[22145  5850]\n",
            " [  514  1491]]\n",
            "Confusion matrix, without normalization\n",
            "[[27995     0]\n",
            " [    0  2005]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qGAozR9E4M4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_inputs = Xsm_train.shape[1]\n",
        "\n",
        "oversample_model = Sequential([\n",
        "    Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),\n",
        "    Dense(32, activation='relu'),\n",
        "    Dense(2, activation='softmax')\n",
        "])"
      ],
      "execution_count": 197,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oRPRl6FNFb_f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "oversample_model.compile(Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 198,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2iizGO5JFixJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "outputId": "d685cbac-2c35-44e4-d9fb-2b105f6e1b5b"
      },
      "source": [
        "oversample_model.fit(Xsm_train, ysm_train, validation_split=0.2, batch_size=30, epochs=20, shuffle=True, verbose=2)"
      ],
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 179166 samples, validate on 44792 samples\n",
            "Epoch 1/20\n",
            " - 8s - loss: 0.4773 - accuracy: 0.7834 - val_loss: 0.7317 - val_accuracy: 0.6010\n",
            "Epoch 2/20\n",
            " - 8s - loss: 0.4750 - accuracy: 0.7845 - val_loss: 0.6854 - val_accuracy: 0.6191\n",
            "Epoch 3/20\n",
            " - 8s - loss: 0.4743 - accuracy: 0.7844 - val_loss: 0.7326 - val_accuracy: 0.5939\n",
            "Epoch 4/20\n",
            " - 8s - loss: 0.4737 - accuracy: 0.7844 - val_loss: 0.6380 - val_accuracy: 0.6384\n",
            "Epoch 5/20\n",
            " - 8s - loss: 0.4730 - accuracy: 0.7848 - val_loss: 0.6692 - val_accuracy: 0.6458\n",
            "Epoch 6/20\n",
            " - 8s - loss: 0.4722 - accuracy: 0.7852 - val_loss: 0.6888 - val_accuracy: 0.6271\n",
            "Epoch 7/20\n",
            " - 8s - loss: 0.4713 - accuracy: 0.7856 - val_loss: 0.6701 - val_accuracy: 0.6301\n",
            "Epoch 8/20\n",
            " - 8s - loss: 0.4710 - accuracy: 0.7856 - val_loss: 0.6853 - val_accuracy: 0.6374\n",
            "Epoch 9/20\n",
            " - 8s - loss: 0.4702 - accuracy: 0.7864 - val_loss: 0.6778 - val_accuracy: 0.6268\n",
            "Epoch 10/20\n",
            " - 8s - loss: 0.4696 - accuracy: 0.7869 - val_loss: 0.6447 - val_accuracy: 0.6352\n",
            "Epoch 11/20\n",
            " - 9s - loss: 0.4688 - accuracy: 0.7868 - val_loss: 0.5979 - val_accuracy: 0.6675\n",
            "Epoch 12/20\n",
            " - 8s - loss: 0.4677 - accuracy: 0.7878 - val_loss: 0.6523 - val_accuracy: 0.6358\n",
            "Epoch 13/20\n",
            " - 8s - loss: 0.4669 - accuracy: 0.7871 - val_loss: 0.7062 - val_accuracy: 0.6026\n",
            "Epoch 14/20\n",
            " - 8s - loss: 0.4657 - accuracy: 0.7878 - val_loss: 0.6724 - val_accuracy: 0.6368\n",
            "Epoch 15/20\n",
            " - 9s - loss: 0.4640 - accuracy: 0.7885 - val_loss: 0.6665 - val_accuracy: 0.6387\n",
            "Epoch 16/20\n",
            " - 8s - loss: 0.4624 - accuracy: 0.7885 - val_loss: 0.7034 - val_accuracy: 0.6117\n",
            "Epoch 17/20\n",
            " - 8s - loss: 0.4600 - accuracy: 0.7895 - val_loss: 0.7157 - val_accuracy: 0.6062\n",
            "Epoch 18/20\n",
            " - 9s - loss: 0.4568 - accuracy: 0.7905 - val_loss: 0.7177 - val_accuracy: 0.6017\n",
            "Epoch 19/20\n",
            " - 9s - loss: 0.4539 - accuracy: 0.7917 - val_loss: 0.5982 - val_accuracy: 0.6697\n",
            "Epoch 20/20\n",
            " - 9s - loss: 0.4502 - accuracy: 0.7927 - val_loss: 0.6455 - val_accuracy: 0.6634\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7f8ac3fc8780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkyCZSc3Fy9E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "oversample_predictions = oversample_model.predict(original_Xtest, batch_size=200, verbose=0)"
      ],
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRSP1XFCGzXP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "oversample_default_predictions = oversample_model.predict_classes(original_Xtest, batch_size=200, verbose=0)"
      ],
      "execution_count": 202,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjC2kb06G-EH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "c00a4e8b-c85d-4341-80eb-bb6fd467cf87"
      },
      "source": [
        "oversample_smote = confusion_matrix(original_ytest, oversample_default_predictions)\n",
        "actual_cm = confusion_matrix(original_ytest, original_ytest)\n",
        "labels = ['No Default', 'Default']\n",
        "\n",
        "fig = plt.figure(figsize=(16,8))\n",
        "\n",
        "fig.add_subplot(221)\n",
        "plot_confusion_matrix(oversample_smote, labels, title=\"OverSample (SMOTE) \\n Confusion Matrix\", cmap=plt.cm.Oranges)\n",
        "\n",
        "fig.add_subplot(222)\n",
        "plot_confusion_matrix(actual_cm, labels, title=\"Confusion Matrix \\n (with 100% accuracy)\", cmap=plt.cm.Greens)"
      ],
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[24351  3644]\n",
            " [  673  1332]]\n",
            "Confusion matrix, without normalization\n",
            "[[27995     0]\n",
            " [    0  2005]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mM0RSPqTHpRY",
        "colab_type": "text"
      },
      "source": [
        "Conclusion:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5dmD7oegI_Mo",
        "colab_type": "text"
      },
      "source": [
        "Implementing SMOTE on our imbalanced dataset helped us with the imbalance of our labels (more no default than default transactions). Nonetheless, I still have to state that sometimes the neural network on the oversampled dataset predicts less correct default transactions than our model using the undersample dataset. However, remember that the removal of outliers was implemented only on the random undersample dataset and not on the oversampled one. Also, in our undersample data our model is unable to detect for a large number of cases non default transactions correctly and instead, misclassifies those non default transactions as default cases. The next step of this analysis will be to do an outlier removal on our oversample dataset and see if our accuracy in the test set improves."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3c7h4-0E7WN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}