First_prediction.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "x2fsyClhl0VM"
      },
      "outputs": [],
      "source": [
        "##Predict if patients will be undergoing thyroid surgery based on their blood work"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dfvsWZ3aXm8X",
        "outputId": "ab1c33fe-6477-4ff7-bec2-f6578dc7c54d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "8O7Hk7xHiqZE"
      },
      "outputs": [],
      "source": [
        "import datetime\n",
        "import os\n",
        "import collections\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from collections import Counter\n",
        "from matplotlib import pyplot as plt\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import (accuracy_score, auc, classification_report,\n",
        "                             confusion_matrix, f1_score, precision_recall_curve,\n",
        "                             precision_score, recall_score, roc_auc_score,\n",
        "                             roc_curve)\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import (cross_validate, train_test_split)\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.model_selection import KFold\n",
        "\n",
        "\n",
        "import keras\n",
        "from keras.callbacks import TensorBoard\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras.models import Sequential\n",
        "from keras.optimizers import Adam\n",
        "\n",
        "from keras.utils.vis_utils import plot_model\n",
        "\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from imblearn.under_sampling import RandomUnderSampler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j96IUyNWk_tT",
        "outputId": "07bd63b1-596c-4273-bced-a8bdd81fadf0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8624, 29)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "dataset_file = \"/content/drive/MyDrive/506/Final Project/thyroid_preprocessed.csv\"\n",
        "df = pd.read_csv(dataset_file)\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "3SUGKjcPlPU6",
        "outputId": "2620d8e7-155f-4bf3-ef2d-50bea981c87d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age_group  age  sex  on_thyroxine  query_on_thyroxine  \\\n",
              "0           1.0   18    0             1                   0   \n",
              "1           1.0   18    1             0                   0   \n",
              "2           1.0   18    0             1                   0   \n",
              "3           1.0   18    0             0                   0   \n",
              "4           1.0   18    1             0                   0   \n",
              "...         ...  ...  ...           ...                 ...   \n",
              "8619        8.0   95    1             0                   0   \n",
              "8620        8.0   95    0             0                   0   \n",
              "8621        8.0   97    1             0                   0   \n",
              "8622        8.0   97    1             0                   0   \n",
              "8623        8.0   97    0             0                   0   \n",
              "\n",
              "      on_antithyroid_meds  sick  pregnant  thyroid_surgery  I131_treatment  \\\n",
              "0                       0     0         0                0               0   \n",
              "1                       0     0         0                0               0   \n",
              "2                       0     0         0                0               0   \n",
              "3                       0     0         0                0               0   \n",
              "4                       0     0         0                0               0   \n",
              "...                   ...   ...       ...              ...             ...   \n",
              "8619                    0     0         0                0               0   \n",
              "8620                    0     0         0                0               0   \n",
              "8621                    0     0         0                0               0   \n",
              "8622                    0     0         0                0               0   \n",
              "8623                    0     0         0                0               0   \n",
              "\n",
              "      query_hypothyroid  query_hyperthyroid  lithium  goitre  tumor  \\\n",
              "0                     1                   0        0       0      0   \n",
              "1                     1                   0        0       0      0   \n",
              "2                     0                   0        0       0      0   \n",
              "3                     0                   0        0       0      0   \n",
              "4                     1                   0        0       0      0   \n",
              "...                 ...                 ...      ...     ...    ...   \n",
              "8619                  0                   0        0       0      0   \n",
              "8620                  0                   0        0       0      0   \n",
              "8621                  0                   0        0       0      0   \n",
              "8622                  0                   0        0       0      0   \n",
              "8623                  0                   0        0       0      0   \n",
              "\n",
              "      hypopituitary  psych  TSH_measured   TSH  T3_measured   T3  \\\n",
              "0                 0      0             1  2.50            1  2.7   \n",
              "1                 0      0             1  0.25            1  2.5   \n",
              "2                 0      0             1  0.33            0  0.0   \n",
              "3                 0      0             0  0.00            1  2.4   \n",
              "4                 0      0             1  1.50            0  0.0   \n",
              "...             ...    ...           ...   ...          ...  ...   \n",
              "8619              0      0             1  1.60            0  0.0   \n",
              "8620              0      0             1  4.40            1  0.4   \n",
              "8621              0      0             1  3.20            1  1.3   \n",
              "8622              0      0             1  2.90            1  0.5   \n",
              "8623              0      0             1  2.30            1  1.8   \n",
              "\n",
              "      TT4_measured    TT4  FTI_measured    FTI  TBG_measured   TBG  \\\n",
              "0                1  131.0             1  149.0             0   0.0   \n",
              "1                1  269.0             1  165.0             0   0.0   \n",
              "2                1  154.0             1  179.0             0   0.0   \n",
              "3                1   86.0             1   85.0             0   0.0   \n",
              "4                1  116.0             1  111.0             0   0.0   \n",
              "...            ...    ...           ...    ...           ...   ...   \n",
              "8619             1   98.0             1  108.0             0   0.0   \n",
              "8620             1   54.0             1   87.0             0   0.0   \n",
              "8621             1   84.0             1   87.0             0   0.0   \n",
              "8622             1  101.0             1  158.0             0   0.0   \n",
              "8623             1   93.0             1  104.0             1  29.0   \n",
              "\n",
              "     diagnosis_name  avg_value  \n",
              "0              none        0.0  \n",
              "1              none        0.0  \n",
              "2              none        0.0  \n",
              "3              none        0.0  \n",
              "4              none        0.0  \n",
              "...             ...        ...  \n",
              "8619           none        0.0  \n",
              "8620           none        0.0  \n",
              "8621           none        0.0  \n",
              "8622           none        0.0  \n",
              "8623           none       29.0  \n",
              "\n",
              "[8624 rows x 29 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age_group</th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>on_thyroxine</th>\n",
              "      <th>query_on_thyroxine</th>\n",
              "      <th>on_antithyroid_meds</th>\n",
              "      <th>sick</th>\n",
              "      <th>pregnant</th>\n",
              "      <th>thyroid_surgery</th>\n",
              "      <th>I131_treatment</th>\n",
              "      <th>query_hypothyroid</th>\n",
              "      <th>query_hyperthyroid</th>\n",
              "      <th>lithium</th>\n",
              "      <th>goitre</th>\n",
              "      <th>tumor</th>\n",
              "      <th>hypopituitary</th>\n",
              "      <th>psych</th>\n",
              "      <th>TSH_measured</th>\n",
              "      <th>TSH</th>\n",
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              "      <th>TBG</th>\n",
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              "      <td>0.25</td>\n",
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              "      <td>2.5</td>\n",
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              "      <td>269.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.90</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>101.0</td>\n",
              "      <td>1</td>\n",
              "      <td>158.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>none</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8623</th>\n",
              "      <td>8.0</td>\n",
              "      <td>97</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.30</td>\n",
              "      <td>1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1</td>\n",
              "      <td>93.0</td>\n",
              "      <td>1</td>\n",
              "      <td>104.0</td>\n",
              "      <td>1</td>\n",
              "      <td>29.0</td>\n",
              "      <td>none</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8624 rows × 29 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d7f729b-3b3a-4791-a34c-28c4578a5b29')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7d7f729b-3b3a-4791-a34c-28c4578a5b29 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7d7f729b-3b3a-4791-a34c-28c4578a5b29');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "pd.set_option('display.max_columns', None)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "PBIALa5LlyVY"
      },
      "outputs": [],
      "source": [
        "##features extracted for the models\n",
        "\n",
        "##X_df = df.loc[:,['age', 'sex', 'TSH', 'T3', 'TT4', 'FTI', 'TBG']]\n",
        "X_df = df.drop(['thyroid_surgery', 'age_group', 'diagnosis_name', 'avg_value'], axis = 1)\n",
        "y_df = df['thyroid_surgery']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "VlOdjghsmqPI"
      },
      "outputs": [],
      "source": [
        "##splitting the data into train/test sets\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_df, y_df, test_size=0.2, random_state=50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "brfj27TTjC3S"
      },
      "outputs": [],
      "source": [
        "##conducting random oversample of the data\n",
        "\n",
        "ros = RandomOverSampler()\n",
        "X_resampled, y_resampled = ros.fit_resample(X_train, y_train)\n",
        "\n",
        "X_train_resampled, X_test_resampled, y_train_resampled, y_test_resampled = train_test_split(X_resampled, y_resampled, test_size = 0.2, random_state = 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3FsMidzCndep",
        "outputId": "5d5bfd05-bfb3-497b-876f-7dd5f4db713a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((6899, 25), (1725, 25))"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "X_train.shape, X_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__3ifabxnfdw",
        "outputId": "ccb511fd-ef6a-4508-b18c-8357024fbe87"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Counter({0: 6807, 1: 92}), Counter({0: 1690, 1: 35}))"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "Counter(y_train), Counter(y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "X29UYDn2nqwZ",
        "outputId": "0d4d99c6-c116-44d7-88d8-22f81e3d8772"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=5000)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=5000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=5000)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "##logistic regression test for random over sample\n",
        "\n",
        "lr_model = LogisticRegression(max_iter = 5000)\n",
        "lr_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8c-t0Xb3njuE",
        "outputId": "df1ff276-362f-41ff-cb67-b1aed65720e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9797101449275363\n"
          ]
        }
      ],
      "source": [
        "y_pred = lr_model.predict(X_test)\n",
        "\n",
        "accuracy = lr_model.score(X_test, y_test)\n",
        "print('Accuracy:', accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eElOVMgZnu1R",
        "outputId": "981988c1-e9fb-4e51-aa99-d2ababe36e3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99      1690\n",
            "           1       0.00      0.00      0.00        35\n",
            "\n",
            "    accuracy                           0.98      1725\n",
            "   macro avg       0.49      0.50      0.49      1725\n",
            "weighted avg       0.96      0.98      0.97      1725\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "M0OnaXN8J3VL",
        "outputId": "4f402d8c-b51f-4d5f-905a-4e1a9478fcb0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 640x480 with 1 Axes>,\n",
              " <Axes: xlabel='predicted label', ylabel='true label'>)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#performing a confusion matrix on the logistic regression\n",
        "\n",
        "plot_confusion_matrix(confusion_matrix(y_test, y_pred), \n",
        "                      show_absolute = True, \n",
        "                      show_normed = True,\n",
        "                      cmap = 'Reds')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "1q52JlSKqCp6",
        "outputId": "d8f787e8-3285-477e-e726-e5a270a2b1ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=50, random_state=25)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=50, random_state=25)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=50, random_state=25)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "##random forest classifier for random over sample\n",
        "\n",
        "rfc = RandomForestClassifier(n_estimators = 50, random_state = 25)\n",
        "rfc.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "65UE8eT7qO19",
        "outputId": "cca77310-79f7-42e0-f8f3-f80801a4cd8d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9797101449275363\n"
          ]
        }
      ],
      "source": [
        "y_pred = rfc.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print('Accuracy:', accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FxqdJP6bKd3v",
        "outputId": "e9b82971-8bac-40e4-c8cb-4e8b856027bb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99      1690\n",
            "           1       0.00      0.00      0.00        35\n",
            "\n",
            "    accuracy                           0.98      1725\n",
            "   macro avg       0.49      0.50      0.49      1725\n",
            "weighted avg       0.96      0.98      0.97      1725\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "SZIlhbZjKglg",
        "outputId": "59518bd7-8c21-487b-ca41-2ce2ff36c950"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 640x480 with 1 Axes>,\n",
              " <Axes: xlabel='predicted label', ylabel='true label'>)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#performing a confusion matrix on the random forest\n",
        "\n",
        "plot_confusion_matrix(confusion_matrix(y_test, y_pred), \n",
        "                      show_absolute = True, \n",
        "                      show_normed = True,\n",
        "                      cmap = 'Reds')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nui8gn9g1i7e",
        "outputId": "883b1269-2d6c-4a94-c3d5-b6fb1be87d7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fold 0: Accuracy = 0.9797101449275363\n",
            "Fold 1: Accuracy = 0.9820289855072464\n",
            "Fold 2: Accuracy = 0.9884057971014493\n",
            "Fold 3: Accuracy = 0.9895652173913043\n",
            "Fold 4: Accuracy = 0.9866589327146171\n"
          ]
        }
      ],
      "source": [
        "##cross validation on Random Forest with the over sample\n",
        "\n",
        "num_folds = 5\n",
        "kf = KFold(n_splits=num_folds, shuffle = True, random_state = 50)\n",
        "rfc = RandomForestClassifier(n_estimators = 50, max_depth = 5)\n",
        "accuracies = []\n",
        "\n",
        "for fold, (train_index, test_index) in enumerate(kf.split(X_df)):\n",
        "    X_train, X_test = X_df.iloc[train_index], X_df.iloc[test_index]\n",
        "    y_train, y_test = y_df.iloc[train_index], y_df.iloc[test_index]\n",
        "    \n",
        "    rfc.fit(X_train, y_train)\n",
        "    y_pred = rfc.predict(X_test)\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    accuracies.append(accuracy)\n",
        "    print(f\"Fold {fold}: Accuracy = {accuracy}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LO8vhdcb3hny",
        "outputId": "eeba1b06-568b-4c88-d72b-4461a4bbc228"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Accuracy = 0.9852738155284306\n"
          ]
        }
      ],
      "source": [
        "avg_accuracy = np.mean(accuracies)\n",
        "print(f\"Average Accuracy = {avg_accuracy}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fNd2ieSK4yI",
        "outputId": "963a853d-e48f-49ca-c0d9-30b3b04816d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      0.99      1701\n",
            "           1       0.00      0.00      0.00        23\n",
            "\n",
            "    accuracy                           0.99      1724\n",
            "   macro avg       0.49      0.50      0.50      1724\n",
            "weighted avg       0.97      0.99      0.98      1724\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "iVkiF82VK8vT",
        "outputId": "ffb913c9-866c-4979-82da-56d5a0ccc0b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 640x480 with 1 Axes>,\n",
              " <Axes: xlabel='predicted label', ylabel='true label'>)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#performing a confusion matrix on the cross validaton random forest\n",
        "\n",
        "plot_confusion_matrix(confusion_matrix(y_test, y_pred), \n",
        "                      show_absolute = True, \n",
        "                      show_normed = True,\n",
        "                      cmap = 'Reds')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "RxtRAnymsgwP"
      },
      "outputs": [],
      "source": [
        "##neural networks for random over sample\n",
        "\n",
        "clf = Sequential()\n",
        "\n",
        "clf.add(Dense(units = 32, \n",
        "              kernel_initializer = 'uniform',\n",
        "              activation = 'relu', \n",
        "              input_dim = X_train.shape[1]))\n",
        "\n",
        "clf.add(Dense(units = 8, \n",
        "              kernel_initializer = 'uniform', \n",
        "              activation = 'relu'))\n",
        "\n",
        "dropout = 0.3\n",
        "clf.add(Dropout(dropout))\n",
        "\n",
        "clf.add(Dense(units = 1, \n",
        "              kernel_initializer = 'uniform', \n",
        "              activation = 'sigmoid'))\n",
        "\n",
        "clf.compile(optimizer = 'adam', \n",
        "            loss = 'binary_crossentropy', \n",
        "            metrics = ['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9xfiHKctfr-",
        "outputId": "86b79101-de50-42ab-87f5-a811e5843c6e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 32)                832       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 8)                 264       \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 8)                 0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,105\n",
            "Trainable params: 1,105\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "clf.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N94FADtTtuf6",
        "outputId": "f2e0a004-b1c4-4a81-d154-d82e43f70b9a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "151/151 [==============================] - 2s 4ms/step - loss: 0.1974 - accuracy: 0.9810 - val_loss: 0.0520 - val_accuracy: 0.9918\n",
            "Epoch 2/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1103 - accuracy: 0.9820 - val_loss: 0.0519 - val_accuracy: 0.9918\n",
            "Epoch 3/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1158 - accuracy: 0.9820 - val_loss: 0.0542 - val_accuracy: 0.9918\n",
            "Epoch 4/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1207 - accuracy: 0.9820 - val_loss: 0.0482 - val_accuracy: 0.9918\n",
            "Epoch 5/50\n",
            "151/151 [==============================] - 1s 3ms/step - loss: 0.1157 - accuracy: 0.9820 - val_loss: 0.0559 - val_accuracy: 0.9918\n",
            "Epoch 6/50\n",
            "151/151 [==============================] - 1s 4ms/step - loss: 0.1137 - accuracy: 0.9820 - val_loss: 0.0512 - val_accuracy: 0.9918\n",
            "Epoch 7/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1142 - accuracy: 0.9820 - val_loss: 0.0486 - val_accuracy: 0.9918\n",
            "Epoch 8/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1109 - accuracy: 0.9820 - val_loss: 0.0526 - val_accuracy: 0.9918\n",
            "Epoch 9/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1119 - accuracy: 0.9820 - val_loss: 0.0503 - val_accuracy: 0.9918\n",
            "Epoch 10/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1104 - accuracy: 0.9820 - val_loss: 0.0493 - val_accuracy: 0.9918\n",
            "Epoch 11/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1085 - accuracy: 0.9820 - val_loss: 0.0492 - val_accuracy: 0.9918\n",
            "Epoch 12/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1058 - accuracy: 0.9820 - val_loss: 0.0491 - val_accuracy: 0.9918\n",
            "Epoch 13/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1066 - accuracy: 0.9820 - val_loss: 0.0494 - val_accuracy: 0.9918\n",
            "Epoch 14/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1052 - accuracy: 0.9820 - val_loss: 0.0486 - val_accuracy: 0.9918\n",
            "Epoch 15/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1074 - accuracy: 0.9820 - val_loss: 0.0527 - val_accuracy: 0.9918\n",
            "Epoch 16/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1060 - accuracy: 0.9820 - val_loss: 0.0490 - val_accuracy: 0.9918\n",
            "Epoch 17/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1082 - accuracy: 0.9820 - val_loss: 0.0511 - val_accuracy: 0.9918\n",
            "Epoch 18/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1033 - accuracy: 0.9820 - val_loss: 0.0515 - val_accuracy: 0.9918\n",
            "Epoch 19/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1020 - accuracy: 0.9820 - val_loss: 0.0501 - val_accuracy: 0.9918\n",
            "Epoch 20/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.1022 - accuracy: 0.9820 - val_loss: 0.0491 - val_accuracy: 0.9918\n",
            "Epoch 21/50\n",
            "151/151 [==============================] - 1s 4ms/step - loss: 0.1012 - accuracy: 0.9820 - val_loss: 0.0496 - val_accuracy: 0.9918\n",
            "Epoch 22/50\n",
            "151/151 [==============================] - 1s 4ms/step - loss: 0.0973 - accuracy: 0.9820 - val_loss: 0.0503 - val_accuracy: 0.9918\n",
            "Epoch 23/50\n",
            "151/151 [==============================] - 1s 4ms/step - loss: 0.0964 - accuracy: 0.9820 - val_loss: 0.0491 - val_accuracy: 0.9918\n",
            "Epoch 24/50\n",
            "151/151 [==============================] - 1s 4ms/step - loss: 0.0970 - accuracy: 0.9820 - val_loss: 0.0497 - val_accuracy: 0.9918\n",
            "Epoch 25/50\n",
            "151/151 [==============================] - 1s 4ms/step - loss: 0.0955 - accuracy: 0.9820 - val_loss: 0.0501 - val_accuracy: 0.9918\n",
            "Epoch 26/50\n",
            "151/151 [==============================] - 1s 4ms/step - loss: 0.0972 - accuracy: 0.9820 - val_loss: 0.0490 - val_accuracy: 0.9918\n",
            "Epoch 27/50\n",
            "151/151 [==============================] - 1s 4ms/step - loss: 0.0901 - accuracy: 0.9820 - val_loss: 0.0507 - val_accuracy: 0.9918\n",
            "Epoch 28/50\n",
            "151/151 [==============================] - 1s 4ms/step - loss: 0.0964 - accuracy: 0.9820 - val_loss: 0.0492 - val_accuracy: 0.9918\n",
            "Epoch 29/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0957 - accuracy: 0.9820 - val_loss: 0.0490 - val_accuracy: 0.9918\n",
            "Epoch 30/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0951 - accuracy: 0.9820 - val_loss: 0.0488 - val_accuracy: 0.9918\n",
            "Epoch 31/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0911 - accuracy: 0.9820 - val_loss: 0.0493 - val_accuracy: 0.9918\n",
            "Epoch 32/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0937 - accuracy: 0.9820 - val_loss: 0.0488 - val_accuracy: 0.9918\n",
            "Epoch 33/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0945 - accuracy: 0.9820 - val_loss: 0.0521 - val_accuracy: 0.9918\n",
            "Epoch 34/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0940 - accuracy: 0.9820 - val_loss: 0.0485 - val_accuracy: 0.9918\n",
            "Epoch 35/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0952 - accuracy: 0.9820 - val_loss: 0.0481 - val_accuracy: 0.9918\n",
            "Epoch 36/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0906 - accuracy: 0.9820 - val_loss: 0.0485 - val_accuracy: 0.9918\n",
            "Epoch 37/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0944 - accuracy: 0.9820 - val_loss: 0.0486 - val_accuracy: 0.9918\n",
            "Epoch 38/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0921 - accuracy: 0.9820 - val_loss: 0.0543 - val_accuracy: 0.9918\n",
            "Epoch 39/50\n",
            "151/151 [==============================] - 0s 2ms/step - loss: 0.0930 - accuracy: 0.9820 - val_loss: 0.0496 - val_accuracy: 0.9918\n",
            "Epoch 40/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0917 - accuracy: 0.9820 - val_loss: 0.0485 - val_accuracy: 0.9918\n",
            "Epoch 41/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0892 - accuracy: 0.9820 - val_loss: 0.0485 - val_accuracy: 0.9918\n",
            "Epoch 42/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0914 - accuracy: 0.9820 - val_loss: 0.0487 - val_accuracy: 0.9918\n",
            "Epoch 43/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0904 - accuracy: 0.9820 - val_loss: 0.0482 - val_accuracy: 0.9918\n",
            "Epoch 44/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0919 - accuracy: 0.9820 - val_loss: 0.0484 - val_accuracy: 0.9918\n",
            "Epoch 45/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0893 - accuracy: 0.9820 - val_loss: 0.0485 - val_accuracy: 0.9918\n",
            "Epoch 46/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0924 - accuracy: 0.9820 - val_loss: 0.0489 - val_accuracy: 0.9918\n",
            "Epoch 47/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0908 - accuracy: 0.9820 - val_loss: 0.0496 - val_accuracy: 0.9918\n",
            "Epoch 48/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0913 - accuracy: 0.9820 - val_loss: 0.0481 - val_accuracy: 0.9918\n",
            "Epoch 49/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0910 - accuracy: 0.9820 - val_loss: 0.0499 - val_accuracy: 0.9918\n",
            "Epoch 50/50\n",
            "151/151 [==============================] - 0s 3ms/step - loss: 0.0912 - accuracy: 0.9820 - val_loss: 0.0496 - val_accuracy: 0.9918\n"
          ]
        }
      ],
      "source": [
        "history = clf.fit(X_train, \n",
        "                  y_train, \n",
        "                  validation_split = 0.3,\n",
        "                  batch_size = 32, \n",
        "                  epochs = 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "vrAKFxQWv3SZ"
      },
      "outputs": [],
      "source": [
        "plt.style.use('ggplot')\n",
        "\n",
        "def plot_history(history):\n",
        "    acc = history.history['accuracy']\n",
        "    val_acc = history.history['val_accuracy']\n",
        "    loss = history.history['loss']\n",
        "    val_loss = history.history['val_loss']\n",
        "    x = range(1, len(acc) + 1)\n",
        "\n",
        "    plt.figure(figsize = (12, 5))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(x, acc, 'b', label = 'Training acc')\n",
        "    plt.plot(x, val_acc, 'r', label = 'Validation acc')\n",
        "    plt.title('Training and validation accuracy')\n",
        "    plt.legend()\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(x, loss, 'b', label = 'Training loss')\n",
        "    plt.plot(x, val_loss, 'r', label = 'Validation loss')\n",
        "    plt.title('Training and validation loss')\n",
        "    plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "kQ_hHBqVv4lU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3834580c-92a2-4397-fb9b-b185293b0d0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "54/54 [==============================] - 0s 1ms/step\n"
          ]
        }
      ],
      "source": [
        "y_pred_prob = clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WrxNdpitv8Yg",
        "outputId": "e2f56eca-5a42-4ed0-9211-48e3c123bfa9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.9263098e-03],\n",
              "       [3.8343523e-10],\n",
              "       [2.5231002e-02],\n",
              "       [6.9429632e-03],\n",
              "       [6.8383380e-03]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "y_pred_prob[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z7gmWhjZv-Wp",
        "outputId": "4d419ec2-771a-424b-e4e9-eddb886c3e86"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "54/54 [==============================] - 0s 1ms/step\n"
          ]
        }
      ],
      "source": [
        "y_pred = (clf.predict(X_test) > 0.5).astype(\"int32\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i-3GYh3cwCHM",
        "outputId": "ddd96221-32fc-4f9c-f7e6-d7791d2e7664"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "y_pred[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CO5nZKr_t5s2",
        "outputId": "32456698-6aca-42d0-9d87-2bfefddfd2f6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      0.99      1701\n",
            "           1       0.00      0.00      0.00        23\n",
            "\n",
            "    accuracy                           0.99      1724\n",
            "   macro avg       0.49      0.50      0.50      1724\n",
            "weighted avg       0.97      0.99      0.98      1724\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "FJSeAyd8wF5N",
        "outputId": "63b78201-39a9-471e-c0b4-704e3609949a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 640x480 with 1 Axes>,\n",
              " <Axes: xlabel='predicted label', ylabel='true label'>)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#performing a confusion matrix on the neural networks\n",
        "\n",
        "plot_confusion_matrix(confusion_matrix(y_test, y_pred), \n",
        "                      show_absolute = True, \n",
        "                      show_normed = True,\n",
        "                      cmap = 'Reds')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "q9G7XrPixpRA"
      },
      "outputs": [],
      "source": [
        "##conducting a random under sample of the data\n",
        "\n",
        "rus = RandomUnderSampler()\n",
        "X_resampled, y_resampled = rus.fit_resample(X_df, y_df)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size = 0.2, random_state = 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GypR2TORyC9z",
        "outputId": "fdbce991-f364-4cc4-d21a-8b88111883c4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((203, 25), (51, 25))"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "X_train.shape, X_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kTm0-R1EyD9y",
        "outputId": "eee82a7c-8c12-4234-f6ba-a4662a5b4f96"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Counter({1: 103, 0: 100}), Counter({0: 27, 1: 24}))"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "Counter(y_train), Counter(y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "tisc7y2xyJAA",
        "outputId": "d05a96e4-c5fb-4960-b165-efadf579a625"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=5000)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=5000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=5000)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "##logistic regression test for random under sample\n",
        "\n",
        "lr_model = LogisticRegression(max_iter = 5000)\n",
        "lr_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07moBNWC0ppn",
        "outputId": "52131629-a725-467e-d37c-207f62670eb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.47058823529411764\n"
          ]
        }
      ],
      "source": [
        "y_pred = lr_model.predict(X_test)\n",
        "\n",
        "accuracy = lr_model.score(X_test, y_test)\n",
        "print('Accuracy:', accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IrlDJDRy0xR0",
        "outputId": "3b9ff8cc-3c03-4fdb-edee-b31a89b6c086"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      0.44      0.47        27\n",
            "           1       0.44      0.50      0.47        24\n",
            "\n",
            "    accuracy                           0.47        51\n",
            "   macro avg       0.47      0.47      0.47        51\n",
            "weighted avg       0.47      0.47      0.47        51\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "prAU34jULeCf",
        "outputId": "7e2b387b-1b2f-404e-decb-47d96e4deec8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 640x480 with 1 Axes>,\n",
              " <Axes: xlabel='predicted label', ylabel='true label'>)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#performing a confusion matrix on the logistic regression\n",
        "\n",
        "plot_confusion_matrix(confusion_matrix(y_test, y_pred), \n",
        "                      show_absolute = True, \n",
        "                      show_normed = True,\n",
        "                      cmap = 'Reds')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "P3T3O-AW01ay",
        "outputId": "c3938c9e-dae6-45a1-fa09-56ad0fb1b3ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=50, random_state=25)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=50, random_state=25)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=50, random_state=25)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "##random forest classifier for random under sample\n",
        "\n",
        "rfc = RandomForestClassifier(n_estimators = 50, random_state = 25)\n",
        "rfc.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HUOeDE151S5U",
        "outputId": "a179d3df-a605-4cc1-9cc9-0a439ad761e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6666666666666666\n"
          ]
        }
      ],
      "source": [
        "y_pred = rfc.predict(X_test)\n",
        "\n",
        "accuracy = rfc.score(X_test, y_test)\n",
        "print('Accuracy:', accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZqBc2dJLvMD",
        "outputId": "4928dc58-4293-4639-ca4f-e59090d04993"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.67      0.68        27\n",
            "           1       0.64      0.67      0.65        24\n",
            "\n",
            "    accuracy                           0.67        51\n",
            "   macro avg       0.67      0.67      0.67        51\n",
            "weighted avg       0.67      0.67      0.67        51\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "Aif1c3FyLzwk",
        "outputId": "4a28076b-4ab2-4d24-b65b-e2b2666a37c4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 640x480 with 1 Axes>,\n",
              " <Axes: xlabel='predicted label', ylabel='true label'>)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#performing a confusion matrix on the random forest\n",
        "\n",
        "plot_confusion_matrix(confusion_matrix(y_test, y_pred), \n",
        "                      show_absolute = True, \n",
        "                      show_normed = True,\n",
        "                      cmap = 'Reds')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "sf0Ev7IZ4GzI"
      },
      "outputs": [],
      "source": [
        "##neural networks for random under sample\n",
        "\n",
        "clf = Sequential()\n",
        "\n",
        "clf.add(Dense(units = 32, \n",
        "              kernel_initializer = 'uniform',\n",
        "              activation = 'relu', \n",
        "              input_dim = X_train.shape[1]))\n",
        "\n",
        "clf.add(Dense(units = 8, \n",
        "              kernel_initializer = 'uniform', \n",
        "              activation = 'relu'))\n",
        "\n",
        "dropout = 0.3\n",
        "clf.add(Dropout(dropout))\n",
        "\n",
        "clf.add(Dense(units = 1, \n",
        "              kernel_initializer = 'uniform', \n",
        "              activation = 'sigmoid'))\n",
        "\n",
        "clf.compile(optimizer = 'adam', \n",
        "            loss = 'binary_crossentropy', \n",
        "            metrics = ['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1aH1CT3h4NMw",
        "outputId": "5c383d88-770a-4db4-a18b-4d847e737f6a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_3 (Dense)             (None, 32)                832       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 8)                 264       \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 8)                 0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,105\n",
            "Trainable params: 1,105\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "clf.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgGB5csQ4RmT",
        "outputId": "2040de22-d8a8-40b3-ade1-71c57e6be7fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "5/5 [==============================] - 2s 56ms/step - loss: 0.6940 - accuracy: 0.4437 - val_loss: 0.6930 - val_accuracy: 0.5738\n",
            "Epoch 2/50\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.6929 - accuracy: 0.5070 - val_loss: 0.6931 - val_accuracy: 0.4918\n",
            "Epoch 3/50\n",
            "5/5 [==============================] - 0s 28ms/step - loss: 0.6930 - accuracy: 0.5493 - val_loss: 0.6933 - val_accuracy: 0.4754\n",
            "Epoch 4/50\n",
            "5/5 [==============================] - 0s 47ms/step - loss: 0.6928 - accuracy: 0.5141 - val_loss: 0.6933 - val_accuracy: 0.4918\n",
            "Epoch 5/50\n",
            "5/5 [==============================] - 0s 50ms/step - loss: 0.6927 - accuracy: 0.5070 - val_loss: 0.6938 - val_accuracy: 0.4918\n",
            "Epoch 6/50\n",
            "5/5 [==============================] - 0s 55ms/step - loss: 0.6917 - accuracy: 0.5352 - val_loss: 0.6944 - val_accuracy: 0.4918\n",
            "Epoch 7/50\n",
            "5/5 [==============================] - 0s 32ms/step - loss: 0.6898 - accuracy: 0.5141 - val_loss: 0.6952 - val_accuracy: 0.4918\n",
            "Epoch 8/50\n",
            "5/5 [==============================] - 0s 28ms/step - loss: 0.6914 - accuracy: 0.5775 - val_loss: 0.6958 - val_accuracy: 0.4918\n",
            "Epoch 9/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.6890 - accuracy: 0.5704 - val_loss: 0.6966 - val_accuracy: 0.4918\n",
            "Epoch 10/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.6908 - accuracy: 0.5845 - val_loss: 0.6968 - val_accuracy: 0.4754\n",
            "Epoch 11/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.6894 - accuracy: 0.5634 - val_loss: 0.6965 - val_accuracy: 0.4754\n",
            "Epoch 12/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.6880 - accuracy: 0.5423 - val_loss: 0.6967 - val_accuracy: 0.4754\n",
            "Epoch 13/50\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.6904 - accuracy: 0.5563 - val_loss: 0.6971 - val_accuracy: 0.4590\n",
            "Epoch 14/50\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.6926 - accuracy: 0.5352 - val_loss: 0.6964 - val_accuracy: 0.4262\n",
            "Epoch 15/50\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.6920 - accuracy: 0.5352 - val_loss: 0.6947 - val_accuracy: 0.4262\n",
            "Epoch 16/50\n",
            "5/5 [==============================] - 0s 44ms/step - loss: 0.6843 - accuracy: 0.6056 - val_loss: 0.6932 - val_accuracy: 0.4918\n",
            "Epoch 17/50\n",
            "5/5 [==============================] - 0s 49ms/step - loss: 0.6869 - accuracy: 0.5563 - val_loss: 0.6924 - val_accuracy: 0.5410\n",
            "Epoch 18/50\n",
            "5/5 [==============================] - 0s 36ms/step - loss: 0.6824 - accuracy: 0.5845 - val_loss: 0.6926 - val_accuracy: 0.5410\n",
            "Epoch 19/50\n",
            "5/5 [==============================] - 0s 28ms/step - loss: 0.6859 - accuracy: 0.5211 - val_loss: 0.6933 - val_accuracy: 0.5246\n",
            "Epoch 20/50\n",
            "5/5 [==============================] - 0s 34ms/step - loss: 0.6851 - accuracy: 0.5141 - val_loss: 0.6938 - val_accuracy: 0.5410\n",
            "Epoch 21/50\n",
            "5/5 [==============================] - 0s 26ms/step - loss: 0.6755 - accuracy: 0.5915 - val_loss: 0.6951 - val_accuracy: 0.5410\n",
            "Epoch 22/50\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.6746 - accuracy: 0.6056 - val_loss: 0.6948 - val_accuracy: 0.4918\n",
            "Epoch 23/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.6665 - accuracy: 0.6408 - val_loss: 0.6963 - val_accuracy: 0.4918\n",
            "Epoch 24/50\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.6638 - accuracy: 0.6338 - val_loss: 0.6984 - val_accuracy: 0.5082\n",
            "Epoch 25/50\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.6685 - accuracy: 0.5775 - val_loss: 0.7002 - val_accuracy: 0.4918\n",
            "Epoch 26/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.6724 - accuracy: 0.5282 - val_loss: 0.7041 - val_accuracy: 0.5410\n",
            "Epoch 27/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.6542 - accuracy: 0.6338 - val_loss: 0.7090 - val_accuracy: 0.5082\n",
            "Epoch 28/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.6661 - accuracy: 0.5634 - val_loss: 0.7130 - val_accuracy: 0.4918\n",
            "Epoch 29/50\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.6564 - accuracy: 0.5775 - val_loss: 0.7100 - val_accuracy: 0.4918\n",
            "Epoch 30/50\n",
            "5/5 [==============================] - 0s 13ms/step - loss: 0.6593 - accuracy: 0.5845 - val_loss: 0.7132 - val_accuracy: 0.5246\n",
            "Epoch 31/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.6614 - accuracy: 0.5563 - val_loss: 0.7197 - val_accuracy: 0.5246\n",
            "Epoch 32/50\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.6558 - accuracy: 0.5845 - val_loss: 0.7298 - val_accuracy: 0.4754\n",
            "Epoch 33/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.6538 - accuracy: 0.6338 - val_loss: 0.7263 - val_accuracy: 0.5246\n",
            "Epoch 34/50\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.6533 - accuracy: 0.5915 - val_loss: 0.7253 - val_accuracy: 0.5246\n",
            "Epoch 35/50\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.6468 - accuracy: 0.6056 - val_loss: 0.7327 - val_accuracy: 0.4918\n",
            "Epoch 36/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.6319 - accuracy: 0.6197 - val_loss: 0.7355 - val_accuracy: 0.5082\n",
            "Epoch 37/50\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.6490 - accuracy: 0.5493 - val_loss: 0.7373 - val_accuracy: 0.4918\n",
            "Epoch 38/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.6395 - accuracy: 0.5915 - val_loss: 0.7399 - val_accuracy: 0.5410\n",
            "Epoch 39/50\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.6441 - accuracy: 0.6127 - val_loss: 0.7511 - val_accuracy: 0.4918\n",
            "Epoch 40/50\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.6337 - accuracy: 0.6197 - val_loss: 0.7500 - val_accuracy: 0.5082\n",
            "Epoch 41/50\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.6462 - accuracy: 0.5563 - val_loss: 0.7499 - val_accuracy: 0.5410\n",
            "Epoch 42/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.6487 - accuracy: 0.6197 - val_loss: 0.7597 - val_accuracy: 0.4918\n",
            "Epoch 43/50\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.6266 - accuracy: 0.6620 - val_loss: 0.7567 - val_accuracy: 0.4918\n",
            "Epoch 44/50\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.6376 - accuracy: 0.6197 - val_loss: 0.7606 - val_accuracy: 0.4918\n",
            "Epoch 45/50\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.6386 - accuracy: 0.5704 - val_loss: 0.7682 - val_accuracy: 0.5410\n",
            "Epoch 46/50\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.6270 - accuracy: 0.6479 - val_loss: 0.7684 - val_accuracy: 0.5246\n",
            "Epoch 47/50\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.6382 - accuracy: 0.6056 - val_loss: 0.7741 - val_accuracy: 0.4918\n",
            "Epoch 48/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.6408 - accuracy: 0.5845 - val_loss: 0.7783 - val_accuracy: 0.5410\n",
            "Epoch 49/50\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.6261 - accuracy: 0.6338 - val_loss: 0.7837 - val_accuracy: 0.5246\n",
            "Epoch 50/50\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.6140 - accuracy: 0.6479 - val_loss: 0.7891 - val_accuracy: 0.5738\n"
          ]
        }
      ],
      "source": [
        "history = clf.fit(X_train, \n",
        "                  y_train, \n",
        "                  validation_split = 0.3,\n",
        "                  batch_size = 32, \n",
        "                  epochs = 50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "t6-5_nuN4U5K"
      },
      "outputs": [],
      "source": [
        "plt.style.use('ggplot')\n",
        "\n",
        "def plot_history(history):\n",
        "    acc = history.history['accuracy']\n",
        "    val_acc = history.history['val_accuracy']\n",
        "    loss = history.history['loss']\n",
        "    val_loss = history.history['val_loss']\n",
        "    x = range(1, len(acc) + 1)\n",
        "\n",
        "    plt.figure(figsize = (12, 5))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(x, acc, 'b', label = 'Training acc')\n",
        "    plt.plot(x, val_acc, 'r', label = 'Validation acc')\n",
        "    plt.title('Training and validation accuracy')\n",
        "    plt.legend()\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(x, loss, 'b', label = 'Training loss')\n",
        "    plt.plot(x, val_loss, 'r', label = 'Validation loss')\n",
        "    plt.title('Training and validation loss')\n",
        "    plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hB0EP_9o4ale",
        "outputId": "aeb01927-1ed8-4429-bb5c-cda880f9eb8a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 4ms/step\n"
          ]
        }
      ],
      "source": [
        "y_pred_prob = clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "00sqU-o_4bIv",
        "outputId": "d10cf60c-0ad1-457d-bfae-d597bdf44f97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.44961497],\n",
              "       [0.48517582],\n",
              "       [0.4773118 ],\n",
              "       [0.47122625],\n",
              "       [0.6150603 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "y_pred_prob[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Woo91WjD4fal",
        "outputId": "27e0606e-b1ad-4d59-d21e-38b614891b0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 6ms/step\n"
          ]
        }
      ],
      "source": [
        "y_pred = (clf.predict(X_test) > 0.5).astype(\"int32\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2pjpEFoX4iHX",
        "outputId": "1acbbe83-6e8d-4064-99aa-bc2ff232e31b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [1]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "y_pred[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VuF0ebEwhSu",
        "outputId": "2123cf91-aaf9-4f74-9304-7ffb04291a5c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.53      0.37      0.43        27\n",
            "           1       0.47      0.62      0.54        24\n",
            "\n",
            "    accuracy                           0.49        51\n",
            "   macro avg       0.50      0.50      0.49        51\n",
            "weighted avg       0.50      0.49      0.48        51\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "oyxGFgd54lWF",
        "outputId": "c86ff205-baf4-4b87-da4b-f43a6ad9e48b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Figure size 640x480 with 1 Axes>,\n",
              " <Axes: xlabel='predicted label', ylabel='true label'>)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#performing a confusion matrix on the neural network\n",
        "\n",
        "plot_confusion_matrix(confusion_matrix(y_test, y_pred), \n",
        "                      show_absolute = True, \n",
        "                      show_normed = True,\n",
        "                      cmap = 'Reds')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}