Machine Learning Projects (Classic)/Predicting_Heart_Disease_ML_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1MH3W8OWDHB2byl_tNgpddK8fRePjr0uo",
      "authorship_tag": "ABX9TyNNUSCisuWO9JRwOjRLrjgf",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kelixirr/Complete-Machine-Learning-And-Data-Science-Course/blob/main/Predicting_Heart_Disease_ML_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Heart Disease Machine Learning Project\n",
        "\n",
        "This is a classification problem in which we are going to indentify the heart disease status of patients based on certain features. This is a binary classification which means we have patients with either heart disease or not.\n",
        "\n",
        "You can download the data from [kaggle](https://www.kaggle.com/datasets/sumaiyatasmeem/heart-disease-classification-dataset)\n",
        "\n",
        "Originally it was taken from https://archive.ics.uci.edu/ml/datasets/heart+Disease here which contains 76 attributes but kaggle datase uses only 14 attributes to make classification.\n",
        "\n",
        "Here we can have any evaluation metric based on the kind of project you are working on in real life. For example, what is the accuracy level you are looking for when you are making prediction from your model? How accurate the prediction should be?\n",
        "\n",
        "Here are some of our features:\n",
        "\n",
        "- age: Displays the age of the individual.\n",
        "\n",
        " - sex: Displays the gender of the individual using the following format : 1 = male 0 = female\n",
        "\n",
        "- cp- Chest-pain type: displays the type of chest-pain experienced by the individual using the following format : 0 = typical angina 1 = atypical angina 2 = non — anginal pain 3 = asymptotic\n",
        "\n",
        "- trestbps- Resting Blood Pressure: displays the resting blood pressure value of an individual in mmHg (unit). anything above 130-140 is typically cause for concern.\n",
        "\n",
        "- chol- Serum Cholestrol: displays the serum cholesterol in mg/dl (unit)\n",
        "\n",
        "- fbs- Fasting Blood Sugar: compares the fasting blood sugar value of an individual with 120mg/dl. If fasting blood sugar > 120mg/dl then : 1 (true) else : 0 (false) '>126' mg/dL signals diabetes\n",
        "\n",
        "- restecg- Resting ECG : displays resting electrocardiographic results 0 = normal 1 = having ST-T wave abnormality 2 = left ventricular hyperthrophy\n",
        "\n",
        "- thalach- Max heart rate achieved : displays the max heart rate achieved by an individual.\n",
        "\n",
        "- exang- Exercise induced angina : 1 = yes 0 = no\n",
        "\n",
        "- oldpeak- ST depression induced by exercise relative to rest: displays the value which is an integer or float.\n",
        "\n",
        "- slope- Slope of the peak exercise ST segment : 0 = upsloping: better heart rate with excercise (uncommon) 1 = flat: minimal change (typical healthy heart) 2 = downsloping: signs of unhealthy heart\n",
        "\n",
        "- ca- Number of major vessels (0–3) colored by flourosopy : displays the value as integer or float.\n",
        "\n",
        "- thal : Displays the thalassemia : 1,3 = normal 6 = fixed defect 7 = reversible defect: no proper blood movement when excercising\n",
        "\n",
        "- target : Displays whether the individual is suffering from heart disease or not : 1 = yes 0 = no"
      ],
      "metadata": {
        "id": "QCgv8l1_MYEp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Data And Tools"
      ],
      "metadata": {
        "id": "-4Tfumqdixl6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2Pt-OdIpCK9I"
      },
      "outputs": [],
      "source": [
        "#importing tools for the project\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# importing models from sklearn library\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "\n",
        "# importing evaluation metrics for the models\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import RocCurveDisplay"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importing data\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/Data/heart-disease.csv\")"
      ],
      "metadata": {
        "id": "XGz9c1B8knBD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exploring Data"
      ],
      "metadata": {
        "id": "JP0Evv-ImQmZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()   # top 5 rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rZamwFeUmNmj",
        "outputId": "7264a68e-c40a-453f-bf12-69ecd68fea40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
              "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
              "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
              "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
              "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   0     1       1  \n",
              "1   0     2       1  \n",
              "2   0     2       1  \n",
              "3   0     2       1  \n",
              "4   0     2       1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0cf511fd-783a-457c-8998-ac6352bdf76e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0cf511fd-783a-457c-8998-ac6352bdf76e')\"\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-0cf511fd-783a-457c-8998-ac6352bdf76e 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-0cf511fd-783a-457c-8998-ac6352bdf76e');\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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJXADNZGmOch",
        "outputId": "39fe70bf-5c12-4823-a4d5-b562180ef81b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vo5YbmhvmYHZ",
        "outputId": "a344fb3b-ed21-48f9-b8a1-b1be68678e85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "target      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RB92gydmmdUB",
        "outputId": "df0b15b0-40da-4985-88c8-2f2d1f6729f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking the number of values in our target variable\n",
        "df.target.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-2G977HEmqZp",
        "outputId": "97f68a21-4655-4325-d808-90b448e9e95b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    165\n",
              "0    138\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This means we have 1 as heart disease and 0 as no heart disease. We can use this to train our model to classify and make actual prediction. Notice that our data samples are quite balanced in each case which is exactly what we should be aiming for."
      ],
      "metadata": {
        "id": "mamHqZ5FnKcl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MmCKwRXmpzao",
        "outputId": "c8bfa0b2-9f25-42c6-fd43-b2e90419e431"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    int64  \n",
            " 12  thal      303 non-null    int64  \n",
            " 13  target    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "I5zecxVrp0X4",
        "outputId": "74d49d6a-3a7c-40e4-94d4-7cc06e20fc97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age         sex          cp    trestbps        chol         fbs  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
              "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
              "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
              "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
              "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
              "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
              "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
              "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
              "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
              "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
              "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
              "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
              "\n",
              "             thal      target  \n",
              "count  303.000000  303.000000  \n",
              "mean     2.313531    0.544554  \n",
              "std      0.612277    0.498835  \n",
              "min      0.000000    0.000000  \n",
              "25%      2.000000    0.000000  \n",
              "50%      2.000000    1.000000  \n",
              "75%      3.000000    1.000000  \n",
              "max      3.000000    1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-16b6da9a-1caa-4aeb-be47-c4cc4ce6948a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16b6da9a-1caa-4aeb-be47-c4cc4ce6948a')\"\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-16b6da9a-1caa-4aeb-be47-c4cc4ce6948a 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-16b6da9a-1caa-4aeb-be47-c4cc4ce6948a');\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.sex.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QyZ8jR61rZPY",
        "outputId": "8cac5f28-e793-4235-f203-4e6189a6d07f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    207\n",
              "0     96\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1 means male and 0 means female"
      ],
      "metadata": {
        "id": "POfQiOaPs9KU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df.target, df.sex)  # comparing two columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "5zWfCHlgs7Tx",
        "outputId": "e18279da-f2ca-4fbd-ca92-57a082f95f1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex      0    1\n",
              "target         \n",
              "0       24  114\n",
              "1       72   93"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a4517920-f795-45d5-aee3-9473a5db6857\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>sex</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24</td>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>72</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4517920-f795-45d5-aee3-9473a5db6857')\"\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-a4517920-f795-45d5-aee3-9473a5db6857 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-a4517920-f795-45d5-aee3-9473a5db6857');\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": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This means more percentage of women are likely to have heart disease as compared to the percentage of men. If you take out the percentage then you will realize that women are more vulnerable than men. It stands at 75%"
      ],
      "metadata": {
        "id": "JEpgwy5PtQNx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(93/207)*100, (72/96)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "akN-emMDtMEf",
        "outputId": "abd3f1e8-f338-4c75-b441-e8bdf873dfb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(44.927536231884055, 75.0)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(44.927536231884055 + 75.0)/2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_JfxR-TMtkTR",
        "outputId": "df30eaab-1114-44c8-bc4d-830470d3dfd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59.96376811594203"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can say that 60% is the chance for a person to have heart disease. So we can try to optimized our model to perform better than this. This can be our baseline."
      ],
      "metadata": {
        "id": "t_KztkQsvJSf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EDA"
      ],
      "metadata": {
        "id": "FJbN8fmzFYeT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby([\"target\"]).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "CVd2_zZOuhzp",
        "outputId": "7be629ae-898e-4138-b00b-0c2cc324132b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age       sex        cp    trestbps        chol       fbs  \\\n",
              "target                                                                    \n",
              "0       56.601449  0.826087  0.478261  134.398551  251.086957  0.159420   \n",
              "1       52.496970  0.563636  1.375758  129.303030  242.230303  0.139394   \n",
              "\n",
              "         restecg     thalach     exang   oldpeak     slope        ca      thal  \n",
              "target                                                                          \n",
              "0       0.449275  139.101449  0.550725  1.585507  1.166667  1.166667  2.543478  \n",
              "1       0.593939  158.466667  0.139394  0.583030  1.593939  0.363636  2.121212  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1bdcc21e-921b-43ee-9c50-ee74a5b8b46a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56.601449</td>\n",
              "      <td>0.826087</td>\n",
              "      <td>0.478261</td>\n",
              "      <td>134.398551</td>\n",
              "      <td>251.086957</td>\n",
              "      <td>0.159420</td>\n",
              "      <td>0.449275</td>\n",
              "      <td>139.101449</td>\n",
              "      <td>0.550725</td>\n",
              "      <td>1.585507</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>2.543478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>52.496970</td>\n",
              "      <td>0.563636</td>\n",
              "      <td>1.375758</td>\n",
              "      <td>129.303030</td>\n",
              "      <td>242.230303</td>\n",
              "      <td>0.139394</td>\n",
              "      <td>0.593939</td>\n",
              "      <td>158.466667</td>\n",
              "      <td>0.139394</td>\n",
              "      <td>0.583030</td>\n",
              "      <td>1.593939</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>2.121212</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1bdcc21e-921b-43ee-9c50-ee74a5b8b46a')\"\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-1bdcc21e-921b-43ee-9c50-ee74a5b8b46a 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-1bdcc21e-921b-43ee-9c50-ee74a5b8b46a');\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": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a plot\n",
        "pd.crosstab(df.target, df.sex).plot(kind=\"bar\", figsize=(10,5), color=[\"red\", \"blue\"])\n",
        "\n",
        "# Add some attributes to it\n",
        "plt.title(\"Heart Disease Frequency\")\n",
        "plt.xlabel(\"0 = No Disease, 1 = Disease\")\n",
        "plt.ylabel(\"Amount\")\n",
        "plt.legend([\"Female\", \"Male\"])\n",
        "plt.xticks(rotation=0); # keep the labels on the x-axis vertical"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "rC17QYcA9fF5",
        "outputId": "517c60fd-26e3-47a1-9328-28de144f9c77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's check the relationship between age and max heart rate using plot"
      ],
      "metadata": {
        "id": "N0rp7MZ__R2s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10,5))\n",
        "\n",
        "ax.scatter(df.age[df.target==1], df.thalach[df.target==1], c=\"red\")\n",
        "ax.scatter(df.age[df.target==0], df.thalach[df.target==0], c=\"blue\")\n",
        "\n",
        "ax.set(title=\"Heart Diseases with Age And Max Heart Rate\", xlabel=\"Age\", ylabel=\"Max Heart Rate\")\n",
        "ax.legend([\"Disease\", \"No Disease\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "ksGCu3X6-0ss",
        "outputId": "d8a371ef-ba08-4c7f-e995-3162ac871ef4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f987cdefcd0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Try to analyze the graph above and see how age and max heart rate has some sort of relationship with heart disease. As you can see people with high hight rate and around 40-50 have maximum number of heart disease. This may also be possible because of the number of data samples in that age range. With age the heart disease is even possible for people with lower heart rates in the graph. Now let's see the data distribution."
      ],
      "metadata": {
        "id": "hcXkRJKHEbB7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(\"target\", axis=1).plot.hist()   # let's check the data distribution"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "lQ6mU3NRAZys",
        "outputId": "5a905ba3-bdbb-4f78-f1ae-c152d041568f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.age.plot.hist()    # age distribution of the population"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "o-jsMUjyEDvc",
        "outputId": "9c1e319d-e6b8-48a0-a74d-f7ede72a4319"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGdCAYAAAAIbpn/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAjqklEQVR4nO3de3BU9f3/8ddiLoRLNhBlNykBooABEZRgYQvaCtEI1EHJWC8wIqZa26hAZJS0CvL1kogDIlNI1GKQ8YLGeqMOeAkYqwaEIILaBhAwwVygSrKQmiWy5/eHP3e6cs2y4ewnPh8zZ4Y95+zJ2zM75jlnz2YdlmVZAgAAMFAHuwcAAAAIFSEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFhRdg/Q1vx+v2pqatS1a1c5HA67xwEAACfBsiwdOHBAycnJ6tDh2Ndd2n3I1NTUKCUlxe4xAABACKqrq9WzZ89jbm/3IdO1a1dJP5yI+Ph4m6cBAAAnw+v1KiUlJfB7/Fjafcj8+HZSfHw8IQMAgGFOdFsIN/sCAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYUXYPAAA/N31mvWn3CK22u2C83SMAR8UVGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLFsD5mvv/5akydPVmJiouLi4nT++edr48aNge2WZWn27NlKSkpSXFycMjIytH37dhsnBgAAkcLWkNm/f79Gjhyp6OhorVq1Sl988YXmz5+vbt26BfaZN2+eFi1apKKiIq1fv16dO3dWZmammpubbZwcAABEgig7f/gjjzyilJQUFRcXB9alpqYG/m1ZlhYuXKh7771XEyZMkCQtX75cLpdLr732mq677rrTPjMAAIgctl6ReeONNzRs2DBdc8016tGjhy688EI99dRTge27du1SXV2dMjIyAuucTqeGDx+u8vLyox7T5/PJ6/UGLQAAoH2y9YrMzp07VVhYqNzcXP35z3/Whg0bdOeddyomJkZTpkxRXV2dJMnlcgU9z+VyBbb9VH5+vubOndvmswOIDH1mvWn3CABsZOsVGb/fr6FDh+rhhx/WhRdeqFtvvVW33HKLioqKQj5mXl6eGhsbA0t1dXUYJwYAAJHE1pBJSkrSwIEDg9YNGDBAVVVVkiS32y1Jqq+vD9qnvr4+sO2nYmNjFR8fH7QAAID2ydaQGTlypCorK4PWbdu2Tb1795b0w42/brdbpaWlge1er1fr16+Xx+M5rbMCAIDIY+s9MjNmzNCvfvUrPfzww/rd736njz/+WE8++aSefPJJSZLD4dD06dP14IMPql+/fkpNTdV9992n5ORkXXXVVXaODgAAIoCtIXPRRRfp1VdfVV5env7v//5PqampWrhwoSZNmhTY5+6771ZTU5NuvfVWNTQ0aNSoUVq9erU6duxo4+QAACASOCzLsuweoi15vV45nU41NjZyvwzQDvGppdNjd8F4u0fAz8zJ/v62/SsKAAAAQkXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxla8jcf//9cjgcQUtaWlpge3Nzs3JycpSYmKguXbooKytL9fX1Nk4MAAAiie1XZM477zzV1tYGlg8++CCwbcaMGVq5cqVKSkpUVlammpoaTZw40cZpAQBAJImyfYCoKLnd7iPWNzY2aunSpXr++ec1evRoSVJxcbEGDBigdevWacSIEad7VAAAEGFsvyKzfft2JScn6+yzz9akSZNUVVUlSaqoqFBLS4syMjIC+6alpalXr14qLy8/5vF8Pp+8Xm/QAgAA2idbQ2b48OFatmyZVq9ercLCQu3atUsXX3yxDhw4oLq6OsXExCghISHoOS6XS3V1dcc8Zn5+vpxOZ2BJSUlp4/8KAABgF1vfWho7dmzg34MHD9bw4cPVu3dvvfTSS4qLiwvpmHl5ecrNzQ089nq9xAwAAO2U7W8t/a+EhAT1799fO3bskNvt1qFDh9TQ0BC0T319/VHvqflRbGys4uPjgxYAANA+RVTIHDx4UF9++aWSkpKUnp6u6OholZaWBrZXVlaqqqpKHo/HxikBAECksPWtpZkzZ+rKK69U7969VVNTozlz5uiMM87Q9ddfL6fTqezsbOXm5qp79+6Kj4/XHXfcIY/HwyeWAACAJJtDZs+ePbr++uv1zTff6KyzztKoUaO0bt06nXXWWZKkxx57TB06dFBWVpZ8Pp8yMzO1ZMkSO0cGAAARxGFZlmX3EG3J6/XK6XSqsbGR+2WAdqjPrDftHuFnYXfBeLtHwM/Myf7+jqh7ZAAAAFqDkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgrCi7BwAARL4+s960e4RW210w3u4RcBpwRQYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGCsiAmZgoICORwOTZ8+PbCuublZOTk5SkxMVJcuXZSVlaX6+nr7hgQAABElIkJmw4YNeuKJJzR48OCg9TNmzNDKlStVUlKisrIy1dTUaOLEiTZNCQAAIo3tIXPw4EFNmjRJTz31lLp16xZY39jYqKVLl2rBggUaPXq00tPTVVxcrI8++kjr1q2zcWIAABApbA+ZnJwcjR8/XhkZGUHrKyoq1NLSErQ+LS1NvXr1Unl5+TGP5/P55PV6gxYAANA+Rdn5w1esWKFNmzZpw4YNR2yrq6tTTEyMEhISgta7XC7V1dUd85j5+fmaO3duuEcFAAARyLYrMtXV1Zo2bZqee+45dezYMWzHzcvLU2NjY2Cprq4O27EBAEBksS1kKioqtHfvXg0dOlRRUVGKiopSWVmZFi1apKioKLlcLh06dEgNDQ1Bz6uvr5fb7T7mcWNjYxUfHx+0AACA9sm2t5bGjBmjrVu3Bq2bOnWq0tLSdM899yglJUXR0dEqLS1VVlaWJKmyslJVVVXyeDx2jAwAACKMbSHTtWtXDRo0KGhd586dlZiYGFifnZ2t3Nxcde/eXfHx8brjjjvk8Xg0YsQIO0YGAAARxtabfU/kscceU4cOHZSVlSWfz6fMzEwtWbLE7rEAAECEcFiWZbX2STt37tTZZ5/dFvOEndfrldPpVGNjI/fLAO1Qn1lv2j0CItTugvF2j4BTcLK/v0O62bdv37669NJL9eyzz6q5uTnkIQEAAE5FSCGzadMmDR48WLm5uXK73frDH/6gjz/+ONyzAQAAHFdIIXPBBRfo8ccfV01NjZ5++mnV1tZq1KhRGjRokBYsWKB9+/aFe04AAIAjnNLfkYmKitLEiRNVUlKiRx55RDt27NDMmTOVkpKiG2+8UbW1teGaEwAA4Ain9KmljRs36umnn9aKFSvUuXNnzZw5U9nZ2dqzZ4/mzp2rCRMm8JYTfrZMvAmVmyMBmCakkFmwYIGKi4tVWVmpcePGafny5Ro3bpw6dPjhAk9qaqqWLVumPn36hHNWAACAICGFTGFhoW6++WbddNNNSkpKOuo+PXr00NKlS09pOAAAgOMJKWS2b99+wn1iYmI0ZcqUUA4PAABwUkK62be4uFglJSVHrC8pKdEzzzxzykMBAACcjJBCJj8/X2eeeeYR63v06KGHH374lIcCAAA4GSG9tVRVVaXU1NQj1vfu3VtVVVWnPBQAe5j4SSsAP28hXZHp0aOHtmzZcsT6Tz/9VImJiac8FAAAwMkIKWSuv/563XnnnVq7dq0OHz6sw4cPa82aNZo2bZquu+66cM8IAABwVCG9tfTAAw9o9+7dGjNmjKKifjiE3+/XjTfeyD0yAADgtAkpZGJiYvTiiy/qgQce0Keffqq4uDidf/756t27d7jnAwAAOKZT+oqC/v37q3///uGaBQAAoFVCCpnDhw9r2bJlKi0t1d69e+X3+4O2r1mzJizDAQAAHE9IITNt2jQtW7ZM48eP16BBg+RwOMI9FwAAwAmFFDIrVqzQSy+9pHHjxoV7HgAAgJMW0sevY2Ji1Ldv33DPAgAA0Cohhcxdd92lxx9/XJZlhXseAACAkxbSW0sffPCB1q5dq1WrVum8885TdHR00PZXXnklLMMBAAAcT0ghk5CQoKuvvjrcswAAALRKSCFTXFwc7jkAAABaLaR7ZCTp+++/17vvvqsnnnhCBw4ckCTV1NTo4MGDYRsOAADgeEK6IvPVV1/piiuuUFVVlXw+ny677DJ17dpVjzzyiHw+n4qKisI9JwAAwBFCuiIzbdo0DRs2TPv371dcXFxg/dVXX63S0tKwDQcAAHA8IV2R+ec//6mPPvpIMTExQev79Omjr7/+OiyDAQAAnEhIV2T8fr8OHz58xPo9e/aoa9eupzwUAADAyQgpZC6//HItXLgw8NjhcOjgwYOaM2cOX1sAAABOm5DeWpo/f74yMzM1cOBANTc364YbbtD27dt15pln6oUXXgj3jAAAAEcVUsj07NlTn376qVasWKEtW7bo4MGDys7O1qRJk4Ju/gUAAGhLIYWMJEVFRWny5MnhnAU4pj6z3rR7BABABAopZJYvX37c7TfeeGNIwwAAALRGSCEzbdq0oMctLS3673//q5iYGHXq1ImQAQAAp0VIn1rav39/0HLw4EFVVlZq1KhR3OwLAABOm5C/a+mn+vXrp4KCgiOu1gAAALSVsIWM9MMNwDU1NeE8JAAAwDGFdI/MG2+8EfTYsizV1tbqr3/9q0aOHBmWwQAAAE4kpJC56qqrgh47HA6dddZZGj16tObPnx+OuQAAAE4opJDx+/3hngMAAKDVwnqPDAAAwOkU0hWZ3Nzck953wYIFofwIAACAEwopZD755BN98sknamlp0bnnnitJ2rZtm8444wwNHTo0sJ/D4QjPlAAAAEcR0ltLV155pS655BLt2bNHmzZt0qZNm1RdXa1LL71Uv/3tb7V27VqtXbtWa9asOe5xCgsLNXjwYMXHxys+Pl4ej0erVq0KbG9ublZOTo4SExPVpUsXZWVlqb6+PpSRAQBAOxRSyMyfP1/5+fnq1q1bYF23bt304IMPtupTSz179lRBQYEqKiq0ceNGjR49WhMmTNDnn38uSZoxY4ZWrlypkpISlZWVqaamRhMnTgxlZAAA0A6F9NaS1+vVvn37jli/b98+HThw4KSPc+WVVwY9fuihh1RYWKh169apZ8+eWrp0qZ5//nmNHj1aklRcXKwBAwZo3bp1GjFiRCijAwCAdiSkKzJXX321pk6dqldeeUV79uzRnj179Pe//13Z2dkhXzE5fPiwVqxYoaamJnk8HlVUVKilpUUZGRmBfdLS0tSrVy+Vl5eH9DMAAED7EtIVmaKiIs2cOVM33HCDWlpafjhQVJSys7P16KOPtupYW7dulcfjUXNzs7p06aJXX31VAwcO1ObNmxUTE6OEhISg/V0ul+rq6o55PJ/PJ5/PF3js9XpbNQ8AADBHSCHTqVMnLVmyRI8++qi+/PJLSdI555yjzp07t/pY5557rjZv3qzGxka9/PLLmjJlisrKykIZS5KUn5+vuXPnhvx8AABgjlP6g3i1tbWqra1Vv3791LlzZ1mW1epjxMTEqG/fvkpPT1d+fr6GDBmixx9/XG63W4cOHVJDQ0PQ/vX19XK73cc8Xl5enhobGwNLdXV1q2cCAABmCClkvvnmG40ZM0b9+/fXuHHjVFtbK0nKzs7WXXfddUoD+f1++Xw+paenKzo6WqWlpYFtlZWVqqqqksfjOebzY2NjAx/n/nEBAADtU0ghM2PGDEVHR6uqqkqdOnUKrL/22mu1evXqkz5OXl6e3n//fe3evVtbt25VXl6e3nvvPU2aNElOp1PZ2dnKzc3V2rVrVVFRoalTp8rj8fCJJQAAICnEe2TefvttvfXWW+rZs2fQ+n79+umrr7466ePs3btXN954o2pra+V0OjV48GC99dZbuuyyyyRJjz32mDp06KCsrCz5fD5lZmZqyZIloYwMAADaoZBCpqmpKehKzI++/fZbxcbGnvRxli5detztHTt21OLFi7V48eJWzwgAANq/kN5auvjii7V8+fLAY4fDIb/fr3nz5unSSy8N23AAAADHE9IVmXnz5mnMmDHauHGjDh06pLvvvluff/65vv32W3344YfhnhEAAOCoQroiM2jQIG3btk2jRo3ShAkT1NTUpIkTJ+qTTz7ROeecE+4ZAQAAjqrVV2RaWlp0xRVXqKioSH/5y1/aYiYAAICT0uorMtHR0dqyZUtbzAIAANAqIb21NHny5BN+4ggAAKCthXSz7/fff6+nn35a7777rtLT04/4jqUFCxaEZTgAAIDjaVXI7Ny5U3369NFnn32moUOHSpK2bdsWtI/D4QjfdAAAAMfRqpDp16+famtrtXbtWkk/fCXBokWL5HK52mQ4AACA42nVPTI//XbrVatWqampKawDAQAAnKyQbvb90U/DBgAA4HRqVcg4HI4j7oHhnhgAAGCXVt0jY1mWbrrppsAXQzY3N+u222474lNLr7zySvgmBAAAOIZWhcyUKVOCHk+ePDmswwAAALRGq0KmuLi4reYAAABotVO62RcAAMBOhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMJatIZOfn6+LLrpIXbt2VY8ePXTVVVepsrIyaJ/m5mbl5OQoMTFRXbp0UVZWlurr622aGAAARBJbQ6asrEw5OTlat26d3nnnHbW0tOjyyy9XU1NTYJ8ZM2Zo5cqVKikpUVlZmWpqajRx4kQbpwYAAJEiys4fvnr16qDHy5YtU48ePVRRUaFLLrlEjY2NWrp0qZ5//nmNHj1aklRcXKwBAwZo3bp1GjFihB1jAwCACBFR98g0NjZKkrp37y5JqqioUEtLizIyMgL7pKWlqVevXiovL7dlRgAAEDlsvSLzv/x+v6ZPn66RI0dq0KBBkqS6ujrFxMQoISEhaF+Xy6W6urqjHsfn88nn8wUee73eNpsZAADYK2KuyOTk5Oizzz7TihUrTuk4+fn5cjqdgSUlJSVMEwIAgEgTESFz++236x//+IfWrl2rnj17Bta73W4dOnRIDQ0NQfvX19fL7XYf9Vh5eXlqbGwMLNXV1W05OgAAsJGtIWNZlm6//Xa9+uqrWrNmjVJTU4O2p6enKzo6WqWlpYF1lZWVqqqqksfjOeoxY2NjFR8fH7QAAID2ydZ7ZHJycvT888/r9ddfV9euXQP3vTidTsXFxcnpdCo7O1u5ubnq3r274uPjdccdd8jj8fCJJQAAYG/IFBYWSpJ+85vfBK0vLi7WTTfdJEl67LHH1KFDB2VlZcnn8ykzM1NLliw5zZMCAIBIZGvIWJZ1wn06duyoxYsXa/HixadhIgAAYJKIuNkXAAAgFIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMFWX3AAAAtIU+s960e4RW210w3u4RjMMVGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsaLsHgCnX59Zb9o9AgAAYWHrFZn3339fV155pZKTk+VwOPTaa68FbbcsS7Nnz1ZSUpLi4uKUkZGh7du32zMsAACIOLaGTFNTk4YMGaLFixcfdfu8efO0aNEiFRUVaf369ercubMyMzPV3Nx8micFAACRyNa3lsaOHauxY8cedZtlWVq4cKHuvfdeTZgwQZK0fPlyuVwuvfbaa7ruuutO56gAACACRezNvrt27VJdXZ0yMjIC65xOp4YPH67y8vJjPs/n88nr9QYtAACgfYrYkKmrq5MkuVyuoPUulyuw7Wjy8/PldDoDS0pKSpvOCQAA7BOxIROqvLw8NTY2Bpbq6mq7RwIAAG0kYkPG7XZLkurr64PW19fXB7YdTWxsrOLj44MWAADQPkVsyKSmpsrtdqu0tDSwzuv1av369fJ4PDZOBgAAIoWtn1o6ePCgduzYEXi8a9cubd68Wd27d1evXr00ffp0Pfjgg+rXr59SU1N13333KTk5WVdddZV9QwMAgIhha8hs3LhRl156aeBxbm6uJGnKlClatmyZ7r77bjU1NenWW29VQ0ODRo0apdWrV6tjx452jQwAACKIw7Isy+4h2pLX65XT6VRjYyP3y/x/fEUBAESm3QXj7R4hYpzs7++IvUcGAADgRAgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLGi7B4AAAD8oM+sN+0eodV2F4y39edzRQYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADG4isKToGJf0oaAID2xIgrMosXL1afPn3UsWNHDR8+XB9//LHdIwEAgAgQ8SHz4osvKjc3V3PmzNGmTZs0ZMgQZWZmau/evXaPBgAAbBbxIbNgwQLdcsstmjp1qgYOHKiioiJ16tRJTz/9tN2jAQAAm0X0PTKHDh1SRUWF8vLyAus6dOigjIwMlZeXH/U5Pp9PPp8v8LixsVGS5PV6wz6f3/ffsB8TAACTtMXv1/89rmVZx90vokPmP//5jw4fPiyXyxW03uVy6d///vdRn5Ofn6+5c+cesT4lJaVNZgQA4OfMubBtj3/gwAE5nc5jbo/okAlFXl6ecnNzA4/9fr++/fZbJSYmyuFwnPZ5vF6vUlJSVF1drfj4+NP+83+uOO/24Lzbg/NuD85727IsSwcOHFBycvJx94vokDnzzDN1xhlnqL6+Pmh9fX293G73UZ8TGxur2NjYoHUJCQltNeJJi4+P54VuA867PTjv9uC824Pz3naOdyXmRxF9s29MTIzS09NVWloaWOf3+1VaWiqPx2PjZAAAIBJE9BUZScrNzdWUKVM0bNgw/fKXv9TChQvV1NSkqVOn2j0aAACwWcSHzLXXXqt9+/Zp9uzZqqur0wUXXKDVq1cfcQNwpIqNjdWcOXOOeLsLbYvzbg/Ouz047/bgvEcGh3WizzUBAABEqIi+RwYAAOB4CBkAAGAsQgYAABiLkAEAAMYiZMKgsLBQgwcPDvxRJI/Ho1WrVgW2Nzc3KycnR4mJierSpYuysrKO+CN/OHUFBQVyOByaPn16YB3nPvzuv/9+ORyOoCUtLS2wnXPedr7++mtNnjxZiYmJiouL0/nnn6+NGzcGtluWpdmzZyspKUlxcXHKyMjQ9u3bbZzYfH369Dni9e5wOJSTkyOJ13skIGTCoGfPniooKFBFRYU2btyo0aNHa8KECfr8888lSTNmzNDKlStVUlKisrIy1dTUaOLEiTZP3b5s2LBBTzzxhAYPHhy0nnPfNs477zzV1tYGlg8++CCwjXPeNvbv36+RI0cqOjpaq1at0hdffKH58+erW7dugX3mzZunRYsWqaioSOvXr1fnzp2VmZmp5uZmGyc324YNG4Je6++8844k6ZprrpHE6z0iWGgT3bp1s/72t79ZDQ0NVnR0tFVSUhLY9q9//cuSZJWXl9s4Yftx4MABq1+/ftY777xj/frXv7amTZtmWZbFuW8jc+bMsYYMGXLUbZzztnPPPfdYo0aNOuZ2v99vud1u69FHHw2sa2hosGJjY60XXnjhdIz4szBt2jTrnHPOsfx+P6/3CMEVmTA7fPiwVqxYoaamJnk8HlVUVKilpUUZGRmBfdLS0tSrVy+Vl5fbOGn7kZOTo/HjxwedY0mc+za0fft2JScn6+yzz9akSZNUVVUliXPelt544w0NGzZM11xzjXr06KELL7xQTz31VGD7rl27VFdXF3TunU6nhg8fzrkPk0OHDunZZ5/VzTffLIfDwes9QhAyYbJ161Z16dJFsbGxuu222/Tqq69q4MCBqqurU0xMzBFfXOlyuVRXV2fPsO3IihUrtGnTJuXn5x+xjXPfNoYPH65ly5Zp9erVKiws1K5du3TxxRfrwIEDnPM2tHPnThUWFqpfv35666239Mc//lF33nmnnnnmGUkKnN+f/tVzzn34vPbaa2poaNBNN90kif/HRIqI/4oCU5x77rnavHmzGhsb9fLLL2vKlCkqKyuze6x2rbq6WtOmTdM777yjjh072j3Oz8bYsWMD/x48eLCGDx+u3r1766WXXlJcXJyNk7Vvfr9fw4YN08MPPyxJuvDCC/XZZ5+pqKhIU6ZMsXm6n4elS5dq7NixSk5OtnsU/A+uyIRJTEyM+vbtq/T0dOXn52vIkCF6/PHH5Xa7dejQITU0NATtX19fL7fbbc+w7URFRYX27t2roUOHKioqSlFRUSorK9OiRYsUFRUll8vFuT8NEhIS1L9/f+3YsYPXextKSkrSwIEDg9YNGDAg8Lbej+f3p5+Y4dyHx1dffaV3331Xv//97wPreL1HBkKmjfj9fvl8PqWnpys6OlqlpaWBbZWVlaqqqpLH47FxQvONGTNGW7du1ebNmwPLsGHDNGnSpMC/Ofdt7+DBg/ryyy+VlJTE670NjRw5UpWVlUHrtm3bpt69e0uSUlNT5Xa7g8691+vV+vXrOfdhUFxcrB49emj8+PGBdbzeI4Tddxu3B7NmzbLKysqsXbt2WVu2bLFmzZplORwO6+2337Ysy7Juu+02q1evXtaaNWusjRs3Wh6Px/J4PDZP3T7976eWLItz3xbuuusu67333rN27dplffjhh1ZGRoZ15plnWnv37rUsi3PeVj7++GMrKirKeuihh6zt27dbzz33nNWpUyfr2WefDexTUFBgJSQkWK+//rq1ZcsWa8KECVZqaqr13Xff2Ti5+Q4fPmz16tXLuueee47YxuvdfoRMGNx8881W7969rZiYGOuss86yxowZE4gYy7Ks7777zvrTn/5kdevWzerUqZN19dVXW7W1tTZO3H79NGQ49+F37bXXWklJSVZMTIz1i1/8wrr22mutHTt2BLZzztvOypUrrUGDBlmxsbFWWlqa9eSTTwZt9/v91n333We5XC4rNjbWGjNmjFVZWWnTtO3HW2+9ZUk66rnk9W4/h2VZlt1XhQAAAELBPTIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABj/T+T2ZypPjgpWwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see that our graph which is normally distributed is kind of heavy towards right side this is just like the graph we created above."
      ],
      "metadata": {
        "id": "A09gFyCAF3A6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's analyze another independent variable chest pain.\n",
        "\n",
        "cp - chest pain type\n",
        "1. 0: Typical angina: chest pain related decrease blood supply to the heart\n",
        "2. 1: Atypical angina: chest pain not related to heart\n",
        "3. 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
        "4. 3: Asymptomatic: chest pain not showing signs of disease"
      ],
      "metadata": {
        "id": "HYibuBajGPQi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df.cp, df.target)"
      ],
      "metadata": {
        "id": "10zb3SzEEI0s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "eb40bfd1-2780-4cf7-9a01-8bc92bb0f330"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target    0   1\n",
              "cp             \n",
              "0       104  39\n",
              "1         9  41\n",
              "2        18  69\n",
              "3         7  16"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-81a4da15-dc2d-4cba-8d86-07bc056b71df\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>target</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>104</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-81a4da15-dc2d-4cba-8d86-07bc056b71df')\"\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-81a4da15-dc2d-4cba-8d86-07bc056b71df 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-81a4da15-dc2d-4cba-8d86-07bc056b71df');\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": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's plot the above graph\n",
        "\n",
        "pd.crosstab(df.cp, df.target).plot(kind=\"bar\", figsize=(10,5),  color=[\"red\", \"blue\"])\n",
        "\n",
        "# Add some attributes to it\n",
        "plt.title(\"Heart Disease Frequency Per Chest Pain Type\")\n",
        "plt.xlabel(\"Type of Chest Pain\")\n",
        "plt.ylabel(\"Frequnecy\")\n",
        "plt.legend([\"Disease\", \"No Disease\"])\n",
        "plt.xticks(rotation=0); # keep the labels on the x-axis vertical"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "TT7ezqOlGuOQ",
        "outputId": "7083663b-f1bf-4be3-9025-7919f7a95ff5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our focused should be on type 0 and 3 as 1 and 2 are not related to heart disease. Try to see the ratio between no disease and disease in 1 and 2 as it seems very few people have the disease due to 1 and in 0 we have many people which means chest pain indeed plays a role but 0 here is a contributing factor for majority."
      ],
      "metadata": {
        "id": "7M6R9-3JI2qU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# now let's check the correlation between different variables\n",
        "correlation_matrix = df.corr()\n",
        "correlation_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "id": "LFgeKoxCHXXL",
        "outputId": "4af9e95d-9ced-4388-dc13-0aa679161ddf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age       sex        cp  trestbps      chol       fbs  \\\n",
              "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
              "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
              "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
              "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
              "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
              "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
              "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
              "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
              "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
              "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
              "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
              "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
              "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
              "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
              "\n",
              "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
              "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
              "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
              "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
              "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
              "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
              "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
              "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
              "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
              "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
              "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
              "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
              "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
              "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
              "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
              "\n",
              "              thal    target  \n",
              "age       0.068001 -0.225439  \n",
              "sex       0.210041 -0.280937  \n",
              "cp       -0.161736  0.433798  \n",
              "trestbps  0.062210 -0.144931  \n",
              "chol      0.098803 -0.085239  \n",
              "fbs      -0.032019 -0.028046  \n",
              "restecg  -0.011981  0.137230  \n",
              "thalach  -0.096439  0.421741  \n",
              "exang     0.206754 -0.436757  \n",
              "oldpeak   0.210244 -0.430696  \n",
              "slope    -0.104764  0.345877  \n",
              "ca        0.151832 -0.391724  \n",
              "thal      1.000000 -0.344029  \n",
              "target   -0.344029  1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6780ddf9-e5d2-4616-808b-f848e8065e05\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.098447</td>\n",
              "      <td>-0.068653</td>\n",
              "      <td>0.279351</td>\n",
              "      <td>0.213678</td>\n",
              "      <td>0.121308</td>\n",
              "      <td>-0.116211</td>\n",
              "      <td>-0.398522</td>\n",
              "      <td>0.096801</td>\n",
              "      <td>0.210013</td>\n",
              "      <td>-0.168814</td>\n",
              "      <td>0.276326</td>\n",
              "      <td>0.068001</td>\n",
              "      <td>-0.225439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>-0.098447</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.049353</td>\n",
              "      <td>-0.056769</td>\n",
              "      <td>-0.197912</td>\n",
              "      <td>0.045032</td>\n",
              "      <td>-0.058196</td>\n",
              "      <td>-0.044020</td>\n",
              "      <td>0.141664</td>\n",
              "      <td>0.096093</td>\n",
              "      <td>-0.030711</td>\n",
              "      <td>0.118261</td>\n",
              "      <td>0.210041</td>\n",
              "      <td>-0.280937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>-0.068653</td>\n",
              "      <td>-0.049353</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.047608</td>\n",
              "      <td>-0.076904</td>\n",
              "      <td>0.094444</td>\n",
              "      <td>0.044421</td>\n",
              "      <td>0.295762</td>\n",
              "      <td>-0.394280</td>\n",
              "      <td>-0.149230</td>\n",
              "      <td>0.119717</td>\n",
              "      <td>-0.181053</td>\n",
              "      <td>-0.161736</td>\n",
              "      <td>0.433798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>0.279351</td>\n",
              "      <td>-0.056769</td>\n",
              "      <td>0.047608</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.123174</td>\n",
              "      <td>0.177531</td>\n",
              "      <td>-0.114103</td>\n",
              "      <td>-0.046698</td>\n",
              "      <td>0.067616</td>\n",
              "      <td>0.193216</td>\n",
              "      <td>-0.121475</td>\n",
              "      <td>0.101389</td>\n",
              "      <td>0.062210</td>\n",
              "      <td>-0.144931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>0.213678</td>\n",
              "      <td>-0.197912</td>\n",
              "      <td>-0.076904</td>\n",
              "      <td>0.123174</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.013294</td>\n",
              "      <td>-0.151040</td>\n",
              "      <td>-0.009940</td>\n",
              "      <td>0.067023</td>\n",
              "      <td>0.053952</td>\n",
              "      <td>-0.004038</td>\n",
              "      <td>0.070511</td>\n",
              "      <td>0.098803</td>\n",
              "      <td>-0.085239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbs</th>\n",
              "      <td>0.121308</td>\n",
              "      <td>0.045032</td>\n",
              "      <td>0.094444</td>\n",
              "      <td>0.177531</td>\n",
              "      <td>0.013294</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.084189</td>\n",
              "      <td>-0.008567</td>\n",
              "      <td>0.025665</td>\n",
              "      <td>0.005747</td>\n",
              "      <td>-0.059894</td>\n",
              "      <td>0.137979</td>\n",
              "      <td>-0.032019</td>\n",
              "      <td>-0.028046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>restecg</th>\n",
              "      <td>-0.116211</td>\n",
              "      <td>-0.058196</td>\n",
              "      <td>0.044421</td>\n",
              "      <td>-0.114103</td>\n",
              "      <td>-0.151040</td>\n",
              "      <td>-0.084189</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.044123</td>\n",
              "      <td>-0.070733</td>\n",
              "      <td>-0.058770</td>\n",
              "      <td>0.093045</td>\n",
              "      <td>-0.072042</td>\n",
              "      <td>-0.011981</td>\n",
              "      <td>0.137230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>-0.398522</td>\n",
              "      <td>-0.044020</td>\n",
              "      <td>0.295762</td>\n",
              "      <td>-0.046698</td>\n",
              "      <td>-0.009940</td>\n",
              "      <td>-0.008567</td>\n",
              "      <td>0.044123</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.378812</td>\n",
              "      <td>-0.344187</td>\n",
              "      <td>0.386784</td>\n",
              "      <td>-0.213177</td>\n",
              "      <td>-0.096439</td>\n",
              "      <td>0.421741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>0.096801</td>\n",
              "      <td>0.141664</td>\n",
              "      <td>-0.394280</td>\n",
              "      <td>0.067616</td>\n",
              "      <td>0.067023</td>\n",
              "      <td>0.025665</td>\n",
              "      <td>-0.070733</td>\n",
              "      <td>-0.378812</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.288223</td>\n",
              "      <td>-0.257748</td>\n",
              "      <td>0.115739</td>\n",
              "      <td>0.206754</td>\n",
              "      <td>-0.436757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>0.210013</td>\n",
              "      <td>0.096093</td>\n",
              "      <td>-0.149230</td>\n",
              "      <td>0.193216</td>\n",
              "      <td>0.053952</td>\n",
              "      <td>0.005747</td>\n",
              "      <td>-0.058770</td>\n",
              "      <td>-0.344187</td>\n",
              "      <td>0.288223</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.577537</td>\n",
              "      <td>0.222682</td>\n",
              "      <td>0.210244</td>\n",
              "      <td>-0.430696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <td>-0.168814</td>\n",
              "      <td>-0.030711</td>\n",
              "      <td>0.119717</td>\n",
              "      <td>-0.121475</td>\n",
              "      <td>-0.004038</td>\n",
              "      <td>-0.059894</td>\n",
              "      <td>0.093045</td>\n",
              "      <td>0.386784</td>\n",
              "      <td>-0.257748</td>\n",
              "      <td>-0.577537</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.080155</td>\n",
              "      <td>-0.104764</td>\n",
              "      <td>0.345877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>0.276326</td>\n",
              "      <td>0.118261</td>\n",
              "      <td>-0.181053</td>\n",
              "      <td>0.101389</td>\n",
              "      <td>0.070511</td>\n",
              "      <td>0.137979</td>\n",
              "      <td>-0.072042</td>\n",
              "      <td>-0.213177</td>\n",
              "      <td>0.115739</td>\n",
              "      <td>0.222682</td>\n",
              "      <td>-0.080155</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.151832</td>\n",
              "      <td>-0.391724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thal</th>\n",
              "      <td>0.068001</td>\n",
              "      <td>0.210041</td>\n",
              "      <td>-0.161736</td>\n",
              "      <td>0.062210</td>\n",
              "      <td>0.098803</td>\n",
              "      <td>-0.032019</td>\n",
              "      <td>-0.011981</td>\n",
              "      <td>-0.096439</td>\n",
              "      <td>0.206754</td>\n",
              "      <td>0.210244</td>\n",
              "      <td>-0.104764</td>\n",
              "      <td>0.151832</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.344029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>-0.225439</td>\n",
              "      <td>-0.280937</td>\n",
              "      <td>0.433798</td>\n",
              "      <td>-0.144931</td>\n",
              "      <td>-0.085239</td>\n",
              "      <td>-0.028046</td>\n",
              "      <td>0.137230</td>\n",
              "      <td>0.421741</td>\n",
              "      <td>-0.436757</td>\n",
              "      <td>-0.430696</td>\n",
              "      <td>0.345877</td>\n",
              "      <td>-0.391724</td>\n",
              "      <td>-0.344029</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6780ddf9-e5d2-4616-808b-f848e8065e05')\"\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-6780ddf9-e5d2-4616-808b-f848e8065e05 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-6780ddf9-e5d2-4616-808b-f848e8065e05');\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": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's visulaize it\n",
        "plt.figure(figsize=(15, 10))\n",
        "sns.heatmap(df.corr(), cmap=\"YlGnBu\", annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "LgH5bPG2Jxtt",
        "outputId": "caebd5fa-52bc-4cb5-8b3e-f07aaee9c60b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Value closer to one means higher correlation where as value colser to 0 means lower correlation. Here our target varibale is either 0 or 1 so it will be difficult to compare the correlation between different varible to heart disease but in cases where you have numerical value you can simply use correlation to find the relationship. Here you can check the correlation betwen one independent varible to another.\n",
        "\n",
        "You can also perform EDA in your own way and try to find some few feature combination or see what are some important features according to you. How the data is distributed and how they are correlated?"
      ],
      "metadata": {
        "id": "cLR3xrdBKq88"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating Training And Test Set"
      ],
      "metadata": {
        "id": "ucuchbnUM3-G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's create training and test set so that we can prepare our model for the real world."
      ],
      "metadata": {
        "id": "rw9rJJjmGf_o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# we will use sklearn training and test set split method\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "X = df.drop(\"target\", axis=1)  # independent variable\n",
        "y = df.target.values           # dependent variable\n",
        "\n",
        "# split the data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,\n",
        "                                                    y,\n",
        "                                                    test_size=0.2)  #percentage of data used for test set"
      ],
      "metadata": {
        "id": "O0_f1zR0Mce6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YT5DOAj8HuiR",
        "outputId": "eec7e9d9-6a66-4884-ece8-c5f2e8b17e47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((242, 13), (61, 13), (242,), (61,))"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fitting The Models"
      ],
      "metadata": {
        "id": "vTYOYBA7W-po"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we have our training and test set. We can now select our models for the classfication problem.\n",
        "There are various models you can choose for the classification problem. Here is a cheat code to select the right models: https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html\n",
        "\n",
        "We will select:\n",
        "1. Logistic Regression - [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)\n",
        "2. K-Nearest Neighbors - [KNeighboursClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)\n",
        "3. RandomForest - [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)"
      ],
      "metadata": {
        "id": "d3YbVK-JIMeu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# now let's create a dictionary to put these models into it\n",
        "\n",
        "models = {\"KNN\": KNeighborsClassifier(),\n",
        "          \"Logistic Regression\": LogisticRegression(),\n",
        "          \"Random Forest\": RandomForestClassifier()\n",
        "          }"
      ],
      "metadata": {
        "id": "W35mCHzNHxzo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a function to evaluate and fit\n",
        "def fit_and_score(models, X_train, X_test, y_train, y_test):\n",
        "  \"\"\"\n",
        "  This function fits and evalautes the different models in models dict using sets\n",
        "\n",
        "  \"\"\"\n",
        "  np.random.seed(42)  # for reprocudibility\n",
        "\n",
        "  model_scores = {}\n",
        "\n",
        "  for type, model in models.items():\n",
        "    model.fit(X_train, y_train)     # fit the models in dict\n",
        "    model_scores[type] = model.score(X_test, y_test)\n",
        "\n",
        "  return model_scores\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "LHJ8S0ExKYed"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models_scores = fit_and_score(models = models,\n",
        "                              X_train = X_train,\n",
        "                              y_train = y_train,\n",
        "                              X_test = X_test,\n",
        "                              y_test = y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77osPD6ZLvHC",
        "outputId": "8a4040fc-b792-48cc-c4d8-7f99a068495b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models_scores   # this is our models score on test data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "05L-Mi7zMHby",
        "outputId": "40d21a1d-f424-44d0-f752-db4b4536922d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'KNN': 0.6885245901639344,\n",
              " 'Logistic Regression': 0.8852459016393442,\n",
              " 'Random Forest': 0.8360655737704918}"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notice we are not using validation set because we don't have much data"
      ],
      "metadata": {
        "id": "bbQGtBO8OTZZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# you can also create a bar graph\n",
        "\n",
        "model_scores_df = pd.DataFrame(models_scores, index=[\"Accuracy\"])\n",
        "model_scores_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "8LqX88iUMP04",
        "outputId": "44cf5731-ab06-4b68-cd1e-fd5a5c4d1baf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               KNN  Logistic Regression  Random Forest\n",
              "Accuracy  0.688525             0.885246       0.836066"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f117afd5-3bd7-4444-aefa-c26cfdd1bb46\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>KNN</th>\n",
              "      <th>Logistic Regression</th>\n",
              "      <th>Random Forest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.688525</td>\n",
              "      <td>0.885246</td>\n",
              "      <td>0.836066</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f117afd5-3bd7-4444-aefa-c26cfdd1bb46')\"\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-f117afd5-3bd7-4444-aefa-c26cfdd1bb46 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-f117afd5-3bd7-4444-aefa-c26cfdd1bb46');\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": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_scores_df.T.plot.bar(color='lightblue');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "Zbd6DXcfMquZ",
        "outputId": "8e7207c5-bb06-49af-dcad-0f368b607aa0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see from the graph above logistic regression is performing better than other models.\n",
        "\n",
        "Now let's hypertune our models and see what we can achieve"
      ],
      "metadata": {
        "id": "rpzS0ztANfgv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperparameter Tuning"
      ],
      "metadata": {
        "id": "wGvv0CsNXEvN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# KNN hyptertining\n",
        "# there are various KNN parameters you can see that in sklearn documentation. We can hypertune n_neighbours which is default at 5 and see changes\n",
        "\n",
        "# train scores list\n",
        "train_scores = []\n",
        "\n",
        "# test scores list\n",
        "test_scores = []\n",
        "\n",
        "# neighbours different values\n",
        "\n",
        "neighbors = range(1, 21)\n",
        "\n",
        "# algorithm\n",
        "\n",
        "knn = KNeighborsClassifier()\n",
        "\n",
        "# looping through different neighbors values\n",
        "\n",
        "for i in neighbors:\n",
        "  knn.set_params(n_neighbors = i)    # setting different params\n",
        "  knn.fit(X_train, y_train)          # fitting the model\n",
        "  train_scores.append(knn.score(X_train, y_train))  # update the training score list\n",
        "  test_scores.append(knn.score(X_test, y_test))     # update the test score list\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "xi3bO1pZM0CR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wwnyxcc3RM8i",
        "outputId": "3d09f4eb-0e18-4c65-db23-034130351430"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.0,\n",
              " 0.8099173553719008,\n",
              " 0.7727272727272727,\n",
              " 0.743801652892562,\n",
              " 0.7603305785123967,\n",
              " 0.7520661157024794,\n",
              " 0.743801652892562,\n",
              " 0.7231404958677686,\n",
              " 0.71900826446281,\n",
              " 0.6942148760330579,\n",
              " 0.7272727272727273,\n",
              " 0.6983471074380165,\n",
              " 0.6900826446280992,\n",
              " 0.6942148760330579,\n",
              " 0.6859504132231405,\n",
              " 0.6735537190082644,\n",
              " 0.6859504132231405,\n",
              " 0.6652892561983471,\n",
              " 0.6818181818181818,\n",
              " 0.6694214876033058]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's use graph\n",
        "plt.plot(neighbors, train_scores, label=\"Train Score\")\n",
        "plt.plot(neighbors, test_scores, label=\"Test Score\")\n",
        "plt.xticks(np.arange(1, 21, 1))\n",
        "plt.xlabel('Number of neighors')\n",
        "plt.ylabel(\"Model Score\")\n",
        "plt.legend()\n",
        "\n",
        "print(f\"Max KNN score on test data: {max(test_scores)*100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "J0IL23syROC5",
        "outputId": "bd763617-3ebb-49a3-e72e-23abe88b8b17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max KNN score on test data: 75.41%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The best score here is still less than our logistic regression so we will discard it.\n",
        "\n",
        "Now let's hypertune other models using Randomized Search CV which test and evaluates the model by trying different combinations\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html"
      ],
      "metadata": {
        "id": "GVEuDynhSnGK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have different parameters in logistic regressiona and random forest classifier as mentioned in sklearn documentation\n"
      ],
      "metadata": {
        "id": "w1mZGiXfTZYi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# logistic regression\n",
        "\n",
        "log_reg_grid = {\"C\": np.logspace(-4, 4, 20),     # c is choosen on logarithmic space\n",
        "                \"solver\": [\"liblinear\"] }        # liblinear includes L1 and L2 penality\n",
        "\n",
        "# Random Forest Classifier\n",
        "\n",
        "rf_grid = {\"n_estimators\": np.arange(10, 100, 50),\n",
        "           \"max_depth\": [None, 3, 5, 10],\n",
        "           \"min_samples_split\": np.arange(2, 20, 2),\n",
        "           \"min_samples_leaf\": np.arange(1, 20, 2)}\n",
        "\n"
      ],
      "metadata": {
        "id": "nH0Yeo17Rb5R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup random seed\n",
        "np.random.seed(42)\n",
        "\n",
        "# Setup random hyperparameter search for LogisticRegression\n",
        "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
        "                                param_distributions=log_reg_grid,\n",
        "                                cv=5,\n",
        "                                n_iter=20,\n",
        "                                verbose=True)\n",
        "\n",
        "# Fit random hyperparameter search model\n",
        "rs_log_reg.fit(X_train, y_train);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-i1x-r7mZdZx",
        "outputId": "b7b27520-ead2-4423-ec22-21fbf12afe49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rs_log_reg.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_OzHJTVWm4h",
        "outputId": "2b615865-e65f-4dd9-f736-82bef58ed581"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'solver': 'liblinear', 'C': 0.23357214690901212}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rs_log_reg.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gkzKgg6ZWzyH",
        "outputId": "e39f6f12-29b5-4d03-8dd0-4f01d51d829e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8852459016393442"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's check for Random Forest Classifier\n",
        "\n",
        "np.random.seed(42)\n",
        "\n",
        "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
        "                           param_distributions=rf_grid,\n",
        "                           cv=5,\n",
        "                           n_iter=20,\n",
        "                           verbose=True)\n",
        "\n",
        "rs_rf.fit(X_train, y_train);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4vxp20LTa1fL",
        "outputId": "8c86e465-000d-4e8e-cf05-6bbb661cd5e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rs_rf.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Iv3F9XubdHN",
        "outputId": "7b878d10-f578-41a1-9894-9913597e8f1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_estimators': 10,\n",
              " 'min_samples_split': 18,\n",
              " 'min_samples_leaf': 17,\n",
              " 'max_depth': 3}"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rs_rf.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p7VEH7ClbgHM",
        "outputId": "ad9527f9-e239-4e13-ad36-a81e5450f22d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.819672131147541"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our Logistic Regression still perfoms better.\n",
        "Now let's tune our model using GridSearchCV:\n",
        "\n",
        "RandomizedSearchCV - tries n_iter combinations of hyperparameters and saves the best.\n",
        "GridSearchCV - tries every single combination of hyperparameters and saves the best."
      ],
      "metadata": {
        "id": "GS0pK5SFbu9C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
        "                          cv=5,\n",
        "                          verbose=True,\n",
        "                          param_grid=log_reg_grid)\n",
        "\n",
        "gs_log_reg.fit(X_train, y_train);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Zzqbpkmbk7M",
        "outputId": "a30f7c41-335b-4f8f-8cb8-17273d5e3eb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gs_log_reg.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1nogeN5Lcr5C",
        "outputId": "3645fbcc-4460-4c21-cbae-3b9a6a6850f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gs_log_reg.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ElH05KtkcuiK",
        "outputId": "4fcc7531-ac4c-438f-f6f2-63cddfd48339"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8852459016393442"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are getting the same result. Now let's check different evalaution metrics for this."
      ],
      "metadata": {
        "id": "o0We13Wpc9e6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation Of Our Classification Model\n",
        "\n",
        "We want:\n",
        "\n",
        "1. ROC curve and AUC score - plot_roc_curve()\n",
        "2. Confusion matrix - confusion_matrix()\n",
        "3. Classification report - classification_report()\n",
        "4. Precision - precision_score()\n",
        "5. Recall - recall_score()\n",
        "6. F1-score - f1_score()"
      ],
      "metadata": {
        "id": "MjuZKZd_dK70"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "These are other metrics in addition to accuracy.\n",
        "\n",
        "- Confusion matrix - Compares the predicted values with the true values in a tabular way, if 100% correct, all values in the matrix will be top left to bottom right (diagnol line).\n",
        "- Precision - Proportion of true positives over total number of samples. Higher precision leads to less false positives.\n",
        "- Recall - Proportion of true positives over total number of true positives and - false negatives. Higher recall leads to less false negatives.\n",
        "- F1 score - Combines precision and recall into one metric. 1 is best, 0 is worst.\n",
        "- Classification report - Sklearn has a built-in function called - classification_report() which returns some of the main classification metrics such as precision, recall and f1-score.\n",
        "- ROC Curve - Receiver Operating Characterisitc is a plot of true positive rate versus false positive rate.\n",
        "- Area Under Curve (AUC) - The area underneath the ROC curve. A perfect model achieves a score of 1.0.\n",
        "___\n",
        "\n",
        "https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/\n",
        "_\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html#sklearn.metrics.precision_score\n",
        "-\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score\n",
        "-\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score\n",
        "_\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html\n",
        "-\n",
        "https://en.wikipedia.org/wiki/Receiver_operating_characteristic\n",
        "_\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html"
      ],
      "metadata": {
        "id": "xOxJsjZ2dY4i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# make prediction on test data\n",
        "\n",
        "y_preds = gs_log_reg.predict(X_test)"
      ],
      "metadata": {
        "id": "5pqXTLA9c1rS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X4mP62xPeafD",
        "outputId": "2331780e-f885-4065-d1eb-f2fabc8eaa72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test   # compare both y_preds and y_test output and see the difference"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97XDLaQGecoz",
        "outputId": "8e223272-0db7-4bee-8847-09a3060f7955"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
              "       0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ROC Curve And AUC Score\n",
        "- It's a way of understanding how your model is performing by comparing the true positive rate to the false positive rate.\n",
        "\n",
        "___\n",
        "Consider a diagnostic test that seeks to determine whether a person has a certain disease. A false positive in this case occurs when the person tests positive, but does not actually have the disease. A false negative, on the other hand, occurs when the person tests negative, suggesting they are healthy, when they actually do have the disease"
      ],
      "metadata": {
        "id": "MjkibZUie3L8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "RocCurveDisplay.from_estimator(gs_log_reg, X_test, y_test);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Xo0Df8Lbeg8K",
        "outputId": "b573c6bb-57a3-4850-91ca-c724f9c5a99a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Confusion Matrix\n",
        "A confusion matrix is a visual way to show where your model made the right predictions and where it made the wrong predictions (or in other words, got confused).\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html"
      ],
      "metadata": {
        "id": "EsZGp6MNgCMK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test, y_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cndtr-_UgBuj",
        "outputId": "1c86095a-edcc-4edf-cba2-50da94654312"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[25  4]\n",
            " [ 3 29]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's plot it\n",
        "fig, ax = plt.subplots(figsize=(3, 3))\n",
        "ax = sns.heatmap(confusion_matrix(y_test, y_preds), annot=True, cbar=False)\n",
        "plt.xlabel(\"true label\")\n",
        "plt.ylabel(\"predicted label\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "JYNxX8sxgW7K",
        "outputId": "ab968317-a072-4a56-fa86-336efe72bfb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(8.222222222222223, 0.5, 'predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "See black boxes to indentify the model predicted errors. Boxes contain the values."
      ],
      "metadata": {
        "id": "hXv1qk_9hlYy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Classification Report\n",
        "A classification report will also give us information of the precision and recall of our model for each class"
      ],
      "metadata": {
        "id": "IXvZApI_htZD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTq-HCQkhSnZ",
        "outputId": "e97951cc-9309-40b9-eb34-fe72d86756cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.86      0.88        29\n",
            "           1       0.88      0.91      0.89        32\n",
            "\n",
            "    accuracy                           0.89        61\n",
            "   macro avg       0.89      0.88      0.88        61\n",
            "weighted avg       0.89      0.89      0.89        61\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Precision - Indicates the proportion of positive identifications (model predicted class 1) which were actually correct. A model which produces no false positives has a precision of 1.0.\n",
        "- Recall - Indicates the proportion of actual positives which were correctly classified. A model which produces no false negatives has a recall of 1.0.\n",
        "- F1 score - A combination of precision and recall. A perfect model achieves an F1 score of 1.0.\n",
        "- Support - The number of samples each metric was calculated on.\n",
        "- Accuracy - The accuracy of the model in decimal form. Perfect accuracy is equal to 1.0.\n",
        "- Macro avg - Short for macro average, the average precision, recall and F1 score between classes. Macro avg doesn’t class imbalance into effort, so if you do have class imbalances, pay attention to this metric.\n",
        "- Weighted avg - Short for weighted average, the weighted average precision, recall and F1 score between classes. Weighted means each metric is calculated with respect to how many samples there are in each class. This metric will favour the majority class (e.g. will give a high value when one class out performs another due to having more samples).\n",
        "\n"
      ],
      "metadata": {
        "id": "bfNoSuheivXI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cross Validation\n"
      ],
      "metadata": {
        "id": "WqvJUg2mj67h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splits your dataset into multiple parts and train and tests your model on each part and evaluates performance as an average.\n",
        "https://scikit-learn.org/stable/modules/cross_validation.html"
      ],
      "metadata": {
        "id": "_Y5dZYmcunNF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gs_log_reg.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z0kCuY_9h4Sa",
        "outputId": "52e9a3af-aa3d-413a-c223-d23c10f35ec2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 0.23357214690901212, 'solver': 'liblinear'}"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import cross-val\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Instantiate best model with best hyperparameters (found with GridSearchCV)\n",
        "clf = LogisticRegression(C =0.23357214690901212,\n",
        "                         solver = \"liblinear\")"
      ],
      "metadata": {
        "id": "KqmcbuZjkE06"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# now let's calculate the cross val accuracy\n",
        "cv_acc = cross_val_score(clf,\n",
        "                         X,\n",
        "                         y,\n",
        "                         cv=5,   # cross val 5 folds\n",
        "                         scoring=\"accuracy\") # accuracy as scoring\n",
        "cv_acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEhHhEY0klkG",
        "outputId": "7c69ef3a-549f-4cc2-b840-df8c9ceb8982"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.81967213, 0.90163934, 0.8852459 , 0.88333333, 0.75      ])"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_acc = np.mean(cv_acc)\n",
        "cv_acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnCgNbMAlHhU",
        "outputId": "6e035d52-29f5-4009-f052-b20edc298f84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8479781420765027"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now let's find other metrics\n",
        "\n",
        "cv_precision = cross_val_score(clf,\n",
        "                               X,\n",
        "                               y,\n",
        "                               cv=5,\n",
        "                               scoring=\"precision\")\n",
        "cv_precision"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S94sitN2ld4z",
        "outputId": "92d66d5e-08b5-422b-e57c-9d3726857239"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.775     , 0.88571429, 0.86111111, 0.86111111, 0.725     ])"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_precision = np.mean(cv_precision)\n",
        "cv_precision"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fIEuAxO-l5Wo",
        "outputId": "bb5e62d6-ff5c-4bbe-8691-cd3f70d480b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8215873015873015"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# recall\n",
        "\n",
        "cv_recall = cross_val_score(clf,\n",
        "                            X,\n",
        "                            y,\n",
        "                            cv=5,\n",
        "                            scoring=\"recall\")\n",
        "cv_recall"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VBo54qdul_XC",
        "outputId": "eba91421-4e5c-4da1-b2f8-916155afb70c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.93939394, 0.93939394, 0.93939394, 0.93939394, 0.87878788])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_recall = np.mean(cv_recall)\n",
        "cv_recall"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7wKDzwGmRN3",
        "outputId": "b70287c5-614d-4524-b096-e6a1a6c36349"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9272727272727274"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f1 score\n",
        "\n",
        "cv_f1 = cross_val_score(clf,\n",
        "                        X,\n",
        "                        y,\n",
        "                        cv=5,\n",
        "                        scoring =\"f1\")\n",
        "cv_f1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lH6fWSKFmW5X",
        "outputId": "d8f79edc-624b-4c40-b0a3-30987bf4e482"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.84931507, 0.91176471, 0.89855072, 0.89855072, 0.79452055])"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_f1 = np.mean(cv_f1)\n",
        "cv_f1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PhcWW914mmY6",
        "outputId": "1745ef27-fc7f-4c42-fe07-6624c2091fe6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8705403543192143"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's visulize\n",
        "cv_metrics = pd.DataFrame({\"cv_acc\":cv_acc, \"cv_precision\": cv_precision, \"cv_recall\":cv_recall, \"cv_f1\": cv_f1}, index=[0])\n",
        "cv_metrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "foCODlaDmuBC",
        "outputId": "fab05f59-d320-440f-a80b-82a7f65e953b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     cv_acc  cv_precision  cv_recall    cv_f1\n",
              "0  0.847978      0.821587   0.927273  0.87054"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-16da8f14-3e15-4f88-b4fd-b2065709300a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cv_acc</th>\n",
              "      <th>cv_precision</th>\n",
              "      <th>cv_recall</th>\n",
              "      <th>cv_f1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.847978</td>\n",
              "      <td>0.821587</td>\n",
              "      <td>0.927273</td>\n",
              "      <td>0.87054</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16da8f14-3e15-4f88-b4fd-b2065709300a')\"\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-16da8f14-3e15-4f88-b4fd-b2065709300a 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-16da8f14-3e15-4f88-b4fd-b2065709300a');\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": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_metrics.T.plot.bar(title=\"Cross val Metrics\", legend=False);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "ixTA2EpZnXdE",
        "outputId": "d92a6cfb-5a09-4e11-f76a-94ad5efd7c10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Importance\n",
        "Feature importance is another way of asking, \"which features contributing most to the outcomes of the model?\""
      ],
      "metadata": {
        "id": "lHUSe1jYo8Kr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# fit the instance of the model\n",
        "clf.fit(X_train, y_train);"
      ],
      "metadata": {
        "id": "KwDvIE88oEYz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check coefficicients\n",
        "clf.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUcFx0RjpVMb",
        "outputId": "00c68dae-a139-456b-c8a9-91cc00edd876"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.00369922, -0.9042409 ,  0.67472826, -0.0116134 , -0.00170364,\n",
              "         0.04787688,  0.33490198,  0.02472938, -0.63120406, -0.5759095 ,\n",
              "         0.47095141, -0.65165348, -0.69984208]])"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "these values are how much each feature contributes to how a model makes a decision on whether patterns in a sample of patients health data leans more towards having heart disease or not."
      ],
      "metadata": {
        "id": "RYHdmABnpcvd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
        "features_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EQOMB_qOpZxb",
        "outputId": "673c59ff-f3e8-4518-becd-a421db5adcab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'age': 0.003699220776580221,\n",
              " 'sex': -0.9042409028785717,\n",
              " 'cp': 0.6747282587404362,\n",
              " 'trestbps': -0.011613401339975146,\n",
              " 'chol': -0.0017036439067759743,\n",
              " 'fbs': 0.047876881148997324,\n",
              " 'restecg': 0.3349019815885189,\n",
              " 'thalach': 0.02472938284108309,\n",
              " 'exang': -0.6312040612837573,\n",
              " 'oldpeak': -0.5759095045469952,\n",
              " 'slope': 0.4709514073081419,\n",
              " 'ca': -0.6516534770577476,\n",
              " 'thal': -0.6998420764664995}"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visulaize\n",
        "\n",
        "features_df = pd.DataFrame(features_dict, index=[0])\n",
        "features_df.T.plot.bar(title=\"feature Importance\", legend=False);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "EuekRobJp1tT",
        "outputId": "7892f8de-2416-4392-8c54-145ec7d6466d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This reflects the relationship between different variables to target varible. Sex has negative value which means as the value of sex increases the target variable decreases."
      ],
      "metadata": {
        "id": "s65TEzlbqrHf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df.sex, df.target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "yqikJBLUqYaD",
        "outputId": "807c1be2-ca02-41e1-d438-78b5d160b70a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target    0   1\n",
              "sex            \n",
              "0        24  72\n",
              "1       114  93"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4305a996-9be9-44b9-9b5f-6be50d4644ea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>target</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>114</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4305a996-9be9-44b9-9b5f-6be50d4644ea')\"\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-4305a996-9be9-44b9-9b5f-6be50d4644ea 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-4305a996-9be9-44b9-9b5f-6be50d4644ea');\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": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see the as the value of sex goes up from 0 to 1 the value of target variable goes down which means if the sex contribution is negative more people are likely to have heart disease which means women here as compared to men the other way.\n",
        "\n",
        "___\n",
        "Now let's see the positive one:\n",
        "slope\n",
        "What is it?\n",
        "slope is the \"slope of the peak exercise ST segment\" where:\n",
        "\n",
        "0: Upsloping: better heart rate with excercise (uncommon)\n",
        "1: Flatsloping: minimal change (typical healthy heart)\n",
        "2: Downslopins: signs of unhealthy heart\n",
        "\n",
        "__\n",
        "\n",
        "According to our model learning the slope has positive correlation or contribution. As slope increase the value of target also increases. Let's check"
      ],
      "metadata": {
        "id": "w6Zh7UR5rFnL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df.slope, df.target)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "jUpI45i0rEJb",
        "outputId": "594d208d-0a98-491f-be81-a8ddc7c18d3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target   0    1\n",
              "slope          \n",
              "0       12    9\n",
              "1       91   49\n",
              "2       35  107"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9550dd16-50d6-4cf0-b501-914e2be1c6a8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>target</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>91</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>35</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9550dd16-50d6-4cf0-b501-914e2be1c6a8')\"\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-9550dd16-50d6-4cf0-b501-914e2be1c6a8 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-9550dd16-50d6-4cf0-b501-914e2be1c6a8');\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": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see the value of slope also increases with target.\n",
        "\n",
        "With this information you can try to see how model is learning and what features are contributing more. You can remove some features if they are not contributing or do some feature engineering if you like."
      ],
      "metadata": {
        "id": "bKWsPMJFsStp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see our model fail to cross our 95% accuracy that we had in mind so what now? Well, you can try different model. you can try collecting more data maybe and maybe you can improve the current model."
      ],
      "metadata": {
        "id": "4pIIVhzUtPVL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# let's save our model\n",
        "from joblib import dump, load\n",
        "dump(gs_log_reg, 'heartDisease.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zoY9Vnd0sLnx",
        "outputId": "8bf80028-a201-4bd1-a0f1-2c547fd81000"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['heartDisease.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_instance = load(\"heartDisease.joblib\")\n",
        "model_instance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "id": "nFHGZrsjuPTM",
        "outputId": "232f1102-08fb-4634-96fe-8711b0f70c6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
              "             param_grid={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
              "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
              "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
              "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
              "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
              "                         'solver': ['liblinear']},\n",
              "             verbose=True)"
            ],
            "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>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
              "             param_grid={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
              "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
              "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
              "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
              "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
              "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
              "             verbose=True)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
              "             param_grid={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
              "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
              "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
              "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
              "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
              "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
              "             verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><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\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    }
  ]
}