Heart Disease Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "HP Project.ipynb",
      "provenance": [],
      "collapsed_sections": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHReUImXmwi0"
      },
      "source": [
        "**About this dataset**<br>\n",
        " \n",
        "1. Age: Age (in years) \n",
        "2. Sex: gender (1 = male; 0 = female) \n",
        "3. ChestPain: Chest Pain type -- 1: typical angina (all criteria present) -- 2: atypical angina (two of three criteria satisfied) -- 3: non-anginal pain (less than one criteria satisfied) -- 4: asymptomatic (none of the criteria are satisfied) 0:\"asymptomatic\", 1: \"typical angina\", 2:\"atypical angina\", 3:\"non-anginal pain\"\n",
        "4. Restbps: Resting Blood pressure (in mmHg, upon admission to the hospital) \n",
        "5. Chol: serum cholesterol in mg/dL \n",
        "6. Fbs: fasting blood sugar > 120 mg/dL (likely to be diabetic) 1 = true; 0 = false \n",
        "7. RestECG: Resting electrocardiogram results -- Value 0: normal -- Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) -- Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria \n",
        "8. MaxHR: Greatest number of beats per minute your heart can possibly reach during all-out strenuous exercise. \n",
        "9. Exang: exercise induced angina (1 = yes; 0 = no) \n",
        "10. Oldpeak: ST depression induced by exercise relative to rest (in mm, achieved by subtracting the lowest ST segment points during exercise and rest) \n",
        "11. Slope: the slope of the peak exercise ST segment, ST-T abnormalities are considered to be a crucial indicator for identifying presence of ischaemia -- Value 1: upsloping -- Value 2: flat -- Value 3: downsloping \n",
        "12. Ca: number of major vessels (0-3) colored by fluoroscopy. Major cardial vessels are as goes: aorta, superior vena cava, inferior vena cava, pulmonary artery (oxygen-poor blood --> lungs), pulmonary veins (oxygen-rich blood --> heart), and coronary arteries (supplies blood to heart tissue). \n",
        "13. thal: 0 = normal; 1 = fixed defect (heart tissue can't absorb thallium both under stress and in rest); 2 = reversible defect (heart tissue is unable to absorb thallium only under the exercise portion of the test) \n",
        "14. target: 0 = no disease, 1 = disease\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGvn9OT55FZy"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "import pickle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1ulvvXRkyM7"
      },
      "source": [
        "df=pd.read_csv('Heart Attack Data Set.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "TxORG83Dk2ZN",
        "outputId": "f17c2b2c-a93b-45dd-fceb-2f462874cbbc"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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>"
            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4ZL3MFNVHEB"
      },
      "source": [
        "data=df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lgIEARYj0zJ9",
        "outputId": "23390a54-1fa9-4c24-a0c2-de7a03cefd26"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "N5oUW-n-k339",
        "outputId": "e116d090-620d-4154-9863-f9074203110c"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<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>"
            ],
            "text/plain": [
              "              age         sex          cp  ...          ca        thal      target\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    0.729373    2.313531    0.544554\n",
              "std      9.082101    0.466011    1.032052  ...    1.022606    0.612277    0.498835\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    0.000000    2.000000    1.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    1.000000    3.000000    1.000000\n",
              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVMdVu50k5_j",
        "outputId": "23533d5a-ec43-4c4d-f0e2-727c959178e7"
      },
      "source": [
        "data.info()"
      ],
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LxeXIq67m3VH",
        "outputId": "3c5bbe20-6ba4-461f-80d6-75664f56c5a7"
      },
      "source": [
        "# checking for null values\n",
        "data.isnull().sum()"
      ],
      "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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MbN_DfYNoMWP",
        "outputId": "35475edd-15d6-4951-f650-f20bb652f4a4"
      },
      "source": [
        "data.nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age          41\n",
              "sex           2\n",
              "cp            4\n",
              "trestbps     49\n",
              "chol        152\n",
              "fbs           2\n",
              "restecg       3\n",
              "thalach      91\n",
              "exang         2\n",
              "oldpeak      40\n",
              "slope         3\n",
              "ca            5\n",
              "thal          4\n",
              "target        2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f-J9T_R008sG",
        "outputId": "7d738a05-79db-4b33-d03a-c99315fea5dd"
      },
      "source": [
        "data['target'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    165\n",
              "0    138\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZoU9fK2_1maX",
        "outputId": "2e01ebce-87eb-4f1c-e5a7-ac474b24cf3f"
      },
      "source": [
        "# Percentage of patients have and do not have heart disease\n",
        "data['target'].value_counts()/data.shape[0]*100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    54.455446\n",
              "0    45.544554\n",
              "Name: target, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "juzQSZKeAgx5"
      },
      "source": [
        "**EXPLORATORY DATA ANALYSIS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "QKuBFSuJy9NY",
        "outputId": "95c627f6-af28-4a85-899d-bca0beedbeb4"
      },
      "source": [
        "# Create a plot to display the percentage of the positive and negative heart disease \n",
        "labels = ['Yes', 'No']\n",
        "values = data['target'].value_counts().values\n",
        "plt.rcParams[\"figure.figsize\"] = (8,4)\n",
        "plt.pie(values, labels=labels, autopct='%1.0f%%', textprops = {\"fontsize\" : 15})\n",
        "plt.title('Heart Disease', fontsize=20)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u1P6f3pWR1C4"
      },
      "source": [
        "We can see that, the dataset contains 14 columns 5 of them are numerical values and 9 of them are categorical values. We can see also there are no missing values in this dataset. As for the data balancing, the data is relatively balanced, 56% of the persons in the dataset have heart disease."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jmf-DLYr4pW",
        "outputId": "a1c244a7-ee93-414e-8919-7466327b9897"
      },
      "source": [
        "df_gender = data.groupby([\"sex\",\"target\"]).size()\n",
        "df_gender"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex  target\n",
              "0    0          24\n",
              "     1          72\n",
              "1    0         114\n",
              "     1          93\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "VipZT-oXsNJO",
        "outputId": "a2425ec0-d506-48ee-ef8c-000f9f21c950"
      },
      "source": [
        "plt.pie(df_gender.values, labels = [\"Female, No_Heart_disease\", \"Female, With_heart_disease\", \n",
        "                                    \"Male, No_Heart_disease\", \"Male, With_Heart_disease\"],autopct='%1.1f%%',radius = 1.5, \n",
        "        textprops = {\"fontsize\" : 15})\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TPgv7akDtA6L"
      },
      "source": [
        "The ratio of male having a heart disease is 30.7%, a little bit higher than female."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "NT4c5Krw0Gt6",
        "outputId": "4b90898a-528d-47f8-d42a-6fd37b61046b"
      },
      "source": [
        "data.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>"
            ],
            "text/plain": [
              "               age       sex        cp  ...        ca      thal    target\n",
              "age       1.000000 -0.098447 -0.068653  ...  0.276326  0.068001 -0.225439\n",
              "sex      -0.098447  1.000000 -0.049353  ...  0.118261  0.210041 -0.280937\n",
              "cp       -0.068653 -0.049353  1.000000  ... -0.181053 -0.161736  0.433798\n",
              "trestbps  0.279351 -0.056769  0.047608  ...  0.101389  0.062210 -0.144931\n",
              "chol      0.213678 -0.197912 -0.076904  ...  0.070511  0.098803 -0.085239\n",
              "fbs       0.121308  0.045032  0.094444  ...  0.137979 -0.032019 -0.028046\n",
              "restecg  -0.116211 -0.058196  0.044421  ... -0.072042 -0.011981  0.137230\n",
              "thalach  -0.398522 -0.044020  0.295762  ... -0.213177 -0.096439  0.421741\n",
              "exang     0.096801  0.141664 -0.394280  ...  0.115739  0.206754 -0.436757\n",
              "oldpeak   0.210013  0.096093 -0.149230  ...  0.222682  0.210244 -0.430696\n",
              "slope    -0.168814 -0.030711  0.119717  ... -0.080155 -0.104764  0.345877\n",
              "ca        0.276326  0.118261 -0.181053  ...  1.000000  0.151832 -0.391724\n",
              "thal      0.068001  0.210041 -0.161736  ...  0.151832  1.000000 -0.344029\n",
              "target   -0.225439 -0.280937  0.433798  ... -0.391724 -0.344029  1.000000\n",
              "\n",
              "[14 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "jnnZPJ4lo1vq",
        "outputId": "ecaed9ff-9c27-417d-c895-e797656e97fb"
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.heatmap(data.corr(), annot=True, cbar=True, cmap='icefire')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f10620a2ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKFyo0bczytT"
      },
      "source": [
        "This heatmap shows the correlations between the dataset attributes, and how the attributes interact with each other. From the heatmap, we can observe that the chest pain type (cp), exercise-induced angina (exang), ST depression induced by exercise relative to rest (oldpeak), the slope of the peak exercise ST segment (slope), number of major vessels (0–3) colored by flourosopy (ca) and thalassemia (thal) are highly correlated with the heart disease (target). We observe also that there is an inverse proportion between heart disease and maximum heart rate (thalch).\n",
        "\n",
        "Moreover, we can see that the age is correlated with number of major vessels (0–3) colored by flourosopy (ca) and maximum heart rate (thalch). There is also a relation between ST depression induced by exercise relative to rest (oldpeak) and the slope of the peak exercise ST segment (slope). Moreover, there is a relation between chest pain type (cp) and exercise-induced angina (exang)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dez8ResBQeN1",
        "outputId": "d86b1959-2fae-4355-a180-7eb7b8298831"
      },
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "sns.countplot(x='sex', data=data, palette='Set1')\n",
        "plt.xticks(ticks=[0, 1], labels = [\"female\", \"male\"])\n",
        "plt.title(\"No. of males and females present in the dataset\", size=15)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ErPtGX5BauV_",
        "outputId": "b39de9ee-5d86-4307-f04a-41b62e891566"
      },
      "source": [
        "pd.crosstab(data.sex, data.target).plot(kind=\"bar\",figsize=(15,6),color=['#1CA53B','#AA1111' ])\n",
        "plt.title('Heart Disease Frequency for Gender', fontsize=15)\n",
        "plt.xlabel('Gender (0 = Female, 1 = Male)', fontsize=15)\n",
        "plt.xticks(rotation=0)\n",
        "plt.legend([\"Don't have Disease\", \"Have Disease\"])\n",
        "plt.ylabel('Frequency',fontsize=15)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X-IhMHFszMfo",
        "outputId": "3ec858f8-7860-4ab4-b810-83ec667c1d36"
      },
      "source": [
        "sns.catplot(x=\"cp\", hue=\"target\", kind=\"count\", data=data, legend=False,  palette=sns.color_palette(['#1CA53B','#AA1111']))\n",
        "plt.legend([\"Don't Have Disease\", \"Have Disease\"])\n",
        "plt.xlabel('Chest Pain Type', fontsize=15)\n",
        "plt.ylabel('Frequency',fontsize=15)\n",
        "plt.title('Heart Disease Frequency According To Chest Pain Type', fontsize=15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Heart Disease Frequency According To Chest Pain Type')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "R9VVk03jtKgl",
        "outputId": "811db530-a1c2-4ea8-dff3-a482376c414d"
      },
      "source": [
        "plt.hist([data[data.target==0].age, data[data.target==1].age], bins = 20, alpha = 0.5, label = [\"No Heart Disease\",\"With Heart Disease\"], color=['#FFC300','#581845'])\n",
        "plt.xlabel(\"Age\", fontsize=15)\n",
        "plt.ylabel(\"Percentage\", fontsize=15)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njRkV0p1b34h"
      },
      "source": [
        "From the above graphs and data we can say that heart diseases can occur in both young and old people, but the chances are higher in old people."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ky3_Y1gOX3XZ",
        "outputId": "39a1dee9-727c-4e89-a153-58bb96c8cf61"
      },
      "source": [
        "# Get min, max and average of the age\n",
        "print('Min age in dataset: ', min(data['age']))\n",
        "print('Max age in dataset: ', max(data['age']))\n",
        "print('Average age: ', data['age'].mean())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min age in dataset:  29\n",
            "Max age in dataset:  77\n",
            "Average age:  54.366336633663366\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fygEsXmdtp6_",
        "outputId": "19d0bd1d-ff74-4ff5-e6e8-befd1ca06522"
      },
      "source": [
        "plt.hist([data[data.target==0].chol, data[data.target==1].chol], bins = 20, alpha = 0.5, label = [\"No Heart Disease\",\"With Heart Disease\"],color=['#FFC300','#581845'])\n",
        "plt.xlabel(\"chol\", fontsize=15)\n",
        "plt.ylabel(\"percentage\", fontsize=15)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "veTREVi2t_Yo"
      },
      "source": [
        "The number of people having heart disease are over 200mg/dl of chol. \n",
        "The normal value of chol should be lower than 200mg/dl."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ROptfE91q8kM",
        "outputId": "94585c65-26cf-4ab6-ce8e-4dcccde658b2"
      },
      "source": [
        "sns.countplot(data= data, x='sex',hue='thal')\n",
        "plt.title('Gender v/s Thalassemia\\n')\n",
        "print('Thalassemia (thal-uh-SEE-me-uh) is an inherited blood disorder that causes your body to have less heamoglobin than normal. Hemoglobin enables red blood cells to carry oxygen')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thalassemia (thal-uh-SEE-me-uh) is an inherited blood disorder that causes your body to have less heamoglobin than normal. Hemoglobin enables red blood cells to carry oxygen\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMO0jyGlr4Kq",
        "outputId": "29a05b8d-bcfc-4d1b-a49f-0ca8acdbb91f"
      },
      "source": [
        "sns.countplot(data= data, x='slope',hue='target')\n",
        "plt.title('Slope v/s Target\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Slope v/s Target\\n')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "tNikPIp5sPsC",
        "outputId": "4765094b-2c73-43e5-9c71-8d7848dcb36d"
      },
      "source": [
        "plt.figure(figsize=(16,7))\n",
        "sns.distplot(data[data['target']==0]['thalach'],kde=False,bins=40)\n",
        "plt.title('thalach of Heart Diseased Patients\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'thalach of Heart Diseased Patients\\n')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6AAAAHICAYAAABQ/YevAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAfkUlEQVR4nO3debhkd13n8c9Xmi0kEDQtkpAmyCYhyDINQdGRbRQBCT4qQpBFHSMgsjwoE3AeQMcFcRsRcQiCQUkCCKgRdCSyukBDEoJkAUFIOisEISTsBL7zR507XG737e1W/2533dfrefrpe+ucOvWrqtPV9b7nV+dWdwcAAAD2t29Z7wEAAACwMQhQAAAAhhCgAAAADCFAAQAAGEKAAgAAMIQABQAAYAgBCrBgquqYquqq2rSP1++qusMax3BqVf36WraxbFt3rqrzquq6qnraPLZ5oKiqz1XVd673OHanqp5YVf884Ha+v6o+vL9vB4D1I0ABDnJVdXFVPXi9x7EfPTvJ27v7sO5+8cqFVfWOqvrvKy67f1Vdtr8GtLvHfLr9r0+B+bmquqyqXldV916+Xncf2t0f21/jHGHZDzyW7uvFVXXyHl73m37Y0d3/1N13ntO4Fv3fBcBBSYACcKC7bZIL1nsQSbKXR5Wv6O5DkxyW5L5JPpTkn6rqQftlcOvv8On+PibJ86rqIes9IAAOPAIU4CBWVX+RZEuSv52OPj172eLHVtX2qvpUVf3Ksuvcp6reXVXXVNWVVfWSqrrRKtt/WFW9v6qurapLq+oFK5Z/X1X967StS6vqicsW37Kq3jxNnd1WVbffxf14RFVdMG3nHVV1l+nytyV5QJKXTPfvTnv7GE3buUVVvWK6v5dX1a9X1Q2mZbevqrdV1X9Oj9VpVXX4suteXFX/o6r+Lcnnq+qMrP6Y76BnLuvu5yX50yS/vWzb//8IYFU9tKounB6vy6vql5at9/BpGvI10+P93cuWnVxV/zFd78Kq+tFly+5QVe+sqs9O9+21y5Z9V1WdVVWfrqoPV9Wjli37tqo6c3re35tk1eduJ/f33Zn9wOC4Xe1rVfWu6SofmB7Hn1x55LqqjqyqN1TV1VX18Vo2BbuqXjAdVf7z6b5fUFVbp2U7/LuoqptU1aun5/maqnpfVd1qT+8XAPMhQAEOYt39uCTbk/zINJ3zRcsWf1+SOyd5UGZHpO4yXf61JM9MckSS75mWP2WVm/h8kscnOTzJw5I8uaoemSRVddskf5/kj5JsTnKPJOctu+6jk/xqklsm+WiS39jZDUxReUaSZ0zb+bvMwuFG3f3AJP+U5KnT/fv3PXlcduLUJNcnuUOSeyb5wSRL03YryW8lOTLJXZIcneQFK67/mMzu/+Hd/Zis/pjvzhuT3KuqbraTZa9I8vPdfViS45K8LUmq6p5JXpnk55N8W5KXJTmzqm48Xe8/knx/kltk9ni/uqpuPS37X0nektlzcJvMnqtMt39WktOTfHtmz9VLq+rY6Xp/nORLSW6d5GemP7tVM/dLctck788u9rXu/q/T1e4+PY6vXbGtb0nyt0k+kOSo6brPqKofWrbaI5K8JrP988wkL5m2vbN/F0+YHqOjp8fxSUm+uCf3C4D5EaAAi+tXu/uL3f2BzN7E3z1Juvuc7n5Pd1/f3RdnFjQ/sLMNdPc7uvuD3f317v63zEJxad0Tk/xjd5/R3V/t7v/s7uUB+lfd/d7uvj7JaZkF6s78ZJI3d/dZ3f3VJL+b5KZJvncv7uuLp6Na11TVNUnetLRgOsr10CTP6O7Pd/cnk/xBZtGV7v7odNtf7u6rk/z+Th6PF3f3pd291mC5IrPgPXwny76a5Niqunl3f6a7z50uPynJy7p7W3d/rbtfleTLmU3rTXf/ZXdfMT1Hr03ykST3WbbN2yY5sru/1N1LJxJ6eJKLu/vPpv3g/UnekOQnpiPDP5bkedPjdX6SV+3BfftUkk9ndpT35O5+697saztx7ySbu/vXuvsr02dlX57peZv8c3f/XXd/LclfZNrHV/HVzMLzDtPjeE53X7uHYwFgTgQowOK6atnXX0hyaDI74lhVb6qqq6rq2iS/mdkRqh1U1fFV9fZpCuRnMztqtLTu0Zkdfdur29+JI5NcsvRNd389yaWZHfXaU0/r7sOX/mQWWEtum+SGSa5cFqgvy+zIX6rqVlX1mmna67VJXp0dH49L92Isu3JUkk5yzU6W/VhmoXzJNG32e5aN/1krAvvozB63VNXjl03PvSazo6dL4392ZsH73mmK6s8s2+bxK7b52CTfkdlR6E0r7vMl2b0juvuW3X2XpZNF7c2+thO3TXLkijE+N8nyabMr97Gb1Oqf0/2LJP+Q5DVVdUVVvaiqbriHYwFgTgQowMGv93L9P8nshDh37O6bZ/amvlZZ9/TMpjYe3d23SPJ/lq17afbis4G7cEVmsZFkNo0zs8C6fA7bTmbj/HJmgbQUqTfv7rtOy38zs8fwbtPj8VPZ8fFY+Rjv7WO+5EeTnNvdn1+5oLvf190nZBbGf53kdcvG/xvLA7u7D+nuM6Zp0C9P8tQk3zbF9/lL4+/uq7r757r7yMym8L60Zp85vTTJO1ds89DufnKSqzObrnz0suFt2cf7uzf72kqXJvn4ijEe1t0P3cPrf9NzNB2l/9XuPjazo+sPz2x6OQADCVCAg98nkuzN75I8LMm1ST5XVd+V5Mm7WffT3f2lqrpPZtNul5yW5MFV9aiq2jSduGa1aba78rokD6uqB01HpJ6VWTD+6z5sawfdfWVmn4P8vaq6eVV9S81OPLQ0FfSwJJ9L8tmqOirJL+/BZvf4MZ8+F3lUVT0/s8+dPncn69yoqh5bVbeYpiFfm+Tr0+KXJ3nSdDS6qupmNTs51GFJbpZZaF09beenMzsCurTdn6iq20zffmZa9+uZTVG+U1U9rqpuOP25d1XdZZrO+sYkL6iqQ6bPhT5hT+7rTuxuX9vV4/jeJNfV7ARQN62qG1TVcbXiV9nswjdtu6oeUFV3m6YYX5vZlNyvr3ZlAPYPAQpw8PutJP9zmqb4S7tdO/mlzELyuszi5rW7WPcpSX6tqq5L8rx846hcunt7ZlNGn5XZZ//Oy64/g7dT3f3hzI46/lFmnyP8kcxOHvOVvd3WLjw+yY2SXJhZiL0+sxPsJLMT99wryWeTvDmz+NqdPXnMj6yqz2UWt+9Lcrck9+/ut6yy/uOSXDxNVX1SZlNi091nJ/m5zE6w85nMTuj0xGnZhUl+L8m7MwuuuyX5l2XbvHeSbdM4zkzy9O7+WHdfl9mJmB6d2RHoqzI7O+/SiY2emtmU6asyO4HTn+3BY7Izu9vXXpDkVdPj+KjlC6YQfnhmnx3+eGb7xp9mdiKhPbHyOfqOzJ73a5NclOSdmU3LBWCg6t7XWUQAAACw5xwBBQAAYAgBCgAAwBACFAAAgCEEKAAAAEMIUAAAAIYQoAAAAAwhQAEAABhCgAIAADCEAAUAAGAIAQoAAMAQAhQAAIAhBCgAAABDCFAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAITaNvLEjjjiijznmmJE3CQAAwCDnnHPOp7p782rLhwboMccck7PPPnvkTQIAADBIVV2yq+Wm4AIAADCEAAUAAGAIAQoAAMAQAhQAAIAhBCgAAABDCFAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAIQQoAAAAQwhQAAAAhhCgAAAADCFAAQAAGGLTeg8AAA4Up2/bPtftnXj8lrlub3+Y932et4PhMQRgzzkCCgAAwBACFAAAgCEEKAAAAEMIUAAAAIYQoAAAAAwhQAEAABhCgAIAADCEAAUAAGAIAQoAAMAQAhQAAIAhBCgAAABDCFAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAIQQoAAAAQwhQAAAAhhCgAAAADCFAAQAAGEKAAgAAMIQABQAAYAgBCgAAwBACFAAAgCEEKAAAAEMIUAAAAIYQoAAAAAyx2wCtqldW1Ser6vxll31rVZ1VVR+Z/r7l/h0mAAAAB7s9OQJ6apKHrLjs5CRv7e47Jnnr9D0AAACsarcB2t3vSvLpFRefkORV09evSvLIOY8LAACABbOvnwG9VXdfOX19VZJbrbZiVZ1UVWdX1dlXX331Pt4cAAAAB7s1n4SouztJ72L5Kd29tbu3bt68ea03BwAAwEFqXwP0E1V16ySZ/v7k/IYEAADAItrXAD0zyROmr5+Q5G/mMxwAAAAW1Z78GpYzkrw7yZ2r6rKq+tkkL0zy36rqI0kePH0PAAAAq9q0uxW6+zGrLHrQnMcCAADAAlvzSYgAAABgTwhQAAAAhhCgAAAADCFAAQAAGEKAAgAAMIQABQAAYAgBCgAAwBACFAAAgCEEKAAAAEMIUAAAAIYQoAAAAAwhQAEAABhCgAIAADCEAAUAAGAIAQoAAMAQAhQAAIAhBCgAAABDCFAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAIQQoAAAAQwhQAAAAhhCgAAAADLFpvQcAwMZw+rbtc9/micdvmfs2AYD9xxFQAAAAhhCgAAAADCFAAQAAGEKAAgAAMIQABQAAYAgBCgAAwBACFAAAgCEEKAAAAEMIUAAAAIYQoAAAAAwhQAEAABhCgAIAADCEAAUAAGAIAQoAAMAQAhQAAIAhBCgAAABDCFAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAIQQoAAAAQwhQAAAAhhCgAAAADCFAAQAAGEKAAgAAMIQABQAAYIg1BWhVPbOqLqiq86vqjKq6ybwGBgAAwGLZ5wCtqqOSPC3J1u4+LskNkjx6XgMDAABgsax1Cu6mJDetqk1JDklyxdqHBAAAwCLatK9X7O7Lq+p3k2xP8sUkb+nut6xcr6pOSnJSkmzZsmVfbw4AgH10+rbtc93eicd7Twfsm7VMwb1lkhOS3C7JkUluVlU/tXK97j6lu7d299bNmzfv+0gBAAA4qK1lCu6Dk3y8u6/u7q8meWOS753PsAAAAFg0awnQ7UnuW1WHVFUleVCSi+YzLAAAABbNPgdod29L8vok5yb54LStU+Y0LgAAABbMPp+EKEm6+/lJnj+nsQAAALDA1vprWAAAAGCPCFAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAIQQoAAAAQwhQAAAAhhCgAAAADCFAAQAAGEKAAgAAMIQABQAAYAgBCgAAwBACFAAAgCEEKAAAAEMIUAAAAIYQoAAAAAwhQAEAABhCgAIAADCEAAUAAGAIAQoAAMAQAhQAAIAhBCgAAABDCFAAAACGEKAAAAAMsWm9BwAAwMHl9G3b57q9E4/fMtftAQcuR0ABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAIQQoAAAAQwhQAAAAhhCgAAAADCFAAQAAGEKAAgAAMIQABQAAYAgBCgAAwBACFAAAgCEEKAAAAEMIUAAAAIYQoAAAAAwhQAEAABhCgAIAADCEAAUAAGAIAQoAAMAQAhQAAIAhBCgAAABDCFAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAIdYUoFV1eFW9vqo+VFUXVdX3zGtgAAAALJZNa7z+Hyb5v93941V1oySHzGFMAAAALKB9DtCqukWS/5rkiUnS3V9J8pX5DAsAAIBFs5YjoLdLcnWSP6uquyc5J8nTu/vzy1eqqpOSnJQkW7ZsWcPNAQAbzenbts99mycef2C/H9kf9xngQLGWz4BuSnKvJH/S3fdM8vkkJ69cqbtP6e6t3b118+bNa7g5AAAADmZrCdDLklzW3dum71+fWZACAADADvY5QLv7qiSXVtWdp4selOTCuYwKAACAhbPWs+D+YpLTpjPgfizJT699SAAAACyiNQVod5+XZOucxgIAAMACW8tnQAEAAGCPCVAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAIQQoAAAAQwhQAAAAhhCgAAAADCFAAQAAGEKAAgAAMIQABQAAYAgBCgAAwBACFAAAgCEEKAAAAEMIUAAAAIYQoAAAAAwhQAEAABhCgAIAADCEAAUAAGAIAQoAAMAQAhQAAIAhBCgAAABDCFAAAACGEKAAAAAMsWm9BwBwMDh92/a5bu/E47fMdXvAnpv3v2cOPPvjOfa6DfPhCCgAAABDCFAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAIQQoAAAAQwhQAAAAhhCgAAAADCFAAQAAGEKAAgAAMIQABQAAYAgBCgAAwBACFAAAgCEEKAAAAEMIUAAAAIYQoAAAAAwhQAEAABhCgAIAADCEAAUAAGAIAQoAAMAQAhQAAIAhBCgAAABDCFAAAACGEKAAAAAMIUABAAAYQoACAAAwxJoDtKpuUFXvr6o3zWNAAAAALKZ5HAF9epKL5rAdAAAAFtiaArSqbpPkYUn+dD7DAQAAYFFtWuP1/3eSZyc5bLUVquqkJCclyZYtW9Z4c8CB4PRt2+e+zROPn+/rw/4Y44Fu3vd53s/JRnQw/FuBA8FGfM2GjWqfj4BW1cOTfLK7z9nVet19Sndv7e6tmzdv3tebAwAA4CC3lim490vyiKq6OMlrkjywql49l1EBAACwcPY5QLv7Od19m+4+Jsmjk7ytu39qbiMDAABgofg9oAAAAAyx1pMQJUm6+x1J3jGPbQEAALCYHAEFAABgCAEKAADAEAIUAACAIQQoAAAAQwhQAAAAhhCgAAAADCFAAQAAGEKAAgAAMIQABQAAYAgBCgAAwBACFAAAgCEEKAAAAEMIUAAAAIYQoAAAAAwhQAEAABhCgAIAADCEAAUAAGAIAQoAAMAQAhQAAIAhBCgAAABDCFAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAITat9wAAkuT0bdvXewhDbbT7u794HIFR5v16c+LxW+a6PThYOAIKAADAEAIUAACAIQQoAAAAQwhQAAAAhhCgAAAADCFAAQAAGEKAAgAAMIQABQAAYAgBCgAAwBACFAAAgCEEKAAAAEMIUAAAAIYQoAAAAAwhQAEAABhCgAIAADCEAAUAAGAIAQoAAMAQAhQAAIAhBCgAAABDCFAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAIQQoAAAAQwhQAAAAhhCgAAAADLHPAVpVR1fV26vqwqq6oKqePs+BAQAAsFg2reG61yd5VnefW1WHJTmnqs7q7gvnNDYAAAAWyD4fAe3uK7v73Onr65JclOSoeQ0MAACAxVLdvfaNVB2T5F1Jjuvua1csOynJSUmyZcuW/3LJJZes+fZg0Z2+bftct3fi8Vvmur15jw8AWJt5/1+fHPjvRzgwVdU53b11teVrPglRVR2a5A1JnrEyPpOku0/p7q3dvXXz5s1rvTkAAAAOUmsK0Kq6YWbxeVp3v3E+QwIAAGARreUsuJXkFUku6u7fn9+QAAAAWERrOQJ6vySPS/LAqjpv+vPQOY0LAACABbPPv4alu/85Sc1xLAAAACywNZ+ECAAAAPaEAAUAAGAIAQoAAMAQAhQAAIAhBCgAAABDCFAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAIQQoAAAAQwhQAAAAhhCgAAAADCFAAQAAGEKAAgAAMIQABQAAYAgBCgAAwBACFAAAgCEEKAAAAEMIUAAAAIYQoAAAAAwhQAEAABhCgAIAADCEAAUAAGAIAQoAAMAQm9Z7AAeS07dtn/s2Tzx+y9y3udHsj+dlo/EYAsBi83/9gWnez8sitIUjoAAAAAwhQAEAABhCgAIAADCEAAUAAGAIAQoAAMAQAhQAAIAhBCgAAABDCFAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEAIUAACAIQQoAAAAQwhQAAAAhhCgAAAADCFAAQAAGEKAAgAAMIQABQAAYAgBCgAAwBACFAAAgCEEKAAAAEMIUAAAAIYQoAAAAAwhQAEAABhCgAIAADCEAAUAAGAIAQoAAMAQawrQqnpIVX24qj5aVSfPa1AAAAAsnn0O0Kq6QZI/TvLDSY5N8piqOnZeAwMAAGCxrOUI6H2SfLS7P9bdX0nymiQnzGdYAAAALJpNa7juUUkuXfb9ZUmOX7lSVZ2U5KTp289V1YfXcJsHnceu9wBWd0SST633IDgg2BdYYl9giX2BJfYFlqx5XziA3xcfNA6Qx3B3+8Jtd3XltQToHunuU5Kcsr9vh71TVWd399b1Hgfrz77AEvsCS+wLLLEvsMS+wJK17gtrmYJ7eZKjl31/m+kyAAAA2MFaAvR9Se5YVberqhsleXSSM+czLAAAABbNPk/B7e7rq+qpSf4hyQ2SvLK7L5jbyNjfTItmiX2BJfYFltgXWGJfYIl9gSVr2hequ+c1EAAAAFjVWqbgAgAAwB4ToAAAAAwhQDeIqnpmVV1QVedX1RlVdZPpBFLbquqjVfXa6WRSLJiqemVVfbKqzl922bdW1VlV9ZHp71tOl1dVvXjaJ/6tqu61fiNn3lbZF36nqj40Pd9/VVWHL1v2nGlf+HBV/dD6jJr9YWf7wrJlz6qqrqojpu+9Liyw1faFqvrF6bXhgqp60bLLvS4sqFX+j7hHVb2nqs6rqrOr6j7T5V4XFlRVHV1Vb6+qC6d//0+fLp/be0cBugFU1VFJnpZka3cfl9lJox6d5LeT/EF33yHJZ5L87PqNkv3o1CQPWXHZyUne2t13TPLW6fsk+eEkd5z+nJTkTwaNkTFOzY77wllJjuvu707y70mekyRVdWxmrxN3na7z0qq6wbihsp+dmh33hVTV0Ul+MMn2ZRd7XVhsp2bFvlBVD0hyQpK7d/ddk/zudLnXhcV2anZ8XXhRkl/t7nsked70feJ1YZFdn+RZ3X1skvsm+YXp3/7c3jsK0I1jU5KbVtWmJIckuTLJA5O8flr+qiSPXKexsR9197uSfHrFxSdk9pwn3/zcn5Dkz3vmPUkOr6pbjxkp+9vO9oXufkt3Xz99+57MfqdzMtsXXtPdX+7ujyf5aJL7DBss+9UqrwtJ8gdJnp1k+RkKvS4ssFX2hScneWF3f3la55PT5V4XFtgq+0Inufn09S2SXDF97XVhQXX3ld197vT1dUkuSnJU5vjeUYBuAN19eWY/vdyeWXh+Nsk5Sa5Z9sbzssx2LjaGW3X3ldPXVyW51fT1UUkuXbae/WJj+Zkkfz99bV/YYKrqhCSXd/cHViyyL2w8d0ry/dPHdN5ZVfeeLrcvbDzPSPI7VXVpZu8lnzNdbl/YAKrqmCT3TLItc3zvKEA3gGmO9glJbpfkyCQ3y06mXrEx9ex3Mfl9TBtcVf1KZtNuTlvvsTBeVR2S5LmZTbGDTUm+NbPpd7+c5HVVVes7JNbJk5M8s7uPTvLMJK9Y5/EwSFUdmuQNSZ7R3dcuX7bW944CdGN4cJKPd/fV3f3VJG9Mcr/MDpFvmta5TZLL12uADPeJpekR099L06suT3L0svXsFxtAVT0xycOTPLa/8cuh7Qsby+0z+yHlB6rq4sye73Or6jtiX9iILkvyxmlK3XuTfD3JEbEvbERPyOx9Y5L8Zb4x5dq+sMCq6oaZxedp3b30/M/tvaMA3Ri2J7lvVR0y/QTzQUkuTPL2JD8+rfOEJH+zTuNjvDMze86Tb37uz0zy+OmMZvdN8tll0y1YQFX1kMw+8/eI7v7CskVnJnl0Vd24qm6X2ckF3rseY2T/6+4Pdve3d/cx3X1MZgFyr+6+Kl4XNqK/TvKAJKmqOyW5UZJPxevCRnRFkh+Yvn5gko9MX3tdWFBTK7wiyUXd/fvLFs3tveOmXS1kMXT3tqp6fZJzM5ti9/4kpyR5c5LXVNWvT5eZVrGAquqMJPdPckRVXZbk+UlemNmUqp9NckmSR02r/12Sh2Z2YokvJPnp4QNmv1llX3hOkhsnOWuaYfee7n5Sd19QVa/L7IdV1yf5he7+2vqMnHnb2b7Q3av9H+B1YYGt8rrwyiSvnH4dx1eSPGGaHeF1YYGtsi/8XJI/nGbMfSmzs5wmXhcW2f2SPC7JB6vqvOmy52aO7x3rG7OtAAAAYP8xBRcAAIAhBCgAAABDCFAAAACGEKAAAAAMIUABAAAYQoACwApVdXhVPWX6+v5V9aa9vP6pVfXju19zh+vt9W0BwMFEgALAjg5P8pT1HgQALBoBCgA7emGS20+/hPt3khxaVa+vqg9V1WlVVUlSVc+rqvdV1flVdcrS5cuttk5V3aGq/rGqPlBV51bV7aer7PS2AGARCFAA2NHJSf6ju++R5JeT3DPJM5Icm+Q7k9xvWu8l3X3v7j4uyU2TPHwn21ptndOS/HF33z3J9ya5crp8tdsCgIOeAAWA3Xtvd1/W3V9Pcl6SY6bLH1BV26rqg0kemOSuO7nuDutU1WFJjuruv0qS7v5Sd39hN7cFAAe9Tes9AAA4CHx52ddfS7Kpqm6S5KVJtnb3pVX1giQ3WX6lPVlnT25rjWMHgAOGI6AAsKPrkhy2m3WWQvJTVXVokp2d9Xan63T3dUkuq6pHJklV3biqDln7sAHgwOanqgCwQnf/Z1X9S1Wdn+SLST6xk3WuqaqXJzk/yVVJ3reX6zwuycuq6teSfDXJT8z/ngDAgaW6e73HAAAAwAZgCi4AAABDCFAAAACGEKAAAAAMIUABAAAYQoACAAAwhAAFAABgCAEKAADAEP8P6bepgUzklbkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xTpi1iFUTwBr",
        "outputId": "b4bb3ee0-140b-4b27-ab5c-18a8f6ec99e8"
      },
      "source": [
        "sns.catplot(x=\"target\", y=\"oldpeak\", hue=\"slope\", kind=\"bar\", data=data);\n",
        "\n",
        "plt.title('ST depression (induced by exercise relative to rest) vs. Heart Disease',size=25)\n",
        "plt.xlabel('Heart Disease',size=20)\n",
        "plt.ylabel('ST depression',size=20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(26.426458333333343, 0.5, 'ST depression')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 402.375x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWkQcjRtUrEy"
      },
      "source": [
        "X = data.iloc[:, :-1].values\n",
        "y = data.iloc[:, -1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bIwNQzEdUu7J"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3m2K_MLvUxeN"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v-r2BK7xVc_w"
      },
      "source": [
        "Machine Learning model used: Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NRLy8PnVZI5",
        "outputId": "ef3590bc-fa86-44f9-dfb2-c36ac2b9f8fd"
      },
      "source": [
        "from sklearn.metrics import classification_report \n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model6 = RandomForestClassifier(random_state=1)# get instance of model\n",
        "model6.fit(x_train, y_train) # Train/Fit model \n",
        "\n",
        "y_pred6 = model6.predict(x_test) # get y predictions\n",
        "print(classification_report(y_test, y_pred6)) # output accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.70      0.78        30\n",
            "           1       0.76      0.90      0.82        31\n",
            "\n",
            "    accuracy                           0.80        61\n",
            "   macro avg       0.82      0.80      0.80        61\n",
            "weighted avg       0.81      0.80      0.80        61\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3uk2UQTM-SZk"
      },
      "source": [
        "**Precision, Recall, F1-score and Support:**<br>\n",
        "Precision : “how many are correctly classified among that class”<br>\n",
        "Recall : “how many of this class you find over the whole number of element of this class”<br>\n",
        "F1-score : harmonic mean of precision and recall values.<br>\n",
        "F1 score reaches its best value at 1 and worst value at 0.<br>\n",
        "F1 Score = 2 x ((precision x recall) / (precision + recall))<br>\n",
        "Support: # of samples of the true response that lie in that class.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P5_1HxNYVl_U"
      },
      "source": [
        "**Making the Confusion Matrix**<br>\n",
        "A confusion matrix, also known as an error matrix, is a table that is often used to describe the performance of a classification model (or “classifier”) on a set of test data for which the true values are known. It allows the visualization of the performance of an algorithm. It allows easy identification of confusion between classes.<br>\n",
        "The key to the confusion matrix is the number of correct and incorrect predictions are summarizedwith count values and broken down by each class not just the number of errors made.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vib7UWToVkvQ",
        "outputId": "41746c8c-0d9f-414b-8956-e0784404d306"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred6)\n",
        "print(cm)\n",
        "print('Accuracy:')\n",
        "accuracy_score(y_test, y_pred6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[21  9]\n",
            " [ 3 28]]\n",
            "Accuracy:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8032786885245902"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eCRL_4NUwDUR",
        "outputId": "8f0323f2-6c25-4b25-a635-a5f92446e47d"
      },
      "source": [
        "from sklearn.metrics import plot_confusion_matrix\n",
        "plot_confusion_matrix(model6, x_test, y_test)\n",
        "ax = plt.subplot()\n",
        "sns.set(font_scale=3.0) #edited as suggested\n",
        "sns.heatmap(cm, annot=True, ax=ax, cmap=\"icefire\", fmt=\"g\");  # annot=True to annotate cells\n",
        "\n",
        "# labels, title and ticks\n",
        "ax.set_xlabel('Predicted labels');\n",
        "ax.set_ylabel('Observed labels');\n",
        "ax.set_title('Confusion Matrix');\n",
        "ax.xaxis.set_ticklabels(['False', 'True']);\n",
        "ax.yaxis.set_ticklabels(['Flase', 'True']);\n",
        "\n",
        "label_font = {'size':'18'}  # Adjust to fit\n",
        "\n",
        "\n",
        "plt.title('Confusion Matrix',fontsize=15)\n",
        "plt.xlabel('Predicted Label',fontsize=15)\n",
        "plt.ylabel('True Label',fontsize=15)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7a1NzQ597gA"
      },
      "source": [
        "**Interpreting the confusion matrix:**<br>\n",
        "21 is the amount of True Positives in our data, while 28 is the amount of True Negatives.<br>\n",
        "9 & 3 are the number of errors.<br>\n",
        "There are 9 Type 1 errors (False Positives)- You predicted positive and it’s false.<br>\n",
        "There are 3 Type 2 errors (False Negatives)- You predicted negative and it’s false."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OkfyteQ4AAVq"
      },
      "source": [
        "**Feature Importance:**<br>\n",
        "Feature Importance provides a score that indicates how helpful each feature was in our model.The higher the Feature Score, the more that feature is used to make key decisions & thus the more important it is."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWBuvzIMVgXs",
        "outputId": "c98b28ab-90c8-4c08-8f80-06c39393a0d6"
      },
      "source": [
        "# get importance\n",
        "importance = model6.feature_importances_\n",
        "\n",
        "# summarize feature importance\n",
        "for i,v in enumerate(importance):\n",
        "    print('Feature: %0d, Score: %.5f' % (i,v))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature: 0, Score: 0.07814\n",
            "Feature: 1, Score: 0.04206\n",
            "Feature: 2, Score: 0.16580\n",
            "Feature: 3, Score: 0.07477\n",
            "Feature: 4, Score: 0.07587\n",
            "Feature: 5, Score: 0.00828\n",
            "Feature: 6, Score: 0.02014\n",
            "Feature: 7, Score: 0.12772\n",
            "Feature: 8, Score: 0.06950\n",
            "Feature: 9, Score: 0.09957\n",
            "Feature: 10, Score: 0.04677\n",
            "Feature: 11, Score: 0.11667\n",
            "Feature: 12, Score: 0.07473\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AYaRkw6vVwOb",
        "outputId": "601342fa-88a1-470f-ab5e-34197c49df0b"
      },
      "source": [
        "index= data.columns[:-1]\n",
        "importance = pd.Series(model6.feature_importances_, index=index)\n",
        "plt.figure(figsize=(8,10))\n",
        "importance.nlargest(13).plot(kind='barh', colormap='Dark2')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1050d1d0d0>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AYzvHR-sAOkD"
      },
      "source": [
        "From the Feature Importance graph above, we can conclude that the top 4 significant features were chest pain type (cp), maximum heart rate achieved (thalach), number of major vessels (ca), and ST depression induced by exercise relative to rest (oldpeak)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W0WMTGcjZUEm"
      },
      "source": [
        "**PREDICTIONS:**<br>\n",
        "He is a 20 year old male, with a chest pain value of 2 (atypical angina), with resting blood pressure of 110.\n",
        "In addition he has a serum cholestoral of 230 mg/dl.\n",
        "He is fasting blood sugar > 120 mg/dl.\n",
        "He has a resting electrocardiographic result of 1.\n",
        "The patients maximum heart rate achieved is 140.\n",
        "Also, he was exercise induced angina.\n",
        "His ST depression induced by exercise relative to rest value was 2.2.\n",
        "The slope of the peak exercise ST segment is flat.\n",
        "He has no major vessels colored by fluoroscopy, and in addition his maximum heart rate achieved is a reversible defect."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iMZ8-YCWV5Ct",
        "outputId": "e58b554a-763a-4061-fd2f-01a93e612438"
      },
      "source": [
        "print(model6.predict(sc.transform([[20,1,2,110,230,1,1,140,1,2.2,2,0,2]])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MRN2o09rV-rQ"
      },
      "source": [
        "y_pred = model6.predict(x_test)\n",
        "x=np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrVUY1npWENp"
      },
      "source": [
        "# Save the model as serialized object pickle\n",
        "with open('model.pkl', 'wb') as file:\n",
        "    pickle.dump(model6, file)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}