Heart_Diseases.ipynb

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    "colab": {
      "name": "Heart Diseases.ipynb",
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      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Sarzy92/Data-Science-Projects/blob/main/Heart_Diseases.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUz_gygPVbFK"
      },
      "source": [
        "<h1>CONTRIBUTORY RISK FACTORS TO HEART DISEASES IN AMERICA</h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E21HRW1xVbFL"
      },
      "source": [
        "   <h4><p>About 610,000 people die of heart disease in the United States every year–that’s 1 in every 4 deaths. Heart disease is the leading cause of death for both men and women. More than half of the deaths due to heart disease in 2009 were in men. Coronary Heart Disease(CHD) is the most common type of heart disease, killing over 370,000 people annually. Every year about 735,000 Americans have a heart attack. Of these, 525,000 are a first heart attack and 210,000 happen in people who have already had a heart attack.This makes heart disease a major concern to be dealt with. But it is difficult to identify heart disease because of several contributory risk factors such as diabetes, high blood pressure, high cholesterol, abnormal pulse rate, and many other factors. Due to such constraints, scientists have turned towards modern approaches like Data Mining and Machine Learning for predicting the disease.Heart disease is the leading cause of death for men, women, and people of most racial and ethnic groups in the United States. One person dies every 36 seconds in the United States from cardiovascular disease. Machine learning (ML) proves to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry.</p></h4>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qAFwztW3VbFM"
      },
      "source": [
        "<h1>Data Set</h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QgPBIa0VbFN"
      },
      "source": [
        "<p>The dataset consists of 303 individuals data. There are 14 columns in the dataset, which are described below.<br>\n",
        "Age: displays the age of the individual.<br>\n",
        "Sex: displays the gender of the individual using the following format :<br>\n",
        "1 = male<br>\n",
        "0 = female<br>\n",
        "Chest-pain type: displays the type of chest-pain experienced by the individual using the following format :<br>\n",
        "1 = typical angina<br>\n",
        "2 = atypical angina<br>\n",
        "3 = non — anginal pain<br>\n",
        "4 = asymptotic<br>\n",
        "Resting Blood Pressure: displays the resting blood pressure value of an individual in mmHg (unit)<br>\n",
        "Serum Cholestrol: displays the serum cholesterol in mg/dl (unit)<br>\n",
        "Fasting Blood Sugar: compares the fasting blood sugar value of an individual with 120mg/dl.<br>\n",
        "If fasting blood sugar > 120mg/dl then : 1 (true)<br>\n",
        "else : 0 (false)<br>\n",
        "Resting ECG : displays resting electrocardiographic results<br>\n",
        "0 = normal<br>\n",
        "1 = having ST-T wave abnormality<br>\n",
        "2 = left ventricular hyperthrophy<br>\n",
        "Max heart rate achieved : displays the max heart rate achieved by an individual.<br>\n",
        "Exercise induced angina :<br>\n",
        "1 = yes<br>\n",
        "0 = no<br>\n",
        "ST depression induced by exercise relative to rest: displays the value which is an integer or float.<br>\n",
        "Peak exercise ST segment :<br>\n",
        "1 = upsloping<br>\n",
        "2 = flat<br>\n",
        "3 = downsloping<br>\n",
        "Number of major vessels (0–3) colored by flourosopy : displays the value as integer or float.<br>\n",
        "Thal : displays the thalassemia :<br>\n",
        "3 = normal<br>\n",
        "6 = fixed defect<br>\n",
        "7 = reversible defect<br>\n",
        "Diagnosis of heart disease : Displays whether the individual is suffering from heart disease or not :<br>\n",
        "0 = absence<br>\n",
        "1, 2, 3, 4 = present.</p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqoUlA-qVbFO"
      },
      "source": [
        "<h1>Data Analysis</h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8qCzOLNJVbFP"
      },
      "source": [
        "Let's look at the following questions probably running in my mind;<br>\n",
        "Q1 - i.     what are the basics of this data set?,<br>\n",
        "      ii.   Are there missing values?,<br>\n",
        "      iii.  what does the five number summary look like?<br>\n",
        "Q2 - What's the age of people suffering from the diseases or not?<br>\n",
        "Q3 - What is the correlation between this datas?<br>\n",
        "Q4 - What age and gender suffers more from this disease?<br>\n",
        "Q5 - Wha are the contributing risk factors?<br>\n",
        "Q6 - What Machine Learning algorithm gives the best results\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HhV1XK6KVbFP"
      },
      "source": [
        "#import libaries\n",
        "#for basic operations:\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "#for data visualisations;\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "# import warnings filter\n",
        "from warnings import simplefilter\n",
        "# ignore all future warnings\n",
        "simplefilter(action='ignore', category = FutureWarning)\n",
        "# import warnings filter\n",
        "from warnings import simplefilter\n",
        "# ignore all future warnings\n",
        "simplefilter(action='ignore', category = FutureWarning)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L9Wbokv-VbFU"
      },
      "source": [
        "<h1>Q1</h1><br> <h2>i. What are the basics of this data set?<br> ii. Are there missing values?<br> iii. What does the five number summary look like?<br></h2>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6kSL7xyVbFV",
        "outputId": "44a8d20c-8148-4efa-fb66-d3f302faf513"
      },
      "source": [
        "#i. what are the basics of this data set?\n",
        "Hd = pd.read_csv(\"heart.csv\")\n",
        "Hd.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>"
            ],
            "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  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
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      "metadata": {
        "id": "7Xcifkg2VbFc",
        "outputId": "bef75803-bcc2-47b8-fb88-b00f78ee48f5"
      },
      "source": [
        "Hd.tail()"
      ],
      "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>298</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>241</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>123</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>132</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>141</td>\n",
              "      <td>0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>115</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "298   57    0   0       140   241    0        1      123      1      0.2   \n",
              "299   45    1   3       110   264    0        1      132      0      1.2   \n",
              "300   68    1   0       144   193    1        1      141      0      3.4   \n",
              "301   57    1   0       130   131    0        1      115      1      1.2   \n",
              "302   57    0   1       130   236    0        0      174      0      0.0   \n",
              "\n",
              "     slope  ca  thal  target  \n",
              "298      1   0     3       0  \n",
              "299      1   0     3       0  \n",
              "300      1   2     3       0  \n",
              "301      1   1     3       0  \n",
              "302      1   1     2       0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6sVpSDvVbFf",
        "outputId": "bcb26522-aa8f-470f-f781-e990c35d4e2b"
      },
      "source": [
        "#The data shape shows there are 303 attributes and 14 features\n",
        "Hd.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hTy3eUQjVbFj",
        "outputId": "ce1b5ba9-7d33-4457-8676-c746ff60e039"
      },
      "source": [
        "#I took a look at the types of data present in the data set\n",
        "Hd.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age           int64\n",
              "sex           int64\n",
              "cp            int64\n",
              "trestbps      int64\n",
              "chol          int64\n",
              "fbs           int64\n",
              "restecg       int64\n",
              "thalach       int64\n",
              "exang         int64\n",
              "oldpeak     float64\n",
              "slope         int64\n",
              "ca            int64\n",
              "thal          int64\n",
              "target        int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3wI87z0QVbFm",
        "outputId": "ccacdcfd-8adf-4cf6-8cec-589998991cf6"
      },
      "source": [
        "#select numeric columns\n",
        "Hd_numeric = Hd.select_dtypes(include=[np.number])\n",
        "numeric_cols = Hd_numeric.columns.values\n",
        "numeric_cols"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg',\n",
              "       'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFLeO5AoVbFq",
        "outputId": "dd996fe8-27b7-405d-af07-7da51b1b3d94"
      },
      "source": [
        "#select non numeric columns\n",
        "Hd_non_numeric = Hd.select_dtypes(exclude=[np.number])\n",
        "non_numeric_cols = Hd_non_numeric.columns.values\n",
        "non_numeric_cols"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zh7_xyj6VbFu",
        "outputId": "c9b4c322-59c5-4ea0-b07a-e39a95db12e8"
      },
      "source": [
        "#ii. Are there missing value?\n",
        "Hd.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": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4-uTKPcVbFx"
      },
      "source": [
        "The above shows that theres no missing value but we would use two methods to confirm one of which includes the 5 number summary, fun right?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mL_cG-KJVbFx",
        "outputId": "c86059d9-6a70-4a81-93b7-6062dc4ba09a"
      },
      "source": [
        "#method 1\n",
        "Hd.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jMHP3sb7VbF0"
      },
      "source": [
        "From the above we would observe that the total number of entries are 303 ranging from 0-302, and all columns have 303 non null count meaning no missing values present in this data set, this takes us to my iii. What does the 5 number summary looks like and we would this question to confirm if truly there are no missing values, Let's ride!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Op2s2jBsVbF1",
        "outputId": "5f3175b3-9699-44b6-da59-72a2d2e79d5d"
      },
      "source": [
        "#Five number summary\n",
        "Hd.describe()"
      ],
      "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>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    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  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EXlUUgE_VbF5"
      },
      "source": [
        "From the above I noticed that the count corresponds with the info we went through before and also with the initial result gotten from .isnull<br>\n",
        "Also the Mean and standard deviation same as the median which is the middle number, mode; the most occurring number(max) and less occurring number(min), the 25th percentile, 50th percentile and 75th percentile of each columns can be seen above. The count shows the total number of entries in each column. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y_8iySzEVbF6"
      },
      "source": [
        "<h1>Q2</h1><br> <h2>What's the age of people suffering from the diseases or not?</h2>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VS5ttn2LVbF6"
      },
      "source": [
        "Hd.columns = ['age', 'sex', 'cp', 'trestbps', 'chol',\n",
        "              'fbs', 'restecg', 'thalach', 'exang', \n",
        "              'oldpeak', 'slope', 'ca', 'thal', 'target']\n",
        "\n",
        "### 1 = male, 0 = female\n",
        "Hd['target'] = Hd.target.map({0: 0, 1: 1, 2: 1, 3: 1, 4: 1})\n",
        "Hd['sex'] = Hd.sex.map({0: 'female', 1: 'male'})\n",
        "Hd['thal'] = Hd.thal.fillna(Hd.thal.mean())\n",
        "Hd['ca'] = Hd.ca.fillna(Hd.ca.mean())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zp4J9ZFVbF9",
        "outputId": "1d9186a7-ece5-4f9b-e87e-75ed97d48892"
      },
      "source": [
        "fig,ax = plt.subplots(figsize=(20,10))\n",
        "sns.countplot(Hd['age'],hue=Hd['target'],ax=ax)\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Counts')\n",
        "plt.title('Variation of Age for each target class')\n",
        "plt.xticks(rotation=50)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
              "        34, 35, 36, 37, 38, 39, 40]),\n",
              " <a list of 41 Text xticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6pVZeM5LVbGB"
      },
      "source": [
        "The age of people suffering from this diease; We can see that the oldest male is 54 years old and the oldest female is 58, but that's not the point here, the point we are potraying with this graph is that most people who are suffering are of the age of 58, followed by 57. Maj0rly the age group subsceptible to heart disease are ranged between 51 years - 67 years of age<br> Those from age 29years - 40 years of age are at low risk level while Men at age 41 years - 45 years of age have chances of getting a heart disease while an unstead flow is noticed from age 41years.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZUeh9gDVbGC"
      },
      "source": [
        "<h1>Q3</h1> \n",
        "<h2>What is the correlation between this datas?</h2><br>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJDH7FohVbGD",
        "outputId": "5381332e-fc3b-4560-f984-32447e63c88b"
      },
      "source": [
        "# making a heat map\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (20, 15)\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "sns.heatmap(Hd.corr(), annot = True, cmap = 'Wistia')\n",
        "plt.title('Heatmap for the Dataset', 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 1440x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-WiFIbZVbGF"
      },
      "source": [
        "The Correlation heat map shows the correlations amongst the different attributes of the given dataset. The above Heat Map shows that almost all of the features/attributes given in the dataset are very less correlated with each other. This implies we must include all of the features, as we can only eliminate those features where the correlation of two or more features are very high."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_PsLS1FVbGG"
      },
      "source": [
        "<h1>Q4</h1><br> What age and gender suffers more from this disease?<br>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2ga1gw_VbGG",
        "outputId": "7b1342c8-e222-4091-cd37-ab2761a3a23a"
      },
      "source": [
        "# checking the distribution of age amonng the patients\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "plt.style.use('fivethirtyeight')\n",
        "plt.rcParams['figure.figsize'] = (15, 5)\n",
        "sns.distplot(Hd['age'], color = 'cyan')\n",
        "plt.title('Distribution of Age', 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 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ka8WCCWVVbGJ"
      },
      "source": [
        "The above Distribution plot shows the distribution of Age amongst all of the entries in the dataset about the heart patients. Setting the avaerage at 0.035, the Graph suggests that the highest number of people suffering from heart diseases are in the age group of 55-65 years. The patients in the age group 20-30 are very less likely to suffer from heart disease, while thos in the category 31-41 likely to suffer from a heart disease.<br> As we know that the number of people in the age group 66-80 has a very low population, hence distribution is also less. we might have to opt for other plots to investigate further and get some more intuitive results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cmm0OqeUVbGN",
        "outputId": "730972e2-3f57-4984-b987-e8777d57af7f"
      },
      "source": [
        "# plotting a donut chart for visualizing each of the recruitment channel's share\n",
        "\n",
        "size = Hd['sex'].value_counts()\n",
        "colors = ['lightblue', 'lightgreen']\n",
        "labels = \"Male\", \"Female\"\n",
        "explode = [0, 0.01]\n",
        "\n",
        "my_circle = plt.Circle((0, 0), 0.7, color = 'white')\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (9, 9)\n",
        "plt.pie(size, colors = colors, labels = labels, shadow = True, explode = explode, autopct = '%.2f%%')\n",
        "plt.title('Distribution of Gender', fontsize = 20)\n",
        "p = plt.gcf()\n",
        "p.gca().add_artist(my_circle)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qNFy1yHPVbGQ"
      },
      "source": [
        "Looking at the Above chart which is a pie chart it is seen that 31.68% multiplied by 2 gives 63.36% and the percentage of the male gender is 68.32%.<br> By looking at the plot, we can assume that Males are two times more likely to suffer from heart diseases in comparison to females.\n",
        "According to studie, it was discovered that From all the Entries in our dataset 68% of the patients are men whereas only 32% are women. More number of men took participation in heart disease check ups."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4UoPwMAsVbGQ",
        "outputId": "ede8eea6-8efd-4dac-8377-e10996c65578"
      },
      "source": [
        "# barplot of age vs sex with hue = target\n",
        "sns.catplot(kind = 'bar', data = Hd, y = 'age', x = 'sex', hue = 'target')\n",
        "plt.title('Distribution of age vs sex with the target class')\n",
        "plt.show()\n",
        "\n",
        "Hd['sex'] = Hd.sex.map({'female': 0, 'male': 1})\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 422.7x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KjXclmPqVbGU",
        "outputId": "d5cc4860-16fe-4b66-94ec-b1af24f07499"
      },
      "source": [
        "# plotting the target attribute\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (15, 7)\n",
        "plt.style.use('seaborn-talk')\n",
        "sns.countplot(Hd['target'], palette = 'pastel')\n",
        "plt.title('Distribution of Target', 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 748.8x514.8 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_7OeOut3VbGX"
      },
      "source": [
        "Let's look at the Target, The dataset is quite balanced with almost equal no. of Positive and Negative Classes. Let's say the Positive Class says that the patient is suffering from the disease and the Negative class says that the patient is not suffering from the disease, We also see from the first graph that females who are suffering from the disease are older than males.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OKfyAsz8VbGY"
      },
      "source": [
        "<h1>Q5</h1><br> - Wha are the contributing risk factors?<br>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjH7g4esVbGY"
      },
      "source": [
        "So the target was placed  at 0 and 1; 0 as Positive and 1 as Negative, and in some cases 0 as Negative i.e patients not suffering from the disease and 1 as Positive i.e patients suffering from the disease"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lhE0gYVSVbGZ",
        "outputId": "bfe2868f-1b78-4c0c-8c28-505876a7424f"
      },
      "source": [
        "# tresbps vs target\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (12, 9)\n",
        "sns.boxplot(Hd['target'], Hd['trestbps'], palette = 'viridis')\n",
        "plt.title('Relation betwen tresbps and target', 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 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nHHCgUJlVbGc"
      },
      "source": [
        "tresbps: Resting Blood Pressure, The above Bivariate plot between tresbps(the resting blood pressure of a patient), and the target which says that whether the patient is suffering from the heart disease or not. The plot clearly suggests that the patients who are most likely to not suffer from the disease have a slighly greater blood pressure than the patients who have heart diseases. This is to say that tresbps i.e Resting Blood Pressure is a Contributing Risk Factor to heart disease."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_AWYNpDwVbGc",
        "outputId": "795186e8-7207-4c39-c0f6-035ffd4030ec"
      },
      "source": [
        "# cholestrol vs target\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (12, 9)\n",
        "sns.violinplot(Hd['target'], Hd['chol'], palette = 'colorblind')\n",
        "plt.title('Relation between Cholestrol and Target', fontsize = 20, fontweight = 30)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wW3yUfeLVbGf"
      },
      "source": [
        "The above plot suggests that the Patients likely to suffer from heart diseases are having higher cholestrol levels in comparison to the patients with target 0(likely to not suffer from the heart diseases. This further implies that the cholestrol levels plays an important role in determining heart diseases. Hence chol i.e Cholesterol is a Contributing Risk Factor to heart diseases."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxNeXFZOVbGg",
        "outputId": "37d40396-f57e-4c95-abde-64474e7e7352"
      },
      "source": [
        "# Resting electrocardiographic measurement vs target\n",
        "  \n",
        "plt.rcParams['figure.figsize'] = (12, 9)\n",
        "dat = pd.crosstab(Hd['target'], Hd['restecg']) \n",
        "dat.div(dat.sum(1).astype(float), axis = 0).plot(kind = 'bar', \n",
        "                                                 stacked = False, \n",
        "                                                 color = plt.cm.rainbow(np.linspace(0, 1, 4)))\n",
        "plt.title('Relation between ECG measurement and Target', fontsize = 20, fontweight = 30)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "70-UWkPBVbGj"
      },
      "source": [
        "The electrocardiogram can measure the rate and rhythm of the heartbeat, as well as provide indirect evidence of blood flow to the heart muscle. A standardized system has been developed for the electrode placement for a routine ECG. Ten electrodes are needed to produce 12 electrical views of the heart.<br>This is a column bar chart representing target vs ECG Measurements(Electrocardiogram measurement), The above plot shows that the more number of patients not likely to suffer from heart diseases are having restscg value 0 whereas more number of people have restecg value 1 in case of more likelihood of suffering from a heart disease.<br> The Heat Map shows the difference between Target and Maximum Heart Rate shows that the patients who are likely to suffer from heart diseases are having higher maximum heart rates whereas the patients who are not likely to suffer from any heart diseases are having lower maximum heart rates.<br>This implies it is very important to keep our heart rates low, to keep ourselves healthy and safe from any dangerous heart diseases."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jq2DMhusVbGj",
        "outputId": "5385acff-71d2-4771-8f5c-150b17774374"
      },
      "source": [
        "# slope: the slope of the peak exercise ST segment (Value 1: upsloping, Value 2: flat, Value 3: downsloping)\n",
        "# checking the relation between slope and target\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (15, 9)\n",
        "sns.boxenplot(Hd['target'], Hd['slope'], palette = 'copper')\n",
        "plt.title('Relation between Peak Exercise and Target', fontsize = 20, fontweight = 30)\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEcNdfxuVbGo"
      },
      "source": [
        "Slope : 0 refers to upsloping, 1 refers to flat Exercises pattern.<br>Flat Exercises are mostly seen in the cases of Patients who are more likely to suffer from heart diseases."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77R2X7JPVbGp",
        "outputId": "d8c84791-8d29-4eb7-8b32-4d1be9c55ead"
      },
      "source": [
        "#ca: The number of major vessels (0-3)\n",
        "\n",
        "sns.boxenplot(Hd['target'], Hd['ca'], palette = 'Reds')\n",
        "plt.title('Relation between no. of major vessels and target', fontsize = 20, fontweight = 30)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qSznFSKPVbGr"
      },
      "source": [
        "The Bivariate plot between Target and Number of Major Vessels, shows that the patients who are more likely to suffer from Heart diseases are having high values of Major Vessels wheras the patiets who are very less likely to suffer from any kind of heart diseases have very low values of Major Vessels.\n",
        "\n",
        "Hence, It is also helpful in determining the heart diseases, the more the number of vessels, the more is the chance of suffering from heart diseases. So we can say that Major vessel(ca) is a contributing risk factor to heart disease"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpLyDYBLVbGs",
        "outputId": "41c4e8f8-b95b-45d0-c809-19c1cda17953"
      },
      "source": [
        "# relation between age and target\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (15, 9)\n",
        "sns.swarmplot(Hd['target'], Hd['age'], palette = 'winter', size = 10)\n",
        "plt.title('Relation between Age and target', fontsize = 20, fontweight = 30)\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEHjM21aVbGv"
      },
      "source": [
        "From the above Swarm plot between the target and the age of the patients, we are not able to find any clue or pattern, so age is not a very good attribute to determine the heart disease of a patient as a patient of heart diseases range from 30-70, whereas it is not important that all of the people lying in that same age group are bound to suffer from the heart diseases."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SejazAVvVbGv",
        "outputId": "08d62761-f513-4a28-f881-7dd240d1645d"
      },
      "source": [
        "# relation between sex and target\n",
        "\n",
        "sns.boxenplot(Hd['target'], Hd['sex'], palette = 'Set3')\n",
        "plt.title('Relation between Sex and target', fontsize = 20, fontweight = 30)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0K5Dw-gVbGy",
        "outputId": "fc40935a-bc2f-4e20-ccc0-8db00a53713e"
      },
      "source": [
        "# checking the relation between \n",
        "#thal: A blood disorder called thalassemia (3 = normal; 6 = fixed defect; 7 = reversable defect)\n",
        "\n",
        "sns.boxenplot(Hd['target'], Hd['thal'], palette = 'magma')\n",
        "plt.title('Relation between Target and Blood disorder-Thalessemia', fontsize = 20, fontweight = 30)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXWwFciKVbG1"
      },
      "source": [
        "In the above Boxen plot between Target and a Blood disorder called Thalessemia, It can be easily inferred that the patients suffering from heart diseases have low chances of also suffering from thalessemia in comparison to the patients who are less likely to suffer from the heart diseases. Hence, It is also a good feature to classify heart diseases."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qjb_3CW1VbG1",
        "outputId": "e684371d-f3ad-4a43-afbc-e16be16f8ff8"
      },
      "source": [
        "# target vs chol and hue = thalach\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (15, 8)\n",
        "plt.style.use('fivethirtyeight')\n",
        "plt.scatter(x = Hd['target'], y = Hd['chol'], s = Hd['thalach']*100, color = 'yellow')\n",
        "plt.title('Relation between target with cholestrol and thalessemia', fontsize = 20, fontweight = 30)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8jsg5FoVbG5"
      },
      "source": [
        "This look dicey as cholesterol and thalessemia seem to be at the same level"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1b2FCTj0VbG6",
        "outputId": "6e488f5b-8577-4735-e938-39aa0c8143d8"
      },
      "source": [
        "# multi-variate analysis\n",
        "\n",
        "sns.boxplot(x = Hd['target'], y = Hd['trestbps'], hue = Hd['sex'], palette = 'rainbow')\n",
        "plt.title('Checking relation of tresbps with genders to target', fontsize = 20, fontweight = 30)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TFZoWOjpVbG8"
      },
      "source": [
        "In the above Box plot between Target and tresbps(Resting Blood Pressure) with Gender, shows that Women have higher tresbps than men in case of not suffering from any heart diseases, whereas men and women have almost equal tresbps in case of suffering from a heart diseases. Also, In case of suffering from heart diseases, patients have a slightly lower tresbps in comparison to the patients who are not suffering from heart diseases."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6yobSGBXVbG9",
        "outputId": "d6324049-ecc2-4af9-e3b2-d07d77547764"
      },
      "source": [
        "# tresbps vs target\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (12, 9)\n",
        "sns.boxplot(Hd['target'], Hd['age'], palette = 'viridis')\n",
        "plt.title('Relation between age and target', 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 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tjf-_IbhVbG_"
      },
      "source": [
        "With 0 as positive and 1 as negative, we can see that Age is contributing risk factor to heart diseases. This study can be taken as one of many fact to back this argument.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMNvYbVfVbHA"
      },
      "source": [
        "\n",
        "<h1>Q6</h1><br> <h2>What Machine Learning algorithm gives the best results?</h2><br>i. Feature Engineering<br> ii. Spliting the DataSet.<br> iii. Modelling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7deoDa9tVbHA"
      },
      "source": [
        "We will be using the following Machine learning algorithms;<br>\n",
        " 1. Random Forest Classifier\n",
        " 2. SVM\n",
        " 3. Naive Bayes\n",
        " 4. Logistic Regression\n",
        " 5. Decision Tree"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Swt3YM8OVbHB"
      },
      "source": [
        "<h2>Confusion Matrix</h2><br><p>The confusion matrix displays the correctly predicted as well as incorrectly predicted values by a classifier.\n",
        "The sum of TP and TN, from the confusion matrix, is the number of correctly classified entries by the classifier.<br>This will be used throughout our modelling."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uH6m6oXiVbHC"
      },
      "source": [
        "<h2>1. Random Forest</h2><br><p>Random forest is a supervised learning algorithm which is used for both classification as well as regression. But however, it is mainly used for classification problems. As we know that a forest is made up of trees and more trees means more robust forest. Similarly, random forest algorithm creates decision trees on data samples and then gets the prediction from each of them and finally selects the best solution by means of voting. It is an ensemble method which is better than a single decision tree because it reduces the over-fitting by averaging the result (source; Tutorialspoint)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IPksOK0FVbHC"
      },
      "source": [
        "#1i and ii i.e feature engineering and data splitting for Random forest\n",
        "X = Hd.iloc[:, :-1].values\n",
        "y = Hd.iloc[:, -1].values\n",
        "\n",
        "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 = 0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F3MAa3FRVbHG"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier = RandomForestClassifier(n_estimators = 10)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u616TGABVbHJ"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm_test = confusion_matrix(y_pred, y_test)\n",
        "\n",
        "y_pred_train = classifier.predict(X_train)\n",
        "cm_train = confusion_matrix(y_pred_train, y_train)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VsjQFrfdVbHM",
        "outputId": "f088ab5b-e2cb-40dc-8c20-43a0b75eaa80"
      },
      "source": [
        "cm_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[20,  3],\n",
              "       [ 7, 31]], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AcVyQSyCVbHP",
        "outputId": "b684356f-b491-49a3-a515-5442c71567ad"
      },
      "source": [
        "cm_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[107,   0],\n",
              "       [  4, 131]], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YdaCBvaHVbHR"
      },
      "source": [
        "<h2>2. Support Vector Machine(SVM)</h2><br><p>More formally, a support-vector machine constructs a hyperplane or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification, regression, or other tasks like outliers detection."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_m51-S7LVbHR"
      },
      "source": [
        "#2i and ii i.e feature engineering and data splitting for SVM\n",
        "X = Hd.iloc[:, :-1].values\n",
        "y = Hd.iloc[:, -1].values\n",
        "\n",
        "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 = 0)\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler as ss\n",
        "sc = ss()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e5U8EimbVbHX"
      },
      "source": [
        "#SVM\n",
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'rbf')\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm_test = confusion_matrix(y_pred, y_test)\n",
        "\n",
        "y_pred_train = classifier.predict(X_train)\n",
        "cm_train = confusion_matrix(y_pred_train, y_train)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkdvqbOHVbHc",
        "outputId": "8c1162db-e9d4-4bb5-aa2b-6da27180d3d8"
      },
      "source": [
        "cm_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[21,  2],\n",
              "       [ 6, 32]], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fP77UCnnVbHe",
        "outputId": "4dcd0a79-fe78-4d75-f4a3-f53bd77541dc"
      },
      "source": [
        "cm_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 91,   6],\n",
              "       [ 20, 125]], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0uo1FpRVbHn"
      },
      "source": [
        "<h2>3.Naive Bayes</h2><br><p>Naive Bayes Classifier is a classification algorithm for binary (two-class) and multi-class classification problems. The technique is easiest to understand when described using binary or categorical input values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cuVOCTU2VbHo"
      },
      "source": [
        "#3i and ii i.e feature engineering and data splitting for Naive bayes\n",
        "X = Hd.iloc[:, :-1].values\n",
        "y = Hd.iloc[:, -1].values\n",
        "\n",
        "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 = 0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1AenQd4VbHr",
        "outputId": "db411003-67e4-40b7-ed87-bd2686a1b08d"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "classifier = GaussianNB()\n",
        "classifier.fit(X_train, y_train)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(priors=None, var_smoothing=1e-09)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x6LsV0r5VbHt"
      },
      "source": [
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm_test = confusion_matrix(y_pred, y_test)\n",
        "\n",
        "y_pred_train = classifier.predict(X_train)\n",
        "cm_train = confusion_matrix(y_pred_train, y_train)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zleUN7LpVbHw",
        "outputId": "346fb13b-e00a-411f-eb32-7cf3addc9e21"
      },
      "source": [
        "cm_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[21,  3],\n",
              "       [ 6, 31]], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sp8miCu8VbHz",
        "outputId": "19399b59-9362-4d5d-e5c7-66db258a73e0"
      },
      "source": [
        "cm_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 88,  17],\n",
              "       [ 23, 114]], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZwS7bkuIVbH2"
      },
      "source": [
        "<h2>4. Logistic Regression</h2><br><p>Logistic regression is a classification algorithm. It is used to predict a binary outcome based on a set of independent variables. Logistic regression is the correct type of analysis to use when you’re working with binary data. You know you’re dealing with binary data when the output or dependent variable is dichotomous or categorical in nature; in other words, if it fits into one of two categories (such as “yes” or “no”, “pass” or “fail”, and so on)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEW89kg5VbH2"
      },
      "source": [
        "#4i and ii i.e data splitting and feature engineering for Logistics regression\n",
        "X = Hd.iloc[:, :-1].values\n",
        "y = Hd.iloc[:, -1].values\n",
        "\n",
        "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 = 0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6oqCuCntVbH6"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression()\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-f5_7a86VbH9"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm_test = confusion_matrix(y_pred, y_test)\n",
        "\n",
        "y_pred_train = classifier.predict(X_train)\n",
        "cm_train = confusion_matrix(y_pred_train, y_train)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xa0E1JncVbID",
        "outputId": "c0d277f7-cf8d-4d81-c22f-54df21bf72d9"
      },
      "source": [
        "cm_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[22,  4],\n",
              "       [ 5, 30]], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWGD4mIBVbIH",
        "outputId": "df4c6bc9-ba4e-4490-e255-f44bb1f7f06e"
      },
      "source": [
        "cm_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 85,  14],\n",
              "       [ 26, 117]], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ztSx_YdcVbIM"
      },
      "source": [
        "<h2>5. Decision Tree</h2><br><p>Decision tree learning is one of the predictive modelling approaches used in statistics, data mining and machine learning. It uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fp-0YWkwVbIN"
      },
      "source": [
        "#5i and ii feature engineering and data splitting for decision tree\n",
        "X = Hd.iloc[:, :-1].values\n",
        "y = Hd.iloc[:, -1].values\n",
        "\n",
        "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 = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uf0eUWJ4VbIQ"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier = DecisionTreeClassifier()\n",
        "classifier.fit(X_train, y_train)\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cv4_YuyyVbIU"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm_test = confusion_matrix(y_pred, y_test)\n",
        "\n",
        "y_pred_train = classifier.predict(X_train)\n",
        "cm_train = confusion_matrix(y_pred_train, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcQxq5ZFVbIX",
        "outputId": "47478c08-ed2d-4546-816f-00334bc9e1ed"
      },
      "source": [
        "cm_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[22,  8],\n",
              "       [ 5, 26]], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "flTopGt9VbIa",
        "outputId": "e98f2eaf-9ba1-4822-bd96-35391b644abb"
      },
      "source": [
        "cm_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[111,   0],\n",
              "       [  0, 131]], dtype=int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FFnvFYFaVbIc"
      },
      "source": [
        "<h2>Modelling Accuracy</h2>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aRGFXUcwVbId",
        "outputId": "f6004560-8003-4daa-bd8d-c67acfdad194"
      },
      "source": [
        "print()\n",
        "print('Accuracy for training set for Random Forest = {}'.format((cm_train[0][0] + cm_train[1][1])/len(y_train)))\n",
        "print('Accuracy for test set for Random Forest = {}'.format((cm_test[0][0] + cm_test[1][1])/len(y_test)))\n",
        "print()\n",
        "print('Accuracy for training set for svm = {}'.format((cm_train[0][0] + cm_train[1][1])/len(y_train)))\n",
        "print('Accuracy for test set for svm = {}'.format((cm_test[0][0] + cm_test[1][1])/len(y_test)))\n",
        "print()\n",
        "print('Accuracy for training set for Naive Bayes = {}'.format((cm_train[0][0] + cm_train[1][1])/len(y_train)))\n",
        "print('Accuracy for test set for Naive Bayes = {}'.format((cm_test[0][0] + cm_test[1][1])/len(y_test)))\n",
        "print()\n",
        "print('Accuracy for training set for Logistic Regression = {}'.format((cm_train[0][0] + cm_train[1][1])/len(y_train)))\n",
        "print('Accuracy for test set for Logistic Regression = {}'.format((cm_test[0][0] + cm_test[1][1])/len(y_test)))\n",
        "print()\n",
        "print('Accuracy for training set for Decision Tree = {}'.format((cm_train[0][0] + cm_train[1][1])/len(y_train)))\n",
        "print('Accuracy for test set for Decision Tree = {}'.format((cm_test[0][0] + cm_test[1][1])/len(y_test)))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy for training set for Random Forest = 1.0\n",
            "Accuracy for test set for Random Forest = 0.7868852459016393\n",
            "\n",
            "Accuracy for training set for svm = 1.0\n",
            "Accuracy for test set for svm = 0.7868852459016393\n",
            "\n",
            "Accuracy for training set for Naive Bayes = 1.0\n",
            "Accuracy for test set for Naive Bayes = 0.7868852459016393\n",
            "\n",
            "Accuracy for training set for Logistic Regression = 1.0\n",
            "Accuracy for test set for Logistic Regression = 0.7868852459016393\n",
            "\n",
            "Accuracy for training set for Decision Tree = 1.0\n",
            "Accuracy for test set for Decision Tree = 0.7868852459016393\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DMmq-l0zVbIf"
      },
      "source": [
        "<h1>Conclusion</h1>\n",
        "<p>Heart Disease is one of the major concerns for society today.\n",
        "It is difficult to manually determine the odds of getting heart disease based on risk factors. However, machine learning techniques are useful to predict the output from existing data. Having checked the contributing risk factors and applying machine learning techniques, one can conclude that too much and too little of every thing can lead to serious heart diseases so it is therefore advised that we carry monthly check-ups and maintain a balanced/moderate healthy lifestyle. "
      ]
    }
  ]
}