Lab 6 - Heart Attack Dataset.ipynb

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        "<a href=\"https://colab.research.google.com/github/ahmedlila/Machine-Learning-Course-Implementations/blob/main/Lab%206%20-%20Heart%20Attack%20Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "![alt text](https://zewailcity.edu.eg/main/images/logo3.png) \n",
        "\n",
        "_Requirements Solved by_  [**Ahmed Lila - 201800251**](mailto:s-ahmedlila@zewailcity.edu.eg) \n"
      ],
      "id": "L3SZVlNInGx6"
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      "source": [
        "# Lab 6 Task | Logistic Regression\n",
        "\n",
        "In this task, you'll be working with the heart attack dataset (https://www.kaggle.com/nareshbhat/health-care-data-set-on-heart-attack-possibility) \n",
        "- Propose at least 2 questions about the dataset. (1 mark)\n",
        "- Use visualizations and relations between variables to support your analysis in answering these questions. (4 marks)\n",
        "- Perform any necessary cleaning steps. (2 marks)\n",
        "- Fit a logistic regression model. (1 mark)\n",
        "- Report model performance. (0.5 marks)\n",
        "- Discuss your findings and observations. (1 mark)\n",
        "- Discuss dataset limitations. (0.5 marks)\n",
        "- 2 bonus marks for organized notebooks with detailed analysis and logical flow of conclusions. "
      ],
      "id": "d44f568a"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MVEiSMUVoH3J"
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      "source": [
        "## About the data"
      ],
      "id": "MVEiSMUVoH3J"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8rsbzd9oLBo"
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      "source": [
        "This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to\n",
        "this date.The \"target\" field refers to the presence of heart disease in the patient. It is integer valued 0 = no/less chance of heart attack and 1 = more chance of heart attack\n",
        "\n",
        "**Attribute Information**\n",
        "- Age [age]\n",
        "- Sex [sex]\n",
        "- Chest pain type (4 values) [cp]\n",
        " - Value 1: typical angina (value=3)\n",
        " - Value 2: atypical angina\n",
        " - Value 3: non-anginal pain\n",
        " - Value 4: asymptomatic (normal) [value=0]\n",
        "- Resting blood pressure [trestbps]\n",
        "- Serum cholestoral in mg/dl [chol]\n",
        "- Fasting blood sugar > 120 mg/dl (1 = true; 0 = false) [fbs]\n",
        " - In general: A fasting blood sugar level below 100 milligrams per deciliter (mg/dL) — 5.6 millimoles per liter (mmol/L) — is considered normal. A fasting blood sugar level from 100 to 125 mg/dL (5.6 to 7.0 mmol/L ) is considered prediabetes. This result is sometimes called impaired fasting glucose. [Source](https://www.mayoclinic.org/diseases-conditions/prediabetes/diagnosis-treatment/drc-20355284#:~:text=hours%20or%20overnight.-,In%20general%3A,sometimes%20called%20impaired%20fasting%20glucose.)\n",
        "- Resting electrocardiographic results  [restecg]\n",
        " - Value 0: normal\n",
        " - Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)\n",
        " - Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria\n",
        "- Maximum heart rate achieved [thalach]\n",
        "- Exercise induced angina [exang]\n",
        "- Oldpeak = ST depression induced by exercise relative to rest [oldpeak]\n",
        "- The slope of the peak exercise ST segment [slope]\n",
        " - Value 1: upsloping\n",
        " - Value 2: flat\n",
        " - Value 3: downsloping\n",
        "- Number of major vessels (0-3) colored by flourosopy [ca] (normal=3)\n",
        "- Thal [thal]\n",
        " - 0 = normal;\n",
        " - 1 = fixed defect;\n",
        " - 2 = reversable defect\n",
        "- Target: 0= less chance of heart attack 1= more chance of heart attack"
      ],
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## Data Analysis Process"
      ],
      "id": "AAC2zzxirGM9"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dQ75FXcWrKAR"
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      "source": [
        "![alt text](https://www.researchgate.net/publication/329930775/figure/fig3/AS:873046667710469@1585161954284/The-fundamental-steps-of-the-exploratory-data-analysis-process_W640.jpg) \n"
      ],
      "id": "dQ75FXcWrKAR"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4q8JNL9Jr8d3"
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      "source": [
        "After doing feature engineering, we start training our data using different models that are compatible with data"
      ],
      "id": "4q8JNL9Jr8d3"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mk62EVS8tm-p"
      },
      "source": [
        "## Import Libraries and Dataset "
      ],
      "id": "mk62EVS8tm-p"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lVNs_d0ar8EU"
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      "source": [
        "#import needed libraries \n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier,ExtraTreeClassifier\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "from sklearn.linear_model import LinearRegression, Lasso, Ridge, LassoCV, RidgeCV, MultiTaskLassoCV, LassoLarsCV\n",
        "from sklearn import datasets\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV, LeaveOneOut\n",
        "from sklearn import tree\n",
        "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, plot_confusion_matrix\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.feature_selection import RFE\n"
      ],
      "id": "lVNs_d0ar8EU",
      "execution_count": null,
      "outputs": []
    },
    {
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          "height": 206
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        "id": "IR3euUM5oKoA",
        "outputId": "417f08bf-3d93-4e3f-9b2e-60c763ac0652"
      },
      "source": [
        "df=pd.read_csv('heart.csv')\n",
        "df.head()"
      ],
      "id": "IR3euUM5oKoA",
      "execution_count": null,
      "outputs": [
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              "  <thead>\n",
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              "      <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",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>63</td>\n",
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              "      <td>145</td>\n",
              "      <td>233</td>\n",
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              "      <td>2.3</td>\n",
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              "      <th>1</th>\n",
              "      <td>37</td>\n",
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              "      <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",
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              "      <td>1</td>\n",
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              "      <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",
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              "      <td>172</td>\n",
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              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
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              "      <td>57</td>\n",
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              "      <td>354</td>\n",
              "      <td>0</td>\n",
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              "      <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",
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            "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]"
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          },
          "metadata": {},
          "execution_count": 87
        }
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8NGS5cOb1joW"
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      "source": [
        "## Research Questions: \n",
        "- Does heart attack only attack the old people?\n",
        "- Does having these syndromes (Exercise induced angina - Maximum heart rate achieved - Chest pain - Number of major vessels) means that the person is suffering from heart attack?\n",
        "- Does heart attack get males more than females?"
      ],
      "id": "8NGS5cOb1joW"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M4mg6oT-yGSp"
      },
      "source": [
        "## Exploratory Data Analysis"
      ],
      "id": "M4mg6oT-yGSp"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R3KdyzYF0SZN"
      },
      "source": [
        "### 1) Distinguish Attributes"
      ],
      "id": "R3KdyzYF0SZN"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEQSUf-foKqi",
        "outputId": "2a45e62a-f6a4-4848-a3f2-8b283e5553d4"
      },
      "source": [
        "df.info()"
      ],
      "id": "KEQSUf-foKqi",
      "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": {
        "id": "f691b952",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "fd863be1-dc55-4a90-895c-bca8c65aac7f"
      },
      "source": [
        "df.describe()"
      ],
      "id": "f691b952",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXEUiRnML_sk"
      },
      "source": [
        "### 2) Missing data and Duplicates"
      ],
      "id": "hXEUiRnML_sk"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mni5kGc5uZP6",
        "outputId": "6ed7eb24-2e42-4c9c-f6ae-b2237728935e"
      },
      "source": [
        "#check for missing data, duplicates\n",
        "print('missing values -> {}'.format(df.isna().sum().sum()))\n",
        "print('duplicate values -> {}'.format(df.duplicated().sum()))"
      ],
      "id": "Mni5kGc5uZP6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "missing values -> 0\n",
            "duplicate values -> 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "si95FBWoyOsM",
        "outputId": "6c892d26-8452-499e-bc24-29e18310e749"
      },
      "source": [
        "# Remove Any duplicates or Missing values\n",
        "#CODE\n",
        "df.drop_duplicates(inplace=True)\n",
        "#TEST\n",
        "print('duplicate values -> {}'.format(df.duplicated().sum()))"
      ],
      "id": "si95FBWoyOsM",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "duplicate values -> 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nCgSuCEv0fgp"
      },
      "source": [
        "### 3) Univariate Analysis"
      ],
      "id": "nCgSuCEv0fgp"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPj9xPWmR6Yv"
      },
      "source": [
        "#### A- Is the data balanced?"
      ],
      "id": "WPj9xPWmR6Yv"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "4JTIX4xXSVPC",
        "outputId": "31ed7839-24aa-45b7-fd5f-d755dc985b21"
      },
      "source": [
        "plt.pie(df['target'].value_counts(), explode = [0.2, 0], shadow = True, autopct='%.0f%%', pctdistance=0.5, labeldistance=1.2)\n",
        "plt.title('Target Variable [People who has heart attack]')\n",
        "plt.show() "
      ],
      "id": "4JTIX4xXSVPC",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncZ9QDs5VBW-"
      },
      "source": [
        "*Yes, 54 and 46 makes our data almost balanced*"
      ],
      "id": "ncZ9QDs5VBW-"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JUcDIrNISVz2"
      },
      "source": [
        "#### B- Q1: Does heart attack only attack old people?"
      ],
      "id": "JUcDIrNISVz2"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "ZPfOUJMdCarv",
        "outputId": "f1d9a9f8-4d05-4fca-d85a-babe3513502b"
      },
      "source": [
        "# Age distribution in dataset\n",
        "sns.histplot( df['age'],bins=20, color='#FA8072', alpha=0.7,edgecolor='black', linewidth=1, kde=True, stat=\"density\" );"
      ],
      "id": "ZPfOUJMdCarv",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B2YSucf7Dcls",
        "outputId": "58b2fa13-f924-4782-de28-fd34e7e996fc"
      },
      "source": [
        "#Average, Mode, and Median \n",
        "print(\"Mean of age= {}, Median of age= {}, Mode of age= {}\" .format(np.round(df['age'].mean(),2), np.round(df['age'].median(),2),int(np.round(df['age'].mode(),2))))"
      ],
      "id": "B2YSucf7Dcls",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean of age= 54.42, Median of age= 55.5, Mode of age= 58\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "ZFh2-cQq29D_",
        "outputId": "2c372fdd-3427-494a-cc49-a57b4a907242"
      },
      "source": [
        "sns.boxplot(x='target',y='age',data=df, palette='Set2');"
      ],
      "id": "ZFh2-cQq29D_",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Toj0Te8JQQRj"
      },
      "source": [
        "From the above plot, it's shown that age has not an effect on having heart attack as they're mutual portion between who have and who haven't "
      ],
      "id": "Toj0Te8JQQRj"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kb9hweGjDt1k"
      },
      "source": [
        "The age don't necessarily affect people to have heart attack"
      ],
      "id": "Kb9hweGjDt1k"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFUAKDq1VmGS"
      },
      "source": [
        "#### C- Q2: Does heart attack get males more than females?"
      ],
      "id": "ZFUAKDq1VmGS"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFubXHpDbcFD"
      },
      "source": [
        "Males and females distribution"
      ],
      "id": "cFubXHpDbcFD"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "4o_juuJRbfwD",
        "outputId": "0d48b469-5986-4a65-abad-45cb574e1a7a"
      },
      "source": [
        "# the information collected from men are twice information collected from women\n",
        "sns.countplot(x='sex',data=df, palette='Set2', linewidth=0.5, edgecolor='black');"
      ],
      "id": "4o_juuJRbfwD",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6JfCdZAkdZZR"
      },
      "source": [
        "As we already have collected a biased data to a specific gender, we would try to separate them. Then, We'll try to get each gender as percentage if it has heart attack or not "
      ],
      "id": "6JfCdZAkdZZR"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "wNwHXNh8g4yD",
        "outputId": "080cc71b-adbd-4b26-b717-249f7509fd46"
      },
      "source": [
        "sns.countplot(x='sex',hue='target',data=df, palette='Set2', linewidth=0.5, edgecolor='black');"
      ],
      "id": "wNwHXNh8g4yD",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLEnZ_0CcDNT"
      },
      "source": [
        "sex_values= df.sex.value_counts()\n",
        "male_ha= 100*df[df['sex']==1].target.value_counts()/sex_values[1] #Percentage of men who are having heart attack\n",
        "female_ha= 100*df[df['sex']==0].target.value_counts()/sex_values[0] #Percentage of women who are having heart attack"
      ],
      "id": "NLEnZ_0CcDNT",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "50MA5BT6fHwX",
        "outputId": "24b6182b-1c92-418c-9939-329d760ca06e"
      },
      "source": [
        "data = [male_ha, female_ha]\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.bar(df.target.unique()-0.15, data[0], color = '#8FB9AA', width = 0.3);\n",
        "ax.bar(df.target.unique()+0.15, data[1], color = '#ED8975', width = 0.3);\n",
        "ax.legend(['Men', 'Women']);\n",
        "plt.xlabel('Have Heart Attack')\n",
        "plt.ylabel('Percentage')\n",
        "plt.title('Men Vs Women who have heart attack [percentages]')\n",
        "plt.show()"
      ],
      "id": "50MA5BT6fHwX",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ttKa/W7durF69umr1mtn66ZUf+HBXa/qe/YOqLcu7oDvAqaeeyvnnn8/QoUPXGn7IIYdwxRVXEBEAPPLIIznKMzOzGuQA7gADBgzgS1/60j8NP++881i1ahW77747u+66K+edd16G6szMrBapYeusltXX10fj+wHPnj2bnXfeOVNFnYPXkZmV8i7o1nX0LmhJMyOivqlx3gI2MzPLwAFsZmaWgQPYzMwsAwewmZlZBg5gMzOzDBzAZmZmGTiA2+krX/kKl1122Zr+Qw45hNNOO21N/1lnncUll1ySozQzM+sEusylKDv6922t/RZs//335+abb+bMM8/k3XffZenSpbz22mtrxk+bNo1LL720Q2syM7Ouw1vA7bTffvsxffp0AJ588kl22203evfuzauvvsrKlSuZPXs2K1asYPjw4QwdOpRTTz2VlStXAjBo0CDOPfdchg0bRn19PQ8//DCHHHII22+/PVddddWaZfzwhz9k7733Zvfdd+f8888HYN68eey8886cfvrp7Lrrrhx88MG89dZb1V8BZma2ThzA7bT11lvTvXt3nn/+eaZNm8bIkSMZMWIE06dPZ8aMGQwZMoTTTjttzS0HV69ezZVXXrnm+QMHDmTWrFkccMABjB07lltvvZX7779/TdBOnjyZOXPm8OCDDzJr1ixmzpzJ1KlTAZgzZw6f//znefLJJ+nTpw+33XZblnVgZmbtV7EAlrSTpFklj9cknSmpr6Qpkuakv1tUqoZK22+//Zg2bdqaAB45cuSa/gEDBjB48GB23HFHAMaMGbMmQKG4gxLA0KFDGTFiBL1796auro6NNtqI5cuXM3nyZCZPnszw4cPZc889efrpp5kzZw4AgwcPZtiwYQDstddezJs3r7oNNzOzdVaxAI6IZyJiWEQMA/YC/g78GjgHuCcihgD3pP5Oaf/992fatGk8/vjj7Lbbbuy7775Mnz6dadOmMWrUqBaf23A7wg022GCtWxNusMEGrF69mojg3HPPZdasWcyaNYu5c+cybty4tZ4LvpWhmVlnVa1d0KOBZyNiPnAkMCENnwAcVaUaOtx+++3HHXfcQd++fenWrRt9+/Zl+fLlTJ8+nWOOOYZ58+Yxd+5cAK677jo++MEPlj3vQw45hGuvvZY33ngDgBdeeIHFixdXpB1mZlZ91ToL+kTgxtTdPyIWpe6XgP5VqqHDDR06lKVLl3LyySevNeyNN95gwIAB/PznP+e4445j9erV7L333nzmM58pe94HH3wws2fPZuTIkQBsuummXH/99XTr1q3D22FmZtVX8dsRStoQeBHYNSJelrQ8IvqUjH81Iv7pOLCkM4AzAAYOHLjX/Pnz1xrvW+21zuvIzEr5doSt62q3IzwMeDgiXk79L0vaKhW2FdDkftWIuDoi6iOivq6urgplmpmZVU81Avgk3tv9DDAJGJO6xwC3V6EGMzOzmlLRAJa0CfAR4Fclg78PfETSHODDqd/MzGy9UtGTsCLiTaBfo2HLKM6K7oj5I6kjZtXlVPrYvpmZrZtOeyWsnj17smzZMgdNEyKCZcuW0bNnz9ylmJlZMzrtzRgGDBjAwoULWbJkSe5SalLPnj0ZMGBA7jLMzKwZnTaAe/ToweDBg3OXYWZm1i6ddhe0mZlZZ+YANjMzy8ABbGZmloED2MzMLAMHsJmZWQYOYDMzswwcwGZmZhk4gM3MzDJwAJuZmWXgADYzM8vAAWxmZpaBA9jMzCwDB7CZmVkGDmAzM7MMHMBmZmYZOIDNzMwycACbmZll4AA2MzPLwAFsZmaWgQPYzMwsAwewmZlZBg5gMzOzDBzAZmZmGTiAzczMMnAAm5mZZVDRAJbUR9Ktkp6WNFvSSEl9JU2RNCf93aKSNZiZmdWiSm8BXw78PiL+FdgDmA2cA9wTEUOAe1K/mZnZeqViASxpc+BA4BqAiHgnIpYDRwIT0mQTgKMqVYOZmVmtquQW8GBgCfBzSY9I+pmkTYD+EbEoTfMS0L+pJ0s6Q9IMSTOWLFlSwTLNzMyqr5IB3B3YE7gyIoYDb9Jod3NEBBBNPTkiro6I+oior6urq2CZZmZm1VfJAF4ILIyIB1L/rRSB/LKkrQDS38UVrMHMzKwmVSyAI+IlYIGkndKg0cBTwCRgTBo2Bri9UjWYmZnVqu4Vnv8XgRskbQg8B3yaIvRvljQOmA8cX+EazMzMak5FAzgiZgH1TYwaXcnlmpmZ1TpfCcvMzCwDB7CZmVkGDmAzM7MMHMBmZmYZOIDNzMwycACbmZll4AA2MzPLwAFsZmaWgQPYzMwsAwewmZlZBg5gMzOzDCp9MwYzs4q7bPItuUvoFD6VuwBbi7eAzczMMnAAm5mZZeAANjMzy8ABbGZmloED2MzMLAMHsJmZWQYOYDMzswwcwGZmZhk4gM3MzDJwAJuZmWXgADYzM8vAAWxmZpaBA9jMzCwDB7CZmVkGDmAzM7MMKno/YEnzgNeBfwCrI6JeUl9gIjAImAccHxGvVrIOMzOzWlONLeAPRcSwiKhP/ecA90TEEOCe1G9mZrZeybEL+khgQuqeAByVoQYzM7OsKh3AAUyWNFPSGWlY/4hYlLpfAvpXuAYzM7OaU9FjwMAHIuIFSe8Dpkh6unRkRISkaOqJKbDPABg4cGCFyzQzM6uuim4BR8QL6e9i4NfAPsDLkrYCSH8XN/PcqyOiPiLq6+rqKlmmmZlZ1VUsgCVtIql3QzdwMPAEMAkYkyYbA9xeqRrMzMxqVSV3QfcHfi2pYTm/jIjfS3oIuFnSOGA+cHwFazAzM6tJFQvgiHgO2KOJ4cuA0ZVarpmZWWfgK2GZmZll4AA2MzPLwAFsZmaWgQPYzMwsAwewmZlZBg5gMzOzDBzAZmZmGTiAzczMMigrgFU4RdK/p/6BkvapbGlmZmZdV7lbwP8NjAROSv2vAz+tSEVmZmbrgXIvRTkiIvaU9AhARLwqacMK1mVmZtallbsFvEpSNyAAJNUB71asKjMzsy6u3AD+L4r7+b5P0kXAn4H/rFhVZmZmXVxZu6Aj4gZJMynuYiTgqIiYXdHKzMzMurCyAlhSX2AxcGPJsB4RsapShVXKZZNvyV1Cp3DmwcflLsHMrEsrdxf0w8AS4K/AnNQ9T9LDkvaqVHFmZmZdVbkBPAU4PCK2jIh+wGHAHcDnKH6iZGZmZm1QbgDvGxF3NfRExGRgZETcD2xUkcrMzMy6sHJ/B7xI0jeAm1L/CcDL6adJ/jmSmZlZG5W7BXwyMAD4TXoMTMO6AcdXpjQzM7Ouq9yfIS0FvtjM6LkdV46Zmdn6odyfIdUBZwO7Aj0bhkfEQRWqy8zMrEsrdxf0DcDTwGDgO8A84KEK1WRmZtbllRvA/SLiGmBVRPwxIk4FvPVrZmbWTuWeBd1wxatFkj4KvAj0rUxJZmZmXV+5AXyhpM2Bs4ArgM2AMytWlZmZWRdXbgC/GhErgBXAhwAk7V+xqszMzLq4co8BX1HmMDMzMytDi1vAkkYC+wF1kr5aMmoziotwtCpdLWsG8EJEHCFpMMUVtfoBM4FPRsQ77SnezMyss2ptC3hDYFOKoO5d8ngNOLbMZXwZKL138MXApRGxA/AqMK4tBZuZmXUFLW4BR8QfgT9KGh8R89s6c0kDgI8CFwFflSSKny+dnCaZAFwAXNnWeZuZmXVm5Z6EtZGkq4FBpc8p40pYl1FcQat36u8HLI+I1al/IbBN2dWamZl1EeUG8C3AVcDPgH+U8wRJRwCLI2KmpFFtLUzSGcAZAAMHDmzr083MzGpauQG8OiLaupt4f+Djkg6nuH70ZsDlQB9J3dNW8ADghaaeHBFXA1cD1NfXRxuXbWZmVtPK/RnSbyV9TtJWkvo2PFp6QkScGxEDImIQcCLwh4j4BHAv753ANQa4vb3Fm5mZdVblbgGPSX+/XjIsgPe3Y5nfAG6SdCHwCHBNO+ZhZmbWqZV7P+DB67KQiLgPuC91Pwfssy7zMzMz6+zK2gUtaWNJ305nQiNpSDrJyszMzNqh3GPAPwfeobgqFhQnTl1YkYrMzMzWA+UG8PYR8QPSbQkj4u+AKlaVmZlZF1duAL8jqRfFiVdI2h5YWbGqzMzMurhyz4I+H/g9sK2kGyh+4zu2UkWZmZl1deWeBT1F0sPAvhS7nr8cEUsrWpmZmVkXVu5Z0P9GcTWsOyPiDmC1pKMqW5qZmVnXVe4x4PMjYkVDT0Qsp9gtbWZmZu1QbgA3NV25x4/NzMyskXIDeIakSyRtnx6XADMrWZiZmVlXVm4Af5HiQhwTgZuAt4HPV6ooMzOzrq7V3ciSugF3RMSHqlCPmZnZeqHVLeCI+AfwrqTNq1CPmZnZeqHcE6neAB6XNAV4s2FgRHypIlWZmZl1ceUG8K/Sw8zMzDpAuVfCmpCuBT0wIp6pcE1mZmZdXrlXwvoYMIvietBIGiZpUiULMzMz68rK/RnSBcA+wHKAiJgFvL9CNZmZmXV55QbwqtJLUSbvdnQxZmZm64tyT8J6UtLJQDdJQ4AvAdMqV5aZmVnX1pYrYe0KrAR+CawAzqxUUWZmZl1di1vAknoCnwF2AB4HRkbE6moUZmZm1pW1tgU8AainCN/DgB9VvCIzM7P1QGvHgHeJiKEAkq4BHqx8SWZmZl1fa1vAqxo6vOvZzMys47S2BbyHpNdSt4BeqV9ARMRmFa3OzMysi2oxgCOiW7UKMTMzW5+U+zOkNpPUU9KDkh6V9KSk76ThgyU9IGmupImSNqxUDWZmZrWqYgFM8ZvhgyJiD2AYcKikfYGLgUsjYgfgVWBcBWswMzOrSRUL4Ci8kXp7pEcABwG3puETgKMqVYOZmVmtquQWMJK6SZoFLAamAM8Cy0vOqF4IbFPJGszMzGpRRQM4Iv4REcOAARR3U/rXcp8r6QxJMyTNWLJkScVqNDMzy6GiAdwgIpYD9wIjgT6SGs6+HgC80Mxzro6I+oior6urq0aZZmZmVVPJs6DrJPVJ3b2AjwCzKYL42DTZGOD2StVgZmZWq8q9HWF7bAVMkNSNIuhvjog7JD0F3CTpQuAR4JoK1mBmZlaTKhbAEfEYMLyJ4c9RHA82MzNbb1XlGLCZmZmtzQFsZmaWgQPYzMwsAwewmZlZBg5gMzOzDBzAZmZmGTiAzczMMnAAm5mZZeAANjMzy8ABbGZmloED2MzMLAMHsJmZWQYOYDMzswwqeTtC68Re+cHZuUvoFPqe/YPcJZhZJ+UtYDMzswwcwGZmZhk4gM3MzDJwAJuZmWXgADYzM8vAAWxmZpaBA9jMzCwDB7CZmVkGDmAzM7MMHMBmZmYZOIDNzMwycACbmZll4AA2MzPLwAFsZmaWQcUCWNK2ku6V9JSkJyV9OQ3vK2mKpDnp7xaVqsHMzKxWVXILeDVwVkTsAuwLfF7SLsA5wD0RMQS4J/WbmZmtVyoWwBGxKCIeTt2vA7OBbYAjgQlpsgnAUZWqwczMrFZV5RiwpEHAcOABoH9ELEqjXgL6V6MGMzOzWlLxAJa0KXAbcGZEvFY6LiICiGaed4akGZJmLFmypNJlmpmZVVVFA1hSD4rwvSEifpUGvyxpqzR+K2BxU8+NiKsjoj4i6uvq6ipZppmZWdVV8ixoAdcAsyPikpJRk4AxqXsMcHulajAzM6tV3Ss47/2BTwKPS5qVhn0T+D5ws6RxwHzg+ArWYGZmVpMqFsAR8WdAzYweXanlmpmZdQa+EpaZmVkGDmAzM7MMHMBmZmYZOIDNzMwycACbmZll4AA2MzPLwAFsZmaWgQPYzMwsAwewmZlZBg5gMzOzDBzAZmZmGTiAzczMMnAAm5mZZeAANjMzy8ABbGZmloED2MzMLAMHsJmZWQYOYDMzswwcwGZmZhk4gM3MzDJwAJuZmWXgADYzM8vAAWxmZpaBA9jMzCwDB7CZmVkGDmAzM7MMHMBmZmYZOIDNzMwyqFgAS7pW0mJJT5QM6ytpiqQ56e8WlVq+mZlZLavkFvB44NBGw84B7omIIcA9qd/MzGy9U7EAjoipwCuNBh8JTEjdE4CjKrV8MzOzWlbtY8D9I2JR6n4J6N/chJLOkDRD0owlS5ZUpzozM7MqyXYSVkQEEC2Mvzoi6iOivq6urpJISc4AAAolSURBVIqVmZmZVV61A/hlSVsBpL+Lq7x8MzOzmlDtAJ4EjEndY4Dbq7x8MzOzmlDJnyHdCEwHdpK0UNI44PvARyTNAT6c+s3MzNY73Ss144g4qZlRoyu1TDMzs87CV8IyMzPLwAFsZmaWgQPYzMwsAwewmZlZBg5gMzOzDBzAZmZmGTiAzczMMnAAm5mZZeAANjMzy8ABbGZmloED2MzMLAMHsJmZWQYOYDMzswwcwGZmZhk4gM3MzDJwAJuZmWXgADYzM8vAAWxmZpaBA9jMzCwDB7CZmVkGDmAzM7MMHMBmZmYZOIDNzMwycACbmZll4AA2MzPLwAFsZmaWgQPYzMwsgywBLOlQSc9ImivpnBw1mJmZ5VT1AJbUDfgpcBiwC3CSpF2qXYeZmVlOObaA9wHmRsRzEfEOcBNwZIY6zMzMsskRwNsAC0r6F6ZhZmZm6w1FRHUXKB0LHBoRp6X+TwIjIuILjaY7Azgj9e4EPFPVQtfNlsDS3EWsI7ehNrgNtaErtAG6Rjs6Wxu2i4i6pkZ0r3YlwAvAtiX9A9KwtUTE1cDV1SqqI0maERH1uetYF25DbXAbakNXaAN0jXZ0hTY0yLEL+iFgiKTBkjYETgQmZajDzMwsm6pvAUfEaklfAO4CugHXRsST1a7DzMwspxy7oImI3wG/y7HsKumUu84bcRtqg9tQG7pCG6BrtKMrtAHIcBKWmZmZ+VKUZmZmWTiAO4CkvpKmSJqT/m7RxDTDJE2X9KSkxySdkKPWxlq7LKikjSRNTOMfkDSo+lW2rIw2fFXSU2m93yNpuxx1tqTcy7NKOkZSSKq5s0DLaYOk49Nr8aSkX1a7xtaU8V4aKOleSY+k99PhOepsiaRrJS2W9EQz4yXpv1IbH5O0Z7VrbE0ZbfhEqv1xSdMk7VHtGjtERPixjg/gB8A5qfsc4OImptkRGJK6twYWAX0y190NeBZ4P7Ah8CiwS6NpPgdclbpPBCbmXt/taMOHgI1T92c7YxvSdL2BqcD9QH3uutvxOgwBHgG2SP3vy113O9pwNfDZ1L0LMC933U2040BgT+CJZsYfDvwfIGBf4IHcNbejDfuVvI8Oq8U2lPPwFnDHOBKYkLonAEc1niAi/hoRc1L3i8BioMkfZ1dROZcFLW3brcBoSapija1ptQ0RcW9E/D313k/x2/NaUu7lWb8LXAy8Xc3iylROG04HfhoRrwJExOIq19iactoQwGape3PgxSrWV5aImAq80sIkRwK/iML9QB9JW1WnuvK01oaImNbwPqI2P9NlcQB3jP4RsSh1vwT0b2liSftQfMN+ttKFtaKcy4KumSYiVgMrgH5Vqa48bb206TiKb/+1pNU2pN2E20bEndUsrA3KeR12BHaU9BdJ90s6tGrVlaecNlwAnCJpIcUvOb5YndI6VFe7HHAtfqbLkuVnSJ2RpLuBf2li1LdKeyIiJDV7ann6pnkdMCYi3u3YKq0lkk4B6oEP5q6lLSRtAFwCjM1cyrrqTrEbehTFFstUSUMjYnnWqtrmJGB8RPxY0kjgOkm7+bOch6QPUQTwB3LX0h4O4DJFxIebGyfpZUlbRcSiFLBN7lqTtBlwJ/CttOsnt3IuC9owzUJJ3Sl2uy2rTnllKevSppI+TPFl6YMRsbJKtZWrtTb0BnYD7kt7//8FmCTp4xExo2pVtqyc12EhxbG6VcDfJP2VIpAfqk6JrSqnDeOAQwEiYrqknhTXJq613ektKeszU+sk7Q78DDgsImrpf1LZvAu6Y0wCxqTuMcDtjSdIl938NcWxl1urWFtLyrksaGnbjgX+EOnMhxrRahskDQf+B/h4DR53hFbaEBErImLLiBgUEYMojnnVUvhCee+l31Bs/SJpS4pd0s9Vs8hWlNOG54HRAJJ2BnoCS6pa5bqbBHwqnQ29L7Ci5BBapyBpIPAr4JMR8dfc9bRb7rPAusKD4pjoPcAc4G6gbxpeD/wsdZ8CrAJmlTyG1UDthwN/pTge/a007D8o/sFD8Q/mFmAu8CDw/tw1t6MNdwMvl6z3SblrbmsbGk17HzV2FnSZr4ModqU/BTwOnJi75na0YRfgLxRnSM8CDs5dcxNtuJHiVxarKPY6jAM+A3ym5HX4aWrj4zX6XmqtDT8DXi35TM/IXXN7Hr4SlpmZWQbeBW1mZpaBA9jMzCwDB7CZmVkGDmAzM7MMHMBmZmYZOIDNOpCkNxr1j5X0kwoub5SkOxoNGy/p2A6c/36tTPMbSfc3GjZW0tYl/WdK2ridNVR0HZrl4gA2syalK5+NorjzTHPT9AH2AjaX9P6SUWMp7vrV4EygXQFs1lU5gM2qRNLHVNxT+RFJd0vqL2kDSfNSkDVMNyeNq5N0m6SH0mP/dixzL0l/lDRT0l0Nd72RdHqa56NpGRun4eMlXSXpAeBmiosffEXSLEkHNLGIo4HfUtw56MQ0j2MpLkJzQ3relynC+F5J96ZprpQ0Q8V9gb9TUu/e6f6uj0p6UFLvRu35qIr7am/Z1nVhVnNyXwnEDz+60gP4B2tf7ex54Cdp3Baw5uI3pwE/Tt2XA59O3SOAu1P3L4EPpO6BwOwmljeK4g5Vpct8heKyoT2AaUBdmvYE4NrU3a9kHhcCX0zd44E7gG6p/wLgay20dwpwAMVlJR8vGX4fJVdYAuYBW5b0N1wtrluadneKO4Q9B+ydxm1Gcb36scBPgH8D/kS6D6wffnT2h2/GYNax3oqIYQ09ksZSbA1CcdH7iWkrdEPgb2n4RODfgZ9TbEVOTMM/DOxScvvlzSRtGhFrHWcG/hQRR5Qsc3zq3IniJg5T0jy6UVzeD2A3SRcCfYBNgbtK5ndLRPyjtYZK6k9xM4U/R0RIWpXuDPREa88Fjpd0BkXAbkVxiccAFkXEQwAR8VpaDsBBFOvx4IbhZp2dA9iseq4ALomISZJGUWxdAkwHdpBUBxxFsUUKxSGifSPi7XYuT8CTETGyiXHjgaMi4tH0JWFUybg3y5z/8RRb9X9LIbkZxe36vtXSkyQNBr5GsaX7avrC0LOVZT0LvJ9iS7uWbkJh1m4+BmxWPZvz3m3fGu4wRUQExZ2yLqHYzdxwa7XJlNzwXdKaLesyPQPUpfvWIqmHpF3TuN7AIkk9gE+0MI/X07RNOQk4NN67S9NepOPATTyvtH8zipBfkbaiDyupdytJe6d6e6cTwQDmA8cAvyhpg1mn5gA2q54LgFskzQSWNho3keKOWRNLhn0JqJf0mKSnKE6IKltEvENxLPhiSQ1372k4o/k84AGKO/s83cJsfgv8W+OTsCQNArajuDViw/L+RhGqIyi2sK9Kz+sFXA38XtK9EfEo8Eha7i9TDQ31ngBckeqdQsmWcUQ8TfFl4RZJ27dlXZjVIt8NyczMLANvAZuZmWXgADYzM8vAAWxmZpaBA9jMzCwDB7CZmVkGDmAzM7MMHMBmZmYZOIDNzMwy+P9I6b13vROiRQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1o5YS9nmshg"
      },
      "source": [
        "Observation: Females are susceptible to get a heart attack than males"
      ],
      "id": "X1o5YS9nmshg"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqjVq932nHWN"
      },
      "source": [
        "#### D- Q3: Does having these syndromes (Exercise induced angina - Maximum heart rate achieved - Chest pain - Number of major vessels) means that the person is susceptible to get a heart attack?"
      ],
      "id": "bqjVq932nHWN"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NsO3XoVRytJb",
        "outputId": "52359721-9c63-4f29-8068-eac5ad364ac2"
      },
      "source": [
        "df.exang.value_counts()"
      ],
      "id": "NsO3XoVRytJb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    203\n",
              "1     99\n",
              "Name: exang, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Um9tVi8nnOLi",
        "outputId": "c3036dbc-5c70-4a53-95aa-35129a201ce4"
      },
      "source": [
        "# Will use this feature later\n",
        "#Inversely Propotional to (Exercise induced angina - Number of major vessels )\n",
        "#Propotional to (Maximum heart rate achieved - Chest pain)\n",
        "heart_syndromes=-df['exang']-df['ca']+(df['cp']/df['cp'].max())+ (df['thalach']/df['thalach'].max())\n",
        "sns.boxplot(x=df['target'],y=heart_syndromes, palette='Set2');"
      ],
      "id": "Um9tVi8nnOLi",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0i4BstjBqfBX"
      },
      "source": [
        "This box plot shows that gethering those 4 major features is a good indicator, where: \n",
        "- The less (excercise exerted - number of major vessels) than normal person,  the person is more susceptible to have a heart attack [Inversely Propotional]\n",
        "- The more (Maximum heart rate achieved - Chest pain) than normal person,  the person is more susceptible to have a heart attack [Propotional]\n"
      ],
      "id": "0i4BstjBqfBX"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3mvipaaJIGZ"
      },
      "source": [
        "## Feature Engineering "
      ],
      "id": "Y3mvipaaJIGZ"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MomUe3yNH2E4"
      },
      "source": [
        "- Feature Selection\n",
        " - Identify data attributes\n",
        " - Filter irrelevant and redundant attributes\n",
        "- Feature Creation\n",
        " - Existing data points into new features (Making sure to avoid collinearity)\n",
        " - Infer new attributes\n",
        "\n",
        "- Feature Transformation\n",
        " - Impute missing feature values: Fortunately, data have no missing values\n",
        " - Scale numerical features: there are some features range from 0:155 and others from 0:4, all features don't have same minimum maximum value."
      ],
      "id": "MomUe3yNH2E4"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ntDIPm-LLx9"
      },
      "source": [
        "### 1) Feature Selection"
      ],
      "id": "4ntDIPm-LLx9"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FonC1fhiIvyj",
        "outputId": "2dbdcbfb-2aef-424d-a5f3-cba10934c683"
      },
      "source": [
        "####### Identify data attributes #######\n",
        "df.info()"
      ],
      "id": "FonC1fhiIvyj",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 302 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       302 non-null    int64  \n",
            " 1   sex       302 non-null    int64  \n",
            " 2   cp        302 non-null    int64  \n",
            " 3   trestbps  302 non-null    int64  \n",
            " 4   chol      302 non-null    int64  \n",
            " 5   fbs       302 non-null    int64  \n",
            " 6   restecg   302 non-null    int64  \n",
            " 7   thalach   302 non-null    int64  \n",
            " 8   exang     302 non-null    int64  \n",
            " 9   oldpeak   302 non-null    float64\n",
            " 10  slope     302 non-null    int64  \n",
            " 11  ca        302 non-null    int64  \n",
            " 12  thal      302 non-null    int64  \n",
            " 13  target    302 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 45.4 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2shS4pg5KIp",
        "outputId": "059b98f7-545b-4199-f9df-76f7b7904bfd"
      },
      "source": [
        "#heatmap for different features\n",
        "fig, ax = plt.subplots(figsize=(15,5))         # Sample figsize in inches\n",
        "sns.heatmap(df.corr(), annot = True, ax=ax, cmap=\"RdPu\");\n",
        "plt.title('Correlation between the variables')\n",
        "plt.xticks(rotation=45);"
      ],
      "id": "I2shS4pg5KIp",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FQDHZIC2I-ls",
        "outputId": "c67ece7d-c87f-4859-e956-3ca199338cb0"
      },
      "source": [
        "#CODE\n",
        "correlation_matrix = df.corr().round(2)\n",
        "#TEST\n",
        "correlation_matrix.target.sort_values()"
      ],
      "id": "FQDHZIC2I-ls",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "exang      -0.44\n",
              "oldpeak    -0.43\n",
              "ca         -0.41\n",
              "thal       -0.34\n",
              "sex        -0.28\n",
              "age        -0.22\n",
              "trestbps   -0.15\n",
              "chol       -0.08\n",
              "fbs        -0.03\n",
              "restecg     0.13\n",
              "slope       0.34\n",
              "thalach     0.42\n",
              "cp          0.43\n",
              "target      1.00\n",
              "Name: target, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9NSMUkuJOr1",
        "outputId": "113b2e1b-3d85-4d05-938b-294508329a55"
      },
      "source": [
        "####### Filter irrelevant and redundant attributes #######\n",
        "# CODE\n",
        "df.drop(columns=['age',\t'sex', 'trestbps',\t'chol',\t'fbs',\t'restecg', 'thal'], inplace=True)\n",
        "#TEST\n",
        "df.head()"
      ],
      "id": "a9NSMUkuJOr1",
      "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>cp</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   cp  thalach  exang  oldpeak  slope  ca  target\n",
              "0   3      150      0      2.3      0   0       1\n",
              "1   2      187      0      3.5      0   0       1\n",
              "2   1      172      0      1.4      2   0       1\n",
              "3   1      178      0      0.8      2   0       1\n",
              "4   0      163      1      0.6      2   0       1"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LbIQji4ZKjjO"
      },
      "source": [
        "## Training/Testing Models"
      ],
      "id": "LbIQji4ZKjjO"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u8S3ZRockMC1"
      },
      "source": [
        "### 1) Train Models on basic data\n",
        " [no feature creation or transformation]"
      ],
      "id": "u8S3ZRockMC1"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RkbK3GEcKmK9"
      },
      "source": [
        "X = df.drop('target',axis=1)\n",
        "y = df['target']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 20, shuffle=True)"
      ],
      "id": "RkbK3GEcKmK9",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D448IHQBLUIt"
      },
      "source": [
        "#### A- Cross Validation Models"
      ],
      "id": "D448IHQBLUIt"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qdrx52Q1VG28"
      },
      "source": [
        "\n",
        "<img src=\"https://scikit-learn.org/stable/_images/grid_search_workflow.png\" width=\"500\">\n"
      ],
      "id": "Qdrx52Q1VG28"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgAY1sA4DwXV"
      },
      "source": [
        "def cross_val_models(X_train, y_train, cv_model):\n",
        "  cv=5\n",
        "  if cv_model=='loocv':\n",
        "    cv=LeaveOneOut()\n",
        "  ############################## MODELS ##############################\n",
        "  #I - KNN\n",
        "  clf = KNeighborsClassifier()\n",
        "  scores1 = cross_val_score(clf, X_train, y_train, cv=cv) #notice the difference\n",
        "  avg_score1= np.round(scores1.mean()*100,2)\n",
        "\n",
        "  #II - DecisionTreeClassifier\n",
        "  clf = DecisionTreeClassifier()\n",
        "  scores2 = cross_val_score(clf, X_train, y_train, cv=cv)\n",
        "  avg_score2= np.round(scores2.mean()*100,2)\n",
        "\n",
        "\n",
        "  #III - ExtraTreeClassifier\n",
        "  clf = ExtraTreeClassifier()\n",
        "  scores4 = cross_val_score(clf, X_train, y_train,cv=cv)\n",
        "  avg_score4= np.round(scores4.mean()*100,2)\n",
        "\n",
        "  #IV - LogisticRegression\n",
        "  clf = LogisticRegression(solver = 'liblinear')\n",
        "  scores5 = cross_val_score(clf, X_train, y_train, cv=cv)\n",
        "  avg_score5= np.round(scores5.mean()*100,2)\n",
        "\n",
        "  #V - RandomForestClassifier\n",
        "  clf = RandomForestClassifier()\n",
        "  scores6 = cross_val_score(clf, X_train, y_train,cv=cv)\n",
        "  avg_score6= np.round(scores6.mean()*100,2)\n",
        "\n",
        "  ######################### Pretty Table #############################\n",
        "  data=[['KNeighborsClassifier', avg_score1],['DecisionTreeClassifier', avg_score2],['ExtraTreeClassifier', avg_score4],['LogisticRegression', avg_score5],['RandomForestClassifier', avg_score6]]\n",
        "  kfolds_models=pd.DataFrame(data,columns=['Model', 'Average Accuracy'])\n",
        "  return kfolds_models.sort_values([\"Average Accuracy\"], ascending=False, ignore_index=True)"
      ],
      "id": "HgAY1sA4DwXV",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "k4eWzfzlLvg6",
        "outputId": "e8038a1c-22e7-4740-e039-7b733885ae80"
      },
      "source": [
        "# 5 Folds - default\n",
        "cross_val_models(X_train,y_train, cv_model=5)"
      ],
      "id": "k4eWzfzlLvg6",
      "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>Model</th>\n",
              "      <th>Average Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>81.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>80.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DecisionTreeClassifier</td>\n",
              "      <td>75.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ExtraTreeClassifier</td>\n",
              "      <td>73.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>KNeighborsClassifier</td>\n",
              "      <td>70.10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Model  Average Accuracy\n",
              "0      LogisticRegression             81.73\n",
              "1  RandomForestClassifier             80.89\n",
              "2  DecisionTreeClassifier             75.09\n",
              "3     ExtraTreeClassifier             73.43\n",
              "4    KNeighborsClassifier             70.10"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RO3-jCdU_FOL"
      },
      "source": [
        "### 2) Feature Engineering and Retrain data"
      ],
      "id": "RO3-jCdU_FOL"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ey3sYI3hLT9u"
      },
      "source": [
        "#### Feature Transformation and Creation"
      ],
      "id": "Ey3sYI3hLT9u"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "un4nkp5xPkEs"
      },
      "source": [
        "Normalization"
      ],
      "id": "un4nkp5xPkEs"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDbvBMKISR3r",
        "outputId": "133e3eef-9b84-4a41-8ba2-4dd4c06e9333"
      },
      "source": [
        "df.columns"
      ],
      "id": "MDbvBMKISR3r",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['cp', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'target'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2fpr2rX5dNO"
      },
      "source": [
        "scaler = preprocessing.MinMaxScaler()\n",
        "#fit on training only, the min/max here may be the min/max of the whole dataset\n",
        "scaler.fit(X_train)\n",
        "X_train_normalized = pd.DataFrame(scaler.transform(X_train), columns=df.columns[0:6])\n",
        "X_test_normalized =  pd.DataFrame(scaler.transform(X_test), columns=df.columns[0:6])\n"
      ],
      "id": "o2fpr2rX5dNO",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VyWqoYngPdiZ",
        "outputId": "06b99160-dad6-4676-cd66-a6fd76eb97fe"
      },
      "source": [
        "#TEST\n",
        "model = LogisticRegression(solver = 'liblinear')\n",
        "model.fit(X_train_normalized,y_train)\n",
        "predictions = model.predict(X_test_normalized)\n",
        "acc = accuracy_score(y_test, predictions)\n",
        "print('Accuracy of Logistic Regression = {}%'.format(np.round(acc*100,2)))"
      ],
      "id": "VyWqoYngPdiZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Logistic Regression = 77.05%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_mNPuH62jUp1"
      },
      "source": [
        "### 3) Compare Models Accuracy"
      ],
      "id": "_mNPuH62jUp1"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8FpPj8cNToVv"
      },
      "source": [
        "Adding new features and trying different models accuracy on 1000 iterations"
      ],
      "id": "8FpPj8cNToVv"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oi1ygSwBYJsu"
      },
      "source": [
        "knn_scores, dtree_scores, extratree_scores, rf_scores, log_scores= [], [], [], [], []\n",
        "for i in range(1000):\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = i, shuffle=True)\n",
        "  #Adding New features: it connects the propotional and inversely propotional indicators in one feature\n",
        "  X_train['heart_problems']=-X_train['exang']-(X_train['ca']/X_train['ca'].max())-2.5*(X_train['oldpeak']/X_train['oldpeak'].max())+(X_train['cp']/X_train['cp'].max())+(X_train['thalach']/X_train['thalach'].max())+(X_train['slope']/X_train['slope'].max())\n",
        "  X_test['heart_problems']=-X_test['exang']-(X_test['ca']/X_train['ca'].max())-2.5*(X_test['oldpeak']/X_train['oldpeak'].max())+(X_test['cp']/X_train['cp'].max())+(X_test['thalach']/X_train['thalach'].max())+(X_test['slope']/X_train['slope'].max())\n",
        "  # To avoid collinearity, we're deleting features that we've used in heart_problem new feature\n",
        "  collinear_cols=['exang', 'ca','cp', 'slope', 'thalach', 'oldpeak'] \n",
        "  X_train.drop(columns=collinear_cols,inplace=True)\n",
        "  X_test.drop(columns=collinear_cols,inplace=True)\n",
        "  ####################### MODELS - After Feature Creation #######################\n",
        "  # I - KNN\n",
        "  knn = KNeighborsClassifier()\n",
        "  knn.fit(X_train,y_train)\n",
        "  predictions = knn.predict(X_test)\n",
        "  acc = accuracy_score(y_test, predictions)\n",
        "  knn_score=np.round(acc*100,2)\n",
        "  knn_scores.append(knn_score)\n",
        "\n",
        "  # II - DecisionTreeClassifier\n",
        "  dtree = DecisionTreeClassifier()\n",
        "  dtree.fit(X_train,y_train)\n",
        "  predictions = dtree.predict(X_test)\n",
        "  acc = accuracy_score(y_test, predictions)\n",
        "  dtree_score=np.round(acc*100,2)\n",
        "  dtree_scores.append(dtree_score)\n",
        "\n",
        "  # III - ExtraTreeClassifier\n",
        "  extratree = ExtraTreeClassifier()\n",
        "  extratree.fit(X_train,y_train)\n",
        "  predictions = extratree.predict(X_test)\n",
        "  acc = accuracy_score(y_test, predictions)\n",
        "  extratree_score=np.round(acc*100,2)\n",
        "  extratree_scores.append(extratree_score)\n",
        "\n",
        "  # IV - RandomForestClassifier\n",
        "  rf = RandomForestClassifier()\n",
        "  rf.fit(X_train,y_train)\n",
        "  predictions = rf.predict(X_test)\n",
        "  acc = accuracy_score(y_test, predictions)\n",
        "  rf_score=np.round(acc*100,2)\n",
        "  rf_scores.append(rf_score)\n",
        "\n",
        "  # V - Logistic Regression\n",
        "  log = LogisticRegression(solver = 'liblinear')\n",
        "  log.fit(X_train,y_train)\n",
        "  predictions = log.predict(X_test)\n",
        "  acc = accuracy_score(y_test, predictions)\n",
        "  log_score=np.round(acc*100,2)\n",
        "  log_scores.append(log_score)"
      ],
      "id": "oi1ygSwBYJsu",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0_WkyOThj-c"
      },
      "source": [
        "#### Logistic Regression:"
      ],
      "id": "B0_WkyOThj-c"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "TULkQQKkh1bK",
        "outputId": "46ef86e2-dec6-4299-b3f0-4faf6dc693fb"
      },
      "source": [
        "sns.histplot(log_scores,bins=10, color='#8FB9AA', alpha=0.7,edgecolor='black', linewidth=1, kde=True, stat=\"density\" );"
      ],
      "id": "TULkQQKkh1bK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AgMwc4BHe6mP",
        "outputId": "fba3780a-5c5b-4b2b-8186-cafd61a41b6c"
      },
      "source": [
        "avg_log=sum(log_scores)/len(log_scores)\n",
        "print('Accuracy of Logistic Regression (One Feature) of 1000 iterations = {}%'.format(np.round(avg_log,2)))"
      ],
      "id": "AgMwc4BHe6mP",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Logistic Regression (One Feature) of 1000 iterations = 81.36%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4y7XZ1Ybg-1h"
      },
      "source": [
        "#### KNeighborsClassifier:"
      ],
      "id": "4y7XZ1Ybg-1h"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "avzC9CdkhEqL",
        "outputId": "c2de79ab-7824-42de-933a-884988ac934b"
      },
      "source": [
        "sns.histplot(knn_scores,bins=10, color='#CD7672', alpha=0.7,edgecolor='black', linewidth=1, kde=True, stat=\"density\" );"
      ],
      "id": "avzC9CdkhEqL",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tBUTksyKyTkTW9fb2TmdXTAEYa2+j76UXqH/nWYTnzXM7TlHwhsNUnXgSkY3rScWtMV8sDueQUS3wSeDTwAbg30gXjIcdyOUDzgY+mvn5ARG5YOpKqvoTVV2lqqvq6+0aw8VAVdm75h58JSU0Xfhet+MUlbpVp5GMRhl87VW3o5hZMt0+iD8ATwMlwPtVdbWq3qmqfwWUHWCzdqBl0uMFmWVZ18n0O1QC/aRbG0+pap+qjgH3kS5GpshFNm1gdNdOmt97Kb5w2O04RaVsyVEEqmvof3mt21HMLJluC+KnqrpMVf9JVTshPQIJQFVXHWCbtcBSEVksIgHgGmDNlHXWANdl7l8JPKbpYRIPAieKSEmmcJwHbJn2XpmClIxN0H7vnwg3z6f21NPcjlN0xOOhZuXJDG9/g/jIsNtxzCyYboH4dpZlzx9sg0yfwg2kv+y3Anep6mYR+aaIrM6sditQKyLbgRuBmzLbRoB/JV1kNgLrVfXeaWY1Bar3maeJ7xuiZfUV1jHtkurlK0CVwVc2uR3FzALfwZ4UkXmkO5LDIrIS2H8mUgXpw00Hpar3kT48NHnZ1yfdjwJXHWDbX5Ee6moMidFRup54nMrjl1G2eInbcYpWeF4TocZ5DGzaaBdjKgIHLRDAe0l3TC8g/Rf9fsPA3zuUyZi36Xr8UVKxCZovvtTtKEWvesVKOh+8n9hghEBVtdtxjIMO2k5X1dtV9Xzgk6p6/qTbalX9/SxlNEUuFonQ+/yz1Kw8hfA8u86D26pPWgFAZNNGl5MYpx3qENPHMod6FonIjVOfV9V/zbKZMTnV+chDoErTRTasdS4I1dVRsqCFyKaNNJ53vttxjIMO1dNXmvlZBpRnuRnjqGhPN/0vr6X+nWcRrK5xO47JqF6+krH2NqJ9doJqITtoC0JVf5z5+Y+zE8eYt+p89GE8Pj+N57/b7ShmkuqTltN+7xoiGzfS9J4L3Y5jHDLdE+X+WUQqRMQvIo+KSK+IfMzpcKa4RXt7iGzaSN2ZZ+IvswbrXBKoqqJs8RIimzbYDK8FbLqDyS9S1X3A+0jPxXQ08DdOhTIGoOvRR9Kth3Pf5XYUk0X18pVEe7qJdnW5HcU4ZLoFYv+hqMuA36rqkEN5jAEg2tvLwMb11L3TWg9zVdWJJ4LHw8CmDW5HMQ6ZboH4k4i8DpwCPCoi9UDUuVim2HU99gji81nrYQ7zl5VTftTRdpipgE13uu+bgDOBVaoaB0Z5+7UdjMmJaF8fAxtepv6MM/GXW+thLqtZsZLYwABje/e4HcU44HAmtDkOuFpEPkF6Yr2LnIlkil3P008gXi+N573L7SjmECpPOBHxeu2kuQI13VFMvwS+R/raDKdmbgeaxdWYGYsPD9O/bi21J6/CX17hdhxzCL5wmIpjjyPyykY0lXI7jsmxQ83FtN8qYJnagUbjsN7nnkGTSRqs7yFvVC9fydCWzYzs2kn5kqPcjmNyaLqHmF4D7NqOxlHJiQl6n3+WymXvIGRXCMwblcuW4fEH7DBTAZpugagDtmSuEb1m/83JYKb49K97ieT4OPNsfp+84g0EqTx+GYOvbkKTSbfjmBya7iGmbzgZwhhNJul5+klKFy2mtLXV7TjmMFWvWEHklY0Mv7mdimOOdTuOyZHpDnN9kvQZ1P7M/bXAegdzmSITeXUTsUjEZgfNUxXHHIcnGCJiJ80VlOmOYvoMcDfw48yi+cA9ToUyxUVV6X7ycUINjVQed7zbccwMePx+qk54B4ObXyOVSLgdx+TIdPsgvgicBewDUNU3gAanQpniMvzGnxnv6KDh3PPsWtN5rHr5CpLj4+z78za3o5gcme6ncUJVY/sfiIgPsCGvJie6n3wcf3kFNStPcTuKOQIVS4/BW1Jih5kKyHQLxJMi8vdAWEQuBH4L/JdzsUyxGGtvY3j7G9SffQ4e33THTJi5SLxeqt9xEkNbNpOKxQ69gZnzplsgbgJ6gVeBzwH3Af/gVChTPLqfegJPMEj96e90O4rJgeoVK0nFYgy9vtXtKCYHpvUnm6qmROQe4B5VtWsMmpyYGBgg8somGs4+F2847HYckwNli5fgKy9nYOMGqk9a7nYcc4QO2oKQtG+ISB+wDdiWuZrc12cnnilkPc88hYjQcNY5bkcxOSIeD9UnrWDftq0kxsfdjmOO0KEOMX2F9OilU1W1RlVrgNOBs0TkK46nMwUrMTpK/0svUr1iJYGqKrfjmByqOfkUNJFg8JVNbkcxR+hQBeLjwLWqunP/AlXdAXwM+ISTwUxh633+OVLxmF0QqACVzF9AsL6B/vXr3I5ijtChCoRfVfumLsz0Q/idiWQKXSoep/e5Z6g47njC85rcjmNyTESoPWUVo7t2MjHQ73YccwQOVSAONlbNxrGZGel/eS2J0RGbVqOA1aw4GYCB9TYjTz47VIFYLiL7styGgRNnI6ApLJpK0fPUE5S0LKRs8RK34xiHBKqrKTvqaAbWr7PrVeexgw5zVVXvbAUxxWHwtVeZ6O9n8SXvQ0TcjlPwfB6Pa//Onzz3bP7qjNMY27PHZujNU3bqqpk1+yflC9bWUXXCO9yOUxQSqRRv3HaLK6+94nNf4Etnn0n/+nVWIPKUzYxmZs3Izh2Mte21SfmKxGgsRtU7TiSyaQOpeNztOGYGHP2UisjFIrJNRLaLyE1Zng+KyJ2Z518UkUVTnl8oIiMi8lUnc5rZ0f3k4/hKy6g95VS3o5hZUrvqNJLj4wxuftXtKGYGHCsQIuIFbgYuAZYB14rIsimrXQ9EVPVo4PvAd6c8/6/A/U5lNLNnvKuTfa9vpf7Ms/H4bYR0sSg/6mgCNTX0vfSi21HMDDjZgjgN2K6qOzJThd8BXD5lncuB2zP37wYukEyPmohcAewENjuY0cyS7qeewOMPUP/OM92OYmaReDzUnno6I29uJ9r3tlOqzBznZIGYD+yd9LgtsyzrOqqaAIaAWhEpA/4O+MeDvYCIfFZE1onIut5em0NwrooNDjKwYT21p56Gr7TU7ThmltWuOhU8HvrXWisi38zVnsJvAN9X1ZGDraSqP1HVVaq6qr6+fnaSmcPW88xTADScc57LSYwbAhWVVB53PP3r1qLJpNtxzGFwskC0Ay2THi/ILMu6TuYqdZVAP+kJAf9ZRHYBXwb+XkRucDCrcUhifJy+l16g+qTlBGtq3I5jXFJ76ukkRoYZ2rrF7SjmMDhZINYCS0VksYgEgGuANVPWWQNcl7l/JfCYpp2jqotUdRHwA+B/qeq/O5jVOKTvxedJTUzYpHxFrvLY4/BXVNBnh5nyimMFItOncAPwILAVuEtVN4vIN0VkdWa1W0n3OWwHbiR95TpTIFKJBD3PPEX50Uspmb/A7TjGReL1Unvq6ezb9rpN4JdHHD2TWlXvI3150snLvj7pfhS46hC/4xuOhDOOG9jwMonhYRo/fK3bUcwcUHfaGXQ9/ii9zz/Hgsve73YcMw1ztZPa5DlNpeh+8gnCzc2ULz3G7TjGBfvngdp/C1ZX8/CWrWx/6AHCAf9bnsvlrbWl5dDhzLTYXEzGEUOvb2Git4dF13zUJuUrUtnmgUqMjTG2excvf/efCFRXO/K6Sz/1aUd+bzGyFoRxRPcTjxOorrYL15u38IbDeIIhYpEBmwY8D1iBKFKtLS2ONfFXLpjP6O5dfPu3v8Pj873t+VAg4NhrH+xm3CciBGpqSE1MkBwbczuOOQQ7xFSk9rS1OTYN9NjePSTHx/nWF7/At7PM2rr0U592ZQpqO/QwN/grKpjo6SYWGbAz6+c4a0GYnEpGoyRGRghU19iU3iYr8XjwV1WRGB4mFbMrF89l9gk2ORXr74fMYQRjDiRQXQMidk7EHGcFwuRMKhYjvm+IQHU14rWr1ZoD8/j9+CsqiQ8Okkok3I5jDsAKhMmZ2MAAAIGaWpeTmHwQqK0FVWKRAbejmAOwAmFyIpVIEBuM4K+ssgsCmWnxBoP4ysqJRyJoKuV2HJOFFQiTE7HIAKim/yo0ZpoCtbVoMklsMOJ2FJOFFQhzxDSVIjYwgK+sHG8w6HYck0d8JSV4w2Fi/Xbi3FxkBcIcsdjAAKRSBOqs9WAOX6C2Dk3EiQ8NuR3FTGEFwhyRdOuhH29pKb5widtxTB7ylZXhCYWY6Ou1VsQcYwXCHJFYZABNJgnaJV/NDIkIwbp6NG6tiLnGCoSZMU2liPVb68EcOWtFzE1WIMyM/aX1UGetB3NkrBUxN1mBMDPyltZDibUezJGzVsTcYwXCzEhsoN9aDyanrBUx91iBMIctlUgw0d+Pr6zcWg8mp6wVMbdYgTCHLdbfB6kUwYYGt6OYAmOtiLnFCoQ5LKlYjNjAAP7KKjtr2jjCWhFzhxUIc1gmentBxM57MI4REUL1DelWRMTmaHKTFQgzbcnx8fT1HmpqbMZW4yhvaSnekpJ0K8JmenWNFQgzLapKtLsL8XoJ1ta5HccUOBEh2NCQnunVrjrnGisQZlriQ0Mkx8cJNjTa1eLMrPCFS/CVlzPR329XnXOJFQhzSJpMMtHTjTccxl9Z6XYcU0SC9Q2QSqVHzplZZwXCHFK0twdNJgnNa0JE3I5jiog3GMRfWUUsEiEVj7sdp+hYgTAHlRwfJx6JEKiuwRsKuR3HFKH9I+YmentdTlJ8rECYA9JUivHODsTns2GtxjUev59AdTXxoUGSExNuxykqViDMAU309ZKamCDc1Gwd08ZVgdo68HiY6O1xO0pRsQJhskqMjRHr78dfVYWvrMztOKbIeXw+gjW1JIaHSY6Pux2naDhaIETkYhHZJiLbReSmLM8HReTOzPMvisiizPILReRlEXk18/PdTuY0b6WpFNHODsTvJ9TQ6HYcYwAI1NQgXi/Rnm6bgmOWOFYgRMQL3AxcAiwDrhWRZVNWux6IqOrRwPeB72aW9wHvV9UTgeuAXzqV07xdtKuLVCxmh5bMnCJeL4G6OpJjYyRGR9yOUxScbEGcBmxX1R2qGgPuAC6fss7lwO2Z+3cDF4iIqOoGVe3ILN8MhEXEZoabBbHBCPGhQQK1dfhKS92OY8xbBKpr8PgDTPT0WCtiFjhZIOYDeyc9bsssy7qOqiaAIaB2yjofAtar6tuGL4jIZ0VknYis67UhcEcsGY0S7erCW1pqo5bMnLR/Co7UxATxwUG34xS8Od1JLSInkD7s9Llsz6vqT1R1laquqrcvtCOiySRjbXsRr5dw83w7Ic7MWb7ycrzhsE3kNwucLBDtQMukxwsyy7KuIyI+oBLozzxeAPwB+ISqvulgzqKnqoy1t6HxOOH5C/D4fG5HMuaA0q2IRjSRINZvE/k5yckCsRZYKiKLRSQAXAOsmbLOGtKd0ABXAo+pqopIFXAvcJOqPutgxqKnqkQ7O0iOjhJqarZLiJq84CvZP5Ffn03k5yDHCkSmT+EG4EFgK3CXqm4WkW+KyOrMarcCtSKyHbgR2D8U9gbgaODrIrIxc7PrWzog1tdHfGiIQF0dgaoqt+MYM22hhkZQtZPnHOTosQRVvQ+4b8qyr0+6HwWuyrLdt4FvO5nNpEcsTfT14q+sJFhnfTgmv3gCAQLVNcQiAwRqau0SuA6Y053UxjmXLjueaGcn3tJSQk3N1ilt8lKgLj0FR7S7y4a9OsAKRBEa2LiBb1z6XrwlJZQsaLHiYPKWJzORZHJ0lMSInTyXa1Ygikxk00Z23flrNrZ3UNKyEPHYfwGT3wLVNXiCwXQrwoa95pR9OxSR3heeZ+dvfkXpwla+/Ls/WHEwBUFECDU2ovG4Xb86x+wbogioKl2PPcLeP9xNxbHHsfT6zzIWs6tzmcLhKy1LD3vt66PBZh/OGTsjqsBpKkX7vf9FzzNPUbPyFFqvutom4DMFKdTQyMjICF+94Hy3oxQMa0EUsFQiwe7f3knPM09Rf9Y5tH74GisOpmB5AgGCdfVccOxSBje/6nacgmAFokAlxsbY/rOfMrB+HU0XXcyC919ufQ6m4AVqa9nW3cPee35vFxbKAfvGKEDRvj62/eiHjO7aSevV19J0wYU2lNUUBRHhWw8+THx4mPYH7nU7Tt6zAlFghne8ybab/43E6ChLP/N5ak9e5XYkY2bV1q5uGs4+l7J50p4AAAvaSURBVL4Xnmdk5w634+Q1KxAFpP/ltWy/5cf4Sss47oa/pmzxErcjGeOKpoveS6Cmll13/pqEHWqaMSsQBUCTSfauuYfdd91B2eIlHPuFvyJYW+d2LGNc4w0EWXztR4kNDbH393fbNBwzZMNc81x8ZISd//kLRna8ScPZ5zL/0vfZSCVjgNKFrTRfdDEdD9xH+THHUHfq6W5HyjvWgnBZa0t6LqSZ3I6f18hDX/0Kfdu28Q9/up+W1Vfg8fmmta0xxaDxvPMpP3opbX+8h2hPt9tx8o61IFy2p62NN2675bC2UVXiQ4NEu7oQr5eSBS388KSTDut3LP3Upw9rfWPykXg8tF59La//4F948xe3cewXvmQXxToM1oLIM5pMMt7Rnp6qO1xC6eIleMNht2MZM2cFKipZ8vFPEhsYYMcvbrMr0B0GKxB5JDk+zujOnST27SNYX0/JwoV2/WhjpqFs8RJaP3wNIzt3sOfuu6zTeprs2yUPqCqxyAATPT3pQ0qtrfhKSt2OZUxeqVlxMhP9/XQ+9AD+qiqa33uJ9ccdghWIOS4VizHe2UFybAxfWRmhpmZrNRgzQ/Pe/R7ig4N0P/4oIkLTRRdbkTgI+6aZo1SVeCSSHnkhQmheE/6qKvvPbMwREBFaPvAhVFN0PfYIiZERWq74oA0NPwArEHNQcnyc8a5OUtEo3tJSwk3NePx+t2MZUxDE42HhB6/CV1ZO9+OPMtHfx+KPXWejm7KwTuo5JJVIMN7ZweiunWgiQbh5PiUtC604GJNj4vEw/+JLaf3wtYzs2sm2m3/IWEe727HmHCsQc4Amk0R7ehjZ/gbxwUECNTWULTkKf2WlHVIyxkG1p6xi6Wc+T3Iiyuv/5wd0PPygDYOdxA4xuSgxNsZnzjyDkTe3o8kkvooKQvUNeAIBt6MZUzTKFi9h2Y1/S9uae+h65CGGNr/GgtVXUL7kKLejuc4KhAsmBgbofe4Z+l58gc+ffSbecJhgXb2d8GZMDvg8nhm3vM89egk3XXgB4z/+Ec/t2MXNTz/D690909p24YIF7N67d0avO1dZgZglmkqx740/0/f8swy9vhWA6uUruOhLX+H+//0dl9MZUzgSqdRhT18zmaZSxCIRzvJ6OXPJIrylpQSqq/GVlR+08BTi9DVWIBykqox3tDOwYT2RjRuID+/DV1bGvPMvoO70MwhUVbP9Ix93O6YxZhLxeAjW1hKoqiIWGSAWiTDe1ob4/PgrK/FXVOANhdyOOSusQOSYqjLe2cHQli1ENm0g2tONeL1UHHscNStPoXLZCXaimzF5QLxegnX1BGrrSAwPExuMEOvvI9bfhycYxFdWhq+sDG+4pGAHk9g3VQ4kYxOM7NjB0NYtDG3dQnxoEEQobW2l5QMfovrE5fhKbWoMY/KRiOCvqMBfUUEqkSCxbx/x4X3E+vuJ9feDx4OvpJQrV5zEeGcnocZGxFMYA0StQMxAKh5ndM9uht/czvCb2xnbuwdNJvEEApQvPZbKCy+i8rhl+MvL3Y5qjMkhj89HoKaGQE0NmkySGB1N30ZG+NpF72HrD76HJxiidOFCyhYtpqRlISXNzfjLK9yOPiNWIA5BUykm+vsZ3bubsb17GN27l/GOdjSZBBFK5i+g4exzKT/qaMqWHGUntRlTJMTr/UvLQlV5941/w8ZHH2Z09y5Gdu2i85GHIDNrrK+snJLmZsJNzQTr6wnV1ROsq8dXVjanD09ZgchobWlhsK+PJbU1LK6rZUltLUvqalk2r5GKTIfUWCzGlq5uNnd2saGtnQ172xiJxVxOboxxm4jQNjhI7cmrqD15FZCeMmess4Pxzg7G2tsZ72xn+Jmn0n9cZniCIUJ1dQTr6ghUVeOvrCRQWZXuDK+sxF9W7urhKkcLhIhcDPwb4AVuUdXvTHk+CPwCOAXoB65W1V2Z574GXA8kgS+p6oNOZBzv6mTvmnv46erLqCub1E8ggicYxBsK4Q2H8YbClAeDzFsuvDuHr1+IQ+OMMeANhylfctRbTrjTZJLYYIRoby8T/X1M9PUS7e1ldO9eBl979S3FA0j3b5SWZm5lb7nvLy3FW1qKv7SMQHUNwdranO+DYwVCRLzAzcCFQBuwVkTWqOqWSatdD0RU9WgRuQb4LnC1iCwDrgFOAJqBR0TkGFWd8q935DyBABqP88yOnVz9ngvwBAN4g0HE55/TTT9jzNxyJCfoAQhQVRKmsbychvIyGsrKaCgvp6akhOqSMFXhMFUlYarDJVSG3zrM9pnde/jSHb89wj14OydbEKcB21V1B4CI3AFcDkwuEJcD38jcvxv4d0n/C18O3KGqE8BOEdme+X3P5zpksKaWY7/4Jb51w1/zias/nOtfb4wpEkd6gt7hUFU0mUQTCTSZ5Ec//yVfcuB1xKlL74nIlcDFqvrpzOOPA6er6g2T1nkts05b5vGbwOmki8YLqvqrzPJbgftV9e4pr/FZ4LOZh8cC2w4Rqw7oO8Jdm6ts3/JPoe4XFO6+FeJ+tapqfbYn8rqTWlV/AvxkuuuLyDpVXeVgJNfYvuWfQt0vKNx9K9T9OhAnu8fbgZZJjxdklmVdR0R8QCXpzurpbGuMMcZBThaItcBSEVksIgHSnc5rpqyzBrguc/9K4DFNH/NaA1wjIkERWQwsBV5yMKsxxpgpHDvEpKoJEbkBeJD0MNefqepmEfkmsE5V1wC3Ar/MdEIPkC4iZNa7i3SHdgL4Yo5GME37cFQesn3LP4W6X1C4+1ao+5WVY53Uxhhj8lthzChljDEm56xAGGOMyaqgC4SIVInI3SLyuohsFZF3isg3RKRdRDZmbpe6nfNwiMixk7JvFJF9IvJlEakRkYdF5I3Mz2q3sx6ug+xbXr9n+4nIV0Rks4i8JiK/EZFQZhDHiyKyXUTuzAzoyCsH2K+fi8jOSe/ZCrdzzoSI/HVmvzaLyJczy/L+szZdBd0HISK3A0+r6i2ZD14J8GVgRFW/5266I5eZzqSd9MmFXwQGVPU7InITUK2qf+dqwCMwZd8+RZ6/ZyIyH3gGWKaq45lBGPcBlwK/V9U7ROT/AptU9T/czHo4DrJf7wL+NPXk1nwiIu8A7iA9i0MMeAD4POmTcwvms3YwBduCEJFK4FzSI6VQ1ZiqDrqbKucuAN5U1d2kpye5PbP8duAK11LlxuR9KxQ+IJw556cE6ATeTXqaGcjf923qfnW4nCdXjgdeVNUxVU0ATwIfpPA+awdUsAUCWAz0AreJyAYRuUVE9k/XeoOIvCIiP8vz5uE1wG8y9xtVtTNzvwtodCdSzkzeN8jz90xV24HvAXtIF4Yh4GVgMPPlA+lJLee7k3Bmsu2Xqj6Uefp/Zt6z72dmbs43rwHniEitiJSQbu21UHiftQMq5ALhA04G/kNVVwKjwE3AfwBHAStI/4f+F9cSHoHMIbPVwNumcMycbJi3xw6z7Fvev2eZonY56T9cmoFS4GJXQ+VAtv0SkY8BXwOOA04FaoC8OwSjqltJzzD9EOnDSxtJX35g8jp5/Vk7lEIuEG1Am6q+mHl8N3CyqnaralJVU8BPSR9fzEeXAOtVtTvzuFtEmgAyP3tcS3bk3rJvBfKevQfYqaq9qhoHfg+cBVRlDs1Afk4pk22/zlTVTk2bAG4jP98zVPVWVT1FVc8FIsCfKazP2kEVbIFQ1S5gr4gcm1l0AbBl/xub8QHSzch8dC1vPQQzedqS64A/znqi3HnLvhXIe7YHOENESkREyPx/BB4nPc0M5Of7lm2/tk76AhXSx+jz8T1DRBoyPxeS7n/4NYX1WTuoQh/FtAK4BQgAO0iPhvkh6UMVCuwCPjfpeGJeyPSl7AGWqOpQZlktcBewENgNfFhVB9xLOTMH2LdfkufvGYCI/CNwNenpYzYAnybd53AH6cMwG4CPZf7qzhsH2K/7gXrS18HZCHxeVUdcCzlDIvI0UAvEgRtV9dFC+axNR0EXCGOMMTNXsIeYjDHGHBkrEMYYY7KyAmGMMSYrKxDGGGOysgJhjDEmKysQxhhjsrICYYwxJqv/B1kDRyz8WhJsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NW_TWIqterZJ",
        "outputId": "67fb1274-3358-4eef-ff86-62d42dd0b4a7"
      },
      "source": [
        "knn_avg= sum(knn_scores)/len(knn_scores)\n",
        "print('Accuracy of KNeighborsClassifier (One Feature) of 1000 iterations = {}%'.format(np.round(knn_avg,2)))"
      ],
      "id": "NW_TWIqterZJ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of KNeighborsClassifier (One Feature) of 1000 iterations = 79.5%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2qccX3PZSQL7"
      },
      "source": [
        "#### Decision Tree Classifier:"
      ],
      "id": "2qccX3PZSQL7"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "CVHrOxijSQL7",
        "outputId": "b0d1e18e-6f3d-4243-f1b3-dc11d1eb90ff"
      },
      "source": [
        "sns.histplot(dtree_scores,bins=10, color='#CDB272', alpha=0.7,edgecolor='black', linewidth=1, kde=True, stat=\"density\" );"
      ],
      "id": "CVHrOxijSQL7",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1TW52ppSQL7",
        "outputId": "301b4541-ba0f-4028-8a7c-f7aaf079000c"
      },
      "source": [
        "avg_dtree=sum(dtree_scores)/len(dtree_scores)\n",
        "print('Accuracy of Decision Tree Classifier (One Feature) of 1000 iterations = {}%'.format(np.round(avg_dtree,2)))"
      ],
      "id": "e1TW52ppSQL7",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Decision Tree Classifier (One Feature) of 1000 iterations = 72.48%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ud73oaMxS1Pa"
      },
      "source": [
        "#### Extra Tree Classifier:"
      ],
      "id": "Ud73oaMxS1Pa"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "6aVR9_bXS1Pb",
        "outputId": "170403cd-5440-4287-ff2a-0e1f3c077a3f"
      },
      "source": [
        "sns.histplot(extratree_scores,bins=10, color='#8FB9AA', alpha=0.7,edgecolor='black', linewidth=1, kde=True, stat=\"density\" );"
      ],
      "id": "6aVR9_bXS1Pb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zbahPiisS1Pb",
        "outputId": "aeb4bc8c-715e-4382-ea8e-1e57eed2984b"
      },
      "source": [
        "avg_extratree=sum(extratree_scores)/len(extratree_scores)\n",
        "print('Accuracy of Extra Tree Classifier (One Feature) of 1000 iterations = {}%'.format(np.round(avg_extratree,2)))"
      ],
      "id": "zbahPiisS1Pb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Extra Tree Classifier (One Feature) of 1000 iterations = 72.39%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MLRwTeHUTJiI"
      },
      "source": [
        "#### Random Forest Classifier:"
      ],
      "id": "MLRwTeHUTJiI"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "HN_5fPaxTJiJ",
        "outputId": "b9daa76f-d3a6-4fea-f7f5-47751406a105"
      },
      "source": [
        "sns.histplot(rf_scores,bins=10, color='#8FB9AA', alpha=0.7,edgecolor='black', linewidth=1, kde=True, stat=\"density\" );"
      ],
      "id": "HN_5fPaxTJiJ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BxHlDyHRTJiJ",
        "outputId": "eecfc3ad-c682-4f79-bd4d-fb3a07b688fc"
      },
      "source": [
        "avg_rf=sum(rf_scores)/len(rf_scores)\n",
        "print('Accuracy of Random Forest Classifier (One Feature) of 1000 iterations = {}%'.format(np.round(avg_rf,2)))"
      ],
      "id": "BxHlDyHRTJiJ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Random Forest Classifier (One Feature) of 1000 iterations = 72.56%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PPXevn_tZAYN"
      },
      "source": [
        "## Findings and Observations"
      ],
      "id": "PPXevn_tZAYN"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9jGdcjw369RS"
      },
      "source": [
        "#### 1) Research Questions:"
      ],
      "id": "9jGdcjw369RS"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uIVgwfcoZJzP"
      },
      "source": [
        "- Q1: Does heart attack only attack the old people?\n",
        " - there's no obvious relation between getting old and being susceptible to have heart attack\n",
        "- Q3: Does heart attack get males more than females?\n",
        " -  No, according to this dataset ,which is too small, Females are susceptible to get a heart attack than males \n",
        "- Does having these syndromes (Exercise induced angina - Maximum heart rate achieved - Chest pain - Number of major vessels) means that the person is suffering from heart attack?\n",
        " - Yes, it seems that it's a strong inficator of getting heart attack by the following relation: \n",
        " -- The less (excercise exerted - number of major vessels) than normal person, the person is more susceptible to have a heart attack [Inversely Propotional]\n",
        " -- The more (Maximum heart rate achieved - Chest pain) than normal person, the person is more susceptible to have a heart attack [Propotional]"
      ],
      "id": "uIVgwfcoZJzP"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hlmySjtp7HTx"
      },
      "source": [
        "#### 2) Findings:"
      ],
      "id": "hlmySjtp7HTx"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4lH__GGAJEPo"
      },
      "source": [
        "- Data hadn't any missing values, and there were one duplicate sample which is re,moved in EDA part"
      ],
      "id": "4lH__GGAJEPo"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L2FNG2njFODs"
      },
      "source": [
        "**Using only one features in the training data, the accuracy of models on test data (selected randomly) in 1000 iterations are:**\n",
        "- **Logistic Regression:** 81.36%\n",
        "- **KNeighborsClassifier:** 79.5% \n",
        "- **Decision Tree Classifier:** 72.48%\n",
        "- **Extra Tree Classifier:** 72.39%\n",
        "- **Random Forest Classifier:** 72.56%"
      ],
      "id": "L2FNG2njFODs"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v-NXh_tWZDH1"
      },
      "source": [
        "## Dataset Limitations"
      ],
      "id": "v-NXh_tWZDH1"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CqSCVGJ-ZKP7"
      },
      "source": [
        "- Dataset is too small, accordingly any visual might not represent (or similar to) the population of people \n",
        "- Bias in gender, where men samples are almost twice of women samples\n",
        "- There are another indicators that affect directly or indirectly on getting heart attack, so the major 6 indicators that affect in the data is not efficient to have a good accuracy in predicting."
      ],
      "id": "CqSCVGJ-ZKP7"
    }
  ]
}