HeartDiseaseAnalysis-and-Prediction.ipynb

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      "metadata": {
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      },
      "source": [
        "# **Final Project of Machine Learning 2021/2022**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oURxWW5IiCNR"
      },
      "source": [
        "## Presentation\n",
        "\n",
        "Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease.\n",
        "\n",
        "This dataset contains data from patients hospitalized for presumed cardiovascular diseases. Inside, we can find both healthy patients and those with heart disease. Obviously, all the data relating to their state of health are present, as we will see later. We have chosen it because in the medical world prevention is never too much and through research it is possible to prevent and treat many diseases that could otherwise be fatal.\n",
        "\n",
        "People with cardiovascular disease or who are at high cardiovascular risk need early detection and management wherein a machine learning model can be of great help.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ewxyqvTNiDkc"
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      "source": [
        "## Dataset explaination\n",
        "\n",
        "* Age: age of the patient [years]\n",
        "* Sex: sex of the patient [M: Male, F: Female]\n",
        "* ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]\n",
        "* RestingBP: resting blood pressure [mm Hg]\n",
        "* Cholesterol: serum cholesterol [mm/dl]\n",
        "* FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]\n",
        "* RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]\n",
        "* MaxHR: maximum heart rate achieved [Numeric value between 60 and 202]\n",
        "* ExerciseAngina: exercise-induced angina [Y: Yes, N: No]\n",
        "* Oldpeak: oldpeak = ST [Numeric value measured in depression]\n",
        "* ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]\n",
        "* HeartDisease: output class [1: heart disease, 0: Normal]\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFy0JfsojAXI"
      },
      "source": [
        "## Work written by\n",
        "\n",
        "*   Christian Stingone (https://github.com/ChriStingo)\n",
        "*   Alex Di Stefano (https://github.com/xDiste)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fACve4PnDUwR"
      },
      "source": [
        "# **Import**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fhut9B_VUA8R"
      },
      "source": [
        "Import useful libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8ul-mHO2_c-X"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import missingno    # cool print for NaN values into dataset\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.metrics import plot_confusion_matrix, classification_report\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "import xgboost\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import *\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e_neB0MuDbpo"
      },
      "source": [
        "# **Read and Correct the Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6dNTNXL2Ui3N"
      },
      "source": [
        "## Read the CSV and display it"
      ]
    },
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
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              "      <th>ExerciseAngina</th>\n",
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              "      <th>ST_Slope</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>172</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>F</td>\n",
              "      <td>NAP</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>156</td>\n",
              "      <td>N</td>\n",
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              "      <td>ATA</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>ST</td>\n",
              "      <td>98</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>F</td>\n",
              "      <td>ASY</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>108</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>M</td>\n",
              "      <td>NAP</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>122</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
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              "      <th>913</th>\n",
              "      <td>45</td>\n",
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              "      <td>TA</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>132</td>\n",
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              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "      <td>141</td>\n",
              "      <td>N</td>\n",
              "      <td>3.4</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>915</th>\n",
              "      <td>57</td>\n",
              "      <td>M</td>\n",
              "      <td>ASY</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>115</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.2</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>916</th>\n",
              "      <td>57</td>\n",
              "      <td>F</td>\n",
              "      <td>ATA</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>LVH</td>\n",
              "      <td>174</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>917</th>\n",
              "      <td>38</td>\n",
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              "      <td>175</td>\n",
              "      <td>0</td>\n",
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              "      <td>173</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
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              "<p>918 rows × 12 columns</p>\n",
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            "text/plain": [
              "     Age Sex ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "0     40   M           ATA  ...      0.0        Up             0\n",
              "1     49   F           NAP  ...      1.0      Flat             1\n",
              "2     37   M           ATA  ...      0.0        Up             0\n",
              "3     48   F           ASY  ...      1.5      Flat             1\n",
              "4     54   M           NAP  ...      0.0        Up             0\n",
              "..   ...  ..           ...  ...      ...       ...           ...\n",
              "913   45   M            TA  ...      1.2      Flat             1\n",
              "914   68   M           ASY  ...      3.4      Flat             1\n",
              "915   57   M           ASY  ...      1.2      Flat             1\n",
              "916   57   F           ATA  ...      0.0      Flat             1\n",
              "917   38   M           NAP  ...      0.0        Up             0\n",
              "\n",
              "[918 rows x 12 columns]"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "df = pd.read_csv('heart.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4u4az6MI-Et"
      },
      "source": [
        "## Dealing to missing and bad values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "459p9W1RSrCL"
      },
      "source": [
        "### Summary of the dataset (**before**)\n",
        "\n",
        "State of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XGUN-mESJLQB",
        "outputId": "09944b57-518a-4974-ef3a-b96d6b9a7567"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "##### Type of columns #####\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 918 entries, 0 to 917\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Age             918 non-null    int64  \n",
            " 1   Sex             918 non-null    object \n",
            " 2   ChestPainType   918 non-null    object \n",
            " 3   RestingBP       918 non-null    int64  \n",
            " 4   Cholesterol     918 non-null    int64  \n",
            " 5   FastingBS       918 non-null    int64  \n",
            " 6   RestingECG      918 non-null    object \n",
            " 7   MaxHR           918 non-null    int64  \n",
            " 8   ExerciseAngina  918 non-null    object \n",
            " 9   Oldpeak         918 non-null    float64\n",
            " 10  ST_Slope        918 non-null    object \n",
            " 11  HeartDisease    918 non-null    int64  \n",
            "dtypes: float64(1), int64(6), object(5)\n",
            "memory usage: 86.2+ KB\n",
            "None\n",
            "\n",
            "##### Nan Value #####\n",
            "Age               0\n",
            "Sex               0\n",
            "ChestPainType     0\n",
            "RestingBP         0\n",
            "Cholesterol       0\n",
            "FastingBS         0\n",
            "RestingECG        0\n",
            "MaxHR             0\n",
            "ExerciseAngina    0\n",
            "Oldpeak           0\n",
            "ST_Slope          0\n",
            "HeartDisease      0\n",
            "dtype: int64\n",
            "\n",
            "##### Chart of missing values #####\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f4173c450>"
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          "metadata": {},
          "execution_count": 4
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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "print(\"##### Type of columns #####\")\n",
        "print(df.info(), end=\"\\n\\n\")\n",
        "\n",
        "print(\"##### Nan Value #####\")\n",
        "print(df.isnull().sum(), end=\"\\n\\n\")\n",
        "\n",
        "print(\"##### Chart of missing values #####\")\n",
        "missingno.matrix(df, figsize=(15, 5))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xx9f2dTtJIIb"
      },
      "source": [
        "### Remove duplicates\n",
        "\n",
        "in this dataset we have a duplicate row. In order to get more accurate predictions we remove this clone"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dgo7PIsGJGbz"
      },
      "outputs": [],
      "source": [
        "df.drop_duplicates(keep='first',inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_99ilDwwD1R"
      },
      "source": [
        "### Check data values\n",
        "\n",
        "Check if there are some errors inside our data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lp5J2ASJwMuD",
        "outputId": "2abdab3c-c8ae-4745-8ddd-0a8e4a748ede"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Age :  [28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77]\n",
            "\n",
            "Sex :  ['F', 'M']\n",
            "\n",
            "ChestPainType :  ['ASY', 'ATA', 'NAP', 'TA']\n",
            "\n",
            "RestingBP :  [0, 80, 92, 94, 95, 96, 98, 100, 101, 102, 104, 105, 106, 108, 110, 112, 113, 114, 115, 116, 117, 118, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 148, 150, 152, 154, 155, 156, 158, 160, 164, 165, 170, 172, 174, 178, 180, 185, 190, 192, 200]\n",
            "\n",
            "Cholesterol :  [0, 85, 100, 110, 113, 117, 123, 126, 129, 131, 132, 139, 141, 142, 147, 149, 152, 153, 156, 157, 159, 160, 161, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 190, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 297, 298, 299, 300, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 315, 316, 318, 319, 320, 321, 322, 325, 326, 327, 328, 329, 330, 331, 333, 335, 336, 337, 338, 339, 340, 341, 342, 344, 347, 349, 353, 354, 355, 358, 360, 365, 369, 384, 385, 388, 392, 393, 394, 404, 407, 409, 412, 417, 458, 466, 468, 491, 518, 529, 564, 603]\n",
            "\n",
            "FastingBS :  [0, 1]\n",
            "\n",
            "RestingECG :  ['LVH', 'Normal', 'ST']\n",
            "\n",
            "MaxHR :  [60, 63, 67, 69, 70, 71, 72, 73, 77, 78, 80, 82, 83, 84, 86, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 184, 185, 186, 187, 188, 190, 192, 194, 195, 202]\n",
            "\n",
            "ExerciseAngina :  ['N', 'Y']\n",
            "\n",
            "Oldpeak :  [-2.6, -2.0, -1.5, -1.1, -1.0, -0.9, -0.8, -0.7, -0.5, -0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.8, 2.9, 3.0, 3.1, 3.2, 3.4, 3.5, 3.6, 3.7, 3.8, 4.0, 4.2, 4.4, 5.0, 5.6, 6.2]\n",
            "\n",
            "ST_Slope :  ['Down', 'Flat', 'Up']\n",
            "\n",
            "HeartDisease :  [0, 1]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for column in df:\n",
        "    print(column, \": \", sorted(df[column].unique()), end=\"\\n\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uIyhPmPqw-P7"
      },
      "source": [
        "As we can see from this print we have wrong values, such as cholesterol and restingBP. These columns have data with a value of 0, this is not possible. To make a correction we will replace these 0s with the average value of the entire column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RE6Ram1hxTBN",
        "outputId": "b2c3acd7-936e-4476-b3a5-cfa0f5097687"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RestpingBP:  [80, 92, 94, 95, 96, 98, 100, 101, 102, 104, 105, 106, 108, 110, 112, 113, 114, 115, 116, 117, 118, 120, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 148, 150, 152, 154, 155, 156, 158, 160, 164, 165, 170, 172, 174, 178, 180, 185, 190, 192, 200]\n",
            "\n",
            "Cholesterol:  [85, 100, 110, 113, 117, 123, 126, 129, 131, 132, 139, 141, 142, 147, 149, 152, 153, 156, 157, 159, 160, 161, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 190, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 297, 298, 299, 300, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 315, 316, 318, 319, 320, 321, 322, 325, 326, 327, 328, 329, 330, 331, 333, 335, 336, 337, 338, 339, 340, 341, 342, 344, 347, 349, 353, 354, 355, 358, 360, 365, 369, 384, 385, 388, 392, 393, 394, 404, 407, 409, 412, 417, 458, 466, 468, 491, 518, 529, 564, 603]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df['RestingBP'][df['RestingBP'] == 0] = int(df['RestingBP'].mean())\n",
        "df['Cholesterol'][df['Cholesterol'] == 0] = int(df['Cholesterol'].mean())\n",
        "\n",
        "print(\"RestpingBP: \", sorted(df['RestingBP'].unique()), end=\"\\n\\n\")\n",
        "print(\"Cholesterol: \", sorted(df['Cholesterol'].unique()), end=\"\\n\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nP21VeQnS4JQ"
      },
      "source": [
        "### Summary of the dataset (**after**)\n",
        "\n",
        "State of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8soeuGNISR2c",
        "outputId": "aa7e64d8-73cf-4315-ef51-16fa6ba77f3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "After:\n",
            "##### Type of columns #####\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 918 entries, 0 to 917\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Age             918 non-null    int64  \n",
            " 1   Sex             918 non-null    object \n",
            " 2   ChestPainType   918 non-null    object \n",
            " 3   RestingBP       918 non-null    int64  \n",
            " 4   Cholesterol     918 non-null    int64  \n",
            " 5   FastingBS       918 non-null    int64  \n",
            " 6   RestingECG      918 non-null    object \n",
            " 7   MaxHR           918 non-null    int64  \n",
            " 8   ExerciseAngina  918 non-null    object \n",
            " 9   Oldpeak         918 non-null    float64\n",
            " 10  ST_Slope        918 non-null    object \n",
            " 11  HeartDisease    918 non-null    int64  \n",
            "dtypes: float64(1), int64(6), object(5)\n",
            "memory usage: 93.2+ KB\n",
            "None\n",
            "\n",
            "##### Nan Value #####\n",
            "Age               0\n",
            "Sex               0\n",
            "ChestPainType     0\n",
            "RestingBP         0\n",
            "Cholesterol       0\n",
            "FastingBS         0\n",
            "RestingECG        0\n",
            "MaxHR             0\n",
            "ExerciseAngina    0\n",
            "Oldpeak           0\n",
            "ST_Slope          0\n",
            "HeartDisease      0\n",
            "dtype: int64\n",
            "\n",
            "##### Chart of missing values #####\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2f3899ea50>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7q5GtuNMjJ9vJyMioi5biWxJB0uO4iJmcF9jN3W9Lrm0ALkfMxSB3/6A9xt4aRGKhG4GnUcKg5YH5gEvcfY8yx9YeqJ13M/sr8DP0HL5I5n0h4J/AzMAa7v5hWKf6uPvXJQx9smHKQDkIeDeY5k4DM1sexUBfiKxIL9e55iikJHgUWZxuBXoD6wF7An2A5dx9TI6PrS7MbBvgHMQ0H+/up8Q33EPQHIQybY8DznL3c5J7F0AZus9091dKGH5GxkQhiQGv7R/g7p9H7P+XwF4eNbDN7EFUymw6YCF3H96ug56KkS2SGRkZEyBiHRvDvXMdM1vRzOaGZgJkrWVyGHBrWDcKNCIXqfUrIkSuBNyF6uZtH1kHV0MZa39pZhuVOb4pjVSINLMtwo2nFzCtu38e7r1FPdBXkAZ3PlRHC3dvrKAQ+RPkmnsisGzJw2lXRNKcU4ArgSMLIbLWhdvdj0MC4+zo3XgNeAc4GClblg0hsiELkdVC6mHh7lfRVCP2UDNbLuKgPRQEzyPPhNmAldPfcPdhwAFZiMyYmhHruOBhupvZNIVbdwiRfdGe1jMRIhcHxqLwjgWzENkcOWtrRkZGMwRTMNbM+iGhak5gBpRI5WR3vz4ECis0e+4+JGIJX0TuoMB4F7jfl0LIJMLMFkTuffe7+wHR1+Dun5nZH1AG2oFljnFKIuayECJvQIXHX0K1Q3c1s7+4++GxNgqr0zfAxyQFyqsEM/spmvPrgFvd/eaSh9TemBuYCTjO3b8sOmss0sU7fp6Z3QPMDyyE5v4x4MVQOjV4U03BjKkUwRQvgmoBD0uZancf7e5nm1kjcDhwqpnt6+7PmFmXeO//F8qXt+O+8dadrETImJphzZPHnQwsASyGss3fELzNN2Y2DFjTzHYEPkChHNMBb2Z31gmRBcmMjIzxKD60oaG7EaW/3gMlT9gNON3Merv7pXWEyZuAm9LfKY2QycOcyNKymJltHvQUlpmiDEKHLFdSY4mcC2VdPNjdXzKzD4AHgB3M7Ft3/3OskYEoq+P7QOU0tGY2P3K7vhT4Y6Flruf21JIrVAfASohBeqSlCxJBoxdipF4DhqTXxPrJQuRUDjObBxgahy+b2TnAbe7+uruPLr7b7v5PM+uGagKfXiNMdnH3N+L3qvidz+ikSITIa1HN62uAF4ClgWvN7CB3PwUlGLwf+BfwNfAVsIkrW3VGDbIgmZGRAYxnlscFw9gTeBm43N0fifMvowD0v5sZiTDZJY7HM9pVZC7c/S4zOwSVOTnPzLq5+7Vx+jjgFXe/srQBTkEkQuRpwDQo8cCzce5LU4Hmy4BDTKUgXkNujkug9Odf1v3hqRCJULgq8ClwhrsPT/p7h8vnQsA37v5ARxMiE1q/B3qgeMcvWxCiCyXDmkg7/4zVxNF6ztBbFXyMQhAWiP//DuxvZrcg1+4vgO8A3P2McHvdH1kmD3X3x9Ifq+J3vrOhAyvBJgumRFErANsAT7n797GnrQoMMrMe0bciSjD1DfC8d+I6kT+GHCOZkdGJYWZzBdNclLfoimoFDkep3d8vrnX3+1As5DPAyaYsfUVcXIfYqNz9bkTjy8CZZrZBaC8XBraCpjT5HQ1mthiwFrAZSsT2ZSgJurv7e4j+P6ONdT4kTK7o7kNb/NGpEMlaXRK5bL9T9IeV8kJUQ+824D4zO7+UgU4BmNliZrZ48gxeQQrl3UNxkmZbBsZnKu4F/APFx2XBsYII6+FIlGH3e+TSvRoqvP5r4DlUK3JpU2w07n46cBpKtLVzGePOmHykQqSZDTKz1cxsKzObpaPuYxOBhdCafzYExuKbfxmwj7uPim9ko7tf4+63ZyHyh5EFyYyMTojwSF0AJcJZPj2F4sWeRLFTM8f1BWPxIE3C5KVmtl57jrstYWbzmdnOZnaEma1W9Lv7fxCNr6AkLGsDg939tcJqW9KQpyjc/QWUPOU5YC0z2zY209EhZHzq7ie5+5rAT4FfeZ0Mn1M7EkHpC7TGNzWz5UNT/RiwMSpjsBNwBYoP/VMZY21LmNmqyIV1xeQZPITimncGVi0Yz1phErmBfRLXZ1QQyXfrURSyMNjdH0KKo3WR4mRb5MZ+upkVSoNCgbBPuw86o1VIhMidkeLgWuBq4F7g6FAQdVjU+Y6Bvvn93f3bCON4HOWC2MvdR4aCfE8zG9CeY6003D233HLrpA3YLv52A2aI/7ujGIG3kBvUdEV/ct9aKNtj17JpmEy6V0JC9DAU/9AI7FxzzbrAf5F1drOk38oefxvQ3+K8Bd2PACOAnyX9XToC7Qk9Dcjy2IgY60aUnXeH5Jrp45pHgH5lj7kVtK4CjELWpV7pGkDF5j9FDNV66dqIOZ8f1YL9d1Xf99wmWA9/jPWwQtLXJ751r6C4sLHA88DyyTV5/ivWgE2BkUhJuCKKib42vneHlT2+KUh3+h2bNvn/IKQU2zv2uKuAaeLc7PFszgZ6l01DVVquI5mR0QlhzbOXdUeWmAeAE9z9g+j7BYqb+QZpr4eHm+Pomt+qVLZGU53Iu4BzUR3Ebmhz2RgF3b+TPJv1gCORa+se7n5jKYNuQ9TM/S9R5s4xKPvmrdG/Acra2IzuqsbbmNkiaH4XR4liHnH3tyKhyBGoxMn/UObWL2ru/S/wrbtv0s7DbhPEer8bOAM4wt2/K2Ici3fXzDYBzkfr4BrgIvReDAZ+iWKmf+Iq8VG3vmzG1I/E4rw8skA+6u6bmBJn3YdcXjdE5T3WRd4qW3kFvTCsKXFcpfantkTs4+ehd3k/d/8s+v+N5ngbd/9fiUOcIqjZ445BnlWnupLHTQs8BcyDvoubxjdxFuBPyANpHXd/taThVw5ZkMzIyMDMrgG2AE5AhaY/CCZ7ayRMfo2EyRFV3pgjgP5+4G9IaP4y+ncA/gL8BGh090+Se9ZHQtUCwEHufkW7D7yNUBMzcx2qBfcZyso7Cvivu28f59cBjkbxkAe6asxVDma2MsrO+hWqdzkHcBZwuLt/9SP3LhP3Xu7ulXNvrVnvx7v7V4kQOQgJC+e43LwWRcqVQcgroTtKuPQCsGsInJV99zMmeP8vR0zzL5AFZiSyxr9U59pKZmc1sz5IYN4feK6KSrDWIFxXnwPucfc9o+8OpFDb2N2fi29EH1d+gMrDmmcgvxrVBj4XuMbd34r+RYH/QwL2rSjB1CBgKWBdd3+2hKFXFjlGMiOjE8Oaisv/AgWcHw7sZWazuvsYFE9xKLLYvGFm/avKSJrZ7Kjw+nDgD65kMkWMyCjk2jUEGGZm15vZxgDuPgRlbf0C2L79R952SBjDE9EGuxUqJj8d2lC3NbOd4toiVnQ4cJyZ9W0h5mSqRVjj7kGxrj9z97lQXdO9kBtTeq0VCSjMrMFUb+8MJIAeX1zTjsNvFeqs969MGQkbzWxpxGDOBHwfQsOLKDPrSki42ATYHNgpC5HVQ71kKjWC1J+QsuBu9G3bthAia6+tghBpZgNC+ZliLVQzszMKkYYEpREEr29mt6O6iYUQOTOwH8pW2iGqOCRC5FnAcihM58zwQOka37EXkbfFXUioHowUZitnIXLS0SEWTkZGxsShVrOcMobuvkfwyYfHtYVl8hpUHmAjFEtWVXyP4rx+hlz3tg+XluVRvajbUDKRcSgt/kJmNtzdH3H3/5iKEz9dztDbDmbWE6U/vwZ42pWlblaU6vxfKNkSIGHSzPZD9QO/KWO8kwszG4zm+3Tg9+4+Kk7dghKH9DezhYGv3f39YDTHmdlCyDq/CdAVMRfjKmiVqbfeR8V6/y/S0v8xcQHrEm7r/4s2HpbrRFYKictyX+AYYF4kTPwbuNpVM/UzlHhnJeAkdx9W2oBbCVPmzYeAg83sqlCCgkoZdcjavynqhRzE8VdmdhdwkJktCwwANnL350Nw3AAJW5dX9f02s97A9J5kVo39bHngFG8qXzYPiouc28zud/d/mNlucYvDBIqWjIlEFiQzMjoJEuaiN8pIuSjwISpI/RxMIEy6mZ0dwuRF7n5e/E7VGOpio/3MzI5AwvAuZnYesjjdjdwXDypcHc1sGHA7yk76CIC7PxHnKkW/KePqmKRrRpR04dpwaVwI0XgXSn8+0sz2Bj509xvc/Z4Sht0qmFl/FBvUBbgoESJBLsqzoZjARYB3zOxkdz8zmKsTUAKaW4ADqmiNm4j1fiVyVx5Z3BOWyu6uLL3NGFPPMZGVQcxdIUQ+jdz2hqE1fyiwo5n9zN0/NLOTUIKlBZJ7q8hMjwJeQgngRpvZjaEUGYs8Crqaah1X5h2eWNS4IC+JvAy6AkPd/X3gr8jqtilwGPC6KVvppuhb90d3v62UwbcSYXV/GLjbzA5O1m4vVN7pa1P87xbIvf8dpGDbwsxGunuHKe1UKnwqyPiTW265TdlGU4bGfsil7SkkPDyKMjauVHP9P9Em/A9gYNnjb6NnUMSED0SbynCUue6c6G9Irp0Z+Bz4c9njntz5Buap6dsaJU3pEfP/TxQTOgK5MPeL65YEbgZ2AbqUTctk0t8dMQ8folIeC0f/Uih51JUoqciWiBFpBLaOa3qheJlivVQyU+VErPeuNdfPiTIYLlX22HNr9dx3jbl8CJg76b8u1sCWcdwTxYp9Xvu9qFqL9Xsr8CUqY2LIAvVo2WNrJ/p3QS7K38Ucv4ySawEsmMz9y8DrqA7wIcn9lcvIjRSFK6FyHqS8SuxvjcDbwAfIlbt3fA+fAC4te/wdpWWLZEZGJ4DLNa8XigH8DJW6eNfM7kElDu41s3U9rE/uvqcpu9lyiAGtLKx5BrcuLkvN8YjR+CVy98GlxS+unQ89p6pmtNsQWR5udvfLzOxWYAbgPnf/yMweRO6d2wND3H1rADObDsXMzAXc7RW1RLmsajehLKQXASeZ2fnI8nwZYqC+BjCz94A7gU3ieX2Hyh4U66Uy1meYpPU+3l3XzAohcgDVXfMZTeiLhIervSnByM+QcuVgd78u9oNRqKbgjIjhrizc/Z3wpDgdCRGjURz0LGY2H1KidkcWekfKov9z90qGa9RYIpdBSfGOR9nXv0eC035mNpO77wdsaWabIcv0p8Dr7v503F/JTMwx5ocAzOwfwMpmtqW7vwH8Dq3trig0o7huGhS+8lLdH82YdJQtyeaWW25t35BVZVqUQKawTPwGuWsuEMfXA+8BuwLPoI13cM3vdIm/ldNWpuNGmvcbkatqQ/TNgCw1I4ArknvmI9xlqK41amGkdX4JeBJ4F1iC5lbXa5DG9i9x/SZIyPoCWKJsGlpBe5fk/+5B18dB65UoQyE0rzP2PHBe2WNvA9onZ73PG+v9BaBb7bOpSqvqN2oKPYvZkeV97zjeJtb/7+O4N0qktVEcF9/5ys17HdoLy+RXwIPIs+ZjJDyMRRa7UfFdrKTHRQ29CwMHIFf8AUn/zMi9/11glx+4v/LvDUoqtBVS/v6bxApfZ22ci1xc5yt73B2l5aytGRkdDJGR8TLkvnEf8JeI/XoRWZ+GmdnfUBzYxu5+IdLgNqBYg/WL33LFTXXx+ApXCRHX5kH7/MBmwJ+BpcIS8ynS4F4ErG9ml5nZbCh77TTA+h5Wm7JomByEpvplYB1U1mMQcIG7D3VZXbvHpdsDV8TfF4CTUWmMVdx9aAlDbzViXhvNrFv8PxpZG3cF3kfM1aww3iLXxcyWQ+6+z5c28DZAK9b7RWi9L+WqE9ngFbHCmrIw9jJlk67cN6otYE2ZhlN+bhRy51vKzPZA7/nhyGoF8jRZE5XDKb7zVpV5h/pZaUGWSWBfpDQdjBRlmwMLISvtcmjvW6Ggu31G3LYwYRm0r+8JDHf3z+Ncg7t/hBItjUZxsHVRpfcm3vfZzGxrM9vWzFYN/mQMUpztjOb3XDObt+be36IyNxuj2pGvt/f4OyzKlmRzyy23tmvAKsgV9Q700XwSbSQX0Txm6gVUW6uInZwdeAV4C7i/bDra4DmkMaHXRXsHaeWfQqUvimsGIiHqkzj/Ik2WmYYyxt8KusdbFoD1UYzgW8gyuVNyXbfk//mB1WINTFs2Da2gPbXG3Y4sLg1J3yZIY30XTVb52ZBr1INU2BrTGdc7ct+8BHlTvA0cCMxb9rja+RkU67sPEhp2TeZ538/Y2J4AACAASURBVJjfRpRQpbhnwVjvt1d1zSd090LuuocjYWne6DfkWXJjvPPb1aO1qvTX0HBGzPHHNMWCW7IOzkRKtP5lj7WVdPZB9X+fQtblUdEeBBZK6N4E8UB3JuthIZSI6fLi2tzacG7KHkBuueXWNg0FnY9CpSsGRl/BWI5FdfRACVbGANsk926JhM+f0gHcfYKmXsBQ5KK6McpS+1vgDWR9qmWuz0dJZgompTJMdYw3ddWcJ/l/CSRIvoRiY4v+7rHxdi977G1A+/g5CwbyC5RU6sBkjgs3189QAp7VkEvnS1TYpTN5Bp1mvSNL2qvBVF6PlAPjgmmurDJkEp9Bqjx4FsXG/QboGf09UP3bRuACZK35HVIuPpPMe6XWfA3djyEF6JtIWLoDWRqLa+eKNf45SkZTmTX+A/RPkAQMWZobY/3PnfT3BK5Fnkk9yh57K2juF+/6PagO8JIocdqpwEdIabZWXNtAc2FyzugfAPQtm5aO2EofQG655db6htK3f0FiTUwYhcWCydojjrsFw3kfcv1ZN/7/V3Jv5YVJJBx/huoAFn09kNX2jWColkue07Q0WfQqxXDUMBWnAzehmJFCQFqSJmFyh4JGlJDhcCocJ5PMWT+UifhSFBf0OcrgeADNhcmNkaW2ETHglbPGtfAcOsV6R5bI11Es1LzI+t4DlTkYBSxX9hjb8Vn0QiU+/o1KPHSrc82ByOo8HLifCGOo2rzX0NQ71vPdNFnhngG+RsqhFZNr50JWqzvLHncr6LXk/xmizVFzTWGZvA4pi5cD9kCJd/Yum4ZW0N4fKQruRuWa0r2uR3zPh8Y3f9GkfxMkZD5BCJO5TaE5KnsAueWWW+tbMIuPIveWX0ZfwSwsDYwEdkwYxw2CGWtEAuhjCUNdWaGi5pnsjhIrFJtLIUz0AA4K2u8DBkV/l/RvVVoNk3EtSuv+G2C29Hysg5dQTbmzgX8hy/RiZdPQBs+gB/B4zOcyyGV1UZSB9H0mFCZ/jkqedAghMmjo8OsdWVg+iG/X3DXn5kbuulvVua8yNP4A7ROUYUIumy/EWi/me6VY738FZoi+GVB8cI/k3kqueeRFcTSyPhbfuCJx3HFIgfQosHxyz8xVXQM13/dtYq/+PPbt84C1k/OnxXs+DglX16N6sRP8VhUaUhi8DtyGssuPT/6X7GtdgY1i/h9P3oOu8Z1/nSxITtl5KnsAueWWW9s0YGXk4jWC5i6MQ4Bna67tgtzbtkOlIoqPbyWZixaexyrIpbewwHVJ6FwSaTA/Ax4ve6xtRO9xyMVnRZrc23oF8z1NHC8N/BcJm88DS5Y97jac64+An9f0zxCM9pfIMlMoVyrPULfwDDr0ekdKgLeCjl1pHus7LaqRdxpKKrQWNZkZKyxMLIKUBMvU9B8IfBn/zwYcFdcNQ9bZV4HedX6vUgJFnfHvDhwV/5+FLFaFgqRw83wAWLMjzH+MfTuU7+BU4DAUAz4GWWK3Sq47niY31yWT/kq5MMeYizjfi5K+NPN4IUz2QEmVxgEbpvNNdmed8vNU9gByyy231rUapngF5AIyHLk2XosSURTJRVpkmqu40fzQuFFMxLXI3Wm1mnM/Q4kYNkOuP/uWTUcrn0FvpKE/OelbGBUbfyyYjQWjfyAwCzBd2eNuQ/pXCiZik6SvEBoHIe398yhmsLLMZNDTqdc7Uow8h2rh/YqI8Q3GuhFZa76nKQHJacBeVDgWON7l3WrnH1geCdaFC99wJGDPglydRwPrlD3+NqC/EBhSIaIHMA8SmncGekX/WrEGRgDnlD32NqJ/NhQjeCqJYgApjz5GCcMWSfoLN9e/EVbbKjbkZfCXoOXMpD8t8VSsjfmQEuU3ZY+7s7UGMjIyKocoqtsP+MTdR8Xxyu5+m5n9AfgjzV0X34802WNb+k2vUOr3AklB9d6IWTTgA3e/3N0/N7NzUIzMbWa2D4qJGwAciuKGHkbWqhnKoaDNMBppX5cxs1WAVZH71wPIvXMV4J9mtrG7f1beMKcYPo+2npnd7+5fJmv9GyRY9Af2QW5vj6UFvauCzrjeg9b9gb+7+/fu/r2ZrYAUJH8BRpjZkiiRzN5E/Vdkmd8IJVlxVFvw3RJIaDVc5XxeNrMewANmdra7X4SUI3siOt8E7vYo3WNmo1Bs7MclDbvVKNZ72lX8E/veIkiAeNrdv4tSHnMCN6AkO7e164DbAGa2IvIg+j7p7o2ya5/r7iPjugZ3f8DMdkZ0DkahC7j7PmY2Frk4T2Nmh3iUBqkKYu7fMrMz0ft8iJnh7nt7U1myxuSWb5EysWcpA+7MKFuSzS233CatoY/qPsjatBRy6foUlfgotPOrIpfWz4Htk3srbY1p4Xn0RcLSO8jy1EjzxEFroHi4scjd63PEhHZHTMdrhIWGCrh81ZtDxGDtHs9hJErAcWhy/hLg3rLH3ga0t2g1Ry5t3wO7kWTuRFaby1Ec2XDgpLLpaOUz6GzrvbA0nkFz74ueSJBqRJaI7evc2xXFVs1eNh2toD+1vsyDXNNHAtvWXFdY4LshC+YDSHiu5Dc/oacvKt1wC1IS7AjMHOfmRDGz18Y8r4ASCp2WroGyaZkEmreM9bxXzVpfCimAjkrXRXz3uyAl0TXR3z255hLgsrLpmgT6rXa+UNmPASSZaWvXSPy/E3LlXrxsOjpbyxbJjIyKwWWReBgxWBcCMyFXr98h5hF3vz/qLB8NnB7Wl8u8qfB0pSwxtQht7NjQQB+DAu0PQJvqJsDRZtYHlTi5x8weB/6BtNefoiyHjuJJeiKGi6n9uaQa+rA8ToOEhf+6+/lm9gAwI/CZu78U102LmLE3zaw7MGZqp7MeEmtcH5Rpdjbksnubu78WfXOjmKnlzOw/aG4PAL5y9xfN7FWk2a8UOut6D5yMapxuAXQ1swO8yTL5E5oSLPU0s27uPqZ4XgDuPry8obcOyZrv6u7j3P1NM/s1yrZ8cXzKrwKI9TEAuXn+AikO1vT61pupGrFHjTWzvsil80vkvjsSedrcbGbHI4XZOchl/SOkNHkHOLj4La+Wp82DSCF8MuBmdqG7j3L3Z83sKWBnM7vS3V8r5tPMBqJ9fxiAu49O1suOxQ9P7fu+mXVB67Zr0NhoZlcCL7j7n8zstLg0tUyOjftmQrkenqeiXgeVRtmSbG655TZ5DVgdaehGAFsk/amWbhVUM+8TovxHR2lIU7khykC6c9I/AGUtHQlcQ53YKGS1uQkx2UuVTctE0ptaJq5ADNPXsQbupibRTFw3CNULHE6kya9yizl/CcWFvYJcmf4LrB7nDTgBufONi7/3olIn06LEO38um45W0N5p1nuMO60ZeCmyPp3NhJbJoUHbbsU5KmBt/RHaC4tcn3iHf0cUlUcWx2uRS/vWBb3Ar5GnysVUv8RHV+RJ8ABS/hRr4QLkebBeHPdFNWEPRjGzlUscB/RJ/p8h5nt0vNd9on8FlO/gSeTG2hDvxS6IB9i85jet3v9Tc0OlacYl6/hTkgRTwKzUWCaRwuS8+DZUfo+rYit9ALnlltvEN5oLE3sgDdynsbmsQp26cCib63PAkLLH3wq6lwE2quk7JjaUr4GNa871j034GyR09UzODUCuwVdQwdIXsWm+g+KilkL140agNOeDk+sORQWZX6PC2VkTxtCQ+9JtyPLYB7lwfxvrf63knkWC8Vo6jhuQIPIRMH/ZNE0EzZ1+vRfML82Ty1waa/3MGhoLYfJDFCdZ2cQ6QU9aG/VF5K55eA3N9YTJfsBC9Z5d1RrytngeOCLp2w5Z3w6L414t3FsZulFJj7+SJEViQmGyIdomSIH2PdrTHwS+Sp9RFRvNhd67kKv6CGCVOudTYfIc5PY8kgopyDpaK30AueWW28Q1mtfBm5OmeMhlg4F6Mhjr8fXhkmuaFfKtSkPCw3TIhfH3NedmB06KDeV0osRFcr4/SkTRCBxbc64vFUwLjuL8hiKBqpjbvkiYOoemzIVdgEOQC9x8ZY23DenujQSp61CZk5SxWDbofwJYt869WyCr/IdTO7PR2dc7KlczmCZrWrOYKWSN/hQpBM5mQmHyXaQ4maY9xz2FnkV3lIn5ARQbWXzX0+exMLJCfwfsWnN/JWMjk/EvgFxVd47jX8baLoTI3rEGVi97rK2g8eRYr7eiZHnpuVSY3Ism5cD0SJC6Brmv/zy5p5JzXrOmb6Qp7vm3NFlkUyX6rDRlcx1NKAxzK2n+yh5Abrnl9uON5okHhgBXImtUsbksR5MwORgJEjMg16D1k9+pnDAZ4y5qhPUCVk36ZwlmYgyyuvSquW9aVPqgMm5OMe4+1Fikon+V2DjXjuMFkeb2GiItfNA7bTDhlbPMBI3b1/TtE0zDh8Avo69r8l4siyx1j6bPLa4ZjKy4lXF76mzrPRn/TSiB0Ho096rojtyS70fC079iLdQTJuctm462WgPIhXunOudSpnpB5N59X9ljbiO600LzQ5HyaBPk8nhkct0K8b7vUPaYJ5POU5FSZDtgjhbmdiaaC5M9f+D3KilE1tBwFFKSLRVr+luUsblfnWczM8oBsWDZ4+7srfQB5JZbbj/caO7m9DJy/dixDhP5E6SpfyU2qceQhr6STGVKe/E/cuX6kMS1LzaU82Kz3bf2uSTXVeY50FRUulagWhNpapcF5kBC5NXJRrsmcA812u2qtBAELgNOr3PuyHgmzwKLRp/RJEwuE+fPr3NvJQTqzrrekzEvjFw5Xylojjl+AXgEmCv6+qI4wA+D8WyRwa5KS9Zx4VWwXqznFerNZzyDaeL/uaoqSCR0TxDHhyzsI+M57Jv0L4JqJw6hgspR5M76BrDxj80bskBeGO/7njSvI1mJ2McfoC21RJ4e7/NPk757aBImC0VpH+RlMkNV13xHa6UPILfccvvxhlK634S0dHPR5OY6YzAU08Xx4sF0vYhiybpFf6U22zpMU8FsbIHc/p6lefH5mYK5HoVipPq011inEP2LIoFqDGGBS849iTL0fY5ixooNdiBKRHEfMFPZNLSC9hnibx9gw5pzR6OYoCsITTTNhcmFatdOFVpnX+81tM+P0vi/gCxR/wuhYY44nyrWLoxn8Leyx91KmgsrXD/kzrpWrOXvaF7Gp1gXXZH15pCa36kUY53sY31RopU7UFz31si63h9lYf4Klf/YCCmUnoj3oiH9nao05JZ5X8x36qa/DBIWL0BKouJbOB1wLhKo96/aPE/E85gnnsn2cZw+k3tQ7PfvgCVRCMcHwGxljzu3mKOyB5Bbbrn9eENWiBeBPZO+n6P4mZdRXMGS0d8TWasK5qRSjDVKDrILYVVD7n13A2vE8abI5em5Osz1P2OznSCDadUaclm7EiWX+GXSv0XQ/zWwRDyfpVHNsOFUtI5WLXOEElDUs8oeh9wfU2GyC82125VZ83m9TygIoPi4V4K2/wFz17ueJkGjsu5tCS1dkaLwLqQQHBjf9Y9q5zeez8PAGWWPvw3o7xNz/UIIDQ/FvF+BlArToCye/0MWq0diziuZlRYpvq4Fnq/pPxglUWtECsRGpAyeJ87PHPvBPmXT0MbP47ig90uaQjaM5q7td8bz+Ahl4l627HHn1tRyHcmMjGqgF2KW5zWzbVHpj1+hmlOvIk3tz83sBXf/nqilFPXDxpYz5MlGA9LK9jOzQ5HLy0dIYMbdb4l6escBf4qaUre6+8dmdgzwJnBzSWNvNYp6X+7+qpn9Ibr/Ff2XIneufsDvkSJhONLYF3Xj/lfKwCcTZtbTVRewqIvWEzENZ6GkCgXtlwG4+1FRI3VvoNHM/uLuL6a/WbE136nXOzTV+jOzJd39OXcfZmYbIYZ7ALCQmb3rqqlo3lRb8Ssz29uD26wigpZeSGAaBlxRvMNmdjbKUHy2mS2IsnQuiDJ5GrJOVQ5pnU+09j9ApVvecdUG3J8m9/693f0cMzsXKUg/jj2u9ncqAXd3M3saWMvMzkQC9EbABigcZX/07q+BMpLuDPzB3T8ys10K2jsQipImSyPLK0ipOLaYX3df18x2Q8qWu9z9jbIGm1EHZUuyudVvtOC60FJ/R2udhc4WaK/rpoMyvL0LvI+SDGyQnHsSOKfssbfhM5gz6Pw6aJu9dl0AmyFLzbPUlEOI81XTVLf0zi9Kk2Vyp2KNIMbzV8CBqL7gLGXTMBk090NJJPaN4+7I8v7bOK5rlY1zxyJG849l09EGz6HTrfc64z8eMdUpzYWb6yuI0W4xnq6qLd7lf8dafg9YoOb82kigLmIF30RZPqsatlBYYPsgr4N/AifXue6AoHfLFn6nsmsgaL8WhScU834oSUKwuOY5pCzsCtWrC/kjzyC1OG6DlChfActF3wRZinObOlu2SE6FKLQwZtYD+cz3Ab509yc8tPYdGaFxLqwTs7r7B4WVpuyxTWkkc98Txcn0A0a6+y3ufrCZXYlqSH3h7u+bWTdgPrTRDCtv5G0Ld38nnkEf5Ko7P/CeuzeGJWKcu99sZo7KQpxnZlu6+0PJb1RGU13QFP/PhSzQY939NXd/0cyORhaIC+JVuAS5Ap1X3qjbBA3AT4H1zWwgsANKQnEDgE9olcWbLJNHm9m7yCpfaXS29d4CeqA1P36Pc/fXzGxD5OJ3GrCvmd3dAWhN4Uio6I9c1ecChplZd3cf7e53mdmTKLnIXEjoeDXWRiUscma2BHq3fx/7W1dgeeTOCcpMWlxbrPdTzGwb5PZ9XS0PUFV+IOj71sx2QFa4AcDj7v5ZzaX9kQLt8WJvKFBF2tM9LpC+51fFt+0o4Goz28bdnwiPqnET/FjG1IWyJdncmjeatHX9kNVpGPqYfI0CsDtNgDEKLr+57HGUNPdPxdx/g7K1DUHpztM4sN6otMGDwONU3yIxvvB8/N0UWBdZYZ9FGUnH18hM7tsCJZ2ppOayZk7PQVanMSgW5AxgQJxbgCbr3HY1v1FZDTWK/bkraH41oTd9LguimKnR1MRMxvnKrf3Out5r5zbp2xJZJOauPY8E6xdQuYS1yh5/K2mvl520G6qT+DqyOM5Y9P/APZXw2kEhGRei7Jt/S9Z9L+TS+RZSHi1Tc18PZI27qWwapsQzqbcGkv8bgD2oydhc1VbzLd8P8Xb/ATYHpk3ObQc8H+th2ZaeVW5TVyt9ALnVmRR9YJ9GgefrIGFhJ6TBuQzoX/YY2+EZNKBEAzfFcWUZ5UmkuyeKk7gXWWoWA1ZCNbTuIBQJSFt5A1I23EdF3ZzS+Y6/fVBWvlVoSoO/BNLCP4tiAIt7ZiWC85O+StIfY78y6PwdsDtSHH0T8z5zXLMoEiIagV+UPeZW0ms0CUqPA5+hWKnf166L+H9BVBe1EViv7PG3kvZOv94TWvshj4qifMvica5Lzd+FkdvvfGWPuw3mvQHVBJ0DmD76ugUj/S4qUl9k7Kz0HAcNA1Htz/eAfyTPoRdSoHyGEkwtm9yzYDyH08oe/2TQa+nfSbx3zvj+fwscVjYtbfAsUiXY1chN/SaaspIfTZT1iWu2Qxl5vwCWLnv8uU3EHJc9gNzqTIo0k6/ExlpsojvGJntwzbUdQsCivobuCBSI3a8jbKYT+Rw2jbkfnMz9VjH3B9Vce1Q8o0LDWzmrTIy71hL7DEomkWoxlwwm5CmkxRwU/9/WEd4BVDPuAxTrWDyPaVAa/C+Bi5NrF0WuYIuUPe42mPPi70+RwmRIMNKHJ9emwuRcwJ+qutZraO606z1oLMqXfIjKOVwS37ljkAJt5jr3dGvvcU6heb8DZSH9EilRto5zDcC28Q4Mo0mYrKxVhqjxiRKpXIqsrqcmz6MHiv/9DFknL0LC5kMoRrBycbHASpN53z7A/yGL3CFJf2XnP6HhHzH3K8bxwTRlqD2Z5sLkLjH/85c97twmYm7LHkBudSZF2fneAHrE8bbxwv0ujqejhQD0KjaaB5HPkfy/NXLp7R/HXerd05Ea8Fvgk4SBKOb+sDieHti1zn2VFrSRm+5zNKW+71HnmqWCwRqL3D6fqTJjWUPbzqhm3KI1/X2CsR4HrJL0V5ZumlvjTkGuTv2ibwGUeKRWmJwNOAjoXvs7VWydfb0HfYOR4uRIlHDl6XgHvohv3ufR9zBKKlXpb1zQ3Aclk3oIZSvdH7gm6P11XNMVJR95M/a/acsccyvpLYTFaVFdxBtpSjBzMs2FyU2RINkI/B0JVZUrYYUUIuOYxORnyCJ9LfJEScv8dAQhcmnkYbdlHB+CBMitkGJwLKojOU9yT4f3vOsoLSfbKRl1ApBBm8f07j7KzDZB7lyHu/uJZtYF+BmwtZk97u7vtPeY2xoeXw0zuwKlxH4bbSjvoI13KzO7AWmvR6b3dEB8hSxRo8xsAzT3R7j7CVECYANgDzN7xN1fKm6qs4aqht2Qq+Pu7v4WgJn9BMWFOvCAuz9rZksBu6J35HxPUoSXNO62QiNipnqCki4B41xJGW5F1ucZiovdfUwpo2wl4ns31sz6odjerxBjPSaSaQwzs/1QYpXfmNk0yB3qdPRsTil+q+Jz3qnWe71kae7+cPx7e1wzEFljPgH+jKySyyOX3oc7wDcOpAwZDezs7sNAzwbFh3aH8eVArkPKho3R3FcSQUsfVPvxPfTO34wE6N2AbmZ2UPA6/0HC4/nI8+CsYs1UZb2b2floj17P3T+chPvM3cdEaa/e7v5V0t8REix+iHiZO83sZ8iTajfgeuSNsDUq59TPzE509/eKZ5BRAZQtyXbmRpO2rRcwOOlfGwlR9yEG84Dk3GLRfxYdyCqHtLA7ogDzS5Cm/oOg/1OkxRwGXId866seI9VSiY9FgZdQHalGYL/k3CIx9xd2pLkP2g5DLr2zoXiaw1GMyNtIeXAHievLjz3HqbXRcomPhVCimUepcekDVkVMWKWTjCT09ETMw52xptMkE4WFYhFkvfgu3v3HaYoD7gga+k6x3mvHjNw6Z6gz38XfC4F7a+6v/HwntFwN3JEcbx3f+UPieACwZJ3nVql5p7mX0R/i+7VY0jcAWWK/QAl40pjJzVBt3CEk5TCm9oZcN0eQhKVM7jOrd1yV1tK4afI6uSTeg17JuTtRMq3PgZnKpiG3SWtdyCgFoZn3sDBeBFxhZmsDuPtd6CO6CmIsLzWzLma2EtpoeyIBw0ObWTkE3ePhSvd9ibuf6+47uvvawPrIvef/kAbzBsSILIEYrkoiLArjzKynmf3czDY0syUBXIXVr0Ab7TDgVjPra2ZromQFvYE9krVTOaRrNqFhONLIX4fm+2Dk/rIkCsZfDSUYagavkJUi3vmirM1KZrasmS0A4O6vABejRAuXmdm8JsyJNLffo422I2BtoC+a31ddmvjVzewY4CIzW9plbd8PWWR+Dfw0rmvwimnoO+t6hwnK2vwZWR8fNbP/mtk8yPqa0vUhsED6zKo23wXCq6B2r+sbDTPbEiXYOtzdT4rr90QeJ/3Tua7CvJvZIma2OshjKJnDeVH5shfiuu7u/jl6r99B4RwnmVk3d/8OvQ87owzGfzGVuJqqYWaFK+6t7v6wR9meSfmNWn7O3SvndVXwtfH/tGY2Y3iV4O5fxxqfHSmTvovr5kffgW2Q8uzjkoafMZnIrq0lwJpqBfZGCXVmQgLC38PN4z/uvkdsQGujcgBf0mSdWyfur+cWO9WjhrlYFtH/CfCSy5Wvwd3HuvtQU524ce5+cVzfDWm8RpdGwGQg6mi5uz/vTa599yK3rYHAB2b2J3c/z92Pi01oKzT3nyMXwA+BDSs+98Xa74Jc+/qhmpjnxTNZHBVm/7W7Pxv3vIJihqtIb29UXP7VZM1fjGqEDgTeNLOT3P1Cd/+zqZbgTihj57tog50RvfMflUNFm2MAssB+CMxuZrujTLUvo2/Bxma2rLu/iZ4B0OQWW8aAJxedbb2nsKQGnJldA/wExX+9jtwXrwUOMLMHE6Z5KNoLZ0RxoZVFzHsf4AwzOxJ52DwM7GlmZwB7Icv0yXHLIBQzerdXyK0vhJ9eyG31UzP7tbvfm8zp68CGZrawu7/s7qNDmBwRz+EvyBPpXeDvoWS9HZUGecOncjd+M7sAWZafBFY3s6Pd/digo8vEKkJS928z+znwlrs/NeVG3rao4etOR7ztIsDzZnaJu18Q78RQYHszOwIpRzdByoYRVVr3GQnKMIN25kaTO2tfxDjdgWIGipT+zyJhobh+LeBA4FAUjF71DJ1pwpzLaWKYPkHJJBauuf564MGyx91KmvsgV76vaXJbuh65766KBMZbmdCVdVHk7rsbsAZNWVyrOvfF2u2LGMpH0OZ7FpECP84XLozd0Eb0EHALFXNxQ/XTrkWxvcW8nxBr/pcoU+eQOvO+BkqwcxmKJalsuYPa5xF/F0ZJVL5HgsNH8Tz6x/duJB0gmVhnW+8FzSjGK+07Nva6FeL4kHgn3kMeJ6skz2pdZK3tEO5tNGXcLpKnzYFKWjQClybXDYr1cX/yLCrl2ogyL3+JylelZWs2R4lVTiRx20eKlT8hN8cjqZj7btBwHlL0LgvMg6yp7wF/SK750fc4nWuUlKgR2Khs+ibzmVwdz+BI5F1xZdBzXHLNEOTK/1W8D0uWPe7cWjHnZQ+gMzbEYF4VTNTcycaxI3JnfBZZIFq6v3If3Do0nI8S6myFtPKbx/FnKNC+YDp/F8+kd5njbQN61wtG4W0U5/pPkppw8QyKGnn7/tDaKZuWyaQ/VaC8Es/irGgfowyW6yTXzYDcmx5GZQ+KGJpK0R/z/ihy4VoA+Gu85wWdg5DA2EyY7AitliGmKQt1V2DFYDR2p3ns1OqxPlYue/ytpL3TrXdkbb0PMcJ9o28m4Gxg7zg+GCWb2RolFvognsXKSLCYGZi1bFraaO4NKQq/BxZKzs+HrHRvo3Iut8QzSOOAK7nHI2FyJFKcrpX0nxjfuJOIskUoK/GDxdqoGt3xvn4LrJnMhXc3qwAAHP5JREFU+bwxpxMtTDKhEDkG2K1s+iaC/nTcBb/2i1jXqxEZtpFitBEJ3b2Te9aM6yr9vueWBclyHrqycj4FnB3HqZVup3jphvIDwmTVWs1HZxGUWGQHmopwL4y0mZcTdaeif18UwD5N2TRMBs0LIkvyJcjSsi7wPLJMvkNNHcB4LoUw+Zuyx99W8530dQHOAR4AFkj6jwqaN0s2pIMRA345SRHvsumaSNp7orItf0au6evF+/wpUpasWXN9h5n3hKa0xMexKHHOtcBWtWsi/nZHFvgHUfmPyghQMf5Ou94L+lEMdyNSBu6J3B27IRfFGZCQ8T7KVFvcd0Xc8wmKgy2dllY8g+51+qZFFqtfxXEhKM6BEizdiJSqv6Vi3kbINX8BlHF2dWDu6F8KCVhPAusm15+AlAgjYq1/hDwTKkFvDe2FUmzupK94lydamGRCIXJc+n5MrS2+6+cBq9f0/x55H0wfx/PHfF9GCJHAMmWPP7c2Xg9lD6AjN+rUBov+bshK8X9JX1p0+6rYjB+q8uaKLA+z1+lfG9UNKjSTCyN3pquSj82v4mO1cPqxrkpDmdteR+6rNyMray+knbubqKEU19YK2UVh7i3KpqMV9Hepdxzr/oyEado21kJRI7Vvcs9yyX2V0FQjq8z9QeeLyPI2TTBad8e87lL7jGLeL47zU702ugXa50fJgoo564diYB5GrvtXBX1719w3AMWKPYaYz8plZ+2s672G5j2RgPA4Ehj2rVnjByImM60V/Dfk+nYbsGDZNEwGzWsANybHfZFAfQhSlK6BhOdDJvL3KjHvyJp8P4pzbow2AtXAngYphb6J9zn1vNkUCRvnxt8qKk12QR5FO9U592PCZEvZ2vehIkJkjHdwzPldNK84cCrwYfw/e6yJq2nyUNg6jjuE63puMe9lD6CjNlSA9XySwrLR30CTpvpTFFxfuEV0QULm7dHeBc6Ic1WLl+gTDNR1TGiFWCqYqTVQspnaj82qyId+cHuPu41oXz420X/UMkcx/2vGBvsuTbFzqTC5BEqZXpnNtYbGxVACiX8BxyfMwszIre/QOP5lbEaHxXEPpLn8dc3vVUKgoMmN8W7kqpda1rvGvD8RzEW9eR+EGKzF2nPcbUT7qjGXhXKkZzyHu4i4KBQHPIak3EH0b49KAZxLNRnLTrne6zyHhZBl8XhkjR+LGOTiu34iskIVCsTpkZJtXyoiQNXQuwpyWz2bJje+Q4H/IY+TUaiUUyOKBTsf1UfdPN715cumYTLp/inyqjkXWSMHooybhQL0LiRELIosk0+QWCbr/F6V3vVTUVzvTbSg5Ke+MHnUD/zm75AQWQkFIk386rrxPf8vEYoQ6+EzFPs6AimJpolzs8XxxSQKtNyq30ofQEdswHSxmRSaumuQP32qnR2IBInnSIKqkVb/HpQG/q+oztKAsmmaRPr7ocQ5jwbz0L/m/FzIxfMhZIm8kmC6g7m4JD5OM5ZNy2TQPmPQdT6JOy5y/UoVBqvH3L9NHaEiua8ym2yMd+XYSF6kSVv9FDBtnL8+mKu9YvM8Irl3OSLGqmw6JoPuBmRxu5cad6dJmXfquMdN7Q0x1N8hJrnwKNgEJZ5YKo6vi+/dBqjcUSOwV5zrGt+E4jlVRqjorOu9znMomOdDkPv2qkjAGk1TfOQgxHjeG2vlOiR4Vi6ZVKz50cDfSerhJednRMlX1kfKglHIgvcmcnVtREJG1RTEs6EcDmdSs6/H+f2CtiEo++4yKGbyUWpc+qvWkLLoY+Dn1CQNqv2f5sLkzchocEKd3+yGlOj7lE3fJD6Lgs6NkMLoHmSl7ok8UBpRmEKfuG52lHDsPZJ44dw6Rit9AB2xIYaxiP24CSUUaETxADsWL1JsrG8iYfHfqEbkMOCZOH9YbMrTlk3TJNDeG7k2/QclkCm0811rrtspnsn7wPrRt0x8bIZTQatM0LAsysq5wUSskdWQwP02sET0V4qxqKFp5WCuTkMJJfohd7ZvgSvjmm2QQNFYbKxIkFgkNqC7atdKFVpslENRbbQWxx+0roaYsXeqPu/JnJ9C80QKixOuX8jd7Q3gJ3G8Ok1KtuNqfq8yz6GTr/ceSGFaWOIK190VkSC9I8rCe0k8o/3i/BooS+ObsU8MKpuWVsz7+DVPk+AwQeIQVN7icZqY7zniOVVx3leP+Vsl6at16z4g1vvByZr4Kr4BlYyPQ55jryOL2wTzVvsMatbEfEiJUNfiSLjyV60l7/4mMd+3o7wQ/RCv+yGq/X0eEjQ/IhSLuXWsVvoAOlpLPh7zI+3V31EiiUNjg22MD+q+yK2zG8pk9mC8fBfQJHwNQQJmn7LpmgT6D0WuLIOob2GbIfl/5/jYvB8f6ReQ9r6yqaBjXr8B+v3ANQVDMQAlYnkMaaznL3v8raB76VjbJ6NY0OI96IGUKk8m1x6BNJMvo7iq04PRepqKZi1EsT+N1JSvaWHee9OUxfcbYPGyx98Wcx59xbdrfmDT+P8R5O6YromnkWXqgXrfiam9deb1jsIWPkZKz1OpYQ5RwqCX4/++yAI9mrC8ohi6aahmArV6a74QopdBQtbGNff8HLmCFgrk1DOpah4nh6KkeBPsb8n3rStSoL9LU9mTlZCre2XWeQ1tByMvswE1/YPjmVyHXH3nqjlffBcmsN5WpcX7vmdNX/Gd7xXfsWHI4+JBlICpDyr9cRsyKvyJCvM3uf1wayCjTeFNxWc/RW5LuwEXuPtfzew0YDtUfPc0lKntcpQS/nh3HwFgZrOa2Z+QJm8ld/+2ncloDZZFBbefTzuj6Ph6wMJm9h5wkLv/y8xeQm5tg5D7y7Pu/n57D7oNMRZtpD2Ar3+kIPEg9PE9GsVPvdk+Q2xbmFlXpJUEGOnu30V/D3cfZWaPocLz66BA/D+b2eso6dJRSIFwL3C4q2Bxg1es8DyyMI8vnN3CvBvgKHZuHPBHNPej2mmMbYZ6cx40jzWz5ZAG+hQzex5pqe9KnkdRb+4g5H3haTHuqR15vbMDysI6AFlWH48C5A+5+w1IcL7FzPZy97PM7DC07k8ws17A6cUzqxJ+YM2PM7NlEBN9ObIyp/gEMdwDoBmPQMXmHfStakDK8WbfueL9jedxD3J77Gdmn7n7QyhzebPC9RXCzEgp0g30riPh8kA0r1+gDL1rmNmm7v5SzbP5Ku6rzHcuwZ7AyWY2o7sfV3yv4hk8iazNy6McALcjz7q93f3Y8oac0a4oW5LtyA0FIzcCv0/6+iJXxicRM/F1XHNAnF8VWfSGUTHLHNpgrkcuW9MjxnlBmrJVjkDaq5FIaJq77DFPgWewPBIo/pj01bPM9o05PrKmv6oa22lpqhV2dEoHUqg0Ju0pZKFaDFi0g9C/IHJpPPmH5j36hwDnxf8TxFdVpdXM+THRtwyysp5LU9zzNcjzYE/k0v4gSrwzvt5e2bS0kvZOtd4R43wMEipOQYLlfcgCdTOwBVIkXJjcM2Osg4+pWMz/D8x7uua/RZk8J6h3HM9rNLBr2eNvA/oHx/52TNJXr57givGMlq3i+12H7iWCnqtRGZO74vge5PbaH7m9fgrcXPZ425j2aVC+jkbg2OjrgSy0j9A8C/P6SJl+F7BGvTWSW8drpQ+gozdUJ+ozJFj1QUkZngJmib4i419a/mMLYJ6yxz6Z9C4fH5y7kVD5MYp5PC3o7Y7cAL8Fzi17vFOA/oEo7vEtEhcnJowjWS0+wpuXPeY2pL1/rOXxyhOUcOVtYP/YZI6LTabI3nl6cn9lN5vYbO9Hbtqb/cC8LxL0Vz7BSp05Pxdpp/+J3HcLl7/ZkPDcGN+C+6lgiY8fob0zrveTgqa9Yj9bFbnp/wcpCxuJbI5xz0BglrLH3sbz3mzN17l2hqD771TMjbUF2n90f0PeGYehhHqVc1/+AdpXR8rwsSg86TfUuGuiJIHPUtG4xx+gPV3zx6GEcY+QlHejSYlQGFBuocKK0twmYX2UPYCO3pAb61ikwX0VCZF146ioYMbGFuhYJWj9BGWyXJXmiTh6x0Z0adljnUL0L4usMk+RCBVxrgHFjz0M3FllRroF2vuj2nCNwVC/gTIQF5tMYYVaDiUiqTxzldC+ZDLvm9Q5PwDFQL9CTSxNlRtKrvDXEB6GkpQ9qbluZRRjVqyFys99J1/v/UNAagSOjr6uwMaxHu5saa+repuYNY9qqr5OeB9EX+XnP9Zy8Z3bvOZcF5Rc5hHk4lhZZUkLtM+AQlL61fRbfN//g2KCu3RA2gth8jtkea2XWKr43q1FlPnJreO3YtIz2hipL7yZPYTigu5HxWzf8g7+4M2sN/rYflzTb+hDfBlwibufXNG4gR+Ema0PXItcl69Cm2qRsXNbJEwv5+5jfiSOsnIws/5II30AmuM9o///27v/GNnOuo7jn2+lVWstrRHFGEspxkhSYxUrQWltEfAP0wLhJhqUFKKgRhKLaaNGkBaLQAXTSFQUYwtyhWjFQg0SQrlV/gCMyA/bBjUVa1poDZa2txS4LX3848y227177+733s7Odub1Sm7m7szszHNmZnf2Peec51SmP7C/vuH6j7V9xA5p9rxfnWnN29szTZN/f6YPV/ZkWht/9hjj0wsb5BxU1UmZjof2G5k2e7t03WUHvb4fo/tJbWrFX+8nZjrm7SuTvG6M8ep1l500xrhrYYObsy1e80/KNOnSiUmeNsY4sJhRzse633P7M23u+bZMMfUjmT48X3t/e2AZ39+TpKqOHWPcP/v/N2TaxPtNmY4Le/VCBzcnVfX4TJML/Vam3XcO2g9yWZ9vDs1kO3MyxiMmkPjLTNPhf2SM8ZicUKVrjHFfpk9rU1XHrXsjPSnTZl8nZAqtLOMvnTHGB6rqmZlmNXx5kl/NtKP+pzJt8vML47E50caWxhj3VNXlmX6/XFRVt40xXjt7ng+Kh2Va/tnz/hOZNnn7zUxvuiPTJq+3ZZo864YFDnEuxhh3VdXrM73GX1NVWfvDerMPSZYlIpOVf73fU1WXZnqN/3ZVfX2Mccns4nsWN7L5O9RrvqpOyfTh4YmZZrS9f9l+z89+z/14kj9M8rJMm3kem2kiqf9I8jPL+v62Zl1EnpZpM/bLk/zeskZkkowx7q6qN2baR/KS2d+4l264ztL9PcfhCck5WvcDdW2SV2U6YOvKfWKzFpFVdW6mQ36cn+ScMcYtixzXvI0xPl1Vz8u0X8n3Z/qj8jNJbp990LDMb7J3zWYePibTH1ljjPG7ix7XThhjfKKqnpvk1CRnZFoT/bEkt47ZzMzLaBYVa59Q/05VPbhCz/kqv943Pu8PjDEuW6atLA5lk2V/fJIzM+1D+oPLGJFrZu9v52faP/b0TO9vNyb5z2V/f1tTVZdkmq30OzJt3v0Hs/OXaiuj9Ta85l8z+/DosoUOioUSkjtgjHFbVb0uyR9V1bPGGB9e9Jh2UlUdl2l/mZMzTbJz1jKuldnMmKb9XjsY80PWDpWwmFHtjHVvOA8muXS2puYvFj2unTDG+L9Mm7d+YtFj2Ukr/pxb9ikmXltVB8YYly96XDthw7JfnOl4oUsdkWvGGHdnOpTPZ9efvwrvbzMfzhTSl40xPpgsd0SuWeWfdw5mH8kdUlWnZtr04UUr8gv2Earq6Zn2n/i7McbnFz0eds7sU/oXJ3nrKr72V9EqP+eWPRcledcY46ZFj2cnVdXJSV6Y5Kpl36yTh23YV3KltjZb5Z93HiYkF2BV32BW7ZcsB1vV1/4qW+XnfBWXfRXWyGxlFZ93VpOfd4QkAAAALccsegDrVdWeqnpLVX2kqu6pqlFV71z0uAAAAHjYbpts51V5+KDet2aa6RIAAIBdZFetkcx0UOPvy3T8pV9Z8FgAAADYxK5aIznG2Lf2/6pa5FAAAAA4hN22RhIAAIBdTkgCAADQIiQBAABo2VX7SD4azjnnnJU8MOYVV1yRJLnwwgsXPJKdZ9kt+6qx7Ku37Ku63IllTyz7qlnlZU+S66+/ftETpcy1Jdae17XneY7m/jhaIwkAAECLkAQAAKBFSAIAANAiJAEAAGjZVZPtVNXzkzx/9uUTZ6fPqKqrZv//4hjjoh0fGAAAAA/ZVSGZ5IwkF2w477TZvyS5JYmQBAAAWKBdtWnrGOOSMUYd5t+pix4jAADAqttVIQkAAMDuJyQBAABoEZIAAAC0CEkAAABahCQAAAAtQhIAAIAWIQkAAECLkAQAAKBFSAIAANAiJAEAAGgRkgAAALQISQAAAFqEJAAAAC1CEgAAgBYhCQAAQIuQBAAAoEVIAgAA0CIkAQAAaBGSAAAAtAhJAAAAWoQkAAAALUISAACAFiEJAABAi5AEAACgRUgCAADQIiQBAABoEZIAAAC0CEkAAABahCQAAAAtQhIAAIAWIQkAAECLkAQAAKBFSAIAANAiJAEAAGgRkgAAALQISQAAAFqEJAAAAC1CEgAAgBYhCQAAQIuQBAAAoEVIAgAA0CIkAQAAaBGSAAAAtAhJAAAAWoQkAAAALUISAACAFiEJAABAi5AEAACgRUgCAADQIiQBAABoEZIAAAC0CEkAAABahCQAAAAtQhIAAIAWIQkAAECLkAQAAKBFSAIAANAiJAEAAGgRkgAAALQISQAAAFqEJAAAAC1CEgAAgBYhCQAAQIuQBAAAoEVIAgAA0CIkAQAAaBGSAAAAtAhJAAAAWoQkAAAALUISAACAFiEJAABAi5AEAACgRUgCAADQIiQBAABoEZIAAAC0CEkAAABahCQAAAAtQhIAAIAWIQkAAECLkAQAAKBFSAIAANAiJAEAAGgRkgAAALQISQAAAFqEJAAAAC1CEgAAgBYhCQAAQIuQBAAAoEVIAgAA0CIkAQAAaBGSAAAAtAhJAAAAWoQkAAAALUISAACAFiEJAABAi5AEAACgRUgCAADQIiQBAABoEZIAAAC0CEkAAABahCQAAAAtQhIAAIAWIQkAAECLkAQAAKBFSAIAANAiJAEAAGgRkgAAALQISQAAAFqEJAAAAC1CEgAAgBYhCQAAQIuQBAAAoEVIAgAA0CIkAQAAaBGSAAAAtAhJAAAAWoQkAAAALUISAACAFiEJAABAi5AEAACgRUgCAADQIiQBAABoEZIAAAC0CEkAAABahCQAAAAtQhIAAIAWIQkAAECLkAQAAKBFSAIAANAiJAEAAGgRkgAAALQISQAAAFqEJAAAAC1CEgAAgBYhCQAAQIuQBAAAoEVIAgAA0CIkAQAAaBGSAAAAtAhJAAAAWoQkAAAALUISAACAFiEJAABAi5AEAACgRUgCAADQIiQBAABoEZIAAAC0CEkAAABahCQAAAAtQhIAAIAWIQkAAECLkAQAAKBFSAIAANAiJAEAAGgRkgAAALQISQAAAFqEJAAAAC1CEgAAgBYhCQAAQIuQBAAAoEVIAgAA0CIkAQAAaBGSAAAAtAhJAAAAWoQkAAAALUISAACAFiEJAABAi5AEAACgRUgCAADQIiQBAABoEZIAAAC0CEkAAABahCQAAAAtQhIAAIAWIQkAAECLkAQAAKBFSAIAANAiJAEAAGgRkgAAALQISQAAAFqEJAAAAC1CEgAAgBYhCQAAQIuQBAAAoEVIAgAA0CIkAQAAaBGSAAAAtAhJAAAAWoQkAAAALUISAACAFiEJAABAi5AEAACgRUgCAADQIiQBAABoEZIAAAC0CEkAAABahCQAAAAtQhIAAIAWIQkAAECLkAQAAKBFSAIAANAiJAEAAGgRkgAAALQISQAAAFqEJAAAAC1CEgAAgBYhCQAAQIuQBAAAoEVIAgAA0CIkAQAAaBGSAAAAtAhJAAAAWoQkAAAALUISAACAFiEJAABAi5AEAACgRUgCAADQIiQBAABoEZIAAAC0CEkAAABahCQAAAAtQhIAAIAWIQkAAECLkAQAAKBFSAIAANAiJAEAAGgRkgAAALQISQAAAFqEJAAAAC1CEgAAgBYhCQAAQIuQBAAAoEVIAgAA0CIkAQAAaBGSAAAAtAhJAAAAWoQkAAAALUISAACAFiEJAABAi5AEAACgRUgCAADQIiQBAABoEZIAAAC0CEkAAABahCQAAAAtQhIAAIAWIQkAAECLkAQAAKBFSAIAANAiJAEAAGgRkgAAALQISQAAAFqEJAAAAC1CEgAAgBYhCQAAQIuQBAAAoEVIAgAA0LKtkKzJy6rq41V1b1V9uar+pap+uaqO2XDdk6rq4qraW1U3VdUDVTWq6tlb3MeTq+qtVfXZqrqvqu6oqo9W1cur6rijWUgAAGD5VdXZVfW+qrpt1iAvWXfZsVX1xqr6zKxnvlBVf1VVpyxwyHM1z8dju2sk35nkz5KcmuRdSf48yfFJ/iTJVRuue2qSy5O8KMm3JvniVjdeVWcm+bckL0tyc5K3JHlPku9J8qdJrq2q2uZYAQCA1XRCkhuS/FqSr2y47PgkP5zkdbPT52XqjQ9U1eN2cpA7aG6Px5ZXqKoXZIrCzyX50THGF2fnH5fkb5O8uKquGWO8Z/YttyR5dpJPjjHurKqrklywxd1ckuRbkrxkjPH2dfd9UZJ/TvLcJGcl+aetxgsAAKymMcb7k7w/SWYdsv6yu5M8Z/15VfVLSW5M8tRMK7aWyjwfj+2skXzB7PTNaxE5u+MDSV49+/IV687/0hjjujHGndu47TWnzU7ft/7MMcaXk1w3+/IJjdsDAADYyomz0y/N+44OHDiQ22+/PTfffHOuvPLKHDhwYN53eSS2/XhsJySfODv9r00uWzvvrKPcj/HG2elPrz+zqo5P8qwk9yX56FHcPgAAwENm/fLmJNeOMW6d530dOHAge/bsyR133JF7770373jHO7Jnz55dFZPdx2M7Ibm2FvLJm1y2tibxcev+fyReleQLSa6a7Qz6hqr64ySfTfJtSfaMMT5/FLcPAACQJJntA/jOJCcleem872/v3r3Zv3//I87bv39/9u7dO++73pYjeTxqjLHVjf7c7EZvzrSP5J2z849NcnWS82dX/bExxkFrDdftI/mcMcaHDnM/351pIp+z1p19f5Irkrx+jDH31c0AAMByqKp7k7xijHHVhvMfl6k7fiDJOWOM2+c9lnPPPfdDSX5yk4s+tG/fvudscv6j7tF+PLYzO9G7k7w4yU8luamq3pvkq5km1PmuJP+T5JQkD25zGQ5SVT+U5Jok/5spJD+VqYZ/PsllSZ5fVWfOdggFAABom60Me3eS07NDEZkk+/btO+yhEBflaB6PLUNyjPH1qjovya9nCrsLMoXk9UlemGmtZDJFYNusgP8602Q6T183+HuTvKGqvjPJhUlemWl2VwAAgINU1QlJvnf25TFJTqmqM5LcmeTzSf4myZlJzksyqmptPpi7xxgbD4/xmDfPx2PLTVu3GNg3Jbk7yT1jjE1nVd1q09aqOj3T1LL/OsZ42iaXn5/kvUn+foxx3hEPFgAAWGpVdU6SfZtc9PZMK6U+d4hvfenGTT6XwTwfj6M98ObPJjku0za1R+obZ6fffojL1wJ190xpBAAA7DpjjOuT1GGucrjLls48H4/tzNqaqjpxk/POSPL7mY4x8oYjHUCSG5LclWk16y9uuI+Tklw0+/K6jd8IAADAztvWpq1V9fEkX8kUffuTPDXTMR+/kuS8McY/brj+m/LwGsZnJnlKkg9mOsRHklwzxrhm3fUvSHJlpiK+Lsknk5ycaUbYJyT5WKadP792REsJAADAo2a7IXlxps1Yn5Lkm5PcluQfMh2W46CDVVbVfyd50mFu8tIxxiUbvufsTJPqPCNThH4tyb9n2gH0ijHGV7deHAAAAObtqCbbAQAAYPVsax9JAAAAWCMkAQAAaBGSAAAAtAhJAAAAWoQkAAAALUISAACAFiEJAABAi5AEAACgRUgCAADQIiQBAABo+X/uzA0A7EM5VgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "print(\"After:\")\n",
        "print(\"##### Type of columns #####\")\n",
        "print(df.info(), end=\"\\n\\n\")\n",
        "\n",
        "print(\"##### Nan Value #####\")\n",
        "print(df.isnull().sum(), end=\"\\n\\n\")\n",
        "\n",
        "print(\"##### Chart of missing values #####\")\n",
        "missingno.matrix(df, figsize=(15, 5))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U-CAaNeuWjM8"
      },
      "source": [
        "# **Observations on the dataset**\n",
        "\n",
        "In this section we want to make some observations on the dataset in order to fully understand the application domain in which we are operating and to be able to better understand how to structure our ML model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "id": "8Zvik9YqNCU_",
        "outputId": "bafacbe4-b28f-4ff1-fb38-b380040c5510"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(15,10))\n",
        "fig.suptitle(\"Data about all the dataset\", y=1.02)\n",
        "for i,col in enumerate(df.columns,1):\n",
        "    plt.subplot(4,3,i)\n",
        "    plt.title(f\"Distribution of {col} Data\")\n",
        "    sns.histplot(df[col],kde=True)\n",
        "    plt.tight_layout()\n",
        "    plt.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "id": "wutkUa3zkJZ1",
        "outputId": "5f706044-2c8e-405e-86c0-ede510beeea4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df_heart_failure = df[df['HeartDisease'] == 1]\n",
        "fig = plt.figure(figsize=(15,10))\n",
        "fig.suptitle(\"Data about only the people with heart attack\", y=1.02)\n",
        "for i,col in enumerate(df_heart_failure.columns,1):\n",
        "    plt.subplot(4,3,i)\n",
        "    plt.title(f\"Distribution of {col} Data\")\n",
        "    sns.histplot(df_heart_failure[col],kde=True, color='red')\n",
        "    plt.tight_layout()\n",
        "    plt.plot()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "xKPDAsxndVG7",
        "outputId": "ee9a69df-55e9-4bde-c995-9585a6a04106"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
        "women = df[df['Sex'] == 'F']\n",
        "men = df[df['Sex'] == 'M']\n",
        "women_chart = [women[women['HeartDisease'] == 1].shape[0] / women.shape[0], women[women['HeartDisease'] == 0].shape[0] / women.shape[0]]\n",
        "men_chart = [men[men['HeartDisease'] == 1].shape[0] / men.shape[0], men[men['HeartDisease'] == 0].shape[0] / men.shape[0]]\n",
        "\n",
        "fig1, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n",
        "ax1.set_title(\"Women Heart Attack\")\n",
        "ax1.pie(women_chart, labels=['Heart attack', 'Safe'], autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "\n",
        "ax2.set_title(\"Men Heart Attack\")\n",
        "ax2.pie(men_chart, labels=['Heart attack', 'Safe'], autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax2.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "QSUE6pmyfzMi",
        "outputId": "9d93d276-8a72-498b-ba07-5f5019b4aa22"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.title(\"Age of women with heart attack\")\n",
        "sns.histplot(women[women['HeartDisease'] == 1]['Age'],kde=True)\n",
        "plt.tight_layout()\n",
        "plt.plot()\n",
        "plt.subplot(1,2,2)\n",
        "plt.title(\"Age of men with heart attack\")\n",
        "sns.histplot(men[men['HeartDisease'] == 1]['Age'],kde=True)\n",
        "plt.tight_layout()\n",
        "plt.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Maybe suffer from gender bias?"
      ],
      "metadata": {
        "id": "4rNmKv63UuSQ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Iin7RsKaTUr"
      },
      "source": [
        "# **Predictions**\n",
        "\n",
        "In this section we want to predict the probability of person to have an heart attack. To do that we choose different ML Models and we want to compare them to find the best one for our purpose. \n",
        "\n",
        "We will evaluate the goodness of 2 predictive models:\n",
        "1.   Random Forest\n",
        "2.   KNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FrbyJ8o_Q9Yx"
      },
      "source": [
        "To compare them we will evaluate their accuracy, i.e. first of all we will train the models on the test cases prepared, then making them associate which elements must have target 0 and which 1. Subsequently we will run the algorithm on elements unknown to him and check if the predicted target will match the actual one. The ratio between correct elements and total elements will be expressed as a percentage, this will tell us the accuracy of the prediction. Subsequently we will show a matrix, this matrix is ​​composed of 2 rows and 2 columns and indicates the predicted values ​​corresponding to the real values. In this way we could evaluate both which targets were predicted correctly and which ones were wrong. This concept anticipates the ROC curve (Receiver operating characteristic) which has a well known trend. Sensitivity and specificity can be represented along the two axes, respectively represented by True Positive Rate (TPR, fraction of true positives) and False Positive Rate (FPR, fraction of false positives). The yellow area is called AUC and tells us if we have many correct values over the wrong ones\n",
        "\n",
        "\n",
        "> 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)\n",
        "\n",
        "\n",
        "In other words, the relationships between true alarms (hit rate) and false alarms are studied. The usefulness of this graph is to allow us to visualize how our result would change if we wanted to obtain a certain number of True Positives in terms of False Negatives."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot ROC-AUC\n",
        "def auc_plot(model, x, y, label):\n",
        "    fpr, tpr, thresholds = roc_curve(y, model.predict_proba(x)[:,1])\n",
        "    auc = roc_auc_score(y,model.predict(x))\n",
        "    print(label, auc)\n",
        "    plt.plot(fpr, tpr, label=label)"
      ],
      "metadata": {
        "id": "U0Xdxv--jS_0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWnRlkEMfUNx"
      },
      "source": [
        "## Preparing the data\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kp1cXIpKyebe"
      },
      "source": [
        "#### Encode, Balance and Split data into Train & Test\n",
        "\n",
        "Unfortunately, our dataset is unbalanced, i.e. the number of elements with Target at 0 is approximately four times the elements with Target at 1 and this could cause several problems for our predictive algorithms working with decision trees. To balance our data we use SMOTE. SMOTE is an oversampling technique where the synthetic samples are generated for the minority class. This algorithm helps to overcome the overfitting problem posed by random oversampling. It takes the minority class and makes KNN among its points. It takes n points, draws a line between them, along the line creates a new point with the correct values ​​to fit on that line. As many lines are drawn and as many points are created as needed to have a balanced dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QFWyP7zuBfpF"
      },
      "source": [
        "Random Forest Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYj_EFFDBip0",
        "outputId": "168d70c1-a8a5-4fe0-bbc1-585444e115d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before Smote:\n",
            "- HeartDisease 0: 333\n",
            "- HeartDisease 1: 401\n",
            "After Smote:\n",
            "- HeartDisease 0: 401\n",
            "- HeartDisease 1: 401\n"
          ]
        }
      ],
      "source": [
        "string_col=df.select_dtypes(\"object\").columns.to_list()\n",
        "string_num= [x for x in df.columns if x not in string_col and x != 'HeartDisease']\n",
        "\n",
        "# Dataset Label Encoding\n",
        "df_rf = df.apply(LabelEncoder().fit_transform)\n",
        "\n",
        "# Split X, Y\n",
        "Y_rf = df_rf['HeartDisease']\n",
        "X_rf = df_rf.drop(['HeartDisease'], axis=1)\n",
        "\n",
        "# Unbalanced Dataset\n",
        "X_train_rf, X_test_rf, y_train_rf, y_test_rf = train_test_split(X_rf, Y_rf, test_size=0.20, random_state=0)\n",
        "\n",
        "# Dataset scaling\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(X_train_rf[string_num])\n",
        "X_train_scaled_rf = X_train_rf\n",
        "X_test_scaled_rf = X_test_rf\n",
        "X_train_scaled_rf[string_num] = scaler.transform(X_train_rf[string_num])\n",
        "X_test_scaled_rf[string_num] = scaler.transform(X_test_rf[string_num])\n",
        "\n",
        "# Apply SMOTE\n",
        "smoteOBJ = SMOTE(random_state=0)\n",
        "X_train_smote_rf, y_train_smote_rf = smoteOBJ.fit_resample(X_train_scaled_rf, y_train_rf)\n",
        "\n",
        "print(\"Before Smote:\\n- HeartDisease 0: {}\".format(sum(y_train_rf == 0)))\n",
        "print(\"- HeartDisease 1: {}\".format(sum(y_train_rf == 1)))\n",
        "print(\"After Smote:\\n- HeartDisease 0: {}\".format(sum(y_train_smote_rf == 0)))\n",
        "print(\"- HeartDisease 1: {}\".format(sum(y_train_smote_rf == 1)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1k35CWiqBeTb"
      },
      "source": [
        "KNN dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LG05PouyykCb",
        "outputId": "fccc2e60-0ff7-42a8-d4d1-8c5b9525641d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before Smote:\n",
            "- HeartDisease 0: 333\n",
            "- HeartDisease 1: 401\n",
            "After Smote:\n",
            "- HeartDisease 0: 401\n",
            "- HeartDisease 1: 401\n"
          ]
        }
      ],
      "source": [
        "df_knn = df\n",
        "\n",
        "string_col=df.select_dtypes(\"object\").columns.to_list()\n",
        "string_num= [x for x in df.columns if x not in string_col and x != 'HeartDisease']\n",
        "\n",
        "# Dataset Label Encoding\n",
        "onehotencoder = OneHotEncoder()\n",
        "for col in string_col:\n",
        "    tmp = onehotencoder.fit_transform(df_knn[col].values.reshape(-1,1)).toarray()\n",
        "    dfOneHot = pd.DataFrame(tmp, columns = [col+str(int(i)) for i in range(tmp.shape[1])])\n",
        "    df_knn = pd.concat([df_knn, dfOneHot], axis=1)\n",
        "    df_knn = df_knn.drop(col, axis=1)  \n",
        "\n",
        "# Split X, Y\n",
        "Y_knn = df_knn['HeartDisease']\n",
        "X_knn = df_knn.drop(['HeartDisease'], axis=1)\n",
        "\n",
        "# Unbalanced Dataset\n",
        "X_train_knn, X_test_knn, y_train_knn, y_test_knn = train_test_split(X_knn, Y_knn, test_size=0.20, random_state=0)\n",
        "\n",
        "# Dataset scaling\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(X_train_knn[string_num])\n",
        "X_train_scaled_knn = X_train_knn\n",
        "X_test_scaled_knn = X_test_knn\n",
        "X_train_scaled_knn[string_num] = scaler.transform(X_train_knn[string_num])\n",
        "X_test_scaled_knn[string_num] = scaler.transform(X_test_knn[string_num])\n",
        "\n",
        "# Apply SMOTE\n",
        "smoteOBJ = SMOTE(random_state=0)\n",
        "X_train_smote_knn, y_train_smote_knn = smoteOBJ.fit_resample(X_train_scaled_knn, y_train_knn)\n",
        "\n",
        "print(\"Before Smote:\\n- HeartDisease 0: {}\".format(sum(y_train_knn == 0)))\n",
        "print(\"- HeartDisease 1: {}\".format(sum(y_train_knn == 1)))\n",
        "print(\"After Smote:\\n- HeartDisease 0: {}\".format(sum(y_train_smote_knn == 0)))\n",
        "print(\"- HeartDisease 1: {}\".format(sum(y_train_smote_knn == 1)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YR0h97vzaa6j"
      },
      "source": [
        "## Random Forests\n",
        "\n",
        "Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time.  Random forests generally outperform decision trees, but their accuracy is lower than gradient boosted trees."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AZLsvTf7a8WZ"
      },
      "source": [
        "### How does it work\n",
        "\n",
        "Before analyzing how random forest works, we must first introduce what decision trees are.\n",
        "A decision tree is a graph of decisions and their possible consequences. They are predictive models, where each internal node represents a variable, an arc towards a child node represents a possible value for that property and a leaf represents the predicted value for the target variable starting from the values ​​of the other properties, which in the tree is represented by the path from the root node to the leaf node.\n",
        "\n",
        "Random forest, like its name implies, consists of a large number of individual decision trees that operate as an ensemble. Each individual tree in the random forest spits out a class prediction and the class with the most votes becomes our model’s prediction. A large number of relatively uncorrelated models (trees) operating as a committee will outperform any of the individual constituent models, indeed the low correlation between models is the key. To do that we use Bootstrap to create many datasets, each one different from the other and, through Random Subset Feature Selection, we create the decision tree. This will allow us to create them on slightly different datasets and therefore we will have different trees, which however learn something from our dataset. Every single tree is not that good, but together I get quite high accuracy. Furthermore, this type of model can be created in parallel.\n",
        "\n",
        "For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction of the individual trees is returned."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lh2-_GKW-vQh"
      },
      "source": [
        "### Execution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ntk328iR567Y"
      },
      "source": [
        "#### Creating the Model and train it\n",
        "\n",
        "With Default hyperparameters. With this model most of the time the default settings approach the optimal solution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "cGTC1nft6Di8",
        "outputId": "dee95c66-352c-465d-9633-9dd521b47a1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.842391304347826\n",
            "Recall: 0.8785046728971962\n",
            "Auc: 0.8353562325524941\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f2f362b6fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MChFuWppZN+BEZmbV50RmZpVXskSWZ11LM7Otci7Om7f5KalJ0rOSfpbuj5E0U9ICSXdI6pNVhhOZmRWXf4HePL4EdFwr91rghog4GFgFTM4qwInMzApTW74tsxxpX+AjwNR0XyRTht2dXjINOCurHPeRmVlhBZ5aDpc0q8P+lIiY0mH/G8D/YuvaIMOA1RHRku4vBEZn3cSJzMyKKdZsXB4R43d0QtJHgWUR8bSkibsTkhOZmRVXm6eWxwNnSPowyUzUewHfBAa3rxkC7AssyirIfWRmVkj7yP7dfWoZEVdExL4RcSBwLvBYRJwPPA6cnV42Cbg/KyYnMjMrTG2Ra9tFlwFflrSApM/spqwvuGlpZsXU4aXxiPgl8Mv088vAhCLfdyIzs8L8rqWZVZ8TmZlVnWtkZlZ9TmRmVmkVW0XJzOxPeIZYM+seolyZzInMzAorW43MI/tr7Mtff507npvL9x978d1jF3x1Cd999EVunP4iV9/2R4aO3NLACG17900dzoUnjuUfJo7l3h+M2Obc3d8bwan7HMmaFU0Niq6E8s5F1oXJrm6JTNLNkpZJmlOve5TRI3cM5Z/PH7PNsbu/uzefO2UsF31oLDMf3YtPXvpmg6Kz7b36Qj9+8eNhfOvnL/G9R19k5vS9WPRKMiHpskW9eeZXA9l79OYGR1k+tZqPrFbqWSP7IXBaHcsvpTkz92Ttqm1b7BvWbf2/eb892srWvdCjvT6/L4cetYF+/YOmZjj8uHX89sHBAHz/X0cz+X8vRmpwkCXUYxJZRDwBrKxX+VXzd5ct4dZZ8zjp46v50XXvaXQ4ljrw0HeY8+QA3l7ZxDsbxFOP7cVbi3vz/x7ai+Hv2cJB73un0SGWT5B09ufZukjD+8gkXShplqRZW9jU6HDq5ofXjuKT48fx2L2DOeNTyxsdjqX2P2QT51y0jCvOO4h/Pv8g3vu+jWzZLG7/9kgu+OqSRodXWrVcfKQWGp7IImJKRIyPiPG96dvocOrusfuG8FcfXtPoMKyD0z6xku88/BLX37eAPQe1csDYd1j6eh8+d8qhXDBhHG8t6c3Fp45l5TI/5H9XyTr7/TfTBfYZs4nFryRJ+rhT1/DGgu6fsKtk9fJmBg9vYdnC3vz2wUF882fz+dint9aaL5gwjm//4kUGDWttYJTl4QGxPcDlN77G4cetY9DQFm6dNY9brh/JhJPWsu9Bm2hrg2WL+vCty/ZtdJjWwVWfPpC1q5pp6h18/uqF7DnICatTsVuTJtZF3RKZpNuAiSSrqCwEroyIzJkeq+6aiw74k2MP3zasAZFYXl//rwWdnv/Rk/O6KJIKKVceq18ii4jz6lW2mTVW2ZqWDe/sN7OKCaAt8m2dkNRP0pOSfi9prqR/S4+PkTRT0gJJd0jqkxWSE5mZFVebp5abgJMi4gjgSOA0SccC1wI3RMTBwCpgclZBTmRmVliNloOLiFiX7vZOtwBOAu5Oj08DzsqKx4nMzAorsBzc8PYB7+l24TblSE2SZgPLgOnAH4HV6eK8AAuB0VnxePiFmRVTbLDr8ogYv9OiIlqBIyUNBu4DDt2VkJzIzKyQZEBsbR9bRsRqSY8DxwGDJTWntbJ9gUVZ33fT0syKa8u5dULSiLQmhqQ9gA8BzwOPA2enl00C7s8KxzUyMyusRjWyUcA0SU0klao7I+JnkuYBt0v6GvAskDmQ3onMzIqp0QvhEfEccNQOjr8MTChSlhOZmRXUg961NLNurGTTHDuRmVkxXqDXzLoF18jMrPLKlcecyMysOLWVq23pRGZmxQSZg127mhOZmRUiouavKO0uJzIzK86JzMwqz4nMzCrNfWRm1h34qaWZVVy4aWlmFRc4kZlZN1CulqUTmZkV53FkZlZ9TmRmVmkR0FqutqUXHzGz4iLybZ2QtJ+kxyXNkzRX0pfS40MlTZc0P/05JCscJzIzK64GiQxoAf4xIsYBxwIXSxoHXA7MiIhDgBnpfqecyMysmADaIt/WWTERSyLimfTzWpKl4EYDZwLT0sumAWdlheQ+MjMrKCBy95ENlzSrw/6UiJiy/UWSDiRZUWkmMDIilqSnlgIjs27iRGZmxQRFOvuXR8T4zi6QtCdwD3BJRLwtaeutIkJSZhvVTUszK642fWRI6k2SxH4cEfemh9+UNCo9PwpYllWOE5mZFVebp5YiWUX8+Yj4eodTDwCT0s+TgPuzwnHT0swKqtlL48cDfwv8QdLs9Ng/AdcAd0qaDLwGnJNVkBOZmRUTQA2m8YmI3wDayemTi5TlRGZmxfkVJTOrtvK9ouREZmbFBET+cWRdwonMzIrLGLXf1ZzIzKw495GZWaVF1OSpZS05kZlZca6RmVm1BdHa2uggtuFEZmbFtE/jUyJOZGZWnIdfmFmVBRCukZlZpUWhiRW7hBOZmRVWts5+RYkeo0p6i2Taju5mOLC80UFYId317+yAiBixOwVIeojkzyeP5RFx2u7cL49SJbLuStKsrOl+rVz8d1YtniHWzCrPiczMKs+JrGv8yfJXVnr+O6sQ95GZWeW5RmZmledEZmaV50RWR5JOk/SipAWSLm90PJZN0s2Slkma0+hYLD8nsjqR1AR8BzgdGAecJ2lcY6OyHH4I1H0Ap9WWE1n9TAAWRMTLEbEZuB04s8ExWYaIeAJY2eg4rBgnsvoZDbzRYX9heszMasyJzMwqz4msfhYB+3XY3zc9ZmY15kRWP08Bh0gaI6kPcC7wQINjMuuWnMjqJCJagM8DDwPPA3dGxNzGRmVZJN0G/A4YK2mhpMmNjsmy+RUlM6s818jMrPKcyMys8pzIzKzynMjMrPKcyMys8pzIKkRSq6TZkuZIuktS/90o64eSzk4/T+3shXZJEyV9YBfu8aqkP1ltZ2fHt7tmXcF7/aukrxSN0boHJ7Jq2RgRR0bEYcBm4LMdT0rapXVKI+LTETGvk0smAoUTmVlXcSKrrl8DB6e1pV9LegCYJ6lJ0nWSnpL0nKTPACjxf9L50R4F9m4vSNIvJY1PP58m6RlJv5c0Q9KBJAnz0rQ2+EFJIyTdk97jKUnHp98dJukRSXMlTQWU9UtI+i9JT6ffuXC7czekx2dIGpEeO0jSQ+l3fi3p0Fr8YVq1eaXxCkprXqcDD6WHjgYOi4hX0mSwJiLeL6kv8FtJjwBHAWNJ5kYbCcwDbt6u3BHAD4AT0rKGRsRKSd8D1kXEf6bX/QS4ISJ+I2l/krcX/hy4EvhNRFwl6SNAnlHxn0rvsQfwlKR7ImIFMACYFRGXSvqXtOzPkywK8tmImC/pGOBG4KRd+GO0bsSJrFr2kDQ7/fxr4CaSJt+TEfFKevyvgcPb+7+AQcAhwAnAbRHRCiyW9NgOyj8WeKK9rIjY2bxcpwDjpHcrXHtJ2jO9x8fT7/5c0qocv9MXJX0s/bxfGusKoA24Iz1+K3Bveo8PAHd1uHffHPewbs6JrFo2RsSRHQ+k/6DXdzwEfCEiHt7uug/XMI5ewLER8c4OYslN0kSSpHhcRGyQ9Eug304uj/S+q7f/MzBzH1n38zDwOUm9AST9maQBwBPA36R9aKOAE3fw3f8GTpA0Jv3u0PT4WmBgh+seAb7QviOpPbE8AXwiPXY6MCQj1kHAqjSJHUpSI2zXC2ivVX6CpMn6NvCKpP+Z3kOSjsi4h/UATmTdz1SS/q9n0gU0vk9S874PmJ+e+xHJDA/biIi3gAtJmnG/Z2vT7qfAx9o7+4EvAuPThwnz2Pr09N9IEuFckibm6xmxPgQ0S3oeuIYkkbZbD0xIf4eTgKvS4+cDk9P45uLpww3PfmFm3YBrZGZWeU5kZlZ5TmRmVnlOZGZWeU5kZlZ5TmRmVnlOZGZWef8fre2aiixMFqUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Balanced Dataset\n",
        "RF = RandomForestClassifier(random_state=0)\n",
        "RF.fit(X_train_smote_rf, y_train_smote_rf)\n",
        "pred = RF.predict(X_test_scaled_rf)\n",
        "print(\"Accuracy:\", accuracy_score(y_test_rf, pred))\n",
        "print(\"Recall:\", recall_score(y_test_rf, pred))\n",
        "print(\"Auc:\", roc_auc_score(y_test_rf, pred))\n",
        "plot_confusion_matrix(RF, X_test_scaled_rf, y_test_rf)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROC-AUC"
      ],
      "metadata": {
        "id": "BcvcXzZsjb7G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "plt.grid()\n",
        "auc_plot(RF, X_test_scaled_rf, y_test_rf, label=\"RF\")\n",
        "plt.legend(loc=\"upper left\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "7OlMC36njaIc",
        "outputId": "665705f5-2246-428c-9c4e-b6789cf597c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RF 0.8353562325524941\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f2f34526e10>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Ck_F-um6Iwl"
      },
      "source": [
        "#### Find the best parameters\n",
        "\n",
        "In this section we want to try to use the same algorithm, but with different parameters. \n",
        "\n",
        "The parameters we are going to use are:\n",
        "*   Max Depth: the bigger the tree, the more chances of having different trees, but I risk an overfit\n",
        "*   Number of Estimators: the number of trees\n",
        "*   Max Features: these are the maximum number of features Random Forest is allowed to try in individual tree. Increasing max_features generally improves the performance of the model as at each node now we have a higher number of options to be considered. However, this is not necessarily true as this decreases the diversity of individual tree which is important in random forest.\n",
        "* Min_samples_leaf: The minimum number of samples required to be at a leaf node.\n",
        "* Min_samples_split: The minimum number of samples required to split an internal node\n",
        "\n",
        "With GridSearchCV we are trying all the possibility we insert into the code and it will return the best one. Then we are trying to execute the model with the parameters returned by this function.\n",
        "\n",
        "\n",
        "> NB: running this cell may take a few minutes, if you don't want to print the actual GridSearchCV state change the value of \"verbose\" variable to 0, otherwise 2\n",
        ">\n",
        "> Set the value to -1 to not execute the GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rR16mUq06LpH",
        "outputId": "6dad2810-c9a5-4f70-fe7f-201e40a84ec5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best previous params:\n",
            "'max_depth': 11,\n",
            "'max_features': 2,\n",
            "'n_estimators': 1000,\n",
            "'min_samples_leaf': 1,\n",
            "'min_samples_split': 3\n"
          ]
        }
      ],
      "source": [
        "# Change the value of this variable as you wish, 0 = no prints, 2 = it will return one line per parameter set\n",
        "verbose = -1 # 0 | 2\n",
        "if verbose != -1:\n",
        "    base_model = RandomForestClassifier()\n",
        "    max_depth = [8, 9, 10, 11, 12]\n",
        "    max_features = [2, 3, 4, 5, 6, 7, 8, 9, 19]\n",
        "    n_estimators = [1000]\n",
        "    min_samples_leaf = [1, 2, 3]\n",
        "    min_samples_split = [2, 3, 4, 8]\n",
        "    params = {\n",
        "        'max_depth': max_depth, \n",
        "        'max_features': max_features,\n",
        "        'n_estimators': n_estimators,\n",
        "        'min_samples_leaf': min_samples_leaf,\n",
        "        'min_samples_split': min_samples_split,\n",
        "        'random_state': [0]\n",
        "    }\n",
        "    grid = GridSearchCV(base_model, params, verbose=verbose, scoring=\"accuracy\")\n",
        "\n",
        "    grid.fit(X_train_smote_rf, y_train_smote_rf)\n",
        "\n",
        "    print(\"\\n\\nBest parameters\")\n",
        "    print(grid.best_params_)\n",
        "else:\n",
        "    print(\"Best previous params:\\n'max_depth': 11,\\n'max_features': 2,\\n'n_estimators': 1000,\\n'min_samples_leaf': 1,\\n'min_samples_split': 3\")\n",
        "    grid = RandomForestClassifier(max_depth=11, max_features=2, n_estimators=1000, min_samples_leaf=1, min_samples_split=3, random_state=0)\n",
        "    grid.fit(X_train_smote_rf, y_train_smote_rf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "srClAOox6gzi",
        "outputId": "064be747-f2fe-49d3-a7a0-2b82c1474b20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8586956521739131\n",
            "Recall: 0.8878504672897196\n",
            "Auc: 0.8530161427357689\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f2f366d6fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "pred = grid.predict(X_test_scaled_rf)\n",
        "print(\"Accuracy:\", accuracy_score(y_test_rf, pred))\n",
        "print(\"Recall:\", recall_score(y_test_rf, pred))\n",
        "print(\"Auc:\", roc_auc_score(y_test_rf, pred))\n",
        "plot_confusion_matrix(grid, X_test_scaled_rf, y_test_rf)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROC-AUC"
      ],
      "metadata": {
        "id": "FdwaH8B1jsze"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "plt.grid()\n",
        "auc_plot(grid, X_test_scaled_rf, y_test_rf, label=\"RF TUNING\")\n",
        "plt.legend(loc=\"upper left\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "fudBWxJjjtnd",
        "outputId": "7d9528d1-7de5-408b-d846-fb1da20b4824"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RF TUNING 0.8530161427357689\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f2f364dd850>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "th-QR3c_-GZ5"
      },
      "source": [
        "### Conclusion\n",
        "The Random Forest offers good results and excellent execution speed. Furthermore already in the version with the default parameters we obtain excellent precision. \n",
        "This result is so good also thanks to the SMOTE method to balance our dataset, as this predictive model works best on datasets that are not too unbalanced. \n",
        "\n",
        "Subsequently we tried to do the tuning of the parameters although this model doesn’t require a particular tuning as the improvements that can be obtained are minor, as in our case.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "So you should be wondering if it is worth running a GridSearchCV, which takes several minutes/hours to run, rather than using the model with the basic parameters.\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cR1Ud6cObKKp"
      },
      "source": [
        "## KNN\n",
        "\n",
        "The k-nearest neighbors is an algorithm used in pattern recognition for the classification of objects based on the characteristics of the objects close to the one considered.\n",
        "KNN algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sN5_y1J_bbLM"
      },
      "source": [
        "### How does it work\n",
        "\n",
        "First of all we have to introduce some fondumental aspects: What is a \"supervised machine learning algorithm\"? A supervised machine learning algorithm is one that relies on labeled input data to learn a function that produces an appropriate output when given new unlabeled data.\n",
        "Supervised machine learning algorithm that can be used to solve both classification and regression problems. It’s easy to implement and understand, but has a major drawback of becoming significantly slows as the size of that data in use grows.\n",
        "KNN works by finding the distances between a query and all the examples in the data, selecting the specified number examples (K) closest to the query, then votes for the most frequent label (in the case of classification) or averages the labels (in the case of regression).\n",
        "Have to find the right K, in order to select the K that’s right for your data, we run the KNN algorithm several times with different values of K and choose the K that reduces the number of errors we encounter while maintaining the algorithm’s ability to accurately make predictions when it’s given data it hasn’t seen before.\n",
        "As we decrease the value of K to 1, our predictions become less stable (warning to overfitting). Inversely, as we increase the value of K, our predictions become more stable due to majority voting / averaging, and thus, more likely to make more accurate predictions, but if we begin to see an increasing number of errors (warning to underfitting). It is at this point that we know we have pushed the K value too far.\n",
        "In cases a majority vote is used between the labels, usually K an odd number is chosen to have a tiebreaker.\n",
        "Main advantagies are: \n",
        "The algorithm is simple and easy to implement.\n",
        "There’s no need to build a model, tune several parameters, or make additional assumptions.\n",
        "The algorithm is versatile. It can be used for classification, regression, and search (as we will see in the next section).\n",
        "But:\n",
        "the algorithm gets significantly slower as the number of examples and/or predictors/independent variables increase."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_yDxmW_WRFS"
      },
      "source": [
        "### Execution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Adl3DPPAW5Lm"
      },
      "source": [
        "#### Creating the Model and train it\n",
        "\n",
        "With Default hyperparameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "0D5UnbI_WV6e",
        "outputId": "c3b63c56-58e4-4da8-c8d6-5130eba1e93c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7934782608695652\n",
            "Recall: 0.8504672897196262\n",
            "Auc: 0.7823765020026703\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f2f3687c790>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Balanced Dataset\n",
        "knn = KNeighborsClassifier()\n",
        "knn.fit(X_train_smote_knn, y_train_smote_knn)\n",
        "pred = knn.predict(X_test_scaled_knn)\n",
        "print(\"Accuracy:\", accuracy_score(y_test_knn, pred))\n",
        "print(\"Recall:\", recall_score(y_test_knn, pred))\n",
        "print(\"Auc:\", roc_auc_score(y_test_knn, pred))\n",
        "plot_confusion_matrix(knn, X_test_scaled_knn, y_test_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROC-AUC"
      ],
      "metadata": {
        "id": "7wL1gJgXj0fC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "plt.grid()\n",
        "auc_plot(knn, X_test_scaled_knn, y_test_knn, label=\"KNN\")\n",
        "plt.legend(loc=\"upper left\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "iq3nJURNj1fH",
        "outputId": "50455193-0074-4a64-9311-4f4db0bc15ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN 0.7823765020026703\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f2f3662d8d0>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eIzIk1_dXrj5"
      },
      "source": [
        "#### Find the best parameters\n",
        "\n",
        "In this section we want to try to use the same algorithm, but with different parameters. \n",
        "\n",
        "The parameters we are going to use are:\n",
        "*   N_neighbors: number of neighbors\n",
        "*   Weights: with uniform all points in each neighborhood are weighted equally, with distance weight points by the inverse of their distance. In this case, closer neighbors of a query point will have a greater influence than neighbors which are further away.\n",
        "\n",
        "With GridSearchCV we are trying all the possibility we insert into the code and it will return the best one. Then we are trying to execute the model with the parameters returned by this function.\n",
        "\n",
        "\n",
        "> NB: running this cell may take a few minutes, if you don't want to print the actual GridSearchCV state change the value of \"verbose\" variable to 0, otherwise 2\n",
        ">\n",
        "> Set the value to -1 to not execute the GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UHK_r6psXsQm",
        "outputId": "5c0ea471-c51c-4a6b-c306-74824ddf033b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best previous params:\n",
            "'n_neighbors': 9,\n",
            "'metric': 'manhattan'\n"
          ]
        }
      ],
      "source": [
        "# Change the value of this variable as you wish, 0 = no prints, 2 = it will return one line per parameter set\n",
        "verbose = -1 # 0 | 2\n",
        "if verbose != -1:\n",
        "\tbase_model = KNeighborsClassifier()\n",
        "\tn_neighbors = [1,3,5,7,9,11]\n",
        "\tmetric = [\"euclidean\", \"manhattan\", \"chebyshev\", \"minkowski\"] \n",
        "\tparams = {\n",
        "\t\t\"n_neighbors\": n_neighbors,\n",
        "\t\t\"metric\": metric,\n",
        "\t}\n",
        "\n",
        "\tgrid2 = GridSearchCV(base_model, params, verbose=verbose, scoring=\"accuracy\")\n",
        "\tgrid2.fit(X_train_smote_knn, y_train_smote_knn)\n",
        "\n",
        "\tprint(\"\\n\\nBest parameters\")\n",
        "\tprint(grid2.best_params_)\n",
        "else:\n",
        "\tprint(\"Best previous params:\\n'n_neighbors': 9,\\n'metric': 'manhattan'\")\n",
        "\tgrid2 = KNeighborsClassifier(n_neighbors=9, metric='manhattan')\n",
        "\tgrid2.fit(X_train_smote_knn, y_train_smote_knn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "ly9bX0bLYUTu",
        "outputId": "fcf8ed34-f399-4e16-a578-e1e71b8179ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8260869565217391\n",
            "Recall: 0.8411214953271028\n",
            "Auc: 0.823158150260954\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f2f36764a10>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "pred = grid2.predict(X_test_scaled_knn)\n",
        "print(\"Accuracy:\", accuracy_score(y_test_knn, pred))\n",
        "print(\"Recall:\", recall_score(y_test_knn, pred))\n",
        "print(\"Auc:\", roc_auc_score(y_test_knn, pred))\n",
        "plot_confusion_matrix(grid2, X_test_scaled_knn, y_test_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROC-AUC"
      ],
      "metadata": {
        "id": "L7OdhiCXj-Rk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "plt.grid()\n",
        "auc_plot(grid2, X_test_scaled_knn, y_test_knn, label=\"KNN TUNING\")\n",
        "plt.legend(loc=\"upper left\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "xvlzAbLZj_aQ",
        "outputId": "3c20eb73-7724-457b-d4e0-e05d5b253631"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN TUNING 0.823158150260954\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f2f3448add0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FPUQ4Jjhf2KW"
      },
      "source": [
        "### Conclusion\n",
        "\n",
        "We must note that its default accuracy is less than the Random Forest, this is caused by the number of neighbors which must be chosen carefully and which can be considered its most important parameter.\n",
        "\n",
        "In fact, in the section dedicated to the tuning of the parameters we can see how the precision increases as the number of neighbors increases.\n",
        "\n",
        "The only disadvantage of this algorithm is that it becomes slow if the number of examples / predictions / independent variables increases. \n",
        "\n",
        "KNN is a lazy learner, there is no function and therefore the model is as large as the data.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Conclusion**\n",
        "\n",
        "Recap all ROC-AUC"
      ],
      "metadata": {
        "id": "4FghDyTmkL9y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "plt.grid()\n",
        "auc_plot(RF, X_test_scaled_rf, y_test_rf, label=\"RF\")\n",
        "auc_plot(grid, X_test_scaled_rf, y_test_rf, label=\"RF TUNING\")\n",
        "auc_plot(knn, X_test_scaled_knn, y_test_knn, label=\"KNN\")\n",
        "auc_plot(grid2, X_test_scaled_knn, y_test_knn, label=\"KNN TUNING\")\n",
        "plt.legend(loc=\"upper left\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "5xSrToinkN8F",
        "outputId": "878ef1a6-fd86-4469-d7ee-844a3199b936"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RF 0.8353562325524941\n",
            "RF TUNING 0.8530161427357689\n",
            "KNN 0.7823765020026703\n",
            "KNN TUNING 0.823158150260954\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f2f34365690>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6eIbtwnC56MS"
      },
      "source": [
        "In this project we prefer the Random Forest (also without tuning the parameters), remembering however that we must first execute the SMOTE function (which uses KNN) to balance the dataset, because it has a better accuracy, recall and AUC. \n",
        "\n",
        "These data, together, give us a good overview of the goodness of this model\n",
        "\n",
        "How to improve: by adding more data (medical or about individual’s life) or adding another ML model, like XGBoost\n",
        "\n",
        "Applications: in any medical area where you want to prevent an heart disease/attack\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "fACve4PnDUwR",
        "e_neB0MuDbpo",
        "6dNTNXL2Ui3N",
        "j4u4az6MI-Et",
        "459p9W1RSrCL",
        "xx9f2dTtJIIb",
        "s_99ilDwwD1R",
        "nP21VeQnS4JQ",
        "U-CAaNeuWjM8",
        "3Iin7RsKaTUr",
        "gWnRlkEMfUNx",
        "Kp1cXIpKyebe",
        "YR0h97vzaa6j",
        "cR1Ud6cObKKp",
        "4FghDyTmkL9y"
      ],
      "name": "Heart Attack ML_FinalProject.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}