HEART DISEASE PREDICTION.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  BRIEF DESCRIPRION ABOUT THE USE CASE \n",
    "  It is known that the most popular cause of dealth globally is the Cardiovascular diseases (CVDs) also known as Heart Disease. With over 18m dealth annually. Given that, it is worth the effort and investing time to research and find ways to curb or reduce the mortality rate caused by CVDs. One of the ways to help is to proactively identify (predict)the patients who are likely to have the CVDs and prevent the disease at early stage. The factors that lead to heart disease such as age, sex, Resting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Import Done!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from datetime import datetime, timedelta\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import pie, axis, show\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "from sklearn.model_selection import train_test_split\n",
    "from datetime import date\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "print(\"Import Done!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_flat_file(file):\n",
    "    dfs = pd.read_csv(file)\n",
    "    return dfs\n",
    "data=read_flat_file('Heart_Disease_Prediction.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Chest pain type</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FBS over 120</th>\n",
       "      <th>EKG results</th>\n",
       "      <th>Max HR</th>\n",
       "      <th>Exercise angina</th>\n",
       "      <th>ST depression</th>\n",
       "      <th>Slope of ST</th>\n",
       "      <th>Number of vessels fluro</th>\n",
       "      <th>Thallium</th>\n",
       "      <th>Heart Disease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>322</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>115</td>\n",
       "      <td>564</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Presence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>128</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>269</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Absence</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex  Chest pain type   BP  Cholesterol  FBS over 120  EKG results  \\\n",
       "0   70    1                4  130          322             0            2   \n",
       "1   67    0                3  115          564             0            2   \n",
       "2   57    1                2  124          261             0            0   \n",
       "3   64    1                4  128          263             0            0   \n",
       "4   74    0                2  120          269             0            2   \n",
       "\n",
       "   Max HR  Exercise angina  ST depression  Slope of ST  \\\n",
       "0     109                0            2.4            2   \n",
       "1     160                0            1.6            2   \n",
       "2     141                0            0.3            1   \n",
       "3     105                1            0.2            2   \n",
       "4     121                1            0.2            1   \n",
       "\n",
       "   Number of vessels fluro  Thallium Heart Disease  \n",
       "0                        3         3      Presence  \n",
       "1                        0         7       Absence  \n",
       "2                        0         7      Presence  \n",
       "3                        1         7       Absence  \n",
       "4                        1         3       Absence  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(270, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  All Observations and features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number observations 270\n",
      "The total features that come with the dataset 13\n"
     ]
    }
   ],
   "source": [
    "print(\"The total number observations\",len(data))\n",
    "print(\"The total features that come with the dataset\",len(data.columns)-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                        0\n",
       "Sex                        0\n",
       "Chest pain type            0\n",
       "BP                         0\n",
       "Cholesterol                0\n",
       "FBS over 120               0\n",
       "EKG results                0\n",
       "Max HR                     0\n",
       "Exercise angina            0\n",
       "ST depression              0\n",
       "Slope of ST                0\n",
       "Number of vessels fluro    0\n",
       "Thallium                   0\n",
       "Heart Disease              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The number of null values in each column i.e column means axis =0\n",
    "data.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Absence     150\n",
       "Presence    120\n",
       "Name: Heart Disease, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Heart Disease'].value_counts() #The distribution of the Heart Disease class that is target variable. If the data is not closely divided we would have applied SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                         41\n",
       "Sex                          2\n",
       "Chest pain type              4\n",
       "BP                          47\n",
       "Cholesterol                144\n",
       "FBS over 120                 2\n",
       "EKG results                  3\n",
       "Max HR                      90\n",
       "Exercise angina              2\n",
       "ST depression               39\n",
       "Slope of ST                  3\n",
       "Number of vessels fluro      4\n",
       "Thallium                     3\n",
       "Heart Disease                2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nunique = data.nunique()\n",
    "#df = data[[col for col in data if nunique[col] > 1 and nunique[col] < 50]] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Distribution graphs (histogram/bar graph) of column data\n",
    "def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):\n",
    "    nunique = df.nunique()\n",
    "    df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]] # For displaying purposes, pick columns that have between 1 and 50 unique values\n",
    "    nRow, nCol = df.shape\n",
    "    columnNames = list(df)\n",
    "    nGraphRow = (nCol + nGraphPerRow - 1) / nGraphPerRow\n",
    "    plt.figure(num = None, figsize = (6 * nGraphPerRow, 8 * nGraphRow), dpi = 80, facecolor = 'w', edgecolor = 'k')\n",
    "    for i in range(min(nCol, nGraphShown)):\n",
    "        plt.subplot(nGraphRow, nGraphPerRow, i + 1)\n",
    "        columnDf = df.iloc[:, i]\n",
    "        if (not np.issubdtype(type(columnDf.iloc[0]), np.number)):\n",
    "            valueCounts = columnDf.value_counts()\n",
    "            valueCounts.plot.bar()\n",
    "        else:\n",
    "            columnDf.hist()\n",
    "        plt.ylabel('counts')\n",
    "        plt.xticks(rotation = 90)\n",
    "        plt.title(f'{columnNames[i]} (column {i})')\n",
    "    plt.tight_layout(pad = 1.0, w_pad = 1.0, h_pad = 1.0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2048 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPerColumnDistribution(data, 13, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xefe5da4438>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = data['Heart Disease'].value_counts()\n",
    "df2.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Heart Disease']=data['Heart Disease'].replace(['Presence','Absence'],[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54    16\n",
       "58    15\n",
       "51    12\n",
       "57    12\n",
       "59    12\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Age.value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Age:  77\n",
      "Minimum Age: 29\n"
     ]
    }
   ],
   "source": [
    "print(\"Maximum Age: \",data.Age.max())\n",
    "print(\"Minimum Age:\",data.Age.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['group_age'] = pd.cut(data['Age'], bins = [0,40,50,60,70,100], include_lowest = True, labels = ['0-40', '41-50','51-60','61-70','71 and above'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df2 = data['group_age'].value_counts()\n",
    "# Fixed to show distribution of bin\n",
    "#df2.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One hot encoding for the nominal features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def onehot_encode(df, column_dict):\n",
    "    df = df.copy()\n",
    "    for column, prefix in column_dict.items():\n",
    "        dummies = pd.get_dummies(df[column], prefix=prefix)\n",
    "        df = pd.concat([df, dummies], axis=1)\n",
    "        df = df.drop(column, axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_inputs(df, scaler):\n",
    "    df = df.copy()\n",
    "    \n",
    "    # One-hot encode the nominal features\n",
    "    nominal_features = ['Chest pain type', 'Slope of ST', 'Thallium']\n",
    "    df = onehot_encode(df, dict(zip(nominal_features, ['CP', 'SL', 'TH'])))\n",
    "    \n",
    "    # Split df into X and y\n",
    "    y = df['Heart Disease'].values\n",
    "    X = df.drop('Heart Disease', axis=1).copy()\n",
    "    \n",
    "    # Scale X\n",
    "    X = pd.DataFrame(scaler.fit_transform(X), columns=X.columns)\n",
    "    \n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = preprocess_inputs(data, RobustScaler())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FBS over 120</th>\n",
       "      <th>EKG results</th>\n",
       "      <th>Max HR</th>\n",
       "      <th>Exercise angina</th>\n",
       "      <th>ST depression</th>\n",
       "      <th>Number of vessels fluro</th>\n",
       "      <th>CP_1</th>\n",
       "      <th>CP_2</th>\n",
       "      <th>CP_3</th>\n",
       "      <th>CP_4</th>\n",
       "      <th>SL_1</th>\n",
       "      <th>SL_2</th>\n",
       "      <th>SL_3</th>\n",
       "      <th>TH_3</th>\n",
       "      <th>TH_6</th>\n",
       "      <th>TH_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.153846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.149254</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.348485</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.923077</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>4.761194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.196970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.238806</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.378788</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.3125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.268657</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.469697</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.3750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.461538</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.358209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.984848</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.3750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age  Sex    BP  Cholesterol  FBS over 120  EKG results    Max HR  \\\n",
       "0  1.153846  0.0  0.00     1.149254           0.0          0.0 -1.348485   \n",
       "1  0.923077 -1.0 -0.75     4.761194           0.0          0.0  0.196970   \n",
       "2  0.153846  0.0 -0.30     0.238806           0.0         -1.0 -0.378788   \n",
       "3  0.692308  0.0 -0.10     0.268657           0.0         -1.0 -1.469697   \n",
       "4  1.461538 -1.0 -0.50     0.358209           0.0          0.0 -0.984848   \n",
       "\n",
       "   Exercise angina  ST depression  Number of vessels fluro  CP_1  CP_2  CP_3  \\\n",
       "0              0.0         1.0000                      3.0   0.0   0.0   0.0   \n",
       "1              0.0         0.5000                      0.0   0.0   0.0   1.0   \n",
       "2              0.0        -0.3125                      0.0   0.0   1.0   0.0   \n",
       "3              1.0        -0.3750                      1.0   0.0   0.0   0.0   \n",
       "4              1.0        -0.3750                      1.0   0.0   1.0   0.0   \n",
       "\n",
       "   CP_4  SL_1  SL_2  SL_3  TH_3  TH_6  TH_7  \n",
       "0   1.0   0.0   1.0   0.0   0.0   0.0   0.0  \n",
       "1   0.0   0.0   1.0   0.0  -1.0   0.0   1.0  \n",
       "2   0.0   1.0   0.0   0.0  -1.0   0.0   1.0  \n",
       "3   1.0   0.0   1.0   0.0  -1.0   0.0   1.0  \n",
       "4   0.0   1.0   0.0   0.0   0.0   0.0   0.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FEEDFORWARD NEURAL NETWORK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "def mean_squared_error(predictions, labels):\n",
    "    N = labels.size \n",
    "    mse = ((predictions - labels)**2).sum() / (2*N)\n",
    "    return mse\n",
    "\n",
    "def accuracy(predictions, labels):\n",
    "    predicions_correct = predictions.round() == labels\n",
    "    accuracy = predicions_correct.mean()\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.reshape((-1,1))\n",
    "y_test = y_test.reshape((-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.7\n",
    "epochs = 18000,\n",
    "n_input = 20,\n",
    "n_hidden = 4,\n",
    "n_output = 1\n",
    "np.random.seed(10)\n",
    "weights_1 = np.random.normal(scale=0.5, size=(20,4))   # (24, 4) n_input, n_hidden\n",
    "weights_2 = np.random.normal(scale=0.5, size=(4,1))  # n_hidden, n_output (4, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/(18000,) - Sum Squared Error(0.1698561475208504), Accuracy(0.35185185185185186)\n",
      "Epoch 1001/(18000,) - Sum Squared Error(0.040850871306535363), Accuracy(0.9027777777777778)\n",
      "Epoch 2001/(18000,) - Sum Squared Error(0.035880848614381), Accuracy(0.9166666666666666)\n",
      "Epoch 3001/(18000,) - Sum Squared Error(0.030060654590067817), Accuracy(0.9444444444444444)\n",
      "Epoch 4001/(18000,) - Sum Squared Error(0.024637129086933607), Accuracy(0.9583333333333334)\n",
      "Epoch 5001/(18000,) - Sum Squared Error(0.0212337451481936), Accuracy(0.9675925925925926)\n",
      "Epoch 6001/(18000,) - Sum Squared Error(0.019542125912861582), Accuracy(0.9675925925925926)\n",
      "Epoch 7001/(18000,) - Sum Squared Error(0.01862110207134582), Accuracy(0.9675925925925926)\n",
      "Epoch 8001/(18000,) - Sum Squared Error(0.018059461471634022), Accuracy(0.9675925925925926)\n",
      "Epoch 9001/(18000,) - Sum Squared Error(0.017688613382476823), Accuracy(0.9675925925925926)\n",
      "Epoch 10001/(18000,) - Sum Squared Error(0.01742906457427932), Accuracy(0.9675925925925926)\n",
      "Epoch 11001/(18000,) - Sum Squared Error(0.017239191154246755), Accuracy(0.9675925925925926)\n",
      "Epoch 12001/(18000,) - Sum Squared Error(0.017095369820024315), Accuracy(0.9675925925925926)\n",
      "Epoch 13001/(18000,) - Sum Squared Error(0.01698332369073948), Accuracy(0.9675925925925926)\n",
      "Epoch 14001/(18000,) - Sum Squared Error(0.016893981532122387), Accuracy(0.9675925925925926)\n",
      "Epoch 15001/(18000,) - Sum Squared Error(0.016821339455539236), Accuracy(0.9675925925925926)\n",
      "Epoch 16001/(18000,) - Sum Squared Error(0.016761286050601155), Accuracy(0.9675925925925926)\n",
      "Epoch 17001/(18000,) - Sum Squared Error(0.01671092311495599), Accuracy(0.9675925925925926)\n",
      "Final Epoch 18000/(18000,) - Sum Squared Error(0.016668195359938933), Accuracy(0.9675925925925926)\n"
     ]
    }
   ],
   "source": [
    "N = y_train.size\n",
    "monitoring = {\"mean_squared_error\": [], \"accuracy\": []}\n",
    "\n",
    "def train(learning_rate, epochs,weights_1, weights_2):\n",
    "    \n",
    "    for epoch in range(18000):\n",
    "        if(epoch==0):\n",
    "            wc1,wc2 = 0.0,0.0\n",
    "\n",
    "        # feedforward\n",
    "        hidden_layer = sigmoid(np.dot(X_train, weights_1))        # (712, 4)\n",
    "        output_layer = sigmoid(np.dot(hidden_layer, weights_2))   # (712, 1)\n",
    "\n",
    "        # monitor training process\n",
    "        acc = accuracy(output_layer, y_train)\n",
    "        mse = mean_squared_error(output_layer, y_train)\n",
    "        monitoring[\"accuracy\"].append(acc)\n",
    "        monitoring[\"mean_squared_error\"].append(mse)\n",
    "\n",
    "        # backpropagation\n",
    "        output_layer_delta = (output_layer - y_train) * output_layer * (1 - output_layer)                 # (712, 1)\n",
    "        hidden_layer_delta = np.dot(output_layer_delta, weights_2.T) * hidden_layer * (1 - hidden_layer)  # (712, 4)\n",
    "        \n",
    "        #Calculation with momentum\n",
    "        # Weight changes(t-1)\n",
    "        wc2_before = wc2 # (4, 1)\n",
    "        wc1_before = wc1 # (24, 4)\n",
    "\n",
    "        # Weight changes(t)\n",
    "        wc2 = learning_rate * np.dot(hidden_layer.T, output_layer_delta)  / N # (4, 1)\n",
    "        wc1 = learning_rate * np.dot(X_train.T, hidden_layer_delta)  / N # (24, 4)\n",
    "        \n",
    "        # weight updates\n",
    "        weights_2 -= wc2 + wc2_before    \n",
    "        weights_1 -= wc1 + wc1_before\n",
    "             \n",
    "        '''\n",
    "        # Calulation without momentum\n",
    "        # weight updates\n",
    "        weights_2 -= (learning_rate * np.dot(hidden_layer.T, output_layer_delta)  / N)    \n",
    "        weights_1 -= (learning_rate * np.dot(x_train.T, hidden_layer_delta)  / N)\n",
    "        '''\n",
    "        \n",
    "        if(epoch%1000==0):\n",
    "            print(\"Epoch {}/{} - Sum Squared Error({}), Accuracy({})\".format(epoch+1,epochs,mse,acc))\n",
    "                    \n",
    "    print(\"Final Epoch {}/{} - Sum Squared Error({}), Accuracy({})\".format(epoch+1,epochs,mse,acc))\n",
    "        \n",
    "\n",
    "train(learning_rate, epochs, weights_1, weights_2)\n",
    "\n",
    "monitoring_df = pd.DataFrame(monitoring)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#testdata\n",
    "\n",
    "# feedforward\n",
    "hidden_layer_inputs = np.dot(X_test, weights_1)\n",
    "hidden_layer_outputs = sigmoid(hidden_layer_inputs)\n",
    "\n",
    "output_layer_inputs = np.dot(hidden_layer_outputs, weights_2)\n",
    "output_layer_outputs = sigmoid(output_layer_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8888888888888888\n"
     ]
    }
   ],
   "source": [
    "acc = accuracy(output_layer_outputs, y_test)\n",
    "print(\"Accuracy: {}\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8888888888888888\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy:',acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DECISION TREE AND RANDOM FOREST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]Decision Tree Classifier Training Accuracy: 1.0\n",
      "[2]Random Forest Classifier Training Accuracy: 0.9953703703703703\n"
     ]
    }
   ],
   "source": [
    "# Define a function which trains models\n",
    "def models(X_train,y_train):\n",
    "    from sklearn.tree import DecisionTreeClassifier\n",
    "    tree = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
    "    tree.fit(X_train, y_train)\n",
    "  #Using Random Forest Classifier\n",
    "    from sklearn.ensemble import RandomForestClassifier\n",
    "    forest = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
    "    forest.fit(X_train, y_train)\n",
    "\n",
    "  \n",
    "  #print model accuracy on the training data.\n",
    "    print('[1]Decision Tree Classifier Training Accuracy:', tree.score(X_train, y_train))\n",
    "    print('[2]Random Forest Classifier Training Accuracy:', forest.score(X_train, y_train))\n",
    "    \n",
    "    \n",
    "  \n",
    "    return tree,forest\n",
    "#get the training results\n",
    "model = models(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 88.89%\n"
     ]
    }
   ],
   "source": [
    "lr_model = LogisticRegression()\n",
    "lr_model.fit(X_train, y_train)\n",
    "print(\"Logistic Regression Accuracy: {:.2f}%\".format(lr_model.score(X_test, y_test) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[23  7]\n",
      " [ 8 16]]\n",
      "Model[0] Testing Accuracy = \"0.7222222222222222\"\n",
      "\n",
      "[[27  3]\n",
      " [ 7 17]]\n",
      "Model[1] Testing Accuracy = \"0.8148148148148148\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "for i in range(len(model)):\n",
    "    cm = confusion_matrix(y_test, model[i].predict(X_test))\n",
    "    TN = cm[0][0]\n",
    "    TP = cm[1][1]\n",
    "    FN = cm[1][0]\n",
    "    FP = cm[0][1]\n",
    "\n",
    "    print(cm)\n",
    "    print('Model[{}] Testing Accuracy = \"{}\"'.format(i, (TP + TN) / (TP + TN + FN + FP)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Absence       0.74      0.77      0.75        30\n",
      "    Presence       0.70      0.67      0.68        24\n",
      "\n",
      "    accuracy                           0.72        54\n",
      "   macro avg       0.72      0.72      0.72        54\n",
      "weighted avg       0.72      0.72      0.72        54\n",
      "\n",
      "0.7222222222222222\n",
      "\n",
      "Model 1\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Absence       0.79      0.90      0.84        30\n",
      "    Presence       0.85      0.71      0.77        24\n",
      "\n",
      "    accuracy                           0.81        54\n",
      "   macro avg       0.82      0.80      0.81        54\n",
      "weighted avg       0.82      0.81      0.81        54\n",
      "\n",
      "0.8148148148148148\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "for i in range(len(model)):\n",
    "    print('Model',i)\n",
    " #Check precision, recall, f1-score\n",
    "    print(classification_report(y_test, model[i].predict(X_test)))\n",
    " #Another way to get the models accuracy on the test data\n",
    "    print(accuracy_score(y_test, model[i].predict(X_test)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest=model[1]\n",
    "feats = {}\n",
    "for feature, importance in zip(X.columns, forest.feature_importances_):\n",
    "    feats[feature] = importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_by_values= sorted(feats.items(), key=lambda x: (x[1],x[0]), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('TH_7', 0.10933415263405595),\n",
       " ('Cholesterol', 0.10667086134587081),\n",
       " ('ST depression', 0.10507043089040627),\n",
       " ('Max HR', 0.10087847450256122),\n",
       " ('Number of vessels fluro', 0.09459063528230699),\n",
       " ('Age', 0.08628413276478357),\n",
       " ('BP', 0.06974148835172059),\n",
       " ('CP_4', 0.06708451674367763),\n",
       " ('Exercise angina', 0.03881818904800802),\n",
       " ('TH_3', 0.03719645445359472),\n",
       " ('SL_1', 0.0342679038305768),\n",
       " ('Sex', 0.030998715183284238),\n",
       " ('SL_2', 0.027913734322008478),\n",
       " ('EKG results', 0.027548387857447065),\n",
       " ('CP_1', 0.019818025062381766),\n",
       " ('CP_3', 0.016060321865694657),\n",
       " ('CP_2', 0.010559421967785145),\n",
       " ('FBS over 120', 0.009078401243522435),\n",
       " ('SL_3', 0.007349812792347156),\n",
       " ('TH_6', 0.0007359398579665286)]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_by_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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