heart_disease_svm_optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "efd311ba",
   "metadata": {},
   "source": [
    "## Optimization of Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfaef2d9",
   "metadata": {},
   "source": [
    "Support vector machine (SVM) is one of the most popular classifer which deals with the classification and regression problem with strong generalization capabilities. Optimization of SVM can be done by tuning of SVM parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85f1a16",
   "metadata": {},
   "source": [
    "Here, the used dataset is heart disease dataset collected from Kaggle (https://www.kaggle.com/johnsmith88/heart-disease-dataset)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9992a7",
   "metadata": {},
   "source": [
    "#### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "492b3e07",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np    #for the analysis of provided data\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt # python library useful for plotting graphs\n",
    "import seaborn as sns # to exploring and understanding of data\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import os #operating system\n",
    "\n",
    "from sklearn.metrics import confusion_matrix #to evaluate the performance of a classification model by the performance metrics calculation \n",
    "from sklearn import metrics, preprocessing # preprocessing to convert the provided raw data into clean data\n",
    "#metrics to measure classification model performance\n",
    "from sklearn.metrics import classification_report #to show the result of classification model\n",
    "\n",
    "from sklearn.svm import SVC # importing support vector classifier (SVC)\n",
    "from sklearn.model_selection import cross_val_score #to estimate the skill of machine learning model\n",
    "from sklearn.model_selection import GridSearchCV #to fit the model on the training set\n",
    "\n",
    "from sklearn.pipeline import make_pipeline # for constructing pipelines\n",
    "from sklearn.feature_selection import SelectKBest, f_regression # SelectKBest to select the features according to the best K score\n",
    "#f_regression to rank the features on the same order\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ae0f8c",
   "metadata": {},
   "source": [
    "#### Importing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "7bb3061c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_heart = pd.read_csv(\"heart_disease.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "5b3f64da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data_heart)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8552df94",
   "metadata": {},
   "source": [
    "##### Finding out the samples and features number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "f4445f3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_heart.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "574052e3",
   "metadata": {},
   "source": [
    "The provided dataset contains 303 samples and 14 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "e0c1f18e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_heart.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c59b4b3",
   "metadata": {},
   "source": [
    "##### Finding out the statistical description¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "9d40575b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_heart.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19988163",
   "metadata": {},
   "source": [
    "##### Checking out datatype information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "d277e4ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "data_heart.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ab7af59",
   "metadata": {},
   "source": [
    "##### Checking for the missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "5abb655f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_heart.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026e4d26",
   "metadata": {},
   "source": [
    "There is no missing value in this dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f3a1e2",
   "metadata": {},
   "source": [
    "##### Data type target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "c36058f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_heart.target.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9fdca9c",
   "metadata": {},
   "source": [
    "Here, 1= Patients with heart disease\n",
    "\n",
    "     0= Patients without heart disease "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "033f4de3",
   "metadata": {},
   "source": [
    "##### Storing in X and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "02d353cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y=data_heart,data_heart.target\n",
    "X.drop('target',axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "834e7cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 13)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a5921103",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303,)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d5215e",
   "metadata": {},
   "source": [
    "##### Data Normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57f3f2a1",
   "metadata": {},
   "source": [
    "Here, the Z-score (Zero mean unit variance) is implemented. The process is to centering the feature at mean 0 with a standard deviation of 1. Z-score normalization is highly impactful for SVM classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2c0ab3bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e9010104",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d095498a",
   "metadata": {},
   "source": [
    "##### Dimensionality reduction by principal component analysis (PCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c88843ff",
   "metadata": {},
   "source": [
    "Among the dimensionality reduction algorithms Principal Component Analysis(PCA) is one of the most popular linear dimension reduction algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1886612a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "# feature extraction\n",
    "pca = PCA(n_components=10) # extracting 10 features out of 13 features  \n",
    "fit = pca.fit(X)\n",
    "X_pca = pca.transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03cdd678",
   "metadata": {},
   "source": [
    "##### Retrieving model accuracy: Spliting dataset into training and testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96829b71",
   "metadata": {},
   "source": [
    "The dataset has ben split into 80% training and 20% test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "030323ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=5,test_size=0.4,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "0c7486c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_set_x shape: (181, 13)\n",
      "train_set_y shape: (181,)\n",
      "test_set_x shape: (122, 13)\n",
      "test_set_y shape: (122,)\n"
     ]
    }
   ],
   "source": [
    "print (\"train_set_x shape: \" + str(X_train.shape))\n",
    "print (\"train_set_y shape: \" + str(y_train.shape))\n",
    "print (\"test_set_x shape: \" + str(X_test.shape))\n",
    "print (\"test_set_y shape: \" + str(y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6689a5f1",
   "metadata": {},
   "source": [
    "##### Creating SVM classifier\n",
    "Creation of SVM classifier and train it on the 80% of the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2a104298",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(probability=True)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_heart_disease = SVC(probability=True)\n",
    "clf_heart_disease.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3558774e",
   "metadata": {},
   "source": [
    "Analyzing the prediction accuracy on 20% test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "929eec7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The classifier accuracy score is 0.81\n"
     ]
    }
   ],
   "source": [
    "classifier_score = clf_heart_disease.score(X_test, y_test)\n",
    "print ('The classifier accuracy score is {:03.2f}'.format(classifier_score))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f512f7",
   "metadata": {},
   "source": [
    "Accessing cross validation score with k value of 10 and 3 fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1b5e9efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf2_heart_disease = make_pipeline(SelectKBest(f_regression, k=10),SVC(probability=True))\n",
    "scores = cross_val_score(clf2_heart_disease, X_pca, y, cv=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13fb4867",
   "metadata": {},
   "source": [
    "Getting average of 10-fold cross-validation score using an SVC estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "e101b9e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 10-fold cross-validation accuracy score for this classifier is 0.83\n"
     ]
    }
   ],
   "source": [
    "n_folds = 10\n",
    "cv_error = np.average(cross_val_score(SVC(), X_pca, y, cv=n_folds))\n",
    "print ('The {}-fold cross-validation accuracy score for this classifier is {:.2f}'.format(n_folds, cv_error))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3562151c",
   "metadata": {},
   "source": [
    "##### Generation of Classification Report\n",
    "In order to access the quality of prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "53b3eb6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.80      0.79        55\n",
      "           1       0.83      0.82      0.83        67\n",
      "\n",
      "    accuracy                           0.81       122\n",
      "   macro avg       0.81      0.81      0.81       122\n",
      "weighted avg       0.81      0.81      0.81       122\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf_heart_disease.fit(X_train, y_train).predict(X_test)\n",
    "class_report = metrics.confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(classification_report(y_test, y_pred ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d1b3150",
   "metadata": {},
   "source": [
    "##### The classifier accuracy is 0.81"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "2f513463",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.matshow(class_report, cmap=plt.cm.Reds, alpha=0.5)\n",
    "for i in range(class_report.shape[1]):\n",
    "     for j in range(class_report.shape[0]):\n",
    "         ax.text(x=i, y=j,\n",
    "                s=class_report[j, i], \n",
    "                va='center', ha='center')\n",
    "plt.xlabel('Predicted Values', )\n",
    "plt.ylabel('Actual Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e4712a3",
   "metadata": {},
   "source": [
    "##### Understanding the Confusion Matrix\n",
    "* The top left box indicates True Negative value (TN).\n",
    "\n",
    "* The top right box indicates False Negative value (FN). \n",
    "\n",
    "* The bottom left box indicates False Positive (FP) value.\n",
    "\n",
    "* The bottom right box indicates True Positive (TP) value..\n",
    "---------------------------------------------------------------------------------\n",
    "- 44 patients are predicted corrected without heart disease.\n",
    "\n",
    "- 11 patients are wrongly predicted without heart disease.\n",
    "\n",
    "- 12 patients are wrongly predicted with heart disease.\n",
    "\n",
    "- 55 patients are predicted correctly with heart disease.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e53bddd6",
   "metadata": {},
   "source": [
    "##### Optimization of SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c5698b",
   "metadata": {},
   "source": [
    "###### For the optimization the following steps can be implied:\n",
    "\n",
    "- Tuning of hyperparameters (C and gamma).\n",
    "\n",
    "   - C hyperparameter is used to control error.\n",
    "               \n",
    "   - gamma hyperparameter control the curving of decision boundary.\n",
    "   \n",
    "               \n",
    "- Tuning the kernel type (SVM-linear, SVM-polynomial, SVM-RBF (Gaussian radial basis function), SVM- sigmoid).\n",
    "\n",
    "- Grid search parameter tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "800836f8",
   "metadata": {},
   "source": [
    "##### Training classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "dae53218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=8, estimator=SVC(),\n",
       "             param_grid={'C': array([1.00000000e-03, 1.77827941e-02, 3.16227766e-01, 5.62341325e+00,\n",
       "       1.00000000e+02]),\n",
       "                         'gamma': array([1.00000000e-03, 1.77827941e-02, 3.16227766e-01, 5.62341325e+00,\n",
       "       1.00000000e+02]),\n",
       "                         'kernel': ['linear', 'poly', 'rbf', 'sigmoid']})"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel_values = [ 'linear' ,  'poly' ,  'rbf' ,  'sigmoid' ]\n",
    "param_grid = {'C': np.logspace(-3, 2, 5), 'gamma': np.logspace(-3, 2, 5),'kernel': kernel_values}\n",
    "\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid, cv=8) # 3 fold grid search cross validation\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "ce4806a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best parameters are {'C': 100.0, 'gamma': 0.01778279410038923, 'kernel': 'sigmoid'} with a score of 0.82\n"
     ]
    }
   ],
   "source": [
    "print(\"The best parameters are %s with a score of %0.2f\"\n",
    "      % (grid.best_params_, grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "9e2bddc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid.best_estimator_.probability = True\n",
    "clf_heart_optimized = grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c9ac1ec",
   "metadata": {},
   "source": [
    "##### Generation of Classification report after Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "aff6e776",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[47  8]\n",
      " [10 57]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.85      0.84        55\n",
      "           1       0.88      0.85      0.86        67\n",
      "\n",
      "    accuracy                           0.85       122\n",
      "   macro avg       0.85      0.85      0.85       122\n",
      "weighted avg       0.85      0.85      0.85       122\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = clf_heart_optimized.fit(X_train, y_train).predict(X_test)\n",
    "c_m_optimized= metrics.confusion_matrix(y_test, y_pred)\n",
    "print(c_m_optimized)\n",
    "print(classification_report(y_test, y_pred ))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.matshow(c_m_optimized, cmap=plt.cm.Reds, alpha=0.3)\n",
    "for i in range(c_m_optimized.shape[0]):\n",
    "     for j in range(c_m_optimized.shape[1]):\n",
    "         ax.text(x=j, y=i,\n",
    "                s=c_m_optimized[i, j], \n",
    "                va='center', ha='center')\n",
    "plt.xlabel('Predicted Values', )\n",
    "plt.ylabel('Actual Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257a0b56",
   "metadata": {},
   "source": [
    "##### Accuracy score after optimization is 0.85"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "119c974c",
   "metadata": {},
   "source": [
    "##### Understanding confusion matrix after optimization\n",
    "\n",
    "- 47 patients are predicted corrected without heart disease.\n",
    "\n",
    "- 8 patients are wrongly predicted without heart disease.\n",
    "\n",
    "- 10 patients are wrongly predicted with heart disease.\n",
    "\n",
    "- 57 patients are predicted correctly with heart disease.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8991d32",
   "metadata": {},
   "source": [
    "##### Plotting Decision Boundaries of SVM with different kernels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "3ed4c1ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn import svm, datasets\n",
    "\n",
    "def decision_plot(X_train, y_train, n_neighbors, weights):\n",
    "       h = .02  # step size in the mesh\n",
    "\n",
    "Xtrain = X_train[:, :2] #taking the first two features.\n",
    "\n",
    "# Creating color maps\n",
    "\n",
    "cmap_light = ListedColormap(['#7fc97f', '#386cb0', '#f0027f'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "\n",
    "C = 3.0  # SVM regularization parameter\n",
    "\n",
    "svm = SVC(kernel='linear', random_state=0, gamma=0.1, C=C).fit(Xtrain, y_train)\n",
    "rbf_svc = SVC(kernel='rbf', gamma=0.8, C=C).fit(Xtrain, y_train)\n",
    "poly_svc = SVC(kernel='poly', degree=2, C=C).fit(Xtrain, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "0c168422",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (16, 10) \n",
    "plt.rcParams['axes.titlesize'] = 'large'\n",
    "    \n",
    "#creating a mesh to plot in\n",
    "x_min, x_max = Xtrain[:, 0].min() - 1, Xtrain[:, 0].max() + 1\n",
    "y_min, y_max = Xtrain[:, 1].min() - 1, Xtrain[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                         np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "# title for the plots\n",
    "titles = ['SVC with linear kernel',\n",
    "          'SVC with RBF kernel',\n",
    "          'SVC with polynomial (degree 2) kernel']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "8bee8e95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, clf in enumerate((svm, rbf_svc, poly_svc)):\n",
    "    plt.subplot(2, 2, i + 1)\n",
    "    plt.subplots_adjust(wspace=0.4, hspace=0.4)\n",
    "\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # Puting the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)\n",
    "\n",
    "    # Ploting the training points\n",
    "    plt.scatter(Xtrain[:, 0], Xtrain[:, 1], c=y_train, cmap=plt.cm.coolwarm)\n",
    "    plt.xlabel('radius_mean')\n",
    "    plt.ylabel('texture_mean')\n",
    "    plt.xlim(xx.min(), xx.max())\n",
    "    plt.ylim(yy.min(), yy.max())\n",
    "    plt.xticks(())\n",
    "    plt.yticks(())\n",
    "    plt.title(titles[i])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74cfd881",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db8afd72",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}