Classification Algorithms Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import tree\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# -------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Processing and Analysis"
   ]
  },
  {
   "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>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>Y</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex ChestPainType ExerciseAngina  Oldpeak ST_Slope  HeartDisease\n",
       "0    M           ATA              N      0.0       Up             0\n",
       "1    F           NAP              N      1.0     Flat             1\n",
       "2    M           ATA              N      0.0       Up             0\n",
       "3    F           ASY              Y      1.5     Flat             1\n",
       "4    M           NAP              N      0.0       Up             0\n",
       "..  ..           ...            ...      ...      ...           ...\n",
       "95   M           ASY              Y      2.0     Flat             1\n",
       "96   M           ATA              N      0.0       Up             0\n",
       "97   M           NAP              N      0.0       Up             0\n",
       "98   M           ASY              N      0.0       Up             0\n",
       "99   M           ATA              N      0.0       Up             0\n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name =  \"Dataset.xlsx\"\n",
    "sheet =  'Heart1'\n",
    "df = pd.read_excel(io=file_name, sheet_name=sheet)\n",
    "df=df.head(100)\n",
    "df=df[['Sex', 'ChestPainType', 'ExerciseAngina', 'Oldpeak', 'ST_Slope','HeartDisease']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    65\n",
       "1    35\n",
       "Name: HeartDisease, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HeartDisease'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex               False\n",
       "ChestPainType     False\n",
       "ExerciseAngina    False\n",
       "Oldpeak           False\n",
       "ST_Slope          False\n",
       "HeartDisease      False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df['Sex']:\n",
    "    if i == 'M':\n",
    "        df['Sex'] = df['Sex'].replace(['M'],0)\n",
    "    elif i == 'F':\n",
    "        df['Sex'] = df['Sex'].replace(['F'],1)\n",
    "        \n",
    "for i in df['ExerciseAngina']:\n",
    "    if i == 'N':\n",
    "        df['ExerciseAngina'] = df['ExerciseAngina'].replace(['N'],0)\n",
    "    elif i == 'Y':\n",
    "        df['ExerciseAngina'] = df['ExerciseAngina'].replace(['Y'],1)\n",
    "        \n",
    "        \n",
    "        \n",
    "for i in df['ST_Slope']:\n",
    "    if i == 'Up':\n",
    "        df['ST_Slope'] = df['ST_Slope'].replace(['Up'],0)\n",
    "    elif i == 'Flat':\n",
    "        df['ST_Slope'] = df['ST_Slope'].replace(['Flat'],1)\n",
    "        \n",
    "        \n",
    "for i in df['ChestPainType']:\n",
    "    if i == 'ATA':\n",
    "        df['ChestPainType'] = df['ChestPainType'].replace(['ATA'],0)\n",
    "    elif i == 'ASY':\n",
    "        df['ChestPainType'] = df['ChestPainType'].replace(['ASY'],1)\n",
    "    elif i == 'NAP':\n",
    "        df['ChestPainType'] = df['ChestPainType'].replace(['NAP'],2)\n",
    "    elif i == 'TA':\n",
    "        df['ChestPainType'] = df['ChestPainType'].replace(['TA'],3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation Matrix\n",
    "corr = df.corr()\n",
    "plt.figure(figsize=(14,10))\n",
    "sns.heatmap(corr, cmap=\"Purples\",annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df['HeartDisease']\n",
    "df = df.drop('HeartDisease', axis =1)\n",
    "\n",
    "X = df; Y = target\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3, random_state = 100)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# -------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "model = linear_model.LogisticRegression(random_state = 0,solver = 'liblinear')\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9142857142857143 Train\n",
      "0.9 Test\n",
      "Report :                precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.90      0.92        20\n",
      "           1       0.82      0.90      0.86        10\n",
      "\n",
      "    accuracy                           0.90        30\n",
      "   macro avg       0.88      0.90      0.89        30\n",
      "weighted avg       0.90      0.90      0.90        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model.score(X_train, y_train),'Train')\n",
    "print(model.score(X_test, y_test),'Test')\n",
    "print(\"Report : \",classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy Score: 90.00')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.heatmap(data=cm,linewidths=.5, annot=True,square = True,  cmap = 'Purples')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "all_sample_title = 'Accuracy Score: {:.2f}'.format(accuracy_score(y_test,y_pred)*100)\n",
    "plt.title(all_sample_title, size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred)\n",
    "plt.plot(fpr,tpr,label=\"auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# -------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model = RandomForestClassifier(n_estimators = 100) \n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9857142857142858 Train\n",
      "0.9 Test\n",
      "Report :                precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.90      0.92        20\n",
      "           1       0.82      0.90      0.86        10\n",
      "\n",
      "    accuracy                           0.90        30\n",
      "   macro avg       0.88      0.90      0.89        30\n",
      "weighted avg       0.90      0.90      0.90        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model.score(X_train, y_train),'Train')\n",
    "print(model.score(X_test, y_test),'Test')\n",
    "print(\"Report : \",classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy Score: 90.00')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.heatmap(data=cm,linewidths=.5, annot=True,square = True,  cmap = 'Purples')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "all_sample_title = 'Accuracy Score: {:.2f}'.format(accuracy_score(y_test,y_pred)*100)\n",
    "plt.title(all_sample_title, size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# -------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 : [0.9714285714285714, 0.9]\n",
      "3 : [0.9428571428571428, 0.9]\n",
      "4 : [0.9285714285714286, 0.9]\n",
      "5 : [0.9142857142857143, 0.9]\n",
      "6 : [0.8857142857142857, 0.9]\n",
      "7 : [0.9, 0.9]\n",
      "8 : [0.9, 0.9]\n",
      "9 : [0.8571428571428571, 0.9]\n",
      "10 : [0.8428571428571429, 0.9]\n",
      "11 : [0.8571428571428571, 0.9]\n",
      "12 : [0.8428571428571429, 0.9]\n",
      "13 : [0.8571428571428571, 0.9]\n",
      "14 : [0.7714285714285715, 0.8333333333333334]\n",
      "15 : [0.7714285714285715, 0.8666666666666667]\n",
      "16 : [0.7857142857142857, 0.8333333333333334]\n",
      "17 : [0.8, 0.8333333333333334]\n",
      "18 : [0.8, 0.8333333333333334]\n",
      "19 : [0.8, 0.8333333333333334]\n",
      "20 : [0.8, 0.8666666666666667]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "K = []\n",
    "training = []\n",
    "test = []\n",
    "scores = {}\n",
    "  \n",
    "for k in range(2, 21):\n",
    "    model = KNeighborsClassifier(n_neighbors = k)\n",
    "    model.fit(X_train, y_train)\n",
    "  \n",
    "    training_score = model.score(X_train, y_train)\n",
    "    test_score = model.score(X_test, y_test)\n",
    "    K.append(k)\n",
    "  \n",
    "    training.append(training_score)\n",
    "    test.append(test_score)\n",
    "    scores[k] = [training_score, test_score]\n",
    "    \n",
    "for keys, values in scores.items():\n",
    "    print(keys, ':', values)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KNeighborsClassifier(n_neighbors = 8)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9 Train\n",
      "0.9 Test\n",
      "Report :                precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.90      0.92        20\n",
      "           1       0.82      0.90      0.86        10\n",
      "\n",
      "    accuracy                           0.90        30\n",
      "   macro avg       0.88      0.90      0.89        30\n",
      "weighted avg       0.90      0.90      0.90        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model.score(X_train, y_train),'Train')\n",
    "print(model.score(X_test, y_test),'Test')\n",
    "print(\"Report : \",classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy Score: 90.00')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.heatmap(data=cm,linewidths=.5, annot=True,square = True,  cmap = 'Purples')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "all_sample_title = 'Accuracy Score: {:.2f}'.format(accuracy_score(y_test,y_pred)*100)\n",
    "plt.title(all_sample_title, size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# -------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "model = GaussianNB()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9 Train\n",
      "0.9333333333333333 Test\n",
      "Report :                precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.90      0.95        20\n",
      "           1       0.83      1.00      0.91        10\n",
      "\n",
      "    accuracy                           0.93        30\n",
      "   macro avg       0.92      0.95      0.93        30\n",
      "weighted avg       0.94      0.93      0.93        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model.score(X_train, y_train),'Train')\n",
    "print(model.score(X_test, y_test),'Test')\n",
    "print(\"Report : \",classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy Score: 93.33')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.heatmap(data=cm,linewidths=.5, annot=True,square = True,  cmap = 'Purples')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "all_sample_title = 'Accuracy Score: {:.2f}'.format(accuracy_score(y_test,y_pred)*100)\n",
    "plt.title(all_sample_title, size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# -------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "model = SVC()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9285714285714286 Train\n",
      "0.9333333333333333 Test\n",
      "Report :                precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.90      0.95        20\n",
      "           1       0.83      1.00      0.91        10\n",
      "\n",
      "    accuracy                           0.93        30\n",
      "   macro avg       0.92      0.95      0.93        30\n",
      "weighted avg       0.94      0.93      0.93        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(model.score(X_train, y_train),'Train')\n",
    "print(model.score(X_test, y_test),'Test')\n",
    "print(\"Report : \",classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy Score: 93.33')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.heatmap(data=cm,linewidths=.5, annot=True,square = True,  cmap = 'Purples')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "all_sample_title = 'Accuracy Score: {:.2f}'.format(accuracy_score(y_test,y_pred)*100)\n",
    "plt.title(all_sample_title, size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# -------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ALL OF THE MODELS ARE WORKING VERY NICELY WITH  ONLY 100 ROWS OF DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NAIVE BAYES & SVM PERFORM THE BEST WITH SIMILAR RESULTS"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gpu_tf",
   "language": "python",
   "name": "gpu_tf"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}