{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Kernel Support Vector Machine (SVM)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url='https://raw.githubusercontent.com/cagriemreakin/Machine-Learning/master/2%20-%20Classification/3%20-%20Kernel%20SVM/sosyal_ag_reklamlari.csv'\n",
    "dataset = pd.read_csv(url)\n",
    "X = dataset.iloc[:, [2, 3]].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Eğitim ve test set olarak ayırma\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ceakn\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train_scaled = sc.fit_transform(X_train)\n",
    "X_test_scaled = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modelimizi training set' e göre eğitme\n",
    "from sklearn.svm import SVC\n",
    "#Nonlinear SVM . kernel = rbf \n",
    "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
    "classifier.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Test setine göre sonuçları tahmin etme\n",
    "y_pred = classifier.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[55,  3],\n",
       "       [ 1, 21]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion Matrix ile hatalı tahminleri bulma\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doğruluk oranı: % 95.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Doğruluk oranı: %\", sum(np.diag(cm))/ cm.sum()*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eaY0I0gSw/FMg5FEabfVxn/AL3K6flrOXb2Xr8U57ewdrltW3LHcaT94aEVRc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x222705e35c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test set tahminlerinin görselleştirilmesi\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test_scaled, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('SVM Test Set')\n",
    "plt.xlabel('Yas')\n",
    "plt.ylabel('Maas')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}