SVMrbf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Social_Network_Ads.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:,[2,3]].values\n",
    "y = df.iloc[:,4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "scx = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = scx.fit_transform(x_train)\n",
    "x_test = scx.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='rbf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = svc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.95      0.96        80\n",
      "           1       0.93      0.96      0.94        52\n",
      "\n",
      "    accuracy                           0.95       132\n",
      "   macro avg       0.95      0.96      0.95       132\n",
      "weighted avg       0.96      0.95      0.95       132\n",
      "\n",
      "\n",
      "\n",
      "[[76  4]\n",
      " [ 2 50]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predict))\n",
    "print('\\n')\n",
    "print(confusion_matrix(y_test,predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "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": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = x_test, 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, svc.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('Kernel SVM (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}