Kernel SVM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Social_Network_Ads.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, [2,3]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User ID', 'Gender', 'Age', 'EstimatedSalary', 'Purchased'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the Dataset into the Training Set and Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting the Classifier to the Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = SVC(kernel = 'rbf', random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Test Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[64,  4],\n",
       "       [ 3, 29]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Training Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_set, y_set = X_train, y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nG5wvHv49AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24db565d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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('Kernel SVM (Training Set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Test Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_set, y_set = X_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24db565dc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('blue', '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(('blue', '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": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}