SVM Lecture.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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')\n",
    "X = dataset.iloc[:, [2, 3]].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User ID', 'Gender', 'Age', 'EstimatedSalary', 'Purchased'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the Data Into the Train Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\atse\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "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": 8,
   "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 SVM to the Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = SVC(kernel = 'linear', random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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='linear',\n",
       "  max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict the Test Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[66,  2],\n",
       "       [ 8, 24]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Traing Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_set, y_set = X_train, y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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fm5thzKXkc3NzpbxNTTA+7m/eMAol6LUu9P5YWftKOD7zzNicU8448yuvD1KW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\n/wNpOkDJG2uAYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa09a57c88>"
      ]
     },
     "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('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": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_set, y_set = X_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9lAaXAdamYKLlJyEMiMgs4OfABhFZB7wUwLnPAJ6Z8P5Zd9skIrJGRLaJyLaR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa09a578d0>"
      ]
     },
     "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('Classifier (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": 0
}