Random Forest Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import the Packages"
   ]
  },
  {
   "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": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Social_Network_Ads.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, [2, 3]].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the Dataset into the Training Set and Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = sc.fit_transform(X_train)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "       [-0.21060859,  2.15757314],\n",
       "       [-1.99318916, -0.04590581],\n",
       "       [ 0.8787462 , -0.77073441],\n",
       "       [-0.80480212, -0.59677555],\n",
       "       [-1.00286662, -0.42281668],\n",
       "       [-0.11157634, -0.42281668],\n",
       "       [ 0.08648817,  0.21503249],\n",
       "       [-1.79512465,  0.47597078],\n",
       "       [-0.60673761,  1.37475825],\n",
       "       [-0.11157634,  0.21503249],\n",
       "       [-1.89415691,  0.44697764],\n",
       "       [ 1.67100423,  1.75166912],\n",
       "       [-0.30964085, -1.37959044],\n",
       "       [-0.30964085, -0.65476184],\n",
       "       [ 0.8787462 ,  2.15757314],\n",
       "       [ 0.28455268, -0.53878926],\n",
       "       [ 0.8787462 ,  1.02684052],\n",
       "       [-1.49802789, -1.20563157],\n",
       "       [ 1.07681071,  2.07059371],\n",
       "       [-1.00286662,  0.50496393],\n",
       "       [-0.90383437,  0.30201192],\n",
       "       [-0.11157634, -0.21986468],\n",
       "       [-0.60673761,  0.47597078],\n",
       "       [-1.6960924 ,  0.53395707],\n",
       "       [-0.11157634,  0.27301877],\n",
       "       [ 1.86906873, -0.27785096],\n",
       "       [-0.11157634, -0.48080297],\n",
       "       [-1.39899564, -0.33583725],\n",
       "       [-1.99318916, -0.50979612],\n",
       "       [-1.59706014,  0.33100506],\n",
       "       [-0.4086731 , -0.77073441],\n",
       "       [-0.70576986, -1.03167271],\n",
       "       [ 1.07681071, -0.97368642],\n",
       "       [-1.10189888,  0.53395707],\n",
       "       [ 0.28455268, -0.50979612],\n",
       "       [-1.10189888,  0.41798449],\n",
       "       [-0.30964085, -1.43757673],\n",
       "       [ 0.48261718,  1.22979253],\n",
       "       [-1.10189888, -0.33583725],\n",
       "       [-0.11157634,  0.30201192],\n",
       "       [ 1.37390747,  0.59194336],\n",
       "       [-1.20093113, -1.14764529],\n",
       "       [ 1.07681071,  0.47597078],\n",
       "       [ 1.86906873,  1.51972397],\n",
       "       [-0.4086731 , -1.29261101],\n",
       "       [-0.30964085, -0.3648304 ],\n",
       "       [-0.4086731 ,  1.31677196],\n",
       "       [ 2.06713324,  0.53395707],\n",
       "       [ 0.68068169, -1.089659  ],\n",
       "       [-0.90383437,  0.38899135],\n",
       "       [-1.20093113,  0.30201192],\n",
       "       [ 1.07681071, -1.20563157],\n",
       "       [-1.49802789, -1.43757673],\n",
       "       [-0.60673761, -1.49556302],\n",
       "       [ 2.1661655 , -0.79972756],\n",
       "       [-1.89415691,  0.18603934],\n",
       "       [-0.21060859,  0.85288166],\n",
       "       [-1.89415691, -1.26361786],\n",
       "       [ 2.1661655 ,  0.38899135],\n",
       "       [-1.39899564,  0.56295021],\n",
       "       [-1.10189888, -0.33583725],\n",
       "       [ 0.18552042, -0.65476184],\n",
       "       [ 0.38358493,  0.01208048],\n",
       "       [-0.60673761,  2.331532  ],\n",
       "       [-0.30964085,  0.21503249],\n",
       "       [-1.59706014, -0.19087153],\n",
       "       [ 0.68068169, -1.37959044],\n",
       "       [-1.10189888,  0.56295021],\n",
       "       [-1.99318916,  0.35999821],\n",
       "       [ 0.38358493,  0.27301877],\n",
       "       [ 0.18552042, -0.27785096],\n",
       "       [ 1.47293972, -1.03167271],\n",
       "       [ 0.8787462 ,  1.08482681],\n",
       "       [ 1.96810099,  2.15757314],\n",
       "       [ 2.06713324,  0.38899135],\n",
       "       [-1.39899564, -0.42281668],\n",
       "       [-1.20093113, -1.00267957],\n",
       "       [ 1.96810099, -0.91570013],\n",
       "       [ 0.38358493,  0.30201192],\n",
       "       [ 0.18552042,  0.1570462 ],\n",
       "       [ 2.06713324,  1.75166912],\n",
       "       [ 0.77971394, -0.8287207 ],\n",
       "       [ 0.28455268, -0.27785096],\n",
       "       [ 0.38358493, -0.16187839],\n",
       "       [-0.11157634,  2.21555943],\n",
       "       [-1.49802789, -0.62576869],\n",
       "       [-1.29996338, -1.06066585],\n",
       "       [-1.39899564,  0.41798449],\n",
       "       [-1.10189888,  0.76590222],\n",
       "       [-1.49802789, -0.19087153],\n",
       "       [ 0.97777845, -1.06066585],\n",
       "       [ 0.97777845,  0.59194336],\n",
       "       [ 0.38358493,  0.99784738]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = sc.transform(X_test)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting Random Forest Classifier to the Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=10, n_jobs=1, oob_score=False, random_state=0,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 14,
     "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": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 1, 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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 0, 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, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[63,  5],\n",
       "       [ 3, 29]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising the Training Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mphmGYSTIddfBJZfAYX+v+x07vPcAZ52VXbvCYCMEwzCMBLniihFlUObwYa8875hCMAzD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16ba31f5d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_set, y_set = X_train, y_train\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', 'blue')))\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', 'blue'))(i), label = j)\n",
    "plt.title('Random Forest Classification (Training Set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising the Test Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UD4lN/5EPvFt3wvD9+tx95obRA2oaEh83Alvbu1F5wrYWVnf2DgqNXD3WX30u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\nREL/PxnaSn0b/i4sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16ba31f5cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'khaki')))\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('Random Forest Classification (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": {
  "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}