Decision Tree Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Packages"
   ]
  },
  {
   "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": 6,
   "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": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "       [  3.50000000e+01,   5.70000000e+04],\n",
       "       [  3.60000000e+01,   6.30000000e+04],\n",
       "       [  4.20000000e+01,   7.30000000e+04],\n",
       "       [  4.30000000e+01,   1.12000000e+05],\n",
       "       [  4.50000000e+01,   7.90000000e+04],\n",
       "       [  4.60000000e+01,   1.17000000e+05],\n",
       "       [  5.80000000e+01,   3.80000000e+04],\n",
       "       [  4.80000000e+01,   7.40000000e+04],\n",
       "       [  3.70000000e+01,   1.37000000e+05],\n",
       "       [  3.70000000e+01,   7.90000000e+04],\n",
       "       [  4.00000000e+01,   6.00000000e+04],\n",
       "       [  4.20000000e+01,   5.40000000e+04],\n",
       "       [  5.10000000e+01,   1.34000000e+05],\n",
       "       [  4.70000000e+01,   1.13000000e+05],\n",
       "       [  3.60000000e+01,   1.25000000e+05],\n",
       "       [  3.80000000e+01,   5.00000000e+04],\n",
       "       [  4.20000000e+01,   7.00000000e+04],\n",
       "       [  3.90000000e+01,   9.60000000e+04],\n",
       "       [  3.80000000e+01,   5.00000000e+04],\n",
       "       [  4.90000000e+01,   1.41000000e+05],\n",
       "       [  3.90000000e+01,   7.90000000e+04],\n",
       "       [  3.90000000e+01,   7.50000000e+04],\n",
       "       [  5.40000000e+01,   1.04000000e+05],\n",
       "       [  3.50000000e+01,   5.50000000e+04],\n",
       "       [  4.50000000e+01,   3.20000000e+04],\n",
       "       [  3.60000000e+01,   6.00000000e+04],\n",
       "       [  5.20000000e+01,   1.38000000e+05],\n",
       "       [  5.30000000e+01,   8.20000000e+04],\n",
       "       [  4.10000000e+01,   5.20000000e+04],\n",
       "       [  4.80000000e+01,   3.00000000e+04],\n",
       "       [  4.80000000e+01,   1.31000000e+05],\n",
       "       [  4.10000000e+01,   6.00000000e+04],\n",
       "       [  4.10000000e+01,   7.20000000e+04],\n",
       "       [  4.20000000e+01,   7.50000000e+04],\n",
       "       [  3.60000000e+01,   1.18000000e+05],\n",
       "       [  4.70000000e+01,   1.07000000e+05],\n",
       "       [  3.80000000e+01,   5.10000000e+04],\n",
       "       [  4.80000000e+01,   1.19000000e+05],\n",
       "       [  4.20000000e+01,   6.50000000e+04],\n",
       "       [  4.00000000e+01,   6.50000000e+04],\n",
       "       [  5.70000000e+01,   6.00000000e+04],\n",
       "       [  3.60000000e+01,   5.40000000e+04],\n",
       "       [  5.80000000e+01,   1.44000000e+05],\n",
       "       [  3.50000000e+01,   7.90000000e+04],\n",
       "       [  3.80000000e+01,   5.50000000e+04],\n",
       "       [  3.90000000e+01,   1.22000000e+05],\n",
       "       [  5.30000000e+01,   1.04000000e+05],\n",
       "       [  3.50000000e+01,   7.50000000e+04],\n",
       "       [  3.80000000e+01,   6.50000000e+04],\n",
       "       [  4.70000000e+01,   5.10000000e+04],\n",
       "       [  4.70000000e+01,   1.05000000e+05],\n",
       "       [  4.10000000e+01,   6.30000000e+04],\n",
       "       [  5.30000000e+01,   7.20000000e+04],\n",
       "       [  5.40000000e+01,   1.08000000e+05],\n",
       "       [  3.90000000e+01,   7.70000000e+04],\n",
       "       [  3.80000000e+01,   6.10000000e+04],\n",
       "       [  3.80000000e+01,   1.13000000e+05],\n",
       "       [  3.70000000e+01,   7.50000000e+04],\n",
       "       [  4.20000000e+01,   9.00000000e+04],\n",
       "       [  3.70000000e+01,   5.70000000e+04],\n",
       "       [  3.60000000e+01,   9.90000000e+04],\n",
       "       [  6.00000000e+01,   3.40000000e+04],\n",
       "       [  5.40000000e+01,   7.00000000e+04],\n",
       "       [  4.10000000e+01,   7.20000000e+04],\n",
       "       [  4.00000000e+01,   7.10000000e+04],\n",
       "       [  4.20000000e+01,   5.40000000e+04],\n",
       "       [  4.30000000e+01,   1.29000000e+05],\n",
       "       [  5.30000000e+01,   3.40000000e+04],\n",
       "       [  4.70000000e+01,   5.00000000e+04],\n",
       "       [  4.20000000e+01,   7.90000000e+04],\n",
       "       [  4.20000000e+01,   1.04000000e+05],\n",
       "       [  5.90000000e+01,   2.90000000e+04],\n",
       "       [  5.80000000e+01,   4.70000000e+04],\n",
       "       [  4.60000000e+01,   8.80000000e+04],\n",
       "       [  3.80000000e+01,   7.10000000e+04],\n",
       "       [  5.40000000e+01,   2.60000000e+04],\n",
       "       [  6.00000000e+01,   4.60000000e+04],\n",
       "       [  6.00000000e+01,   8.30000000e+04],\n",
       "       [  3.90000000e+01,   7.30000000e+04],\n",
       "       [  5.90000000e+01,   1.30000000e+05],\n",
       "       [  3.70000000e+01,   8.00000000e+04],\n",
       "       [  4.60000000e+01,   3.20000000e+04],\n",
       "       [  4.60000000e+01,   7.40000000e+04],\n",
       "       [  4.20000000e+01,   5.30000000e+04],\n",
       "       [  4.10000000e+01,   8.70000000e+04],\n",
       "       [  5.80000000e+01,   2.30000000e+04],\n",
       "       [  4.20000000e+01,   6.40000000e+04],\n",
       "       [  4.80000000e+01,   3.30000000e+04],\n",
       "       [  4.40000000e+01,   1.39000000e+05],\n",
       "       [  4.90000000e+01,   2.80000000e+04],\n",
       "       [  5.70000000e+01,   3.30000000e+04],\n",
       "       [  5.60000000e+01,   6.00000000e+04],\n",
       "       [  4.90000000e+01,   3.90000000e+04],\n",
       "       [  3.90000000e+01,   7.10000000e+04],\n",
       "       [  4.70000000e+01,   3.40000000e+04],\n",
       "       [  4.80000000e+01,   3.50000000e+04],\n",
       "       [  4.80000000e+01,   3.30000000e+04],\n",
       "       [  4.70000000e+01,   2.30000000e+04],\n",
       "       [  4.50000000e+01,   4.50000000e+04],\n",
       "       [  6.00000000e+01,   4.20000000e+04],\n",
       "       [  3.90000000e+01,   5.90000000e+04],\n",
       "       [  4.60000000e+01,   4.10000000e+04],\n",
       "       [  5.10000000e+01,   2.30000000e+04],\n",
       "       [  5.00000000e+01,   2.00000000e+04],\n",
       "       [  3.60000000e+01,   3.30000000e+04],\n",
       "       [  4.90000000e+01,   3.60000000e+04]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "       1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1,\n",
       "       1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1,\n",
       "       0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "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 Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 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 Classifier to the Training Set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "            max_features=None, 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",
       "            presort=False, random_state=0, splitter='best')"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 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",
       "       1, 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, 1, 0, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[62,  6],\n",
       "       [ 3, 29]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Training Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vS/aMUnkXi6w8C5ZdzLDLZzUsWR/feK08uZdlXVvY3lZkdtF79u6O7syyfJo9o6sRZGYQ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\ntcbtiGNlfH0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x217005299b0>"
      ]
     },
     "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', '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('Decision Tree (Training Set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visulaizing the Test Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uYAGAmU0GxgATzGyCu0c8IlugtEs0VJJCA22+p/ahBfA2OOvKw6+G7GQHma1J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\nAv8farW2zHAaunUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x217007f4278>"
      ]
     },
     "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', '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', 'yellow'))(i), label = j)\n",
    "plt.title('Decision Tree (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
}