DecisionTree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows     :  7043\n",
      "Columns  :  21\n",
      "Rows     :  7032\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 468x187.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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      "Intercept: [-0.96827337]\n",
      "Regression: [[ 0.23227207 -0.03411993 -0.16560479 -0.03193762 -0.8841879   0.10755879\n",
      "   0.27887738 -0.52600566 -0.26126309 -0.12779354 -0.50309034 -0.66593924\n",
      "   0.34245058 -0.13372566  0.02568169]]\n",
      "Accuracy of logistic regression classifier on test set: 0.80\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.89      0.86      1010\n",
      "           1       0.67      0.56      0.61       397\n",
      "\n",
      "   micro avg       0.80      0.80      0.80      1407\n",
      "   macro avg       0.75      0.73      0.74      1407\n",
      "weighted avg       0.79      0.80      0.79      1407\n",
      "\n"
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      "/home/palak/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning:\n",
      "\n",
      "Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "\n"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/palak/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning:\n",
      "\n",
      "Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.76\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.72      0.75      1037\n",
      "           1       0.74      0.81      0.77      1029\n",
      "\n",
      "   micro avg       0.76      0.76      0.76      2066\n",
      "   macro avg       0.77      0.76      0.76      2066\n",
      "weighted avg       0.77      0.76      0.76      2066\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/palak/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:246: FutureWarning:\n",
      "\n",
      "The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "\n",
      "/home/palak/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:246: FutureWarning:\n",
      "\n",
      "The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of decision tree unpruned classifier on test set: 0.77\n",
      "Accuracy of random forest classifier on test set: 0.77\n",
      "Accuracy of random forest classifier on test set: 0.90\n",
      "Accuracy of random forest classifier on test set: 0.75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/palak/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/forest.py:246: FutureWarning:\n",
      "\n",
      "The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "empty range for randrange()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m~/Customer_Churn_Prediction/Data_Analysis.ipynb\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn_tree\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mn_trees\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 270\u001b[0;31m     \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meval_algorithm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtelcom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_forest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_folds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_tree\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    271\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Trees: %d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mn_tree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Scores: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Customer_Churn_Prediction/Data_Analysis.ipynb\u001b[0m in \u001b[0;36meval_algorithm\u001b[0;34m(dset, algorithm, k, *args)\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0meval_algorithm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malgorithm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m     \u001b[0mfolds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_validation_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m     \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Customer_Churn_Prediction/Data_Analysis.ipynb\u001b[0m in \u001b[0;36mcross_validation_split\u001b[0;34m(dset, k)\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfold\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mfold_size\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m             \u001b[0mfold\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdset_cpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdset_cpy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mdset_split\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/random.py\u001b[0m in \u001b[0;36mrandrange\u001b[0;34m(self, start, stop, step, _int)\u001b[0m\n\u001b[1;32m    188\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mistart\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_randbelow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mistart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"empty range for randrange()\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m         \u001b[0;31m# stop argument supplied.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: empty range for randrange()"
     ]
    }
   ],
   "source": [
    "%run Data_Analysis.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'values'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-8422650aeb8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtelcom1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtelcom1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'values'"
     ]
    }
   ],
   "source": [
    "telcom1 = telcom1.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "\n",
    "import CSVReader\n",
    "import random\n",
    "from math import log, sqrt\n",
    "\n",
    "\n",
    "class DecisionTreeClassifier:\n",
    "\n",
    "    class DecisionNode:\n",
    "        def __init__(self, col=-1, value=None, results=None, tb=None, fb=None):\n",
    "            self.col = col\n",
    "            self.value = value\n",
    "            self.results = results\n",
    "            self.tb = tb\n",
    "            self.fb = fb\n",
    "\n",
    "    \"\"\"\n",
    "    :param  max_depth:          Maximum number of splits during training\n",
    "    :param  random_features:    If False, all the features will be used to\n",
    "                                train and predict. Otherwise, a random set of\n",
    "                                size sqrt(nb features) will be chosen in the\n",
    "                                features.\n",
    "                                Usually, this option is used in a random\n",
    "                                forest.\n",
    "    \"\"\"\n",
    "    def __init__(self, max_depth=-1, random_features=False):\n",
    "        self.root_node = None\n",
    "        self.max_depth = max_depth\n",
    "        self.features_indexes = []\n",
    "        self.random_features = random_features\n",
    "\n",
    "    \"\"\"\n",
    "    :param  rows:       The data used to rain the decision tree. It must be a\n",
    "                        list of lists. The last vaue of each inner list is the\n",
    "                        value to predict.\n",
    "    :param  criterion:  The function used to split data at each node of the\n",
    "                        tree. If None, the criterion used is entropy.\n",
    "    \"\"\"\n",
    "    def fit(self, rows, criterion=None):\n",
    "        if len(rows) < 1:\n",
    "            raise ValueError(\"Not enough samples in the given dataset\")\n",
    "\n",
    "        if not criterion:\n",
    "            criterion = self.entropy\n",
    "        if self.random_features:\n",
    "            self.features_indexes = self.choose_random_features(rows[0])\n",
    "            rows = [self.get_features_subset(row) + [row[-1]] for row in rows]\n",
    "\n",
    "        self.root_node = self.build_tree(rows, criterion, self.max_depth)\n",
    "\n",
    "    \"\"\"\n",
    "    Returns a prediction for the given features.\n",
    "    :param  features:   A list of values\n",
    "    \"\"\"\n",
    "    def predict(self, features):\n",
    "        if self.random_features:\n",
    "            if not all(i in range(len(features))\n",
    "                       for i in self.features_indexes):\n",
    "                raise ValueError(\"The given features don't match\\\n",
    "                                 the training set\")\n",
    "            features = self.get_features_subset(features)\n",
    "\n",
    "        return self.classify(features, self.root_node)\n",
    "\n",
    "    \"\"\"\n",
    "    Randomly selects indexes in the given list.\n",
    "    \"\"\"\n",
    "    def choose_random_features(self, row):\n",
    "        nb_features = len(row) - 1\n",
    "        return random.sample(range(nb_features), int(sqrt(nb_features)))\n",
    "\n",
    "    \"\"\"\n",
    "    Returns the randomly selected values in the given features\n",
    "    \"\"\"\n",
    "    def get_features_subset(self, row):\n",
    "        return [row[i] for i in self.features_indexes]\n",
    "\n",
    "    \"\"\"\n",
    "    Divides the given dataset depending on the value at the given column index.\n",
    "    :param  rows:   The dataset\n",
    "    :param  column: The index of the column used to split data\n",
    "    :param  value:  The value used for the split\n",
    "    \"\"\"\n",
    "    def divide_set(self, rows, column, value):\n",
    "        split_function = None\n",
    "        if isinstance(value, int) or isinstance(value, float):\n",
    "            split_function = lambda row: row[column] >= value\n",
    "        else:\n",
    "            split_function = lambda row: row[column] == value\n",
    "\n",
    "        set1 = [row for row in rows if split_function(row)]\n",
    "        set2 = [row for row in rows if not split_function(row)]\n",
    "\n",
    "        return set1, set2\n",
    "\n",
    "    \"\"\"\n",
    "    Returns the occurence of each result in the given dataset.\n",
    "    :param  rows:   A list of lists with the output at the last index of\n",
    "                    each one\n",
    "    \"\"\"\n",
    "    def unique_counts(self, rows):\n",
    "        results = {}\n",
    "        for row in rows:\n",
    "            r = row[len(row) - 1]\n",
    "            if r not in results:\n",
    "                results[r] = 0\n",
    "            results[r] += 1\n",
    "        return results\n",
    "\n",
    "    \"\"\"\n",
    "    Returns the entropy in the given rows.\n",
    "    :param  rows:   A list of lists with the output at the last index of\n",
    "                    each one\n",
    "    \"\"\"\n",
    "    def entropy(self, rows):\n",
    "        log2 = lambda x: log(x) / log(2)\n",
    "        results = self.unique_counts(rows)\n",
    "        ent = 0.0\n",
    "        for r in results.keys():\n",
    "            p = float(results[r]) / len(rows)\n",
    "            ent = ent - p * log2(p)\n",
    "        return ent\n",
    "\n",
    "    \"\"\"\n",
    "    Recursively creates the decision tree by splitting the dataset until no\n",
    "    gain of information is added, or until the max depth is reached.\n",
    "    :param  rows:   The dataset\n",
    "    :param  func:   The function used to calculate the best split and stop\n",
    "                    condition\n",
    "    :param  depth:  The current depth in the tree\n",
    "    \"\"\"\n",
    "    def build_tree(self, rows, func, depth):\n",
    "        if len(rows) == 0:\n",
    "            return self.DecisionNode()\n",
    "        if depth == 0:\n",
    "            return self.DecisionNode(results=self.unique_counts(rows))\n",
    "\n",
    "        current_score = func(rows)\n",
    "        best_gain = 0.0\n",
    "        best_criteria = None\n",
    "        best_sets = None\n",
    "        column_count = len(rows[0]) - 1\n",
    "\n",
    "        for col in range(0, column_count):\n",
    "            column_values = {}\n",
    "            for row in rows:\n",
    "                column_values[row[col]] = 1\n",
    "            for value in column_values.keys():\n",
    "                set1, set2 = self.divide_set(rows, col, value)\n",
    "\n",
    "                p = float(len(set1)) / len(rows)\n",
    "                gain = current_score - p * func(set1) - (1 - p) * func(set2)\n",
    "                if gain > best_gain and len(set1) > 0 and len(set2) > 0:\n",
    "                    best_gain = gain\n",
    "                    best_criteria = (col, value)\n",
    "                    best_sets = (set1, set2)\n",
    "\n",
    "        if best_gain > 0:\n",
    "            trueBranch = self.build_tree(best_sets[0], func, depth - 1)\n",
    "            falseBranch = self.build_tree(best_sets[1], func, depth - 1)\n",
    "            return self.DecisionNode(col=best_criteria[0],\n",
    "                                     value=best_criteria[1],\n",
    "                                     tb=trueBranch, fb=falseBranch)\n",
    "        else:\n",
    "            return self.DecisionNode(results=self.unique_counts(rows))\n",
    "\n",
    "    \"\"\"\n",
    "    Makes a prediction using the given features.\n",
    "    :param  observation:    The features to use to predict\n",
    "    :param  tree:           The current node\n",
    "    \"\"\"\n",
    "    def classify(self, observation, tree):\n",
    "        if tree.results is not None:\n",
    "            return list(tree.results.keys())[0]\n",
    "        else:\n",
    "            v = observation[tree.col]\n",
    "            branch = None\n",
    "            if isinstance(v, int) or isinstance(v, float):\n",
    "                if v >= tree.value:\n",
    "                    branch = tree.tb\n",
    "                else:\n",
    "                    branch = tree.fb\n",
    "            else:\n",
    "                if v == tree.value:\n",
    "                    branch = tree.tb\n",
    "                else:\n",
    "                    branch = tree.fb\n",
    "            return self.classify(observation, branch)\n",
    "    \n",
    "    def test_tree():\n",
    "        #data = CSVReader.read_csv(\"income.csv\")\n",
    "        data= telcom1\n",
    "        tree = DecisionTreeClassifier(random_features=True)\n",
    "        tree.fit(data)\n",
    "        print(tree.predict([39, 'State-gov', 'Bachelors', 13, 'Never-married',\n",
    "                        'Adm-clerical', 'Not-in-family', 'White', 'Male',\n",
    "                        2174, 0, 40, 'United-States']))\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    test_tree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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