decision_tree1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "X= [\n",
    "    ['Green', 3],\n",
    "    ['Yellow', 3],\n",
    "    ['Red', 1],\n",
    "    ['Red', 1 ],\n",
    "    ['Yellow', 3]\n",
    "]\n",
    "y=['Apple', 'Apple', 'Grape', 'Grape', 'Lemon']\n",
    "#features\n",
    "x1=[X[a][0] for a in range(len(X))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encoding categorical data\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "lab=LabelEncoder()\n",
    "y=lab.fit_transform(y)\n",
    "lab1=LabelEncoder()\n",
    "x1=lab1.fit_transform(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing the encoded data in X\n",
    "for i in range(len(x1)):\n",
    "    X[i][0]=x1[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "header = [\"color\", \"diameter\", \"label\"]\n",
    "training_data = [\n",
    "    ['Green', 3, 'Apple'],\n",
    "    ['Yellow', 3, 'Apple'],\n",
    "    ['Red', 1, 'Grape'],\n",
    "    ['Red', 1, 'Grape'],\n",
    "    ['Yellow', 3, 'Lemon'],\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unique(rows,cols):\n",
    "    return set([rows[i][cols] for i in range(len(rows))])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "color = set(['Green', 'Red', 'Yellow'])\n",
      "diameter = set([1, 3])\n",
      "label = set(['Lemon', 'Grape', 'Apple'])\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(header)):\n",
    "        print header[i],'=',unique(training_data,i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def  class_counts(rows):\n",
    "    \"\"\"Counts the number of each type of example in a dataset.\"\"\"\n",
    "    count={}\n",
    "    for i in range(len(rows)):\n",
    "        label=rows[i][-1]\n",
    "        if label not in count:\n",
    "            count[label]=0\n",
    "        count[label]+=1\n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Apple': 2, 'Grape': 2, 'Lemon': 1}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_counts(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now spliting the data into dependent and independent variables\n",
    "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": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing the DecisionTreeClassifier and fitting the data\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier()\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([[2, 3], [1, 1], [2, 3]], [[1, 1], [0, 3]], array([0, 1, 2]), array([1, 0]))"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 1]), array([1, 0]))"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred,y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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