IMU_updated.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "imu_data = pd.read_csv(\"Dataset/Dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1.374342</th>\n",
       "      <th>-9.06491</th>\n",
       "      <th>-5.224414</th>\n",
       "      <th>-0.324076</th>\n",
       "      <th>0.16569</th>\n",
       "      <th>0.282479</th>\n",
       "      <th>-3.702203</th>\n",
       "      <th>32.476425</th>\n",
       "      <th>36.428134</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.324061</td>\n",
       "      <td>-9.108008</td>\n",
       "      <td>-4.994559</td>\n",
       "      <td>-0.350175</td>\n",
       "      <td>0.112426</td>\n",
       "      <td>0.269429</td>\n",
       "      <td>-4.936271</td>\n",
       "      <td>33.363761</td>\n",
       "      <td>37.625305</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.422228</td>\n",
       "      <td>-9.153500</td>\n",
       "      <td>-5.025685</td>\n",
       "      <td>-0.342185</td>\n",
       "      <td>0.121214</td>\n",
       "      <td>0.268098</td>\n",
       "      <td>-4.054794</td>\n",
       "      <td>32.831359</td>\n",
       "      <td>36.086086</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.388708</td>\n",
       "      <td>-9.036178</td>\n",
       "      <td>-5.114275</td>\n",
       "      <td>-0.361893</td>\n",
       "      <td>0.098843</td>\n",
       "      <td>0.250254</td>\n",
       "      <td>-4.759976</td>\n",
       "      <td>33.896160</td>\n",
       "      <td>36.428134</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.297723</td>\n",
       "      <td>-9.167866</td>\n",
       "      <td>-5.006531</td>\n",
       "      <td>-0.351240</td>\n",
       "      <td>0.077005</td>\n",
       "      <td>0.260907</td>\n",
       "      <td>-5.465157</td>\n",
       "      <td>33.896160</td>\n",
       "      <td>34.375846</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.288146</td>\n",
       "      <td>-9.160683</td>\n",
       "      <td>-5.104698</td>\n",
       "      <td>-0.337658</td>\n",
       "      <td>0.100441</td>\n",
       "      <td>0.242531</td>\n",
       "      <td>-4.054794</td>\n",
       "      <td>33.896160</td>\n",
       "      <td>37.112232</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1.374342  -9.06491  -5.224414  -0.324076   0.16569  0.282479  -3.702203  \\\n",
       "0  1.324061 -9.108008  -4.994559  -0.350175  0.112426  0.269429  -4.936271   \n",
       "1  1.422228 -9.153500  -5.025685  -0.342185  0.121214  0.268098  -4.054794   \n",
       "2  1.388708 -9.036178  -5.114275  -0.361893  0.098843  0.250254  -4.759976   \n",
       "3  1.297723 -9.167866  -5.006531  -0.351240  0.077005  0.260907  -5.465157   \n",
       "4  1.288146 -9.160683  -5.104698  -0.337658  0.100441  0.242531  -4.054794   \n",
       "\n",
       "   32.476425  36.428134  1  \n",
       "0  33.363761  37.625305  1  \n",
       "1  32.831359  36.086086  1  \n",
       "2  33.896160  36.428134  1  \n",
       "3  33.896160  34.375846  1  \n",
       "4  33.896160  37.112232  1  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imu_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "imu_shuffle_data = shuffle(imu_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sample Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1.374342</th>\n",
       "      <th>-9.06491</th>\n",
       "      <th>-5.224414</th>\n",
       "      <th>-0.324076</th>\n",
       "      <th>0.16569</th>\n",
       "      <th>0.282479</th>\n",
       "      <th>-3.702203</th>\n",
       "      <th>32.476425</th>\n",
       "      <th>36.428134</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1633</th>\n",
       "      <td>-2.145314</td>\n",
       "      <td>-8.061688</td>\n",
       "      <td>2.743895</td>\n",
       "      <td>-0.728836</td>\n",
       "      <td>0.230561</td>\n",
       "      <td>0.149976</td>\n",
       "      <td>-4.407385</td>\n",
       "      <td>53.062576</td>\n",
       "      <td>16.589338</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>-0.335205</td>\n",
       "      <td>10.001091</td>\n",
       "      <td>-0.778155</td>\n",
       "      <td>0.413399</td>\n",
       "      <td>-0.074940</td>\n",
       "      <td>0.018578</td>\n",
       "      <td>17.805835</td>\n",
       "      <td>10.825476</td>\n",
       "      <td>-24.114400</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>-0.220278</td>\n",
       "      <td>9.512648</td>\n",
       "      <td>-0.045492</td>\n",
       "      <td>-0.239599</td>\n",
       "      <td>1.191131</td>\n",
       "      <td>-0.398166</td>\n",
       "      <td>14.456223</td>\n",
       "      <td>3.194403</td>\n",
       "      <td>9.235302</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>-0.158025</td>\n",
       "      <td>9.910106</td>\n",
       "      <td>-0.677594</td>\n",
       "      <td>0.417394</td>\n",
       "      <td>-0.073875</td>\n",
       "      <td>0.036422</td>\n",
       "      <td>17.276948</td>\n",
       "      <td>11.357876</td>\n",
       "      <td>-24.285423</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>-0.272953</td>\n",
       "      <td>9.711378</td>\n",
       "      <td>0.471682</td>\n",
       "      <td>-0.127212</td>\n",
       "      <td>1.098718</td>\n",
       "      <td>-0.432788</td>\n",
       "      <td>11.635497</td>\n",
       "      <td>1.419734</td>\n",
       "      <td>13.339881</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1.374342   -9.06491  -5.224414  -0.324076   0.16569  0.282479  \\\n",
       "1633 -2.145314  -8.061688   2.743895  -0.728836  0.230561  0.149976   \n",
       "1957 -0.335205  10.001091  -0.778155   0.413399 -0.074940  0.018578   \n",
       "1023 -0.220278   9.512648  -0.045492  -0.239599  1.191131 -0.398166   \n",
       "1914 -0.158025   9.910106  -0.677594   0.417394 -0.073875  0.036422   \n",
       "1081 -0.272953   9.711378   0.471682  -0.127212  1.098718 -0.432788   \n",
       "\n",
       "      -3.702203  32.476425  36.428134  1  \n",
       "1633  -4.407385  53.062576  16.589338  7  \n",
       "1957  17.805835  10.825476 -24.114400  8  \n",
       "1023  14.456223   3.194403   9.235302  5  \n",
       "1914  17.276948  11.357876 -24.285423  8  \n",
       "1081  11.635497   1.419734  13.339881  5  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imu_shuffle_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Lenght:: 2409\n",
      "Dataset Shape:: (2409, 10)\n"
     ]
    }
   ],
   "source": [
    "print (\"Dataset Lenght:: \"+ str(len(imu_shuffle_data)))\n",
    "print (\"Dataset Shape:: \"+str(imu_shuffle_data.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### seperating label column and feature colum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = imu_shuffle_data.values[:,0:9]\n",
    "Y = imu_shuffle_data.values[:,-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting test data and training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size = 0.3, random_state = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree classifier with gini index as criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=5, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=100,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_gini = DecisionTreeClassifier(criterion = \"gini\", random_state = 100,\n",
    "                               max_depth=3, min_samples_leaf=5)\n",
    "clf_gini.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree classifier with information gain as criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=3,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=5, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=100,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_entropy = DecisionTreeClassifier(criterion = \"entropy\", random_state = 100,\n",
    " max_depth=3, min_samples_leaf=5)\n",
    "clf_entropy.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample prediction result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_gini.predict([[-1.053502,-9.151105,-4.968222,-0.525817,-0.22892,-0.382496,-35.96426,42.592033,5.81482\n",
    "]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### prediction using gini index as criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7., 8., 8., 7., 8., 8., 8., 8., 8., 8., 8., 6., 8., 8., 8., 8., 8.,\n",
       "       8., 8., 6., 8., 8., 8., 8., 8., 7., 8., 8., 8., 8., 8., 8., 6., 8.,\n",
       "       8., 7., 8., 8., 8., 8., 7., 8., 4., 4., 4., 4., 8., 8., 4., 8., 8.,\n",
       "       7., 6., 6., 8., 8., 4., 7., 6., 8., 7., 8., 8., 8., 8., 8., 8., 7.,\n",
       "       8., 8., 8., 8., 4., 7., 8., 4., 4., 8., 8., 6., 8., 8., 6., 8., 8.,\n",
       "       4., 7., 8., 6., 4., 8., 8., 8., 6., 8., 8., 8., 8., 8., 6., 8., 8.,\n",
       "       8., 8., 8., 8., 4., 8., 8., 8., 8., 6., 8., 8., 8., 8., 8., 8., 4.,\n",
       "       7., 8., 8., 8., 8., 8., 8., 8., 8., 8., 8., 8., 8., 4., 8., 8., 8.,\n",
       "       8., 8., 8., 8., 8., 8., 8., 6., 4., 8., 7., 8., 8., 8., 6., 8., 8.,\n",
       "       8., 8., 4., 8., 8., 8., 8., 6., 8., 8., 8., 8., 4., 8., 4., 4., 8.,\n",
       "       8., 8., 8., 8., 8., 8., 8., 8., 4., 8., 8., 8., 8., 8., 8., 8., 8.,\n",
       "       8., 8., 8., 7., 8., 4., 8., 8., 4., 8., 8., 8., 8., 4., 8., 6., 8.,\n",
       "       8., 8., 8., 8., 8., 8., 8., 8., 8., 8., 8., 8., 8., 8., 6., 8., 8.,\n",
       "       8., 8., 8., 7., 7., 8., 8., 4., 8., 8., 7., 4., 8., 8., 8., 8., 8.,\n",
       "       8., 8., 8., 8., 8., 8., 7., 8., 7., 8., 8., 7., 8., 8., 6., 8., 8.,\n",
       "       8., 6., 6., 8., 8., 8., 8., 6., 4., 8., 7., 8., 8., 8., 8., 8., 7.,\n",
       "       8., 8., 8., 4., 8., 8., 7., 8., 8., 4., 8., 8., 8., 8., 8., 8., 8.,\n",
       "       8., 4., 8., 8., 8., 8., 8., 4., 4., 8., 8., 7., 8., 8., 8., 6., 7.,\n",
       "       8., 8., 8., 7., 4., 8., 8., 8., 8., 8., 8., 8., 6., 8., 8., 8., 8.,\n",
       "       7., 6., 8., 8., 8., 4., 8., 8., 8., 8., 6., 7., 4., 8., 8., 8., 8.,\n",
       "       8., 8., 7., 8., 8., 8., 8., 8., 7., 7., 6., 8., 6., 8., 8., 8., 6.,\n",
       "       8., 8., 8., 8., 8., 8., 4., 8., 8., 8., 8., 8., 8., 6., 8., 4., 8.,\n",
       "       8., 7., 8., 8., 8., 8., 8., 4., 8., 8., 7., 8., 7., 8., 8., 8., 8.,\n",
       "       8., 8., 7., 8., 6., 8., 8., 8., 8., 8., 8., 8., 6., 6., 8., 8., 8.,\n",
       "       8., 7., 8., 8., 8., 7., 6., 8., 8., 8., 8., 8., 6., 8., 6., 8., 8.,\n",
       "       8., 7., 4., 7., 8., 8., 8., 8., 8., 8., 7., 6., 8., 4., 4., 7., 7.,\n",
       "       8., 8., 6., 8., 4., 4., 8., 8., 8., 8., 8., 8., 8., 8., 8., 8., 8.,\n",
       "       6., 4., 8., 8., 8., 7., 8., 8., 8., 6., 6., 8., 8., 8., 8., 8., 4.,\n",
       "       8., 8., 4., 6., 6., 8., 8., 8., 7., 7., 4., 7., 8., 8., 8., 8., 4.,\n",
       "       8., 6., 4., 8., 8., 8., 8., 8., 8., 7., 8., 8., 4., 8., 8., 8., 8.,\n",
       "       8., 7., 8., 4., 8., 8., 4., 8., 7., 8., 8., 8., 8., 8., 8., 8., 8.,\n",
       "       8., 7., 8., 8., 7., 4., 4., 8., 8., 6., 7., 8., 8., 8., 8., 8., 4.,\n",
       "       4., 8., 6., 8., 4., 4., 7., 7., 8., 8., 8., 8., 8., 8., 4., 6., 6.,\n",
       "       8., 8., 7., 8., 8., 8., 8., 8., 8., 8., 6., 7., 7., 6., 8., 7., 8.,\n",
       "       8., 6., 8., 7., 7., 4., 8., 8., 8., 8., 8., 8., 8., 8., 8., 8., 8.,\n",
       "       8., 8., 8., 8., 4., 8., 8., 8., 4., 8., 8., 8., 8., 8., 8., 8., 6.,\n",
       "       8., 6., 6., 7., 8., 7., 8., 7., 8., 7., 8., 8., 8., 4., 7., 6., 6.,\n",
       "       8., 8., 8., 8., 8., 8., 8., 7., 8., 6., 8., 8., 8., 8., 8., 8., 8.,\n",
       "       8., 8., 8., 8., 8., 8., 8., 6., 4., 8., 8., 4., 8., 8., 8., 8., 8.,\n",
       "       8., 6., 6., 7., 4., 8., 8., 8., 7., 8., 8., 8., 8., 7., 4., 8., 4.,\n",
       "       8., 8., 8., 8., 8., 6., 6., 8., 8., 8., 4., 4., 4., 7., 8., 8., 4.,\n",
       "       8., 7., 8., 8., 8., 8., 4., 6., 7., 8., 6., 8., 8., 4., 8., 8., 8.,\n",
       "       6., 4., 8., 8., 8., 6., 8., 8., 8.])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = clf_gini.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction usin Information gain as criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7., 5., 4., 7., 2., 8., 3., 2., 4., 1., 1., 6., 3., 3., 5., 5., 5.,\n",
       "       4., 3., 6., 5., 3., 2., 3., 3., 7., 4., 1., 3., 3., 3., 1., 6., 8.,\n",
       "       2., 7., 2., 3., 3., 5., 7., 8., 4., 4., 4., 4., 1., 3., 4., 8., 5.,\n",
       "       7., 6., 6., 2., 1., 4., 7., 6., 3., 6., 8., 2., 8., 5., 1., 5., 7.,\n",
       "       8., 5., 8., 3., 4., 7., 8., 4., 4., 3., 5., 6., 5., 3., 6., 4., 1.,\n",
       "       4., 7., 2., 6., 4., 3., 3., 1., 6., 4., 8., 8., 3., 1., 6., 5., 2.,\n",
       "       3., 1., 1., 2., 4., 5., 4., 5., 3., 6., 2., 3., 3., 5., 3., 4., 4.,\n",
       "       7., 3., 1., 3., 3., 2., 2., 3., 4., 3., 5., 3., 3., 4., 3., 8., 3.,\n",
       "       3., 2., 5., 4., 2., 1., 1., 6., 4., 5., 7., 8., 3., 3., 6., 8., 3.,\n",
       "       1., 4., 4., 8., 5., 4., 1., 6., 2., 5., 2., 8., 4., 3., 4., 4., 4.,\n",
       "       1., 8., 1., 8., 4., 3., 1., 3., 4., 1., 3., 5., 3., 5., 1., 3., 3.,\n",
       "       1., 4., 1., 7., 5., 4., 2., 8., 4., 8., 3., 3., 1., 4., 2., 6., 3.,\n",
       "       3., 1., 3., 5., 5., 5., 3., 5., 8., 5., 3., 1., 5., 1., 6., 8., 5.,\n",
       "       2., 1., 1., 7., 7., 1., 1., 4., 1., 8., 7., 4., 2., 1., 3., 3., 3.,\n",
       "       1., 3., 2., 8., 3., 3., 7., 1., 7., 5., 3., 7., 8., 2., 6., 4., 5.,\n",
       "       3., 6., 6., 3., 3., 5., 3., 6., 4., 2., 7., 2., 3., 8., 8., 2., 7.,\n",
       "       3., 2., 5., 4., 3., 2., 7., 3., 3., 4., 5., 3., 2., 5., 1., 1., 4.,\n",
       "       3., 4., 4., 5., 8., 8., 1., 4., 4., 5., 4., 7., 3., 4., 2., 6., 7.,\n",
       "       2., 3., 3., 7., 4., 3., 3., 5., 5., 4., 8., 2., 6., 3., 8., 5., 3.,\n",
       "       7., 6., 8., 8., 1., 4., 1., 3., 3., 1., 6., 7., 4., 3., 2., 2., 1.,\n",
       "       4., 1., 7., 2., 3., 2., 1., 2., 7., 7., 6., 3., 6., 3., 1., 3., 6.,\n",
       "       8., 3., 8., 3., 1., 3., 4., 3., 2., 5., 3., 2., 8., 6., 1., 4., 1.,\n",
       "       3., 7., 5., 2., 4., 3., 8., 4., 3., 5., 7., 3., 7., 1., 4., 2., 8.,\n",
       "       5., 3., 7., 1., 6., 3., 3., 2., 5., 3., 3., 1., 6., 6., 1., 4., 4.,\n",
       "       8., 7., 3., 3., 2., 7., 6., 5., 8., 3., 2., 2., 6., 5., 6., 2., 8.,\n",
       "       3., 7., 4., 7., 3., 3., 4., 1., 1., 3., 7., 6., 5., 4., 4., 7., 7.,\n",
       "       3., 2., 6., 3., 4., 4., 3., 2., 3., 3., 2., 3., 3., 3., 2., 5., 2.,\n",
       "       6., 4., 1., 8., 5., 7., 4., 8., 8., 6., 6., 2., 8., 5., 5., 8., 4.,\n",
       "       3., 1., 4., 6., 6., 8., 3., 3., 7., 7., 4., 7., 2., 5., 3., 3., 4.,\n",
       "       2., 6., 4., 2., 3., 2., 3., 5., 5., 7., 3., 8., 4., 5., 8., 2., 3.,\n",
       "       2., 7., 3., 4., 8., 4., 4., 1., 7., 3., 4., 1., 4., 1., 8., 1., 5.,\n",
       "       8., 7., 8., 5., 7., 4., 4., 3., 1., 6., 7., 5., 8., 4., 5., 3., 4.,\n",
       "       4., 4., 6., 4., 4., 4., 7., 7., 3., 8., 2., 2., 2., 1., 4., 6., 6.,\n",
       "       4., 3., 7., 2., 1., 1., 8., 3., 1., 3., 6., 7., 7., 6., 1., 7., 1.,\n",
       "       8., 6., 2., 7., 7., 4., 3., 5., 4., 3., 5., 5., 3., 8., 8., 3., 3.,\n",
       "       8., 8., 5., 2., 4., 1., 3., 3., 4., 2., 3., 1., 3., 3., 3., 1., 6.,\n",
       "       3., 6., 6., 7., 1., 7., 2., 7., 3., 7., 3., 8., 1., 4., 7., 6., 6.,\n",
       "       5., 5., 5., 5., 5., 2., 2., 7., 1., 6., 1., 2., 2., 8., 5., 2., 3.,\n",
       "       1., 2., 3., 5., 3., 3., 5., 6., 4., 3., 3., 4., 3., 1., 3., 4., 8.,\n",
       "       1., 6., 6., 7., 4., 1., 1., 8., 7., 4., 2., 3., 2., 7., 4., 3., 4.,\n",
       "       2., 5., 1., 3., 5., 6., 6., 5., 5., 3., 4., 4., 4., 7., 5., 1., 4.,\n",
       "       3., 7., 5., 3., 2., 5., 4., 6., 7., 3., 6., 2., 4., 4., 3., 8., 3.,\n",
       "       6., 4., 4., 3., 3., 6., 2., 1., 3.])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_en = clf_entropy.predict(X_test)\n",
    "y_pred_en"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 38.17427385892116\n"
     ]
    }
   ],
   "source": [
    "print (\"Accuracy is \"+str( accuracy_score(y_test,y_pred)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 81.74273858921161\n"
     ]
    }
   ],
   "source": [
    "print (\"Accuracy is \"+ str(accuracy_score(y_test,y_pred_en)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix using gini index as criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  0  0  0  0  0  0 83  0  0]\n",
      " [ 0  0  0  0  0  0  0 74  0  0]\n",
      " [ 0  0  0  0  0  0  0 78  0  0]\n",
      " [ 0  0  0 73  0  0  0  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 81  0  0]\n",
      " [ 0  0  0  0  0 65  0  0  0  0]\n",
      " [ 0  0  0  0  0  0 72  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 66  0  0]\n",
      " [ 0  0  0  0  0  0  0 87  0  0]\n",
      " [ 0  0  0  1  0  0  0 43  0  0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "labels = [1,2,3,4,5,6,7,8,9,10]\n",
    "cm_gini = confusion_matrix(y_test, y_pred,labels)\n",
    "print(cm_gini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(cm_gini)\n",
    "plt.title('Confusion matrix using gini index as criterion')\n",
    "fig.colorbar(cax)\n",
    "ax.set_xticklabels([''] + labels)\n",
    "ax.set_yticklabels([''] + labels)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix using information gain as criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[83  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 74  0  0  0  0  0  0  0  0]\n",
      " [ 0  0 78  0  0  0  0  0  0  0]\n",
      " [ 0  0  0 73  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 81  0  0  0  0  0]\n",
      " [ 0  0  0  0  0 65  0  0  0  0]\n",
      " [ 0  0  0  0  0  1 71  0  0  0]\n",
      " [ 0  0  0  0  0  0  0 66  0  0]\n",
      " [ 0  1 86  0  0  0  0  0  0  0]\n",
      " [ 0  3  0 41  0  0  0  0  0  0]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred_en,labels)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(cm)\n",
    "plt.title('Confusion matrix Using Information gain as Criterion')\n",
    "fig.colorbar(cax)\n",
    "ax.set_xticklabels([''] + labels)\n",
    "ax.set_yticklabels([''] + labels)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}