Model-Based Feature Selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Libraries"
   ]
  },
  {
   "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 seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv('./train.csv')\n",
    "test  = pd.read_csv('./test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tBodyAcc-mean()-X</th>\n",
       "      <th>tBodyAcc-mean()-Y</th>\n",
       "      <th>tBodyAcc-mean()-Z</th>\n",
       "      <th>tBodyAcc-std()-X</th>\n",
       "      <th>tBodyAcc-std()-Y</th>\n",
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       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>angle(tBodyAccMean,gravity)</th>\n",
       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>angle(X,gravityMean)</th>\n",
       "      <th>angle(Y,gravityMean)</th>\n",
       "      <th>angle(Z,gravityMean)</th>\n",
       "      <th>subject</th>\n",
       "      <th>Activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>-0.983111</td>\n",
       "      <td>-0.913526</td>\n",
       "      <td>-0.995112</td>\n",
       "      <td>-0.983185</td>\n",
       "      <td>-0.923527</td>\n",
       "      <td>-0.934724</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.710304</td>\n",
       "      <td>-0.112754</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>-0.464761</td>\n",
       "      <td>-0.018446</td>\n",
       "      <td>-0.841247</td>\n",
       "      <td>0.179941</td>\n",
       "      <td>-0.058627</td>\n",
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       "      <td>STANDING</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>-0.016411</td>\n",
       "      <td>-0.123520</td>\n",
       "      <td>-0.998245</td>\n",
       "      <td>-0.975300</td>\n",
       "      <td>-0.960322</td>\n",
       "      <td>-0.998807</td>\n",
       "      <td>-0.974914</td>\n",
       "      <td>-0.957686</td>\n",
       "      <td>-0.943068</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.861499</td>\n",
       "      <td>0.053477</td>\n",
       "      <td>-0.007435</td>\n",
       "      <td>-0.732626</td>\n",
       "      <td>0.703511</td>\n",
       "      <td>-0.844788</td>\n",
       "      <td>0.180289</td>\n",
       "      <td>-0.054317</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.279653</td>\n",
       "      <td>-0.019467</td>\n",
       "      <td>-0.113462</td>\n",
       "      <td>-0.995380</td>\n",
       "      <td>-0.967187</td>\n",
       "      <td>-0.978944</td>\n",
       "      <td>-0.996520</td>\n",
       "      <td>-0.963668</td>\n",
       "      <td>-0.977469</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.760104</td>\n",
       "      <td>-0.118559</td>\n",
       "      <td>0.177899</td>\n",
       "      <td>0.100699</td>\n",
       "      <td>0.808529</td>\n",
       "      <td>-0.848933</td>\n",
       "      <td>0.180637</td>\n",
       "      <td>-0.049118</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.279174</td>\n",
       "      <td>-0.026201</td>\n",
       "      <td>-0.123283</td>\n",
       "      <td>-0.996091</td>\n",
       "      <td>-0.983403</td>\n",
       "      <td>-0.990675</td>\n",
       "      <td>-0.997099</td>\n",
       "      <td>-0.982750</td>\n",
       "      <td>-0.989302</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.482845</td>\n",
       "      <td>-0.036788</td>\n",
       "      <td>-0.012892</td>\n",
       "      <td>0.640011</td>\n",
       "      <td>-0.485366</td>\n",
       "      <td>-0.848649</td>\n",
       "      <td>0.181935</td>\n",
       "      <td>-0.047663</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.276629</td>\n",
       "      <td>-0.016570</td>\n",
       "      <td>-0.115362</td>\n",
       "      <td>-0.998139</td>\n",
       "      <td>-0.980817</td>\n",
       "      <td>-0.990482</td>\n",
       "      <td>-0.998321</td>\n",
       "      <td>-0.979672</td>\n",
       "      <td>-0.990441</td>\n",
       "      <td>-0.942469</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.699205</td>\n",
       "      <td>0.123320</td>\n",
       "      <td>0.122542</td>\n",
       "      <td>0.693578</td>\n",
       "      <td>-0.615971</td>\n",
       "      <td>-0.847865</td>\n",
       "      <td>0.185151</td>\n",
       "      <td>-0.043892</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 563 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
       "0           0.288585          -0.020294          -0.132905         -0.995279   \n",
       "1           0.278419          -0.016411          -0.123520         -0.998245   \n",
       "2           0.279653          -0.019467          -0.113462         -0.995380   \n",
       "3           0.279174          -0.026201          -0.123283         -0.996091   \n",
       "4           0.276629          -0.016570          -0.115362         -0.998139   \n",
       "\n",
       "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
       "0         -0.983111         -0.913526         -0.995112         -0.983185   \n",
       "1         -0.975300         -0.960322         -0.998807         -0.974914   \n",
       "2         -0.967187         -0.978944         -0.996520         -0.963668   \n",
       "3         -0.983403         -0.990675         -0.997099         -0.982750   \n",
       "4         -0.980817         -0.990482         -0.998321         -0.979672   \n",
       "\n",
       "   tBodyAcc-mad()-Z  tBodyAcc-max()-X    ...     \\\n",
       "0         -0.923527         -0.934724    ...      \n",
       "1         -0.957686         -0.943068    ...      \n",
       "2         -0.977469         -0.938692    ...      \n",
       "3         -0.989302         -0.938692    ...      \n",
       "4         -0.990441         -0.942469    ...      \n",
       "\n",
       "   fBodyBodyGyroJerkMag-kurtosis()  angle(tBodyAccMean,gravity)  \\\n",
       "0                        -0.710304                    -0.112754   \n",
       "1                        -0.861499                     0.053477   \n",
       "2                        -0.760104                    -0.118559   \n",
       "3                        -0.482845                    -0.036788   \n",
       "4                        -0.699205                     0.123320   \n",
       "\n",
       "   angle(tBodyAccJerkMean),gravityMean)  angle(tBodyGyroMean,gravityMean)  \\\n",
       "0                              0.030400                         -0.464761   \n",
       "1                             -0.007435                         -0.732626   \n",
       "2                              0.177899                          0.100699   \n",
       "3                             -0.012892                          0.640011   \n",
       "4                              0.122542                          0.693578   \n",
       "\n",
       "   angle(tBodyGyroJerkMean,gravityMean)  angle(X,gravityMean)  \\\n",
       "0                             -0.018446             -0.841247   \n",
       "1                              0.703511             -0.844788   \n",
       "2                              0.808529             -0.848933   \n",
       "3                             -0.485366             -0.848649   \n",
       "4                             -0.615971             -0.847865   \n",
       "\n",
       "   angle(Y,gravityMean)  angle(Z,gravityMean)  subject  Activity  \n",
       "0              0.179941             -0.058627        1  STANDING  \n",
       "1              0.180289             -0.054317        1  STANDING  \n",
       "2              0.180637             -0.049118        1  STANDING  \n",
       "3              0.181935             -0.047663        1  STANDING  \n",
       "4              0.185151             -0.043892        1  STANDING  \n",
       "\n",
       "[5 rows x 563 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tBodyAcc-mean()-X</th>\n",
       "      <th>tBodyAcc-mean()-Y</th>\n",
       "      <th>tBodyAcc-mean()-Z</th>\n",
       "      <th>tBodyAcc-std()-X</th>\n",
       "      <th>tBodyAcc-std()-Y</th>\n",
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       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>angle(tBodyAccMean,gravity)</th>\n",
       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>angle(X,gravityMean)</th>\n",
       "      <th>angle(Y,gravityMean)</th>\n",
       "      <th>angle(Z,gravityMean)</th>\n",
       "      <th>subject</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.257178</td>\n",
       "      <td>-0.023285</td>\n",
       "      <td>-0.014654</td>\n",
       "      <td>-0.938404</td>\n",
       "      <td>-0.920091</td>\n",
       "      <td>-0.667683</td>\n",
       "      <td>-0.952501</td>\n",
       "      <td>-0.925249</td>\n",
       "      <td>-0.674302</td>\n",
       "      <td>-0.894088</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.705974</td>\n",
       "      <td>0.006462</td>\n",
       "      <td>0.162920</td>\n",
       "      <td>-0.825886</td>\n",
       "      <td>0.271151</td>\n",
       "      <td>-0.720009</td>\n",
       "      <td>0.276801</td>\n",
       "      <td>-0.057978</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.286027</td>\n",
       "      <td>-0.013163</td>\n",
       "      <td>-0.119083</td>\n",
       "      <td>-0.975415</td>\n",
       "      <td>-0.967458</td>\n",
       "      <td>-0.944958</td>\n",
       "      <td>-0.986799</td>\n",
       "      <td>-0.968401</td>\n",
       "      <td>-0.945823</td>\n",
       "      <td>-0.894088</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.594944</td>\n",
       "      <td>-0.083495</td>\n",
       "      <td>0.017500</td>\n",
       "      <td>-0.434375</td>\n",
       "      <td>0.920593</td>\n",
       "      <td>-0.698091</td>\n",
       "      <td>0.281343</td>\n",
       "      <td>-0.083898</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.275485</td>\n",
       "      <td>-0.026050</td>\n",
       "      <td>-0.118152</td>\n",
       "      <td>-0.993819</td>\n",
       "      <td>-0.969926</td>\n",
       "      <td>-0.962748</td>\n",
       "      <td>-0.994403</td>\n",
       "      <td>-0.970735</td>\n",
       "      <td>-0.963483</td>\n",
       "      <td>-0.939260</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.640736</td>\n",
       "      <td>-0.034956</td>\n",
       "      <td>0.202302</td>\n",
       "      <td>0.064103</td>\n",
       "      <td>0.145068</td>\n",
       "      <td>-0.702771</td>\n",
       "      <td>0.280083</td>\n",
       "      <td>-0.079346</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.270298</td>\n",
       "      <td>-0.032614</td>\n",
       "      <td>-0.117520</td>\n",
       "      <td>-0.994743</td>\n",
       "      <td>-0.973268</td>\n",
       "      <td>-0.967091</td>\n",
       "      <td>-0.995274</td>\n",
       "      <td>-0.974471</td>\n",
       "      <td>-0.968897</td>\n",
       "      <td>-0.938610</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.736124</td>\n",
       "      <td>-0.017067</td>\n",
       "      <td>0.154438</td>\n",
       "      <td>0.340134</td>\n",
       "      <td>0.296407</td>\n",
       "      <td>-0.698954</td>\n",
       "      <td>0.284114</td>\n",
       "      <td>-0.077108</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.274833</td>\n",
       "      <td>-0.027848</td>\n",
       "      <td>-0.129527</td>\n",
       "      <td>-0.993852</td>\n",
       "      <td>-0.967445</td>\n",
       "      <td>-0.978295</td>\n",
       "      <td>-0.994111</td>\n",
       "      <td>-0.965953</td>\n",
       "      <td>-0.977346</td>\n",
       "      <td>-0.938610</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.846595</td>\n",
       "      <td>-0.002223</td>\n",
       "      <td>-0.040046</td>\n",
       "      <td>0.736715</td>\n",
       "      <td>-0.118545</td>\n",
       "      <td>-0.692245</td>\n",
       "      <td>0.290722</td>\n",
       "      <td>-0.073857</td>\n",
       "      <td>2</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 563 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
       "0           0.257178          -0.023285          -0.014654         -0.938404   \n",
       "1           0.286027          -0.013163          -0.119083         -0.975415   \n",
       "2           0.275485          -0.026050          -0.118152         -0.993819   \n",
       "3           0.270298          -0.032614          -0.117520         -0.994743   \n",
       "4           0.274833          -0.027848          -0.129527         -0.993852   \n",
       "\n",
       "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
       "0         -0.920091         -0.667683         -0.952501         -0.925249   \n",
       "1         -0.967458         -0.944958         -0.986799         -0.968401   \n",
       "2         -0.969926         -0.962748         -0.994403         -0.970735   \n",
       "3         -0.973268         -0.967091         -0.995274         -0.974471   \n",
       "4         -0.967445         -0.978295         -0.994111         -0.965953   \n",
       "\n",
       "   tBodyAcc-mad()-Z  tBodyAcc-max()-X    ...     \\\n",
       "0         -0.674302         -0.894088    ...      \n",
       "1         -0.945823         -0.894088    ...      \n",
       "2         -0.963483         -0.939260    ...      \n",
       "3         -0.968897         -0.938610    ...      \n",
       "4         -0.977346         -0.938610    ...      \n",
       "\n",
       "   fBodyBodyGyroJerkMag-kurtosis()  angle(tBodyAccMean,gravity)  \\\n",
       "0                        -0.705974                     0.006462   \n",
       "1                        -0.594944                    -0.083495   \n",
       "2                        -0.640736                    -0.034956   \n",
       "3                        -0.736124                    -0.017067   \n",
       "4                        -0.846595                    -0.002223   \n",
       "\n",
       "   angle(tBodyAccJerkMean),gravityMean)  angle(tBodyGyroMean,gravityMean)  \\\n",
       "0                              0.162920                         -0.825886   \n",
       "1                              0.017500                         -0.434375   \n",
       "2                              0.202302                          0.064103   \n",
       "3                              0.154438                          0.340134   \n",
       "4                             -0.040046                          0.736715   \n",
       "\n",
       "   angle(tBodyGyroJerkMean,gravityMean)  angle(X,gravityMean)  \\\n",
       "0                              0.271151             -0.720009   \n",
       "1                              0.920593             -0.698091   \n",
       "2                              0.145068             -0.702771   \n",
       "3                              0.296407             -0.698954   \n",
       "4                             -0.118545             -0.692245   \n",
       "\n",
       "   angle(Y,gravityMean)  angle(Z,gravityMean)  subject  Activity  \n",
       "0              0.276801             -0.057978        2  STANDING  \n",
       "1              0.281343             -0.083898        2  STANDING  \n",
       "2              0.280083             -0.079346        2  STANDING  \n",
       "3              0.284114             -0.077108        2  STANDING  \n",
       "4              0.290722             -0.073857        2  STANDING  \n",
       "\n",
       "[5 rows x 563 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7352, 563)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2947, 563)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for Any NaN Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2728e7ca0f0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C/fHaBep8Hyw/82isF7NyfS8jGvL/T0MulEvQmF+DGPyo9H8Hosc/RUz/3yG30T7kI35y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\ny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yEz9DCE+axAN2atb9DJm4o+hTSH30Oe5PlN0zwrpfTV/ocBCq7fjqRoL6cLlB5sR+fX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\n0EGmlEaRiT0Jz22a5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Z1G7PfbwusRiiPQvxtBFsG1PHC1UBz14tA4h9DFP1r+j4bv3TvP\nBYMGHfs7EEPeiObzl/tFTGTBvQoJ5SFUb/nqMI4jkdB4MyUb8NoBxwLMMuVZmqWfBYpVzt6CnFAb\nkBMMxCRG8/sOahIQ6/tGBmWzPu59t7Ky9udWVjOFrU9Fg0a9OLqlLQomJqcMFPWSMyV/BbUiewNi\nohcMEDGREJwEcqpaq3cnl04+78EIvnj1VGOJNAtfzNIsPfRpd7SygpIYAd3drGe0ldUANGjUy1Q0\ncNRLZsiXI1x9DGUtrkFRIOfTP2LiLARR3IiY8CGozOeT0LMYR4x+KJ/z+QirHohmNeVZmqWHPu2u\nVlauVTyKGPHubGXVjxIzoyA+AmXpvRRZE/3oIhRqeBnK1LsJhf79L6YIqXOBo3zs0Wien0xJfhkH\n/gtlhN5Hwb0HolmmPEuz9BCnjGO6ypr/zFw3hp+6lZWxYeju8OFKcOsQjBFjmA1xGHf+LDOTIj4I\nnYXKbX4Daa4bkTC5GDHO2xHj/gClddMt+fOt4fu3oo4jz6Z39IRpLrmueD7XXSmlmyjV3gYd980I\nU3bWZSePZTHqjP78lNLHm6bp1SVlEs3GKc/SLP0M0DQqrdXhY44/7kX1MdsQjDAO/CiltEutrB6q\nFGLS/wlp1kMoMeWlACmlowY8z1ko6WUOgjQuRcLlpyg07lAkJC4atAPJLKY8S7P0s0G9klr6tbKC\n9lZWC8Jr8nvDGAuRNno9M9vK6qFGp6L5OgFVhPsSiulejPD7KalyFH4LFX56OmqzZXjoTcA5KaWB\nW63NwhezNEs/GzRopTX3soPuKmuma8JrwyLx/e5qZfVQo02ofshzkXXwGFSvYj1T49HRUXgEqnlx\nCbJcVlMgouGU0kemw5BhlinP0iz9rNDuaGXVVtRnd7WyeqhRQnj1CLIYNoa/qfBoaHcU7qBg+duB\n8dwHc1o0y5RnaZZ+Nmh3tbKyBr67W1k91Mh9+y5FWP0L/Ncnmy/SJEchyiQ8AgnPBSibb2AHn2nW\n0TdLs/QwoIdqK6uHK/VwFG5GgnMcCcZ1qB3UGdM596ymPEuz9PCgh2orq4cr1Y7C9RRM/h5kZYwj\nC2RaNBt9MUuz9PCgaxBjfhxizNei2F8onVRMrpXc9nobgj6mVa/h55A2IRjpuchZelj4fBHqur0m\npTRQJEekWU15lmbp4UFjyHzehLQ1t7JyFbO6rZXfbwzvN1IyAUeZuXZWD0eKjkIntHjOPouy+VY2\nTTNt+GdWU56lWXp40KmUVvegoj6m6LCr9/w+PV4/jtKYdJYmk+tfuDPJm4AXowiW01AxonFguGma\nzmyR+1mapZ8/esCL+vw8UyyUnz/6g6ZpjgP+Glkb84B3A38CfJBpFLmfhS9maZYeHvRgFPWZpf+/\nvftlqTsMwzj+bQrKEDYtFpP3bC5o8d3ICTIQVoRZBBd8BwObwXSw2BR8BYJRhTsPB4tjzeLCrYjt\nPPKAP+T7iSdc8eZ3nj/X89I8sEc1+Y2AL8BPYCUidiYNcShL78NblPropVPqqvYZtfF3QJ3QGFNP\nQk3Ec8qS1ElE3AL7wE1mXkfEFbAGXGbm+iQZrilLUgePfRi/qH8jdxGxSDXIbdFwxNChLEl9nFBN\ncZfU6YuLzDyIiHngcNIQly8kqYOIuAA2qbX46affM/NHS44bfZLUxzW10brE84WcP60hLl9IUh+r\nwEeql/kzMJWZG60hfilLUh9jnkuKloHFiFhuDXEoS1IfX6m3+e6pF0hmgKPWEIeyJPXxGzgGPlA9\nIrPAQmuIa8qS1Mdf4DtwTg3jB15RgeqXsiT1MQLugG9UD8YmMNca4lCWpA4y8x/1ZuI61T8SwKfW\nHIeyJPUzosqddqkTGNutAd7ok6QB8UtZkgbEoSxJA+JQlqQBcShL0oA4lCVpQP4DWrzmBdlXb00A\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2728e7cacc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2728f56fa90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l71uQFLNP1hIEqlSOwSB3fhEx2vVoAo9ARHsh8rvZz7We8WyDhZ52LKnuRi/d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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2728f532198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(test.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Datasets looks great for both the test and train! Onwards! However, the words have been overlapped. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7352, 94)\n"
     ]
    }
   ],
   "source": [
    "features = train.iloc[:,0:562]\n",
    "label = train['Activity']\n",
    "clf = ExtraTreesClassifier()\n",
    "clf = clf.fit(features, label)\n",
    "model = SelectFromModel(clf, prefit=True)\n",
    "New_features = model.transform(features)\n",
    "print(New_features.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# L1-based Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7352, 110)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "lsvc = LinearSVC(C=0.01, penalty=\"l1\", dual=False).fit(features, label)\n",
    "model_2 = SelectFromModel(lsvc, prefit=True)\n",
    "New_features_2 = model_2.transform(features)\n",
    "print(New_features_2.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The L1-based feature selection will keep more features within the training set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting Classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "Classifiers = [DecisionTreeClassifier(),RandomForestClassifier(n_estimators=200),\n",
    "               GradientBoostingClassifier(n_estimators=200)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "import timeit\n",
    "test_features= test.iloc[:,0:562]\n",
    "Time_1=[]\n",
    "Model_1=[]\n",
    "Out_Accuracy_1=[]\n",
    "for clf in Classifiers:\n",
    "    start_time = timeit.default_timer()\n",
    "    fit=clf.fit(features,label)\n",
    "    pred=fit.predict(test_features)\n",
    "    elapsed = timeit.default_timer() - start_time\n",
    "    Time_1.append(elapsed)\n",
    "    Model_1.append(clf.__class__.__name__)\n",
    "    Out_Accuracy_1.append(accuracy_score(test['Activity'],pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tree-Based Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_features= model.transform(test.iloc[:,0:562])\n",
    "Time_2=[]\n",
    "Model_2=[]\n",
    "Out_Accuracy_2=[]\n",
    "for clf in Classifiers:\n",
    "    start_time = timeit.default_timer()\n",
    "    fit=clf.fit(New_features,label)\n",
    "    pred=fit.predict(test_features)\n",
    "    elapsed = timeit.default_timer() - start_time\n",
    "    Time_2.append(elapsed)\n",
    "    Model_2.append(clf.__class__.__name__)\n",
    "    Out_Accuracy_2.append(accuracy_score(test['Activity'],pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L1-Based Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_features= model_2.transform(test.iloc[:,0:562])\n",
    "Time_3=[]\n",
    "Model_3=[]\n",
    "Out_Accuracy_3=[]\n",
    "for clf in Classifiers:\n",
    "    start_time = timeit.default_timer()\n",
    "    fit=clf.fit(New_features_2,label)\n",
    "    pred=fit.predict(test_features)\n",
    "    elapsed = timeit.default_timer() - start_time\n",
    "    Time_3.append(elapsed)\n",
    "    Model_3.append(clf.__class__.__name__)\n",
    "    Out_Accuracy_3.append(accuracy_score(test['Activity'],pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3mPKY89tWIjMz6xWtdAEtImkDAEmjgYMj4t72FsvMzNqtlRbAxIhYunEhIj4JtLoWwMzM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\noGY5UBCXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2728e85a0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind =  np.arange(3)   # the x locations for the groups\n",
    "width = 0.3       # the width of the bars\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(ind, Out_Accuracy_1, width, color='r')\n",
    "rects2 = ax.bar(ind + width, Out_Accuracy_2, width, color='g')\n",
    "rects3 = ax.bar(ind + width + width ,Out_Accuracy_3, width, color='b')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_title('Accuracy by Models and Selection Process')\n",
    "ax.set_xticks(ind + width)\n",
    "ax.set_xticklabels(Model_3,rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C5qTL5Y9XaubM+dV4eslUXFzEtGlzcx1G3snWeS2u9UfMrqycA71na6yyZFpZ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\nRERQMhAREZQMREQEJQMREUHJQEREgP8HbYXDdGHJffcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2728e890dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind =  np.arange(3)   # the x locations for the groups\n",
    "width = 0.5       # the width of the bars\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(ind, Time_1, width, color='r')\n",
    "rects2 = ax.bar(ind + width, Time_2, width, color='y')\n",
    "rects3 = ax.bar(ind + width + width ,Time_3, width, color='b')\n",
    "ax.set_ylabel('Running Time')\n",
    "ax.set_title('Time by Models and Selection Process')\n",
    "ax.set_xticks(ind + width)\n",
    "ax.set_xticklabels(Model_3,rotation=45)\n",
    "ax.legend((rects1[0], rects2[0],rects3[0]), ('No Selection', 'Tree_Based','L1_Based'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
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