Human_Activity_Prediction_Jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Importing all the required modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear algebra import\n",
    "import numpy as np\n",
    "\n",
    "# Library to handle dataset\n",
    "import pandas as pd\n",
    "\n",
    "# ML models imports\n",
    "\n",
    "# Metrics to Evaluate the models\n",
    "from sklearn.metrics import *\n",
    "\n",
    "# Visualization Libraries imports\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scikitplot as skplt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('dataset/train.csv')\n",
    "df_test = pd.read_csv('dataset/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <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",
       "      <td>0</td>\n",
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       "      <td>-0.995279</td>\n",
       "      <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",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.278419</td>\n",
       "      <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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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  ...  fBodyBodyGyroJerkMag-kurtosis()  \\\n",
       "0         -0.923527         -0.934724  ...                        -0.710304   \n",
       "1         -0.957686         -0.943068  ...                        -0.861499   \n",
       "2         -0.977469         -0.938692  ...                        -0.760104   \n",
       "3         -0.989302         -0.938692  ...                        -0.482845   \n",
       "4         -0.990441         -0.942469  ...                        -0.699205   \n",
       "\n",
       "   angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
       "0                    -0.112754                              0.030400   \n",
       "1                     0.053477                             -0.007435   \n",
       "2                    -0.118559                              0.177899   \n",
       "3                    -0.036788                             -0.012892   \n",
       "4                     0.123320                              0.122542   \n",
       "\n",
       "   angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \\\n",
       "0                         -0.464761                             -0.018446   \n",
       "1                         -0.732626                              0.703511   \n",
       "2                          0.100699                              0.808529   \n",
       "3                          0.640011                             -0.485366   \n",
       "4                          0.693578                             -0.615971   \n",
       "\n",
       "   angle(X,gravityMean)  angle(Y,gravityMean)  angle(Z,gravityMean)  subject  \\\n",
       "0             -0.841247              0.179941             -0.058627        1   \n",
       "1             -0.844788              0.180289             -0.054317        1   \n",
       "2             -0.848933              0.180637             -0.049118        1   \n",
       "3             -0.848649              0.181935             -0.047663        1   \n",
       "4             -0.847865              0.185151             -0.043892        1   \n",
       "\n",
       "   Activity  \n",
       "0  STANDING  \n",
       "1  STANDING  \n",
       "2  STANDING  \n",
       "3  STANDING  \n",
       "4  STANDING  \n",
       "\n",
       "[5 rows x 563 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the first five samples of  train data\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <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()-Y</th>\n",
       "      <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",
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       "      <td>0</td>\n",
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       "      <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",
       "      <td>1</td>\n",
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       "      <td>-0.013163</td>\n",
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       "      <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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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  ...  fBodyBodyGyroJerkMag-kurtosis()  \\\n",
       "0         -0.674302         -0.894088  ...                        -0.705974   \n",
       "1         -0.945823         -0.894088  ...                        -0.594944   \n",
       "2         -0.963483         -0.939260  ...                        -0.640736   \n",
       "3         -0.968897         -0.938610  ...                        -0.736124   \n",
       "4         -0.977346         -0.938610  ...                        -0.846595   \n",
       "\n",
       "   angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
       "0                     0.006462                              0.162920   \n",
       "1                    -0.083495                              0.017500   \n",
       "2                    -0.034956                              0.202302   \n",
       "3                    -0.017067                              0.154438   \n",
       "4                    -0.002223                             -0.040046   \n",
       "\n",
       "   angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \\\n",
       "0                         -0.825886                              0.271151   \n",
       "1                         -0.434375                              0.920593   \n",
       "2                          0.064103                              0.145068   \n",
       "3                          0.340134                              0.296407   \n",
       "4                          0.736715                             -0.118545   \n",
       "\n",
       "   angle(X,gravityMean)  angle(Y,gravityMean)  angle(Z,gravityMean)  subject  \\\n",
       "0             -0.720009              0.276801             -0.057978        2   \n",
       "1             -0.698091              0.281343             -0.083898        2   \n",
       "2             -0.702771              0.280083             -0.079346        2   \n",
       "3             -0.698954              0.284114             -0.077108        2   \n",
       "4             -0.692245              0.290722             -0.073857        2   \n",
       "\n",
       "   Activity  \n",
       "0  STANDING  \n",
       "1  STANDING  \n",
       "2  STANDING  \n",
       "3  STANDING  \n",
       "4  STANDING  \n",
       "\n",
       "[5 rows x 563 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7352 entries, 0 to 7351\n",
      "Columns: 563 entries, tBodyAcc-mean()-X to Activity\n",
      "dtypes: float64(561), int64(1), object(1)\n",
      "memory usage: 31.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# Checking count etc\n",
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-skewness()</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.274488</td>\n",
       "      <td>-0.017695</td>\n",
       "      <td>-0.109141</td>\n",
       "      <td>-0.605438</td>\n",
       "      <td>-0.510938</td>\n",
       "      <td>-0.604754</td>\n",
       "      <td>-0.630512</td>\n",
       "      <td>-0.526907</td>\n",
       "      <td>-0.606150</td>\n",
       "      <td>-0.468604</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.307009</td>\n",
       "      <td>-0.625294</td>\n",
       "      <td>0.008684</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.008726</td>\n",
       "      <td>-0.005981</td>\n",
       "      <td>-0.489547</td>\n",
       "      <td>0.058593</td>\n",
       "      <td>-0.056515</td>\n",
       "      <td>17.413085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.070261</td>\n",
       "      <td>0.040811</td>\n",
       "      <td>0.056635</td>\n",
       "      <td>0.448734</td>\n",
       "      <td>0.502645</td>\n",
       "      <td>0.418687</td>\n",
       "      <td>0.424073</td>\n",
       "      <td>0.485942</td>\n",
       "      <td>0.414122</td>\n",
       "      <td>0.544547</td>\n",
       "      <td>...</td>\n",
       "      <td>0.321011</td>\n",
       "      <td>0.307584</td>\n",
       "      <td>0.336787</td>\n",
       "      <td>0.448306</td>\n",
       "      <td>0.608303</td>\n",
       "      <td>0.477975</td>\n",
       "      <td>0.511807</td>\n",
       "      <td>0.297480</td>\n",
       "      <td>0.279122</td>\n",
       "      <td>8.975143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.999873</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.995357</td>\n",
       "      <td>-0.999765</td>\n",
       "      <td>-0.976580</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.262975</td>\n",
       "      <td>-0.024863</td>\n",
       "      <td>-0.120993</td>\n",
       "      <td>-0.992754</td>\n",
       "      <td>-0.978129</td>\n",
       "      <td>-0.980233</td>\n",
       "      <td>-0.993591</td>\n",
       "      <td>-0.978162</td>\n",
       "      <td>-0.980251</td>\n",
       "      <td>-0.936219</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.542602</td>\n",
       "      <td>-0.845573</td>\n",
       "      <td>-0.121527</td>\n",
       "      <td>-0.289549</td>\n",
       "      <td>-0.482273</td>\n",
       "      <td>-0.376341</td>\n",
       "      <td>-0.812065</td>\n",
       "      <td>-0.017885</td>\n",
       "      <td>-0.143414</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.277193</td>\n",
       "      <td>-0.017219</td>\n",
       "      <td>-0.108676</td>\n",
       "      <td>-0.946196</td>\n",
       "      <td>-0.851897</td>\n",
       "      <td>-0.859365</td>\n",
       "      <td>-0.950709</td>\n",
       "      <td>-0.857328</td>\n",
       "      <td>-0.857143</td>\n",
       "      <td>-0.881637</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.343685</td>\n",
       "      <td>-0.711692</td>\n",
       "      <td>0.009509</td>\n",
       "      <td>0.008943</td>\n",
       "      <td>0.008735</td>\n",
       "      <td>-0.000368</td>\n",
       "      <td>-0.709417</td>\n",
       "      <td>0.182071</td>\n",
       "      <td>0.003181</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.288461</td>\n",
       "      <td>-0.010783</td>\n",
       "      <td>-0.097794</td>\n",
       "      <td>-0.242813</td>\n",
       "      <td>-0.034231</td>\n",
       "      <td>-0.262415</td>\n",
       "      <td>-0.292680</td>\n",
       "      <td>-0.066701</td>\n",
       "      <td>-0.265671</td>\n",
       "      <td>-0.017129</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.126979</td>\n",
       "      <td>-0.503878</td>\n",
       "      <td>0.150865</td>\n",
       "      <td>0.292861</td>\n",
       "      <td>0.506187</td>\n",
       "      <td>0.359368</td>\n",
       "      <td>-0.509079</td>\n",
       "      <td>0.248353</td>\n",
       "      <td>0.107659</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.916238</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.989538</td>\n",
       "      <td>0.956845</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998702</td>\n",
       "      <td>0.996078</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 562 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  \\\n",
       "count        7352.000000        7352.000000        7352.000000   \n",
       "mean            0.274488          -0.017695          -0.109141   \n",
       "std             0.070261           0.040811           0.056635   \n",
       "min            -1.000000          -1.000000          -1.000000   \n",
       "25%             0.262975          -0.024863          -0.120993   \n",
       "50%             0.277193          -0.017219          -0.108676   \n",
       "75%             0.288461          -0.010783          -0.097794   \n",
       "max             1.000000           1.000000           1.000000   \n",
       "\n",
       "       tBodyAcc-std()-X  tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  \\\n",
       "count       7352.000000       7352.000000       7352.000000       7352.000000   \n",
       "mean          -0.605438         -0.510938         -0.604754         -0.630512   \n",
       "std            0.448734          0.502645          0.418687          0.424073   \n",
       "min           -1.000000         -0.999873         -1.000000         -1.000000   \n",
       "25%           -0.992754         -0.978129         -0.980233         -0.993591   \n",
       "50%           -0.946196         -0.851897         -0.859365         -0.950709   \n",
       "75%           -0.242813         -0.034231         -0.262415         -0.292680   \n",
       "max            1.000000          0.916238          1.000000          1.000000   \n",
       "\n",
       "       tBodyAcc-mad()-Y  tBodyAcc-mad()-Z  tBodyAcc-max()-X  ...  \\\n",
       "count       7352.000000       7352.000000       7352.000000  ...   \n",
       "mean          -0.526907         -0.606150         -0.468604  ...   \n",
       "std            0.485942          0.414122          0.544547  ...   \n",
       "min           -1.000000         -1.000000         -1.000000  ...   \n",
       "25%           -0.978162         -0.980251         -0.936219  ...   \n",
       "50%           -0.857328         -0.857143         -0.881637  ...   \n",
       "75%           -0.066701         -0.265671         -0.017129  ...   \n",
       "max            0.967664          1.000000          1.000000  ...   \n",
       "\n",
       "       fBodyBodyGyroJerkMag-skewness()  fBodyBodyGyroJerkMag-kurtosis()  \\\n",
       "count                      7352.000000                      7352.000000   \n",
       "mean                         -0.307009                        -0.625294   \n",
       "std                           0.321011                         0.307584   \n",
       "min                          -0.995357                        -0.999765   \n",
       "25%                          -0.542602                        -0.845573   \n",
       "50%                          -0.343685                        -0.711692   \n",
       "75%                          -0.126979                        -0.503878   \n",
       "max                           0.989538                         0.956845   \n",
       "\n",
       "       angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
       "count                  7352.000000                           7352.000000   \n",
       "mean                      0.008684                              0.002186   \n",
       "std                       0.336787                              0.448306   \n",
       "min                      -0.976580                             -1.000000   \n",
       "25%                      -0.121527                             -0.289549   \n",
       "50%                       0.009509                              0.008943   \n",
       "75%                       0.150865                              0.292861   \n",
       "max                       1.000000                              1.000000   \n",
       "\n",
       "       angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \\\n",
       "count                       7352.000000                           7352.000000   \n",
       "mean                           0.008726                             -0.005981   \n",
       "std                            0.608303                              0.477975   \n",
       "min                           -1.000000                             -1.000000   \n",
       "25%                           -0.482273                             -0.376341   \n",
       "50%                            0.008735                             -0.000368   \n",
       "75%                            0.506187                              0.359368   \n",
       "max                            0.998702                              0.996078   \n",
       "\n",
       "       angle(X,gravityMean)  angle(Y,gravityMean)  angle(Z,gravityMean)  \\\n",
       "count           7352.000000           7352.000000           7352.000000   \n",
       "mean              -0.489547              0.058593             -0.056515   \n",
       "std                0.511807              0.297480              0.279122   \n",
       "min               -1.000000             -1.000000             -1.000000   \n",
       "25%               -0.812065             -0.017885             -0.143414   \n",
       "50%               -0.709417              0.182071              0.003181   \n",
       "75%               -0.509079              0.248353              0.107659   \n",
       "max                1.000000              0.478157              1.000000   \n",
       "\n",
       "           subject  \n",
       "count  7352.000000  \n",
       "mean     17.413085  \n",
       "std       8.975143  \n",
       "min       1.000000  \n",
       "25%       8.000000  \n",
       "50%      19.000000  \n",
       "75%      26.000000  \n",
       "max      30.000000  \n",
       "\n",
       "[8 rows x 562 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking train data stats\n",
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Check Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicates in train data :  0\n"
     ]
    }
   ],
   "source": [
    "print('Duplicates in train data : ',sum(df_train.duplicated()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicates in test data :  0\n"
     ]
    }
   ],
   "source": [
    "print('Duplicates in test data : ',sum(df_test.duplicated()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Check Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of missing values in train data :  0\n"
     ]
    }
   ],
   "source": [
    "print('Total number of missing values in train data : ', df_train.isnull().values.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of missing values in test data :  0\n"
     ]
    }
   ],
   "source": [
    "print('Total number of missing values in test data : ', df_test.isnull().values.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Check for Class Imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   0.,  200.,  400.,  600.,  800., 1000., 1200., 1400., 1600.]),\n",
       " <a list of 9 Text xticklabel objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.title('Distribution of Activities')\n",
    "sns.countplot(y=df_train.Activity, palette='rainbow')\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution across classes is almost Uniform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per dataset description activites are split into two categories:\n",
    "\n",
    "**Static**: Activites with no almost motion i.e. SITTING, STANDING, LAYING  \n",
    "**Dynamic**: Activites with significant motion i.e. WALKING, WALKING_DOWNSTAIRS, WALKING_UPSTAIRS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Analyzing the Difference between two categories of Activites:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can consider tBodyAccMag-mean() feature to differentiate among these two categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.1, 9, 'Dynamic Activities')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 875.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "facetgrid = sns.FacetGrid(df_train, hue='Activity', size=5,aspect=2)\n",
    "facetgrid.map(sns.distplot,'tBodyAccMag-mean()', hist=False).add_legend()\n",
    "plt.annotate(\"Static Activities\", xy=(-.996,21), xytext=(-0.9, 23),arrowprops={'arrowstyle': '-', 'ls': 'dashed'})\n",
    "plt.annotate(\"Static Activities\", xy=(-.999,26), xytext=(-0.9, 23),arrowprops={'arrowstyle': '-', 'ls': 'dashed'})\n",
    "plt.annotate(\"Static Activities\", xy=(-0.985,12), xytext=(-0.9, 23),arrowprops={'arrowstyle': '-', 'ls': 'dashed'})\n",
    "plt.annotate(\"Dynamic Activities\", xy=(-0.2,3.25), xytext=(0.1, 9),arrowprops={'arrowstyle': '-', 'ls': 'dashed'})\n",
    "plt.annotate(\"Dynamic Activities\", xy=(0.1,2.18), xytext=(0.1, 9),arrowprops={'arrowstyle': '-', 'ls': 'dashed'})\n",
    "plt.annotate(\"Dynamic Activities\", xy=(-0.01,2.15), xytext=(0.1, 9),arrowprops={'arrowstyle': '-', 'ls': 'dashed'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This density plot shows an important threshold of seperation between these two activites.  \n",
    "    if tBodyAccMag-mean < -0.5:   \n",
    "        'Static activities'  \n",
    "    else if tBodyAccMag-mean > -0.5:  \n",
    "        'Dynamic activities'  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets take a closer look at density plots of these two categories of activites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28da52ab908>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"Static Activities\")\n",
    "sns.distplot(df_train[df_train[\"Activity\"]==\"SITTING\"]['tBodyAccMag-mean()'],hist = False, label = 'Sitting')\n",
    "sns.distplot(df_train[df_train[\"Activity\"]==\"STANDING\"]['tBodyAccMag-mean()'],hist = False,label = 'Standing')\n",
    "sns.distplot(df_train[df_train[\"Activity\"]==\"LAYING\"]['tBodyAccMag-mean()'],hist = False, label = 'Laying')\n",
    "plt.axis([-1.02, -0.5, 0, 35])\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"Dynamic Activities\")\n",
    "sns.distplot(df_train[df_train[\"Activity\"]==\"WALKING\"]['tBodyAccMag-mean()'],hist = False, label = 'Sitting')\n",
    "sns.distplot(df_train[df_train[\"Activity\"]==\"WALKING_DOWNSTAIRS\"]['tBodyAccMag-mean()'],hist = False,label = 'Standing')\n",
    "sns.distplot(df_train[df_train[\"Activity\"]==\"WALKING_UPSTAIRS\"]['tBodyAccMag-mean()'],hist = False, label = 'Laying')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize this insight using box-plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5]), <a list of 6 Text xticklabel objects>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(x='Activity', y='tBodyAccMag-mean()',data=df_train, showfliers=False)\n",
    "plt.ylabel('Body Acceleration Magnitude mean')\n",
    "plt.title(\"Boxplot of tBodyAccMag-mean() column across various activities\")\n",
    "plt.axhline(y=-0.7, xmin=0.05,dashes=(3,3))\n",
    "plt.axhline(y=0.020, xmin=0.35, dashes=(3,3))\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Using box-plot we are getting the insight that:  \n",
    "\n",
    "    if tBodyAccMag-mean()<=-0.8:  \n",
    "        Activity = \"static\"  \n",
    "    else if tBodyAccMag-mean()>=-0.6:  \n",
    "        Activity = \"dynamic\"  \n",
    "    \n",
    "2. We can also observe that acceleration is greater while walking downstairs    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Analysis of angle between X and Y axes and gravity mean feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5]), <a list of 6 Text xticklabel objects>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UDktlQM1npZTen1J6BkBVVXdRTne2H9DPplzv9dq63CrKL+kX1cvu5O8pX5pn1+/3OEpLwnhxjmq6rj4L7Exp5fpZ/WUwnluAI1NKc1pbFutTbv9KWbeLKRfHj6mqqvWUL+K9q6r6bYdi7wWOTymdl1I6pN5njk4pfTKlNHoq9AbglSmlx6WUDqnfS5MvpLEcQbmGdLzhEs4BXphS+lBK6fEppUemlE5pOQ32N8CbUkp/XO9nrwNeD/z1OHV+E3heSinX2+K7KD90+uVGyrb19pTS/imlP6R8Fq3+ifLd85WU0jPrcsemlF44Qd1/A/xRferwoJRSprRq/11VVavr97+w3q/2TSk9nbIurgNIKZ1ar9snpJT2BV5J6U19XYfljbqI0qrzJ5RrJVtbNLt1TBl9fxN9/t+knPY9qt5+zqPllO5E66CDGyj7yqEppYMprd0PJoz1j5+/A85KKb2hfp9PSCm9u6WOWyjHmnkppVkppTFzi7rVfhEluXwxG7dyjuUWYM+U0tPrejc53VzH99fAX9ffUY9MKT0mpXRCSmlhvV6ekVL6i5TS0+oYn0vp8DHRZz88+nWxnn/N/yhflFXL3/2UXyPvAB7SUm4nNgzhsYpNh/D4GOVX184t055FOd1y3DjLryi9vr5cL/t2Wi5Or8vcysa9WceNpS7zasrOuobxhyZpUtcVTNABgpJ0BKVn2V3ARylfsLe2lPkUmw4D8yrqId9apr2Ncv3QSsqvzdPr9bR7Pf8INh2aZF9KC9vo+/g1JYHZv6XMycAv2z6fdbQMQdMy752UC6vn1c/PGiPO0aFJVlGul8t1XE/uFGfTddVS9kt1HW/vsO20doA4mtKiu2qMWEd78757jHrG/Xxpu3C6nnYY5cvvt5TTXNdTepI+tJ7/OEpPy5WU7fdP6vKf6rRd19Pah4JIlO34xAb78gvqz2ElpZXn25QWhdF63smGfeJmJh6aZGb9npbV28JHKcn/RNv0Ru+hnvZxNu7UtMk6p8MF6W1l3kC5mH8l5Xq5o2m7KJ/S8vOleh3cT+l40tqbdW2Huk+uP8fVlP3v/S2f516UITaW1NvXUkoL7871/JfWn/c99TJ/TktP0XHez8x6/T6430z1mDLW9Iaf/8OAz9TvYRllu2/tADHuOujw/uZSjmG/oxzb/4rSmte+jb+FkvitpvRG/nzL/KdQWlxX0mFokjH287to6ZQz1n5cr/tFlB66FeMPTXIq5TKJB+r18yM29Dp+DOXU8R1sOPZ+sH35w/w32kVf6iiV22OdVFXVv/Y7lkGVUnov8JaqqnafYj0zKRfrvruqqi9P8rWfpgz3cdQ4ZV7NhvHW7p1KrN2Wyh1NvkxJTodm4Oe6hegvKL1k1/U7Hklbn4f2OwBp2NQJ19spv/R+R7lo+J2UX+VTUlXVmpTSyUzyupv6tMZz61hap7+D0vpzN+V6poWUX9QDk8jVp07mUU7HLRqmRK62LWVcMRM5SX1hy5wmZMvcxlLpUn8ppcfYwyinRT4NfLDavJ6cPVO31j2f0ttyhHJq6y+rzhfUb3Gp3NPxTMr1gn9Yde7FKkkag8mcJEnSEBuY06w55wspt5RZFhGPHWN+ooysfQzl4tVTIuJ/63knU37ZA7wvIqY6CKEkSdJQGJhkjtKz5yN07qr8Qjbca+5plJ6ZT8s57wb8JRvu8fbjnPMlEXHPBMuzSVKSJA2TMQcSH5hkLiK+k3Peb5wixwOfjogK+GHOeZec816UoRW+ERF3A+Scv0HpCv/ZiZa5dOnSKcctSZLUa3PmtI8XvcEwDRq8N+UC7lFL6mmdpkuSJE17A9My18BYTYvVONM3kXM+nfq2JhHBrFlNbm0pSZI0uIYpmVtCGal61D6U0a2XUE61tk6/YqwKIuJ8NtyYu1q+fLxbwkmSJA2G8U6zDlMydwnwxpzzxZQOEPdFxO0558uAv845j97b8yjg3Z0qkSRJmk4GJpnLOX+W0sI2K+e8hNJDdSZARHycMtr+McBNlKFJXlPPuzvnfA5wZV3V2aOdISRJkqa7rXnQ4MrerJIkaRjUp1nHHJpkmHqzSpIkqY3JnCRJ0hAzmZMkSRpiJnOSJElDzGROkiRpiJnMSZIkDTGTOUmSpCFmMidJkjTETOYkSZKG2MDczkvScFu0aBEjIyNTrufOO+8EYI899phyXQBz585l/vz5XalLkgaRyZykgbJq1ap+hyBJQ8V7s0oaKAsXLgRgwYIFfY5EkgaH92aVJEmapkzmJEmShpjJnCRJ0hAzmZMkSRpiJnOSJElDzGROkiRpiJnMSZIkDTGTOUmSpCFmMidJkjTETOYkSZKGmMmcJEnSEDOZkyRJGmImc5IkSUPMZE6SJGmImcxJkiQNMZM5SZKkIWYyJ0mSNMRM5iRJkoaYyZwkSdIQM5mTJEkaYiZzkiRJQ8xkTpIkaYiZzEmSJA0xkzlJkqQhZjInSZI0xB7a7wBG5ZyPBj4MzAAuiIhz2+afBxxZP90BmB0Ru9Tz1gHX1PMWR8RxWyZqSZKk/hqIZC7nPAP4KPB8YAlwZc75koi4brRMRLytpfybgCe2VLEyIg7ZUvFKkiQNikE5zXoocFNE3BwRq4GLgePHKX8i8NktEpkkSdIAG4iWOWBvYKTl+RLgaWMVzDnvC+wPXN4yebuc81XAWuDciPhyh9eeDpwOEBHMmjWrC6FL6qaZM2cCuH9KUkODksylMaZVHcqeAHwhIta1TJsXEUtzzgcAl+ecr4mIX7W/MCLOB84frX/58uVTClpS961ZswYA909J2mDOnDkd5w3KadYlwNyW5/sASzuUPYG2U6wRsbT+fzNwBRtfTydJkjRtDUrL3JXAgTnn/YHbKAnb/PZCOedHArsCP2iZtitwf0SsyjnPAp4JfGCLRC1JktRnA9EyFxFrgTcClwHXl0lxbc757Jxz6zAjJwIXR0TrKdhHA1flnH8KfJtyzdx1SJIkbQVSVXW6NG3aq5Yu7XQmV1K/LFy4EIAFCxb0ORJJGhz1NXNj9TEYjJY5SZIkbR6TOUmSpCFmMidJkjTETOYkSZKGmMmcJEnSEDOZkyRJGmImc5IkSUPMZE6SJGmImcxJkiQNMZM5SZKkIWYyJ0mSNMRM5iRJkoaYyZwkSdIQM5mTJEkaYiZzkiRJQ8xkTpIkaYiZzEmSJA0xkzlJkqQhlqqq6ncM/VItXbq03zFIfbVo0SJGRkb6HcZGFi9eDMC8efP6HMmm5s6dy/z58/sdhqSt0Jw5cwDSWPMeumVDkTRIRkZGuPlXv2aXnffqdygbVDMBuHv56j4HsrF777u93yFI0phM5qSt3C4778VzDjut32EMvMu/e0G/Q5CkMTVK5nLOuwHvAA4BdmydFxGH9yAuSZIkNdC0ZW4RsC0QwP29C0eSJEmT0TSZewbw8IhY1ctgJEmSNDlNhyb5GbBPLwORJEnS5DVtmbsc+HrO+V+AO1pnRMSFXY9KkiRJjTRN5g4DlgDPb5teASZzkiRJfdIomYuII3sdiCRJkiZv0uPM5ZwTLSMQR8T6rkYkSZKkxpqOM7c38BHgcGCXttkzuh2UJEmSmmnam/XjwGrgucAK4EnAJcAZPYpLkiRJDTRN5p4BvDYirgaqiPgpcCrw9p5FJkmSpAk1TebWAWvrx/fmnB8O/A7YuydRSZIkqZGmydyPgGPqx5cBnwO+CFzVi6AkSZLUTNPerCexIfF7K+X06sOAD/UiKEmSJDXTdJy5e1serwTe17OIJEmS1FjToUm2Bd4LnAjsHhE755yPAg6KiI90I5Cc89HAhylDnVwQEee2zT8F+CBwWz3pIxFxQT3vZODMevr7IuKibsQkTXfLli1jxW9Xcvl3L+h3KAPv3vtuZ+367fsdhiRtouk1c+cBjwVeSbmFF8C1wOu7EUTOeQbwUeCFwMHAiTnng8co+rmIOKT+G03kdgP+EngacCjwlznnXbsRlyRJ0qBres3cS4BHRMTvcs7rASLitnow4W44FLgpIm4GyDlfDBwPXNfgtS8AvhERd9ev/QZwNPDZLsUmTVuzZ8/moQ9ZzXMOO63foQy8y797AbvN2qbfYUjSJpomc6vby9bDk9zVpTj2BkZani+htLS1+6Oc8+HAL4G3RcRIh9eOmWTmnE8HTgeICGbNmtWF0KXhNXPmTMrurSZmzpzpcUPSwGmazH0euCjn/DaAnPNelJ6sF3cpjjTGtKrt+X8An42IVTnnM4CLgOc0fC0AEXE+cP5omeXLl29muNL0sGbNmn6HMFTWrFmDxw1J/TBnzpyO85peM/fnwK3ANZR7s94ILAX+aoqxjVoCzG15vk9d/4Mi4q6IWFU//WfgyU1fK0mSNF01HZpkNWV8ubfWp1eXR8SYrV+b6UrgwJzz/pTeqicA81sL5Jz3iojb66fHAdfXjy8D/rql08NRwLu7GJskSdLAGjeZyznP6zBrbs4ZgIhYPNUgImJtzvmNlMRsBnBhRFybcz4buCoiLgHenHM+jnJbsbuBU+rX3p1zPoeSEAKcPdoZQpIkabqbqGXuVjZcf9bp2rQZ3QgkIv4T+M+2ae9tefxuOrS4RcSFwIXdiEOSJGmYTHTN3M8o18edCewLzGz7s5++JElSH42bzEXEIcDLgN2A71Fazk4AtomIdRGxrvchSpIkqZMJe7NGxM8j4p3A/sDfA8cCt+ecn9Tr4CRJkjS+pkOTABwIPBt4OvAT4J6eRCRJkqTGJurNuhtwInAy8DDgM8Dh3ejBKkmSpKmbqDfrUuAWShL3w3raI3LOjxgtEBGX9yg2SZIkTWCiZO4OYDvgj+u/dhVwQLeDkiRJUjPjJnMRsd8WikOSJEmboVEHiJzzm3POs3odjCRJkian0b1ZgedR7n96BeX6uS+33PRekiRJfdIomYuI43LOu1MGDH4r8PGc878Dn46I7/QyQEm9de99t3P5dy/odxgPWvG7uwDY8fd273MkG7v3vtvZbda+/Q5DkjaRqqqauFSbnPPjKS10jwVGgH8GPhwRK7obXk9VS5cu7XcMUl8tWrSIkZGRfoexkcWLy8hH8+bN63Mkm5o7dy7z58/vdxiStkJz5swBSGPNa3qaFYCc83OBVwHHA1cBHwAWA28BvgYcNpVAJW1Zg5iYLFy4EIAFCxb0ORJJGg6Nkrmc899STrHeB3waODMibmuZ/0O8I4QkSdIW17RlbjvgJRFx5VgzI2JNzvkp3QtLkiRJTTS9N+v6sRK5nPOHRh9HxC+6FpUkSZIaaZrMndJh+kldikOSJEmbYdzTrDnn146Wa3k86gBgeU+ikiRJUiMTXTM32vK2DRu3wlXAncDJvQhKkiRJzUx0b9YjAXLO74uIM7dMSJIkSWqqYzKXc04RMTqi8HtzzmNeXxcR63sSmSRJkiY0XsvcfcBO9eO1lFOrrVI9bUYP4pIkSVID4yVzj2l5vH+vA5EkSdLkdUzmIqL1ho27RsTVWyAeSZIkTULTO0B8I+f8f8AiYFFE3NzDmCRJktRQ02RuT+Bo4ETg6pzztZTE7nMRsaxXwUmSJGl8jZK5iFgHfBX4as55e+B44PXA3wLb9i48SZIkjafp7bwAyDlvBxwLvAJ4CvDdXgQlSZKkZhq1zOWcjwHmA8cB1wEXA6+PiDt6GJskSZIm0PSaub8FPgs8MSJ+1cN4JEmSNAlNr5k7uNeBSJIkafKatsyRcz4EOAyYRbn7AwAR8d4exCVJkqQGGnWAyDmfDnwfeA6wAHgc8HbgEb0LTZIkSRNp2pv1z4CjI+IlwMr6/8uANT2LTJIkSRNqmszNjojRYUjW55wfEhFfA17co7gkSZLUQNNkbknOeb/68S+B43POhwGrexKVJEmSGmnaAeIDwKOBW4GzgS8A2wBv7lYgOeejgQ8DM4ALIuLctvl/CpwGrAX+D3htRPy6nrcOuKYuujgijutWXJIkSYNswpa5nHMCvgN8A6A+vborsGtEfKwbQeScZwAfBV4IHAycmHNuHw7lJ8BTIuLxlGTyAy3zVkbEIfWfiZwkSdpqTNgyFxFVzvka4GEt01bT3VOshwI3RcTNADnniyn3f72uZZnfbin/Q+BVXVy+JEnSUGp6mvUnwEHAL3oUx7r79cwAACAASURBVN7ASMvzJcDTxil/KvC1lufb5ZyvopyCPTcivjzWi+ohVk4HiAhmzZo1paAldd/MmTMB3D8lqaGmydwVwNdzzp+iJF3V6IyIuLALcaQxplVjTCPn/CrgKcCzWybPi4ilOecDgMtzzteMdduxiDgfOH+0/uXLl08xbEndtmZNGfHI/VOSNpgzZ07HeU17sz4TuIWSQL0KOKn+69apziXA3Jbn+wBL2wvlnJ8HvAc4LiJWjU6PiKX1/5spiecTuxSXJEnSQGt6b9YjexzHlcCBOef9gduAE4D5rQVyzk8EPkEZvHhZy/RdgfsjYlXOeRYl8WztHCFJkjRtNUrmcs5jtuBFxPpuBBERa3PObwQuowxNcmFEXJtzPhu4KiIuAT4I7Ah8PucMG4YgeTTwiZzzekpL47kRcd2YC5IkSZpmUlWNeWnaRupEaayCaymnQ78I/GVErOhueD1VLV26yZlcSX22cOFCABYsWNDnSCRpcNTXzI3Vx6DxNXNvAi4HjqK0hL0A+Bblnq2vB54BfGiqgUqSJGlymvZm/VPgSRFxX/38l/VQID+OiN+vx6H7cU8ilDQUFi1axMjIyMQFJ7B48WJgQwvdVM2dO5f58+dPXFCShlTTZG4nYAfgvpZpOwA714/vALbvYlyStlLbbrttv0OQpKHSNJn7NPCNnPOHKePM7QO8Bbionn8UcEP3w5M0LGz9kqT+aJrMvRO4kTJkyBzgdsq9VP+5nv9tyvhukiRJ2oIa9WadpuzNKkmShkI3erNKkiRpAJnMSZIkDTGTOUmSpCFmMidJkjTEOvZmre+LOqGIeG/3wpEkSdJkjDc0ydyWx9sBfwRcCfwamAccCvx770KTJEnSRDomcxHxmtHHOeeLgRMj4t9bpr0UeHlvw5MkSdJ4ml4z90Lgy23TvgIc091wJEmSNBlNk7mbgDe0TfsT4FfdDUeSJEmT0fR2XqcBX8o5/xlwG7A3sBZ4aa8CkyRJ0sQa384r5zwTeDqwF+XerD+IiDU9jK3XvJ2XJEkaCuPdzmvS92bNOW90ajYi1m92ZP1lMidJkobCeMlco9OsOecnAR8FHk8ZpmS0wgqYMfUQJUmStDmaXjN3EfAfwGuB+3sXjiRJkiajaTK3L/CeiJjcOVlJkiT1VNOhSb4EHNXLQCRJkjR5TVvmtqMMTfI94I7WGRHx6q5HJUmSpEaaJnPX1X+SJEkaIJMemmQamdZDk6z74J9v9Dw947k85JnPtYxlLGMZy1hmqygz3Ux5aBKAnPM2wCOBWa2VRcTlU4xPkiRJm6lRy1zO+VnA54FtgZ2A3wAPA0Yi4oCeRtg707plTpIkTR/jtcw17c16HvCBiNgN+G39/xzgn7oSoSRJkjZL02TuIODDbdPOBd7W3XAkSZI0GU2Tufsop1cBbs85HwzsCuzYk6gkSZLUSNNk7ovAMfXjTwLfBn5MuY5OkiRJfbJZQ5PUHSIeBlwWEeu7HtWWYQcISZI0FMbrAOE4c5IkSQOuG71ZJUmSNIBM5iRJkoaYyZwkSdIQa3w7r1Y55yOBdRHxnW4FknM+mjKW3Qzggog4t23+tsCngScDdwGviIhb63nvBk4F1gFvjojLuhWXJEnSIGvUMpdz/u+c8zPrxwuAi4HP5pz/fPxXNpNzngF8FHghcDBwYj2WXatTgXsi4hGUO1IsrF97MHAC8BjgaOCf6vokSZKmvaanWR8L/LB+/MfAEcAfAGd0KY5DgZsi4uaIWE1JFo9vK3M8cFH9+AvAc3POqZ5+cUSsiohbgJvq+iRJkqa9pqdZHwJUOeffB1JEXA+Qc961S3HsDYy0PF8CPK1TmYhYm3O+D9i9nv7DttfuPdZCcs6nA6fXdTBr1qyuBC9JktQvTZO57wEfAfYCvgRQJ3bLuxTHWOOmtA+A16lMk9cCEBHnA+ePllm+vFvhS5Ik9U49ztyYmp5mPQW4F/gZcFY97VGUDgvdsASY2/J8H6B9RN8Hy+ScHwrsDNzd8LWSJEnTUqOWuYi4C/jztmlf7WIcVwIH5pz3B26jdGiY31bmEuBk4AfAy4DLI6LKOV8CLMo5/z0wBzgQ+J8uxiZJkjSwOiZzOef3RMT768dndyoXEe+dahD1NXBvBC6jDE1yYURcWy/3qoi4BPgk8Jmc802UFrkT6tdem3MO4DpgLfCGiFg31ZgkSZKGQcd7s+acPxYRr68f/0unCiLiNT2Krde8N6skSRoK492btWMytxUwmZMkSUNhvGSu6aDBrxpjWqrvvCBJkqQ+adqb9S9zzp8bHVcu53wAZbiSY3oWmSRJkibUNJk7BPgNcE3O+RxKb9FLgWf3KjBJkiRNrFEyFxG/owxNcjfwHuA/gHMjYn0PY5MkSdIEml4z9yLgp8C3gccDBwHfrceFkyRJUp80vZ3Xx4GTI+IbADnnwygtdFdR7o8qSZKkPmiazD0+Iu4ZfVKfXj0n59zNu0BIkiRpkprezuvBRC7nnNgwzsnVvQhKkiRJzTRK5nLOewP/SOm9ukvb7BndDkqSJEnNNB2a5OPAGuC5wArgSZQb35/Ro7gkSZLUQNNk7hnAayPiaqCKiJ8CpwJv71lkkiRJmlDTZG4dsLZ+fG/O+eHA74C9exKVJEmSGmmazP2IDbfuugz4HPBFytAkkiRJ6pOmQ5OcxIbE762U06sPAz7Ui6AkSZLUTKqqqt8x9Eu1dOnSfscgSZI0oTlz5sCGoeE20vQ064Nyzr+ZakCSJEnqjkknc3TICiVJkrTlbU4yJ0mSpAGxOcncwV2PQpIkSZtl3GQu5/zS9mkRMVLPm5lzPqdXgUmSJGliE7XMnZdz/nzOeXbrxJzzs4BrgD/oWWSSJEma0ETjzD0G+CBwbc757cCXgA8ALwcWRMQnexyfJEmSxtFonLmc87OBLwDbA98EzoiIO3ocW685zpwkSRoK440zN+EdIHLOuwOvA9YAV1M6QBwEDHsyp2ls0aJFjIyMTLmeO++8E4A99thjynUBzJ07l/nz53elLkmSYOIOECcA1wMPAAdHxPOBs4Ev5Jw/nnPeaQvEKPXNqlWrWLVqVb/DkCSpo3FPs+acbwFOj4hvtE2fDXwEeEZE7NPbEHvG06ya0MKFCwFYsGBBnyORJG3NpnKa9bER8bv2iRGxDMg55+OnHp4kSZI217inWcdK5Nrmf6W74UiSJGkyvJ2XJEnSEDOZkyRJGmImc5IkSUNswnHmWtW9WHdsnRYRN3c1IkmSJDXWKJnLOR8NfBLYk427xVbAjB7EJUmSpAaatsx9FDgHuCgiVvYwHkmSJE1C02RuV+ATETHxjVwlSZK0xTRN5j4JvAa4sNsB5Jx3Az4H7AfcCuSIuKetzCHAx4CdgHXA+yPic/W8TwHPBu6ri58SEVd3O05JkqRB1DSZ+wPgzTnndwF3tM6IiMOnGMO7gG9FxLl1/e8C2u+ddD/w6oi4Mec8B/hxzvmyiLi3nv/OiPjCFOOQJEkaOk2TuQvqv144HjiifnwRcAVtyVxE/LLl8dKc8zLg4cC9SJIkbcUaJXMRcVEPY9gjIm6vl3N7PfxJRznnQ4FtgF+1TH5/zvm9wLeAd0XEqg6vPR04vV4Ws2bN6kb8msZmzpwJ4LYiSRpYTYcmScBpwInArIh4fM75cGDPiIgGr/8mZViTdu+ZTLA5572AzwAnR8T6evK7Kad+twHOp7TqnT3W6yPi/LoMQLV8+fLJLF5boTVr1gDgtiJJ6qc5c+Z0nNf0NOvZwPOBDwEfr6ctAc4DJkzmIuJ5neblnO/MOe9Vt8rtBSzrUG4n4KvAmRHxw5a6b68frso5/wvwjgbvR5IkaVpoejuvU4BjI+JiykDBALcAB3QhhkuAk+vHJwNfaS+Qc94G+BLw6Yj4fNu8ver/CfhD4OddiEmSJGkoNG2ZmwGsqB+PJnM7tkybinOByDmfCiwGXg6Qc34KcEZEnAZk4HBg95zzKfXrRocg+bec88Mpd6a4GjijCzFJkiQNhVRVE48DnHO+AFgNvA24Hdidcop1m4j4k55G2DvV0qVL+x2DBtzChQsBWLCgfbQcSZK2nPqauTTWvKanWf8UmEMZmHdnSovcvmw6HpwkSZK2oKZDk/wG+MN62JB9gZGIuGOCl0mSJKnHmrbMkXPeHXgBcGRE3JFznpNz3qd3oUmSJGkijZK5nPOzgRuAVwJ/UU8+kHK/VEmSJPVJ096sHwJeERHfyjnfU0/7EXBob8KSJEmCRYsWMTIyMuV67rzzTgD22GOPKdcFMHfuXObPn9+VuqaqaTK3X0R8q3482v119SReL0mS1DerVo15p89poWkydl3O+QURcVnLtOcB1/QgJkmSJICutX5N56GmmiZzbwcuzTl/Fdg+5/wJ4MXA8T2LTJIkSRNq1AGivhfqE4BrgQspt/I6NCKu7GFskiRJmkCjlrmc884RcRvwgR7HI0mSpEloepr1jpzzL4D/rv++ExF39S4sSZIkNdF00OBdKdfN3Qu8Gfh1zvmanPNHehaZJEmSJtT0mrkHIuJyymnWvwE+AcwDXtbD2CRJkjSBptfMnQs8G9gb+H/Ad4CnR8R1PYxNkiRJE2h6zdwbgTsot++6ArgyItb2KihJkiQ10/SauV2Ak4D1wJmUa+a+kXM+s2eRSZIkaUKNWubqVrgf1D1afwk8B3g1cDjwvt6FJ0mSpPE0vWbuHyjXzB0IXEW5Zu5llOvnJEmS1CdNr5m7G3gr8IOIeKCH8UiSJGkSml4zt1tEfLs9kcs5f6gHMUmSJKmhpsncKR2mn9SlOCRJkrQZxj3NmnN+7Wi5lsejDgCW9yQqSZIkNTLRNXOjLW/bsHErXAXcCZzci6AkSZLUzLjJXEQcCZBzfl9EOKacJEnSgGk6ztyZOefdgWOAPSPigznnOcBDImJJTyOUJElSR406QOScnw3cALwSeG89+UDK7b0kSZLUJ017s34IeEVEHA2M3pP1R8ChPYlKkiRJjTRN5vaLiG/Vj6v6/2qaDzosSZKkHmiazF2Xc35B27TnAdd0OR5JkiRNQtOWtbcDl+acvwpsn3P+BPBi4PieRSZJkqQJNWqZi4gfAk8ArgUuBG4BDo2IK3sYmyRJkibQ9DQrEXEb8EHgLGChQ5JIkiT1X6PTrDnnXYB/BF4OzATW5Jw/D7wlIu7uYXySJEkaR9OWuX8BtgeeCDys/r8t5ZSrJEmS+qRpB4gjgb0iYmX9/Pqc8ynA0p5EJUmSpEaatszdAOzXNm1ePV2SJEl90rFlLuf82pan3wL+K+f8GWAEmAu8CvjMVAPIOe8GfI6SLN4K5Ii4Z4xy69gwrt3iiDiunr4/cDGwG/C/wEkRsXqqcXXTokWLGBkZmXI9d955JwB77LHHlOsCmDt3LvPnz+9KXZIkqT/Ga5k7qeXv6cBN9f9c//9V/X+q3gV8KyIOpCSN7+pQbmVEHFL/HdcyfSFwXv36e4BTuxDTQFq1ahWrVq3qdxiSJGmAdGyZi4gjt1AMxwNH1I8vAq4AFjR5Yc45Ac8BRpuXLqIMnfKxbgY4Vd1q/Vq4cCEACxY0Wj2SJGkrMAj3Vt0jIm4HiIjbc86zO5TbLud8FbAWODcivgzsDtwbEWvrMkuAvTstKOd8OnB6vSxmzZrVrfewRcycORNg6OIeZq5zSZoepvPxfIskcznnbwJ7jjHrPZOoZl5ELM05HwBcnnO+BvjNGOWqThVExPnA+aPlli9fPonF99+aNWsAGLa4h5nrXJKmh2E/ns+ZM6fjvC2SzEXE8zrNyznfmXPeq26V2wtY1qGOpfX/m3POV1DGuvt3YJec80Pr1rl9cLgUSZK0FRmE06yXACcD59b/v9JeIOe8K3B/RKzKOc8Cngl8ICKqnPO3gZdRerSO+XpJmo7sKS8JJpHM5ZwfTUma9oyIN+ScHwVsExE/m2IM5wKRcz4VWEy5ZRg556cAZ0TEacCjgU/knNdTeuCeGxHX1a9fAFycc34f8BPgk1OMR5K2KvaSl4Zb03uzvhz4KPBFSs/RNwA7UhKxjqdQm4iIu4DnjjH9KuC0+vH/Ax7X4fU3A4dOJQZJGkb2lN/ybA3VIGp6B4izgaMi4gxgXT3tp8ATehKVJEnTmOOGqpuanmadTUneYENv0Ypxeo5KkjTd2BqqQdS0Ze7HlDtBtDoB+J/uhiNJkqTJaNoy92bKvVlPBX4v53wZcBBwVM8ikyRJ0oQaJXMR8Yu69+qxwKXACHBpRKzoZXCSJGn4dKujSDctXrwY2HCKe5BMtQNL46FJIuJ+IDZ7SZIkaaswMjLCr265gR13a3o1V++tS+sBuPO+G/scycZW3L1+ynV0TOZyzt+lQQeHiDh8ylFIkqRpZcfdHsKTj92u32EMvB9f+sCU6xivZe6CKdcuSZKknuqYzEXERVsyEMlrLCbHQUIlSdD8DhCv7TBrFbAE+GFEOPqhpmRkZIRf3HQL7Lh7v0PZYF0C4Bd3/KbPgbRZcVe/I5AkDYimHSBeDTwduJOSvO0D7AFcBewHkHM+vr4Fl7T5dtyd6pBj+x3FwEtXX9rvECRJA6JpMnct8MWI+IfRCTnnNwKPAp4FvAf4R0rCJ0mSpC2kaZ/h+cBH2qZ9DHhlRFTAB4GDuxmYJEmSJtY0mbsTeHHbtBcBy+rH2wFruhWUJEmSmpnM7bw+n3P+OeXuD3OBxwIvr+c/jXKaVZIkSVtQ09t5/VfO+QDgGGAO8J/AVyPirtH5wH/1LEpJkiSNaTK387oL+EwPY5EkSdIkNR1nbn/g/cAhwI6t8yJiXg/ikiRJQ2rZsmX8dsX6rtyqarr77V3rSauWTVxwHE1b5hYBvwLeDtw/pSVKkiSpa5omc48BnhkR63sZjCRJGn6zZ8+m2vY+nnzsdv0OZeD9+NIHmL3z7CnV0XRoku8AT5zSkiRJktR1TVvmbgUuyzl/EbijdUZEvLfbQUmSJKmZpsnc7wH/AcykjDEnSZKkAdB0nLnXjDU959z0NK0kSZJ6oPE4c61yzo8DXg28kjKIsCRJkvqgcTKXc344MB84GXgC8D3gLT2KS5IkSQ2Mm8zlnGcCxwGnAC8AbgI+C+wLvDwipjbKnSRJkqZkomve7gQ+AdwA/EFEHBwR5wCrex6ZJEmSJjRRMvczYBfgacBTc8679j4kSZIkNTXuadaIOCLnvC+ls8M7gH/IOf8XZaiSmVsgvr5ZtGgRIyMj/Q5jI4sXLwZg4cKFfY5kU3PnzmX+/Pn9DkOSpK3OhB0gIuLXwDnAOTnnZ1ESu/XAT3POF0bEn/U4xr4YGRnh17+8gb1m9DuSDWauK/9X/+qG/gbS5vZ1/Y5AGh7+UJwcfyhKE5vU0CQR8T3geznnNwMvoSR209ZeM+C0nR1KbyIX3Octe6WmRkZGuPHGG9l+++37HcqD1qxZA8CSJUv6HMnGVq5c2e8QpKGwWePMRcQDlF6tn+1uOJI0/W2//fYceOCB/Q5j4N144439DkEaCjY7SZIkDbHNapmTJGlYeJ3i5Hid4vAxmZMkTWsjIyMsvvVG5s3ert+hPGi7GeU6Re4fsCRz2QNdq2vF3ev58aXdq2+q7v9Nub57h50G66TkirvXs8fOU6uj78lcznk34HPAfsCtQI6Ie9rKHAmc1zLpUcAJEfHlnPOngGcD99XzTomIq3sctnpg2bJlsOJ3pKsv7Xcog2/FXSzr4kFXmu7mzd6Od5+wf7/DGHh/c/EtXaln7ty5XamnmxbfV1pD99h5Xp8j2dgeO099ffU9mQPeBXwrIs7NOb+rfr6gtUBEfBs4BB5M/m4C/qulyDsj4gtbKF5JkjSOQTxNO3pKe8GCBROUHD6DkMwdDxxRP74IuIK2ZK7Ny4CvRcT9vQ1LW9rs2bO5e/1vqA45tt+hDLx09aXMnr1Tv8OQJA2AQThxvEdE3A5Q/589QfkT2HRIlPfnnH+Wcz4v57xtL4KUJEkaRFukZS7n/E1gzzFmvWeS9ewFPA64rGXyu4E7gG2A8ymtemd3eP3pwOkAEcGsWbM6LmvmzJmsnkxwW7mZM2eOuz6b1qHmurHOteW5nU9Ot44ta7oUz9Zguh5bRve96fjetkgyFxHP6zQv53xnznmviLi9TtaWjVNVBr4UEQ/ul6OtesCqnPO/UO4h2ymO8ykJH0C1fPnyjgsaHRFdzaxZs4bx1mfTOtRcN9a5trzbbruNlStXOiBuAytXruS2227z2LKFTddjy+h2MKzvbc6cOR3nDcJp1kuAk+vHJwNfGafsibSdYq0TQHLOCfhD4Oc9iFGSJGkgDUIHiHOByDmfCiwGXg6Qc34KcEZEnFY/3w+YC/x32+v/Lef8cCABVwNnbKG4JWnSZs+ezerVq72dVwM33ngjs2dPdBm1pL4ncxFxF/DcMaZfBZzW8vxWYO8xyj2nl/FJkiQNsr4nc5Ik9dKyZct44P4HujYg7nS2eNkDbLfDeJeuaxCZzHWwbNkyVq6FC+5b3+9QBt7ta2H7Ze78kiT1g8mcJGlamz17Nty/ytt5NfA3F98CO3id4rAxmetg9uzZrP7tPZy28yB0+B1sF9y3nm28SFmSpL4wU5EkSRpiJnOSJElDzGROkiRpiJnMSZIkDTGTOUmSpCFmb1ZJ2sJWrlzJjTfe2O8wHrRq1SoAtt122z5HsrGVK1f2OwRpKJjMjeP2dYM1aPBd68r/3Wf0N452t6+DffsdhDQk5s6d2+8QNrF48WIA9tlnnz5HsqlBXF/SoDGZ62AQDyBr6gPuNvPm9TmSje3LYK4vaRDNnz+/3yFsYuHChQAsWLCgz5FI2hwmcx14wJUkScPAZE6SNO0tXvZAuVXVgFh2z2oAZu+6TZ8j2djiZQ8wb79+R6HJMpmTJE1rg3gZyAPLy2Uz7DBYsc3bbzDXl8ZnMqfBsuIu0tWX9juKDVbeV/5vv3N/42i34i5gp35HIQ0FL5vRdGcyp4ExiL8GFy++F4B5ew5a4rTTQK4vSeq2RYsWMTIyMuV6RnttjybSUzV37tyB+aFgMqeBMSg7RSt/PUvS9DBo4yh2k8mcJEkaWIP4Q3/QeDsvSZKkIWYyJ0mSNMRM5iRJkoaYyZwkSdIQM5mTJEkaYiZzkiRJQ8xkTpIkaYiZzEmSJA0xkzlJkqQhZjInSZI0xEzmJEmShpjJnCRJ0hAzmZMkSRpiJnOSJElD7KH9DkCStHkWLVrEyMjIlOtZvHgxAAsXLpxyXQBz585l/vz5XalL0sRM5iRpK7ftttv2OwRJU2AyJ0lDytYvSTAAyVzO+eXAWcCjgUMj4qoO5Y4GPgzMAC6IiHPr6fsDFwO7Af8LnBQRq7dA6Bpgnn6SJG0tBqEDxM+BlwLf6VQg5zwD+CjwQuBg4MSc88H17IXAeRFxIHAPcGpvw9XWZNttt/UUlCRpoPW9ZS4irgfIOY9X7FDgpoi4uS57MXB8zvl64DnAaFPHRZRWvo/1Kl4NB1u/JElbi74ncw3tDbSeM1sCPA3YHbg3Ita2TN97C8c2IU/5SdL04PFcg2iLJHM5528Ce44x6z0R8ZUGVaQxplXjTO8Ux+nA6QARwaxZsxoseuq23357Zs6cOeV6dthhB4Cu1AUlri21DiRpOvB4rkG0RZK5iHjeFKtYAsxteb4PsBRYDuySc35o3To3Or1THOcD59dPq+XLl08xrGZe8pKXbJHlbI4ttQ4kaTrweK5+mTNnTsd5g9ABookrgQNzzvvnnLcBTgAuiYgK+DbwsrrcyUCTlj5JkqRpoe/JXM75JTnnJcDTga/mnC+rp8/JOf8nQN3q9kbgMuD6MimuratYAPxpzvkmyjV0n9zS70GSJKlfUlV1vMRsuquWLu14RlaSJGlg1KdZx+or0P+WOUmSJG0+kzlJkqQhZjInSZI0xEzmJEmShpjJnCRJ0hAzmZMkSRpiJnOSJElDzGROkiRpiJnMSZIkDTGTOUmSpCFmMidJkjTETOYkSZKGWKqqqt8x9MtW+8YlSdJQSmNN3Jpb5tIw/uWcf9zvGLa2P9e563xr+HOdu863hr9psM7HtDUnc5IkSUPPZE6SJGmImcz9//buPNquqs72+PcGaVIaQECQIiCERpROQXq1lEZ8JVCCOjEoUhp0WOUTAREQFXxosCHgK5rCQgoRBWSi8kR9SCjbQgIKSmcH5VOkFUUQ0EK6+/5YO3IM916RnH3WufvMzxhnjLv3Tm5mNoeb31l7rd+afk6rHWAE5Z4PXu754OWeD17u+eB18p6P8gKIiIiIiGkvI3MRERER01iKuYiIiIhpLMVcRERExDSWYi4iIiJiGntK7QAxOUmbAOvbvrA5/hiwUnP5ZNvfrxauoyStCKxh+8bm+DXAzObyxbZ/VS1cR+V9HqNI0qrAi4Ff2r6qdp4ukrQHcK3tm5rjo4BXATcB77D985r5+ikjc8Ptw8Bveo53A74CfAM4qkqi7lsA7Nhz/CFga8oP3f9VJVH35X0+YJLmSXpXz/Gtku6VdJ+kf6qZraskfVnSps3XawLXA28CPi3poKrhums+8GsASbsDr6fc8wuBj1fM1Xcp5obbmrYv6zm+1/bnbX8aWK1WqI7bGvhUz/F9tt9u+wBg00qZui7v88F7K3BGz/GdtlcEngHMrROp89azfX3z9RuBS2zvAWxLKTCi/8Zt/6H5em/g321fZft0ynu9M1LMDbdZvQe2t+s5XH3AWUbFU2z3Nl/cr+frlQcdZkTkfT54M2zf1XN8PoDtB3hsWkH010M9X+8M/F8A2/cBj1ZJ1H1jkp4maQblnn+t59oKlTK1IsXccLtN0rZLnpS0HXBbhTyj4FFJz1x8sPiTtKS1yA/ctuR9Pngr9R7YPhag+Udv1SqJuu9mSW+XtBewJfBVAEkzgWWrJuuu/w1cDVwJ/Nj2lQCSng/cXjNYv2UBxHA7HDhP0pnA4kngWwH7A/vUCtVxFZbgggAAF2pJREFUxwFfkvRO4AfNuS0pc+mOq5aq2/I+H7yFkj5o+71LnD8GWFgj0AiYR7m/uwD72L6nOb8d8MlqqTrM9hmSLqaM8F/Tc+kO4B+rhGpJtvMacpLWAN4GbNKc+iFwSlZVtkfSy4EjeeyeXw982PZF9VJ1W97ngyXpqcDplDmii/+R24IygnGA7ftrZRtFkp61eMVltE/Ss4FDbb+5dpZ+STEXETGiJM3hsQL6R7Z/VjNP10naHlgL+LbtOyVtDhwBvMj22nXTdU9zfxcAfwv8H+Ak4F8pi06Ot/2xivH6KsXcEJP0DWCy/0DjtnceZJ5R0PQhmsy47Q8MLMyIyPt88CStM9V1278cVJZRIek4YHfKHK4NgC8D/wwcC/xbs/gk+kjSFcCpwCLg5cBhwDnA+7p2vzNnbrgdOsG57ShvyDsHnGVU/H6Cc0+lzHdZFUgx1395nw/eVygF9FjPuXFKu4bVgWVqhOq4VwDPt/2ApKdTFvdsvrhBebRiedtnNl//VNKhwBG2H6mYqRUZmZsmJP0d8D5geeDYzN9qn6RZwDsohZwpw/IpLlqU93kdktalLETZBTjR9kl1E3WPpKtsb9VzfLXt59XM1HWSfkLpm7j4Q8vZwL6Lj7u0u0xG5oacpN0o/7g9AMy3/Y3KkTpP0irAIcDrKA2Et7R9d91U3Zb3eR2SNgTeQzOHCDjQ9kNT/654ktaXdGHP8bq9x7b3rJCp6+4ATpjkeBzYaeCJWpKRuSEm6XuUxx7HUZ75/5kufaoYFs28lr2B0yirKbOqr2V5nw9es63UeyiLHz4KnNvFR0/DpBl1npTtbw0qS3RPirkhJumbTD0xvDOfKoaFpEeBPwIP8+f3foxyz1esEqzD8j4fPEmPADdT5s49roizfeDAQ0X0maS9p7pu+wuDytK2PGYdYrZfUjvDqLGdXVEGLO/zKuYxeQEdLZB0HRPf88UfFDcfcKRRsMcU18aBFHPRvlH6VDEsmvlyk7L920FlGRV5nw9ezwq/GJzdawcYNbbfONm1plF5Z6SYG24j86liiFzF41s2LDYOzBlsnJGQ9/mASfoSU4zMZTJ+/022w4OkHSkrLN822ESjR9JKwKso9/s5lAbOnZBibohN9akiWvOSbKszcO/PPR+4BbUDjDJJz6MUFAJ+Tj6wtEbSTGBPyv3eEpgFvBL4ds1c/ZZibsg1e8i9Bdi4OfVj4DTbN9RL1WkXUP6Hj8H5mqTTgQW2H64dZkQsZ/uSiS5I+giQlZV9Jmkj4LWUvmd3AecBY7ZfWjVYh0k6G3gxsBA4Gfg68F+2v1kzVxsy2XuINfv4fRO4n9Iq4xOUHQq+KWm7itG6bKLHq9Gu5wNrAFdJenHtMCPiFEmv6D0haYakM4Et6kTqvJ8AOwN72H5h05g57WDatSlwN2UQ5CdN+51OLvxJa5IhJuki4CNLfopo+hUdYft/VAnWYZLuBD472fW0bGiPpK2ArwG3AI+SVX6taXZ8+CpwpO0vNI+izgfuBfZP4+D+k7QXZWRuB8q9/yxwuu31qgbrOEkbUx6x7kPZHnBjYDPbd1QN1mcp5oaYpBtsbzTJtZ/afvagM3WdpJuAoya7bvtTA4wzMiTtBPwLcDFwCqWYAyafOB5LR9Jsyv0+CdgPuML2IXVTdZ+kp1LmbM2l7EDwKeAC2wurBhsBkl5Aue+vAW6xvUPlSH2TOXPD7b4prk20IXwsvbtSsA2WpM9SVpXta/u62nlGgaTF80IPA84CLgE+s/h8dt3oP0lPsf2w7d9T9gg9u2mF9BrgCMq8rugjSf/T9smLj21fCVwp6VDKXLrOSDE33NaWdOIE58fo0JLqIbNm7QAj6Gu2PzHRBUlr2P7VoAONgON7vr6WMmdx8blO7Vk5RL7LEourmr6V/9a8ov/eRFn48Gdsj9OxRT4p5obbu6a4duXAUoyWTs2jmA6WLOS63AtqWEy1gjKLq1qTxVXRmhRzQyyP+6rIJNIKRqUX1DRhYJ3aITroGZImnZNo+4RBhhkRm0u6d4LzndtrO8XcEJP0SabegHzeIPOMiNmTPNoGspq1DaPUC2qayAhSO5YBnkbu7yBdZ/v5tUMMQoq54fblCc6tAxxE+cEQ/ffflC29YnAe1wtKUkZI68m9b8ftto+pHSK6KcXcELP9+cVfS5oDHEkZwfgw8O+1cnVcVrMOmO0tenpB/UfT62+WpGd2rRfUsJhib9YxYNUBxxkVGZEbvPNrBxiUFHNDTtJzgPdQuuQfB7w1Wx616sHaAUaR7Z9Q+vsd1dML6ruSOtULaohMtTdr9m1txz9IWnZxQ+Zmq8a/B26ynb1Z2/FrSRvavlHSGHAGZXHVL4B/7FILnhRzQ0zS+cALKD9cD6Zs/bKiJOBPy9qjv97W04Prcbr0P/+w6ukF9S7gHbXzdJHtCdsySFqbsktBp9o2DInPAPOAGyVtACyi9JvbXdLWtt9dNV03vQM4s/l6LrA5sB5lcORfgBfVidV/KeaG29aURyGHAu9szi0eqh8H5tQI1XELKPe29z73Sv+tAbH9qKSDgY/VztJlklajNK6dS2kDc0HdRJ31dNs3Nl/vD5xr++2SlqPM000x138P92xNtztwlu27KNM5PloxV9+lmBtittetnWEEHQ7cbPt2AEn789iw/PvrxRpZmWfUAkmzgL0o8xQ3ohRwc2zPrhqs23o/GO5EmTaD7QclPTrxb4ml9KikNSkLrHYG5vdcm1knUjtSzE0zktanPAaZa3vT2nk66OPALgCSXgx8CHg78DzgNODV9aKNpKysbMedlB0J3gtcanu82Qg+2nOtpAXArcAGNNt3SVq5aqpuO4rSYH8Z4ELbPwSQ9HfA/6sZrN9SzE0DzSeLfSifojenFBhzq4bqrmV65iLuA5zWrCr+vKSrK+bqLEn3MfnKyk59eh4iR1I+FJ4KnCPpvMp5RsGbKXO41gVeZvsPzfnnkkUnrbD9ZUnPAmbZvrvn0pWUn++dMTY+ng++w0rSmylF22xKV3YDX7S9XtVgHSbpeuB5th+W9BPgLba/vfhaRkOjS5qWR3Mphd2GwNHABbZvqBosog8kbUh5nL0BcB1wqO1b66ZqR0bmhtsplBVP+zYr/Egz1dadC3xL0m8oDYT/E6BZffa7msEi+kXSQcClwNW25wPzJW1GKewuAtavma+LJH2DqXf02XmQeUbEGcBZlG0B9wROAvaumqglKeaG299SVpmdIGkNysjcsnUjdZvt+ZK+BqwJLLS9+IfvDMrcuYgumA2cCGws6VrgMuA7wALbR1ZN1l2HTnBuO+AwyhzG6L9Ztj/RfH2cpM62lspj1mlC0myahQ/A31AeheSHbkQ8aU1bjBcAOwDbN697bD+3arCOaybgvw9YHjjW9kWVI3VSM1VmLo+tij+bMvd8DLrVNzQjc0NM0na2LwewfQtlkuyCpnP4a6uGi4gumAmsCKzUvG6jzC2KFkjajVLEPQDMt/2NypG67g7ghEmOx+lQ39CMzA0xSd+3PeluBBERT4ak04BNgPuAK4DLgcuXWPEXfSTpe8AzKBPyFy15vUujRDF4GZmLiBg961Ae8d1I6Xt2C3BP1UTd93vgfkqvyiX7VXZqlGhYSFpyscM48BvKwp/7KkRqTUbmhpikeyircCZke88BxomIDmk2Ht+EMl9uB2BT4LfAIttH18wW0Q+SPjnB6VUo/Vrn2f76gCO1JiNzw+3XwPG1Q0RE9zQrta9vPjT+rnntDmxD6TcXfSTpGko7mMuA79j+Rd1E3Wf7jROdbxoJG9h2sInak2JuuN1v+1u1Q0REt0g6kDIatyPwEKUtySJKX64sgGjH6yj3fFfgaElPpRR2lwGX2b6iZrhRYvsmSZ1q85VibrjdLemZtu8AkPQGyqbvNwHv79l2KiLir7Eu8DngYNu3V84yEmxfD1xP2eMZSatRuhIcROlUsEy9dKOl6Qjxx9o5+inF3HBbGXgQ/rTp+4fJpu8RsZRsH1I7w6iRtAzwfB4bEV2fsvjkdCZY3RpLT9KXePyuG6tQmsK/fvCJ2pNibrjNyKbvERGdcC/wY8o2jUfY/nnlPKNgwRLH48BdwI22H6yQpzUp5obbUyQ9xfbDwM7AW3qvVcoUERF/vQMoO2wcALyx6Tu3iLJ6uJObv9f2ROecS1pke/u287QpBcFwy6bvEREdYPtcys90JP0NZdXwjsCHJC1n+1k18424FWoHWFop5oZYNn2PiOiOZgXrtjw2b25r4GbKauKoZ9o33E3T4IiIiJZJ+gFl540raXrNUbZQu79qsOjE1pkZmYuIiGjf/sB1PU9YYniM1Q6wtDIyFxERMQCSNgXeRdlGbRz4EXC87WurBhtxkjZt+gBOWynmIiIiWibpHyitMj5EedQ6BmwFvBs41PYXK8brJEnzgFVsH9cc3wrMotz7w2yfWjNfP+Uxa0RERPuOAXZdYk/WayR9Hfhi84r+eivw8p7jO22vJWkFYCHQmWJuRu0AERERI2DZJQo5AJpzndondIjMsH1Xz/H5ALYfAGbWidSOFHMRERHte0jSOkuelPQs4OEKeUbBSr0Hto8FkDQDWLVKopbkMWtERET7jgb+Q9KxwFWUBRBbA0cAh9cM1mELJX3Q9nuXOH8M5TFrZ2QBRERExABI2gJ4J2U16xjwQ2CB7WuqBuuopknz6ZSiefE93oKyAOWALvX4SzEXERERnSVpDqWABviR7Z/VzNOGFHMREREDIGl/4EBg4+bUj4ETbZ9VL1V3TTRHsZftXw4qS9syZy4iIqJlkt4AHAQcAnyf8ph1S+A4SaSga8VXKHMTe3d4GAeeAawOLFMjVBtSzEVERLTvn4G9lmhP8nVJrwI+C6SY6zPbm/UeS1qXsthkF+DYGpnaktYkERER7Vtxij5zKw48zQiRtKGkM4GLKCuJn2v7pLqp+isjcxEREe377yd5LZ6kZi/c91AWP3wUmGf7kbqp2pFiLiIion3PkXTtBOfHgDmDDjMirgFupsyd2wbYRtKfLto+sFKuvksxFxER0b7n1A4wguZRFjx0XlqTREREDAlJi2xvXztHTC8ZmYuIiBgeK9QO0BWSvsQUI3O29xxgnFalmIuIiBgeeVzWPwtqBxiUFHMRERHRRcvZvmSiC5I+AnxrwHlakz5zERERw2PsL/+SeIJOkfSK3hOSZjQ957aoE6kdKeYiIiKGx361A3TIy4DjJe0NIGkmcCGwHLBHzWD9ltWsERERLZM0D1jF9nHN8a3ALMpI3GG2T62Zr6skzQYuBk6iFMpX2D6kbqr+y8hcRERE+94KnNFzfKftFSmbvs+tE6nbJG0JrA4cBsynNBD+jKQtm2udkQUQERER7Zth+66e4/MBbD/QPP6L/ju+5+trgTV6zo0DOw08UUtSzEVERLRvpd4D28dCmZAPrFolUcfZfulk1yRtN8gsbctj1oiIiPYtlPTBCc4fAywcdJjAtQP0U0bmIiIi2vcu4HRJ/0XZAB5Ke4wrgQOqpRpdnWoBk2IuIiKiZbZ/D8yVNAfYpDn9I9s/qxhrlHWqlUdak0RERLRM0jpTXbf9y0FlGRVT7M06Buxk+6kDjtSajMxFRES07yuUwqL38d44pTXJ6sAyNUJ13FR7s3Zq39YUcxERES2zvVnvsaR1gcOBXYBja2TqOtsT7r0qaW3gtXRob9YUcxEREQMiaUPgPcC2lJ5nB9p+qG6q7pO0GvAaSoPmtYAL6ibqrxRzERERLZO0KaWI2wT4KDDP9iN1U3WbpFnAXsC+wEaUAm6O7dlVg7UgxVxERET7rqFsJ/UVYBtgG0l/umj7wEq5uuxO4LvAe4FLbY9L2qtyplakmIuIiGjfPDrWDmMaOJIyN+5U4BxJ51XO05q0JomIiIjOanr7zaUUdhsCRwMX2L6harA+yshcREREy6boeQaA7T0HGGckSDoIuBS42vZ8YL6kzSiF3UXA+jXz9VOKuYiIiPZ1qq/ZNDEbOBHYWNK1wGXAd4AFto+smqzP8pg1IiKiZZJ2tX3JJNc+YvvwQWcaFZKWA14A7ABs37zusf3cqsH6aEbtABERESPgFEmv6D0haYakM4Et6kQaGTOBFYGVmtdtwBVVE/VZHrNGRES072XAVyUtb/sLkmYC5wP3AnvUjdZNkk6j9PW7j1K8XQacYPvuqsFakGIuIiKiZbZ/IWkX4GJJqwP7AVfYPqRytC5bB1geuBG4FbgFuKdqopZkzlxERETLJG3ZfLkmcBZwCWUnCABsf79Grq6TNEYZnduheW0K/BZYZPvomtn6KSNzERER7Tu+5+trgTV6zo0DOw080QiwPQ5cL+ke4HfNa3fKLhwp5iIiIuKJsf3Sya5J2m6QWUaFpAMpo3E7Ag9R2pIsAs4ArqsYre9SzEVERNRlyvyu6K91gc8BB9u+vXKWVqWYi4iIqGusdoAuGqXFJekzFxERUVdWIsZSychcREREy6bYm3UMWHXAcaJjUsxFRES0b6q9WbNvayyV9JmLiIioRNLawGttH1c7S0xfGZmLiIgYIEmrAa8B5gJrARfUTRTTXYq5iIiIlkmaBewF7AtsRCng5tieXTVYdEJWs0ZERLTvTmAeMB9Y3/Y7gQfrRoquSDEXERHRviOBFYBTgXdLWr9ynuiQLICIiIgYEElzKHPlXgtsSNkf9ALbN1QNFtNa5sxFRES0TNJBwKXA1bbnA/MlbUYp7C4CMlIXT1pG5iIiIlomaQFl0/eNgWuBy2g2frf925rZYvpLMRcRETEgkpYDXkAp7LZvXvfYfm7VYDGt5TFrRETE4MwEVgRWal63AddVTRTTXkbmIiIiWibpNGAT4D7gCuBy4HLbd1cNFp2Q1iQRERHtWwdYHrgDuBW4BbinaqLojIzMRUREDICkMcro3A7Na1Pgt5RFEEfXzBbTW4q5iIiIAZI0G9iRUtDtDqxqe+W6qWI6ywKIiIiIlkk6kFK87Qg8RNOWBDiDLICIpZRiLiIion3rAp8DDrZ9e+Us0TF5zBoRERExjWU1a0RERMQ0lmIuIiIiYhpLMRcRsRQkvU7Swif4a38o6SUtR4qIEZM5cxEx0iR9E9gCeKbtP/6FX7su8HNgWdsPL+Wf+35gA9uvX5rvExGRkbmIGFlNcfYiYBzYs26aiIgnJyNzETGyJB0F7EbZK3Mj27s352cCHwReDaxM6QO2K/BTYG3g98232BV4NnCA7RdK+jhwv+1De/6MLwLfsn2CpF8AB1DaQl0IjAF/BH7W/HlH2N6q5/e+E3iR7Ve2cwciogsyMhcRo+wNwNnNazdJazTnFwBbUZq8rgIcBjwKvLi5vrLtp9letMT3OwfYp9m2CUlPB14GfLb3F9n+KnAscF7zfbagFHfrSXpOzy99PfDpvvxNI6KzUsxFxEiS9ELgWYBtX0UZHdtX0gzgTcA7bN9q+xHbl/2l+XSN/6Q8sn1Rc/xqyr6bt/2l39h8//MoBRySNqE0mv3yX/c3i4hRk2IuIkbV/sBC279pjs9pzq0GrEAp7v4qtscpo3Bzm1P7Ukb9nqhPUQrKMWC/8i2fUBEZESMs23lFxMhp5sQJWEbSHc3p5Snz49YEHgDWB65Z4rc+kUnG5wILJX0Y2BbYa5Jf97jvZftySQ9SRvb2bV4REVNKMRcRo+iVwCPAZsCDPedNmUd3BnCCpP2AXwHbAN8Hfk2ZOzcHuGGib2z7B5J+DZwOXGz7nkky/ArYVdIM24/2nD8LOBl42PalT/LvFxEjJMVcRIyi/YFP2v5l70lJJwMnAhsCHwC+BzyNMkK3m+0/SJoPfEfSssDLJ/n+5wLHUEb/JnM+ZX7cXZJ+bnvL5vynmz/7A0/qbxYRIyetSSIihkjzCPhOYEvbN9bOExHDLwsgIiKGyz8B30shFxFPVB6zRkQMiaap8BhlTl9ExBOSx6wRERER01ges0ZERERMYynmIiIiIqaxFHMRERER01iKuYiIiIhpLMVcRERExDT2/wGpjPekkUwKaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(x='Activity', y='angle(X,gravityMean)', data=df_train, showfliers=False)\n",
    "plt.axhline(y=0.08, xmin=0.1, xmax=0.9,dashes=(3,3))\n",
    "plt.ylabel(\"Angle between X-axis and gravityMean\")\n",
    "plt.title('Box plot of angle(X,gravityMean) column across various activities')\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This analysis gives an insight of seperation of Laying activites from other activities. We can observe that:  \n",
    "    \n",
    "    if angle(X,gravityMean) > 0.01:  \n",
    "        Activity = \"LAYING\"\n",
    "    else:\n",
    "        Activity = \"OTHER\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly if we analyze the angle between Y axes and gravity mean feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x28da56fd208>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(x='Activity', y='angle(Y,gravityMean)', data = df_train, showfliers=False)\n",
    "plt.ylabel(\"Angle between Y-axis and gravityMean\")\n",
    "plt.title('Box plot of angle(Y,gravityMean) column across various activities')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.axhline(y=-0.35, xmin=0.01, dashes=(3,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that activity 'LAYING' stands out from other activites but with some classification error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Visualizing data using t-Distributed Stochastic Neighbor Embedding (t-SNE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize the the seperability of all the activites using t-SNE in a 2D space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n",
      "[t-SNE] Computing 151 nearest neighbors...\n",
      "[t-SNE] Indexed 7352 samples in 0.274s...\n",
      "[t-SNE] Computed neighbors for 7352 samples in 32.199s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 4000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 5000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 6000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 7000 / 7352\n",
      "[t-SNE] Computed conditional probabilities for sample 7352 / 7352\n",
      "[t-SNE] Mean sigma: 1.437672\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 74.058258\n",
      "[t-SNE] KL divergence after 1000 iterations: 1.283563\n"
     ]
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "X_for_tsne = df_train.drop(['subject', 'Activity'], axis=1)\n",
    "\n",
    "%time\n",
    "tsne = TSNE(random_state = 42, n_components=2, verbose=1, perplexity=50, n_iter=1000).fit_transform(X_for_tsne)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x28da5a4f988>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.scatterplot(x =tsne[:, 0], y = tsne[:, 1], hue = df_train[\"Activity\"],palette=\"bright\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Importing classification models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn. linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Defining global reusable function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def best_grid_search_results(model):\n",
    "    print(\"Best estimator : \", model.best_estimator_)\n",
    "    print(\"Best set of parameters : \", model.best_params_)\n",
    "    print(\"Best score : \", model.best_score_)\n",
    "    \n",
    "def plot_confusion_matrix(y_test, y_true):\n",
    "    cm = confusion_matrix(y_test,y_pred)\n",
    "    df_cm = pd.DataFrame(cm, columns=np.unique(y_true), index = np.unique(y_true))\n",
    "    df_cm.index.name = 'Actual'\n",
    "    df_cm.columns.name = 'Predicted'\n",
    "    plt.figure(figsize = (10,7))\n",
    "    plt.ylim([1.5, -.5])\n",
    "    sns.set(font_scale=1.4)#for label size\n",
    "    sns.heatmap(df_cm, cmap=\"Blues\", annot=True,annot_kws={\"size\": 12})# font size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Preparing Training and Test data for Machine Learning Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data size :  (7352, 561)\n",
      "Test data size :  (2947, 561)\n"
     ]
    }
   ],
   "source": [
    "X_train = df_train.drop(['subject', 'Activity'], axis=1)\n",
    "y_train = df_train.Activity\n",
    "X_test = df_test.drop(['subject', 'Activity'], axis=1)\n",
    "y_test = df_test.Activity\n",
    "print('Training data size : ', X_train.shape)\n",
    "print('Test data size : ', X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Comparison of Classifiers without Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.961995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.969121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SVC</td>\n",
       "      <td>0.940278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.857482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.902273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.925687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Score\n",
       "Model                           \n",
       "LogisticRegression      0.961995\n",
       "LinearSVC               0.969121\n",
       "SVC                     0.940278\n",
       "DecisionTreeClassifier  0.857482\n",
       "KNeighborsClassifier    0.902273\n",
       "RandomForestClassifier  0.925687"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifiers = [\n",
    "    LogisticRegression(),\n",
    "    LinearSVC(tol=0.00005),\n",
    "    SVC(),\n",
    "    DecisionTreeClassifier(),\n",
    "    KNeighborsClassifier(7), # 6 different labels 0-6\n",
    "    RandomForestClassifier(200)\n",
    "]\n",
    "\n",
    "names = []\n",
    "scores = []\n",
    "\n",
    "for clf in classifiers:\n",
    "    clf = clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    \n",
    "    names.append(clf.__class__.__name__)\n",
    "    scores.append(accuracy_score(y_pred, y_test))\n",
    "\n",
    "score_df = pd.DataFrame({'Model': names, 'Score': scores}).set_index('Model')\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'LogisticRegression'),\n",
       " Text(0, 0, 'LinearSVC'),\n",
       " Text(0, 0, 'SVC'),\n",
       " Text(0, 0, 'DecisionTreeClassifier'),\n",
       " Text(0, 0, 'KNeighborsClassifier'),\n",
       " Text(0, 0, 'RandomForestClassifier')]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = score_df.plot.bar()\n",
    "ax.set_xticklabels(score_df.index, rotation=45, fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without hyperparameter tuning, we can see that Logistic regression has the highest accuracy. Now lets check which classifier performs best after hyperparameter tuning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.5 Comparison of Classifiers with Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for storing the scores of classifiers after hyperparameter tuning\n",
    "scores_tuned = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Classifier Accuracy:  0.9623345775364778\n",
      "Classification Report: \n",
      "                     precision    recall  f1-score   support\n",
      "\n",
      "            LAYING       1.00      1.00      1.00       537\n",
      "           SITTING       0.97      0.87      0.92       491\n",
      "          STANDING       0.90      0.98      0.94       532\n",
      "           WALKING       0.95      1.00      0.97       496\n",
      "WALKING_DOWNSTAIRS       1.00      0.97      0.98       420\n",
      "  WALKING_UPSTAIRS       0.98      0.95      0.96       471\n",
      "\n",
      "          accuracy                           0.96      2947\n",
      "         macro avg       0.97      0.96      0.96      2947\n",
      "      weighted avg       0.96      0.96      0.96      2947\n",
      "\n"
     ]
    }
   ],
   "source": [
    "params = {'C':np.arange(10,61,10), 'penalty':['l2','l1']}\n",
    "lr = LogisticRegression()\n",
    "lr_tuned = GridSearchCV(lr, param_grid=params, cv=5)\n",
    "lr_tuned.fit(X_train, y_train)\n",
    "y_pred = lr_tuned.predict(X_test)\n",
    "\n",
    "lr_accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print('LR Classifier Accuracy: ', lr_accuracy)\n",
    "print('Classification Report: \\n', classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.941240478781284]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_tuned.append(lr_tuned.best_score_)\n",
    "scores_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator :  LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
      "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n",
      "Best set of parameters :  {'C': 10, 'penalty': 'l2'}\n",
      "Best score :  0.941240478781284\n"
     ]
    }
   ],
   "source": [
    "best_grid_search_results(lr_tuned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Support Vector Machine**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Linear SVM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear SVM Classifier Accuracy:  0.9647098744485918\n",
      "Classification Report: \n",
      "                     precision    recall  f1-score   support\n",
      "\n",
      "            LAYING       1.00      1.00      1.00       537\n",
      "           SITTING       0.98      0.86      0.92       491\n",
      "          STANDING       0.89      0.98      0.93       532\n",
      "           WALKING       0.96      1.00      0.98       496\n",
      "WALKING_DOWNSTAIRS       1.00      0.98      0.99       420\n",
      "  WALKING_UPSTAIRS       0.98      0.96      0.97       471\n",
      "\n",
      "          accuracy                           0.96      2947\n",
      "         macro avg       0.97      0.96      0.96      2947\n",
      "      weighted avg       0.97      0.96      0.96      2947\n",
      "\n"
     ]
    }
   ],
   "source": [
    "params = {'C':np.arange(1,12,2)}\n",
    "lr_svm = LinearSVC(tol=0.00005)\n",
    "lr_svm_tuned = GridSearchCV(lr_svm, param_grid=params)\n",
    "lr_svm_tuned.fit(X_train, y_train)\n",
    "y_pred = lr_svm_tuned.predict(X_test)\n",
    "\n",
    "lr_svm_accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "\n",
    "print('Linear SVM Classifier Accuracy: ', lr_svm_accuracy)\n",
    "print('Classification Report: \\n', classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.941240478781284, 0.9465451577801959]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_tuned.append(lr_svm_tuned.best_score_)\n",
    "scores_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator :  LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
      "          intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "          multi_class='ovr', penalty='l2', random_state=None, tol=5e-05,\n",
      "          verbose=0)\n",
      "Best set of parameters :  {'C': 1}\n",
      "Best score :  0.9465451577801959\n"
     ]
    }
   ],
   "source": [
    "best_grid_search_results(lr_svm_tuned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Kernel SVM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kernel SVM Classifier Accuracy:  0.9416355615880556\n",
      "Classification Report: \n",
      "                     precision    recall  f1-score   support\n",
      "\n",
      "            LAYING       1.00      0.98      0.99       537\n",
      "           SITTING       0.97      0.91      0.94       491\n",
      "          STANDING       0.94      0.95      0.95       532\n",
      "           WALKING       0.98      0.92      0.95       496\n",
      "WALKING_DOWNSTAIRS       0.81      0.92      0.86       420\n",
      "  WALKING_UPSTAIRS       0.95      0.96      0.95       471\n",
      "\n",
      "          accuracy                           0.94      2947\n",
      "         macro avg       0.94      0.94      0.94      2947\n",
      "      weighted avg       0.95      0.94      0.94      2947\n",
      "\n"
     ]
    }
   ],
   "source": [
    "params = {'C':[2,4,8,16],'gamma': [0.125, 0.250, 0.5, 1]}\n",
    "kernel_svm = SVC(kernel='rbf')\n",
    "kernel_svm_tuned = GridSearchCV(kernel_svm, param_grid=params)\n",
    "kernel_svm_tuned.fit(X_train, y_train)\n",
    "y_pred = kernel_svm_tuned.predict(X_test)\n",
    "\n",
    "kernel_svm_accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print('Kernel SVM Classifier Accuracy: ', kernel_svm_accuracy)\n",
    "print('Classification Report: \\n', classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.941240478781284, 0.9465451577801959, 0.8813928182807399]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_tuned.append(kernel_svm_tuned.best_score_)\n",
    "scores_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator :  SVC(C=2, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma=0.125, kernel='rbf',\n",
      "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "    tol=0.001, verbose=False)\n",
      "Best set of parameters :  {'C': 2, 'gamma': 0.125}\n",
      "Best score :  0.8813928182807399\n"
     ]
    }
   ],
   "source": [
    "best_grid_search_results(kernel_svm_tuned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Decision Tree Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Classifier Accuracy:  0.8096369189005769\n",
      "Classification Report: \n",
      "                     precision    recall  f1-score   support\n",
      "\n",
      "            LAYING       1.00      1.00      1.00       537\n",
      "           SITTING       0.78      0.81      0.79       491\n",
      "          STANDING       0.82      0.79      0.80       532\n",
      "           WALKING       0.65      0.88      0.75       496\n",
      "WALKING_DOWNSTAIRS       0.94      0.62      0.75       420\n",
      "  WALKING_UPSTAIRS       0.77      0.71      0.74       471\n",
      "\n",
      "          accuracy                           0.81      2947\n",
      "         macro avg       0.83      0.80      0.80      2947\n",
      "      weighted avg       0.83      0.81      0.81      2947\n",
      "\n"
     ]
    }
   ],
   "source": [
    "params = {'max_depth':np.arange(2,10,2)}\n",
    "dtc = DecisionTreeClassifier()\n",
    "dtc_tuned = GridSearchCV(dtc, param_grid=params)\n",
    "dtc_tuned.fit(X_train, y_train)\n",
    "y_pred = dtc_tuned.predict(X_test)\n",
    "\n",
    "dtc_accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print('Decision Tree Classifier Accuracy: ', dtc_accuracy)\n",
    "print('Classification Report: \\n', classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.941240478781284, 0.9465451577801959, 0.8813928182807399, 0.844532100108814]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_tuned.append(dtc_tuned.best_score_)\n",
    "scores_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator :  DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=4,\n",
      "                       max_features=None, max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, presort=False,\n",
      "                       random_state=None, splitter='best')\n",
      "Best set of parameters :  {'max_depth': 4}\n",
      "Best score :  0.844532100108814\n"
     ]
    }
   ],
   "source": [
    "best_grid_search_results(dtc_tuned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. K-Nearest Neighbor**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbor Classifier Accuracy:  0.9077027485578555\n",
      "Classification Report: \n",
      "                     precision    recall  f1-score   support\n",
      "\n",
      "            LAYING       1.00      1.00      1.00       537\n",
      "           SITTING       0.92      0.82      0.86       491\n",
      "          STANDING       0.85      0.93      0.89       532\n",
      "           WALKING       0.84      0.99      0.91       496\n",
      "WALKING_DOWNSTAIRS       0.96      0.78      0.86       420\n",
      "  WALKING_UPSTAIRS       0.90      0.90      0.90       471\n",
      "\n",
      "          accuracy                           0.91      2947\n",
      "         macro avg       0.91      0.90      0.90      2947\n",
      "      weighted avg       0.91      0.91      0.91      2947\n",
      "\n"
     ]
    }
   ],
   "source": [
    "params = {'n_neighbors': np.arange(1, 25)}\n",
    "knn = KNeighborsClassifier()\n",
    "knn_tuned = GridSearchCV(knn, param_grid=params, cv=5)\n",
    "knn_tuned.fit(X_train, y_train)\n",
    "y_pred = knn_tuned.predict(X_test)\n",
    "\n",
    "knn_accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print('K-Nearest Neighbor Classifier Accuracy: ', knn_accuracy)\n",
    "print('Classification Report: \\n', classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.941240478781284,\n",
       " 0.9465451577801959,\n",
       " 0.8813928182807399,\n",
       " 0.844532100108814,\n",
       " 0.9031556039173014]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_tuned.append(knn_tuned.best_score_)\n",
    "scores_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator :  KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=None, n_neighbors=16, p=2,\n",
      "                     weights='uniform')\n",
      "Best set of parameters :  {'n_neighbors': 16}\n",
      "Best score :  0.9031556039173014\n"
     ]
    }
   ],
   "source": [
    "best_grid_search_results(knn_tuned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. Random Forests Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forests Classifier Accuracy:  0.8934509670851714\n",
      "Classification Report: \n",
      "                     precision    recall  f1-score   support\n",
      "\n",
      "            LAYING       1.00      1.00      1.00       537\n",
      "           SITTING       0.89      0.82      0.86       491\n",
      "          STANDING       0.85      0.91      0.88       532\n",
      "           WALKING       0.81      0.98      0.89       496\n",
      "WALKING_DOWNSTAIRS       0.96      0.76      0.85       420\n",
      "  WALKING_UPSTAIRS       0.89      0.86      0.87       471\n",
      "\n",
      "          accuracy                           0.89      2947\n",
      "         macro avg       0.90      0.89      0.89      2947\n",
      "      weighted avg       0.90      0.89      0.89      2947\n",
      "\n"
     ]
    }
   ],
   "source": [
    "params = {'n_estimators': np.arange(20,101,10), 'max_depth':np.arange(2,16,2)}\n",
    "rf_classifier = RandomForestClassifier()\n",
    "rf_classifier_tuned = GridSearchCV(rf_classifier, param_grid=params)\n",
    "rf_classifier_tuned.fit(X_train, y_train)\n",
    "y_pred = rf_classifier_tuned.predict(X_test)\n",
    "\n",
    "rf_accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print('Random Forests Classifier Accuracy: ', rf_accuracy)\n",
    "print('Classification Report: \\n', classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.941240478781284,\n",
       " 0.9465451577801959,\n",
       " 0.8813928182807399,\n",
       " 0.844532100108814,\n",
       " 0.9031556039173014,\n",
       " 0.9144450489662677]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_tuned.append(rf_classifier_tuned.best_score_)\n",
    "scores_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator :  RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "                       max_depth=6, max_features='auto', max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=40,\n",
      "                       n_jobs=None, oob_score=False, random_state=None,\n",
      "                       verbose=0, warm_start=False)\n",
      "Best set of parameters :  {'max_depth': 6, 'n_estimators': 40}\n",
      "Best score :  0.9144450489662677\n"
     ]
    }
   ],
   "source": [
    "best_grid_search_results(rf_classifier_tuned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.6 Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.941240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.946545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SVC</td>\n",
       "      <td>0.881393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.844532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.903156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.914445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Score\n",
       "Model                           \n",
       "LogisticRegression      0.941240\n",
       "LinearSVC               0.946545\n",
       "SVC                     0.881393\n",
       "DecisionTreeClassifier  0.844532\n",
       "KNeighborsClassifier    0.903156\n",
       "RandomForestClassifier  0.914445"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_df = pd.DataFrame({'Model': names, 'Score': scores_tuned}).set_index('Model')\n",
    "score_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'LogisticRegression'),\n",
       " Text(0, 0, 'LinearSVC'),\n",
       " Text(0, 0, 'SVC'),\n",
       " Text(0, 0, 'DecisionTreeClassifier'),\n",
       " Text(0, 0, 'KNeighborsClassifier'),\n",
       " Text(0, 0, 'RandomForestClassifier')]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = score_df.plot.bar()\n",
    "ax.set_xticklabels(score_df.index, rotation=45, fontsize=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these results it's evident that Linear SVM has the best score after hyperparameter tuning. Now lets retrain the model with best parameters and save it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.7 Saving the best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear SVM best parameters are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_svm_tuned.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear SVM Classifier Accuracy:  0.9647098744485918\n",
      "Classification Report: \n",
      "                     precision    recall  f1-score   support\n",
      "\n",
      "            LAYING       1.00      1.00      1.00       537\n",
      "           SITTING       0.98      0.86      0.91       491\n",
      "          STANDING       0.89      0.98      0.93       532\n",
      "           WALKING       0.97      1.00      0.98       496\n",
      "WALKING_DOWNSTAIRS       1.00      0.98      0.99       420\n",
      "  WALKING_UPSTAIRS       0.98      0.97      0.97       471\n",
      "\n",
      "          accuracy                           0.96      2947\n",
      "         macro avg       0.97      0.96      0.96      2947\n",
      "      weighted avg       0.97      0.96      0.96      2947\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lr_svm = LinearSVC(tol=0.00005, C=7)\n",
    "lr_svm.fit(X_train, y_train)\n",
    "y_pred = lr_svm.predict(X_test)\n",
    "lr_svm_accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "\n",
    "print('Linear SVM Classifier Accuracy: ', lr_svm_accuracy)\n",
    "print('Classification Report: \\n', classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['best_model']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "filename = 'best_model'\n",
    "joblib.dump(lr_svm, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['STANDING' 'STANDING' 'STANDING' ... 'WALKING_UPSTAIRS'\n",
      " 'WALKING_UPSTAIRS' 'WALKING_UPSTAIRS']\n"
     ]
    }
   ],
   "source": [
    "loaded_model = joblib.load(filename)\n",
    "\n",
    "result = loaded_model.predict(X_test)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-meanFreq()</th>\n",
       "      <th>fBodyBodyGyroJerkMag-skewness()</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>0.071645</td>\n",
       "      <td>-0.33037</td>\n",
       "      <td>-0.705974</td>\n",
       "      <td>0.006462</td>\n",
       "      <td>0.16292</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 561 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",
       "\n",
       "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
       "0         -0.920091         -0.667683         -0.952501         -0.925249   \n",
       "\n",
       "   tBodyAcc-mad()-Z  tBodyAcc-max()-X  ...  fBodyBodyGyroJerkMag-meanFreq()  \\\n",
       "0         -0.674302         -0.894088  ...                         0.071645   \n",
       "\n",
       "   fBodyBodyGyroJerkMag-skewness()  fBodyBodyGyroJerkMag-kurtosis()  \\\n",
       "0                         -0.33037                        -0.705974   \n",
       "\n",
       "   angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
       "0                     0.006462                               0.16292   \n",
       "\n",
       "   angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \\\n",
       "0                         -0.825886                              0.271151   \n",
       "\n",
       "   angle(X,gravityMean)  angle(Y,gravityMean)  angle(Z,gravityMean)  \n",
       "0             -0.720009              0.276801             -0.057978  \n",
       "\n",
       "[1 rows x 561 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = X_test.iloc[0:1, :]\n",
    "sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['STANDING']\n"
     ]
    }
   ],
   "source": [
    "result = loaded_model.predict(sample)\n",
    "print(result)"
   ]
  }
 ],
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