PAMAP2_research.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# The PAMAP2 Physical Activity Monitoring Report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Data Science Research Methods PAMAP2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outcome of the report:\n",
    "\n",
    "Develop hardware and/or software which can determine:\n",
    "\n",
    "- the **amount** of exercise using start/end times and heartrates;\n",
    "- the **type** of exercise\n",
    "\n",
    "In order to develop this, it is neccessary to do the following:\n",
    "\n",
    "### Table of contents\n",
    "\n",
    "**1. [Data extraction](#dataextraction)**\n",
    "- 1.1 PAMAP2 Physical Activity Monitoring dataset \n",
    "- 1.2 Uploading data files\n",
    "- 1.3 Merging the files\n",
    "- 1.4 Importing extra files\n",
    "\n",
    "**2. [Pre-processing the data](#preprop)**\n",
    "- 2.1 Cleaning columns\n",
    "- 2.2 Removing and filtering data\n",
    "\n",
    "**3. [Dev and Test set](#devtest)**\n",
    "- 3.1 Splitting into Dev and Test sets\n",
    "\n",
    "**4. [Exploratory Data Analysis](#explore)**\n",
    "- 4.1 Averages\n",
    "- 4.2 Correlations\n",
    "- 4.3 Exploring Heartrate\n",
    "- 4.4 Exploring Temperature\n",
    "- 4.5 Exploring Moving and Non-Moving Activities\n",
    "- 4.6 Subject Exploration\n",
    "\n",
    "**5. [Hypothesis Testing](#hypo)**\n",
    "- 5.1 Our first testable hypothesis\n",
    "- 5.2 Our second testable hypothesis\n",
    "- 5.3 Our third testable hypothesis\n",
    "\n",
    "**6. [Modeling Introduction](#model)**\n",
    "- 6.1 Scaling the data \n",
    "- 6.2 Random Forrest Calssifier\n",
    "- 6.3 Logistic Regression\n",
    "\n",
    "**7. [Dimensionality Reduction and Principal Componant Analysis](#pca)**\n",
    "\n",
    "**8. [K Means Clustering](#kmeans)**\n",
    "- 8.1 How many clusters should we use?\n",
    "- 8.2 Evaluating Silhuoette and ARI score\n",
    "- 8.3 Evaluating the clusters\n",
    "\n",
    "**9. [Summary](#summary)**\n",
    "\n",
    "**10. [References](#ref)**\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "\n",
    "## Part 1: Data Extraction <a class=\"anchor\" id=\"dataextraction\"></a>\n",
    "\n",
    "### 1.1 PAMAP2 Physical Activity Monitoring dataset \n",
    "\n",
    "For this report, I will be using data from the *PAMAP2 Physical Activity Monitoring Data Set* [[1](#1)]. This data set recorded 9 subjects wearing 3 inertial measurements units and a heartrate monitor. The subjects performed 18 different physical activities (walking, cycling, rope jumping, etc.). There are two sets of data, optional and protocol. All of the subjects undertook the protocol activities whereas only a few completed a few of the optional.\n",
    "\n",
    "### 1.2 Uploading data files\n",
    "\n",
    "As the data was in either `.dat` format, with no headers, a header list was needed before uploading the files. The headers that needed to be removed were numbers for ease later on. A data frame was created for each subject, all had *protocol* data frame and five subjects also took part in the *optional*.\n",
    "\n",
    "Each of the data-files contains 54 columns per row, the columns contain the following data:\n",
    "\n",
    "- 1 timestamp (s)\n",
    "- 2 activityID\n",
    "- 3 heart rate (bpm)\n",
    "- 4-20 IMU hand\n",
    "- 21-37 IMU chest\n",
    "- 38-54 IMU ankle\n",
    "\n",
    "The IMU sensory data contains the following columns:\n",
    "\n",
    "- 1 temperature (°C)\n",
    "- 2-4 3D-acceleration data (ms-2), scale: ±16g, resolution: 13-bit\n",
    "- 5-7 3D-acceleration data (ms-2), scale: ±6g, resolution: 13-bit* *(to be removed)*\n",
    "- 8-10 3D-gyroscope data (rad/s)\n",
    "- 11-13 3D-magnetometer data (μT)\n",
    "- 14-17 orientation (invalid in this data collection) *(to be removed)*\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Please note</b> the units of the columns for future reference\n",
    "</div>\n"
   ]
  },
  {
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    "#standard imports for when working with pandas\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import glob\n",
    "import random\n",
    "from numpy.random import RandomState\n",
    "import sklearn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from IPython.display import display\n",
    "from sklearn.cluster import KMeans \n",
    "from sklearn.decomposition import PCA\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.metrics import homogeneity_score, completeness_score, \\\n",
    "v_measure_score, adjusted_rand_score, adjusted_mutual_info_score, silhouette_score\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "#setting headers for the dat files\n",
    "a= ['timestamp', \n",
    "    'activityID',\n",
    "    'heartrate_bpm',\n",
    "    'imu_hand_temp',\n",
    "    'imu_hand_3d_acceleration_1',\n",
    "          'imu_hand_3d_acceleration_2', \n",
    "          'imu_hand_3d_acceleration_3',\n",
    "    #the numbers are columns that will be removed\n",
    "    #this is stated in the readme.pdf\n",
    "    '5',\n",
    "    '6',\n",
    "    '7',\n",
    "          'imu_hand_3d_gyroscope_1',\n",
    "          'imu_hand_3d_gyroscope_2', \n",
    "          'imu_hand_3d_gyroscope_3', \n",
    "          'imu_hand_3d_magnetometer_1',\n",
    "          'imu_hand_3d_magnetometer_2',\n",
    "          'imu_hand_3d_magnetometer_3',\n",
    "    '14',\n",
    "    '15',\n",
    "    '16',\n",
    "    '17',\n",
    "          'imu_chest_temp', \n",
    "          'imu_chest_3d_acceleration_1',\n",
    "          'imu_chest_3d_acceleration_2',\n",
    "          'imu_chest_3d_acceleration_3',\n",
    "    '5b',\n",
    "    '6b',\n",
    "    '7b',\n",
    "          'imu_chest_3d_gyroscope_1',\n",
    "          'imu_chest_3d_gyroscope_2',\n",
    "          'imu_chest_3d_gyroscope_3',\n",
    "          'imu_chest_3d_magnetometer_1',\n",
    "          'imu_chest_3d_magnetometer_2',\n",
    "          'imu_chest_3d_magnetometer_3',\n",
    "    '14b',\n",
    "    '15b',\n",
    "    '16b',\n",
    "    '17b',\n",
    "\n",
    "          'imu_ankle_temp',\n",
    "          'imu_ankle_3d_acceleration_1',\n",
    "          'imu_ankle_3d_acceleration_2',\n",
    "          'imu_ankle_3d_acceleration_3',\n",
    "    '5c',\n",
    "    '6c',\n",
    "    '7c',\n",
    "          'imu_ankle_3d_gyroscope_1',\n",
    "          'imu_ankle_3d_gyroscope_2',\n",
    "          'imu_ankle_3d_gyroscope_3', \n",
    "          'imu_ankle_3d_magnetometer_1',\n",
    "          'imu_ankle_3d_magnetometer_2',\n",
    "          'imu_ankle_3d_magnetometer_3',\n",
    "    '14c',\n",
    "    '15c',\n",
    "    '16c',\n",
    "    '17c']\n",
    "\n",
    "activityIDkey = {0: 'transient',\n",
    "              1: 'lying',\n",
    "              2: 'sitting',\n",
    "              3: 'standing',\n",
    "              4: 'walking',\n",
    "              5: 'running',\n",
    "              6: 'cycling',\n",
    "              7: 'Nordic_walking',\n",
    "              9: 'watching_TV',\n",
    "              10: 'computer_work',\n",
    "              11: 'car driving',\n",
    "              12: 'ascending_stairs',\n",
    "              13: 'descending_stairs',\n",
    "              16: 'vacuum_cleaning',\n",
    "              17: 'ironing',\n",
    "              18: 'folding_laundry',\n",
    "              19: 'house_cleaning',\n",
    "              20: 'playing_soccer',\n",
    "              24: 'rope_jumping' }\n",
    "\n",
    "#protocol files\n",
    "df101p = pd.read_table('101_p.dat', sep=' ', names=a)\n",
    "df102p = pd.read_table('102_p.dat', sep=' ', names=a)\n",
    "df103p = pd.read_table('103_p.dat', sep=' ', names=a)\n",
    "df104p = pd.read_table('104_p.dat', sep=' ', names=a)\n",
    "df105p = pd.read_table('105_p.dat', sep=' ', names=a)\n",
    "df106p = pd.read_table('106_p.dat', sep=' ', names=a)\n",
    "df107p = pd.read_table('107_p.dat', sep=' ', names=a)\n",
    "df108p = pd.read_table('108_p.dat', sep=' ', names=a)\n",
    "df109p = pd.read_table('109_p.dat', sep=' ', names=a)\n",
    "\n",
    "#optional files\n",
    "df101o = pd.read_table('101_o.dat', sep=' ', names=a)\n",
    "df105o = pd.read_table('105_o.dat', sep=' ', names=a)\n",
    "df106o = pd.read_table('106_o.dat', sep=' ', names=a)\n",
    "df108o = pd.read_table('108_o.dat', sep=' ', names=a)\n",
    "df109o = pd.read_table('109_o.dat', sep=' ', names=a)\n",
    "\n"
   ]
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   "source": [
    "### 1.3 Merging the data files\n",
    "\n",
    "As these files were stored separately, data merging is a necessary step. All the files have the same header values so it was a matter of combining the sets. As each data file is from separate subjects, a subject header was added. This process will be useful when analysing trends within the whole data set."
   ]
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  {
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   "source": [
    "#Creating a list of the data frames\n",
    "\n",
    "mylist = [df101p,df102p,df103p,df104p,df105p,df106p,df107p,df108p,df109p,df101o,df105o,df106o,df108o,df109o]\n",
    "\n",
    "#list of subjects\n",
    "subject_names = ['101p','102p','103p','104p','105p','106p','107p','108p','109p','101o','105o','106o','106o','109o']\n",
    "subjects = []\n",
    "\n",
    "#adding a subject column to the dat files\n",
    "x=0\n",
    "for w in mylist:\n",
    "    w['subject']=subject_names[x]\n",
    "    x+=1\n",
    "    subjects.append(w)\n",
    "    \n",
    "#adding to the data frame    \n",
    "df = pd.concat(subjects)\n",
    "\n",
    "#checking the column has been added\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Importing other files\n",
    "\n",
    "There were 2 other files which may be needed for further analysis. They were converted into `csv` format and then uploaded. For the `Subject_Information.csv` a column for BMI may be needed further down the line, so was added using the BMI equation:\n",
    "\n",
    "$$BMI = \\frac{kg}{m^2}$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "subjectinfo = 'Subject_Information.csv'\n",
    "#subject info df\n",
    "sidf = pd.read_csv(subjectinfo,index_col=None)\n",
    "#inserting BMI column\n",
    "sidf.insert(5, column='BMI', value=sidf['Weight (kg)']/((sidf['Height (cm)']/100)**2))\n",
    "\n",
    "\n",
    "#activities df\n",
    "actdf = pd.read_csv('PerformedActivitiesSummaries.csv',index_col=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "## Part 2: Pre-processing the data <a class=\"anchor\" id=\"preprop\"></a>\n",
    "\n",
    "\n",
    "### 2.1 Cleaning columns\n",
    "\n",
    "As stated in the `readme.pdf`, columns 5-7 were 'not precisely calibrated by the first one. Moreover, due to high impacts caused by certain movements with acceleration over 6g, it gets saturated sometimes. Therefore, the use of the data from the first accelerometer (with the scale of 16g is recommended.'. Columns 14-17 (orientation) is also invalid in this data collection and should be removed for the analysis.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing the uneccessary columns\n",
    "\n",
    "titles = ['subject',\n",
    "          'timestamp', \n",
    "          'activityID',\n",
    "          'heartrate_bpm',\n",
    "          'imu_hand_temp',\n",
    "          'imu_hand_3d_acceleration_1',\n",
    "          'imu_hand_3d_acceleration_2', \n",
    "          'imu_hand_3d_acceleration_3',\n",
    "          'imu_hand_3d_gyroscope_1',\n",
    "          'imu_hand_3d_gyroscope_2', \n",
    "          'imu_hand_3d_gyroscope_3', \n",
    "          'imu_hand_3d_magnetometer_1',\n",
    "          'imu_hand_3d_magnetometer_2',\n",
    "          'imu_hand_3d_magnetometer_3',\n",
    "          'imu_chest_temp', \n",
    "          'imu_chest_3d_acceleration_1',\n",
    "          'imu_chest_3d_acceleration_2',\n",
    "          'imu_chest_3d_acceleration_3',\n",
    "          'imu_chest_3d_gyroscope_1',\n",
    "          'imu_chest_3d_gyroscope_2',\n",
    "          'imu_chest_3d_gyroscope_3',\n",
    "          'imu_chest_3d_magnetometer_1',\n",
    "          'imu_chest_3d_magnetometer_2',\n",
    "          'imu_chest_3d_magnetometer_3',\n",
    "          'imu_ankle_temp',\n",
    "          'imu_ankle_3d_acceleration_1',\n",
    "          'imu_ankle_3d_acceleration_2',\n",
    "          'imu_ankle_3d_acceleration_3',\n",
    "          'imu_ankle_3d_gyroscope_1',\n",
    "          'imu_ankle_3d_gyroscope_2',\n",
    "          'imu_ankle_3d_gyroscope_3', \n",
    "          'imu_ankle_3d_magnetometer_1',\n",
    "          'imu_ankle_3d_magnetometer_2',\n",
    "          'imu_ankle_3d_magnetometer_3',]\n",
    "df=df[titles]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
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   ],
   "source": [
    "#What does the uncleaned data frame look like?\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Removing and filtering data\n",
    "\n",
    "The IMU data is collected at a sampling frequency of 100Hz which means it stores 100 data points per second. The smallest granularity is the HR-monitor which has a sampling frequency of 9Hz. This meant removing all the rows containing `NaN` and thus kept every 11th data point - also a method of **down sampling** [[4](#4)].\n",
    "\n",
    "Having looked more closely at the data, I have decided to remove the optional data. These data points included activities which, I think, are not of value for developing this particular hardware/software. The pdf also stated that the all points with `ActivityID = 0` should be discarded. This data was only covering transient events.\n",
    "\n",
    "I have removed certain points within heartrate and temperature which would be errors. Heartrate below 55 and above 205 [[2](#2)] and temperature below 30 [[3](#3)]. "
   ]
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   "cell_type": "code",
   "execution_count": 6,
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   ],
   "source": [
    "#removing the optional data\n",
    "df=df.set_index('subject')\n",
    "df=df.drop(['101o','105o','106o','106o','109o'],axis=0)\n",
    "\n",
    "#removing activity 0\n",
    "df=df[df['activityID']>0]\n",
    "\n",
    "#removing heartrate below 55 as the normal resting heartrate is 60-100\n",
    "#maximum heartrate is 220 minus the age, our minimum age is 23 so this would be 197 (set the max to 205)\n",
    "df=df[df['heartrate_bpm']>55]\n",
    "df=df[df['heartrate_bpm']<205]\n",
    "\n",
    "#removing temperature below 30\n",
    "df=df[df['imu_chest_temp']>30]\n",
    "df=df[df['imu_hand_temp']>30]\n",
    "df=df[df['imu_ankle_temp']>30]\n",
    "\n",
    "#what is the new shape?\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.reset_index(drop = False)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>activityID</th>\n",
       "      <th>heartrate_bpm</th>\n",
       "      <th>imu_hand_temp</th>\n",
       "      <th>imu_hand_3d_acceleration_1</th>\n",
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       "      <th>imu_ankle_3d_magnetometer_1</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101p</td>\n",
       "      <td>37.70</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.375</td>\n",
       "      <td>2.30106</td>\n",
       "      <td>7.25857</td>\n",
       "      <td>6.09259</td>\n",
       "      <td>-0.069961</td>\n",
       "      <td>-0.018328</td>\n",
       "      <td>...</td>\n",
       "      <td>30.75</td>\n",
       "      <td>9.77578</td>\n",
       "      <td>-1.88582</td>\n",
       "      <td>0.095775</td>\n",
       "      <td>0.001351</td>\n",
       "      <td>-0.048878</td>\n",
       "      <td>-0.006328</td>\n",
       "      <td>-60.2040</td>\n",
       "      <td>-37.1225</td>\n",
       "      <td>-57.8847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101p</td>\n",
       "      <td>37.81</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.375</td>\n",
       "      <td>2.24615</td>\n",
       "      <td>7.48180</td>\n",
       "      <td>5.55219</td>\n",
       "      <td>-0.431227</td>\n",
       "      <td>0.002685</td>\n",
       "      <td>...</td>\n",
       "      <td>30.75</td>\n",
       "      <td>9.73536</td>\n",
       "      <td>-2.03681</td>\n",
       "      <td>0.019118</td>\n",
       "      <td>0.005819</td>\n",
       "      <td>-0.017912</td>\n",
       "      <td>0.015243</td>\n",
       "      <td>-60.7820</td>\n",
       "      <td>-36.2116</td>\n",
       "      <td>-58.4891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101p</td>\n",
       "      <td>37.92</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.375</td>\n",
       "      <td>2.30000</td>\n",
       "      <td>7.10681</td>\n",
       "      <td>6.09309</td>\n",
       "      <td>0.075692</td>\n",
       "      <td>-0.030792</td>\n",
       "      <td>...</td>\n",
       "      <td>30.75</td>\n",
       "      <td>9.73745</td>\n",
       "      <td>-1.84729</td>\n",
       "      <td>0.056553</td>\n",
       "      <td>0.015376</td>\n",
       "      <td>-0.011798</td>\n",
       "      <td>0.011387</td>\n",
       "      <td>-61.4531</td>\n",
       "      <td>-36.2983</td>\n",
       "      <td>-58.4845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101p</td>\n",
       "      <td>38.03</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.375</td>\n",
       "      <td>2.49455</td>\n",
       "      <td>7.52335</td>\n",
       "      <td>6.17157</td>\n",
       "      <td>-0.259058</td>\n",
       "      <td>-0.267895</td>\n",
       "      <td>...</td>\n",
       "      <td>30.75</td>\n",
       "      <td>9.58444</td>\n",
       "      <td>-1.84538</td>\n",
       "      <td>-0.060796</td>\n",
       "      <td>-0.012672</td>\n",
       "      <td>-0.009233</td>\n",
       "      <td>0.005823</td>\n",
       "      <td>-62.0065</td>\n",
       "      <td>-36.7217</td>\n",
       "      <td>-58.3608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101p</td>\n",
       "      <td>38.14</td>\n",
       "      <td>1</td>\n",
       "      <td>101.0</td>\n",
       "      <td>30.375</td>\n",
       "      <td>2.71654</td>\n",
       "      <td>8.30596</td>\n",
       "      <td>4.78671</td>\n",
       "      <td>0.377115</td>\n",
       "      <td>-0.023688</td>\n",
       "      <td>...</td>\n",
       "      <td>30.75</td>\n",
       "      <td>9.69963</td>\n",
       "      <td>-1.92299</td>\n",
       "      <td>0.056635</td>\n",
       "      <td>0.050959</td>\n",
       "      <td>-0.042562</td>\n",
       "      <td>-0.009289</td>\n",
       "      <td>-60.4311</td>\n",
       "      <td>-37.5412</td>\n",
       "      <td>-59.0013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  subject  timestamp  activityID  heartrate_bpm  imu_hand_temp  \\\n",
       "0    101p      37.70           1          100.0         30.375   \n",
       "1    101p      37.81           1          100.0         30.375   \n",
       "2    101p      37.92           1          100.0         30.375   \n",
       "3    101p      38.03           1          100.0         30.375   \n",
       "4    101p      38.14           1          101.0         30.375   \n",
       "\n",
       "   imu_hand_3d_acceleration_1  imu_hand_3d_acceleration_2  \\\n",
       "0                     2.30106                     7.25857   \n",
       "1                     2.24615                     7.48180   \n",
       "2                     2.30000                     7.10681   \n",
       "3                     2.49455                     7.52335   \n",
       "4                     2.71654                     8.30596   \n",
       "\n",
       "   imu_hand_3d_acceleration_3  imu_hand_3d_gyroscope_1  \\\n",
       "0                     6.09259                -0.069961   \n",
       "1                     5.55219                -0.431227   \n",
       "2                     6.09309                 0.075692   \n",
       "3                     6.17157                -0.259058   \n",
       "4                     4.78671                 0.377115   \n",
       "\n",
       "   imu_hand_3d_gyroscope_2  ...  imu_ankle_temp  imu_ankle_3d_acceleration_1  \\\n",
       "0                -0.018328  ...           30.75                      9.77578   \n",
       "1                 0.002685  ...           30.75                      9.73536   \n",
       "2                -0.030792  ...           30.75                      9.73745   \n",
       "3                -0.267895  ...           30.75                      9.58444   \n",
       "4                -0.023688  ...           30.75                      9.69963   \n",
       "\n",
       "   imu_ankle_3d_acceleration_2  imu_ankle_3d_acceleration_3  \\\n",
       "0                     -1.88582                     0.095775   \n",
       "1                     -2.03681                     0.019118   \n",
       "2                     -1.84729                     0.056553   \n",
       "3                     -1.84538                    -0.060796   \n",
       "4                     -1.92299                     0.056635   \n",
       "\n",
       "   imu_ankle_3d_gyroscope_1  imu_ankle_3d_gyroscope_2  \\\n",
       "0                  0.001351                 -0.048878   \n",
       "1                  0.005819                 -0.017912   \n",
       "2                  0.015376                 -0.011798   \n",
       "3                 -0.012672                 -0.009233   \n",
       "4                  0.050959                 -0.042562   \n",
       "\n",
       "   imu_ankle_3d_gyroscope_3  imu_ankle_3d_magnetometer_1  \\\n",
       "0                 -0.006328                     -60.2040   \n",
       "1                  0.015243                     -60.7820   \n",
       "2                  0.011387                     -61.4531   \n",
       "3                  0.005823                     -62.0065   \n",
       "4                 -0.009289                     -60.4311   \n",
       "\n",
       "   imu_ankle_3d_magnetometer_2  imu_ankle_3d_magnetometer_3  \n",
       "0                     -37.1225                     -57.8847  \n",
       "1                     -36.2116                     -58.4891  \n",
       "2                     -36.2983                     -58.4845  \n",
       "3                     -36.7217                     -58.3608  \n",
       "4                     -37.5412                     -59.0013  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking this works\n",
    "#removing the heartrate has downsampled to only use data every 0.11 seconds\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject                        0\n",
       "timestamp                      0\n",
       "activityID                     0\n",
       "heartrate_bpm                  0\n",
       "imu_hand_temp                  0\n",
       "imu_hand_3d_acceleration_1     0\n",
       "imu_hand_3d_acceleration_2     0\n",
       "imu_hand_3d_acceleration_3     0\n",
       "imu_hand_3d_gyroscope_1        0\n",
       "imu_hand_3d_gyroscope_2        0\n",
       "imu_hand_3d_gyroscope_3        0\n",
       "imu_hand_3d_magnetometer_1     0\n",
       "imu_hand_3d_magnetometer_2     0\n",
       "imu_hand_3d_magnetometer_3     0\n",
       "imu_chest_temp                 0\n",
       "imu_chest_3d_acceleration_1    0\n",
       "imu_chest_3d_acceleration_2    0\n",
       "imu_chest_3d_acceleration_3    0\n",
       "imu_chest_3d_gyroscope_1       0\n",
       "imu_chest_3d_gyroscope_2       0\n",
       "imu_chest_3d_gyroscope_3       0\n",
       "imu_chest_3d_magnetometer_1    0\n",
       "imu_chest_3d_magnetometer_2    0\n",
       "imu_chest_3d_magnetometer_3    0\n",
       "imu_ankle_temp                 0\n",
       "imu_ankle_3d_acceleration_1    0\n",
       "imu_ankle_3d_acceleration_2    0\n",
       "imu_ankle_3d_acceleration_3    0\n",
       "imu_ankle_3d_gyroscope_1       0\n",
       "imu_ankle_3d_gyroscope_2       0\n",
       "imu_ankle_3d_gyroscope_3       0\n",
       "imu_ankle_3d_magnetometer_1    0\n",
       "imu_ankle_3d_magnetometer_2    0\n",
       "imu_ankle_3d_magnetometer_3    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160919, 34)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What is the shape of our new dataframe?\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Part 3: Development and Test set <a class=\"anchor\" id=\"devtest\"></a>\n",
    "\n",
    "\n",
    "### 3.1 Splitting into Dev and Test sets \n",
    "\n",
    "In order to make sure no assumptions are being made about the data,  the data will be split the data into dev and test sets. \n",
    "\n",
    "50% of the data will be used for the exploratory data analysis and then 50% for testing the hypothesis. This will help with a clearer diagnosis and a hypothesis with no p-hacking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80460, 34)\n",
      "(80459, 34)\n"
     ]
    }
   ],
   "source": [
    "#resetting the index because the merged frame kept old indexs \n",
    "df=df.reset_index(drop = True)\n",
    "\n",
    "#setting a random.seed so that the sample doesn't change each time the kernal re-runs\n",
    "random.seed(2447)\n",
    "\n",
    "#splitting the data into dev and test set \n",
    "#using the random seed\n",
    "dfd = df.sample(frac=0.5, random_state=2447)\n",
    "dft =df.loc[~df.index.isin(dfd.index)]\n",
    "\n",
    "#Checking that the data has been split evenly \n",
    "print(dfd.shape)\n",
    "print(dft.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Part 4: Exploratory Data Analysis (EDA) <a class=\"anchor\" id=\"explore\"></a>\n",
    "\n",
    "\n",
    "Using our *development* data frame to start exploring our set. As the goal of the report is to develop a hardware/software which categorises activity types,there will be a focus on activityIDs for the EDA. For reference, each of the IDs corresponds to an activity the subject took part in.\n",
    "\n",
    "**Activity IDs:**\n",
    "> 1 lying |\n",
    "> 2 sitting |\n",
    "> 3 standing |\n",
    "> 4 walking |\n",
    "> 5 running |\n",
    "> 6 cycling |\n",
    "> 7 Nordic walking |\n",
    "> 9 watching TV |\n",
    "> 12 ascending stairs |\n",
    "> 13 descending stairs |\n",
    "> 16 vacuum cleaning |\n",
    "> 17 ironing |\n",
    "> 24 rope jumping |\n",
    "\n",
    "For potential later analysis, the following activityIDs have been grouped:\n",
    "\n",
    "**Moving* Activity IDs**\n",
    "\n",
    "> 4 walking |\n",
    "> 5 running |\n",
    "> 6 cycling |\n",
    "> 7 Nordic walking |\n",
    "> 12 ascending stairs |\n",
    "> 13 descending stairs |\n",
    "> 16 vacuum cleaning |\n",
    "> 24 rope jumping |\n",
    "\n",
    "**Non-Moving Activity IDs**\n",
    "\n",
    "> 1 lying |\n",
    "> 2 sitting |\n",
    "> 3 standing |\n",
    "> 9 watching TV |\n",
    "> 17 ironing |\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>*Please note</b> moving is defined as moving in leg positions here.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80460\n"
     ]
    }
   ],
   "source": [
    "#checking shape\n",
    "\n",
    "data_size = len(dfd)\n",
    "print(data_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Averages\n",
    "\n",
    "In order to get an initial idea from the data, means were taken across all variables. These means will be used throughout the report. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                      1567.338595\n",
       "activityID                        8.124683\n",
       "heartrate_bpm                   104.965896\n",
       "imu_hand_temp                    33.123041\n",
       "imu_hand_3d_acceleration_1       -4.793968\n",
       "imu_hand_3d_acceleration_2        3.418039\n",
       "imu_hand_3d_acceleration_3        3.710944\n",
       "imu_hand_3d_gyroscope_1          -0.012079\n",
       "imu_hand_3d_gyroscope_2           0.039067\n",
       "imu_hand_3d_gyroscope_3          -0.004945\n",
       "imu_hand_3d_magnetometer_1       20.359893\n",
       "imu_hand_3d_magnetometer_2      -13.581101\n",
       "imu_hand_3d_magnetometer_3      -24.649830\n",
       "imu_chest_temp                   36.378754\n",
       "imu_chest_3d_acceleration_1       0.356891\n",
       "imu_chest_3d_acceleration_2       8.243940\n",
       "imu_chest_3d_acceleration_3      -1.123665\n",
       "imu_chest_3d_gyroscope_1          0.005183\n",
       "imu_chest_3d_gyroscope_2          0.010114\n",
       "imu_chest_3d_gyroscope_3         -0.022967\n",
       "imu_chest_3d_magnetometer_1       4.692329\n",
       "imu_chest_3d_magnetometer_2     -30.764418\n",
       "imu_chest_3d_magnetometer_3       3.001267\n",
       "imu_ankle_temp                   33.780625\n",
       "imu_ankle_3d_acceleration_1       9.250442\n",
       "imu_ankle_3d_acceleration_2      -0.392088\n",
       "imu_ankle_3d_acceleration_3      -2.675513\n",
       "imu_ankle_3d_gyroscope_1          0.014836\n",
       "imu_ankle_3d_gyroscope_2         -0.028612\n",
       "imu_ankle_3d_gyroscope_3          0.003990\n",
       "imu_ankle_3d_magnetometer_1     -31.190873\n",
       "imu_ankle_3d_magnetometer_2       2.306686\n",
       "imu_ankle_3d_magnetometer_3      17.494876\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking mean\n",
    "\n",
    "dfd.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1567.3385950783259\n",
      "3888.22\n",
      "37.7\n"
     ]
    }
   ],
   "source": [
    "print(dfd['timestamp'].mean())\n",
    "print(dfd['timestamp'].max())\n",
    "print(dfd['timestamp'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mean Heartrate per ActivityID')"
      ]
     },
     "execution_count": 15,
     "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": [
    "#activityID barchart boxplot\n",
    "\n",
    "df_hr_act = dfd['heartrate_bpm'].groupby(dfd['activityID']).mean()\n",
    "\n",
    "#sorting the bar chart into ascending heartrates\n",
    "df_hr_act.sort_values(ascending=False, inplace = True )\n",
    "df_hr_act.index = df_hr_act.index.map(activityIDkey)\n",
    "df_hr_act.plot(kind='bar')\n",
    "plt.title('Mean Heartrate per ActivityID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>timestamp</th>\n",
       "      <th>heartrate_bpm</th>\n",
       "      <th>imu_hand_temp</th>\n",
       "      <th>imu_hand_3d_acceleration_1</th>\n",
       "      <th>imu_hand_3d_acceleration_2</th>\n",
       "      <th>imu_hand_3d_acceleration_3</th>\n",
       "      <th>imu_hand_3d_gyroscope_1</th>\n",
       "      <th>imu_hand_3d_gyroscope_2</th>\n",
       "      <th>imu_hand_3d_gyroscope_3</th>\n",
       "      <th>imu_hand_3d_magnetometer_1</th>\n",
       "      <th>...</th>\n",
       "      <th>imu_ankle_temp</th>\n",
       "      <th>imu_ankle_3d_acceleration_1</th>\n",
       "      <th>imu_ankle_3d_acceleration_2</th>\n",
       "      <th>imu_ankle_3d_acceleration_3</th>\n",
       "      <th>imu_ankle_3d_gyroscope_1</th>\n",
       "      <th>imu_ankle_3d_gyroscope_2</th>\n",
       "      <th>imu_ankle_3d_gyroscope_3</th>\n",
       "      <th>imu_ankle_3d_magnetometer_1</th>\n",
       "      <th>imu_ankle_3d_magnetometer_2</th>\n",
       "      <th>imu_ankle_3d_magnetometer_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>activityID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>206.580091</td>\n",
       "      <td>75.396550</td>\n",
       "      <td>32.732742</td>\n",
       "      <td>3.756578</td>\n",
       "      <td>2.018568</td>\n",
       "      <td>6.385424</td>\n",
       "      <td>-0.008764</td>\n",
       "      <td>0.008986</td>\n",
       "      <td>0.002238</td>\n",
       "      <td>-0.171106</td>\n",
       "      <td>...</td>\n",
       "      <td>32.994020</td>\n",
       "      <td>0.544743</td>\n",
       "      <td>-6.392343</td>\n",
       "      <td>-3.672087</td>\n",
       "      <td>0.012183</td>\n",
       "      <td>-0.005562</td>\n",
       "      <td>0.006445</td>\n",
       "      <td>-17.854605</td>\n",
       "      <td>20.721140</td>\n",
       "      <td>0.023530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>506.136985</td>\n",
       "      <td>80.035071</td>\n",
       "      <td>33.266343</td>\n",
       "      <td>-1.376973</td>\n",
       "      <td>4.317854</td>\n",
       "      <td>5.179943</td>\n",
       "      <td>-0.002374</td>\n",
       "      <td>0.009722</td>\n",
       "      <td>-0.005765</td>\n",
       "      <td>3.519832</td>\n",
       "      <td>...</td>\n",
       "      <td>33.639048</td>\n",
       "      <td>8.866678</td>\n",
       "      <td>-0.310780</td>\n",
       "      <td>-2.492304</td>\n",
       "      <td>0.005227</td>\n",
       "      <td>-0.006177</td>\n",
       "      <td>0.005814</td>\n",
       "      <td>-22.735097</td>\n",
       "      <td>2.460055</td>\n",
       "      <td>21.006176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>732.984055</td>\n",
       "      <td>88.367582</td>\n",
       "      <td>33.640534</td>\n",
       "      <td>-7.091734</td>\n",
       "      <td>3.217199</td>\n",
       "      <td>2.683870</td>\n",
       "      <td>0.005618</td>\n",
       "      <td>0.000884</td>\n",
       "      <td>-0.005340</td>\n",
       "      <td>27.557579</td>\n",
       "      <td>...</td>\n",
       "      <td>33.897290</td>\n",
       "      <td>9.368908</td>\n",
       "      <td>-0.827263</td>\n",
       "      <td>-2.087657</td>\n",
       "      <td>0.004788</td>\n",
       "      <td>-0.004786</td>\n",
       "      <td>0.004478</td>\n",
       "      <td>-22.396515</td>\n",
       "      <td>-0.328439</td>\n",
       "      <td>24.521866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2435.133555</td>\n",
       "      <td>112.452830</td>\n",
       "      <td>32.482349</td>\n",
       "      <td>-10.012624</td>\n",
       "      <td>2.470108</td>\n",
       "      <td>1.894922</td>\n",
       "      <td>-0.049856</td>\n",
       "      <td>0.025401</td>\n",
       "      <td>-0.008440</td>\n",
       "      <td>38.179269</td>\n",
       "      <td>...</td>\n",
       "      <td>33.813064</td>\n",
       "      <td>11.856911</td>\n",
       "      <td>0.506397</td>\n",
       "      <td>-3.172319</td>\n",
       "      <td>-0.000571</td>\n",
       "      <td>-0.107478</td>\n",
       "      <td>-0.067086</td>\n",
       "      <td>-37.209953</td>\n",
       "      <td>-0.895906</td>\n",
       "      <td>15.694330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3393.563315</td>\n",
       "      <td>159.094118</td>\n",
       "      <td>32.022837</td>\n",
       "      <td>-6.709552</td>\n",
       "      <td>4.249012</td>\n",
       "      <td>1.576684</td>\n",
       "      <td>-0.008865</td>\n",
       "      <td>0.336436</td>\n",
       "      <td>-0.015374</td>\n",
       "      <td>19.141812</td>\n",
       "      <td>...</td>\n",
       "      <td>33.106315</td>\n",
       "      <td>13.463779</td>\n",
       "      <td>2.422553</td>\n",
       "      <td>-3.904547</td>\n",
       "      <td>0.053800</td>\n",
       "      <td>0.006211</td>\n",
       "      <td>0.153229</td>\n",
       "      <td>-38.448878</td>\n",
       "      <td>-6.351850</td>\n",
       "      <td>13.274418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2986.473326</td>\n",
       "      <td>124.414501</td>\n",
       "      <td>32.420728</td>\n",
       "      <td>-4.311831</td>\n",
       "      <td>2.986762</td>\n",
       "      <td>7.529086</td>\n",
       "      <td>-0.056278</td>\n",
       "      <td>-0.005275</td>\n",
       "      <td>0.091610</td>\n",
       "      <td>29.415642</td>\n",
       "      <td>...</td>\n",
       "      <td>33.058510</td>\n",
       "      <td>9.280793</td>\n",
       "      <td>1.465412</td>\n",
       "      <td>-2.080847</td>\n",
       "      <td>0.123938</td>\n",
       "      <td>0.042004</td>\n",
       "      <td>0.002720</td>\n",
       "      <td>-41.304175</td>\n",
       "      <td>-5.650927</td>\n",
       "      <td>14.040563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2917.563502</td>\n",
       "      <td>123.505952</td>\n",
       "      <td>31.934922</td>\n",
       "      <td>-4.217568</td>\n",
       "      <td>5.043965</td>\n",
       "      <td>2.498619</td>\n",
       "      <td>0.022999</td>\n",
       "      <td>0.049373</td>\n",
       "      <td>-0.004116</td>\n",
       "      <td>18.412181</td>\n",
       "      <td>...</td>\n",
       "      <td>33.527651</td>\n",
       "      <td>12.274516</td>\n",
       "      <td>0.564078</td>\n",
       "      <td>-3.387095</td>\n",
       "      <td>0.028892</td>\n",
       "      <td>-0.122246</td>\n",
       "      <td>0.014809</td>\n",
       "      <td>-37.493414</td>\n",
       "      <td>-0.099960</td>\n",
       "      <td>14.412029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1806.156140</td>\n",
       "      <td>129.516093</td>\n",
       "      <td>33.516326</td>\n",
       "      <td>-8.739140</td>\n",
       "      <td>3.673413</td>\n",
       "      <td>1.606860</td>\n",
       "      <td>-0.337135</td>\n",
       "      <td>0.174845</td>\n",
       "      <td>0.105914</td>\n",
       "      <td>38.048284</td>\n",
       "      <td>...</td>\n",
       "      <td>34.176930</td>\n",
       "      <td>9.734887</td>\n",
       "      <td>1.454661</td>\n",
       "      <td>-3.142101</td>\n",
       "      <td>0.373505</td>\n",
       "      <td>0.144148</td>\n",
       "      <td>-0.020877</td>\n",
       "      <td>-35.639611</td>\n",
       "      <td>-4.919154</td>\n",
       "      <td>14.046774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1906.774399</td>\n",
       "      <td>129.079505</td>\n",
       "      <td>33.321219</td>\n",
       "      <td>-6.221895</td>\n",
       "      <td>2.871335</td>\n",
       "      <td>3.648378</td>\n",
       "      <td>0.254417</td>\n",
       "      <td>-0.106310</td>\n",
       "      <td>-0.191100</td>\n",
       "      <td>31.944753</td>\n",
       "      <td>...</td>\n",
       "      <td>34.161960</td>\n",
       "      <td>10.923985</td>\n",
       "      <td>0.952553</td>\n",
       "      <td>-2.639927</td>\n",
       "      <td>-0.394677</td>\n",
       "      <td>-0.246644</td>\n",
       "      <td>0.066514</td>\n",
       "      <td>-36.954259</td>\n",
       "      <td>-4.284169</td>\n",
       "      <td>20.225516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1360.611224</td>\n",
       "      <td>104.275056</td>\n",
       "      <td>34.170433</td>\n",
       "      <td>-7.207438</td>\n",
       "      <td>3.589560</td>\n",
       "      <td>1.895451</td>\n",
       "      <td>0.002921</td>\n",
       "      <td>0.001622</td>\n",
       "      <td>-0.006121</td>\n",
       "      <td>30.343557</td>\n",
       "      <td>...</td>\n",
       "      <td>34.457738</td>\n",
       "      <td>9.586215</td>\n",
       "      <td>0.389842</td>\n",
       "      <td>-1.730479</td>\n",
       "      <td>-0.013429</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>0.007299</td>\n",
       "      <td>-24.024955</td>\n",
       "      <td>4.003300</td>\n",
       "      <td>11.079963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1025.299373</td>\n",
       "      <td>89.951334</td>\n",
       "      <td>34.021504</td>\n",
       "      <td>-3.369453</td>\n",
       "      <td>3.734848</td>\n",
       "      <td>5.484141</td>\n",
       "      <td>0.002415</td>\n",
       "      <td>0.020066</td>\n",
       "      <td>0.002729</td>\n",
       "      <td>3.616550</td>\n",
       "      <td>...</td>\n",
       "      <td>34.275966</td>\n",
       "      <td>9.596536</td>\n",
       "      <td>-0.490532</td>\n",
       "      <td>-1.923180</td>\n",
       "      <td>0.009569</td>\n",
       "      <td>-0.003516</td>\n",
       "      <td>0.001835</td>\n",
       "      <td>-38.879560</td>\n",
       "      <td>5.640664</td>\n",
       "      <td>34.161695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3690.652449</td>\n",
       "      <td>167.553450</td>\n",
       "      <td>31.339604</td>\n",
       "      <td>-5.400178</td>\n",
       "      <td>3.266017</td>\n",
       "      <td>0.821490</td>\n",
       "      <td>0.169402</td>\n",
       "      <td>0.572490</td>\n",
       "      <td>-0.156181</td>\n",
       "      <td>29.768727</td>\n",
       "      <td>...</td>\n",
       "      <td>32.929824</td>\n",
       "      <td>10.025842</td>\n",
       "      <td>0.840319</td>\n",
       "      <td>-2.971924</td>\n",
       "      <td>-0.016533</td>\n",
       "      <td>0.002711</td>\n",
       "      <td>-0.004906</td>\n",
       "      <td>-42.845427</td>\n",
       "      <td>-4.900916</td>\n",
       "      <td>21.431987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              timestamp  heartrate_bpm  imu_hand_temp  \\\n",
       "activityID                                              \n",
       "1            206.580091      75.396550      32.732742   \n",
       "2            506.136985      80.035071      33.266343   \n",
       "3            732.984055      88.367582      33.640534   \n",
       "4           2435.133555     112.452830      32.482349   \n",
       "5           3393.563315     159.094118      32.022837   \n",
       "6           2986.473326     124.414501      32.420728   \n",
       "7           2917.563502     123.505952      31.934922   \n",
       "12          1806.156140     129.516093      33.516326   \n",
       "13          1906.774399     129.079505      33.321219   \n",
       "16          1360.611224     104.275056      34.170433   \n",
       "17          1025.299373      89.951334      34.021504   \n",
       "24          3690.652449     167.553450      31.339604   \n",
       "\n",
       "            imu_hand_3d_acceleration_1  imu_hand_3d_acceleration_2  \\\n",
       "activityID                                                           \n",
       "1                             3.756578                    2.018568   \n",
       "2                            -1.376973                    4.317854   \n",
       "3                            -7.091734                    3.217199   \n",
       "4                           -10.012624                    2.470108   \n",
       "5                            -6.709552                    4.249012   \n",
       "6                            -4.311831                    2.986762   \n",
       "7                            -4.217568                    5.043965   \n",
       "12                           -8.739140                    3.673413   \n",
       "13                           -6.221895                    2.871335   \n",
       "16                           -7.207438                    3.589560   \n",
       "17                           -3.369453                    3.734848   \n",
       "24                           -5.400178                    3.266017   \n",
       "\n",
       "            imu_hand_3d_acceleration_3  imu_hand_3d_gyroscope_1  \\\n",
       "activityID                                                        \n",
       "1                             6.385424                -0.008764   \n",
       "2                             5.179943                -0.002374   \n",
       "3                             2.683870                 0.005618   \n",
       "4                             1.894922                -0.049856   \n",
       "5                             1.576684                -0.008865   \n",
       "6                             7.529086                -0.056278   \n",
       "7                             2.498619                 0.022999   \n",
       "12                            1.606860                -0.337135   \n",
       "13                            3.648378                 0.254417   \n",
       "16                            1.895451                 0.002921   \n",
       "17                            5.484141                 0.002415   \n",
       "24                            0.821490                 0.169402   \n",
       "\n",
       "            imu_hand_3d_gyroscope_2  imu_hand_3d_gyroscope_3  \\\n",
       "activityID                                                     \n",
       "1                          0.008986                 0.002238   \n",
       "2                          0.009722                -0.005765   \n",
       "3                          0.000884                -0.005340   \n",
       "4                          0.025401                -0.008440   \n",
       "5                          0.336436                -0.015374   \n",
       "6                         -0.005275                 0.091610   \n",
       "7                          0.049373                -0.004116   \n",
       "12                         0.174845                 0.105914   \n",
       "13                        -0.106310                -0.191100   \n",
       "16                         0.001622                -0.006121   \n",
       "17                         0.020066                 0.002729   \n",
       "24                         0.572490                -0.156181   \n",
       "\n",
       "            imu_hand_3d_magnetometer_1  ...  imu_ankle_temp  \\\n",
       "activityID                              ...                   \n",
       "1                            -0.171106  ...       32.994020   \n",
       "2                             3.519832  ...       33.639048   \n",
       "3                            27.557579  ...       33.897290   \n",
       "4                            38.179269  ...       33.813064   \n",
       "5                            19.141812  ...       33.106315   \n",
       "6                            29.415642  ...       33.058510   \n",
       "7                            18.412181  ...       33.527651   \n",
       "12                           38.048284  ...       34.176930   \n",
       "13                           31.944753  ...       34.161960   \n",
       "16                           30.343557  ...       34.457738   \n",
       "17                            3.616550  ...       34.275966   \n",
       "24                           29.768727  ...       32.929824   \n",
       "\n",
       "            imu_ankle_3d_acceleration_1  imu_ankle_3d_acceleration_2  \\\n",
       "activityID                                                             \n",
       "1                              0.544743                    -6.392343   \n",
       "2                              8.866678                    -0.310780   \n",
       "3                              9.368908                    -0.827263   \n",
       "4                             11.856911                     0.506397   \n",
       "5                             13.463779                     2.422553   \n",
       "6                              9.280793                     1.465412   \n",
       "7                             12.274516                     0.564078   \n",
       "12                             9.734887                     1.454661   \n",
       "13                            10.923985                     0.952553   \n",
       "16                             9.586215                     0.389842   \n",
       "17                             9.596536                    -0.490532   \n",
       "24                            10.025842                     0.840319   \n",
       "\n",
       "            imu_ankle_3d_acceleration_3  imu_ankle_3d_gyroscope_1  \\\n",
       "activityID                                                          \n",
       "1                             -3.672087                  0.012183   \n",
       "2                             -2.492304                  0.005227   \n",
       "3                             -2.087657                  0.004788   \n",
       "4                             -3.172319                 -0.000571   \n",
       "5                             -3.904547                  0.053800   \n",
       "6                             -2.080847                  0.123938   \n",
       "7                             -3.387095                  0.028892   \n",
       "12                            -3.142101                  0.373505   \n",
       "13                            -2.639927                 -0.394677   \n",
       "16                            -1.730479                 -0.013429   \n",
       "17                            -1.923180                  0.009569   \n",
       "24                            -2.971924                 -0.016533   \n",
       "\n",
       "            imu_ankle_3d_gyroscope_2  imu_ankle_3d_gyroscope_3  \\\n",
       "activityID                                                       \n",
       "1                          -0.005562                  0.006445   \n",
       "2                          -0.006177                  0.005814   \n",
       "3                          -0.004786                  0.004478   \n",
       "4                          -0.107478                 -0.067086   \n",
       "5                           0.006211                  0.153229   \n",
       "6                           0.042004                  0.002720   \n",
       "7                          -0.122246                  0.014809   \n",
       "12                          0.144148                 -0.020877   \n",
       "13                         -0.246644                  0.066514   \n",
       "16                          0.001929                  0.007299   \n",
       "17                         -0.003516                  0.001835   \n",
       "24                          0.002711                 -0.004906   \n",
       "\n",
       "            imu_ankle_3d_magnetometer_1  imu_ankle_3d_magnetometer_2  \\\n",
       "activityID                                                             \n",
       "1                            -17.854605                    20.721140   \n",
       "2                            -22.735097                     2.460055   \n",
       "3                            -22.396515                    -0.328439   \n",
       "4                            -37.209953                    -0.895906   \n",
       "5                            -38.448878                    -6.351850   \n",
       "6                            -41.304175                    -5.650927   \n",
       "7                            -37.493414                    -0.099960   \n",
       "12                           -35.639611                    -4.919154   \n",
       "13                           -36.954259                    -4.284169   \n",
       "16                           -24.024955                     4.003300   \n",
       "17                           -38.879560                     5.640664   \n",
       "24                           -42.845427                    -4.900916   \n",
       "\n",
       "            imu_ankle_3d_magnetometer_3  \n",
       "activityID                               \n",
       "1                              0.023530  \n",
       "2                             21.006176  \n",
       "3                             24.521866  \n",
       "4                             15.694330  \n",
       "5                             13.274418  \n",
       "6                             14.040563  \n",
       "7                             14.412029  \n",
       "12                            14.046774  \n",
       "13                            20.225516  \n",
       "16                            11.079963  \n",
       "17                            34.161695  \n",
       "24                            21.431987  \n",
       "\n",
       "[12 rows x 32 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What are the averages per activity?\n",
    "\n",
    "dfd.groupby(['activityID']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Correlations\n",
    "\n",
    "Firstly, I would like to know the correlation between the data point of the development data set. Correlations are a good starting point to get a quick handle on the data set we have. In the graph below, the data points with a strong correlation is green. The descending line through the middle is to be ignored as it is just saying there as a string correlation between two of the same variables. \n",
    "\n",
    "We can see that there is also a correlation between chest and hand temperature and between the magnetometers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-18899f0e143a>:11: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  sns.heatmap(df_corr, mask=np.zeros_like(df_corr, dtype=np.bool), cmap = \"PiYG\",ax=ax)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from pandas.plotting import scatter_matrix\n",
    "df_corr = dfd.corr()\n",
    "\n",
    "#dropping activity ID for correlation \n",
    "df_corr = df_corr.drop(['activityID'], axis = 1)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(15, 10))\n",
    "#plotting the correlations \n",
    "#setting the right colour to represent strong correlation as green \n",
    "sns.heatmap(df_corr, mask=np.zeros_like(df_corr, dtype=np.bool), cmap = \"PiYG\",ax=ax)\n",
    "plt.title('Correlations within the data set')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-18-14b4fa581bfa>:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  meansearies = dfd.groupby('activityID')['heartrate_bpm', 'imu_hand_temp', 'imu_hand_3d_acceleration_1',\n"
     ]
    },
    {
     "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>heartrate_bpm</th>\n",
       "      <th>imu_hand_temp</th>\n",
       "      <th>imu_hand_3d_acceleration_1</th>\n",
       "      <th>imu_hand_3d_acceleration_2</th>\n",
       "      <th>imu_hand_3d_acceleration_3</th>\n",
       "      <th>imu_hand_3d_gyroscope_1</th>\n",
       "      <th>imu_hand_3d_gyroscope_2</th>\n",
       "      <th>imu_hand_3d_gyroscope_3</th>\n",
       "      <th>imu_hand_3d_magnetometer_1</th>\n",
       "      <th>imu_hand_3d_magnetometer_2</th>\n",
       "      <th>...</th>\n",
       "      <th>imu_ankle_temp</th>\n",
       "      <th>imu_ankle_3d_acceleration_1</th>\n",
       "      <th>imu_ankle_3d_acceleration_2</th>\n",
       "      <th>imu_ankle_3d_acceleration_3</th>\n",
       "      <th>imu_ankle_3d_gyroscope_1</th>\n",
       "      <th>imu_ankle_3d_gyroscope_2</th>\n",
       "      <th>imu_ankle_3d_gyroscope_3</th>\n",
       "      <th>imu_ankle_3d_magnetometer_1</th>\n",
       "      <th>imu_ankle_3d_magnetometer_2</th>\n",
       "      <th>imu_ankle_3d_magnetometer_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>activityID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.396550</td>\n",
       "      <td>32.732742</td>\n",
       "      <td>3.756578</td>\n",
       "      <td>2.018568</td>\n",
       "      <td>6.385424</td>\n",
       "      <td>-0.008764</td>\n",
       "      <td>0.008986</td>\n",
       "      <td>0.002238</td>\n",
       "      <td>-0.171106</td>\n",
       "      <td>9.263335</td>\n",
       "      <td>...</td>\n",
       "      <td>32.994020</td>\n",
       "      <td>0.544743</td>\n",
       "      <td>-6.392343</td>\n",
       "      <td>-3.672087</td>\n",
       "      <td>0.012183</td>\n",
       "      <td>-0.005562</td>\n",
       "      <td>0.006445</td>\n",
       "      <td>-17.854605</td>\n",
       "      <td>20.721140</td>\n",
       "      <td>0.023530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.035071</td>\n",
       "      <td>33.266343</td>\n",
       "      <td>-1.376973</td>\n",
       "      <td>4.317854</td>\n",
       "      <td>5.179943</td>\n",
       "      <td>-0.002374</td>\n",
       "      <td>0.009722</td>\n",
       "      <td>-0.005765</td>\n",
       "      <td>3.519832</td>\n",
       "      <td>-14.512305</td>\n",
       "      <td>...</td>\n",
       "      <td>33.639048</td>\n",
       "      <td>8.866678</td>\n",
       "      <td>-0.310780</td>\n",
       "      <td>-2.492304</td>\n",
       "      <td>0.005227</td>\n",
       "      <td>-0.006177</td>\n",
       "      <td>0.005814</td>\n",
       "      <td>-22.735097</td>\n",
       "      <td>2.460055</td>\n",
       "      <td>21.006176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88.367582</td>\n",
       "      <td>33.640534</td>\n",
       "      <td>-7.091734</td>\n",
       "      <td>3.217199</td>\n",
       "      <td>2.683870</td>\n",
       "      <td>0.005618</td>\n",
       "      <td>0.000884</td>\n",
       "      <td>-0.005340</td>\n",
       "      <td>27.557579</td>\n",
       "      <td>-19.091959</td>\n",
       "      <td>...</td>\n",
       "      <td>33.897290</td>\n",
       "      <td>9.368908</td>\n",
       "      <td>-0.827263</td>\n",
       "      <td>-2.087657</td>\n",
       "      <td>0.004788</td>\n",
       "      <td>-0.004786</td>\n",
       "      <td>0.004478</td>\n",
       "      <td>-22.396515</td>\n",
       "      <td>-0.328439</td>\n",
       "      <td>24.521866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>112.452830</td>\n",
       "      <td>32.482349</td>\n",
       "      <td>-10.012624</td>\n",
       "      <td>2.470108</td>\n",
       "      <td>1.894922</td>\n",
       "      <td>-0.049856</td>\n",
       "      <td>0.025401</td>\n",
       "      <td>-0.008440</td>\n",
       "      <td>38.179269</td>\n",
       "      <td>-10.292968</td>\n",
       "      <td>...</td>\n",
       "      <td>33.813064</td>\n",
       "      <td>11.856911</td>\n",
       "      <td>0.506397</td>\n",
       "      <td>-3.172319</td>\n",
       "      <td>-0.000571</td>\n",
       "      <td>-0.107478</td>\n",
       "      <td>-0.067086</td>\n",
       "      <td>-37.209953</td>\n",
       "      <td>-0.895906</td>\n",
       "      <td>15.694330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>159.094118</td>\n",
       "      <td>32.022837</td>\n",
       "      <td>-6.709552</td>\n",
       "      <td>4.249012</td>\n",
       "      <td>1.576684</td>\n",
       "      <td>-0.008865</td>\n",
       "      <td>0.336436</td>\n",
       "      <td>-0.015374</td>\n",
       "      <td>19.141812</td>\n",
       "      <td>-22.759069</td>\n",
       "      <td>...</td>\n",
       "      <td>33.106315</td>\n",
       "      <td>13.463779</td>\n",
       "      <td>2.422553</td>\n",
       "      <td>-3.904547</td>\n",
       "      <td>0.053800</td>\n",
       "      <td>0.006211</td>\n",
       "      <td>0.153229</td>\n",
       "      <td>-38.448878</td>\n",
       "      <td>-6.351850</td>\n",
       "      <td>13.274418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>124.414501</td>\n",
       "      <td>32.420728</td>\n",
       "      <td>-4.311831</td>\n",
       "      <td>2.986762</td>\n",
       "      <td>7.529086</td>\n",
       "      <td>-0.056278</td>\n",
       "      <td>-0.005275</td>\n",
       "      <td>0.091610</td>\n",
       "      <td>29.415642</td>\n",
       "      <td>-13.181587</td>\n",
       "      <td>...</td>\n",
       "      <td>33.058510</td>\n",
       "      <td>9.280793</td>\n",
       "      <td>1.465412</td>\n",
       "      <td>-2.080847</td>\n",
       "      <td>0.123938</td>\n",
       "      <td>0.042004</td>\n",
       "      <td>0.002720</td>\n",
       "      <td>-41.304175</td>\n",
       "      <td>-5.650927</td>\n",
       "      <td>14.040563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>123.505952</td>\n",
       "      <td>31.934922</td>\n",
       "      <td>-4.217568</td>\n",
       "      <td>5.043965</td>\n",
       "      <td>2.498619</td>\n",
       "      <td>0.022999</td>\n",
       "      <td>0.049373</td>\n",
       "      <td>-0.004116</td>\n",
       "      <td>18.412181</td>\n",
       "      <td>-20.049217</td>\n",
       "      <td>...</td>\n",
       "      <td>33.527651</td>\n",
       "      <td>12.274516</td>\n",
       "      <td>0.564078</td>\n",
       "      <td>-3.387095</td>\n",
       "      <td>0.028892</td>\n",
       "      <td>-0.122246</td>\n",
       "      <td>0.014809</td>\n",
       "      <td>-37.493414</td>\n",
       "      <td>-0.099960</td>\n",
       "      <td>14.412029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>129.516093</td>\n",
       "      <td>33.516326</td>\n",
       "      <td>-8.739140</td>\n",
       "      <td>3.673413</td>\n",
       "      <td>1.606860</td>\n",
       "      <td>-0.337135</td>\n",
       "      <td>0.174845</td>\n",
       "      <td>0.105914</td>\n",
       "      <td>38.048284</td>\n",
       "      <td>-12.523232</td>\n",
       "      <td>...</td>\n",
       "      <td>34.176930</td>\n",
       "      <td>9.734887</td>\n",
       "      <td>1.454661</td>\n",
       "      <td>-3.142101</td>\n",
       "      <td>0.373505</td>\n",
       "      <td>0.144148</td>\n",
       "      <td>-0.020877</td>\n",
       "      <td>-35.639611</td>\n",
       "      <td>-4.919154</td>\n",
       "      <td>14.046774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>129.079505</td>\n",
       "      <td>33.321219</td>\n",
       "      <td>-6.221895</td>\n",
       "      <td>2.871335</td>\n",
       "      <td>3.648378</td>\n",
       "      <td>0.254417</td>\n",
       "      <td>-0.106310</td>\n",
       "      <td>-0.191100</td>\n",
       "      <td>31.944753</td>\n",
       "      <td>-13.415866</td>\n",
       "      <td>...</td>\n",
       "      <td>34.161960</td>\n",
       "      <td>10.923985</td>\n",
       "      <td>0.952553</td>\n",
       "      <td>-2.639927</td>\n",
       "      <td>-0.394677</td>\n",
       "      <td>-0.246644</td>\n",
       "      <td>0.066514</td>\n",
       "      <td>-36.954259</td>\n",
       "      <td>-4.284169</td>\n",
       "      <td>20.225516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>104.275056</td>\n",
       "      <td>34.170433</td>\n",
       "      <td>-7.207438</td>\n",
       "      <td>3.589560</td>\n",
       "      <td>1.895451</td>\n",
       "      <td>0.002921</td>\n",
       "      <td>0.001622</td>\n",
       "      <td>-0.006121</td>\n",
       "      <td>30.343557</td>\n",
       "      <td>-10.566788</td>\n",
       "      <td>...</td>\n",
       "      <td>34.457738</td>\n",
       "      <td>9.586215</td>\n",
       "      <td>0.389842</td>\n",
       "      <td>-1.730479</td>\n",
       "      <td>-0.013429</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>0.007299</td>\n",
       "      <td>-24.024955</td>\n",
       "      <td>4.003300</td>\n",
       "      <td>11.079963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>89.951334</td>\n",
       "      <td>34.021504</td>\n",
       "      <td>-3.369453</td>\n",
       "      <td>3.734848</td>\n",
       "      <td>5.484141</td>\n",
       "      <td>0.002415</td>\n",
       "      <td>0.020066</td>\n",
       "      <td>0.002729</td>\n",
       "      <td>3.616550</td>\n",
       "      <td>-25.505825</td>\n",
       "      <td>...</td>\n",
       "      <td>34.275966</td>\n",
       "      <td>9.596536</td>\n",
       "      <td>-0.490532</td>\n",
       "      <td>-1.923180</td>\n",
       "      <td>0.009569</td>\n",
       "      <td>-0.003516</td>\n",
       "      <td>0.001835</td>\n",
       "      <td>-38.879560</td>\n",
       "      <td>5.640664</td>\n",
       "      <td>34.161695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>167.553450</td>\n",
       "      <td>31.339604</td>\n",
       "      <td>-5.400178</td>\n",
       "      <td>3.266017</td>\n",
       "      <td>0.821490</td>\n",
       "      <td>0.169402</td>\n",
       "      <td>0.572490</td>\n",
       "      <td>-0.156181</td>\n",
       "      <td>29.768727</td>\n",
       "      <td>-17.692110</td>\n",
       "      <td>...</td>\n",
       "      <td>32.929824</td>\n",
       "      <td>10.025842</td>\n",
       "      <td>0.840319</td>\n",
       "      <td>-2.971924</td>\n",
       "      <td>-0.016533</td>\n",
       "      <td>0.002711</td>\n",
       "      <td>-0.004906</td>\n",
       "      <td>-42.845427</td>\n",
       "      <td>-4.900916</td>\n",
       "      <td>21.431987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            heartrate_bpm  imu_hand_temp  imu_hand_3d_acceleration_1  \\\n",
       "activityID                                                             \n",
       "1               75.396550      32.732742                    3.756578   \n",
       "2               80.035071      33.266343                   -1.376973   \n",
       "3               88.367582      33.640534                   -7.091734   \n",
       "4              112.452830      32.482349                  -10.012624   \n",
       "5              159.094118      32.022837                   -6.709552   \n",
       "6              124.414501      32.420728                   -4.311831   \n",
       "7              123.505952      31.934922                   -4.217568   \n",
       "12             129.516093      33.516326                   -8.739140   \n",
       "13             129.079505      33.321219                   -6.221895   \n",
       "16             104.275056      34.170433                   -7.207438   \n",
       "17              89.951334      34.021504                   -3.369453   \n",
       "24             167.553450      31.339604                   -5.400178   \n",
       "\n",
       "            imu_hand_3d_acceleration_2  imu_hand_3d_acceleration_3  \\\n",
       "activityID                                                           \n",
       "1                             2.018568                    6.385424   \n",
       "2                             4.317854                    5.179943   \n",
       "3                             3.217199                    2.683870   \n",
       "4                             2.470108                    1.894922   \n",
       "5                             4.249012                    1.576684   \n",
       "6                             2.986762                    7.529086   \n",
       "7                             5.043965                    2.498619   \n",
       "12                            3.673413                    1.606860   \n",
       "13                            2.871335                    3.648378   \n",
       "16                            3.589560                    1.895451   \n",
       "17                            3.734848                    5.484141   \n",
       "24                            3.266017                    0.821490   \n",
       "\n",
       "            imu_hand_3d_gyroscope_1  imu_hand_3d_gyroscope_2  \\\n",
       "activityID                                                     \n",
       "1                         -0.008764                 0.008986   \n",
       "2                         -0.002374                 0.009722   \n",
       "3                          0.005618                 0.000884   \n",
       "4                         -0.049856                 0.025401   \n",
       "5                         -0.008865                 0.336436   \n",
       "6                         -0.056278                -0.005275   \n",
       "7                          0.022999                 0.049373   \n",
       "12                        -0.337135                 0.174845   \n",
       "13                         0.254417                -0.106310   \n",
       "16                         0.002921                 0.001622   \n",
       "17                         0.002415                 0.020066   \n",
       "24                         0.169402                 0.572490   \n",
       "\n",
       "            imu_hand_3d_gyroscope_3  imu_hand_3d_magnetometer_1  \\\n",
       "activityID                                                        \n",
       "1                          0.002238                   -0.171106   \n",
       "2                         -0.005765                    3.519832   \n",
       "3                         -0.005340                   27.557579   \n",
       "4                         -0.008440                   38.179269   \n",
       "5                         -0.015374                   19.141812   \n",
       "6                          0.091610                   29.415642   \n",
       "7                         -0.004116                   18.412181   \n",
       "12                         0.105914                   38.048284   \n",
       "13                        -0.191100                   31.944753   \n",
       "16                        -0.006121                   30.343557   \n",
       "17                         0.002729                    3.616550   \n",
       "24                        -0.156181                   29.768727   \n",
       "\n",
       "            imu_hand_3d_magnetometer_2  ...  imu_ankle_temp  \\\n",
       "activityID                              ...                   \n",
       "1                             9.263335  ...       32.994020   \n",
       "2                           -14.512305  ...       33.639048   \n",
       "3                           -19.091959  ...       33.897290   \n",
       "4                           -10.292968  ...       33.813064   \n",
       "5                           -22.759069  ...       33.106315   \n",
       "6                           -13.181587  ...       33.058510   \n",
       "7                           -20.049217  ...       33.527651   \n",
       "12                          -12.523232  ...       34.176930   \n",
       "13                          -13.415866  ...       34.161960   \n",
       "16                          -10.566788  ...       34.457738   \n",
       "17                          -25.505825  ...       34.275966   \n",
       "24                          -17.692110  ...       32.929824   \n",
       "\n",
       "            imu_ankle_3d_acceleration_1  imu_ankle_3d_acceleration_2  \\\n",
       "activityID                                                             \n",
       "1                              0.544743                    -6.392343   \n",
       "2                              8.866678                    -0.310780   \n",
       "3                              9.368908                    -0.827263   \n",
       "4                             11.856911                     0.506397   \n",
       "5                             13.463779                     2.422553   \n",
       "6                              9.280793                     1.465412   \n",
       "7                             12.274516                     0.564078   \n",
       "12                             9.734887                     1.454661   \n",
       "13                            10.923985                     0.952553   \n",
       "16                             9.586215                     0.389842   \n",
       "17                             9.596536                    -0.490532   \n",
       "24                            10.025842                     0.840319   \n",
       "\n",
       "            imu_ankle_3d_acceleration_3  imu_ankle_3d_gyroscope_1  \\\n",
       "activityID                                                          \n",
       "1                             -3.672087                  0.012183   \n",
       "2                             -2.492304                  0.005227   \n",
       "3                             -2.087657                  0.004788   \n",
       "4                             -3.172319                 -0.000571   \n",
       "5                             -3.904547                  0.053800   \n",
       "6                             -2.080847                  0.123938   \n",
       "7                             -3.387095                  0.028892   \n",
       "12                            -3.142101                  0.373505   \n",
       "13                            -2.639927                 -0.394677   \n",
       "16                            -1.730479                 -0.013429   \n",
       "17                            -1.923180                  0.009569   \n",
       "24                            -2.971924                 -0.016533   \n",
       "\n",
       "            imu_ankle_3d_gyroscope_2  imu_ankle_3d_gyroscope_3  \\\n",
       "activityID                                                       \n",
       "1                          -0.005562                  0.006445   \n",
       "2                          -0.006177                  0.005814   \n",
       "3                          -0.004786                  0.004478   \n",
       "4                          -0.107478                 -0.067086   \n",
       "5                           0.006211                  0.153229   \n",
       "6                           0.042004                  0.002720   \n",
       "7                          -0.122246                  0.014809   \n",
       "12                          0.144148                 -0.020877   \n",
       "13                         -0.246644                  0.066514   \n",
       "16                          0.001929                  0.007299   \n",
       "17                         -0.003516                  0.001835   \n",
       "24                          0.002711                 -0.004906   \n",
       "\n",
       "            imu_ankle_3d_magnetometer_1  imu_ankle_3d_magnetometer_2  \\\n",
       "activityID                                                             \n",
       "1                            -17.854605                    20.721140   \n",
       "2                            -22.735097                     2.460055   \n",
       "3                            -22.396515                    -0.328439   \n",
       "4                            -37.209953                    -0.895906   \n",
       "5                            -38.448878                    -6.351850   \n",
       "6                            -41.304175                    -5.650927   \n",
       "7                            -37.493414                    -0.099960   \n",
       "12                           -35.639611                    -4.919154   \n",
       "13                           -36.954259                    -4.284169   \n",
       "16                           -24.024955                     4.003300   \n",
       "17                           -38.879560                     5.640664   \n",
       "24                           -42.845427                    -4.900916   \n",
       "\n",
       "            imu_ankle_3d_magnetometer_3  \n",
       "activityID                               \n",
       "1                              0.023530  \n",
       "2                             21.006176  \n",
       "3                             24.521866  \n",
       "4                             15.694330  \n",
       "5                             13.274418  \n",
       "6                             14.040563  \n",
       "7                             14.412029  \n",
       "12                            14.046774  \n",
       "13                            20.225516  \n",
       "16                            11.079963  \n",
       "17                            34.161695  \n",
       "24                            21.431987  \n",
       "\n",
       "[12 rows x 31 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Observing all the means per activity \n",
    "\n",
    "meansearies = dfd.groupby('activityID')['heartrate_bpm', 'imu_hand_temp', 'imu_hand_3d_acceleration_1',\n",
    "       'imu_hand_3d_acceleration_2', 'imu_hand_3d_acceleration_3',\n",
    "       'imu_hand_3d_gyroscope_1', 'imu_hand_3d_gyroscope_2',\n",
    "       'imu_hand_3d_gyroscope_3', 'imu_hand_3d_magnetometer_1',\n",
    "       'imu_hand_3d_magnetometer_2', 'imu_hand_3d_magnetometer_3',\n",
    "       'imu_chest_temp', 'imu_chest_3d_acceleration_1',\n",
    "       'imu_chest_3d_acceleration_2', 'imu_chest_3d_acceleration_3',\n",
    "       'imu_chest_3d_gyroscope_1', 'imu_chest_3d_gyroscope_2',\n",
    "       'imu_chest_3d_gyroscope_3', 'imu_chest_3d_magnetometer_1',\n",
    "       'imu_chest_3d_magnetometer_2', 'imu_chest_3d_magnetometer_3',\n",
    "       'imu_ankle_temp', 'imu_ankle_3d_acceleration_1',\n",
    "       'imu_ankle_3d_acceleration_2', 'imu_ankle_3d_acceleration_3',\n",
    "       'imu_ankle_3d_gyroscope_1', 'imu_ankle_3d_gyroscope_2',\n",
    "       'imu_ankle_3d_gyroscope_3', 'imu_ankle_3d_magnetometer_1',\n",
    "       'imu_ankle_3d_magnetometer_2', 'imu_ankle_3d_magnetometer_3'].mean()\n",
    "sortedact = meansearies.sort_values('activityID', ascending=True)\n",
    "\n",
    "sortedact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does temperature have any correlation with heartrate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-edc5eb7c4442>:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  actseries=dfd.groupby('activityID')['heartrate_bpm','imu_hand_temp','imu_chest_temp','imu_ankle_temp'].mean()\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>heartrate_bpm</th>\n",
       "      <th>imu_hand_temp</th>\n",
       "      <th>imu_chest_temp</th>\n",
       "      <th>imu_ankle_temp</th>\n",
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       "  <tbody>\n",
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       "      <th>24</th>\n",
       "      <td>167.553450</td>\n",
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       "      <td>34.583254</td>\n",
       "      <td>32.929824</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>35.076276</td>\n",
       "      <td>33.106315</td>\n",
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       "      <td>37.042163</td>\n",
       "      <td>34.176930</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>129.079505</td>\n",
       "      <td>33.321219</td>\n",
       "      <td>37.026681</td>\n",
       "      <td>34.161960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>124.414501</td>\n",
       "      <td>32.420728</td>\n",
       "      <td>36.780196</td>\n",
       "      <td>33.058510</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>123.505952</td>\n",
       "      <td>31.934922</td>\n",
       "      <td>36.443980</td>\n",
       "      <td>33.527651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>112.452830</td>\n",
       "      <td>32.482349</td>\n",
       "      <td>37.017302</td>\n",
       "      <td>33.813064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>104.275056</td>\n",
       "      <td>34.170433</td>\n",
       "      <td>37.053034</td>\n",
       "      <td>34.457738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>89.951334</td>\n",
       "      <td>34.021504</td>\n",
       "      <td>36.666473</td>\n",
       "      <td>34.275966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88.367582</td>\n",
       "      <td>33.640534</td>\n",
       "      <td>36.167544</td>\n",
       "      <td>33.897290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.035071</td>\n",
       "      <td>33.266343</td>\n",
       "      <td>35.831983</td>\n",
       "      <td>33.639048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.396550</td>\n",
       "      <td>32.732742</td>\n",
       "      <td>35.094228</td>\n",
       "      <td>32.994020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            heartrate_bpm  imu_hand_temp  imu_chest_temp  imu_ankle_temp\n",
       "activityID                                                              \n",
       "24             167.553450      31.339604       34.583254       32.929824\n",
       "5              159.094118      32.022837       35.076276       33.106315\n",
       "12             129.516093      33.516326       37.042163       34.176930\n",
       "13             129.079505      33.321219       37.026681       34.161960\n",
       "6              124.414501      32.420728       36.780196       33.058510\n",
       "7              123.505952      31.934922       36.443980       33.527651\n",
       "4              112.452830      32.482349       37.017302       33.813064\n",
       "16             104.275056      34.170433       37.053034       34.457738\n",
       "17              89.951334      34.021504       36.666473       34.275966\n",
       "3               88.367582      33.640534       36.167544       33.897290\n",
       "2               80.035071      33.266343       35.831983       33.639048\n",
       "1               75.396550      32.732742       35.094228       32.994020"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grouping by activity\n",
    "actseries=dfd.groupby('activityID')['heartrate_bpm','imu_hand_temp','imu_chest_temp','imu_ankle_temp'].mean()\n",
    "\n",
    "#We want to present it in acending heartrates\n",
    "groupedact = actseries.sort_values('heartrate_bpm', ascending=False)\n",
    "\n",
    "#printing a table for inspection\n",
    "groupedact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-5c1aace6e27e>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  actseries=dfd.groupby('activityID')['heartrate_bpm','imu_hand_temp','imu_chest_temp','imu_ankle_temp'].mean()\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>heartrate_bpm</th>\n",
       "      <th>imu_hand_temp</th>\n",
       "      <th>imu_chest_temp</th>\n",
       "      <th>imu_ankle_temp</th>\n",
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       "      <th>16</th>\n",
       "      <td>104.275056</td>\n",
       "      <td>34.170433</td>\n",
       "      <td>37.053034</td>\n",
       "      <td>34.457738</td>\n",
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       "      <th>17</th>\n",
       "      <td>89.951334</td>\n",
       "      <td>34.021504</td>\n",
       "      <td>36.666473</td>\n",
       "      <td>34.275966</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88.367582</td>\n",
       "      <td>33.640534</td>\n",
       "      <td>36.167544</td>\n",
       "      <td>33.897290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>129.516093</td>\n",
       "      <td>33.516326</td>\n",
       "      <td>37.042163</td>\n",
       "      <td>34.176930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>129.079505</td>\n",
       "      <td>33.321219</td>\n",
       "      <td>37.026681</td>\n",
       "      <td>34.161960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.035071</td>\n",
       "      <td>33.266343</td>\n",
       "      <td>35.831983</td>\n",
       "      <td>33.639048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.396550</td>\n",
       "      <td>32.732742</td>\n",
       "      <td>35.094228</td>\n",
       "      <td>32.994020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>112.452830</td>\n",
       "      <td>32.482349</td>\n",
       "      <td>37.017302</td>\n",
       "      <td>33.813064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>124.414501</td>\n",
       "      <td>32.420728</td>\n",
       "      <td>36.780196</td>\n",
       "      <td>33.058510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>159.094118</td>\n",
       "      <td>32.022837</td>\n",
       "      <td>35.076276</td>\n",
       "      <td>33.106315</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>31.934922</td>\n",
       "      <td>36.443980</td>\n",
       "      <td>33.527651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>167.553450</td>\n",
       "      <td>31.339604</td>\n",
       "      <td>34.583254</td>\n",
       "      <td>32.929824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            heartrate_bpm  imu_hand_temp  imu_chest_temp  imu_ankle_temp\n",
       "activityID                                                              \n",
       "16             104.275056      34.170433       37.053034       34.457738\n",
       "17              89.951334      34.021504       36.666473       34.275966\n",
       "3               88.367582      33.640534       36.167544       33.897290\n",
       "12             129.516093      33.516326       37.042163       34.176930\n",
       "13             129.079505      33.321219       37.026681       34.161960\n",
       "2               80.035071      33.266343       35.831983       33.639048\n",
       "1               75.396550      32.732742       35.094228       32.994020\n",
       "4              112.452830      32.482349       37.017302       33.813064\n",
       "6              124.414501      32.420728       36.780196       33.058510\n",
       "5              159.094118      32.022837       35.076276       33.106315\n",
       "7              123.505952      31.934922       36.443980       33.527651\n",
       "24             167.553450      31.339604       34.583254       32.929824"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actseries=dfd.groupby('activityID')['heartrate_bpm','imu_hand_temp','imu_chest_temp','imu_ankle_temp'].mean()\n",
    "\n",
    "#We want to present it in acending temperatures\n",
    "groupedact = actseries.sort_values('imu_hand_temp', ascending=False)\n",
    "\n",
    "#Print the activities for inspection\n",
    "groupedact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-21-63380775f82b>:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  handseries=dfd.groupby('activityID')['heartrate_bpm','imu_hand_temp','imu_hand_3d_gyroscope_1', 'imu_hand_3d_gyroscope_2',\n"
     ]
    },
    {
     "data": {
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       "      <th>heartrate_bpm</th>\n",
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       "      <th>activityID</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>167.553450</td>\n",
       "      <td>31.339604</td>\n",
       "      <td>0.169402</td>\n",
       "      <td>0.572490</td>\n",
       "      <td>-0.156181</td>\n",
       "      <td>29.768727</td>\n",
       "      <td>-17.692110</td>\n",
       "      <td>-9.761562</td>\n",
       "      <td>-5.400178</td>\n",
       "      <td>3.266017</td>\n",
       "      <td>0.821490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>89.951334</td>\n",
       "      <td>34.021504</td>\n",
       "      <td>0.002415</td>\n",
       "      <td>0.020066</td>\n",
       "      <td>0.002729</td>\n",
       "      <td>3.616550</td>\n",
       "      <td>-25.505825</td>\n",
       "      <td>-38.048815</td>\n",
       "      <td>-3.369453</td>\n",
       "      <td>3.734848</td>\n",
       "      <td>5.484141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>104.275056</td>\n",
       "      <td>34.170433</td>\n",
       "      <td>0.002921</td>\n",
       "      <td>0.001622</td>\n",
       "      <td>-0.006121</td>\n",
       "      <td>30.343557</td>\n",
       "      <td>-10.566788</td>\n",
       "      <td>-18.295363</td>\n",
       "      <td>-7.207438</td>\n",
       "      <td>3.589560</td>\n",
       "      <td>1.895451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>129.079505</td>\n",
       "      <td>33.321219</td>\n",
       "      <td>0.254417</td>\n",
       "      <td>-0.106310</td>\n",
       "      <td>-0.191100</td>\n",
       "      <td>31.944753</td>\n",
       "      <td>-13.415866</td>\n",
       "      <td>-23.848863</td>\n",
       "      <td>-6.221895</td>\n",
       "      <td>2.871335</td>\n",
       "      <td>3.648378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>129.516093</td>\n",
       "      <td>33.516326</td>\n",
       "      <td>-0.337135</td>\n",
       "      <td>0.174845</td>\n",
       "      <td>0.105914</td>\n",
       "      <td>38.048284</td>\n",
       "      <td>-12.523232</td>\n",
       "      <td>-18.333501</td>\n",
       "      <td>-8.739140</td>\n",
       "      <td>3.673413</td>\n",
       "      <td>1.606860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>123.505952</td>\n",
       "      <td>31.934922</td>\n",
       "      <td>0.022999</td>\n",
       "      <td>0.049373</td>\n",
       "      <td>-0.004116</td>\n",
       "      <td>18.412181</td>\n",
       "      <td>-20.049217</td>\n",
       "      <td>-20.410378</td>\n",
       "      <td>-4.217568</td>\n",
       "      <td>5.043965</td>\n",
       "      <td>2.498619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>124.414501</td>\n",
       "      <td>32.420728</td>\n",
       "      <td>-0.056278</td>\n",
       "      <td>-0.005275</td>\n",
       "      <td>0.091610</td>\n",
       "      <td>29.415642</td>\n",
       "      <td>-13.181587</td>\n",
       "      <td>-42.915979</td>\n",
       "      <td>-4.311831</td>\n",
       "      <td>2.986762</td>\n",
       "      <td>7.529086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>159.094118</td>\n",
       "      <td>32.022837</td>\n",
       "      <td>-0.008865</td>\n",
       "      <td>0.336436</td>\n",
       "      <td>-0.015374</td>\n",
       "      <td>19.141812</td>\n",
       "      <td>-22.759069</td>\n",
       "      <td>-21.450725</td>\n",
       "      <td>-6.709552</td>\n",
       "      <td>4.249012</td>\n",
       "      <td>1.576684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>112.452830</td>\n",
       "      <td>32.482349</td>\n",
       "      <td>-0.049856</td>\n",
       "      <td>0.025401</td>\n",
       "      <td>-0.008440</td>\n",
       "      <td>38.179269</td>\n",
       "      <td>-10.292968</td>\n",
       "      <td>-18.520008</td>\n",
       "      <td>-10.012624</td>\n",
       "      <td>2.470108</td>\n",
       "      <td>1.894922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88.367582</td>\n",
       "      <td>33.640534</td>\n",
       "      <td>0.005618</td>\n",
       "      <td>0.000884</td>\n",
       "      <td>-0.005340</td>\n",
       "      <td>27.557579</td>\n",
       "      <td>-19.091959</td>\n",
       "      <td>-13.933189</td>\n",
       "      <td>-7.091734</td>\n",
       "      <td>3.217199</td>\n",
       "      <td>2.683870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.035071</td>\n",
       "      <td>33.266343</td>\n",
       "      <td>-0.002374</td>\n",
       "      <td>0.009722</td>\n",
       "      <td>-0.005765</td>\n",
       "      <td>3.519832</td>\n",
       "      <td>-14.512305</td>\n",
       "      <td>-23.228745</td>\n",
       "      <td>-1.376973</td>\n",
       "      <td>4.317854</td>\n",
       "      <td>5.179943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.396550</td>\n",
       "      <td>32.732742</td>\n",
       "      <td>-0.008764</td>\n",
       "      <td>0.008986</td>\n",
       "      <td>0.002238</td>\n",
       "      <td>-0.171106</td>\n",
       "      <td>9.263335</td>\n",
       "      <td>-34.671473</td>\n",
       "      <td>3.756578</td>\n",
       "      <td>2.018568</td>\n",
       "      <td>6.385424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            heartrate_bpm  imu_hand_temp  imu_hand_3d_gyroscope_1  \\\n",
       "activityID                                                          \n",
       "24             167.553450      31.339604                 0.169402   \n",
       "17              89.951334      34.021504                 0.002415   \n",
       "16             104.275056      34.170433                 0.002921   \n",
       "13             129.079505      33.321219                 0.254417   \n",
       "12             129.516093      33.516326                -0.337135   \n",
       "7              123.505952      31.934922                 0.022999   \n",
       "6              124.414501      32.420728                -0.056278   \n",
       "5              159.094118      32.022837                -0.008865   \n",
       "4              112.452830      32.482349                -0.049856   \n",
       "3               88.367582      33.640534                 0.005618   \n",
       "2               80.035071      33.266343                -0.002374   \n",
       "1               75.396550      32.732742                -0.008764   \n",
       "\n",
       "            imu_hand_3d_gyroscope_2  imu_hand_3d_gyroscope_3  \\\n",
       "activityID                                                     \n",
       "24                         0.572490                -0.156181   \n",
       "17                         0.020066                 0.002729   \n",
       "16                         0.001622                -0.006121   \n",
       "13                        -0.106310                -0.191100   \n",
       "12                         0.174845                 0.105914   \n",
       "7                          0.049373                -0.004116   \n",
       "6                         -0.005275                 0.091610   \n",
       "5                          0.336436                -0.015374   \n",
       "4                          0.025401                -0.008440   \n",
       "3                          0.000884                -0.005340   \n",
       "2                          0.009722                -0.005765   \n",
       "1                          0.008986                 0.002238   \n",
       "\n",
       "            imu_hand_3d_magnetometer_1  imu_hand_3d_magnetometer_2  \\\n",
       "activityID                                                           \n",
       "24                           29.768727                  -17.692110   \n",
       "17                            3.616550                  -25.505825   \n",
       "16                           30.343557                  -10.566788   \n",
       "13                           31.944753                  -13.415866   \n",
       "12                           38.048284                  -12.523232   \n",
       "7                            18.412181                  -20.049217   \n",
       "6                            29.415642                  -13.181587   \n",
       "5                            19.141812                  -22.759069   \n",
       "4                            38.179269                  -10.292968   \n",
       "3                            27.557579                  -19.091959   \n",
       "2                             3.519832                  -14.512305   \n",
       "1                            -0.171106                    9.263335   \n",
       "\n",
       "            imu_hand_3d_magnetometer_3  imu_hand_3d_acceleration_1  \\\n",
       "activityID                                                           \n",
       "24                           -9.761562                   -5.400178   \n",
       "17                          -38.048815                   -3.369453   \n",
       "16                          -18.295363                   -7.207438   \n",
       "13                          -23.848863                   -6.221895   \n",
       "12                          -18.333501                   -8.739140   \n",
       "7                           -20.410378                   -4.217568   \n",
       "6                           -42.915979                   -4.311831   \n",
       "5                           -21.450725                   -6.709552   \n",
       "4                           -18.520008                  -10.012624   \n",
       "3                           -13.933189                   -7.091734   \n",
       "2                           -23.228745                   -1.376973   \n",
       "1                           -34.671473                    3.756578   \n",
       "\n",
       "            imu_hand_3d_acceleration_2  imu_hand_3d_acceleration_3  \n",
       "activityID                                                          \n",
       "24                            3.266017                    0.821490  \n",
       "17                            3.734848                    5.484141  \n",
       "16                            3.589560                    1.895451  \n",
       "13                            2.871335                    3.648378  \n",
       "12                            3.673413                    1.606860  \n",
       "7                             5.043965                    2.498619  \n",
       "6                             2.986762                    7.529086  \n",
       "5                             4.249012                    1.576684  \n",
       "4                             2.470108                    1.894922  \n",
       "3                             3.217199                    2.683870  \n",
       "2                             4.317854                    5.179943  \n",
       "1                             2.018568                    6.385424  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#grouping with activity looking at the hand data\n",
    "handseries=dfd.groupby('activityID')['heartrate_bpm','imu_hand_temp','imu_hand_3d_gyroscope_1', 'imu_hand_3d_gyroscope_2',\n",
    "       'imu_hand_3d_gyroscope_3', 'imu_hand_3d_magnetometer_1',\n",
    "       'imu_hand_3d_magnetometer_2', 'imu_hand_3d_magnetometer_3', 'imu_hand_3d_acceleration_1',\n",
    "       'imu_hand_3d_acceleration_2', 'imu_hand_3d_acceleration_3'].mean()\n",
    "\n",
    "\n",
    "#We want to present in ascending activity \n",
    "groupedhands= handseries.sort_values('activityID', ascending=False)\n",
    "\n",
    "#print the activities for inspection\n",
    "groupedhands"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Exploring heartrate\n",
    "\n",
    "In the correlations, it was evident that there is an increase in heartrate during some exercises. This needs further investigation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104.96589609743972\n",
      "57.0\n",
      "202.0\n"
     ]
    }
   ],
   "source": [
    "#What does the heartrate data look like?\n",
    "\n",
    "print(dfd['heartrate_bpm'].mean())\n",
    "print(dfd['heartrate_bpm'].min())\n",
    "print(dfd['heartrate_bpm'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Activity ID')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cL0nSDLDhhhty2223rdL/zNVeVXHbbbdNuvTFZNaOEa+RERh1er0kafbabrvtWLJkyUOPr9HMt+GGG7Lddtut1DFrR/CSJGmWW3/99SddgV2zh5caJUmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoxeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjc4ddwLSMjUEy7CoktVQ17AokaY1zxEuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNNA9eSZ6Y5JwkVyW5IslbWtcgSZI0DMO4q3Ep8PaquijJpsBYkrOr6soh1CJJktRM8xGvqrqxqi7q3t8FXAVs27oOSZKk1oY6xyvJPGAP4MIJ2hYmGU0yekvzyiRJkta8oQWvJJsAXwSOqao7x7dX1YlVtaCqFmzVvjxJkqQ1bijBK8n69ELXKVX1pWHUIEmS1Now7moM8Engqqr6p9b9S5IkDcswRryeDRwNHJBkUfd6wRDqkCRJaqr5chJVdT7gE68lSdI6x5XrJUmSGjF4SZIkNTKMletX3sgIjI4OuwpJkqTV4oiXJElSIwYvSZKkRgxekiRJjRi8JEmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoxeEmSJDVi8JIkSWrE4CVJktTI3GEXMC1jY5AMuwpJs1HVsCuQtA5xxEuSJKkRg5ckSVIjBi9JkqRGmgevJBsm+VGSS5JckeR9rWuQJEkahmFMrr8fOKCq7k6yPnB+km9W1QVDqEWSJKmZ5sGrqgq4u/u4fvfytiJJkjTrDWWOV5L1kiwCbgbOrqoLh1GHJElSS0MJXlX1YFXNB7YD9k6yy/h9kixMMppk9JbmFUqSJK15Q72rsap+DZwLHDxB24lVtaCqFmzVujBJkqQBGMZdjVsl2bx7vxFwIHB16zokSZJaG8ZdjdsAJydZj17w+3xVfX0IdUiSJDU1jLsaLwX2aN2vJEnSsLlyvSRJUiMGL0mSpEYMXpIkSY0MY3L9yhsZgdHRYVchSZK0WhzxkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoxeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNzB12AdMyNgbJsKuQtK6oGnYFkmYpR7wkSZIaMXhJkiQ1YvCSJElqxOAlSZLUyMCCV5JPJbk5yeV9205Psqh7LU6yaFD9S5IkzTSDvKvxM8BHgf+zfENVHbH8fZL/DdwxwP4lSZJmlIEFr6o6L8m8idqSBPhT4IBB9S9JkjTTDGuO1z7ATVX108l2SLIwyWiS0VsaFiZJkjQowwpeRwKnTbVDVZ1YVQuqasFWjYqSJEkapOYr1yeZCxwGjLTuW5IkaZiGMeJ1IHB1VS0ZQt+SJElDM8jlJE4Dfgg8LcmSJK/uml7KCi4zSpIkzUaDvKvxyEm2v2pQfUqSJM1krlwvSZLUiMFLkiSpkeZ3Na6SkREYHR12FZIkSavFES9JkqRGDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRgxekiRJjRi8JEmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqZG5wy5gWsbGIBl2FZI0s1UNuwJJK+CIlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoZWPBK8qkkNye5fNz2Nyf5SZIrkvz9oPqXJEmaaQY54vUZ4OD+DUn2B/4E2K2qdgb+cYD9S5IkzSgDC15VdR5w+7jNfwH8z6q6v9vn5kH1L0mSNNO0nuP1VGCfJBcm+W6SvSbbMcnCJKNJRm9pWKAkSdKgtF5AdS7wWOCZwF7A55PsUPXIVf+q6kTgRIAFiasCSpKktV7rEa8lwJeq50fAMmDLxjVIkiQNRevg9WXgAIAkTwUeBdzauAZJkqShGNilxiSnAfsBWyZZArwX+BTwqW6Jid8Cr5zoMqMkSdJsNLDgVVVHTtL08kH1KUmSNJO5cr0kSVIjBi9JkqRGWi8nsWpGRmB0dNhVSJIkrRZHvCRJkhoxeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRuZO1ZhkC+BlwNO7TVcBp1XVbYMu7GHGxiBp2qUkaRJVw65AWmtNOuKVZEfgcmAEuAb4KbAXcFmSp092nCRJkiY21YjX+4G3VNXn+zcmeTHwAeDFgyxMkiRptplqjteu40MXQFV9EdhlcCVJkiTNTlMFr3tWsU2SJEkTmOpS4+OTvG2C7QG2Wt2Ok6wHjAK/qKpDVvd8kiRJM91UweskYNNJ2j6xBvp+C727JDdbA+eSJEma8SYNXlX1vkF1mmQ74IX0JulPNKomSZI060wavJL881QHVtVfrka/HwLexeQjaiRZCCwEeNJqdCRJkjRTTHWpcWwQHSY5BLi5qsaS7DfZflV1InAiwILE1fokSdJab6pLjScPqM9nA4cmeQGwIbBZks9V1csH1J8kSdKM0PxZjVX1V1W1XVXNA14KfMfQJUmS1gU+JFuSJKmRFQavJI8bVOdVda5reEmSpHXFdEa8LkxyRpIXJMnAK5IkSZqlphO8nkrv7sKjgf9M8sEkTx1sWZIkSbNPqqa/UkOS/YHPARsDlwDHVtUPB1TbQxYsWFCjo6OD7kaSJGm1JRmrqgUTtU21jtfyg7cAXk5vxOsm4M3AV4H5wBnA9musUkmSpFlshcEL+CHwWeC/VtWSvu2jST4+mLIkSZJmn+nM8Xp3Vb2/P3QlORygqv7XwCqTJEmaZaYTvI6dYNtfrelCJEmSZrupHpL9R8ALgG3HPTB7M2DpoAuTJEmabaaa43UDMAocysMfmH0X8NZBFiVJkjQbTfWQ7EuAS5KcUlWOcEmSJK2mqS41fr6q/hS4OMkjFvuqqt0GWpkkSdIsM9Wlxrd0f/osRUmSpDVgqkuNN3ZvDwM+X1W/aFOSJEnS7DSd5SQ2A85K8r0kb0yy9aCLkiRJmo1WGLyq6n1VtTPwRuAJwHeT/MfAK5MkSZplpjPitdzNwC+B24DHD6YcSZKk2WuFwSvJXyQ5F/g2sCXwWu9olCRJWnnTeUj2k4FjqmrRgGuRJEma1VL1iCW6eg3JZlV1Z5LHTdReVbcPtLI+C5IabdWZJGntMcn/w6RhSjJWVQsmaptqxOtUemt4jQEFpK+tgB3WWIWSJEnrgKnW8Tqk+3P7duVIkiTNXtOZXP/t6WyTJEnS1CYNXkk27OZ3bZnksUke173m0VvPa5UkeVqSRX2vO5Mcs6rnkyRJWltMNcfrdcAx9ELWGL+b43Un8C+r2mFV/QSYD5BkPeAXwJmrej5JkqS1xVRzvD4MfDjJm6vqIwPq/3nAz6rq5wM6vyRJ0owxnZXrlyXZfPmH7rLjG9ZQ/y8FTpuoIcnCJKNJRm9ZQ51JkiQN06TreD20Q7KoquaP23ZxVe2xWh0njwJuAHauqpum2td1vCRJE3IdL81AU63jNZ0RrzlJHlrDq5uX9ag1UNcfARetKHRJkiTNFtN5ZNC/A59P8nF6C6e+HvjWGuj7SCa5zChJkjQbTedS4xxgIXAgvTsbzwJOqqplq9xp8mjgemCHqrpjRft7qVGSNCEvNWoGmupS4wqD1wQnew5wZFW9cU0UNx0GL0nShAxemoFW9VmN/SeYT+/S4BHAdcCX1lh1kiRJ64hJg1eSp9Jb7uFI4DbgdHojZPs3qk2SJGlWmWrE62rge8AfV9V/AiR5a5OqxhsZgVEvNkqSpLXbVMtJvBj4JXBOkpOSPI/fPTZIkiRJK2nS4FVVZ1bVEcDTgXOBtwJbJ/lYkoMa1SdJkjRrrHAB1aq6p6pOqapDgO2ARcCxgy5MkiRptpnOyvUPqarbq+qEqjpgUAVJkiTNVisVvCRJkrTqDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRgxekiRJjRi8JEmSGpk77AKmZWwMkmFXIUmayaqGXYG0Qo54SZIkNWLwkiRJasTgJUmS1IjBS5IkqZGhBK8kmyf5QpKrk1yV5FnDqEOSJKmlYd3V+GHgW1X1kiSPAh49pDokSZKaaR68kmwG7Au8CqCqfgv8tnUdkiRJrQ3jUuMOwC3Ap5NcnOQTSTYev1OShUlGk4ze0r5GSZKkNW4YwWsusCfwsaraA7gHOHb8TlV1YlUtqKoFW7WuUJIkaQCGEbyWAEuq6sLu8xfoBTFJkqRZrXnwqqpfAtcneVq36XnAla3rkCRJam1YdzW+GTilu6PxWuDPhlSHJElSM0MJXlW1CFgwjL4lSZKGxZXrJUmSGjF4SZIkNTKsOV4rZ2QERkeHXYUkSdJqccRLkiSpEYOXJElSIwYvSZKkRgxekiRJjRi8JEmSGjF4SZIkNWLwkiRJasTgJUmS1IjBS5IkqRGDlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoxeEmSJDVi8JIkSWpk7rALmJaxMUiGXYUkaTaqGnYFWoc44iVJktSIwUuSJKkRg5ckSVIjBi9JkqRGBha8knwqyc1JLu/b9v4klyZZlOSsJE8YVP+SJEkzzSBHvD4DHDxu2z9U1W5VNR/4OvCeAfYvSZI0owwseFXVecDt47bd2fdxY8B7eCVJ0jqj+TpeST4AvAK4A9h/iv0WAgsBntSmNEmSpIFqPrm+qv6mqp4InAK8aYr9TqyqBVW1YKt25UmSJA3MMO9qPBV48RD7lyRJaqpp8ErylL6PhwJXt+xfkiRpmAY2xyvJacB+wJZJlgDvBV6Q5GnAMuDnwOsH1b8kSdJMM7DgVVVHTrD5k4PqT5IkaaZz5XpJkqRGDF6SJEmNNF/Ha5WMjMDo6LCrkCRJWi2OeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRgxekiRJjcwddgHTMjYGybCrkCTNZlXDrkDrAEe8JEmSGjF4SZIkNWLwkiRJamRgwSvJp5LcnOTyvm3/kOTqJJcmOTPJ5oPqX5IkaaYZ5IjXZ4CDx207G9ilqnYDrgH+aoD9S5IkzSgDC15VdR5w+7htZ1XV0u7jBcB2g+pfkiRpphnmHK8/B745xP4lSZKaGkrwSvI3wFLglCn2WZhkNMnoLe1KkyRJGpjmwSvJK4FDgKOqJl+trqpOrKoFVbVgq3blSZIkDUzTleuTHAz8N+C5VfWbln1LkiQN2yCXkzgN+CHwtCRLkrwa+CiwKXB2kkVJPj6o/iVJkmaagY14VdWRE2z+5KD6kyRJmulcuV6SJKkRg5ckSVIjBi9JkqRGmt7VuMpGRmB0dNhVSJIkrRZHvCRJkhoxeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSI3OHXcC0jI1BMuwqJEnSmlY17AqacsRLkiSpEYOXJElSIwYvSZKkRgxekiRJjQxlcn2SxcBdwIPA0qpaMIw6JEmSWhrmXY37V9WtQ+xfkiSpKS81SpIkNTKs4FXAWUnGkiycaIckC5OMJhm9pXFxkiRJg5AawsJlSZ5QVTckeTxwNvDmqjpvsv0XJDXarjxJktTKLFxANcnYZPPXhzLiVVU3dH/eDJwJ7D2MOiRJklpqHrySbJxk0+XvgYOAy1vXIUmS1Now7mrcGjgzvWcvzgVOrapvDaEOSZKkppoHr6q6Fti9db+SJEnD5nISkiRJjRi8JEmSGhnmyvXTNzICoy4oIUmS1m6OeEmSJDVi8JIkSWrE4CVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmNGLwkSZIaMXhJkiQ1YvCSJElqxOAlSZLUiMFLkiSpEYOXJElSIwYvSZKkRgxekiRJjcwddgHTMjYGybCrkCRJa6uqYVcAOOIlSZLUjMFLkiSpEYOXJElSIwYvSZKkRgYWvJI8Mck5Sa5KckWSt4xrf0eSSrLloGqQJEmaSQZ5V+NS4O1VdVGSTYGxJGdX1ZVJngj8IfD/Bti/JEnSjDKwEa+qurGqLure3wVcBWzbNR8PvAuYGfd2SpIkNdBkjleSecAewIVJDgV+UVWXrOCYhUlGk4ze0qJISZKkARv4AqpJNgG+CBxD7/Lj3wAHrei4qjoROBFgQeLImCRJWusNdMQryfr0QtcpVfUl4PeB7YFLkiwGtgMuSvJ7g6xDkiRpJhjYiFeSAJ8ErqqqfwKoqsuAx/ftsxhYUFW3DqoOSZKkmWKQI17PBo4GDkiyqHu9YID9SZIkzWgDG/GqqvOBKZ9sXVXzBtW/JEnSTOPK9ZIkSY0YvCRJkhoZ+HISa8TICIyODrsKSZKk1eKIlyRJUiMGL0mSpEYMXpIkSY0YvCRJkhoxeEmSJDVi8JIkSWrE4CVJktRIqmrYNaxQkluAn3cftwR8qLYkSZqpnlxVW03UsFYEr35JRqtqwbDrkCRJWlleapQkSWrE4CVJktTI2hi8Thx2AZIkSatirZvjJUmStLZaG0e8JEmS1koGL0mSpEYMXpIkSY2sMHglmZfk8jXdcZL5SV6wCsfNS/KyVexzvyRfX5VjJUmSVtdQRrySzAXmAxMGr659MvOAVQpekiRJwzRVwOm3XpKTgP8C/AL4E+AJwL8AWwG/AV5bVVcn+WPg3cCjgNuAo6rqpiTHdcfMo/fIn+cAGyV5DvB3wI797Un+GvgssHFXw5uq6gfA/wR2TLIIOBn4527bfsAGwL9U1QlTfJfNkpwJPA04D3hDVS1LcjdwArA/8CvgpVV1S5JzgYuBke67vgL4K2BX4PSqevc0f0NJkrSOm+6I11PoBZqdgV8DL6a3ntabq2oEeAfwr92+5wPPrKo9gP8LvKvvPCPAn1TVy4D30Asu86vq9Anabwb+sKr2BI6gF7AAjgW+1x13PPBq4I6q2gvYC3htku2n+C57A2+nF5x+Hzis274xcFHX33eB9/Yd89uq2hf4OPAV4I3ALsCrkmyxgt9OkiQJmP6I13VVtah7P0ZvVOq/AGckWb7PBt2f2wGnJ9mG3qjXdX3n+WpV3TtFP/3t6wMfTTIfeBB46iTHHATsluQl3efH0AuK102y/4+q6lqAJKfRG3n7ArAMWB4APwd8qb+u7s/LgCuq6sbu+GuBJ9Ib2ZMkSZrSdIPX/X3vHwS2Bn5dVfMn2PcjwD9V1VeT7Acc19d2zwr66W9/K3ATsDu9kbn7Jjkm9Ebe/n0F515u/Iqxk60g2799+fdfxsN/i2VM/zeUJEnruFWdXH8ncF2SwwHSs3vX9hh688AAXjnFOe4CNp2i/THAjVW1DDgaWG+S4/4d+Isk63e1PDXJxkxu7yTbJ5lD7xLm+d32OcDyUbOX9W2XJElaI1bnrsajgFcnuQS4gt6Ee+iNcJ2R5Hv0JtFP5hxgpySLkhwxQfu/Aq9McgG9y4zLR8MuBZYmuSTJW4FPAFcCF3XLXpzA1KNQP6Q3Gf9yepcjz+y23wPsnGQMOAD42ynOIUmStNJ8VmMnyd1Vtcmw65AkSbOXK9dLkiQ1MitHvJLsSm8NsH73V9UzhlGPJEkSzNLgJUmSNBN5qVGSJKkRg5ckSVIjBi9JA9U9B7X/86uSfHQNnXtekpetwnGbJ3nDKhyXJN9JslnX9+Ure45p9vMfSR47iHNLGi6Dl6S1UpK59B5fNmHw6tonszmw0sELeAFwSVXduQrHrozPsmr1SZrhfNyNpKFJshW9h88/qdt0TFV9P8newIeAjYB7gT+rqp8keRXwQmBDeg+2fzSwY5JFwMnAr/rbkxxK78H2j6X3/Nd3V9VX6C2i/PvdcWdX1TuTvBP4U3rPnT2zqt47QclHASf2fZ6b5GRgD+Aa4BVV9Zski+k9+3X/br+XVdV/JvlM932eDjwZ+DN6T/h4FnBhVb2q2/+rwPeAD0z7x5S0VjB4SRq0jbqAs9zj+N2D5z8MHF9V5yd5Er1HgO0IXA3sW1VLkxwIfBB4cXfMs4Ddqur27nmw76iqQ6B3GXNc+1zgRVV1Z5ItgQuSfBU4Fthl+fNmkxwEPAXYm97zX7+aZN+qOm/cd3k28Lq+z08DXt2FxU/RG6X6x67tzqraO8kr6IXIQ7rtj6X3dIxDga9153wN8OMk86tqUVX9KskGSbaoqtum9StLWisYvCQN2r3LAw48FI4WdB8PpPfosOXNmyXZlN6zWk9O8hR6D6xfv+98Z1fV7VP0198e4INJ9qX3UPttga0nOOag7nVx93kTekFsfPB6XFXd1ff5+qr6fvf+c8Bf8rvgdVrfn8f3HfO1qqoklwE3VdVlAEmuoHfpdFG3383AEwCDlzSLGLwkDdMc4FlVdW//xiQfAc6pqhclmQec29d8D1Prbz8K2AoYqaoHukuAG05wTIC/q6oTVnDupUnmVNWy7vP4hRBrGu/v7/5c1vd++ef+/yZvSO+ypKRZxMn1kobpLOBNyz8kmd+9fQzwi+79q6Y4/i5g0ynaHwPc3IWu/enNq5rouH8H/jzJJl0d2yZ5/ATn+wmwQ9/nJyV5Vvf+SOD8vrYj+v784RQ1PkJ6Q4C/ByxemeMkzXwGL0nD9JfAgiSXJrkSeH23/e+Bv0vyfWC9KY6/lN4o1CVJ3jpB+ynd+UfpjX5dDdDNm/p+ksuT/ENVnQWcCvywuwT4BSYOdP8G7Nf3+SrglUkupTd37WN9bRskuRB4CzBRbVMZAS6oqqUreZykGc5HBknSNCXZBvg/VfWHK9hvMbCgqm5dxX4+DHy1qr69KsdLmrkc8ZKkaaqqG4GTkmw24K4uN3RJs5MjXpIkSY044iVJktSIwUuSJKkRg5ckSVIjBi9JkqRGDF6SJEmN/H8d0Q042yXBZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualising the heartrate means per activity \n",
    "\n",
    "heart_dfd=dfd.groupby(['activityID']).mean()\n",
    "heart_dfd.sort_values('heartrate_bpm', ascending=False, inplace = True )\n",
    "heart_dfd[['heartrate_bpm']].plot.barh(stacked=True, title = 'Heartrate (bpm) per Activity'\n",
    "                                       , color='r', figsize=(10,7));\n",
    "dfd['heartrate_bpm'].plot(kind='box')\n",
    "plt.xlabel('Heartrate (bpm)')\n",
    "plt.ylabel('Activity ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#looking at the heartrate range over activityIDs with histograms\n",
    "\n",
    "dfd.hist(column='heartrate_bpm', by='activityID', figsize=(11,8), bins=50, color='red')\n",
    "dfd.index = dfd.index.map(activityIDkey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Activity ID')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using heartrate and activity\n",
    "heart_activity=dfd.loc[:,['heartrate_bpm','activityID']]\n",
    "\n",
    "# get frequencies\n",
    "counts1=heart_activity.groupby('activityID').count().reset_index('activityID')\n",
    "\n",
    "#set the number we consider to be 'big' in terms of films made so that we can plot heartrate\n",
    "n=50 \n",
    "\n",
    "#Making the index by frequencies\n",
    "bigcounts1=counts1[counts1.heartrate_bpm>n]['activityID']\n",
    "bigheart1_df = heart_activity[heart_activity.activityID.isin(bigcounts1)]\n",
    "bigheart1_df.boxplot(by='activityID',figsize=(12,11), color='red', notch=True, vert=True)\n",
    "#setting labels \n",
    "plt.ylabel('Heartrate (bpm)')\n",
    "plt.xlabel('Activity ID')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Exploring Temperature\n",
    "\n",
    "When exploring temperature, the focus will mainly be on **chest temperature** as this is the most consistent and will have the least influence from external sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Activity ID')"
      ]
     },
     "execution_count": 26,
     "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": [
    "#looking at the temperatues within each activity\n",
    "#mean temperature per activity\n",
    "chest_dfd=dfd.groupby(['activityID']).mean()\n",
    "chest_dfd.sort_values('imu_chest_temp', ascending=False, inplace = True )\n",
    "chest_dfd[['imu_chest_temp']].plot.barh(stacked=True, title = 'Chest Temperature per Activity'\n",
    "                                       ,  color='orange', figsize=(10,7));\n",
    "\n",
    "dfd['imu_chest_temp'].plot(kind='box')\n",
    "plt.xlabel('Chest Temperature')\n",
    "plt.ylabel('Activity ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Activity ID')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using heartrate and activity\n",
    "temp_activity=dfd.loc[:,['imu_chest_temp','activityID']]\n",
    "\n",
    "# get frequencies\n",
    "counts2=temp_activity.groupby('activityID').count().reset_index('activityID')\n",
    "\n",
    "#set the number we consider to be 'big' in terms of films made so that we can plot temperatues\n",
    "n=50 \n",
    "\n",
    "#Making the index by frequencies\n",
    "bigcounts2=counts2[counts2.imu_chest_temp>n]['activityID']\n",
    "bigtemp1_df = temp_activity[temp_activity.activityID.isin(bigcounts2)]\n",
    "bigtemp1_df.boxplot(by='activityID',figsize=(12,11), color='peru', notch=True, vert=True)\n",
    "#setting labels \n",
    "plt.ylabel('Chest Temperature')\n",
    "plt.xlabel('Activity ID')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Exploring Moving and Non-Moving Activities\n",
    "\n",
    "Remembering the note above that I have defined moving activities as those where the subject is moving in leg positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80460, 34)\n",
      "(43861, 34)\n",
      "(36599, 34)\n"
     ]
    }
   ],
   "source": [
    "print(dfd.shape)\n",
    "\n",
    "#grouping the activities\n",
    "\n",
    "movingact = ['4','5','6','7','12','13','16','24']\n",
    "stillact = ['1','2','3','9','17']\n",
    "\n",
    "#creating two dataframes - one for moving and one for still\n",
    "\n",
    "movingdfd=dfd.loc[(dfd['activityID']==4)|(dfd['activityID']==5)|(dfd['activityID']==6)|\n",
    "                  (dfd['activityID']==7)|(dfd['activityID']==12)|(dfd['activityID']==13)|(dfd['activityID']==16)|\n",
    "                  (dfd['activityID']==24)]\n",
    "\n",
    "stilldfd=dfd.loc[(dfd['activityID']==1)|(dfd['activityID']==2)|(dfd['activityID']==3)|\n",
    "                  (dfd['activityID']==9)|(dfd['activityID']==17)]\n",
    "\n",
    "#checking the shapes of the new dataframes\n",
    "print(movingdfd.shape)\n",
    "print(stilldfd.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GREEN is moving activities\n",
      "BLUE is still activities\n",
      "The average heartrate for moving activities is 122.59832197168328\n",
      "The average heartrate for still activities is 83.83483155277466\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "#plotting two boxplots showing the heartrate for moving and still activities\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16,10))\n",
    "plt.title(\"Heartrate (bpm) for Moving Activities\")\n",
    "ax = sns.boxplot(y=movingdfd[\"heartrate_bpm\"],color='g')\n",
    "ax = sns.boxplot(y=stilldfd[\"heartrate_bpm\"])\n",
    "\n",
    "\n",
    "#What are the average heartrates?\n",
    "print('GREEN is moving activities')\n",
    "print('BLUE is still activities')\n",
    "print('The average heartrate for moving activities is '+ str(movingdfd['heartrate_bpm'].mean()))\n",
    "print('The average heartrate for still activities is '+ str(stilldfd['heartrate_bpm'].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 Exploring the Subjects\n",
    "\n",
    "*Who are the subjects?*\n",
    "It is important to know who the subjects are that were being tested.\n",
    "\n",
    "Age range: 23-32\n",
    "Avg BMI: \n",
    "Avg Height:\n",
    "Avg Weight:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Subject ID</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age (years)</th>\n",
       "      <th>Height (cm)</th>\n",
       "      <th>Weight (kg)</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Resting HR (bpm)</th>\n",
       "      <th>Max HR (bpm)</th>\n",
       "      <th>Dominant Hand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>182</td>\n",
       "      <td>83</td>\n",
       "      <td>25.057360</td>\n",
       "      <td>75</td>\n",
       "      <td>193</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102</td>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>169</td>\n",
       "      <td>78</td>\n",
       "      <td>27.309968</td>\n",
       "      <td>74</td>\n",
       "      <td>195</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>187</td>\n",
       "      <td>92</td>\n",
       "      <td>26.309017</td>\n",
       "      <td>68</td>\n",
       "      <td>189</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>194</td>\n",
       "      <td>95</td>\n",
       "      <td>25.241790</td>\n",
       "      <td>58</td>\n",
       "      <td>196</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105</td>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>180</td>\n",
       "      <td>73</td>\n",
       "      <td>22.530864</td>\n",
       "      <td>70</td>\n",
       "      <td>194</td>\n",
       "      <td>right</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Subject ID     Sex  Age (years)  Height (cm)  Weight (kg)        BMI  \\\n",
       "0         101    Male           27          182           83  25.057360   \n",
       "1         102  Female           25          169           78  27.309968   \n",
       "2         103    Male           31          187           92  26.309017   \n",
       "3         104    Male           24          194           95  25.241790   \n",
       "4         105    Male           26          180           73  22.530864   \n",
       "\n",
       "   Resting HR (bpm)  Max HR (bpm) Dominant Hand  \n",
       "0                75           193         right  \n",
       "1                74           195         right  \n",
       "2                68           189         right  \n",
       "3                58           196         right  \n",
       "4                70           194         right  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sidf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9 entries, 0 to 8\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Subject ID        9 non-null      int64  \n",
      " 1   Sex               9 non-null      object \n",
      " 2   Age (years)       9 non-null      int64  \n",
      " 3   Height (cm)       9 non-null      int64  \n",
      " 4   Weight (kg)       9 non-null      int64  \n",
      " 5   BMI               9 non-null      float64\n",
      " 6   Resting HR (bpm)  9 non-null      int64  \n",
      " 7   Max HR (bpm)      9 non-null      int64  \n",
      " 8   Dominant Hand     9 non-null      object \n",
      "dtypes: float64(1), int64(6), object(2)\n",
      "memory usage: 776.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "sidf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Activity</th>\n",
       "      <th>subject101</th>\n",
       "      <th>subject102</th>\n",
       "      <th>subject103</th>\n",
       "      <th>subject104</th>\n",
       "      <th>subject105</th>\n",
       "      <th>subject106</th>\n",
       "      <th>subject107</th>\n",
       "      <th>subject108</th>\n",
       "      <th>subject109</th>\n",
       "      <th>Sum</th>\n",
       "      <th>Nr. of subjects</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 - lying</td>\n",
       "      <td>271.86</td>\n",
       "      <td>234.29</td>\n",
       "      <td>220.43</td>\n",
       "      <td>230.46</td>\n",
       "      <td>236.98</td>\n",
       "      <td>233.39</td>\n",
       "      <td>256.10</td>\n",
       "      <td>241.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1925.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2 - sitting</td>\n",
       "      <td>234.79</td>\n",
       "      <td>223.44</td>\n",
       "      <td>287.60</td>\n",
       "      <td>254.91</td>\n",
       "      <td>268.63</td>\n",
       "      <td>230.40</td>\n",
       "      <td>122.81</td>\n",
       "      <td>229.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1851.80</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3 - standing</td>\n",
       "      <td>217.16</td>\n",
       "      <td>255.75</td>\n",
       "      <td>205.32</td>\n",
       "      <td>247.05</td>\n",
       "      <td>221.31</td>\n",
       "      <td>243.55</td>\n",
       "      <td>257.50</td>\n",
       "      <td>251.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1899.23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4 - walking</td>\n",
       "      <td>222.52</td>\n",
       "      <td>325.32</td>\n",
       "      <td>290.35</td>\n",
       "      <td>319.31</td>\n",
       "      <td>320.32</td>\n",
       "      <td>257.20</td>\n",
       "      <td>337.19</td>\n",
       "      <td>315.32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2387.53</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5 - running</td>\n",
       "      <td>212.64</td>\n",
       "      <td>92.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>246.45</td>\n",
       "      <td>228.24</td>\n",
       "      <td>36.91</td>\n",
       "      <td>165.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>981.92</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Activity  subject101  subject102  subject103  subject104  subject105  \\\n",
       "0     1 - lying      271.86      234.29      220.43      230.46      236.98   \n",
       "1   2 - sitting      234.79      223.44      287.60      254.91      268.63   \n",
       "2  3 - standing      217.16      255.75      205.32      247.05      221.31   \n",
       "3   4 - walking      222.52      325.32      290.35      319.31      320.32   \n",
       "4   5 - running      212.64       92.37        0.00        0.00      246.45   \n",
       "\n",
       "   subject106  subject107  subject108  subject109      Sum  Nr. of subjects  \n",
       "0      233.39      256.10      241.64         0.0  1925.15              NaN  \n",
       "1      230.40      122.81      229.22         0.0  1851.80              NaN  \n",
       "2      243.55      257.50      251.59         0.0  1899.23              NaN  \n",
       "3      257.20      337.19      315.32         0.0  2387.53              NaN  \n",
       "4      228.24       36.91      165.31         0.0   981.92              NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part 5: Hypothesis testing <a class=\"anchor\" id=\"hypo\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our hypothesis testing, we will use the `test` data frame.\n",
    "\n",
    "\n",
    "**Our 3 Hypotheses:** \n",
    "\n",
    "> 1. ***If*** higher heartrate is related to moving, ***then*** exercising will have a higher heartbeat than average \n",
    "(105bpm).\n",
    "\n",
    "> 2. ***If*** higher heartrate is related to intense activities, ***then*** running and rope jumping will have a higher heartbeat than moving activity average (122bpm).\n",
    "\n",
    "> 3. ***If*** higher temperature is related to running, ***then*** exercising will imply a higher temperature than average.\n",
    "\n",
    "> 4. ***If*** timestamp is related to activity starting, ***then*** timestamp greater than the minimum is related to starting an activity.\n",
    "\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Please note</b> the average heartrate for our data set is 105bpm and temperature is 36 Celsius.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 5.1 Our first testable hypothesis\n",
    "\n",
    "***If*** higher heartrate is related to moving, ***then*** exercising will have a higher heartbeat than average (105bpm)..\n",
    "\n",
    "In order to test this hypothesis, we will need to conduct an upper-tailed test. This is because we are hypothesising that there is an increase in heartrate when moving.\n",
    "\n",
    "***\n",
    "\n",
    "> $H_0: μ = μ_0$ Our null hypothesis, that there is no change.\n",
    "\n",
    "> $H_1: μ > μ_0$ The research hypothesis, that **higher heartrate** implies a moving exercise.\n",
    "\n",
    "$α =0.05$\n",
    "\n",
    "$μ_0 = 105bpm $ This is the average heartrate across the bpm data.\n",
    "\n",
    "***\n",
    "The significance level ($α$) is the probability that we will reject the null hypothesis, assuming its true. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean for each moving exercise heartrate from data:  activityID\n",
      "4     112.286981\n",
      "5     157.041115\n",
      "6     124.733731\n",
      "7     123.720963\n",
      "12    129.560105\n",
      "13    129.217310\n",
      "16    104.100440\n",
      "24    168.020750\n",
      "Name: heartrate_bpm, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#creating two data frames, moving and not moving \n",
    "#both for the test data frame\n",
    "movingdft=dft.loc[(dft['activityID']==4)|(dft['activityID']==5)|(dft['activityID']==6)|\n",
    "                  (dft['activityID']==7)|(dft['activityID']==12)|(dft['activityID']==13)|(dft['activityID']==16)|\n",
    "                  (dft['activityID']==24)]\n",
    "\n",
    "stilldft=dft.loc[(dft['activityID']==1)|(dft['activityID']==2)|(dft['activityID']==3)|\n",
    "                  (dft['activityID']==9)|(dft['activityID']==17)]\n",
    "\n",
    "#importing scipy\n",
    "from scipy import stats\n",
    "movingheart_means=movingdft.groupby(\"activityID\")[\"heartrate_bpm\"].mean()\n",
    "movingheart_std=movingdft.groupby(\"activityID\")[\"heartrate_bpm\"].std()\n",
    "movingheart_count=movingdft.groupby(\"activityID\")[\"heartrate_bpm\"].count()\n",
    "\n",
    "#here I am look at the mean value hearrate for each activity \n",
    "\n",
    "P_G = (movingheart_means-105)/(movingheart_std/np.sqrt(movingheart_count))\n",
    "print('Mean for each moving exercise heartrate from data: ',movingheart_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P_G is: 167.13448375089035\n",
      "Mean for moving exercise heartrate from data:  122.48493727680615\n",
      "one tail p value from normal:  0.0\n"
     ]
    }
   ],
   "source": [
    "#taking mean, std and count of the moving rate\n",
    "movingheart_means=movingdft[\"heartrate_bpm\"].mean()\n",
    "movingheart_std=movingdft[\"heartrate_bpm\"].std()\n",
    "movingheart_count=movingdft[\"heartrate_bpm\"].count()\n",
    "\n",
    "#here I am calculating the p value, I am taking the mean heartrate value from the moving activities\n",
    "#average heartrate is 105\n",
    "#then dividing this value by the moving activity standard deviation \n",
    "\n",
    "P_G = (movingheart_means-105.00)/(movingheart_std/np.sqrt(movingheart_count))\n",
    "print('P_G is: '+ str(P_G))\n",
    "p_value_from_normal=(1-stats.norm.cdf(P_G))\n",
    "print('Mean for moving exercise heartrate from data: ',movingheart_means)\n",
    "print ('one tail p value from normal: ', p_value_from_normal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, I conducted a z-test that is looking at the probability that our hypothesis is wrong, i.e. the null hypothesis is true. The p-value (*probability value*) is a number describing how likely it is that your data would have occurred by random chance (i.e. that the null hypothesis is true).\n",
    "\n",
    "Our p value (0.0) is less than the significance level ($α =0.05$). This indicates very strong evidence that we should reject our null hypothesis. Therefore, we reject the null hypothesis and accept our first hypothesis that a **higher heartrate** indicates **moving exercise**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P_G is: -358.10972101573606\n",
      "Mean for still exercise heartrate from data:  84.01316111488784\n",
      "one tail p value from normal:  1.0\n"
     ]
    }
   ],
   "source": [
    "#taking mean, std and count of the still rate\n",
    "stillheart_means=stilldft[\"heartrate_bpm\"].mean()\n",
    "stillheart_std=stilldft[\"heartrate_bpm\"].std()\n",
    "stillheart_count=stilldft[\"heartrate_bpm\"].count()\n",
    "\n",
    "#here I am calculating the p value, I am taking the mean heartrate value from the still activities\n",
    "#average heartrate is 105\n",
    "#then dividing this value by the still activity standard deviation \n",
    "\n",
    "P_G = (stillheart_means-105.00)/(stillheart_std/np.sqrt(stillheart_count))\n",
    "print('P_G is: '+ str(P_G))\n",
    "p_value_from_normal=(1-stats.norm.cdf(P_G))\n",
    "print('Mean for still exercise heartrate from data: ',stillheart_means)\n",
    "print ('one tail p value from normal: ', p_value_from_normal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Our second testable hypothesis\n",
    "\n",
    "***If*** higher heartrate is related to intense activities, ***then*** running and rope jumping will have a higher heartbeat than moving activity average (122bpm).\n",
    "\n",
    "In order to test this hypothesis, we will need to conduct an upper-tailed test. This is because we are hypothesising that there is an increase in heartrate when running and ropejumping.\n",
    "\n",
    "***\n",
    " \n",
    "> $H_0: μ = μ_0$ Our null hypothesis, that there is no change.\n",
    "\n",
    "> $H_1: μ > μ_0$ The research hypothesis, that **higher heartrate** implies a intense activities.\n",
    "\n",
    "$α =0.05$\n",
    "\n",
    "$μ_0 = 122bpm $\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "    <b>Please note</b> the average heart rate for <i>moving</i> activities is 122bpm.\n",
    "</div>\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "runningropedft = dft.loc[(dft.activityID==5)|(dft.activityID==24)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P_G is: 105.62339432314663\n",
      "Mean heartrate when running or ropejumping:  160.31982840800762\n",
      "one tail p value from normal:  0.0\n"
     ]
    }
   ],
   "source": [
    "#running temp\n",
    "\n",
    "runningtemp_means=runningropedft[\"heartrate_bpm\"].mean()\n",
    "runningtemp_std=runningropedft[\"heartrate_bpm\"].std()\n",
    "runningtemp_count=runningropedft[\"heartrate_bpm\"].count()\n",
    "\n",
    "#here I am calculating the p value, I am taking the mean running temp and taking away the average\n",
    "#then diving this value by the running temp standard diviation\n",
    "\n",
    "P_G = (runningtemp_means-122)/(runningtemp_std/np.sqrt(runningtemp_count))\n",
    "print('P_G is: '+ str(P_G))\n",
    "p_value_from_normal=(1-stats.norm.cdf(P_G))\n",
    "print('Mean heartrate when running or ropejumping: ',runningtemp_means)\n",
    "print ('one tail p value from normal: ', p_value_from_normal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our p value (0.0) is less than the significance level ($α =0.05$). This indicates very strong evidence that we should reject our null hypothesis. Therefore, we reject the null hypothesis and accept our first hypothesis that a **heartrate above 122bpm** indicates **intense exercise** (such as running, rope-jumping)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Our third testable hypothesis\n",
    "\n",
    "***If*** higher temperature is related to running, ***then*** exercising will imply a higher temperature than average.\n",
    "\n",
    "In order to test this hypothesis, we will need to conduct an upper-tailed test. This is because we are hypothesising that there is an increase in temperature when running.\n",
    "\n",
    "***\n",
    "\n",
    "> $H_0: μ = μ_0$ Our null hypothesis, that there is no change.\n",
    "\n",
    "> $H_1: μ > μ_0$ The research hypothesis, that **higher temperature** implies a running.\n",
    "\n",
    "$α =0.05$\n",
    "\n",
    "$μ_0 = 36 $ This is the average temperature across our data.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Please note</b> running is ActivityID 5\n",
    "</div>\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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       "      <th>timestamp</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101p</td>\n",
       "      <td>37.81</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.3750</td>\n",
       "      <td>2.246150</td>\n",
       "      <td>7.48180</td>\n",
       "      <td>5.55219</td>\n",
       "      <td>-0.431227</td>\n",
       "      <td>0.002685</td>\n",
       "      <td>...</td>\n",
       "      <td>30.7500</td>\n",
       "      <td>9.73536</td>\n",
       "      <td>-2.036810</td>\n",
       "      <td>0.019118</td>\n",
       "      <td>0.005819</td>\n",
       "      <td>-0.017912</td>\n",
       "      <td>0.015243</td>\n",
       "      <td>-60.7820</td>\n",
       "      <td>-36.2116</td>\n",
       "      <td>-58.4891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>101p</td>\n",
       "      <td>38.25</td>\n",
       "      <td>1</td>\n",
       "      <td>101.0</td>\n",
       "      <td>30.3750</td>\n",
       "      <td>2.549540</td>\n",
       "      <td>7.63122</td>\n",
       "      <td>5.55623</td>\n",
       "      <td>-0.487667</td>\n",
       "      <td>-0.019958</td>\n",
       "      <td>...</td>\n",
       "      <td>30.7500</td>\n",
       "      <td>9.66059</td>\n",
       "      <td>-1.808200</td>\n",
       "      <td>-0.021657</td>\n",
       "      <td>0.005473</td>\n",
       "      <td>-0.003154</td>\n",
       "      <td>0.025621</td>\n",
       "      <td>-60.5521</td>\n",
       "      <td>-36.5522</td>\n",
       "      <td>-58.3708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>101p</td>\n",
       "      <td>38.36</td>\n",
       "      <td>1</td>\n",
       "      <td>101.0</td>\n",
       "      <td>30.3750</td>\n",
       "      <td>2.824070</td>\n",
       "      <td>6.14490</td>\n",
       "      <td>5.06502</td>\n",
       "      <td>-0.781563</td>\n",
       "      <td>0.198873</td>\n",
       "      <td>...</td>\n",
       "      <td>30.7500</td>\n",
       "      <td>9.69872</td>\n",
       "      <td>-1.884700</td>\n",
       "      <td>0.017799</td>\n",
       "      <td>0.011453</td>\n",
       "      <td>-0.000398</td>\n",
       "      <td>0.001243</td>\n",
       "      <td>-61.1082</td>\n",
       "      <td>-36.4321</td>\n",
       "      <td>-57.6223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>101p</td>\n",
       "      <td>38.47</td>\n",
       "      <td>1</td>\n",
       "      <td>101.0</td>\n",
       "      <td>30.3750</td>\n",
       "      <td>2.736260</td>\n",
       "      <td>7.94195</td>\n",
       "      <td>6.52017</td>\n",
       "      <td>-0.472414</td>\n",
       "      <td>0.279868</td>\n",
       "      <td>...</td>\n",
       "      <td>30.7500</td>\n",
       "      <td>9.58774</td>\n",
       "      <td>-1.846340</td>\n",
       "      <td>0.055012</td>\n",
       "      <td>0.003170</td>\n",
       "      <td>-0.019596</td>\n",
       "      <td>0.001829</td>\n",
       "      <td>-61.7855</td>\n",
       "      <td>-36.2931</td>\n",
       "      <td>-57.9867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>101p</td>\n",
       "      <td>38.91</td>\n",
       "      <td>1</td>\n",
       "      <td>101.0</td>\n",
       "      <td>30.3750</td>\n",
       "      <td>-0.530751</td>\n",
       "      <td>7.62191</td>\n",
       "      <td>6.04895</td>\n",
       "      <td>0.179548</td>\n",
       "      <td>1.051120</td>\n",
       "      <td>...</td>\n",
       "      <td>30.7500</td>\n",
       "      <td>9.65520</td>\n",
       "      <td>-1.996760</td>\n",
       "      <td>-0.174900</td>\n",
       "      <td>0.012554</td>\n",
       "      <td>-0.019851</td>\n",
       "      <td>0.008941</td>\n",
       "      <td>-61.7860</td>\n",
       "      <td>-36.1844</td>\n",
       "      <td>-57.8617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160907</th>\n",
       "      <td>108p</td>\n",
       "      <td>3887.13</td>\n",
       "      <td>24</td>\n",
       "      <td>201.0</td>\n",
       "      <td>30.8125</td>\n",
       "      <td>1.549900</td>\n",
       "      <td>-9.75252</td>\n",
       "      <td>-4.44530</td>\n",
       "      <td>0.026142</td>\n",
       "      <td>-0.165929</td>\n",
       "      <td>...</td>\n",
       "      <td>31.6875</td>\n",
       "      <td>9.33841</td>\n",
       "      <td>-0.029067</td>\n",
       "      <td>-3.779190</td>\n",
       "      <td>-0.057015</td>\n",
       "      <td>0.065142</td>\n",
       "      <td>0.046579</td>\n",
       "      <td>-32.2368</td>\n",
       "      <td>12.3835</td>\n",
       "      <td>38.8660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160908</th>\n",
       "      <td>108p</td>\n",
       "      <td>3887.24</td>\n",
       "      <td>24</td>\n",
       "      <td>201.0</td>\n",
       "      <td>30.8125</td>\n",
       "      <td>1.564860</td>\n",
       "      <td>-8.95345</td>\n",
       "      <td>-4.17836</td>\n",
       "      <td>-0.554971</td>\n",
       "      <td>-0.259998</td>\n",
       "      <td>...</td>\n",
       "      <td>31.6875</td>\n",
       "      <td>9.27205</td>\n",
       "      <td>0.121033</td>\n",
       "      <td>-3.510670</td>\n",
       "      <td>0.011600</td>\n",
       "      <td>0.055103</td>\n",
       "      <td>0.023033</td>\n",
       "      <td>-31.3352</td>\n",
       "      <td>12.2110</td>\n",
       "      <td>39.2949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160911</th>\n",
       "      <td>108p</td>\n",
       "      <td>3887.56</td>\n",
       "      <td>24</td>\n",
       "      <td>201.0</td>\n",
       "      <td>30.8125</td>\n",
       "      <td>0.754323</td>\n",
       "      <td>-8.81963</td>\n",
       "      <td>-6.88507</td>\n",
       "      <td>0.247418</td>\n",
       "      <td>-0.280137</td>\n",
       "      <td>...</td>\n",
       "      <td>31.6875</td>\n",
       "      <td>9.34600</td>\n",
       "      <td>0.158849</td>\n",
       "      <td>-3.548740</td>\n",
       "      <td>-0.299871</td>\n",
       "      <td>0.027859</td>\n",
       "      <td>0.147208</td>\n",
       "      <td>-32.6293</td>\n",
       "      <td>12.9184</td>\n",
       "      <td>39.0058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160913</th>\n",
       "      <td>108p</td>\n",
       "      <td>3887.78</td>\n",
       "      <td>24</td>\n",
       "      <td>201.0</td>\n",
       "      <td>30.8125</td>\n",
       "      <td>0.440724</td>\n",
       "      <td>-9.23551</td>\n",
       "      <td>-2.77022</td>\n",
       "      <td>2.233370</td>\n",
       "      <td>0.320604</td>\n",
       "      <td>...</td>\n",
       "      <td>31.6875</td>\n",
       "      <td>9.38396</td>\n",
       "      <td>-0.373912</td>\n",
       "      <td>-3.429280</td>\n",
       "      <td>-0.021327</td>\n",
       "      <td>0.008685</td>\n",
       "      <td>0.053233</td>\n",
       "      <td>-32.5020</td>\n",
       "      <td>12.7825</td>\n",
       "      <td>38.7228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160918</th>\n",
       "      <td>108p</td>\n",
       "      <td>3888.33</td>\n",
       "      <td>24</td>\n",
       "      <td>201.0</td>\n",
       "      <td>30.8125</td>\n",
       "      <td>-1.531630</td>\n",
       "      <td>-9.62899</td>\n",
       "      <td>-1.83625</td>\n",
       "      <td>-0.151713</td>\n",
       "      <td>-0.007082</td>\n",
       "      <td>...</td>\n",
       "      <td>31.6875</td>\n",
       "      <td>9.44911</td>\n",
       "      <td>-0.333527</td>\n",
       "      <td>-3.776170</td>\n",
       "      <td>0.133438</td>\n",
       "      <td>-0.039959</td>\n",
       "      <td>-0.091760</td>\n",
       "      <td>-31.0977</td>\n",
       "      <td>12.9712</td>\n",
       "      <td>39.1530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80459 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject  timestamp  activityID  heartrate_bpm  imu_hand_temp  \\\n",
       "1         101p      37.81           1          100.0        30.3750   \n",
       "5         101p      38.25           1          101.0        30.3750   \n",
       "6         101p      38.36           1          101.0        30.3750   \n",
       "7         101p      38.47           1          101.0        30.3750   \n",
       "11        101p      38.91           1          101.0        30.3750   \n",
       "...        ...        ...         ...            ...            ...   \n",
       "160907    108p    3887.13          24          201.0        30.8125   \n",
       "160908    108p    3887.24          24          201.0        30.8125   \n",
       "160911    108p    3887.56          24          201.0        30.8125   \n",
       "160913    108p    3887.78          24          201.0        30.8125   \n",
       "160918    108p    3888.33          24          201.0        30.8125   \n",
       "\n",
       "        imu_hand_3d_acceleration_1  imu_hand_3d_acceleration_2  \\\n",
       "1                         2.246150                     7.48180   \n",
       "5                         2.549540                     7.63122   \n",
       "6                         2.824070                     6.14490   \n",
       "7                         2.736260                     7.94195   \n",
       "11                       -0.530751                     7.62191   \n",
       "...                            ...                         ...   \n",
       "160907                    1.549900                    -9.75252   \n",
       "160908                    1.564860                    -8.95345   \n",
       "160911                    0.754323                    -8.81963   \n",
       "160913                    0.440724                    -9.23551   \n",
       "160918                   -1.531630                    -9.62899   \n",
       "\n",
       "        imu_hand_3d_acceleration_3  imu_hand_3d_gyroscope_1  \\\n",
       "1                          5.55219                -0.431227   \n",
       "5                          5.55623                -0.487667   \n",
       "6                          5.06502                -0.781563   \n",
       "7                          6.52017                -0.472414   \n",
       "11                         6.04895                 0.179548   \n",
       "...                            ...                      ...   \n",
       "160907                    -4.44530                 0.026142   \n",
       "160908                    -4.17836                -0.554971   \n",
       "160911                    -6.88507                 0.247418   \n",
       "160913                    -2.77022                 2.233370   \n",
       "160918                    -1.83625                -0.151713   \n",
       "\n",
       "        imu_hand_3d_gyroscope_2  ...  imu_ankle_temp  \\\n",
       "1                      0.002685  ...         30.7500   \n",
       "5                     -0.019958  ...         30.7500   \n",
       "6                      0.198873  ...         30.7500   \n",
       "7                      0.279868  ...         30.7500   \n",
       "11                     1.051120  ...         30.7500   \n",
       "...                         ...  ...             ...   \n",
       "160907                -0.165929  ...         31.6875   \n",
       "160908                -0.259998  ...         31.6875   \n",
       "160911                -0.280137  ...         31.6875   \n",
       "160913                 0.320604  ...         31.6875   \n",
       "160918                -0.007082  ...         31.6875   \n",
       "\n",
       "        imu_ankle_3d_acceleration_1  imu_ankle_3d_acceleration_2  \\\n",
       "1                           9.73536                    -2.036810   \n",
       "5                           9.66059                    -1.808200   \n",
       "6                           9.69872                    -1.884700   \n",
       "7                           9.58774                    -1.846340   \n",
       "11                          9.65520                    -1.996760   \n",
       "...                             ...                          ...   \n",
       "160907                      9.33841                    -0.029067   \n",
       "160908                      9.27205                     0.121033   \n",
       "160911                      9.34600                     0.158849   \n",
       "160913                      9.38396                    -0.373912   \n",
       "160918                      9.44911                    -0.333527   \n",
       "\n",
       "        imu_ankle_3d_acceleration_3  imu_ankle_3d_gyroscope_1  \\\n",
       "1                          0.019118                  0.005819   \n",
       "5                         -0.021657                  0.005473   \n",
       "6                          0.017799                  0.011453   \n",
       "7                          0.055012                  0.003170   \n",
       "11                        -0.174900                  0.012554   \n",
       "...                             ...                       ...   \n",
       "160907                    -3.779190                 -0.057015   \n",
       "160908                    -3.510670                  0.011600   \n",
       "160911                    -3.548740                 -0.299871   \n",
       "160913                    -3.429280                 -0.021327   \n",
       "160918                    -3.776170                  0.133438   \n",
       "\n",
       "        imu_ankle_3d_gyroscope_2  imu_ankle_3d_gyroscope_3  \\\n",
       "1                      -0.017912                  0.015243   \n",
       "5                      -0.003154                  0.025621   \n",
       "6                      -0.000398                  0.001243   \n",
       "7                      -0.019596                  0.001829   \n",
       "11                     -0.019851                  0.008941   \n",
       "...                          ...                       ...   \n",
       "160907                  0.065142                  0.046579   \n",
       "160908                  0.055103                  0.023033   \n",
       "160911                  0.027859                  0.147208   \n",
       "160913                  0.008685                  0.053233   \n",
       "160918                 -0.039959                 -0.091760   \n",
       "\n",
       "        imu_ankle_3d_magnetometer_1  imu_ankle_3d_magnetometer_2  \\\n",
       "1                          -60.7820                     -36.2116   \n",
       "5                          -60.5521                     -36.5522   \n",
       "6                          -61.1082                     -36.4321   \n",
       "7                          -61.7855                     -36.2931   \n",
       "11                         -61.7860                     -36.1844   \n",
       "...                             ...                          ...   \n",
       "160907                     -32.2368                      12.3835   \n",
       "160908                     -31.3352                      12.2110   \n",
       "160911                     -32.6293                      12.9184   \n",
       "160913                     -32.5020                      12.7825   \n",
       "160918                     -31.0977                      12.9712   \n",
       "\n",
       "        imu_ankle_3d_magnetometer_3  \n",
       "1                          -58.4891  \n",
       "5                          -58.3708  \n",
       "6                          -57.6223  \n",
       "7                          -57.9867  \n",
       "11                         -57.8617  \n",
       "...                             ...  \n",
       "160907                      38.8660  \n",
       "160908                      39.2949  \n",
       "160911                      39.0058  \n",
       "160913                      38.7228  \n",
       "160918                      39.1530  \n",
       "\n",
       "[80459 rows x 34 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#what does the test set look like?\n",
    "\n",
    "dft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#creating a dataframe that is just for running\n",
    "\n",
    "runningdft = dft.loc[(dft.activityID==5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>subject</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>activityID</th>\n",
       "      <th>heartrate_bpm</th>\n",
       "      <th>imu_hand_temp</th>\n",
       "      <th>imu_hand_3d_acceleration_1</th>\n",
       "      <th>imu_hand_3d_acceleration_2</th>\n",
       "      <th>imu_hand_3d_acceleration_3</th>\n",
       "      <th>imu_hand_3d_gyroscope_1</th>\n",
       "      <th>imu_hand_3d_gyroscope_2</th>\n",
       "      <th>...</th>\n",
       "      <th>imu_ankle_temp</th>\n",
       "      <th>imu_ankle_3d_acceleration_1</th>\n",
       "      <th>imu_ankle_3d_acceleration_2</th>\n",
       "      <th>imu_ankle_3d_acceleration_3</th>\n",
       "      <th>imu_ankle_3d_gyroscope_1</th>\n",
       "      <th>imu_ankle_3d_gyroscope_2</th>\n",
       "      <th>imu_ankle_3d_gyroscope_3</th>\n",
       "      <th>imu_ankle_3d_magnetometer_1</th>\n",
       "      <th>imu_ankle_3d_magnetometer_2</th>\n",
       "      <th>imu_ankle_3d_magnetometer_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19585</th>\n",
       "      <td>101p</td>\n",
       "      <td>3136.13</td>\n",
       "      <td>5</td>\n",
       "      <td>125.0</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>2.371540</td>\n",
       "      <td>8.24138</td>\n",
       "      <td>5.74395</td>\n",
       "      <td>-0.279118</td>\n",
       "      <td>-0.207532</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3750</td>\n",
       "      <td>9.71245</td>\n",
       "      <td>-1.992100</td>\n",
       "      <td>-0.791775</td>\n",
       "      <td>0.077495</td>\n",
       "      <td>0.039526</td>\n",
       "      <td>0.004155</td>\n",
       "      <td>-98.1005</td>\n",
       "      <td>7.55577</td>\n",
       "      <td>-22.9352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19588</th>\n",
       "      <td>101p</td>\n",
       "      <td>3136.45</td>\n",
       "      <td>5</td>\n",
       "      <td>125.0</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>1.876260</td>\n",
       "      <td>4.69394</td>\n",
       "      <td>7.44194</td>\n",
       "      <td>-1.114220</td>\n",
       "      <td>1.748620</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3750</td>\n",
       "      <td>9.67232</td>\n",
       "      <td>-1.876990</td>\n",
       "      <td>-0.908670</td>\n",
       "      <td>-0.050110</td>\n",
       "      <td>-0.006431</td>\n",
       "      <td>-0.008428</td>\n",
       "      <td>-98.5550</td>\n",
       "      <td>7.79725</td>\n",
       "      <td>-23.3003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19590</th>\n",
       "      <td>101p</td>\n",
       "      <td>3136.67</td>\n",
       "      <td>5</td>\n",
       "      <td>125.0</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>-0.127646</td>\n",
       "      <td>4.17893</td>\n",
       "      <td>7.49063</td>\n",
       "      <td>0.389019</td>\n",
       "      <td>1.525470</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3750</td>\n",
       "      <td>9.66803</td>\n",
       "      <td>-2.065870</td>\n",
       "      <td>-1.023310</td>\n",
       "      <td>0.029171</td>\n",
       "      <td>-0.030504</td>\n",
       "      <td>-0.017898</td>\n",
       "      <td>-97.9870</td>\n",
       "      <td>7.87298</td>\n",
       "      <td>-22.1901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19598</th>\n",
       "      <td>101p</td>\n",
       "      <td>3137.55</td>\n",
       "      <td>5</td>\n",
       "      <td>125.0</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>-2.762890</td>\n",
       "      <td>10.00490</td>\n",
       "      <td>3.27487</td>\n",
       "      <td>-0.064850</td>\n",
       "      <td>-0.092200</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3750</td>\n",
       "      <td>9.86721</td>\n",
       "      <td>-1.233990</td>\n",
       "      <td>-0.756303</td>\n",
       "      <td>-0.002413</td>\n",
       "      <td>-0.005103</td>\n",
       "      <td>-0.027196</td>\n",
       "      <td>-97.9014</td>\n",
       "      <td>1.69414</td>\n",
       "      <td>-22.3805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19599</th>\n",
       "      <td>101p</td>\n",
       "      <td>3137.66</td>\n",
       "      <td>5</td>\n",
       "      <td>125.0</td>\n",
       "      <td>31.0000</td>\n",
       "      <td>-0.817240</td>\n",
       "      <td>8.11934</td>\n",
       "      <td>2.04029</td>\n",
       "      <td>-1.010910</td>\n",
       "      <td>-0.155790</td>\n",
       "      <td>...</td>\n",
       "      <td>33.3750</td>\n",
       "      <td>9.67218</td>\n",
       "      <td>-1.268530</td>\n",
       "      <td>-1.028220</td>\n",
       "      <td>0.000648</td>\n",
       "      <td>-0.017953</td>\n",
       "      <td>-0.004464</td>\n",
       "      <td>-98.0189</td>\n",
       "      <td>2.56940</td>\n",
       "      <td>-21.5037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160127</th>\n",
       "      <td>108p</td>\n",
       "      <td>3628.89</td>\n",
       "      <td>5</td>\n",
       "      <td>195.0</td>\n",
       "      <td>30.8125</td>\n",
       "      <td>0.679521</td>\n",
       "      <td>2.74683</td>\n",
       "      <td>9.27798</td>\n",
       "      <td>-0.017814</td>\n",
       "      <td>-0.023487</td>\n",
       "      <td>...</td>\n",
       "      <td>31.6875</td>\n",
       "      <td>9.06824</td>\n",
       "      <td>-0.329228</td>\n",
       "      <td>-4.011640</td>\n",
       "      <td>0.060811</td>\n",
       "      <td>-0.026769</td>\n",
       "      <td>-0.018568</td>\n",
       "      <td>-17.3033</td>\n",
       "      <td>11.47190</td>\n",
       "      <td>26.0129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160130</th>\n",
       "      <td>108p</td>\n",
       "      <td>3629.22</td>\n",
       "      <td>5</td>\n",
       "      <td>195.0</td>\n",
       "      <td>30.8125</td>\n",
       "      <td>0.530237</td>\n",
       "      <td>2.63485</td>\n",
       "      <td>9.35309</td>\n",
       "      <td>-0.006221</td>\n",
       "      <td>-0.042546</td>\n",
       "      <td>...</td>\n",
       "      <td>31.6875</td>\n",
       "      <td>9.14219</td>\n",
       "      <td>-0.291413</td>\n",
       "      <td>-4.049700</td>\n",
       "      <td>-0.024199</td>\n",
       "      <td>0.053295</td>\n",
       "      <td>0.037101</td>\n",
       "      <td>-18.3303</td>\n",
       "      <td>11.91020</td>\n",
       "      <td>26.2924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160131</th>\n",
       "      <td>108p</td>\n",
       "      <td>3629.33</td>\n",
       "      <td>5</td>\n",
       "      <td>195.0</td>\n",
       "      <td>30.8125</td>\n",
       "      <td>0.601805</td>\n",
       "      <td>2.63326</td>\n",
       "      <td>9.23871</td>\n",
       "      <td>-0.002547</td>\n",
       "      <td>0.029761</td>\n",
       "      <td>...</td>\n",
       "      <td>31.6875</td>\n",
       "      <td>9.18512</td>\n",
       "      <td>-0.293256</td>\n",
       "      <td>-3.856300</td>\n",
       "      <td>-0.010813</td>\n",
       "      <td>-0.007312</td>\n",
       "      <td>0.037356</td>\n",
       "      <td>-18.2049</td>\n",
       "      <td>12.16230</td>\n",
       "      <td>26.5762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160135</th>\n",
       "      <td>108p</td>\n",
       "      <td>3629.77</td>\n",
       "      <td>5</td>\n",
       "      <td>195.0</td>\n",
       "      <td>30.8125</td>\n",
       "      <td>0.570697</td>\n",
       "      <td>2.82459</td>\n",
       "      <td>9.39155</td>\n",
       "      <td>0.011127</td>\n",
       "      <td>0.038150</td>\n",
       "      <td>...</td>\n",
       "      <td>31.6875</td>\n",
       "      <td>9.14640</td>\n",
       "      <td>-0.330666</td>\n",
       "      <td>-3.895060</td>\n",
       "      <td>0.023179</td>\n",
       "      <td>-0.007246</td>\n",
       "      <td>0.007512</td>\n",
       "      <td>-17.6928</td>\n",
       "      <td>12.00760</td>\n",
       "      <td>26.4364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160138</th>\n",
       "      <td>108p</td>\n",
       "      <td>3630.09</td>\n",
       "      <td>5</td>\n",
       "      <td>195.0</td>\n",
       "      <td>30.8125</td>\n",
       "      <td>0.644139</td>\n",
       "      <td>2.70986</td>\n",
       "      <td>9.35454</td>\n",
       "      <td>-0.004483</td>\n",
       "      <td>-0.001148</td>\n",
       "      <td>...</td>\n",
       "      <td>31.6250</td>\n",
       "      <td>9.18622</td>\n",
       "      <td>-0.293577</td>\n",
       "      <td>-3.817700</td>\n",
       "      <td>0.044398</td>\n",
       "      <td>-0.009639</td>\n",
       "      <td>0.006757</td>\n",
       "      <td>-18.0830</td>\n",
       "      <td>12.28410</td>\n",
       "      <td>26.2929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2943 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       subject  timestamp  activityID  heartrate_bpm  imu_hand_temp  \\\n",
       "19585     101p    3136.13           5          125.0        31.0000   \n",
       "19588     101p    3136.45           5          125.0        31.0000   \n",
       "19590     101p    3136.67           5          125.0        31.0000   \n",
       "19598     101p    3137.55           5          125.0        31.0000   \n",
       "19599     101p    3137.66           5          125.0        31.0000   \n",
       "...        ...        ...         ...            ...            ...   \n",
       "160127    108p    3628.89           5          195.0        30.8125   \n",
       "160130    108p    3629.22           5          195.0        30.8125   \n",
       "160131    108p    3629.33           5          195.0        30.8125   \n",
       "160135    108p    3629.77           5          195.0        30.8125   \n",
       "160138    108p    3630.09           5          195.0        30.8125   \n",
       "\n",
       "        imu_hand_3d_acceleration_1  imu_hand_3d_acceleration_2  \\\n",
       "19585                     2.371540                     8.24138   \n",
       "19588                     1.876260                     4.69394   \n",
       "19590                    -0.127646                     4.17893   \n",
       "19598                    -2.762890                    10.00490   \n",
       "19599                    -0.817240                     8.11934   \n",
       "...                            ...                         ...   \n",
       "160127                    0.679521                     2.74683   \n",
       "160130                    0.530237                     2.63485   \n",
       "160131                    0.601805                     2.63326   \n",
       "160135                    0.570697                     2.82459   \n",
       "160138                    0.644139                     2.70986   \n",
       "\n",
       "        imu_hand_3d_acceleration_3  imu_hand_3d_gyroscope_1  \\\n",
       "19585                      5.74395                -0.279118   \n",
       "19588                      7.44194                -1.114220   \n",
       "19590                      7.49063                 0.389019   \n",
       "19598                      3.27487                -0.064850   \n",
       "19599                      2.04029                -1.010910   \n",
       "...                            ...                      ...   \n",
       "160127                     9.27798                -0.017814   \n",
       "160130                     9.35309                -0.006221   \n",
       "160131                     9.23871                -0.002547   \n",
       "160135                     9.39155                 0.011127   \n",
       "160138                     9.35454                -0.004483   \n",
       "\n",
       "        imu_hand_3d_gyroscope_2  ...  imu_ankle_temp  \\\n",
       "19585                 -0.207532  ...         33.3750   \n",
       "19588                  1.748620  ...         33.3750   \n",
       "19590                  1.525470  ...         33.3750   \n",
       "19598                 -0.092200  ...         33.3750   \n",
       "19599                 -0.155790  ...         33.3750   \n",
       "...                         ...  ...             ...   \n",
       "160127                -0.023487  ...         31.6875   \n",
       "160130                -0.042546  ...         31.6875   \n",
       "160131                 0.029761  ...         31.6875   \n",
       "160135                 0.038150  ...         31.6875   \n",
       "160138                -0.001148  ...         31.6250   \n",
       "\n",
       "        imu_ankle_3d_acceleration_1  imu_ankle_3d_acceleration_2  \\\n",
       "19585                       9.71245                    -1.992100   \n",
       "19588                       9.67232                    -1.876990   \n",
       "19590                       9.66803                    -2.065870   \n",
       "19598                       9.86721                    -1.233990   \n",
       "19599                       9.67218                    -1.268530   \n",
       "...                             ...                          ...   \n",
       "160127                      9.06824                    -0.329228   \n",
       "160130                      9.14219                    -0.291413   \n",
       "160131                      9.18512                    -0.293256   \n",
       "160135                      9.14640                    -0.330666   \n",
       "160138                      9.18622                    -0.293577   \n",
       "\n",
       "        imu_ankle_3d_acceleration_3  imu_ankle_3d_gyroscope_1  \\\n",
       "19585                     -0.791775                  0.077495   \n",
       "19588                     -0.908670                 -0.050110   \n",
       "19590                     -1.023310                  0.029171   \n",
       "19598                     -0.756303                 -0.002413   \n",
       "19599                     -1.028220                  0.000648   \n",
       "...                             ...                       ...   \n",
       "160127                    -4.011640                  0.060811   \n",
       "160130                    -4.049700                 -0.024199   \n",
       "160131                    -3.856300                 -0.010813   \n",
       "160135                    -3.895060                  0.023179   \n",
       "160138                    -3.817700                  0.044398   \n",
       "\n",
       "        imu_ankle_3d_gyroscope_2  imu_ankle_3d_gyroscope_3  \\\n",
       "19585                   0.039526                  0.004155   \n",
       "19588                  -0.006431                 -0.008428   \n",
       "19590                  -0.030504                 -0.017898   \n",
       "19598                  -0.005103                 -0.027196   \n",
       "19599                  -0.017953                 -0.004464   \n",
       "...                          ...                       ...   \n",
       "160127                 -0.026769                 -0.018568   \n",
       "160130                  0.053295                  0.037101   \n",
       "160131                 -0.007312                  0.037356   \n",
       "160135                 -0.007246                  0.007512   \n",
       "160138                 -0.009639                  0.006757   \n",
       "\n",
       "        imu_ankle_3d_magnetometer_1  imu_ankle_3d_magnetometer_2  \\\n",
       "19585                      -98.1005                      7.55577   \n",
       "19588                      -98.5550                      7.79725   \n",
       "19590                      -97.9870                      7.87298   \n",
       "19598                      -97.9014                      1.69414   \n",
       "19599                      -98.0189                      2.56940   \n",
       "...                             ...                          ...   \n",
       "160127                     -17.3033                     11.47190   \n",
       "160130                     -18.3303                     11.91020   \n",
       "160131                     -18.2049                     12.16230   \n",
       "160135                     -17.6928                     12.00760   \n",
       "160138                     -18.0830                     12.28410   \n",
       "\n",
       "        imu_ankle_3d_magnetometer_3  \n",
       "19585                      -22.9352  \n",
       "19588                      -23.3003  \n",
       "19590                      -22.1901  \n",
       "19598                      -22.3805  \n",
       "19599                      -21.5037  \n",
       "...                             ...  \n",
       "160127                      26.0129  \n",
       "160130                      26.2924  \n",
       "160131                      26.5762  \n",
       "160135                      26.4364  \n",
       "160138                      26.2929  \n",
       "\n",
       "[2943 rows x 34 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runningdft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P_G is: -39.46602837318915\n",
      "Mean temperature when running:  35.095268433571185\n",
      "one tail p value from normal:  1.0\n"
     ]
    }
   ],
   "source": [
    "#running temp\n",
    "\n",
    "runningtemp_means=runningdft['imu_chest_temp'].mean()\n",
    "runningtemp_std=runningdft['imu_chest_temp'].std()\n",
    "runningtemp_count=runningdft['imu_chest_temp'].count()\n",
    "\n",
    "#here I am calculating the p value, I am taking the mean running temp and taking away the average\n",
    "#then diving this value by the running temp standard diviation\n",
    "\n",
    "P_G = (runningtemp_means-36)/(runningtemp_std/np.sqrt(runningtemp_count))\n",
    "print('P_G is: '+ str(P_G))\n",
    "p_value_from_normal=(1-stats.norm.cdf(P_G))\n",
    "print('Mean temperature when running: ',runningtemp_means)\n",
    "print ('one tail p value from normal: ', p_value_from_normal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our p value (1.0) is *greater* than the significance level ($α =0.05$). This indicates very strong evidence that we should reject our testable hypothesis. Therefore, we accept the null hypothesis and reject our first hypothesis that a **higher temperature** indicates **running**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Our forth testable hypothesis\n",
    "\n",
    "***If*** higher temperature is related to household chores, ***then*** ironing and vacuum cleaning will imply a higher temperature than average.\n",
    "\n",
    "In order to test this hypothesis, we will need to conduct an upper-tailed test. This is because we are hypothesising that there is an increase in temperature when doing household chores.\n",
    "\n",
    "***\n",
    "\n",
    "> $H_0: μ = μ_0$ Our null hypothesis, that there is no change.\n",
    "\n",
    "> $H_1: μ > μ_0$ The research hypothesis, that **higher temperature** implies household chores.\n",
    "\n",
    "$α =0.05$\n",
    "\n",
    "$μ_0 = 36 $ This is the average temperature across our data.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Please note</b> household chores are defined as ActivityID 16 (vacuum cleaning) and 17 (ironing).\n",
    "</div>\n",
    "\n",
    "***\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "choresdft = dft.loc[(dft.activityID==16)|(dft.activityID==17)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P_G is: 121.92883706722087\n",
      "Mean temperature when doing chores:  36.83189792875779\n",
      "one tail p value from normal:  0.0\n"
     ]
    }
   ],
   "source": [
    "#chores temp\n",
    "\n",
    "chorestemp_means=choresdft['imu_chest_temp'].mean()\n",
    "chorestemp_std=choresdft['imu_chest_temp'].std()\n",
    "chorestemp_count=choresdft['imu_chest_temp'].count()\n",
    "\n",
    "#here I am calculating the p value, I am taking the mean chores temp value and taking away the average\n",
    "\n",
    "P_G = (chorestemp_means-36)/(chorestemp_std/np.sqrt(chorestemp_count))\n",
    "print('P_G is: '+ str(P_G))\n",
    "p_value_from_normal=(1-stats.norm.cdf(P_G))\n",
    "print('Mean temperature when doing chores: ',chorestemp_means)\n",
    "print ('one tail p value from normal: ', p_value_from_normal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our p value (0.0) is *less* than the significance level ($α =0.05$). This indicates very strong evidence that we should reject our null hypothesis. Therefore, we accept our first hypothesis that a **temperature greater than 36** indicates **they have done chores**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- \n",
    "\n",
    "## Part 6: Modeling Introduction <a class=\"anchor\" id=\"model\"></a>\n",
    "\n",
    "*How should we choose between differing explanations of data?* Myung, (2000)[[5](#5)] stated that we select the one, amond candidate models, that *best* captures the underlying regularities. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Supervised Learning\n",
    "\n",
    "Supervised learning is defined by its use of labeled datasets to train algorithms that will classify data and predict outcomes. A model is trained, using the *development* data, until it can detect the relationships and patterns between input and output data labels. Then, when using unseen *test* data, the model will yield accurate results. \n",
    "\n",
    "Here, we will train two models and check their performance. The focus will be on two modeling algorithms, *Random Forest* and *Logistic Regression*.\n",
    "\n",
    "Some definitions:\n",
    "\n",
    "- True Positive: TP\n",
    "- False Positive: FP\n",
    "\n",
    "**Accuracy:**\n",
    "\n",
    "The *accuracy* is only telling when the dataset is balanced between `dev` and `test`. As ours is, we can use this as a judge of each model.\n",
    "\n",
    "**Precision:**\n",
    "\\begin{eqnarray*}\n",
    "P=\\frac{TP}{TP+FP}\n",
    "\\end{eqnarray*}\n",
    "\n",
    "The *precision* is intuitively the ability of the classifier to not label a sample as positive if it is negative.\n",
    "\n",
    "**Recall:** \n",
    "\\begin{eqnarray*}\n",
    "R=\\frac{TP}{TP+FN}\n",
    "\\end{eqnarray*}\n",
    "\n",
    "The *recall* is intuitively the ability of the classifier to find all the positive samples.\n",
    "\n",
    "**F1-score:**\n",
    "\\begin{eqnarray*}\n",
    "F1 = \\frac{2\\times P\\times R}{P+R}\n",
    "\\end{eqnarray*}\n",
    "\n",
    "The *F1 score* can be interpreted as a weighted harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. This score weights the recall more than the precision. If the f1 score is 1.0 this means recall and precision are equally important. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 Scaling the data set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>activityID</th>\n",
       "      <th>heartrate_bpm</th>\n",
       "      <th>imu_hand_temp</th>\n",
       "      <th>imu_hand_3d_acceleration_1</th>\n",
       "      <th>imu_hand_3d_acceleration_2</th>\n",
       "      <th>imu_hand_3d_acceleration_3</th>\n",
       "      <th>imu_hand_3d_gyroscope_1</th>\n",
       "      <th>imu_hand_3d_gyroscope_2</th>\n",
       "      <th>imu_hand_3d_gyroscope_3</th>\n",
       "      <th>imu_hand_3d_magnetometer_1</th>\n",
       "      <th>...</th>\n",
       "      <th>imu_ankle_temp</th>\n",
       "      <th>imu_ankle_3d_acceleration_1</th>\n",
       "      <th>imu_ankle_3d_acceleration_2</th>\n",
       "      <th>imu_ankle_3d_acceleration_3</th>\n",
       "      <th>imu_ankle_3d_gyroscope_1</th>\n",
       "      <th>imu_ankle_3d_gyroscope_2</th>\n",
       "      <th>imu_ankle_3d_gyroscope_3</th>\n",
       "      <th>imu_ankle_3d_magnetometer_1</th>\n",
       "      <th>imu_ankle_3d_magnetometer_2</th>\n",
       "      <th>imu_ankle_3d_magnetometer_3</th>\n",
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       "      <th>NaN</th>\n",
       "      <td>2</td>\n",
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       "      <td>0.833333</td>\n",
       "      <td>0.632428</td>\n",
       "      <td>0.758890</td>\n",
       "      <td>0.461104</td>\n",
       "      <td>-0.058651</td>\n",
       "      <td>-0.050517</td>\n",
       "      <td>-0.083170</td>\n",
       "      <td>-0.534968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.311316</td>\n",
       "      <td>-0.645522</td>\n",
       "      <td>-0.071654</td>\n",
       "      <td>-0.015038</td>\n",
       "      <td>-0.078372</td>\n",
       "      <td>0.017949</td>\n",
       "      <td>-0.012521</td>\n",
       "      <td>-0.099432</td>\n",
       "      <td>0.268470</td>\n",
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       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.684211</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.483698</td>\n",
       "      <td>-0.197967</td>\n",
       "      <td>0.541603</td>\n",
       "      <td>0.115393</td>\n",
       "      <td>-0.087513</td>\n",
       "      <td>0.038696</td>\n",
       "      <td>-0.430248</td>\n",
       "      <td>...</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>-6.597898</td>\n",
       "      <td>-2.366013</td>\n",
       "      <td>-0.477952</td>\n",
       "      <td>-0.049021</td>\n",
       "      <td>-0.151122</td>\n",
       "      <td>-0.002073</td>\n",
       "      <td>0.836053</td>\n",
       "      <td>0.754093</td>\n",
       "      <td>-0.807183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>16</td>\n",
       "      <td>-0.263158</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>-0.294699</td>\n",
       "      <td>-0.984344</td>\n",
       "      <td>-0.565588</td>\n",
       "      <td>-0.287114</td>\n",
       "      <td>1.915038</td>\n",
       "      <td>0.359981</td>\n",
       "      <td>1.168434</td>\n",
       "      <td>...</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.098957</td>\n",
       "      <td>0.346768</td>\n",
       "      <td>-0.063735</td>\n",
       "      <td>-0.794492</td>\n",
       "      <td>1.158063</td>\n",
       "      <td>0.381517</td>\n",
       "      <td>0.935655</td>\n",
       "      <td>-0.027952</td>\n",
       "      <td>0.888731</td>\n",
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       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>12</td>\n",
       "      <td>1.657895</td>\n",
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       "      <td>-0.551654</td>\n",
       "      <td>0.097152</td>\n",
       "      <td>-0.250036</td>\n",
       "      <td>-2.064097</td>\n",
       "      <td>1.834339</td>\n",
       "      <td>-1.857976</td>\n",
       "      <td>-0.266234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>1.176198</td>\n",
       "      <td>-0.251395</td>\n",
       "      <td>-0.152227</td>\n",
       "      <td>-0.114978</td>\n",
       "      <td>1.398601</td>\n",
       "      <td>-0.999322</td>\n",
       "      <td>-0.976194</td>\n",
       "      <td>0.327421</td>\n",
       "      <td>-0.610844</td>\n",
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       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>12</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>-0.400000</td>\n",
       "      <td>-0.307912</td>\n",
       "      <td>0.441735</td>\n",
       "      <td>-0.418997</td>\n",
       "      <td>-0.474059</td>\n",
       "      <td>0.853304</td>\n",
       "      <td>0.070826</td>\n",
       "      <td>-0.837059</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.347826</td>\n",
       "      <td>0.022563</td>\n",
       "      <td>1.354814</td>\n",
       "      <td>-0.858045</td>\n",
       "      <td>1.136667</td>\n",
       "      <td>1.048552</td>\n",
       "      <td>2.380406</td>\n",
       "      <td>-0.860425</td>\n",
       "      <td>-0.525029</td>\n",
       "      <td>-1.203777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     activityID  heartrate_bpm  imu_hand_temp  imu_hand_3d_acceleration_1  \\\n",
       "NaN           2      -0.605263       0.833333                    0.632428   \n",
       "NaN           1      -0.684211       0.200000                    1.483698   \n",
       "NaN          16      -0.263158       0.733333                   -0.294699   \n",
       "NaN          12       1.657895       0.200000                   -0.551654   \n",
       "NaN          12       0.473684      -0.400000                   -0.307912   \n",
       "\n",
       "     imu_hand_3d_acceleration_2  imu_hand_3d_acceleration_3  \\\n",
       "NaN                    0.758890                    0.461104   \n",
       "NaN                   -0.197967                    0.541603   \n",
       "NaN                   -0.984344                   -0.565588   \n",
       "NaN                    0.097152                   -0.250036   \n",
       "NaN                    0.441735                   -0.418997   \n",
       "\n",
       "     imu_hand_3d_gyroscope_1  imu_hand_3d_gyroscope_2  \\\n",
       "NaN                -0.058651                -0.050517   \n",
       "NaN                 0.115393                -0.087513   \n",
       "NaN                -0.287114                 1.915038   \n",
       "NaN                -2.064097                 1.834339   \n",
       "NaN                -0.474059                 0.853304   \n",
       "\n",
       "     imu_hand_3d_gyroscope_3  imu_hand_3d_magnetometer_1  ...  imu_ankle_temp  \\\n",
       "NaN                -0.083170                   -0.534968  ...        0.000000   \n",
       "NaN                 0.038696                   -0.430248  ...        0.347826   \n",
       "NaN                 0.359981                    1.168434  ...        0.478261   \n",
       "NaN                -1.857976                   -0.266234  ...        0.521739   \n",
       "NaN                 0.070826                   -0.837059  ...       -0.347826   \n",
       "\n",
       "     imu_ankle_3d_acceleration_1  imu_ankle_3d_acceleration_2  \\\n",
       "NaN                    -0.311316                    -0.645522   \n",
       "NaN                    -6.597898                    -2.366013   \n",
       "NaN                     0.098957                     0.346768   \n",
       "NaN                     1.176198                    -0.251395   \n",
       "NaN                     0.022563                     1.354814   \n",
       "\n",
       "     imu_ankle_3d_acceleration_3  imu_ankle_3d_gyroscope_1  \\\n",
       "NaN                    -0.071654                 -0.015038   \n",
       "NaN                    -0.477952                 -0.049021   \n",
       "NaN                    -0.063735                 -0.794492   \n",
       "NaN                    -0.152227                 -0.114978   \n",
       "NaN                    -0.858045                  1.136667   \n",
       "\n",
       "     imu_ankle_3d_gyroscope_2  imu_ankle_3d_gyroscope_3  \\\n",
       "NaN                 -0.078372                  0.017949   \n",
       "NaN                 -0.151122                 -0.002073   \n",
       "NaN                  1.158063                  0.381517   \n",
       "NaN                  1.398601                 -0.999322   \n",
       "NaN                  1.048552                  2.380406   \n",
       "\n",
       "     imu_ankle_3d_magnetometer_1  imu_ankle_3d_magnetometer_2  \\\n",
       "NaN                    -0.012521                    -0.099432   \n",
       "NaN                     0.836053                     0.754093   \n",
       "NaN                     0.935655                    -0.027952   \n",
       "NaN                    -0.976194                     0.327421   \n",
       "NaN                    -0.860425                    -0.525029   \n",
       "\n",
       "     imu_ankle_3d_magnetometer_3  \n",
       "NaN                     0.268470  \n",
       "NaN                    -0.807183  \n",
       "NaN                     0.888731  \n",
       "NaN                    -0.610844  \n",
       "NaN                    -1.203777  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split,cross_val_score,StratifiedShuffleSplit\n",
    "from sklearn.metrics import precision_score,recall_score, f1_score, confusion_matrix,roc_auc_score,roc_curve, accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler,RobustScaler\n",
    "\n",
    "#dropping columns we do not need\n",
    "dfds = dfd.drop([\"timestamp\", \"subject\"],1)\n",
    "\n",
    "#apply scaling to all columns except subject and activity \n",
    "scaler = RobustScaler()\n",
    "df_scaled = dfds.copy()\n",
    "df_scaled_test = dft.copy()\n",
    "\n",
    "df_scaled.iloc[:,1:41] = scaler.fit_transform(df_scaled.iloc[:,1:41])\n",
    "df_scaled_test.iloc[:,1:41] = scaler.fit_transform(df_scaled_test.iloc[:,1:41])\n",
    "\n",
    "df_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dev data set \n",
    "x_dev = df_scaled.drop('activityID', axis=1).values\n",
    "#y is the acitivity labels\n",
    "y_dev = df_scaled['activityID'].values\n",
    "\n",
    "# Test Dataset\n",
    "x_test = df_scaled.drop('activityID', axis=1).values\n",
    "#y is the activity labels\n",
    "y_test = df_scaled['activityID'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to calculate the accuracy,precision,recall,error and f1 score\n",
    "def metricscalc (y_true,y_pred):\n",
    "    a = accuracy_score(y_true, y_pred)\n",
    "    e = 1-a\n",
    "    p = precision_score(y_true, y_pred,average=None).mean()\n",
    "    r = recall_score(y_true, y_pred, average=None).mean()\n",
    "    f1 = f1_score(y_true, y_pred, average=None).mean()\n",
    "    print(\"Accuracy: %.3f \\nError: %.3f \\nPrecesion: %.3f \\nRecall: %.3f \\nF1 score:%.3f \\n\"% (a,e,p,r,f1) )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 Random Forest Classifier\n",
    "\n",
    "*Random Forest* algorithm can be used for regression and classification which makes it an adaptable modeling algorithm. The random forest classifier works by creating a set of decision trees from our randomly selected *development* set. The algorithms creates and combines decision trees together, the accuracy of its predictions will be better the more trees there are, i.e. the forest is more robust [[6](#1)]. Random forest also works well with datasets with high dimensionality, which the PAMAP2 dataset set is.\n",
    "\n",
    "This classifier was imported from `sklearn` and then trained and tested using our split, balanced data. The results showed the classifier is accurate and precise for this large data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.000 \n",
      "Error: 0.000 \n",
      "Precesion: 1.000 \n",
      "Recall: 1.000 \n",
      "F1 score:1.000 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#importing random forest from sklearn\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier(n_jobs =2)\n",
    "#fitting the x dev and y dev to the random forest classifier\n",
    "#training on dev data\n",
    "rfc.fit(x_dev,y_dev)\n",
    "\n",
    "#testing on test data \n",
    "y_pred_rf = rfc.predict(x_test)\n",
    "metricscalc(y_test,y_pred_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 Logistic Regression\n",
    "\n",
    "*Logistic regression* algorithm is another type of supervised learning classification. This algorithm is used to predict the probability of a target variable and is an extension for linear regression model for classification problems. \n",
    "\n",
    "The model, which was imported from sklearn, was trained using the `development` data then `tested` using test.  This model had high precision and accuracy which, as we have a balanced data set, implies that it is a good model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.809 \n",
      "Error: 0.191 \n",
      "Precesion: 0.783 \n",
      "Recall: 0.769 \n",
      "F1 score:0.774 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "#importing the logistic regression function from sklearn \n",
    "log_reg = LogisticRegression()\n",
    "#training the model on dev data \n",
    "log_reg.fit(X=x_dev, y=y_dev )\n",
    "\n",
    "#using the test data to test for the scores\n",
    "y_pred_lr = log_reg.predict(x_test)\n",
    "metricscalc(y_test, y_pred_lr)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Part 7: Dimensionality Reduction and Principal Component Analysis <a class=\"anchor\" id=\"pca\"></a>\n",
    "\n",
    "Reducing the number of dimensions that are used in our analysis will help the report more efficiently. Principal Component Analysis helps to detect the correlation between variables. If a strong correlation exists between our variables, we can then attempt to reduce the the dimensionality. As this protocol data is high-dimensional data, finding the directions of maximum variance would then make it possible to project it onto a smaller dimensional subspace.\n",
    "\n",
    "Issues with PCA are that it can be quite slow and the directions of projection are dependent on data so it is necessary to have access to all of the data at once. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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": [
    "#function to plot the scatter\n",
    "def plot_points(data, name='Scatter',axis_eq=True):\n",
    "    \"\"\"Plot the given 2d data on a scatter plot\"\"\"\n",
    "    plt.scatter(data[:,0], data[:,1], marker='.', color='b')\n",
    "    plt.title(name)8\n",
    "    plt.xlabel('variable 1')\n",
    "    plt.ylabel('variable 2')\n",
    "    plt.axis('equal')\n",
    "\n",
    "#only using relevent data points \n",
    "#removing time, subject and activity\n",
    "df1=df.loc[:,'heartrate_bpm':'imu_ankle_3d_magnetometer_3']\n",
    "#creating and array\n",
    "df2=df1.to_numpy()\n",
    "\n",
    "# We need to 'centre' the data, by subtracting the mean of each column.\n",
    "df2 = df2 - df2.mean(axis=0)\n",
    "\n",
    "# Slice the data by taking x = first column, y = second column.\n",
    "x = df2[:, 0] #heartrate\n",
    "y = df2[:, 12] #imu_chest_3d_acceleration_1\n",
    "\n",
    "# Plot the data\n",
    "plot_points(df2,axis_eq=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['heartrate_bpm', 'imu_hand_temp', 'imu_hand_3d_acceleration_1',\n",
       "       'imu_hand_3d_acceleration_2', 'imu_hand_3d_acceleration_3',\n",
       "       'imu_hand_3d_gyroscope_1', 'imu_hand_3d_gyroscope_2',\n",
       "       'imu_hand_3d_gyroscope_3', 'imu_hand_3d_magnetometer_1',\n",
       "       'imu_hand_3d_magnetometer_2', 'imu_hand_3d_magnetometer_3',\n",
       "       'imu_chest_temp', 'imu_chest_3d_acceleration_1',\n",
       "       'imu_chest_3d_acceleration_2', 'imu_chest_3d_acceleration_3',\n",
       "       'imu_chest_3d_gyroscope_1', 'imu_chest_3d_gyroscope_2',\n",
       "       'imu_chest_3d_gyroscope_3', 'imu_chest_3d_magnetometer_1',\n",
       "       'imu_chest_3d_magnetometer_2', 'imu_chest_3d_magnetometer_3',\n",
       "       'imu_ankle_temp', 'imu_ankle_3d_acceleration_1',\n",
       "       'imu_ankle_3d_acceleration_2', 'imu_ankle_3d_acceleration_3',\n",
       "       'imu_ankle_3d_gyroscope_1', 'imu_ankle_3d_gyroscope_2',\n",
       "       'imu_ankle_3d_gyroscope_3', 'imu_ankle_3d_magnetometer_1',\n",
       "       'imu_ankle_3d_magnetometer_2', 'imu_ankle_3d_magnetometer_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For index references\n",
    "\n",
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvalues are: [2.36263648e+08 1.41518918e+08 1.01722556e+08 6.41704896e+07\n",
      " 5.42623235e+07 5.16813543e+07 3.57057618e+07 2.92388541e+07\n",
      " 2.00844409e+07 1.43449145e+07 8.31771077e+06 5.60369974e+06\n",
      " 4.56611070e+06 3.59411654e+06 2.07190599e+06 2.00278471e+06\n",
      " 1.26836684e+06 9.49658512e+05 6.49319842e+05 4.88890957e+05\n",
      " 4.19626063e+05 3.09295561e+05 2.18949341e+05 1.59539625e+05\n",
      " 9.07673194e+03 2.41214363e+04 3.59351313e+04 5.68423958e+04\n",
      " 6.69873862e+04 1.01072995e+05 1.01370597e+05]\n",
      "The largest eigenvalue is at index : 0\n",
      "The smallest eigenvalue is at index : 24\n"
     ]
    }
   ],
   "source": [
    "#computing the covariance matrix\n",
    "Sigma = df2.T.dot(df2)\n",
    "\n",
    "#find eigenvectors and eigenvalues\n",
    "u, v = np.linalg.eig(Sigma) \n",
    "\n",
    "print('Eigenvalues are: {}'.format(u))\n",
    "\n",
    "# np.argsort orders in ascending order, [-1] indexes the final element of the list\n",
    "max_eigen_index = np.argsort(u)[-1] \n",
    "min_eigen_index = np.argsort(u)[0]\n",
    "\n",
    "#What is the largest eigenvalue?\n",
    "print('The largest eigenvalue is at index : {}'.format(max_eigen_index))\n",
    "\n",
    "#What is the smallest eigenvalue?\n",
    "print('The smallest eigenvalue is at index : {}'.format(min_eigen_index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What does this mean?**\n",
    "\n",
    "> *Eigenvectors* are unit vectors with length or magnitude equal to 1. They are often referred to as right vectors, which simply means a column vector.\n",
    "\n",
    "> *Eigenvalues* are coefficients applied to eigenvectors that give the vectors their length or magnitude.\n",
    "\n",
    "Above, it states that the largest eigenvalue is at the index 0 and the smallest is at 24. Each eigenvector is associated with one eigenvalue. If there are eigenvalues close to zero, they represent components that may be discarded. The eigenvalues explain the variance of the data along the new feature axes, as the index 0 has the highest variance (as it is the largest eigenvalue) this would be a good focus for **clustering** next. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "#plotting the points on our data set\n",
    "transformed=df2.dot(v)\n",
    "plot_points(transformed, name='transformed')\n",
    "\n",
    "Sigma = transformed.T.dot(transformed)\n",
    "\n",
    "#eigenvalue and eigenvector\n",
    "u1,v1 = np.linalg.eig(Sigma)\n",
    "total=sum(u1)\n",
    "prop=[x/total for x in u1]\n",
    "\n",
    "plt.arrow(0, 0, 300*prop[0]*v1[0,0], 300*prop[0]*v1[1,0] , color='r', head_width=5)\n",
    "plt.arrow(0, 0, 300*prop[1]*v1[0,1],300*prop[1]*v1[1,1] , color='g', head_width=5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same PCA method has been done below using the sklearn.decomposition library[[6](#4)]. Below I have looked at the amount of variance ratio. Plotting this, against the number of components, is a telling way to see how many to use. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12665c820>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#trying PCA with sklearn\n",
    "\n",
    "#using the sklearn function\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA()\n",
    "#fitting pca to the fit\n",
    "pca.fit(df2)\n",
    "var= pca.explained_variance_ratio_\n",
    "var1=np.cumsum(np.round(pca.explained_variance_ratio_, decimals=4)*100)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14,9))\n",
    "\n",
    "plt.title(\"PCA Variance against num of Componmnets\")\n",
    "plt.ylabel(\"Variance %\")\n",
    "plt.xlabel(\"Number of componments\")\n",
    "l = plt.axhline(94, color=\"red\")\n",
    "plt.grid()\n",
    "plt.plot(var1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure above suggest that the number of components to use is around 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "\n",
    "## Part 8: K Means Clustering <a class=\"anchor\" id=\"kmeans\"></a>\n",
    "\n",
    "##### Unsupervised learning\n",
    "\n",
    "K-means clustering is a simple method of unsupervised learning which, in contrast with *supervised learning*, makes inferences from the dataset without knowing the labels. The labels for our data set would be the activity number and so we will start by removing this. Removing the labels allows us to create clusters based on other information within the data. *Means* in K-Means clustering refers to finding the mean (i.e average) within the data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1 How many clusters should we use?\n",
    "\n",
    "First, as this is an unsupervised learning model, it is necessary to remove all labels from the data set that could be pre-created clusters. For this data, I have removed `subject` and `activityID`. We then normalize the data set using the `StandardScaler`. To check, the normalized data should have a mean of 0 and standard deviation of 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Activity labels: [1, 2, 3, 17, 16, 12, 13, 4, 7, 6, 5, 24]\n",
      "The mean: 6.239856631572901e-17\n",
      "The standard deviation: 1.0000000000000002\n"
     ]
    }
   ],
   "source": [
    "#normalising the data\n",
    "#checking our mean and standard deviation \n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#converting the labels into strings\n",
    "labels = df['activityID']\n",
    "#dropping unneeded columns for clustering \n",
    "df3 = df.drop(['subject','timestamp', 'activityID'], axis = 1)\n",
    "\n",
    "labels_keys = labels.unique().tolist()\n",
    "labels = np.array(labels)\n",
    "\n",
    "#Checking the activity labels are correct\n",
    "print('Activity labels: ' + str(labels_keys))\n",
    "\n",
    "#using the standardisation function to normalise df3\n",
    "df3 = StandardScaler().fit_transform(df3)\n",
    "\n",
    "#checking when the normalized data has a mean of zero and sd of 1\n",
    "print('The mean: ' +str(np.mean(df3)))\n",
    "print('The standard deviation: '+str(np.std(df3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  1,  1, ..., 24, 24, 24])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What are the labels?\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#checking the optimal k value \n",
    "\n",
    "#Number of potential clusters \n",
    "ks = range(1, 9)\n",
    "inertias = []\n",
    "\n",
    "#KMeans imported from sklearn\n",
    "for k in ks:\n",
    "    model = KMeans(n_clusters=k)\n",
    "    model.fit(df3)\n",
    "    inertias.append(model.inertia_)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.plot(ks, inertias, '-o')\n",
    "plt.xlabel('Number of clusters, k')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('Number of clusters vs Inertia')\n",
    "\n",
    "#plotting x labels with number of clusters\n",
    "plt.xticks(ks)\n",
    "\n",
    "#showing the graph\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the best cluster is at the elbow of the graph, 2 or 3 clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this function applies k-means clustering alrorithm on a dataset \n",
    "#and prints the crosstab of cluster and actual labels \n",
    "#and clustering performance parameters (ARI and silhouette).\n",
    "\n",
    "def k_means(n_clust, data_frame, true_labels):\n",
    "    k_means = KMeans(n_clusters = n_clust, n_init=30)\n",
    "    k_means.fit(data_frame)\n",
    "    c_labels = k_means.labels_\n",
    "    df = pd.DataFrame({'clust_label': c_labels, 'orig_label': true_labels.tolist()})\n",
    "    ct = pd.crosstab(df['clust_label'], df['orig_label'])\n",
    "    y_clust = k_means.predict(data_frame)\n",
    "    display(ct)\n",
    "    #printing the values for each of the outcomes\n",
    "    print('% 9s' % 'Inertia  ARI     Silhouette')\n",
    "    print('%i %.3f   %.3f'\n",
    "      %(k_means.inertia_,\n",
    "      adjusted_rand_score(true_labels, y_clust),\n",
    "      silhouette_score(data_frame, y_clust, metric='euclidean')))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>orig_label</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>24</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clust_label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>15971</td>\n",
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       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1570</td>\n",
       "      <td>16834</td>\n",
       "      <td>17256</td>\n",
       "      <td>19782</td>\n",
       "      <td>5812</td>\n",
       "      <td>8749</td>\n",
       "      <td>14319</td>\n",
       "      <td>10698</td>\n",
       "      <td>9569</td>\n",
       "      <td>15997</td>\n",
       "      <td>21696</td>\n",
       "      <td>2544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "orig_label      1      2      3      4     5     6      7      12    13  \\\n",
       "clust_label                                                               \n",
       "0            15971     47      0      0    21     1      8      1    16   \n",
       "1             1570  16834  17256  19782  5812  8749  14319  10698  9569   \n",
       "\n",
       "orig_label      16     17    24  \n",
       "clust_label                      \n",
       "0                0      0    28  \n",
       "1            15997  21696  2544  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inertia  ARI     Silhouette\n",
      "4425678 0.041   0.192\n"
     ]
    }
   ],
   "source": [
    "#Looking at Silhouette and ARI for 2 clusters\n",
    "k_means(n_clust=2, data_frame=df3, true_labels=labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "orig_label      1      2      3      4     5     6      7     12    13    16  \\\n",
       "clust_label                                                                    \n",
       "0              973    523   1814  15373  4954  6957  10006  9015  5955  8961   \n",
       "1            15967      3      0      0    17     1      7     0    16     0   \n",
       "2              601  16355  15442   4409   862  1792   4314  1684  3614  7036   \n",
       "\n",
       "orig_label      17    24  \n",
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       "2            21438    18  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inertia  ARI     Silhouette\n",
      "4185998 0.158   0.119\n"
     ]
    }
   ],
   "source": [
    "#Looking at Silhouette and ARI for 3 clusters\n",
    "k_means(n_clust=3, data_frame=df3, true_labels=labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "orig_label      1      2      3      4     5     6     7     12    13    16  \\\n",
       "clust_label                                                                   \n",
       "0            15960      0      0      0     9     1     8     0    15     0   \n",
       "1              462    424    366   7168  2956  4387  5724  3069  3138  3582   \n",
       "2              200  15568   5426    705   250  2711  2556   478  2332  3259   \n",
       "3              919    889  11464  11909  2618  1651  6039  7152  4100  9156   \n",
       "\n",
       "orig_label      17    24  \n",
       "clust_label               \n",
       "0                0    16  \n",
       "1               26  1547  \n",
       "2            19937    54  \n",
       "3             1733   955  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inertia  ARI     Silhouette\n",
      "4016688 0.192   0.083\n"
     ]
    }
   ],
   "source": [
    "#Looking at Silhouette and ARI for 4 clusters\n",
    "k_means(n_clust=4, data_frame=df3, true_labels=labels)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluating the different cluster sizes**\n",
    "\n",
    "*Silhouette score*\n",
    "\n",
    "In order to evaluate the quality of each of our clusters, the silhouette score needs to be calculated. The silhouette score is calculated for each sample of different clusters. \n",
    "The value of the silhouette score varies from -1 to 1. If the score is 1, each cluster is concentrated and well-separated from other clusters [[8](#8)]. A value near 0 represents overlapping clusters with samples very close to the boundaries of the neighbouring clusters. A negative score indicates that the samples could have been assigned to the wrong clusters. We have a score of 0.192,0.119 and 0.083* when there are 2,3 and 4 clusters respectively. This implies overlapping clusters, as also confirmed by the graph below.\n",
    "\n",
    "*Adjusted rand score (ARI)*\n",
    "\n",
    "The ARI score is used to determine whether two cluster results are similar to each other. This value is equal to 0 when points are assigned into clusters randomly and it equals to 1 when two cluster results are same. Our ARI scores are 0.041, 0.158 and 0.192* respectively.\n",
    "\n",
    "*Which cluster should we use?*\n",
    "\n",
    "None of the silhouette and ARI scores are very telling as they are all below 0.2. However, 3 clusters has the *best* overall score so I will choose this for the number of clusters to use below. \n",
    "\n",
    "*What are the disadvantages of k-means?*\n",
    "\n",
    "The k-means algorithm must access every point in the dataset and thus can be relatively slow as the number of samples grow. As this large dataset has been down sampled, it is not too slow but this is good to note for any further projects.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>*Please note</b> these scores may change if the kernal is re-run. \n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2 Visualising the clusters\n",
    "\n",
    "From the ARI and silhouette score, I have chosen to use  clusters and will visualise these clusters below. As predicted from the low ARI and Silhouette score, the clusters are overlapping and not distinct. This could be prevented, if done again, by the overlapping k-means algorithm which is an extension of this traditional k-means algorithm below [[9](#1)]. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 ... 1 1 1]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x191ff26d0>"
      ]
     },
     "execution_count": 63,
     "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": [
    "#Plotting the clusters\n",
    "\n",
    "from sklearn import cluster\n",
    "\n",
    "#specifiying the number of clusters\n",
    "K=3\n",
    "clf=cluster.KMeans(init='random',n_clusters=K)\n",
    "clf.fit(df3)  \n",
    "\n",
    "print(clf.predict(df3))\n",
    "\n",
    "#importing collections from a library\n",
    "from collections import defaultdict\n",
    "\n",
    "#setting a function to divide the data into labels\n",
    "#coordinating the points to be put in appropriate lists \n",
    "def divide(data,labels):\n",
    "    xclusters=defaultdict(list)\n",
    "    yclusters=defaultdict(list)\n",
    "    for datapoint,label in zip(data,labels):\n",
    "        x=datapoint[0]\n",
    "        y=datapoint[1]\n",
    "        xclusters[label].append(x)\n",
    "        yclusters[label].append(y)\n",
    "    return xclusters,yclusters\n",
    "\n",
    "#dividing clusters \n",
    "clusters=divide(df3,clf.predict(df3))\n",
    "\n",
    "#plotting the clusters \n",
    "plt.scatter(clusters[0][0],clusters[1][0],alpha=0.4)\n",
    "plt.scatter(clusters[0][1],clusters[1][1],alpha=0.4)\n",
    "plt.scatter(clusters[0][2],clusters[1][2],alpha=0.4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Part 9: Summary <a class=\"anchor\" id=\"summary\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In summary, using evidence from the PAMAP2 data set, this report investigated and analysed information on subjects partaking in physical activities. The dataset recorded 18 data points over time.  The goal of this report was to derive actionable insights to support the development of a hardware/software which can determine the type of physical activity carried out by an individual. \n",
    "\n",
    "**Handling missing and dirty data**\n",
    "\n",
    "In order to have clean and filtered data, pre-processing steps were needed. This meant cleaning the data that could have been error, removal of `NaN` values and down sampling. Then, to make sure no assumptions are being made about the data, the data was split 50% 50% into `dev` and `test` set.\n",
    "\n",
    "\n",
    "**Thorough exploratory data analysis**\n",
    "\n",
    "To get to grips with the dataset, initial exploration of averages and correlations was important. This showed that there was a correlations between the average `heartrate` and the type of activity. This then lead to a further analysis of the type of activity and heartrate which gave strong evidence that the heartrate changes when the subjects were changing activities. After this, some exploration of moving and not-moving activities was necessary. This then showed that , when moving, the subjects mean heartrate was *higher* then when still. This was then remembered as a potential hypothesis. \n",
    "\n",
    "Temperature also stood out as something to explore in the EDA, there were slight correlations within temperature but the distinction between activities was not as clear as heartrate. There were also correlations with `magnetometer_3`, and activity type but it was not as strong so did not look further, this could be a good step if we were to take this report for further anaysis though. \n",
    "\n",
    "\n",
    "The next step was to **develop and test 4 hypotheses**:\n",
    "\n",
    " 1. ***If*** higher heartrate is related to moving, ***then*** exercising will have a higher heartbeat than average (105bpm).\n",
    " \n",
    " > This was **<span style=\"color:green\">true</span>**\n",
    " \n",
    " 2. ***If*** higher heartrate is related to intense activities, ***then*** running and rope jumping will have a higher heartbeat than moving activity average (122bpm).\n",
    " \n",
    " > This was **<span style=\"color:green\">true</span>**\n",
    "\n",
    " 3. ***If*** higher temperature is related to running, ***then*** exercising will imply a higher temperature than average.\n",
    " \n",
    " > This was **<span style=\"color:red\">false</span>**\n",
    "\n",
    " 4. ***If*** higher temperature is related to household chores, ***then*** ironing and vacuum cleaning will imply a higher temperature than average.\n",
    " \n",
    " >This was **<span style=\"color:green\">true</span>**\n",
    "\n",
    "\n",
    "The final step, **to develop and test at least one model**:\n",
    "\n",
    "*Supervised Learning*\n",
    "\n",
    "Supervised learning is a method of modeling and is defined by its use of labeled datasets. As this report is making actionable insights into type of activity, this was the label to base the models on. Both *Random Forest Classifier* and the *Logistic Regression* models had a good accuracy, precision and recall. We could rate them by accuracy because our development and test data was balanced. Both were trained on the `development` data then tested on the `test` set. Having high scores indicates that these models classified and predicted outcomes accurately. This shows that there is are distinct differences between the activities.\n",
    "\n",
    "*Unsupervised learning*\n",
    "\n",
    "K-means was the form of unsupervised learning, where unlike supervised, we removed the labels. We used the K-mean algorithm imported from *sklearn* to assign the data into k-groups. Firstly, we determined the number of clusters to use based on the ARI and Silhouette score. For this data, both scores were relatively low which implied overlapping clusters. However, the silhouette score and ARI were the best when k=3, which implies 3 clusters. When visualising this, overlap was apparent.  Potential solutions to this could be the overlapping k-means algorithm.\n",
    "\n",
    "\n",
    "**In conclusion, what are the insights from the data assuming the goal is to develop a hardware/software?**\n",
    "\n",
    "> If the heartrate is above **105bpm** the subject is moving, and below the subject is still. \n",
    "\n",
    "> If the heartrate is above **122bpm** the subject is doing an intense activity such as rope-jumping or running.\n",
    "\n",
    "> If the temperature is above **36 celsius** the subject could be doing household chores.\n",
    "\n",
    "This was a large data set which could provide more actionable insights, I am sure. Further analysis would be needed but these are a few of the insights which were found in this PAMAP2 dataset analysing subjects during physical activities which will, hopefully, help develop a hardware/software.\n",
    "\n",
    "***\n",
    "\n",
    "<span style=\"color:green\">\n",
    "    \n",
    "Therefore, some potential hardware/software ideas for a developer..\n",
    "\n",
    "***Hardware:*** Special Bra/Chest Piece with chest thermometer and heartrate monitor. \n",
    "\n",
    "***Software:*** Application on the phone which records the chest temperature and heartrate. The software will take into account how long the subject has been taking park in each activity. </span>\n",
    "\n",
    "***"
   ]
  },
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   "cell_type": "markdown",
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    "***\n",
    "\n",
    "## Part 10: References\n",
    "\n",
    "1. Archive.ics.uci.edu. (2012). UCI Machine Learning Repository: PAMAP2 Physical Activity Monitoring Data Set <a class=\"anchor\" id=\"1\"></a>\n",
    "\n",
    "http://archive.ics.uci.edu/ml/datasets/pamap2+physical+activity+monitoring\n",
    "\n",
    "2. Heartrate averages. What is normal?  Laskowski M.D., E. R. <a class=\"anchor\" id=\"2\"></a>\n",
    "\n",
    "https://www.mayoclinic.org/healthy-lifestyle/fitness/expert-answers/heart-rate/faq-20057979#:~:text=A%20normal%20resting%20heart%20rate%20for%20adults%20ranges%20from%2060,to%2040%20beats%20per%20minute.\n",
    "\n",
    "3. Body temperature norms, MedlinePlus [Accessed Dec 2021] <a class=\"anchor\" id=\"3\"></a>\n",
    "\n",
    "https://medlineplus.gov/ency/article/001982.htm#:~:text=The%20average%20normal%20body%20temperature,by%20an%20infection%20or%20illness.\n",
    "\n",
    "4. Imbalanced data [Accessed Dec 2021]\n",
    "\n",
    "https://towardsdatascience.com/imbalanced-data-when-details-matter-16bd3ec7ef74\n",
    "\n",
    "5. The Importance of Complexity in Model Selection Myung, J. (2000)\n",
    "\n",
    "https://www.sciencedirect.com/science/article/abs/pii/S002224969991283X\n",
    "\n",
    "\n",
    "6. Sklearn decomposition [Accessed Dec 2021]  <a class=\"anchor\" id=\"4\"></a>\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html\n",
    "\n",
    "7. Random Forest Classifier Scikit-Learn [Accessed Jan 2021]\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html \n",
    "\n",
    "\n",
    "8. Advanced Machine Learning with Python (2016) Hearty, J. <a class=\"anchor\" id=\"5\"></a>\n",
    "\n",
    "https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781784398637/1/ch01lvl1sec11/introducing-k-means-clustering\n",
    "\n",
    "9. Overlapping K-Means [Accessed Jan 2022]\n",
    "\n",
    "https://www.sciencedirect.com/science/article/abs/pii/S0957417416305097#:~:text=One%20of%20the%20simplest%20and,to%20the%20initial%20cluster%20centroids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Submission length is 4015\n"
     ]
    }
   ],
   "source": [
    "##counting all of the markdown cells in the notebook saved at filepath\n",
    "\n",
    "import io\n",
    "from nbformat import current\n",
    "\n",
    "filepath=\"Report2final_244788_DSRM.ipynb\"\n",
    "question_count=0\n",
    "\n",
    "with io.open(filepath, 'r', encoding='utf-8') as f:\n",
    "    nb = current.read(f, 'json')\n",
    "\n",
    "word_count = 0\n",
    "for cell in nb.worksheets[0].cells:\n",
    "    if cell.cell_type == \"markdown\":\n",
    "        word_count += len(cell['source'].replace('#', '').lstrip().split(' '))\n",
    "print(\"Submission length is {}\".format(word_count-question_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "263.797px"
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   "toc_section_display": true,
   "toc_window_display": true
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