{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Health Analytics based on Apple Health Data\n", "\n", "The project takes Raw Data from iPhone and converts the datapoints like Step Count, Sleep, Flights Climbed to build a Health Analytics and Prediction System that can be useful to understand patterns. We have also collected user **User Information** for all our 25 User Base whose data we have have analysed in this Project.\n", "\n", "This project will be extremely helpful to understand User Health Patterns and help users self-reflect their Health Metrics with respect to their friends and community\n", "\n", "**Libraries used:**\n", "\n", "1. Pandas\n", "2. Numpy\n", "3. Matplotlib\n", "4. Seaborn\n", "5. Sklearn\n", "6. Pyplot\n", "7. CalMap" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from datetime import datetime\n", "from matplotlib import pyplot as plt\n", "import os.path\n", "from pathlib import Path\n", "import seaborn as sns; sns.set(style=\"ticks\", color_codes=True)\n", "import calmap" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read the data from CSV for all the 25 Users that we collected XML and converted to CSV" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def read_files(filename, number_folders, delimiter = ';'):\n", " data = []\n", " for i in range(1,number_folders+1):\n", " file = \"users/\"+str(i)+\"/\"+str(filename)\n", " my_file = Path(file)\n", " if my_file.is_file():\n", " df = pd.read_csv(file, delimiter = ';')\n", " data.append(df)\n", " else:\n", "# data.append([\"Not Found\"])\n", " print(i, filename, \"Not found\")\n", " return data\n", "\n", "data = read_files('HKQuantityTypeIdentifierStepCount.csv', 25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Pre-Processing and Feature Engineering\n", "\n", "In this step we preporcess the data and perform feature engineering as follows:\n", "\n", "1. Drop Unuseful Columns\n", "2. Remove -400 from all time fields like creationDate, startDate,endDate\n", "3. Split date time into separate features: Date and Time\n", "4. Drop Unuseful Time fields\n", "5. Create new features" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def preprocess(data, drop_columns):\n", " \"\"\"\n", " Input:\n", " list of df (dataframe): input list of all dataframes\n", " Output:\n", " list of df (dataframe): input list of all dataframes\n", " \"\"\"\n", " number_folders = len(data)\n", " for i in range(1,number_folders+1):\n", " # drop unuseful columns\n", " data[i-1].drop(drop_columns,axis = 1, inplace=True)\n", " # remove the string -400 from date field\n", " data[i-1]['creationDate'] = data[i-1]['creationDate'].astype(str).str[:-6]\n", " data[i-1]['startDate'] = data[i-1]['startDate'].astype(str).str[:-6]\n", " data[i-1]['endDate'] = data[i-1]['endDate'].astype(str).str[:-6]\n", " return data\n", "\n", "column_names = ['type','sourceName','sourceVersion','device','unit']\n", "data_clean = preprocess(data, column_names)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The below method is used to split date time features separately for the purpose of extracting day, time, hour of the day" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def time_transformation(df):\n", " \"\"\"\n", " Input:\n", " df (dataframe): input dataframe\n", " Output:\n", " df (dataframe): return a dataframe with formatted datetime columns\n", " \"\"\"\n", " df['CREATION_DATE'] = df['creationDate'].apply(lambda x : x.split(' ')[0])\n", " df['CREATION_DATE'] = pd.to_datetime(df['CREATION_DATE'],format='%Y-%m-%d')\n", " df['CREATION_TIME'] = df['creationDate'].apply(lambda x : x.split(' ')[1])\n", " df['CREATION_TIME'] = pd.to_datetime(df['CREATION_TIME'],format='%H:%M:%S').apply(lambda x:x.time())\n", " \n", " df['START_DATE'] = df['startDate'].apply(lambda x : x.split(' ')[0])\n", " df['START_DATE'] = pd.to_datetime(df['START_DATE'],format='%Y-%m-%d')\n", " df['START_TIME'] = df['startDate'].apply(lambda x : x.split(' ')[1])\n", " df['START_TIME'] = pd.to_datetime(df['START_TIME'],format='%H:%M:%S').apply(lambda x:x.time())\n", " \n", " df['END_DATE'] = df['endDate'].apply(lambda x : x.split(' ')[0])\n", " df['END_DATE'] = pd.to_datetime(df['END_DATE'],format='%Y-%m-%d')\n", " df['END_TIME'] = df['endDate'].apply(lambda x : x.split(' ')[1])\n", " df['END_TIME'] = pd.to_datetime(df['END_TIME'],format='%H:%M:%S').apply(lambda x:x.time())\n", " return df" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def perfom_time_transform(data):\n", " number_folders = len(data)\n", " for i in range(1,number_folders+1):\n", " data[i-1] = time_transformation(data[i-1]) \n", " data[i-1].drop((['creationDate','startDate','endDate','CREATION_DATE','CREATION_TIME',\n", " 'END_TIME','START_DATE','START_TIME']), axis=1, inplace=True)\n", " return data\n", "\n", "data_transform = perfom_time_transform(data)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " id avg\n", "0 1 5073.455556\n", "1 2 6606.426190\n", "2 3 8978.688889\n", "3 4 3232.273333\n", "4 5 4884.705208" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "life_time_avg.head(5)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from datetime import datetime, date\n", "\n", "def sleep_time_transform(data):\n", " number_folders = len(data)\n", " for i in range(1,number_folders+1):\n", " data[i-1] = time_transformation(data[i-1])\n", " carry = datetime.strptime('0000','%H%M').time()\n", " endtime = datetime.strptime('2359','%H%M').time()\n", " starttime = datetime.strptime('0000','%H%M').time()\n", " tot1=0\n", " tot2=0\n", " for index, row in data[i-1].iterrows():\n", " if row['START_DATE'] == row['END_DATE']:\n", " diff = datetime.combine(date.min, row['END_TIME']) - datetime.combine(date.min, row['START_TIME'])\n", " tot1 = diff.total_seconds()\n", " data[i-1].set_value(index,'value', tot1)\n", " else:\n", " carry = (datetime.combine(date.min, endtime) - datetime.combine(date.min, row['START_TIME'])) + (datetime.combine(date.min, row['END_TIME']) - datetime.combine(date.min, starttime))\n", " tot2 = carry.total_seconds()\n", " data[i-1].set_value(index,'value',tot2)\n", " carry = datetime.strptime('0000','%H%M').time()\n", " tot1=0\n", " tot2=0\n", " # dropping columns \n", " data[i-1].drop((['creationDate','startDate','endDate','CREATION_DATE','CREATION_TIME', 'END_TIME',\n", " 'START_DATE','START_TIME']), axis=1, inplace=True)\n", " data[i-1]['value'] = data[i-1]['value']/3600\n", "# print(data[i-1].shape)\n", " data[i-1] = data[i-1].groupby('END_DATE', as_index=False).sum()\n", "# date[i-1].reset_index()\n", "# print(data[i-1].shape)\n", "# print(data[i-1].head())\n", " return data\n", "\n", "# print(sleep_analysis_life_time_avg)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n", "4 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n", "5 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n", "6 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n", "7 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n", "8 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n", "10 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n", "11 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n", "12 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n", "13 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n", "16 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n", "20 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n", "22 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n", "25 HKCategoryTypeIdentifierSleepAnalysis.csv Not found\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:16: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n", " app.launch_new_instance()\n", "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:20: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " id avg\n", "0 1 2.183262\n", "1 2 4.100364\n", "2 3 3.759584\n", "3 4 2.027935\n", "4 5 2.102282" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "distance_analysis = read_files('HKQuantityTypeIdentifierDistanceWalkingRunning.csv', 25)\n", "distance_analysis_clean = preprocess(distance_analysis, ['type','sourceName','sourceVersion','device','unit'])\n", "distance_analysis_transform = perfom_time_transform(distance_analysis_clean)\n", "distance_analysis_features = create_date_features(distance_analysis_transform)\n", "distance_analysis_life_time_avg = calculate_average_steps(distance_analysis_features, \n", " 'HKQuantityTypeIdentifierDistanceWalkingRunning.csv', 25)\n", "distance_analysis_life_time_avg.head(5)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idavg
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" ], "text/plain": [ " id avg\n", "0 1 5073.455556\n", "1 2 6606.426190\n", "2 3 8978.688889\n", "3 4 3232.273333\n", "4 5 4884.705208" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "steps_analysis = read_files('HKQuantityTypeIdentifierStepCount.csv', 25)\n", "steps_analysis_clean = preprocess(steps_analysis, ['type','sourceName','sourceVersion','device','unit'])\n", "steps_analysis_transform = perfom_time_transform(steps_analysis_clean)\n", "steps_analysis_features = create_date_features(steps_analysis_transform)\n", "steps_analysis_life_time_avg = calculate_average_steps(steps_analysis_features, \n", " 'HKQuantityTypeIdentifierStepCount.csv', 25)\n", "steps_analysis_life_time_avg.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Merging User Data with features created form the Apple Health\n", "\n", "We did a survey of all our users to understand their **Activities**, **Profession, City, Sex, Eating Habits, etc** and create a better Healthy Analysis System. \n", "\n", "We wish to merge features such as:\n", "* Step Count\n", "* Distance of walking or running\n", "* Sleep analysis" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "user_information = pd.read_csv(\"all_users.csv\", delimiter = ',')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idNameAgeSexProfessionNationalityCityRunningSwimmingCyclingGymmingCommuteCommute_TimeVegNon_Veg
01Bhavya Sharma23MaleStudentIndianPittsburgh0001Walk1010
12Ayush Jain25MaleStudentIndianPittsburgh0001Walk1001
23Rohan Panikkar26MaleStudentIndianPittsburgh0000Walk2001
34Om Khard24MaleFreelance EditorIndianMumbai1101Bus6011
45Vidushi Dikshit24FemaleSoftware EngineerIndianKansas0001Car4501
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" ], "text/plain": [ " id Name Age Sex Profession Nationality \\\n", "0 1 Bhavya Sharma 23 Male Student Indian \n", "1 2 Ayush Jain 25 Male Student Indian \n", "2 3 Rohan Panikkar 26 Male Student Indian \n", "3 4 Om Khard 24 Male Freelance Editor Indian \n", "4 5 Vidushi Dikshit 24 Female Software Engineer Indian \n", "\n", " City Running Swimming Cycling Gymming Commute Commute_Time Veg \\\n", "0 Pittsburgh 0 0 0 1 Walk 10 1 \n", "1 Pittsburgh 0 0 0 1 Walk 10 0 \n", "2 Pittsburgh 0 0 0 0 Walk 20 0 \n", "3 Mumbai 1 1 0 1 Bus 60 1 \n", "4 Kansas 0 0 0 1 Car 45 0 \n", "\n", " Non_Veg \n", "0 0 \n", "1 1 \n", "2 1 \n", "3 1 \n", "4 1 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "user_information.head(5)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# renaming columns\n", "steps_analysis_life_time_avg.rename(columns={\"avg\": \"steps\"}, inplace = True)\n", "distance_analysis_life_time_avg.rename(columns={\"avg\": \"distance\"}, inplace = True)\n", "sleep_analysis_life_time_avg.rename(columns={\"avg\": \"sleep\"}, inplace = True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "merged = pd.merge(user_information, steps_analysis_life_time_avg, on='id', how='left')\n", "merged = pd.merge(merged, distance_analysis_life_time_avg, on='id', how='left')\n", "merged = pd.merge(merged, sleep_analysis_life_time_avg, on='id', how='left')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idNameAgeSexProfessionNationalityCityRunningSwimmingCyclingGymmingCommuteCommute_TimeVegNon_Vegstepsdistancesleep
01Bhavya Sharma23MaleStudentIndianPittsburgh0001Walk10105073.4555562.1832620.000000
12Ayush Jain25MaleStudentIndianPittsburgh0001Walk10016606.4261904.1003644.130765
23Rohan Panikkar26MaleStudentIndianPittsburgh0000Walk20018978.6888893.7595843.579188
34Om Khard24MaleFreelance EditorIndianMumbai1101Bus60113232.2733332.0279350.000000
45Vidushi Dikshit24FemaleSoftware EngineerIndianKansas0001Car45014884.7052082.1022820.000000
\n", "
" ], "text/plain": [ " id Name Age Sex Profession Nationality \\\n", "0 1 Bhavya Sharma 23 Male Student Indian \n", "1 2 Ayush Jain 25 Male Student Indian \n", "2 3 Rohan Panikkar 26 Male Student Indian \n", "3 4 Om Khard 24 Male Freelance Editor Indian \n", "4 5 Vidushi Dikshit 24 Female Software Engineer Indian \n", "\n", " City Running Swimming Cycling Gymming Commute Commute_Time Veg \\\n", "0 Pittsburgh 0 0 0 1 Walk 10 1 \n", "1 Pittsburgh 0 0 0 1 Walk 10 0 \n", "2 Pittsburgh 0 0 0 0 Walk 20 0 \n", "3 Mumbai 1 1 0 1 Bus 60 1 \n", "4 Kansas 0 0 0 1 Car 45 0 \n", "\n", " Non_Veg steps distance sleep \n", "0 0 5073.455556 2.183262 0.000000 \n", "1 1 6606.426190 4.100364 4.130765 \n", "2 1 8978.688889 3.759584 3.579188 \n", "3 1 3232.273333 2.027935 0.000000 \n", "4 1 4884.705208 2.102282 0.000000 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploratory Data Analysis\n", "\n", "We will not look at patterns and relationships for different sub-groups of data for example:\n", "\n", "1. Patterns by Nationality\n", "2. Eating Habits - Veg or Non-Veg\n", "3. Patterns by Hobbies\n", "4. Patterns by Activity" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Lets look at the pairplots first to derive relationships between attributes in our users\n", "pairplot = merged.drop((['id','Name']),axis=1)\n", "\n", "g = sns.pairplot(pairplot, hue = 'Nationality')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Univariate Plots\n", "\n", "The univariate plots are helpful to undertand the distrbution of our features" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pairplot.hist()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Density Plots\n", "\n", "We also did Density plots to understand the distributions better according to density" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pairplot.plot(kind='density', subplots=True, layout=(4,3), sharex=False)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlation Matrix Plot\n", "\n", "From the correlation matrix, we can see that Swimming and Cycling are Highly Correlated (Negatively). Also age and profession are positively correlated. This could help us in grouping users to understand their patterns" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "correlations = pairplot.corr()\n", "# plot correlation matrix\n", "fig = plt.figure()\n", "fig.set_size_inches(30, 15)\n", "ax = fig.add_subplot(111)\n", "cax = ax.matshow(correlations, vmin=-1, vmax=1)\n", "fig.colorbar(cax)\n", "ticks = np.arange(0,11,1)\n", "ax.set_xticks(ticks)\n", "ax.set_yticks(ticks)\n", "ax.set_xticklabels(pairplot.columns.tolist())\n", "ax.set_yticklabels(pairplot.columns.tolist())\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Model : Unsupervised Learning\n", "\n", "We decided to use Unsupervised learning because we do not have any Ground Truth data available. The motivation behind using unsupervised learning is to understand **patterns** of User health behaviour and recommend them useful insights to help them **self-reflect** and improve to healthier living patterns\n", "\n", "We will be using KMeans clustering to perform our Unsupervised learning" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "from sklearn.cluster import KMeans\n", "from sklearn.preprocessing import StandardScaler" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## One Hot Encoding" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "merged['Profession']=merged['Profession'].apply(lambda x:x.replace(' ','_'))\n", "merged['City']=merged['City'].apply(lambda x:x.replace(' ','_'))\n", "merged.drop((['id','Name']),axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "def one_hot(df, list_var):\n", " list_df = pd.get_dummies(df[list_var])\n", " df = pd.concat([df,list_df],axis=1) \n", " return df" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "def get_object_type(df):\n", " cat_col = []\n", " for col in df.columns:\n", " if df[col].dtype == 'object':\n", " cat_col.append(col)\n", " return cat_col" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "cat_col = get_object_type(pairplot)\n", "clean_cat_col= []\n", "for col in cat_col:\n", " if col != 'steps' and col != 'steps':\n", " clean_cat_col.append(col)\n", "\n", "merged_clean = one_hot(merged,clean_cat_col)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AgeRunningSwimmingCyclingGymmingCommute_TimeVegNon_Vegstepsdistance...City_Jersey_CityCity_KansasCity_MumbaiCity_PerthCity_PittsburghCity_SFOCommute_BusCommute_CarCommute_TrainCommute_Walk
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" ], "text/plain": [ " Age Running Swimming Cycling Gymming Commute_Time Veg Non_Veg \\\n", "0 23 0 0 0 1 10 1 0 \n", "1 25 0 0 0 1 10 0 1 \n", "2 26 0 0 0 0 20 0 1 \n", "3 24 1 1 0 1 60 1 1 \n", "4 24 0 0 0 1 45 0 1 \n", "\n", " steps distance ... City_Jersey_City City_Kansas \\\n", "0 5073.455556 2.183262 ... 0 0 \n", "1 6606.426190 4.100364 ... 0 0 \n", "2 8978.688889 3.759584 ... 0 0 \n", "3 3232.273333 2.027935 ... 0 0 \n", "4 4884.705208 2.102282 ... 0 1 \n", "\n", " City_Mumbai City_Perth City_Pittsburgh City_SFO Commute_Bus \\\n", "0 0 0 1 0 0 \n", "1 0 0 1 0 0 \n", "2 0 0 1 0 0 \n", "3 1 0 0 0 1 \n", "4 0 0 0 0 0 \n", "\n", " Commute_Car Commute_Train Commute_Walk \n", "0 0 0 1 \n", "1 0 0 1 \n", "2 0 0 1 \n", "3 0 0 0 \n", "4 1 0 0 \n", "\n", "[5 rows x 32 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_clean.drop((['Sex','Profession','Nationality','City','Commute']),axis=1,inplace=True)\n", "merged_clean.head()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "#change the object data type of categorical data\n", "def change_dtype(df,numeric_names,to_type):\n", " \"\"\"\n", " input:\n", " df (dataframe): input dataframe\n", " numeric_names (list): names of numeric data\n", " to_type (str): target type. 'category', 'str', 'bool'\n", " \n", " \"\"\"\n", " numeric_col = numeric_names\n", " categorical_col = list(df.columns.difference(numeric_col))\n", "\n", " for c in categorical_col:\n", " df[c] = df[c].astype(to_type)\n", " \n", " return df" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Age int64\n", "Running int64\n", "Swimming int64\n", "Cycling int64\n", "Gymming int64\n", "Commute_Time int64\n", "Veg int64\n", "Non_Veg int64\n", "steps float64\n", "distance float64\n", "sleep float64\n", "Sex_Female uint8\n", "Sex_Male uint8\n", "Profession_Entrepreneur uint8\n", "Profession_Freelance_Editor uint8\n", "Profession_Software_Engineer uint8\n", "Profession_Student uint8\n", "Nationality_China uint8\n", "Nationality_Indian uint8\n", "Nationality_Taiwanese uint8\n", "City_Adelaide uint8\n", "City_Delhi uint8\n", "City_Jersey_City uint8\n", "City_Kansas uint8\n", "City_Mumbai uint8\n", "City_Perth uint8\n", "City_Pittsburgh uint8\n", "City_SFO uint8\n", "Commute_Bus uint8\n", "Commute_Car uint8\n", "Commute_Train uint8\n", "Commute_Walk uint8\n", "dtype: object" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_clean.dtypes" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AgeRunningSwimmingCyclingGymmingCommute_TimeVegNon_Vegstepsdistance...City_Jersey_CityCity_KansasCity_MumbaiCity_PerthCity_PittsburghCity_SFOCommute_BusCommute_CarCommute_TrainCommute_Walk
023000110105073.4555562.183262...0000100001
125000110016606.4261904.100364...0000100001
226000020018978.6888893.759584...0000100001
324110160113232.2733332.027935...0010001000
424000145014884.7052082.102282...0100000100
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5 rows × 32 columns

\n", "
" ], "text/plain": [ " Age Running Swimming Cycling Gymming Commute_Time Veg Non_Veg \\\n", "0 23 0 0 0 1 10 1 0 \n", "1 25 0 0 0 1 10 0 1 \n", "2 26 0 0 0 0 20 0 1 \n", "3 24 1 1 0 1 60 1 1 \n", "4 24 0 0 0 1 45 0 1 \n", "\n", " steps distance ... City_Jersey_City City_Kansas \\\n", "0 5073.455556 2.183262 ... 0 0 \n", "1 6606.426190 4.100364 ... 0 0 \n", "2 8978.688889 3.759584 ... 0 0 \n", "3 3232.273333 2.027935 ... 0 0 \n", "4 4884.705208 2.102282 ... 0 1 \n", "\n", " City_Mumbai City_Perth City_Pittsburgh City_SFO Commute_Bus \\\n", "0 0 0 1 0 0 \n", "1 0 0 1 0 0 \n", "2 0 0 1 0 0 \n", "3 1 0 0 0 1 \n", "4 0 0 0 0 0 \n", "\n", " Commute_Car Commute_Train Commute_Walk \n", "0 0 0 1 \n", "1 0 0 1 \n", "2 0 0 1 \n", "3 0 0 0 \n", "4 1 0 0 \n", "\n", "[5 rows x 32 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_clean.head()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from copy import deepcopy\n", "import numpy as np\n", "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "plt.rcParams['figure.figsize'] = (16, 9)\n", "plt.style.use('ggplot')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 5073.455556\n", "1 6606.426190\n", "2 8978.688889\n", "3 3232.273333\n", "4 4884.705208\n", "Name: steps, dtype: float64\n", "0 0.000000\n", "1 4.130765\n", "2 3.579188\n", "3 0.000000\n", "4 0.000000\n", "Name: sleep, dtype: float64\n", "0 2.183262\n", "1 4.100364\n", "2 3.759584\n", "3 2.027935\n", "4 2.102282\n", "Name: distance, dtype: float64\n" ] } ], "source": [ "print(merged_clean[\"steps\"].head())\n", "print(merged_clean[\"sleep\"].head())\n", "print(merged_clean[\"distance\"].head())" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['Age', 'Running', 'Swimming', 'Cycling', 'Gymming', 'Commute_Time',\n", " 'Veg', 'Non_Veg', 'steps', 'distance', 'sleep', 'Sex_Female',\n", " 'Sex_Male', 'Profession_Entrepreneur', 'Profession_Freelance_Editor',\n", " 'Profession_Software_Engineer', 'Profession_Student',\n", " 'Nationality_China', 'Nationality_Indian', 'Nationality_Taiwanese',\n", " 'City_Adelaide', 'City_Delhi', 'City_Jersey_City', 'City_Kansas',\n", " 'City_Mumbai', 'City_Perth', 'City_Pittsburgh', 'City_SFO',\n", " 'Commute_Bus', 'Commute_Car', 'Commute_Train', 'Commute_Walk'],\n", " dtype='object')\n" ] } ], "source": [ "cols = merged_clean.columns\n", "print(cols)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "cluster = KMeans(n_clusters = 6)" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "merged_clean[\"cluster\"] = cluster.fit_predict(merged_clean)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "from scipy.spatial.distance import cdist\n", "# k means determine k\n", "distortions = []\n", "K = range(1,10)\n", "for k in K:\n", " kmeanModel = KMeans(n_clusters=k).fit(merged_clean)\n", " kmeanModel.fit(merged_clean)\n", " distortions.append(sum(np.min(cdist(merged_clean, kmeanModel.cluster_centers_, 'euclidean'), axis=1)) / merged_clean.shape[0])\n", "\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the elbow\n", "plt.plot(K, distortions, 'bx-')\n", "plt.xlabel('k')\n", "plt.ylabel('Distortion')\n", "plt.title('The Elbow Method showing the optimal k')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# PCA Component Creation to Visualize Clusters better\n", "\n", "In the below approach, we are creating 2 PCA components, x and y reduce our dimensionality and visualize our clusters better." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "\n", "pca = PCA(n_components = 2)\n", "\n", "merged_clean[\"x\"] = pca.fit_transform(merged_clean[cols])[:,0]\n", "merged_clean[\"y\"] = pca.fit_transform(merged_clean[cols])[:,1]\n", "merged_clean = merged_clean.reset_index()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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5 rows × 36 columns

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" ], "text/plain": [ " index Age Running Swimming Cycling Gymming Commute_Time Veg \\\n", "20 20 29 1 0 0 0 30 1 \n", "21 21 23 0 0 0 1 30 0 \n", "22 22 23 0 0 0 1 30 0 \n", "23 23 24 1 1 1 1 15 1 \n", "24 24 29 0 0 0 0 20 0 \n", "\n", " Non_Veg steps ... City_Perth City_Pittsburgh City_SFO \\\n", "20 0 7682.252941 ... 0 1 0 \n", "21 1 3951.920833 ... 0 1 0 \n", "22 1 5483.919608 ... 0 1 0 \n", "23 0 5092.648485 ... 0 1 0 \n", "24 1 6647.680000 ... 0 1 0 \n", "\n", " Commute_Bus Commute_Car Commute_Train Commute_Walk cluster \\\n", "20 0 0 0 1 2 \n", "21 0 0 0 1 3 \n", "22 0 0 0 1 5 \n", "23 0 0 0 1 1 \n", "24 0 0 0 1 0 \n", "\n", " x y \n", "20 2005.328686 9.375952 \n", "21 -1724.992209 -2.710310 \n", "22 -193.001967 2.131796 \n", "23 -584.218441 -14.121743 \n", "24 970.791726 -3.761315 \n", "\n", "[5 rows x 36 columns]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_clean.tail()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "final_clusters = merged_clean[['Age','steps','x','y']]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/vnd.plotly.v1+html": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n", "init_notebook_mode()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now create a scatter plot for all of the 6 Clusters that we have chosen using the Elbow approach with lowest decrease in losses." ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "marker": { "color": "rgba(15,152,152,0.5)", "line": { "color": "rgb(0,0,0)", "width": 1 }, "size": 10 }, "mode": "markers", "name": "Cluster 1", "type": "scatter", "x": [ 929.5673558037972, 770.3876419499859, 970.791726354481 ], "y": [ -14.199603744343172, 5.482825273902316, -3.7613150139503007 ] }, { "marker": { "color": "rgba(180,18,180,0.5)", "line": { "color": "rgb(0,0,0)", "width": 1 }, "size": 10 }, "mode": "markers", "name": "Cluster 2", "type": "scatter", "x": [ -603.3995047239753, -792.2612586433485, -840.6202239004308, -693.8226931532614, -1057.8248239147922, -798.2933254142713, -1232.4634663307731, -949.8500421055809, -791.8264690374339, -584.2184409801187 ], "y": [ -19.111934735938156, 15.310185208568756, 0.05291181976939672, -4.387872669047419, -20.52438235804893, 45.23409069975479, -0.9901264752402309, -0.1898647329021422, 0.2529855525943868, -14.121743028532391 ] }, { "marker": { "color": "rgba(132,132,132,0.8)", "line": { "color": "rgb(0,0,0)", "width": 1 }, "size": 10 }, "mode": "markers", "name": "Cluster 3", "type": "scatter", "x": [ 1910.108593709318, 2069.9610386080826, 2005.3286864700403 ], "y": [ 10.374653845195164, 9.43982066340518, 9.375951612497188 ] }, { "marker": { "color": "rgba(122,122,12,0.8)", "line": { "color": "rgb(0,0,0)", "width": 1 }, "size": 10 }, "mode": "markers", "name": "Cluster 4", "type": "scatter", "x": [ -2444.7308933340146, -2097.0868655865793, -1724.9922086216868 ], "y": [ 25.06536243375068, -3.647475041951041, -2.710309841219584 ] }, { "marker": { "color": "rgba(210,20,30,0.5)", "line": { "color": "rgb(0,0,0)", "width": 1 }, "size": 10 }, "mode": "markers", "name": "Cluster 5", "type": "scatter", "x": [ 3301.782746192508, 2999.658712431589 ], "y": [ 3.3413428171831594, -2.6545935056592564 ] }, { "marker": { "color": "rgba(240,60,70,0.5)", "line": { "color": "rgb(0,0,0)", "width": 1 }, "size": 10 }, "mode": "markers", "name": "Cluster 5", "type": "scatter", "x": [ 3301.782746192508, 2999.658712431589 ], "y": [ 3.3413428171831594, -2.6545935056592564 ] } ], "layout": {} }, "text/html": [ "
" ], "text/vnd.plotly.v1+html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trace0 = go.Scatter(x =final_clusters[merged_clean.cluster == 0][\"x\"],\n", " y = final_clusters[merged_clean.cluster == 0][\"y\"],\n", " name = \"Cluster 1\",\n", " mode = \"markers\",\n", " marker = dict(size = 10,\n", " color = \"rgba(15,152,152,0.5)\",\n", " line = dict(width = 1, color = \"rgb(0,0,0)\")))\n", "trace1 = go.Scatter(x =final_clusters[merged_clean.cluster == 1][\"x\"],\n", " y = final_clusters[merged_clean.cluster == 1][\"y\"],\n", " name = \"Cluster 2\",\n", " mode = \"markers\",\n", " marker = dict(size = 10,\n", " color = \"rgba(180,18,180,0.5)\",\n", " line = dict(width = 1, color = \"rgb(0,0,0)\")))\n", "trace2 = go.Scatter(x =final_clusters[merged_clean.cluster == 2][\"x\"],\n", " y = final_clusters[merged_clean.cluster == 2][\"y\"],\n", " name = \"Cluster 3\",\n", " mode = \"markers\",\n", " marker = dict(size = 10,\n", " color = \"rgba(132,132,132,0.8)\",\n", " line = dict(width = 1, color = \"rgb(0,0,0)\")))\n", "trace3 = go.Scatter(x =final_clusters[merged_clean.cluster == 3][\"x\"],\n", " y = final_clusters[merged_clean.cluster == 3][\"y\"],\n", " name = \"Cluster 4\",\n", " mode = \"markers\",\n", " marker = dict(size = 10,\n", " color = \"rgba(122,122,12,0.8)\",\n", " line = dict(width = 1, color = \"rgb(0,0,0)\")))\n", "trace4 = go.Scatter(x =final_clusters[merged_clean.cluster == 4][\"x\"],\n", " y = final_clusters[merged_clean.cluster == 4][\"y\"],\n", " name = \"Cluster 5\",\n", " mode = \"markers\",\n", " marker = dict(size = 10,\n", " color = \"rgba(210,20,30,0.5)\",\n", " line = dict(width = 1, color = \"rgb(0,0,0)\")))\n", "trace5 = go.Scatter(x =final_clusters[merged_clean.cluster == 4][\"x\"],\n", " y = final_clusters[merged_clean.cluster == 4][\"y\"],\n", " name = \"Cluster 5\",\n", " mode = \"markers\",\n", " marker = dict(size = 10,\n", " color = \"rgba(240,60,70,0.5)\",\n", " line = dict(width = 1, color = \"rgb(0,0,0)\")))\n", "\n", "\n", "\n", "data = [trace0,trace1,trace2,trace3,trace4,trace5]\n", "iplot(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating New Features to identify specific clusters\n", "\n", "These features will be helpful to understand the business value and make inferences from our clusters by comparing all features against the ones which are interpretable in different clusters" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "merged_clean[\"0\"] = merged_clean.cluster == 0\n", "merged_clean[\"1\"] = merged_clean.cluster == 1\n", "merged_clean[\"2\"] = merged_clean.cluster == 2\n", "merged_clean[\"3\"] = merged_clean.cluster == 3\n", "merged_clean[\"4\"] = merged_clean.cluster == 4\n", "merged_clean[\"5\"] = merged_clean.cluster == 5" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Age\n", "False 23 6\n", " 24 5\n", " 25 3\n", " 26 3\n", " 22 2\n", " 28 1\n", " 29 1\n", " 48 1\n", "True 25 1\n", " 28 1\n", " 29 1\n", "Name: Age, dtype: int64" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_clean.groupby(['0']).Age.value_counts()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 Running\n", "False 0 11\n", " 1 4\n", "True 0 7\n", " 1 3\n", "Name: Running, dtype: int64" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_clean.groupby(['1']).Running.value_counts()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2 Running\n", "False 0 17\n", " 1 5\n", "True 1 2\n", " 0 1\n", "Name: Running, dtype: int64" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_clean.groupby(['2']).Running.value_counts()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3 Running\n", "False 0 16\n", " 1 6\n", "True 0 2\n", " 1 1\n", "Name: Running, dtype: int64" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_clean.groupby(['3']).Running.value_counts()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4 Running\n", "False 0 16\n", " 1 7\n", "True 0 2\n", "Name: Running, dtype: int64" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_clean.groupby(['4']).Running.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cluster Analysis and Recommendations\n", "\n", "In this phase we interpret each of the clusters separately to understand the useful and **meaningful patterns** in our data. \n", "\n", "We were able to find out insights by highest **average steps, sleep and distance patterns.**\n", "\n", "In the below section, we give an example of a sample analysis of clusters by **highest average steps**" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['index', 'Age', 'Running', 'Swimming', 'Cycling', 'Gymming',\n", " 'Commute_Time', 'Veg', 'Non_Veg', 'steps', 'distance', 'sleep',\n", " 'Sex_Female', 'Sex_Male', 'Profession_Entrepreneur',\n", " 'Profession_Freelance_Editor', 'Profession_Software_Engineer',\n", " 'Profession_Student', 'Nationality_China', 'Nationality_Indian',\n", " 'Nationality_Taiwanese', 'City_Adelaide', 'City_Delhi',\n", " 'City_Jersey_City', 'City_Kansas', 'City_Mumbai', 'City_Perth',\n", " 'City_Pittsburgh', 'City_SFO', 'Commute_Bus', 'Commute_Car',\n", " 'Commute_Train', 'Commute_Walk', 'cluster', 'x', 'y', '0', '1', '2',\n", " '3', '4', '5', 'Sex', 'Profession', 'Nationality', 'City', 'Commute',\n", " 'Name'],\n", " dtype='object')" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged_new = user_information[['Sex', 'Profession', 'Nationality','City','Commute','Name']].copy()\n", "\n", "merged_clean_new = merged_clean\n", "\n", "analysis_df = pd.concat((merged_clean_new,merged_new),axis=1)\n", "\n", "analysis_df.columns" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Sex \n", "False Male 13\n", " Female 9\n", "True Male 2\n", " Female 1\n", "Name: Sex, dtype: int64" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df.groupby(['0']).Sex.value_counts()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 Sex \n", "False Male 10\n", " Female 5\n", "True Female 5\n", " Male 5\n", "Name: Sex, dtype: int64" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df.groupby(['1']).Sex.value_counts()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indexAgeRunningSwimmingCyclingGymmingCommute_TimeVegNon_Vegsteps...2345SexProfessionNationalityCityCommuteName
0023000110105073.455556...FalseFalseFalseFalseMaleStudentIndianPittsburghWalkBhavya Sharma
1125000110016606.426190...FalseFalseFalseFalseMaleStudentIndianPittsburghWalkAyush Jain
2226000020018978.688889...FalseFalseTrueFalseMaleStudentIndianPittsburghWalkRohan Panikkar
3324110160113232.273333...FalseTrueFalseFalseMaleFreelance EditorIndianMumbaiBusOm Khard
4424000145014884.705208...FalseFalseFalseFalseFemaleSoftware EngineerIndianKansasCarVidushi Dikshit
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5 rows × 48 columns

\n", "
" ], "text/plain": [ " index Age Running Swimming Cycling Gymming Commute_Time Veg \\\n", "0 0 23 0 0 0 1 10 1 \n", "1 1 25 0 0 0 1 10 0 \n", "2 2 26 0 0 0 0 20 0 \n", "3 3 24 1 1 0 1 60 1 \n", "4 4 24 0 0 0 1 45 0 \n", "\n", " Non_Veg steps ... 2 3 4 5 Sex \\\n", "0 0 5073.455556 ... False False False False Male \n", "1 1 6606.426190 ... False False False False Male \n", "2 1 8978.688889 ... False False True False Male \n", "3 1 3232.273333 ... False True False False Male \n", "4 1 4884.705208 ... False False False False Female \n", "\n", " Profession Nationality City Commute Name \n", "0 Student Indian Pittsburgh Walk Bhavya Sharma \n", "1 Student Indian Pittsburgh Walk Ayush Jain \n", "2 Student Indian Pittsburgh Walk Rohan Panikkar \n", "3 Freelance Editor Indian Mumbai Bus Om Khard \n", "4 Software Engineer Indian Kansas Car Vidushi Dikshit \n", "\n", "[5 rows x 48 columns]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df.head()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df[analysis_df.cluster==0][\"Commute\"].count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From our analysis we can infer that Commute has maximum counts in **Cluster 2** (Cluster Index 1), but it is mixed form, **Walk, Bus, Train and Car**. This does not point us to any valuable insight but only helps us understand the nature of the cluster" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 Commute\n", "False Walk 9\n", " Bus 4\n", " Car 2\n", "True Walk 4\n", " Bus 3\n", " Train 2\n", " Car 1\n", "Name: Commute, dtype: int64" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df.groupby(['1']).Commute.value_counts()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0\n", "False 5555.514915\n", "True 6567.138175\n", "Name: steps, dtype: float64" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df.groupby(['0']).steps.mean()" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1\n", "False 6233.210009\n", "True 4842.459252\n", "Name: steps, dtype: float64" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df.groupby(['1']).steps.mean()" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2\n", "False 5404.844824\n", "True 7672.052171\n", "Name: steps, dtype: float64" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df.groupby(['2']).steps.mean()" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3\n", "False 5961.760429\n", "True 3588.004405\n", "Name: steps, dtype: float64" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df.groupby(['3']).steps.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the below two analysis steps, we can identify **Cluster 5 (Cluster Index 4)** has **highest mean** of average **steps**. Also, the only means to commute in this cluster is by **Walk**" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4\n", "False 5402.935067\n", "True 8827.618056\n", "Name: steps, dtype: float64" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df.groupby(['4']).steps.mean()" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5\n", "False 5693.400613\n", "True 5590.332442\n", "Name: steps, dtype: float64" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df.groupby(['5']).steps.mean()" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 Commute\n", "False Walk 9\n", " Bus 4\n", " Car 2\n", "True Walk 4\n", " Bus 3\n", " Train 2\n", " Car 1\n", "Name: Commute, dtype: int64" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df.groupby(['1']).Commute.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Winners\n", "\n", "From our example of using one of the metrics **Step Count**, Cluster 5 (Cluster Index = 4) gives two users, Rohan Panikkar and Devang Varia who have the best Step averages. We can infer that these users tend to be healthier on average by looking at their datapoints" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2 Rohan Panikkar\n", "8 Devang Varia\n", "Name: Name, dtype: object" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df[analysis_df.cluster==4][\"Name\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analysis of Winners" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indexAgeRunningSwimmingCyclingGymmingCommute_TimeVegNon_Vegsteps...2345SexProfessionNationalityCityCommuteName
2226000020018978.688889...FalseFalseTrueFalseMaleStudentIndianPittsburghWalkRohan Panikkar
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1 rows × 48 columns

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" ], "text/plain": [ " index Age Running Swimming Cycling Gymming Commute_Time Veg \\\n", "2 2 26 0 0 0 0 20 0 \n", "\n", " Non_Veg steps ... 2 3 4 5 Sex \\\n", "2 1 8978.688889 ... False False True False Male \n", "\n", " Profession Nationality City Commute Name \n", "2 Student Indian Pittsburgh Walk Rohan Panikkar \n", "\n", "[1 rows x 48 columns]" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df.loc[analysis_df['index'] == 2]" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indexAgeRunningSwimmingCyclingGymmingCommute_TimeVegNon_Vegsteps...2345SexProfessionNationalityCityCommuteName
8825000115018676.547222...FalseFalseTrueFalseMaleStudentIndianPittsburghWalkDevang Varia
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" ], "text/plain": [ " index Age Running Swimming Cycling Gymming Commute_Time Veg \\\n", "8 8 25 0 0 0 1 15 0 \n", "\n", " Non_Veg steps ... 2 3 4 5 Sex \\\n", "8 1 8676.547222 ... False False True False Male \n", "\n", " Profession Nationality City Commute Name \n", "8 Student Indian Pittsburgh Walk Devang Varia \n", "\n", "[1 rows x 48 columns]" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis_df.loc[analysis_df['index'] == 8]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## No. of Steps for User 8 (Devang Varia)\n", "\n", "When we check for both users, we inferred that User 8 has a better Steps Pattern and is clearly our winner" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/anaconda3/lib/python3.6/site-packages/calmap/__init__.py:294: FutureWarning:\n", "\n", "how in .resample() is deprecated\n", "the new syntax is .resample(...).sum()\n", "\n", "/anaconda3/lib/python3.6/site-packages/calmap/__init__.py:146: MatplotlibDeprecationWarning:\n", "\n", "The get_axis_bgcolor function was deprecated in version 2.0. Use get_facecolor instead.\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib\n", "steps_analysis_features[8] = steps_analysis_features[8][steps_analysis_features[8].value != 0]\n", "steps_analysis_features[8].index=pd.to_datetime(steps_analysis_features[8].END_DATE)\n", "events = pd.Series(steps_analysis_features[8]['value'])\n", "fig,ax=calmap.calendarplot(events, monthticks=True, cmap='GnBu', vmin=0, \n", " vmax=max(steps_analysis_features[8]['value']),fig_kws=dict(figsize=(12, 8)));\n", "c = fig.add_axes([1.0, 0.2, 0.02, 0.6])\n", "normalize = matplotlib.colors.Normalize(0,max(steps_analysis_features[8]['value']))\n", "cb = matplotlib.colorbar.ColorbarBase(c, cmap='GnBu', norm=normalize)\n", "cb.set_label('# of Steps')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Future Enhancements\n", "\n", "In the future, we could create a HealthScore which can be calculated based on insights from our Clusters and then label our **Ground Truth label**. This will be very useful to use for a **Classification** algorithims which can then be used for **Real-Time** analysis of Health Index for users on a Daily Basis.\n", "\n", "Through this project, we hope to use the insights to help users in self-reflection of their data and then improve their overall health." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# References\n", "\n", "1. https://developer.apple.com/documentation/healthkit\n", "\n", "2. http://www.ryanpraski.com/apple-health-data-how-to-export-analyze-visualize-guide/\n", "\n", "3. http://ericwolter.com/projects/health-export.html\n", "\n", "4. https://github.com/amandasolis/Fitbit/blob/master/FitbitSummaryPlots.ipynb\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }