FITBIT CONSUMER BEHAVIOUR ANALYSIS

INTRODUCTION¶

Fitbit is a line of wireless-enabled wearable technology, physical fitness monitors and activity trackers such as smartwatches, pedometers and monitors for heart rate, quality of sleep, and stairs climbed as well as related software. It operated as an American consumer electronics and fitness company from 2007 to 2021.

The Fitbit brand name was originally owned by Fitbit, Inc., founded by James Park and Eric Freidman. The company was acquired by Google in January 2021 and was absorbed into the company's hardware division.

In 2019, Fitbit was the fifth largest wearable technology company in shipments.The company has sold more than 120 million devices and has 29 million users in over 100 countries

OBJECTIVE¶

Imagine you are a data analyst at "Health Trackers Inc.," a fictional company operating in the Fitbit industry. Your company is dedicated to understanding consumer behavior to enhance product offerings and optimize marketing strategies. You have been tasked with analyzing a comprehensive dataset obtained from Fitbit users to uncover trends and insights. The business objective is to identify key trends, understand their implications for customers, and leverage these insights to shape an effective marketing strategy.

BUSINESS TASK¶

Analyze FitBit Fitness Tracker App data to gain insights into how consumers use the FitBit app and discover trends and insights for the marketing team.

BUSINESS OBJECTIVES¶

  1. What are the trends identified?
  2. How could these trends apply to customers?
  3. How could these trends help influence marketing strategy?

Data Source:¶

  1. The data is given to us by our mentor available on Google Drove. It contains personal fitness tracker data from 30 FitBit users who have given consent to use their data.
  2. There are 18 csv files in total, but the datasets used according to the product analysis are daily activity, hourly calorie, hourly steps, sleep day, daily_calories, daily_intensity, weight_log and daily_steps datasets.

Sorting the Data¶

To have a quick review of the csv data open them in MS Excel.The first thing I noticed was that the data was organized in a long format. I also noticed that the dailyActivity_merged data contains some metrics that can give us some insights like the total steps taken by the Fitbit users, the active minutes spent, and the calories burned. With that we can establish some correlation between calories burned and the steps taken. The hourly calories and the hourly steps contain info about the activity hour that will help give some insight about calories and the time of the day. Then I created a separate folder on my desktop to store the files, since I was going to use Jupyter Notebook to process the data.

Loading Libraries¶

Lets start by loading the libraries for data processing and vizualization

In [2]:
%matplotlib inline
import pandas as pd
import numpy as np
import datetime as dt
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings (action='ignore', category=FutureWarning)
warnings.filterwarnings (action='ignore', category=UserWarning)

Loading Datasets¶

In [3]:
daily_activity = pd.read_csv(r'C:\Users\Harshitha GR\Desktop\prepinsta\week 8\dailyActivity_merged.csv')
hourly_steps = pd.read_csv(r'C:\Users\Harshitha GR\Desktop\prepinsta\week 8\hourlySteps_merged.csv')
hourly_calories = pd.read_csv(r'C:\Users\Harshitha GR\Desktop\prepinsta\week 8\hourlyCalories_merged.csv')
sleepday = pd.read_csv(r'C:\Users\Harshitha GR\Desktop\prepinsta\week 8\sleepDay_merged.csv')
daily_calories=pd.read_csv(r'C:\Users\Harshitha GR\Desktop\prepinsta\week 8\dailyCalories_merged.csv')
daily_intensity = pd.read_csv(r'C:\Users\Harshitha GR\Desktop\prepinsta\week 8\dailyIntensities_merged.csv')
weight_log = pd.read_csv(r'C:\Users\Harshitha GR\Desktop\prepinsta\week 8\weightLogInfo_merged.csv')
daily_steps= pd.read_csv(r'C:\Users\Harshitha GR\Desktop\prepinsta\week 8\dailySteps_merged.csv')
heartrate_secs = pd.read_csv(r'C:\Users\Harshitha GR\Desktop\prepinsta\week 8\heartrate_seconds_merged.csv')

Data Exploration¶

Lets explore the data by viewing some information about them

In [4]:
daily_activity.head()
Out[4]:
Id ActivityDate TotalSteps TotalDistance TrackerDistance LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance LightActiveDistance SedentaryActiveDistance VeryActiveMinutes FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories
0 1503960366 4/12/2016 13162 8.50 8.50 0.0 1.88 0.55 6.06 0.0 25 13 328 728 1985
1 1503960366 4/13/2016 10735 6.97 6.97 0.0 1.57 0.69 4.71 0.0 21 19 217 776 1797
2 1503960366 4/14/2016 10460 6.74 6.74 0.0 2.44 0.40 3.91 0.0 30 11 181 1218 1776
3 1503960366 4/15/2016 9762 6.28 6.28 0.0 2.14 1.26 2.83 0.0 29 34 209 726 1745
4 1503960366 4/16/2016 12669 8.16 8.16 0.0 2.71 0.41 5.04 0.0 36 10 221 773 1863
In [5]:
hourly_steps.head()
Out[5]:
Id ActivityHour StepTotal
0 1503960366 4/12/2016 12:00:00 AM 373
1 1503960366 4/12/2016 1:00:00 AM 160
2 1503960366 4/12/2016 2:00:00 AM 151
3 1503960366 4/12/2016 3:00:00 AM 0
4 1503960366 4/12/2016 4:00:00 AM 0
In [6]:
hourly_calories.head()
Out[6]:
Id ActivityHour Calories
0 1503960366 4/12/2016 12:00:00 AM 81
1 1503960366 4/12/2016 1:00:00 AM 61
2 1503960366 4/12/2016 2:00:00 AM 59
3 1503960366 4/12/2016 3:00:00 AM 47
4 1503960366 4/12/2016 4:00:00 AM 48
In [7]:
sleepday.head()
Out[7]:
Id SleepDay TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
0 1503960366 4/12/2016 12:00:00 AM 1 327 346
1 1503960366 4/13/2016 12:00:00 AM 2 384 407
2 1503960366 4/15/2016 12:00:00 AM 1 412 442
3 1503960366 4/16/2016 12:00:00 AM 2 340 367
4 1503960366 4/17/2016 12:00:00 AM 1 700 712
In [8]:
daily_calories.head()
Out[8]:
Id ActivityDay Calories
0 1503960366 4/12/2016 1985
1 1503960366 4/13/2016 1797
2 1503960366 4/14/2016 1776
3 1503960366 4/15/2016 1745
4 1503960366 4/16/2016 1863
In [9]:
daily_intensity.head()
Out[9]:
Id ActivityDay SedentaryMinutes LightlyActiveMinutes FairlyActiveMinutes VeryActiveMinutes SedentaryActiveDistance LightActiveDistance ModeratelyActiveDistance VeryActiveDistance
0 1503960366 4/12/2016 728 328 13 25 0.0 6.06 0.55 1.88
1 1503960366 4/13/2016 776 217 19 21 0.0 4.71 0.69 1.57
2 1503960366 4/14/2016 1218 181 11 30 0.0 3.91 0.40 2.44
3 1503960366 4/15/2016 726 209 34 29 0.0 2.83 1.26 2.14
4 1503960366 4/16/2016 773 221 10 36 0.0 5.04 0.41 2.71
In [10]:
weight_log.head()
Out[10]:
Id Date WeightKg WeightPounds Fat BMI IsManualReport LogId
0 1503960366 5/2/2016 11:59:59 PM 52.599998 115.963147 22.0 22.650000 True 1462233599000
1 1503960366 5/3/2016 11:59:59 PM 52.599998 115.963147 NaN 22.650000 True 1462319999000
2 1927972279 4/13/2016 1:08:52 AM 133.500000 294.317120 NaN 47.540001 False 1460509732000
3 2873212765 4/21/2016 11:59:59 PM 56.700001 125.002104 NaN 21.450001 True 1461283199000
4 2873212765 5/12/2016 11:59:59 PM 57.299999 126.324875 NaN 21.690001 True 1463097599000
In [11]:
daily_steps.head()
Out[11]:
Id ActivityDay StepTotal
0 1503960366 4/12/2016 13162
1 1503960366 4/13/2016 10735
2 1503960366 4/14/2016 10460
3 1503960366 4/15/2016 9762
4 1503960366 4/16/2016 12669
In [12]:
heartrate_secs.head()
Out[12]:
Id Time Value
0 2022484408 4/12/2016 7:21:00 AM 97
1 2022484408 4/12/2016 7:21:05 AM 102
2 2022484408 4/12/2016 7:21:10 AM 105
3 2022484408 4/12/2016 7:21:20 AM 103
4 2022484408 4/12/2016 7:21:25 AM 101

See how many unique users are present in each data¶

In [13]:
print("Daily Activity Dataset: ", daily_activity.Id.nunique(), "unique users")
print("Hourly Steps Dataset: ", hourly_steps.Id.nunique(), "unique users")
print("Hourly Calories Dataset: ", hourly_calories.Id.nunique(), "unique users")
print("SleepDay Dataset: ", sleepday.Id.nunique(), "unique users")
print("Daily Calories Dataset: ", daily_calories.Id.nunique(), "unique users")
print("Daily Intensity Dataset: ", daily_intensity.Id.nunique(), "unique users")
print("Weight Logged Dataset: ",weight_log.Id.nunique(), "unique users")
print("Daily Steps Dataset:", daily_steps.Id.nunique(), "unique users")
print("Heart Rate Dataset: ", heartrate_secs.Id.nunique(), "unique users")

Daily Activity Dataset:  33 unique users
Hourly Steps Dataset:  33 unique users
Hourly Calories Dataset:  33 unique users
SleepDay Dataset:  24 unique users
Daily Calories Dataset:  33 unique users
Daily Intensity Dataset:  33 unique users
Weight Logged Dataset:  8 unique users
Daily Steps Dataset: 33 unique users
Heart Rate Dataset:  14 unique users

View the data types in each dataset¶

In [14]:
daily_activity.dtypes
Out[14]:
Id                            int64
ActivityDate                 object
TotalSteps                    int64
TotalDistance               float64
TrackerDistance             float64
LoggedActivitiesDistance    float64
VeryActiveDistance          float64
ModeratelyActiveDistance    float64
LightActiveDistance         float64
SedentaryActiveDistance     float64
VeryActiveMinutes             int64
FairlyActiveMinutes           int64
LightlyActiveMinutes          int64
SedentaryMinutes              int64
Calories                      int64
dtype: object
In [15]:
hourly_steps.dtypes
Out[15]:
Id               int64
ActivityHour    object
StepTotal        int64
dtype: object
In [16]:
hourly_calories.dtypes
Out[16]:
Id               int64
ActivityHour    object
Calories         int64
dtype: object
In [17]:
sleepday.dtypes
Out[17]:
Id                     int64
SleepDay              object
TotalSleepRecords      int64
TotalMinutesAsleep     int64
TotalTimeInBed         int64
dtype: object
In [18]:
daily_calories.dtypes
Out[18]:
Id              int64
ActivityDay    object
Calories        int64
dtype: object
In [19]:
daily_intensity.dtypes
Out[19]:
Id                            int64
ActivityDay                  object
SedentaryMinutes              int64
LightlyActiveMinutes          int64
FairlyActiveMinutes           int64
VeryActiveMinutes             int64
SedentaryActiveDistance     float64
LightActiveDistance         float64
ModeratelyActiveDistance    float64
VeryActiveDistance          float64
dtype: object
In [20]:
weight_log.dtypes
Out[20]:
Id                  int64
Date               object
WeightKg          float64
WeightPounds      float64
Fat               float64
BMI               float64
IsManualReport       bool
LogId               int64
dtype: object
In [21]:
daily_steps.dtypes
Out[21]:
Id              int64
ActivityDay    object
StepTotal       int64
dtype: object
In [22]:
heartrate_secs.dtypes
Out[22]:
Id        int64
Time     object
Value     int64
dtype: object

View their shapes¶

In [23]:
print("Shape of Daily Activity:", daily_activity. shape)
print("Shape of Hourly Steps: ", hourly_steps.shape)
print("Shape of Hourly Calories:", hourly_calories.shape)
print("Shape of SleepDay: ", sleepday. shape)
print("Shape of Daily Calories:",daily_calories.shape)
print("Shape of Daily Intensity:",daily_intensity.shape)
print("Shape of Weight Logged: ",weight_log.shape)
print("Shape of Daily Steps: ", daily_steps.shape)
print("Shape of Heart Rate:", heartrate_secs.shape)

Shape of Daily Activity: (940, 15)
Shape of Hourly Steps:  (22099, 3)
Shape of Hourly Calories: (22099, 3)
Shape of SleepDay:  (413, 5)
Shape of Daily Calories: (940, 3)
Shape of Daily Intensity: (940, 10)
Shape of Weight Logged:  (67, 8)
Shape of Daily Steps:  (940, 3)
Shape of Heart Rate: (2483658, 3)

Checking if the datasets have missing values¶

In [24]:
print("Daily Activity Dataset has", daily_activity. isnull().values.sum(), "missing values")
print("Hourly Steps Dataset has",hourly_steps.isnull().values.sum(), "missing values")
print("Hourly Calories Dataset has", hourly_calories.isnull().values.sum(), "missing values")
print("SleepDay Dataset has", sleepday.isnull().values.sum(), "missing values")
print("Daily Calories Dataset has", daily_calories.isnull().values.sum(), "missing values")
print("Daily Intensity Dataset has", daily_intensity.isnull().values.sum(), "missing values")
print("Daily Steps Dataset has", daily_steps.isnull().values.sum(), "missing values")
print("Heart Rate Dataset has", heartrate_secs. isnull().values.sum(), "missing values")

Daily Activity Dataset has 0 missing values
Hourly Steps Dataset has 0 missing values
Hourly Calories Dataset has 0 missing values
SleepDay Dataset has 0 missing values
Daily Calories Dataset has 0 missing values
Daily Intensity Dataset has 0 missing values
Daily Steps Dataset has 0 missing values
Heart Rate Dataset has 0 missing values

Data Transformation¶

In [25]:
daily_activity ["ActivityDate"]= pd.to_datetime (daily_activity ["ActivityDate"])
hourly_steps ["ActivityHour"]=pd.to_datetime (hourly_calories ["ActivityHour"]) 
(hourly_calories ["ActivityHour"])=pd.to_datetime (hourly_calories["ActivityHour"])
hourly_calories ["ActivityHour"] = sleepday ["SleepDay"] = pd.to_datetime (sleepday ["SleepDay"])

Checking for changes

In [26]:
print("Daily Activity data type is", daily_activity ["ActivityDate"].dtypes, "data type") 
print("Hourly Steps data type is", hourly_steps ["ActivityHour"].dtypes, "data type") 
print("Hourly Calories data type is", hourly_calories ["ActivityHour"].dtypes, "data type") 
print("Sleepday data type is", sleepday ["SleepDay"].dtypes, "data type")

Daily Activity data type is datetime64[ns] data type
Hourly Steps data type is datetime64[ns] data type
Hourly Calories data type is datetime64[ns] data type
Sleepday data type is datetime64[ns] data type

merging hourly_steps with hourly_calories

In [27]:
for col in hourly_steps.columns, hourly_calories.columns:
    print (col)

Index(['Id', 'ActivityHour', 'StepTotal'], dtype='object')
Index(['Id', 'ActivityHour', 'Calories'], dtype='object')

Merging based on Id and Activity column

In [28]:
merge_df = pd.merge(hourly_steps, hourly_calories, on = ['Id','ActivityHour'], how ='inner')
merge_df.head()
Out[28]:
Id ActivityHour StepTotal Calories
0 1503960366 2016-04-12 373 81
1 1503960366 2016-04-12 373 69
2 1503960366 2016-04-12 373 72
3 1503960366 2016-04-12 373 50
4 1503960366 2016-04-12 373 214

Creating new columns

In [29]:
daily_activity["WeekDay"] = daily_activity ["ActivityDate"].dt.day_name() 
merge_df ["WeekDay"] = merge_df ["ActivityHour"].dt.day_name()
sleepday ["WeekDay"]=sleepday ["SleepDay"].dt.day_name()
merge_df.head()
Out[29]:
Id ActivityHour StepTotal Calories WeekDay
0 1503960366 2016-04-12 373 81 Tuesday
1 1503960366 2016-04-12 373 69 Tuesday
2 1503960366 2016-04-12 373 72 Tuesday
3 1503960366 2016-04-12 373 50 Tuesday
4 1503960366 2016-04-12 373 214 Tuesday
In [30]:
merge_df["DateHour"] = merge_df["ActivityHour"].dt.hour
merge_df.head()
Out[30]:
Id ActivityHour StepTotal Calories WeekDay DateHour
0 1503960366 2016-04-12 373 81 Tuesday 0
1 1503960366 2016-04-12 373 69 Tuesday 0
2 1503960366 2016-04-12 373 72 Tuesday 0
3 1503960366 2016-04-12 373 50 Tuesday 0
4 1503960366 2016-04-12 373 214 Tuesday 0
In [31]:
merge_df["ActivityDay"] = merge_df["ActivityHour"].dt.hour
merge_df.head()
Out[31]:
Id ActivityHour StepTotal Calories WeekDay DateHour ActivityDay
0 1503960366 2016-04-12 373 81 Tuesday 0 0
1 1503960366 2016-04-12 373 69 Tuesday 0 0
2 1503960366 2016-04-12 373 72 Tuesday 0 0
3 1503960366 2016-04-12 373 50 Tuesday 0 0
4 1503960366 2016-04-12 373 214 Tuesday 0 0
In [32]:
merge_df = merge_df.drop("ActivityHour", axis=1)
merge_df.head()
Out[32]:
Id StepTotal Calories WeekDay DateHour ActivityDay
0 1503960366 373 81 Tuesday 0 0
1 1503960366 373 69 Tuesday 0 0
2 1503960366 373 72 Tuesday 0 0
3 1503960366 373 50 Tuesday 0 0
4 1503960366 373 214 Tuesday 0 0
In [33]:
merge_df["ActivityDay"] = pd.to_datetime(merge_df["ActivityDay"])
In [34]:
sleepday = sleepday.rename(columns={'sleepday':'ActivityDay'})
sleepday.head()
Out[34]:
Id SleepDay TotalSleepRecords TotalMinutesAsleep TotalTimeInBed WeekDay
0 1503960366 2016-04-12 1 327 346 Tuesday
1 1503960366 2016-04-13 2 384 407 Wednesday
2 1503960366 2016-04-15 1 412 442 Friday
3 1503960366 2016-04-16 2 340 367 Saturday
4 1503960366 2016-04-17 1 700 712 Sunday
In [35]:
for col in merge_df.columns, sleepday.columns:
    print(col)

Index(['Id', 'StepTotal', 'Calories', 'WeekDay', 'DateHour', 'ActivityDay'], dtype='object')
Index(['Id', 'SleepDay', 'TotalSleepRecords', 'TotalMinutesAsleep',
       'TotalTimeInBed', 'WeekDay'],
      dtype='object')

Second time merging

In [36]:
merge_df2 = pd.merge(merge_df, sleepday, on=['Id', 'WeekDay'], how='inner')
merge_df2.head(5)
Out[36]:
Id StepTotal Calories WeekDay DateHour ActivityDay SleepDay TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
0 1503960366 373 81 Tuesday 0 1970-01-01 2016-04-12 1 327 346
1 1503960366 373 81 Tuesday 0 1970-01-01 2016-04-19 1 304 320
2 1503960366 373 81 Tuesday 0 1970-01-01 2016-04-26 1 245 274
3 1503960366 373 81 Tuesday 0 1970-01-01 2016-05-03 1 273 296
4 1503960366 373 81 Tuesday 0 1970-01-01 2016-05-10 1 383 403
In [37]:
new_cols = ["Id", "ActivityDate",
"WeekDay", "TotalSteps",
"TotalDistance", "VeryActiveDistance",
"ModeratelyActiveDistance", "LightActiveDistance", "SedentaryActiveDistance", 
"VeryActiveMinutes", "FairlyActiveMinutes", "LightlyActiveMinutes", "SedentaryMinutes", 
"TotalActiveMinutes", "TotalMinutes", "TotalActiveHours", "Calories"]
daily_activity = daily_activity.reindex(columns=new_cols)
In [38]:
daily_activity[["TotalActiveMinutes","TotalMinutes","TotalActiveHours"]].head()
Out[38]:
TotalActiveMinutes TotalMinutes TotalActiveHours
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
In [41]:
daily_activity["TotalActiveMinutes"] = daily_activity["VeryActiveMinutes"] + daily_activity["FairlyActiveMinutes"] + daily_activity["LightlyActiveMinutes"]
daily_activity["TotalMinutes"] = daily_activity["TotalActiveMinutes"] + daily_activity["SedentaryMinutes"]
daily_activity["TotalActiveHours"] = round(daily_activity["TotalActiveMinutes"]/60)
In [42]:
daily_activity[["TotalActiveMinutes","TotalMinutes","TotalActiveHours"]].head()
Out[42]:
TotalActiveMinutes TotalMinutes TotalActiveHours
0 366 1094 6.0
1 257 1033 4.0
2 222 1440 4.0
3 272 998 5.0
4 267 1040 4.0

Analyzing the Data¶

Our data processing is complete and now we analyze it for insights. We have narrowed down our datasetsinto 3 dataframes:

  1. daily_activity
  2. merge_df
  3. merge_df2
In [43]:
daily_activity.describe()
Out[43]:
Id ActivityDate TotalSteps TotalDistance VeryActiveDistance ModeratelyActiveDistance LightActiveDistance SedentaryActiveDistance VeryActiveMinutes FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes TotalActiveMinutes TotalMinutes TotalActiveHours Calories
count 9.400000e+02 940 940.000000 940.000000 940.000000 940.000000 940.000000 940.000000 940.000000 940.000000 940.000000 940.000000 940.000000 940.000000 940.000000 940.000000
mean 4.855407e+09 2016-04-26 06:53:37.021276672 7637.910638 5.489702 1.502681 0.567543 3.340819 0.001606 21.164894 13.564894 192.812766 991.210638 227.542553 1218.753191 3.775532 2303.609574
min 1.503960e+09 2016-04-12 00:00:00 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2.000000 0.000000 0.000000
25% 2.320127e+09 2016-04-19 00:00:00 3789.750000 2.620000 0.000000 0.000000 1.945000 0.000000 0.000000 0.000000 127.000000 729.750000 146.750000 989.750000 2.000000 1828.500000
50% 4.445115e+09 2016-04-26 00:00:00 7405.500000 5.245000 0.210000 0.240000 3.365000 0.000000 4.000000 6.000000 199.000000 1057.500000 247.000000 1440.000000 4.000000 2134.000000
75% 6.962181e+09 2016-05-04 00:00:00 10727.000000 7.712500 2.052500 0.800000 4.782500 0.000000 32.000000 19.000000 264.000000 1229.500000 317.250000 1440.000000 5.000000 2793.250000
max 8.877689e+09 2016-05-12 00:00:00 36019.000000 28.030001 21.920000 6.480000 10.710000 0.110000 210.000000 143.000000 518.000000 1440.000000 552.000000 1440.000000 9.000000 4900.000000
std 2.424805e+09 NaN 5087.150742 3.924606 2.658941 0.883580 2.040655 0.007346 32.844803 19.987404 109.174700 301.267437 121.776307 265.931767 2.057646 718.166862
In [44]:
merge_df.describe()
Out[44]:
Id StepTotal Calories DateHour ActivityDay
count 4.050000e+02 405.000000 405.000000 405.0 405
mean 1.503960e+09 138.740741 78.358025 0.0 1970-01-01 00:00:00
min 1.503960e+09 0.000000 47.000000 0.0 1970-01-01 00:00:00
25% 1.503960e+09 17.000000 49.000000 0.0 1970-01-01 00:00:00
50% 1.503960e+09 101.000000 66.000000 0.0 1970-01-01 00:00:00
75% 1.503960e+09 202.000000 86.000000 0.0 1970-01-01 00:00:00
max 1.503960e+09 459.000000 318.000000 0.0 1970-01-01 00:00:00
std 0.000000e+00 131.524720 40.482519 0.0 NaN
In [45]:
merge_df2.describe()
Out[45]:
Id StepTotal Calories DateHour ActivityDay SleepDay TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
count 1.458000e+03 1458.000000 1458.000000 1458.0 1458 1458 1458.000000 1458.000000 1458.000000
mean 1.503960e+09 138.581619 78.421125 0.0 1970-01-01 00:00:00 2016-04-27 00:25:40.740740864 1.085048 358.879287 381.639918
min 1.503960e+09 0.000000 47.000000 0.0 1970-01-01 00:00:00 2016-04-12 00:00:00 1.000000 245.000000 264.000000
25% 1.503960e+09 17.000000 49.000000 0.0 1970-01-01 00:00:00 2016-04-19 00:00:00 1.000000 304.000000 320.750000
50% 1.503960e+09 101.000000 66.000000 0.0 1970-01-01 00:00:00 2016-04-28 00:00:00 1.000000 340.000000 367.000000
75% 1.503960e+09 202.000000 85.000000 0.0 1970-01-01 00:00:00 2016-05-05 00:00:00 1.000000 383.000000 403.000000
max 1.503960e+09 459.000000 318.000000 0.0 1970-01-01 00:00:00 2016-05-11 00:00:00 2.000000 700.000000 712.000000
std 0.000000e+00 132.844315 39.842151 0.0 NaN NaN 0.279049 96.539915 94.137431

Visualizing the data¶

In [47]:
Corr1 = ["TotalDistance","TotalSteps","SedentaryMinutes","TotalActiveMinutes","Calories"]

fig, ax = plt.subplots(figsize = (15,8))
ax = sns.heatmap(daily_activity[Corr1].corr(), annot=True, fmt="0.2f", linewidths=0.5, cmap="icefire")
ax.set_title('Correlation Matrix')
Out[47]:
Text(0.5, 1.0, 'Correlation Matrix')
No description has been provided for this image

Comparing TotalSteps and Sedentary Minutes to Calories

In [52]:
fig, (ax0, ax1) = plt.subplots(nrows=2, ncols=1, figsize=(10,10), sharex=True)

scatter = ax0.scatter(x=daily_activity["Calories"], 
                    y = daily_activity["TotalSteps"], 
                    color = "lightgreen")

ax0.set(title="Calories Burnt by Total Steps", ylabel = "Total Steps")

ax0.legend(*scatter.legend_elements(), title =None)

scatter = ax1.scatter(x= daily_activity["Calories"],
                    y = daily_activity["SedentaryMinutes"],
                     color = "skyblue")

ax1.set(title="Sedentary Minutes and Calories", ylabel = "sedentary minutes")

ax1.legend(*scatter.legend_elements(), title = None)
Out[52]:
<matplotlib.legend.Legend at 0x1de88f57f90>
No description has been provided for this image

Showing amount of calories burnt for everystep taken in a scatter plot

In [53]:
plt.style.use("default")
plt.figure(figsize=(8,6)) # specify size of the chart
plt.scatter (daily_activity ["TotalSteps"], daily_activity ["Calories"],
             alpha = 0.8, c = daily_activity ["Calories"],
             cmap = "Spectral")
# add annotations and visuals
median_calories = 2134
median_steps = 7405
2
plt.colorbar(orientation = "vertical")
plt.axvline (median_steps, color = "Blue", label = "Median steps")
plt.axhline (median_calories, color = "Red", label = "Median calories burned")
plt.xlabel("Steps taken")
plt.ylabel("Calories burned")
plt.title("Calories burned for every step taken")
plt.grid(True)
plt. legend()
plt.show()
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Relation between Sedentary Minutes VS Total Active Minutes shown below

In [54]:
fig, ax = plt.subplots(figsize=(8, 5))
x = np.array(["Sedentary Minutes", "Total Active Minutes"])
y = np.array([daily_activity ["SedentaryMinutes"].mean(),
daily_activity ["TotalActiveMinutes"].mean()])
# Bar plot
bars = ax.bar(x, y, width=0.8, color="cyan")
# Adding annotations
for bar in bars:
    yval = bar.get_height()
    ax.text(bar.get_x() + bar.get_width()/2, yval + 0.1, round (yval, 2), ha='center', va='bottom')
plt.title("Sedentary Minutes Vs Total Active Minutes")
plt.show()
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Showing the total Calories burnt by each day of the week in a bar chart

In [55]:
fig, ax = plt.subplots (figsize=(10,5))
plt.bar(merge_df. WeekDay, merge_df.Calories, color = "seagreen")
plt.xlabel("Week Day")
plt.ylabel("Calories Burned")
plt.title("Calories Burned by Day of the Week")
plt.grid()
plt.show()
21
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Out[55]:
21

Showing the total steps walked everyday for the week in a bar chart

In [56]:
fig, ax = plt.subplots (figsize=(10,5))
plt.bar(daily_activity. WeekDay, daily_activity. TotalSteps,
color = "pink")
plt.xlabel("WEEK DAY")
plt.ylabel("TOTAL STEPS TAKEN")
plt.title("TOTAL STEPS TAKEN BY THE DAY OF THE WEEK")
plt.grid()
plt.show()
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Showing the total minutes spent asleep daily in a week

In [58]:
fig, ax = plt.subplots(figsize=(10,5))
x = merge_df2["WeekDay"]
y = merge_df2["TotalMinutesAsleep"]
plt.title("TOTAL TIME SLEPT BY DAY OF THE WEEK")
plt.xlabel("WEEKDAY")
plt.ylabel("TOTAL MINUTES SLEPT")
plt.bar(x, y, width = 0.8, color = "turquoise")
plt.show()
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Showing the percentage of activities done in minutes in a pie chart

In [61]:
# calculating total of individual minutes column
very_active_mins = daily_activity ["VeryActiveMinutes"].sum()
fairly_active_mins = daily_activity ["FairlyActiveMinutes"].sum()
lightly_active_mins = daily_activity ["LightlyActiveMinutes"].sum()
sedentary_mins = daily_activity ["SedentaryMinutes"].sum()
# plotting pie chart

slices = [very_active_mins, fairly_active_mins, lightly_active_mins, sedentary_mins]
labels = ["Very active minutes", "Fairly active minutes", "Lightly active minutes", "Sedentary minutes"]
colours = ["lightcoral", "yellowgreen", "lightskyblue", "darkorange"]
explode = [0, 0, 0, 0.1]
plt.style.use("default")
plt.pie(slices, labels = labels,
colors = colours, wedgeprops = {"edgecolor": "black"},
explode = explode, autopct = "%1.1f%%")
plt.title("Percentage of Activity in Minutes")
plt.tight_layout()
plt.show()
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Recommendations

In this final step, we will be delivering our insights and providing recommendations based on our analysis.

Here, we will revisit our business questions and share with you our business recommendations.

  1. Promotion: The data we explored revealed that consumers are less active between 8p.m. and 10 p. hortly before they retire for the night. This is the Beal moment for Health Trackers Inc. to schedule their advertising initiatives for optimal results. Google AdWords, for example, allows internet advertisers to schedule when their adverts are shown with its ad scheduling function. Such functions can allow Health Trackers Inc. to effectively reach its consumers. Scheduling adverts will also help Bellabeat save money on advertisements.

  2. Retention: The objective of any membership program is not limited to generating a regular stream of new members. Retention is crucial! According to an online report, eighty percent of customers quit their subscription to a member site within the first two years of joining the site. For FitBit to retain its consumers, it must focus on personalized contents that offer value. For instance, the data revealed a positive correlation between the number of steps walked and the number of calories burnt. The more steps a person takes each day, the more calories they burn.

  3. The data also revealed that one of the issues people have is that they are hardly able to reach the minimum 10,000 steps per day recommended for healthy adults. To address this issue, Health Trackers Inc. can provide fascinating tips to assist its consumers achieve their daily objectives. This may be published periodically on its website. Additionally, it can create engaging challenges to motivate its users to walk regularly. This will not only drive engagement, but also provide enormous value to them.

  4. I also noted that people spend far more time being inactive than being active. Inactivity is related with several health problems, including obesity, heart disease, stroke, etc. Health Trackers Inc. can create explainer videos and blogs to educate its users about the negative impacts of sedentary lifestyle and how to prevent them. This is also a fantastic opportunity for FitBi provide users with lifestyle-related health data, which links to the FitBit app to offer users with insights on their daily wellbeing.

  5. How could these trends help influence FitBit marketing strategy? HealthTrackers Inc. marketing team can encourage users by educating and equipping them with knowledge about fitness benefits, suggest different types of exercise (ie. simple 10 minutes exercise on weekday and a more intense exercise on weekends) and calories intake and burnt rate information on the FitBit app.

On weekends, FitBit app can also prompt notification to encourage users to exercise.