Daily Activity Table

[msa1979msa]

**I cleaned each dataset using Excel. I took the following steps within each dataset: Sorted and filtered data by Id to obtain how many unique users there were within the dataset. Formatted date data into MM/DD/YY date format Checked Id entries and other columns for LEN to make sure the data was correct and uniform

Uploaded FitBit Fitness Tracker Data into BigQuery dailyActivity dailyCalories dailySteps sleepDay weightLoginfo**

**

> Daily Activity Table

**

SELECT * from msa-1979-project.Bellabeat_Dataset.dailyActivity Check the data record • Correct data type • Total 940 rows • Total 33 IDs/users • Recording data from 4/12/16 —5/12/16

SELECT Id, ActivityDate, TotalSteps, TotalDistance Calories from msa-1979-project.Bellabeat_Dataset.dailyActivity where TotalSteps = 0 order by Id Here, we have seen some data on 0 steps and calories

Check duplicate SELECT Id, ActivityDate from msa-1979-project.Bellabeat_Dataset.dailyActivity group by Id, ActivityDate HAVING count(*) >1 Comments: No duplicate found

Check 0 values in the steps column select TotalSteps from msa-1979-project.Bellabeat_Dataset.dailyActivity Where TotalSteps = 0

[msa1979msa]

Sleep Day Table

SELECT * FROM msa-1979-project.Bellabeat_Dataset.sleepDay Check the data record • 413 rows • 24 users • data types are correct • missing data input on many days from many users

Duplicate rows exist in the record and need to remove Removed duplicates values from the record By using Excel sheet

SELECT Id, SleepDay FROM msa-1979-project.Bellabeat_Dataset.sleepDay group by Id, SleepDay having count(*) >1

[msa1979msa]

WeighLogInfo Table

SELECT * FROM msa-1979-project.Bellabeat_Dataset.weightLoginfo Check the data record

67 rows 56 distinct Log ID (not sure what that is) data types are correct 8 users No duplicate values found

[msa1979msa]

dailySteps Table

SELECT * FROM msa-1979-project.Bellabeat_Dataset.dailySteps

Check the date record Total 940 rows in dailySteps data record No duplicate values found

[msa1979msa]

Analysis section

The next section covers the analysis

[msa1979msa]

SELECT COUNT (DISTINCT Id) FROM msa-1979-project.Bellabeat_Dataset.table-name

**So I checked all the tables by mentioning the full path so I have got the details of Id total unique values from each table we can use a join statement as well to get the details in one time query

• Daily_Activity = 33 • Day_Sleep = 24 • Hourly_Calories = 33 • Hourly_Intensities = 33 • Hourly_Steps = 33 • Weight_Log_Info = 8

I have used both platform for cleaning data Excel and SQL, I chose to work with SQL to break down the data instead of remaining in Succeed (despite the fact that I returned a couple of times for perceptions). Why SQL? SQL and Succeed are the two most excellent abilities being mentioned (and utilized) in an information examiner's work. So I'm expecting to grandstand that range of commands here. Also, I would like to work on the same dataset in programming R as well.**

[msa1979msa]

MAXIMUM steps and burn calories

SELECT max(TotalSteps) as Total_steps, max(Calories) as Total_calories_Burn FROM msa-1979-project.Bellabeat_Dataset.dailyActivity

AVERAGE steps and burn calories

SELECT avg(TotalSteps) as Total_steps, AVG(Calories) as Total_calories_Burn FROM msa-1979-project.Bellabeat_Dataset.dailyActivity

Daily Maximum activity analysis sorted of days

Select MAX(TotalSteps) as max_steps, MAX(TotalDistance) as max_distance, MAX(Calories) as max_calories, day_of_week From msa-1979-project.Bellabeat_Dataset.daliyActivity Group By day_of_week order by (max_steps) DESC

Activities and calories comparison total sum of each activity

Select Id, SUM(TotalSteps) as total_steps, SUM(VeryActiveMinutes) as total_very_active_mins, Sum(FairlyActiveMinutes) as total_fairly_active_mins, SUM(LightlyActiveMinutes) as total_lightly_active_mins, SUM(Calories) as total_calories From msa-1979-project.Bellabeat_Dataset.daliyActivity Group By Id

Analysis with minutes made new column avg_sleep_time_hour

SELECT Id, Avg(TotalMinutesAsleep)/60 as avg_sleep_time_hour, Avg(TotalTimeInBed)/60 as avg_time_bed_hour, AVG(TotalTimeInBed - TotalMinutesAsleep) as wasted_bed_time_min FROM msa-1979-project.Bellabeat_Dataset.sleepDay Group by Id

The analysis is based on calories, steps, and as well dates, so we limit the data here

Select Distinct temp1.Id, temp1.ActivityDate, sum(temp1.METs) as sum_mets, temp2.Calories From msa-1979-project.Bellabeat_Dataset.minuteMETsNarrow as temp1 inner join msa-1979-project.Bellabeat_Dataset.dailyActivity as temp2 on temp1.Id = temp2.Id and temp1.ActivityDate = temp2.ActivityDate Group By temp1.Id, temp1.ActivityDate, temp2.Calories Order by ActivityDate LIMIT 15

[msa1979msa]

Visualize the data

The section is based on visualization. Where we would analyze data by using different graphs.

[msa1979msa]

Activity analyze data by time and days

Here we have seen that mostly people start workout at 7:00 AM

[msa1979msa]

Here we have seen that comparison of two visualization of total steps and burning calories according to Id

[msa1979msa]

Here we have seen the three activity modes very active, light active and fairly active logs

[msa1979msa]

Final conclusion and analysis report writing

This section shall cover all the aspects of data and deep analysis report

[msa1979msa]

My last conclusion about this analysis

Subsequent to playing out the assortment, change, cleaning, association, and examination of the given datasets, we have sufficient authentic proof to propose replies to the business-related questions that were inquired.

We can deduce that the term and the degree of power of the exercises performed are extraordinarily dependent on how many calories it consumed. METs give an incredible understanding of the force of activities performed and how many calories it consumed each moment. While a large portion of the customers accomplishes satisfactory measures of rest, it is seen that a bit of part of the clients either sleep late or under-sleeping. Shoppers are additionally bound to perform low-focused energy exercises from 7:00 AM - 8:00 PM over the course of the day.

To configure new promoting systems to more readily zero in on opening new development opportunities and fostering the business, we need to allude to the examination given above and remember those realities. The suggestions I would give to assist with tackling this business-related situation are displayed underneath.

Recommendations

Feature the MET following component on the savvy gadgets as a promoting system and make mindfulness on MET values. For it permits clients to follow their degree of power of exercises and gives a continuous understanding of how many calories they consume consistently.

Think about setting day-to-day/week-by-week calorie difficulties and grant focuses on clients in view of the top entertainers. Where the focus can be gathered and reclaimed as a rebate for their next item bought.