Data Preparation

Before analyzing the data, I organized and prepared the datasets to improve consistency and reliability.

1. Dataset Organization: I uploaded and organized the datasets in Google Drive and created copies to preserve the original raw datasets.

2. Missing-Value Checks: I reviewed each dataset for blank or missing values before analysis.

3. Date Standardization: I standardized inconsistent date formats and separated date-time fields into distinct Date and Time columns to improve organization and facilitate analysis.

4. Data Consistency: I reviewed columns for irregular values and ensured the datasets were appropriately organized for analysis.

Data Analysis

To answer the business questions, the analysis examined four areas:

ANALYSIS AREA

• Tracking behaviour: I compared automatic tracking records with manually logged records by using a formula to count records marked TRUE and FALSE in the relevant columns.

• Time of activity: I analyzed the Hourly Calories dataset and created a pivot table to calculate the average calories burned for each hour. Results were sorted in descending order to identify periods with the highest average calorie expenditure.

• Body-fat adoption: I reviewed the Weight dataset, which contained a relatively small number of records, making it possible to identify the limited number of records that included body-fat measurements.

• Days of activity: After reviewing other relevant datasets, i analyzed the Daily Activity dataset to identify the days when users were most active. A Day of the Week column was created, followed by a pivot table calculating the average Very Active Minutes for each day.