Data Analyst with a Bachelor Degree in Information Systems from Kafrelsheikh University. Skilled in Python, SQL, Power BI, Tableau, Excel, data analysis, data cleaning, data modeling, web scraping, statistics, and machin…
Data Analyst with a Bachelor Degree in Information Systems from Kafrelsheikh University. Skilled in Python, SQL, Power BI, Tableau, Excel, data analysis, data cleaning, data modeling, web scraping, statistics, and machine learning-related analytical methods.
Majority of users (81.3%) are using the Fitbit app to track sedentary activities and not using it for tracking their health habits.
Users prefer to track their activities during weekdays compared to weekends - perhaps because they spend more time outside on weekdays and stay in on weekends.
Both companies develop products focused on providing women with their health, habit, and fitness data and encouraging them to understand their current habits and make healthy decisions. These common trends surrounding health and fitness can very well be applied to Bellabeat customers.
Bellabeat marketing team can encourage users by educating and equipping them with knowledge about fitness benefits, suggest different types of exercise (i.e., simple 10 minutes of exercise on weekdays and a more intense exercise on weekends), and calories intake and burnt rate information on the Bellabeat app.
On weekends, The Bellabeat app can also prompt notification to encourage users to exercise.
Collected data from The New York Times website.
Reduced errors from scraping by more than 90% through data cleaning and feature engineering.
Applied Natural Language Processing to identify named entities for location of each news.
Applied statistical classification model to predict a qualitative variable ‘Category of news’.
Organized data into usable format ‘Json’ ready to be pulled over from the app.
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