In this project, I analyzed a real-world dataset from the Google Play Store to uncover insights about app market trends, user behavior, and category performance.
Using Python and popular data analysis libraries, I performed comprehensive data cleaning, transformation, and visualization to turn messy app data into meaningful business insights.
๐ Key Highlights:
๐งน Cleaned and preprocessed messy, inconsistent data (null values, duplicates, type issues)
๐ Categorized apps based on genre, type, and content rating
๐ Analyzed patterns in app ratings, install counts, review volumes, pricing, and size
๐ Built visualizations to reveal trends across app categories, popularity, and monetization models
๐ฏ Interpreted data to suggest user preferences and potential growth areas for developers
๐ ๏ธ Tools & Techniques Used:
Python (Pandas, NumPy)
Data Visualization (Matplotlib, Seaborn)
Exploratory Data Analysis (EDA)
Data Cleaning and Feature Engineering
๐ Outcome:
This project enhanced my skills in data wrangling, EDA, visualization, and storytelling with data. It also provided real-world exposure to analyzing large datasets from the tech industry.