AppleStore Data Cleaning

Pure Python Data Processing: AppleStore.csv Sanitation

Conducted a comprehensive data quality audit and sanitization pass on a 7,197-row dataset of Apple App Store applications. Identified and resolved edge cases including duplicate app titles, unrated applications, extreme price outliers, and unformatted file sizes to yield a 100% clean, analysis-ready dataset. ​Key Highlights: ​Unrated App Handling: Isolated 929 unrated applications (user_rating == 0.0) to avoid skewing average rating metrics in downstream analysis. ​Pricing Tier Segmentation: Categorized records into Free (4,056 apps) and Paid (3,141 apps) subsets to enable targeted financial modeling. ​Schema Validation: Executed type-casting and string-sanitization routines to eliminate non-standard characters across app names and versions. ​Tech Stack: Python, Jupyter Notebook, Data Quality Frameworks.