Steam Game Recommendation System

A Python-based recommendation engine built to practice data science and machine learning concepts using real-world Steam gaming data.

Purpose

This project was created as a learning exercise to gain hands-on experience with:

Technologies Used

• Python: Core programming language
• SQLite: Local database for caching and analysis
• Pandas/NumPy: Data manipulation and numerical computing
• Scikit-learn: Machine learning algorithms and text processing
• VADER Sentiment: Natural language processing for review analysis
• Steam Web API: External data source

Key Learning Areas Practiced

• API Integration: Handling HTTP requests, JSON parsing, and API rate limiting
• Database Operations: CRUD operations, joins, data integrity, and performance optimization
• Machine Learning: Feature engineering, similarity calculations, and recommendation algorithms
• Data Processing: ETL pipelines, data cleaning, and statistical analysis
• Error Handling: Robust exception handling and data validation

Built as a practical exercise in applying data science concepts to real-world gaming data.