Chess Move Data Analysis

This project converts raw chess game logs into structured datasets, segments games into opening, middlegame, and endgame phases, and performs analysis to identify player weaknesses and outcome patterns.

Raw chess logs are difficult to inspect manually at scale, making it hard to identify phase-specific patterns and outcome signals.

Built the preprocessing pipeline, phase segmentation logic, exploratory analysis, and visual reporting structure.

• Raw game logs are parsed and cleaned into structured tabular datasets.
• Move counts and game metadata segment each game into opening, middlegame, and endgame phases.
• Analysis notebooks generate aggregate player and outcome trends.
• Optional modeling experiments explore predictive signals from engineered features.

• Processing a large volume of irregular game logs reliably.
• Choosing phase segmentation rules that are explainable and consistent.
• Presenting analysis in a way that is useful beyond charts alone.

• 700,000+ game logs processed
• Phase-wise performance analysis
• Structured data pipeline

• Data cleaning decisions become part of the model story.
• Readable analysis is a product skill, not just a notebook skill.