Next-generation analytics & ML-powered churn prediction for Solana gaming. Self-training models predict player churn 14 days in advance. Live dashboard + REST API analyzing 60M+ on-chain transactions across 12 games.

This project involves an A/B test to evaluate the impact of moving the Cookie Cats' first gate from level 30 to level 40 on player retention after 1 day (retention_1) and 7 days (retention_7) following installation.

Data-driven A/B testing analysis for Cookie Cats mobile game to optimise player retention through statistical hypothesis testing, interactive visualisations & a Flask dashboard. Determines optimal gate placement (Level 30 vs 40) using 90,189 player records with Chi-square tests, effect size analysis & business impact quantification.

This repository provides an AI-powered player engagement system with personalized recommendations, in-game rewards, and generative content for gaming scenarios.

Impact analysis of player retention by movement of gates in the cookie cats game

A complete Streamlit + Machine Learning + SHAP + NLP project to analyze, predict, and improve player retention in games. This project simulates a game environment, models churn behavior, and provides insights using SHAP, NLP word clouds, and strategy simulators.

Mobile game A/B test analysis (n=90,000). Used statistical hypothesis testing (Z-test & Mann-Whitney U) to measure the impact of gate placement on player retention.