Capstone project for Data Analytics II at Lagos Business School, analyzing 3,000+ client complaints in the basic education system using advanced techniques including text analytics, Monte Carlo simulation, Prophet forecasting, survival analysis, and association rules in R with Quarto.

This report presents an advanced operational analytics study of an EdTech complaints management system, focusing on optimizing service delivery and resource allocation. Utilizing a dataset of over 3,000 support tickets submitted from across from over 200 schools, five sophisticated analytical techniques were applied: **Text Analytics** (including Bigram TF-IDF and LDA topic modeling), **Monte Carlo Simulation**, **Advanced Forecasting** with Prophet, **Survival Analysis**, and **Association Rule Mining**.