ABOUT THE PROJECT

This project analyzes 31,000+ train ticket transactions to uncover patterns and drivers behind customer refund requests. By examining journey status (on-time, delayed, cancelled), ticket types, pricing, and reasons for delays, the analysis provides actionable insights into how disruptions affect refund behavior and revenue loss.

PROJECT TOOLS, SKILLS AND ACTIVITIES

Conducted EDA on 31,000+ train records using Python (Pandas, Seaborn) and Power BI. Built KPIs, trend charts, and interactive dashboards to visualize refunds, delays, and cancellations. Applied DAX for custom metrics and also applied statistical testing for actionable insights. Cleaned and prepared dataset and also transformed time-series, categorical, missing data and new columns for analysis. Published insights via Power BI Service and embedded them on a GitHub Pages portfolio.

PROJECT LINKS