I built an AI churn prediction and retention analytics project using the IBM Telco dataset, comparing Logistic Regression and Random Forest. The Random Forest model achieved 78.54% accuracy, and I used SHAP insights and the Gemini API to generate retention recommendations.
For a retail decision-support system, I analyzed 34,500 transactions across 7,903 customers, using MySQL and Python for KPI analysis and automating executive reporting with the Gemini API. I also built a four-page Power BI dashboard covering revenue, average order value, returns, delivery time, and margins.
As a Financial Analyst Intern at R. Gopal & Associates, I updated Form 3CD clauses, calculated capital gains using Excel, and worked with Tally ERP data on reconciliation. My research includes an implicit transit cost index study in Pune and a VAR econometrics analysis of women’s economic independence and marriage and divorce rates.
