Predicting the default customers

Building an PD, LGD and EAD Model for Financial Modeling.

Portfolio of course work for my Master's in Data Science.

To provide complete workflow from Inferential Analytics, Predictive Analytics, Prescriptive Analytics and Evaluate the performance of prescriptions

Build a predictive model to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

Machine Learning Project to understand the lifecycle metrics of a loan in order to minimize delinquent loans

Streamlit legal ops automation: ETL + KPI dashboard (matters, utilization, AR aging, deadlines). Python, Pandas, SQL, CI.

six-projects-revamped-cloud-buttons

Housing price drivers with OLS + regularization + causal baseline; robust diagnostics. Python, scikit-learn, CI.

Credit Risk analysis and predictive modelling of the German credit dataset. This repository holds all the R-scripts and markdown files for my report on the same

Real-return analysis across inflation episodes with bootstrapped CIs and allocation sketches. Python, Pandas, CI.

Reproducible OHLCV pipeline: baselines + SARIMAX/Prophet, uncertainty & drawdowns. Python, statsmodels, CI.

Collection of data science projects demonstrating machine learning and analytics skills

Studio green-light model: TMDB/OMDb pipeline, classification/quantile, decision rubric. Python, scikit-learn, CI.

Streamlit-based tool for mutual fund analytics with CAGR, Sharpe/Sortino, drawdowns, CAPM, Prophet forecasting, fund scoring, and K-Means clustering for benchmarking.

Performing Exploratory Data analysis for loan application approvals and understanding Risk analytics

Loan Defaulter's Prediction using Statistical Analysis

Big data analytics project analyzing truck fleet data to identify risk factors, dangerous driving patterns, and provide actionable insights for minimizing traffic accidents and improving fleet safety.

Dynamic stock selection and portfolio optimization using Efficient Frontier, VaR/CVaR risk modeling, CAPM benchmarking, backtesting, and ML-based market regime detection.