projects
E-Commerce Fulfillment & Delivery Optimization Analytics
This project analyzes 100K+ order fulfillment records to identify operational bottlenecks, calculate On-Time In-Full (OTIF) delivery performance, and model revenue exposure resulting from Service Level Agreement (SLA) breaches. Using MySQL, the analysis evaluates last-mile logistics constraints, seller reliability scores, and external factors like weather and traffic congestion to provide actionable supply chain insights.
Supply Chain Inventory Analytics Power BI Dashboard
Built an end-to-end Supply Chain & Inventory Analytics Dashboard in Power BI to monitor operational KPIs, inventory velocity, and logistics cost distributions across regional networks.
Forecasting Electricity Price using Autoregressive and LSTM Model
Developed and optimized PyTorch LSTM model for forecasting Location Marginal Price and compared with autoregressive models. Implemented feature engineering and time-series analysis. LSTM model outperformed traditional statistical methods, achieving a MAE of 3.92 and RMSE of 9.21—outperforming the benchmark SARIMAX model (MAE=4.05, RMSE=9.87).
Power Consumption Analysis and Prediction
Capstone project for data analytics course applying learned methodologies to energy consumption analysis. Practiced end-to-end analytics workflow - data wrangling, exploratory analysis, visualization, and predictive modeling. Key deliverables included clustering analysis of daily load patterns, regression modeling, and feature importance analysis for thermal comfort. The project demonstrated application of course concepts including data preprocessing, statistical analysis, machine learning implementation, and result interpretation in a practical domain.
Home Credit Default Risk
This course project, developed for the Data Mining II course in the Industrial and Systems Engineering (ISE) program, focuses on predicting loan default risk using real‑world financial and demographic data from the Home Credit dataset. The objective was to build reliable machine learning models like XGboost and LightGBM that identify high‑risk applicants while minimizing false rejections, enabling data‑driven credit decision‑making.
Sales Customer Segmentation
Built two interactive Tableau dashboards a primary sales model analyzing $21M+ in revenue to track customer demographics, profit trends, and product performance, and a supplemental Netflix content trend analyzer. Demonstrated end-to-end business intelligence capabilities by transforming complex data into actionable visual insights across commercial and media analytics.
Pattern Analysis of 9 years of US Vehicle Accidents
Pattern analysis of 9 years of US vehicle accident data (2.8M records) investigating environmental factors impact on traffic behavior. Conducted extensive EDA using R and ggplot2, performed text mining on accident descriptions, and developed interactive visualizations in Tableau. Built a Streamlit web app for data exploration, identifying key patterns including rush hour peaks, fair weather accidents, and high-risk locations to propose data-driven road safety policies