Streamlit

Demo | Demo (Streamlit) | GitHub

This is a nested logit discrete choice model implemented in PyTorch and deployed as a Streamlit app (containerized with Docker). The public ModeCanada dataset is used to model travel mode choice across train, car, bus, air using a two-level nest structure (Land vs. Air) where Land = {train, car, bus} and Air = {air}. Heterogeneous effects introduced via income and urban features. The project is framed as a hypothetical travel-agency e-ticketing case study, where train recall is prioritized to reduce missed public transport demand signals.

Demo (Streamlit) | GitHub

This is a time series forecasting pipeline built using Prophet to predict qty and revenue per category using daily ecommerce book sales data from 2020-2022. The model was tuned using a grid search with a time-based holdout split (train before 2022-01-01, test from 2022-01-01 onward) over key Prophet hyperparameters (seasonality mode, changepoint/seasonality priors, yearly seasonality), and enhanced with holiday effects and additional custom seasonalities (monthly/semester). Accuracy was measured using WAPE and MAE at both daily and weekly-aggregated levels, with a 7-day seasonal naive baseline (t-7) used for context.

Demo (Streamlit) | GitHub

A Streamlit application for analyzing customer retention patterns using cohort analysis.