Data Science Case Studies
Automotive aftermarket pricing analytics
Price Optimization Model for Airbnb, which helps Airbnb hosts set the right price for their Airbnb listing and provides customers, the benefit of cost. This is a Regression Analysis problem.
Synthetic personal-lines insurance portfolio built as a governed digital twin, with dataset freezing, validation gates, and actuarial realism.
End-to-end Machine Learning project that predicts optimal product discount and estimates revenue impact using LightGBM and Streamlit.
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End-to-end machine learning project to predict whether hotel guests will cancel their booking. Includes pricing analysis and recommendations for increasing revenue and reducing cancellations.
Daily RGM price intelligence for Coca-Cola CSD at Retail store Ireland — zero-cost automated pipeline
Invoice-to-POS pricing model for beverage cost, pour size, markup, and manager review.
Reproducible pricing governance analytics for discount leakage, margin exposure, and customer intervention prioritization.
End-to-end pricing analytics pipeline modeling gross-to-net optimizations, distributor discount structures, rebate ROI, and competitor gaps with interactive dashboard.
Enterprise-grade decision support system for grain pricing and demand forecasting using machine learning, time-series forecasting, pricing scenario simulation, and business intelligence dashboards.
Competitive pricing and promotion benchmarking across 5K+ SKUs with Tableau readouts.
Revenue management and pricing analytics - Price elasticity modeling, promotional ROI analysis, A/B testing for media industry
Airline pricing, route profitability, and booking behavior analytics dashboard using Python and Power BI.
Overhead per bid is roughly constant; the return is not. Scope-level unit economics of a construction subcontractor bid pipeline: cost to win, gross profit per overhead hour, hours sensitivity, and a minimum-GP decision rule, on an anonymized real ledger.
This project analyzes NYC property sales data from September 2016 to September 2017. The goal was to clean, explore, and understand the factors that influence property prices in New York City.
Machine learning project for retail price optimization using Decision Tree Regressor and pricing analytics.
Surge-pricing dark-pattern analysis on food delivery, quantifying fee inflation via Tableau.
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