Data Science Case Studies
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Updated
Jan 31, 2021 - Jupyter Notebook
Data Science Case Studies
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.
Automotive aftermarket pricing analytics
End-to-end Machine Learning project that predicts optimal product discount and estimates revenue impact using LightGBM and Streamlit.
Synthetic personal-lines insurance portfolio built as a governed digital twin, with dataset freezing, validation gates, and actuarial realism.
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Daily RGM price intelligence for Coca-Cola CSD at Retail store Ireland — zero-cost automated pipeline
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.
End-to-end pricing analytics pipeline modeling gross-to-net optimizations, distributor discount structures, rebate ROI, and competitor gaps with interactive dashboard.
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
Segment-level pricing diagnostics and forecasting for marketplace transaction data
Reproducible pricing governance analytics for discount leakage, margin exposure, and customer intervention prioritization.
Airline pricing, route profitability, and booking behavior analytics dashboard using Python and Power BI.
Laptop Price Analysis Dashboard — Excel-based data analysis project exploring how hardware specifications (CPU, RAM, GPU, storage, brand) impact laptop pricing using pivot tables and interactive dashboards.
Pricing & yield analytics on a real warehouse portfolio using SQL and pandas — rate premium, +50% growth, and repricing recommendations.
Surge-pricing dark-pattern analysis on food delivery, quantifying fee inflation via Tableau.
Machine learning project for retail price optimization using Decision Tree Regressor and pricing analytics.
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