Porsche Dealership Site Selection - AI-Driven Market Intelligence System

An end-to-end data intelligence system that identifies optimal Porsche dealership locations across the US by analyzing IRS ZIP-level income data and applying machine learning to model luxury consumer behavior. Problem Selecting the wrong dealership location is a multi-million dollar mistake. Traditional site selection relies on gut instinct and basic demographics. This system replaces that with data-driven market intelligence.

What It Does Analyzes IRS ZIP-level income and demographic data to map wealth concentration across the US Engineers domain-specific features to model luxury consumer purchasing behavior Predicts high-potential dealership regions using a trained ML model Combines rule-based scoring with ML predictions for explainable, actionable decisions

Key Features Passive Income Ratio - custom engineered feature capturing wealth beyond active employment Financial Stability Score - measures income consistency and long-term purchasing power Random Forest Model - trained and validated with feature importance analysis Hybrid Decision System - combines ML predictions with rule-based scoring for business interpretability

Tech Stack Python, ML - Scikit-learn, Random Forest, Pandas, NumPy, Matplotlib, Seaborn, Data Source - IRS ZIP-level income data

Results Identified high-value luxury markets using a combination of income stability and passive wealth indicators Hybrid decision system balances ML accuracy with rule-based interpretability for real business use.