This project leverages a dataset from Melissa to predict the ideal residential community within Rancho Santa Margarita (RSM), California. By masking geographic identifiers and training on diverse lifestyle predictors, the model generates a predicted latitude and longitude for a user based on their personal, financial, and behavioral profile.

While the scope for this project is localized to Rancho Santa Margarita, the underlying idea serves for building an AI tool to find your community!

Use Case: Door-to-door sales or local service providers (e.g., solar, landscaping). Impact: Instead of knocking door to door in entire cities, sales teams can target specific neighborhoods where the "Lifestyle Fingerprint" matches their ideal customer profile (e.g., high DIY interest + home ownership).

Use Case: College "Fit" Prediction. Impact: Using a student's GPA, social interests, and educational goals as predictors, an AI can recommend specific campus communities or universities where the student is statistically most likely to thrive.

These categories and respective predictors allow the model to distinguish between neighborhoods:

Predictors Included

Socio Demographics- Marital status, Single parent, Number of children, Grandchildren, Household size, Veteran status.

Financial Profile- Net worth, Home ownership vs. Renting, Credit card usage, Foreign investments, Number of vehicles.

Values & Beliefs- Religious involvement, Political active, Charitable giving, Environmental consciousness.

Interests & Hobbies- Photography, Auto work, Movie collecting, Beauty/Cosmetics, DIY, Home improvement, Self-improvement.

Lifestyle & Tech- Health conscious, Pet ownership (Cat/Dog), TV cable subscription, Wireless/Cellular owner, Online education.

Outdoor & Fitness- Fishing, Camping/Hiking, Hunting/Shooting, Outdoor groups.

Python Skitlearn pandas numpy