California House Price Prediction AI (Ensemble + XGBoost)

and XGBoost.

The application predicts California housing prices based on key socioeconomic and geographical features, while integrating an interactive map for location-based visualization.

? Key Features:

Ensemble Machine Learning Model

XGBoost Regressor for high accuracy prediction

Real-time price prediction

Uncertainty estimation (Standard Deviation output)

Interactive location-based map visualization

User-friendly input interface

Example data loading functionality

? Input Features Used:

Median Income

House Age

Average Rooms

Average Bedrooms

Population

Average Occupancy

Latitude & Longitude

? Technical Stack:

Python

XGBoost

Scikit-learn

Pandas & NumPy

Map integration (Geospatial visualization)

Machine Learning Ensemble Techniques

? Project Objective:

The goal of this project was to design a production-style AI system capable of delivering accurate housing price predictions while providing uncertainty estimation and location awareness.

The system demonstrates strong understanding of:

Regression modeling

Ensemble learning

Feature engineering

Model evaluation

AI deployment with UI integration