Real Estate Price Prediction Model

• Tech Stack: Python, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
• Project Focus: Predicting property prices based on location, size, and market trends
• GitHub Repository: Project Link

This project focuses on developing a machine learning model to predict real estate prices based on historical data and market trends. It follows a structured approach for scalability and efficiency:

• Data Pipeline: Organized data directories for raw, interim, processed, and external data sources, ensuring a clean and reusable workflow.
• Feature Engineering: Scripts in the src/features directory to extract critical features like location score, square footage, and year built.
• Model Development: Trained regression models such as Linear Regression and Random Forest to predict property prices, with evaluation metrics like RMSE and R².
• Visualization and Reporting: Exploratory Data Analysis and results visualized using Matplotlib and Seaborn, with reports generated in HTML and PDF formats under the reports directory.
• Reproducibility: The environment can be replicated using requirements.txt, and the project can be set up easily with setup.py.

The model leverages location data, property attributes, and market conditions to provide accurate price predictions, assisting buyers, sellers, and real estate agents in making informed decisions.