Deep Learning for House Price Prediction

House Price Prediction using ANN (PyTorch)

This project utilizes an Artificial Neural Network (ANN) built with PyTorch to predict house prices based on a variety of residential property features. Input features include square footage, bedrooms, bathrooms, garage type and area, presence of amenities like fireplace and pool, location data (city, zip code) and more.

The life cycle includes:

• Data Preprocessing: Managing missing values, encoding categorical data (like garage type), and feature scaling.
• Model Building: Designing and training a feedforward ANN using PyTorch for regression tasks.
• Evaluation: Using MAE, MSE, RMSE, and R² Score to assess model performance.
• Model Saving: Storing the trained model using `.pt` format for future inference.
• Deployment: Integration into a frontend app to provide real-time price estimates based on user inputs.
• Monitoring: Keeping track of model performance and updating with new housing market trends.

Challenges: Handling large variance in house features, dealing with outliers, multicollinearity, and ensuring generalization across diverse locations and property types.