House Price Prediction with PyTorch

Oct, 2024_ Project Link

• This project utilizes PyTorch to build a regression model that predicts house prices based on features such as location, size, number of rooms, and other relevant attributes.

• It demonstrates the full machine learning pipeline, including data preprocessing (handling missing values, normalization, and feature engineering), model building, training, and evaluation using Mean Squared Error (MSE).

• The project monitors model performance by tracking loss metrics and visualizes the loss curve across training epochs to help understand the model’s learning progression.