House Price Prediction with PyTorch

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.