A simple Feed Forward Neural Network trained to predict the price of houses on the houseprice.csv dataset.

Multivariate linear regression for house price prediction implemented using PyTorch tensors and trained in Google Colab.

A machine learning project focused on accurately predicting house prices in King County, WA, leveraging a Multi-Layer Perceptron (MLP) with extensive data preprocessing and hyperparameter optimization using Optuna and PyTorch Lightning. This project demonstrates a significant improvement in prediction accuracy through iterative refinement.

built a PyTorch-based house price prediction model by preprocessing data, encoding categorical features with embeddings, combining them with continuous variables in a feed-forward neural network, training the model using Adam and RMSE loss, evaluating it on a test set, and saving/loading the trained model for reuse.