House Price

This project involves building a neural network model to predict house prices using a dataset of housing features. The data is preprocessed by selecting relevant columns, handling missing values, and normalizing the features. The model, consisting of four dense layers, is trained using the Adam optimizer and evaluated for performance using R-squared and loss metrics.