Python House Price Predictor

Using housing data on residential homes in Ames, Iowa sourced from Kaggle, a platform that data scientists use to post datasets relevant for building AI or machine learning models, creating a machine learning model that can predict the final house prices of all given homes.

Details

Configured a deep learning, feed forward neural network model using Python’s PyTorch machine learning library. Compiled relevant categorical and continuous data then converted them to tensors while training the model on a root mean square error function. Trained the model using 5000 epochs to minimize error and ensure accurate house price predictions.