Predicting Sales Price for Homes at Ames Iowa

Presented by:
Sijia Liang (Pace University)

The goal of this project is to find the best predictive model to predict sale prices for houses at Ames, Iowa using a high dimension dataset with exploratory data analysis and data pre-processing. This project compares the performance of lasso regression, gradient descent decision tree boosting model, and multi-layer perceptron neural network. Cross validations were used to tune the hyperparameters. We found that the gradient boosting model performs the best on this housing dataset.