In this project I was tasked with analyzing available data on Houses. I was given 79 different explanatory variables which included their lot size, number of bedrooms, and number of bathrooms to name a few. The goal was to predict the sales prices of other houses using these variables. For this project I utilized the power of machine learning, Random Forest Regression and XGBoost algorithms,to predict the sales prices. The final (best) model these machine learning techniques calculated was then used to predict the house prices and were submitted to the Kaggle competition site and was given a score determining how well the model worked.