In this project, my goal was to build a predictive model to answer the House Prices Kaggle Competition, in which we are provided with diverse data concerning houses such as quality, number of bathrooms, areas, in order to predict the final sale price. The large majority of the work was spent on exploratory data analysis and feature engineering, with some data cleaning to do in both the training and test set.

A machine learning project in which I used fitbit-like sensor data from gyroscopes and accelerometers to predict the performance of a weightlifting exercise. This project goes through the following key steps : Raw Data gathering and cleaning Model building - RandomForest Cross validation and Testing Link to the GitHub repository