Here, I investigated the factors that affect a student's performance in high school. I trained and tested several supervised machine learning models on a given dataset to predict how likely a student is to pass. Then, I selected the best model based on the amount of computation resources used to make a reliable prediction

I mostly used the Python libraries Pandas and Sklearn to wrangle and built the model and Seaborn to visualize the data. This project is connected to the Machine Learning Engineer Nanodegree program, from Udacity.