Student Performance Indicator: An End-to-End Machine Learning Pipeline for Predicting Academic Outcomes

With all the amount of data that is now available about the students in a school environment, there is no way one could analyze such data manually. The Student Performance Predictor is a web application I designed to help determine the final score that a particular student will get from mathematics class, basing on his demographics and background. The whole machine learning pipeline was implemented by me using the Python language. After experimenting with various models in Jupyter Notebooks and having my kernel crash quite a few times, I managed to find the most accurate one – Random Forest Regressor with an 80% accuracy rate. Next, I embedded this algorithm in my application, which uses the Flask server. User only needs to input some values in three fields to get the prediction instantly.