Demand Forecasting using Machine Learning

This project is based on exploring and understanding the data using analytical tools and machine learning models to forecast the demand for a specific attribute of the educational organization. The database used is a sample de-identified aggregate course data to understand the demand for courses and predict the number of sections each course will require in future semesters. Exploratory data analysis is done to determine the most relevant factors that affects the number of sections for a course. Time series forecasting models are used to make initial predictions for number of course sections needed to meet the prospective demand of the course.