Commodities like energy, base metals, other industrial inputs, etc. are a major asset class – most industries rely heavily on them to conduct business. Businesses monitor the price of these commodities to gauge the market movements and thus make more informed decisions and investments. The aim of this project is to forecast the price of a particular commodity using machine learning techniques.


Participants will work on publicly available data-sets. They will be required to use data pre-processing techniques and feature engineering in the course of the project, as well as perform traditional time series techniques like ARIMA and deep learning methods.


This project will help students understand machine learning algorithms and give them hands on training for the same.

Duration: 3-6 Months (Online – Offline, Instructor-based)