This project attempts to utilize methods in Unsupervised Learning to deduce and infer similarities in the trading behaviour of commodity contracts. Specifically, it uses Hierarchical Clustering and K-Means Algorithms to investigate if different commodity contracts may be clustered into reasonable sub-groups.
Open the Command Prompt and git clone to a local folder with:
git clone https://github.com/NicholasTanWeiHong/unsupervised-learning-with-commodity-prices.git
Then, the code may be accessed from unsupervised-learning-with-commodity-prices.R
Alternatively, open unsupervised-learning-with-commodity-prices.md in GitHub to view the analysis as an RMarkdown Report.