Forecasting Malaysian gold price using random forest / Muhammad Nur Firmanrulah Samsudin

• Main Author: Samsudin, Muhammad Nur Firmanrulah
• Format: Thesis
• Language: English
• Published: 2024
• Subjects: Algorithms
• Online Access: https://ir.uitm.edu.my/id/eprint/96337/1/96337.pdf; https://ir.uitm.edu.my/id/eprint/96337/

Gold is a yellow valuable metal that is used to make coins, jewellery, attractive artefacts, and many other things. Gold is the most popular and outperforms other metals when used as an investment instruments. The gold prices are influenced by supply and demand. Estimating its future pricing remains a difficult undertaking due to the complex and volatile structure of financial markets. Previously, manual prediction being done to forecast gold prices. Developing this model can save their time in predicting gold prices. Random forest appears to be the best model for predicting gold prices. Dataset is gathered from multiple sources and being merge into one file. Dataset being split into training and testing for ratio 90/10. This ratio being chosen after some experiment being held. The training set will use to generate subset for each decision tree. After that, random forest will be created to add tree into forest until number of trees reached.