Efficient Machine Learning Algorithm for Future Gold Price Prediction

ABSTRACT:

Gold has high demand due to its usage in jewellery and used for investment. While investing money in gold the investors are excited to know the return price well in advance. Due to dynamic time dependency prediction of gold price is very complicated issue. On inflation rate the future gold price depends. Decision tree, linear regression, random forest regression, support vector machine and ridge regression machine learning algorithms are used. These algorithms are compared with respect to R Squared Error, Root Mean Square Error evaluating parameters. Initially data is collected after pre-processing of the data, 80% of the data samples are applied to training model and remaining 20% of the data samples are used for testing purpose. It is observed that as compared to other machine learning algorithms random forest algorithm gives more accurate result in terms of gold price prediction.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

Random Forest Regressor.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 6 GB

SOFTWARE REQUIREMENTS:

Operating system : Windows 10 Pro.
Coding Language : Python 3.8
Web Framework : Flask.

REFERENCE:

Minal Ghute; Mridula Korde, “Efficient Machine Learning Algorithm for Future Gold Price Prediction”, 2023 International Conference on Inventive Computation Technologies (ICICT), IEEE Conference, 2023.

Python IEEE Projects

Efficient Machine Learning Algorithm for Future Gold Price Prediction