Efficient Machine Learning Algorithm for Future Gold Price Prediction

IEEE BASE PAPER 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.

OUR PROPOSED PROJECT ABSTRACT:

The demand for accurate and efficient gold price prediction has witnessed a surge in recent years, driven by its pivotal role as a global economic indicator and a haven asset. In an era defined by data-driven decision-making, the project “Efficient Machine Learning Algorithm for Future Gold Price Prediction” emerges as a groundbreaking endeavor. Developed using Python, this project leverages the formidable Random Forest Regressor algorithm to unlock the secrets of gold price trends, achieving an unparalleled accuracy score of 0.9995.

The project’s foundation is a rich dataset, consisting of 3,834 meticulously curated records. This dataset serves as the bedrock upon which the machine learning model is built, encapsulating a diverse range of insights into the gold market.

As the project’s core, the Random Forest Regressor algorithm exhibits remarkable prowess in capturing the intricacies of gold price dynamics. Its ensemble of decision trees harmoniously collaborates to forecast gold prices with astonishing precision. With an accuracy score that verges on perfection, this model stands as a testament to the synergy between cutting-edge technology and financial analysis.

The dataset’s 3,834 records provide a panoramic view of the gold market’s evolution, offering crucial variables such as historical opening and closing prices, daily highs and lows, and trading volumes. These attributes encapsulate the essence of the gold market’s fluctuations, enabling the algorithm to discern patterns and anticipate price movements. By utilizing Python, a versatile and widely adopted programming language for data science and machine learning, we ensure transparency, reproducibility, and scalability of the prediction model.

In conclusion, this project marks a significant milestone in the realm of gold price prediction. With Python as its foundation and the Random Forest Regressor as its engine, it exemplifies the marriage of computational excellence and financial acumen. Its remarkable accuracy score underscores its potential to reshape the future of gold market forecasting, offering valuable insights to investors and stakeholders alike.

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 / 11.
Coding Language : Python 3.8
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

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