gold-price-prediction's Introduction

GOLD PRICE PREDICTION

Gold Price Prediction with ML Algorithms such as Long-Short Term Memory and Random Forest Regression.

PROBLEM STATEMENT

In today’s dynamic market, gold prices have a continuous fluctuation which results in volatile investment opportunities.
The price fluctuations may result in financial loss, improper resource allocation, and difficulties in planning for investments in gold.

PROPOSED SOLUTION

The Objective is to leverage the data sets containing historical data on gold prices over a period of time and by using various visualization techniques to observe the spread and pattern of data.
Implementing a model for gold price prediction providing accurate and decentralized forecasts through collective intelligence.

ABSTRACT

Gold, a timeless asset, is a key element in investment and trade. Its market, driven by diverse factors, requires accurate prediction. This study offers a predictive model using historical data and various indicators. A refined machine learning model is trained, providing user-friendly real-time predictions, benefiting investors, businesses, and policymakers. This model is a valuable tool for the ever-changing gold market.

TECHNOLOGY STACK

Technology: Long Short-Term Memory (LSTM) and Random Forest (RF) – ML Algorithms
Tool: Jupyter Notebook (IBM Z PLATFORM)

IBM Z IMPLEMENTATION

We've deployed our project on the Z platform with a focus on professionalism and reliability. This includes high availability, load balancing, redundancy, efficient resource use, comprehensive testing, compliance, disaster recovery, lifecycle management, user training, cost control, feedback loops, third-party integration validation, regular data backups, resource allocation monitoring, and a scalability plan. These measures ensure smooth operation and security on the Z platform.

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