Gold-Price-Prediction

Screenshots

• Splash Screen

• Main Screen

Introduction:

Gold is a highly valued commodity, and predicting its price is a valuable tool for investors and people who want to buy gold. However, predicting the price of gold has traditionally been a challenging task due to its volatile nature. In this project, I developed a machine learning model embedded in an app to predict gold prices.

Problem:

The main challenge in predicting the price of gold is its volatility. It's difficult to account for all the factors that impact its price, including political and economic events, currency fluctuations, and supply and demand. As a result, traditional methods of predicting gold prices are often unreliable.

Solution:

• I developed a machine learning model that leverages historical gold price data and other relevant features like Oil Price, Standard and Poor’s (S&P) 500 index, Dow Jones Index US Bond rates (10 years), Euro USD exchange rates, prices of precious metals Silver and Platinum and other metals such as Palladium and Rhodium, prices of US Dollar Index, Eldorado Gold Corporation and Gold Miners ETF, to predict the price of gold.
• The model uses a random-forest algorithm. It's trained on a large dataset of gold price data spanning from November 18th 2011 to January 1st 2020 from various sources, with a focus on ensuring that the model can accurately predict price fluctuations.

Benefits:

Using this app, investors can make informed decisions about buying and selling gold, resulting in potentially higher profits. The machine learning model can help provide more accurate and timely predictions.

Demonstration:

Currently working on the UI of mobile application for the same in Android Studio using Flutter

Impact:

• In testing, the model achieved an accuracy rate of 80%, which is significantly higher than traditional methods.
• R squared error : 0.9887421869729113

Result: