Best Gold Price Prediction Using Machine Learning

Gold Price Prediction

Overview

This project focuses on predicting gold prices in INR using Machine Learning and time-series forecasting methods. It makes use of historical gold price data along with the USD/INR exchange rate to train multiple models for accurate predictions. A Gradio-based interface is integrated into the system, allowing users to easily input values and receive forecasts in an interactive and user-friendly way.By providing reliable gold price predictions, the project is especially useful for investors, traders, and researchers. It helps them analyze market trends, reduce uncertainty, and make smarter financial decisions backed by data-driven insights.

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Project Details

Introduction

Gold prices are highly sensitive to global economic conditions, exchange rates, and financial market fluctuations. Because of this volatility, predicting gold prices is a complex yet essential task. This project applies machine learning models such as Linear Regression, Ridge Regression, and Random Forest to forecast gold prices using historical data.

The project provides not only the trained models but also an interactive web-based application where users can input features and visualize predictions compared with historical data.

Directory Structure

Gold-price-prediction-main/
├── README.md                # Documentation  
├── requirements.txt          # Dependencies  
├── assets/                   # Images and plots  
│   └── plots/                # Generated plots  
├── data/                     # Dataset files  
│   ├── Gold vs USDINR.csv    # Gold price dataset  
│   └── USDINR.csv            # USD/INR dataset  
├── models/                   # Saved trained models  
│   ├── Regression_model.pkl  
│   ├── best_lin_reg_ridge_model.pkl  
│   ├── best_random_forest_model.pkl  
│   ├── ridge_model.pkl  
│   └── scaler.pkl  
├── notebook/                 # Jupyter notebooks  
│   └── Gold_price_usdinr_prediction.ipynb  
├── src/                      # Application source code  
│   ├── app.py                # Gradio-based app  
│   └── .gradio/              # SSL certificate files  
└── .gradio/                  # Config directory  

Dataset

• Source: ExchangeRate.org
• Description: Contains gold prices in INR and USD/INR exchange rates over time.
• Sample Columns:
  • Date
  • Gold Price in INR
  • USD/INR Exchange Rate

Features

This project includes:

• Preprocessed historical gold and currency exchange data.
• Multiple machine learning models for comparison.
• Visualization of training results and model accuracy.
• Evaluation using industry-standard metrics (MSE, RMSE, R²).
• A Gradio-based interactive application for predictions.

Modeling Techniques

The following machine learning algorithms were applied:

1. Linear Regression – Provides baseline predictions.
2. Ridge Regression – Regularized regression to reduce overfitting.
3. Random Forest Regressor – An ensemble model for robust forecasting.

Evaluation Metrics

The models were tested with these metrics:

• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R-Squared Score (R²)

Results

App Demo

The project comes with a Gradio app (app.py) that provides:

• User-friendly interface for gold price prediction.
• Visualization of predicted values against historical data.
• Easy integration for research and academic use.

Installation

1. Install dependencies: pip install -r requirements.txt
2. Open the notebook for analysis: jupyter notebook notebook/Gold_price_usdinr_prediction.ipynb
3. Run the application: python src/app.py

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