Gold is one of the most valuable financial commodities and its price fluctuates daily based on the economy stock market currency strength and global demand.
In this project machine learning is used to study historical gold price data and predict future prices.
The goal is to analyze market attributes correlate them with gold value and train a regression model that can forecast price movement.
This helps traders analysts and investors make more informed decisions using data driven insights.
Project Overview
- Predicts gold prices using machine learning regression
- Random Forest Regressor used for training and prediction
- Built entirely in Python for financial forecasting
Libraries Used
- Pandas for handling datasets
- NumPy for numerical computation
- Matplotlib and Seaborn for visualization
- Scikit Learn for model training and evaluation
- Metrics module for R² score accuracy measurement
Dataset and Fields
- Dataset loaded from CSV containing historical market features
- Date column dropped and remaining values used as numeric features
- Correlation matrix analyzed to observe strongest price influencing factors
Preprocessing Steps
- Removed Date column to simplify numeric processing
- Checked for missing values and ensured clean structure
- Created independent variables x and target y where y is GLD price
Model Building
- Chosen algorithm RandomForestRegressor with one hundred estimators
- Data split into training and test sets using train test split
- Model trained on x train to learn patterns in price movement
Evaluation and Accuracy
- Predictions generated by model on test data
- R² score calculated to evaluate model performance
- Higher score indicates stronger accuracy and generalization capability
Prediction Visualization
- Actual price values plotted against predicted values
- Graph shows how closely model follows real price trends
- Visualization useful for performance interpretation and forecasting confidence
Key Learnings
- Random Forest performs well for non linear financial data
- Correlation analysis helps understand major price influencers
- Visualization gives real view of prediction behaviour over time
Future Scope
- Add more market variables like crude oil stock indices currency index
- Test with advanced models like XGBoost LightGBM or LSTM deep learning
- Deploy with Streamlit or Flask for live gold price forecasting dashboard