https://github.com/anish15ag/gold-price-prediction-usingml

Machine Learning (ML) project for predicting Gold Prices using Random Forest Regression. Also includes in training the model and data processing
https://github.com/anish15ag/gold-price-prediction-usingml

machine-learning

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Machine Learning (ML) project for predicting Gold Prices using Random Forest Regression. Also includes in training the model and data processing

• Host: GitHub
• URL: https://github.com/anish15ag/gold-price-prediction-usingml
• Owner: Anish15AG
• Created: 2024-09-04T17:51:17.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-13T12:42:45.000Z (4 months ago)
• Last Synced: 2024-11-06T03:47:11.412Z (about 2 months ago)
• Topics: machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 117 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Gold Price Prediction Using Machine Learning

This project demonstrates the prediction of gold prices using a machine learning model (Random Forest Regressor). The model is trained on historical gold price data to predict future values with a focus on correlation analysis and error measurement (R squared).

## Dataset

The dataset used contains historical data of gold prices and related financial parameters, loaded from a CSV file.

The date approximation is of around 10 years of data.

## Libraries Used

- **pandas**: Data manipulation and analysis

- **numpy**: Numerical computations

- **matplotlib**: Data visualization

- **seaborn**: Advanced data visualization

- **scikit-learn**: Machine learning models and metrics

## Steps Involved

1. **Data Loading**: The dataset is loaded using `pandas` from a CSV file.

2. **Data Cleaning**: Checked for missing values and converted columns to numeric types.

3. **Exploratory Data Analysis (EDA)**:

    - Used correlation matrix to find relationships between variables.

    - Visualized the distribution of gold prices.

4. **Model Training**:

    - Features (`X`) were extracted by removing the `Date` and `GLD` columns.

    - The target (`Y`) is the `GLD` (gold price).

    - The data is split into training and testing sets using an 80/20 split.

5. **Prediction**:

    - The Random Forest Regressor model is trained on the training set.

    - The model predicts the values on the test set, and the performance is evaluated using the R-squared error.

6. **Visualization**:

    - Comparison of actual vs predicted gold prices using a line plot.

## Model Performance

- The model's accuracy is measured using the R-squared error, where a higher score indicates better performance.

## Visualizations

- **Correlation Heatmap**: Shows the relationships between different financial factors and gold prices.

- **Actual vs Predicted Plot**: Compares the model's predictions with actual values.

## Requirements

- Python 3.9.6 or above

- Libraries: `pandas`, `numpy`, `matplotlib`, `seaborn`, `scikit-learn`