Gold Price Predictions with Machine Learning
Implementation with Random Forest Regression, Python
This report outlines the development and evaluation of a predictive model for gold prices (GLD) using a Random Forest Regressor.
Accuracy = 0.9897338861925125
Accuracy and Performance
The Random Forest Regressor model demonstrates strong performance, with an R-squared error of approximately 0.9897 when evaluated against test data. This high R-squared value indicates a robust fit of the model to the data.
A visual comparison of actual gold prices with predicted prices further illustrates the effectiveness of the model in aligning with real-world data.
Data and Application
Data Loading and Exploration: Loaded the dataset and conducted exploratory data analysis to understand the dataset's structure and quality.
Data Preprocessing: Ensured data quality through preprocessing steps like handling missing values and addressing outliers.
Model Training: Utilized the Random Forest Regressor for training. Fine-tune the model's hyperparameters for optimal performance.
Model Evaluation: Assessed the model's accuracy and performance using metrics like the R-squared error.
Visualizations: Visualized the model's predictions to gain a practical understanding of its alignment with actual data with the help of Python libraries like matplotlib and seaborn.
Usage
The code can be used as a starting point for predicting gold prices based on historical data and related features. Potential use cases include financial analysis, investment decisions, and market forecasting.
Range
Minimum Value: 73.21
Maximum Value: 177.19