This repository contains a Python implementation of a Gold Price Prediction model using the Random Forest Regressor algorithm. The model is designed to predict the future prices of gold based on historical data and various features.
Gold has been a valuable and sought-after commodity for centuries. Predicting its price accurately can be beneficial for investors, traders, and economists. This project aims to build a predictive model using the Random Forest Regressor algorithm to forecast the future prices of gold based on historical data.
The Random Forest Regressor algorithm is an ensemble learning method that combines multiple decision trees to create a robust model. It is capable of handling non-linear relationships and capturing complex patterns in the data, making it suitable for predicting gold prices.
Dataset used in this project is downloaded from kaggle : Download dataset
After training the model, it is crucial to evaluate its performance. In this project I have used R Squared Score to access the model's accuracy.
This project can be further improved in several ways. Some potential enhancements include:
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Adding more features: Explore additional economic and market indicators that may influence gold prices.
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Hyperparameter tuning: Optimize the Random Forest Regressor model by tuning hyperparameters using techniques like grid search or random search.
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Feature selection: Apply feature selection techniques to identify the most relevant features for prediction.
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Visualizations: Create informative visualizations to better understand the relationship between gold prices and the selected features.
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Real-time predictions: Extend the model to make real-time predictions by incorporating streaming data or APIs.
Contributions are welcome! If you have any ideas, bug fixes, or improvements, please open an issue or submit a pull request.