This project aims to predict the price of gold using historical data and the Random Forest algorithm.
The dataset used in this project is obtained from [source]. It contains historical data of gold prices and other relevant financial indicators.
Python Streamlit scikit-learn
EDA has been conducted to explore the dataset. This includes analyzing the distribution of gold prices, identifying outliers, and understanding the relationships between different features.
The Random Forest algorithm has been used to train the machine learning model for gold price prediction. The Random Forest algorithm is a powerful ensemble learning method used for regression tasks.
The model is evaluated using R-squared score. R Squared Error: 0.9892091367803421. The evaluation results help in understanding the performance of the model.
The final model provides predictions for gold prices with a certain accuracy. The results are visualized using plots and graphs to show the actual vs. predicted prices.
Contributions are welcome! If you have any improvements or suggestions, feel free to open a pull request or create an issue.
The application is deployed using Streamlit. You can access it here = https://ml-project-8-gold-price-prediction-s7ye4yjuuzal5idhfppksf.streamlit.app/