This repository contains a Jupyter Notebook for a gold price prediction project. The notebook explores historical gold price data and implements machine learning models to predict future gold prices.
The Gold Price Prediction project aims to demonstrate how to use machine learning techniques to predict the price of gold. It utilizes historical gold price data and various features to train models that can forecast future gold prices.
The Jupyter Notebook in this repository provides step-by-step instructions and code snippets to guide you through the process of analyzing the dataset, preprocessing the data, training the models, and evaluating their performance.
The project uses a historical gold price dataset, which can be obtained from various sources such as financial websites, APIs, or databases. The dataset typically includes features such as date, opening price, closing price, high price, low price, and trading volume.
To run the notebook and experiment with the code, follow these steps:
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Clone this repository to your local machine using the following command: git clone https://github.com/alzx1/Gold-Price-Prediction.git
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Ensure you have the necessary dependencies installed. You can install them using pip: pip install -r requirements.txt
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Launch Jupyter Notebook: jupyter notebook
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Open the
gold-price-prediction.ipynbnotebook in Jupyter.
The notebook provides detailed explanations of each step involved in the gold price prediction, including:
- Data loading and exploration
- Data preprocessing and feature engineering
- Splitting the dataset into training and testing sets
- Training different regression models (e.g., linear regression, random forest regression)
- Evaluating and comparing the model performance
- Forecasting future gold prices using the trained models
Follow the instructions in the notebook cells to execute the code and observe the results. Feel free to modify the code and experiment with different approaches or models.
Contributions to this project are welcome. If you have any suggestions, bug reports, or improvements, please open an issue or submit a pull request. We appreciate your contributions!
This project is licensed under the MIT License.