This repository contains a Jupyter Notebook, Crypto_predict.ipynb, dedicated to predicting cryptocurrency prices using historical price data and machine learning models. The project aims to demonstrate the application of various data science techniques in the financial domain, specifically in the volatile cryptocurrency market.
The Crypto_predict.ipynb notebook explores several key areas:
- Data Collection: How to gather cryptocurrency price data from various sources.
- Data Preprocessing: Preparing the data for analysis, including cleaning and normalization.
- Feature Engineering: Identifying and creating features that can help in predicting cryptocurrency prices.
- Model Building: Developing machine learning models to predict future prices of cryptocurrencies.
- Evaluation: Assessing the performance of the models using appropriate metrics.
Ensure you have the following installed:
- Python 3.x
- Jupyter Notebook or JupyterLab
- Required Python packages:
- pandas
- numpy
- scikit-learn
- matplotlib
- seaborn
- any other packages mentioned in the notebook
- Clone this repository to your local machine:
git clone <repository-url>- Navigate to the cloned directory:
cd <repository-name>- Install the required Python packages:
pip install -r requirements.txt(Note: You might want to create a virtual environment for this project to keep dependencies organized.)
After installation, open the Jupyter Notebook to explore the project:
jupyter notebook Crypto_predict.ipynbContributions to improve the project are welcome. Please follow these steps to contribute:
- Fork the repository.
- Create your feature branch (
git checkout -b feature/AmazingFeature). - Commit your changes (
git commit -am 'Add some AmazingFeature'). - Push to the branch (
git push origin feature/AmazingFeature). - Open a Pull Request.
Distributed under the MIT License. See LICENSE for more information.
Project Link: https://github.com/your_username/repo_name