Bitcoin Market Pattern Analysis

This project aims to analyze historical Bitcoin price and volume data, using pattern analysis and clustering techniques to identify trends and recurring patterns in market behavior.

Features

• Data Loading: Import historical Bitcoin price and volume data.
• Visualization: Price and volume charts for initial analysis.
• Pattern Analysis: Use clustering algorithms (K-Means, DBSCAN) to identify patterns in the market.
• Insights: Generate insights to predict potential rises or falls in the Bitcoin price based on the identified patterns.

Technologies Used

• Python
• Pandas
• Matplotlib, Seaborn
• Scikit-learn

How to Run the Project

1. Clone the repository to your local machine:

git clone <repository_URL>

2. Install the dependencies:

pip install -r requirements.txt

3. Run the .ipynb notebook:

• Open the bitcoin_pattern_analysis.ipynb file (or whatever name you choose) in Jupyter Notebook or Google Colab.

Data Used

The data used in this project comes from [https://www.kaggle.com/datasets/mczielinski/bitcoin-historical-data]. This data includes historical Bitcoin price and trading volume information.

Contribution

Feel free to contribute to the project! If you have any improvements or ideas to improve the analysis, please open a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for more information.