CryptoClustering Project

Overview

Python and unsupervised learning techniques are used to predict if cryptocurrencies are affected by 24-hour or 7-day price changes. Clustering analysis is used on the cryptocurrency market data, imported from the /Resources/crypto_market_data.csv file, to group similar cryptocurrencies based on their price change patterns.

Purpose and Goals

The purpose of this project is to gain insights into the cryptocurrency market and identify clusters of cryptocurrencies that exhibit similar price change behaviors, potentially uncovering hidden relationships and trends which influence the cryptocurrency market.

Tools and Skills Used

To achieve the project's goals, we will be using the following tools and skills:

1. Python: To write the code and perform data analysis and manipulation.
2. Jupyter Notebook: For a structured and interactive environment to run the code and document the analysis.
3. Pandas: For data loading, preprocessing, and manipulation.
4. NumPy: For mathematical operations and array manipulations.
5. Scikit-learn: For implementing the K-means clustering algorithm and PCA.
6. HvPlot: To create interactive and visually appealing plots.

Conclusion

The CryptoClustering project aims to gain insights into the cryptocurrency market by performing unsupervised learning and clustering analysis. By identifying similar price change behaviors among cryptocurrencies, we can gain a deeper understanding of the market dynamics and potentially make more informed investment decisions. The combination of Python, Pandas, Scikit-learn, and HvPlot provides a powerful toolkit to achieve these goals efficiently and effectively.