Ethereum Price Prediction using Machine Learning

This project aims to predict the future price of Ethereum using various machine learning models, including deep learning. The project is implemented using Python and Jupyter notebooks.

Introduction

Ethereum is a decentralized, open-source blockchain platform that enables the creation of smart contracts and decentralized applications. With its growing popularity, Ethereum has become a hot topic for investors, and predicting its price movements is crucial for informed decision making.

Requirements

The following packages are required to run the project:

• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• TensorFlow
• Dataset

Methodology

The project uses multiple machine learning models, including linear regression, decision trees, random forests, and deep neural networks. The models are trained using a 80-20 split of the data, with 80% used for training and 20% used for testing. The performance of each model is evaluated using mean squared error and root mean squared error.

Results

The results of the project show that random forests outperform other machine learning models in terms of accuracy. The model was able to predict the future price of Ethereum with a high degree of accuracy, demonstrating the potential of machine learning in the finance industry.

Conclusion

This project demonstrates the capability of machine learning in solving real-world problems and provides valuable insights for Ethereum enthusiasts and investors. The code and notebooks can be used as a starting point for further research and development in the field of crypto price prediction.