Ethereum Price Prediction using Hidden Markov Model (HMM)

This repository contains a Jupyter Notebook that demonstrates how to predict the price of Ethereum (ETH) over the next week using a Hidden Markov Model (HMM).

Overview

The code utilizes historical price data fetched from the Coingecko API and applies a Gaussian Hidden Markov Model to capture the underlying states of the market. The model is then used to simulate future price changes and provide a prediction for the next week.

Key Features

• Fetching historical price data for Ethereum (ETH)
• Visualizing price trends
• Preprocessing data by calculating logarithmic returns
• Training a Gaussian Hidden Markov Model
• Simulating future price changes
• Converting log returns to price levels

Dependencies

• NumPy
• pandas
• matplotlib
• seaborn
• requests
• hmmlearn

Usage

Simply run the Jupyter Notebook to execute the code and view the predictions.

License

Feel free to use, modify, and distribute the code as per your requirements.

Contact

For any queries or suggestions, please feel free to reach out.