Ethereum Gas Price Prediction Model

Overview

This repository contains a machine learning model developed to predict gas prices on the Ethereum blockchain. The model uses historical data to forecast future gas prices, helping users optimize transaction costs and timing. The notebook can be opened and run directly in Google Colab, providing an accessible and cloud-based environment for experimentation and learning.

Table of Contents

• Introduction
• Dataset
• Setup
• Running on Google Colab
• Contents

Introduction

In this notebook, you will learn how to use a machine learning model to predict gas prices on the Ethereum network. The model leverages techniques such as feature engineering, model tuning, and evaluation to provide accurate predictions. This project is particularly useful for developers, traders, and anyone interested in optimizing Ethereum transaction costs.

Dataset

The dataset used for training and testing the model consists of historical Ethereum gas prices. You can access the dataset here. This dataset contains various features that are essential for accurate gas price prediction, such as block number, timestamp, gas limit, gas used, and gas price.

Setup

To run this notebook, you don’t need to install anything locally. You can open and execute the notebook directly in Google Colab, which provides a cloud-based environment with all necessary dependencies pre-installed.

Running on Google Colab

Follow these steps to run the notebook on Google Colab:

1. Open the Notebook on GitHub:

• Navigate to the notebook file (Gas_Cost_Optimization.ipynb) on the GitHub repository.

2. Open with Colab:

• Once you are viewing the notebook file on GitHub, you will see an Open in Colab button if available, or you can manually open it in Colab by replacing github.com in the URL with colab.research.google.com/github/. For example:

  https://colab.research.google.com/github/your-username/your-repo-name/blob/main/Gas_Cost_Optimization.ipynb
  

3. Run the Notebook:

• After the notebook opens in Colab, you can run each cell sequentially by clicking on the play button next to each code cell.

4. Save Your Work (Optional):

• If you want to save your work, you can either save a copy to your Google Drive by selecting File > Save a copy in Drive, or you can download the notebook by selecting File > Download .ipynb.

Contents

• Data Loading and Exploration: How to load and explore Ethereum gas price data.
• Feature Engineering: techniques to create features that improve model performance.
• Model Training and Comparison: raining the model and comparing different machine learning algorithms.
• Hyperparameter Tuning: Fine-tuning the model for better performance.
• Ensembling and Stacking: Advanced techniques to improve predictions.
• Model Blending: Combining multiple models to improve prediction performance.
• Prediction: Using the blended model to make accurate gas price predictions.