## Goal

Develop an **AI-based model** to monitor and predict Ethereum gas fees in real-time, allowing users to time their transactions for cost savings. The agent should be capable of analyzing gas price fluctuations and offering insights for optimal transaction windows, helping users reduce expenses on gas-heavy networks like Ethereum.

## Scope of Work

### Data Collection & Preprocessing

• Collect historical and real-time gas price data from sourceslike Etherscan or other public blockchain analytics APIs.

• Analyze network congestion patterns and transaction types to understand factors influencing gas prices.

• Clean, aggregate, and preprocess data to identify trends and patterns for effective prediction modeling.

### Model Development

• Develop a predictive model using machine learning techniques in Python (e.g., LSTM or Prophet) to forecast short-term gas price trends over next 7 days. 

• Consider real-time adjustments based on user-specified factors, such as urgency and maximum acceptable gas fees.

Possible model architectures could include:

• Recurrent Neural Networks (RNNs), LSTMs, or GRUs for time series forecasting.

• Transformer-based models for capturing complex patterns in gas fee fluctuations.

### Evaluation & Testing

• Validate the model against historical data to measure the accuracy and reliability of gas fee predictions.

• Test the model's accuracy against historical gas fee data and assess performance with metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE).

•  Test the API app for response speed and ensure it aligns with real-time network changes.

### Deployment Readiness

• Prepare the agent for deployment by packaging it as a microservice, integrating API calls to get predictions and trends .
 
• Document deployment steps, including data source configuration, and make it accessible in a shared drive or GitHub repository and include it on the bepro deliverable

### Requirements

• Experience with time-series forecasting, blockchain data analytics, and real-time alerting systems using RNN , Transformers,…

• Proficiency in handling transaction data and implementing machine learning models for prediction.

### Deliverables

• **Codebase**: Documented Python code, including data processing, model architecture and the pythorch trained model.

• **Trained Model**: Ready-to-deploy model (.pth file) capable of real-time predictions.

• **Documentation**: Detailed README with installation steps, data requirements, and usage guidelines

• **Performance Report**: Evaluation metrics, testing outcomes, and improvement recommendations.
 

Smart Gas Fee Predictor Model for Optimal Transaction Timing on Ethereum

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