Crypto Forecasting ML Model and AI Chatbot

Inspiration

The volatility and complexity of cryptocurrency markets make it difficult for investors to make informed decisions. We wanted to leverage machine learning (ML) to create a forecasting model that provides insights into market trends. Additionally, we built an AI chatbot to make the information accessible and user-friendly.

What We Learned

Throughout this project, we gained hands-on experience in:

• Collecting and preprocessing financial data from cryptocurrency exchanges.
• Implementing and fine-tuning ML models for time-series forecasting.
• Deploying AI chatbots that interact naturally with users.
• Overcoming data biases and ensuring model reliability.

How We Built It

1. Data Collection & Preprocessing:

   • Gathered historical price data from recommended datasets.
   • Cleaned and normalized the data to improve ML model performance.

2. Machine Learning Model:

   • Used a combination of LSTM (Long Short-Term Memory) networks and Random Forest models for prediction.
   • Trained the model on past price trends and technical indicators.
   • Evaluated performance using RMSE (Root Mean Square Error) and other statistical metrics.

3. AI Chatbot:

   • Built using OpenAI's GPT framework to respond to queries about market trends.
   • Integrated with our forecasting model to provide real-time predictions.
   • Deployed on a web-based platform for ease of access.

Challenges We Faced

• Data Quality: Inconsistent and missing data points required extensive preprocessing.
• Model Optimization: Ensuring the model generalizes well without overfitting.
• Chatbot Integration: Making the chatbot provide accurate and relevant responses based on ML forecasts.

Conclusion

This project was a valuable learning experience that combined ML, finance, and AI chatbot development. The final product provides users with an interactive and intelligent tool to navigate cryptocurrency markets. Moving forward, we aim to enhance our model with more advanced architectures and integrate sentiment analysis for better forecasting accuracy.

Built With

• ai
• lstm
• machine-learning
• neuralnetworks
• python
• randomforest
• streamlit