I built a Bitcoin price forecasting model that achieves 0.55% error, here is everything I learned

1. 1Collect and preprocess 2 million minutes of Coinbase data
2. 2Build and train a stacked LSTM model using the collected data
3. 3Tune hyperparameters to optimize model performance
4. 4Evaluate model error and compare to baseline models
5. 5Deploy the model for real-time Bitcoin price forecasting

Data scientists and machine learning engineers can benefit from this walkthrough to improve their forecasting models, while software engineers can learn from the mistakes and solutions presented

💡 Using a stacked LSTM model with careful hyperparameter tuning can achieve high accuracy in Bitcoin price forecasting

🚀 Built a Bitcoin price forecasting model with 0.55% error using stacked LSTM on 2M minutes of Coinbase data! 📊