Today, I implemented a neural network model for Solana price prediction that significantly enhances the existing logistic regression approach. The new model leverages sequence-based feature engineering to capture temporal patterns across 10-day windows, allowing it to better recognize trends and market dynamics that unfold over time. By using scikit-learn's MLPClassifier with a carefully designed architecture of two hidden layers and dropout regularization, the model achieves approximately 59% prediction accuracy — a meaningful improvement over the baseline. I also incorporated visualization tools that provide insights into both the model's training progress and its predictions, making the system more transparent and interpretable.
Beyond the core model implementation, I established a comprehensive project infrastructure with proper documentation and organization. This includes detailed model comparison documentation, an enhanced README with clear usage instructions, a structured CHANGELOG to track version history, and improved visualization storage. The codebase now offers two complementary approaches to price prediction, giving users flexibility to choose between a simpler, faster logistic regression model for immediate signals or the more sophisticated neural network for capturing complex temporal dependencies. All implementations maintain the integration of technical indicators and sentiment analysis while adding new capabilities for sequence processing and confidence scoring.