Project Overview

FX Sprinter Forecaster is a financial analysis tool built to explore the application of machine learning in the forex market. Using Python and AI, this project focuses on building and evaluating a model to deliver predictive insights for EUR/USD price movements based on historical data.

Tech Stack & Performance Metrics

• Core Technology: Python, Machine Learning
• Algorithm: K Nearest Neighbor (KNN)
• Training Accuracy: 98.02%
• Test Accuracy: 29.70%

Analysis & Learning Outcomes

The model achieved a high training accuracy of 98.02%, indicating a strong ability to learn from the provided dataset. The test accuracy of 29.70% highlights a classic case of model overfitting and underscores the inherent challenge of predicting volatile financial markets. This project served as a critical investigation into model validation, demonstrating the importance of techniques to prevent overfitting and the need for rigorous testing against unseen data.