Adaptive Learning Path Generator with Performance Tracking

Design a Python-based adaptive learning recommendation system using pandas and scikit-learn that dynamically adjusts educational content difficulty based on individual learner performance metrics. The system should track user progress across multiple skill domains, calculate learning efficiency scores, and generate personalized learning paths with complexity scaling. Implement machine learning algorithms to predict optimal learning sequences and include visualization capabilities using matplotlib to display learning progression and skill gaps.