[20] Implement predictive analytics system for early identification of at-risk participants

[daaimah123]

Problem: We need a system to predict participant success and identify those who may be at risk of falling behind or dropping out.

Implementation Details:

1. Design a machine learning model for predicting participant success
2. Implement data preprocessing pipelines for model input
3. Develop a training and evaluation system for the predictive model
4. Create an automated system for running predictions on current participant data
5. Implement an alert system for flagging at-risk participants
6. Develop an interface for instructors to view and act on predictive insights
7. Create a feedback loop for improving model accuracy over time

Technical Concepts:

• Machine learning model design and implementation
• Data preprocessing and feature engineering
• Model training and evaluation techniques
• Automated machine learning pipelines
• Alert systems and notification design

Dependencies: Issue 19

Acceptance Criteria:

• Predictive model accurately identifies at-risk participants
• Data preprocessing pipeline correctly handles various input data types
• Model training and evaluation system allows for easy updates and improvements
• Automated prediction system runs efficiently without impacting system performance
• Alert system notifies relevant personnel about at-risk participants in a timely manner
• Instructor interface clearly presents predictive insights and suggested actions
• Feedback loop demonstrably improves model accuracy over time