Student Performance Predictive Model Using Machine Learning

Develop a comprehensive predictive model using scikit-learn to forecast student academic performance based on multi-dimensional data. Include preprocessing steps for handling missing educational data, feature engineering for learning behaviors, and model evaluation using cross-validation. The model should predict end-of-semester grades with at least 85% accuracy, incorporating variables like attendance, previous semester grades, extracurricular activities, and learning platform engagement metrics.