Editing Openai/9ae7f6c5-b14d-453e-bcee-53f73d7fccf9 (section)

===== 4. Model Selection and Development =====
* Algorithm Selection: Choose appropriate machine learning algorithms for the task. Common algorithms for predictive modeling include: - Logistic Regression: For binary classification (e.g., dropout vs. non-dropout). - Random Forests: For handling complex datasets with many features. - Gradient Boosting Machines (GBM): For improving prediction accuracy by combining multiple weak learners. - Neural Networks: For capturing non-linear relationships and complex patterns in the data.
* Model Training: Train the selected models using historical data, with labeled examples of students who did or did not drop out. Split the data into training and validation sets to evaluate the model's performance.
* Hyperparameter Tuning: Optimize the model’s hyperparameters to improve its accuracy and generalization to new data.