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

===== 3. Feature Engineering =====
* Feature Selection: Identify and select relevant features (data points) that are likely to indicate a risk of dropping out. These might include: - Attendance patterns (e.g., frequent absences or tardiness). - Academic performance metrics (e.g., grades, test scores). - Socio-economic indicators (e.g., household income, parental education). - Behavioral data (e.g., disciplinary actions, participation in extracurricular activities).
* Feature Transformation: Create new features or modify existing ones to better capture the underlying patterns in the data. For example, calculating the rate of change in attendance over time or the correlation between academic performance and socio-economic status.