Student Performance Analysis and Intervention Strategy

Objective: Analyze student academic data to identify at-risk students and recommend targeted interventions to improve performance.

? Tools & Tech:

Python (Pandas, Matplotlib, Scikit-learn)

Excel or Google Sheets

Power BI or Tableau for dashboards

Dataset Includes:

Student demographics (age, gender, socioeconomic status)

Attendance records

Grades across subjects

Participation in extracurriculars

Final exam results

Workflow:

Clean and preprocess the data

Perform correlation analysis between attendance, activities, and grades

Use classification models (e.g., decision tree) to predict risk of failure

Visualize performance trends across different groups

Recommend interventions (e.g., tutoring, mentorship)

Outcome: Identified that students with <80% attendance and no extracurriculars were 3× more likely to fail. Proposed a mentorship program and tracked improvement over time.