Summary of Findings on Drivers Impacting Student Test Scores

Based on the analysis conducted on the dataset, we aimed to determine the primary variables and drivers that influence student performance in various subjects including English, Math, Science, Social Science, and Art Culture. The results of the analysis are stored in a CSV file that outlines feature importance derived from a Random Forest regression model.

Key Findings

Top Variables Influencing Scores:

• Student Group in Arts (stu_group_arts): This feature has the highest importance score of 0.575. This suggests that students grouped in the arts curriculum perform significantly better across various subjects compared to others.
• Student Group in Science (stu_group_science): With an importance score of 0.158, this variable is also a notable driver of higher test scores, indicating that students in science groups are positively correlated with higher performance.
• Student ID (id): Surprisingly, this variable has low predictive value (importance of 0.033), suggesting it doesn’t significantly influence student performance.
• Attendance: Attendance has a modest impact (importance of 0.022), implying that regular presence in class may contribute to better academic results.
• Study Time: The importance score for study time is 0.013, indicating that while it has some impact, it may not be the most critical driver for performance.
• Family Size and Age: These features show lower importance scores (0.011 and 0.008, respectively), suggesting they play a minor role compared to educational grouping and attendance.
• Mother's Job Status: A marginal influence of 0.004 indicates that this variable might not significantly affect student scores.

Insights and Trends

The data strongly suggests that student grouping (arts and science) is a central factor in determining academic success, emphasizing the importance of a well-structured curriculum that aligns with students' performance. While attendance and study time show some positive correlation with scores, their lower importance implies that other factors, particularly educational grouping, overshadow them. Demographic factors like family size and age appear to have minimal influence, which may indicate that academic environment and engagement play a more crucial role in student outcomes.

Recommendations for the User

• Focus on Educational Groupings: If you aim to improve student outcomes, consider enhancing resources and support within arts and science groupings.
• Monitor Attendance: Since attendance has a recognizable impact, interventions to promote consistent classroom attendance could be beneficial.
• Consider Additional Factors: Explore qualitative aspects such as student motivation and engagement that might not be fully captured by the quantitative data.

Usage of Results

The findings are encapsulated in a file named feature_importance.csv, which can be found in the 'results' folder. This file details the importance of each feature assessed in the model. Further advancements could include using the trained Random Forest model, available as random_forest_model.pkl, for predictive analyses related to student scores across different educational initiatives.

If you wish to delve deeper into these findings or make data-driven decisions, you can review these files or consult with data professionals who can help interpret the model's results in practical terms.