Python Student Performance Analysis

The project focuses on analyzing student performance data to uncover trends and insights that can help educational institutions make informed decisions. Leveraging Python programming and data analysis libraries such as Pandas, Numpy, Matplotlib, and Seaborn, I conducted a thorough examination of various factors influencing student academic achievement.

Data Source: kaggle.com

Through data cleaning, exploratory data analysis, and visualization techniques, several key findings were discovered:

1. Gender Disparity: Female students consistently outperform male students academically.

2. Lunch Type Impact: Students with standard lunch tend to perform better compared to those with non-standard lunch arrangements.

3. Parental Education Influence: A significant portion of parents have attended some college or possess an Associate's degree, suggesting a potential correlation between parental education level and student performance.

4. Test Preparation Courses: Completion of test preparation courses is associated with improved student performance.

These insights provide valuable guidance for educators, policymakers, and stakeholders in enhancing educational strategies and support systems to ensure all students have equitable opportunities for success.