● Developed a Python-based analysis of a 30,641-record dataset to explore the impact of socio-economic and educational factors on student performance. ● Employed Pandas for data preprocessing, and Matplotlib/Seaborn for insightful visualizations of gender distribution and parental education effects on scores. ● Demonstrated key findings through statistical analysis and visual data presentations, highlighting the minimal impact of parental marital status on student outcomes. ● Enhanced data integrity and analysis accuracy through meticulous data cleaning and preprocessing.