Student Performance Data Analysis Using Python

Conducted an exploratory data analysis (EDA) project on a real-world dataset of student performance. Utilized Python libraries such as Pandas, Matplotlib, and Seaborn to perform data cleaning, preprocessing, and visualization. Investigated correlations between various factors (e.g., gender, parental education level, test preparation) and students’ scores in math, reading, and writing. Presented insights using charts and summary statistics to highlight trends and potential predictors of academic success.