This repository contains an Exploratory Data Analysis (EDA) of a student performance dataset. The goal of this analysis is to understand the key factors that influence students' academic performance, such as study habits, parental involvement, socioeconomic factors, and more.
Understanding student performance is essential for improving educational outcomes. This EDA explores:
Grades & Performance Trends: Identifying patterns in student scores. Study Habits & Attendance: Analyzing the impact of study hours and school attendance. Parental & Socioeconomic Influence: Examining how parental education, family support, and economic status affect academic success. Subject-wise Analysis: Investigating performance variations in different subjects.
Students with higher study time tend to score better. Parental education and support significantly influence student performance. Socioeconomic status impacts access to resources and learning opportunities. Attendance and engagement levels correlate with academic success.
Dataset: Student performance data containing attributes like study time, parental education, grades, absences, etc. Tech Stack: Python, Pandas, Matplotlib, Seaborn. Methods Used: Data cleaning, correlation analysis, visualization, trend analysis, and statistical insights.
Predicting student success using machine learning. Analyzing the impact of online vs. offline learning on grades. Identifying strategies to improve student performance.
🔗 Feel free to explore, contribute, or share insights!