Student Performance Analysis

Student Performance Insights

This project explores a dataset related to student performance to uncover how various factors such as attendance, grades, extracurricular activities, parental education, and study habits influence academic outcomes.

The analysis is conducted using Python in a Jupyter Notebook, utilizing essential data science libraries including:

Pandas and NumPy for data manipulation

Matplotlib and Seaborn for data visualization

Key Project Highlights:

Data Cleaning & Exploration: Loaded and examined the dataset using Pandas, performed initial visual exploration using Seaborn heatmaps and plots.

Visualization: Graphical analysis of the impact of parental education and student activities on performance.

Insights: The project reveals patterns that can help in designing interventions for improving student outcomes.

This project serves as a hands-on example of applying data science to real-world educational problems, combining data exploration, visualization, and interpretation to draw meaningful conclusions.