This Python project analyzes student performance using data analysis and visualization techniques. The goal is to explore factors affecting student grades and provide insights through charts and graphs.
- Data Cleaning & Preprocessing: Handle missing values and prepare data for analysis.
- Exploratory Data Analysis (EDA): Understand the data distributions and relationships.
- Visualizations: Generate histograms, boxplots, correlation heatmaps, and other charts.
- Insights & Recommendations: Identify trends and patterns that impact student performance.
- Python
- Libraries: Pandas, NumPy, Matplotlib, Seaborn
- Google Colab for interactive analysis
- Clone the repository:
https://github.com/Nadaalnajjar2015/Student-Performance-Analysis-Python