This repository contains a data analysis project focused on evaluating student performance using various factors like demographic information, grades, and study habits. The goal of this project is to uncover trends and insights that can help improve student outcomes.
The Student Performance Analysis project explores the relationships between different factors and student performance. The analysis includes descriptive statistics, visualizations, and insights into how different factors such as gender, parental education level, and test preparation impact student success.
The dataset used in this analysis includes information on:
- Student demographics (age, gender, parental education)
- Academic performance (math, reading, and writing scores)
- Study habits (test preparation, study time, etc.)
The data was cleaned and prepared before analysis to ensure accuracy.
This project was created using the following technologies:
- Python 3.x
- Pandas for data manipulation
- NumPy for numerical computations
- Matplotlib and Seaborn for data visualization
- Jupyter Notebook for interactive exploration
Key areas of analysis include:
- Impact of parental education on student performance
- Gender-based performance differences
- Correlation between study habits and scores
- Identification of key factors influencing student success
Visualizations and statistical models are used to support findings.
To explore the analysis:
- Clone this repository:
git clone https://github.com/your-username/student-performance
- Navigate to the project folder.
- Open the Jupyter Notebook (Student_Performance_Analysis.ipynb) to view the code, analysis, and visualizations.
- Run the notebook or scripts in your local Python environment.
Contributions are welcome! Feel free to fork this repository and submit a pull request with improvements or additional analyses.