https://github.com/naso7y/students-performance-analysis
A project analyzing students' academic performance to identify trends and factors affecting outcomes. Built with Python, using data visualization and statistical techniques to derive actionable insights.
https://github.com/naso7y/students-performance-analysis
data-analysis data-visualization machine-learning python
Last synced: about 2 months ago
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A project analyzing students' academic performance to identify trends and factors affecting outcomes. Built with Python, using data visualization and statistical techniques to derive actionable insights.
- Host: GitHub
- URL: https://github.com/naso7y/students-performance-analysis
- Owner: NASO7Y
- Created: 2024-12-09T23:36:10.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-22T14:00:45.000Z (about 1 year ago)
- Last Synced: 2025-06-16T23:36:53.948Z (10 months ago)
- Topics: data-analysis, data-visualization, machine-learning, python
- Language: Jupyter Notebook
- Homepage:
- Size: 142 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Students Performance Analysis
This repository contains the **Students Performance Analysis** project, which focuses on analyzing and visualizing students' academic performance. The project uses modern tools to uncover insights and patterns that can aid in improving educational outcomes.
## Features
- 📊 **Data Visualization:** Detailed charts and graphs for performance insights.
- 📈 **Statistical Analysis:** Identifies trends and correlations in the data.
- 🧠**Machine Learning Models:** Predict student performance based on various factors.
- 📋 **Customizable Analysis:** Easily adapt the analysis for different datasets.
## Tech Stack
- **Programming Language:** Python
- **Libraries:**
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
## Installation
1. Clone the repository:
```bash
git clone https://github.com/NASO7Y/Students-Performance-Analysis.git
cd Students-Performance-Analysis
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Prepare your dataset in CSV format. Ensure it includes columns like `Gender`, `Test Scores`, `Study Hours`, etc.
2. Run the analysis script:
```bash
python main.py
```
3. View results and visualizations in the output directory.
## Example Outputs
- **Correlation Heatmap:** Shows relationships between variables.
- **Performance Prediction:** Machine learning model predicts scores based on input factors.
## Contributing
Contributions are welcome! To contribute:
1. Fork the repository.
2. Create a new branch (`feature/new-analysis`).
3. Commit your changes and submit a pull request.
## Contact
For questions or feedback, feel free to open an issue or reach out to [NASO7Y](https://github.com/NASO7Y).
Email: ahmed.noshy2004@gmail.com
LinkedIn: [LinkedIn](https://www.linkedin.com/in/nos7y/)