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:

   git clone https://github.com/NASO7Y/Students-Performance-Analysis.git
   cd Students-Performance-Analysis

2. Install the required dependencies:

   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:

   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.

Email: [email protected]

LinkedIn: LinkedIn