Student-Result-Analysis-Data-Analysis-Using-Python.ipynb

Project Overview

This project is designed to analyze student results using Python and data analysis techniques. It aims to provide insights into performance trends, identify areas needing improvement, and promote data-driven decisions in educational environments.

Repository Structure

/
|-- Student Result Analysis Project with Python & Data Analysis.ipynb  # Main Jupyter notebook containing the analysis
|-- Expanded data with more features.csv/                              # Datasets used in the analysis
|-- README.md                                                          # This file

Installation

Prerequisites

• Python 3.8 or higher
• Jupyter Notebook or JupyterLab

Libraries

Install the required Python libraries with pip:

pip install numpy pandas matplotlib seaborn scikit-learn

Clone the Repository

To get a local copy up and running, clone the repository using:

git clone https://github.com/Harshit0699/Student-Result-Analysis-Data-Analysis-Using-Python.git
cd Student-Result-Analysis-Data-Analysis-Using-Python

Usage

Launch the Jupyter Notebook to explore the datasets and visualizations:

jupyter notebook Student Result Analysis Project with Python & Data Analysis.ipynb

Navigate through the notebook to understand the different data analysis performed.

Contributing

Contributions are what make the open source community such a powerful place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE file for more information.

Contact

GitHub Profile: Harshit0699

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