📊 Analyzing Academic-Professional Correlations

This project aims to analyze the potential correlations between academic performance, extracurricular activities, and professional prospects using a dataset of student information. The analysis will provide insights into factors that may influence students' career trajectories and help identify areas for improvement in academic programs and career counseling services.

📁 Dataset

The dataset used in this project contains information about students, including their academic performance (GPA), extracurricular activities, family background, and expected salary after graduation. The dataset is provided in a CSV file named StudentData.csv.

🧪 Analysis

The analysis is performed using Python and several data analysis libraries, including Pandas, NumPy, Matplotlib, and Seaborn. The main analysis steps are outlined in the Analysis.ipynb Jupyter Notebook file, which includes the following:

1. 🔍 Data Exploration: Exploring the dataset, handling missing values, and understanding the distribution of various features.
2. 🔗 Correlation Analysis: Investigating the relationships between academic performance (GPA), extracurricular activities, family income, and expected salary.
3. 📊 Visualization: Creating informative visualizations to represent the findings and patterns in the data.
4. 💡 Insights and Conclusions: Summarizing the key insights and conclusions drawn from the analysis.

🚀 Getting Started

To run the analysis locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/ommagdum/Analyzing-Academic-Professional-Correlations

2. Navigate to the project directory:

   cd Analyzing-Academic-Professional-Correlations

3. Open the Analysis.ipynb file in a Jupyter Notebook environment.
4. Run the notebook cells sequentially to execute the analysis.

🤝 Contributing

Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

📜 License

This project is licensed under the MIT License.

🙏 Acknowledgments

The dataset used in this project was provided by Cloud Counselage Pvt. Ltd. Special thanks to the organization for the amazing data analysis libraries used in this project.