📊 Student Performance Dataset Project

📌 Project Overview

This project focuses on generating and analyzing a synthetic student performance dataset. The goal is to understand how different factors such as study hours, attendance, and extracurricular activities impact exam scores.

The project includes dataset creation, loading, and basic analysis using Python.

---

📁 Files in This Repository

• dataset_generator.ipynb → Jupyter Notebook version of the project
• dataset_generator.py → Python script for dataset generation
• student_performance_dataset.csv → Generated dataset used for analysis
• README.md → Project documentation

---

📊 Dataset Generation / Loading Process

• The dataset is created using Python libraries like NumPy and Pandas.
• Randomized but realistic student-related features are generated such as:
  • Study hours per day
  • Attendance rate
  • Extracurricular activity hours
  • Exam scores
• The final dataset is saved as a .csv file for further analysis.

---

🧰 Libraries Used

• Python 3
• Pandas
• NumPy
• Matplotlib (if used for visualization)
• Seaborn (if used for visualization)

---

🚀 Steps to Run the Project

Option 1: Jupyter Notebook

1. Open dataset_generator.ipynb in Jupyter Notebook or Google Colab
2. Run all cells sequentially
3. The dataset will be generated and displayed

Option 2: Python Script

python dataset_generator.py