📊 Student Performance Data Analysis

A Data Science assingnment that explores student performance using descriptive statistics, random sampling, and data visualization with Python.

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📌 Overview

This project analyzes the Student Performance Analysis Dataset using Python and Jupyter Notebook.

The notebook demonstrates a complete data analysis workflow, including data loading, exploratory analysis, descriptive statistics, random sampling, frequency comparison, and visualization.

This project represents the beginning of my Data Science journey and focuses on building strong fundamentals in data analysis.

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🚀 Features

• Dataset Exploration
• Data Summary
• Mean Calculation
• Median Calculation
• Mode Calculation
• Standard Deviation
• Random Sampling (20%)
• Frequency Count Analysis
• Original vs Sample Comparison
• Statistical Visualizations

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📂 Project Structure

student-performance-data-analysis/
│
├── dataset/
│   └── Student Performance Analysis Dataset.csv
│
├── assignment.ipynb
├── requirements.txt
├── README.md
└── .gitignore

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🛠️ Tech Stack

Category	Technologies
Language	Python
Environment	Jupyter Notebook
Libraries	Pandas, NumPy
Visualization	Matplotlib, Seaborn

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📊 Statistical Analysis

The following statistical measures were computed:

• Mean
• Median
• Mode
• Standard Deviation

A random sample of 20% of the dataset was generated and compared with the original dataset to evaluate statistical consistency.

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📈 Visualizations

The notebook includes:

• Histogram
• Bar Chart
• Box Plot
• Scatter Plot
• Correlation Heatmap
• Original vs Sample Frequency Comparison

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📸 Sample Output

Replace this image with a screenshot from your notebook.

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⚙️ Installation

Clone the repository

git clone https://github.com/jayrathi77/student-performance-data-analysis.git

Navigate into the project

cd student-performance-data-analysis

Install dependencies

pip install -r requirements.txt

Launch Jupyter Notebook

jupyter notebook

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📚 Learning Outcomes

This project helped me understand:

• Exploratory Data Analysis (EDA)
• Descriptive Statistics
• Random Sampling
• Frequency Analysis
• Data Visualization
• Python for Data Science

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🎯 Future Improvements

• Data Cleaning
• Feature Engineering
• Machine Learning Models
• Predictive Analytics
• Interactive Dashboards

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👨‍💻 Author

Jay Jiwanlal Rathi

B.Tech Computer Engineering
Vishwakarma Institute of Technology (VIT), Pune

Connect with me

• GitHub: https://github.com/jayrathi77
• LinkedIn: https://www.linkedin.com/in/jay-rathi-8128a036b/

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⭐ Support

If you found this project useful, consider giving it a ⭐ on GitHub.