Student Grades Data Analysis Using JSON

📌 Project Description

This project analyzes student grades using a dataset stored in JSON format. The dataset includes:

• Student ID
• Class ID
• Exam Scores
• Quiz Scores
• Homework Scores

The objective is to extract, clean, and analyze this data using Python, Pandas, and Matplotlib/Seaborn for visualization.

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📂 Table of Contents

• Project Description
• Technologies Used
• Dataset Overview
• Project Workflow
• Installation & Setup
• Usage
• Results & Visualization
• Future Improvements
• Contributors

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🛠 Technologies Used

🔹 Python
🔹 Pandas
🔹 NumPy
🔹 Matplotlib
🔹 Seaborn
🔹 Requests (for data fetching)
🔹 BeautifulSoup (for HTML parsing)

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📊 Dataset Overview

The dataset is obtained from a publicly available JSON file. Each student has three types of scores:

• Exam Score
• Quiz Score
• Homework Score

Example JSON entry:

{
  "_id": {"$oid": "50906d7fa3c412bb040eb577"},
  "student_id": 10,
  "class_id": 2,
  "scores": [
    {"type": "exam", "score": 89.23},
    {"type": "quiz", "score": 76.55},
    {"type": "homework", "score": 91.12}
  ]
}

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🔄 Project Workflow

✅ Step 1: Fetch JSON data from the remote URL
✅ Step 2: Convert the JSON file into a Pandas DataFrame
✅ Step 3: Extract individual scores (exam, quiz, homework)
✅ Step 4: Clean and preprocess the dataset
✅ Step 5: Perform statistical analysis
✅ Step 6: Visualize results using Matplotlib and Seaborn

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⚙ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/VIPULbunny/student-grades-json-analysis.git
cd student-grades-json-analysis

2️⃣ Install Dependencies

Ensure you have Python 3 installed. Then, install the required packages:

pip install pandas numpy matplotlib seaborn requests beautifulsoup4

3️⃣ Run the Script

Execute the Python script:

python analysis.py

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🚀 Usage

1. Run the script to fetch the JSON data and process it.
2. View the statistical summary of student grades.
3. Visualize the results using histograms and scatter plots.

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📈 Results & Visualization

📌 Distribution of Exam Scores

📌 Relationship Between Exam & Quiz Scores

🔮 Future Improvements

🚀 Add advanced data visualizations (box plots, heatmaps)
🚀 Implement machine learning models for predicting student performance
🚀 Integrate a web dashboard using Streamlit or Dash

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👨‍💻 Contributors

💡 Vipul Solanki(https://github.com/VIPULbunny)

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📜 License

This project is licensed under the MIT License.