{"id":25236928,"url":"https://github.com/vipulbunny/student-grades-data-analysis-using-json","last_synced_at":"2026-04-28T13:39:24.369Z","repository":{"id":276165806,"uuid":"928432449","full_name":"VIPULbunny/Student-Grades-Data-Analysis-Using-JSON","owner":"VIPULbunny","description":"This project focuses on analyzing student grades using a dataset stored in JSON format. The dataset includes student IDs, class IDs, and different types of scores (exam, quiz, and homework). 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The dataset includes:  \n- **Student ID**\n- **Class ID**\n- **Exam Scores**\n- **Quiz Scores**\n- **Homework Scores**  \n\nThe objective is to extract, clean, and analyze this data using **Python, Pandas, and Matplotlib/Seaborn** for visualization.  \n\n---\n\n## **📂 Table of Contents**  \n- [Project Description](#-project-description)  \n- [Technologies Used](#-technologies-used)  \n- [Dataset Overview](#-dataset-overview)  \n- [Project Workflow](#-project-workflow)  \n- [Installation \u0026 Setup](#-installation--setup)  \n- [Usage](#-usage)  \n- [Results \u0026 Visualization](#-results--visualization)  \n- [Future Improvements](#-future-improvements)  \n- [Contributors](#-contributors)  \n\n---\n\n## **🛠 Technologies Used**  \n🔹 Python  \n🔹 Pandas  \n🔹 NumPy  \n🔹 Matplotlib  \n🔹 Seaborn  \n🔹 Requests (for data fetching)  \n🔹 BeautifulSoup (for HTML parsing)  \n\n---\n\n## **📊 Dataset Overview**  \nThe dataset is obtained from a publicly available JSON file. Each student has three types of scores:  \n- **Exam Score**  \n- **Quiz Score**  \n- **Homework Score**  \n\nExample JSON entry:  \n```json\n{\n  \"_id\": {\"$oid\": \"50906d7fa3c412bb040eb577\"},\n  \"student_id\": 10,\n  \"class_id\": 2,\n  \"scores\": [\n    {\"type\": \"exam\", \"score\": 89.23},\n    {\"type\": \"quiz\", \"score\": 76.55},\n    {\"type\": \"homework\", \"score\": 91.12}\n  ]\n}\n```\n\n---\n\n## **🔄 Project Workflow**  \n\n✅ **Step 1:** Fetch JSON data from the remote URL  \n✅ **Step 2:** Convert the JSON file into a Pandas DataFrame  \n✅ **Step 3:** Extract individual scores (exam, quiz, homework)  \n✅ **Step 4:** Clean and preprocess the dataset  \n✅ **Step 5:** Perform statistical analysis  \n✅ **Step 6:** Visualize results using Matplotlib and Seaborn  \n\n---\n\n## **⚙ Installation \u0026 Setup**  \n\n### **1️⃣ Clone the Repository**  \n```bash\ngit clone https://github.com/VIPULbunny/student-grades-json-analysis.git\ncd student-grades-json-analysis\n```\n\n### **2️⃣ Install Dependencies**  \nEnsure you have Python 3 installed. Then, install the required packages:  \n```bash\npip install pandas numpy matplotlib seaborn requests beautifulsoup4\n```\n\n### **3️⃣ Run the Script**  \nExecute the Python script:  \n```bash\npython analysis.py\n```\n\n---\n\n## **🚀 Usage**  \n1. **Run the script** to fetch the JSON data and process it.  \n2. **View the statistical summary** of student grades.  \n3. **Visualize the results** using histograms and scatter plots.  \n\n---\n\n## **📈 Results \u0026 Visualization**  \n\n### **📌 Distribution of Exam Scores**  \n![image](https://github.com/user-attachments/assets/ca727ee0-0731-422c-abcd-343fd44737de)\n \u003c!-- Replace with actual image URL --\u003e\n\n### **📌 Relationship Between Exam \u0026 Quiz Scores**  \n![image](https://github.com/user-attachments/assets/82a91844-50e4-44cd-ae59-8a1d9e323738)\n---\n\n## **🔮 Future Improvements**  \n🚀 Add advanced **data visualizations** (box plots, heatmaps)  \n🚀 Implement **machine learning models** for predicting student performance  \n🚀 Integrate a **web dashboard** using Streamlit or Dash  \n\n---\n\n## **👨‍💻 Contributors**  \n💡 Vipul Solanki(https://github.com/VIPULbunny)  \n\n---\n\n## **📜 License**  \nThis project is licensed under the **MIT License**.  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvipulbunny%2Fstudent-grades-data-analysis-using-json","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvipulbunny%2Fstudent-grades-data-analysis-using-json","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvipulbunny%2Fstudent-grades-data-analysis-using-json/lists"}