https://github.com/vipulbunny/student-grades-data-analysis-using-json
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). The objective is to extract, clean, and analyze the data using Python, Pandas, and Matplotlib/Seaborn for visualization.
https://github.com/vipulbunny/student-grades-data-analysis-using-json
data-cleaning data-science dataanalysis json-handling matplotlib matplotlib-pyplot pandas python python3 seaborn student-performance-analysis web-scraping
Last synced: 2 months ago
JSON representation
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). The objective is to extract, clean, and analyze the data using Python, Pandas, and Matplotlib/Seaborn for visualization.
- Host: GitHub
- URL: https://github.com/vipulbunny/student-grades-data-analysis-using-json
- Owner: VIPULbunny
- Created: 2025-02-06T16:23:11.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-08T09:46:38.000Z (4 months ago)
- Last Synced: 2025-04-05T18:13:42.365Z (2 months ago)
- Topics: data-cleaning, data-science, dataanalysis, json-handling, matplotlib, matplotlib-pyplot, pandas, python, python3, seaborn, student-performance-analysis, web-scraping
- Language: Python
- Homepage:
- Size: 10.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## **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.
---
## **📂 Table of Contents**
- [Project Description](#-project-description)
- [Technologies Used](#-technologies-used)
- [Dataset Overview](#-dataset-overview)
- [Project Workflow](#-project-workflow)
- [Installation & Setup](#-installation--setup)
- [Usage](#-usage)
- [Results & Visualization](#-results--visualization)
- [Future Improvements](#-future-improvements)
- [Contributors](#-contributors)
---
## **🛠 Technologies Used**
🔹 Python
🔹 Pandas
🔹 NumPy
🔹 Matplotlib
🔹 Seaborn
🔹 Requests (for data fetching)
🔹 BeautifulSoup (for HTML parsing)
---
## **📊 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:
```json
{
"_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}
]
}
```
---
## **🔄 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
---
## **⚙ Installation & Setup**
### **1️⃣ Clone the Repository**
```bash
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:
```bash
pip install pandas numpy matplotlib seaborn requests beautifulsoup4
```
### **3️⃣ Run the Script**
Execute the Python script:
```bash
python analysis.py
```
---
## **🚀 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.
---
## **📈 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
---
## **👨💻 Contributors**
💡 Vipul Solanki(https://github.com/VIPULbunny)
---
## **📜 License**
This project is licensed under the **MIT License**.