📊 QuickViz - Student Performance Visualization Welcome to QuickViz! QuickViz is a web-based, interactive platform that simplifies data analysis and visualization, tailored specifically for student performance data. With an intuitive interface, educators and administrators can effortlessly explore datasets, uncover insights, and make data-driven decisions.
🌟 Key Features
📂 CSV Upload & Merge: Upload multiple CSV files and let QuickViz handle merging and formatting, saving you valuable time.
🔍 Custom Column Selection: Target your analysis by selecting specific columns from merged datasets.
📈 Diverse Plot Options: Instantly generate bar charts, line graphs, pie charts, and more.
👥 Participant Data Extraction: View and analyze participant details with ease.
🔒 Secure Access: User authentication ensures that only registered users can access the data.
💡 User-friendly Interface: Responsive design with Bootstrap for seamless usage across devices.
🛠️ Tech Stack
Technology Purpose
Python Backend development
Flask Web framework for API & app structure
HTML/CSS Frontend development
Bootstrap Responsive UI design
Pandas Data processing and analysis
Matplotlib Data visualization
Seaborn Advanced styling for visualizations
Flask-Login Authentication and session management
🚀 Getting Started
- Prerequisites Python 3.x Pip (package installer for Python)
- Installation Clone the repository:
bash Copy code git clone https://github.com/yourusername/QuickViz.git cd QuickViz Install dependencies:
bash Copy code pip install -r requirements.txt Run the app:
bash Copy code python app.py Now, open your browser and go to http://127.0.0.1:5000.
💻 How to Use QuickViz Log In / Sign Up: Secure your data by logging in. Create a free account if you don’t already have one. Upload & Merge CSVs: Navigate to the dashboard and upload your CSV files. Select Columns & Visualize: Choose the columns you’re interested in and pick your preferred chart type. Explore Data: Dive into the insights with our interactive charts and participant details. 🛠️ Dependencies Flask: For backend web framework Pandas: For data handling Matplotlib & Seaborn: For visualization Flask-Login: For user authentication Jinja2: For dynamic HTML rendering Datetime, OS, Sys: For file management and system operations 🖼️ Screenshots Dashboard View Visualization Example 🤝 Contributing We welcome contributions! Here’s how you can help:
Fork the repository Create a feature branch (git checkout -b feature-name) Commit your changes (git commit -m 'Add a feature') Push to the branch (git push origin feature-name) Open a Pull Request
💬 Connect with Us! We’d love to hear your feedback, ideas, and questions. Reach out to us on:
LinkedIn: https://www.linkedin.com/in/nareshkanna-shanmugam-a4462a269/ Let’s make data analysis accessible and insightful together!