The goal of this project is to build a web application that allows users to visualize and interact with large datasets in real-time. The application will utilize Django as the backend framework, focusing on providing users with an intuitive and dynamic interface to explore, analyze, and manipulate data visually.
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User Authentication and Authorization: Implement a secure user registration and login system using Django's authentication system.
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Data Upload and Management: Allow users to upload datasets in various formats (e.g., CSV, JSON) and store them in a database. Implement data management functionalities like editing, deleting, and organising datasets.
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Real-time Data Visualization: Utilise Matplotlib and seaborn to create interactive and visually appealing charts, graphs, and visualisations based on the uploaded datasets. Enable users to customise the visualisations and explore data in real-time.
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Filtering and Aggregation: Implement powerful filtering and aggregation functionalities to enable users to focus on specific subsets of data and perform calculations on the data.
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Data Export: Provide users the option to export visualizations and data in different formats (e.g., PDF, PNG, CSV) for reporting and sharing purposes.
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Performance Optimization: Enhance the application's performance by implementing caching mechanisms, lazy loading, and server-side optimizations to handle large datasets efficiently.
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Interactive Dashboard: Create a dashboard that allows users to save and organise their favourite visualisations and layouts.
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Data Insights: Implement data analysis tools like clustering, pattern recognition, or anomaly detection to offer users valuable insights from their datasets.
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Responsive Design: Ensure the application is responsive and works seamlessly across different devices and screen sizes.
Client (Frontend):
- HTML
- CSS
- JavaScript
- Bootstrap
Server (Backend):
- Django,
- Python (Pandas, Matplotlib, Seaborn)
Other Technologies:
- Docker (for containerization)
- CI/CD Pipeline (GitHub Actions for automated build and deployment)
Clone the project
git clone https://github.com/vivekdevkar123/VisuData.gitGo to the project directory
cd VisuDataRun Container and Start the server
sudo docker-compose upGo to the below address to access the website. http://127.0.0.1:8000/
If you have any feedback, please reach out to us at mrvivekdevkar123@gmail.com