SAD Analytics A Web Application for Data Analysis and Visualization

Welcome to our Data Analysis and Visualization Web Application repository! This web application allows users to upload data files, perform data analysis, and generate visualizations in a user-friendly environment.

Features

• User Authentication: Secure user authentication system to ensure data privacy and access control.
• Data Upload: Users can upload data files in various formats for analysis and visualization.
• Data Analysis: Perform data analysis tasks such as summarization, aggregation, and statistical calculations.
• Visualization: Generate charts, graphs, and visualizations based on the uploaded data for insights.
• AI-driven Preview: AI-powered mechanism to preview the uploaded data and automatically generate initial visualizations.
• Responsive Design: Responsive user interface design for seamless access across different devices and screen sizes.

Installation

1. Clone the repository to your local machine:

git clone https://github.com/dhimant2299/Data-Visualization-and-Analysis-App.git

2. Navigate to the project directory:

cd WebProj-main

3. Install dependencies:

npm install

4. Run the server:

node app.js

5. Navigate to the Python directory:

cd python

6. Install dependencies

pip install -r requirements.txt 

7. Run the application

streamlit run app.p

This creates a new local host at 8501.

8. Access the web application in your browser at http://localhost:4000.

Usage

1. Register a new account or log in with existing credentials.
2. Upload a data file using the provided interface. Click on Load data which provides a preview of the table with the top 5 rows and several columns.
3. Explore the data analysis and visualization options available.
4. Use the AI-driven preview feature to automatically generate initial visualizations.
5. Interact with the visualizations to gain insights from the data. You can also download, zoom in, zoom out the visualizations.
6. Experiment with different data analysis techniques and visualization types to analyze your data effectively.

Technologies Used

• Frontend: Node.js, Express.js, Streamlit
• Backend: Node.js, Streamlit
• Database: SQLite3
• AI Framework: Sklearn
• Data Visualization: Streamlit, matplotlib, pandas, plotly, seaborn

Contributors

• Sarbagya Malla
• Aswin Lohani
• Dhimant Adhikari