IntelliScraper: A Powerful Web Scraping Tool with Advanced Data Visualization
In the world of data analysis, obtaining structured information from websites is a crucial first step. IntelliScraper offers a comprehensive solution that combines web scraping capabilities with powerful data processing and visualization features.
Project Architecture
IntelliScraper is built on a modular architecture that handles the complete data pipeline from extraction to visualization. The project collects data from websites, processes and cleans it using pandas, stores it in a MySQL database, and presents it through an interactive dashboard created with Streamlit.
Technologies Used
For Web Scraping:
- Beautiful Soup
- Selenium
- Requests
For Data Processing:
- Pandas
For Visualization:
- Matplotlib
- Seaborn
- Plotly
- Streamlit
For Data Storage:
- MySQL
- CSV export capability
Installation and Setup
The project can be easily deployed by following these steps:
- Clone the repository from GitHub
- Navigate to the project directory
- Create and activate a virtual environment to manage dependencies
- Install required packages using the requirements.txt file
- Run the scraper application to extract and store data
- Launch the Streamlit dashboard to visualize the data
Key Components
The project contains several important files:
- config.py: Contains configuration settings
- dashboard.py: The Streamlit application for visualization
- DataVista.py: Handles database operations including table creation and data insertion
- renderer.py: The main entry point for scraping operations
- scraper.py: Core scraping functionality
Data Visualization Features
The Streamlit dashboard provides a rich set of visualization options:
- Table Views: Categorized data tables with filtering capabilities
- CSV Export: One-click download of scraped data
- Geographic Visualization: Map-based representation of data by country
- Time Series Analysis: Temporal trends in the data
- Commodity Analysis: Breakdown by product categories
- Correlation Matrix: Relationships between different data parameters
Interactive Filtering
The dashboard allows users to filter data by various parameters including:
- Year
- Country
- Type of product
- Trade metrics (weight vs quantity)
Deployment Options
While the project runs locally, it’s designed to be deployable on servers for broader access. The Streamlit integration makes it particularly suitable for cloud deployment with minimal configuration changes.
IntelliScraper represents a comprehensive data pipeline solution that combines the power of Python’s web scraping libraries with advanced data visualization capabilities, all within a user-friendly interface that requires minimal technical knowledge to operate.