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Overview

The project aims to create a repository of active traders by extracting information available on social media platforms, especially LinkedIn and Twitter, using web scraping techniques, natural language processing (NLP), and automation tools.

Features

  • Extract trader information from social media platforms.
  • Perform social profiling using NLP techniques.
  • Automate email communication and follow-ups with traders.
  • Implement AI-based chatbots for additional communication channels.
  • Analyze and clean web scraping outputs for data quality.
  • Optimize data processing pipeline for efficiency.
  • Store and maintain extracted data in a PostgreSQL database.

Requirements

  • Python 3.x
  • Scrapy
  • Selenium
  • BeautifulSoup
  • psycopg2 (for PostgreSQL database interaction)

Installation

  1. Clone the repository to your local machine. 
    git clone https://github.com/yourusername/algobulls-web-scraping.git
cd algobulls-web-scraping
  2. Install the required dependencies. 
    pip install -r requirements.txt

Usage

  1. Set up your PostgreSQL database and configure the connection parameters in the code.
  2. Run the appropriate scripts or modules to perform web scraping, data analysis, or automation tasks. 
    python main.py
  3. Refer to the project documentation for detailed usage instructions and examples.

Contributing

Contributions to the project are welcome! If you'd like to contribute, please follow these steps:

  1. Fork the repository.
  2. Create a new branch (git checkout -b feature-branch).
  3. Make your changes and commit them (git commit -am 'Add new feature').
  4. Push to the branch (git push origin feature-branch).
  5. Create a new pull request.