PrashantBarthwal

Summary:
Developed a Python script for web scraping and text extraction from URLs stored in an Excel file. The script utilizes the Pandas library to read Excel data into a DataFrame and the Requests library to fetch web page content. BeautifulSoup is employed for HTML parsing, enabling extraction of titles and text from web pages

Key Features:
• Data Handling: Utilized Pandas for efficient handling of Excel data, enabling seamless integration of URL and URL ID information into the script
• Web Scraping: Employed Requests library for making HTTP requests to URLs, ensuring robustness with a user-agent header to mimic browser behavior
• HTML Parsing: Leveraged BeautifulSoup library for parsing HTML content, facilitating extraction of titles and text from web pages through intuitive methods like find() and find_all()
• File Management: Organized extracted data into structured text files, with each file containing a title and corresponding text extracted from a web page. Implemented file creation and directory management functionalities to ensure seamless file writing operations

Technologies Used:
• Python
• Pandas
• Requests
• BeautifulSoup

Applications:
• Data Extraction
• Web Scraping
• Natural Language Processing (NLP)
• Automation

• Developed a Python script leveraging the transformers library (version 4.4.2) to perform table question-answering tasks
• Utilized the TAPAS (Table-based Pre-training for Answering Selection) model (google/tapas-base-finetuned-wtq) for accurate and efficient question-answering on tabular data
• Employed the pipeline function to create a streamlined workflow for table question-answering tasks
• Processed tabular data from CSV files, performing data cleaning operations such as correcting typographical errors in specific columns (e.g., ‘location’)
• Generated insightful answers to complex queries posed to the tabular data, facilitating rapid data analysis and decision-making
• Demonstrated proficiency in data manipulation techniques using pandas library for data wrangling and preprocessing tasks
• Optimized code efficiency by saving modified data back to CSV files for future use and easy retrieval