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Gulzar Soft is a visionary IT service provider, pioneering innovative solutions that empower businesses to thrive in the digital age.
This project involved developing advanced web scraping projects scripts to extract detailed property information from real estate websites. The data included property listings, prices (including installment plans), locations, community names, property features, and high-quality images.
The solution was designed to be accurate and scalable, ensuring consistent access to actionable property data for:
By focusing on compliance with platform policies and using Python-based tools, we ensured the highest standards in data accuracy and usability.
We created scalable and efficient web scraping scripts to extract structured data from e-commerce websites. Key data points collected include product names, prices, availability, reviews, detailed descriptions, and images. This automated data extraction process supported multiple use cases such as:
By leveraging tools like Beautiful Soup, Scrapy, and Selenium, we ensured the scraping process handled complex website architectures. The extracted data was clean, accurate, and ready for actionable insights while adhering to website terms of service.
In this project, we implemented a system for Information Extraction (IE) from raw news articles. The system automatically extracts structured data such as entities, relationships, events, and metadata from unstructured text, making it easier for businesses to analyze and utilize news content.
Objective
The goal of this project is to extract key pieces of information from news articles, such as:
This data is utilized for:
Tools & Technologies Used
Key Features
Extract additional metadata such as the news source, category, importance rating, and timeliness rating.
