At Gulzarsoft, we specialize in web scraping and automated data extraction, offering solutions tailored to industry-specific needs. Our expertise includes E-Commerce (e.g., Amazon Scraping) Real Estate (e.g., Property Listing Aggregation) Market Research (e.g., Competitor Analysis).We deliver structured, clean, and actionable datasets by combining cutting-edge tools like Scrapy, Selenium, and Beautiful Soup,
Please reach out to us today to discuss your web scraping needs and achieve your business goals with reliable data solutions.
In this project, the client provided a list of specific regions from which they needed restaurant data extracted from OpenTable. I developed a custom web scraping solution using Python and Scrapy, tailored to navigate the site’s structure and extract the required information efficiently.
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.
NEWS BRIEF demonstrates how NLP techniques can be applied to extract valuable structured data from raw, unstructured news articles. By automating the extraction process, businesses can save time and resources while gaining deeper insights into ongoing events and trends.
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.
We are a creative powerhouse that develops elegant, high-performing online web scraping using Python that are customized to your particular business needs. Our web data extraction and custom web scraping solutions stand out and deliver impressive outcomes.
Our project approach drives measurable results, supports marketers in achieving their goals, and addresses customer needs within their budget..
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In this project, the client provided a list of specific regions from which they needed restaurant data extracted from OpenTable. I developed a custom web scraping solution using Python and Scrapy, tailored to navigate the site’s structure and extract the required information efficiently.
The extracted data included key restaurant details such as name, location, cuisine, ratings, and reservation availability. I ensured that the output met the client’s dual requirements by delivering the data both as a CSV file and by dumping it into a PostgreSQL database for easier integration with their internal systems.
The project was delivered successfully and on time. The client was extremely satisfied with the quality of the work, awarding it 5 stars and leaving a positive review. It was a great experience working on this project and contributing to the client’s data automation workflow.
Client Name: Aaron DeCoste
Website: https://www.opentable.com/
Skills Used: Python, Scrapy, PostgreSQL, CSV, Web Scraping, Data Pipeline Automation
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.
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.
