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Real Estate Web Scraping Project Using Python

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:

  • Real Estate Professionals to understand market trends.
  • Investors to identify lucrative opportunities.
  • Analysts to support data-driven decision-making.

By focusing on compliance with platform policies and using Python-based tools, we ensured the highest standards in data accuracy and usability.

  • Market Analytics Insights
  • Efficiency Boost and Automated data collection
  • Data-Driven Strategies

E-Commerce Website Scraping Project

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:

  • Price Monitoring to track competitor pricing trends.
  • Market Research to analyze customer preferences.
  • Inventory Tracking for better stock management.

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.

  • Enhanced Decision-Making
  • Enabled clients to monitor and Competitive Edge
  • Streamlined Operations

NEWS BRIEF

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:

  • Translation Translate the text into Chinese
  • Entities (e.g., persons, organizations, locations)
  • Events mentioned in the article
  • Sentiments from various perspectives
  • Metadata such as news source, category, and credibility ratings

This data is utilized for:

  • Automated content analysis
  • Market research
  • Media monitoring
  • News aggregation

Tools & Technologies Used

  • Python: The primary programming language for text processing.
  • Natural Language Processing (NLP) Libraries:
    • spaCy: For Named Entity Recognition (NER), dependency parsing, and relation extraction.
    • OpenAi: For translation, extracting sentiments and collecting metadata 

Key Features

  1. Entity Extraction:
    • Identify and extract entities such as people, organizations, locations, and dates.
  2. Language Translation:
    • Translate the title and description to Chinese and in any other language that client requires.
  3. Event Extraction:
    • Detect significant events, such as demands for extradition or political developments.
  1. Sentiment Extraction:
    • Extract sentiment from different comments, categorizing them into neutral, left-wing, and right-wing perspectives.
  2. Metadata Extraction:

Extract additional metadata such as the news source, category, importance rating, and timeliness rating.

  • Identifying key people, organizations, and events
  • Understanding the sentiment and political leanings of different perspectives
  • Analyzing trends in news content for further research or reporting