I specialize in web scraping and data extraction, leveraging advanced techniques for efficient data gathering and analysis. With expertise in automation using Selenium, I streamline tasks and enhance productivity. My skills extend to data analysis with Python, providing insights from complex datasets, and front-end development, creating user-friendly interfaces. My portfolio showcases high-quality solutions across these domains.
This project extracts essential business details from yellowpages.com.au and yellowpages.com, including:
The automated scraping process aids in market research, lead generation, and contact database creation, providing valuable insights for targeted outreach.
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Scrape TikTok data: author username, follower count, video URL, and more.
Scrape public LinkedIn user and company data.
Scrape public group/page data: post text, comments, group name, and URL.
Scrape Twitter data: tweet text, likes, retweets, views, and more.
Additional scraping services for other platforms are available.
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This project scrapes property and agent data from websites like Zillow, Realtor.com, Microburbs, Realtor.ca, and Walk Score. The extracted data includes property listings, agent details, and location scores, creating comprehensive datasets for market analysis, property valuation, and agent profiling. The automated approach helps users make informed decisions regarding property investments and agent selection.
A sample JSON file is attached to the repository.
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This project extracts business details from Google Maps listings, including name, email (if available), phone number, website URL, rating, and review count. The automated web scraping process collects structured data for market research, lead generation, and business analysis, making data collection efficient and enabling informed decision-making.
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I have completed 200+ web scraping projects using Python and Selenium, covering a wide range of industries such as real estate, e-commerce, and social media. Below are some key projects.
A fully responsive digital agency website, designed for all devices. Built with HTML, CSS, and Bootstrap for seamless performance and modern design.
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This bot automates the process of checking visa appointment availability on the VFS Global website, bypassing Cloudflare protection and solving reCAPTCHA challenges. It also rotates proxies to avoid IP bans and sends email notifications to clients about appointment status.
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This project aims to build a spam detection model using machine learning, utilizing a dataset of text messages labeled as "ham" (non-spam) or "spam." The key features of the project include:
This project analyzes unemployment data, focusing on the Estimated Unemployment Rate, Estimated Employed, and Estimated Labour Participation Rate at regional and state levels. It processes the data to generate insights and visualizes the trends using bar charts and an interactive map.
Key Features:
This project predicts sales based on advertising budgets (TV, Radio, Newspaper) using a linear regression model. It includes data analysis, visualization, model training, and a Flask web application for real-time predictions.
Key Features:
This project predicts the selling price of cars using a Random Forest Regressor model, based on features like present price, year of manufacture, kilometers driven, and fuel type.
Key Features:
This project builds a machine learning model to classify tumors as "Malignant" or "Benign" based on various tumor characteristics like radius, perimeter, and area.
Key Features:
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