Yahia Mahmoud

The Dream2000 Products Scraper is a meticulously crafted web scraping solution to extract extensive product details from the Dream2000 eCommerce platform. My role in this project involved conceptualizing, developing, and optimizing the scraper to navigate the dynamic website, overcoming challenges such as asynchronous content loading and multilingual support.

Leveraging Python, Beautiful Soup, and Selenium, I ensured the scraper's adaptability to the site's structure, enabling it to systematically collect product information, including categories, subcategories, links, images, prices, and descriptions. Implementing a checkpoint system mitigated potential data loss and provided reliability during prolonged scraping sessions.

This project showcases my expertise in web scraping, data processing with Pandas, and problem-solving in handling diverse website structures. The Dream2000 Products Scraper is a testament to my commitment to delivering robust, versatile, and user-friendly solutions for extracting valuable insights from complex online platforms.

This project involved a comprehensive initiative aimed at optimizing the integration of real estate data into the Coldwell Banker system. Leveraging Python and Pandas, the project focuses on extracting valuable insights from diverse datasets, ensuring accuracy, and aligning data with Coldwell Banker's standards. The script streamlines the integration process through meticulous data cleaning, mapping, and purpose-specific Microsoft Excel sheets.

My involvement in the project encompassed the data processing script's design, development, and refinement. I played a pivotal role in crafting the logic for data cleaning, implementing mapping strategies, and orchestrating the creation of Microsoft Excel sheets. Additionally, I spearheaded the project's overarching goal of comparing new projects with existing ones and refining project names based on a predefined mapping schema. My commitment to data accuracy and consistency has been instrumental in the project's success, contributing to the seamless integration of real estate data into Coldwell Banker's system.

This project dives deep into The Movie Database (TMDB) dataset using Python, unveiling intricate details of the film industry. Employing Pandas, Matplotlib, and Seaborn, the analysis unfolds multifaceted insights, from genre popularity evolution to complex correlations between revenue, popularity, and vote averages. Unveiling the influence of stars and directors on movie ratings adds a layer of sophistication. The project serves as a comprehensive guide for movie enthusiasts and data aficionados, illuminating the dynamic landscape of cinema. Despite inherent limitations like dropped rows and missing values, the project highlights Python's prowess in extracting meaningful narratives from diverse datasets.

I developed Manga Downloader to enable users to download and save their favorite manga and manhwa series for offline reading. It was implemented in Python, utilizing Beautiful Soup for web scraping and the Requests library for seamless connections with web pages. In addition, image processing libraries like PIL or OpenCV handle downloaded images for tasks such as resizing and cropping.

Key features include a vast selection of manga and manhwa titles, chapter-specific downloads for offline reading, and a user-friendly offline reading experience. Automation supports batch downloads of multiple chapters or entire series, saving time for avid readers. This project, driven by a passion for manga and manhwa, leverages Python and diverse libraries to provide a delightful offline reading experience for enthusiasts worldwide.