Welcome to the Data Scraping Tasks repository! This repository showcases my data scraping Tasks, where I have used Python and various libraries (like BeautifulSoup, Scrapy, and Requests) to extract, process, and analyze data from the web. Each project is stored in its respective folder with detailed explanations, code, and datasets.
This project extracts book information (title, price, rating, availability, etc.) from the Books to Scrape website. The dataset can be useful for e-commerce analysis, book categorization, and price comparisons.
In this project, quotes, authors, and tags are scraped from the Quotes to Scrape website. This dataset is great for sentiment analysis or NLP experiments.
This project focuses on extracting detailed information about countries, including names, capitals, and population. The dataset can support geographical and demographic analysis.
4. Etsy Product
Data is scraped from Etsy product listings, including product names, prices, and seller details. This project is useful for market research and competitor analysis in the e-commerce space.
5. Hockey Teams
A project that collects information about hockey teams, their players, and match statistics. It is ideal for sports analytics and fan engagement projects.
This project extracts financial data about the largest companies in the USA by revenue. The dataset supports financial analysis and insights into the U.S. economy.
Scrapes data from the Moviesjoy website, including movie titles, genres, and ratings. The dataset can be leveraged for building recommendation systems or film research.
This project demonstrates how to scrape structured data from Wikipedia pages. It is a versatile tool for extracting general knowledge or niche information.
Scrapes data about the population of countries worldwide. This dataset is perfect for demographic studies and visualization projects.
Similar to the previous project but includes additional attributes like growth rate, density, and urban population percentages for deeper insights into global population trends.
- Clean and Organized: Each project includes a folder with code, README file, and scraped dataset for easy navigation and replication.
- Scalable Codebase: Designed to handle dynamic content and adapt to changes in website structures.
- Reusable Scripts: Scripts can be reused and customized for scraping similar data from other sources.
- Libraries:
BeautifulSoup,Scrapy,toscrap,Requests,Pandas - Languages: Python
- Applications: Data analysis, visualization, and insights generation
- Clone this repository:
git clone https://github.com/drisskhattabi6/Data-Scraping-Tasks.git
- Navigate to the desired project folder.
- Follow the instructions in the project's README file to run the scraping script.
If you have any questions or suggestions, feel free Contact me. Happy Scraping! 🚀