What are some examples of web scraping portfolio projects for data science?

1 Why web scraping?

Web scraping can help you answer various questions and solve problems using data from the web. For example, you can use web scraping to monitor prices and trends, compare products and services, gather customer reviews and feedback, scrape social media data, extract information from news articles, and more. Web scraping can also help you enrich your existing datasets with additional features and variables, or create new datasets from scratch.

3 What to web scrape?

The choice of what to web scrape depends on your interests, goals, and data availability. You can web scrape almost any website that has public and accessible data, as long as you respect the terms and conditions and the ethical guidelines of web scraping. However, some websites may have anti-scraping measures, such as rate limits, IP bans, or dynamic content, that can make web scraping more challenging. You may need to use advanced techniques, such as rotating proxies, headers, and user agents, or using headless browsers, to overcome these obstacles.

4 Examples of web scraping projects

Data science portfolio projects that involve web scraping can be a great way to showcase your skills and creativity. For instance, you could scrape job listings from Indeed or Glassdoor to analyze the skills, salaries, and locations of data science jobs. Alternatively, you could scrape product reviews from Amazon or eBay and use sentiment analysis, topic modeling, or recommendation systems. You could also scrape hotel or restaurant ratings and reviews from TripAdvisor or Yelp to create a dashboard or report that summarizes customer satisfaction and feedback. Additionally, you could scrape stock prices, news articles, or tweets and apply time series analysis, natural language processing, or machine learning to predict market movements or sentiment. Lastly, you could scrape sports data from ESPN or Wikipedia and use exploratory data analysis, visualization, or statistical modeling to find insights and patterns.

5 Tips for web scraping projects

To make your web scraping projects more successful and impressive, it's important to plan your project before you start. Define your research question, data sources, data requirements, and analysis methods. Documenting your web scraping process and code is also essential. Use comments, docstrings, and markdown cells to explain your logic, assumptions, and results. Additionally, it's important to respect the website's rules and etiquette. Follow the robots.txt file, limit your request frequency, use proper headers and user agents, and avoid scraping sensitive or copyrighted data. Furthermore, handle errors and exceptions gracefully by using try-except blocks, logging, and retries. Lastly, test and validate your data with assertions, checks, and sanity checks to ensure accuracy, completeness, and consistency.