Data Collection Techniques used in this project

  • Web Scraping avec Python

  • Surveys and questionnaires

  • Interviews

  • Observations

  • Focus groups

  • Experimentation

  • Sensor data collection

  • Social media monitoring

. HTTP Requests: Web scraping starts with sending HTTP requests to the target website. This can be done using libraries like requests in Python.

  1. HTML Parsing: Once the web page is fetched, the HTML content needs to be parsed to extract the relevant data. Libraries like Beautiful Soup or lxml can be used for this purpose.

  2. Locating Elements: Web scraping often requires locating specific elements within the HTML structure, such as tags, classes, or IDs. CSS selectors or XPath expressions can be used to identify these elements.

  3. Data Extraction: After locating the desired elements, the data can be extracted using various methods. This may involve extracting text, attributes, or even navigating through nested elements.

  4. Handling Dynamic Content: Some websites use JavaScript to load content dynamically. To scrape such websites, you may need to use tools like Selenium or Scrapy that can interact with JavaScript-driven pages.