The demand for Machine Learning and AI models has surged in today's market, leading to an increased need for diverse and extensive datasets. While many datasets are readily available, some individuals prefer to create their datasets to cater to specific requirements. Web scraping presents a viable option for accomplishing this task.
The internet is a treasure trove of vast and varied data across different domains, waiting to be harnessed and utilized to train cutting-edge machine learning models. In this blog, we will delve into the process of scraping data from Myntra, an Indian fashion e-commerce company, using the Selenium WebDriver.
With the Selenium WebDriver, we can automate the data extraction process from Myntra's website, enabling us to gather fashion-related information such as product details, images, prices, and customer reviews. By harnessing this data, we can create custom datasets that cater to our machine-learning projects' specific needs, thus enhancing our models' performance and accuracy.
Through this blog, you will gain a comprehensive understanding of the web scraping process using Selenium WebDriver, empowering you to curate unique datasets tailored to your AI and Machine Learning endeavors. Let's embark on this exciting journey of data extraction from Myntra and unlock the full potential of your ML and AI projects!
In addition to the aforementioned Python libraries, to perform web scraping on Myntra using Selenium WebDriver, you will also need to install Chromedriver. This essential component can be directly obtained from the official website: https://chromedriver.chromium.org/. However, ensure that you select the appropriate chromedriver version that matches your current Google Chrome version installed on your system.
Integrating chromedriver into your web scraping setup allows you to seamlessly interact with Google Chrome, enabling automated data extraction from Myntra's website. This synergy between Python, Selenium, and chromedriver will empower you to create robust and custom datasets for training state-of-the-art machine learning models, revolutionizing your AI projects with rich and relevant fashion-related data.
The code provided above allows you to retrieve and load the URL, which in turn opens a Chrome window displaying the response page. By using Selenium WebDriver, we can automate this process. Here's the code snippet for achieving that:
After loading the response page with the Selenium WebDriver, we can interact with the search bar by entering text and clicking to retrieve the search results. To achieve this, we can use the following code:
If your dataset collection's purpose is solely to retrieve the thumbnails, you can extract them directly from the response page. However, if you require additional metadata along with the images, you can proceed with further steps to scrape the relevant data.
After loading the response page with the Selenium WebDriver, you can use various methods to locate and extract the image thumbnails from the page's HTML content. These image URLs can be stored for later use.
On the other hand, if you also need metadata like product names, prices, customer reviews, and other details, you will need to interact with and navigate through the page using Selenium WebDriver. You can identify the relevant HTML elements containing this information, extract their data, and combine it with the previously retrieved image URLs to form a comprehensive dataset.
By proceeding with metadata extraction, you can create a more enriched dataset with images and relevant information. This dataset can then be used to train your machine learning models with a broader scope, enhancing their performance and accuracy for various fashion analysis and recommendation systems tasks.
The provided code fetches all the product links on the response page and then searches for the "NEXT->" button. If the button is found, the code navigates to the next page and repeats the process. If the button does not exist, it halts the driver connection. Below is a rephrased version of the process:
The code retrieves all product links from the response page and checks for the presence of a "NEXT->" button. If the button is present, it proceeds to the next page and repeats the procedure. If the button is not found, it stops the driver connection, concluding the scraping process. By utilizing this method, you can efficiently crawl through multiple pages of the website, collecting product links and associated data for your dataset.
In Step IV, after fetching the product links, we can use those links to access the individual product pages and extract the name, title, and price of each product. Here's how you can do that:
In this code snippet, we attempt to load all the specifications of the product and then retrieve the key-value pairs to add the metadata. Here's the process described in more detail:
To utilize image data for Deep Learning and Computer Vision with state-of-the-art models, you can retrieve the product images using the following code snippet:
In conclusion, the metadata collected from web scraping is saved as a JSON file. Web scraping proves to be an invaluable tool for exploring websites and extracting valuable datasets. In this article, we have successfully demonstrated how to scrape data from the Myntra website.
The dataset obtained from this process can serve multiple purposes, such as generating new clothing designs using GAN models or aiding machine learning tasks like image classification.
However, it is essential to stress that the work carried out through web scraping should be strictly used for research purposes and with ethical considerations. Engaging in unethical practices or violating the terms and conditions of websites is strongly discouraged.
Always comply with the website's policies and legal regulations when scraping. Responsible and ethical use of web scraping is crucial in maintaining a respectful and positive impact on the internet and its users.
For more information about scraping Myntra data using Python or Selenium, contact Actowiz Solutions now! You can also contact us for all your mobile app scraping, instant data scraper or web scraping service requirements.
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