In today's data-driven world, accessing and processing information efficiently is paramount. This project tackles the common challenge of data acquisition from dynamic websites, specifically Flipkart's laptop listings. Facing the hurdles of complex HTML structures and potential JavaScript rendering, this scraper leverages the power of Python, Selenium to automate the extraction of crucial product data. It showcases my ability to:
- Automate data collection: Efficiently gather large datasets from dynamic websites.
- Handle HTML parsing: Extract relevant information from complex web page structures.
- Clean and structure data: Transform raw data into a usable format for analysis.
This project is not just a script; it's a demonstration of how I can leverage programming to solve real-world data acquisition challenges.
- Robust Scraping: Utilizes
requestsandSeleniumto reliably extract data even with website changes. - Comprehensive Data Extraction: Gathers laptop names, prices, specifications (processor, RAM, storage, etc.), ratings, and other relevant details.
- Data Cleaning and Transformation: Implements data cleaning techniques to handle missing values, inconsistencies, and format data for analysis.
- Structured Output: Saves the extracted data into a Pandas DataFrame, which can be easily exported to CSV or other formats.
- Modular Design: The code is structured for easy understanding and modification.
- Scalability: The code can be modified to scrape other categories or websites.
- Python 3.8+
pippackage manager- Required Python libraries:
requests,Selenium,pandas(install usingpip install package_name)
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Open and run the Jupyter Notebook
Scrape-Flipkart-Laptop-Data.ipynb.jupyter notebook Scrape-Flipkart-Laptop-Data.ipynb
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Follow the instructions within the notebook to execute the scraping process.
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The scraped data will be saved as a CSV file (or within the notebook's dataframe) in the project directory.