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This repository contains a Python project for scraping skincare product data from Sociolla.com. It collects details on over 7,500 products, including brand, price, user interactions, and availability, supporting analysis of market trends and customer preferences.

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Scrapping Sociolla: Exploring Skincare Products Dataset

Overview:

This dataset encompasses details on over 7,500 skincare products gathered from Sociolla.com, a prominent online beauty and skincare platform. The dataset spans a diverse array of offerings from more than 300 distinct brands, providing a comprehensive view of the skincare landscape.

Key Features:

1.Brand Information:

  • Columns: 'brand_name'
  • Description: The name of the skincare brand associated with each product, allowing for brand-specific analyses and market insights.

2.Product Details:

  • Columns: 'product_name', 'product_id', 'default_category', 'categories'
  • Description: In-depth information about each skincare product, including its name, unique identifier, default category, and additional categories for detailed product classification.

3.Pricing Information:

  • Columns: 'price_range', 'price_by_combinations'
  • Description: Insights into the pricing structure of skincare products, with a general price range and specific pricing based on different product variations.

4.Customer Engagement:

  • Columns: 'beauty_point_earned', 'total_in_wishlist'
  • Description: Metrics indicating customer engagement, such as beauty points earned through purchases and the number of users who have added the product to their wishlist.

5.User Ratings and Reviews:

  • Columns: 'rating_types_str', 'average_rating', 'total_reviews', 'average_rating_by_types'
  • Description: Comprehensive data on customer feedback, including the types of ratings, overall average ratings, total reviews, and average ratings for specific aspects or types.

6.Customer Recommendations and Repurchase Intent:

  • Columns: 'total_recommended_count', 'total_repurchase_maybe_count', 'total_repurchase_no_count', 'total_repurchase_yes_count'
  • Description: Insights into customer recommendations and repurchase intent, with counts indicating the level of satisfaction and potential for repeat business.

7.Product Availability:

  • Columns: 'active_date'
  • Description: Information on the date when each product became active or available on Sociolla.com, providing a timeline for product availability.

8.Direct Access to Product:

  • Columns: 'url'
  • Description: The URL link to each product on Sociolla.com, facilitating direct access for additional information or purchase.

Scope of Analysis:

Researchers, analysts, and skincare enthusiasts can leverage this dataset to explore trends, patterns, and insights within the skincare industry, covering aspects such as brand performance, pricing strategies, customer engagement, and product feedback.

Use Cases:

  • Brand-specific market analysis.
  • Pricing strategy evaluations.
  • Customer engagement and satisfaction assessments.
  • Academic research on skincare trends and consumer behavior.

Data Collection Method:

The dataset was obtained by scraping Sociolla.com, ensuring the inclusion of a broad range of products available on the platform.

Disclaimer:

This dataset is intended for research and analytical purposes only. Any commercial use must adhere to Sociolla.com's terms and conditions.

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This repository contains a Python project for scraping skincare product data from Sociolla.com. It collects details on over 7,500 products, including brand, price, user interactions, and availability, supporting analysis of market trends and customer preferences.

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