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This project examines the cosmetics segment using a dataset with product prices, categories, ratings, brands, and sales figures. Python and Power BI were used for data processing and visualization, revealing trends in consumer preferences, pricing strategies, and market dynamics.

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E-Commerce-Cosmetics Product Analysis

This project presents a comprehensive analysis of the cosmetics market using a dataset containing various product attributes, including price, category, rating, brand, and sales figures. The goal of this analysis is to uncover key trends, consumer preferences, and market dynamics within the cosmetics industry.

Dataset

The dataset, cosmetic_dataset.xlsx, contains detailed information on cosmetic products, including:

  • Price: The cost of each product, allowing for an analysis of pricing strategies and market positioning.
  • Category: Different categories like lips, eyes, face, skincare, and hair, helping to understand which segments dominate the market.
  • Rating: Customer ratings, providing insights into overall product satisfaction.
  • Brand: Information about different brands and their product lines.
  • Sales Figures: Data on product sales, enabling the analysis of revenue distribution across categories.

Key Insights

  • Price Distribution: The analysis reveals a right-skewed price distribution, with most products priced lower, and a few high-end products.
  • Category Representation: The "lips" and "eyes" categories are the most prevalent, while "hair" and "face" are less represented, indicating market focus and consumer demand.
  • Customer Satisfaction: Most products receive high ratings, particularly in the "eyes" and "lips" categories, although the "face" category shows room for improvement.
  • Brand Analysis: Sephora Collection leads with the most extensive product line, with other brands like Anastasia Beverly Hills and Too Faced also having a strong presence.
  • Sales Analysis: The body category generates the highest sales, with eyes and skincare also performing well. Specific subcategories within the body category dominate revenue, suggesting targeted consumer preferences.

Analysis & Visualization

The project includes several charts and visualizations to represent the data insights effectively. These include:

  • Price distribution histograms.
  • Frequency distribution of product categories.
  • Average ratings across different categories and subcategories.
  • Scatter plots showing the relationship between price and ratings.
  • Bar charts comparing product distribution and sales across different countries.

Conclusion

The analysis highlights the strengths and opportunities within the cosmetics market, such as the high customer satisfaction in certain categories and the potential for growth in underperforming segments. It also identifies key trends that brands can leverage to enhance their market presence and revenue.

About

This project examines the cosmetics segment using a dataset with product prices, categories, ratings, brands, and sales figures. Python and Power BI were used for data processing and visualization, revealing trends in consumer preferences, pricing strategies, and market dynamics.

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