https://github.com/hafidaso/insights-into-luxury-fashion

This project conducts a comprehensive analysis of a dataset containing product listings from SSENSE, a renowned retailer in designer fashion and high-end streetwear.
https://github.com/hafidaso/insights-into-luxury-fashion

data-science python

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This project conducts a comprehensive analysis of a dataset containing product listings from SSENSE, a renowned retailer in designer fashion and high-end streetwear.

• Host: GitHub
• URL: https://github.com/hafidaso/insights-into-luxury-fashion
• Owner: hafidaso
• Created: 2024-01-24T17:48:15.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-01-24T17:51:02.000Z (11 months ago)
• Last Synced: 2024-01-24T19:04:25.625Z (11 months ago)
• Topics: data-science, python
• Language: Jupyter Notebook
• Homepage:
• Size: 729 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Insights into Luxury Fashion: An Analytical Dive into the SSENSE Product Portfolio

## Description

This project conducts a comprehensive analysis of a dataset containing product listings from SSENSE, a renowned retailer in designer fashion and high-end streetwear. The dataset includes data about brands, product descriptions, prices, and target genders, providing insights into trends, pricing strategies, brand positioning, and gender segmentation in the luxury fashion e-commerce sector.

## Contents

### 1. Dataset Description

- **Source**: SSENSE

- **Features**: Brand, Description, Price in USD, Target Gender

### 2. Analytical Approach

- Price Distribution and Statistics

- Brand Analysis: Frequency and Pricing Strategies

- Gender-Based Insights: Product Count and Pricing Differences

- Product Category Analysis: Common Categories and Price Comparisons

### 3. Key Findings and Insights

- Market segmentation by gender and product categories

- Variation in pricing across different brands and categories

- Brand-specific strategies in product diversity and targeting

- Predominance of men's products in the dataset, with notable brand-specific exceptions

### 4. Technologies Used

- **Data Analysis**: Python, Pandas

- **Data Visualization**: Matplotlib, Seaborn

### 5. Conclusion

- Summary of insights on luxury fashion market dynamics

- Implications for marketing strategies and inventory management in luxury fashion retail

### 6. Future Scope

- Expanding the analysis with more datasets

- Incorporating consumer behavior and sales data for a more comprehensive understanding

## Author Information

### [Hafida Belayd](https://www.linkedin.com/in/hafida-belayd/)