Explorative data analysis of fashion product dataset

This exploratory data analysis project involved examining a comprehensive dataset of fashion products to identify trends, patterns, and insights that could help optimize product development and marketing strategies.

I led the research and analysis, working with a dataset containing over 50,000 fashion products with attributes including pricing, materials, styles, customer ratings, and sales performance.

Key Findings

• Identified seasonal trend patterns across different product categories
• Discovered correlations between specific materials and customer satisfaction
• Analyzed price elasticity variations across different fashion segments
• Mapped the relationship between product attributes and return rates
• Created predictive models for product performance based on attribute combinations

Methodology

The analysis utilized a combination of statistical methods and machine learning techniques. I began with descriptive statistics to understand the dataset's composition, followed by correlation analyses to identify relationships between variables.

For deeper insights, I applied clustering algorithms to segment products based on multiple attributes, and used regression analysis to model the impact of various factors on sales performance. The findings were visualized using interactive dashboards that allowed stakeholders to explore the data from different perspectives.