Project Overview

This interactive dashboard, built on Hex, transforms a detailed H&M product dataset from Kaggle into a powerful tool for exploring fashion trends and product insights. Sourced from H&M’s official website, the dataset includes metadata like product names, prices, colors, material compositions, and detailed descriptions. My aim was to create an engaging, user-friendly platform that highlights pricing patterns, color preferences, and product details while experimenting with natural language processing (NLP) to summarize descriptions.

Dataset

The dataset offers a structured snapshot of H&M’s product catalog, with key fields:

• Product Details: Product ID, name, brand, URL, and price.
• Colors: Primary color, available variants, and shades.
• Sustainability: Material compositions, including indicators for eco-friendly materials like recycled cotton.
• Descriptions: Detailed product descriptions for NLP analysis.
• Categories: Main category codes and other metadata.

This rich dataset is perfect for e-commerce analytics, fashion trend analysis, or sustainability research.

Approach

I used Hex’s cloud-based platform to blend SQL, Python, and no-code visualizations into a seamless dashboard. The process involved:

1. Data Preparation: Uploaded the Kaggle CSV to Hex and ensured data consistency (e.g., numerical prices, clean color names).
2. Analysis: Employed Pandas for data exploration and NLTK for NLP to generate concise product description summaries.
3. Visualization: Created interactive charts with Plotly and Hex’s drag-and-drop tools to showcase key insights.
4. Interactivity: Added filters for categories, price ranges, and colors to enable dynamic exploration.

Dashboard Features

The dashboard provides a curated set of tools for fashion enthusiasts, analysts, and data scientists:

• Price Distribution: An interactive histogram revealing how prices vary across products, with filters to focus on specific categories or price ranges.
• Color Breakdown: A pie chart showing the percentage of products in primary colors (Black, White, Others), highlighting popular color trends in H&M’s catalog.
• Product Explorer: A dynamic table to browse products by name, price, color, and other attributes, making it easy to dive into specific items.
• NLP Summaries: Automated summaries of product descriptions, powered by NLTK, that extract key terms (e.g., “soft,” “stylish”) to capture the essence of each item.

Additionally, the summary section includes Sustainability Insights, noting the presence of eco-friendly materials like recycled cotton in product compositions, supporting greener fashion exploration.

Impact and Applications

This dashboard is designed for multiple use cases:

• E-commerce Analytics: Analyze pricing strategies and category performance to understand market dynamics.
• Fashion Trends: Identify popular colors to spot styling trends.
• Sustainability Awareness: Highlight eco-friendly products to promote sustainable shopping.
• NLP Experimentation: Test NLP techniques for summarizing or tagging product descriptions, useful for recommendation systems.

Whether you’re a data enthusiast, a fashion lover, or an advocate for sustainability, this project offers valuable insights in an accessible format.

Technologies Used

• Hex: Platform for data ingestion, analysis, and dashboard creation.
• Python: Pandas for data processing, Plotly for visualizations, and NLTK for NLP tasks.
• SQL: For querying and filtering the dataset.
• Kaggle: Source of the H&M product dataset.