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Introduction

In today's digital age, fashion data analysis is crucial. It transforms vast amounts of information from e-commerce, social media, and market trends into actionable insights. This enables fashion brands to predict trends, optimize pricing, personalize experiences, and make informed decisions.

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Previously, during my marketing minor study, we conducted a project focused on Amazon, an online retail platform. Our approach was straightforward, concentrating on easily accessible product information. We collected data on key aspects such as product prices, types (e.g., dresses, hoodies, skirts), and brand names. Our main objective was to understand how these basic features influenced product sales.

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To analyze the data, we employed a multivariate regression model. This statistical technique allowed us to examine the relationships between multiple independent variables (price, product type, brand) and the dependent variable (sales). We aimed to quantify how changes in these factors correlated with changes in sales figures.

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For presenting our findings, we utilized basic data visualization techniques. These included scatter plots to show price-sales relationships, bar charts to compare sales across different product types.

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My updated project plans to significantly expand on this foundation. I'll incorporate more advanced techniques such as sentiment analysis of customer reviews, style classification using AI, detailed price and brand analysis, and trend forecasting. This comprehensive approach will provide deeper insights into the fashion market, enabling more accurate predictions and nuanced understanding of customer behavior and market trends.

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Updated Project

Fashion Analytics: Unveiling Trends with AI

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  • In the fast-paced world of fashion, staying ahead of trends is crucial. This project will utilize the power of artificial intelligence to analyze and predict fashion trends from online retail data. This innovative approach combines web scraping, natural language processing, and machine learning to provide valuable insights into the fashion industry.

 

The Data Behind the Trends

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  • The project begins with a weekly data collection from Amazon, a popular retail platform. Using web scraping techniques, the system gathers a wealth of information, including product details, prices, customer reviews, and sales figures. This raw data is then cleaned and organized, setting the stage for in-depth analysis.​​​

 

Decoding Fashion Sentiments

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  • One of the most fascinating aspects of this project is its ability to understand customer sentiments. By applying advanced natural language processing to product reviews, the system should be able to determine not just whether a review is positive or negative, but also detect specific emotions like joy or disappointment, or even if the size fit.

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The Science of Style

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  • The project goes beyond simple categorization, using cutting-edge AI to classify fashion styles. It employs large language models to extract style keywords from product descriptions and uses image recognition to analyze product photos. In this way, we will be able to analyze more than basic feature from text, but also a lot more advance feature like the color of the product, the texture…

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Price and Sale Dynamics

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  • Understanding the relationship between price, brand, and consumer behavior is crucial in the fashion market. Previously we only collect data for once, but now we plan to collect time series data, thus we can study the trend, and anlayze if certain event, or social meida, or season effect on the sale.

 

Predicting the Next Big Thing

 

  • By combining all this data - from sentiment analysis to style classification to pricing dynamics - the project creates predictive models to forecast sales and upcoming trends. Compared with classic regression model we used before. We plan to use advanced machine learning techniques like Random Forest and Gradient Boosting, it can identify the key factors that drive sales in different product categories much more effectively.

 

From Data to Decisions

 

  • All of these insights are planned to be brought together in an interactive dashboard using tools like tablular, allowing fashion industry professionals to visualize current trends and future predictions.

 

The Future of Fashion Analytics

 

  • This project represents a significant leap forward in fashion analytics. As the fashion industry continues to evolve, tools like this will be essential for brands and retailers looking to stay ahead in a competitive and fast-changing market.

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Detailed Proposal

Data Collection:

  • Implement a web scraping script to extract data from Amazon weekly

  • Store data in a structured format (e.g., CSV or database)

  • Key data points: product details, price, reviews, sales figures

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Data Preprocessing:

  • Clean and normalize the collected data

  • Handle missing values and outliers

  • Organize data into a format suitable for analysis

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Feature Engineering:

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  • Sentiment Analysis:

    • Apply advanced NLP techniques to analyze product reviews

    • Generate multi-dimensional sentiment scores (positive, negative, neutral)

    • Implement aspect-based sentiment analysis to capture sentiments on specific product features (e.g., fit, quality, design)

    • Analyze review helpfulness and its correlation with sentiment

    • Track sentiment trends over time for each product​

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  • Style Classification:

    • Utilize a pre-trained LLM (e.g., GPT-4) to extract style keywords from product descriptions

    • Implement image recognition models to classify styles based on product photos

    • Implement color analysis to extract dominant colors and color schemes

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  • Price Analysis:

    • Develop a price elasticity model for different product categories

    • Analyze price fluctuations and their impact on sales

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  • Brand Analysis:

    • Create brand equity scores based on customer loyalty and perception

    • Analyze brand collaboration impact on product popularity

    • Track brand mention frequency and sentiment in reviews and social media

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  • Product Attributes:

    • Extract and categorize materials used in products

    • Analyze the impact of sustainability claims on product popularity

    • Create a trendiness index based on current fashion trends

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Exploratory Data Analysis (EDA):

  • Visualize relationships between variables

  • Identify patterns and trends in the data

  • Generate insights on price distribution, popular styles, and sentiment correlations

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Predictive Modeling:

  • Develop regression models to predict sales:

    1. Multiple Linear Regression

    2. Random Forest Regression

    3. Gradient Boosting Regression

  • Feature importance analysis to understand key factors affecting sales

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Trend Forecasting:

  • Time series analysis of style popularity

  • Implement models like ARIMA for trend prediction

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Visualization and Reporting:

  • Create an interactive dashboard to display insights

  • Generate weekly reports on trends and predictions

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Continuous Improvement:

  • Regularly update the model with new data

  • Explore advanced techniques like deep learning for improved accuracy

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