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I analyzed a large retail dataset to uncover “Hidden Gem” products items receiving highly positive customer feedback despite having relatively low visibility.
Using Python and Pandas, I cleaned and combined product, customer review, and metadata from multiple CSV and JSON sources. I then applied VADER sentiment analysis to thousands of customer reviews to measure customer sentiment beyond traditional star ratings.
To identify overlooked products, I developed a custom Hidden Gem Score combining average rating, customer sentiment, and review volume. The analysis identified approximately 2.8% of products as strong hidden gems, highlighting products with strong customer satisfaction but comparatively limited engagement.
Key skills: Python · Pandas · Data Cleaning · Sentiment Analysis · NLP · Data Visualization · Feature Engineering · Business Analytics
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