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Built a Python-based analytics system to uncover patterns in retail product data. Used Decision Tree Classifier for product trend analysis, K-Means clustering for customer segmentation, and the Apriori algorithm for market basket analysis (association rule mining). Included data cleaning/preprocessing and visualization of key insights using Pandas, NumPy, and Scikit-learn.
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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
Built for product teams who need sharper visibility into user behavior, this Product Analytics Dashboard brings activity timelines, feature engagement analysis, user segments, custom metrics, filters, AI support, and an insight builder into one clean SaaS interface. With a soft light-mode layout, blue data visualization, structured navigation, and actionable analytics panels, the dashboard makes product performance easier to monitor, compare, and turn into better decisions.
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