A prominent ecommerce platform for home supplies wanted to increase cross-sell and upsell opportunities but struggled with limited, unclean data, privacy concerns, and difficulty capturing user behavior. Aspire Systems built a recommendation engine using clustering, nearest-neighbor algorithms, logistic regression, and predictive offers to enable hyper-personalized marketing. Technologies included Python with Numpy, Pandas, Scikit-learn, SciPy, collaborative filtering, association rules, and cosine similarity for product matching. The solution improved cross-selling, helped customers find relevant products easily, boosted targeted marketing, and enhanced customer engagement.

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