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Product Recommendation Case Study

In this project, the power of machine learning takes center stage, driving the creation of intelligent product recommendations. By meticulously analyzing prior user behavior, the system adeptly crafts personalized suggestions, increasing the likelihood of prompting additional purchases.

Business Case - What products should we recommend?

Problem

Customers are not given suitable suggestions when making a selection, hindering additional purchases.

Solution

Leveraging machine learning on customer data to generate recommendations from prior behavior patterns.

Outcome

Providing customers with data-driven recommendations, continuously adapting to changing tastes over time.

Methodology

In this sample business case, I am going to use apriori to systematically analyze sales in order to make associations between items which can then be used to recommend products to customers.

Recommendations

I put a dynamic dashboard here to show the output of the Apriori algorythm when different items are selected, showing the confidence of each product pairing. This could be further manipulated by only recommending products that are in the same type(such as only recommending meats what a meat is selected) or capping the recommendations to streamline the user experience. For the purposes of this visualization, the results have not been culled, manipulated, or capped, to better convey the behavior of the algorythm.

Acknowledgements

Thank you to Super Data Science for supplying the data used in this project.



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