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Machine learning offers businesses a range of benefits, including predictive analytics to forecast future trends and outcomes, customer segmentation to tailor marketing strategies, and personalized recommendations to enhance customer satisfaction. Additionally, machine learning can help detect fraudulent activities, automate repetitive tasks, and streamline business processes, leading to increased efficiency and cost savings. Natural language processing can also be used to automate customer service interactions and analyze customer feedback. Overall, machine learning can improve decision-making, increase efficiency, and give businesses a competitive advantage in the market.


  • Modeling of product stock levels

      Run on Colab

    Part of the Cognizant Artificial Intelligence Internship program

    Groceries are highly perishable items. If you overstock, you are wasting money on excessive storage and waste, but if you understock, then you risk losing customers.

    • In this project, we work with a client Gala Groceries, who has contacted Cognizant for logistics advice about product storage
    • Specifically, they are interested in wanting to know how better stock the items that they sell.
    • Our role is to take on this project as a data scientist and understand what the client actually needs. This will result in the formulation/confirmation of a new project statement, in which we will be focusing on predicting stock levels of products.
    • Such a model would enable the client to estimate their product stock levels at a given time & make subsequent business decisions in a more effective manner reducing understocking and overstocking losses.
    • We also explore how well different models perform and give feedback to the client about what most significantly affects the stock levels.

  • Retaining customer clients vulnerable to churn

      GitHub Repository

    Part of the BCG Data Science Internship program

    In this project, we work with another client who is a major gas and electricity utility that supplies to small and medium sized enterprises

    • The energy market has had a lot of change in recent years and there are more options than ever for customers to choose from
    • They are concerned about their customers leaving for better offers from other energy providers
    • We investigate whether price sensitivity is the most influential factor for a customer churning
    • Conduct feature engineering that is used to test hypotheses related to customer churn
    • And finally we utilise predictive modelling so that it can be used to highlight customers at risk of churn

Thank you for reading!

Any questions or comments about the posts below can be addressed to the mldsai-info channel or to me directly shtrauss2, on shtrausslearning or shtrausslearning