Challenge:Our client manages a lot of commercial real estate facilities in the US. Their aim was to reduce tenant churn. They could do so by making educated guesses about whether the tenants will churn and offering them appropriate discounts or other promotions and by understanding better how different factors influence tenant decisions. SolutionUsing Machine Learning, we built a predictive pipeline that evaluates the probability of tenant churn and provides an analysis of how different features influence that probability. To get to this stage, we had to provide business analysis, figure out which features should this backend system have, have model training, and deploy the server. ResultsThe final result of the project is an ML pipeline that predicts probability of tenant churn, can continuously improve its accuracy by training on more data, and provides extensive model explainability. The system proved its efficiency by meeting customer’s historic data and being more accurate than it.
