Churn Prediction Model Using Machine Learning

Client churn is a major problem and one of the most important enterprises for large companies. Especially in the telecommunications industry, companies are trying to develop tools to predict implicit customer churn because of its direct impact on company profits. Thus, changing factors that increase client churn is important to take necessary conduct to reduce this churn. The main donation of our work is to develop a churn vaticination model which assists telecom drivers to prognosticate guests who are most likely subject to churn. The model developed in this work uses machine literacy ways on a big data platform and builds a new way of features engineering and selection. To measure the performance of the model, This work also linked churn factors that are essential in determining the root causes of churn. By knowing the significant churn factors from guests’ data, CRM can ameliorate productivity, recommend applicable elevations to the group of likely churn guests grounded on analogous patterns, and exorbitantly ameliorate marketing juggernauts of the company.