Customer retention is key priority for any business. Multiple factors drive customer churn and understanding of these factors can help proactive management of customer churn. Combination of data processing and statistics can help in understanding the possible reasons and identifying customers at risk.For this example we have used telecom data set from IBM community website ( https://community.watsonanalytics.com/resources/).Customersgo through a complex decision making process before subscribing to any one of the numerous Telecom service.Goal of predictive model in this blog is to identify set of customers who have high probability of unsubscribing from the service. For this model, we are using personal details, demographic information, pricing, and plan information. We will also identify set of independent variables that are related to customer unsubscribing from service.
Source code for this exercise is available at https://github.com/Innovyt/Machine-Learning Model/blob/master/Telecom%20Customer%20Churn%20Analysis.ipynb
For this exercise, we are using logistic regression algorithm. Logistic regression is useful in establishing a relationship between binary outcome and a group of continuous and/or categorical predictor variables. It also determines the percentage of variance in the dependent variable explained by independent variable.
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