INTEGRATING RANDOM FOREST AND LOGISTIC REGRESSION TO FORECAST CUSTOMER ATTRITION IN THE TELECOM SECTOR
Abstract
Since customers are the foundation of any successful business, companies must prioritize making sure they are satisfied. However, due to increased corporate competition, the importance of customers’ and marketing tactics more informed conduct in the past few years, client attrition is a significant problem and is acknowledged as one of the top concerns among businesses. Organizations must take a number of measures to address the problems with churn brought on by the services they offer. Customer attrition strategies are essential in the fiercely competitive and rapidly changing telecom industry. Utilizing machine learning methods, assess the possibility that a client will leave a firm. This research uses logistic regression, random forest, and big data to predict customer attrition in the telecom sector. A large-scale logistic regression analysis has been used to assess the probability of churn as a function of a variable set or customer attribute. Similarly, based on how close a feature is to customers in each class, random forest is employed to ascertain if or not a customer churns. This research makes use of information from the Kaggle website to forecast and examine churn. According to the results of the study show that 0.84 percent is the area under the curve., and the forecast precision rates for consumer churn using linear regression and random forest are 0.80 and 0.79 percent, respectively.
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