Andere Kunden interessierten sich auch für
![Customer Churn Prediction Using Machine learning technique]( "Customer Churn Prediction Using Machine learning technique")
Customer Churn Prediction Using Machine learning technique53,99 €![Breast Cancer Prediction Using Genetic algorithm in machine learning]( "Breast Cancer Prediction Using Genetic algorithm in machine learning")
Breast Cancer Prediction Using Genetic algorithm in machine learning26,99 €![Missing Child Prediction Using Machine Learning Algorithms]( "Missing Child Prediction Using Machine Learning Algorithms")
Missing Child Prediction Using Machine Learning Algorithms29,99 €![Prediction of breast cancer using various machine learning methods]( "Prediction of breast cancer using various machine learning methods")
Prediction of breast cancer using various machine learning methods36,99 €![DeepCOPD: COPD DETECTION USING DEEP LEARNING]( "DeepCOPD: COPD DETECTION USING DEEP LEARNING")
DeepCOPD: COPD DETECTION USING DEEP LEARNING29,99 €![Proper wear of facemask using machine learning to fight Covid 19 virus]( "Proper wear of facemask using machine learning to fight Covid 19 virus")
Proper wear of facemask using machine learning to fight Covid 19 virus29,99 €![Framework for Intelligent CDSS to Diagnose Chronic Myeloid Leukemia]( "Framework for Intelligent CDSS to Diagnose Chronic Myeloid Leukemia")
Framework for Intelligent CDSS to Diagnose Chronic Myeloid Leukemia32,99 €
The churn prediction is an important problem, which can be found in businesses with a constant stream of customers that use subscription services: banking, telecommunication and entertainment industry. The focus of business development shifts from attracting the new customers to retention of the old ones and therefore modeling users' outflow can be used to plan company's tactics and strategies. However, it is often more important not to just know the outflow indicators on a macro level, but to predict the churn probability for every customer to spot interventions. The object of this book is the process of the customer churn. The subject of research can be defined as automated models for the churn prediction and its practical use. The models were designed on a basis of state-of-the-art machine learning methods: the ensemble trees methods and the logistic regression. The Metric for the model evaluation is proposed; process of the model optimization on this metric is well-described.The main result of this work is the cohort-based ensemble model for the churn prediction and automation of the model construction pipeline.