Customer churn prediction system: a machine learning approach

  title={Customer churn prediction system: a machine learning approach},
  author={Praveen Lalwani and Manas Kumar Mishra and Jasroop Singh Chadha and Pratyush Sethi},
Customer Churns Prediction Model Based on Machine Learning Techniques: A Systematic Review
The customer churn prediction model is required by many companies to predict the risk of customer churn and take necessary actions to prevent churn. Recently, machine-learning techniques are highly
A Hybrid Two-Level Support Vector Machine-Based Method for Churn Analysis
A two-level churn analysis is proposed to classify the customer churn or not, and predict how much the customer has paid for the service, demonstrating that both the churn classification and charge prediction can be performed at the same time with a higher degree of accuracy.
Predicting credit card customer churn using support vector machine based on Bayesian optimization
  • K. D. Ünlü
  • Computer Science
    Communications Faculty Of Science University of Ankara Series A1Mathematics and Statistics
  • 2021
A hybrid machine learning algorithm to predict credit card customer churn is employed and the proposed model is Support Vector Machine with Bayesian Optimization (BO).
Predicting special care during the COVID-19 pandemic: a machine learning approach
An analytical approach based on statistics and machine learning that uses lab exam data coming from patients to predict whether patients are going to require special care (hospitalisation in regular or special-care units) and the number of days the patients will stay under such care.


Customer churn prediction in telecom using machine learning in big data platform
This work develops a churn prediction model which assists telecom operators to predict customers who are most likely subject to churn and builds a new way of features’ engineering and selection on big data platform.
Novel machine learning approach for classification of high-dimensional microarray data
A new (artificial bee colony) ABC-based feature selection approach for microarray data is proposed based on two stages: ICA-based extraction approach to reduce the size of data and ABC- based wrapper approach to optimize the reduced feature vectors.
  • International Journal of Pure and Applied Mathematics
  • 2018
Artificial Neural Network Classification of High Dimensional Data with Novel Optimization Approach of Dimension Reduction
The experimental result shows that a combination of ICA with genetic bee colony algorithm shows superior performance as it heuristically removes non-contributing features to improve the performance of classifiers.
BERA: a biogeography-based energy saving routing architecture for wireless sensor networks
A biogeography-based energy saving routing architecture (BERA) is proposed for CH selection and routing with an efficient encoding scheme of a habitat and by formulating a novel fitness function that uses residual energy and distance as its metrics.
CRWO: Clustering and routing in wireless sensor networks using optics inspired optimization
This study proposes an OIO based CH selection algorithm that is extensively tested and compared with some of the existing algorithms based on both conventional and nature inspired routing techniques to depict the superiority of the proposed algorithm over its comparatives.
GSA-CHSR: Gravitational Search Algorithm for Cluster Head Selection and Routing in Wireless Sensor Networks
A new CH selection strategy is developed with an efficient encoding scheme by formulating a novel fitness function based on the residual energy, intra-cluster distance, and CH balancing factor, and a GSA-based routing algorithm is devised by considering residual energy and distance as parameters to be optimized.
Improved credit card churn prediction based on rough clustering and supervised learning techniques
This work performs data processing techniques and proposes modified rough K-means algorithm used for clustering credit card holders and in next stage hold-out method divides the cluster data into testing and training clusters and evaluates the work using precision, recall, specification, accuracy, and misclassification error.
  • International Research Journal of Engineering and Technology (IRJET)
  • 2016
A Survey on Decision Tree Algorithms of Classification in Data Mining
Various algorithms of Decision tree (ID3, C4.5, CART), their characteristic, challenges, advantage and disadvantage, are focused on.