Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers

Abstract

In India, the banks have a formidable edge in maintaining their customer retention ratio for past few decades. Downfall makes the private banks to reduce their operations and the nationalised banks merge with other banks. The researchers have used the traditional and ensemble algorithms with relevant feature engineering techniques to better classify the customers. The proposed algorithm uses a Meta classifier instead of an ensemble algorithm with an adaptive genetic algorithm for feature selection. Churn prediction is the number of customers who wants to terminate their services in the banking sector. The model considers twelve attributes like credit score, geography, gender, age, etc, to predict customer churn. The project consists of five modules as follows. First is the pre-processing module that identifies the missing data and fills the value with mean and mode. Second is the data transformation module where, the categorical data is converted into numerical data using label encoding to fasten the computations. The converted numerical data is normalized using the standard scalar technique. The feature selection module identifies the essential attributes using DragonFly and Firefly (Hybrid Fly) algorithms. The classification module designs an intelligent Meta learner, which combines the Ensemble Algorithm Extreme Gradient Boosting (XGBOOST) with base classifiers as "Extra Tree Classifier" and "Logistic Regression" to predict the churn customers.

LiDAR Dynamic Target Detection Based on Multidimensional Features

“…By extracting the characteristics of the point cloud cluster and using a machine learning method to detect the motion state of each point cloud cluster in each frame point cloud, high-precision and high-efficiency LiDAR dynamic target detection is achieved. Among many machine learning algorithms, XGBoost [ 24 ] has the advantages of flexibility, accuracy, and efficiency and has been optimized by relevant experts and scholars from the perspectives of data processing, multilabel classification, and hyperparameter tuning [ 25 , 26 , 27 , 28 ]. Therefore, this algorithm is widely used to address various classification and regression problems [ 29 , 30 , 31 ].…”