Abstract. Customer satisfaction questionnaires are a rich and strong source of information for companies to seek loyalty, customer and client retention, ...

Oct 21, 2019 · The present paper evaluates the BAGGING ensemble model using the well-renowned k-nn algorithm as the base learner.

The present paper evaluates the BAGGING ensemble model using the well-renowned k -nn algorithm as the base learner and results indicate that the BAGGING ...

By having a database on customer satisfaction, the company can utilize the data for machine learning modelling. The model generated can predict customer ...

We perform an experimental study using a real database of 129,890 samples from airline companies, in order to verify the benefits of ensemble models for ...

They analyze the performance of the k-nearest neighbor (k-NN) model for regression as base classifier in the BAGGING (Bootstrap AGGregatING) ensemble model. You ...

Predicting Airline Customer Satisfaction using k-nn Ensemble Regression Models. Resource URI: https://dblp.l3s.de/d2r/resource/publications/journals/rcs ...

Several advanced machine learning and statistical models have been employed to estimate the customer satisfaction score; however, there is no single model that ...

This study uses several classification models such as KNN, Logistic Regression, Gaussian NB, Decision Trees and Random Forest which will later be compared. The ...

At this stage, we will make predictions with different machine learning algorithms. The algorithms we will use are: Logistic Regression; KNN; Naive Bayes ...