| dc.description.abstract | The concept of customer churn in telecommunications is important because it can help
organizations better understand their customers' needs and preferences. It can also be
used to develop more effective marketing strategies and improve customer service.
Using customer churn prediction models can also help companies reduce the cost of
customer acquisition and retention. This paper describes how to build a data analysis
model that predicts early subscribers leaving a telecommunications network using
different machine learning algorithms commonly used such as Random Forest, Support
Vector Machine, Artificial Neural Network, Logistics Regression, and K-Nearest
Neighbor. The dataset was also subjected to resampling techniques for dealing with
imbalanced data, such as Synthetic Minority Oversampling Technique Tomek Link
(SMOTE Tomek), Synthetic Minority Oversampling Technique ENN (SMOTE ENN)
and Adaptive Synthetic Sampling Approach for Imbalanced Learning (ADASYN).
Finally, the test set results have been evaluated using a confusion matrix and an AUC
curve. | en_US |
