Predictive and Analytics using Data Mining and Machin Learning for Customer Churn Prediction

This research aims to predict and analyze customer churn using Data Mining and Machine Learning methods. The background of this research
is based on the importance of understanding the factors that influence customer decisions to churn, as well as improving the effectiveness of
customer retention strategies in a business context. The method used in this research involves the use of a customer bank dataset that includes
information about customers who left in the past month, services registered by customers, customer account information, and demographic info
about customers. The factors most influential to churn were identified through heatmap analysis, including MonthlyCharges, PaperlessBilling,
SeniorCitizen, PaymentMethod, MultipleLines, and PhoneService. This research compares the performance of several machine learning
algorithms, including Random Forest, Logistic Regression, Adaboost, and Extreme Gradient Boosting (XGBoost), to predict customer churn.
Accuracy metrics and confusion matrix results are used to evaluate the performance of these algorithms. The results showed that XGBoost proved
to be the best algorithm in predicting customer churn with high accuracy. The factors that have been correctly identified do not provide missed
precision, showing a significant influence on customer churn decisions. The novelty and uniqueness of this research lies in focusing on the factors
that have the most influence on customer churn and comparing the performance of machine learning algorithms. This research provides more
specific and relevant insights for companies in developing effective customer retention strategies. However, this research has some limitations.
One of them is the use of a dataset limited to a customer bank, so the generalizability of the findings of this research may be limited to that
business context. In addition, other factors that are not the focus of this research may also contribute to the prediction of customer churn.