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ABSTRACT
Now a -days there are a lot of service providers are available in every business. There
is no shortage of customers in any options. Mainly, in the banking sector when want to
keep their money safely they have a lot of options. As a result, customer churn and
loyalty of customers have become a major problem for most banks. In this paper, a
method that predicts customer churn in banking using Machine learning with ANN.
This research promotes the exploration of the likelihood of churning by customer
loyalty.
The Random Forest, SVM, KNN, and Decision Tree Machine Learning algorithms are
used in this study. Keras and TensorFlow are ANN concepts that are also used in this
study. This study is done on a dataset called churn modeling. The dataset was
collected from Kaggle. The results are compared to find an appropriate model with
higher accuracy. As a result, the Random Forest algorithm achieved higher accuracy
than other algorithms. And accuracy was nearly 87%. so The least accuracy was
achieved by the Decision tree algorithm and it was 78.3% accuracy.
The number of service providers are being increasing very rapidly in every business.
In these days, there is no shortage of options for customers in the banking sector
when choosing where to put their money. As a result, customer churn and
engagement have become one of the top issues for most banks. In this project, a
method to predict the customer churn in a Bank, using machine learning techniques,
which is a branch of artificial intelligence is proposed. hence The research promotes the
exploration of the likelihood of churn by analyzing customer behavior. so Customer Churn
has become a major problem in all industries including the banking industry and
banks have always tried to track customer interaction so that they can detect the
customers who are likely to leave the bank.
keywords : churn prediction using machine learning.
