摘要：Though banks hold an abundance of data on their customers in general, it is not unusual for them
to track the actions of the creditors regularly to improve the services they offer to them and understand why
a lot of them choose to exit and shift to other banks. Analyzing customer behavior can be highly beneficial
to the banks as they can reach out to their customers on a personal level and develop a business model that
will improve the pricing structure, communication, advertising, and benefits for their customers and
themselves. Features like the amount a customer credits every month, his salary per annum, the gender of
the customer, etc. are used to classify them using machine learning algorithms like K Neighbors Classifier
and Random Forest Classifier. On classifying the customers, banks can get an idea of who will be
continuing with them and who will be leaving them in the near future. Our study determines to remove the
features that are independent but are not influential to determine the status of the customers in the future
without the loss of accuracy and to improve the model to see if this will also increase the accuracy of the
results.
其他摘要：Though banks hold an abundance of data on their customers in general, it is not unusual for them to track the actions of the creditors regularly to improve the services they offer to them and understand why a lot of them choose to exit and shift to other banks. Analyzing customer behavior can be highly beneficial to the banks as they can reach out to their customers on a personal level and develop a business model that will improve the pricing structure, communication, advertising, and benefits for their customers and themselves. Features like the amount a customer credits every month, his salary per annum, the gender of the customer, etc. are used to classify them using machine learning algorithms like K Neighbors Classifier and Random Forest Classifier. On classifying the customers, banks can get an idea of who will be continuing with them and who will be leaving them in the near future. Our study determines to remove the features that are independent but are not influential to determine the status of the customers in the future without the loss of accuracy and to improve the model to see if this will also increase the accuracy of the results.
