Banking Sales Services Optimization

The project aims to optimize banking sales services using machine learning and deep learning models. Three focus areas were used to optimize the bank´s services. First is to predict the estimated salary of bank customers and offer them better services corresponding to their income. The second area is to predict customers who might leave the bank and focus more on them so that they will keep the bank´s services. The third area of research is to find a group of similar customers and classify them as valuable/non-valuable and loyal/non-loyal so that the bank can focus on valuable and loyal customers. To achieve this research result, different regression models such as Decision Tree Regression, Random Forest Regression, Polynomial and Support Vector Regression were used to predict the estimated salary of the customers. Several classification models such as Logistic, K-NN, Naive Bayes, Decision Trees Classifier, Random Forest Classifier, and SVM were used to predict the churn rate i.e. customers who might leave the bank. Later XGBoost model was used for better performance and faster execution to predict churn rate. A deep learning model - Artificial Neural Network has been used for better accuracy. To find a similar group of customers, unsupervised machine learning techniques such as K-Means and Hierarchical clustering were used. Several different evaluation techniques such as confusion matrix, R-Squared, Adjusted R-Squared and SSE were used to assess the performance of the models. K Cross-validation was used to confirm whether a model is over fitting or under fitting.