Predictive analytics for banking user data using AWS Machine Learning cloud service

Abstract

The aim of the project is to develop a Machine Learning model to perform predictive analytics on the banking dataset. The banking data set consists of details about customers like and whether the customer will buy a product provided by the bank or not. The data set is obtained from University of California Irvine Machine Learning Repository. This data set is used to create a binary classification model using Amazon Web Service(AWS) Machine Learning platform. 70 % of the data is used to train the binary classification model and 30 % of the dataset is used to test the model. Depending upon the test result we evaluate the essential parameters like precision, recall, accuracy and false positive rates. These parameters evaluate the efficiency of our model. Once we design our model we test our model using two features in AWS Machine learning. One, using real time prediction where we give real time input data and test our model. Two, we do batch prediction, where we have a set of customer data and we upload our data to evaluate our prediction.