Predictive Modeling and Analysis of Logistic Regression and k-Nearest Neighbor for Personal Loan Campaign

Science is the knowledge which a person understand and that can be taught to a computer. Extensive research has been made to develop appropriate machine learning algorithms for different classification or function approximation problems. Some of the machine learning methods depends on the characteristics of the data set and the requirements of the business domain. This case study provides the predictive performance of different classification methods for identifying the potential customers who have a higher probability of purchasing the loan. For this we will build two statistical model, a logistic regression model and a k-nearest neighbor model. The model is a way of showing the relationships between various factors in the real world and in the data set or raw data. It is also important to select the technique which performs best on the data set. This study works on the following two techniques to build the model and provides a guideline for similar comparison studies and to find the best one out.