Regression analysis based decision support system with relationship extraction

Abstract

Regression analysis is a widely used statistical technique for estimating the relationship between two variables. These two variables are called independent and dependent variables. The regression techniques are classified into two broad categories such as linear and logistic regression. Based on the input dataset, these two techniques are chosen and implemented. Many organizations and institutions are trying to use the decision support system for extracting the relationship between the employees’ salaries based on the target achieved and the years of experience. In this paper, the relationship extraction between two variables is analysed and studied. Based on the Experience, the salary of employees is predicted. Here the model extracts the relationship among the variables first, next to that forecasting of new observations is carried out. In this phased approach, the data pre-processing is carried out to clean the noise on the dataset. Followed by, fitting the model to train the train set and testing test. The third phase predicts the results based on the two variables to draw some observations. As a final step, visualization is employed on training and testing datasets. To implement this proposed work, the employee database from an organization is considered. This dataset contains 115 technical and non-technical staff details with their profile information.