Comparative Analysis of Classification Models on Income Prediction

Abstract

Predictive Analytics is the underlying technology that can simply be described as an approach to scientifically utilize the past to predict the future to help coveted results. It is the branch of cutting edge analytics which is utilized to make predictions about unfamiliar events. Predictive analytics utilizes different procedures from information mining, insights, modeling, machine learning and artificial Intelligence. It includes extraction of data from information and is utilized to predict patterns and behavior patterns. It can be connected to an unfamiliar event or interest whether past, present or future. It helps being used of statistical algorithms information and machine learning strategies to distinguish the probability of future results in light of chronicled information. Income Determination is an important application of predictive analytics where customer segmentation takes place based on different demographical data. In this paper, we attempt to identify this purpose with a novel approach using different classification techniques to minimize the risk and cost involved to predict certain income levels. Here we have demonstrated the performance of each algorithm particularly on identification of customers using classification techniques. In addition, we provide an investigation analysis on true positives, false negatives, scored labels and scored probabilities.