Exploratory Data Analysis and Visualization for Business Analytics

Chunhua Deming; Sreekanth Dekkati; Harshith Desamsetti

doi:10.18034/ajase.v7i1.53

Authors

Chunhua Deming National University of Singapore, Singapore
Sreekanth Dekkati System Administrator, MUFG Bank, Arizona, USA
Harshith Desamsetti School of Computer Science, Northern Illinois University, USA

Keywords:

Data Analysis

Business Analytics

Data Visualization

EDA

Abstract

The advantages of doing exploratory data analysis have been discussed in this article. We have gone through the typical data preprocessing procedures to understand the data and prepare it for modeling. In this research, we intend to become familiar with the most extensively utilized predictive modeling techniques and the fundamental principles underlying these techniques. Developing statistical or machine-learning models to generate predictions based on data is an example of predictive modeling, which is creating these models. We will use one typical example to make our research more tangible and demonstrate how the performance of various models varies when applied to the same data set. Exploratory data analysis will be covered in depth here, so prepare for that! In the following, we will discuss comprehending and validating the data.

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