High Impact Factor : 4.396 icon | Submit Manuscript Online icon |

Application of Classification Algorithms on Predicting the Outcome of Bank Marketing


Aarya Vardhan Reddy Paakaala , MVSR Engineering College; Sai Gautam Mandapati, KMIT


Classification, Bank Marketing Dataset, K-NN Algorithm, Naive Bayes, Logistic Regression, Decision Tree, Random Forest


In the current world, direct marketing has become an effective tool to promote the products and services directly to the customers who are interested in them. Consequently, it has generated a widespread popularity among enterprises to research about it comprehensively. There are numerous factors which affect the demand for a product or a service. Several factors include the changes in customer's preferences, technological advancements, and price and income levels. Our objective is to provide how classification algorithms could be used to predict the outcome of a direct bank marketing strategy which would help the enterprises understand the mindset of the customers. We mainly focus on the K-NN classifier and Naive Bayes classifier and divert into other classification algorithms such as logistic regression, decision tree, and random forest. Several parameters are given as an input for the classification algorithms to get a detailed overview and accurate values of the predictions.

Other Details

Paper ID: IJSRDV6I60242
Published in: Volume : 6, Issue : 6
Publication Date: 01/09/2018
Page(s): 320-323

Article Preview

Download Article