This paper is published in Volume-5, Issue-3, 2019
Area
Computer Science Engineering, Machine Learning
Author
Poulomi Saha
Org/Univ
Independent Researcher, India
Pub. Date
21 June, 2019
Paper ID
V5I3-1966
Publisher
Keywords
Classification, Decision Tree, K-Nearest Neighbor (K-NN), Logistic regression, Machine learning, Naive bayes, Random forest, Support Vector Machine (SVM)

Citationsacebook

IEEE
Poulomi Saha. Performance analysis of the Machine Learning Classifiers to predict the behaviour of the customers, when a new product is launched in the market, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Poulomi Saha (2019). Performance analysis of the Machine Learning Classifiers to predict the behaviour of the customers, when a new product is launched in the market. International Journal of Advance Research, Ideas and Innovations in Technology, 5(3) www.IJARIIT.com.

MLA
Poulomi Saha. "Performance analysis of the Machine Learning Classifiers to predict the behaviour of the customers, when a new product is launched in the market." International Journal of Advance Research, Ideas and Innovations in Technology 5.3 (2019). www.IJARIIT.com.

Abstract

Here in this study, I will analyze the correlation of the buyers with their age and salary using various machine learning classifier algorithms. This study will predict, who will buy a new item faster as soon as it is launched in the market and how it will be related to the age and salary of the people, who are buying it. The aim of this study is to investigate six different types of Machine Learning, Classifier algorithms (namely Logistic Regression, SVM, Naive Bayes, KNN,Decision Tree, Random Forest and to show their comparative analysis and to predict whether a person will buy a certain product as soon as it is launched in the market. Experiments are performed on the Social_Network_Ads data set which is sourced from Kaggle the online community of data scientists and machine learning engineers. The performance of all the above algorithms is evaluated on the various metrics like recall, precision, F1_score and confusion matrix. Results are then compared.
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