A Comparative Analysis of Logistic Regression , SVM, and Hyperparameter Tuning Algorithms for Predictive Modeling in Social Network Advertisement Effectiveness

Neelima Sheikh; Mahadev Bag

doi:10.59367/7psmwx26

Authors

Neelima Sheikh RITEE, Chatona, Raipur (C.G.)
Mahadev Bag RITEE, Chatona, Raipur (C.G.)

DOI:

https://doi.org/10.59367/7psmwx26

Keywords:

Social network advertising, Predictive modeling, Logistic Regression, SVM, Hyperparameter tuning, Grid Search CV

Abstract

This paper presents a comparative study of three popular machine learning algorithms, namely Logistic Regression, SVM and Gradient Search Cross Validation (GridSearchCV), for predictive modeling in the context of social network advertisement effectiveness. The study evaluates the performance of these algorithms in predicting whether a user will make a purchase based on their age, salary, and previous purchase behavior. Additionally, the paper explores the impact of hyperparameter tuning on the predictive accuracy of these models. The experimental results demonstrate the effectiveness of each algorithm and provide insights into the optimal configuration for achieving the highest prediction accuracy.

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