Prediction of Ads Click through rate using Machine Learning

TITLE	Prediction of Ads Click through rate using Machine Learning
ABSTRACT	Although online advertising has revolutionized the way companies interact with their customers, not every impression results in a click. For marketing techniques to be optimized, it is crucial to predict whether a person would click on an advertisement. This project creates a web-based machine learning model to forecast ad click-through behavior by utilizing Random Forest and Logistic Regression. Age, daily internet usage, area income, and amount of time spent on the website are among the demographic and behavioral data used to train the model. A straightforward web interface may be used to access the model, which is hosted on Flask. Histograms, heatmaps, pairplots, and income- age comparisons are used to depict key findings and show significant click-through trends.
AUTHOR	Anas Adnan Ali, Dr. Sunitha G P
PUBLICATION DATE	2025-08-28 21:03:14
VOLUME	13
ISSUE	3
DOI	DOI: 10.15662/IJMSERH.2025.1303059
PDF	pdf/2025/7/59_Prediction of Ads Click through rate using Machine Learning.pdf
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