This repository aims to analyse user behaviour and user information to predict whether a user would press on an add presented infront of them, using Machine Learning.

This repository is dedicated to the comprehensive analysis of user behavior and user information with the primary objective of predicting the likelihood of a user clicking on an advertisement presented before them. Leveraging a Logistic Regression Machine Learning model, this endeavor seeks to discern patterns and correlations within the dataset to make informed predictions regarding user engagement with advertisements.

For additional details and comprehensive documentation, refer to the accompanying Jupyter notebook, where further insights, methodologies, and results are elucidated. The notebook serves as a repository of knowledge, encapsulating the intricacies of the analysis conducted and the rationale behind employing Logistic Regression as the chosen machine learning model.