In this project, I will use publicly available datasets, SQL databases, and Python to predict customer behavior and optimize profit. By calculating conversion rates for different customer segments, the insights gained will enhance ad campaign targeting and effectiveness. I will explore machine learning models such as Logistic Regression, Decision Trees, Random Forest, and K-Nearest Neighbors (KNN), investigating algorithm efficiency, the impact of combining models, and the challenges of overfitting, aiming to balance model complexity with predictive accuracy.