This project explores the application of logistic regression to predict whether users will click on online advertisements based on demographic and behavioral data. Using a dataset containing information such as age, daily internet usage, income, and engagement metrics, we conducted extensive exploratory data analysis (EDA) to uncover key patterns and relationships. After cleaning and transforming the data, including feature engineering to extract temporal components, we built a logistic regression model to predict ad clicks. The model achieved a strong balance between precision and recall, indicating its effectiveness in identifying factors influencing user behavior. Key findings suggest that user age, daily internet usage, and time spent on site significantly impact the likelihood of clicking on ads. This analysis demonstrates the power of predictive modeling in digital marketing and highlights potential areas for future model enhancement using more advanced machine learning techniques.

bt

NESO public domain half-hour data