Click-Through Rate (CTR) Prediction
Machine Learning for Digital Advertising
Predicting ad click-through behavior from large-scale user interaction data
I built machine learning models to predict whether a user would click on an advertisement using behavioral and contextual features from millions of ad interactions. The project focused on feature engineering, model comparison, and improving performance on an imbalanced classification problem.
Role
Machine Learning Researcher
Domain
Digital Advertising · Marketing Analytics
Stack
Python · CatBoost · Logistic Regression
Methods
Classification · Imbalanced Learning
Project Snapshot
Problem
Most users do not click on ads, which creates a highly imbalanced classification problem where accuracy alone can be misleading.
Solution
I engineered behavioral features and compared logistic regression with gradient-boosted models to improve click prediction.
Outcome
Improved ROC-AUC from 0.71 to 0.81 using feature engineering and class imbalance strategies.
Overview
Click-through rate prediction is a core problem in digital advertising because even small improvements in prediction quality can improve ad targeting, budget allocation, and campaign performance.
This project used large-scale advertising interaction data to predict whether an impression would result in a click. Because click events were rare, the main challenge was not just building a classifier, but building one that could learn meaningful signals from an imbalanced dataset.
The Challenge
Class imbalance
Click events were much less common than non-click events, making standard accuracy a poor measure of model quality.
Large-scale data
The dataset contained millions of ad interactions, requiring efficient preprocessing and modeling workflows.
Feature engineering
Raw interaction data needed to be transformed into useful behavioral and contextual predictors.
Model evaluation
The project required metrics such as ROC-AUC, recall, precision, and F1 rather than relying only on accuracy.
Modeling Pipeline
Data Ingestion
Loaded large-scale advertising interaction data.
Preprocessing
Cleaned variables, handled missing values, and prepared categorical features.
Feature Engineering
Created behavioral and contextual predictors from user interaction patterns.
Imbalance Strategy
Applied class imbalance techniques to improve rare-click prediction.
Model Training
Compared logistic regression and CatBoost classification models.
Evaluation
Evaluated models using ROC-AUC, recall, precision, and F1.
End-to-end classification workflow for predicting rare ad click events.
Technical Stack
- Python
- Pandas
- Scikit-learn
- CatBoost
- Logistic Regression
- Feature Engineering
- Classification
- Imbalanced Data
- ROC-AUC
- F1 Score
What I Built
Feature Engineering Pipeline
Created behavioral predictors from ad interaction data to capture patterns related to user engagement and click likelihood.
Baseline Model
Built a logistic regression model to establish an interpretable benchmark for click prediction.
Gradient-Boosted Model
Trained a CatBoost classifier to better capture nonlinear relationships and categorical feature interactions.
Evaluation Framework
Compared models using metrics appropriate for imbalanced classification, including ROC-AUC, recall, precision, and F1.
Key Results
Final ROC-AUC after feature engineering and imbalance handling
ROC-AUC improvement from 0.71 to 0.81
Ad interactions modeled
What I Learned
This project taught me that model performance in imbalanced classification depends heavily on how the problem is framed. A model can appear accurate while failing to identify the minority class, so metrics like recall, F1, and ROC-AUC are essential for evaluating real performance.
I also learned how much feature engineering matters in advertising prediction. CatBoost improved performance because it could capture nonlinear relationships and categorical interactions that simpler models missed.
Full Technical Report
The complete report includes the modeling workflow, feature engineering process, class imbalance strategy, and model evaluation results.