Predicting User Ad Clicks Using Machine Learning 📊🧠
Author: Swapnil Herwadkar
Tech Stack: Python, Scikit-Learn, XGBoost, SMOTE, Pandas, Matplotlib
🔍 Overview This project presents a machine learning pipeline for predicting whether a user will click on an online ad, based on behavioral and demographic data. By applying robust preprocessing, handling class imbalance with SMOTE, and optimizing models like XGBoost, this project aims to improve digital ad targeting and maximize click-through rates.
🎯 Objectives Predict ad click-through behavior using user features and ad context.
Handle severe class imbalance to better identify the minority (clicked) class.
Engineer interaction features to enhance model performance.
Evaluate and compare model performance using classification metrics.
🛠️ Methods & Techniques
- 🧹 Data Preprocessing Dropped irrelevant features (e.g., ID, full name).
One-hot encoding for categorical variables (e.g., device type, ad position).
Created interaction terms to capture contextual behavior (e.g., device_type × time_of_day).
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⚖️ Class Imbalance Handling Applied SMOTE (Synthetic Minority Over-sampling Technique) to balance the dataset and improve recall for rare events (ad clicks).
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🤖 Models Used Logistic Regression (baseline)
Random Forest (ensemble model)
XGBoost (best performer after hyperparameter tuning)
- 🎯 Evaluation Metrics Accuracy, Precision, Recall
Confusion Matrix
ROC-AUC Curve
📈 Key Results XGBoost achieved the highest performance post-SMOTE and hyperparameter tuning.
Visualization of class balance before/after SMOTE showed significant improvements in model learning.
Feature interactions (e.g., ad_position × browsing_history) played a key role in performance gains.
📌 Visualizations Bar plots for performance metrics
Confusion matrices per model
ROC curves comparing classifiers
Class distribution before and after SMOTE
💡 Insights & Recommendations Time of Day, Device Type, and Ad Position are highly predictive features.
SMOTE was critical in overcoming class imbalance and improving minority class recall.
This framework can be deployed in real-time ad platforms to dynamically adjust ad placement and content.
🚀 Future Improvements Incorporate time zone and session-based behavior.
Deploy as a REST API for real-time predictions.
Experiment with deep learning models for higher granularity.