A machine learning mobile ad monetization optimizer.
- A data product that predicts the mobile ad revenue prior to receiving the ad from ad networks.
- Using Ad Vantage, mobile apps or ad exchanges can optimize revenue.
- Advertisers can better understand their targeting demographics, publisher content relevance and user behavior.
- Prediction accuracy: 79%.
- Random Forest ROC Plot AUC: 0.85.
- Logistic Regression ROC Plot AUC: 0.76.
- Naive Bayes Gaussian AUC: 0.60.
- Naive Bayes Multinomial AUC: 0.73.
- Cross-Validation F1 Score Random Forest: 0.89.
- Revenue Lift from "Random" baseline: 109%.
Logs of raw ad tags received from ad networks to classify five targeted revenue buckets.
- The Classifiers were Random Forest, Logistic Regression (One-vs-All), Naive Bayes (One-vs-All).
- Tuned Classifiers with Grid Search, conducted Feature Engineering and cross-validation.
- A/B testing to verify revenue lift; or lift chart.
- Feature extraction, create device user session feature, behavior pattern.
- Core revenue group: top 20% apps, devices and locations.
- Train on more time-consuming models to improve accuracy.
- Build scalability.
- Code files at:
project_code - Project Presentation:
Jun Zhang presso mobile ads.pdf - Some Visualization and more project details to come at:
http://junkateannzhang.herokuapp.com/