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Optimizing Ad Conversions with DS / Yael Kiselman (DigitalTurbine)
Optimize ad conversions using data science techniques like XGBoost, Optuna, and SHAP, and learn how to deploy models in production while addressing the cold start problem.
- Optimize Ad Conversions: Optimize ad conversions by predicting user behavior and tailoring models to individual users.
- Use Lag Features: Use lag features, such as time series data, to improve model performance and capture sequential dependencies.
- XGBoost: Use XGBoost, a powerful machine learning algorithm, to handle large datasets and optimize conversion rates.
- Optuna: Use Optuna, a Bayesian optimization library, to search for the best hyperparameters and optimize model performance.
- Pandas and Spark: Use Pandas and Spark for data processing and analysis, and leverage PySpark for big data processing.
- Grouping Users: Group users based on similarity or features to improve model performance and reduce data complexity.
- Lift and Conversion Rate: Measure lift and conversion rate to evaluate model performance and optimize ad campaigns.
- SHAP: Use SHAP, a machine learning explainability library, to interpret model predictions and understand feature importance.
- Data Engineering: Perform data engineering tasks, such as data preprocessing and feature engineering, to prepare data for modeling.
- Model Deployment: Deploy models in production and monitor performance to identify areas for improvement.
- Cold Start Problem: Address the cold start problem by using techniques such as preloading apps and using historical data to improve model performance.
- Real-time Processing: Leverage real-time processing capabilities to enable fast and efficient data processing and analysis.