Esplorazione delle feature

Usando lo stesso insieme di dati Avazu, esplorerai come sono distribuiti i valori di device_type e banner_pos, e come il CTR varia in base a questi.

Dati di esempio in forma di DataFrame sono caricati come df. Le variabili X e y che hai creato nell'esercizio precedente sono disponibili nel tuo workspace. pandas come pd è anch'esso disponibile nel tuo workspace.

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Prevedere il CTR con il Machine Learning in Python

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# Distribution of values for device type
print("Distribution of device type: ")
print(X.device_type.____()/len(X))

# Sample CTR by device type 
print("CTR by device type: ")
print(df.____('device_type')['click'].____/len(y))

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Prevedere il CTR con il Machine Learning in Python

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Chances are you’re on this page because you clicked a link. In this chapter, you’ll learn why click-through-rates (CTR) are integral to targeted advertising, how to perform basic DataFrame manipulation, and how you can use machine learning models to predict CTR.

Exercise 1: Introduzione ai click-through rate Exercise 2: Primi passi Exercise 3: Esplorazione delle feature

Esercizio in corso

Exercise 4: Prima valutazione dei dati Exercise 5: Panoramica dei modelli di Machine Learning Exercise 6: Regressione logistica per il tumore al seno Exercise 7: Regressione logistica per immagini Exercise 8: Un secondo modello di prova Exercise 9: Previsione del CTR con alberi decisionali Exercise 10: Implementazione del modello Exercise 11: Un primo modello di CTR Exercise 12: Oltre la sola accuratezza

This chapter provides the foundations for exploratory data analysis (EDA). Using sample data you’ll use the pandas library to look at columns and data types, explore missing data, and use hashing to perform feature engineering on categorical features. All of which are important when exploring features for more accurate CTR prediction.

Exercise 1: Exploratory data analysis Exercise 2: A first look Exercise 3: Checking for missing values Exercise 4: Distributions by CTR Exercise 5: Feature engineering Exercise 6: Analyzing datetime columns Exercise 7: Converting categorical variables Exercise 8: Creating new features Exercise 9: Standardizing features Exercise 10: Log normalization Exercise 11: Understanding standardization Exercise 12: Standard scaling

It’s time to dive deeper. Find out how you can use measures of model performance including precision and recall to answer real-world questions, such as evaluating ROI on ad spend. You’ll also learn ways to improve upon those evaluation metrics, such as ensemble methods and hyperparameter tuning.

Exercise 1: Applications of metric evaluation Exercise 2: Four categories of outcomes Exercise 3: Evaluating four categories Exercise 4: ROI on ad spend Exercise 5: Model evaluation Exercise 6: Precision and recall Exercise 7: Baseline Exercise 8: Classifier comparison Exercise 9: Tuning models Exercise 10: Regularization Exercise 11: Cross validation Exercise 12: Model selection Exercise 13: Ensembles and hyperparameter tuning Exercise 14: Understanding hyperparameter tuning Exercise 15: Random forests Exercise 16: Grid search

Profits can be heavily impacted by your campaign’s CTR. In this chapter, you’ll learn how deep learning can be used to reduce that risk. You’ll focus on multi-layer perceptron (MLP) and neural network models, and learn how these can be used to capture the complex relationship between variables to more accurately predict CTR. Lastly, you’ll explore how to apply the basics of hyperparameter tuning and regularization to classification models.

Exercise 1: Introduction to deep learning Exercise 2: Understanding MLPs Exercise 3: Beginning model Exercise 4: MLPs for CTR Exercise 5: Hyperparameter tuning in deep learning Exercise 6: Hyperparameter tuning in MLPs Exercise 7: Varying hyperparameters Exercise 8: MLP Grid Search Exercise 9: Model evaluation Exercise 10: F-beta score Exercise 11: Low precision and high AUC Exercise 12: Precision, ROI, and AUC Exercise 13: Model review and comparison Exercise 14: Model comparison warmup Exercise 15: Evaluating precision and ROI Exercise 16: Total scoring Exercise 17: Wrap-up video