Creating new features

Feature engineering includes also the actual creation of new features. Creating new features is important since the model relies on such features for prediction accuracy. In this exercise, you will check on properties of three columns which appear as integers in the data but represent categorical values. These three columns are: search_engine_type, product_type, and advertiser_type. You will create count features for those 3 columns, as well as device_id and site_id. These count features represent the number of clicks for each of those columns and will be used later on for prediction.

The pandas module is available as pd in your workspace and the sample DataFrame is loaded as df.

This exercise is part of the course

Predicting CTR with Machine Learning in Python

Exercise instructions

Print the total number of values and the number of unique values for each feature in the feature_list list.
Create new features from the features in new_feature_list by counting the number of clicks for each feature using .transform().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Get counts of total and unique values for given features
feature_list = ["search_engine_type", "product_type", "advertiser_type"]
for feature in feature_list:
	print(df[feature].____)
	print(df[feature].____)

# Define new features as counts
new_feature_list = ['device_id', 'site_id'] + feature_list
for new_feature in new_feature_list:
  df[new_feature + '_count'] = df.____(
    new_feature)['click'].____("count")
print(df.head(5))

Edit and Run Code

This exercise is part of the course

Predicting CTR with Machine Learning in Python

IntermediateSkill Level

5.0+

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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: Introduction to click-through rates Exercise 2: Beginning steps Exercise 3: Feature exploration Exercise 4: First evaluation of data Exercise 5: Overview of machine learning models Exercise 6: Logistic regression for breast cancer Exercise 7: Logistic regression for images Exercise 8: A second toy model Exercise 9: CTR prediction using decision trees Exercise 10: Model implementation Exercise 11: A first CTR model Exercise 12: Beyond only accuracy

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

Current Exercise

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