Specifieke datatypes selecteren

Vaak bevat een gegevensset kolommen met verschillende datatypes (zoals de set waar je nu mee werkt). De meeste Machine Learning-modellen vereisen dat je consistente datatypes over je features heen gebruikt. Ook zijn de meeste feature engineering-technieken maar op één type data tegelijk toepasbaar. Daarom wil je in een DataFrame vaak alleen de kolommen van bepaalde typen kunnen selecteren.

De DataFrame (so_survey_df) uit de vorige oefening is beschikbaar in je werkruimte.

Deze oefening maakt deel uit van de cursus

Feature engineering voor Machine Learning in Python

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Oefeninstructies

Maak een subset van so_survey_df die alleen de numerieke (int en float) kolommen bevat.
Print de kolomnamen in so_survey_df_num.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create subset of only the numeric columns
so_numeric_df = so_survey_df.____(____=[____])

# Print the column names contained in so_survey_df_num
print(so_numeric_df.____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Feature engineering voor Machine Learning in Python

SkillTag.level.intermediateSkillTag.label

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In this chapter, you will explore what feature engineering is and how to get started with applying it to real-world data. You will load, explore and visualize a survey response dataset, and in doing so you will learn about its underlying data types and why they have an influence on how you should engineer your features. Using the pandas package you will create new features from both categorical and continuous columns.

Exercise 1: Waarom kenmerken genereren?Exercise 2: Je data leren kennen Exercise 3: Specifieke datatypes selecteren

Huidige oefening

Exercise 4: Werken met categorische features Exercise 5: One-hot encoding en dummyvariabelen Exercise 6: Omgaan met ongebruikelijke categorieën Exercise 7: Numerieke variabelen Exercise 8: Kolommen binariseren Exercise 9: Waarden indelen in bins

This chapter introduces you to the reality of messy and incomplete data. You will learn how to find where your data has missing values and explore multiple approaches on how to deal with them. You will also use string manipulation techniques to deal with unwanted characters in your dataset.

Exercise 1: Why do missing values exist?Exercise 2: How sparse is my data?Exercise 3: Finding the missing values Exercise 4: Dealing with missing values (I)Exercise 5: Listwise deletion Exercise 6: Replacing missing values with constants Exercise 7: Dealing with missing values (II)Exercise 8: Filling continuous missing values Exercise 9: Imputing values in predictive models Exercise 10: Dealing with other data issues Exercise 11: Dealing with stray characters (I)Exercise 12: Dealing with stray characters (II)Exercise 13: Method chaining

In this chapter, you will focus on analyzing the underlying distribution of your data and whether it will impact your machine learning pipeline. You will learn how to deal with skewed data and situations where outliers may be negatively impacting your analysis.

Exercise 1: Data distributions Exercise 2: What does your data look like? (I)Exercise 3: What does your data look like? (II)Exercise 4: When don't you have to transform your data?Exercise 5: Scaling and transformations Exercise 6: Normalization Exercise 7: Standardization Exercise 8: Log transformation Exercise 9: When can you use normalization?Exercise 10: Removing outliers Exercise 11: Percentage based outlier removal Exercise 12: Statistical outlier removal Exercise 13: Scaling and transforming new data Exercise 14: Train and testing transformations (I)Exercise 15: Train and testing transformations (II)

Finally, in this chapter, you will work with unstructured text data, understanding ways in which you can engineer columnar features out of a text corpus. You will compare how different approaches may impact how much context is being extracted from a text, and how to balance the need for context, without too many features being created.

Exercise 1: Encoding text Exercise 2: Cleaning up your text Exercise 3: High level text features Exercise 4: Word counts Exercise 5: Counting words (I)Exercise 6: Counting words (II)Exercise 7: Limiting your features Exercise 8: Text to DataFrame Exercise 9: Term frequency-inverse document frequency Exercise 10: Tf-idf Exercise 11: Inspecting Tf-idf values Exercise 12: Transforming unseen data Exercise 13: N-grams Exercise 14: Using longer n-grams Exercise 15: Finding the most common words Exercise 16: Wrap-up