Missende waarden vervangen door constanten

Hoewel het volledig verwijderen van missende data in veel situaties een juiste aanpak kan zijn, kan dit ertoe leiden dat er veel informatie uit je modellen verdwijnt.

Je kunt categorische kolommen tegenkomen waarbij een missende waarde op zichzelf al betekenisvol is, zoals iemand die in een enquête een vraag niet wil beantwoorden. In dit soort gevallen kun je alle missende waarden opvullen met een geheel nieuwe categorie, bijvoorbeeld 'Geen antwoord gegeven'.

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Feature engineering voor Machine Learning in Python

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# Print the count of occurrences
print(so_survey_df['Gender']____)

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Feature engineering voor Machine Learning in Python

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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: Why generate features?Exercise 2: Getting to know your data Exercise 3: Selecting specific data types Exercise 4: Dealing with categorical features Exercise 5: One-hot encoding and dummy variables Exercise 6: Dealing with uncommon categories Exercise 7: Numeric variables Exercise 8: Binarizing columns Exercise 9: Binning values

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: Waarom bestaan ontbrekende waarden?Exercise 2: Hoe schaars is mijn data?Exercise 3: Ontbrekende waarden vinden Exercise 4: Omgaan met ontbrekende waarden (I)Exercise 5: Listwise deletion Exercise 6: Missende waarden vervangen door constanten

Huidige oefening

Exercise 7: Omgaan met missende waarden (II)Exercise 8: Ontbrekende continue waarden invullen Exercise 9: Waarden imputeren in voorspellende modellen Exercise 10: Omgaan met andere databronproblemen Exercise 11: Omgaan met losse tekens (I)Exercise 12: Omgaan met losse tekens (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