Ontbrekende waarden identificeren

Verloop is een belangrijk issue voor bedrijven: als een medewerker vertrekt, kost het niet alleen geld om iemand nieuw te werven en op te leiden, maar gaat er ook veel impliciete kennis en cultuur verloren die lastig te herstellen is.

De gegevensset attrition bevat informatie over personeelsverloop, zoals Age, WorkLifeBalance, DistanceFromHome, StockOptionLevel en nog 27 andere variabelen. Voordat je verdergaat met je analyse, wil je eventuele ontbrekende waarden opsporen.

Het pakket naniar en de gegevensset attrition zijn al voor je geladen.

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Feature engineering in R

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# Explore missing data on the attrition dataset
___(attrition)

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Deze oefening maakt deel uit van de cursus

Feature engineering in R

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Begin de cursus gratis

Raw data does not always come in its best shape for analysis. In this opening chapter, you will get a first look at how to transform and create features that enhance your model's performance and interpretability.

Exercise 1: Wat is feature engineering?Exercise 2: Een voorlopig model Exercise 3: Handmatig een feature engineeren Exercise 4: Nieuwe features maken met domeinkennis Exercise 5: Je gegevens klaarmaken voor analyse Exercise 6: Een workflow bouwen Exercise 7: De informatie-inhoud van ruwe data vergroten Exercise 8: Ontbrekende waarden identificeren

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Exercise 9: Missende waarden imputeren en dummyvariabelen maken Exercise 10: Het model fitten en beoordelen Exercise 11: Hotelboekingen voorspellen

In this chapter, you’ll learn that, beyond manually transforming features, you can leverage tools from the tidyverse to engineer new variables programmatically. You’ll explore how this approach improves your models' reproducibility and is especially useful when handling datasets with many features.

Exercise 1: Why transform existing features?Exercise 2: Glancing at your data Exercise 3: Normalizing and log-transforming Exercise 4: Fit and augment Exercise 5: Customize your model assessment Exercise 6: Common feature transformations Exercise 7: Common transformations Exercise 8: Plain recipe Exercise 9: Box-Cox transformation Exercise 10: Yeo-Johnson transformation Exercise 11: Advanced transformations Exercise 12: Baseline Exercise 13: step_poly()Exercise 14: step_percentile()Exercise 15: Who's staying?

You’ll now learn how models often benefit from reducing dimensionality and extracting features from high-dimensional data, including converting text data into numeric values, encoding categorical data, and ranking the predictive power of variables. You’ll explore methods including principal component analysis, kernel principal component analysis, numerical extraction from text, categorical encodings, and variable importance scores.

Exercise 1: Reducing dimensionality Exercise 2: Prepping the stage Exercise 3: Digging into the structure Exercise 4: Percent of variance explained Exercise 5: Visualizing variance explained Exercise 6: Feature hashing Exercise 7: Investigating education field Exercise 8: Into the matrix Exercise 9: Exploring the hashing Exercise 10: Visualizing the hashing Exercise 11: Encoding categorical data using supervised learning Exercise 12: Setting up your workflow Exercise 13: Fitting, augmenting, and assessing Exercise 14: Binding models together Exercise 15: Variable Importance Exercise 16: Create a workflow Exercise 17: Fit and augment Exercise 18: Which is the main predictor?

You’ll wrap up the course by learning about feature engineering and machine learning techniques. You’ll begin by focusing on the problems associated with using all available features in a model and the importance of identifying irrelevant and redundant features and learning to remove these features using embedded methods such as lasso and elastic-net. Next, you’ll explore shrinkage methods such as lasso, ridge, and elastic-net, which can be used to regularize feature weights or select features by setting coefficients to zero. Finally, you’ll finish by focusing on creating an end-to-end feature engineering workflow and reviewing and practicing the previously learned concepts and functions in a small project.

Exercise 1: Reducing the model's features Exercise 2: Sifting through variable importance Exercise 3: Assessing model performance using all available predictors Exercise 4: Building a reduced model Exercise 5: Shrinkage methods Exercise 6: Manual regularization with Lasso Exercise 7: Tuning the penalty Exercise 8: Finalizing the model Exercise 9: Putting it all together Exercise 10: Prep and split Exercise 11: Preprocess Exercise 12: Model Exercise 13: Assess Exercise 14: Congratulations!