Individuare i valori mancanti

Avere un riepilogo su quanta parte dei tuoi dati è mancante può essere utile, ma spesso avrai bisogno di trovare le posizioni esatte di questi valori mancanti. Usando lo stesso sottoinsieme dei dati di StackOverflow dell’esercizio precedente (sub_df), vedrai come contrassegnare un valore come mancante.

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Feature Engineering per il Machine Learning in Python

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Esercizio pratico interattivo

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# Print the top 10 entries of the DataFrame
print(sub_df.____)

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Questo esercizio fa parte del corso

Feature Engineering per il 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: Perché esistono i valori mancanti?Exercise 2: Quanto è sparso il mio insieme di dati?Exercise 3: Individuare i valori mancanti

Esercizio in corso

Exercise 4: Gestire i valori mancanti (I)Exercise 5: Eliminazione per lista (listwise deletion)Exercise 6: Sostituire i valori mancanti con costanti Exercise 7: Gestire i valori mancanti (II)Exercise 8: Compilare i valori mancanti continui Exercise 9: Imputazione dei valori nei modelli predittivi Exercise 10: Gestire altri problemi nei dati Exercise 11: Gestire i caratteri indesiderati (I)Exercise 12: Gestire i caratteri fuori posto (II)Exercise 13: Chaining dei metodi

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