Metnini temizleme

Yapılandırılmamış metin verileri çoğu analizde doğrudan kullanılamaz. Serbest biçimli uzun bir metinden, bir Machine Learning modelinin alabileceği doğru biçimde sayısal sütunlar kümesine geçmek için birden fazla adım gerekir. Bu sürecin ilk adımı, veriyi standartlaştırmak ve analiz hattında ileride sorun çıkarabilecek karakterleri temizlemektir.

Bu bölümde, Amerika Birleşik Devletleri başkanlarının göreve başlama konuşmalarını içeren ve speech_df olarak yüklenen yeni bir veri kümesiyle çalışacaksın; konuşmalar text sütununda saklanıyor.

Bu egzersiz

Python ile Machine Learning için Özellik Mühendisliği

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Print the first 5 rows of the text column
print(____)

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Machine Learning için Özellik Mühendisliği

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: 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: Metni kodlama Exercise 2: Metnini temizleme

Geçerli Egzersiz

Exercise 3: Yüksek düzey metin özellikleri Exercise 4: Kelime sayımları Exercise 5: Kelimeleri sayma (I)Exercise 6: Kelimeleri sayma (II)Exercise 7: Özelliklerini sınırlama Exercise 8: Metinden DataFrame'e Exercise 9: Terim sıklığı - ters belge sıklığı Exercise 10: Tf-idf Exercise 11: Tf-idf değerlerini inceleme Exercise 12: Görülmemiş veriyi dönüştürme Exercise 13: N-gramlar Exercise 14: Daha uzun n-gram'lar kullanma Exercise 15: En yaygın sözcükleri bulma Exercise 16: Kapanış