Kapan Anda tidak perlu mentransformasikan data Anda?

Meskipun memastikan semua data berada pada skala yang sama dianjurkan untuk sebagian besar analisis, pada model Machine Learning berikut, normalisasi data tidak selalu diperlukan?

Latihan ini merupakan bagian dari kursus

Rekayasa Fitur untuk Machine Learning di Python

Latihan interaktif langsung

Ubah teori menjadi aksi dengan salah satu latihan interaktif kami

Latihan ini merupakan bagian dari kursus

Rekayasa Fitur untuk Machine Learning di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: Distribusi data Exercise 2: Seperti apa data Anda? (I)Exercise 3: Seperti apa data Anda? (II)Exercise 4: Kapan Anda tidak perlu mentransformasikan data Anda?

Latihan Saat Ini

Exercise 5: Penskalaan dan transformasi Exercise 6: Normalisasi Exercise 7: Standardization Exercise 8: Transformasi log Exercise 9: Kapan Anda dapat menggunakan normalisasi?Exercise 10: Menghapus pencilan Exercise 11: Penghapusan pencilan berbasis persentase Exercise 12: Penghapusan pencilan secara statistik Exercise 13: Menskalakan dan mentransformasikan data baru Exercise 14: Transformasi data train dan test (I)Exercise 15: Transformasi pelatihan dan pengujian (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