Mengimputasi nilai dalam model prediktif

Saat bekerja dengan model prediktif, Anda sering memiliki DataFrame train dan test yang terpisah. Dalam kasus seperti ini, Anda perlu memastikan tidak ada informasi dari test set yang bocor ke train set. Saat mengisi nilai yang hilang pada data yang akan digunakan dalam situasi ini, bagaimana sebaiknya Anda menangani kedua himpunan data tersebut?

Latihan ini adalah bagian dari kursus

Rekayasa Fitur untuk Machine Learning di Python

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Mulai berolahraga

Latihan ini adalah 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: Mengapa nilai yang hilang ada?Exercise 2: Seberapa jarang data saya?Exercise 3: Menemukan nilai yang hilang Exercise 4: Menangani nilai yang hilang (I)Exercise 5: Penghapusan secara keseluruhan (listwise deletion)Exercise 6: Mengganti nilai hilang dengan konstanta Exercise 7: Menangani nilai hilang (II)Exercise 8: Mengisi nilai hilang kontinu Exercise 9: Mengimputasi nilai dalam model prediktif

Latihan Saat Ini

Exercise 10: Menangani masalah data lainnya Exercise 11: Menangani karakter tak diinginkan (I)Exercise 12: Menangani karakter liar (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