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.

Why generate features?

Getting to know your data

Selecting specific data types

Dealing with categorical features

One-hot encoding and dummy variables

Dealing with uncommon categories

Numeric variables

Binarizing columns

Binning values

Creating Features

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.

Mengapa nilai yang hilang ada?

Seberapa jarang data saya?

Menemukan nilai yang hilang

Menangani nilai yang hilang (I)

Penghapusan secara keseluruhan (listwise deletion)

Mengganti nilai hilang dengan konstanta

Menangani nilai hilang (II)

Mengisi nilai hilang kontinu

Mengimputasi nilai dalam model prediktif

Menangani masalah data lainnya

Menangani karakter tak diinginkan (I)

Menangani karakter liar (II)

Method chaining

Dealing with Messy Data

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.

Data distributions

What does your data look like? (I)

What does your data look like? (II)

When don't you have to transform your data?

Scaling and transformations

Normalization

Standardization

Log transformation

When can you use normalization?

Removing outliers

Percentage based outlier removal

Statistical outlier removal

Scaling and transforming new data

Train and testing transformations (I)

Train and testing transformations (II)

Conforming to Statistical Assumptions

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.

Encoding text

Cleaning up your text

High level text features

Word counts

Counting words (I)

Counting words (II)

Limiting your features

Text to DataFrame

Term frequency-inverse document frequency

Tf-idf

Inspecting Tf-idf values

Transforming unseen data

N-grams

Using longer n-grams

Finding the most common words

Wrap-up

Dealing with Text Data

Stack Overflow Survey Responses (Modified)

US Presidential Inauguration Addresses

Setiap hari Anda mendengar terobosan luar biasa tentang bagaimana penerapan terbaru Machine Learning mengubah dunia. Sering kali pemberitaan tersebut mengabaikan fakta bahwa diperlukan banyak proses pembersihan data dan rekayasa fitur sebelum model canggih apa pun dapat digunakan. Dalam kursus ini, Anda akan mempelajari cara melakukannya. Anda akan bekerja dengan survei Stack Overflow Developers dan pidato pelantikan presiden AS sejarah, untuk memahami cara terbaik melakukan praproses dan merekayasa fitur dari data kategorikal, kontinu, dan tidak terstruktur. Kursus ini akan memberi Anda pengalaman langsung tentang cara menyiapkan data apa pun untuk model Machine Learning Anda sendiri.

Supervised Learning with scikit-learn

Pelajari persiapan data untuk model ML dengan memproses dan merekayasa fitur dari data kategorikal, kontinu, dan tak terstruktur.

Menangani nilai yang hilang (I)

Create Your Free Account