Verini tanı

Pandas, Python'da tabular verilerle çalışmak için en popüler paketlerden biridir. Genellikle pd kısaltmasıyla içe aktarılır ve read_csv() kullanarak bir CSV'yi (veya diğer sınırlayıcı dosyaları) yüklemek için kullanılabilir.

Bu kursun ilk üç bölümünde Stackoverflow anket yanıt verilerinin değiştirilmiş bir alt kümesiyle çalışacaksın. Bu veri kümesi, StackOverflow web sitesinin binlerce kullanıcısının ayrıntılarını ve tercihlerini kaydeder.

Bu egzersiz

Python ile Machine Learning için Özellik Mühendisliği

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import pandas
import ____ as ____

# Import so_survey_csv into so_survey_df
so_survey_df = ____(so_survey_csv)

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: Neden özellik üretiyoruz?Exercise 2: Verini tanı

Geçerli Egzersiz

Exercise 3: Belirli veri tiplerini seçme Exercise 4: Kategorik özelliklerle çalışmak Exercise 5: One-hot encoding ve kukla değişkenler Exercise 6: Nadir kategorilerle başa çıkma Exercise 7: Sayısal değişkenler Exercise 8: Sütunları ikili değerlere dönüştürme Exercise 9: Değerleri bölmelere ayırma (binning)

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: 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