2. Data Wrangling and Feature Engineering

Sections

Navigate through the learning materials and practice exercises.

1. 2

   Data Wrangling And Feature Engineering

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   Data wrangling and feature engineering are essential processes in data...

2. 2.1

   Understanding Data Wrangling

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   Data wrangling is the process of cleaning and transforming raw data into a...

3. 2.1.1

   What Is Data Wrangling?

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   Data wrangling is the process of cleaning and transforming raw data into a...

4. 2.1.2

   Importance Of Data Wrangling

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   Data wrangling is crucial for ensuring high-quality data, leading to...

5. 2.1.3

   Common Data Wrangling Steps

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   This section outlines the essential steps of data wrangling, focusing on how...

6. 2.2

   Handling Missing Values

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   This section discusses the types of missing values in data and techniques to...

7. 2.2.1

   Types Of Missingness

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   This section discusses the types of missing data in datasets, specifically...

8. 2.2.2

   Techniques To Handle Missing Data

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   This section covers various techniques for addressing missing data,...

9. 2.3

   Data Transformation Techniques

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   This section covers various techniques for transforming and preparing data...

10. 2.3.1

    Normalization And Standardization

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    Normalization and standardization are critical data transformation...

11. 2.3.2

    Log Transformation

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    Log transformation is a technique used to compress skewed data, making it...

12. 2.3.3

    Binning

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    Binning is the process of converting numeric data into categorical bins to...

13. 2.3.4

    One-Hot Encoding

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    One-hot encoding is a technique used to convert categorical variables into a...

14. 2.3.5

    Label Encoding

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    Label encoding is a technique used to convert categorical variables into...

15. 2.4

    Feature Engineering

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    Feature engineering involves the creation and modification of variables to...

16. 2.4.1

    What Is Feature Engineering?

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    Feature engineering is the process of creating or modifying variables to...

17. 2.4.2

    Why Is It Important?

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    Feature engineering is essential in improving model accuracy and aiding...

18. 2.5

    Types Of Feature Engineering Techniques

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    This section explores various feature engineering techniques, focusing on...

19. 2.5.1

    Feature Extraction

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    Feature extraction is the process of deriving new features from raw data to...

20. 2.5.2

    Feature Transformation

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    Feature transformation involves altering the distribution of features to...

21. 2.5.3

    Feature Selection

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    Feature selection is the process of identifying and selecting the most...

22. 2.5.4

    Feature Construction

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    Feature construction is the process of creating new, meaningful features...

23. 2.6

    Dealing With Outliers

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    This section discusses how to detect and treat outliers in datasets, which...

24. 2.6.1

    Detection Techniques

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    Detection techniques help identify outliers in datasets.

25. .2.6.2

    Treatment Options

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    The section on Treatment Options discusses methods for addressing outliers in data.

26. 2.7

    Data Pipelines

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    Data pipelines automate the processes of data wrangling and feature...

27. 2.8

    Tools And Libraries For Data Wrangling And Feature Engineering

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    This section covers essential tools and libraries used for data wrangling...

28. 2.3

    Summary

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    Data wrangling and feature engineering are essential steps in data science...