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Numerical Variables

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In this course, you will learn how to apply basic and more advanced feature engineering to tabular data with python, during this course,  we will see a range of different techniques and methods to handle many common cases within the data set, so in result, you will create great features so that your machine learning models can predict good results.

Feature Engineering with Python

## Definition
Arbitrary Value Imputation is another easy way to impute the missing data values (NA). For this, you need to select an arbitrary value that is not too close or similar to the mean and median and not within the normal values of the variable. Generally, we use values such as 0, 999, -999 (or other combinations of 9s), or -1 (if the distribution is positive).

# Definition
Arbitrary Value Imputation is another easy way to impute the missing data values (NA). For this, you need to select an arbitrary value that is not too close or similar to the mean and median and not within the normal values of the variable. Generally, we use values such as 0, 999, -999 (or other combinations of 9s), or -1 (if the distribution is positive).

In this lesson, we will learn about the technique of arbitrary values imputation to eliminate missing values, assumptions, advantages, and limitations. Finally, a sample code showing how to use this method in practice.


Arbitrary value imputation

In this lesson, we will learn about the technique of arbitrary values imputation to eliminate missing values, assumptions, advantages, and limitations. Finally, a sample code showing how to use this method in practice.

Introduction

Variable Types

Common Concerns in Datasets

Handling & Imputing Missing Values

Encoding Categorical Variables

Transforming Variables

Variable Discretization

Handling Outliers

Feature Scaling

Engineering Geospatial Data

Handling Date-Time and Mixed Variables

Resampling Imbalanced Data

Advanced Feature Engineering Techniques

Conclusion

Arbitrary value imputation

Definition