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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

This is a categorical variable that holds dates or times as values. Datetime variables can contain dates or times, or both. 

For example, date of birth (05-07-1987, 18-07-1993), date of graduation (25-07-2019), etc.

Here is an example of variables containing date in a column and separate column for time variable:



# Definition

This is a categorical variable that holds dates or times as values. Datetime variables can contain dates or times, or both. 

For example, date of birth (05-07-1987, 18-07-1993), date of graduation (25-07-2019), etc.

Here is an example of variables containing date in a column and separate column for time variable:



A special kind of categorical variable is sometimes present in data sets called date-time variables. In this lesson, we will explore these date-time variables and see some examples of what they can be.


Dates & times variables

A special kind of categorical variable is sometimes present in data sets called date-time variables. In this lesson, we will explore these date-time variables and see some examples of what they can be.

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

Dates & times variables

Definition