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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

## Handling date-time and mixed variables
Earlier in this course, we saw the different types of variables, and we talked about particular data types called date-time and mixed variables. These features appear in many datasets, and only by engineering these variables, you will be able to help your models learn faster and make more accurate predictions. Therefore, this chapter will review a range of methods you can use to handle date-time and mixed variables.

## Engineering variables of date and time
Dates and times are rich sources of information that can be used with machine learning models.

In real-world datasets, we can find date-time variables in many formats, for example:
- Time of death (21:06:45)
- Birthday date (04-04-1986, 15-10-1986)
- Invoice date (15–09–2021 19:47:29)
There are many features we can extract from date-time variables. We will cover that in the section below.

# Handling date-time and mixed variables
Earlier in this course, we saw the different types of variables, and we talked about particular data types called date-time and mixed variables. These features appear in many datasets, and only by engineering these variables, you will be able to help your models learn faster and make more accurate predictions. Therefore, this chapter will review a range of methods you can use to handle date-time and mixed variables.

# Engineering variables of date and time
Dates and times are rich sources of information that can be used with machine learning models.

In real-world datasets, we can find date-time variables in many formats, for example:
- Time of death (21:06:45)
- Birthday date (04-04-1986, 15-10-1986)
- Invoice date (15–09–2021 19:47:29)
There are many features we can extract from date-time variables. We will cover that in the section below.

We saw previously in this course that there is a variable type called date-time variables. We will discuss these variables while exploring a few examples in this lesson.


Engineering variables of date and time

We saw previously in this course that there is a variable type called date-time variables. We will discuss these variables while exploring a few examples in this lesson.

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

Engineering variables of date and time

Handling date-time and mixed variables