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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

## Date Variable
The presence of date variables in a dataset allows us to extract many vital features to enrich the feature's set. Some of the features we can extract are:
- Day of the week (Sunday, Monday).
- Day of the month (22,19).
- Month (July, April), or numbers (7,4).
- Year (1954, 1993, 2021).
- Week of the year (3 would represent the third week of January).
- Season of the year (Spring, Winter).
- Quarter.
- Semester.
- If a day is a weekend (True for Saturday, False for Monday)
- Public holiday or not.
- Leap year or not.
- Difference between two dates.

We can choose to generate any features we need according to the context of our project.

Let us extract some of the above features by running this Python code:

# Date Variable
The presence of date variables in a dataset allows us to extract many vital features to enrich the feature's set. Some of the features we can extract are:
- Day of the week (Sunday, Monday).
- Day of the month (22,19).
- Month (July, April), or numbers (7,4).
- Year (1954, 1993, 2021).
- Week of the year (3 would represent the third week of January).
- Season of the year (Spring, Winter).
- Quarter.
- Semester.
- If a day is a weekend (True for Saturday, False for Monday)
- Public holiday or not.
- Leap year or not.
- Difference between two dates.

We can choose to generate any features we need according to the context of our project.

Let us extract some of the above features by running this Python code:

Date variables are the kind of variables that hold much information that can be useful for the model. In this lesson, we will learn what essential pieces of information we can get from these date variables and how to extract them in some lines of code.


Date variable

Date variables are the kind of variables that hold much information that can be useful for the model. In this lesson, we will learn what essential pieces of information we can get from these date variables and how to extract them in some lines of code.

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

Date variable

Date Variable