Dockerfile.tar.gz

Numerical Variables

Default Job

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

## Challenge Defifnition

Before going further, let us try to apply the engineering to the date and time variables, you will get a dataset with two dates and times:
* Purchase Date
* Loan Date

Your goal is to extract some date features from the Loan Date feature, and some time features from the Purchase Date Features (See the comment below).

You will be presented with a code execution widget below, in which you will find some steps to complete, complete all the steps, and then make sure to see at the end the solution to the challenge as I would approach it:

# Challenge Defifnition

Before going further, let us try to apply the engineering to the date and time variables, you will get a dataset with two dates and times:
* Purchase Date
* Loan Date

Your goal is to extract some date features from the Loan Date feature, and some time features from the Purchase Date Features (See the comment below).

You will be presented with a code execution widget below, in which you will find some steps to complete, complete all the steps, and then make sure to see at the end the solution to the challenge as I would approach it:

In this challenge, we will practice dealing with Date and time features.

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

Coding challenge

Challenge Defifnition