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

In this challenge, you will be presented with data of credit card transactions, and label for it transaction were fraud or not, which is an example for the imbalanced dataset, you will try to balance the dataset using some techniques you learned in this chapter.

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

In this challenge, you will be presented with data of credit card transactions, and label for it transaction were fraud or not, which is an example for the imbalanced dataset, you will try to balance the dataset using some techniques you learned in this chapter.

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:

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