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 Definition

Let us start the first challenge with the imputation of missing values, we will be using the same titanic dataset, but with more techniques and more variables.

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 Definition

Let us start the first challenge with the imputation of missing values, we will be using the same titanic dataset, but with more techniques and more variables.

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:

Chance to practice the missing value imputation methods on a dataset with different columns.

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 Definition