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

## Automated feature engineering
Typically, feature engineering is a drawn-out manual process, relying on domain knowledge, intuition, and data manipulation. 

This process can be highly tedious, and the final features will be limited both by human subjectivity and time. Automated feature engineering aims to help the data scientist by automatically creating many candidates features out of a dataset from which the best can be selected and used for training with the help of a great concept called deep feature synthesis and an exceptional tool called Featuretools.

### Deep feature synthesis
Deep Feature Synthesis (DFS) is an automated method for performing feature engineering on relational and temporal data proposed by James Max Kanter and Kalyan Veeramachaneni in December 2015.
This algorithm follows relationships in the data to a base field and then sequentially applies mathematical functions along that path to create the final feature. By stacking calculations sequentially, we observe that we can define each new feature as having a certain depth, d. Hence, the algorithm is called Deep Feature Synthesis.

You can read the original paper [here](https://groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/uploads/Site/DSAA_DSM_2015.pdf)

Here is how the automated feature engineering works:


# Automated feature engineering
Typically, feature engineering is a drawn-out manual process, relying on domain knowledge, intuition, and data manipulation. 

This process can be highly tedious, and the final features will be limited both by human subjectivity and time. Automated feature engineering aims to help the data scientist by automatically creating many candidates features out of a dataset from which the best can be selected and used for training with the help of a great concept called deep feature synthesis and an exceptional tool called Featuretools.

## Deep feature synthesis
Deep Feature Synthesis (DFS) is an automated method for performing feature engineering on relational and temporal data proposed by James Max Kanter and Kalyan Veeramachaneni in December 2015.
This algorithm follows relationships in the data to a base field and then sequentially applies mathematical functions along that path to create the final feature. By stacking calculations sequentially, we observe that we can define each new feature as having a certain depth, d. Hence, the algorithm is called Deep Feature Synthesis.

You can read the original paper [here](https://groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/uploads/Site/DSAA_DSM_2015.pdf)

Here is how the automated feature engineering works:


In this lesson, we will talk about what we call automated feature engineering, what it is, how it works, and its principal components.

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

Automated feature engineering

Automated feature engineering