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

## Features as they are
It is possible to use longitude and latitude, but the two variables are on a different scale level. In that case, we can either perform normalization and include them in the feature set or use a model that does not require any normalization, like tree-based algorithms, where we need to round the features to 3 or 4 decimal places.

Here is how we can do this in pandas:




# Features as they are
It is possible to use longitude and latitude, but the two variables are on a different scale level. In that case, we can either perform normalization and include them in the feature set or use a model that does not require any normalization, like tree-based algorithms, where we need to round the features to 3 or 4 decimal places.

Here is how we can do this in pandas:




In this lesson, we will learn how to use the geospatial features represented as latitude and longitude and how to do that in practice with a few lines of code.


Latitude & longitude

In this lesson, we will learn how to use the geospatial features represented as latitude and longitude and how to do that in practice with a few 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

Latitude & longitude

Features as they are