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Numerical Variables

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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

## Engineering geospatial data
Sometimes, we may run into geospatial features that often appear as longitude and latitude in machine learning projects. 
Many engineers tend to eliminate these features, so they are rarely considered, as they could negatively affect the performance and predictions of our model if not well represented. However, knowing how to handle them properly can bring out many informative features that help the model learn more precisely. 
In this part of the course, we will cover everything you need to start working with this kind of data, from visualizing it to extracting and designing useful new features that will boost your model's performance.

A table showing normal latitude and longitude is presented below:



# Engineering geospatial data
Sometimes, we may run into geospatial features that often appear as longitude and latitude in machine learning projects. 
Many engineers tend to eliminate these features, so they are rarely considered, as they could negatively affect the performance and predictions of our model if not well represented. However, knowing how to handle them properly can bring out many informative features that help the model learn more precisely. 
In this part of the course, we will cover everything you need to start working with this kind of data, from visualizing it to extracting and designing useful new features that will boost your model's performance.

A table showing normal latitude and longitude is presented below:



Geospatial data are features that can improve the predictive model’s results by a large margin if they are well represented and handled; For that, we will see in this lesson some new libraries that allow us to engineer them correctly. This lesson will explore a range of methods we can use to visualize our geospatial data to derive some insights alongside some python code snippets.



Setup & visualisation

Geospatial data are features that can improve the predictive model’s results by a large margin if they are well represented and handled; For that, we will see in this lesson some new libraries that allow us to engineer them correctly. This lesson will explore a range of methods we can use to visualize our geospatial data to derive some insights alongside some python code snippets.

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

Setup & visualisation

Engineering geospatial data