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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

## Distance feature
The distance feature technique is very effective when dealing with the problem of determining ETA (Estimated Time of Arrival). 

Datasets for such problems usually contain departure and arrival coordinates. Using those coordinates to provide your model with a new informative feature, the distance feature, can improve the model's performance by a large margin because the distance directly impacts the time of arrival. 

The formula used here to calculate the distance is the Harvsine Formula. Since it considers that the earth's shape to be spherical and not flat, the distance would be more realistic.

If you do not have two coordinates, you can always calculate the distance to a fixed point (e.g., origin), which depends on the context of your problem. 

Run the code below to apply the Harvsine Formula to our dataset:

# Distance feature
The distance feature technique is very effective when dealing with the problem of determining ETA (Estimated Time of Arrival). 

Datasets for such problems usually contain departure and arrival coordinates. Using those coordinates to provide your model with a new informative feature, the distance feature, can improve the model's performance by a large margin because the distance directly impacts the time of arrival. 

The formula used here to calculate the distance is the Harvsine Formula. Since it considers that the earth's shape to be spherical and not flat, the distance would be more realistic.

If you do not have two coordinates, you can always calculate the distance to a fixed point (e.g., origin), which depends on the context of your problem. 

Run the code below to apply the Harvsine Formula to our dataset:

The distance feature is a significant and reliable source of information for the model in some cases. We will learn in this lesson how to derive this feature from longitude and latitude. 


Distance feature

The distance feature is a significant and reliable source of information for the model in some cases. We will learn in this lesson how to derive this feature from longitude and latitude.

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

Distance feature

Distance feature