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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

## Linear model assumptions
Linear models make few assumptions regarding data. These assumptions can be summarized as follows:

### Linearity
Linearity is when the relationship between the variables (Xs) and the target (Y) is linear, this linear relationship can be evaluated with scatter plots.


### Homoscedasticity
Homoscedasticity means to be of “The same Variance.” For it to exist in a data set, the points must not be distant from the line.

As shown in the pictures below, the one on the right shows homoscedasticity, and the other two on the left shows heteroscedasticity, which is the opposite of homoscedasticity.

We can determine homoscedasticity using a few tests and plots, such as:
- Residuals plot
- Levene’s test
- Barlett’s test

If we have data where homoscedasticity is not satisfied, we can improve the homogeneity of variance by applying non-linear transformations (e.g., logarithm-transformation) or feature scaling.




# Linear model assumptions
Linear models make few assumptions regarding data. These assumptions can be summarized as follows:

## Linearity
Linearity is when the relationship between the variables (Xs) and the target (Y) is linear, this linear relationship can be evaluated with scatter plots.


## Homoscedasticity
Homoscedasticity means to be of “The same Variance.” For it to exist in a data set, the points must not be distant from the line.

As shown in the pictures below, the one on the right shows homoscedasticity, and the other two on the left shows heteroscedasticity, which is the opposite of homoscedasticity.

We can determine homoscedasticity using a few tests and plots, such as:
- Residuals plot
- Levene’s test
- Barlett’s test

If we have data where homoscedasticity is not satisfied, we can improve the homogeneity of variance by applying non-linear transformations (e.g., logarithm-transformation) or feature scaling.




Linear models usually make a few assumptions about the data that are important to know, like linearity, homoscedasticity, normality, and more. We will learn all about that in this lesson.


Linear model assumptions

Linear models usually make a few assumptions about the data that are important to know, like linearity, homoscedasticity, normality, and more. We will learn all about that in this lesson.

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

Linear model assumptions

Linear model assumptions

Linearity

Homoscedasticity