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

## Variable distribution
Probability distribution or variable distribution is a function that shows the possible values for a variable and how often they occur.

For example, if we consider the variable weight. The variable distribution, in this case, describes how frequently we get a value of 55 Kg, or 78 Kg, or 100 KG. 
As you can probably guess, it is more likely to obtain values between 55–78 Kg than values around or bigger than 100 Kg.

## Properties of probability distributions

- P(x) is the probability that the random variable takes x as a value.
- The sum of all probabilities for all possible values must equal 1.
- The probability for a particular value or range of values must be between 0 and 1.
We distinguish some different probability distributions, like:
- Discrete, like Binomial and Poisson.
- Continuous, like Gaussian, skewed, and many others.

## Distributions and model performance
- Linear models presume that the independent variables have a normal distribution.


# Variable distribution
Probability distribution or variable distribution is a function that shows the possible values for a variable and how often they occur.

For example, if we consider the variable weight. The variable distribution, in this case, describes how frequently we get a value of 55 Kg, or 78 Kg, or 100 KG. 
As you can probably guess, it is more likely to obtain values between 55–78 Kg than values around or bigger than 100 Kg.

# Properties of probability distributions

- P(x) is the probability that the random variable takes x as a value.
- The sum of all probabilities for all possible values must equal 1.
- The probability for a particular value or range of values must be between 0 and 1.
We distinguish some different probability distributions, like:
- Discrete, like Binomial and Poisson.
- Continuous, like Gaussian, skewed, and many others.

# Distributions and model performance
- Linear models presume that the independent variables have a normal distribution.


Many models, techniques, and machine learning methods rely on variable distribution. This lesson will cover variable distribution, its properties, and its impact on our model.


Variable distribution

Many models, techniques, and machine learning methods rely on variable distribution. This lesson will cover variable distribution, its properties, and its impact on our model.

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

Variable distribution

Variable distribution