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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

## Definition
This is the most common method of data imputation, where you just replace all the missing values (NA) with the mean or median of the column. 

> This method only works with numerical variables.



## What to Consider
Here are some points to consider when using this method:
- If the variable has a normal distribution, the mean and median are almost the same.
- If the variable has a skewed distribution, then the median is the best representation.
This method can be applied when:
- The data is missing completely at random.
- No more than 5% of the variable contains missing data.

# Definition
This is the most common method of data imputation, where you just replace all the missing values (NA) with the mean or median of the column. 

> This method only works with numerical variables.



# What to Consider
Here are some points to consider when using this method:
- If the variable has a normal distribution, the mean and median are almost the same.
- If the variable has a skewed distribution, then the median is the best representation.
This method can be applied when:
- The data is missing completely at random.
- No more than 5% of the variable contains missing data.

The purpose of this lesson is to cover one of the most used techniques for imputing missing data within a numerical variable which is: mean and median imputation while exploring its advantages and disadvantages and learning how to implement it in a few lines of code.


Mean or median imputation

The purpose of this lesson is to cover one of the most used techniques for imputing missing data within a numerical variable which is: mean and median imputation while exploring its advantages and disadvantages and learning how to implement it in 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

Mean or median imputation

Definition

What to Consider