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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

## Advanced missing value imputation
we saw a range of easy and simple methods to handle missing data earlier in this course. Now, it is time for some advanced techniques for missing value imputation.

### Iterative imputation
Iterative imputation is a technique of imputing missing data using regression and classification estimators to model each feature as a function of other features. Each feature is imputed in a round-robin fashion, previous predictions being used in new ones. This process is repeated several times to increase the quality of imputation. Compared to simple mean/median/mode imputation, it often can create synthetic values that are closer to real values at the cost of additional processing time.

Using round-robin at each step:
1. The algorithm chooses a feature as output y and all the rest of the features as inputs X.
2. Train a regressor and fit it on (X, y) for known y.
3. The regressor is used to predict the missing values of y.
4. Repeat until the defined max_iteration is reached.

> IterativeImputer is still experimental in Sklearn, but predictions or the API might change.

Run the following Python code to apply this technique:

# Advanced missing value imputation
we saw a range of easy and simple methods to handle missing data earlier in this course. Now, it is time for some advanced techniques for missing value imputation.

## Iterative imputation
Iterative imputation is a technique of imputing missing data using regression and classification estimators to model each feature as a function of other features. Each feature is imputed in a round-robin fashion, previous predictions being used in new ones. This process is repeated several times to increase the quality of imputation. Compared to simple mean/median/mode imputation, it often can create synthetic values that are closer to real values at the cost of additional processing time.

Using round-robin at each step:
1. The algorithm chooses a feature as output y and all the rest of the features as inputs X.
2. Train a regressor and fit it on (X, y) for known y.
3. The regressor is used to predict the missing values of y.
4. Repeat until the defined max_iteration is reached.

> IterativeImputer is still experimental in Sklearn, but predictions or the API might change.

Run the following Python code to apply this technique:

Other than the standard missing data imputation techniques we saw previously, there are other advanced approaches to impute missing values. We will be talking about that in this lesson.


Advanced missing value imputation

Other than the standard missing data imputation techniques we saw previously, there are other advanced approaches to impute missing values. We will be talking 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

Advanced missing value imputation

Advanced missing value imputation

Iterative imputation