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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

## Other techniques
In addition to the resampling techniques we saw previously, many other approaches and advanced techniques can deal with this issue. Such as:

1. Collect more data: we can collect more data from other sources to build a more robust model.
2. Choose a different performance metric: Since the accuracy metric is misleading, as we have seen before, it is not the metric to use when dealing with imbalanced datasets. There are other metrics to use for such a case, like Confusion Matrix, Precision & Recall, F1 Score, ROC Curves.
3. Use different algorithms: Some algorithms perform better than others when dealing with imbalanced datasets.
4. Use penalized models: Some algorithms allow you to give them a different perspective on the problem, like penalized-SVM and logistic regression, by adding costs to force the model to pay attention to the minority class.

## Resources

Check these resources if you are interested in learning more about imbalanced datasets:
- https://androidkt.com/set-class-weight-for-imbalance-dataset-in-keras/
- https://imbalanced-learn.org/stable/
- https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.under_sampling.CondensedNearestNeighbour.html

In ```imbalanced-learn``` many other methods use sophisticated methods and more complex algorithms such as [SVM](https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SVMSMOTE.html) and [K-Means](https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.KMeansSMOTE.html) and combine them with SMOTE, make sure to check them out.

# Other techniques
In addition to the resampling techniques we saw previously, many other approaches and advanced techniques can deal with this issue. Such as:

1. Collect more data: we can collect more data from other sources to build a more robust model.
2. Choose a different performance metric: Since the accuracy metric is misleading, as we have seen before, it is not the metric to use when dealing with imbalanced datasets. There are other metrics to use for such a case, like Confusion Matrix, Precision & Recall, F1 Score, ROC Curves.
3. Use different algorithms: Some algorithms perform better than others when dealing with imbalanced datasets.
4. Use penalized models: Some algorithms allow you to give them a different perspective on the problem, like penalized-SVM and logistic regression, by adding costs to force the model to pay attention to the minority class.

# Resources

Check these resources if you are interested in learning more about imbalanced datasets:
- https://androidkt.com/set-class-weight-for-imbalance-dataset-in-keras/
- https://imbalanced-learn.org/stable/
- https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.under_sampling.CondensedNearestNeighbour.html

In ```imbalanced-learn``` many other methods use sophisticated methods and more complex algorithms such as [SVM](https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SVMSMOTE.html) and [K-Means](https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.KMeansSMOTE.html) and combine them with SMOTE, make sure to check them out.

Resampling techniques are not the only solution to imbalanced datasets; therefore, this lesson aims to introduce a few other methods you can use to make your dataset more balanced.

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

Other techniques & resources

Other techniques