![](./assets/colab.png)
![Image](./assets/iris smote.jpeg)
In this tutorial we use an SVM and SMOTE and the Iris data to study how the decision regions change with the amount of oversampling
![](./assets/colab.png)
![Image](./assets/iris rose.jpeg)
In this tutorial we study the `s` parameter in rose and the effect of increasing it.
![](./assets/colab.png)
![Image](./assets/churn smote.jpeg)
In this tutorial we apply SMOTE and random forest to predict customer churn based on continuous attributes.
![](./assets/colab.png)
![Image](./assets/mushy.jpeg)
In this tutorial we use a purely categorical dataset to predict mushroom odour.
![](./assets/colab.png)
![Image](./assets/churn smoten.jpeg)
In this tutorial we extend the SMOTE tutorial to include both categorical and continuous data for churn prediction
![](./assets/colab.png)
![Image](./assets/bmi.jpeg)
In this tutorial we oberve the effects of the hyperparameters found in ENN undersampling with an SVM model