Techniques to Work with Imbalanced Data for Machine Learning in Python

Learn seven effective techniques for handling imbalanced data in machine learning using Python. Explore methods such as upsampling minority classes, downsampling majority classes, combining over and under sampling, penalizing algorithms for misclassification of minority classes, generating synthetic data with SMOTE and ADASYN, and adding appropriate weights to deep learning models. Understand the importance of selecting proper metrics, analyzing confusion matrices, and using ROC_AUC scores. Gain practical insights through examples in image segmentation, feature generation, and label creation. Download accompanying code from the provided GitHub repository to apply these techniques in your own projects.

Intro
What is imbalance
Top 7 techniques
Image segmentation
Generate features
Create labels
Unique and counts
Accuracy
ROCAUC Score
Upsampling
moti
smote
results
Deep learning
Class weights
Adding weights
Manual class weights
Summary