solegalli/machine-learning-imbalanced-data

Code repository for the online course Machine Learning with Imbalanced Data

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When building machine learning models, especially for rare events like fraud detection or disease diagnosis, you often encounter imbalanced datasets where one outcome is far less common. This project helps you address this by providing techniques to balance your data, leading to more accurate and reliable predictions. Data scientists and machine learning engineers will find this useful for improving their model performance.

188 stars. No commits in the last 6 months.

Use this if your machine learning model struggles with predicting minority classes due to an uneven distribution of outcomes in your dataset.

Not ideal if your primary goal is to learn basic machine learning model building rather than specifically tackling imbalanced data challenges.

data-science machine-learning-engineering predictive-modeling fraud-detection medical-diagnosis

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