Handling Imbalanced Data in machine learning classification (Python) - 2

Welcome to our Handling Imbalanced Data in machine learning classification series. You'll work on a highly imbalanced example dataset in Python. In this Part 2 video, we'll learn 6 popular techniques to deal with the imbalanced data problem in Python. 00:00 Overview 01:21 Collecting a bigger sample 02:15 Oversampling (e.g., random, SMOTE) 09:55 Undersampling (e.g., random, K-Means, Tomek links) 15:05 Combining over and undersampling 16:42 Weighing classes differently 19:07 Changing algorithms GitHub Repo with code: https://github.com/liannewriting/YouTube-videos-public/tree/main/imbalanced-data-machine-learning-abalone19 Source of the dataset: https://sci2s.ugr.es/keel/dataset.php?cod=115 Please download from GitHub, since we've made minor changes to the original dataset. Technologies that will be used: ☑️ JupyterLab (Notebook) ☑️ pandas ☑️ sklearn ☑️ imbalanced-learn (imblearn) Links mentioned in the video ►Logistic Regression Example in Python: Step-by-Step Guide: https://www.justintodata.com/logistic-regression-example-in-python/ ►Shrinkage effect: https://imbalanced-learn.org/stable/auto_examples/over-sampling/plot_shrinkage_effect.html# ►SMOTE: Synthetic Minority Over-sampling Technique: https://arxiv.org/abs/1106.1813 ►Decision Tree Model in Machine Learning: Practical Tutorial with Python: https://www.justintodata.com/decision-tree-model-in-machine-learning-tutorial-python/ ►Unlocking Random Forest in Machine Learning: https://www.justintodata.com/random-forest-machine-learning/ ►Paper with comparisons (Survey of Imbalanced Data Methodologies): https://arxiv.org/pdf/2104.02240.pdf There's also an article version of the same content. If you prefer reading, please check it out. How to handle Imbalanced Data in machine learning classification: https://www.justintodata.com/imbalanced-data-machine-learning-classification/ Get access to more data science materials, check out our website Just into Data: https://justintodata.com/