Handling Imbalanced Data in machine learning classification Python 2

YOUR LINK HERE:

http://youtube.com/watch?v=Bt5g7c2s38M

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/YouT... • Source of the dataset: https://sci2s.ugr.es/keel/dataset.php... 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... • ►Shrinkage effect: https://imbalanced-learn.org/stable/a... • ►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... • ►Unlocking Random Forest in Machine Learning: https://www.justintodata.com/random-f... • ►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/imbalanc... • Get access to more data science materials, check out our website Just into Data: https://justintodata.com/

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