Handling Imbalanced Data in machine learning classification (Python) - 1 скачать в хорошем качестве

Handling Imbalanced Data in machine learning classification (Python) - 1

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 1 video, we'll learn: what is imbalanced data what are the proper evaluation metrics for it set up our example of a highly imbalanced dataset ready for modeling. 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. Please check out the Part 2 video to learn 6 popular techniques to deal with the imbalanced data problem in Python. ✔️Collecting a bigger sample ✔️Oversampling (e.g., random, SMOTE) ✔️Undersampling (e.g., random, K-Means, Tomek links) ✔️Combining over and undersampling ✔️Weighing classes differently ✔️Changing algorithms Technologies that will be used: ☑️ JupyterLab (Notebook) ☑️ pandas ☑️ sklearn ☑️ imbalanced-learn (imblearn) Links mentioned in the video ►8 popular Evaluation Metrics for Machine Learning Models: https://www.justintodata.com/machine-... ►FREE Python crash course - basics: https://www.justintodata.com/learn-py... ►Python for Data Analysis with projects: https://www.udemy.com/course/python-f... ►Logistic Regression for Machine Learning: complete Tutorial: https://www.justintodata.com/logistic... ►Logistic Regression Example in Python: Step-by-Step Guide: https://www.justintodata.com/logistic... 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/