100 Days of Machine Learning | CampusX - Imbalanced Data in Machine Learning | Undersampling | Oversampling | SMOTE

Imbalanced data refers to datasets where the distribution of classes is heavily skewed, with one class significantly outnumbering the others. Dealing with imbalanced data is crucial as it can lead to biased models that perform poorly on minority classes. Addressing Class Imbalance with Undersampling, Oversampling, SMOTE, and Ensemble Methods. Imbalanced datasets pose challenges for machine learning models, but techniques like undersampling (reducing majority class samples), oversampling (increasing minority class samples), SMOTE (Synthetic Minority Over-sampling Technique), and ensemble methods (combining multiple models) help mitigate bias and improve predictive performance on minority classes. Code - https://colab.research.google.com/drive/1oHwgsV6_DHt-m-Hxl8DuM-BQbKJrqvcK?usp=sharing ============================ Did you like my teaching style? Check my affordable mentorship program at : https://learnwith.campusx.in DSMP FAQ: https://docs.google.com/document/d/1OsMe9jGHoZS67FH8TdIzcUaDWuu5RAbCbBKk2cNq6Dk/edit#heading=h.gvv0r2jo3vjw ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] ✨ Hashtags✨ #Datascience #Machinelearning #Imbalanceddata #CampusX ⌚Time Stamps⌚ 00:00 - Intro 00:54 - What is Imbalanced Data? 04:10 - Problems with Imbalanced Data 08:00 - Imbalanced Data Demo 11:13 - Why studying imbalanced data is important? 16:58 - Undersampling 25:56 - Oversampling 31:06 - SMOTE 42:43 - Ensemble Learning 47:06 - Cost Sensitive Learning 51:30 - Other techniques