100 Days of Machine Learning | CampusX - Binning and Binarization | Discretization | Quantile Binning | KMeans Binning

Binning involves grouping continuous data into discrete intervals, aiding in feature engineering. Techniques like Quantile Binning divide data based on quantiles, while KMeans Binning uses clustering. Binarization transforms numerical values into binary format, simplifying data representation. These methods contribute to efficient data handling and analysis. Code Used : https://github.com/campusx-official/100-days-of-machine-learning/tree/main/day32-binning-and-binarization ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 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 Instagram: https://www.instagram.com/campusx.official E-mail us at [email protected] ⌚Time Stamps⌚ 00:00 - Intro 00:14 - Encoding Numerical Features 03:37 - What is Discretization? 06:14 - Types of Discretization 08:05 - Equal Width/Uniform Binning 11:50 - Equal Frequency / Quantile Binning 14:47 - KMeans Binning 19:57 - Encoding the discretized variable 21:45 - Example thorugh Code 31:00 - Custom/Domain based Binning 32:32 - Binarization 34:04 - Example