Binning and Binarization Discretization Quantile Binning KMeans Binning

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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/1... • ============================ • Do you want to learn from me? • Check my affordable mentorship program at : https://learnwith.campusx.in/s/store • ============================ • 📱 Grow with us: • CampusX' LinkedIn: / campusx-official • CampusX on Instagram for daily tips: / campusx.official • My LinkedIn: / nitish-singh-03412789 • Discord: / discord • Instagram: / 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

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