Binning and Binarization | Discretization | Quantile Binning | KMeans Binning
Channel: CampusX
Date: 2021-04-19

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

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⌚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