The binning method is used to smooth data or process noisy data. In this method, the data is first sorted and then the sorted values are spread across multiple segments or cells. Because binning methods refer to a neighborhood of values, they perform local smoothing.

** There are three approaches to performing smoothing: **

Smoothing by bin mean s:In smoothing by bin mean s, each value in a bin is replaced by the mean value of the bin.

Smoothing by bin mean -median-mode-in-python-without-libraries/">median:In this method each bin value is replaced by its bin mean -median-mode-in-python-without-libraries/">median value.

Smoothing by bin boundary:In smoothing by bin boundaries, the minimum and maximum values in a given bin are identified as the bin boundaries. Each bin value is then replaced by the closest boundary value.

** Fit :**

- Sort an array of a given dataset.
- Divides the range into N bins, each containing approximately the same number of samples (division by equal depth).
- Store the mean / mean -median-mode-in-python-without-libraries/">median / bounds in each row.

** Examples :**

Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34Smoothing by bin mean s:- Bin 1: 9, 9, 9, 9 - Bin 2: 23, 23, 23, 23 - Bin 3: 29, 29, 29, 29Smoothing by bin boundaries:- Bin 1: 4, 4, 4, 15 - Bin 2: 21, 21, 25, 25 - Bin 3: 26, 26, 26, 34Smoothing by bin mean -median-mode-in-python-without-libraries/">median :- Bin 1: 9 9, 9, 9 - Bin 2: 24, 24, 24, 24 - Bin 3: 29, 29, 29, 29

Below is Python implementation for the above algorithm —

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