# Binning in Data Mining

Data binning, bucketing is a data pre-processing method used to minimize the effects of small observation errors. The original data values are divided into small intervals known as bins and then they are replaced by a general value calculated for that bin. This has a smoothing effect on the input data and may also reduce the chances of overfitting in case of small datasets

There are 2 methods of dividing data into bins ”

1. Equal Frequency Binning : bins have equal frequency.
2. Equal Width Binning : bins have equal width with a range of each bin are defined as [min + w], [min + 2w] …. [min + nw] where w = (max – min) / (no of bins).

Equal frequency

```Input :[5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]
Output :
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]

```

Equal Width

```Input :[5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215]
Output :
[10, 11, 13, 15, 35, 50, 55, 72]



```

Code : Implementation of Bining Technique

 `#equal frequency ` `def` `equifreq(arr1, m): ` `     `  `    ``a ``=` `len``(arr1) ` `    ``n ``=` `int``(a ``/` `m) ` `    ``for` `i ``in` `range``(``0``, m): ` `        ``arr ``=` `[] ` `        ``for` `j ``in` `range``(i ``*` `n, (i ``+` `1``) ``*` `n): ` `            ``if` `j >``=` `a: ` `                ``break` `            ``arr ``=` `arr ``+` `[arr1[j]] ` `        ``print``(arr) ` ` `  `#equal width ` `def` `equiwidth(arr1, m): ` `    ``a ``=` `len``(arr1) ` `    ``w ``=` `int``((``max``(arr1) ``-` `min``(arr1)) ``/` `m) ` `    ``min1 ``=` `min``(arr1) ` `    ``arr ``=` `[] ` `    ``for` `i ``in` `range``(``0``, m ``+` `1``): ` `        ``arr ``=` `arr ``+` `[min1 ``+` `w ``*` `i] ` `    ``arri``=``[] ` `     `  `    ``for` `i ``in` `range``(``0``, m): ` `        ``temp ``=` `[] ` `        ``for` `j ``in` `arr1: ` `            ``if` `j > arr[i] ``and` `j < arr[i``+``1``]: ` `                ``temp ``+``=` `[j] ` `        ``arri ``+``=` `[temp] ` `    ``print``(arri)  ` ` `  `#data to be binned ` `data ``=` `[``5``, ``10``, ``11``, ``13``, ``15``, ``35``, ``50``, ``55``, ``72``, ``92``, ``204``, ``215``] ` `#no of bins ` `m ``=` `3`  ` `  `print``(``"equal frequency binning"``) ` `equifreq(data, m) ` ` `  `print``(``"\n\nequal width binning"``) ` `equiwidth(data, ``3``) `

Output :

```equal frequency binning
[5, 10, 11, 13]
[15, 35, 50, 55]
[72, 92, 204, 215]

equal width binning
[[10, 11, 13, 15, 35, 50, 55, 72], , ]
```

My Personal Notes arrow_drop_up Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.

Article Tags :
Practice Tags :

Be the First to upvote.

Please write to us at contribute@geeksforgeeks.org to report any issue with the above content.