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 the case of small datasets
There are 2 methods of dividing data into bins:  

1. Equal Frequency Binning: bins have an 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:
[5, 10, 11, 13, 15, 35, 50, 55, 72]
[92]
[204, 215]

Code : Implementation of Binning Technique:

Python

# 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
[[5, 10, 11, 13, 15, 35, 50, 55, 72], [92], [204, 215]]