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]
[92]
[204]

Code : Implementation of Bining Technique

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#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)

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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], [92], [204]]


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