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How to create new feature based on clustering result

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0

$begingroup$

I'm still a beginner in machine learning and I want to know how to code this situation based on python and machine learning (clustering).

I have data like:

id Column1 duration(seconde) column3
1 aaa 20 bbb
2 ccc 01 ddd
3 eee 150 fff
4 ggg 25 hhh

I want to group my data according to the duration column value and create new column containing a category name based on duration cluster. I want to get this result:

id Column1 duration(seconde) column3 NewColCategorie
1 aaa 20 bbb Cat2 
2 ccc 01 ddd Cat1
3 eee 150 fff Cat3
4 ggg 25 hhh Cat2
5 iii 175 jjj Cat3

python pandas numpy

share|improve this question

edited Mar 20 at 9:39

Blenzus

446

asked Mar 13 at 19:20

NirmineNirmine

276

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Are you applying a clustering model or just making clusters based on specific range of values?
  $endgroup$
  – bkshi
  Mar 14 at 4:46
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  I want to apply clustering but i don't know how to programme it. with the number of centroids =3
  $endgroup$
  – Nirmine
  Mar 14 at 10:17
  
  

  
  
  
  

add a comment | 

0

$begingroup$

I'm still a beginner in machine learning and I want to know how to code this situation based on python and machine learning (clustering).

I have data like:

id Column1 duration(seconde) column3
1 aaa 20 bbb
2 ccc 01 ddd
3 eee 150 fff
4 ggg 25 hhh

I want to group my data according to the duration column value and create new column containing a category name based on duration cluster. I want to get this result:

id Column1 duration(seconde) column3 NewColCategorie
1 aaa 20 bbb Cat2 
2 ccc 01 ddd Cat1
3 eee 150 fff Cat3
4 ggg 25 hhh Cat2
5 iii 175 jjj Cat3

python pandas numpy

share|improve this question

edited Mar 20 at 9:39

Blenzus

446

asked Mar 13 at 19:20

NirmineNirmine

276

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Are you applying a clustering model or just making clusters based on specific range of values?
  $endgroup$
  – bkshi
  Mar 14 at 4:46
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  I want to apply clustering but i don't know how to programme it. with the number of centroids =3
  $endgroup$
  – Nirmine
  Mar 14 at 10:17
  
  

  
  
  
  

add a comment | 

$begingroup$

I'm still a beginner in machine learning and I want to know how to code this situation based on python and machine learning (clustering).

I have data like:

id Column1 duration(seconde) column3
1 aaa 20 bbb
2 ccc 01 ddd
3 eee 150 fff
4 ggg 25 hhh

I want to group my data according to the duration column value and create new column containing a category name based on duration cluster. I want to get this result:

id Column1 duration(seconde) column3 NewColCategorie
1 aaa 20 bbb Cat2 
2 ccc 01 ddd Cat1
3 eee 150 fff Cat3
4 ggg 25 hhh Cat2
5 iii 175 jjj Cat3

python pandas numpy

share|improve this question

edited Mar 20 at 9:39

Blenzus

446

asked Mar 13 at 19:20

NirmineNirmine

276

$endgroup$

id Column1 duration(seconde) column3
1 aaa 20 bbb
2 ccc 01 ddd
3 eee 150 fff
4 ggg 25 hhh

id Column1 duration(seconde) column3 NewColCategorie
1 aaa 20 bbb Cat2 
2 ccc 01 ddd Cat1
3 eee 150 fff Cat3
4 ggg 25 hhh Cat2
5 iii 175 jjj Cat3

share|improve this question

edited Mar 20 at 9:39

Blenzus

446

asked Mar 13 at 19:20

NirmineNirmine

276

share|improve this question

edited Mar 20 at 9:39

Blenzus

446

asked Mar 13 at 19:20

NirmineNirmine

276

edited Mar 20 at 9:39

Blenzus

446

edited Mar 20 at 9:39

Blenzus

446

asked Mar 13 at 19:20

NirmineNirmine

276

asked Mar 13 at 19:20

NirmineNirmine

276

1 Answer
1

active

oldest

votes

2

$begingroup$

To do clustering you can use sklearn's KMeans Clustering function - sklearn.cluster.KMeans with n_clusters=3 and other parameters as default. This will give you 3 clusters. After you have trained your model you can use the .labels_ attribute of the trained model to classify every example. You can do this in the following way:

>>> from sklearn.cluster import KMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
... [10, 2], [10, 4], [10, 0]])
>>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
>>> kmeans.labels_
array([1, 1, 1, 0, 0, 0], dtype=int32)

To create a new column based on category cluster you can simply add the kmeans.labels_ array as a column to your original dataframe:

>>> df['categories'] = kmeans.labels_

share|improve this answer

answered Mar 17 at 6:18

bkshibkshi

638111

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Thanks you very much it is helpful
  $endgroup$
  – Nirmine
  Mar 17 at 19:11
  

  
  
  
  

add a comment | 

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2

$begingroup$

To do clustering you can use sklearn's KMeans Clustering function - sklearn.cluster.KMeans with n_clusters=3 and other parameters as default. This will give you 3 clusters. After you have trained your model you can use the .labels_ attribute of the trained model to classify every example. You can do this in the following way:

>>> from sklearn.cluster import KMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
... [10, 2], [10, 4], [10, 0]])
>>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
>>> kmeans.labels_
array([1, 1, 1, 0, 0, 0], dtype=int32)

To create a new column based on category cluster you can simply add the kmeans.labels_ array as a column to your original dataframe:

>>> df['categories'] = kmeans.labels_

share|improve this answer

answered Mar 17 at 6:18

bkshibkshi

638111

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Thanks you very much it is helpful
  $endgroup$
  – Nirmine
  Mar 17 at 19:11
  

  
  
  
  

add a comment | 

2

$begingroup$

To do clustering you can use sklearn's KMeans Clustering function - sklearn.cluster.KMeans with n_clusters=3 and other parameters as default. This will give you 3 clusters. After you have trained your model you can use the .labels_ attribute of the trained model to classify every example. You can do this in the following way:

>>> from sklearn.cluster import KMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
... [10, 2], [10, 4], [10, 0]])
>>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
>>> kmeans.labels_
array([1, 1, 1, 0, 0, 0], dtype=int32)

To create a new column based on category cluster you can simply add the kmeans.labels_ array as a column to your original dataframe:

>>> df['categories'] = kmeans.labels_

share|improve this answer

answered Mar 17 at 6:18

bkshibkshi

638111

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Thanks you very much it is helpful
  $endgroup$
  – Nirmine
  Mar 17 at 19:11
  

  
  
  
  

add a comment | 

$begingroup$

To do clustering you can use sklearn's KMeans Clustering function - sklearn.cluster.KMeans with n_clusters=3 and other parameters as default. This will give you 3 clusters. After you have trained your model you can use the .labels_ attribute of the trained model to classify every example. You can do this in the following way:

>>> from sklearn.cluster import KMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
... [10, 2], [10, 4], [10, 0]])
>>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
>>> kmeans.labels_
array([1, 1, 1, 0, 0, 0], dtype=int32)

To create a new column based on category cluster you can simply add the kmeans.labels_ array as a column to your original dataframe:

>>> df['categories'] = kmeans.labels_

share|improve this answer

answered Mar 17 at 6:18

bkshibkshi

638111

$endgroup$

>>> from sklearn.cluster import KMeans
>>> import numpy as np
>>> X = np.array([[1, 2], [1, 4], [1, 0],
... [10, 2], [10, 4], [10, 0]])
>>> kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
>>> kmeans.labels_
array([1, 1, 1, 0, 0, 0], dtype=int32)

>>> df['categories'] = kmeans.labels_

share|improve this answer

answered Mar 17 at 6:18

bkshibkshi

638111

answered Mar 17 at 6:18

bkshibkshi

638111

answered Mar 17 at 6:18

bkshibkshi

638111