Perform k-means clustering over multiple columns
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I am trying to perform k-means clustering on multiple columns. My data set is composed of 4 numerical columns and 1 categorical column. I already researched previous questions but the answers are not satisfactory.
I know how to perform the algorithm on two columns, but I'm finding it quite difficult to apply the same algorithm on 4 numerical columns.
I am not really interested in visualizing the data for now, but in having the clusters displayed in the table.The picture shows that the first row belongs to cluster number 2, and so on. That is exactly what I need to achieve, but using 4 numerical columns, therefore each row must belong to a certain cluster.
Do you have any idea on how to achieve this? Any idea would be of great help. Thanks in advance! :
python clustering pandas
asked 12 hours ago
MaddyMaddy
1
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add a comment |
$begingroup$
I am trying to perform k-means clustering on multiple columns. My data set is composed of 4 numerical columns and 1 categorical column. I already researched previous questions but the answers are not satisfactory.
I know how to perform the algorithm on two columns, but I'm finding it quite difficult to apply the same algorithm on 4 numerical columns.
I am not really interested in visualizing the data for now, but in having the clusters displayed in the table.The picture shows that the first row belongs to cluster number 2, and so on. That is exactly what I need to achieve, but using 4 numerical columns, therefore each row must belong to a certain cluster.
Do you have any idea on how to achieve this? Any idea would be of great help. Thanks in advance! :
python clustering pandas
asked 12 hours ago
MaddyMaddy
1
New contributor
Maddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
Note that the age attribute is effectively ignored. You get the same result using only income. Because the data is not appropriately prepared for this analysis.
$endgroup$
– Anony-Mousse
4 hours ago
add a comment |
$begingroup$
I am trying to perform k-means clustering on multiple columns. My data set is composed of 4 numerical columns and 1 categorical column. I already researched previous questions but the answers are not satisfactory.
I know how to perform the algorithm on two columns, but I'm finding it quite difficult to apply the same algorithm on 4 numerical columns.
I am not really interested in visualizing the data for now, but in having the clusters displayed in the table.The picture shows that the first row belongs to cluster number 2, and so on. That is exactly what I need to achieve, but using 4 numerical columns, therefore each row must belong to a certain cluster.
Do you have any idea on how to achieve this? Any idea would be of great help. Thanks in advance! :
python clustering pandas
asked 12 hours ago
MaddyMaddy
1
New contributor
Maddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am trying to perform k-means clustering on multiple columns. My data set is composed of 4 numerical columns and 1 categorical column. I already researched previous questions but the answers are not satisfactory.
I know how to perform the algorithm on two columns, but I'm finding it quite difficult to apply the same algorithm on 4 numerical columns.
I am not really interested in visualizing the data for now, but in having the clusters displayed in the table.The picture shows that the first row belongs to cluster number 2, and so on. That is exactly what I need to achieve, but using 4 numerical columns, therefore each row must belong to a certain cluster.
Do you have any idea on how to achieve this? Any idea would be of great help. Thanks in advance! :
python clustering pandas
python clustering pandas
asked 12 hours ago
MaddyMaddy
1
New contributor
Maddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 12 hours ago
MaddyMaddy
1
New contributor
Maddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 12 hours ago
MaddyMaddy
1
New contributor
Maddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 12 hours ago
MaddyMaddy
1
asked 12 hours ago
MaddyMaddy
1
1
New contributor
Maddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Maddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Maddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
Note that the age attribute is effectively ignored. You get the same result using only income. Because the data is not appropriately prepared for this analysis.
$endgroup$
– Anony-Mousse
4 hours ago
add a comment |
$begingroup$
Note that the age attribute is effectively ignored. You get the same result using only income. Because the data is not appropriately prepared for this analysis.
$endgroup$
– Anony-Mousse
4 hours ago
$begingroup$
Note that the age attribute is effectively ignored. You get the same result using only income. Because the data is not appropriately prepared for this analysis.
$endgroup$
– Anony-Mousse
4 hours ago
$begingroup$
Note that the age attribute is effectively ignored. You get the same result using only income. Because the data is not appropriately prepared for this analysis.
$endgroup$
– Anony-Mousse
4 hours ago
add a comment |
1 Answer
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oldest
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$begingroup$
There is no difference in methodology between 2 and 4 columns. If you have issues then they are probably due to the contents of your columns. K-Means wants numerical columns, with no null/infinite values and avoid categorical data. Here I do it with 4 numerical features:
import pandas as pd
from sklearn.datasets.samples_generator import make_blobs
from sklearn.cluster import KMeans
X, _ = make_blobs(n_samples=10, centers=3, n_features=4)
df = pd.DataFrame(X, columns=['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4'])
kmeans = KMeans(n_clusters=3)
y = kmeans.fit_predict(df[['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4']])
df['Cluster'] = y
print(df.head())
Which outputs:
Feat_1 Feat_2 Feat_3 Feat_4 Cluster
0 0.005875 4.387241 -1.093308 8.213623 2
1 8.763603 -2.769244 4.581705 1.355389 1
2 -0.296613 4.120262 -1.635583 7.533157 2
3 -1.576720 4.957406 2.919704 0.155499 0
4 2.470349 4.098629 2.368335 0.043568 0
answered 12 hours ago
Simon LarssonSimon Larsson
673113
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Thanks for your help Simon!
$endgroup$
– Maddy
11 hours ago
add a comment |
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1 Answer
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1 Answer
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active
oldest
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oldest
votes
$begingroup$
There is no difference in methodology between 2 and 4 columns. If you have issues then they are probably due to the contents of your columns. K-Means wants numerical columns, with no null/infinite values and avoid categorical data. Here I do it with 4 numerical features:
import pandas as pd
from sklearn.datasets.samples_generator import make_blobs
from sklearn.cluster import KMeans
X, _ = make_blobs(n_samples=10, centers=3, n_features=4)
df = pd.DataFrame(X, columns=['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4'])
kmeans = KMeans(n_clusters=3)
y = kmeans.fit_predict(df[['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4']])
df['Cluster'] = y
print(df.head())
Which outputs:
Feat_1 Feat_2 Feat_3 Feat_4 Cluster
0 0.005875 4.387241 -1.093308 8.213623 2
1 8.763603 -2.769244 4.581705 1.355389 1
2 -0.296613 4.120262 -1.635583 7.533157 2
3 -1.576720 4.957406 2.919704 0.155499 0
4 2.470349 4.098629 2.368335 0.043568 0
answered 12 hours ago
Simon LarssonSimon Larsson
673113
$endgroup$
$begingroup$
Thanks for your help Simon!
$endgroup$
– Maddy
11 hours ago
add a comment |
$begingroup$
There is no difference in methodology between 2 and 4 columns. If you have issues then they are probably due to the contents of your columns. K-Means wants numerical columns, with no null/infinite values and avoid categorical data. Here I do it with 4 numerical features:
import pandas as pd
from sklearn.datasets.samples_generator import make_blobs
from sklearn.cluster import KMeans
X, _ = make_blobs(n_samples=10, centers=3, n_features=4)
df = pd.DataFrame(X, columns=['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4'])
kmeans = KMeans(n_clusters=3)
y = kmeans.fit_predict(df[['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4']])
df['Cluster'] = y
print(df.head())
Which outputs:
Feat_1 Feat_2 Feat_3 Feat_4 Cluster
0 0.005875 4.387241 -1.093308 8.213623 2
1 8.763603 -2.769244 4.581705 1.355389 1
2 -0.296613 4.120262 -1.635583 7.533157 2
3 -1.576720 4.957406 2.919704 0.155499 0
4 2.470349 4.098629 2.368335 0.043568 0
answered 12 hours ago
Simon LarssonSimon Larsson
673113
$endgroup$
$begingroup$
Thanks for your help Simon!
$endgroup$
– Maddy
11 hours ago
add a comment |
$begingroup$
There is no difference in methodology between 2 and 4 columns. If you have issues then they are probably due to the contents of your columns. K-Means wants numerical columns, with no null/infinite values and avoid categorical data. Here I do it with 4 numerical features:
import pandas as pd
from sklearn.datasets.samples_generator import make_blobs
from sklearn.cluster import KMeans
X, _ = make_blobs(n_samples=10, centers=3, n_features=4)
df = pd.DataFrame(X, columns=['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4'])
kmeans = KMeans(n_clusters=3)
y = kmeans.fit_predict(df[['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4']])
df['Cluster'] = y
print(df.head())
Which outputs:
Feat_1 Feat_2 Feat_3 Feat_4 Cluster
0 0.005875 4.387241 -1.093308 8.213623 2
1 8.763603 -2.769244 4.581705 1.355389 1
2 -0.296613 4.120262 -1.635583 7.533157 2
3 -1.576720 4.957406 2.919704 0.155499 0
4 2.470349 4.098629 2.368335 0.043568 0
answered 12 hours ago
Simon LarssonSimon Larsson
673113
$endgroup$
There is no difference in methodology between 2 and 4 columns. If you have issues then they are probably due to the contents of your columns. K-Means wants numerical columns, with no null/infinite values and avoid categorical data. Here I do it with 4 numerical features:
import pandas as pd
from sklearn.datasets.samples_generator import make_blobs
from sklearn.cluster import KMeans
X, _ = make_blobs(n_samples=10, centers=3, n_features=4)
df = pd.DataFrame(X, columns=['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4'])
kmeans = KMeans(n_clusters=3)
y = kmeans.fit_predict(df[['Feat_1', 'Feat_2', 'Feat_3', 'Feat_4']])
df['Cluster'] = y
print(df.head())
Which outputs:
Feat_1 Feat_2 Feat_3 Feat_4 Cluster
0 0.005875 4.387241 -1.093308 8.213623 2
1 8.763603 -2.769244 4.581705 1.355389 1
2 -0.296613 4.120262 -1.635583 7.533157 2
3 -1.576720 4.957406 2.919704 0.155499 0
4 2.470349 4.098629 2.368335 0.043568 0
answered 12 hours ago
Simon LarssonSimon Larsson
673113
edited 11 hours ago
answered 12 hours ago
Simon LarssonSimon Larsson
673113
answered 12 hours ago
Simon LarssonSimon Larsson
673113
answered 12 hours ago
Simon LarssonSimon Larsson
673113
673113
$begingroup$
Thanks for your help Simon!
$endgroup$
– Maddy
11 hours ago
add a comment |
$begingroup$
Thanks for your help Simon!
$endgroup$
– Maddy
11 hours ago
$begingroup$
Thanks for your help Simon!
$endgroup$
– Maddy
11 hours ago
$begingroup$
Thanks for your help Simon!
$endgroup$
– Maddy
11 hours ago
add a comment |
Maddy is a new contributor. Be nice, and check out our Code of Conduct.
Maddy is a new contributor. Be nice, and check out our Code of Conduct.
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$begingroup$
Note that the age attribute is effectively ignored. You get the same result using only income. Because the data is not appropriately prepared for this analysis.
$endgroup$
– Anony-Mousse
4 hours ago