Guidance needed with dimension reduction for clustering - some numerical, lots of categorical data
$begingroup$
I've my data in a Pandas df with 25.000 rows and 1.500 columns without any NaNs. Of the columns about 30 contain numerical data which I standardized with StandardScaler(). The rest are cols with binary values which originated from cols with categorical data. (used pd.get_dummies() for this)
Now I'd like to reduce the dimensions. I'm already running
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
pca.fit(df)
for three hours and I asked my self if my approach was correct. I also saw two variants of PCA, one for sparse data. Does it mean that it doesn't make sense to run PCA in such a mixed scenario?
As I was up to now busy with cleaning and transforming my data, I'd like to understand what a good strategy would be to eliminate irrelevant columns.
I'd appreciate some hints to move forward.
python scikit-learn pandas pca dimensionality-reduction
asked Nov 15 '18 at 11:54
zinyosrimzinyosrim
101
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bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I've my data in a Pandas df with 25.000 rows and 1.500 columns without any NaNs. Of the columns about 30 contain numerical data which I standardized with StandardScaler(). The rest are cols with binary values which originated from cols with categorical data. (used pd.get_dummies() for this)
Now I'd like to reduce the dimensions. I'm already running
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
pca.fit(df)
for three hours and I asked my self if my approach was correct. I also saw two variants of PCA, one for sparse data. Does it mean that it doesn't make sense to run PCA in such a mixed scenario?
As I was up to now busy with cleaning and transforming my data, I'd like to understand what a good strategy would be to eliminate irrelevant columns.
I'd appreciate some hints to move forward.
python scikit-learn pandas pca dimensionality-reduction
asked Nov 15 '18 at 11:54
zinyosrimzinyosrim
101
$endgroup$
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I've my data in a Pandas df with 25.000 rows and 1.500 columns without any NaNs. Of the columns about 30 contain numerical data which I standardized with StandardScaler(). The rest are cols with binary values which originated from cols with categorical data. (used pd.get_dummies() for this)
Now I'd like to reduce the dimensions. I'm already running
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
pca.fit(df)
for three hours and I asked my self if my approach was correct. I also saw two variants of PCA, one for sparse data. Does it mean that it doesn't make sense to run PCA in such a mixed scenario?
As I was up to now busy with cleaning and transforming my data, I'd like to understand what a good strategy would be to eliminate irrelevant columns.
I'd appreciate some hints to move forward.
python scikit-learn pandas pca dimensionality-reduction
asked Nov 15 '18 at 11:54
zinyosrimzinyosrim
101
$endgroup$
I've my data in a Pandas df with 25.000 rows and 1.500 columns without any NaNs. Of the columns about 30 contain numerical data which I standardized with StandardScaler(). The rest are cols with binary values which originated from cols with categorical data. (used pd.get_dummies() for this)
Now I'd like to reduce the dimensions. I'm already running
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
pca.fit(df)
for three hours and I asked my self if my approach was correct. I also saw two variants of PCA, one for sparse data. Does it mean that it doesn't make sense to run PCA in such a mixed scenario?
As I was up to now busy with cleaning and transforming my data, I'd like to understand what a good strategy would be to eliminate irrelevant columns.
I'd appreciate some hints to move forward.
python scikit-learn pandas pca dimensionality-reduction
python scikit-learn pandas pca dimensionality-reduction
asked Nov 15 '18 at 11:54
zinyosrimzinyosrim
101
asked Nov 15 '18 at 11:54
zinyosrimzinyosrim
101
asked Nov 15 '18 at 11:54
zinyosrimzinyosrim
101
asked Nov 15 '18 at 11:54
zinyosrimzinyosrim
101
asked Nov 15 '18 at 11:54
zinyosrimzinyosrim
101
101
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
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There are many ways to get rid of redundant dimensions. The choice wheter to do it or not depends on what kind of problem you want so solve and what kind of algorithm you plan to choose.
answered Nov 15 '18 at 12:05
Michael_SMichael_S
112
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1 Answer
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$begingroup$
There are many ways to get rid of redundant dimensions. The choice wheter to do it or not depends on what kind of problem you want so solve and what kind of algorithm you plan to choose.
answered Nov 15 '18 at 12:05
Michael_SMichael_S
112
$endgroup$
add a comment |
$begingroup$
There are many ways to get rid of redundant dimensions. The choice wheter to do it or not depends on what kind of problem you want so solve and what kind of algorithm you plan to choose.
answered Nov 15 '18 at 12:05
Michael_SMichael_S
112
$endgroup$
add a comment |
$begingroup$
There are many ways to get rid of redundant dimensions. The choice wheter to do it or not depends on what kind of problem you want so solve and what kind of algorithm you plan to choose.
answered Nov 15 '18 at 12:05
Michael_SMichael_S
112
$endgroup$
There are many ways to get rid of redundant dimensions. The choice wheter to do it or not depends on what kind of problem you want so solve and what kind of algorithm you plan to choose.
answered Nov 15 '18 at 12:05
Michael_SMichael_S
112
answered Nov 15 '18 at 12:05
Michael_SMichael_S
112
answered Nov 15 '18 at 12:05
Michael_SMichael_S
112
answered Nov 15 '18 at 12:05
Michael_SMichael_S
112
112
add a comment |
add a comment |
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