How to measure correlation between several categorical features and a numerical label in Python?
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I have for a few weeks measured the time it takes for a product to be released through a automated release pipeline. I have several different categorical features such as "Product Category", "Product Owner". Then I also have some numerical categories such as "Hour of day when started the job", "Number of sub products" and more. In total I have around 16 different categories, where each category can take around 10 different values.
I will now try and train a regression model to see if I can predict the lead time(time it takes for the product to go through the pipeline) based on these features.
I would want to see if there are any of these features which are more correlated with the lead time than others. As I have understod it I have to seperate the numerical and categorical features and perform tests seperately on them. For numerical variables I have read about pearsonr and for correlating categorical and numerical variables I have read about ANOVA but I can't seem to find any way of implementing ANOVA in Python. Has anyone any experience with this? And if not, any other tips on how to do this?
Example data frame
product_category product_owner #sub_products hour lead_Time_min
A Bill 5 13 123
B Lisa 14 19 40
B Lisa 2 16 20
D Eric 1 11 10
C Ben 7 11 4
C Lisa 14 10 25
B Lisa 2 19 252
python regression pandas correlation
asked 9 hours ago
KSPRKSPR
111
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add a comment |
$begingroup$
I have for a few weeks measured the time it takes for a product to be released through a automated release pipeline. I have several different categorical features such as "Product Category", "Product Owner". Then I also have some numerical categories such as "Hour of day when started the job", "Number of sub products" and more. In total I have around 16 different categories, where each category can take around 10 different values.
I will now try and train a regression model to see if I can predict the lead time(time it takes for the product to go through the pipeline) based on these features.
I would want to see if there are any of these features which are more correlated with the lead time than others. As I have understod it I have to seperate the numerical and categorical features and perform tests seperately on them. For numerical variables I have read about pearsonr and for correlating categorical and numerical variables I have read about ANOVA but I can't seem to find any way of implementing ANOVA in Python. Has anyone any experience with this? And if not, any other tips on how to do this?
Example data frame
product_category product_owner #sub_products hour lead_Time_min
A Bill 5 13 123
B Lisa 14 19 40
B Lisa 2 16 20
D Eric 1 11 10
C Ben 7 11 4
C Lisa 14 10 25
B Lisa 2 19 252
python regression pandas correlation
asked 9 hours ago
KSPRKSPR
111
$endgroup$
add a comment |
$begingroup$
I have for a few weeks measured the time it takes for a product to be released through a automated release pipeline. I have several different categorical features such as "Product Category", "Product Owner". Then I also have some numerical categories such as "Hour of day when started the job", "Number of sub products" and more. In total I have around 16 different categories, where each category can take around 10 different values.
I will now try and train a regression model to see if I can predict the lead time(time it takes for the product to go through the pipeline) based on these features.
I would want to see if there are any of these features which are more correlated with the lead time than others. As I have understod it I have to seperate the numerical and categorical features and perform tests seperately on them. For numerical variables I have read about pearsonr and for correlating categorical and numerical variables I have read about ANOVA but I can't seem to find any way of implementing ANOVA in Python. Has anyone any experience with this? And if not, any other tips on how to do this?
Example data frame
product_category product_owner #sub_products hour lead_Time_min
A Bill 5 13 123
B Lisa 14 19 40
B Lisa 2 16 20
D Eric 1 11 10
C Ben 7 11 4
C Lisa 14 10 25
B Lisa 2 19 252
python regression pandas correlation
asked 9 hours ago
KSPRKSPR
111
$endgroup$
I have for a few weeks measured the time it takes for a product to be released through a automated release pipeline. I have several different categorical features such as "Product Category", "Product Owner". Then I also have some numerical categories such as "Hour of day when started the job", "Number of sub products" and more. In total I have around 16 different categories, where each category can take around 10 different values.
I will now try and train a regression model to see if I can predict the lead time(time it takes for the product to go through the pipeline) based on these features.
I would want to see if there are any of these features which are more correlated with the lead time than others. As I have understod it I have to seperate the numerical and categorical features and perform tests seperately on them. For numerical variables I have read about pearsonr and for correlating categorical and numerical variables I have read about ANOVA but I can't seem to find any way of implementing ANOVA in Python. Has anyone any experience with this? And if not, any other tips on how to do this?
Example data frame
product_category product_owner #sub_products hour lead_Time_min
A Bill 5 13 123
B Lisa 14 19 40
B Lisa 2 16 20
D Eric 1 11 10
C Ben 7 11 4
C Lisa 14 10 25
B Lisa 2 19 252
python regression pandas correlation
python regression pandas correlation
asked 9 hours ago
KSPRKSPR
111
asked 9 hours ago
KSPRKSPR
111
asked 9 hours ago
KSPRKSPR
111
asked 9 hours ago
KSPRKSPR
111
asked 9 hours ago
KSPRKSPR
111
111
add a comment |
add a comment |
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