Is there an algorithm that imputes missing values based on n nearest columns? (KNN hybrid)
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I have a dataset of 70 columns that have missing values. Each column has a few columns (3-5) that it is significantly more correlated than the others but each column's correlated columns are very different from other columns. I would like to perform a tweaked version of KNN imputation and before I start writing this from scratch, I'd like to know if there's something similar out there so I don't go about reinventing the wheel.
On initialisation, a correlation matrix is obtained between all the variables. For every missing cell that needs to be imputed, the algorithm retrieves from the correlation matrix the top n highest correlated columns and uses only those n columns to select the k nearest neighbours and use the mean value of those neighbours.
Thanks and apologies if I am not very clear with my question. I'll try to clarify if you have any doubts.
python k-nn
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JoshuaJoshua
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I have a dataset of 70 columns that have missing values. Each column has a few columns (3-5) that it is significantly more correlated than the others but each column's correlated columns are very different from other columns. I would like to perform a tweaked version of KNN imputation and before I start writing this from scratch, I'd like to know if there's something similar out there so I don't go about reinventing the wheel.
On initialisation, a correlation matrix is obtained between all the variables. For every missing cell that needs to be imputed, the algorithm retrieves from the correlation matrix the top n highest correlated columns and uses only those n columns to select the k nearest neighbours and use the mean value of those neighbours.
Thanks and apologies if I am not very clear with my question. I'll try to clarify if you have any doubts.
python k-nn
asked yesterday
JoshuaJoshua
111
New contributor
Joshua is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I have a dataset of 70 columns that have missing values. Each column has a few columns (3-5) that it is significantly more correlated than the others but each column's correlated columns are very different from other columns. I would like to perform a tweaked version of KNN imputation and before I start writing this from scratch, I'd like to know if there's something similar out there so I don't go about reinventing the wheel.
On initialisation, a correlation matrix is obtained between all the variables. For every missing cell that needs to be imputed, the algorithm retrieves from the correlation matrix the top n highest correlated columns and uses only those n columns to select the k nearest neighbours and use the mean value of those neighbours.
Thanks and apologies if I am not very clear with my question. I'll try to clarify if you have any doubts.
python k-nn
asked yesterday
JoshuaJoshua
111
New contributor
Joshua is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have a dataset of 70 columns that have missing values. Each column has a few columns (3-5) that it is significantly more correlated than the others but each column's correlated columns are very different from other columns. I would like to perform a tweaked version of KNN imputation and before I start writing this from scratch, I'd like to know if there's something similar out there so I don't go about reinventing the wheel.
On initialisation, a correlation matrix is obtained between all the variables. For every missing cell that needs to be imputed, the algorithm retrieves from the correlation matrix the top n highest correlated columns and uses only those n columns to select the k nearest neighbours and use the mean value of those neighbours.
Thanks and apologies if I am not very clear with my question. I'll try to clarify if you have any doubts.
python k-nn
python k-nn
asked yesterday
JoshuaJoshua
111
New contributor
Joshua is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
JoshuaJoshua
111
New contributor
Joshua is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
JoshuaJoshua
111
New contributor
Joshua is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
JoshuaJoshua
111
asked yesterday
JoshuaJoshua
111
111
New contributor
Joshua is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Joshua is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Joshua is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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