How about scaling data first by MinMaxScaler and then cluster?
What I thought is scaling can scale then into 0~1 section, and it can
ignore the quantity of each data
After scaling, it shows the increasing/decreasing ratio between each points.
Then cluster then by the eucledian distance should wo
SMOTENC will internally one hot encode the features, generate new features,
and finally decode.
So you need to do something like:
from imblearn.pipeline import make_pipeline, Pipeline
num_indices1 = list(X.iloc[:,np.r_[0:94,95,97,100:123]].columns.values)
cat_indices1 = list(X.iloc[:,np.r_[94,96
