I have features with 10 numeric type, and other 10 categorical, with a lot of values, at the end, using one-hot encoding I got a matrix of 600 columns. My problem is with accuracy which is 0.7, knowing that other peers got more that 0.9.
Target data is binary, and is not evenly distributed at all. Trying blindly after pre-processing
from sklearn.linear_model import LogisticRegression and
sklearn.svm scored using
Back to basics, I run this
train['cible'].value_counts() / train['cible'].count()
1 0.970791 0 0.029209 Name: cible, dtype: float64
Quite interesting I think, but how can I improve accuracy. Any hints ?
Note: I will edit and add False Positive Rate and True Positive Rate as I lost output, after scaling, missing data imputation and retraining the model which takes couple of hours.