Data nature:
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.
Problem:
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 roc_auc_score
: .7
and .75
.
Back to basics, I run this
train['cible'].value_counts() / train['cible'].count()
and got
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.