--- a
+++ b/analysis/ml/ScaledLogisticRegression.py
@@ -0,0 +1,30 @@
+import multiprocessing
+
+if __name__ == '__main__':
+    multiprocessing.set_start_method('forkserver')
+    import sys
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.preprocessing import StandardScaler
+    from sklearn.pipeline import Pipeline
+    import pdb
+    from evaluate_model import evaluate_model
+    import numpy as np
+
+    dataset = sys.argv[1]
+    save_file = sys.argv[2]
+    random_seed = int(sys.argv[3])
+    rare = eval(sys.argv[4])
+
+    # create the classifier
+    clf = Pipeline([('scale',StandardScaler()),
+                     ('lr', LogisticRegression(solver='saga',
+                                               max_iter=1000,
+                                               random_state=random_seed))
+                     ])
+
+    hyper_params = {
+            'lr__C': np.logspace(-2,1,20),
+            'lr__penalty': ['l1','l2'] 
+            }
+    # evaluate the model
+    evaluate_model(dataset, save_file, random_seed, clf, 'ScaleLR', hyper_params, False,rare=rare)