diff --git a/imports.py b/imports.py
index e1d03a2..40888f2 100644
--- a/imports.py
+++ b/imports.py
@@ -206,4 +206,4 @@ categorical_features_df = my_df[categorical_features_names]
 categorical_features_df.shape
 
 all_features_df = my_df[numerical_features_names + categorical_features_names]
-all_features_df.shape
+all_features_df.shape
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diff --git a/my_data9.py b/my_data9.py
new file mode 100644
index 0000000..6265051
--- /dev/null
+++ b/my_data9.py
@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Mar  5 12:57:32 2022
+
+@author: tanu
+"""
+#%%
+# data, etc for now  comes from my_data6.py and/or my_data5.py
+
+#%% try combinations
+#import sys, os
+#os.system("imports.py")
+def precision(y_true,y_pred):
+    return precision_score(y_true,y_pred,pos_label = 1)
+def recall(y_true,y_pred):
+    return recall_score(y_true, y_pred, pos_label = 1)
+def f1(y_true,y_pred):
+    return f1_score(y_true, y_pred, pos_label = 1)
+
+#%%
+numerical_features_df.shape
+categorical_features_df.shape
+all_features_df.shape
+#%%
+target = target1
+#target = target3
+X_trainN, X_testN, y_trainN, y_testN = train_test_split(numerical_features_df, 
+                                                    target, 
+                                                    test_size = 0.33, 
+                                                    random_state = 42)
+
+X_trainC, X_testC, y_trainC, y_testC = train_test_split(categorical_features_df, 
+                                                    target, 
+                                                    test_size = 0.33, 
+                                                    random_state = 42)
+
+X_train, X_test, y_train, y_test = train_test_split(all_features_df, 
+                                                    target, 
+                                                    test_size = 0.33, 
+                                                    random_state = 42)
+#%%
+
+#%%
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', MinMaxScaler()  , numerical_features_names)
+        #,('cat', OneHotEncoder(), categorical_features_names)
+        ])
+
+preprocessor.fit(numerical_features_df)
+preprocessor.transform(numerical_features_df)
+#%%
+model_log = Pipeline(steps = [
+    ('preprocess', preprocessor)
+    #,('log_reg', linear_model.LogisticRegression())
+    ,('log_reg', LogisticRegression(
+        class_weight = 'unbalanced'))
+    ])
+model = model_log
+#%%
+seed = 42
+model_rf = Pipeline(steps = [
+    ('preprocess', preprocessor)
+    ,('rf',  RandomForestClassifier(
+                          min_samples_leaf=50,
+                          n_estimators=150,
+                          bootstrap=True,
+                          oob_score=True,
+                          n_jobs=-1,
+                          random_state=seed,
+                          max_features='auto'))
+    ])
+model = model_rf
+#%%
+model.fit(X_trainN, y_trainN)
+y_pred = model.predict(X_testN)
+y_pred
+
+acc  = make_scorer(accuracy_score)
+prec = make_scorer(precision)
+rec  = make_scorer(recall)
+f1   = make_scorer(f1)
+
+output = cross_validate(model, X_trainN, y_trainN
+                        , scoring = {'acc' : acc
+                                  ,'prec': prec
+                                  ,'rec' : rec
+                                  ,'f1'  : f1}
+                        , cv = 10
+                        , return_train_score = False)
+pd.DataFrame(output).mean()
+
+
+#%%
+
+selector_logistic = RFECV(estimator = model
+                       , cv = 10
+                       , step = 1)
+
+X_trainN, X_testN, y_trainN, y_testN = train_test_split(numerical_features_df 
+                                                    , target1 
+                                                    , test_size = 0.33
+                                                    , random_state = 42)
+
+selector_logistic_xtrain = selector_logistic.fit_transform(X_trainN, y_trainN)
+print(sel_rfe_logistic.get_support())
+X_trainN.columns
+
+print(sel_rfe_logistic.ranking_)
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