added testing_lazypredict that runs 30 ML models in one go

testing_lazypredict_p1.py

@@ -0,0 +1,39 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 14 10:46:44 2022
+
+@author: tanu
+"""
+# Link: https://laptrinhx.com/how-to-run-30-machine-learning-models-with-2-lines-of-code-1521663246/
+import pyforest
+import warnings
+warnings.filterwarnings("ignore")
+from sklearn import metrics
+from sklearn.metrics import accuracy_score
+import lazypredict
+from lazypredict.Supervised import LazyClassifier
+
+#%%
+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)
+
+
+#%%
+clf = LazyClassifier(verbose=0,ignore_warnings=True)
+modelsN, predictionsN = clf.fit(X_trainN, X_testN, y_trainN, y_testN)
+mm_lpN = modelsN
+
+#%%
+# DOESN't work as need to incorporate pipeline(one hot encoder)
+models, predictions = clf.fit(X_train, X_test, y_train, y_test)
+mm_lp = models
+
+model1 = Pipeline(steps = [('preprocess', MinMaxScaler())
+                                , ('multiModels', clf) ])
+
+models, predictions = model1.fit(X_trainN, X_testN, y_trainN, y_testN)