best model

requirements.txt

@@ -1,7 +1,8 @@
-joblib==0.16.0
-numpy==1.19.1
-python-dateutil==2.8.1
-pytz==2020.1
-scipy==1.5.2
-six==1.15.0
-threadpoolctl==2.1.0
+joblib==0.16.0
+numpy==1.19.1
+python-dateutil==2.8.1
+pytz==2020.1
+scipy==1.5.2
+six==1.15.0
+threadpoolctl==2.1.0
+scikit-learn

train.py

@@ -1,19 +1,19 @@
-from sklearn.datasets import make_regression
-from sklearn.linear_model import LinearRegression
-import pickle
-import os
-
-X, y = make_regression(10000,n_features = 10)
-
-# Train a model
-reg = LinearRegression().fit(X, y.ravel())
-# Print out training r2
-print(reg.score(X,y.ravel() ))
-
-# Write the model to a file
-if not os.path.isdir("models/"):
-    os.mkdir("models")
-
-filename = 'models/model.pkl'
-pickle.dump(reg, open(filename, 'wb'))
-
+from sklearn.datasets import make_regression
+from sklearn.linear_model import Lasso
+import pickle
+import os
+
+X, y = make_regression(10000,n_features = 11)
+
+# Train a model
+reg = LinearRegression().fit(X, y.ravel())
+# Print out training r2
+print(reg.score(X,y.ravel() ))
+
+# Write the model to a file
+if not os.path.isdir("models/"):
+    os.mkdir("models")
+
+filename = 'models/model.pkl'
+pickle.dump(reg, open(filename, 'wb'))
+