diff --git a/meteos/cluster/binary/meteos-script-1.6.0.py b/meteos/cluster/binary/meteos-script-1.6.0.py
index 30f44c3..9c34f6d 100644
--- a/meteos/cluster/binary/meteos-script-1.6.0.py
+++ b/meteos/cluster/binary/meteos-script-1.6.0.py
@@ -62,6 +62,8 @@ from pyspark.mllib.recommendation import Rating
 from pyspark.mllib.regression import LabeledPoint
 from pyspark.mllib.regression import LinearRegressionModel
 from pyspark.mllib.regression import LinearRegressionWithSGD
+from pyspark.mllib.regression import RidgeRegressionModel
+from pyspark.mllib.regression import RidgeRegressionWithSGD
 from pyspark.mllib.tree import DecisionTree
 from pyspark.mllib.tree import DecisionTreeModel
 from pyspark.mllib.util import MLUtils
@@ -185,29 +187,40 @@ class RecommendationController(ModelController):
         return model.predict(parsedData[0], parsedData[1])
 
 
-class LinearRegressionModelController(ModelController):
+class RegressionModelController(ModelController):
 
-    def __init__(self):
-        super(LinearRegressionModelController, self).__init__()
+    def __init__(self, train_name, model_name):
+        super(RegressionModelController, self).__init__()
+        self.train_class = eval(train_name)
+        self.model_class = eval(model_name)
+        self.model_params = {}
+
+    def _parse_model_params(self, params):
+
+        p = {}
+        p['iterations'] = int(params.get('numIterations', 100))
+        p['step'] = float(params.get('step', 0.00000001))
+        p['miniBatchFraction'] = float(params.get('miniBatchFraction', 1.0))
+        p['convergenceTol'] = float(params.get('convergenceTol', 0.001))
+        if self.__class__.__name__ == 'LinearRegressionModelController':
+            p['regParam'] = float(params.get('regParam', 0.0))
+        elif self.__class__.__name__ == 'RidgeRegressionModelController':
+            p['regParam'] = float(params.get('regParam', 0.01))
+
+        self.model_params = p
 
     def create_model(self, data, params):
 
-        iterations = int(params.get('numIterations', 10))
-        step = float(params.get('step', 0.00000001))
+        self._parse_model_params(params)
 
         points = data.map(self.parsePoint)
-        return LinearRegressionWithSGD.train(points,
-                                             iterations=iterations,
-                                             step=step)
+        return getattr(self.train_class, 'train')(points, **self.model_params)
 
     def create_model_libsvm(self, data, params):
 
-        iterations = int(params.get('numIterations', 10))
-        step = float(params.get('step', 0.00000001))
+        self._parse_model_params(params)
 
-        return LinearRegressionWithSGD.train(data,
-                                             iterations=iterations,
-                                             step=step)
+        return getattr(self.train_class, 'train')(data, **self.model_params)
 
     def evaluate_model(self, context, model, data):
 
@@ -224,7 +237,7 @@ class LinearRegressionModelController(ModelController):
         return result
 
     def load_model(self, context, path):
-        return LinearRegressionModel.load(context, path)
+        return getattr(self.model_class, 'load')(context, path)
 
     def predict(self, model, params):
         return model.predict(params.split(','))
@@ -233,6 +246,24 @@ class LinearRegressionModelController(ModelController):
         return self.predict(model, params)
 
 
+class LinearRegressionModelController(RegressionModelController):
+
+    def __init__(self):
+        train_name = 'LinearRegressionWithSGD'
+        model_name = 'LinearRegressionModel'
+        super(LinearRegressionModelController, self).__init__(train_name,
+                                                             model_name)
+
+
+class RidgeRegressionModelController(RegressionModelController):
+
+    def __init__(self):
+        train_name = 'RidgeRegressionWithSGD'
+        model_name = 'RidgeRegressionModel'
+        super(RidgeRegressionModelController, self).__init__(train_name,
+                                                             model_name)
+
+
 class LogisticRegressionModelController(ModelController):
 
     def __init__(self):
@@ -456,6 +487,8 @@ class MeteosSparkController(object):
             self.controller = LogisticRegressionModelController()
         elif model_type == 'LinearRegression':
             self.controller = LinearRegressionModelController()
+        elif model_type == 'RidgeRegression':
+            self.controller = RidgeRegressionModelController()
         elif model_type == 'DecisionTreeRegression':
             self.controller = DecisionTreeModelController()
         elif model_type == 'Word2Vec':