diff --git a/.travis.yml b/.travis.yml
index 65843fc..97730fe 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -9,4 +9,4 @@ install:
   - pip install -r requirements.txt
 # command to run tests
 script:
-  - python boston_housing_pred.py linear_regression < testing.txt
+  - python boston-housing-0_1_1.py linear_regression < testing.txt
diff --git a/README.md b/README.md
index 70ec709..00142f6 100644
--- a/README.md
+++ b/README.md
@@ -17,30 +17,35 @@ To install the `requirements` for the script do:
 ```sh
 $ pip install -r requirements.txt
 ```
-Download the script `boston_housing_pred.py`.
+Download the script `boston-housing-0_1_1.py`.
 
 ## Usage example
 
 You can run the programm with:
 ```sh
-$ py boston_housing_pred.py linear_regression
+$ py boston-housing-0_1_1.py linear_regression
 ```  
 To see the help(for extra options) do:
 ```sh
-$ py boston_housing_pred.py -h
+$ py boston-housing-0_1_1.py -h
 ```  
 
 <!--_For more examples and usage, please refer to the [Wiki][wiki]._-->
 
 ## Release History
 
+*   0.1.1
+    *   added functionality to load models without training
+    *   plots are now outsourced and handled by a different kernel
+    *   dataset gets automatically downloaded when missing
+    *   v_data shows 4 different plots to describe the data(previously 2).
+    
 *   0.1.0  
     *   The first proper release
     *   Realese of readme (Thanks @dbader)
-    
-    
+       
 *   0.0.1  
-    *   Work in progress  
+    *   Work in progress
 
 ## Roadmap (planned updates)
 
@@ -51,8 +56,6 @@ $ py boston_housing_pred.py -h
     *   svm
     *   neural network
 
-*   Add saved model can be loaded
-
 *   Upload pre-trained models 
 
 ## Meta
diff --git a/boston_housing_pred.py b/boston-housing-0_1_1.py
similarity index 92%
rename from boston_housing_pred.py
rename to boston-housing-0_1_1.py
index 25f20b8..2a637a8 100644
--- a/boston_housing_pred.py
+++ b/boston-housing-0_1_1.py
@@ -3,6 +3,7 @@
 Started: 08.09.2019
 Lang: Phyton
 Description: Prediction of boston housing market with lienar - regression.
+version: 0.1.1
 
 Dataset:
 Housing Values in Suburbs of Boston
@@ -26,6 +27,7 @@
 import pandas as pd
 import seaborn as sns
 import urllib
+import operator
 
 # TODO: fix train and test loss
 # TODO: add verify  verify a single preictions
@@ -62,6 +64,10 @@ def get_Data() -> object:
             checker_dataset_exist = False
 
 
+# used to remove trailing whitespace from file
+def chomped_lines(it):
+    return map(operator.methodcaller('rstrip', '\r\n'), it)
+
 # visualize data
 def visualize_Data(df: object) -> None:
     """
@@ -163,17 +169,24 @@ def visualize(args, df_data, parameter_list: list) -> None:
     x_train_loose = parameter_list[7]
 
     # prints Mean loss of last epoch
-    print(" ")
-    print("Mean-train loss of last epoch: ", str(round(train_loss_history[-1], 6)))
-    print("Mean-test loss of last epoch:  ", str(round(test_loss_history[-1], 6)))
-    print("Time needed for training:      ", str(round(evaluation_time, 4)) + "s.")
+    if not args.infile:
+        print(" ")
+        print("Mean-train loss of last epoch: ", str(round(train_loss_history[-1], 6)))
+        print("Mean-test loss of last epoch:  ", str(round(test_loss_history[-1], 6)))
+        print("Time needed for training:      ", str(round(evaluation_time, 4)) + "s.")
 
     # communication is key
-    if args.fd == "intermediate" or args.fd == "full":
+    if (args.fd == "intermediate" or args.fd == "full") and not args.infile:
         print(" ")
         print("Value of W1 after training:   ", w1)
         print("Value of Bias after training: ", bias)
         print(" ")
+    elif args.infile:
+        print(" ")
+        print("Value of W1:   ", w1)
+        print("Value of Bias: ", bias)
+        print(" ")
+
     if args.v_data:
         visualize_Data(df_data)
 
@@ -375,7 +388,7 @@ def getter_viszualtion(self) -> list:
                         type=str, choices=["linear_regression", "polynomial_regression"])
     parser.add_argument("--infile", help="If file specified model will load weights from it."
                                        "Else it will normally train.(default: no file loaded)"
-                        , metavar="FILE", type=argparse.FileType('r', encoding='UTF-8'))
+                        , metavar="FILE", type=str) # type=argparse.FileType('r', encoding='UTF-8')
     # implemented
     parser.add_argument("--v_data", metavar="VISUALIZE_DATA",
                         help="Set it to True if you want to get a visualization of the data.(default: %(default)s)",
@@ -416,7 +429,28 @@ def getter_viszualtion(self) -> list:
         print("--------------------------------------")
 
         model = LinearRegression(preproc_data(df_data))  # create our model
-        model.train()  # train our model
+
+        if not args.infile:
+            model.train()  # train our model
+        else:
+            try:
+                filename = str(args.infile)
+                file_list = []
+                with open(filename, "r") as infile:
+                    for line in chomped_lines(infile):
+                        file_list.append(line)
+            except FileNotFoundError as e:
+                print("Errot file not found: ", str(e))
+                if visualize_process.is_alive():
+                    visualize_process.terminate()
+                sys.exit(1)  # exit the script sucessful
+
+            try:
+                model.w1 = float(file_list[1])
+                model.bias = float(file_list[2])
+            except ValueError as e:
+                print(str(e))
+
 
         # if save parameter is true model gets saved
         if args.save: