diff --git a/dev-0/out.tsv b/dev-0/out.tsv
index c451e36..c1fed03 100644
--- a/dev-0/out.tsv
+++ b/dev-0/out.tsv
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diff --git a/model.pkl b/model.pkl
index c5e106d..bbcc4ee 100644
Binary files a/model.pkl and b/model.pkl differ
diff --git a/predict.py b/predict.py
index 988fa7e..90ddf01 100644
--- a/predict.py
+++ b/predict.py
@@ -17,7 +17,25 @@ def read_data_file(filepath):
 
 def dataframe_to_arrays(dataframe):
     dataframe1 = dataframe.copy(deep=True)
-    dataframe1["type"] = dataframe1["type"].astype('category').cat.codes
+    #dataframe1["type"] = dataframe1["type"].astype('category').cat.codes
+
+
+    dataframe1 = pd.get_dummies(dataframe1, columns=['type'])
+    if 'type_apartamentowiec' not in dataframe1:
+        dataframe1['type_apartamentowiec'] = 0
+    if 'type_blok' not in dataframe1:
+        dataframe1['type_blok'] = 0
+    if 'type_dom wolnostojący' not in dataframe1:
+        dataframe1['type_dom wolnostojący'] = 0
+    if 'type_loft' not in dataframe1:
+        dataframe1['type_loft'] = 0
+    if 'type_kamienica' not in dataframe1:
+        dataframe1['type_kamienica'] = 0
+    if 'type_plomba' not in dataframe1:
+        dataframe1['type_plomba'] = 0
+    if 'type_szeregowiec' not in dataframe1:
+        dataframe1['type_szeregowiec'] = 0
+
     return dataframe1
 
 #PREDICT_FILE_PATH = 'dev-0/in.tsv'
@@ -31,7 +49,7 @@ def main():
 
     for index, row in data.iterrows():
         #print(row[0], row[1])
-        x = torch.tensor([float(row[0]), float(row[1]), 1])
+        x = torch.tensor([float(row[0]), float(row[1]), 1, float(row[3]), float(row[4]),float(row[5]),float(row[6]),float(row[7]),float(row[8])])
         y = x @ w
         print(y.item())
 
diff --git a/test-A/out.tsv b/test-A/out.tsv
index 837d29a..c682f4e 100644
--- a/test-A/out.tsv
+++ b/test-A/out.tsv
@@ -1,418 +1,418 @@
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diff --git a/train.py b/train.py
index fc8d14c..98bc1f9 100644
--- a/train.py
+++ b/train.py
@@ -24,14 +24,18 @@ def read_data_file(filepath):
 
 def dataframe_to_arrays(dataframe):
     dataframe1 = dataframe.copy(deep=True)
-    dataframe1["type"] = dataframe1["type"].astype('category').cat.codes
+    #dataframe1["type"] = dataframe1["type"].astype('category').cat.codes
+
+    dataframe1 = pd.get_dummies(dataframe1, columns =['type'])
+    print(dataframe1.columns)
+    input_cols = dataframe1.columns.values[1:]
+    output_cols = dataframe1.columns.values[:1]
     inputs_array = dataframe1[input_cols].to_numpy()
     targets_array = dataframe1[output_cols].to_numpy()
     return inputs_array, targets_array
 
 data =  read_data_file(TRAIN_FILE_PATH)
-input_cols = data.columns.values[1:]
-output_cols = data.columns.values[:1]
+
 
 
 
@@ -40,11 +44,10 @@ inputs_array_training, targets_array_training = dataframe_to_arrays(data)
 inputs_training = torch.from_numpy(inputs_array_training).type(torch.float32)
 targets_training = torch.from_numpy(targets_array_training).type(torch.float32)
 
-
-weights = torch.tensor([1.0, 1.0,300000.0], requires_grad=True)
-learning_rate = torch.tensor(0.00000000001)
+weights = torch.tensor([1.0, 300000.0,1.0, 1.0, 1.0, 1.0,1.0,1.0,1.0], requires_grad=True)
+learning_rate = torch.tensor(0.0000000000005)
 print("training started")
-for i in range(20000):
+for i in range(300):
     y_predicted = inputs_training @ weights
     cost = torch.sum((y_predicted - targets_training) ** 2)
     cost.backward()