diff --git a/dev-0/out.tsv b/dev-0/out.tsv
index bc38ef9..c451e36 100644
--- a/dev-0/out.tsv
+++ b/dev-0/out.tsv
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diff --git a/model.pkl b/model.pkl
index e4ca2a9..c5e106d 100644
Binary files a/model.pkl and b/model.pkl differ
diff --git a/predict.py b/predict.py
index 4cf3cb0..988fa7e 100644
--- a/predict.py
+++ b/predict.py
@@ -20,6 +20,7 @@ def dataframe_to_arrays(dataframe):
     dataframe1["type"] = dataframe1["type"].astype('category').cat.codes
     return dataframe1
 
+#PREDICT_FILE_PATH = 'dev-0/in.tsv'
 PREDICT_FILE_PATH = 'test-A/in.tsv'
 
 def main():
diff --git a/test-A/out.tsv b/test-A/out.tsv
index 36c42d6..837d29a 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 72fb5c2..fc8d14c 100644
--- a/train.py
+++ b/train.py
@@ -2,10 +2,8 @@ import torch
 import torch.nn as nn
 import numpy as np
 import pandas as pd
-import sys
-import torch.nn.functional as F
 import pickle
-from torch.utils.data import TensorDataset, DataLoader
+
 
 TRAIN_FILE_PATH = 'train/train.tsv'
 
@@ -15,7 +13,7 @@ def read_data_file(filepath):
     df = pd.read_csv(filepath, sep='\t', header=None, index_col=None)
     dataframe = df.iloc[:, [0,8,11]]
     dataframe.columns = ['price','biggy','type']
-    #print(dataframe.size[0])
+
     for x in range(len(dataframe)):
         dataframe['biggy'].loc[x] = dataframe['biggy'].loc[x].replace(" ","")
         #such dumb solution, well, but at least it works
@@ -42,17 +40,17 @@ 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)
 
-print(inputs_training)
-w = torch.tensor([7201.61492633873, 1,7201.500], requires_grad=True)
-learning_rate = torch.tensor(0.000000000005)
+
+weights = torch.tensor([1.0, 1.0,300000.0], requires_grad=True)
+learning_rate = torch.tensor(0.00000000001)
 print("training started")
-for i in range(10000):
-    y_predicted = inputs_training @ w
+for i in range(20000):
+    y_predicted = inputs_training @ weights
     cost = torch.sum((y_predicted - targets_training) ** 2)
     cost.backward()
     with torch.no_grad():
-        w -= learning_rate * w.grad
-    w.requires_grad = True
+        weights -= learning_rate * weights.grad
+    weights.requires_grad = True
 
-print(w)
-pickle.dump(w, open('model.pkl', 'wb'))
\ No newline at end of file
+print(weights)
+pickle.dump(weights, open('model.pkl', 'wb'))
\ No newline at end of file