diff --git a/sacred.py b/sacred.py
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+import sacred
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import pandas as pd
+import numpy as np
+import sys
+import os
+from sklearn.model_selection import train_test_split
+from sklearn import preprocessing
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+from sacred.observers import MongoObserver
+
+ex = Experiment("s478815", interactive=True)
+ex.observers.append(FileStorageObserver('experiment'))
+
+#ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password@localhost:27017', db_name='sacred'))  
+
+@ex.config
+def my_config():
+    epochs = 1000
+
+# Model
+class Model(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(1,1) 
+
+    def forward(self, x):
+        y_predicted = torch.sigmoid(self.linear(x))
+        return y_predicted
+
+data = pd.read_csv('data.csv')
+data.dropna()
+
+training_data = data.sample(frac=0.9, random_state=25)
+testing_data = data.drop(training_data.index)
+
+print(f"No. of training examples: {training_data.shape[0]}")
+print(f"No. of testing examples: {testing_data.shape[0]}")
+
+training_data = training_data[['sqft_living', 'price']]
+
+testing_data = testing_data[['sqft_living', 'price']]
+
+training_data[['price']] = training_data[['price']] / 10000000
+training_data[['sqft_living']] = training_data[['sqft_living']] / 10000
+
+testing_data[['price']] = testing_data[['price']] / 10000000
+testing_data[['sqft_living']] = testing_data[['sqft_living']] / 10000
+
+# Tensory
+X_training = training_data[['sqft_living']].to_numpy()
+X_testing = testing_data[['sqft_living']].to_numpy()
+y_training = training_data[['price']].to_numpy()
+y_testing = testing_data[['price']].to_numpy()
+
+torch.from_file
+X_training = torch.from_numpy(X_training.astype(np.float32))
+X_testing = torch.from_numpy(X_testing.astype(np.float32))
+y_training = torch.from_numpy(y_training.astype(np.float32))
+y_testing = torch.from_numpy(y_testing.astype(np.float32))
+
+
+model = Model()
+criterion = nn.BCELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+
+@ex.automain
+def my_main(epochs, _run):
+# Trening
+#num_epochs = EPOCHS
+    for epochs in range(epochs):
+        y_predicted = model(X_training)
+        loss = criterion(y_predicted,y_training)
+        loss.backward()
+        optimizer.step()
+        optimizer.zero_grad()
+
+        if (epochs%100==0):
+            print(f'epoch:{epochs+1},loss = {loss.item():.4f}')
+            
+        with torch.no_grad():
+            y_predicted = model(X_testing)
+            y_predicted_cls = y_predicted.round()
+            acc = y_predicted_cls.eq(y_testing).sum()/float(y_testing.shape[0])
+            print(f'{acc:.4f}')
+            result = open("output",'w+')
+            result.write(f'{y_predicted}')
+
+        torch.save(model, "modelP.pkl")
+        
+        _run.info['accuracy'] = accuracy_score(y_testing, np.argmax(y_predicted, axis=1))
+        
+        
+ex.run()
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