diff --git a/sacred.py b/sacred.py
deleted file mode 100644
index 42342e9..0000000
--- a/sacred.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import tensorflow as tf
-from sacred import Experiment
-from sacred.observers import FileStorageObserver
-import pandas as pd
-import sklearn
-import sklearn.model_selection
-import numpy as np
-
-ex = Experiment('452662')
-ex.observers.append(FileStorageObserver.create('my_runs'))
-#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
-
-def normalize(df,feature_name):
-    result = df.copy()
-    max_value = df[feature_name].max()
-    min_value = df[feature_name].min()
-    result[feature_name] = (df[feature_name] - min_value) / (max_value - min_value)
-    return result
-
-
-@ex.automain
-def run_experiment():
-    cars = pd.read_csv('zbior_ium/Car_Prices_Poland_Kaggle.csv')
-
-    cars = cars.drop(73436) #wiersz z błednymi danymi
-    
-    cars_normalized = normalize(cars,'vol_engine')
-    
-    cars_train, cars_test = sklearn.model_selection.train_test_split(cars_normalized, test_size=23586, random_state=1)
-    cars_dev, cars_test = sklearn.model_selection.train_test_split(cars_test, test_size=11793, random_state=1)
-    cars_train.rename(columns = {list(cars_train)[0]: 'id'}, inplace = True)
-    cars_train.to_csv('train.csv')
-    cars_test.to_csv('test,csv')
-    
-    feature_cols = ['year', 'mileage', 'vol_engine']
-    inputs = tf.keras.Input(shape=(len(feature_cols),))
-
-    x = tf.keras.layers.Dense(10, activation='relu')(inputs)
-    x = tf.keras.layers.Dense(10, activation='relu')(x)
-    outputs = tf.keras.layers.Dense(1, activation='linear')(x)
-
-    model = tf.keras.Model(inputs=inputs, outputs=outputs)
-
-    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
-                  loss='mse', metrics=['mae'])
-
-    model.fit(cars_train[feature_cols], cars_train['price'], epochs=100)
-
-    ex.add_resource('train_data.csv')
-    ex.add_resource('test_data.csv')
-
-    ex.add_artifact(__file__)
-
-    model.save('model.h5')
-    ex.add_artifact('model.h5')
-
-    metrics = model.evaluate(cars_train[feature_cols], cars_train['price'])
-    ex.log_scalar('mse', metrics[0])
-    ex.log_scalar('mae', metrics[1])
\ No newline at end of file