diff --git a/sacred_model.py b/sacred_model.py
new file mode 100644
index 0000000..6b4942a
--- /dev/null
+++ b/sacred_model.py
@@ -0,0 +1,54 @@
+from sacred import Experiment
+from sacred.observers import FileStorageObserver
+import pandas as pd
+import numpy as np
+from tensorflow.keras import Sequential
+from tensorflow.keras.layers import Dense
+from sklearn.preprocessing import MinMaxScaler
+
+ex = Experiment("car_price_prediction")
+
+ex.observers.append(FileStorageObserver('my_runs'))
+
+
+@ex.config
+def my_config():
+    data_path = './data/car_prices_train.csv'
+    epochs = 20
+    batch_size = 32
+    model_path = './car_prices_predict_model.h5'
+
+
+@ex.main
+def train_model(data_path, epochs, batch_size, model_path, _run):
+    train_data = pd.read_csv(data_path)
+    train_data.dropna(inplace=True)
+
+    y_train = train_data['sellingprice'].astype(np.float32)
+    X_train = train_data[['year', 'condition', 'transmission']]
+    scaler_x = MinMaxScaler()
+    X_train['condition'] = scaler_x.fit_transform(X_train[['condition']])
+    scaler_y = MinMaxScaler()
+    y_train = scaler_y.fit_transform(y_train.values.reshape(-1, 1))
+    X_train = pd.get_dummies(X_train, columns=['transmission'])
+
+    model = Sequential([
+        Dense(64, activation='relu'),
+        Dense(32, activation='relu'),
+        Dense(1)
+    ])
+
+    model.compile(optimizer='adam', loss='mean_squared_error')
+    model.summary(print_fn=lambda x: _run.info.setdefault('model_summary', []).append(x))
+    history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size)
+
+    for epoch, loss in enumerate(history.history['loss']):
+        _run.log_scalar("loss", loss, epoch)
+
+    model.save(model_path)
+
+
+if __name__ == "__main__":
+    ex.run()
+
+