diff --git a/MLflow/full.py b/MLflow/full.py
new file mode 100644
index 0000000..f79b841
--- /dev/null
+++ b/MLflow/full.py
@@ -0,0 +1,141 @@
+import subprocess
+import zipfile
+import os
+import pandas as pd
+import re
+from sklearn.model_selection import train_test_split
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import mlflow
+
+def download_kaggle_dataset(dataset_id, destination_folder):
+    try:
+        result = subprocess.run(["kaggle", "datasets", "download", "-d", dataset_id, "-p", destination_folder], check=True, capture_output=True, text=True)
+        zip_filename = re.search(r"(\S+\.zip)", result.stdout).group(1)
+        print(f"Dataset {dataset_id} successfully downloaded.")
+        return os.path.join(destination_folder, zip_filename)
+    except subprocess.CalledProcessError as e:
+        print(f"Error downloading dataset {dataset_id}: {e}")
+        return None
+
+def unzip_file(zip_filepath, destination_folder):
+    try:
+        with zipfile.ZipFile(zip_filepath, 'r') as zip_ref:
+            zip_ref.extractall(destination_folder)
+            print(f"Files extracted to {destination_folder}.")
+    except Exception as e:
+        print(f"Error unzipping file {zip_filepath}: {e}")
+
+def combine_csv_files(train_file, test_file, output_file):
+    try:
+        train_df = pd.read_csv(train_file)
+        test_df = pd.read_csv(test_file)
+        combined_df = pd.concat([train_df, test_df], ignore_index=True)
+        combined_df.to_csv(output_file, index=False)
+        print(f"Combined CSV files saved to {output_file}.")
+    except Exception as e:
+        print(f"Error combining CSV files: {e}")
+
+def split_data(data, train_ratio, dev_ratio, random_seed=42):
+    train_data, temp_data = train_test_split(data, train_size=train_ratio, random_state=random_seed)
+    dev_data, test_data = train_test_split(temp_data, train_size=dev_ratio / (1 - train_ratio), random_state=random_seed)
+    return train_data, dev_data, test_data
+
+class SimpleNN(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(SimpleNN, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, output_size)
+        self.softmax = nn.Softmax(dim=1)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = self.fc2(x)
+        return self.softmax(x)
+
+def main():
+    with mlflow.start_run():
+        dataset_id = "iabhishekofficial/mobile-price-classification"
+        destination_folder = "/app/data"
+        zip_filepath = download_kaggle_dataset(dataset_id, destination_folder)
+
+        if zip_filepath is not None:
+            unzip_file(zip_filepath, destination_folder)
+            train_file = os.path.join(destination_folder, "train.csv")
+            test_file = os.path.join(destination_folder, "test.csv")
+            output_file = os.path.join(destination_folder, "combined.csv")
+            combine_csv_files(train_file, test_file, output_file)
+
+            data = pd.read_csv(output_file)
+            train_data, dev_data, test_data = split_data(data, train_ratio=0.6, dev_ratio=0.2)
+
+            output_dir = "/app/output"
+            os.makedirs(output_dir, exist_ok=True)
+            train_data.to_csv(os.path.join(output_dir, 'Train1.csv'), index=False)
+            dev_data.to_csv(os.path.join(output_dir, 'Dev1.csv'), index=False)
+            test_data.to_csv(os.path.join(output_dir, 'Test1.csv'), index=False)
+
+            print(f"Liczba wierszy w pliku Train1.csv: {len(train_data)}")
+            print(f"Liczba wierszy w pliku Dev1.csv: {len(dev_data)}")
+            print(f"Liczba wierszy w pliku Test1.csv: {len(test_data)}")
+
+        train_file_path = os.path.join(output_dir, 'Train1.csv')
+        train_data = pd.read_csv(train_file_path)
+        train_data = train_data.dropna(subset=['price_range'])
+
+        valid_values = {0.0, 1.0, 2.0, 3.0}
+        assert set(train_data['price_range'].unique()) <= valid_values, "Unexpected values in price_range"
+
+        input_size = len(train_data.columns) - 2  
+        hidden_size = 50
+        output_size = len(valid_values)
+
+        # Logowanie parametrów
+        mlflow.log_param("input_size", input_size)
+        mlflow.log_param("hidden_size", hidden_size)
+        mlflow.log_param("output_size", output_size)
+
+        model = SimpleNN(input_size, hidden_size, output_size)
+
+        criterion = nn.CrossEntropyLoss()
+        optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+        epochs = 15
+        for epoch in range(epochs):
+            inputs = torch.tensor(train_data.drop(['price_range', 'id'], axis=1).values, dtype=torch.float32)
+            labels = torch.tensor(train_data['price_range'].values, dtype=torch.long)
+
+            optimizer.zero_grad()
+            outputs = model(inputs)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+
+            # Logowanie metryk
+            mlflow.log_metric("loss", loss.item(), step=epoch)
+
+            print(f"Epoch {epoch+1}/{epochs}, Loss: {loss.item()}")
+
+        save_path = "model.pth"
+        torch.save(model.state_dict(), save_path)
+
+        model.load_state_dict(torch.load("model.pth"))
+        model.eval() 
+
+        test_file_path = os.path.join(output_dir, 'Test1.csv')
+        test_data = pd.read_csv(test_file_path)
+
+        inputs = torch.tensor(test_data.drop(['price_range', 'id'], axis=1).values, dtype=torch.float32)
+        with torch.no_grad():
+            predictions = model(inputs)
+        predicted_classes = torch.argmax(predictions, dim=1)
+
+        predicted_classes_df = pd.DataFrame(predicted_classes.numpy(), columns=['Predicted_Price_Range'])
+        predicted_classes_df['Actual_Price_Range'] = test_data['price_range'].values
+
+        output_path = 'predictions.csv'
+        predicted_classes_df.to_csv(output_path, index=False)
+
+if __name__ == "__main__":
+    main()