diff --git a/work-with-data/datasets/datasets-tutorial/train-dataset/train.py b/work-with-data/datasets/datasets-tutorial/train-dataset/train.py
deleted file mode 100644
index 9fbca891..00000000
--- a/work-with-data/datasets/datasets-tutorial/train-dataset/train.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import azureml.dataprep as dprep
-import azureml.core
-import pandas as pd
-import logging
-import os
-import datetime
-import shutil
-
-from azureml.core import Workspace, Datastore, Dataset, Experiment, Run
-from sklearn.model_selection import train_test_split
-from azureml.core.compute import ComputeTarget, AmlCompute
-from azureml.core.compute_target import ComputeTargetException
-from sklearn.tree import DecisionTreeClassifier
-
-run = Run.get_context()
-workspace = run.experiment.workspace
-
-dataset_name = 'clean_Titanic_tutorial'
-
-dataset = Dataset.get(workspace=workspace, name=dataset_name)
-df = dataset.to_pandas_dataframe()
-
-x_col = ['Pclass', 'Sex', 'SibSp', 'Parch']
-y_col = ['Survived']
-x_df = df.loc[:, x_col]
-y_df = df.loc[:, y_col]
-
-x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=223)
-
-data = {"train": {"X": x_train, "y": y_train},
-
-        "test": {"X": x_test, "y": y_test}}
-
-clf = DecisionTreeClassifier().fit(data["train"]["X"], data["train"]["y"])
-
-print('Accuracy of Decision Tree classifier on training set: {:.2f}'.format(clf.score(x_train, y_train)))
-print('Accuracy of Decision Tree classifier on test set: {:.2f}'.format(clf.score(x_test, y_test)))