--- a
+++ b/scripts/data/preparation.py
@@ -0,0 +1,38 @@
+from sklearn.model_selection import train_test_split
+import warnings
+import pandas as pd
+import os
+
+warnings.filterwarnings('ignore')
+
+# Load processed datasets
+try:
+    original = pd.read_csv(open(os.path.join(os.path.dirname(__file__), '../../data/processed/original.csv'), 'r'))
+    synthetic = pd.read_csv(open(os.path.join(os.path.dirname(__file__), '../../data/processed/synthetic.csv'), 'r'))
+except FileNotFoundError as err:
+    print(f'Ann error occoured: {err}')
+
+# mix both original and synthetic sets
+data = pd.concat([original, synthetic], keys=[1, 2]).drop_duplicates().dropna()
+
+# Select relevant features
+def feature_selection():
+    feature = data[['GENDER', 'AGE', 'SMOKING', 'YELLOW_FINGERS', 'FATIGUE', 'WHEEZING', 'COUGHING', 'SHORTNESS OF BREATH', 'SWALLOWING DIFFICULTY', 'CHEST PAIN', 'CHRONIC DISEASE']]
+    label = data['LUNG_CANCER']
+   
+    return pd.concat([feature, label], axis=1).drop_duplicates().dropna()
+
+# Split train/test holdout sets
+def data_split(feature, label):
+    X_train, X_test, y_train, y_test = train_test_split(feature, label, test_size=0.2, random_state=42, stratify=label)
+
+    train_data = pd.concat([X_train, y_train], axis=1)
+    test_data = pd.concat([X_test , y_test], axis=1)
+
+    train_data.to_csv('../../data/input/train.csv', index=False)
+    test_data.to_csv('../../data/input/test.csv', index=False)
+
+    return f'Training set: {X_train.shape[0]} and Testing set: {X_test.shape[0]} save.'
+
+if __name__ == '__main__':
+   print(data_split(feature_selection().drop('LUNG_CANCER', axis='columns'), feature_selection()['LUNG_CANCER']))
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