get and split data

main.py

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+import subprocess
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+process = subprocess.Popen("kaggle datasets download -d alwinjoseph/stress-detection-of-medical-partitioners",
+                           shell=True, stdout=subprocess.PIPE)
+process.wait()
+process = subprocess.Popen("tar -xf stress-detection-of-medical-partitioners.zip", shell=True, stdout=subprocess.PIPE)
+process.wait()
+
+data = pd.read_csv('data.csv')
+data_train, data_test = train_test_split(data, test_size=50000, random_state=1, stratify=data["Alcohol_usage"])
+print("All data")
+print("Number of rows ", data.shape[0])
+print("Number of columns ", data.shape[1])
+
+print("\nTrain data")
+print("Number of rows ", data_train.shape[0])
+print("Number of columns ", data_train.shape[1])
+
+print("\nTest data")
+print("Number of rows ", data_test.shape[0])
+print("Number of columns ", data_test.shape[1])
+
+print("\n")
+print(data.describe(include='all'))
+
+print("\n")
+print(data["Alcohol_usage"].value_counts())