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