37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
#!/usr/bin/python
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from kaggle import api
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from pandas import read_csv, DataFrame
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from sklearn.model_selection import train_test_split
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def download_and_save_dataset():
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api.authenticate()
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api.dataset_download_files('shivamb/real-or-fake-fake-jobposting-prediction', unzip=True)
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def split_dataset(data: DataFrame):
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train_ratio, validation_ratio, test_ratio = 0.6, 0.2, 0.2
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data_x, data_y = data.iloc[:, :-1], data.iloc[:, -1:]
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x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=1 - train_ratio, random_state=123)
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x_val, x_test, y_val, y_test = train_test_split(x_test, y_test,
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test_size=test_ratio / (test_ratio + validation_ratio),
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random_state=123)
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return x_train, x_val, x_test, y_train, y_val, y_test
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def main():
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# download_and_save_dataset()
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df = read_csv('./fake_job_postings.csv')
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print(df.describe(include='all'))
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print(df.shape)
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x_train, x_val, x_test, y_train, y_val, y_test = split_dataset(df)
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print(x_train.shape, x_val.shape, x_test.shape)
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print(y_train.shape, y_val.shape, y_test.shape)
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if __name__ == '__main__':
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main()
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