2022-04-03 20:19:58 +02:00
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# coding=<UTF-8>
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2022-04-03 19:10:49 +02:00
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import pandas as pd
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from sklearn.model_selection import train_test_split
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# Data preproccesing
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no_shows=pd.read_csv('KaggleV2-May-2016.csv')
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# Usunięcie negatywnego wieku
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no_shows = no_shows.drop(no_shows[no_shows["Age"] < 0].index)
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# Usunięcie kolumn PatientId oraz AppointmentID
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no_shows.drop(["PatientId", "AppointmentID"], inplace=True, axis=1)
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# Zmiena wartości kolumny No-show z Yes/No na wartość boolowską
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no_shows["No-show"] = no_shows["No-show"].map({'Yes': 1, 'No': 0})
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# Normalizacja kolumny Age
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no_shows["Age"]=(no_shows["Age"]-no_shows["Age"].min())/(no_shows["Age"].max()-no_shows["Age"].min())
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X = no_shows.drop(columns=['No-show'])
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y = no_shows['No-show']
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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