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@ -4,7 +4,7 @@ from sklearn.preprocessing import MinMaxScaler
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model = tf.keras.models.load_model('model.h5')
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data = pd.read_csv('Athletes_winter_games.csv', sep=';')
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data = pd.read_csv('data.csv', sep=';')
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data = pd.get_dummies(data, columns=['Sex', 'Medal'])
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data = data.drop(columns=['Name', 'Team', 'NOC', 'Games', 'Year', 'Season', 'City', 'Sport', 'Event'])
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2
train.py
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train.py
@ -5,7 +5,7 @@ import tensorflow as tf
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from imblearn.over_sampling import SMOTE
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smote = SMOTE(random_state=42)
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data = pd.read_csv('Athletes_winter_games.csv', sep=';')
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data = pd.read_csv('data.csv', sep=';')
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print('Total rows:', len(data))
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print('Rows with medal:', len(data.dropna(subset=['Medal'])))
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