34 lines
1.0 KiB
Python
34 lines
1.0 KiB
Python
import tensorflow as tf
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import pandas as pd
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train_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')
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X_train = train_data[['Sex']]
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y_train = train_data['Medal']
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X_train.loc[:, 'Sex'] = X_train['Sex'].map({'M': 0, 'F': 1})
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y_train = y_train.map({'Bronze': 0, 'Silver': 1, 'Gold': 1}).fillna(0).astype('float32')
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X_train = X_train.astype('float32')
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y_train = y_train.astype('float32')
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model = tf.keras.Sequential([
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tf.keras.layers.Dense(16, activation='relu', input_shape=(X_train.shape[1],)),
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tf.keras.layers.Dense(1, activation='sigmoid')
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])
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model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
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model.fit(X_train, y_train, epochs=10)
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model.save('model.h5')
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test_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')
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test_data.loc[:, 'Sex'] = test_data['Sex'].map({'M': 0, 'F': 1})
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test_data = test_data[['Sex']].astype('float32')
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predictions = model.predict(test_data)
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pd.DataFrame(predictions).to_csv('predictions.csv', index=False, header=False) |