47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MinMaxScaler
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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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print('Total rows:', len(data))
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print('Rows with medal:', len(data.dropna(subset=['Medal'])))
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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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scaler = MinMaxScaler()
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data = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
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X = data.filter(regex='Sex|Age')
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y = data.filter(regex='Medal')
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y = pd.get_dummies(y)
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X = X.fillna(0)
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y = y.fillna(0)
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y = y.values
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X_resampled, y_resampled = smote.fit_resample(X, y)
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X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)
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model = tf.keras.models.Sequential()
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model.add(tf.keras.layers.Dense(64, input_dim=X_train.shape[1], activation='relu'))
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model.add(tf.keras.layers.Dense(32, activation='relu'))
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model.add(tf.keras.layers.Dense(y.shape[1], activation='softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
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model.fit(X_train, y_train, epochs=10, batch_size=32)
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loss, accuracy = model.evaluate(X_test, y_test)
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print('Test accuracy:', accuracy)
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model.save('model.h5')
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