1. import tensorflow as tf
2. from sklearn.model_selection import train_test_split
3.  
4. X = df.drop('W/L', axis=1)
5. y = df['W/L']  
6.  
7. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2137)
8. model = tf.keras.Sequential([
9.     tf.keras.layers.Input(shape=(X_train.shape[1],)),  
10.     tf.keras.layers.Dense(16384, activation='relu'),
11.     tf.keras.layers.Dense(4096, activation='relu'),  
12.     tf.keras.layers.Dense(1024, activation='relu'),    
13.     tf.keras.layers.Dense(256, activation='relu'),  
14.     tf.keras.layers.Dense(64, activation='relu'),
15.     tf.keras.layers.Dense(32, activation='relu'),
16.     tf.keras.layers.Dense(1, activation='sigmoid')    
17. ])
18.  
19. model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
20. history = model.fit(X_train, y_train, epochs=10, batch_size=100, validation_data=(X_test, y_test))