import tensorflow as tf
from sklearn.model_selection import train_test_split

X = df.drop('W/L', axis=1) 
y = df['W/L']  

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2137)
model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(X_train.shape[1],)),  
    tf.keras.layers.Dense(16384, activation='relu'),
    tf.keras.layers.Dense(4096, activation='relu'),  
    tf.keras.layers.Dense(1024, activation='relu'),     
    tf.keras.layers.Dense(256, activation='relu'),   
    tf.keras.layers.Dense(64, activation='relu'), 
    tf.keras.layers.Dense(32, activation='relu'), 
    tf.keras.layers.Dense(1, activation='sigmoid')     
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=10, batch_size=100, validation_data=(X_test, y_test))