In complex models like neural networks, you can use embeddings to convert player names into dense numerical vectors. This is especially useful when you have a large number of unique player names and want to capture relationships or similarities between players. Here’s an outline of how to implement embeddings: - Convert player names into categorical indices. - Use a neural network embedding layer that learns a vector representation for each player. Example using **Keras** embeddings: ``` python from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Embedding, Dense, Flatten # Example: Assuming player_name has been converted to indices num_players = 1000 # Total number of unique players embedding_dim = 10 # Dimension of embedding space model = Sequential([ Embedding(input_dim=num_players, output_dim=embedding_dim, input_length=1), Flatten(), Dense(1) # Output layer for regression ]) model.compile(optimizer='adam', loss='mean_squared_error') ```