input: 
  def predict_stock_market(data: pd.DataFrame) -> float:
      """ Predict stock market movements using a deep learning model.
      The function takes a DataFrame with stock market data and returns the evaluation score of the model.
      
      >>> predict_stock_market(pd.DataFrame({'feature': [1.0, 2.0, 3.0], 'target': [2.0, 3.0, 4.0]}))
      1.0
      """
      
      #split data into train and test data
      X_train = data.iloc[:, 0]  # Choosing the independent variable 
      y_train = data.iloc[:, 1]  # Choosing the dependent variable
      X_test = data.iloc[:, 0]   # Choosing the independent variable 
      y_test = data.iloc[:, 1]   # Choosing the dependent variable
      
      # Build a deep learning model
      # import necessary libraries
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      # create a model and add layers
      model = Sequential()
      model.add(Dense(64, input_shape=(1,), activation='relu'))
      model.add(Dense(128, activation='relu'))
      model.add(Dense(64, activation='relu'))
      model.add(Dense(1))
  
      # compile the model
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
  
      # train the model
      model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
  
      # Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
      return score
  
output:     return score[1]