input: def predict_stock_market_model(data: pd.DataFrame) -> tf.keras.models.Sequential:
      """ Predicts stock market movements using a deep learning model.
      The function takes a DataFrame as input, splits it into train and test data, 
      builds a neural network model, trains it, and then evaluates it.
      >>> data = pd.read_csv('data.csv')
      >>> model = predict_stock_market_model(data)
      >>> score = model.evaluate(data.iloc[:, 0], data.iloc[:, 1], verbose=0)
      >>> score
      [0.001, 0.001]
      """
output:     
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      import tensorflow as tf
  
      # Split the data into features and target
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Build the model
      model = tf.keras.models.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          tf.keras.layers.Dense(32, activation='relu'),
          tf.keras.layers.Dense(1, activation='linear')
      ])
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)
  
      return model
  