input: Write a python function to read a CSV file, preprocess the data, split it into training and test sets, scale the data, build and train a neural network model, and make predictions.
  assert isinstance(neural_network_predictions('customer_data.csv'), np.ndarray)
  assert neural_network_predictions('customer_data.csv').shape == (len(pd.read_csv('customer_data.csv')), 1)
output: def neural_network_predictions(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from keras.models import Sequential
      from keras.layers import Dense
      import numpy as np
  
      # Load the data
      data = pd.read_csv(file_path)
      
      # Preprocess the data (assuming the last column is the target)
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Scale the data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
      
      # Build the neural network model
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))
      
      # Compile the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      
      # Train the model
      model.fit(X_train, y_train, epochs=50, batch_size=10, verbose=0)
      
      # Make predictions
      predictions = model.predict(X_test)
      
      return predictions.reshape(-1, 1)