input: Write a python function to split a dataset into training and test sets, scale the features, and build a TensorFlow Sequential model for regression.
  assert len(create_model(pd.read_csv('building.csv'))[0].layers) == 3  # Check if the model has 3 layers
  assert create_model(pd.read_csv('building.csv'))[1].shape[1] == len(pd.read_csv('building.csv').columns) - 1  # Check if the input shape matches the number of features
  assert create_model(pd.read_csv('building.csv'))[2].shape[1] == 1  # Check if the output layer has one neuron
output: import pandas as pd
  import numpy as np
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
  import tensorflow as tf
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  
  def create_model(data):
      # Assuming the last column is the target variable
      X = data.iloc[:, :-1].values
      y = data.iloc[:, -1].values
      
      # Splitting dataset 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)
      
      # Scaling the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
      
      # Building the TensorFlow Sequential 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))
      
      return model, X_train, y_train, X_test, y_test