input: Write a python function to create a DataFrame, split data into training and testing sets, train a linear regression model, and make predictions.
  assert len(predictions) == len(x_test)
  assert isinstance(predictions, np.ndarray)
output: import pandas as pd
  import numpy as np
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LinearRegression
  
  def create_and_train_model(data):
      df = pd.DataFrame(data)
      X = df.iloc[:, :-1]  # All columns except the last one
      y = df.iloc[:, -1]   # Only the last column
  
      x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = LinearRegression()
      model.fit(x_train, y_train)
      
      predictions = model.predict(x_test)
      return predictions