input: Write a python function to split data into training and test sets, fit a Logistic Regression model, make predictions, and evaluate performance.
  assert logistic_regression_model('data.csv') > 0.5  # Assuming a sample accuracy threshold
  assert isinstance(logistic_regression_model('data.csv'), float)  # Check if the output is a float
output: def logistic_regression_model(data_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
  
      # Load the data
      data = pd.read_csv(data_file)
  
      # Assuming the last column is the target variable
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split 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)
  
      # Fit a Logistic Regression model
      model = LogisticRegression()
      model.fit(X_train, y_train)
  
      # Make predictions
      y_pred = model.predict(X_test)
  
      # Evaluate performance
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy