input: Write a python function to evaluate the accuracy of a logistic regression model on a given dataset.
  assert evaluate_model_accuracy("data.csv") > 0.7
  assert evaluate_model_accuracy("another_data.csv") > 0.5
output: def evaluate_model_accuracy(file_path):
      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 dataset
      data = pd.read_csv(file_path)
      
      # Assume the last column is the target variable and the rest are features
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      # Split the dataset 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)
      
      # Create and train the logistic regression model
      model = LogisticRegression(max_iter=1000)
      model.fit(X_train, y_train)
      
      # Predict on the test set
      y_pred = model.predict(X_test)
      
      # Evaluate the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy