input: Write a python function to train and evaluate a logistic regression model on a given dataset.
  assert train_evaluate_model('transactions.csv') > 0.7
  assert train_evaluate_model('small_dataset.csv') > 0.6
  assert train_evaluate_model('large_dataset.csv') > 0.8
output: def train_evaluate_model(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
      from sklearn.preprocessing import StandardScaler
  
      # Load the dataset
      data = pd.read_csv(file_path)
  
      # Assume the last column is the target variable and others are features
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split the data into train and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Train the logistic regression model
      model = LogisticRegression()
      model.fit(X_train, y_train)
  
      # Evaluate the model
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy