input: Write a python function to build and evaluate a RandomForestClassifier on a loan dataset.
  assert build_and_evaluate_model('loan_data.csv') >= 0.7
  assert isinstance(build_and_evaluate_model('loan_data.csv'), float)
output: def build_and_evaluate_model(csv_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
      
      # Load the dataset
      data = pd.read_csv(csv_path)
      
      # Assuming the target variable is 'Loan_Status' and features are all other columns
      X = data.drop('Loan_Status', axis=1)
      y = data['Loan_Status']
      
      # Split the data 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)
      
      # Initialize the RandomForestClassifier
      model = RandomForestClassifier(random_state=42)
      
      # Train the model
      model.fit(X_train, y_train)
      
      # Make predictions
      y_pred = model.predict(X_test)
      
      # Calculate accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy