input: Write a python function to train a RandomForestRegressor model using a given dataset and evaluate its accuracy.
  assert train_and_evaluate_model('crypto_data.csv')<1000
  assert train_and_evaluate_model('crypto_data_v2.csv')>500
output: def train_and_evaluate_model(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestRegressor
      from sklearn.metrics import mean_squared_error
  
      # Load dataset
      data = pd.read_csv(file_path)
  
      # Assume the target variable is the last column 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)
  
      # Initialize the RandomForestRegressor
      model = RandomForestRegressor(n_estimators=100, random_state=42)
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Make predictions
      y_pred = model.predict(X_test)
  
      # Evaluate the model
      mse = mean_squared_error(y_test, y_pred)
      return mse