input: Write a python function to load a CSV file, split the data into features and target, and train a RandomForestClassifier model.
  assert isinstance(model_training('data.csv'), RandomForestClassifier)
  assert model_training('data.csv').max_depth == 5
  assert model_training('data.csv').n_estimators == 100
output: def model_training(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
  
      # Load the CSV file
      data = pd.read_csv(file_path)
      
      # Assuming the last column is the target and the rest are features
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      # Split the 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)
      
      # Initialize the RandomForestClassifier with specific parameters
      model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=42)
      
      # Train the model
      model.fit(X_train, y_train)
      
      return model