input: Write a python function to predict fatalities using linear regression on a given dataset.
  assert predict_fatalities('data.csv')[0] is not None
  assert isinstance(predict_fatalities('data.csv')[1], float)
  assert len(predict_fatalities('data.csv')[2]) > 0
output: def predict_fatalities(file_path):
      import pandas as pd
      from sklearn.linear_model import LinearRegression
      
      # Load the dataset
      df = pd.read_csv(file_path)
      
      # Assuming the dataset has columns 'feature1', 'feature2', ..., 'fatalities'
      features = df.drop(columns=['fatalities'])
      target = df['fatalities']
      
      # Train a linear regression model
      model = LinearRegression()
      model.fit(features, target)
      
      # Predict using the model
      predictions = model.predict(features)
      
      # Return the model, an example prediction, and all predictions
      return model, predictions[0], predictions.tolist()