text: import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.multioutput import MultiOutputClassifier
  from sklearn.preprocessing import LabelEncoder
  from sklearn.svm import SVC
  
  encoder = LabelEncoder()
  
  column_names = [
      "class", "largest_spot_size", "spot_distribution", "activity",
      "evolution", "past24hour_flare_activity", "historically_complex",
      "region_became_historically_complex_on_pass", "area", "largest_spot_area",
      "common_flare", "moderate_flare", "severe_flare"
  ]
  
  flare_1 = pd.read_csv("flare1.csv", sep=" ", names=column_names).apply(LabelEncoder().fit_transform)
  flare_2 = pd.read_csv("flare2.csv", sep=" ", names=column_names).apply(LabelEncoder().fit_transform)
  
  # Clean the datasets
  df = pd.concat([flare_1, flare_2])
  
  X = df.drop(["common_flare", "moderate_flare", "severe_flare"], axis=1).values
  y = df[["common_flare", "moderate_flare", "severe_flare"]].values
  
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
  # Create Multiclassifier model
  model = MultiOutputClassifier(SVC(kernel='linear'))
  
  model.fit(X_train, y_train)
  
  # Print stats
  print(f"Accuracy: {model.score(X_test, y_test)}")
  