code: import numpy as np
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import KNeighborsClassifier
  
  # NOTE: Make sure that the outcome column is labeled 'target' in the data file
  tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
  features = tpot_data.drop('target', axis=1)
  training_features, testing_features, training_target, testing_target = \
              train_test_split(features, tpot_data['target'], random_state=42)
  
  # Average CV score on the training set was: 0.9996457287206185
  exported_pipeline = KNeighborsClassifier(n_neighbors=2, p=1, weights="distance")
  # Fix random state in exported estimator
  if hasattr(exported_pipeline, 'random_state'):
      setattr(exported_pipeline, 'random_state', 42)
  
  exported_pipeline.fit(training_features, training_target)
  results = exported_pipeline.predict(testing_features)
  
apis: pandas.read_csv sklearn.neighbors.KNeighborsClassifier sklearn.model_selection.train_test_split