ML supervised projects baseball

Good day,
Project: Predict Baseball Strike Zones With Machine Learning.
I wrote the code below to find the best score by looping through gamma and C.
The player_strike_zone_features(dataset,features) function takes a list of players and a list of features,
The code should then plot the players with the various feature comparison for the best score.

Problem: When feature list is changed in the for loop an error occur
ValueError: With n_samples=0, test_size=0.25 and train_size=None, the resulting train set will be empty. Adjust any of the aforementioned parameters.

The following featurelist will work:
featureslist =[[‘plate_x’, ‘plate_z’, ‘strikes’]]
featureslist =[[‘plate_x’, ‘plate_z’]]

This does not work:
featureslist =[[‘plate_x’, ‘plate_z’, ‘strikes’],[‘plate_x’, ‘plate_z’]]
featureslist =[[‘plate_x’, ‘plate_z’, ‘strikes’],[‘plate_x’, ‘plate_z’, ‘strikes’]] # Repeating the same list does not work
What am I doing wrong.

Thanks in advance.

import codecademylib3_seaborn
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from svm_visualization import draw_boundary
from players import aaron_judge, jose_altuve, david_ortiz

fig, ax = plt.subplots()

def player_strike_zone_features(dataset,features):
  dataset.type ={'S':1,'B':0})
  dataset=dataset.dropna(subset = features + ['type'])
  plt.scatter(x = dataset.plate_x, y =  dataset.plate_z, c = dataset.type, cmap =, alpha = 0.5 )
  training_set, validation_set = train_test_split(dataset, random_state = 1)
  bestAccuracy = {"score":0,"gamma":1, "C":1}
  for gamma in range(1,7):
    for C in range(1,7):
      classifier = SVC(kernel = 'rbf',gamma = gamma, C = C)[features],training_set['type'])
      if score > bestAccuracy["score"]:
        bestAccuracy["score"]= score
  classifier = SVC(kernel = 'rbf',gamma = bestAccuracy["gamma"], C = bestAccuracy["C"])[features],training_set['type'])
  if len(features) == 2:
    draw_boundary(ax, classifier)
  ax.set_ylim(-2, 6)
  ax.set_xlim(-3, 3)
  bestAccuracy["score"]=(bestAccuracy["score"] * 100).round(2) # Format score to percent
  ax.set_title("Name: {}.\n Features: {}\n Best: {}".format(dataset.player_name.unique()[0], features, bestAccuracy))

playerlist = [aaron_judge,jose_altuve,david_ortiz]
featureslist =[['plate_x', 'plate_z', 'strikes']]
#featureslist =[['plate_x', 'plate_z', 'strikes'],['plate_x', 'plate_z']] # Does not work

for player in playerlist: 
  for features in featureslist: