Ошибка при проведении регрессивного анализа с помощью нейронной сети

# Используя набор данных, провести регрессивный анализ с помощью нейронной сети. 
# Данные: https://www.kaggle.com/gregorut/videogamesales

from google.colab import drive 
drive.mount('/content/drive/')
root_folder = "drive/My Drive/lab6"

import sklearn
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler 
from sklearn.neural_network import MLPClassifier
import pandas as pd

from sklearn.preprocessing import LabelEncoder

#encoder = LabelEncoder()
#words = ['Rank','Name', 'Platform', 'Year', 'Genre', 'NA_Sales', 'EU_Sales', 'JP_Sales', 'Other_Sales', 'Global_Sales']
#print(encoder.fit_transform(words))

allColumns = pd.read_csv(root_folder + "/vgsales.csv", encoding= 'unicode_escape')

X = allColumns.drop(columns=['Global_Sales'])

encoder = LabelEncoder()

Y = encoder.fit_transform(allColumns['Global_Sales'])

X['Name'] = encoder.fit_transform(allColumns['Name'])

X['Platform'] = encoder.fit_transform(allColumns['Platform'])

X['Genre'] = encoder.fit_transform(allColumns['Genre'])

X['Publisher'] = encoder.fit_transform(allColumns['Publisher']) #тут ошибка argument must be a string or number

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)

nn = MLPClassifier(hidden_layer_sizes=(10, 10))

nn.fit(X_train, Y_train)
scaler = MinMaxScaler()

X_train = scaler.fit_transform(X_train)

X_test = scaler.fit_transform(X_test)

mlp = MLPRegressor(
    hidden_layer_sizes=(20),
    activation='logistic',
    solver='lbfgs,'#adam на замену
)
nn.fit(X_train, Y_train)

print("Точность на обучающем наборе: ", nn.score(X_train, Y_train))


print("Точность на тестовом наборе: ", nn.score(X_test,Y_test))

Возникают ошибки, как решить?