View difference between Paste ID: N0htnps7 and Ky3TBXNW

 from sklearn import datasets, linear_model
 from sklearn.metrics import r2_score, mean_squared_error
 from sklearn.preprocessing import PolynomialFeatures
 from sklearn.linear_model import LinearRegression
 from sklearn.pipeline import Pipeline
 from sklearn.model_selection import cross_val_score
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 
 ## подкл выборку
 
 x_train = pd.read_table ("tic/ticdata2000.txt", header = None).iloc [0:4000, 0:85]
 x_test = x_train.iloc[0:4000, 84]
 
 
 y_train = pd.read_table ("tic/ticeval2000.txt", header = None)
 y_test = pd.read_table ("tic/tictgts2000.txt", header = None)
 
 
 ## обучение выборки
 regression = linear_model.LinearRegression()
 regression.fit (x_train, y_train)
 
 
 train_predict = regression.predict (x_train)
 
 print('Коэффициенты: \n', regression.coef_)
 print("Cреднеквадратичная ошибка: %.2f" % mean_squared_error(y_train, train_predict))
 print('Оценка отклонения: %.2f' % r2_score(y_train, train_predict))
 
 ## Проверка точности модели по тестовой выборке и запись в результирующий файл
 y_pr = pd.DataFrame(train_predict)
 y_test = y_test.reset_index(drop = True)
 res = pd.concat([y_pr, y_test], axis=1)
-res.to_csv("result.txt", index = False)
+res.to_csv("result.txt", index = False)