ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it?

cv = KFold(n_splits = 10)
all_loss = []

for i in range(9):  # 1st for loop over polynomial orders 
    poly_order = i
    X_train = make_polynomial(x, poly_order) 
    loss_at_order = [] # initiate a set to collect loss for CV
    for train_index, test_index in cv.split(X_train):
        print('TRAIN:', train_index, 'TEST:', test_index)
        X_train_cv, X_test_cv = X_train[train_index], X_test[test_index]  
        t_train_cv, t_test_cv = t[train_index], t[test_index]
        reg.fit(X_train_cv, t_train_cv)
        loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold
    all_loss.append(np.mean(loss_at_order)) # collect loss at order
plt.plot(np.log(all_loss), 'bo-') # plot log(loss) at order
plt.xlabel('Polynomial Order') # always label x&y-axis
plt.ylabel('Log Loss') # always label x&y-axis

---

ValueError                                Traceback (most recent call last)
Input In [51], in <cell line: 4>()
     11         t_train_cv, t_test_cv = t[train_index], t[test_index]
     12         reg.fit(X_train_cv, t_train_cv)
---> 13         loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold
     14     all_loss.append(np.mean(loss_at_order)) # collect loss at order
     15 plt.plot(np.log(all_loss), 'bo-') # plot log(loss) at order

File ~\anaconda3\lib\site-packages\sklearn\linear_model\_base.py:362, in LinearModel.predict(self, X)
    348 def predict(self, X):
    349     """
    350     Predict using the linear model.
    351 
   (...)
    360         Returns predicted values.
    361     """
--> 362     return self._decision_function(X)

File ~\anaconda3\lib\site-packages\sklearn\linear_model\_base.py:345, in LinearModel._decision_function(self, X)
    342 def _decision_function(self, X):
    343     check_is_fitted(self)
--> 345     X = self._validate_data(X, accept_sparse=["csr", "csc", "coo"], reset=False)
    346     return safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_

File ~\anaconda3\lib\site-packages\sklearn\base.py:585, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params)
    582     out = X, y
    584 if not no_val_X and check_params.get("ensure_2d", True):
--> 585     self._check_n_features(X, reset=reset)
    587 return out

File ~\anaconda3\lib\site-packages\sklearn\base.py:400, in BaseEstimator._check_n_features(self, X, reset)
    397     return
    399 if n_features != self.n_features_in_:
--> 400     raise ValueError(
    401         f"X has {n_features} features, but {self.__class__.__name__} "
    402         f"is expecting {self.n_features_in_} features as input."
    403     )

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input.

source https://stackoverflow.com/questions/74145897/valueerror-x-has-10-features-but-linearregression-is-expecting-1-features-as-i