The last line of code is running errors.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

dataset = pd.read_csv('50_Startups.csv')
X = dataset.iloc[:, :-1].values
Y = dataset.iloc[:, 4].values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
LabEn = LabelEncoder()
X[:, 3] = LabEn.fit_transform(X[:, 3])
from sklearn.compose import ColumnTransformer
ctrans = ColumnTransformer([("encoder", OneHotEncoder(), [3])], remainder='passthrough')
X = np.array(ctrans.fit_transform(X))
X = X[:, 1:]

from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, Y_train)
Y_pred = regressor.predict(X_test)

import statsmodels.regression.linear_model as sm
X = np.append(arr= np.ones((50, 1)).astype(int), values= X, axis=1)
X_opt = X[:, [0, 1, 2, 3, 4, 5]]
ols = sm.OLS(endog=Y, exog= X_opt).fit()

Reproduce the supplied Python script, including the final sm.OLS call, and capture the exact exception and traceback. Check the shapes and contents of Y and X_opt at that point, then confirm a corrected example by rerunning the script through model fitting; the issue is resolved when the final line runs without the reported error.

Written by the indexing model from the issue text.