Untitled

import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score

df = pd.read_csv('visitors.csv')

X_train, X_test, y_train, y_test = train_test_split(df[['interests', 'n_stands']], df['rating'], test_size=0.2)

regModel = LinearRegression()
regModel.fit(X_train, y_train)
y_pred = regModel.predict(X_test)

accuracy = regModel.score(X_test, y_test)
mae = mean_absolute_error(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_test, y_pred)

print('Accuracy:', accuracy)
print('MAE:', mae)
print('MSE:', mse)
print('RMSE:', rmse)
print("R-squared score:", r2)