Hello,

I`m doing a rmse project, I have two vectors:

**test_set**: values 0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5 and 5

**forecast_rating**: values: 2.939235 3.738641 3.419572, etc.

But I need to create a **forecast_rating_2** vector, but like **test_set** observations, like 0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5 and 5.

set.seed(1)

test_set %>%

left_join(edx1 %>%

group_by(movieId) %>%

summarise(fe = mean(rating - mu)), by = "movieId")

join <- test_set %>%

left_join(edx1 %>%

group_by(movieId) %>%

summarise(fe = mean(rating - mu)), by = "movieId")

forecast_rating <- mu + join$fe

forecast_rating

# NOW RMSE:

mu <- mean(edx1$rating)

mu

set.seed(1)

difference <- test_set_r-forecast_rating

rmse <- sqrt(mean(difference^2))

rmse

## 0.94

(0.94 IS NOT GOOD, BECAUSE I NEED TO HAVE LESS THAN 0.9, so I think if I have a **forecast_rating_2** vector, I will be more close to a 0.8 approximations.

Thank you!

Any recommendation will be very useful

thanks a lot