https://app.dataquest.io/m/140/multivariate-k-nearest-neighbors/8/calculating-mse-using-scikit-learn

I am new to scikit-learn and machine learning and only just started with the data scientist path. I usually work on the DataQuest screen as well as on a local jupyter notebook. I’m getting different predictions and mse / rmse values and just wanted to confirm this is to be expected and not something I have done wrong?

DataQuest - first few predictions:

My first few predictions:

DataQuest MSE and RMSE

My MSE and RMSE

The code I use to validate DataQuest and in my local environment are exactly the same.

If differences are to be expected, is there any way in which I could set a random seed to avoid confusion moving forward?

I also noticed that the first predictions are run using the default metric ‘minkowski’, whereas in the MSE / RMSE calculation screen, the code switches to ‘euclidean’ metric. Could anyone explain the difference?

My code:

```
train_df = normalised_listings.iloc[0:2792].copy()
test_df = normalised_listings.iloc[2792:].copy()
knn = KNeighborsRegressor(algorithm="brute")
train_features = train_df[["accommodates", "bathrooms"]] # training data - feature columns
train_target = train_df["price"] # training data - target column
knn.fit(train_features, train_target)
predictions = knn.predict(test_df[['accommodates', 'bathrooms']])
from sklearn.metrics import mean_squared_error
two_features_mse = mean_squared_error(test_df["price"], predictions)
two_features_rmse = np.sqrt(two_features_mse)
print(two_features_mse, two_features_rmse)
```

Thank you for your help!