The evaluation metric for this competition is [Mean Squared Error (MSE)](https://en.wikipedia.org/wiki/Mean_squared_error). MSE is a commonly used measure of the differences between predicted and observed values. It is calculated as the average of the squared differences between the prediction and the actual observation. The formula for MSE is given by:

$$\text{MSE} = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$

where 
$$y_i \text{ = actual value}, \newline
\hat{y}_i \text{ = predicted value, and} \newline
n \text{ = the number of observations.}$$

MSE gives a relatively high weight to large errors, making it particularly useful when large errors are especially undesirable.

## Submission File
For each ID in the test set, you must predict a particular value. The file should contain a header and have the following format:

    Index,value
    2,5.63
    5,3.14
    6,7.89
    etc.