The goal of this task is to predict the retention of each volunteer, which is defined as the number of future participations during the prediction time span from May 2021 to February 2022. 

## Submission Format

Please ensure that your submission files adhere to the following format requirements:

- The files should have two columns named `Id` and `Predicted`.
- The `Id` column should consist of integers representing the unique ID of each participant.
- Each `Predicted` value in the file represents the predicted number of times the corresponding participant will volunteer.

The file should contain a header and have the following format. Also, a submission sample is given at the [Data](https://www.kaggle.com/competitions/MLSP2023-volunteers-01/data?select=sample_submission_retention.csv) page
```
Id,Predicted
186335,3
148229,9

```

## Root Mean Square Error (RMSE)


The evaluation metric for this competition is [The root mean square error (RMSE)](https://en.wikipedia.org/wiki/Root-mean-square_deviation). The RMSE usually measures the average difference between a statistical model's predicted values and the actual values. It provides a quantitative measure of the accuracy of a regression model.

RMSE is particularly useful when the magnitude of errors needs to be emphasized, as it penalizes larger errors more severely than [mean absolute error (MAE)](https://en.wikipedia.org/wiki/Mean_absolute_error).

### Formula

The RMSE is calculated using the following formula:

![RMSE Formula](https://latex.codecogs.com/svg.latex?%5Cdpi%7B120%7D%20RMSE%20%3D%20%5Csqrt%7B%5Cfrac%7B%5Csum_%7Bi%3D1%7D%5E%7Bn%7D%28y_i%20-%20%5Chat%7By_i%7D%29%5E2%7D%7Bn%7D%7D)

where:
- ![y_i](https://latex.codecogs.com/svg.latex?%5Cdpi%7B120%7D%20y_i) represents the actual value of the target variable for the i-th observation.
- ![y_hat_i](https://latex.codecogs.com/svg.latex?%5Cdpi%7B120%7D%20%5Chat%7By_i%7D) represents the predicted value of the target variable for the i-th observation.
- n is the total number of observations.

RMSE is always non-negative, and a value of 0 (almost never achieved in practice) would indicate a perfect fit to the data. In general, a lower RMSE is better than a higher one. However, comparisons across different types of data would be invalid because the measure is dependent on the scale of the numbers used.