## Goal
It is your job to predict there's a car accident or not. For each film name in the test set, you must predict the outcome of the `car accident` variable. 

## Metric
The evaluation metric for this competition is [**Mean F1-Score**](https://en.wikipedia.org/wiki/F-score). The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision **p** and recall **r**.

Precision is the ratio of true positives **tp** to all predicted positives **tp** **+** **fp**. Recall is the ratio of true positives **tp** to all actual positives **tp** **+** **fn**. The **F1-score** is given by:

$$ \text{F1} = 2\frac{\text{p} \cdot \text{r}}{\text{p}+\text{r}}\ \ \mathrm{where}\ \ \text{p} = \frac{\text{tp}}{\text{tp}+\text{fp}},\ \ \text{r} = \frac{\text{tp}}{\text{tp}+\text{fn}} $$

The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.

## Submission Format

Please submit the file in .csv format that includes two features: `film name` and `car accident`. You may refer to the dataset for an example submission file.