# In-Class Contest - Interest Rate Prediction



## Contest Objective
The objective of the contest is to develop predictive models that accurately estimate the interest rate based on various input variables. Participants are expected to create models that generalize well to unseen data and minimize the RMSE between the predicted interest rates and the actual interest rates.

## Evaluation Metric: RMSE
RMSE is a commonly used evaluation metric for regression tasks. It measures the average deviation between the predicted values and the actual values, taking the square root of the mean of the squared differences. In the context of this contest, the RMSE will be calculated to assess the accuracy of the interest rate predictions.

## Submission Format
Participants are required to submit a CSV file containing their predictions for the interest rates corresponding to the test dataset provided by Kaggle. The submission file should include a unique identifier for each test instance along with the predicted interest rate.

The submission format should follow the guidelines specified in the contest instructions. Failure to adhere to the format may result in disqualification.

## Evaluation Process
Once the participants submit their predictions, the Kaggle platform will calculate the RMSE by comparing the predicted interest rates to the actual interest rates for a separate test dataset, which is not available to the participants during the contest.

The submissions will be ranked based on the RMSE scores, with lower scores indicating better predictions. The leaderboard will display the rankings and scores of the participants throughout the contest duration.

## Final Evaluation and Winner Selection
At the end of the contest period, the final evaluation will be conducted based on the performance of the participants' models on a holdout dataset, which is kept hidden until the contest ends. The participants with the lowest RMSE scores on this holdout dataset will be considered the winners of the Kaggle contest.

## Conclusion
The Kaggle contest on interest rate prediction involves developing accurate models to estimate interest rates based on provided input variables. The evaluation metric, RMSE, will be used to assess the accuracy of the predictions. Participants are required to submit their predictions in the specified format, and the final winners will be determined based on their performance on the holdout dataset.

Good luck to all the participants!