# STDEV

Student Evaluation

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20
1
5

🛠 Contribute : Found a typo? Or any other change in the description that you would like to see ? Please consider sending us a pull request in the public repo of the challenge here.

## 🕵️ Introduction

There is always a course which everyone loves and aces easily but there are certain courses where getting an A grade is almost impossible.Let's try to predict them. We give you a feedback data for courses and ask you to predict the `rating` of the course.

Understand with code! Here is `getting started code` for you.`😄`

## 💾 Dataset

This data set contains a total `5820` evaluation scores provided by students from `Gazi University in Ankara (Turkey)`. There is a total of `28` course specific questions and additional `5` attributes. All the questions are all `Likert-type`, meaning that the values are taken from `{1,2,3,4,5}`, where 5 represents completely agreeing with the question.

For simplification, attributes have been stored in csv file. The file has `34` columns, the last column is the `avg. rating of the course` and the rest `33` contain other information about the course which can be found here.

## 📁 Files

Following files are available in the `resources` section:

• `train.csv` - (`4656` samples) This csv file contains the attributes describing the course along with the course ratings from [1-5].

• `test.csv` - (`1164` samples) File that will be used for actual evaluation for the leaderboard score but does not have the course rating values.

## 🚀 Submission

• Prepare a csv containing header as `rating` and predicted value of the course rating as digit `[1-5]` with name as `submission.csv`.
• Sample submission format available at `sample_submission.csv`.

Make your first submission here 🚀 !!

## 🖊 Evaluation Criteria

During evaluation F1 score will be used to test the efficiency of the model where,

$F1 = 2 * \frac{precision*recall}{precision+recall}$

## 📚 References

• Gunduz, G. & Fokoue, E. (2013). UCI Machine Learning Repository [[Web Link]]. Irvine, CA: University of California, School of Information and Computer Science.
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