STDEV

🛠 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,

🔗 Links

• 💪 Challenge Page : https://www.aicrowd.com/challenges/stdev
• 🗣️ Discussion Forum : https://www.aicrowd.com/challenges/stdev/discussion
• 🏆 leaderboard : https://www.aicrowd.com/challenges/stdev/leaderboards

📱 Contact

• Faizan Farooq Khan

📚 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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