{ "fetched_at": "2026-10-20T08:04:03Z", "source_url": "https://www.kaggle.com/competitions/caisx/data", "pages": [ { "name": "data-description", "content": "

About the Data

\n
\n

What files do I need?

\n\n\n
\n

What is the data format?

\n

The training data format is a 36 column by 835 row dataframe where each row is a unique student and each column is an attribute about that student.

\n

The test data format is a 35 column (Final grades, G3, removed for prediction purposes) by 209 row dataframe where you will assign a test grade based a large amount of potential factors, such as the type of model used, which data you've chosen to train the model with, etc...

\n
\n

What am I predicting?

\n

You are predicting the final grades (G3) from students in a school using all data that is available to you. This prediction will help answer the questions such as:\n

\n

and much more...

\n\n\n

Data fields

\n
# Attributes for train_data dataset:\n1 school - student's school (binary: 'GP' - Gabriel Pereira or 'MS' - Mousinho da Silveira)\n2 sex - student's sex (binary: 'F' - female or 'M' - male)\n3 age - student's age (numeric: from 15 to 22)\n4 address - student's home address type (binary: 'U' - urban or 'R' - rural)\n5 famsize - family size (binary: 'LE3' - less or equal to 3 or 'GT3' - greater than 3)\n6 Pstatus - parent's cohabitation status (binary: 'T' - living together or 'A' - apart)\n7 Medu - mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education)\n8 Fedu - father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education)\n9 Mjob - mother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')\n10 Fjob - father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')\n11 reason - reason to choose this school (nominal: close to 'home', school 'reputation', 'course' preference or 'other')\n12 guardian - student's guardian (nominal: 'mother', 'father' or 'other')\n13 traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)\n14 studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)\n15 failures - number of past class failures (numeric: n if 1<=n<3, else 4)\n16 schoolsup - extra educational support (binary: yes or no)\n17 famsup - family educational support (binary: yes or no)\n18 paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)\n19 activities - extra-curricular activities (binary: yes or no)\n20 nursery - attended nursery school (binary: yes or no)\n21 higher - wants to take higher education (binary: yes or no)\n22 internet - Internet access at home (binary: yes or no)\n23 romantic - with a romantic relationship (binary: yes or no)\n24 famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)\n25 freetime - free time after school (numeric: from 1 - very low to 5 - very high)\n26 goout - going out with friends (numeric: from 1 - very low to 5 - very high)\n27 Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)\n28 Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)\n29 health - current health status (numeric: from 1 - very bad to 5 - very good)\n30 absences - number of school absences (numeric: from 0 to 93)\n\n# these grades are related with the course subject, Math or Portuguese:\n31 G1 - first period grade (numeric: from 0 to 20)\n31 G2 - second period grade (numeric: from 0 to 20)\n32 G3 - final grade (numeric: from 0 to 20, output target)\n\n#identification of either Math or Portuguese classes.\n33 math - grade obtained from Math class (binary: yes or no)\n34 portuguese - grade obtained from Portuguese class (binary: yes or no)\n\n\n
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "

CAIS X 2021/2022

\n

\n || Website || \n Discord ||\n Facebook || \n Youtube ||\n Instagram ||\n

\n\n

Welcome to Carleton Artificial Intelligence Society's official undergraduate machine learning competition! In this competition you will compete for cash prizes while hopefully learning a lot about machine learning and artificial intelligence.

\n

This competition will be run from January 1st 2022 12:00 am EST to April 1st 2022 at 11:59 pm EST.\n

\n

Please read the rules below. If this is your first time, please read up on how to submit your first Kaggle notebook as well. \n

\n

Other than that, if you have any questions, feel free to post it in the discussion forum, or on our discord!\n

\n
\n

Prizes and Judging

\n\n

\nCash prizes of $150, $75 and $25 will be awarded to first, second and third place.

\n\n

\nWhen the competition ends, the top 5 scores (public + private score) will be chosen for further judging on the overall quality and professionalism of their code (this includes commenting, proper formatting and how easy it is to understand what the code is doing) by our team, as well as being scrutinized for plagiarism or other forms of cheating*. Therefore, score only counts for 50% of the judging passed the primary round. The other 50% depends on overall code and comment quality.\n
\n

\n\n

\nThis means that if you're first place in terms of score, it does not mean you will win first prize. Your code, alongside having a high score, needs to be efficient and high quality, having proper coding practices like comments and organization in order to win.\n
\n
\nAs well, in order to receive any prizes, your code needs to be open source in your choice of platform (Kaggle, GitHub, etc...) after the winners have been announced.\n
\n
\n*If 3 winners cannot be chosen from the top 5 because of rule breaks, 6th place will be evaluated, and so on until 3 winners can reasonably be announced.\n
\n

\n
\n

The Kaggle Basics

\n\n

1. Join the Competition

\n\n

Read about the challenge description, accept the Competition Rules and gain access to the competition dataset.

\n\n

2. Get to Work

\n\n

Download the data, build models on it locally or on Kaggle Kernels (our no-setup, customizable Jupyter Notebooks environment with free GPUs) and generate a prediction file.

\n

\n

\n\n

3. Make a Submission

\n

Upload your prediction as a submission on Kaggle and receive an accuracy score.

\n

\n

\n\n

4. Check the Leaderboard

\n

See how your model ranks against other Kagglers on our leaderboard.

\n

\n

\n\n

5. Improve Your Score

\n

Check out the discussion forum to find lots of tutorials and insights from other competitors.

\n

\n

\n\nExtra. Kaggle Lingo Video\n

You may run into unfamiliar lingo as you dig into the Kaggle discussion forums and public notebooks. Check out Dr. Rachael Tatman’s video on Kaggle Lingo to get up to speed!\n

\n

\n
\n\n

How to Submit your Prediction to Kaggle\n

Once you’re ready to make a submission and get on the leaderboard:

\n

1. Click on the “Submit Predictions” button

\n\n

\n

2. Upload a CSV file in the submission file format. You’re able to submit up to 20 submissions a day.

\n\n

\n

\n
\n

Submission File Format:

\n

\n

\n\n

You should submit a csv file with exactly 209 entries (rows) plus a header row. Your \n submission will show an error if you have extra columns (beyond studentID and \n G3) or rows.

\n

The file should have exactly 2 columns:

\n \n\n
\n\n

Where to get help if needed?

\n

For Competition Help: CAIS X Discussion Forum\n
\nTechnical Help: Kaggle Contact Us Page

\n

Kaggle doesn’t have a dedicated support team so you’ll typically find that you receive a response more quickly by asking your question in the appropriate forum. If you share your knowledge, you'll find that others will share a lot in turn!

", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "

The evaluation metric for this competition is Root Mean Square Error (RMSE). The Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are; RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around the line of best fit.\n

\n

Submission Format

\n

For every student in the dataset, submission files should contain two columns: studentID and G3. See the example below

\n

The file should contain a header and have the following format:

\n
StudentID,G3
901,15
902,8
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "#**Rules** \n##**1. Kaggle** \nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts. All Kaggle.com rules are expected to be followed: https://www.kaggle.com/terms.\n\n##**2. No private sharing outside teams** \nPrivately sharing code or data outside of teams is not permitted. It’s okay to share code if made available to all participants on the forums.\n\n##**3. Team Limits and Submission Limits** \nThe maximum team size is 3. The prize will be split between all members of the team evenly, unless team requests otherwise. \n\nYou may submit up to 10 entries per day. Only the last one submitted will be used for judging.\n\n##**4. Eligibility** \nIn order to be eligible to participate in the competition, you must be currently a Carleton University, University of Ottawa, Algonquin College or Collège La Cité student in the 2022 winter semester (January-April) and must be able to provide reasonable proof (I.e. a valid ID and most recent transcript with date is reasonable). In order to be eligible to win a prize, you may be asked to reproduce your scores with your models. So it may be in your best interest to save your models persistently.\n\n##**5. External Data** \nYou may use data other than the competition data to develop and test your models and Submissions.\n\n##**6. Use of Open Source** \nUnless otherwise stated in the specific competition rules above, if open-source code is used in the model to generate the submission, then you must only use open source code licensed under an Open Source Initiative-approved license (see www.opensource.org) that in no event limits commercial use of such code or model containing or depending on such code.\n\n##**7. Disqualification and Rule Changes** \nCAIS executives reserve the right to disqualify any participant from the competition if it is reasonably believed that the participant has attempted to undermine the legitimate operation of the competition by cheating, deception, or other unfair playing practices or abuses, threatens or harasses any other participants. CAIS executives reserve the right to add or modify any rules listed above. Participants will be notified when any addition or modification has been made to the competition.\n", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }