Machine Learning (ML)

Parts of Machine Learning problems

An ML problem comprises the following:

  • Problem statement
  • Dataset
  • Submission
  • Evaluation metrics

Problem statement

A problem statement is a short description of the problem for which you are required to provide a solution or an answer. It comprises the following:

  • Description of the problem
  • Input and output, format
  • Input constraints


A dataset comprises:

  • Training dataset
  • Test dataset
  • Sample submission file

Training data set

A training data set is the data that you will use to train your models.

Test data set

A test data set is a data that you will use to predict your answer.

Sample submission file

A sample submission file consists of the format that you should follow while creating your submission file.

Note: All the variables in the test and training datasets are also described in the Data section.

Master file

This file is a part of the training data set of a specific question.

Note: The format of the test data, training data, and sample submission should be a .csv file.

Test cases

The types of test cases are as follows:

Offline test cases

It contains all the test data based on which the final evaluation (after the challenge gets over) is done.

Online test cases

It contains some part of the test data based on which you are evaluated instantly after you submit your solution.


You must submit your prediction or submission file in the format as described in the sample submission file.

There are two types of submissions that you should make:

  • Online submission
  • Offline submission

Online submissions

When you submit your solution, it is run against online test cases.

You can make multiple submissions. These submissions will be evaluated instantly based on which leaderboard is updated.

Offline submissions

 IMP: The submission file should be a .csv file.

When you submit your final solution, it is run against offline test cases. Once you make this submission, you cannot edit it.

Evaluation metrics

Your submission is evaluated based on many evaluation metrics which depends on the types of problem. The evaluation metric that will be used to evaluate your submission is described in the problem.

There are many evaluation metrics such as Mean Absolute Error (MAE), F1 scoring, etc.

New submission

You must upload the submission file and source code of the solution here.

Note: You can submit your source code after the test or challenge is over.

To submit your submission file, follow these steps:

  1. In the Upload File section, click Upload File.
  2. Navigate to the folder where you have saved your submission file.
  3. Select the file and click Open.

To submit your source code, follow these steps:

  1. In the Upload Source Code section, click Upload File.
  2. Navigate to the folder where you have saved your source code.
  3. Select the file and click Open.
  4. In the Your Answer text box, write additional information (if any) related to your answer.

To submit your solution, click Submit.

You have successfully submitted your solution.

All submissions

You can make multiple submissions for a problem. View all your submissions here.

You can also refresh your submissions by clicking Refresh All Submissions List. Your last submission will appear on the top after you refresh this list.