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Problem statement

Military conflict is an intense state of violence. In such situations, it is crucial for a nation to stay alert, cope with it, and mitigate its implications. A country has set up the DEFCON (Defense Readiness Condition) warning system. This alert system is used to gauge the level of alertness of the defense forces. It consists of five levels of readiness for the military forces to be prepared for the consequences of the conflict. The DEFCON system allows the nation’s forces to be a step ahead of its rivals.

You are given synthesized data that can be used to build a model. This model should be able to predict the DEFCON level raised as a result of the conflict accurately.


The dataset consists of parameters such as number of allied nations, percentage of forces mobilized, number of aircraft carriers traveling to neutralize the threat, the distance of the closest threat to country’s border, and so on.

The benefits of practicing this problem by using Machine Learning techniques are as follows:

  • This challenge will encourage you to apply your Machine Learning skills to build models to predict the DEFCON level for a certain military conflict.
  • This challenge will help you enhance your knowledge of classification. It is one of the basic building blocks of Machine Learning.

We challenge you to build a model that predicts the DEFCON level raised as a result of a military conflict.


  • 1st Prize - 250 USD

  • 2nd Prize - 150 USD

  • 3rd Prize - 75 USD


Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experiences without being explicitly programmed. Machine Learning is a science that determines patterns in data. These patterns provide a deeper meaning to problems. First, it helps you to understand the problems better and then solve the same with elegance.

Here is the new HackerEarth Machine Learning Challenge—Predict the DEFCON level.

This challenge is designed to help you improve your Machine Learning skills by competing and learning from fellow participants.

Why should you participate?

  • To analyze and implement multiple algorithms and determine which is more appropriate for a problem
  • To get hands-on experience in Machine Learning problems

Who should participate?

  • Working professionals
  • Data science or Machine Learning enthusiasts
  • College students (if you understand the basics of predictive modeling)


Machine Learning practice


  • In order to claim your prizes, your HackerEarth profile must be completed more than 50%
  • Only participants currently residing in the US are eligible for the prize money
  • The prizes will be disbursed in the second week of the following month


  1. Your output will be evaluated only for 50% of the test data while the contest is running. Once the contest is over, output for the remaining 50% of the data will be evalu...
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