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Problem statement

Ho ho ho! ‘Tis the season to be jolly!

The holiday season is just around the corner—Christmas trees have been decorated, lights and wreaths hung, streets all decked up, Santa costumes rented out, and holiday cards in the mailbox. In light of this holiday cheer, retail brands, big and small, want to earn considerable profits, and therefore, are investing significantly in advertising. These brands have approached an advertising agency to plan and execute ad campaigns that will help them increase the footfall in their stores.

You have been hired by this advertising agency to assess the scope of revenue that can be generated by a proposed ad. Based on the demographic information provided, you need to predict whether the revenue generated will cover costs to produce and air the ad. This will help guide decision making for the firm, as they will want to pursue ads that are likely to generate a net gain for their clients— thereby bolstering the advertising firm’s reputation.

Data set

The dataset contains all the essential parameters that determine whether an advertisement can successfully incur any gains for the brand. For this particular dataset, the training set includes details such as country, time, and duration of airing the advertisement, sex that was mainly targeted for the ad, genre of the ad and industry that the product belonged to, ratings metric to gauge how much of the targeted demographic watched the ad, economic health during which the ad was aired, relationship status of the ad's most responsive customers, expenses of product/service offered in the ad, and whether or not a refund is offered in case of customer dissatisfaction.

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 that can predict the net gains an advertisement can incur.

  • This challenge will help you enhance your knowledge of regression actively. Regression is one of the basic building blocks of Machine Learning.

This is a binary classification problem where you need to predict whether an ad buy will lead to a netgain.


  • 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 experience without being explicitly programmed. Machine Learning is a science that determines patterns in data. These patterns provide deeper meaning to problems and help you to first understand problems better and then solve the same with elegance.

Here is the new HackerEarth’s Machine Learning Challenge - predict an ad's success.

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)



  • In order to be able to claim your prizes, your HackerEarth profile must be completed more than 50%.
  • Only the 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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