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ABOUT CHALLENGE

Announcement

Unfortunately, since datasets for this challenge were readily available online, there were several instances of malpractice. Thus, the dataset has been updated and your test has been reset. Please download the updated dataset again and submit your predictions for the same.

We apologize for the inconvenience caused.

Problem statement

According to a CDC Report, over 2.4 million cases of sexually transmitted diseases (STDs) were reported in the United States of America in 2018. The report also highlighted a whopping 22% increase from 2017 to 2018 in the number of newborn deaths due to Syphilis.

In the light of upcoming World Health Day, we intend to raise awareness about various STDs and drugs used to cure them. 

A new pharmaceutical startup has recently been acquired by one of the world's largest MNCs. For the acquisition process, the startup is required to tabulate all drugs that have been sold and account for each drug's effectiveness. Your task is to develop a sophisticated NLP-based Machine Learning model to predict the base score of a certain drug in a provided case.

Dataset

The dataset consists of certain parameters such as the drug's name, reviews by patients, popularity and use cases of the drug, and more. 

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 effectiveness of a certain drug to cure an STD
  • This challenge will help you enhance your knowledge of Natural Language Processing (NLP)

We challenge you to build a model that determines how effective an STD drug is in certain circumstances.

Prizes

1st Prize - 150 USD

2nd Prize - 100 USD

3rd Prize - 50 USD

Overview

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 deeper meaning to problems. First, it helps you understand the problems better and, then, solve the same with elegance.

Here is the new HackerEarth Machine Learning Challenge—How effective is the STD drug?

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)

Tutorials

Machine Learning practice

Note:

  • To claim your prize, more than 50% of your HackerEarth profile must be complete
  • The prizes will be disbursed in the second week of the following month

GUIDELINES

  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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