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ABOUT CHALLENGE

To win in the marketplace, you must first win in the workplace.
~ Doug Conant

And rightly so! Happy and healthy employees are indisputably more productive at work, and in turn, help the business flourish profoundly.

However, since working from home has become the new strange normal, over 69% of employees have been showing burnout symptoms globally (source: Monster poll). This rate of burnout is indeed alarming and the likes of Google and Cisco (even HackerEarth!) are taking steps that ensure wellness and reduce burnout among employees.

On the occasion of World Mental Health Day this October 10th, your organization has stepped in to help its employees plan their weekly calendars appropriately so as to prevent burning out. Your task, as a Machine Learning engineer, is to build a Machine Learning model that predicts burnout rate based on numerous factors such as WFH setup, resources, mental fatigue score, and the like.

Join #machine-learning channel on Slack

Dataset

The dataset consists of parameters such as date of joining, gender, company type, WFH setup availability, designation, resource allocation, and mental fatigue score.

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

  • This challenge encourages you to apply your Machine Learning skills to build models that can predict the burnout rate in employees.
  • This challenge will help you enhance your knowledge of regression. Regression is one of the basic building blocks of Machine Learning.

We challenge you to build a model that predicts the burnout rate among employees.

Prizes

Considering these unprecedented times that the world is facing due to the Coronavirus pandemic, we wish to do our bit and contribute the prize money for the welfare of society.

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

Here’s presenting HackerEarth’s Machine Learning challenge: Are your employees burning out?

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
 

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 eval...
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