SETTER: Vivek

Get a room: ML Hackathon

Online 1305 Participating LIVE

OPENS AT: Jul 22, 04:30 PM

CLOSES ON: Aug 21, 04:30 PM

DURATION: 30 days

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ABOUT CHALLENGE

Problem Statement

Finding the correct property to live in is a crucial task while moving to a new city/location. An inappropriate property can make our life miserable. Can AI help us find better places?

Task

You have given a relevant dataset about various properties in the USA. Your task is to identify the habitability score of the property.   

Dataset description

The dataset contains the following files: 

  • train.csv: 39496 x 15
  • test.csv: 10500 x 14
  • sample_submission.csv: 5 x 2 

 The columns provided in the dataset are as follows:

Column Description
Property_ID Represents a unique identification of a property
Property_Type Represents the type of the property( Apartment, Bungalow, etc) 
Property_Area Represents the area of the property in square feets
Number_of_Windows Represents the number of windows available in the property
Number_of_Doors Represents the number of doors available in the property
Furnishing Represents the furnishing type ( Fully Furnished, Semi Furnished, or Unfurnished )
Frequency_of_Powercuts Represents the average number of power cuts per week
Power_Backup Represents the availability of power backup
Water_Supply Represents the availability of water supply ( All time, Once in a day - Morning, Once in a day - Evening, and Once in two days) 
Traffic_Density_Score Represents the density of traffic on a scale of  1 to  10
Crime_Rate Represents the crime rate in the neighborhood ( Well below average, Slightly below average, Slightly above average, and  Well above average )
Dust_and_Noise Represents the quantity of dust and noise in the neighborhood ( High, Medium, Low )
Air_Quality_Index Represents the Air Quality Index of the neighborhood
Neighborhood_Review Represents the average ratings given to the neighborhood by the people 
Habitability_score Represents the habitability score of the property

 

 We challenge you to build a model that successfully predicts the habitability score of a property.

Join #machine-learning channel on Slack

 

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 Get a room: ML Hackathon

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

PRIZES

There are great prizes to be won

First Prize

USD 500 

Second Prize

USD 300 

Third Prize

USD 200 

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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