Predicting interest level of Rental Listings on RentHop

Predicting interest level of Rental Listings on RentHop

In this data science project, we will predict the number of inquiries a new listing receives based on the listing's creation date and other features.
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Ray Han linkedin profile url

Tech Leader | Stanford / Yale University

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

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James Peebles linkedin profile url

Data Analytics Leader, IQVIA

This is one of the best of investments you can make with regards to career progression and growth in technological knowledge. I was pointed in this direction by a mentor in the IT world who I highly... Read More

What will you learn

Featuring rental listing data
Real Estate data understanding
Better handle fraud control
Identify potential listing quality issues
Prediction Model Building
Evaluation using log loss method

Project Description

Finding the perfect place to call your new home should be more than browsing through endless listings. RentHop makes apartment search smarter by using data to sort rental listings by quality. But while looking for the perfect apartment is difficult enough, structuring and making sense of all available real estate data programmatically is even harder.

Two Sigma invites you to apply your talents in this recruiting competition featuring rental listing data from RentHop. We will predict the number of inquiries a new listing receives based on the listing’s creation date and other features. Doing so will help RentHop better handle fraud control, identify potential listing quality issues, and allow owners and agents to better understand renters’ needs and preferences.

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Curriculum For This Mini Project

3-Mar-2017
02h 41m
4-Mar-2017
02h 51m