Predicting rental listing popularity : 2 Sigma connect Renthop
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Renting a perfect apartment can be a hassle. There are plenty of features people care about when it comes to finding the apartment, such as price, hardwood floor, dog park, laundry room, etc. Being able to predict people’s interest level on an apartment will help the rental agency better handle fraud control, identify potential listing quality issues, and allow owners and agents to understand renters’ needs and preferences. RentHop, an apartment search engine, along with 2 Sigma, introduced this multiple classification problem in the Kaggle community. It provides the opportunity to use owners’ data to predict the interest level of their apartments on its website. This report attempts to find a pattern of people’s interest level towards rental listing on the website using the dataset from the Kaggle competition. Multiple features are derived from the original dataset. Several common data mining and machine learning techniques are used to improve the accuracy of the predicting model. The final result is evaluated using Log loss function.