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One of the biggest problems when people prepare to post a new listing on airbnb is, how much should one ask for? Of course the most intuitive way is to check how other similar postings price their apartment/house. So in this assignment, we are going to predict the listing price based on the listing characteristics π₯π₯, in this way to optimize user experience and lower the bar to be a new hostπ !
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Predicting the actual price could be simple (well you can just go search online π), so we cut the pricing into three different bins for classification π. For each listing, we recommend a pricing range to the new host rather than a fix price (how nice is that!). So we define three categories: beginner, plus, premium based on the created listing. Respectively we use 0, 1, 2 to denote these three categories.
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This is a multimodality task which is to estimate the price of an Airbnb listing using multi class classification based on the photo and description provided.
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The dataset contains listings of different areas in Montreal during 2019. It comes with rich information for each listing, including a link to the thumbnails etc. we will follow a multi-objective (multi-task) multi-modality solution.
esraak-fouad / airbnb-price-category-prediction-using-multi-modality-text-image-model Goto Github PK
View Code? Open in Web Editor NEWIt is a Kaggle competition: The problem is determining the appropriate listing price for a new Airbnb listing, which is a common challenge for hosts. To accomplish this, we will use the listing's characteristics and images to predict its price. you can see my Kaggle competitions here:https://www.kaggle.com/competitions/copy-of-cisc-873-dm-w23-a4