AirBnB Listing Price Prediction

Abstract: Airbnb maintains and hosts a marketplace, accessible to consumers on its website or app. Users can arrange lodging, primarily homestays, and tourism experiences or list their spare rooms, properties, or part of it for rental. However, there is no certain way in which a host can decide the price for their listing. After analyzing the data from the Boston AirBnB, a model is built that can predict the price of a listing based on the features that affect the price of the listings. This model will be helpful for the hosts on AirBnB as they can put the right price for their listing, rather than depending on the neighboring prices. Data to use : [login to view URL] We will be using the [login to view URL] file which contains 3586 rows and 95 columns. Listings dataset has various features/columns such as neighborhood, property type, bedrooms, bathrooms, beds, price, reviews, ratings, etc. Prepare the data, including data cleaning, transformations, and aggregation. Store the data in an SQL database. Query the data to make initial discoveries: develop at least four SQL queries using different command, such as BETWEEN, IS NULL, LIKE, DISTINCT, MIN, MAX, COUNT, GROUP BY, HAVING, ORDER BY, etc. Visualize the data based on the queries you’ve performed. Use Spark to perform an initial predictive model for your data. You may use the same dataset to train and test your model, and you may use a linear regression model. Report Title: Clear and succinct title that describes your project. Introduction: Summarize the purpose of the report and summarize the data/subject. Include important contextual information about the reason for the report. Summarize your analysis questions, your conclusions, and briefly outline the report. Body (four sections): Data Section: Include written descriptions of data and follow with relevant spreadsheets. Methods Section: Explain how you gathered and analyzed data. Analysis Section: Explain what you analyzed. Include any charts and visualizations here. Results: Describe the results of your analysis. Conclusions: Restate the questions from your introduction. Restate important results. Include any recommendations for additional data as needed.