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A short data analysis project of AirBnB listings in Berlin. Aspects of the analysis include data wrangling, exploratory analysis of the price, location, property, user reviews data and basic topic extraction using user reviews.
A data-based approach to analyse and compare prices and characteristics of Airbnbs listings in Montreal and Toronto and identify the key factors affecting their prices.
This is the first step towards building my first full web application: the AirBnB clone. This first step is very important because i will use what i build during this project with all other following projects: HTML/CSS templating, database storage, API, front-end integration...
I participated in a Kaggle competition where I predicted the prices of AirBnb rental listings in NYC using supervised machine learning techniques such as linear regression, trees and bootstrap (random forest and boosting).
This project focuses on how pricing of AirBnB houses differ according to neighborhoods. It also looks on how important the description of the house is and how a customer is swayed by it. The Data set has been taken from Kaggle.