Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/182307
Title: Extracción de datos y modelo predictor para el precio de alquiler de viviendas de Barcelona
Author: Fernández Batalla, Oscar
Director/Tutor: Seguí Mesquida, Santi
Keywords: Habitatge de lloguer
Anàlisi de regressió
Programari
Treballs de fi de grau
Preus
Aprenentatge automàtic
Extracció de dades de llocs web
Rental housing
Regression analysis
Computer software
Pricing
Machine learning
Bachelor's thesis
Web scraping
Issue Date: 19-Jun-2021
Abstract: [en] In this work, a solution is presented for the real-estate sector, which can be implemented not only for professionals but also for individuals. Due to the difficulty that entails defining the rental price of a property and the fact that the criteria used to this end are always variable, a technological solution was provided in order to facilitate this challenge. The main aim of this work remains on establishing a system that will be able of predicting, through its features, the rental price of a house located in Barcelona. On account of the criteria already mentioned, which fluctuates depending on the other houses, it was intended to elaborate a system that could be able to be fed consistently with the most recent examples from the market. For this purpose, it has been followed the typical processes of data science discipline and machine learning, as well as the techniques for the data mining contained in a web environment. Obtaining information has been achieved using a program based on “web scraping”, capable of surfing the website of a real estate portal and drawing all the attributes of the several published advertisements. The analysis and the data manipulation, along with the implementation of the predictive models, has been performed with the “Jupyter Notebook” environment. For the code development of all the described functionalities, it has been used the “Python” language, together with various libraries targeted towards these objectives. Concerning the data analysis, it has been explained in this document all considerations about data cleansing, data transformation and other modifications that have been required for the purpose of obtaining data quality. Furthermore, there is also an explanation about the different predictive models which have been created, and the results returned. At the end of this document, the results obtained are compared and the conclusions are provided, in which it is considered whether these results fulfil the ultimate purpose of the work.
Note: Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2021, Director: Santi Seguí Mesquida
URI: http://hdl.handle.net/2445/182307
Appears in Collections:Treballs Finals de Grau (TFG) - Enginyeria Informàtica
Programari - Treballs de l'alumnat

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