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QBUS6180 Statistical Learning and Data Mining
创建日期：2024-05-08 17:20:14
所属分类：统计学statistics
标签： QBUS6180
Statistical Learning and Data Mining

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QBUS6180

Statistical Learning and Data Mining

Regression Project: Airbnb Pricing Analytics


1. Overview

In this project your team will analyse data from Airbnb rentals in Sydney to provide market
advice to hosts, real estate investors, and other stakeholders. Your team will have two tasks:
the first will be to build a predictive model for vacation rental prices and the second will be to
uncover interesting facts from the data that can help your clients make better decisions.

2. Problem description

Airbnb is a global platform that runs an online marketplace for short term
travel rentals.

As a team of data scientists and business analysts working at a market intelligence and
consulting company targeting the Airbnb market, you are tasked with developing an advice
service for hosts, property managers, and real estate investors.1

To achieve your project’s goals, you are provided with a dataset containing detailed
information on a number of existing Airbnb listings in Sydney. Your team has two tasks:2

1. To develop a predictive model for the daily prices of Airbnb rentals based on state-of-
the-art machine learning techniques. This model will and allow the company to advise
hosts on pricing and to help owners and investors to predict the potential revenue of
Airbnb rentals (which also depends on the occupancy rate).

2. To obtain at least three insights that can help hosts to make better decisions. What
are the best hosts doing?

We will refer to these tasks as statistical learning and data mining respectively.

As part of the contract, you are asked to write a report according to the instructions given
below.


1 A real example is Airdna. Airbnb itself has a large data science and analytics team.
2 This is similar to Airdna: https://www.airdna.co/airbnb-hosts.
BUSINESS SCHOOL
Page 2 of 5

3. Understanding the data


3.1 Training, validation, and test sets

The data are split into two files, a training dataset and a second dataset for validation and
evaluation. The second omits the price values.

We will run a Kaggle competition as part of the assignment. Kaggle randomly splits the
observations in the second file into validation (50%) and test (50%) cases, but you will not
know which ones are which. You get a score equal to the RMSLE computed on the validation
cases when you submit to the competition. These scores are displayed on the Public
Leaderboard and provide an ongoing ranking of teams. You can use the scores of your
submissions to help you select the best predictive model.

You will select one of your submissions to be used as the final model at the end of the
competition. Once the competition is over, Kaggle will rank the teams’ final submissions based
on the test cases only. Those will be displayed on the Private Leaderboard. Your goal is to
achieve the best possible score on the Private Leaderboard at the end of the
competition.

Be careful not to overfit the validation cases in an attempt to improve your public ranking.

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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