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QBUS6180
Statistical Learning and Data Mining
Semester 1, 2022


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 (www.airbnb.com) 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.
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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.


3.2 Data description

Each row corresponds to a separate Airbnb listing in Sydney. Because the dataset was scraped
from Airbnb, a detailed description of the variables is not available. However, you can identify
their meanings from the context.
The response variable, price, is the last column in the training dataset. It gives the price per
night for each listing in Australian Dollars. The latitude and longitude variables specify the
geographic location of each property. Some variables are binary, with the word “true” recorded
as “t” and “false” recorded as “f”.

Since this is a real dataset, you will encounter several practical issues, such as redundant
columns and missing values. Overcoming these practical problems is part of the assessment.


4. Statistical Learning (Task 1)

Requirements:

• Your report must provide validation or cross-validation metrics based on the training
set.
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• Your report must provide the Kaggle Public Leaderboard scores for at least five
different sets of predictions, including your final model. You need to submit to Kaggle
to get each validation score. The five sets of predictions should all come from different
machine learning methods.
• At least one of your models should be a linear model.
• At least one of your models should be a tree-based model.
• At least one of your models should be a model average or model stack.
• Identify one of your five models as a benchmark.
Note that these are only minimum requirements. Refer to the rubric for the details on the
marking criteria.

Suggested:

• Try to build at least some features based on text data.


5. Data Mining (Task 2)

Business question: What are the best hosts doing?

Requirements:

• Extract at least three quantitative insights from the data that address the business
question.
• The meaning of “best hosts” is for the group to decide based on the context of the
project. Your clients are hosts and real estate investors, so they’d probably be
interested in maximising their property income. Therefore, you want to consider
outcomes that relate to that, such as price and revenue.

Notes:

• This task is open-ended as is the nature of data mining applications. Here you should
think creatively and explore the data in a way that is interesting for you. The ability to
explore open-ended problems is important for industry work in data science.
• Remember that association is not causation. Do not oversell your insights.



6. Written report

The purpose of the report is to describe, explain, and justify your solution to the clients. You
can assume that the clients have training in business analytics. However, please do not assume
that they are experts on the methods used in your project.
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Preparing the report will involve careful consideration of what should go in the main text (15
pages). The main text should focus on the highlights of the project. Note that there is no page
limit for the appendix. It’s ok to put extra material (such as additional figures and tables) in
the appendix and refer to it in the main text.

Requirements:

• The report should discuss problem formulation, exploratory data analysis, feature
engineering, methodology, and results.

• Write about the data mining task in a separate section.

• In the problem formulation section, discuss the business problem from the perspective
of decision theory. In particular, is Airbnb pricing a prediction problem? In what ways
can machine learning meaningfully help hosts to increase revenue or reduce costs?

• Discuss three models in detail in the methodology section. One model should be your
best linear model, the other your best nonlinear model, and the third is the model
stack (or average).

• When you submit the report on Canvas, include the Python code that generates all the
results that appear on the report as an additional attachment.

Suggested outline:

1. Introduction: write a few paragraphs introducing the project and overview the
methodology and main results. Use plain English and avoid technical language as
much as possible in this section (write it for a broad audience).
2. Problem formulation and objectives: state the problem to be solved and the goals of
the project.
3. Exploratory data analysis: provide essential information about the data, discuss
potential issues and highlight the most interesting findings. Due to a possible lack of
space, you may want to refer to the appendix for most EDA plots.
4. Feature engineering.
5. Methodology: focus on the three models specified above. Explain the rationale for
using these learning algorithms and explain the choices that you’ve made regarding
configuration, training and hyperparameter optimisation. This part is allowed to be
more technical than the rest of the report.
6. Results.
7. What are the best hosts doing?

7. Kaggle Competition

We’ll post the link to join the competition on Canvas.
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You will need to create a Kaggle account identifiable by your name to access the competition
and make submissions. After creating an account and logging into Kaggle, use the provided
link to get to the competition page. Click on “Join Competition”, located in a light blue box
near the top right corner of the page, then click to accept the competition rules.

Each group should create a team on Kaggle. The group leader can create a team by joining
the competition and then going into the “Team” tab, which will appear near the top of the
competition page. The leader can then invite other group members using their (Kaggle)
names. The name of the Kaggle team must be identical to the group name on Canvas, i.e. the
team number must match the group number. Each student in the group must sign up and
be identifiable as a member of a Kaggle team.

Requirement: the Kaggle team must be set up and have a valid submission by the first
prediction deadline posted on Canvas.

The purpose of the Kaggle competition is to incorporate feedback by allowing you to compare
your performance with that of other groups. Participation in the competition is part of the
assessment. Make sure that your final submission is correct. Your ranking in the competition
will typically not affect your marks directly, as long as we can establish that your participation
represents a genuine effort to submit good predictions and improve them over the course of
the competition.

Real-world relevance: employers highly value the ability to participate in a Kaggle
competition. Some companies in Australia go as far as to set up a Kaggle competition just for
recruitment.

Bonus marks: The team with the best performance on the Private Leaderboard will receive
ten bonus marks on the assignment. To qualify for the bonus, the choice of the final model
needs to be well justified in the report, and your Python code must reproduce the winning
predictions. Furthermore, the group would need to post a description of their winning solution
on Ed. Please do this as soon as you can once the competition is finished.

Attention! You have to manually select which submission Kaggle will use to compute
the test (Private Leaderboard) results. It will not necessarily pick the best submission for
you.