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CMT309 Computational Data Science
创建日期：2024-04-07 16:26:06
标签： CMT309
Computational Data Science

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Module Code: CMT309
Module Title: Computational Data Science

This assignment is worth 70% of the total marks available for this module. If coursework is
submitted late (and where there are no extenuating circumstances):

1 - If the assessment is submitted no later than 24 hours after the deadline, the
mark for the assessment will be capped at the minimum pass mark;
2 - If the assessment is submitted more than 24 hours after the deadline, a mark
of 0 will be given for the assessment.
Submission Instructions
Your coursework should be submitted via Learning Central by the above deadline. You have
to upload the following files:

Description Type Name
Cover sheet Compulsory One PDF (.pdf) file Student_number.pdf
Your solution to Part 1 and Part 2 Compulsory One Jupyter Notebook (.ipynb) file Part1_2.ipynb
HTML version of part1_2.ipynb Compulsory One HTML (.html) file Part1_2.html
Your solution to Part 3 Compulsory One Jupyter Notebook (.ipynb) file Part3.ipynb
HTML version of part3.ipynb Compulsory One HTML (.html) file Part3.html

For the filename of the Cover Sheet, replace ‘Student_number’ by your student number, e.g.
“C1234567890.pdf”. Make sure to include your student number as a comment in all of the
Python files! Any deviation from the submission instructions (including the number and types
of files submitted) may result in a reduction of marks for the assessment or question part.

You can submit multiple times on Learning Central. ONLY files contained in the last attempt
will be marked, so make sure that you upload all files in the last attempt.
Staff reserve the right to invite students to a meeting to discuss the Coursework submissions.

Assignment

Start by downloading the following files from the Assessment tab in Learning Central:

• Part1_2.ipynb (Jupyter Notebook file for Part 1 and Part 2)
• Part3.ipynb (Jupyter Notebook file for Part 3)
• listings.csv
• reviews.csv

Then answer the following questions. You can use any Python expression or package that
was used in the lectures and practical sessions. Additional packages are not allowed unless
instructed in the question. You answer the questions by filling in the appropriate sections in
the Jupyter Notebook. Export your final Jupyter Notebooks as HTML to produce the
corresponding HTML files. Before submitting your Jupyter Notebooks and HTML files,
make sure to restart the kernel and execute each cell such that all outputs and figures
are visible.

Scenario

In this assignment you slip into the role of a Data Scientist who has been hired by Airbnb.
Airbnb is an online market place for vacation and short-term rentals of rooms or flats which
operates in many countries in the world (see https://en.wikipedia.org/wiki/Airbnb). Airbnb
collects data on the listing and users interacting with the platform. Let us define these terms
first:

- User: A user is someone using the Airbnb platform (guest or host).
- Guest: A guest is a user who uses the Airbnb platform to book a room, flat or house.
- Host: A host is a user who offers a room, flat, or house for rent.
- Listing: A listing is a room, flat, or house offered for rent. In the dataset, each row
corresponds to a listing.

Since we do not have access to Airbnb’s internal database, we will instead use data published
by the website Inside Airbnb. The data has been acquired by web-scraping publicly available
data from Airbnb. It is available online (http://insideairbnb.com/about.html) but you do not
need to download any data from this website since we have downloaded and renamed all
datasets you need and made them available on Learning Central.

In our scenario, you are responsible for the Amsterdam branch of Airbnb operations. Your
main task is to provide insights on the data collected in Amsterdam as well as write
algorithms to improve the experience of Airbnb users. The assignment is split into three
parts. In the first two parts, you will focus on the numerical parts of the data. In the last part,
you will focus on the text data.

• Part 1: Pre-processing and exploratory analysis
You start by reading the csv file into a Pandas DataFrame, cleaning the data and
removing unwanted columns, performing conversions and calculating new columns.
Then, you will perform exploratory analysis to look at the properties and distribution of
the data, and answer a couple of questions your manager put forward to you.
• Part 2: Statistical analysis and recommender systems
Starting from the pre-processed DataFrame, you will perform statistical analysis using t-
tests and linear regression to identify variables that significantly affect the price of the
rent. Then, you will design a series of recommender systems that have been requested by
users: a function that helps in setting the price for someone offering a new property, and a
function that helps in selecting a city to visit given a particular budget.
• Part 3: Text analysis and ethics
You will mostly work with unstructured text data in a Pandas Dataframe representing
user reviews.
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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 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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