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ICM317 - Machine Learning and Big Data in Finance
创建日期：2024-04-11 14:22:15
所属分类：经济Economics
标签： ICM317
Machine Learning and Big Data in Finance

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ICM317 - Machine Learning and Big Data in Finance

Introduction:

This is Part-1 of your project and accounts for 20% of the overall ICM317 assessment.
The project covers material from Lectures 1 to 10 of ICM317. In addition, you need to
complete Part-2 of the project which accounts for an additional 20% of the overall
ICM317 assessment, details of Part-2 are below. Part-2 will cover material from
Lectures 11 to 20 of ICM317. The two parts of the project are completely independent
of each other. You should work alone on this project and make your own individual
submission.


Project Background: You work in the Debt Origination team at Hyper Big Bank. Your
team helps companies to raise money by finding buyers for Commercial Bonds.

For those unfamiliar with Commercial Bonds, they work like this. Let’s say a company
called Superstar Manufacturing wants to open a new factory and they need to raise
money for the project. Superstar Manufacturing could approach the Debt Origination
team at Hyper Big Bank to create a Bond Issuance. For example, Hyper Big Bank could
work with Superstar Manufacturing (The Bond Issuer) to create 5-year Bonds, each
bond will have a Face Value of $100 and pay a Coupon of $4 per year (for example).
Hyper Big Bank will then try and find buyers for the Bonds, the money raised (minus a
fee from Hyper Big Bank) will be passed to Superstar Manufacturing (The Bond Issuer).
Over the course of the next 5 years, Superstar Manufacturing will pay $4 per year to
each Bond Holder for each Bond that they own, this will be equivalent to Interest Paid.
At the end of 5 years, Superstar Manufacturing will pay $100 (The Face Value) to each
Bond Holder for each Bond that they own. The Bond Buyer has a risk that Superstar
Manufacturing will not be able to maintain payments and then the Bond Buyer has a
risk of losing money. For this reason, Bond Buyers would prefer some Bonds over
others depending on the level of the Coupon ($4 per year in this case), the initial Bond
Price and the Bond Buyers perceived probability of the Bond Issuer defaulting.

The Debt Origination team is working on Four Bond issuances for Four Companies, we
will call the Four Bonds [BondA, BondB, BondC, BondD]. Sales of BondA, BondB and
BondC are going very well, however there is little interest from clients of Hyper Big
Bank to buy BondD.

The Debt Origination Team Manager has asked you as the Data Science Specialist in
the team to work on a targeted pitch campaign to increase sales of BondD. The idea is
to reach out to some of the other Bond Buyers ([BondA, BondB, BondC]) to encourage
them to also buy BondD. The Debt Origination team has so-far collected Data from
2,000 Customers each of whom has purchased one of [BondA, BondB, BondC] and
some of whom have also purchased BondD. The task for you is to build a Machine
Learning model to predict which customers who have purchased one of [BondA,
BondB, BondC] are predicted to also purchase BondD. Your working model can then
be used for a targeted pitch campaign to select future buyers of one of [BondA, BondB,
BondC] to encourage them to also purchase BondD.

The model you will build is an example of a Cross-Selling Model. We know some
customers who buy one of [BondA, BondB, BondC] have also bought BondD. There
may be some Features of the Customer (Original Bond Purchase, Location, Wealth,
Risk Appetite etc.) that makes them more likely to buy certain combinations of the
available Bonds. Building a Machine Learning model is a highly efficient way of finding
out which existing customers we should be trying to cross-sell BondD to.

The Data Set is provided to you in an Excel (CSV) file. The customer information has
been anonymized to protect customer privacy. You are provided with the customer
information in 12 columns (each a Feature) which are labelled [Feat0, Feat1, Feat2, …
Feat11]. Of the 2000 available rows only 1500 have been provided to you. An
Independent Model Validation Team (Dr Mininder Sethi) has kept aside the further
500 rows for their own testing of the model and information that you will provide.


Project Questions: The Debt Origination Team Manager would like to know if you can
build a Machine Learning Model to predict if a customer who has purchased one of
[BondA, BondB, BondC] will also buy BondD. This is a Classification Problem with two
classes [Class 0=No (Not a Buyer of BondD), Class 1=Yes (Is a Buyer of BondD)]. In
particular, the Debt Origination Team Manager would like you to answer the following
questions.

1- Is it possible to build a predictive Machine Learning Model?
2- If so, which Machine Learning Model would you recommend and why?
3- How accurate is your proposed model on the Data provided?
4- If we are to start collecting Data for less than the current 12 Features then
which Features would you recommend and why?. How many Features do we
need to collect Data for?, what is the lowest possible number of Features to
maintain 70% accuracy?
5- One of Features (Feat3) is the Bond that the customer had originally purchased
(one of [BondA, BondB, BondC]). Are previous purchases of any of BondA,
BondB or BondC in particular more indicative of a purchase of BondD?
The Debt Origination Team Manager would like to see your answers to the questions above
in an executive (summary) report. The Debt Origination Team Manager would also
appreciate any further insights on the Data that you can provide beyond the questions
above. Extra insights might include advice on collecting future Data. The Debt Origination
Team Manager is particularly sensitive about attempted cross-selling of BondD to customers
who are unlikely to follow through with a purchase, so accuracy of your model when it
predicts Class 1=Yes (Is a Buyer of BondD) is important.

The Debt Origination Team Manager would appreciate the provision of technical details about
your Data decisions. In particular, the manager would be interested to know why you have
chosen a particular Machine Learning model, how you deal with missing Data and outliers and
your construction of a Training Set and Testing Set.

You should make the following submissions.

1- An executive report. The report should be no longer than 9 pages and contain no more
than 3000 words. The report should also contain at least 4 visualizations (charts of
some kind). You may add more visualizations if you wish to, but the final report length
should not exceed 9 pages. If your report exceeds 9 pages, then only the first 9 pages
will be considered for grading. If your report exceeds 3000 words, then only the first
3000 words will be considered for grading.

2- A single Jupyter Notebook that can be used to reproduce the numerical results of your
report. The Jupyter Notebook should also be able to reproduce the visualizations in
your report. Your Jupyter Notebook may contain extra information or insights that are
not in your report. You may use such extra information to develop your own thoughts.
However, any information inside the Jupyter Notebook that is not added into your
written report will NOT be used for grading.


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MGMTMFE 405 25579 1ECE 273 M122 COMP4434 1ELEC ELEC5620M STAT0010 BFF2401 MXB361 MATH3811 CS330 CMT307 FIT1033 CptS 583 UP 431 MATH6005 AF5343 CS2435 ECOM30002 ES50061 CS 2506 ECMT6002 CODE 00099F MT4539 ESSAY ICM317 MATH1231 MFIT5010 BMA547 MATH334 CIVL2201 MATH39032 T-04 CIVE97119 EMESTER CAB302 CAB301 59PM STAT802 ISYS3412 RM3 COSC 2123 AcF 311 MATH2022 1FINAL 4CCS1PHC ICM 142 ENVS3023 Math 4023 I1 ACSE8 FIN B385F CS-275 STA 4109-01 A31021 EE140 MT1 EE124 ECO-6004B Inf2-IADS CS126 IS2184 UL18 MATH5092 ECOM009 COMP 6211 ELEC202 MKT3019 IAB230 FIT5046 584-S21 COMP3210 CO250 A29401 MATH 136 MGT6174 ECMT6007 DATA2001 MAT 443 MATH10222 PHDE0071 ELE00123M COSC1295 MAT00045H MATH32032 1004GRC MAS223 COMP10001 DATA3404 CIVL2110 STAT2008 FNCE10002 EEE6431 COM6515 COMP9120 NBS8185 ECTE942 ECON 405 MATH314301 STAT 2011 COMPSCI 350 COMP20007 CMPSC-132 ECON5002 STA 35 MTH6108 FIT5147 MTH6150 100C FINA 5260 DESC9115 ECON4998 NBS8257 GMM ECON 6070 BSA2 ECE599 STATS 102B STA120 INFS601 BFC3241 MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 CIVL2010 ECMT2150 CS 333 CP1406 KIT308 Economics 701 ECON3740 ECE 5759 BFC3340 FINC2012 COMP4500 MATH3871 FIT3080 18-819C Economic ECO202Y5Y PHY100 FINC-780 Econ 100B EC602 IE582 EE450 COMP9517 WRDS 150B ECON 570 LECTURE 2 ECA5101 ECE 4420 ADAD9311 FIT3031 CS 544 MF 703 CS 6140 KIT501 ENGSCI 344 CSC171 IE 332 FINM8007 21S2 COSC 1114 FE545 E471 SIT102 CSC108 MAT301 IAE 101 COP3503 STAT2004 EC 303 CS1026 STAT1203 MATH3171 ISOM5160 IFN657 FIT2014 ACCT2102 DSCI550 STATS 330 ELEC421 COMP3670 QBUS6840 MEES:698B MAST30027 MIET1077 Economics IFT 6758 FIT5122 BUSN5101 CptS 111 HRMT5502 Math2018 PP 312 INFS3200 CZ3005 CSSE1001 SENG 2011 MKTG 553Q BUFN 640 COMP507 ETF5952 DATA 311 ECE4043 BANK3011 finance M3H623544 HW1 INFS 7410 AI6103 STAT3888 INFS3208 ACCT7103 ECOS3013 PSYCH 20B F79MA STAT3006 ENGG952 2X MGSC 7000 ECE4132 MSF 526 MATH 3033 CDS 511 EEEE4120 EEET2465 ECA5103 MATH2070 ELEC3104 BU51019 EEE8147 ENGR20003 COSC 1107 ETF3200 FNCE 435 MSFI 435 ISyE6420 GENG4405 HW ETC5523 IE 7315 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