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案例中心
CSCI316 Big Data Mining Techniques and Implementation
创建日期：2024-03-30 09:46:10
标签： CSCI316
Project Specification
Project Specification

You are required to prepare a set of presentation slides which must include (1) the full name
and student number of each student in the group, the contribution (in percent) of each group
member, (2) a description of the task, (3) description of your approach and how the
methodology was implemented; (4) the strengths and weaknesses of the approach or
implementation; (5) your results and an analysis of the results; (6) a brief summary and a
conclusion. The summary should state new and interesting things that you learned and
discovered while working on this project. The conclusion should summarize your main findings
and statements about possible future work (e.g., how you plan to improve your models and
approach in future).

Below is the recommended structure of your slides:
• Introduction (define the problem and the goal)
• Methods (approach, and discussion of strengths and weaknesses)
• Implementation (methods, key-issues, how these were addressed and sample codes)
• Results (include illustrative Figures and Tables and explanations)
• Discussion and Conclusions


The Task

Definition of the task:
You are to implement an end-to-end data mining project to analyse the provided dataset. The
objective is to implement a workflow to predict the targe variable of the data (i.e., classification
or regression). This workflow must include two stages, as illustrated in Figure 1.


Figure 1. The workflow stages

Stage1: Data exploration
This stage includes the use of Spark’s DataFrame and RDD APIs in Python to explore the data.
Understand the dataset by querying a few important statistic measures of the data. Visualise the
data and explain your findings. It is important that you demonstrate an in-depth understanding
on the data that you are analysing. (Note. You cannot use Pandas and Scikit-Learn in this stage.)
Data
Explorative
Analysis
Self-
implemented
ML process
ML process in
Spark MLlib
Results
ML process in
TensorFlow-
Keras

Stage2: Predictive analysis
This stage includes three machine learning (ML) processes. Each process must include at least
three kinds of ML models (such as decision tree, random forest, naïve Bayesian, feedforward
network, etc.). The models must be evaluated with common metrics (such as accuracy,
precision, recall and ROC).

Specific requirements of Process One:
• All ML models and evaluation methods in this process must be implemented from
scratch in Python. You can use any Python modules except the ML libraries.
Specific requirements of Process Two:
• This process is built with the ML library of Spark (i.e., pyspark.ml and pyspark.mllib)
Specific requirements of Process Three:
• This process is built with TensorFlow and Keras.


Deliverables
• Slides
• Presentation
• Python Source Codes

In your slides, you must explain the detailed pipeline design and evaluation outcomes, as well
as any other interesting findings or lessons learned. Any claim that you make in the slides must
be supported by the implementation in your submitted Python source codes.
案例分类
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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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