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INFS4203/7203 data analysis
创建日期：2024-06-03 14:44:20
标签： INFS4203
data analysis

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INFS4203/7203 Project

The assignment aims to assess your ability to apply data mining techniques to solve real-world problems.
This is an individual task, and completion should be based on your own design.
For this assignment, you will individually complete a project proposal and implement it to develop data
mining models applicable to test data. You can choose either:
• A data-oriented project, or
• A competition-oriented project.
To complete the project, you need to submit a comprehensive proposal in Phase 1, clearly describing the
data pre-processing, tuning, model training, and evaluation techniques you plan to apply. Based on this
proposal, in Phase 2, you will submit a project implementation and a report on the final test results.
V1.0 2
Track 1: Data-oriented project
In this data-oriented project, our dataset named train.csv is designed to closely simulate real-world
scenarios, reflecting the inherent complexities found in natural data. It originates from the CIFAR-10
dataset, but we have deliberately introduced various
realistic challenges, such as missing values, a diverse range of data scales, and outliers. To create this
dataset, we employed a neural network to extract features from the original CIFAR-10 data and made
certain modifications to the resultant features. As a result, the dataset exhibits a compelling resemblance
to naturally occurring data, offering an excellent opportunity to study and develop robust solutions
applicable to real-world data analysis.
In the "train.csv" file, each row, except for the first one, represents a single data point, and there are a
total of 2180 data points in this dataset. The first 100 columns (Num_Col_0 to Num_Col_99) contain
numerical features, while the subsequent 28 columns (Cat_Col_100 to Cat_Col_127) contain nominal
features. The last column "Label" indicates the corresponding label for each data point. The dataset
includes a total of 10 classes, which are denoted by numbers 0 to 9, representing the following categories:
airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck, respectively.
The main objective based on the provided labeled data is to develop a classifier capable of accurately
classifying a data point into one of the ten classes for unseen data. The classifier's performance will be
evaluated by the teaching team using the test data released in Week 9, where the ground truth labels
are only accessible to the teaching team.
Phase 1: project proposal (15 marks)
In the first phase of the project, you are required to submit a proposal by 16:00 on 15th September 2023.
The proposal should outline your overall plan for the project, including details about the learning process
and the timeline for Phase 2. It's important to note that you do not need to submit any actual codes or
report any training, validation, or test results in the Phase 1 proposal. Additionally, an abstract is not
required in the proposal. The focus should be on providing a clear and comprehensive outline of your
approach and timeline for the project in Phase 2.

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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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