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SIT720 Machine Learning Assessment Task 2: Problem solving task
创建日期：2024-07-03 17:12:34
标签： SIT720
Machine Learning Assessment Task 2: Problem solving task
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Datafile: Download the dataset (.csv) from the SCADI .
Data Description: This dataset contains 206 attributes of 70 children with physical and motor disability based
on ICF-CY. For more information click this link.

1. Determine the number of subgroups from the dataset using attributes 3 to 205 i.e., exclude attributes 1,
2 and 206. Is this number same as number of classes presented by attribute 206? Explain and justify
your findings. 4 marks

2. Is this data facing curse of dimensionality? If so, then how to solve this problem. Explain with a two-
dimensional plot and report relevant loss of information. 4 marks

3. After applying principal component analysis (PCA) on a given dataset, it was found that the percentage
of variance for the first N components is X%. How is this percentage of variance computed? 2 marks

Background

Obesity has become a global epidemic that has doubled since 1980, with serious consequences for health in
children, teenagers, and adults. Obesity levels in individuals may relate to their eating habits and physical
condition. In this assessment, you will be analysing and creating ML models based on a given dataset that
contains attributes of individuals with relation to obesity levels.

Dataset filename: obesity_levels.csv

Dataset description: This dataset include data for the estimation of obesity levels in individuals based on their
eating habits and physical condition. The data contains 17 attributes and 2111 records.

Features and labels: The attribute names are listed below. The description of the attributes can be found in this
article (web-link).

I. Gender
II. Age
III. Height
IV. Weight
V. family_history_with_overweight (family history of overweight)
VI. FAVC (frequent high caloric food)
VII. FCVC (vegetables per meal)
VIII. NCP (number of main meals per day)
IX. CAEC (any food between meals)
X. SMOKE (smoking)
XI. CH2O (daily water intake)
XII. SCC (daily consumed calories)
XIII. FAF (frequency of physical activity)
XIV. TUE (technology usage)
XV. CALC (consumption of alcohol)
XVI. MTRANS (means of transport)
XVII. NObeyesdad (obesity levels, i.e. Insufficient Weight, Normal Weight, Overweight Level I, Overweight
Level II, Obesity Type I, Obesity Type II and Obesity Type III)

4. Create a machine learning (ML) model for predicting “weight” using all features except “NObeyesdad”
and report observed performance. Explain your results based on following criteria:

a. What model have you selected for solving this problem and why?
b. Have you made any assumption for the target variable? If so, then why?
c. What have you done with text variables? Explain.
d. Have you optimised any model parameters? What is the benefit of this action?
e. Have you applied any step for handling overfitting or underfitting issue? What is that?

5. Create a ML model for classifying subjects into two classes applying following constraints on above
dataset. 12 marks

• Use “NObeyesdad” as target variable and rest of them as predictor variables.
• drop samples with value “Insufficient Weight” for “NObeyesdad”
• Group Normal Weight, Overweight Level I, and Overweight Level II into a class, and the other three
labels (Obesity Type I, II, III) as the other class.

a. Report classification performance scores. Select scores that you think best for describing the model
performance with appropriate justification.
b. Have you taken any step to check generalisability of the model? What is that and how it ensures
generalisability.
c. Can you design and develop any other model for solving this problem? If so, then why have you used
the reported one? Give your justification.

N. B. Use of multiple models to compare results will increase your chances to get higher
marks. This part is for students who are targeting HD – Higher Distinction.

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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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