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SEHS4696 Machine Learning for Data Mining
创建日期：2024-04-20 14:29:04
标签： SEHS4696
Machine Learning for Data Mining

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SEHS4696 Machine Learning for Data Mining

Individual Assignment (30%)
Objective
This assignment aims to use different supervised machine learning algorithms to
develop predictive models and evaluate and compare their performance.

The dataset

A reduced version of an epidemiology dataset of positive and negative COVID-19
cases from Mexico is used in this assignment. The dataset was originally collected by
the General Directorate of Epidemiology, Secretariat of Health in Mexico. The dataset
contains the lab Reverse Transcription Polymerase Chain Reaction (RT-PCR) testing
results for COVID-19 cases in Mexico. The dataset consists of 263,007 records and
originally it has 41 features. However, for the purpose of this assignment, the number
of features is reduced to just 11, including 10 input features plus 1 binary outcome
“result” of the RT-PCR (i.e., the label), as follows:

Feature Description Data type
Age ≥ 0 integer
Sex 0 = female, 1 = male integer
Pneumonia 0 = negative, 1 = positive integer
Diabetes 0 = negative, 1 = positive integer
Asthma 0 = negative, 1 = positive integer
Hypertension 0 = negative, 1 = positive integer
CVDs 0 = negative, 1 = positive integer
Obesity 0 = negative, 1 = positive integer
CKDs 0 = negative, 1 = positive integer
Tobacco 0 = negative, 1 = positive integer
Result 0 = negative, 1 = positive integer


Its first 5 records are as follows:

Age Sex Pneumonia Diabetes Ashma Hypertension CVDs Obesity CKDs Tabacco Result
74 0 0 1 0 1 0 1 0 0 0
71 1 0 1 0 1 0 1 0 1 0
50 0 1 0 0 0 0 0 0 0 1
25 1 0 0 0 0 0 1 0 0 1
28 1 0 0 0 0 0 0 0 0 0

This reduced dataset “mexico_covid19.csv” is available from Moodle.

Things to do

1. Develop models for predicting COVID-19 infection in Python using the above
labelled dataset “mexico_covid19.csv” with the following supervised machine
learning algorithms:
(i) logistic regression
(ii) support vector machines
(iii) k-nearest neighbors (kNN)
(iv) naïve Bayes
(v) decision trees
(vi) random forests
(vii) artificial neural networks
(60 marks)

2. Make an evaluation and comparison of the performance of these algorithms in
solving this specific prediction problem using the metrics of accuracy, sensitivity,
specificity, precision, recall, F1-score, etc.

(40 marks)

You are required to use Jupyter Notebook or Google Colab to do this assignment.

Things to submit

1. A report containing (1) and (2) above
2. Your notebook (.ipynb file)
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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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