FIT3152代做、Data analytics代写、R程序语言调试、R代写

日期：2020-06-03 11:26

FIT3152 Data analytics: Assignment 2

This assignment is worth 20% of your final marks in FIT3152.

Due: Sunday 7th June 2020 at Midnight GMT+10

Note: Students are expected to work individually on this assignment.

How to submit: Submit your written report as a pdf file (.pdf). and R working as an R

script (.R), or

Submit your report comprising both written answers and script as an R

Markdown file in HTML format (.html).

Use the naming convention: Firstname.Lastname.studentID.{pdf, R, html} Upload the one or

two files to Moodle. Do not zip. Do not submit the data file.

Objective:

The objective of this assignment is to gain familiarity with classification models using R.

You will be using a modified version of the Kaggle competition data: Predict rain tomorrow

in Australia. https://www.kaggle.com/jsphyg/weather-dataset-rattle-package The data

contains a number of meteorological observations as attributes, and the class attribute “Rain

Tomorrow”. Details of the decision attributes follow the assignment description.

You are expected to use R for your analysis, and may use any R package. Clear your

workspace, set the number of significant digits to a sensible value, and use ‘WAUS’ as the

default data frame name for the whole data set. Read your data into R using the following

code:

rm(list = ls())

WAUS <- read.csv("WAUS2020.csv")

L <- as.data.frame(c(1:49))

set.seed(88888888) # Your Student ID is the random seed

L <- L[sample(nrow(L), 10, replace = FALSE),] # sample 10 locations

WAUS <- WAUS[(WAUS$Location %in% L),]

WAUS <- WAUS[sample(nrow(WAUS), 2000, replace = FALSE),] # sample 2000 rows

We want to obtain a model that may be used to predict whether it is going to rain tomorrow

for 10 locations in Australia.

Assignment questions:

1. Explore the data: What is the proportion of rainy days to fine days.? Obtain

descriptions of the predictor (independent) variables – mean, standard deviations,

etc. for real-valued attributes. Is there anything noteworthy in the data? Are there

any attributes you need to consider omitting from your analysis? (1 Mark)

2. Document any pre-processing required to make the data set suitable for the model

fitting that follows. (1 Mark)

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3. Divide your data into a 70% training and 30% test set by adapting the following

code (written for the iris data). Use your student ID as the random seed.

set.seed(XXXXXXXX) #Student ID as random seed

train.row = sample(1:nrow(iris), 0.7*nrow(iris))

iris.train = iris[train.row,]

iris.test = iris[-train.row,]

4. Implement a classification model using each of the following techniques. For this

question you may use each of the R functions at their default settings, or with minor

adjustments to set factors etc. (5 Marks)

? Decision Tree

? Na?ve Bayes

? Bagging

? Boosting

? Random Forest

5. Using the test data, classify each of the test cases as ‘will rain tomorrow’ or ‘will not

rain tomorrow’. Create a confusion matrix and report the accuracy of each model.

(1 Mark)

6. Using the test data, calculate the confidence of predicting ‘will rain tomorrow’ for

each case and construct an ROC curve for each classifier. You should be able to plot

all the curves on the same axis. Use a different colour for each classifier. Calculate

the AUC for each classifier. (1 Mark)

7. Create a table comparing the results in parts 5 and 6 for all classifiers. Is there a

single “best” classifier? (1 Mark)

8. Examining each of the models, determine the most important variables in predicting

whether or not it will rain tomorrow. Which variables could be omitted from the data

with very little effect on performance? Give reasons. (2 Marks)

9. Create the best tree-based classifier you can. You may do this by adjusting the

parameters, and/or cross-validation of the basic models in Part 4, or using an

alternative tree-based learning algorithm. Show that your model is better than the

others using appropriate measures. Describe how you created your improved model,

and why you chose that model. What factors were important in your decision? State

why you chose the attributes you used. (4 Marks)

10. Using the insights from your analysis so far, implement an Artificial Neural

Network classifier and report its performance. Comment on attributes used and your

data pre-processing required. How does this classifier compare with the others? Can

you give any reasons? (2 Marks)

11. Write a brief report (suggested length 6 pages) summarizing your results in parts 1 –

10. Use commenting (# ----) in your R script, where appropriate, to help a reader

understand your code. Alternatively combine working, comments and reporting in R

Markdown. (2 Marks)

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Description of the data:

Attributes 1:3, Day, Month, Year of the observation

Attribute 4, Location: the location of the observation

Attribute 5, MinTemp: the daily minimum temperature in degrees celsius

Attribute 6, MaxTemp: the daily maximum temperature in degrees celsius

Attribute 7, Rainfall: the rainfall recorded for the day in mm

Attribute 8, Evaporation: the evaporation (mm) in the 24 hours to 9am

Attribute 9, Sunshine: hours of bright sunshine over the day.

Attribute 10, WindGust: direction of the strongest wind gust over the

day.

Attribute 11, WindGustSpeed: speed (km/h) of the strongest wind gust

over the day.

Attribute 12, WindDir9am: direction of the wind at 9am

Attribute 13, WindDir3pm: direction of the wind at 3pm

Attribute 14, WindSpeed9am: speed (km/hr) averaged over 10 minutes

prior to 9am

Attribute 15, WindSpeed3pm: speed (km/hr) averaged over 10 minutes

prior to 3pm

Attribute 16, Humidity9am: humidity (percent) at 9am

Attribute 17, Humidity3pm: humidity (percent) at 3pm

Attribute 18, Pressure9am: atmospheric pressure (hpa) reduced to mean

sea level at 9am

Attribute 19, Pressure3pm: atmospheric pressure (hpa) reduced to mean

sea level at 3pm

Attribute 20, Cloud9am: fraction of sky obscured by cloud at 9am. This

is measured in "oktas", which are a unit of eigths. It records how many

eigths of the sky are obscured by cloud. A 0 measure indicates

completely clear sky whilst an 8 indicates that it is completely

overcast.

Attribute 21, Cloud3pm: fraction of sky obscured by cloud at 3pm.

Attribute 22, Temp9am: temperature (degrees C) at 9am

Attribute 23, Temp3pm: temperature (degrees C) at 3pm

Attribute 24, RainToday: boolean: 1 if precipitation (mm) in the 24

hours to 9am exceeds 1mm, otherwise 0

Attribute 25, RainTomorrow: the target variable. Did it rain tomorrow?