辅导FIT3152、讲解Data analytics、辅导R、R编程语言调试
- 首页 >> Matlab编程 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)
2
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
• Naïve 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)
3
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?
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)
2
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
• Naïve 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)
3
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?