FIT3152 - Gain Familiarity with Classification Models Using R - Report Writing - IT Assignment Help

Assignment Task

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 next-day rain in Australia but predicting whether or not the following day will be cloudy. The data contains a number of meteorological observations as attributes, and the class attribute “CloudTomorrow”. 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("CloudPredict2021.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 be cloudy tomorrow for 10 locations in Australia.

Assignment questions:
1. Explore the data: What is the proportion of cloudy days to clear 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?
2. Document any pre-processing required to make the data set suitable for the model fitting that follows.
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.

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.
• Decision Tree • Naïve Bayes • Bagging • Boosting • Random Forest
5. Using the test data, classify each of the test cases as ‘cloudy tomorrow’ or ‘not cloudy tomorrow’. Create a confusion matrix and report the accuracy of each model.

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