---
title: "161.777 Practical Data Mining"
subtitle: "Project 2025"
author: "YOUR NAME GOES HERE"
output: html_document
---

```{r setup, echo=TRUE, warning=FALSE, message=FALSE}
# Add any other packages you need to load here.
library(tidyverse)
library(arules)

# Read in the data
ice.train <- read_csv("https://www.massey.ac.nz/~jcmarsha/161777/assessment/data/ice-train.csv")
ice.test  <- read_csv("https://www.massey.ac.nz/~jcmarsha/161777/assessment/data/ice-test.csv")
campy.train <- read_csv("https://www.massey.ac.nz/~jcmarsha/161777/assessment/data/campy-train.csv") |>
  mutate(Source = factor(Source))
campy.test  <- read_csv("https://www.massey.ac.nz/~jcmarsha/161777/assessment/data/campy-test.csv")
wv_survey <- read_csv("https://www.massey.ac.nz/~jcmarsha/161777/assessment/data/wv_survey2014.csv")
flags <- read.transactions("https://www.massey.ac.nz/~jcmarsha/161777/assessment/data/flags.txt", cols=1)
```

## Exercise 1: Predicting ice thickness

```{r}

```

### 1.1 Ice Thickness Methodology

WRITE UP YOUR METHODOLOGY HERE


## Exercise 2: Predicting the source of *campylobacter*

```{r}

```

### 2.1 Campylobacter Methodology

WRITE UP YOUR METHODOLOGY HERE


## Exercise 3: Clustering the New Zealand World Values Survey

### 3.1 Initial exploration of the dataset

```{r}

```

### 3.2 Normalised dataset

```{r}

```

### 3.3 Dendrogram

```{r}

```

### 3.4 k-means

```{r}

```

### 3.5 Visualisation of heirarchical clustering

```{r}

```

### 3.6 Visualisation of k-means clusters

```{r}

```


## Exercise 4

### 4.1 Frequency of colour use

```{r}

```

### 4.2 Number of rules

```{r}

```

### 4.3 Highest support rules

```{r}

```

### 4.4 Lift greater than 2

```{r}

```

### 4.5 Choiseul independence flag

```{r}

```