Predictive Analytics Unit 9: Clustering and Segmentation

class: left, top, title-slide

.title[
# Predictive Analytics Unit 9: Clustering and Segmentation
]
.author[
### Ken Arnold<br>Calvin University
]

---

## Unsupervised Learning

* So far we have been doing *supervised* learning, where have a *target* we're trying to predict.
  * "How much will these homes sell for?"
  * "How long will this person spend watching this video?"
* **Unsupervised** learning works when we don't have an exact target to predict, or we want to explore relationships in the data.
  * "What general types of homes are on the market right now?"
  * "What are some different segments of our customer base?"
  * "[Are there distinct types of Covid-19 symptoms?](https://covid.joinzoe.com/us-post/covid-clusters)"
* **Clustering** is one very common type of unsupervised learning.

---

## Clustering

Goal: put observations into groups

* Those in the *same* group should be *similar to each other*
* Those in *different* groups should be *different*.

Crucial questions:

* How many groups?
* How do we define "similar" / "different"?

---

![](img/kmeans_1.jpg)

.floating-source[Artwork by [@allison_horst](https://github.com/allisonhorst/stats-illustrations)]
---

![](img/kmeans_2.jpg)

---
![](img/kmeans_3.jpg)

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![](img/kmeans_4.jpg)

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![](img/kmeans_5.jpg)

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![](img/kmeans_6.jpg)

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![](img/kmeans_7.jpg)

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![](img/kmeans_8.jpg)

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![](img/kmeans_9.jpg)

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![](img/kmeans_10.jpg)
---

![](img/kmeans_11.jpg)

---
![](img/kmeans_12.jpg)

---

## *Many* types of clustering algorithms

.floating-source[Source: [sklearn documentation](https://scikit-learn.org/stable/modules/clustering.html)]

---

```r
set.seed(20201120)
data_for_clustering <- ames_all %>% 
  select(Latitude, Longitude) %>% 
  #select(Year_Built, Gr_Liv_Area) %>% 
  mutate(
    #Gr_Liv_Area = rescale(Gr_Liv_Area, to = c(0, 1)),
    #Year_Built = rescale(Year_Built, to = c(0, 1))
  )
clustering_results <- kmeans(
  data_for_clustering, nstart = 4, centers = 3
)

ames_with_clusters <- ames_all %>% 
  mutate(cluster = as.factor(clustering_results$cluster))
```

```r
glance(clustering_results)
```

```
# A tibble: 1 × 4
  totss tot.withinss betweenss  iter
  <dbl>        <dbl>     <dbl> <int>
1  2.43        0.687      1.75     3
```

```r
tidy(clustering_results)
```

```
# A tibble: 3 × 5
  Latitude Longitude  size withinss cluster
     <dbl>     <dbl> <int>    <dbl> <fct>  
1     42.0     -93.7   801    0.251 1      
2     42.0     -93.6   845    0.326 2      
3     42.1     -93.6   766    0.110 3      
```

---

.small-code[

```r
latlong_plot <- 
  ggplot(ames_with_clusters, aes(y = Latitude, x = Longitude, color = cluster)) +
    geom_point(alpha = .5)

year_area_plot <- 
  ggplot(ames_with_clusters, aes(x = Gr_Liv_Area, y = Year_Built, color = cluster)) +
    geom_point(alpha = .5)

library(patchwork)
latlong_plot + year_area_plot + plot_layout(guides='collect')
```

<img src="slides09cluster_files/figure-html/cluster-plots-1.png" width="90%" style="display: block; margin: auto;" />
]