wine_segmentation_nemo.Rmd

---
title: " Wine Quality Segmentation: K Means CLustering "
author: "Neha More"
date: "December 1, 2017"
output: html_document
---

###Problem Statement:
Wine quality depends on a lot of factors like alcohol content,presence of sulphates,its pH values etc.The taste,smell and potency of the wine is defined by its chemical ingredients and its percentages in wines. A restaurant needs to classify its wines into different categories depending on its ingredients and label it accordingly for its different category of customers. 

###Aim: 
Using K-means clustering algorithm classify the wines into appropriate distinguished optimal clusters having similar properties in each cluster.

###Data Dictionary:
winequality-red.csv consists of 1599 observations of wines having 12 variables.
Use variables pH, alcohol, sulphates and total.sulpur.dioxide to segment the dataset appropriately using k means clustering algorithm.


###Initial Setup:

```{r}
library(dplyr)
library(ggplot2)
library(cluster)
```

###Step 1 : Reading and standardizing dataset as per the requirements.

```{r}
wine=read.csv("winequality-red.csv",sep=";")
glimpse(wine) #1599 obs and 12 variables.
```

#####Lets select four variables which we think will help us to form good clusters.

```{r}
wine=wine %>%
  select(pH,sulphates,alcohol,total.sulfur.dioxide)
glimpse(wine)
```

#####Lets further standardise each variables of the dataset wine.

```{r}
md=function(x){
  return((x-mean(x))/sd(x))
}
```

```{r}
wine_std=wine %>%
  mutate(pH=md(pH),
         sulphates=md(sulphates),
         alcohol=md(alcohol),
         total.sulfur.dioxide=md(total.sulfur.dioxide))
```

#####Lets check the summary of wine and wine_std datasets:

```{r}
summary(wine)
summary(wine_std)
```


#####Thus out dataset is standardized and can be further used for implementing algorithm.


###Step 2: Using K mean clustering for k=1 to 15 and determine best value of K.

####We will find clusters for values of k=1 to 15 and record the values of SSW for each K and then plot (K Vs SSW)

```{r}
mydata <- wine_std

```
```{r}
wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))
#finding within sum of squares
```
```{r}
for (i in 2:15) wss[i] <- sum(kmeans(mydata,centers=i)$withinss)
#for k=2 to 15,kmeans function takes two arguments(data, and i) and thus finding SSW for each K=i(2 to 15) and storing it in vector wss[i]
```


#####Lets plot SSW vs K values:

```{r}
plot(1:15, wss, type="b", xlab="Number of Clusters",
     ylab="Within groups sum of squares",col="mediumseagreen",pch=12)
#
```



#####The plot shows that K=5/6 is perfect choice of K.

#####Thus the plot shows that the number of groups to choose is 5.

#####Hence lets run K means algorithm for k=5 and find clusters in mydata.(i.e. wine_std)

###Step 3: Use k=5 and make 5 clusters by K means algorithm in dataset wine_std.

```{r}
fit <- kmeans(mydata,5 ) 
#running kmeans for mydata dataset with k=5 and storing the result in fit.
```
fit$cluster will give the cluster in which the obs go to.Lets Store it in mydata dataset with header cluster.
```{r}
mydata$cluster=fit$cluster
#or same as:
wine_std$cluster=fit$cluster
```


###Step 4: Making Pair wise profiling plots and labelling wines with respect to its ingredients:


#####Plotting alcohol Vs pH clusters:
```{r}
ggplot(wine_std,aes(pH,alcohol,color=as.factor(cluster)))+geom_point()

```



#####Plotting pH vs Sulphates groups:
```{r}
ggplot(wine_std,aes(pH,sulphates,color=as.factor(cluster)))+geom_point()

```


#####Plotting pH vs total sulfur dioxide groups:
```{r}
ggplot(wine_std,aes(pH,total.sulfur.dioxide,color=as.factor(cluster)))+geom_point()

```


  
#####Plotting alcohol vs sulphates groups:
```{r}
ggplot(wine_std,aes(alcohol,sulphates,color=as.factor(cluster)))+geom_point()

```


#####Plotting alcohol vs total.sulpur.dioxide groups:
```{r}
ggplot(wine_std,aes(alcohol,total.sulfur.dioxide,color=as.factor(cluster)))+geom_point()

```



#####Plotting sulphates vs total.sulfur.dioxide groups:
```{r}
ggplot(wine_std,aes(sulphates,total.sulfur.dioxide,color=as.factor(cluster)))+geom_point()

```

###Inferences from plots:

>  Cluster 1:low pH,high sulphates,low alcohol

>  Cluster 2:high pH,low sulphates,high alcohol,low total.sulpur.dioxide

>  Cluster 3:Low alcohol,low sulphates,high total.sulpur.dioxide

>  Cluster 4:high alcohol,low pH,low total.sulpur.dioxide

>  Cluster 5:Low alcohol,low sulphates,low total.sulphur.dioxide


###Step 5:Numerical inferences:
```{r}
apply(wine,2,function(x)tapply(x,wine_std$cluster,mean))
```
##**Conclusion:**
#####*Thus we see that:*

#####*pH is high in cluster 2 and low in cluster 1.*

#####*Sulphates is high in cluster 1 and low in cluster 3.*

#####*Alcohol is high in cluster 2 & 4 and low in Rest of the clusters(1,3,5).*

#####*Total.sulput.dioxide is high in cluster 3 and low in cluster 4.*


###Step 6: Plotting silhouette.
#####Lets plot silhouette and determine whether the 5 clusters members were well-assigned:

```{r}
diss=daisy(wine_std)
sk=silhouette(wine_std$cluster,diss)
plot(sk)
```

#####Average silhouette width is 0.4.


####*Thus we can say, clusters are well-assigned respectively.*