#k-means Clustering

#importing libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#importing dataset
dataset = pd.read_csv('Mall_Customers.csv')
X = dataset.iloc[:,[3,4]].values

#using the elbow method to find optimal number of clusters
from sklearn.cluster import KMeans
wcss = []
for i in range(1,11):
    kmeans = KMeans(n_clusters = i,init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)
    kmeans.fit(X)
    wcss.append(kmeans.inertia_)
plt.plot(range(1,11),wcss)
plt.title('The Elbow Method')
plt.xlabel('Number of Clusters')
plt.ylabel('WCSS(Within Cluster Sum of Squares)')
plt.show()

#Applying KMeans to dataset
kmeans = KMeans(n_clusters = 5,init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)
y_kmeans = kmeans.fit_predict(X)

#Visualising the results
plt.scatter(X[y_kmeans==0, 0], X[y_kmeans==0,1],s = 100, c = 'red', label = 'Careful')
plt.scatter(X[y_kmeans==1, 0], X[y_kmeans==1,1],s = 100, c = 'blue', label = 'Standard')
plt.scatter(X[y_kmeans==2, 0], X[y_kmeans==2,1],s = 100, c = 'green', label = 'Target')
plt.scatter(X[y_kmeans==3, 0], X[y_kmeans==3,1],s = 100, c = 'cyan', label = 'Careless')
plt.scatter(X[y_kmeans==4, 0], X[y_kmeans==4,1],s = 100, c = 'magenta', label = 'Sensible')
plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=300,c = 'yellow',label = 'Centroid' )
plt.title('Cluster of Clients')
plt.xlabel('Annual income($)')
plt.ylabel('Spending Score(1-100)')
plt.legend()
plt.show()