import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

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


# Using the elbow method to find the 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 = 10, 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')
plt.show()

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

#Visualizing the clusters

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 = 'Centroids')

plt.title('Cluster of Client')
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100')
plt.legend()
plt.show()