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()