Assignment_4_K_Means_Clust.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans


mall_data = pd.read_csv("Mall_Customers.csv")
mall_data.head()

#missing values
mall_data.info()


# We can use a heatmap to check correlation between the variables.
corr = mall_data.corr()
plt.figure(figsize=(8,8))
sns.heatmap(corr,cbar=True,square=True,fmt='.1f',annot=True,cmap='Reds')


# Which gender shops more?
plt.figure(figsize=(10,10))
sns.countplot(x="Gender", data=mall_data)


# People of what ages shop more?
plt.figure(figsize=(16,10))
sns.countplot(x="Age", data=mall_data)

# Is there really no relationship between annual income and spending score?
plt.figure(figsize=(20,8))
sns.barplot(x='Annual Income (k$)',y='Spending Score (1-100)',data=mall_data)


# For our model, we can choose whatever variables we think are relevant or necessary, we needn't choose all.
# I'm going to choose age, annual income and spending score columns for my clustering model.
X = mall_data.iloc[:,[2,3,4]].values
X

#Now, we need to find the optimal number of clusters for this dataset. 
#To do that, we will use WCSS (within-cluster-sum-of-squares) parameter. 
#WCSS is the sum of squares of the distances of each data point in all clusters to their respective centroids. 
#The idea is to minimise the sum.
wcss = []
for i in range(1,11): # It will find wcss value for different number of clusters (for 1 cluster, for 2...until 10 clusters) and put it in our list
    kmeans = KMeans(n_clusters=i, init='k-means++', random_state=50)
    kmeans.fit(X)
    wcss.append(kmeans.inertia_)
wcss

# elbow graph
sns.set()
plt.plot(range(1,11),wcss)
plt.xlabel("Number of clusters")
plt.ylabel("WCSS value")
plt.show()



#Building the model
kmeans = KMeans(n_clusters = 5, init = 'k-means++',random_state = 0)


# we need a label for each datapoint relative to their clusters 
#(will be split into 5 clusters and each will be labelled 0-4)
y = kmeans.fit_predict(X)


# 3d scatterplot using matplotlib

fig = plt.figure(figsize = (10,10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X[y == 0,0],X[y == 0,1],X[y == 0,2], s = 40 , color = 'red', label = "cluster 1")
ax.scatter(X[y == 1,0],X[y == 1,1],X[y == 1,2], s = 40 , color = 'blue', label = "cluster 2")
ax.scatter(X[y == 2,0],X[y == 2,1],X[y == 2,2], s = 40 , color = 'green', label = "cluster 3")
ax.scatter(X[y == 3,0],X[y == 3,1],X[y == 3,2], s = 40 , color = 'yellow', label = "cluster 4")
ax.scatter(X[y == 4,0],X[y == 4,1],X[y == 4,2], s = 40 , color = 'purple', label = "cluster 5")
ax.set_xlabel('Age of a customer-->')
ax.set_ylabel('Anual Income-->')
ax.set_zlabel('Spending Score-->')
ax.legend()
plt.show()