customer segmentation for Loans using Kmeans clustering Algorithm

#importing necessary libraries import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import sklearn as sk

loan = pd.read_csv('train_ctrUa4K.csv')

loan.head()

loan.describe()

loan["LoanAmount"]= loan["LoanAmount"].fillna(value=loan["LoanAmount"].median())

data = loan.iloc[:, [6, 8]].values

from sklearn.cluster import KMeans wcss = [] for i in range(1, 11): #Test 1 to 10 differnt clusters #Loop over the indented statements 10 times to see how distance reduces till it becomes steady kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42) kmeans.fit(data) wcss.append(kmeans.inertia_)

plt.plot(range(1, 11), wcss) # plot the ten different wcss against the number of clusters plt.title('The Elbow Method') plt.xlabel('Number of clusters') plt.ylabel('WCSS') plt.show()

kmeans = KMeans(n_clusters = 3, init = 'k-means++', random_state = 42) y_kmeans = kmeans.fit_predict(data)

plt.scatter(data[y_kmeans == 0, 0], data[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1') plt.scatter(data[y_kmeans == 1, 0], data[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2') plt.scatter(data[y_kmeans == 2, 0], data[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3') plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids') plt.title('Clusters of customers') plt.xlabel('Annual Income (k$)') plt.ylabel('Spending Score (1-100)') plt.legend() plt.show()