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@PulkitS01 PulkitS01/cluster_count.py Secret
Created Aug 9, 2019

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K-Means implementation
frame = pd.DataFrame(data_scaled)
frame['cluster'] = pred
frame['cluster'].value_counts()
color=['blue','green','cyan']
for k in range(K):
data=X[X["Cluster"]==k+1]
plt.scatter(data["ApplicantIncome"],data["LoanAmount"],c=color[k])
plt.scatter(Centroids["ApplicantIncome"],Centroids["LoanAmount"],c='red')
plt.xlabel('Income')
plt.ylabel('Loan Amount (In Thousands)')
plt.show()
data = pd.read_csv('clustering.csv')
data.head()
# statistics of the data
data.describe()
# fitting multiple k-means algorithms and storing the values in an empty list
SSE = []
for cluster in range(1,20):
kmeans = KMeans(n_jobs = -1, n_clusters = cluster, init='k-means++')
kmeans.fit(data_scaled)
SSE.append(kmeans.inertia_)
# converting the results into a dataframe and plotting them
frame = pd.DataFrame({'Cluster':range(1,20), 'SSE':SSE})
plt.figure(figsize=(12,6))
plt.plot(frame['Cluster'], frame['SSE'], marker='o')
plt.xlabel('Number of clusters')
plt.ylabel('Inertia')
# k means using 5 clusters and k-means++ initialization
kmeans = KMeans(n_jobs = -1, n_clusters = 5, init='k-means++')
kmeans.fit(data_scaled)
pred = kmeans.predict(data_scaled)
# inertia on the fitted data
kmeans.inertia_
# defining the kmeans function with initialization as k-means++
kmeans = KMeans(n_clusters=2, init='k-means++')
# fitting the k means algorithm on scaled data
kmeans.fit(data_scaled)
# Step 3 - Assign all the points to the closest cluster centroid
# Step 4 - Recompute centroids of newly formed clusters
# Step 5 - Repeat step 3 and 4
diff = 1
j=0
while(diff!=0):
XD=X
i=1
for index1,row_c in Centroids.iterrows():
ED=[]
for index2,row_d in XD.iterrows():
d1=(row_c["ApplicantIncome"]-row_d["ApplicantIncome"])**2
d2=(row_c["LoanAmount"]-row_d["LoanAmount"])**2
d=np.sqrt(d1+d2)
ED.append(d)
X[i]=ED
i=i+1
C=[]
for index,row in X.iterrows():
min_dist=row[1]
pos=1
for i in range(K):
if row[i+1] < min_dist:
min_dist = row[i+1]
pos=i+1
C.append(pos)
X["Cluster"]=C
Centroids_new = X.groupby(["Cluster"]).mean()[["LoanAmount","ApplicantIncome"]]
if j == 0:
diff=1
j=j+1
else:
diff = (Centroids_new['LoanAmount'] - Centroids['LoanAmount']).sum() + (Centroids_new['ApplicantIncome'] - Centroids['ApplicantIncome']).sum()
print(diff.sum())
Centroids = X.groupby(["Cluster"]).mean()[["LoanAmount","ApplicantIncome"]]
#import libraries
import pandas as pd
import numpy as np
import random as rd
import matplotlib.pyplot as plt
# importing required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.cluster import KMeans
# Step 1 and 2 - Choose the number of clusters (k) and select random centroid for each cluster
#number of clusters
K=3
# Select random observation as centroids
Centroids = (X.sample(n=K))
plt.scatter(X["ApplicantIncome"],X["LoanAmount"],c='black')
plt.scatter(Centroids["ApplicantIncome"],Centroids["LoanAmount"],c='red')
plt.xlabel('AnnualIncome')
plt.ylabel('Loan Amount (In Thousands)')
plt.show()
# standardizing the data
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data)
# statistics of scaled data
pd.DataFrame(data_scaled).describe()
X = data[["LoanAmount","ApplicantIncome"]]
#Visualise data points
plt.scatter(X["ApplicantIncome"],X["LoanAmount"],c='black')
plt.xlabel('AnnualIncome')
plt.ylabel('Loan Amount (In Thousands)')
plt.show()
# reading the data and looking at the first five rows of the data
data=pd.read_csv("Wholesale customers data.csv")
data.head()
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