# Import libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import DataFrame, Series
import seaborn as sns
#
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

# # Create a data set in 2 dimensions using make_blobs
#
# The function make_blobs stores the data in x and the cluster each point belongs to in y
#
(x,y)= make_blobs (
n_samples =200, n_features=2,centers=5,cluster_std=0.5,
shuffle=True,random_state=0
)
plt.scatter(
x[:,0],x[:,1],
c='white', edgecolor='black',marker='o', s=50)
plt.show()

# Calculate centroids of blobs; remember that the array y contains cluster number
n = len(y)
n_blobs=5
n_points_blob = 40.
blob_mean = np.zeros( ( n_blobs,2) )
#
# Loop to sum up x and y coordinates of each point in blob
for i in range (0,n):
blob=y[i] # given blob index (0,1,2,3,4)
blob_mean [blob,0] = blob_mean [blob,0] + x[i,0]
blob_mean [blob,1] = blob_mean [blob,1] + x[i,1]
# Loop to divide by number of points in blob and print out
for k in range (0,n_blobs) :
blob_mean [k,:] = blob_mean [k,:] / n_points_blob
print (f"Blob {k+1} has centroid: ( { blob_mean [k,0] }, { blob_mean [k,1] } ) " )

```
Blob 1 has centroid: ( 0.9148227503116141, 4.280336857833884 )
Blob 2 has centroid: ( 2.054873265582555, 1.1110568334393494 )
Blob 3 has centroid: ( -1.543516197703645, 2.8134551104965806 )
Blob 4 has centroid: ( -1.3675452152752192, 7.891534958387625 )
Blob 5 has centroid: ( 9.210599887923255, -2.473923314565855 )
```

# Create KMeans model and fit data in array x which is in the correct shape: 200 by 2
km = KMeans (n_clusters = 3, init = 'random', random_state = 0)
km.fit(x)

# Plot clusters using colors and compare visually with blob plot above
plt.scatter(
x[:,0],x[:,1],c=km.labels_,cmap='rainbow'
)
plt.scatter(
km.cluster_centers_[:,0],km.cluster_centers_[:,1],
marker='*',c='black',label='centroids'
)
plt.legend(scatterpoints=1)
plt.grid()
plt.show()
plt.scatter(
x[:,0],x[:,1],
c='white', edgecolor='black',marker='o', s=50)
plt.show()

# Print out final clusters centroids and print out actual centroids to compare
#
print (" KMeans Centroids ", " Original Centroids ")
print (f"Blob {k+1} has centroid: ( { blob_mean [k,0] }, { blob_mean [k,1] } ) " )
# Note that Kmeans cluster 3 corresponds to our cluster 5 and vice versa

```
KMeans Centroids Original Centroids
Blob 5 has centroid: ( 9.210599887923255, -2.473923314565855 )
```

# 1 iteration
centroids = np.array( [ [-3,-2], [-2,12], [3,8], [12,0], [2,12] ])
print ()
print ("******************************************")
print ("Results for 1st iteration of KMeans")
km = KMeans (n_clusters =5, init =centroids, max_iter =1)
km.fit(x)
print( "Centroids are ", km.cluster_centers_ )
plt.scatter(
x[:,0],x[:,1],c=km.labels_,cmap='rainbow' )
plt.title("KMeans results for 1 iteration")
plt.grid()
plt.show()

```
******************************************
Results for 1st iteration of KMeans
Centroids are [[ 0.12781901 1.96326963]
[-1.58138962 8.0665792 ]
[ 0.53251796 4.92290316]
[ 9.21059989 -2.47392331]
[ 2.989047 1.35068599]]
/shared-libs/python3.7/py/lib/python3.7/site-packages/sklearn/cluster/_kmeans.py:1146: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.
self._check_params(X)
```

# 2 iterations
centroids = np.array( [ [-3,-2], [-2,12], [3,8], [12,0], [2,12] ])
print ()
print ("******************************************")
print ("Results for 2nd iteration of KMeans")
km = KMeans (n_clusters =5, init =centroids, max_iter =1)
km.fit(x)
print( "Centroids are ", km.cluster_centers_ )
plt.scatter(
x[:,0],x[:,1],c=km.labels_,cmap='rainbow' )
plt.title("KMeans results for 1 iteration")
plt.grid()
plt.show()

```
******************************************
Results for 2nd iteration of KMeans
Centroids are [[ 0.12781901 1.96326963]
[-1.58138962 8.0665792 ]
[ 0.53251796 4.92290316]
[ 9.21059989 -2.47392331]
[ 2.989047 1.35068599]]
/shared-libs/python3.7/py/lib/python3.7/site-packages/sklearn/cluster/_kmeans.py:1146: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.
self._check_params(X)
```

# Import libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import DataFrame, Series
import seaborn as sns
# Import models
from sklearn.cluster import KMeans

# Create dataframe from file insurance.csv
filename = 'insurance.csv'
df = pd.read_csv ('insurance.csv')
df.head (3)

ageint64

sexobject

0

19

female

1

18

male

2

28

male

#Create a new dataframe which only has the two features (age and bmi)and the charges
#
dfmod =df.drop ( columns = ['sex' , 'children', 'smoker', 'region'] )

# Determine how many data instances there are
print (f' there are {len(dfmod)} data instances')
# so 1338 data instances

```
there are 1338 data instances
```

# Find the maximum & minimum value of the charges
charges = df.charges
print (f' the minimum charge is {min(charges)}')
print (f' the maximum charge is {max(charges)}')

```
the minimum charge is 1121.8739
the maximum charge is 63770.42801
```

# Print out the data instance where the max occurs
df[df.charges == 63770.42801]

ageint64

sexobject

543

54

female

# Print out all data instances where charges are >60,000
df [df.charges > 60000]

ageint64

sexobject

543

54

female

1230

52

male

1300

45

male

# Print out all data instances where bmi > 35 and charges > 50,000
df [(df.bmi >35) & (df.charges >50000)]

ageint64

sexobject

34

28

male

543

54

female

577

31

female

819

33

female

# Delete all data instances where the charges are more than $60,000
df= df.drop ([543, 1230, 1300])
# Print out all data instances where charges are >60000 to check
df [df.charges > 60000]

ageint64

sexobject

# Scale costs between $0 and $100; round costs to 2 decimal places (for cents)
# print out the first few entries to check
df [df.charges <= 100]

ageint64

sexobject

# Add column to dataframe with scaled charges and remove column with full charges
print ('¯\_(ツ)_/¯')

```
¯\_(ツ)_/¯
```

# Create 2D array and use KMeans with 4 clusters
print ('¯\_(ツ)_/¯ ' , ' ¯\_(ツ)_/¯')
print ('¯\_(ツ)_/¯ ' , ' ¯\_(ツ)_/¯')

```
¯\_(ツ)_/¯ ¯\_(ツ)_/¯
¯\_(ツ)_/¯ ¯\_(ツ)_/¯
```

# Print out the labels
#
# add column to dataframe giving cluster labels
print ('¯\_(ツ)_/¯')

```
¯\_(ツ)_/¯
```

# Set white background grid for Seaborn plots
sns.set_style ( "whitegrid")
print ('¯\_(ツ)_/¯')

```
¯\_(ツ)_/¯
```

# Create scatterplot of charges vs bmi with cluster indiciated by hue using Seaborn's relplot
sns.relplot (x='charges', y= 'bmi', data=df, hue = 'charges')

# Create scatterplot of charges vs age with cluster indiciated by hue using Seaborn's relplot
sns.relplot (x='charges', y= 'age', data=df, hue = 'charges')