K-Medoids and the Weather Data

As you can see, there was no such clear peek as in the example. That means that both 3 and 4 clusters may be a good choice!

Let's see what will be the result of using the K-Medoids algorithm for the weather data we used in the previous section. Let's start with defining the optimal number of clusters.

Завдання

Swipe to start coding

Given cities' average temperatures dataset data. The numerical columns are 3 - 14. Table

Your tasks are:

Using for loop iterate over n_cl. Within the loop:

Create KMedoids model with j clusters named model.
Fit the 2-15 columns of data to the model. Watch out that indices in Python start from 0.
Add silhouette score value to the silhouettes list. Remember to pass two parameters: the data used for fitting (the same 3-15 columns) and predicted by model labels.

Visualize the results using lineplot of sns. Pass n_cl as x parameter and silhouettes as y. Do not forget to apply the .show() method to display the plot.

Рішення

# Import the libraries

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

from sklearn_extra.cluster import KMedoids

from sklearn.metrics import silhouette_score

# Read the data

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/138ab9ad-aa37-4310-873f-0f62abafb038/Cities+weather.csv', index_col = 0)

# Creating lists

n_cl = range(2, 10)

silhouettes = []

# Calculate the scores for different number of clusters

for j in n_cl:

model = KMedoids(n_clusters = j)

model.fit(data.iloc[:,2:-1])

silhouettes.append(silhouette_score(data.iloc[:,2:-1], model.labels_))

# Visualize the results

g = sns.lineplot(x = n_cl, y = silhouettes)

g.set_xlabel('Number of clusters')

g.set_ylabel('Silhouette score')

plt.show()

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Секція 2. Розділ 4

single

# Import the libraries

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

from sklearn_extra.cluster import KMedoids

from sklearn.metrics import silhouette_score

# Read the data

data = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/138ab9ad-aa37-4310-873f-0f62abafb038/Cities+weather.csv', index_col = 0)

# Creating lists

n_cl = range(2, 10)

silhouettes = []

# Calculate the scores for different number of clusters

for j in ___:

model = ___(___ = ___)

model.fit(data.___[:,___])

silhouettes.append(___(___.___[:,___], model.___))

# Visualize the results

g = sns.___(x = ___, y = ___)

g.set_xlabel('Number of clusters')

g.set_ylabel('Silhouette score')

___

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