Is 4 the Optimal Number of Clusters?
The last chart (displayed below) left the question about an optimal number of clusters unanswered. Seems like 4 is the 'local maximum', but the value 5 is not significantly lower than 4. We need to consider both cases.
Let's watch the scatter plot of average January vs July temperatures in the case of 4 clusters.
123456789101112131415161718# Import the libraries import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.cluster import SpectralClustering # 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) # Create the model model = SpectralClustering(n_clusters = 4, affinity = 'nearest_neighbors') # Fit the data and predict the labels data['prediction'] = model.fit_predict(data.iloc[:,2:14]) # Visualize the results sns.scatterplot(x = 'Jan', y = 'Jul', hue = 'prediction', data = data) plt.show()
The clustering seems logical, it splits the cities into different disjoint groups. But what if we build the same chart but for 5 clusters? That will be your task!
Swipe to start coding
- Import
SpectralClusteringfunction fromsklearn.cluster. - Create a
SpectralClusteringmodel with 5 clusters using the'nearest_neighbors'affinity. - Fit the 3-14 columns of
datato themodeland predict the labels. Save the result within the'prediction'column of data. - Build the
seabornscatter plot with average January (column'Jan') vs July (column'Jul') temperatures for each cluster (column'prediction').
Solución
¡Gracias por tus comentarios!
single
Pregunte a AI
Pregunte a AI
Pregunte lo que quiera o pruebe una de las preguntas sugeridas para comenzar nuestra charla
Resumir este capítulo
Explicar el código en file
Explicar por qué file no resuelve la tarea
Awesome!
Completion rate improved to 3.57Is 4 the Optimal Number of Clusters?
Desliza para mostrar el menú
The last chart (displayed below) left the question about an optimal number of clusters unanswered. Seems like 4 is the 'local maximum', but the value 5 is not significantly lower than 4. We need to consider both cases.
Let's watch the scatter plot of average January vs July temperatures in the case of 4 clusters.
123456789101112131415161718# Import the libraries import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from sklearn.cluster import SpectralClustering # 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) # Create the model model = SpectralClustering(n_clusters = 4, affinity = 'nearest_neighbors') # Fit the data and predict the labels data['prediction'] = model.fit_predict(data.iloc[:,2:14]) # Visualize the results sns.scatterplot(x = 'Jan', y = 'Jul', hue = 'prediction', data = data) plt.show()
The clustering seems logical, it splits the cities into different disjoint groups. But what if we build the same chart but for 5 clusters? That will be your task!
Swipe to start coding
- Import
SpectralClusteringfunction fromsklearn.cluster. - Create a
SpectralClusteringmodel with 5 clusters using the'nearest_neighbors'affinity. - Fit the 3-14 columns of
datato themodeland predict the labels. Save the result within the'prediction'column of data. - Build the
seabornscatter plot with average January (column'Jan') vs July (column'Jul') temperatures for each cluster (column'prediction').
Solución
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
Awesome!
Completion rate improved to 3.57single
