Answer to Question #299974 in Python for suresh

Question

- Load iris data from load_iris function from sklearn.datasets package.

- From the dataset extract the data property.

- Train an AgglomerativeClustring model based on the data.

- Plot dendrogram to visualize the clustering linkage

Answer 1

import numpy as np
import pandas as pd 
from pandas import Series, DataFrame
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

url = 'https://raw.githubusercontent.com/Sketchjar/MachineLearningHD/main/iris.csv'
df = load_iris(url)
iris.head()
iris.drop('Id',axis=1,inplace=True)
fig = iris[iris.Species == 'Iris-setosa'].plot(kind='scatter', x='SepalLengthCm', y='SepalWidthCm', color='orange', label='Setosa')
iris[iris.Species == 'Iris-versicolor'].plot(kind='scatter', x='SepalLengthCm', y='SepalWidthCm', color='blue', label='Versicolor', ax=fig)
iris[iris.Species == 'Iris-virginica'].plot(kind='scatter', x='SepalLengthCm', y='SepalWidthCm', color='green', label='Virginica', ax=fig)

fig.set_xlabel('Sepal Length')
fig.set_ylabel('Sepal Width')
fig.set_title('Sepal Length Vs Width')

fig=plt.gcf()
fig.set_size_inches(10, 7)
plt.show()