Created
May 19, 2016 17:49
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Visualization prior to running kmeans
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| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| %matplotlib inline | |
| iris = pd.read_csv("https://raw.githubusercontent.com/Thinkful-Ed/curric-data-001-data-sets/master/iris/iris.data.csv", names = ['Sepal_l', 'Sepal_w', 'petal_l', 'petal_w', 'class']) | |
| # Make class categorical variable | |
| iris['class'] = pd.Categorical(iris['class']).codes | |
| # Plot Sepal_length and Sepal_width | |
| plt.scatter(iris['Sepal_l'], iris['Sepal_w'], c=iris['class']) | |
| plt.xlabel('Sepal length') | |
| plt.ylabel('Sepal Width') | |
| plt.show() | |
| # Plot petal_length and petal_width | |
| plt.scatter(iris['petal_l'], iris['petal_w'], c=iris['class']) | |
| plt.xlabel('Petal length') | |
| plt.ylabel('Petal Width') | |
| plt.show() | |
| # Plot sepal_length and petal_width | |
| plt.scatter(iris['Sepal_l'], iris['petal_w'], c=iris['class']) | |
| plt.xlabel('Sepal Length') | |
| plt.ylabel('Petal Width') | |
| plt.show() | |
| # Plot petal_length and sepal_width | |
| plt.scatter(iris['petal_l'], iris['Sepal_w'], c=iris['class']) | |
| plt.xlabel('Petal Length') | |
| plt.ylabel('Sepal Width') | |
| plt.show() |
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