Marketing Analytics: Prevedere il churn dei clienti in Python
Mark Peterson
Director of Data Science, Infoblox
seaborn library allows you to easily create informative and attractive plots
Builds on top of matplotlib
import matplotlib.pyplot as plt import seaborn as snssns.distplot(telco['Account_Length'])plt.show()

sns.boxplot(x = 'Churn',
y = 'Account_Length',
data = telco)
plt.show()

sns.boxplot(x = 'Churn',
y = 'Account_Length',
data = telco)
plt.show()

sns.boxplot(x = 'Churn',
y = 'Account_Length',
data = telco)
plt.show()

sns.boxplot(x = 'Churn',
y = 'Account_Length',
data = telco)
plt.show()

sns.boxplot(x = 'Churn',
y = 'Account_Length',
data = telco)
plt.show()

sns.boxplot(x = 'Churn',
y = 'Account_Length',
data = telco)
plt.show()

sns.boxplot(x = 'Churn',
y = 'Account_Length',
data = telco)
plt.show()

sns.boxplot(x = 'Churn',
y = 'Account_Length',
data = telco,
sym="")
plt.show()

sns.boxplot(x = 'Churn',
y = 'Account_Length',
data = telco,
hue = 'Intl_Plan')
plt.show()

Marketing Analytics: Prevedere il churn dei clienti in Python