Marketing Analytics : prédire l’attrition client en Python
Mark Peterson
Director of Data Science, Infoblox
La bibliothèque seaborn permet de créer facilement des graphiques clairs et esthétiques
S'appuie sur 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 : prédire l’attrition client en Python