target_visualization.py

# create a figure
fig = plt.figure(figsize=(10, 6)) 
ax = fig.add_subplot(111)

# proportion of observation of each class
prop_response = df_telco['Churn'].value_counts(normalize=True)

# create a bar plot showing the percentage of churn
prop_response.plot(kind='bar', 
                   ax=ax,
                   color=['springgreen','salmon'])

# set title and labels
ax.set_title('Proportion of observations of the response variable',
             fontsize=18, loc='left')
ax.set_xlabel('churn',
              fontsize=14)
ax.set_ylabel('proportion of observations',
              fontsize=14)
ax.tick_params(rotation='auto')

# eliminate the frame from the plot
spine_names = ('top', 'right', 'bottom', 'left')
for spine_name in spine_names:
    ax.spines[spine_name].set_visible(False)