# Positive & Negative Correlation:
dataset2 = dataset[['SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'PaperlessBilling',
        'MonthlyCharges', 'TotalCharges']]

correlations = dataset2.corrwith(dataset.Churn)
correlations = correlations[correlations!=1]

positive_correlations = correlations[correlations >0].sort_values(ascending = False)
negative_correlations =correlations[correlations<0].sort_values(ascending = False)

print('Most Positive Correlations: \n', positive_correlations)
print('\nMost Negative Correlations: \n', negative_correlations)


# Correlation Plot among Independent Variables:
#Set and compute the Correlation Matrix:
sn.set(style="white")
corr = dataset2.corr()

#Generate a mask for the upper triangle:
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True

#Set up the matplotlib figure and a diverging colormap:
f, ax = plt.subplots(figsize=(18, 15))
cmap = sn.diverging_palette(220, 10, as_cmap=True)

#Draw the heatmap with the mask and correct aspect ratio:
sn.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})