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# Define a dictionary for the target mapping | |
target_map = {'Yes':1, 'No':0} | |
# Use the pandas apply method to numerically encode our attrition target variable | |
attrition["Attrition_numerical"] = attrition["Attrition"].apply(lambda x: target_map[x]) | |
# creating a list of only numerical values | |
numerical = [u'Age', u'DailyRate', u'DistanceFromHome', | |
u'Education', u'EmployeeNumber', u'EnvironmentSatisfaction', | |
u'HourlyRate', u'JobInvolvement', u'JobLevel', u'JobSatisfaction', | |
u'MonthlyIncome', u'MonthlyRate', u'NumCompaniesWorked', | |
u'PercentSalaryHike', u'PerformanceRating', u'RelationshipSatisfaction', | |
u'StockOptionLevel', u'TotalWorkingYears', | |
u'TrainingTimesLastYear', u'WorkLifeBalance', u'YearsAtCompany', | |
u'YearsInCurrentRole', u'YearsSinceLastPromotion',u'YearsWithCurrManager'] | |
data = [ | |
go.Heatmap( | |
z= attrition[numerical].astype(float).corr().values, # Generating the Pearson correlation | |
x=attrition[numerical].columns.values, | |
y=attrition[numerical].columns.values, | |
colorscale='Viridis', | |
reversescale = False, | |
# text = True , | |
opacity = 1.0 | |
) | |
] | |
layout = go.Layout( | |
title='Pearson Correlation of numerical features', | |
xaxis = dict(ticks='', nticks=36), | |
yaxis = dict(ticks='' ), | |
width = 900, height = 700, | |
) | |
fig = go.Figure(data=data, layout=layout) | |
py.iplot(fig, filename='labelled-heatmap') |
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