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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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