<jupyter_start><jupyter_text>Original data<jupyter_code>df[['Age','DistanceFromHome','Education','MonthlyIncome',
'NumCompaniesWorked', 'PercentSalaryHike','TotalWorkingYears', 'TrainingTimesLastYear',
'YearsAtCompany','YearsSinceLastPromotion', 'YearsWithCurrManager']].describe()<jupyter_output><empty_output><jupyter_text>Sample from original data of size 25<jupyter_code># df[['Age','DistanceFromHome','Education','MonthlyIncome',
# 'NumCompaniesWorked', 'PercentSalaryHike','TotalWorkingYears', 'TrainingTimesLastYear',
# 'YearsAtCompany','YearsSinceLastPromotion', 'YearsWithCurrManager']].sample(25).describe()<jupyter_output><empty_output><jupyter_text>Attrited<jupyter_code>df_1 = df.loc[df['Attrition'] == 'Yes']
df_1_desc=df_1[['Age','DistanceFromHome','Education','MonthlyIncome',
'NumCompaniesWorked', 'PercentSalaryHike','TotalWorkingYears', 'TrainingTimesLastYear',
'YearsAtCompany','YearsSinceLastPromotion', 'YearsWithCurrManager']].describe()
df_1_desc
# df_1[['Age','DistanceFromHome','Education','MonthlyIncome',
# 'NumCompaniesWorked', 'PercentSalaryHike','TotalWorkingYears', 'TrainingTimesLastYear',
# 'YearsAtCompany','YearsSinceLastPromotion', 'YearsWithCurrManager']].sample(70).describe() #Taking a sample out of population of attrited employees<jupyter_output><empty_output><jupyter_text>Not attrited<jupyter_code>df_2 = df.loc[df['Attrition'] == 'No']
df_2[['Age','DistanceFromHome','Education','MonthlyIncome',
'NumCompaniesWorked', 'PercentSalaryHike','TotalWorkingYears', 'TrainingTimesLastYear',
'YearsAtCompany','YearsSinceLastPromotion', 'YearsWithCurrManager']].describe()

# df_2[['Age','DistanceFromHome','Education','MonthlyIncome',
# 'NumCompaniesWorked', 'PercentSalaryHike','TotalWorkingYears', 'TrainingTimesLastYear',
# 'YearsAtCompany','YearsSinceLastPromotion', 'YearsWithCurrManager']].sample(70).describe() #Taking a sample out of popultion of non-attrited employees<jupyter_output><empty_output><jupyter_text>Number of Comapnies Worked more than 4<jupyter_code>df_3 = df_1.loc[df_1['NumCompaniesWorked'] > 4]
df_3[['Age','DistanceFromHome','Education','MonthlyIncome',
 'PercentSalaryHike','TotalWorkingYears', 'TrainingTimesLastYear',
'YearsAtCompany','YearsSinceLastPromotion', 'YearsWithCurrManager']].describe()

# df_3[['Age','DistanceFromHome','Education','MonthlyIncome',
# 'NumCompaniesWorked', 'PercentSalaryHike','TotalWorkingYears', 'TrainingTimesLastYear',
# 'YearsAtCompany','YearsSinceLastPromotion', 'YearsWithCurrManager']].sample(100).describe()<jupyter_output><empty_output><jupyter_text>Number of trainings taken last year more than 3<jupyter_code>df_4 = df_1.loc[df_1['TrainingTimesLastYear'] > 3]
df_4[['Age','DistanceFromHome','Education','MonthlyIncome',
 'PercentSalaryHike','TotalWorkingYears',
'YearsAtCompany','YearsSinceLastPromotion', 'YearsWithCurrManager']].describe()

# df_4[['Age','DistanceFromHome','Education','MonthlyIncome',
#  'PercentSalaryHike','TotalWorkingYears',
# 'YearsAtCompany','YearsSinceLastPromotion', 'YearsWithCurrManager']].sample(25).describe()<jupyter_output><empty_output>
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