Analyze and determine based on the different performance Vector below (Gender and attrition) why is this company is losing customers (attrition rate) by using the primary attribute of gender and attribution rate to identify patterns/reasons that people are leaving ( analyze the data results and identify anomalies or reasons tobelieve people are leaving the company (lack of benefits, high APR, gender preferences, age groups, etc.)

ParameterSet - Gender

Parameter set:

Performance:

PerformanceVector [

-----accuracy: 87.68%

ConfusionMatrix:

True: 0 1

0: 832 0

1: 258 1005

-----AUC: 0.944 (positive class: 1)

-----precision: 79.57% (positive class: 1)

ConfusionMatrix:

True: 0 1

0: 832 0

1: 258 1005

-----true_positive: 1005.000 (positive class: 1)

ConfusionMatrix:

True: 0 1

0: 832 0

1: 258 1005

-----true_negative: 832.000 (positive class: 1)

ConfusionMatrix:

True: 0 1

0: 832 0

1: 258 1005

*****sensitivity: 100.00% (positive class: 1)

ConfusionMatrix:

True: 0 1

0: 832 0

1: 258 1005

-----specificity: 76.33% (positive class: 1)

ConfusionMatrix:

True: 0 1

0: 832 0

1: 258 1005

-----positive_predictive_value: 79.57% (positive class: 1)

ConfusionMatrix:

True: 0 1

0: 832 0

1: 258 1005

-----negative_predictive_value: 100.00% (positive class: 1)

ConfusionMatrix:

True: 0 1

0: 832 0

1: 258 1005

]

Decision Tree.criterion = gini_index

Decision Tree.maximal_depth = 21

Decision Tree.minimal_leaf_size = 80

ParameterSet - attrition

Parameter set:Performance:PerformanceVector [-----accuracy: 92.17%ConfusionMatrix:True:010:1660451:119271-----AUC: 0.915 (positive class: 1)-----precision: 69.49% (positive class: 1)ConfusionMatrix:True:010:1660451:119271-----true_positive: 271.000 (positive class: 1)ConfusionMatrix:True:010:1660451:119271-----true_negative: 1660.000 (positive class: 1)ConfusionMatrix:True:010:1660451:119271*****sensitivity: 85.76% (positive class: 1)ConfusionMatrix:True:010:1660451:119271-----specificity: 93.31% (positive class: 1)ConfusionMatrix:True:010:1660451:119271-----positive_predictive_value: 69.49% (positive class: 1)ConfusionMatrix:True:010:1660451:119271-----negative_predictive_value: 97.36% (positive class: 1)ConfusionMatrix:True:010:1660451:119271]Decision Tree.criterion= gini_indexDecision Tree.maximal_depth= 60Decision Tree.minimal_leaf_size= 100