Table 7 The correlation coefficient between the finally selected features and the target class label
From: Machine learning based customer churn prediction in home appliance rental business
Feature list | Coefficients |
|---|---|
Rental Model Category Usage days Rental Model Color Sale Price 4th year rental fee 5th year rental fee 1st year rental fee 2nd year rental fee 3rd year rental fee Total rental fee Commitment date diff from creation Commitment date diff from start Call Count Monthly discount amount Call Type 004 Sale Charge Manager age_max Last Manager Age Manager age_mean Call Type 008 Discount reason code_40 Manager Age_min Manager Age_range Call Type 048 Not Visit Manager Age_std Sales Type Weekend Visit Non Period Visit Discount date diff from start Discount application amount_max Discount application amount_mean Discount type ST Period visit Visit Change Discount Type SP Discount Application Amount_min Rental model function Discount reason code_90 Call Type 020 Transfer change route Transfer Type Discount reason code_10 Discount reason code_88 Address_2nd offset Address_1st offset Commitment max sequences Normal repair Commitment date diff from creation to start Shibang repair Heavy repair Discount reason code_77 Discount type SC | 0.5 0.493 0.452 0.335 0.334 0.334 0.331 0.331 0.331 0.325 0.277 0.276 0.275 0.2 0.182 0.169 0.142 0.136 0.135 0.133 0.133 0.125 0.12 0.117 0.116 0.116 0.115 0.109 0.107 0.098 0.096 0.095 0.094 0.09 0.089 0.084 0.078 0.052 0.048 0.046 0.038 0.038 0.035 0.026 0.017 0.016 0.015 0.007 0.004 0.002 0.002 0.001 0.001 |