Homework for Group 3

Models

1. rfm with group average: rfm_gpmean

2. rfm with logit regression: rfm_logit

Last round result coef std err z P>|z| [0.025 0.975]

const -3.2474 0.119 -27.215 0.000 -3.481 -3.014 r -0.0594 0.103 -0.574 0.566 -0.262 0.143 m 0.6000 0.119 5.048 0.000 0.367 0.833 f 0.0298 0.142 0.210 0.834 -0.249 0.309

3. rfm with random forest: rfm_tree

Index(['r', 'm', 'f'], dtype='object') [0.0946486 0.60019611 0.30515528] Index(['r', 'm', 'f'], dtype='object') [0.13636322 0.47501402 0.38862276] Index(['r', 'm', 'f'], dtype='object') [0.17568842 0.44757385 0.37673774] Index(['r', 'm', 'f'], dtype='object') [0.09505596 0.41968756 0.48525649]

4. eight factor random forest: f8_tree

Index(['r', 'm', 'f', 'inc', 'yr', 'edu', 'vf', 'child'], dtype='object') [0.01348723 0.34449994 0.09046977 0.28865613 0.01628517 0.00869951 0.08001782 0.15788443] Index(['r', 'm', 'f', 'inc', 'yr', 'edu', 'vf', 'child'], dtype='object') [0.01250737 0.29825681 0.11651353 0.34731074 0.01535743 0.00328625 0.07719467 0.1295732 ] Index(['r', 'm', 'f', 'inc', 'yr', 'edu', 'vf', 'child'], dtype='object') [0.0260904 0.31956531 0.12986956 0.33822537 0.02920092 0.00866598 0.04993307 0.09844938] Index(['r', 'm', 'f', 'inc', 'yr', 'edu', 'vf', 'child'], dtype='object') [0.01779839 0.35813637 0.20127974 0.25168858 0.03264069 0.00517402 0.02622572 0.10705648]

Requires

Python >= 3.6. Only depend on popular packages like numpy, pandas, statmodels, and sklearn.