I got a bunch of only nans in the lecture about creating a scorecard.

min_score=300

max_score=850

In [131]: min_score

min_score=scorecard_df.groupby(['Original_names'])['Coef'].min()

min_score

Out[131]:

Original_names
Intercept                     -1.286588
acc_now_delinq                 0.000000
addr_state                     0.000000
annual_inc                    -0.068937
dti                            0.000000
emp_length                     0.000000
grade                          0.000000
home_ownership                 0.000000
initial_list_status            0.000000
inq_last_6mths                 0.000000
int_rate                       0.000000
mths_since_earliest_cr_line    0.000000
mths_since_issue_d            -0.060627
mths_since_last_delinq         0.000000
mths_since_last_record        -0.049872
purpose                        0.000000
term                           0.000000
verification_status           -0.010562
Name: Coef, dtype: float64

In [130]: min_score_sum

min_score_sum=scorecard_df.groupby(['Original_names'])['Coef'].min().sum()

min_score_sum

Out[130]:

-1.4765851105053986

In [125]: max_score

max_score=scorecard_df.groupby(['Original_names'])['Coef'].max()

max_score

Out[125]:

Original_names
Intercept                     -1.286588
acc_now_delinq                 0.235443
addr_state                     0.524189
annual_inc                     0.577045
dti                            0.389218
emp_length                     0.126174
grade                          0.912940
home_ownership                 0.106218
initial_list_status            0.054820
inq_last_6mths                 0.694467
int_rate                       0.867411
mths_since_earliest_cr_line    0.131432
mths_since_issue_d             1.091989
mths_since_last_delinq         0.239036
mths_since_last_record         0.286678
purpose                        0.300414
term                           0.078659
verification_status            0.084432
Name: Coef, dtype: float64

In [126]: ore_sum

max_score_sum=scorecard_df.groupby(['Original_names'])['Coef'].max().sum()

max_score_sum

Out[126]:

5.413976291458666

In [127]: _df

scorecard_df['Score']=scorecard_df['Coef']*(max_score-min_score)/(max_score_sum-min_score_sum)

In [132]:

scorecard_df.Score.unique()

Out[132]:

array([nan])

Scorecard

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