Prediction of skier days with Oracle Data Mining

create table t_test_data as
select * from (
select ora_hash(date_id, 1, 11) hashvalue, t.*
from t_all_data t
order by date_id
)
where hashvalue = 1;
create table t_training_data as
select * from (
select ora_hash(date_id, 1, 11) as hashvalue, t.*
from t_all_data t
order by date_id
)
where hashvalue = 0;

create table t_training_data as
select * from (
select *
from t_all_data SAMPLE (80) SEED (11)
);
create table t_test_data as
select * from (
select *
from t_all_data
MINUS
select *
from t_training_data
);

create table model_settings (
setting_name varchar2(30 char),
setting_value varchar2(30 char)
);

begin
insert into lds_model_settings (setting_name, setting_value)
values (dbms_data_mining.algo_name,
dbms_data_mining.algo_generalized_linear_model);
values (dbms_data_mining.odms_missing_value_treatment,
dbms_data_mining.odms_missing_value_mean_mode);
commit;
end;
/
values (dbms_data_mining.prep_auto,
dbms_data_mining.prep_auto_on);

begin
dbms_data_mining.create_model(
model_name => 'skier_model’,
mining_function => dbms_data_mining.regression,
data_table_name => 't_training_data’,
case_id_column_name => 'date_id’,
target_column_name => 'amount_skiers’,
settings_table_name => 'model_settings’
);
end;
/

select
t.date_id,
prediction(skier_model using *) predicted_skiers
from t_test_data t ;

select *
from table
(dbms_data_mining.get_model_details_glm ('skier_model'));
coefficient
std_error
p-value

select *
from table
(dbms_data_mining.get_model_details_global
('skier_model'));
f-value
error_sum_squares
r2

Prediction of skier days with Oracle Data Mining

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Prediction of skier days with Oracle Data Mining