glimpse(enrolment4)
Rows: 398
Columns: 13
learner_id <chr> "46a4c71e-8819-4c5a-8164-2f786186b9fb", "091df104-705f-4ee2-a67b-9d90043c4f56", "357e0aed-eb55-4a0d-ac9b-ce5007d855d9", "09ef787d-6b55-4ed~
enrolled_at "2017-11-09 11:18:18 UTC", "2017-10-03 21:22:04 UTC", "2017-12-19 15:46:51 UTC", "2017-10-19 14:26:58 UTC", "2017-12-29 01:04:57 UTC", "20~
unenrolled_at <chr> "2018-10-19 10:31:59 UTC", "2018-10-08 16:27:33 UTC", "2018-09-29 20:27:55 UTC", "", "", "2018-08-11 12:44:38 UTC", "2018-08-10 11:49:07 U~
role "learner", "learner", "learner", "learner", "learner", "learner", "learner", "learner", "learner", "learner", "learner", "learner", "learn~
fully_participated_at <chr> "", "", "", "", "2018-09-12 06:54:19 UTC", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "2017-12-28 00:43:38 UTC", ~
purchased_statement_at "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""~
gender <chr> "female", "female", "male", "male", "male", "female", "female", "female", "male", "female", "female", "male", "male", "male", "female", "f~
country "GR", "GB", "EG", "GB", "CN", "GB", "UA", "NG", "NG", "MK", "AU", "BA", "IN", "NG", "EG", "GB", "ZA", "ZW", "NG", "PK", "GB", "KE", "NG", ~
age_range <chr> "26-35", ">65", "18-25", ">65", "36-45", "56-65", "26-35", "36-45", "26-35", "18-25", "46-55", "18-25", "18-25", "18-25", "18-25", "56-65"~
highest_education_level "university_masters", "university_degree", "secondary", "university_degree", "university_masters", "Unknown", "university_degree", "univer~
employment_status <chr> "full_time_student", "retired", "full_time_student", "retired", "working_full_time", "working_part_time", "self_employed", "working_full_t~
employment_area "accountancy_banking_and_finance", "Unknown", "it_and_information_services", "Unknown", "accountancy_banking_and_finance", "teaching_and_e~
$ detected_country "GR", "GB", "EG", "GB", "CN", "GB", "NO", "NG", "NG", "MK", "AU", "BA", "IN", "ZA", "EG", "GB", "GB", "ZW", "NG", "PK", "GB", "US", "NG", ~
I am wondering if i can use any of the fit model approaches like Regression ,Bayes,Lasso and predict if i will have in the future new enrolments?
If yes , how this could be