GetosMonkey/Kaggle_LabourMarketPrediction

The dataset tracks young South African jobseekers across multiple survey rounds. Each row is one participant's observation in a given round, with their prior-round history summarised inline in the "lag" columns. Rounds 1–8 form the training data; Round 9 is the evaluation cohort you must predict. # Kaggle_LabourMarketPrediction ## Notes on the data we're working with: ### Target employed_status — the outcome to predict. 1 = employed, 0 = unemployed. This column appears in train.csv and is removed from test.csv. ### A note on the lag columns Columns ending in \_lag describe the participant's previous round. They are empty for participants appearing for the first time, who have no earlier round to reference. How you handle this missingness is an important part of the challenge, and note that train and test can differ in how completely some columns are populated. ### Columns #### Identification and wave timeline anonymised_id — unique participant identifier and submission key current_round — the survey round of this observation lag_round — the round number of the participant's previous observation days_since_last_obs — days elapsed since the previous observation total_historical_rounds — number of rounds the participant has appeared in survey_date — date the observation was recorded sample — the survey wave/cohort label for this observation sample_first — the survey wave label of the participant's first appearance Labour market outcomes and historical lags employed_lag — employment status in the previous round status_lag — detailed labour market status in the previous round status_broad_lag — broad status in the previous round, including a "studying" category tenure_lag — job tenure (in days) recorded at the previous round Demographics and geography age — participant's age at the observation gender — gender race — population group sa_citizen — South African citizen (True/False) province — province of residence district — district municipality municipality — local municipality Educational attainment education_level — highest education level attained education_schooling_grade_twelve_equiv — whether the participant holds a Grade 12 equivalent institution_type — type of tertiary institution attended (populated …