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    2021.05.25

    Parking Demand Prediction

    Algorithm | Structured | Regression | Traffic | MAE

    • moneyIcon Prize : Total 1,300만원
    • 2021.06.10 ~ 2021.07.30 18:00 + Google Calendar
    • 2,281 Users Completed
    Practice
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    Setup D-0

     

    [차없는 513-1, Private 31위] Catboost를 이용한 주차 수요 예측

    통통통
    2021.08.05 00:30 7,966 Views language
    comments 2

    <외부 데이터>
    -인구 밀도
    https://www.index.go.kr/potal/main/EachDtlPageDetail.do?idx_cd=1007
    - 초등학교 수 
    https://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1YL21231&conn_path=I2
    - 마트 수
    https://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1KB9010&conn_path=I2

    Code
    2 Comments
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    0 / 1000
    장어
    2021.08.05 09:20

    건축년도에 대한 외부데이터만 생각하고 있었는데 인구밀도나 초등학교 같은 것도 좋은 선택지였군요..
    코드 정리 감사합니다. 잘 봤습니다!  

    inas
    2021.08.05 13:36

    다양한 모델링을 적용해 보았으나 Public 점수를 참고하느라 Catboost 모델만 단독으로 사용하게 된점이 좀 아쉬웠습니다. Catboost 모델도 파라미터 최적을 위해 Optuna와 GridSearchCV 등을 사용해 탐색해 보고, 이에 기반한 앙상블 모델을 만들었지만 공유된 코드의 단순한 기본 형태가 점수가 더 좋게 나와 이 부분 역시 아쉬운 점으로 남았습니다.

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