{ "id": 437264, "title": "Time series materials for reference", "url": "/competitions/child-mind-institute-detect-sleep-states/discussion/437264", "author_name": "", "post_date": "2023-09-06T05:00:22.636282600Z", "votes": 46, "comment_count": 13, "views": 0, "content": "

Hello all,

\n

Wishing you the best for the assignment. Hope the below resources are helpful to onboard onto the assignment effectively-

\n

TPS April 2022

\n
    \n
  1. Overview- https://www.kaggle.com/competitions/tabular-playground-series-apr-2022
    2. \n
  2. Code-
    \na. https://www.kaggle.com/code/ambrosm/tpsapr22-eda-which-makes-sense
    \nb. https://www.kaggle.com/code/lordozvlad/tps-apr-pytorch-bidirectional-lstm
    \nc. https://www.kaggle.com/code/ambrosm/tpsapr22-best-model-without-nn
    \nd. https://www.kaggle.com/code/kellibelcher/time-series-classification-with-lstms-sensor-eda
    \ne. https://www.kaggle.com/code/ravi20076/tpsapr22-lgbm-lstm
    3. \n
  3. High scoring approaches-
    \na. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322259 -- winning solution
    \nb. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322257 -- 2nd place
    \nc. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322269 -- this is an interesting read, 3rd place
    \nd. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322558 -- 4th place
    \ne. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322277 -- 5th place
    4. \n
\n

TPS March 2022, Sep 2022 and Jan 2022
\nThey are regression problems and are similar to an extent, these could be used as a general reference

\n
    \n
  1. Overview-
    \na. https://www.kaggle.com/competitions/tabular-playground-series-jan-2022
    \nb. https://www.kaggle.com/competitions/tabular-playground-series-mar-2022
    \nc. https://www.kaggle.com/competitions/tabular-playground-series-sep-2022
    2. \n
  2. Code-
    \na. https://www.kaggle.com/code/mikailduzenli/tps-2022-notebook
    \nb. https://www.kaggle.com/code/paddykb/tps-2022-09-compare-to-best-public-notebook
    \nc. https://www.kaggle.com/code/azminetoushikwasi/time-series-analysis-forecasting
    \nd. https://www.kaggle.com/code/samuelcortinhas/tps-sept-22-timeseries-analysis
    \ne. https://www.kaggle.com/code/ambrosm/tpsjan22-03-linear-model
    \nf. https://www.kaggle.com/code/ambrosm/tpsjan22-01-eda-which-makes-sense
    \ng. https://www.kaggle.com/code/e0xextazy/tabular-playground-march-2022-11
    \nh. https://www.kaggle.com/code/ambrosm/tpsmar22-eda-which-makes-sense
    \ni. https://www.kaggle.com/code/kellibelcher/time-series-forecasting-arima-ets-boosting
    3. \n
\n

Community competition- Predict Sleep Cycles

\n
    \n
  1. Overview- https://www.kaggle.com/competitions/kaggle-pog-series-s01e04
    2. \n
  2. Code-
    \na. https://www.kaggle.com/code/oscarm524/rob-sleep-prediction-eda-baseline
    \nb. https://www.kaggle.com/code/henriupton/from-zzz-s-to-xgboost-rob-mulla-s-sleep-hours
    \nc. https://www.kaggle.com/code/timtadros/1st-place-solution
    \nd. https://www.kaggle.com/code/airqualityanthony/pogchamps4-prophet-arima-xgboost-ensemble
    \ne. https://www.kaggle.com/code/sujaykapadnis/simple-blend-arima
    \nf. https://www.kaggle.com/code/danpresil1/extracting-data-from-apple-health-app-s-export-xml
    3. \n
  3. High scoring solutions-
    \na. https://www.kaggle.com/competitions/kaggle-pog-series-s01e04/discussion/406334 --winning approach -this is an interesting read
    \nb. https://www.kaggle.com/competitions/kaggle-pog-series-s01e04/discussion/406474 -- 2nd place solution
    \nc. https://www.kaggle.com/competitions/kaggle-pog-series-s01e04/discussion/406546 -- 4th place solution
    4. \n
\n

Other adjutant resources
\nThe below discussion posts allude to several useful kernels and reference materials that can also be considered as references-
\na. https://www.kaggle.com/competitions/tabular-playground-series-sep-2022/discussion/349358
\nb. https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/discussion/372780
\nc. https://www.kaggle.com/discussions/general/375723
\nd. https://www.kaggle.com/competitions/widsdatathon2023/discussion/376574
\ne. https://www.kaggle.com/competitions/playground-series-s3e20/discussion/428298

\n

Competition metric- average precision
\na. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html -- scikit learn article
\nb. https://hasty.ai/docs/mp-wiki/metrics/average-precision
\nc. https://www.baeldung.com/cs/precision-vs-average-precision -- this is an interesting read
\nd. https://deepchecks.com/question/what-is-a-good-average-precision/
\ne. https://glassboxmedicine.com/2020/07/14/the-complete-guide-to-auc-and-average-precision-simulations-and-visualizations/

\n

Best regards and happy learning!

", "messages": [ { "id": "2425647", "postDate": "09/06/2023 05:00:22", "content": "

Hello all,

\n

Wishing you the best for the assignment. Hope the below resources are helpful to onboard onto the assignment effectively-

\n

TPS April 2022

\n
    \n
  1. Overview- https://www.kaggle.com/competitions/tabular-playground-series-apr-2022
    2. \n
  2. Code-
    \na. https://www.kaggle.com/code/ambrosm/tpsapr22-eda-which-makes-sense
    \nb. https://www.kaggle.com/code/lordozvlad/tps-apr-pytorch-bidirectional-lstm
    \nc. https://www.kaggle.com/code/ambrosm/tpsapr22-best-model-without-nn
    \nd. https://www.kaggle.com/code/kellibelcher/time-series-classification-with-lstms-sensor-eda
    \ne. https://www.kaggle.com/code/ravi20076/tpsapr22-lgbm-lstm
    3. \n
  3. High scoring approaches-
    \na. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322259 -- winning solution
    \nb. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322257 -- 2nd place
    \nc. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322269 -- this is an interesting read, 3rd place
    \nd. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322558 -- 4th place
    \ne. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322277 -- 5th place
    4. \n
\n

TPS March 2022, Sep 2022 and Jan 2022
\nThey are regression problems and are similar to an extent, these could be used as a general reference

\n
    \n
  1. Overview-
    \na. https://www.kaggle.com/competitions/tabular-playground-series-jan-2022
    \nb. https://www.kaggle.com/competitions/tabular-playground-series-mar-2022
    \nc. https://www.kaggle.com/competitions/tabular-playground-series-sep-2022
    2. \n
  2. Code-
    \na. https://www.kaggle.com/code/mikailduzenli/tps-2022-notebook
    \nb. https://www.kaggle.com/code/paddykb/tps-2022-09-compare-to-best-public-notebook
    \nc. https://www.kaggle.com/code/azminetoushikwasi/time-series-analysis-forecasting
    \nd. https://www.kaggle.com/code/samuelcortinhas/tps-sept-22-timeseries-analysis
    \ne. https://www.kaggle.com/code/ambrosm/tpsjan22-03-linear-model
    \nf. https://www.kaggle.com/code/ambrosm/tpsjan22-01-eda-which-makes-sense
    \ng. https://www.kaggle.com/code/e0xextazy/tabular-playground-march-2022-11
    \nh. https://www.kaggle.com/code/ambrosm/tpsmar22-eda-which-makes-sense
    \ni. https://www.kaggle.com/code/kellibelcher/time-series-forecasting-arima-ets-boosting
    3. \n
\n

Community competition- Predict Sleep Cycles

\n
    \n
  1. Overview- https://www.kaggle.com/competitions/kaggle-pog-series-s01e04
    2. \n
  2. Code-
    \na. https://www.kaggle.com/code/oscarm524/rob-sleep-prediction-eda-baseline
    \nb. https://www.kaggle.com/code/henriupton/from-zzz-s-to-xgboost-rob-mulla-s-sleep-hours
    \nc. https://www.kaggle.com/code/timtadros/1st-place-solution
    \nd. https://www.kaggle.com/code/airqualityanthony/pogchamps4-prophet-arima-xgboost-ensemble
    \ne. https://www.kaggle.com/code/sujaykapadnis/simple-blend-arima
    \nf. https://www.kaggle.com/code/danpresil1/extracting-data-from-apple-health-app-s-export-xml
    3. \n
  3. High scoring solutions-
    \na. https://www.kaggle.com/competitions/kaggle-pog-series-s01e04/discussion/406334 --winning approach -this is an interesting read
    \nb. https://www.kaggle.com/competitions/kaggle-pog-series-s01e04/discussion/406474 -- 2nd place solution
    \nc. https://www.kaggle.com/competitions/kaggle-pog-series-s01e04/discussion/406546 -- 4th place solution
    4. \n
\n

Other adjutant resources
\nThe below discussion posts allude to several useful kernels and reference materials that can also be considered as references-
\na. https://www.kaggle.com/competitions/tabular-playground-series-sep-2022/discussion/349358
\nb. https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/discussion/372780
\nc. https://www.kaggle.com/discussions/general/375723
\nd. https://www.kaggle.com/competitions/widsdatathon2023/discussion/376574
\ne. https://www.kaggle.com/competitions/playground-series-s3e20/discussion/428298

\n

Competition metric- average precision
\na. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html -- scikit learn article
\nb. https://hasty.ai/docs/mp-wiki/metrics/average-precision
\nc. https://www.baeldung.com/cs/precision-vs-average-precision -- this is an interesting read
\nd. https://deepchecks.com/question/what-is-a-good-average-precision/
\ne. https://glassboxmedicine.com/2020/07/14/the-complete-guide-to-auc-and-average-precision-simulations-and-visualizations/

\n

Best regards and happy learning!

", "rawMarkdown": "Hello all,\n\nWishing you the best for the assignment. Hope the below resources are helpful to onboard onto the assignment effectively-\n\n**TPS April 2022**
\n1. Overview- https://www.kaggle.com/competitions/tabular-playground-series-apr-2022
\n2. Code-
\na. https://www.kaggle.com/code/ambrosm/tpsapr22-eda-which-makes-sense
\nb. https://www.kaggle.com/code/lordozvlad/tps-apr-pytorch-bidirectional-lstm
\nc. https://www.kaggle.com/code/ambrosm/tpsapr22-best-model-without-nn
\nd. https://www.kaggle.com/code/kellibelcher/time-series-classification-with-lstms-sensor-eda
\ne. https://www.kaggle.com/code/ravi20076/tpsapr22-lgbm-lstm
\n3. High scoring approaches-
\na. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322259 -- winning solution
\nb. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322257 -- 2nd place
\nc. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322269 -- this is an interesting read, 3rd place
\nd. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322558 -- 4th place
\ne. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322277 -- 5th place
\n\n**TPS March 2022, Sep 2022 and Jan 2022**
\nThey are regression problems and are similar to an extent, these could be used as a general reference
\n1. Overview-
\na. https://www.kaggle.com/competitions/tabular-playground-series-jan-2022
\nb. https://www.kaggle.com/competitions/tabular-playground-series-mar-2022
\nc. https://www.kaggle.com/competitions/tabular-playground-series-sep-2022
\n2. Code-
\na. https://www.kaggle.com/code/mikailduzenli/tps-2022-notebook
\nb. https://www.kaggle.com/code/paddykb/tps-2022-09-compare-to-best-public-notebook
\nc. https://www.kaggle.com/code/azminetoushikwasi/time-series-analysis-forecasting
\nd. https://www.kaggle.com/code/samuelcortinhas/tps-sept-22-timeseries-analysis
\ne. https://www.kaggle.com/code/ambrosm/tpsjan22-03-linear-model
\nf. https://www.kaggle.com/code/ambrosm/tpsjan22-01-eda-which-makes-sense
\ng. https://www.kaggle.com/code/e0xextazy/tabular-playground-march-2022-11
\nh. https://www.kaggle.com/code/ambrosm/tpsmar22-eda-which-makes-sense
\ni. https://www.kaggle.com/code/kellibelcher/time-series-forecasting-arima-ets-boosting
\n\n**Community competition- Predict Sleep Cycles**
\n1. Overview- https://www.kaggle.com/competitions/kaggle-pog-series-s01e04
\n2. Code-
\na. https://www.kaggle.com/code/oscarm524/rob-sleep-prediction-eda-baseline
\nb. https://www.kaggle.com/code/henriupton/from-zzz-s-to-xgboost-rob-mulla-s-sleep-hours
\nc. https://www.kaggle.com/code/timtadros/1st-place-solution
\nd. https://www.kaggle.com/code/airqualityanthony/pogchamps4-prophet-arima-xgboost-ensemble
\ne. https://www.kaggle.com/code/sujaykapadnis/simple-blend-arima
\nf. https://www.kaggle.com/code/danpresil1/extracting-data-from-apple-health-app-s-export-xml
\n2. High scoring solutions-
\na. https://www.kaggle.com/competitions/kaggle-pog-series-s01e04/discussion/406334 --winning approach -this is an interesting read
\nb. https://www.kaggle.com/competitions/kaggle-pog-series-s01e04/discussion/406474 -- 2nd place solution
\nc. https://www.kaggle.com/competitions/kaggle-pog-series-s01e04/discussion/406546 -- 4th place solution
\n\n**Other adjutant resources**
\nThe below discussion posts allude to several useful kernels and reference materials that can also be considered as references-
\na. https://www.kaggle.com/competitions/tabular-playground-series-sep-2022/discussion/349358
\nb. https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/discussion/372780
\nc. https://www.kaggle.com/discussions/general/375723
\nd. https://www.kaggle.com/competitions/widsdatathon2023/discussion/376574
\ne. https://www.kaggle.com/competitions/playground-series-s3e20/discussion/428298
\n\n**Competition metric- average precision**
\na. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html -- scikit learn article
\nb. https://hasty.ai/docs/mp-wiki/metrics/average-precision
\nc. https://www.baeldung.com/cs/precision-vs-average-precision -- this is an interesting read
\nd. https://deepchecks.com/question/what-is-a-good-average-precision/
\ne. https://glassboxmedicine.com/2020/07/14/the-complete-guide-to-auc-and-average-precision-simulations-and-visualizations/
\n\nBest regards and happy learning!", "votes": null }, { "id": "2425680", "postDate": "09/06/2023 05:37:03", "content": "

Thanks for bringing together this wealth of resources in a single article. @ravi20076
\nScrolled through a few of the links—really cool stuff!

", "rawMarkdown": "Thanks for bringing together this wealth of resources in a single article. @ravi20076 \nScrolled through a few of the links—really cool stuff!", "votes": null }, { "id": "2426161", "postDate": "09/06/2023 12:49:43", "content": "

Thanks for sharing such awesome and quality resources.

", "rawMarkdown": "Thanks for sharing such awesome and quality resources.", "votes": null }, { "id": "2427180", "postDate": "09/07/2023 05:40:30", "content": "

Thanks a lot @ravi20076 for bringing the resources together. I have started working on this competition and these resources will definitely help.

", "rawMarkdown": "Thanks a lot @ravi20076 for bringing the resources together. I have started working on this competition and these resources will definitely help.", "votes": null }, { "id": "2427279", "postDate": "09/07/2023 06:25:35", "content": "

Wow! Really useful resources. Thank you @ravi20076 !

", "rawMarkdown": "Wow! Really useful resources. Thank you @ravi20076 !", "votes": null }, { "id": "2427691", "postDate": "09/07/2023 12:04:01", "content": "

Thanks a lot, @ravi20076 for sharing these valuable resources with us.

", "rawMarkdown": "Thanks a lot, @ravi20076 for sharing these valuable resources with us.", "votes": null }, { "id": "2428292", "postDate": "09/07/2023 19:01:04", "content": "

Thanks a lot !

", "rawMarkdown": "Thanks a lot !", "votes": null }, { "id": "2432635", "postDate": "09/11/2023 04:01:23", "content": "

Thanks for sharing, This content means a lot for me!

", "rawMarkdown": "Thanks for sharing, This content means a lot for me!", "votes": null }, { "id": "2433112", "postDate": "09/11/2023 10:55:35", "content": "

Wow, that's a big compilation out there. Thanks @ravi20076

", "rawMarkdown": "Wow, that's a big compilation out there. Thanks @ravi20076", "votes": null }, { "id": "2436041", "postDate": "09/13/2023 09:59:02", "content": "

thanks for sharing. 👏 it is very useful

", "rawMarkdown": "thanks for sharing. 👏 it is very useful", "votes": null }, { "id": "2445535", "postDate": "09/18/2023 23:10:15", "content": "

Thank you for sharing. very helpful!

", "rawMarkdown": "Thank you for sharing. very helpful!", "votes": null }, { "id": "2445601", "postDate": "09/19/2023 01:13:06", "content": "

Wow this is such a comprehensive library. Thank tou @ravi20076 for sharing this a lot!

", "rawMarkdown": "Wow this is such a comprehensive library. Thank tou @ravi20076 for sharing this a lot!", "votes": null }, { "id": "2480989", "postDate": "10/13/2023 17:28:53", "content": "

Good collection. Thanks for sharing!

", "rawMarkdown": "Good collection. Thanks for sharing!", "votes": null }, { "id": "2529348", "postDate": "11/18/2023 06:33:45", "content": "

thanks for sharing. So amazing that u can find so many resources!

", "rawMarkdown": "thanks for sharing. So amazing that u can find so many resources!", "votes": null } ], "comments": [ { "id": 2425680, "author_name": "ddosad", "author_url": "", "post_date": "09/06/2023 05:37:03", "content": "

Thanks for bringing together this wealth of resources in a single article. @ravi20076
\nScrolled through a few of the links—really cool stuff!

", "votes": null, "replies": [] }, { "id": 2426161, "author_name": "indrasn0wal", "author_url": "", "post_date": "09/06/2023 12:49:43", "content": "

Thanks for sharing such awesome and quality resources.

", "votes": null, "replies": [] }, { "id": 2427180, "author_name": "rishabh15virgo", "author_url": "", "post_date": "09/07/2023 05:40:30", "content": "

Thanks a lot @ravi20076 for bringing the resources together. I have started working on this competition and these resources will definitely help.

", "votes": null, "replies": [] }, { "id": 2427279, "author_name": "tomasluna", "author_url": "", "post_date": "09/07/2023 06:25:35", "content": "

Wow! Really useful resources. Thank you @ravi20076 !

", "votes": null, "replies": [] }, { "id": 2427691, "author_name": "moizbohra", "author_url": "", "post_date": "09/07/2023 12:04:01", "content": "

Thanks a lot, @ravi20076 for sharing these valuable resources with us.

", "votes": null, "replies": [] }, { "id": 2428292, "author_name": "ericka42", "author_url": "", "post_date": "09/07/2023 19:01:04", "content": "

Thanks a lot !

", "votes": null, "replies": [] }, { "id": 2432635, "author_name": "inductiveanks", "author_url": "", "post_date": "09/11/2023 04:01:23", "content": "

Thanks for sharing, This content means a lot for me!

", "votes": null, "replies": [] }, { "id": 2433112, "author_name": "hnooruddin", "author_url": "", "post_date": "09/11/2023 10:55:35", "content": "

Wow, that's a big compilation out there. Thanks @ravi20076

", "votes": null, "replies": [] }, { "id": 2436041, "author_name": "mehmetisik", "author_url": "", "post_date": "09/13/2023 09:59:02", "content": "

thanks for sharing. 👏 it is very useful

", "votes": null, "replies": [] }, { "id": 2445535, "author_name": "ryotapy", "author_url": "", "post_date": "09/18/2023 23:10:15", "content": "

Thank you for sharing. very helpful!

", "votes": null, "replies": [] }, { "id": 2445601, "author_name": "rimbax", "author_url": "", "post_date": "09/19/2023 01:13:06", "content": "

Wow this is such a comprehensive library. Thank tou @ravi20076 for sharing this a lot!

", "votes": null, "replies": [] }, { "id": 2480989, "author_name": "adebayo", "author_url": "", "post_date": "10/13/2023 17:28:53", "content": "

Good collection. Thanks for sharing!

", "votes": null, "replies": [] }, { "id": 2529348, "author_name": "yijiam", "author_url": "", "post_date": "11/18/2023 06:33:45", "content": "

thanks for sharing. So amazing that u can find so many resources!

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "2425647": "Hello all,\n\nWishing you the best for the assignment. Hope the below resources are helpful to onboard onto the assignment effectively-\n\n**TPS April 2022**
\n1. Overview- https://www.kaggle.com/competitions/tabular-playground-series-apr-2022
\n2. Code-
\na. https://www.kaggle.com/code/ambrosm/tpsapr22-eda-which-makes-sense
\nb. https://www.kaggle.com/code/lordozvlad/tps-apr-pytorch-bidirectional-lstm
\nc. https://www.kaggle.com/code/ambrosm/tpsapr22-best-model-without-nn
\nd. https://www.kaggle.com/code/kellibelcher/time-series-classification-with-lstms-sensor-eda
\ne. https://www.kaggle.com/code/ravi20076/tpsapr22-lgbm-lstm
\n3. High scoring approaches-
\na. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322259 -- winning solution
\nb. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322257 -- 2nd place
\nc. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322269 -- this is an interesting read, 3rd place
\nd. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322558 -- 4th place
\ne. https://www.kaggle.com/competitions/tabular-playground-series-apr-2022/discussion/322277 -- 5th place
\n\n**TPS March 2022, Sep 2022 and Jan 2022**
\nThey are regression problems and are similar to an extent, these could be used as a general reference
\n1. Overview-
\na. https://www.kaggle.com/competitions/tabular-playground-series-jan-2022
\nb. https://www.kaggle.com/competitions/tabular-playground-series-mar-2022
\nc. https://www.kaggle.com/competitions/tabular-playground-series-sep-2022
\n2. Code-
\na. https://www.kaggle.com/code/mikailduzenli/tps-2022-notebook
\nb. https://www.kaggle.com/code/paddykb/tps-2022-09-compare-to-best-public-notebook
\nc. https://www.kaggle.com/code/azminetoushikwasi/time-series-analysis-forecasting
\nd. https://www.kaggle.com/code/samuelcortinhas/tps-sept-22-timeseries-analysis
\ne. https://www.kaggle.com/code/ambrosm/tpsjan22-03-linear-model
\nf. https://www.kaggle.com/code/ambrosm/tpsjan22-01-eda-which-makes-sense
\ng. https://www.kaggle.com/code/e0xextazy/tabular-playground-march-2022-11
\nh. https://www.kaggle.com/code/ambrosm/tpsmar22-eda-which-makes-sense
\ni. https://www.kaggle.com/code/kellibelcher/time-series-forecasting-arima-ets-boosting
\n\n**Community competition- Predict Sleep Cycles**
\n1. Overview- https://www.kaggle.com/competitions/kaggle-pog-series-s01e04
\n2. Code-
\na. https://www.kaggle.com/code/oscarm524/rob-sleep-prediction-eda-baseline
\nb. https://www.kaggle.com/code/henriupton/from-zzz-s-to-xgboost-rob-mulla-s-sleep-hours
\nc. https://www.kaggle.com/code/timtadros/1st-place-solution
\nd. https://www.kaggle.com/code/airqualityanthony/pogchamps4-prophet-arima-xgboost-ensemble
\ne. https://www.kaggle.com/code/sujaykapadnis/simple-blend-arima
\nf. https://www.kaggle.com/code/danpresil1/extracting-data-from-apple-health-app-s-export-xml
\n2. High scoring solutions-
\na. https://www.kaggle.com/competitions/kaggle-pog-series-s01e04/discussion/406334 --winning approach -this is an interesting read
\nb. https://www.kaggle.com/competitions/kaggle-pog-series-s01e04/discussion/406474 -- 2nd place solution
\nc. https://www.kaggle.com/competitions/kaggle-pog-series-s01e04/discussion/406546 -- 4th place solution
\n\n**Other adjutant resources**
\nThe below discussion posts allude to several useful kernels and reference materials that can also be considered as references-
\na. https://www.kaggle.com/competitions/tabular-playground-series-sep-2022/discussion/349358
\nb. https://www.kaggle.com/competitions/godaddy-microbusiness-density-forecasting/discussion/372780
\nc. https://www.kaggle.com/discussions/general/375723
\nd. https://www.kaggle.com/competitions/widsdatathon2023/discussion/376574
\ne. https://www.kaggle.com/competitions/playground-series-s3e20/discussion/428298
\n\n**Competition metric- average precision**
\na. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html -- scikit learn article
\nb. https://hasty.ai/docs/mp-wiki/metrics/average-precision
\nc. https://www.baeldung.com/cs/precision-vs-average-precision -- this is an interesting read
\nd. https://deepchecks.com/question/what-is-a-good-average-precision/
\ne. https://glassboxmedicine.com/2020/07/14/the-complete-guide-to-auc-and-average-precision-simulations-and-visualizations/
\n\nBest regards and happy learning!", "2425680": "Thanks for bringing together this wealth of resources in a single article. @ravi20076 \nScrolled through a few of the links—really cool stuff!", "2426161": "Thanks for sharing such awesome and quality resources.", "2427180": "Thanks a lot @ravi20076 for bringing the resources together. I have started working on this competition and these resources will definitely help.", "2427279": "Wow! Really useful resources. Thank you @ravi20076 !", "2427691": "Thanks a lot, @ravi20076 for sharing these valuable resources with us.", "2428292": "Thanks a lot !", "2432635": "Thanks for sharing, This content means a lot for me!", "2433112": "Wow, that's a big compilation out there. Thanks @ravi20076", "2436041": "thanks for sharing. 👏 it is very useful", "2445535": "Thank you for sharing. very helpful!", "2445601": "Wow this is such a comprehensive library. Thank tou @ravi20076 for sharing this a lot!", "2480989": "Good collection. Thanks for sharing!", "2529348": "thanks for sharing. So amazing that u can find so many resources!" }, "source": "meta" }