Prediction By Simulation: Lets play Rocket League!by @aatiffraz
Bonus: Here is a research paper on the subject : AI-enabled prediction of video game player performance using the data from heterogeneous sensors
\nAll due credits to the original contributors. Please upvote their work if you like it.
\nThanks
", "messages": [ { "id": "1975080", "postDate": "10/06/2022 15:46:29", "content": "Prediction By Simulation: Lets play Rocket League!by @aatiffraz
Bonus: Here is a research paper on the subject : AI-enabled prediction of video game player performance using the data from heterogeneous sensors
\nAll due credits to the original contributors. Please upvote their work if you like it.
\nThanks
", "rawMarkdown": "1. [TPS-2022-10 Fastai](https://www.kaggle.com/code/paddykb/tps-2022-10-fastai) by @paddykb \n\n2. [Rocket League | XGBoost + Feat Engineering + CV](https://www.kaggle.com/code/chazzer/rocket-league-xgboost-feat-engineering-cv) by @chazzer\n\n3. [[TPS OCT 22] LAMA (LightAutoML) FE+Sampling](https://www.kaggle.com/code/mukaseevru/tps-oct-22-lama-lightautoml-fe-sampling) by @mukaseevru\n\n4. [TP Oct 2022 - Lightgbm](https://www.kaggle.com/code/stautxie/tp-oct-2022-lightgbm) by @stautxie\n\n5. [Tabular Playground Oct 2022 - Catboost](https://www.kaggle.com/code/stautxie/tabular-playground-oct-2022-catboost) by @stautxie\n\n6. [Tabular Playground Oct 2022 - TabNet](https://www.kaggle.com/code/stautxie/tabular-playground-oct-2022-tabnet) by @stautxie\n\n7. [TPS-OCT22, GBDT Classifier](https://www.kaggle.com/code/cv13j0/tps-oct22-gbdt-classifier) by @cv13j0\n\n8. [Tps Oct22: Animation EDA + Keras Baseline](https://www.kaggle.com/code/jcaliz/tps-oct22-animation-eda-keras-baseline) by @jcaliz\n\n9. [Prediction By Simulation: Lets play Rocket League!](https://www.kaggle.com/code/aatiffraz/prediction-by-simulation-lets-play-rocket-league)by @aatiffraz\n\n10. [Pycaret](https://www.kaggle.com/code/infrarosso/tps-oct-2022-pycaret-model-analysis) by @infrarosso\n\n11. [All data+PCA+mean impute+LogisticRegression](https://www.kaggle.com/code/act18l/all-data-pca-mean-impute-logisticregression) by @act18l\n\n12. [TPSOCT22 CTB Online Learning](https://www.kaggle.com/code/alvinleenh/tpsoct22-ctb-online-learning) by @alvinleenh\n\n\n**Bonus**: [Here is a research paper on the subject](https://link.springer.com/article/10.1007/s11042-022-13464-0) : AI-enabled prediction of video game player performance using the data from heterogeneous sensors\n\n\nAll due credits to the original contributors. Please upvote their work if you like it.\n\nThanks", "votes": null }, { "id": "1975118", "postDate": "10/06/2022 15:58:27", "content": "Thanks!
\nGreat work everyone, I see a lot of diversity in approaches, which is always nice to see
Hello @kritidoneria, thanks for the post and mention; IHello @kritidoneria, thanks for the post and mention, I will continue improving my notebook and review the other content that you are recommending
", "rawMarkdown": "Hello @kritidoneria, thanks for the post and mention; IHello @kritidoneria, thanks for the post and mention, I will continue improving my notebook and review the other content that you are recommending", "votes": null }, { "id": "1980056", "postDate": "10/10/2022 01:13:44", "content": "Thanks for the quick summary guide @kritidoneria. I have created a notebook on online learning with CTB here. Let me know how can I make it beginner friendly too 😉
", "rawMarkdown": "Thanks for the quick summary guide @kritidoneria. I have created a notebook on online learning with CTB [here](https://www.kaggle.com/code/alvinleenh/tpsoct22-ctb-online-learning). Let me know how can I make it beginner friendly too 😉", "votes": null }, { "id": "2033093", "postDate": "11/17/2022 03:05:24", "content": "Dear @kritidoneria thanks for sharing the recap on Oct 22 , beginner friendly competition . Ofcourse credits to authors
", "rawMarkdown": "Dear @kritidoneria thanks for sharing the recap on Oct 22 , beginner friendly competition . Ofcourse credits to authors", "votes": null } ], "comments": [ { "id": 1975118, "author_name": "chazzer", "author_url": "", "post_date": "10/06/2022 15:58:27", "content": "Thanks!
\nGreat work everyone, I see a lot of diversity in approaches, which is always nice to see
Hello @kritidoneria, thanks for the post and mention; IHello @kritidoneria, thanks for the post and mention, I will continue improving my notebook and review the other content that you are recommending
", "votes": null, "replies": [] }, { "id": 1980056, "author_name": "alvinleenh", "author_url": "", "post_date": "10/10/2022 01:13:44", "content": "Thanks for the quick summary guide @kritidoneria. I have created a notebook on online learning with CTB here. Let me know how can I make it beginner friendly too 😉
", "votes": null, "replies": [] }, { "id": 2033093, "author_name": "mragpavank", "author_url": "", "post_date": "11/17/2022 03:05:24", "content": "Dear @kritidoneria thanks for sharing the recap on Oct 22 , beginner friendly competition . Ofcourse credits to authors
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1975080": "1. [TPS-2022-10 Fastai](https://www.kaggle.com/code/paddykb/tps-2022-10-fastai) by @paddykb \n\n2. [Rocket League | XGBoost + Feat Engineering + CV](https://www.kaggle.com/code/chazzer/rocket-league-xgboost-feat-engineering-cv) by @chazzer\n\n3. [[TPS OCT 22] LAMA (LightAutoML) FE+Sampling](https://www.kaggle.com/code/mukaseevru/tps-oct-22-lama-lightautoml-fe-sampling) by @mukaseevru\n\n4. [TP Oct 2022 - Lightgbm](https://www.kaggle.com/code/stautxie/tp-oct-2022-lightgbm) by @stautxie\n\n5. [Tabular Playground Oct 2022 - Catboost](https://www.kaggle.com/code/stautxie/tabular-playground-oct-2022-catboost) by @stautxie\n\n6. [Tabular Playground Oct 2022 - TabNet](https://www.kaggle.com/code/stautxie/tabular-playground-oct-2022-tabnet) by @stautxie\n\n7. [TPS-OCT22, GBDT Classifier](https://www.kaggle.com/code/cv13j0/tps-oct22-gbdt-classifier) by @cv13j0\n\n8. [Tps Oct22: Animation EDA + Keras Baseline](https://www.kaggle.com/code/jcaliz/tps-oct22-animation-eda-keras-baseline) by @jcaliz\n\n9. [Prediction By Simulation: Lets play Rocket League!](https://www.kaggle.com/code/aatiffraz/prediction-by-simulation-lets-play-rocket-league)by @aatiffraz\n\n10. [Pycaret](https://www.kaggle.com/code/infrarosso/tps-oct-2022-pycaret-model-analysis) by @infrarosso\n\n11. [All data+PCA+mean impute+LogisticRegression](https://www.kaggle.com/code/act18l/all-data-pca-mean-impute-logisticregression) by @act18l\n\n12. [TPSOCT22 CTB Online Learning](https://www.kaggle.com/code/alvinleenh/tpsoct22-ctb-online-learning) by @alvinleenh\n\n\n**Bonus**: [Here is a research paper on the subject](https://link.springer.com/article/10.1007/s11042-022-13464-0) : AI-enabled prediction of video game player performance using the data from heterogeneous sensors\n\n\nAll due credits to the original contributors. Please upvote their work if you like it.\n\nThanks", "1975118": "Thanks!\nGreat work everyone, I see a lot of diversity in approaches, which is always nice to see", "1975204": "Hello @kritidoneria, thanks for the post and mention; IHello @kritidoneria, thanks for the post and mention, I will continue improving my notebook and review the other content that you are recommending", "1980056": "Thanks for the quick summary guide @kritidoneria. I have created a notebook on online learning with CTB [here](https://www.kaggle.com/code/alvinleenh/tpsoct22-ctb-online-learning). Let me know how can I make it beginner friendly too 😉", "2033093": "Dear @kritidoneria thanks for sharing the recap on Oct 22 , beginner friendly competition . Ofcourse credits to authors" }, "source": "meta" }