abhisheks008 / ML-Crate

ML-Crate stands as the ultimate hub for a multitude of exciting ML projects, serving as the go-to resource haven for passionate and dedicated ML enthusiasts!πŸŒŸπŸ’« Devfolio URL, https://devfolio.co/projects/mlcrate-98f9
https://quine.sh/repo/abhisheks008-ML-Crate-409463050
MIT License
192 stars 216 forks source link

Predictive Maintenance Equipment Instruments #633

Closed ldneal closed 2 months ago

ldneal commented 3 months ago

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Predictive Maintenance Equipment Instrument :red_circle: Aim : To check the maintenance of Equipment in Instrument Power Plant :red_circle: Dataset : :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.


πŸ“ Follow the Guidelines to Contribute in the Project :


:red_circle::yellow_circle: Points to Note :


:white_check_mark: To be Mentioned while taking the issue :


Happy Contributing πŸš€

All the best. Enjoy your open source journey ahead. 😎

github-actions[bot] commented 3 months ago

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

ldneal commented 3 months ago

Sir I want to be work in this project would you assign me

abhisheks008 commented 3 months ago

You are already assigned to an issue. Please complete that one first.

ldneal commented 3 months ago

Ok Sir

siddhant4ds commented 3 months ago

@abhisheks008 If this issue is still available, I would like to work on it.

Since there is no dataset associated with this issue, I suggest this one: Predictive Maintenance Dataset (AI4I 2020)

Full name: Siddhant Tiwari GitHub profile link: https://github.com/siddhant4ds Participant ID: sid4ds (Devfolio), sid4ds (Discord) Participant role: SSOC-3 Contributor Approach:

  1. EDA and preprocessing of the dataset to make it usable for different ML models
  2. Models implemented - linear models, SVM, RandomForest, Gradient Boosting trees (XGBoost, LightGBM), Neural networks (MLP)
  3. After all models are implemented, try ensembling methods like weighted-averaging and hill-climbing to improve performance.
  4. Will try SOTA/experimental models like TabPFN.
abhisheks008 commented 3 months ago

Assigned @siddhant4ds

github-actions[bot] commented 2 months ago

Hello @siddhant4ds! Your issue #633 has been closed. Thank you for your contribution!