Closed abhisheks008 closed 8 months ago
@abhisheks008 i would like to work on this issue. Could you please assign it to me?
Full name : Avdhesh Varshney GitHub Profile Link : https://github.com/Avdhesh-Varshney Participant ID (If not, then put NA) : Approach for this Project :
What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.) KWOC
One issue at a time @Avdhesh-Varshney
Hi @abhisheks008 Full name : Keshav Arora GitHub Profile Link : https://github.com/CoderOMaster Participant Role : JWOC 2024 Approach for this Project: Will implement decision tree,random forest,knn classifier to find best algorithm. Feature scaling, encoding (if required),ANN can be also be tried. Regards
hi abhisekh Full name: Yuvika Singh git hub profile link:https://github.com/Yuvika-14/Yuvikademo.git approch: ensemble methods, gradient boosting, neural networks. Take care of the missing data if there are any categorical values then will use one hot encoder or label encoder depending upon the issue.
hi abhisekh Full name: Yuvika Singh git hub profile link:https://github.com/Yuvika-14/Yuvikademo.git approch: ensemble methods, gradient boosting, neural networks. Take care of the missing data if there are any categorical values then will use one hot encoder or label encoder depending upon the issue.
This is part of JWOC event.
Hi @abhisheks008 Full name : Keshav Arora GitHub Profile Link : https://github.com/CoderOMaster Participant Role : JWOC 2024 Approach for this Project: Will implement decision tree,random forest,knn classifier to find best algorithm. Feature scaling, encoding (if required),ANN can be also be tried. Regards
Issue assigned to you @CoderOMaster
ML-Crate Repository (Proposing new issue)
:red_circle: Project Title : Vehicle Live Risk Prediction :red_circle: Aim : Create a prediction model for vehicle live risk based on given dataset. :red_circle: Dataset : https://www.kaggle.com/datasets/punyamodi/vehicle-live-risk-prediction :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.
π Follow the Guidelines to Contribute in the Project :
requirements.txt
- This file will contain the required packages/libraries to run the project in other machines.Model
folder, theREADME.md
file must be filled up properly, with proper visualizations and conclusions.:red_circle::yellow_circle: Points to Note :
:white_check_mark: To be Mentioned while taking the issue :
Happy Contributing π
All the best. Enjoy your open source journey ahead. π