Closed adi271001 closed 3 months ago
Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! π
Full name : Aditi Kala GitHub Profile Link : https://www.github.com/why-aditi Participant ID (If not, then put NA) : NA Approach for this Project : Data cleaning, preprocessing, training DL models both CNN and pretrained ones What is your participant role? SSOC'24
@abhisheks008 i have done the requested changes on the previous issue please assign me this issue if everything checks out
Full name : Nithish Paidimarri GitHub Profile Link : https://www.github.com/Nithish-456 Participant ID (If not, then put NA) : NA Approach for this Project : Begin with exploratory data analysis (EDA), Feature engineering , Enhancing the accuracy by hyperparameter tuning (Grid Search ) through different classification algorithms like Random Forest Classifier, Gradient Boosting, XGBoost, SVM etc.. What is your participant role? (SSOC'24)
Hi @adi271001 thanks for creating this issue. Can you please share your approach and what are the models you are planning to implement for this problem statement?
Approach for this Project : data cleaning , preprocessing , eda , using ml models like knn , logistic regression , decision tree , random forest , svm and then test the model
Approach for this Project : data cleaning , preprocessing , eda , using ml models like knn , logistic regression , decision tree , random forest , svm and then test the model
Implement 6-7 models for this problem statement along with the EDA implementations.
Assigned this issue to you @adi271001
Hello @adi271001! Your issue #655 has been closed. Thank you for your contribution!
ML-Crate Repository (Proposing new issue)
:red_circle: Project Title : cheese classification :red_circle: Aim : to classify cheese using various ml algorithms :red_circle: Dataset : https://www.kaggle.com/datasets/jainaru/cheese-across-the-world :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