Open FreeSpirit11 opened 1 month ago
Hi, I have raised this issue . Please assign it to me.
Hi @Kushal997-das , It is not a level 1 issue. Please assign it level 2.
@FreeSpirit11 Will see PR then will decide. Complete this project ASAP else will close.
Is your feature request related to a problem? Please describe.
The current IPL Prediction model in Project-Guidance/Machine Learning and Data Science/Intermediate/IPL Prediction/Regularisation - RIDGE_LASSO_HYBRID.ipynb lacks several advanced features that could significantly improve its performance and interpretability. Specifically, it does not include thorough feature selection, hyperparameter tuning, comprehensive feature engineering, outlier handling, enhanced model evaluation metrics, or ensemble methods.
Describe the solution you'd like.
I would like to enhance the existing model by implementing the following features:
Describe alternatives you've considered.
As an alternative, I considered:
Add any other context or screenshots about the feature request here.
Implementing these features will require modifications to the existing Regularisation - RIDGE_LASSO_HYBRID.ipynb file, including additional code for feature engineering, hyperparameter tuning with GridSearchCV, and evaluating model performance with ensemble methods. Visualizations such as feature importance plots from Random Forest and Gradient Boosting models will also be included.
Below is a brief outline of the changes to be made:
Feature Selection:
Hyperparameter Tuning:
Feature Engineering:
Outlier Handling:
Model Evaluation:
Ensemble Methods:
These enhancements aim to improve the overall robustness and accuracy of the IPL Prediction model.