Enhancing Predictive Calc with Additional Models and Hyperparameter Tuning

[murtaza-sadri-19]

To further improve the capabilities of Predictive Calc under GSSoC'24 Extd, I propose adding new machine learning algorithms in form of projects and implementing hyperparameter tuning techniques. By incorporating a wider range of models and optimizing their parameters, we can enhance the accuracy, flexibility, and overall performance of the tool. This will provide users with more options to choose the best model for their specific prediction tasks and ensure that the models are fine-tuned to achieve optimal results.

I wish to contribute this to your repository. Kindly assign me this.

[yashasvini121]

Hi @murtaza-sadri-19, for now, please be more specific about your proposal.
Describe one problem you plan to tackle and the model you'd create for it. For example, the project currently includes a House Price Prediction model using regression. You could develop multiple models or document both successful and rejected models in a Jupyter notebook. Also, mention the estimated time that you will require.

Make sure to review the add new model template to follow the proper issue format.

[murtaza-sadri-19]

Like I'm Planning to implement Hyperparameter tuning in a model which would classify Breast cancer tumor into two classes viz. Benign/Malignant. And I would also apply ensemble learning techniques over the dataset.

I would require approx 3-4 days to complete the same. And regarding the format I would make sure, that I comply to that.

[yashasvini121]

Ok, great, go ahead!

[yashasvini121]

Updates @murtaza-sadri-19?

[murtaza-sadri-19]

Updates @murtaza-sadri-19?
I have resolved the conflicts and made changes in PR #129

[yashasvini121]

Closing this issue as a similar one has been created under #33 by you.