Cervical Cancer Risk Prediction

[sharayuanuse]

**Is your feature request related to a problem?

Predicting cervical cancer risk is a challenging task due to the complexity of the dataset, which contains various medical, demographic, and lifestyle factors (e.g., pregnancies, smoking habits, and sexually transmitted diseases). Inconsistent or incomplete data can lead to inaccurate predictions and missed diagnoses.

Describe the solution you'd like

• Implement data imputation techniques to handle missing values effectively.
• Scale numerical features using normalization or standardization techniques to ensure all features contribute equally to model training.
• Apply outlier detection and removal methods to minimize the impact of noisy data points.
• Introduce feature selection techniques to remove irrelevant or redundant features, which can help reduce overfitting and improve model performance.

Describe alternatives you've considered

• Applying only basic scaling methods without feature selection, which may result in overfitting.
• Reducing outliers using quantile-based trimming instead of more complex techniques like Isolation Forests

[github-actions]

Thanks for creating the issue in ML-Nexus!🎉
Before you start working on your PR, please make sure to:

• ⭐ Star the repository if you haven't already.
• Pull the latest changes to avoid any merge conflicts.
• Attach before & after screenshots in your PR for clarity.
• Include the issue number in your PR description for better tracking.
  Don't forget to follow @UppuluriKalyani – Project Admin – for more updates!
  Tag @Neilblaze,@SaiNivedh26 for assigning the issue to you.
  Happy open-source contributing!☺️

[github-actions]

Thanks for raising this issue! However, we believe a similar issue already exists. Kindly go through all the open issues and ask to be assigned to that issue.