Perinatal health Risk Predictors Using Classification And Random Forest

[vivekvardhan2810]

Is your feature request related to a problem? Please describe.

The Feature Has the potential to improve prenatal diagnosis of birth defects and outcomes in assisted reproductive technology too.

Describe the solution you'd like

I would like to develop a machine learning-based classification system for Perinatal health Risk Predictors.

Dataset Link : https://archive.ics.uci.edu/dataset/863/maternal+health+risk

Model : Train and compare various ML models, including Decision Tree, KNN, SVC, Random Forest, Logistic Regression, Bagging Classifier, AdaBoost Classifier, and Naive Bayes.

Data Preprocessing: This activity includes the following steps.

● Handling missing values
● Handling categorical data
● Handling outliers
● Scaling Techniques
● Splitting dataset into training and test set

Evaluation: Use GridSearchCV for hyperparameter tuning and evaluate model performance using confusion matrix and classification report, Choose the best-performing model based on accuracy and other performance metrics, typically Random Forest in this case.

Describe alternatives you've considered

No response.

Additional context

No response.

Code of Conduct

• I agree to follow this project's Code of Conduct

[github-actions]

Congratulations, @vivekvardhan2810! 🎉 Thank you for creating your issue. Your contribution is greatly appreciated and we look forward to working with you to resolve the issue. Keep up the great work!

We will promptly review your changes and offer feedback. Keep up the excellent work! Kindly remember to check our contributing guidelines

[SrijanShovit]

Closing as not checked the repo and proposed same project with different name.

[vivekvardhan2810]

Why did you Close this Issue Sir
As i am already working this issue
@SrijanShovit, may i know the reason