🧑‍💻: Diabetes Prediction Model Performance

[PrasDev4]

Title

Hyperparameter Tuning for Improved Diabetes Prediction Model Performance

Enhancement Aim

The aim of this enhancement is to improve the accuracy of the existing diabetes prediction model using Random Forest by performing hyperparameter tuning with GridSearchCV. This will help identify the optimal hyperparameters for the model, leading to better prediction results.

Changes

Changes:

• Hyperparameter Tuning: Introduced GridSearchCV to optimize hyperparameters such as n_estimators, max_depth, and 
   min_samples_split for the Random Forest model.
  

• Model Building: After tuning, rebuilt the Random Forest model using the best hyperparameters identified by the grid search.
  

• Model Evaluation: Evaluated the model’s performance on the test data, showing an improvement in accuracy from 0.975 to 
  0.995.
  

Screenshots 📷

Guidelines

• I have read the guidelines
• I have the link to my latest merged PR

Full Name

Prasanna Devireddy

Participant Role

SWOC

[github-actions]

🙌 Thank you for bringing this issue to our attention! We appreciate your input and will investigate it as soon as possible.

Feel free to join our community on Discord to discuss more!