This project employs machine learning algorithms to analyze health metrics from the Pima Indians Diabetes Database and predict the likelihood of diabetes onset. By training various models on the dataset, the project aims to identify patterns and relationships between the input features (such as glucose levels, BMI, etc.) and the target variable (diabetes occurrence). The trained models then use these patterns to make predictions on new, unseen data, enabling proactive healthcare interventions for individuals identified as at-risk for diabetes.
- Random Forest
- Decision Tree
- XGBoost
- Support Vector Machine (SVM)
The dataset used for this project is the Pima Indians Diabetes Database, which is publicly available and contains various health metrics such as glucose levels, BMI, blood pressure, etc., along with the diabetes status of individuals. This dataset has been widely used in machine learning research for predicting diabetes onset.
- Lakshmi J
- Likith Abhilash C
- Neha S
- Soujanya S