Featured
2023

Diabetes Prediction

Developed a machine learning model for diabetes prediction using multiple algorithms including Logistic Regression, Naive Bayes, Random Forest, and Support Vector Classification (SVC). The model analyzes clinical parameters such as pregnancy status, glucose levels, blood pressure, BMI, and age to predict diabetes risk. Achieved 80% accuracy across different algorithms, demonstrating the model's reliability for clinical decision support.

Machine Learning
Python
Healthcare
Logistic Regression
Random Forest
SVC
Source Code

Project Overview

Developed a machine learning model for diabetes prediction using multiple algorithms including Logistic Regression, Naive Bayes, Random Forest, and Support Vector Classification (SVC). The model analyzes clinical parameters such as pregnancy status, glucose levels, blood pressure, BMI, and age to predict diabetes risk. Achieved 80% accuracy across different algorithms, demonstrating the model's reliability for clinical decision support.

Technologies Used

Machine LearningPythonHealthcareLogistic RegressionRandom ForestSVC

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