Diabetes Prediction

Predictive model using Logistic Regression to assess diabetes risk based on health parameters.

Diabetes Prediction Project

Project Overview

This project uses a healthcare dataset to predict diabetes outcomes based on patient health indicators. The data was cleaned, normalized, and trained using Logistic Regression and Random Forest models to ensure accuracy and generalization.

Tools & Techniques

Python Pandas Scikit-learn Matplotlib Seaborn

Key Outcomes

  • Achieved 87% prediction accuracy using Logistic Regression.
  • Enhanced recall using balanced dataset preprocessing.
  • Visualized results with ROC curve and confusion matrix.
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