Diagnostic Risk Prediction Using Support Vector Machines (SVM) 🩺📊

📌 Project Overview

This project builds a predictive model to classify whether a patient has diabetes based on specific diagnostic and clinical measurements. By leveraging Support Vector Machines (SVM), the project demonstrates how high-dimensional health data can be classified with high precision.

🛠️ Tech Stack & Libraries

• Language: Python
• Algorithm: Support Vector Classifier (SVC)
• Libraries: Scikit-Learn, Pandas, NumPy, Matplotlib, Seaborn

🔍 Key Insights & Pipeline

• Hyperplane Optimization: Implemented Support Vector Classifier (SVC) to find the optimal decision boundary separating the target classes.
• Data Standardization: Applied feature scaling to ensure that clinical attributes with large scales (like Insulin or Glucose levels) do not skew the distance-based SVM model.
• Performance Evaluation: Evaluated the classifier's performance using robust metrics to balance precision and recall.

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💡 Domain Transferability (Automotive Application): The multi-feature classification logic used in this distance-based algorithm (SVM) is directly transferable to Automotive Telematics & EV Battery Analytics. In the automotive industry, these exact multi-attribute predictive models are deployed to analyze complex sensor readings to predict Electric Vehicle (EV) battery degradation over time, or to classify driver risk profiles based on braking, acceleration, and speed patterns for smart insurance systems.