This project focuses on predicting the likelihood of diabetes in individuals based on key health indicators, leveraging the power of Support Vector Machines (SVM) for accurate classification. The objective was to assist in early detection and enable timely medical intervention.

• Methodology:

  • Data Preprocessing: Cleaned and standardized the dataset to handle missing values and ensure uniform scaling of features like glucose levels, BMI, and age.

  • Feature Selection: Identified relevant features using statistical analysis to improve model performance and reduce noise.

  • Model Training: Utilized an SVM classifier with a radial basis function (RBF) kernel for non-linear decision boundaries.

  • Hyperparameter Tuning: Optimized parameters like the regularization factor (C) and kernel coefficient (gamma) using grid search and cross-validation.

• Key Insights and Results:

  • Achieved an accuracy of XX% and a precision-recall balance, demonstrating robust model performance.

  • Highlighted key predictors such as glucose levels and BMI, emphasizing their significance in diabetes risk.

  • Presented the findings through clear visualizations and confusion matrix analysis.

• Skills and Tools:

  • Machine Learning (SVM), Python (Pandas, Scikit-learn, Matplotlib, Seaborn), Data Preprocessing, and Model Evaluation.

This project demonstrates the application of advanced machine learning techniques to solve real-world healthcare challenges and provides actionable insights for clinical decision-making.

Github Project link:https://github.com/sm98code/ML.git