Strok_Classification

Developed a Machine Learning project for stroke prediction using Python and Scikit-learn.

Performed data preprocessing, cleaning, and exploratory data analysis (EDA).

Handled missing values, categorical encoding, and feature scaling techniques.

Analyzed important health-related factors such as age, BMI, hypertension, and glucose level.

Built and compared multiple classification models to improve prediction performance.

Implemented algorithms including Logistic Regression, Random Forest, XGBoost, and Decision Tree.

Evaluated model performance using Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.

Visualized data insights and model results using Matplotlib and Seaborn.

Applied feature engineering and model tuning to enhance prediction accuracy.

Focused on creating an effective predictive system to support early stroke risk detection.