Stroke AI Model

The Stroke AI Model is a Python-based project that aims to predict the likelihood of a person experiencing a stroke. The project involves analyzing stroke-related data, performing data cleaning and preprocessing, and developing multiple AI models. The data analysis phase focuses on understanding the dataset and addressing any issues. Data preprocessing includes handling missing values, outlier detection, and encoding categorical variables. The project employs the SMOTE technique to balance the dataset. Various AI models, such as Random Forest, Decision Tree, Gaussian Naive Bayes, Support Vector Machine, and XGBoost, are trained and evaluated using the preprocessed data. The project's goal is to provide early detection and prevention measures for stroke based on the developed AI models.

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