Model Development and Deployment Description: This project involved developing, training, and deploying a suite of major machine learning classification models to predict the presence of heart disease based on the features in the provided heart.csv dataset.
🎯 Objectives
Build a robust predictive model for heart disease diagnosis.
Compare performance of different Machine Learning algorithms.
Deploy the model using Flask for an interactive web application
🧠 Machine Learning Models Used
The following algorithms were implemented and evaluated:
Model Description K-Nearest Neighbors (KNN) A simple distance-based algorithm for classification. Logistic Regression A linear model commonly used for binary classification. Naive Bayes A probabilistic model based on Bayes' theorem. Decision Tree Classifier A tree-based model that splits data based on feature importance. Random Forest Classifier An ensemble model using multiple decision trees for better accuracy. AdaBoost Classifier A boosting algorithm that combines weak learners into a strong model. Gradient Boosting Classifier An advanced ensemble model that optimizes prediction errors iteratively. XGBoost Classifier An optimized gradient boosting model offering high accuracy and speed. Support Vector Machine (SVC) A margin-based classifier that performs well on high-dimensional data.