Heart-Disease-Prediction and Heart_disease_prediction
These are competitors offering alternative implementations of the same machine learning task—both train classification models to predict heart disease risk—so users would typically choose one based on algorithm preference (K-Neighbors vs. multi-algorithm comparison) rather than use them together.
About Heart-Disease-Prediction
kb22/Heart-Disease-Prediction
The project involves training a machine learning model (K Neighbors Classifier) to predict whether someone is suffering from a heart disease with 87% accuracy.
Implements a complete ML pipeline using scikit-learn's KNeighborsClassifier with hyperparameter tuning via GridSearchCV to optimize the k-value selection. The workflow encompasses data preprocessing, feature scaling with StandardScaler, train-test splitting, and model evaluation using confusion matrices and classification metrics. Targets the pandas/scikit-learn ecosystem for rapid prototyping of medical classification tasks.
About Heart_disease_prediction
chayandatta/Heart_disease_prediction
Heart Disease prediction using 5 algorithms
Compares five classification algorithms (Logistic Regression, Random Forest, Naive Bayes, KNN, Decision Tree) on the UCI Heart Disease dataset, with hyperparameter tuning to optimize accuracy across models. Delivered as a Jupyter notebook enabling interactive exploration of model performance and feature importance visualization. Targets ML beginners seeking hands-on experience with supervised learning fundamentals and algorithm comparison workflows.
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