This project involves a classification task using various machine learning algorithms on a heart disease dataset sourced from Kaggle. The dataset comprises features related to heart health, with the target variable indicating the presence or absence of heart disease. Implemented machine learning algorithms include Logistic Regression, Naive Bayes, Random Forest, Extreme Gradient Boost, K-Nearest Neighbors, Decision Tree, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory networks.
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Dataset Overview: Explore and understand the heart disease dataset from Kaggle, analyzing features and the target variable.
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Algorithm Implementation: Implement multiple machine learning algorithms to classify heart disease instances.
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Evaluation: Assess each model's performance using a confusion matrix, accuracy score, and a detailed classification report.
