This project focuses on the application of various machine learning algorithms to analyze a dataset called heart.csv for classification and clustering tasks. The dataset contains the following columns:

• age: Age of the individual
• sex: Gender (0 for female, 1 for male)
• cp: Chest pain type
• trestbps: Resting blood pressure
• chol: Serum cholesterol level
• fbs: Fasting blood sugar > 120 mg/dl (1 for true, 0 for false)
• restecg: Resting electrocardiographic results
• thalach: Maximum heart rate achieved
• exang: Exercise-induced angina (1 for yes, 0 for no)
• oldpeak: ST depression induced by exercise relative to rest
• slope: Slope of the peak exercise ST segment
• ca: Number of major vessels colored by fluoroscopy
• thal: Thalassemia type
• target: Target variable (1 for presence of heart disease, 0 for absence)

In the classification task, various machine learning algorithms will be applied to predict the presence or absence of heart disease based on the given features. Some of the algorithms that can be explored include:

• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machines
• K-Nearest Neighbors (KNN)
• GaussianNB
• DecisionTreeClassifier
• RandomForestClassifier
• AdaBoostClassifier
• BaggingClassifier

In the clustering task, we aim to group individuals based on similar characteristics. Some clustering algorithms to consider are:

• K-Means Clustering
• Hierarchical Clustering

1. Clone this repository.
2. Install the required libraries and dependencies.
3. Run the Jupyter notebooks or Python scripts to perform classification and clustering.
4. Analyze the results and make improvements as needed.

The heart.csv dataset is the source of our data and can be found in the project folder.

Parsa Khavarinejad This is a project for the data mining course - Tarbiat Modares University

Feel free to add any additional sections or details as needed for your project. Happy coding!