K-Cross Fold Validation and Decision Tree Analysis using RapidMiner

This project focuses on data analysis and classification using the powerful data mining tool, RapidMiner. It can be divided into two main sections.

First Section: Data Preprocessing In the initial section of the project, the focus is on data preprocessing. The key activities include:

• Handling missing data.
• Utilizing RapidMiner for data preprocessing and feature selection.
• Preparing the dataset for further analysis.

Second Section: Classification with Decision Trees The second section of the project involves classification tasks using decision trees within RapidMiner. Here are the main components:

• Classification of heart disease using a multi-class approach.
• Data collection and linking data files to heart-related data.
• Implementing k-fold cross-validation to evaluate model performance.
• Dividing the dataset into 20-80 ratios for training and testing.
• Analyzing the results and using them for model validation and improvement.

For more details and to explore the code repository, please visit GitHub Repository.