We have taken a credit card fraud dataset from Kaggle and applied ... accuracy using both KNN and Decision Tree machine learning algorithms. We have got $\mathbf{9 9. 8 4 \%}$ accuracy using the ...

This study investigates the use of a decision tree classification model, combined with Principal Component Analysis (PCA), to distinguish between Assam and Bhutan ethnic groups based on specific ...

Python based Jupyter Notebook Project to Predict Potential Customer for Term Deposit Marketing Campaign in Banking Institution using Logistic Regression, K-NN, Decision Tree & Random Forest Supervised ...

Decision trees are useful for relatively small datasets that have a relatively simple underlying structure, and when the trained model must be easily interpretable, explains Dr. James McCaffrey of ...

There are several tools and code libraries that you can use to perform binary classification using a decision tree. The scikit-learn library (also called scikit or sklearn) is based on the Python ...

In this study, a decision tree-based categorization of breast cancer in histological images is presented for the first time. Both benign and malignant breast growths can eventually develop into breast ...

The datasets for Alzheimer's Disease is available on both OASIS and Kaggle which is used for training all patient's data using various machine learning algorithms such as SVM, Random Forest classifier ...