GitHub repository showcasing Machine Learning code: KNN, KMeans, Random Forest, Decision Tree, Apriori, Conflict Serializable, Naive Bayes used for skin detection and UCI dataset evaluation to check accuracy. Extensively tested on reliable datasets like breast_cancer and iris, providing valuable insights for ML training and testing.

Find if CS or not. And if not then print ALL cycles in the graph.

A Python tool to check if a given transaction schedule is conflict serializable. It analyzes the schedule, builds a dependency graph, detects cycles, and determines whether the schedule can be serialized. Supports input from Excel files and provides a graphical representation of the dependency graph

This GitHub repository contains machine learning algorithms implemented in Python. The included algorithms cover a range of tasks, such as classification, clustering, association rule mining, and skin detection. The code is tested on reliable datasets like breast_cancer and iris, providing crucial insights and accuracy evaluation.

A DBMS tool to generate transaction schedules and test conflict-serializability, with equivalent serial schedules output.