Welcome to my personal CENG3511 repository at Mugla Sitki Kocman University. This repository contains a collection of projects that I developed for the CENG3511 course. Each project focuses on a specific topic and includes implementations of various algorithms and programs.

1. Graph Search Algorithms: This project implements graph search algorithms such as Uniform Cost Search, Depth First Search, and Breadth First Search. These algorithms are essential for traversing graphs and finding optimal paths.

2. Constraint Solving Programs: In this project, constraint satisfaction problems are solved using various techniques. It includes implementations of Kakuros and Futoshiki programs, which are puzzle games that require finding valid solutions while satisfying specific constraints.

3. Knapsack Genetic Algorithm: The Knapsack Genetic Algorithm project addresses the Knapsack problem, which involves selecting items to maximize the total value within a limited capacity. It utilizes genetic algorithms to optimize the item selection process.

4. KNN Algorithm: This project focuses on mobile price classification using the K-nearest neighbors (KNN) algorithm. By analyzing the features of mobile devices and comparing them to a training dataset, the KNN algorithm predicts the price range of a given mobile device.

5. K-means Algorithm: The K-means Algorithm project demonstrates the application of the K-means clustering algorithm. It groups a set of data points into K clusters based on their similarities, aiming to minimize the within-cluster sum of squares. This algorithm is commonly used in data analysis and pattern recognition tasks.

The repository is structured as follows:

• graph-search-algorithms: Contains implementations of graph search algorithms.
• constraint-solving-programs: Includes implementations of Kakuros and Futoshiki programs.
• knapsack-genetic-algorithm: Contains code related to the Knapsack Genetic Algorithm.
• knn_algorithm: Includes code for mobile price classification using the KNN algorithm.
• kmeans_algorithm: Contains code for the K-means clustering algorithm.
• README.md: This readme file providing an overview of the projects.

Please refer to the individual project directories for specific instructions on running and using the code. Each project may have its own set of dependencies, input data requirements, and execution steps. Make sure to review the README files within each project directory for detailed information.

• train.csv: This file contains the training data used in the KNN Algorithm project and should be provided as input for the program.
• test.csv: This file contains the test data used in the KNN Algorithm project and should be provided as input for the program.

The code in this repository is licensed under the MIT License. You are free to use, modify, and distribute the code for academic and personal purposes. However, please note that the code is provided as-is, and the author is not liable for any consequences arising from its use.

I would like to express our gratitude to the course instructors and Mugla Sitki Kocman University for their guidance and support during the development of these projects. Their expertise and dedication have been invaluable in our learning journey.