A CLI tool for generating interview questions by difficulty level and quantity, with support for multiple output formats including text, JSON, and arrays.

Selects a Random Wishlisted Game from your Steam Wishlist.

Truth and Dare - Spinning Bottle Game:

An ios application that can help to randomly pick a name and is applicable to all kinds of situations requiring fair and random selection.

Randomly routes your Minecraft connection to servers from a predefined list!

A React application that displays user reviews in a card layout. Users can navigate through reviews with previous/next buttons, jump to a random review with "surprise me," and see each reviewer's photo, name, job, and quote, built for learning React fundamentals: state with `useState`, list indexing, event handlers, and conditional rendering

The aim of this project is to achieve maximum success by the algorithm in the rock-paper-scissors game with the reinforcement learning.

Generates two clash-free class schedules using a genetic algorithm with random selection, crossover, and mutation. Designed for 3 teachers over 3 days and 3 periods.

The purpose of this study is to predict which ad will be the most preferred by the customers over the fictitious ads clicked by the users.

Can't decide? 🤷 Well, The CHOOSINATOR loooooooooves choosing, feel free to let it choooooose for you.

Similar to a wheel for random selection, but presented as a list

This program uses Python's random module to generate random integers and make random choices. It generates random values in different ranges and simulates a coin flip and dice roll, showcasing the randint() and choice() functions.

This repository contains the source code of research paper titled: "A Generalized Quantum Algorithm for Assuring Fairness in Random Selection Among 2ⁿ Participants."

An efficient random item sampler that ensures O(1) sampling complexity, and equal selection probability for all items across cycles. Each cycle ensures unique, non-repeating item selections, with each item sampled only once per cycle. Upon cycle completion, the sampler automatically refreshes, initiating a new cycle to repeat the process.

Implementation of Q-Learning and Double Q-Learning for optimal pathfinding in large, dynamic environments. Uses reward shaping and adaptive exploration. Compares RL performance with Dijkstra and random selection, showing Q-Learning's scalability and superior cumulative rewards.