AymanDevOps

Best-City-Savings

City Savings is a project designed to help users identify the most financially beneficial city to reside in, considering specific factors over a 20-year period. By leveraging a combination of economic data and advanced algorithms, CitySavings enables users to make informed decisions about where to live to optimize their savings and investments.

Cost-Of-Living

Cost Of Living is a coding project that demonstrates the way we understand and compare the cost of living in cities worldwide. With the aim to provide users with valuable insights into the affordability of different locations, this application enables users to explore, calculate, and visualize the cost of living across every city in the world.

K-Means-Fitness-Clustering

This repository contains a Python implementation of K-Means clustering to group individuals based on their fitness levels, ranging from fit to unfit. By utilizing the powerful K-Means algorithm, we aim to efficiently categorize people into distinct clusters according to their fitness attributes.

Multi-Threaded-Matrix-Multiplication

This project aims to revolutionize matrix multiplication through the power of multi-threaded parallel computing. By harnessing the full potential of modern CPUs, we have developed a highly scalable and efficient matrix multiplication algorithm that drastically improves performance on large datasets.

Expense-tracker

Expense Tracker is a user-friendly coding project designed to help individuals efficiently manage their expenses. This project provides a simple command-line interface that allows users to track various types of expenses and generate expense reports.

Single-Threaded-Matrix-Multiplication

This coding project explored the concept of single-threaded matrix multiplication and aims to highlight the efficiency differences between single-threaded and multi-threaded implementations. Matrix multiplication is a fundamental operation in computer science, making it an ideal candidate to analyze the impact of parallel processing.

AymanDevOps

CodeWars

complex-api-company

complex-api2-calorie-checker

AymanDevOps/CodeWars

AymanDevOps/demo-day-LaundryMotion

AymanDevOps/savage-auth

AymanDevOps/To-Do-List-FullStack

AymanDevOps/NBA-stats-full-stack

AymanDevOps/personal-express

AymanDevOps/savage-demo

AymanDevOps/name-generator

AymanDevOps/node-palindrome-checker

AymanDevOps/node-coin-flip

AymanDevOps/matching-card-game

AymanDevOps/AymanDevOps

AymanDevOps/complex-api-company

AymanDevOps/complex-nasa-api

AymanDevOps/complex-api2-calorie-checker

AymanDevOps/simple-api2-crypto

AymanDevOps/simple-api-nba

AymanDevOps/weather-api

AymanDevOps/simple-nasa-api

AymanDevOps/Week-7-Homework

AymanDevOps/morning-challenge-tic-tac-toe

AymanDevOps/todo-list

AymanDevOps/slot-machine-2019-week05

AymanDevOps/Expense-tracker

Expense Tracker is a user-friendly coding project designed to help individuals efficiently manage their expenses. This project provides a simple command-line interface that allows users to track various types of expenses and generate expense reports.

AymanDevOps/Cost-Of-Living

Cost Of Living is a coding project that demonstrates the way we understand and compare the cost of living in cities worldwide. With the aim to provide users with valuable insights into the affordability of different locations, this application enables users to explore, calculate, and visualize the cost of living across every city in the world.

AymanDevOps/Single-Threaded-Matrix-Multiplication

This coding project explored the concept of single-threaded matrix multiplication and aims to highlight the efficiency differences between single-threaded and multi-threaded implementations. Matrix multiplication is a fundamental operation in computer science, making it an ideal candidate to analyze the impact of parallel processing.

AymanDevOps/Multi-Threaded-Matrix-Multiplication

This project aims to revolutionize matrix multiplication through the power of multi-threaded parallel computing. By harnessing the full potential of modern CPUs, we have developed a highly scalable and efficient matrix multiplication algorithm that drastically improves performance on large datasets.

AymanDevOps/Top-K-Students

Top K Students is a coding project aimed at efficiently determining the top 2 students from a finite set of numbers using advanced Top K algorithms. The project focuses on scalability, making it suitable for large datasets and extending its use to identify the most prospective students in the entire country.

AymanDevOps/K-Means-Fitness-Clustering

This repository contains a Python implementation of K-Means clustering to group individuals based on their fitness levels, ranging from fit to unfit. By utilizing the powerful K-Means algorithm, we aim to efficiently categorize people into distinct clusters according to their fitness attributes.

AymanDevOps/Best-City-Savings

City Savings is a project designed to help users identify the most financially beneficial city to reside in, considering specific factors over a 20-year period. By leveraging a combination of economic data and advanced algorithms, CitySavings enables users to make informed decisions about where to live to optimize their savings and investments.