S3Emulator is a lightweight server that mimics the services of Amazon S3. It can be helpful for development and testing purposes. By reducing network traffic, it saves both the time and the money.

A full mimic for amazon store with most of its functionalities

Amazon Website Clone built with HTML, CSS, and Bootstrap. A fully responsive and sleek clone that mimics the user interface and experience of Amazon's e-commerce platform. 🚀 Perfect for practice with layout design, responsive web development, and Bootstrap components.

Neuromorphic computing is a new technology which uses very-large-scale integration (VLSI) systems containing electrical analogs that mimic neurobiological structures. Such technology can be simulated using the Python framework snnTorch, a derivative of the deep learning framework PyTorch that has the capacity to run spiking neural networks (SNNs) which in turn can be run on neuromorphic chips. This project serves to explore the use of the snnTorch framework on a networking dataset from the Barcelona Neural Networking Center. Not only are the performance results and construction of a spiking neural network seen from this experiment, but also important characteristics of types of data that can be collected that will better fit the spatio-temporal data characteristics that are ideal to be run in an SNN. Moreover, cloud computing tools from Amazon Web Service (AWS) such as S3 and SageMaker were used to aid in the completion of running deep learning experiments. This work serves as an artificial intelligence proof of concept for the snnTorch framework and cloud computing in the deep learning environment.

# Starbucks Promotions Project ### This project is the Capstone Project of Udacity's Machine Learning Engineering Nanodegree program.    ## Problem Statement This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks. Not all users receive the same offer, and that is the challenge to solve with this data set. The task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type. This data set is a simplified version of the real Starbucks app because the underlying simulator only has one product whereas Starbucks actually sells dozens of products. Starbucks collects the customer data to understand their behaviour on the rewards and offers sent via the mobile-app. Once every few days, Starbucks sends the personalised offers to its customers. These customers can respond positively/negatively/neutrally. A key thing to note is that not all the customers receive the same offer. The task of this project is to combine transaction, demographic and offer data of the past (which is already provided) to determine which demographic groups respond best to which offer types. In order to develop this project, we needed to use some tools, packages, systems and services that could help us achieve our goals. #### Libraries First of all, we used **Python** to write our scripts not only for algorithm training and serving but also for the orchestration of the whole process. Important packages within this environment are listed below: This project is developed in Python 3.6. You will need install some libraries in order to run the code. Libraries are: * `pandas` so we could work with tabular data in dataframes; * `Ploty` so we could visualize our Dataset; * `matplotlib` for Dataset visualization; * `numpy` so we could easily manipulate arrays and data structures; * `seaborn` and `matplotlib` so we could generate insightful visualizations; * `sklearn` so we could build and develop our model pipeline; * `imblearn` so we could apply SMOTE to our training data; * `xgboost` so we could have our main classifier; * `sagemaker` so we could easily interact with AWS. * `json` for reading our Dataset Files. * `boto3` Finally, we used AWS environment in order to launch training jobs, deploy our model and serve predictions. The main services used are also listed below: * __AWS SageMaker__: training, hyperparameter tuning and endpoint serving; * __Amazon S3__: saving our data and model artifacts; ## Files Descriptions This project is structured as follows: #### 01. Proposal Project proposal documentation. #### 02. Data_Cleaning_[Dataset] Folder to perform data preparation and Dataset Cleaning and Prepare the Final Data for Further using in model algorithms. #### 03. Pre-processing Dataset Visualization Folder to perform final Pre-processing Dataset to be used in Visualization and exploration. #### 04. Dataset_Visualization Folder to perform Visualizations for the Pre-processed Dataset. #### 06. ORG_Starbucks_Capstone_Project.ipynb Jupyter notebook file that deploy final model and create an endpoint and orchestrates the end-to-end process in AWS SageMaker and also interacts with other services.

Full-stack web development project that mimics Amazon's e-commerce functionalities

A full-stack web application designed to mimic the basic functionalities of Amazon

This is a React + Vite application that mimics the functionality and design of Amazon.com. It uses React Router for navigation.

🛒 Amazon Clone A fully responsive Amazon clone built using HTML and CSS, showcasing a visually accurate and functional e-commerce layout. This project highlights the use of modern CSS techniques to create an engaging and user-friendly interface that mimics the look and feel of the Amazon website

A customaziable smart button that is powered by Amazon's Web Services. The button is an introduction to AWS IoT and was designed to mimic the AWS Dash Button.

Amazon Clone Project This project is a web-based replica of Amazon's e-commerce platform, built using HTML and CSS. The goal is to mimic the layout, design, and responsiveness of Amazon's website, focusing on the following features: - A home page with a navigation bar, search bar, and product showcase .

The Amazon Clone is a fully responsive front-end project built using HTML and CSS to replicate the UI/UX of Amazon’s e-commerce platform. This project focuses on user interface design, layout structuring, and responsive web development. It accurately mimics Amazon’s homepage, product listing, and navigation system, providing a realistic shopping ex

The Amazon Clone project is a web development exercise aimed at replicating the user interface and basic functionality of the Amazon e-commerce platform using HTML and CSS. This project focuses on the front-end aspect of web development, providing a visually appealing and responsive design that mimics the layout and style of the original Amazon web

This project is a fully functional clone of the Amazon e-commerce platform, designed to demonstrate web development skills and provide a learning resource for aspiring developers. The clone mimics core features of the Amazon website, including product listings, a shopping cart, user authentication, and an intuitive user interface.

This app is the mimic version of Amazon shopping site which is build with react, express and mongoDB.

mimic amazon checkout in woocommerce

Application mimics the fundamental functions of Amazon - React, Redux toolkit, Firebase

A Nodejs based app that mimics an Amazon-like storefront using mySQL

An Amazon clone website is a web application designed to mimic the functionality and user interface of the popular e-commerce platform, Amazon.

Simple Amazon clone using HTML and CSS. A static design that mimics the basic structure and styling of the Amazon homepage, without mobile optimization

Website focused on using database operations (MySQL and PHP) to mimic an online shopping website (similar to Amazon).

An Amazon clone built with HTML and CSS that recreates key features like the homepage, product listings, and a simple shopping layout to mimic the look of Amazon

This repository houses a comprehensive e-commerce project developed using Django, designed to mimic the functionalities of the renowned Amazon platform.

A responsive Amazon Clone website built using HTML, CSS, and . This project mimics the frontend layout of Amazon’s homepage, showcasing skills in modern web design, responsive layouts, and UI component structuring.

A front-end clone of Amazon's homepage built using HTML and CSS. This responsive layout mimics the structure, design, and functionality of Amazon's UI including navigation, search bar, product sections, and footer.

This project is a replica of the user interface of Amazon built using HTML and CSS. It mimics the layout and styling of the Amazon website, providing a familiar and intuitive browsing experience for users.

A fully responsive Amazon Clone created using pure HTML & CSS, with a strong focus on layout design using Flexbox and CSS Grid. a pixel-perfect, responsive static website that mimics the front-end design of Amazon

A fully responsive front-end clone of Amazon’s homepage built using only HTML and CSS. It includes a navigation bar, product showcase, and footer section — designed to mimic Amazon’s layout and style with a clean, modern UI.

The Amazon clone project implemented using HTML and CSS is e-commerce website that mimics the layout and design of Amazon. It includes features like a search bar, product listings, product cards with images and a user-friendly navigation menu.

A basic UI clone of Amazon's homepage, featuring product listings, a search bar, and a shopping cart interface. Built using HTML and CSS to mimic Amazon's look and feel. Ideal for learning web development and UI design fundamentals.