Web Development and Predictive Analytics Portfolio

Welcome to my dynamic portfolio, showcasing cutting-edge projects from my Web Development and Predictive Analytics Nanodegrees. This repository is packed with full-stack solutions, ranging from microservices to interactive front-end interfaces and robust data analytics models.

What's Inside?

• Microservices Architecture: Dive into backend API services crafted during my Nanodegree. More about backend services: cloudapp, petstore, vehicles-api, jira-proxy.
• Load Balancer and Reverse Proxy: Utilize Nginx to efficiently distribute traffic among servers and enhance performance and reliability of applications. Read more.
• Interactive Next.js 14 Frontend: Experience the sleek Next.js interface designed to interact seamlessly with the backend services. Explore the frontend.
• Advanced ML Pipeline: Leverage my Python-based machine learning pipeline for dynamic customer segmentation, developed during my Predictive Analytics Nanodegree. See ML details.
• Integrated External APIs: Enhance functionality with third-party services like OpenAI and Jira through customized proxy APIs to navigate CORS issues. Details on API integration.
• Real-Time Kafka Chat: Engage with the Kafka-powered chat application, demonstrating real-time messaging capabilities. Chat interface.
• Efficient Logging and CI/CD: Implement robust logging with Log4j and streamline deployments using Jenkins. Learn about CI/CD processes.

Example view with ML pipeline and other tabs:

Quick Start

Setup with Docker

Setup and start databases and esential services with docker-compose:

docker-compose -f docker-compose-infrastructure.yml up -d

Build and start the Java based services, the Python based ml-pipeline and the Next.js based frontend:

docker-compose -f docker-compose-app.yml up -d

Runs the app in the production mode.
Open http://localhost:5001 to view it in your browser.

If everything is correctly started, you should see a login page with optional Dark Mode:

And you should be able to register and log in, after starting the backend services, cloudapp is a must, the rest is optional, and see the current front-end of the api integrations from the services above:

1. Machine learning system for Customer Segmentation

MLOps interface for Customer Segmentation API, the user is able to:

• Add new customer data point to the database.
• Sample reference database with user specified samples.
• Sample reference database with predefined 10-20-50-100-200 amount of samples. All these steps will retrigger the segmentation process and then the pictures and tables will update with the new results.

View results:

• Pictures: correlation between parameters and the different segments
• Table: current db from postgres.

2. Shop interface for Cloudapp web store REST API,

The user is able to:

• Create new items.
• Add existing items to the cart.
• See and clear the cart.
• Submit cart and check order history.

Shop API documentation:

• Items
• Cart
• Order

3. Pet Store interface for the Pet Store's REST API

The user is able to:

• Add new customer.
• Add a new Pet to existing customers.
• Add new employees with skills and schedules.
• Check availability based on skills and schedules.
• Plan a new schedule for an employee and assign it to a pet.

4. Maps

Map interface for integrating Open Street Map with the Vehicle location service's REST API. The user is able to:

• Click on the map to add new vehicle locations.
• Click on existing locations and check basic info and delete the location.

Vehicels API documentation

5. OpenAI

OpenAI interface for communicating with the OpenAI API, the user is able to:

• Send a prompt to ChatGPT and receive a response..
• Send a prompt to DallE and receive an image response.

6. Jira

Jira interface for communicating with the Jira API, to use it:

• Register
• Create Personal Access Token
• Use it for requests

The user is able to:

• Create/list/update/delete Jira ticket

7. Notes and Files

A service for creating personal notes and uploading personal files.

• Notes API documentation
• Files API documentation

8. Chat

A Kafka based chat service, the user is able to:

• Create new chat rooms, furthermore share and enter chat room id

• Talk to other users in chat rooms

Optional API services

If OpenAI and Jira functionality is to be used, follow the instructions below:

OpenAI API key:

To be stored in the .env file in the frontend/react-apiview-app root directory in this format:

NEXT_PUBLIC_OPENAI_KEY==xxxxxxxxxxxxxx

Jira API key, how to register and how to get an API key

Frontend: Add .env file at frontend/react-apiview-app root directory in this format:

NEXT_PUBLIC_JIRA_DOMAIN = 'https://xxxx.atlassian.net'
NEXT_PUBLIC_JIRA_KEY = Y3......2edd (note: no single quotation)

NOTE: the next-frontend Docker image needs to be rebuilt after editing the .env file.

Certificates

Web Development Nanodegree certficiate Predictive Analytics for Business Nanodegree certficiate