flyrank

AI-Powered Content Opportunity Scoring using Machine Learning | FlyRank ML Internship Capstone | Random Forest Regression | Search Intelligence

A Machine Learning repository developed during my FlyRank AI Internship, focused on data engineering and model analytics.

Repository for collected work at FlyRank Internship

Persistent Task Management REST API built with Node.js, Express.js, SQLite, and Swagger UI. Developed during the FlyRank Backend AI Engineering Internship using real-world educational workflows from Navigant Education Consultants.

Secure REST API built with FastAPI and Supabase Authentication featuring JWT authentication, protected routes, reusable authorization dependencies, and Swagger documentation.

A CRUD REST API built with Python and FastAPI, backed by PostgreSQL running in Docker. Swaps in a Postgres repository behind the same service/route layer used in the in-memory and SQLite versions — full stack starts with one command: docker compose up.

Beginner-friendly Express.js CRUD Task API with in-memory storage, validation, Swagger UI, filtering, search, pagination, and AI comparison.

A secure REST API built with Express.js and Supabase Auth featuring user signup, login, JWT authentication, protected routes, logout, and Swagger documentation. [ WEEK - 04 ]

AI-powered multi-platform social campaign publishing platform built for the FlyRank Backend AI Engineering Internship Capstone.

A CRUD REST API built with Python and FastAPI, backed by SQLite for persistent storage. Same endpoints as the in-memory version — now tasks survive a server restart.

My work and assignments for the FlyRank Machine Learning Internship, documenting weekly notebooks, experiments, and the capstone project.

A CRUD REST API built with Python and FastAPI — create, read, update, and delete tasks using in-memory storage. Includes Swagger UI docs and proper HTTP status codes (200, 201, 204, 400, 404).

This repository contains a minimal backend server built during my Backend AI Engineering internship training at FlyRank AI. The goal of this task is to practically experience the core Request-Response loop by setting up a lightweight server from scratch.

Implementation of PostgreSQL integration, switching from in-memory storage to a persistent DB repository, and containerizing the stack with Docker Compose as part of the FlyRank training project.

Minimal Express server with two GET endpoints (/api/hello, /api/status), both returning JSON. Tested locally with curl and browser, both endpoints return correct JSON responses.

Assignments and projects for the FlyRank General AI Fluency internship track.

Capstone project and ML pipeline for the FlyRank Machine Learning Internship 2026.