Overview
Designed and implemented a Stripe API–based system to manage products in bulk. The system streamlined product creation, updates, and synchronization with Stripe, reducing manual dashboard operations and improving consistency across product records.

Key Responsibilities

• Designed the system flow for bulk product management using Stripe APIs.
• Integrated Stripe’s product and pricing endpoints for programmatic control.
• Implemented backend logic to handle batch operations safely and efficiently.
• Validated API behavior and error handling during bulk requests.
• Ensured secure handling of API keys and environment separation.
• Structured product data to maintain consistency between local records and Stripe.

Tech Stack

• Payments API: Stripe
• Backend: Server-side logic (API integration)
• Data Handling: Structured product and pricing data
• Security: Environment-based API key management

Overview
End-to-end implementation of an API-driven system built from approved documentation and credentials. Responsible for architecture design, endpoint validation, backend implementation, frontend interaction, and secure payment processing.

Key Responsibilities

• Designed system architecture and integration flow.
• Validated all API endpoints in Postman using test keys prior to production.
• Implemented server-side logic in PHP (learned and applied during development).
• Built a minimal frontend interface for user–API interaction.
• Integrated a payment gateway with secure data handling.
• Implemented persistent transaction logging for auditing and reconciliation.
• Managed API credentials and environment separation.

Tech Stack

• Backend: PHP
• API Testing: Postman, Paystack payment gateway API tests
• Payments: Paystack Gateway Integration
• Database: MySQL
• Frontend: Custom UI for API interaction; HTML, CSS, Javascript

Overview

This was my BSc final year project, where I independently designed and built a facial recognition system from research through implementation. While academic in nature, the project was treated as a full system build rather than a purely theoretical study.

What I did

• Selected the problem space and reviewed existing facial recognition approaches and prior research
• Defined a clear methodology and system architecture
• Implemented a working facial recognition pipeline
• Handled data preparation, model training, testing, and evaluation
• Documented limitations, performance trade-offs, and improvement paths

Tools & techniques

• Python
• Machine learning workflows
• Data cleaning and preprocessing
• Algorithmic reasoning and evaluation metrics

Why it matters

This project reflects how I approach problems generally: identify inefficiencies, study existing solutions, and build practical systems under real constraints. It also laid the foundation for my later work in automation, data processing, and applied machine learning outside of academic settings.