Ibrahim Nasir Bello

A Software Engineering graduate with a Postgraduate Diploma in Data Science, shaped by hands-on experience across internships and national service. I work best in structured environments where I can think clearly, learn quickly, and contribute consistently. With a calm, practical approach to challenges, I’m focused on developing my technical strengths while adding steady value to teams and organisations.

Real-Time Deep Learning System for Fraud Detection

• Built a real-time fraud detection system using machine learning and deep learning models.
• Processed and analysed transaction data, handling class imbalance with SMOTE.
• Compared Neural Networks with traditional ML models using standard evaluation metrics.
• Deployed the model via a FastAPI interface to support real-time predictions.

Airbnb Price Prediction Using Machine Learning

• Worked on a group machine learning project analysing factors influencing Airbnb listing prices in New York City.
• Took responsibility for implementing and evaluating Support Vector Machine (SVM) and XGBoost regression models.
• Preprocessed data using feature scaling (StandardScaler) and performed a 70/30 train-test split.
• Tuned key SVM hyperparameters (C and gamma) to balance model complexity and predictive performance.
• Evaluated model performance using R² score, Mean Squared Error (MSE), and cross-validation.
• Interpreted residual and predicted-vs-actual plots to assess model reliability and generalisation.

CycleConnect – Bike Rental Web Application

• Developed the Reviews microservice using FastAPI, handling creation and retrieval of user ride reviews.
• Designed and implemented corresponding frontend components to integrate review functionality into the user interface.
• Built RESTful API endpoints and validated functionality using Postman and OpenAPI (Swagger).
• Containerised the microservice using Docker and integrated it into a multi-service Docker Compose setup.
• Contributed to system testing, documentation, and report writing as part of a six-member development team.

Relational Database Design and Implementation for Retail Inventory System

• Designed an Entity-Relationship Diagram (ERD) for a retail inventory system based on business requirements.
• Defined and implemented relational tables using SQL DDL with appropriate primary and foreign key constraints.
• Normalised an unstructured dataset through 1NF, 2NF, and 3NF to eliminate redundancy and ensure data integrity.
• Created and tested SQL queries to support pricing, stock monitoring, reordering, and supplier analysis.
• Validated the database design and queries using MySQL Workbench with sample test data.

• Pyhton
• HTML
• CSS
• SQL
• MS Word
• MS Excel