# Technical Portfolio Overview

A collection of diverse software engineering projects spanning web applications, mobile development, machine learning, and fintech solutions.

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## 1. **APLETI: African Prime Leadership Training Institute**
**Repository:** [Nixon-Mburu/apleti](https://github.com/Nixon-Mburu/apleti)

### Project Description
APLETI is a comprehensive professional training and consultancy platform built to develop ethical, empowered leaders with integrity. The platform serves as a learning management system (LMS) connecting students with professional training courses, certifications, and expert instructors.

### Key Features
- **Dual Portal System:** Separate student and instructor dashboards with role-based access
- **Course Management:** Create, manage, and deliver professional training courses
- **Certification System:** M-Pesa integrated certificate requests and verification
- **Email Verification:** Student authentication with email verification codes
- **Student Enrollment:** Track progress, manage course enrollments, and access study materials
- **Quiz & Assessment:** Take quizzes and receive graded evaluations
- **Media Management:** Integrated media viewer for course materials
- **Responsive Design:** Fully mobile-responsive interface with breakpoints for tablets and mobile devices

### Technology Stack
- **Frontend:** React.js with Vite, React Router for navigation, responsive CSS
- **Backend:** Flask (Python) with SQLAlchemy ORM
- **Database:** PostgreSQL (on Render)
- **Authentication:** JWT (JSON Web Tokens) with Flask-JWT-Extended
- **Email Service:** Mailgun for transactional emails
- **Deployment:** Frontend on Vercel/similar, Backend on Render

### Architecture Highlights
- Microservices-style blueprint structure for backend services (student_auth, courses, certificates)
- SPA (Single Page Application) routing with frontend build optimization
- CORS-enabled API with credential support
- Environment-based configuration management
- Comprehensive error handling and logging

### Languages Used
- JavaScript (React frontend)
- Python (Flask backend)
- CSS (responsive styling)

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## 2. **Bloc: Social-First Payments Platform**
**Repository:** [Nixon-Mburu/Bloc](https://github.com/Nixon-Mburu/Bloc)

### Project Description
Bloc reimagines digital payments by replacing traditional payment identifiers (paybills, Till Numbers) with user-friendly usernames and handles. It's a social-first approach to financial transactions that prioritizes user experience and accessibility.

### Key Features
- **Username-Based Payments:** Send money using handles instead of paybills or account numbers
- **Social Integration:** Community-oriented payment experience
- **Frontend/Backend Architecture:** Modular full-stack application
- **Documentation:** Comprehensive platform documentation and wiring guides

### Technology Stack
- **Frontend:** React with Vite build tooling
- **Backend:** Python-based services
- **Styling:** CSS for responsive UI
- **Documentation:** Markdown-based guides for implementation

### Project Status
- Recently implemented pseudo ledger system for transaction tracking
- Active backend wiring and frontend refinements in progress

### Languages Used
- JavaScript (38.8%) - React frontend
- CSS (28.1%) - Styling
- Python (18.7%) - Backend services
- TeX (14.1%) - Documentation

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## 3. **Recommender: AI-Powered Movie Recommendation Engine**
**Repository:** [Nixon-Mburu/Recommender](https://github.com/Nixon-Mburu/Recommender)

### Project Description
A machine learning-powered web application that provides personalized movie recommendations to users based on diverse criteria. The system leverages neural networks and collaborative filtering to suggest movies aligned with user preferences.

### Key Features
- **Movie Browsing:** Browse comprehensive movie catalog via OMDB API integration
- **Smart Search:** Search movies by title, genre, year, or other attributes
- **Detailed Information:** View comprehensive movie information and ratings
- **Neural Network Recommendations:** PyTorch-based ML model for personalized suggestions
- **User Preferences:** Filter recommendations based on diverse criteria

### Technology Stack
- **Frontend:** React.js for dynamic UI
- **Backend:** Flask for API services
- **Machine Learning:** PyTorch for neural network model
- **External API:** OMDB API for movie data
- **Deployment:** Heroku-ready with Procfile

### Architecture
- Modular frontend/backend separation
- REST API for recommendation queries
- ML model integration for prediction
- Technical report documentation included

### Languages Used
- Python (44.1%) - ML models and backend
- JavaScript (25.4%) - React frontend
- CSS (22.6%) - Styling
- TeX (7.3%) - Documentation

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## 4. **List Sensors: Android Device Sensor Explorer**
**Repository:** [Nixon-Mburu/list_sensors](https://github.com/Nixon-Mburu/list_sensors)

### Project Description
A native Android application that discovers, catalogs, and monitors all sensors available on an Android device. Users can view sensor specifications, real-time readings, and historical data with persistent storage.

### Key Features
- **Sensor Detection:** Automatically detect all available device sensors
- **Real-Time Monitoring:** Capture and display live sensor readings with timestamps
- **Sensor Specifications:** View detailed sensor metadata and specifications
- **Database Storage:** SQLite database for sensor metadata and historical readings
- **Data Persistence:** Store and retrieve sensor readings over time
- **User-Friendly UI:** Intuitive interface for sensor information display

### Technical Objectives
1. Sensor Detection - Comprehensive device sensor discovery
2. Database Implementation - SQLite schema for sensor metadata
3. Real-time Monitoring - Continuous sensor data capture
4. User Interface - Intuitive material design UI
5. Data Persistence - Reliable data storage and retrieval

### Technology Stack
- **Language:** Kotlin/Java (Android native)
- **Build System:** Gradle with Kotlin DSL
- **Database:** SQLite with Android Room (implied)
- **IDE:** Android Studio (project structure)

### Project Deliverables
- Complete Android application package
- Assignment report with technical specifications
- Gradle build configuration

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## 5. **Grab: Carpool Matching Platform**
**Repository:** [Nixon-Mburu/grab](https://github.com/Nixon-Mburu/grab)

### Project Description
A React-based matching application designed to help students find and coordinate carpool arrangements with fellow students. The platform uses intelligent algorithms to match students based on routes, schedules, and preferences.

### Key Features
- **Smart Matching Algorithm:** Sophisticated algorithm for pairing compatible carpool participants
- **Student Profiles:** Create profiles with location and schedule preferences
- **Route Matching:** Find students traveling similar routes
- **Ride Coordination:** Schedule and organize shared rides
- **User-Friendly Interface:** React-powered responsive UI

### Technology Stack
- **Frontend:** React.js for dynamic user interface
- **Backend:** Python for matching algorithms and API
- **Architecture:** Full-stack web application
- **Package Management:** NPM for frontend dependencies

### Use Case
Ideal for university and educational institution settings where students need economical transportation solutions.

### Languages Used
- Python (98.1%) - Matching algorithm and backend
- C++ (1.3%) - Performance-critical components
- JavaScript (0.1%) - Frontend

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## 6. **Terra-AI-2026: AI-Powered Geospatial Analysis Platform**
**Repository:** [npm-DevPatel/Terra-AI-2026](https://github.com/npm-DevPatel/Terra-AI-2026)

### Project Description
Terra-AI is an advanced AI platform for geospatial intelligence and environmental analysis. The project integrates mapping technologies with AI capabilities to provide insights on terrain, climate, and geographic data.

### Key Features
- **Interactive Mapping:** MapLibre-GL integration for advanced map visualization
- **AI Analytics:** Machine learning-powered insights on geographic data
- **Insights Dashboard:** Comprehensive dashboard for data visualization
- **Multi-layer Analysis:** Support for multiple data layers and overlays
- **Real-time Updates:** Dynamic data rendering and user interactions

### Technology Stack
- **Frontend:** React/Vite with MapLibre-GL for mapping
- **Backend:** Python-based analytics engine
- **Mapping:** MapLibre-GL for interactive map rendering
- **Deployment:** Vercel-hosted ([terra-ai-2026.vercel.app](https://terra-ai-2026.vercel.app/))
- **Build Tools:** Vite for fast development and production builds

### Architecture Highlights
- Separated frontend and backend services
- Dynamic UI configuration with theme patching
- Tree-structure utility for data hierarchies
- Collaborative development environment (2 contributors)

### Recent Development
- MapLibre-GL integration and conflict resolution (5 months ago)
- Insights page implementation
- UI improvements and theming enhancements

### Languages Used
- JavaScript (77.5%) - React frontend with Vite
- Python (19.3%) - Backend analytics
- CSS (2.8%) - Styling
- HTML (0.4%) - Structure

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## Summary Statistics

| Project | Type | Primary Language | Status |
|---------|------|------------------|--------|
| APLETI | Full-Stack LMS | Python/JavaScript | Active |
| Bloc | Fintech Platform | JavaScript | Development |
| Recommender | ML Web App | Python | Complete |
| List Sensors | Mobile App | Kotlin/Java | Complete |
| Grab | Web Platform | Python | Complete |
| Terra-AI | Geospatial AI | JavaScript | Deployed |

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## Technical Skills Demonstrated

### Backend Development
- Python (Flask, SQLAlchemy)
- REST API design
- Database management (PostgreSQL, SQLite)
- Authentication & Authorization (JWT)
- Microservices architecture

### Frontend Development
- React.js with modern tooling (Vite)
- Responsive CSS design
- Single Page Application (SPA) development
- Interactive mapping (MapLibre-GL)
- State management

### Machine Learning
- PyTorch neural networks
- Recommendation algorithms
- Predictive modeling
- Data processing pipelines

### Mobile Development
- Native Android development
- Kotlin/Java
- Gradle build system
- Sensor APIs
- Local data persistence

### DevOps & Deployment
- Heroku deployment
- Render PostgreSQL
- Vercel hosting
- Environment configuration
- Logging and monitoring

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## Key Takeaways

This portfolio demonstrates:
- **Full-Stack Expertise:** End-to-end development from mobile to web to AI
- **Diverse Tech Stack:** Proficiency across Python, JavaScript, Kotlin, and specialized frameworks
- **Scalable Architecture:** Experience building systems for multiple users and data volumes
- **User-Centric Design:** Focus on responsive, intuitive interfaces
- **AI/ML Integration:** Capability to incorporate machine learning into production systems
- **Collaborative Development:** Successfully working on shared codebases with documentation

Each project showcases different aspects of modern software engineering, from infrastructure design to user experience optimization.