My Approach: FuelFindr

FuelFindr is a location-based web application I built to explore how real-time maps, external APIs, authentication, and deployment come together in a production-style app. The focus of this project was hands-on implementation, rapid prototyping, and solving real integration issues rather than building a purely conceptual ML system.

The goal was to design a clean, intuitive interface that helps users discover nearby fuel stations visually, while learning how to securely integrate third-party APIs and deploy a live product.

Vision and Innovation

FuelFindr is built around a simple but impactful vision:
make fuel discovery faster, clearer, and less frustrating for everyday drivers.

Most fuel-finding experiences today are cluttered, slow, or focused only on prices. FuelFindr rethinks this by prioritizing location awareness, visual clarity, and decision simplicity, helping users quickly understand where to go rather than overwhelming them with raw data.

Application Core Features

1. Nearby Fuel Station Discovery
   Displays nearby gas stations on an interactive map using real-time location search, helping users quickly find stations around their area.

2. Fuel Type Filtering
   Allows users to filter stations by fuel type (Petrol, Diesel, Premium, EV) to match their vehicle needs.

3. Map-Based Visualization
   An intuitive, map-first interface with custom station markers that visually differentiate availability and station status.

4. Station Status Indicators
   Uses clear visual labels (Available, Low Stock, Recommended) to help users make faster decisions at a glance.

5. Search by City or Address
   Enables users to search stations using a city name, address, or ZIP code for flexible location discovery.

6. Responsive Web Experience
   Fully responsive design optimized for desktop and mobile browsers, ensuring usability across devices.

7. Authentication-Ready User Flow
   Secure sign-in system prepared for future personalization, history tracking, and predictive features.

8. Scalable Architecture
   Built with a modular frontend and API-driven backend, allowing easy integration of pricing data, analytics, and ML features in later stages.

Machine Learning & Analytics Integration

• User Interaction Analytics
  Tracks user searches, filter selections, and map interactions to understand how users explore fuel stations and optimize the UI based on usage patterns.

• Station Popularity Scoring (Rule + Data Driven)
  Computes basic station rankings using factors like proximity, availability status, and user interaction frequency, forming the foundation for future ML-based recommendations.

• Predictive Feature Readiness
  Application architecture is designed to support predictive models for fuel demand, refill timing, and station congestion once sufficient historical data is collected.

• Data-Driven UI Decisions
  Analytics insights are used to refine marker visibility, default filters, and search behavior, improving user experience through measured feedback loops.

• Location-Based Trend Analysis
  Aggregates station data by region to analyze demand patterns and peak activity zones, enabling smarter scaling and regional optimization.

• ML Pipeline Planning
  Designed a future ML workflow including data ingestion, feature engineering, model training, and inference APIs for scalable deployment.

Predictive Refueling System

FuelFindr is designed to estimate when a user may need their next refuel by analyzing user-provided refueling inputs and historical usage patterns. Based on factors such as average daily mileage, previous refueling intervals, and fuel consumption estimates, the system generates a projected refill window rather than an exact date.

Example:
A user records a refill of 12 gallons at $3.45 per gallon. Based on an estimated driving average of 35 miles per day, FuelFindr projects the next refuel in approximately 7 days and highlights nearby open stations for convenience.

This feature positions FuelFindr as a personal refueling assistant, focusing on practical planning and decision support, with a foundation that can evolve into more advanced predictive models as usage data grows.

Application Tools

• Frontend & UI: Lovable.dev (visual builder + React-based components)

• Maps & Location: Google Maps JavaScript API, Places API

• Authentication: Supabase Auth

• Email Delivery: Resend SMTP (email verification & OTP)

• Hosting & Deployment: Lovable hosting + custom domain

• DNS & Security: Cloudflare DNS, API key restrictions, referrer-based access control