DriveWise Pro

Inspiration

Fuel has started wars. It has collapsed economies, shifted global power, and defined the 20th century. Yet in 2025, the average driver still cannot answer a simple question — what does my car actually cost to run? We found that absurd. DriveWise Pro exists because that question deserves a real answer.

What it does

DriveWise Pro is an AI-powered fuel intelligence cockpit that predicts real-world MPG across five driving environments using a trained Random Forest model, calculates the exact ROI and break-even point of any vehicle modification before a single dollar is spent, and explains every prediction in plain English through a fully transparent AI layer. A Digital Garage lets users save, compare, and export unlimited vehicle configurations — turning gut-feel decisions into data-driven ones.

How we built it

We trained a Random Forest Regressor on the UCI Auto-MPG dataset using four vehicle inputs — cylinders, weight, horsepower, and acceleration — and wrapped it in a four-module Streamlit application with a single shared prediction pipeline to guarantee consistency across every page. Built with Python, scikit-learn, Folium, and pandas. Fully offline capable.

Challenges we ran into

Maintaining a consistent ML prediction across four independent pages without any data drift was harder than expected. We solved it by architecting a single compute_metrics() function as the absolute source of truth — every module calls it, nothing diverges.

Accomplishments that we're proud of

We built an AI that doesn't just predict — it explains itself. Feature importance breakdowns, plain-English verdicts, and live what-if sensitivity analysis mean that a fleet manager with zero data science background gets the same insight as an engineer. That accessibility is what we're most proud of.

What we learned

Explainability matters as much as accuracy. A model users trust is infinitely more useful than a model that's merely correct. We also learned that a single source of truth in the codebase isn't just good engineering — it's the only way multi-page ML apps stay consistent.

What's next for DriveWise

The vision is simple: a world where no fuel decision is ever made without intelligence behind it.

In the near term that means a solo driver knowing their real commute cost. A mechanic closing more sales with ROI proof. A fleet manager cutting operating costs by 20% without buying a single new vehicle. In the long term it means something bigger — DriveWise embedded at the infrastructure level. City planners modelling entire transit fleets across terrain types. Governments optimising logistics networks using real vehicle data. Insurance companies pricing policies based on actual driving environment risk, not postcodes. And beyond that — as the world transitions to electric vehicles, the same intelligence layer applies. Range anxiety, charging ROI, route efficiency across battery drain curves. The model changes. The problem doesn't.

Built With

• folium
• pandas
• python
• scikit-learn
• streamlit