Machine Learning Case Study

Predict Your Ride prices
Before You Book.

An AI-powered intelligence layer forecasting fares and wait times across Uber, Ola, and Rapido using Real-Time Random Forest Regression.

ML Pipeline Code Launch Live App API Docs

Beyond Real-Time Pricing

We don't just show current prices. Our model simulates market dynamics to project costs 120 minutes into the future, finding the optimal booking window.

Predictive Price Matrix

2-Hour Forward Projection (INR)

Uber Ola Rapido

System Architecture

Production-ready API responses and mobile-first client implementation.

Mobile UI

FastAPI Swagger Specification

Swagger

Platform JSON Output

Response

Forecast Analytics

Analysis

The ML Engine

FareCast replaces static rule-based pricing with a **Random Forest Regressor** deployed via FastAPI. We process highly granular environmental variables to predict non-linear surge patterns before they happen.

Regression

Predicts continuous price and wait time values dynamically.

FastAPI Backend

High-performance asynchronous request handling.

Engineered Features

Distance (KM) Traffic Density Weather (Temp) Zone Type Humidity Decimal Hour
RF

Random Forest Model

Deployed on Render Cloud