🚚 Hyperlocal Delivery ETA Optimizer Using Weather & Traffic

A machine learning-powered platform to accurately predict delivery times in hyperlocal logistics by fusing Google Maps, live weather, and historical trip data.

📍 Built for logistics, Q-commerce, and smart urban mobility solutions.

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✨ Features

• 🔮 ETA Prediction using XGBoost (or LSTM)
• 🛰️ Real-time inputs from Google Maps & OpenWeather API
• 🛣️ Incorporates traffic level, distance, temperature, and rainfall
• 🧠 Option to train your own model with past trip data
• 🌐 FastAPI backend for prediction API
• 💻 Next.js frontend to collect trip data and display ETA
• 🚀 Fully ready for local and cloud deployment

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🧰 Tech Stack

Layer	Technology
Frontend	Next.js (TypeScript, Tailwind CSS)
Backend	FastAPI + Uvicorn
ML Model	XGBoost / LSTM (customizable)
Data	Google Maps API, OpenWeatherMap API, Custom Trip Logs
Dev Tools	Python 3.10+, Jupyter, Pandas, Scikit-learn, Joblib

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🛠️ Getting Started

✅ Prerequisites

• Python 3.10+ (Download: python.org)
• Node.js 18+ (for frontend)

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📦 Backend Setup

# Clone repo
# Create virtual env in backend
cd backend
python -m venv venv (git bash)
source venv\Scripts\activate  # On Windows

# Install dependencies
pip install -r requirements.txt

#Create Model
cd backend
python src/train_model.py

# Run the Backend
cd backend
uvicorn src.main:app --reload