Keerthik1622

Keerthik1622 / truck-delay-prediction

Public

End-to-end Machine Learning pipeline for Truck Delay Prediction using XGBoost, Flask API, MLflow, and Lightning AI deployment.

26
0
85% credibility
Found May 23, 2026 at 26 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This is a machine learning system that predicts whether truck deliveries will arrive late. It learns from historical shipment data to identify patterns—like how weather, traffic, truck age, driver experience, and route type influence delays. Once trained, it provides instant predictions with confidence scores, helping logistics managers make proactive decisions about which shipments need attention.

How It Works

1
🔍 You discover the tool

You hear about a prediction tool that can tell you which truck shipments will arrive late, helping you plan better.

2
📦 You get everything ready

You download the project and install the tools needed to run it on your computer.

3
You connect your data
🗄️
Use your own databases

Connect to your existing MySQL and PostgreSQL databases containing your shipment and route records.

🎭
Try with sample data first

Use the built-in demo data to test everything without setting up any databases yet.

4
🧠 The system learns your patterns

The pipeline studies thousands of past shipments and figures out what factors lead to delays—like weather, traffic, or driver experience.

5
🚀 You launch the prediction service

With one command, you start a web service that listens for prediction requests from your team or other systems.

6
❓ You ask 'Will this truck be delayed?'

You send shipment details—like distance, truck type, cargo weight, weather, and traffic—and instantly get back a prediction with confidence level.

✅ You make better decisions

You can now identify high-risk shipments before they happen, reroute trucks, or alert customers proactively.

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AI-Generated Review

What is truck-delay-prediction?

This is a production-ready Python pipeline that predicts whether truck shipments will arrive late. It pulls data from MySQL and PostgreSQL databases, engineers features like driver risk scores and distance bins, trains multiple models (XGBoost, LightGBM, Random Forest), and deploys a Flask REST API for real-time predictions. The whole thing runs via a single command and logs everything to MLflow for experiment tracking.

Why is it gaining traction?

The project solves a real operational headache: logistics companies need to anticipate delays before they happen. It stands out because you can run the entire pipeline without touching a database -- just flip an environment variable and it generates realistic synthetic data. The Flask API delivers predictions with confidence scores and risk levels, which is exactly what an operations dashboard needs. Hot-reload support means you can retrain and redeploy without restarting the server.

Who should use this?

Backend engineers building logistics or supply chain systems will find this immediately useful. Data scientists who want a reference architecture for end-to-end ML pipelines will appreciate the clean separation between ETL, training, and deployment. If you're evaluating whether to add delay prediction to an existing system, this gives you a working prototype in under an hour.

Verdict

At 26 stars this is a small but well-structured project with solid fundamentals -- config-driven design, MLflow tracking, and a working Flask API. The credibility score of 0.85 reflects that maturity level. Start here if you want a working example of an end-to-end ML system, but plan to adapt it for production use rather than deploying it as-is.

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