🚚 Build Delivery Delays app with KNN Gen AI | Streamlit App

In this video, we demonstrate a machine learning-powered app built with Streamlit that predicts whether a delivery will be delayed based on operational factors like distance, traffic, weather, and more! Using the K-Nearest Neighbors (KNN) Classifier, we analyze important variables such as driver experience, vehicle age, road condition, and package weight to forecast if a delivery will be on time or delayed. Learn how we built this intuitive app and how businesses can use AI to improve their logistics operations. 🚀
🔍 Variables we used in the model:
Delivery Distance
Traffic Congestion
Weather Condition
Driver Experience
Number of Stops
Road Condition Score
Vehicle Age
Package Weight
Fuel Efficiency
Warehouse Processing Time
This app helps logistics companies and delivery services forecast delivery times more accurately and manage resources efficiently. Watch now to understand how this model works and how you can build your own predictive models!
👉 What you'll learn:
How to use KNN to classify delivery delays
How Streamlit simplifies building machine learning apps
Key factors affecting delivery times
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App available at delivery-raxxy...