# ETA Prediction for Deliveries/Rides

Building an end-to-end machine learning project for ETA (Estimated Time of Arrival) prediction for deliveries or rides is a highly practical case that involves a mix of time-series forecasting, regression modeling, and real-time inference. Here’s a step-by-step roadmap tailored to such a project.

The ETA prediction system forecasts the time required for a delivery or ride to reach its destination, accounting for real-time factors like traffic, weather, and route characteristics.

Companies like Uber and Doordash use advanced hybrid models that pair physical routing calculations with post-processing ML models for highest accuracy.​<br>

<https://chatgpt.com/c/690ed949-e1dc-8321-a721-65150e9aa5bb>

<br>

* <https://careersatdoordash.com/blog/deep-learning-for-smarter-eta-predictions/>
* <https://bytes.swiggy.com/personalising-the-swiggy-homepage-layout-part-i-1048dba5e703>
* <https://bytes.swiggy.com/personalising-the-swiggy-homepage-layout-part-ii-450c55a40058>

Personalization<br>

* [New-User Product Recommendations for Q-Commerce via Hierarchical Cross-Domain Learning | by Bhavi Chawla | Swiggy Bytes — Tech Blog](https://bytes.swiggy.com/new-user-product-recommendations-for-q-commerce-via-hierarchical-cross-domain-learning-0a7f97b25405)
* <https://bytes.swiggy.com/contextual-bandits-for-ads-recommendations-ec210775fcf>

Recommendation

* [Predicting Food Delivery Time at Cart | by Shubham Grover | Swiggy Bytes](https://bytes.swiggy.com/predicting-food-delivery-time-at-cart-cda23a84ba63)
* <https://bytes.swiggy.com/how-ml-powers-when-is-my-order-coming-part-i-4ef24eae70da?source=publication_content_feed----9556560f659-----13----------------------------------->
* <https://bytes.swiggy.com/how-ml-powers-when-is-my-order-coming-part-ii-eae83575e3a9?source=publication_content_feed----9556560f659-----11----------------------------------->
* <https://bytes.swiggy.com/optimizing-the-picking-process-to-enable-faster-deliveries-for-instamart-93de0fe9d819>

Swiggy

* [The accurate ETA to customer satisfaction (Part One)](https://blog.zomato.com/the-accurate-eta-to-customer-satisfaction-part-one)
* [Predicting your order’s Food Preparation Time](https://blog.zomato.com/predicting-fpt-optimally)
* [The Deep Tech Behind Estimating Food Preparation Time](https://blog.zomato.com/food-preparation-time)
* [Eliminating Bottlenecks in Real-Time Data Streaming: A Zomato Ads Flink Journey](https://blog.zomato.com/eliminating-bottlenecks-in-real-time-data-streaming-a-zomato-ads-flink-journey)

Zomato<br>

* <https://medium.com/@aryanakm01/zepto-delivery-the-algorithmic-brilliance-behind-10-minute-deliveries-daea75f0604f>
* [Scraping ETA & Delivery Time Accuracy via Rider Tracking APIs (Zepto, Blinkit, Getir, Gorillas)](https://www.realdataapi.com/scrape-delivery-eta-tracking-via-rider-tracking-apis.php)\ <br>


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