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-delivery-rider-response-challenge-organized-by-galsenai-and-solved-by-the-misfits-team

This is a private hackathon open to Senegalese participants. If you would like to participate, please fill out this form Indabaxsenegal_Hackathon_2021 and the secret code will be emailed to you. How to prepare for the hackathon Practice on a challenge and make your first Zindi submission. Watch this YouTube video. Make a team in preparation for UmojaHack. Watch this YouTube video. Sendy links customers who have delivery needs with vetted transporters (from bikes to trucks), using a web and mobile application platform as well as an API. Customers select their vehicle of choice, get their price quote upfront and pay using various payment options. The system optimises the route and dispatches the order to the closest available drivers and riders (called Partners). The objective of this challenge is to create a machine learning model that will predict whether a rider will accept, decline or ignore an order sent to them. Picking the best rider to service the order will improve the experience of the customer and potentially save on time since the rider won’t cancel, creating a more efficient service overall. The datasets provided by Sendy includes dispatch details and rider metrics based on orders made via the Sendy platform. The challenge is to predict whether a Partner will accept, reject or ignore an order that has been dispatched to them. A Partner will receive an order through the phone application and has a few seconds to accept the order. Alternatively, the Partner can actively reject the order. If the Partner doesn’t take an action we consider the order ignored. After a few seconds, Sendy will dispatch the order to the next available Partner. The training dataset provided here is a subset of over 200 000 order dispatches and only includes direct orders (i.e. Sendy “express” orders) placed with bikes in Nairobi. All data in this subset have been fully anonymised while preserving the distribution.WiMLDS createso, opportunities for members to engage in technical and professional conversations in a positive, supportive environment by hosting talks by women and gender minority individuals working in data science or machine learning, as well as hosting technical workshops, networking events and hackathons. We are inclusive to anyone who supports our cause regardless of gender identity or technical background.

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ap-system-implementation

DDBS implementing Availabily and Partitionning in Python

capitaindata.github.io

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github-slideshow

A robot powered training repository :robot:

ivy

The Unified Machine Learning Framework

programming

python

tp_optimisation_ml_data

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