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.

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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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. Projet Overview The dataset provided by Sendy includes order dispatch details and rider metrics based on orders made on the Sendy platform. The challenge is to predict the reaction of a partner rider to an order: is a rider most likely to ignore, decline or accept the dispatch they receive? Sendy provides an API as well as a web and mobile application platform to link customers who have delivery needs with vetted transporters. The customers select their vehicle of choice, get their price quote upfront and pay using various payment options. The system optimises the route, looks for the closest available riders and dispatches the orders in the most efficient way. The training dataset provided here is a subset of over 200,000 dispatches and only includes direct orders (i.e. Sendy “express” orders) with bikes in Nairobi. All data in this subset have been fully anonymized while preserving the distribution. Objectives. Build a machine learning model that will predict whether a rider will accept, decline or ignore an order sent to them. Variable definitions Dispatch Data ID - Unique ID for each order request order_id – Unique number identifying the order client_id - Unique number identifying the customer on a platform client_type - Specifies the customer type (Business or Personal) rider_id - Unique number to uniquely identify the rider rider_license_status - Identifies riders who have a license to access restricted areas i.e. 0 (Cannot access a restricted area) and 1 (Can access a restricted area) rider_carrier_type - Identifies the box option that a rider currently has i.e. 0 (No Box option) and 1 (Box option) rider_amount - The earnings a partner would earn if they successfully complete an order. order_license_status - Identifies orders that require a pick-up or drop-off in a restricted area i.e. 0 (Restricted area) and 1 (Non-Restricted area) order_carrier_type - Identifies the box option the customer specified while placing their orders i.e. 0 (No box option), 1 (Box option), 2 (Any option) vendor_type – For this competition limited to bikes. However, in practice, Sendy’s service extends to Vans and Trucks. Pickup Latitude and Longitude (pickup_lat and pickup_long) - Latitude and longitude of pick up location Destination Latitude and Longitude (drop_off_lat and drop_off_long) - Latitude and longitude of delivery location Rider Latitude and Longitude (rider_lat and rider_long) - Latitude and longitude of the Rider at the time of dispatch. target - The reaction of a rider in regards to a particular dispatch. Did a rider ignore (0), decline (1) or accept (2) a dispatch? Dispatch times dispatch_day - Day of Month i.e. 1-31 dispatch_day_of_week - Weekday (Monday = 1) dispatch_time - Time of day the dispatch was sent out to the riders