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.
This challenge aims to predict the estimated time of delivery of orders, from the point of driver pickup to the point of arrival at final destination.
link to the competition: https://zindi.africa/hackathons/edsa-sendy-logistics-challenge
Our solution will help Sendy improve customer communication and the reliability of their service. Moreover, the solution will allow Sendy to minimise the cost of doing business through better resource management and order scheduling.
- Exploratory Data Analysis
- Feature Selection
- Feature Engineering
- Building Various Machine Learning Models
- Model Evaluation and Selection: rmse used as a performance metric
- Submission
Currently ranked among the top 40%
Link to project Trello board: https://trello.com/invite/b/Jvt3ICOf/6936f63598f23bdfa917f7bf23176ceb/regression-predict
- Rohini Jagath
- Nicole Meinie
- Confidence Ledwaba
- Pilasande Pakkies
If you would like to contribute to our repository please contact nicole.meinie@gmail.com
