🔍 Retailing and AI-based logistics optimizations.
🌟 The idea is to provide a system to support companies in the rental field (outdoor rental), such as electric scooters, bicycles, or other means of transportation. We will leverage regressors that learn from historical data series of past rentals and provide a prediction regarding the demand for the upcoming days.
Some determining factors are mainly related to:
- 🌦️ Weather (uncertain weather, rain, wind, humidity, etc.)
- 📅 Day of the week ( working day, holiday, weekend, etc.)
- 🌙 Time of day (night, day, etc.)
🎯 The aim is to provide a support tool to estimate with a certain degree of probability the days/times when regular maintenance can be scheduled or increase the number of vehicles in case of periods with higher demand, or distribution in different rental points.