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Food delivery supported through advanced applications has emerged as one of the fastest growing developments in the e-commerce space. We all love to order online, one thing that we don't like to experience is variable pricing for delivery charges. Delivery charges highly depend on the availability of riders in your area, demand of orders in your area, and distance covered. Due to driver unavailability, there is a surge in delivery pricing and many customers drop off resulting in loss to the company.
To tackle such issues if we track the number of hours a particular delivery executive is active, we can efficiently allocate certain drivers to a particular area depending on demand.
In this machine learning project you will work on creating a robust prediction model of Rossmann's daily sales using store, promotion, and competitor data.
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.
The goal of this machine learning project is to predict which products existing customers will use next month based on their past behaviour and that of similar customers.