From Business Understanding to Deployment: An application of Machine Learning Algorithms to Forecast Customer Visits per Hour to a Fast-Casual Restaurant in Dublin

This research project identifies the significant factors that affects the number of customer visits to a fast-casual restaurant every hour and proceeds to develop several machine learning models to forecast customer visits. The core value proposition of fast-casual restaurants is quality food delivered at speed which means they have to prepare meals in advance of customers visit but the problem with this approach is in forecasting future demand, under estimating demand could lead to inadequate meal preparation which would leave customers unsatisfied while over estimation of demand could lead to wastage especially with restaurants having to comply with food safety regulations whereby heated food not consumed within 90 minutes has to be discarded. Hourly forecasting of demand as opposed to monthly or even daily forecasting is important to help the manager of the fast-casual restaurant optimize resources and reduce wastage. Approaches to forecasting demand can be broadly categorized into qualitative and quantitative methods. Quantitative methods can be further divided into time series and regression-based methods. The regression-based approach which is used for this study enabled the researcher to gather data on several factors hypothesized to have an impact on the number of customer visits to the fast-casual restaurant every hour, carry out an experiment to test for the significance of these factors and to develop several predictive machine learning models capable of predicting the number of customer visits every hour. The results of the experiments carried out shows that hour, day, public holidays, temperature, humidity, rain and windspeed are significant factors in predicting the number of hourly customer visits. Multiple linear regression, regression tree, random forest and gradient boosting machine learning algorithms were also trained to predict the number of customer visits with the Gradient boosting algorithm achieving the lowest Mean Absolute Percentage Error(MAPE) of 18.82%

An Automated Negotiation System for eCommerce Store Owners to Enable Flexible Product Pricing

If a store owner wishes to sell a product online, they traditionally have two options for deciding on a price. They can sell the product at a fixesd price like the products sold on sites like Amazon, or they can put the product in an auction and let demand from customers drive the final sales price like the products sold on sites like eBay. Both options have their pros and cons. An alternative option for deciding on a final sales price for the product is to enable negotiation on the product. With this, there is a dynamic nature to the price; each customer can negotiate with the store owner on the price which allows the final sales price to both change over time and on a customer by customer basis. The issue with enabling negotiation in the context of eCommerce is the time investment needed from the store owner. A store owner cannot negotiate every time an offer comes in from a potential customer, the potential time investment would not be acceptable. Using software agents to automate the process of negotiation for the seller is a potential solution to enabling negotiation in eCommerce for store owners. In this research, a system such as the one just described is developed in a way that mirrors real life negotiations more closely and after evaluation, is found to be a potential solution for the enabling of negotiation in eCommerce