1 Background Developing a predictive model for ATM cash demand is an important task for every bank. Suppose that you are employed by a bank, and your task is to optimise the bank's cash management by making smarter decisions about reloading its ATM network. The variable Withdraw in the dataset ATM_training.csv is the total cash amount withdrawn per day from an ATM, recorded from the ATM network of a bank. The response variable and covariate variables are described in the following table. Variable Description Withdraw The total cash withdrawn a day (in 1000 local currency) Shops Number of shops/restaurants within a walkable distance (in 100) ATMS Number of other ATMs within a walkable distance (in 10) Downtown =1 if the ATM is in downtown, 0 if not Weekday = 1 if the day is weekday, 0 if not Center =1 if the ATM is located in a center (shopping, airport, etc), 0 if not High =1 if the ATM has a high cash demand in the last month, 0 if not Your task is to develop a model for predicting the cash demand Withdraw based on the covariates. The test dataset ATM_test.csv (not provided) has the same structure as the training data ATM_training.csv