TheMainstreamSeer

In the previous post "predicting payment days for accounts receivables at hospitals", we created several model nuggets that could predict the payment days based on inputs such as age, income, employment, marital status, etc and on the observed values of the actual number of days that we taken for payment.  In today's post, we will use one of those model nuggets to to predict the number of days for a new set of data.  This process, called "scoring the model" in SPSS is really the main objective of the entire modeling exercise. In order to do this, we first need a new set of data that contain the same fields as the data that was used in developing the model.  An easy way to test this is by using the same file that was used to train the model but removing the observed data from the file.  From the image below, you will see that the field "payment days" is missing: We then run this data through the Auto Data Prep node.  This is important because we used...

In today's post, we explore the use of data mining algorithms in creating a SKU (Stock Keeping Unit) level sales forecast.  There are several ways of creating a sales forecast including time series forecasting, simulation and scenario building.  Ignoring these methods, we will instead use a neural net model to create a SKU level sales forecast.  We will then test the accuracy of the forecast against actual data and try to explain why neural net models are great at at making predictions when the exact nature of the relationship between inputs and the output is not known.  This is important because in developing our model, in addition to historical sales data, we will use data about customer demographics as well as unemployment and inflation data.  At the outset, while high level conclusions can be drawn about the impact that demographics and other data can have on the sales forecast, the exact nature of the relationship between these inputs (historical sales...

Academic performance of students in schools and colleges is an important factor in determining their overall success and sustainability.  Traditionally, schools and colleges have measured this after the fact i.e. after students go through tests, exams, etc and are assigned grades based on their performance.  With data mining tools, schools and colleges could predict academic performance of students before the fact.  By mining data about historical performance of students, their demographics, etc educational institutions could create predictive models to determine whether a specific student (with a unique profile) is likely to pass or fail an exam.  Universities offering admissions to prospective students could determine before hand whether a student will likely succeed or not in the program that they propose to enroll in.  This could be used by Admissions Committees to improve the quality of students that they offer admissions to and therefore improve the standi...