Chris is a business analyst who likes to practice data modeling in her free time.
She particularly enjoys building analytical models to achieve marketing objectives. For example: clustering models for auto segmentation, propensity models for customer lifetime value predictions, and attribution models for channel evaluations.
This is a blog for Chris to practice her analytical skills and connect with like-minded people.
All tagged Python
To practice product positioning, I found a perfect data set from the 1995 Wisconsin Dell Case. This study draws data from 1698 face-to-face interviews conducted with visitors to this entertainment resort. We have the demographic data of these visitors and data about whether they had participated in or were likely to participate in any of 33 entertainment activities. The response for these activities constitute a binary response matrix, which is perfect for our multidimensional scaling.
There are several ways to conduct clustering analysis. For this study, I chose to use a partitioning method that requires that input variables have meaningful magnitude or be binary categorical variables. Except for age, the demographic variables in this data are multi-category variables, so we need to perform an extra step to convert multi-category variables into binary categorical variables.
As we can see from the data, we already know the real choices of these 333 commuters. Now it’s time for us to build a predictive model and see how it works on the training data. Today we’ll use a linear combination of the four explanatory variables to predict commuter choice. We will also add a code in the end to evaluate the predictive accuracy of our model.
