Assignment 1 Linear Modelling with Sales Data
Developing linear models with R to make sales predictions and creating a report that engaged the audience while communicating outcomes
- Author
- Joshua McCarthy
- Published
- Sun, Apr 19 2020
- Last Updated
- Sun, Apr 19 2020
Context
This assignment was a first chance to experiment with linear models for a business challenge. Reading through the brief and based on the instructors comments, I felt that, while the technical challenge of delivering an accurate prediction for next month is important, an equal amount of value was being placed on our ability to communicate effectively with the audience and reflected a real world output.
Using all the notes and answers you have above, wrap up all your work into a report for the manager of the automotive firm that follows the CRISP-DM methodology. Remember to consider your audience!
I saw this as an opportunity to experiment with how I might develop a data science focused but user accessible report. Dedicating a portion of the exploration time to evaluating the needs of a sales manager in this context, drawing on professional experience and a needs, wants, solutions design framework.
Report
The report appendices with predictions has been omitted
![Report Page 1, problems, solutions, predictive modelling](/projectimages/dam/damat1a/report_01e.jpg)
Report Page 1, problems, solutions, predictive modelling
![Report Page 2, understanding from data, clients, billables, security](/projectimages/dam/damat1a/report_01e2.jpg)
Report Page 2, understanding from data, clients, billables, security
![Report Page 3, variable evaluation detail, linear modelling process, rmse](/projectimages/dam/damat1a/report_01e3.jpg)
Report Page 3, variable evaluation detail, linear modelling process, rmse
![Report Page 4, outcomes, deliverables, effects of variable engineering, 46% predictions less than 15% RMSE](/projectimages/dam/damat1a/report_01e4.jpg)
Report Page 4, outcomes, deliverables, effects of variable engineering, 46% predictions less than 15% RMSE