SPSS Modeler Predictive Analytics
Budget: $30 – $250 CAD
I am working on a two-part assignment that blends business reasoning with hands-on modelling.
First, you will receive a short list of conceptual questions. I need succinct, convincing answers that show you can translate an undefined business issue into a sound analytic approach, balancing both descriptive exploration and forward-looking predictive analysis.
Next comes the practical phase. I will hand over a raw dataset together with the high-level business challenge. Your task is to explore, clean and model the data, arriving at statistically grounded predictions that can be explained to management. The emphasis is on solid statistical thinking rather than a particular domain such as sales forecasting or risk scoring, so pick whichever techniques—regression, classification, clustering, time-series or hybrids—best justify the evidence.
SPSS Modeler is my default platform, and a fully documented .str stream would be ideal, but if you prefer Python, R or another tool just make sure the workflow is reproducible and that I receive everything needed to run it end-to-end.
Deliverables
• Clear, well-argued responses to the initial questions.
• An exploratory analysis file or stream showing each step.
• The final predictive model with performance metrics, variable importance and business implications.
• A brief stakeholder-friendly summary deck or PDF.
Acceptance criteria: the code/stream runs on my machine without modification, conclusions are backed by relevant statistics and visuals, and recommendations directly answer the stated business problem.
Please include your estimated turnaround time and the toolset you intend to use when you respond.
First, you will receive a short list of conceptual questions. I need succinct, convincing answers that show you can translate an undefined business issue into a sound analytic approach, balancing both descriptive exploration and forward-looking predictive analysis.
Next comes the practical phase. I will hand over a raw dataset together with the high-level business challenge. Your task is to explore, clean and model the data, arriving at statistically grounded predictions that can be explained to management. The emphasis is on solid statistical thinking rather than a particular domain such as sales forecasting or risk scoring, so pick whichever techniques—regression, classification, clustering, time-series or hybrids—best justify the evidence.
SPSS Modeler is my default platform, and a fully documented .str stream would be ideal, but if you prefer Python, R or another tool just make sure the workflow is reproducible and that I receive everything needed to run it end-to-end.
Deliverables
• Clear, well-argued responses to the initial questions.
• An exploratory analysis file or stream showing each step.
• The final predictive model with performance metrics, variable importance and business implications.
• A brief stakeholder-friendly summary deck or PDF.
Acceptance criteria: the code/stream runs on my machine without modification, conclusions are backed by relevant statistics and visuals, and recommendations directly answer the stated business problem.
Please include your estimated turnaround time and the toolset you intend to use when you respond.