Machine Learning
Time Series Forecasting – Jupiter Python
⦁ What use case has your group selected? What are we trying to sell and to whom?
a. Who is the user we are building the product for? What does the user want to accomplish? – Jobs to be Done .
b. What data do we need to create a forecasting app/tool/product? – Address these 2 points in a word document.
⦁ Why would somebody buy this product or service? – Discovery Hypothesis
a. What is the value proposition? What problem does the product solve for customers?
b. What economic benefits can a customer expect? Time or money savings?
c. What makes the product better and different from best available alternatives?
d. What other products, services or infrastructure is needed to get full value from the product?
⦁ Who are potential buyers of the product or service? – Buyer Persona
⦁ Presentation and storytelling.
⦁ Wireframe of your time series app using Balsamiq or any other tool of choice.
⦁ Jupyter notebook with the details about the data used for the analysis, insights generated, and forecasts created.
