In this multi-part liveProject series, you’ll harness the power of machine learning to make predictions about future rainfall. The Weather Department of Australia is having trouble handling meteorological data manually, and your challenge is to build an end-to-end machine learning model that can make on-the-fly predictions. You’ll use common Python data tools to clean and classify your dataset for analysis, train and evaluate your model, and then deploy your model to a remote server using Flask and Heroku. Work from beginning to end, or dive into whichever section will best augment your skills.
In this liveProject, you’ll deploy a machine learning model to production so it can be easily used by colleagues. You’ll create a virtual environment for deploying your application, use Flask to build a local web service that returns predictions, and Heroku for remote deployment.
This liveProject is for Python data scientists who want to expand their capabilities in preparing data for machine learning. To begin this liveProject you will need to be familiar with the following:
Note: The final milestone of Project 3 Deploy a Predictive Model uses Heroku to deploy the completed application. Heroko incurs a cost. There is intermittent use, and the Eco option ($5) will be sufficient to get the app working as Eco covers 1000 hours, and we will be using far less than that for this project.
In this liveProject, you’ll learn skills for cleaning and exploring data in preparation for training a machine learning model.
geekle is based on a wordle clone.