One way to determine this is to click on your service from the resource list in the IBM Cloud dashboard. You also must determine the location of your Watson Machine Learning service. IMPORTANT: The generated API Key is temporary and will disappear after a few minutes, so it is important to copy and save the value for when you need to import it into your notebook. Enter a name for your key, and then click Create.Ĭopy the API key because it is required when you run the notebook. From the main dashboard, click the Manage menu option, and select Access (IAM).Ĭlick Create an IBM Cloud API key. To access your Watson Machine Learning service, create an API key from the IBM Cloud console. To run the following Jupyter Notebook, you must first create an API key to access your Watson Machine Learning service, and create a deployment space to deploy your model to. NOTE: The Watson Machine Learning service is required to run the notebook. If you have finished setting up your environment, continue with the next step, creating the notebook. You must complete these steps before continuing with the learning path.
The notebook is defined in terms of 40 Python cells and requires familiarity with the main libraries used: Python scikit-learn for machine learning, Python numpy for scientific computing, Python pandas for managing and analyzing data structures, and matplotlib and seaborn for visualization of the data. Use Watson Machine Learning to save and deploy the model so that it can be accessed Select the model that’s the best fit for the given data set, and analyze which features have low and significant impact on the outcome of the prediction. Train the model by using various machine learning algorithms for binary classification.Įvaluate the various models for accuracy and precision using a confusion matrix. Split the data into training and test data to be used for model training and model validation. Prepare the data for machine model building (for example, by transforming categorical features into numeric features and by normalizing the data). The data set has a corresponding Customer Churn Analysis Jupyter Notebook (originally developed by Sandip Datta, which shows the archetypical steps in developing a machine learning model by going through the following essential steps:Īnalyze the data by creating visualizations and inspecting basic statistic parameters (for example, mean or standard variation).
We start with a data set for customer churn that is available on Kaggle.
JUPYTER NOTEBOOK ONLINE DEMO HOW TO
This tutorial explains how to set up and run Jupyter Notebooks from within IBM® Watson™ Studio.