No-Code Machine Learning Using Amazon AWS SageMaker Canvas - Adding Data

The video tutorial introduces a project on customer churn prediction using a dataset from Kaggle. It describes the dataset's columns, which include various customer metrics, and explains how to determine if a customer will leave their telco provider. The tutorial covers data preparation, including importing data from S3, and guides viewers through creating a model using SageMaker Canvas. The session concludes with a brief overview of the next steps in model building.

What is the primary goal of the customer churn prediction project?

• To develop a new marketing strategy

• To analyze customer spending habits

• To predict if a customer will switch telco providers

• To increase customer satisfaction

Which platform was used to source the customer churn prediction data?

• AWS Marketplace

• Google Cloud

• GitHub

• Kaggle

What type of information is included in the dataset columns?

• Voicemail plan details and call statistics

• Customer satisfaction scores

• Billing information

• Internet usage patterns

What is the first step in preparing the model for customer churn prediction?

• Creating a new marketing plan

• Importing data from South three

• Analyzing customer feedback

• Developing a customer loyalty program

What is the final step mentioned before building the model in the next lecture?

• Selecting the dataset

• Analyzing the data

• Testing the model

• Deploying the model