Skills application
Instruction and application
In ProgressFeature engineering for churn prediction
In this activity, you will apply feature engineering techniques to the Telco Customer Churn dataset to uncover key drivers of customer retention and attrition. You’ll work through data preprocessing, feature creation and interaction modelilng to refine the dataset and improve model performance.
These skills are essential for building predictive models that provide actionable business insights, helping companies to identify at-risk customers and develop data-driven retention strategies.
Challenge instructions and resources
To complete this activity, you’ll need both the Jupyter Notebook and the Telco Customer Churn dataset. Use the links below to access and download the required files:
mv-l6-m4-w1.ipynbBank Customer Churn Prediction with missing values.csv
Work on the challenges
Follow the instructions in the Jupyter Notebook to apply feature engineering techniques to the Telco Customer Churn dataset, including data cleaning, encoding, scaling and creating new features.
Collaborate in the breakout room
Discuss and share insights with fellow apprentices as you work on the activity challenges. While the activity is designed for individual completion, feel free to ask questions, collaborate and compare approaches with your group.
Regroup and share
Return to the main session after 25 minutes to discuss key takeaways and insights from the exercise.
Action item: Activity share out
What feature engineering techniques did you apply, and how did they impact the model’s ability to predict customer churn?