Skills application
Instruction and application
In ProgressHyperparameter tuning practical demo
In this skills application demo, we'll be applying techniques to address class imbalance in a classification model for RingNet Telecom.
Learning this skill brings value to the workplace because addressing class imbalance, ensures your models are accurate and unbiased. This then leads to more reliable predictions and better decision-making.
Challenge instructions and resources
Select the link below to download and access the Jupyter Notebook for this activity. It includes the challenge instructions and the files needed to complete the challenge.
Additional contextBusinessRingNet is a telecommunications company that provides home phone and internet services to customers across the UK.Business ProblemRingNet is facing challenges retaining customers as more people are opting for phone and internet services that are not tethered to their homes. High churn rates not only impact RingNet's revenue but also hinder their long-term growth strategy.TaskTo address this issue, RingNet is implementing targeted strategies to tailor retention initiatives, enhance customer satisfaction, and ultimately reduce churn rates, fostering a loyal customer base. RingNet has recognized the need to harness the power of machine learning to predict churn and implement targeted strategies to mitigate it effectively; they are looking to implement a predictive model that can accurately forecast customer churn based on demographic and behavioral information.RoleYou are a data analyst working on the Data & Insight team at RingNet. Your manager has asked your team to utilize machine learning skills and knowledge to create a predictive model that can accurately forecast customer churn based on demographic and behavioral information.DatasetRingNet retains a comprehensive database that encompasses detailed customer information, including demographics, contract type, tenure metrics, and mobile usage.##Activity instructions
Work on the challenges
Follow the instructions in the Jupyter Notebook to work on the challenges. Use the provided resources to refine the accuracy of the model and improve it's overall performance.
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.
Share your findings
Submit your completed Jupyter Notebook and summarise your findings.
Regroup
Return to the main session after 20 minutes to discuss key takeaways and insights from the exercise.