Sprint Challenge: Applied Modeling

Sprint Challenge Overview

This sprint challenge will assess your understanding of the concepts covered throughout this sprint on Applied Modeling. You'll demonstrate your ability to:

  • Define a machine learning problem appropriately
  • Engineer meaningful features for your models
  • Handle class imbalance effectively
  • Apply and interpret permutation importance
  • Build and interpret ensemble models
  • Leverage model interpretation tools to explain predictions

Challenge Details

For this challenge, you'll work with a dataset related to customer churn prediction. Your task is to build a model that accurately predicts whether a customer will churn (discontinue their service) based on various features. This is a common business problem with significant impacts on revenue and customer satisfaction.

Your Tasks:

  1. Explore and clean the dataset
  2. Engineer relevant features that might help predict churn
  3. Address any class imbalance issues appropriately
  4. Build multiple models and compare their performance
  5. Use permutation importance to identify the most influential features
  6. Apply model interpretation techniques to explain your best model's predictions
  7. Present your findings and recommendations in a clear, concise manner

Evaluation Criteria:

  • Technical accuracy and appropriate methodology
  • Quality of feature engineering and data preparation
  • Effectiveness of handling class imbalance
  • Appropriate model selection and evaluation
  • Clarity and depth of model interpretation
  • Quality of business insights and recommendations
  • Code quality and documentation

Accessing the Challenge

The sprint challenge will be released at the scheduled time through your learning platform. You'll have a specified timeframe to complete and submit your work.

Resources:

Challenge Repository

Note: The links above will become active when the challenge is released. Please ensure you have all necessary libraries and tools installed and functioning properly before the challenge begins.

Preparation Tips

To prepare for this sprint challenge, we recommend:

  • Review all module materials, especially concepts related to model interpretation and feature engineering
  • Practice with the code-along exercises to reinforce your understanding
  • Ensure your development environment is properly set up and all required libraries are installed
  • Review common evaluation metrics for classification problems and when to use each
  • Practice explaining model outputs and visualizations as if presenting to a non-technical stakeholder
  • Get a good night's sleep before the challenge!

Remember that the sprint challenge is designed to test your understanding of the core concepts, not to trick you with obscure details. Focus on applying the fundamental principles you've learned throughout the sprint.