Feature Engineering for Customer Churn Prediction
A telecom company wants to predict customer churn using a dataset with 10,000 customers and 50 features, including demographics, usage metrics, and service calls. The dataset has missing values in several columns (up to 20% missing in 'average monthly data usage'), and some categorical variables with high cardinality (e.g., 'zip code' with 1,000 unique values). The team plans to use Alteryx Machine Learning to build a model. They are unsure how to handle missing values and high-cardinality categorical variables, and whether to create new features like 'average call duration per month' from existing ones.
1. What strategies would you recommend for handling missing values in this dataset? Consider the nature of the missing data and the modeling technique.
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First, analyze the pattern of missingness: is it missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR)? For 'average monthly data usage', missingness might be related to churn (e.g., customers who churn may stop using services). We can create a binary indicator feature for missingness, which may itself be predictive. For imputation, use median imputation for numeric features (robust to outliers) and mode for categorical. Alternatively, use model-based imputation (e.g., k-NN imputation or iterative imputation) if the missingness is substantial and related to other features. In Alteryx Machine Learning, the 'Impute' tool can handle missing values with various methods. For tree-based models (e.g., XGBoost), missing values can be handled natively by the algorithm, so imputation might not be necessary, but it's still good to flag missingness. For linear models, imputation is required. Evaluate the impact of different imputation strategies on model performance using cross-validation.
2. How would you handle the high-cardinality categorical variable 'zip code'? What are the trade-offs?
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High-cardinality categorical variables like zip code can lead to overfitting and the curse of dimensionality if one-hot encoded. Options include: (1) Target encoding (mean encoding) where each category is replaced by the average target value (churn rate) for that category, with smoothing to avoid overfitting. This is efficient but requires careful cross-validation to prevent leakage. (2) Frequency encoding: replace category with its frequency. (3) Grouping rare categories into an 'Other' category based on frequency threshold. (4) Using the first few digits of the zip code to reduce cardinality (e.g., 3-digit prefix). (5) For tree-based models, one can use ordinal encoding or leave it as categorical if the model supports it (e.g., CatBoost). In Alteryx Machine Learning, we can use the 'Multi-Field Formula' or 'Formula' tool to create new features, and the 'Categorical' tool to group or encode. The trade-off is between information loss (grouping) and overfitting risk (target encoding). Use validation to choose the best approach.