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Create advanced features for a churn prediction model. Techniques: 1. Temporal features (days since last purchase, purchase frequency). 2. Aggregations (total spend, average order value). 3. Categorical encoding (one-hot, target encoding). 4. Interaction features (tenure × monthly charges). 5. Featu...
Master feature engineering and data preprocessing techniques for improved machine learning model performance. Data quality assessment: 1. Missing data analysis: missing completely at random (MCAR), missing at random (MAR), patterns identification. 2. Outlier detection: IQR method (Q1-1.5*IQR, Q3+1.5...
