You receive this dataset summary for a machine learning project predicting customer churn: ``` Total records: 50,000 Features: 45 Target: churned (1) vs retained (0) Class balance: 5% churned, 95% retained Sample statistics: - age: mean=34.2, std=12.1, min=-5, max=150 - account_balance: mean=$2,340, std=$15,000, 5000 missing values - last_login: 30% missing, format varies (MM/DD/YYYY and YYYY-MM-DD mixed) - customer_id: unique count = 48,500 (out of 50,000 records) - country: 200 unique values including "USA", "usa", "United States", "US" Correlation with target: - age: 0.02 - account_balance: 0.45 - days_since_last_login: 0.67 - total_purchases: 0.52 ``` What data quality issues do you identify? What would you do before training a model?