The Patient Churn Prediction dataset is a categorical dataset for the healthcare field containing 2,000 patient records designed to help identify patients at risk of churn so that retention measures can be taken in advance. Dataset composition:

• Includes patient demographic information such as age, sex, geographic location, and duration of relationship with healthcare provider.
• Service utilization metrics include annual visit frequency, missed appointments, days since last interaction, and nursing specialty.
• Patient satisfaction indicators, such as overall satisfaction score, waiting time satisfaction, staff interaction satisfaction, and service provider rating.
• Financial and participation factors, such as type of insurance, average out-of-pocket expenses, billing issue identification, patient portal usage, referral behavior, and distance to the facility. Data Fields:
• The target variable is the churn status, which indicates whether a patient has churned (0 = retained, 1 = churned).