{
  "session_count": {
    "description": "Total number of user sessions recorded within the selected time window.",
    "business_insight": "Higher session_count indicates stronger engagement. Increasing this feature usually reduces churn probability.",
    "range": [0, 500],
    "unit": "sessions",
    "data_type": "numeric"
  },
  "recency": {
    "description": "Number of days since the user last opened or interacted with the app.",
    "business_insight": "Higher recency means longer inactivity and higher churn risk. Decreasing this value implies users are returning more frequently.",
    "range": [0, 365],
    "unit": "days",
    "data_type": "numeric"
  },
  "timestamp": {
    "description": "Date and time of the user's latest app activity. Useful for calculating recency or analyzing temporal churn patterns.",
    "business_insight": "Timestamp itself is not directly used for prediction but can help explain seasonal or temporal trends when analyzing churn patterns.",
    "data_type": "datetime"
  },
    "userid": {
    "description": "Unique identifier for each user in the dataset.",
    "business_insight": "Used to identify specific users when listing churn predictions."
    },
    "ChurnProbability": {
    "description": "Predicted probability that a user will churn based on the Random Forest model.",
    "business_insight": "Higher probability values indicate users at greater risk of churn. Useful for ranking top churn-prone users."
    }

}