A manager at the bank is disturbed with more and more customers leaving their credit card services. They would really appreciate if one could predict for them who is gonna get churned so they can proactively go to the customer to provide them better services and turn customers' decisions in the opposite direction
- CLIENTNUM: unique identifier of the customer
- Attrition_Flag:customer activity(if the customer acount is closed 1 else 0)
- Customer_Age: the age of the customer
- Gender: gender of the customer
- Dependent_count: Demographic variable - Number of dependents
- Education_Level: Demographic variable - Educational Qualification of the account holder (example: high school, college graduate, etc.)
- Marital_Status: Demographic variable - Married, Single, Divorced, Unknown
- Income_Category: Demographic variable - Annual Income Category of the account holder (< $40K, $40K - 60K, $60K - $80K, $80K-$120K, > $120K, Unknown)
- Card_Category: Product Variable - Type of Card (Blue, Silver, Gold, Platinum)
- Months_on_book: Period of relationship with bank
- Total_Relationship_Count:Total no. of products held by the customer
- Months_Inactive_12_mon: No. of months inactive in the last 12 months
- Contacts_Count_12_mon: No. of Contacts in the last 12 months
- Credit_Limit: Credit Limit on the Credit Card
- Total_Revolving_Bal: Total Revolving Balance on the Credit Card
- Avg_Open_To_Buy: Open to Buy Credit Line (Average of last 12 months)
- Total_Amt_Chng_Q4_Q1: Change in Transaction Amount (Q4 over Q1)
- Total_Trans_Amt: Total Transaction Amount (Last 12 months)
- Total_Trans_Ct: Total Transaction Count (Last 12 months)
- Total_Ct_Chng_Q4_Q1: Change in Transaction Count (Q4 over Q1)
- Avg_Utilization_Ratio: Average Card Utilization Ratio