Analyze the data and come up with a predictive model to determine if a customer will leave the credit card services or not and the reason behind it
Background & Context
The Thera bank recently saw a steep decline in the number of users of their credit card, credit cards are a good source of income for banks because of different kinds of fees charged by the banks like annual fees, balance transfer fees, and cash advance fees, late payment fees, foreign transaction fees, and others. Some fees are charged to every user irrespective of usage, while others are charged under specified circumstances.
Customers’ leaving credit cards services would lead bank to loss, so the bank wants to analyze the data of customers and identify the customers who will leave their credit card services and reason for same – so that bank could improve upon those areas
You as a Data scientist at Thera bank need to come up with a classification model that will help the bank improve its services so that customers do not renounce their credit cards
You need to identify the best possible model that will give the required performance
Objective
Explore and visualize the dataset. Build a classification model to predict if the customer is going to churn or not Optimize the model using appropriate techniques Generate a set of insights and recommendations that will help the bank Data Dictionary:
CLIENTNUM: Client number. Unique identifier for the customer holding the account Attrition_Flag: Internal event (customer activity) variable - if the account is closed then "Attrited Customer" else "Existing Customer" Customer_Age: Age in Years Gender: Gender of the account holder Dependent_count: Number of dependents Education_Level: Educational Qualification of the account holder - Graduate, High School, Unknown, Uneducated, College(refers to a college student), Post-Graduate, Doctorate. Marital_Status: Marital Status of the account holder Income_Category: Annual Income Category of the account holder Card_Category: Type of Card Months_on_book: Period of relationship with the 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 between the customer and bank in the last 12 months Credit_Limit: Credit Limit on the Credit Card Total_Revolving_Bal: The balance that carries over from one month to the next is the revolving balance Avg_Open_To_Buy: Open to Buy refers to the amount left on the credit card to use (Average of last 12 months) Total_Trans_Amt: Total Transaction Amount (Last 12 months) Total_Trans_Ct: Total Transaction Count (Last 12 months) Total_Ct_Chng_Q4_Q1: Ratio of the total transaction count in 4th quarter and the total transaction count in 1st quarter Total_Amt_Chng_Q4_Q1: Ratio of the total transaction amount in 4th quarter and the total transaction amount in 1st quarter Avg_Utilization_Ratio: Represents how much of the available credit the customer spent