Name: Credit Card Customers Prediction
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

Credit Card Customers Prediction

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.

I got this dataset from a website with the URL as https://leaps.analyttica.com/home. I have been using this for a while to get datasets and accordingly work on them to produce fruitful results. The site explains how to solve a particular business problem.

Now, this dataset consists of 10,000 customers mentioning their age, salary, marital_status, credit card limit, credit card category, etc. There are nearly 18 features.

We have only 16.07% of customers who have churned. Thus, it's a bit difficult to train our model to predict churning customers.

Data Dictionary

Column	Description
CLIENTNUM	Client number. Unique identifier for the customer holding the account
Attrition_Flag	Internal event (customer activity) variable - if the account is closed then 1 else 0
Customer_Age	Demographic variable - Customer's Age in Years
Gender	Demographic variable - M=Male, F=Female
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, >
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
Naive_Bayes_Classifier_attribution	Naive Bayes
Naive_Bayes_Classifier_attribution	Naive Bayes

Tables

Bankchurners

@kaggle.whenamancodes_credit_card_customers_prediction.bankchurners

386 KB
10127 rows
23 columns


CREATE TABLE bankchurners (
  "clientnum" BIGINT,
  "attrition_flag" VARCHAR,
  "customer_age" BIGINT,
  "gender" VARCHAR,
  "dependent_count" BIGINT,
  "education_level" VARCHAR,
  "marital_status" VARCHAR,
  "income_category" VARCHAR,
  "card_category" VARCHAR,
  "months_on_book" BIGINT,
  "total_relationship_count" BIGINT,
  "months_inactive_12_mon" BIGINT,
  "contacts_count_12_mon" BIGINT,
  "credit_limit" DOUBLE,
  "total_revolving_bal" BIGINT,
  "avg_open_to_buy" DOUBLE,
  "total_amt_chng_q4_q1" DOUBLE,
  "total_trans_amt" BIGINT,
  "total_trans_ct" BIGINT,
  "total_ct_chng_q4_q1" DOUBLE,
  "avg_utilization_ratio" DOUBLE,
  "naive_bayes_classifier_attrition_flag_card_category_co_121d9ef8" DOUBLE,
  "naive_bayes_classifier_attrition_flag_card_category_co_eaea8887" DOUBLE
);