decision_making/finance/classification/Credit_Card_customers_B2/info.json

{
    "name": "Credit Card customers",
    "source": "https://www.kaggle.com/datasets/sakshigoyal7/credit-card-customers/data",
    "data_intro": "this dataset consists of 10,000 customers mentioning their age, salary, marital_status, credit card limit, credit card category, etc,try to predict what kind of client is likely to leave",
    "is_splited": false,
    "overall_size": 10127,
    "train_size": 0,
    "test_size": 0,
    "c_classes": 6,
    "n_classes": 17,
    "task_type": "classification",
    "target": {
        "Attrition_Flag": "Given a customer's all other data, predict whether the customer will be Existing Customer or Attrited Customer"
    },
    "cat_feature_intro": {
        "Attrition_Flag": "- Attrition_Flag:Internal event (customer activity) variable - if the account is closed then 1 else 0,Existing Customer,Attrited Customer",
        "Gender": "- Gender: Demographic variable - M=Male, F=Female",
        "Education_Level": "- Education_Level:Demographic variable - Educational Qualification of the account holder (example: high school, college graduate, etc.)",
        "Marital_Status": "- Marital_Status:Demographic variable - Married, Single, Divorced, Unknown",
        "Income_Category": "- Income_Category: Demographic variable - Annual Income Category of the account holder (< $40K, $40K - 60K, $60K - $80K, $80K-$120K, > $120K, Unknown)",
        "Card_Category": "- Card_Category: Product Variable - Type of Card (Blue, Silver, Gold, Platinum)"
    },
    "num_feature_intro": {
        "CLIENTNUM": "- CLIENTNUM:Client number. Unique identifier for the customer holding the account",
        "Customer_Age": "- Customer_Age:Demographic variable - Customer's Age in Years",
        "Dependent_count": "- Dependent_count:Demographic variable - Number of dependents",
        "Months_on_book": "- Months_on_book:Period of relationship with bank",
        "Total_Relationship_Count": "- Total_Relationship_Count:Total no. of products held by the customer",
        "Months_Inactive_12_mon": "- Months_Inactive_12_mon:No. of months inactive in the last 12 months",
        "Contacts_Count_12_mon": "- Contacts_Count_12_mon:No. of Contacts in the last 12 months",
        "Credit_Limit": "- Credit_Limit:Credit Limit on the Credit Card",
        "Total_Revolving_Bal": "- Total_Revolving_Bal:Total Revolving Balance on the Credit Card",
        "Avg_Open_To_Buy": "- Avg_Open_To_Buy:Open to Buy Credit Line (Average of last 12 months)",
        "Total_Amt_Chng_Q4_Q1": "- Total_Amt_Chng_Q4_Q1:Change in Transaction Amount (Q4 over Q1)",
        "Total_Trans_Amt": "- Total_Trans_Amt:Total Transaction Amount (Last 12 months)",
        "Total_Trans_Ct": "- Total_Trans_Ct:Total Transaction Count (Last 12 months)",
        "Total_Ct_Chng_Q4_Q1": "- Total_Ct_Chng_Q4_Q1:Change in Transaction Count (Q4 over Q1)",
        "Avg_Utilization_Ratio": "- Avg_Utilization_Ratio:Average Card Utilization Ratio",
        "Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1": "- Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1:Naive Bayes",
        "Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2": "- Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2:Naive Bayes"
    },
    "evaluation_metric": null,
    "num_feature_value": {
        "Avg_Open_To_Buy": [
            3.0,
            34516.0
        ],
        "Avg_Utilization_Ratio": [
            0.0,
            0.999
        ],
        "CLIENTNUM": [
            708082083.0,
            828343083.0
        ],
        "Contacts_Count_12_mon": [
            0.0,
            6.0
        ],
        "Credit_Limit": [
            1438.3,
            34516.0
        ],
        "Customer_Age": [
            26.0,
            73.0
        ],
        "Dependent_count": [
            0.0,
            5.0
        ],
        "Months_Inactive_12_mon": [
            0.0,
            6.0
        ],
        "Months_on_book": [
            13.0,
            56.0
        ],
        "Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1": [
            7.6642e-06,
            0.99958
        ],
        "Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2": [
            0.00041998,
            0.99999
        ],
        "Total_Amt_Chng_Q4_Q1": [
            0.0,
            3.397
        ],
        "Total_Ct_Chng_Q4_Q1": [
            0.0,
            3.714
        ],
        "Total_Relationship_Count": [
            1.0,
            6.0
        ],
        "Total_Revolving_Bal": [
            0.0,
            2517.0
        ],
        "Total_Trans_Amt": [
            510.0,
            18484.0
        ],
        "Total_Trans_Ct": [
            10.0,
            139.0
        ]
    },
    "cat_feature_value": {
        "Attrition_Flag": [
            "Attrited Customer",
            "Existing Customer"
        ],
        "Card_Category": [
            "Blue",
            "Gold",
            "Platinum",
            "Silver"
        ],
        "Education_Level": [
            "College",
            "Doctorate",
            "Graduate",
            "High School",
            "Post-Graduate",
            "Uneducated",
            "Unknown"
        ],
        "Gender": [
            "F",
            "M"
        ],
        "Income_Category": [
            "$120K +",
            "$40K - $60K",
            "$60K - $80K",
            "$80K - $120K",
            "Less than $40K",
            "Unknown"
        ],
        "Marital_Status": [
            "Divorced",
            "Married",
            "Single",
            "Unknown"
        ]
    }
}