Credit Card Customer Data Description This dataset contains information about credit card customers, including their account balances, transaction behaviors, and payment patterns. The data is structured in a tabular format with the following columns:

CUST_ID: Unique identifier for each customer. BALANCE: Current balance on the credit card account. BALANCE_FREQUENCY: Frequency of the balance updates (range: 0 to 1). PURCHASES: Total purchases made with the credit card. ONEOFF_PURCHASES: Total one-off purchases made with the credit card. INSTALLMENTS_PURCHASES: Total purchases made in installments. CASH_ADVANCE: Total cash advances taken from the credit card. PURCHASES_FREQUENCY: Frequency of purchases made (range: 0 to 1). ONEOFF_PURCHASES_FREQUENCY: Frequency of one-off purchases (range: 0 to 1). PURCHASES_INSTALLMENTS_FREQUENCY: Frequency of installment purchases (range: 0 to 1). CASH_ADVANCE_FREQUENCY: Frequency of cash advances taken (range: 0 to 1). CASH_ADVANCE_TRX: Total number of cash advance transactions. PURCHASES_TRX: Total number of purchase transactions. CREDIT_LIMIT: Credit limit assigned to the customer’s account. PAYMENTS: Total payments made towards the credit card balance. MINIMUM_PAYMENTS: Minimum payments required. PRC_FULL_PAYMENT: Percentage of full payments made (range: 0 to 1). TENURE: Duration (in months) the account has been active. Insights This dataset can be used to analyze customer spending behavior, assess credit risk, and identify trends in credit card usage. It provides a comprehensive overview of customer transactions, payment patterns, and credit management.

Use Cases Customer Segmentation: Group customers based on their spending habits and payment behaviors. Credit Risk Assessment: Evaluate risk levels associated with different customer profiles. Predictive Modeling: Develop models to predict future spending or likelihood of default.

The credit card penetration in Thailand was forecast to continuously increase between 2024 and 2029 by in total 36.8 percentage points. After the fifteenth consecutive increasing year, the credit card penetration is estimated to reach 67.53 percent and therefore a new peak in 2029. Notably, the credit card penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the credit card penetration in countries like Malaysia and Philippines.

The credit card penetration in Brazil was forecast to continuously increase between 2024 and 2029 by in total 16.6 percentage points. After the twelfth consecutive increasing year, the credit card penetration is estimated to reach 62.27 percent and therefore a new peak in 2029. The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The credit card penetration in Canada was forecast to continuously increase between 2024 and 2029 by in total 1.4 percentage points. After the seventh consecutive increasing year, the credit card penetration is estimated to reach 84.55 percent and therefore a new peak in 2029. The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the credit card penetration in countries like United States and Mexico.

The decision to give credit to a particular borrower is a very important decision for various financial institutions as this affects their revenue and profit. There is always a risk of default (not paying), this risk can be reduced by using data to identify the potential customers who will pay back and the ones who will default on their loan.

This dataset contains demographic and payment status data from a bank. The dataset can be used to practice and hone your exploratory data analysis and machine learning skills

The “Customer Credit Card Data” dataset provides valuable insights into credit card usage patterns and financial behavior. Each record represents an individual credit card holder, and the dataset includes the following features:

Id | Features | Description --|:---------|:----------- 1|**Cust_Id:** | Identification of credit card holder
2|**Balance:** | A credit card balance or Total amount left in their account to make purchases 3|**Balance_Frequency:** | How frequently the balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated) 4|**Purchases:** | Total amount of purchases made from account 5|**One_Off_Purchases:** | Maximum purchase amount done in one-go 6|**Installments_Purchases:** | Amount of purchase done in installment 7|**Cash_Advance:** | Cash in advance given by the user 8|**Purchases_Frequency:** | How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased)
9|**One_Off_Purchases_Frequency:** | How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased)
10|**Purchases_Installments_Frequency:** | How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done)
11|**Cash_Advance_Frequency:** | How frequently the cash in advance being paid
12|**Cash_Advance_Trx:** | Number of Transactions made with "Cash in Advanced"
13|**Purchases_Trx:** | Number of purchase transactions made
14|**Credit_Limit:** | Limit of Credit Card for user
15|**Payments:** | Total amount of payments done by user
16|**Minimum_Payments:** | Minimum amount of payments made by user 17|**Prc_Full_Payment:** | Percentage of full payment paid by user
18|**Tenure:** | Tenure of credit card service for user

This dataset is valuable for analyzing credit card behavior, identifying trends, and building predictive models related to credit usage. Researchers, analysts, and financial institutions can leverage this data to gain deeper insights into customer profiles and optimize credit card services.

The number of credit cards in use in India was forecast to continuously increase between 2024 and 2029 by in total *** million cards (+**** percent). After the fifteenth consecutive increasing year, the number is estimated to reach ***** million cards and therefore a new peak in 2029. Notably, the number of credit cards in use of was continuously increasing over the past years.Shown is the estimated number of credit cards currently in use.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

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Data is the new oil, and this dataset is a wellspring of knowledge waiting to be tapped😷!

Don't forget to upvote and share your insights with the community. Happy data exploration!🥰

** For more related datasets: ** https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report/data

Description: Welcome to the world of credit card transactions! This dataset provides a treasure trove of insights into customers' spending habits, transactions, and more. Whether you're a data scientist, analyst, or just someone curious about how money moves, this dataset is for you.

Features: - Customer ID: Unique identifiers for every customer. - Name: First name of the customer. - Surname: Last name of the customer. - Gender: The gender of the customer. - Birthdate: Date of birth for each customer. - Transaction Amount: The dollar amount for each transaction. - Date: Date when the transaction occurred. - Merchant Name: The name of the merchant where the transaction took place. - Category: Categorization of the transaction.

Why this dataset matters: Understanding consumer spending patterns is crucial for businesses and financial institutions. This dataset is a goldmine for exploring trends, patterns, and anomalies in financial behavior. It can be used for fraud detection, marketing strategies, and much more.

Acknowledgments: We'd like to express our gratitude to the contributors and data scientists who helped curate this dataset. It's a collaborative effort to promote data-driven decision-making.

Let's Dive In: Explore, analyze, and visualize this data to uncover the hidden stories in the world of credit card transactions. We look forward to seeing your innovative analyses, visualizations, and applications using this dataset.

The number of credit cards in use in the United Kingdom was forecast to continuously decrease between 2024 and 2029 by in total 0.02 million cards (-0.03 percent). The number is estimated to amount to 63.62 million cards in 2029. Shown is the estimated number of credit cards currently in use.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The credit card penetration in France was forecast to continuously increase between 2024 and 2029 by in total *** percentage points. According to this forecast, in 2029, the credit card penetration will have increased for the eighth consecutive year to ***** percent. The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the credit card penetration in countries like Belgium and Netherlands.

Background

In today's era of big data, credit scoring is not only used in financial scenes such as handling credit cards and loans, but similar scoring products have touched all aspects of our lives, such as deposit free by charging treasure, payment after taxi use, and even in recruitment, marriage and love scenes. As the pioneer of financial technology, the bank has hundreds of millions of APP users every month. App services not only cover financial scenarios such as capital transaction, financial management and credit, but also extend to non-financial scenarios such as meal ticket, shadow ticket, travel and information. It can build users' credit score and provide users with better and convenient services based on credit score.

Research Target

Detail data description has been provided in Data Description.xlsx. We provide six data sets (training data set and scoring data set), including

• user tag data: train_tag.csv and test_tag.csv
• transaction behavior data in the past 60 days: train_trd.csv and test_trd.csv
• app behavior data in the past 30 days: train_beh.csv and test_beh.csv

We hope to build a credit default prediction model based on the training data set and effective feature extraction, and apply the model to the scoring data set to output the default probability of each user in the scoring data set.

The number of debit cards in use in the United Kingdom was forecast to continuously increase between 2024 and 2029 by in total 4.8 million cards (+4.47 percent). After the eleventh consecutive increasing year, the number is estimated to reach 112.2 million cards and therefore a new peak in 2029. Shown is the estimated number of debit cards currently in use.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

This dataset contains a cleaned version of this dataset from UCI machine learning repository on credit card approvals.

Missing values have been filled and feature names and categorical names have been inferred, resulting in more context and it being easier to use.

Your task is to predict which people in the dataset are successful in applying for a credit card.

The credit card penetration in Saudi Arabia was forecast to continuously increase between 2024 and 2029 by in total **** percentage points. After the fifteenth consecutive increasing year, the credit card penetration is estimated to reach ***** percent and therefore a new peak in 2029. Notably, the credit card penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the credit card penetration in countries like Lebanon and United Arab Emirates.

The debit card penetration in the United Kingdom was forecast to continuously decrease between 2024 and 2029 by in total 0.2 percentage points. According to this forecast, in 2029, the debit card penetration will have decreased for the eighth consecutive year to 94.82 percent. The penetration rate refers to the share of the total population who use debit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

About Dataset This dataset had adapted from 'Credit Card Churn Prediction: https://www.kaggle.com/datasets/anwarsan/credit-card-bank-churn ' for visualization in our university project. We have modified customer information, spending behavior, and also added revenue targets.

Business Context: Analytics driving every industry based on a variety of technology platforms which collect information from various sources by analysing what customers certainly want. The Credit Card industry is also data rich industry and data can be leveraged in infinite ways to understand customer behaviour. The data from a credit card processor shows the consumer types and their business spending behaviours. Therefore, companies can develop the marketing campaigns that directly address consumers’ behaviour. In return, this helps to make better sales and the revenue undoubtedly grows greater sales. Understanding the consumption pattern for credit cards at an individual consumer level is important for customer relationship management. This understanding allows banks to customize for consumers and make strategic marketing plans. Thus it is imperative to study the relationship between the characteristics of the consumers and their consumption patterns. Business Objectives: One of the leading banks provided below data a. Customer Demographics b. Customer Behavioural data (information on liabilities, assets and history of transactions with the bank for each customer). Data has been provided for a particular set of customers' credit card spend in the previous 3 months (April, May & June) and their expected average spend in the coming 3 months (July, August & September) c. Credit consumption Data Dictionary a. CustomerDemographics.csv ID – Customer ID - Unique ID for every Customer Account_type - Account Type (current or saving) Gender- Gender of customer (M or F) Age - Age of customer Income – Income Levels (High/Medium/Low) Emp_Tenure_Years – Experience – Employment Tenure of customer in Years Tenure_with_Bank – Number of years with bank Region_code Code assigned to region of residence (has order) NetBanking_Flag – Whether customer is using net banking for the transactions Avg_days_between_transaction – Average days between two transactions b. CustomerBehaviorData.csv ID – Customer ID - Unique ID for every Customer CC_cons_apr - Credit card spend in April DC_cons_apr - Debit card spend in April CC_cons_may - Credit card spend in May DC_cons_may - Debit card spend in May CC_cons_jun - Credit card spend in June DC_cons_jun - Debit card spend in June CC_count_apr - Number of credit card transactions in April CC_count_may - Number of credit card transactions in May CC_count_jun - Number of credit card transactions in June DC_count_apr - Number of debit card transactions in April DC_count_may - Number of debit card transactions in May DC_count_jun - Number of debit card transactions in June Card_lim - Maximum Credit Card Limit allocated Personal_loan_active - Active personal loan with other bank Vehicle_loan_active - Active Vehicle loan with other bank Personal_loan_closed - Closed personal loan in last 12 months Vehicle_loan_closed - Closed vehicle loan in last 12 months Investment_1 - DEMAT investment in june Investment_2 - Fixed deposit investment in june Investment_3 - Life Insurance investment in June Investment_4 - General Insurance Investment in June Debit_amount_apr - Total amount debited for April Credit_amount_apr - Total amount credited for April Debit_count_apr- Total number of times amount debited in april Credit_count_apr - Total number of times amount credited in april Max_credit_amount_apr - Maximum amount credited in April Debit_amount_may - Total amount debited for May Credit_amount_may - Total amount credited for May Credit_count_may - Total number of times amount credited in May Debit_count_may - Total number of times amount debited in May Max_credit_amount_may - Maximum amount credited in May Debit_amount_jun - Total amount debited for June Credit_amount_jun - Total amount credited for June Credit_count_jun - Total number of times amount credited in June Debit_count_jun - Total number of times amount debited in June Max_credit_amount_jun - Maximum amount credited in June Loan_enq - Loan enquiry in last 3 months (Y or N) Emi_active - Monthly EMI paid to other bank for active loans c. CreditConsumptionData.csv ID – Customer ID - Unique ID for every Customer cc_cons (Target) - Average Credit Card Spend in next three months Note: Some customers are having missing values for credit consumption. You need to build the model using customer’s data where credit consumption is non- missing’s. You need to predict the credit consumption for next three months for the customers having missing values. Model Evaluation Metric: You should validate model using Root Mean Square Percentage Error (RMSPE) between the predicted credit card consumption and Actual Credit Consumption. Expected Outputs: a. Detailed code with comments b. Data Exploratory analysis c. Model validation outputs d. Model documentation with all the details e. Predicted values for customers where target variable having missing values

This dataset represents credit card usage and financial behaviour among 1,000 Indian consumers residing in Ranchi. It was collected to support research and academic projects analyzing:

The impact of credit card rates (interest, annual fees, late payment fees) on consumer financial behavior across demographics.

The psychological and behavioral effects of credit card usage, such as impulsive spending, debt accumulation, and financial stress.

Consumer awareness of hidden charges and regulations affecting credit card usage.

The dataset combines demographic, financial, behavioral, and psychological variables to provide a comprehensive overview of credit card usage patterns in India.

Columns / Data Dictionary Column Name Description Customer_ID Unique identifier for each customer Age Age of the customer (18–70) Gender Male, Female, Other Income_Level Income group: Low, Medium, High Education Highest education level Location Urban, Semi-Urban, Rural Credit_Limit Credit limit assigned (₹20,000 – ₹5,00,000) Interest_Rate Annual interest rate (%) Annual_Fee Annual fee charged (₹0 – ₹5,000) Late_Payment_Fee Penalty fee for late payments Hidden_Charges_Awareness Whether the customer is aware of hidden charges (Yes/No) Regulation_Awareness Awareness of regulatory changes (High/Medium/Low) Monthly_Spending Average monthly spending Impulse_Purchases Whether impulse purchases are made (Yes/No) Debt_Accumulation Level of debt accumulation (Low/Moderate/High) Repayment_Behavior Repayment type (On-time/Partial/Default) Credit_Score_Category Credit score category (Poor/Fair/Good/Excellent) Stress_Level Stress level due to credit card usage (Low/Medium/High) Satisfaction_With_Credit_Card Customer satisfaction rating (1–5) Dependency_On_Credit Dependency level on credit (Low/Medium/High) Inspiration / Use Cases

Research on credit card debt and consumer behavior in India

Machine learning projects: classification or prediction of repayment behavior

Financial analytics and risk assessment modeling

Understanding psychological factors influencing spending and stress among Indian consumers

Kenntnisse über Datenschutzgesetze und Kenntnis der unabhängigen Datenschutzbehörde. Kenntnisse über den Schutz persönlicher Daten. Themen: Interesse am Schutz persönlicher Daten, die in privaten und öffentlichen Organisationen gespeichert werden; Vertrauen in ausgewählte Institutionen im eigenen Lande bezüglich des Datenschutzes; Meinung zum Schutz persönlicher Daten: ausreichender Datenschutz im eigenen Land, Einschätzung des allgemeinen Bewusstseins über den Schutz persönlicher Daten, Beunruhigung über das Hinterlassen persönlicher Daten im Internet, Vertrauen in die Datenschutzgesetzgebung; Kenntnis der Datenschutzbehörde und deren Aufgaben: Annahme von Beschwerden von Privatpersonen, Verhängen von Sanktionen, eigene Kontaktaufnahme zu dieser Behörde; Kenntnisse der Pflichten von datenhaltenden Organisationen gegenüber dem Befragten; Kenntnistest der Rechte des Befragten hinsichtlich der Verwendung seiner persönlichen Daten: erforderliche Zustimmung, Widerspruchsrecht, Auskunftsrecht, Recht auf Korrektur oder Löschung von Daten, Rechtsmittel gegen Verstöße, Schadensersatzforderung bei ungesetzlicher Verwendung; Meinung über Übertragungssicherheit von Daten im Internet; Kenntnis über Technologien, die die Sammlung persönlicher Daten vom eigenen Computer einschränken (Cookies, Firewall); Verwendung dieser Technologien; Gründe für eine Nichtnutzung; Einstellung zur Überwachung von: Telefongesprächen, Internetnutzung, Kreditkartennutzung und Daten von Flugpassagieren zur Terrorismusbekämpfung (Split: umgedrehte Antwortvorgaben); Kenntnis über Verbot der Weitergabe persönlicher Daten an Nicht-EU-Länder, mit unzureichendem Datenschutz; Kenntnis über strengere Datenschutzregelungen für empfindliche Daten. Demographie: Geschlecht; Alter; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Urbanisierungsgrad; Haushaltszusammensetzung und Haushaltsgröße; Besitz eines Mobiltelefons; Festnetztelefon im Haushalt. Zusätzlich verkodet wurde: Befragten-ID; Interviewsprache; Interviewer-ID; Land; Interviewdatum; Interviewdauer (Interviewbeginn und Interviewende); Interviewmodus (Mobiltelefon oder Festnetz); Region; Gewichtungsfaktor. Attitudes towards the protection of personal data. Topics: concern with regard to the protection of personal information by private and public organisations; trust in the following institutions regarding the use of personal information in a proper way: travel companies, medical services, insurance companies, credit card companies, financial institutions, employers, police, social security, tax authorities, local authorities, credit reference agencies, mail order companies, non-profit organisations, market and opinion research companies; attitude towards the following statements on the protection of personal data in the own country: is properly protected, low awareness of people on the subject, worry about leaving personal information on the internet, appropriate legislation to cope with growing number of personal information on the internet; awareness of the national authority to monitor the application of data protection laws; responsibility of the national authority to hear individuals; ability of the authority to pose sanctions; personal contact to authority; awareness of the obligation of data collectors to provide information on identity, purpose, and further data sharing; knowledge test concerning the storage of personal data: need for personal consent with regard to the use of personal information, right to oppose the use, legal assurance to access personal data, right to correct or remove data, national laws allow access to courts to seek remedies for breaches of data protection laws, right for compensation caused by unlawful use of personal data; assessment of the security of transmitting personal data over the internet; awareness of technologies to limit the collection of personal data from personal computer; use of these technologies; reasons for not using; attitude towards selected measures to fight international terrorism: monitor telephone calls, monitor internet use, monitor credit card use, monitor flight passenger data; awareness of the assurance that personal data of EU citizens can only be transferred outside the EU to countries which ensure an adequate level or protection; awareness of stricter data protection rules applied for sensitive data. Demography: sex; age; age at end of education; occupation; professional position; type of community; household composition and household size; own a mobile phone and fixed (landline) phone. Additionally coded was: respondent ID; language of the interview; interviewer ID; country; date of interview; time of the beginning of the interview; duration of the interview; type of phone line; region; weighting factor.

The dataset comprises a broad range of variables to understand the full picture of consumers’ financial health: family socio-demographics, total income, total expenses, employment information, as well as all credit details. The features considered for the analyses were: socio-demographic characterization (marital status, level of education completed, number of people in the household), the perceived causes for over-indebtedness (from a predetermined pool of causes), and data concerning their economic situation, including the total income and expenses of the household as well as data concerning their credits and debts (amount of the monthly installments for credit cards, housing credit, car credit, personal credit and other types of credit or debts; total monthly installment concerning all credits). Each household is represented by one record (one observation) of the dataset with many features to describe their characteristics and behavior