In this notebook we will explore different approaches for clustering using the credit card dataset available on kaggle.

Customers segmentation based on their credit card usage behavior.

Customer Segmentation for Credit Card Users (Banking domain): Defined a marketing strategy by developing a customer segmentation profile using K-means cluster and factor analysis. Used the derived KPI's to gain insights on the behavioral segments of credit card customers. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables.

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The most common way for clustering using Python in Credit Card dataset (with explanation)

Segmentation in marketing is a technique used to divide customers or other entities into groups based on attributes such as behaviour or demographics. It is useful to identify segments of customers who may respond in a similar way to specific marketing techniques such as email subject lines or display advertisements. As it gives businesses the ability to tailor marketing messages and timing to generate better response rates and provide improved consumer experiences.In the following post, I will be using a dataset containing a number of behavioural attributes for credit card customers. The dataset can be downloaded from the Kaggle website. I will be using the scikit-learn python machine learning library to apply an unsupervised machine learning technique known as clustering to identify segments that may not immediately be apparent to human cognition. The dataset consists of 18 features about the behaviour of credit card customers. These include variables such as the balance currently on the card, the number of purchases that have been made on the account, the credit limit, and many others. A complete data dictionary can be found in the repository.

Credit Card Segmentation DATA AVAILABLE: ⮚ CC GENERAL.csv BUSINESS CONTEXT: This case requires trainees to develop a customer segmentation to define marketing strategy. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables. Expectations from the Trainees: EXPECTATIONS: ✔ Advanced data preparation: Build an ‘enriched’ customer profile by deriving “intelligent” KPIs such as: ∙ Monthly average purchase and cash advance amount ∙ Purchases by type (one-off, installments) ∙ Average amount per purchase and cash advance transaction, ∙ Limit usage (balance to credit limit ratio), ∙ Payments to minimum payments ratio etc. ✔ Advanced reporting: Use the derived KPIs to gain insight on the customer profiles. ✔ Identification of the relationships/ affinities between services. ✔ Clustering: Apply a data reduction technique factor analysis for variable reduction technique and a clustering algorithm to reveal the behavioural segments of credit card holders ✔ Identify cluster characterisitics of the cluster using detailed profiling. ✔ Provide the strategic insights and implementation of strategies for given set of cluster characteristics DATA DICTIONARY: CUST_ID: Credit card holder ID BALANCE: Monthly average balance (based on daily balance averages) BALANCE_FREQUENCY: Ratio of last 12 months with balance PURCHASES: Total purchase amount spent during last 12 months ONEOFF_PURCHASES: Total amount of one-off purchases INSTALLMENTS_PURCHASES: Total amount of installment purchases CASH_ADVANCE: Total cash-advance amount PURCHASES_ FREQUENCY: Frequency of purchases (Percent of months with at least one purchase) ONEOFF_PURCHASES_FREQUENCY: Frequency of one-off-purchases PURCHASES_INSTALLMENTS_FREQUENCY: Frequency of installment purchases CASH_ADVANCE_ FREQUENCY: Cash-Advance frequency AVERAGE_PURCHASE_TRX: Average amount per purchase transaction CASH_ADVANCE_TRX: Average amount per cash-advance transaction PURCHASES_TRX: Average amount per purchase transaction CREDIT_LIMIT: Credit limit PAYMENTS: Total payments (due amount paid by the customer to decrease their statement balance) in the period MINIMUM_PAYMENTS: Total minimum payments due in the period. PRC_FULL_PAYMEN: Percentage of months with full payment of the due statement balance TENURE: Number of months as a customer

using pca and Clustering

Kaggle - Credit Card Dataset for Clustering

Application of K-Means Clustering for the Credit Card Dataset

Clustering & hyperparameter tuning to identify customer segments for a bank's marketing campaign. Dataset contains credit card usage behavior of 9000 customers.

Credit Card Customer Segmentation using Unsupervised Learning. This project applies the K-Means clustering algorithm on a credit card dataset to group customers based on spending patterns, balance, and transaction behavior. It helps identify different customer segments for better analysis and decision-making.

This project employs K-Means clustering on the 'credit_card' dataset to segment customers based on spending behaviors, balance management, and payment habits. The goal is to identify distinct customer groups for targeted marketing strategies and optimized credit offerings.

Credit Card Dataset for Clustering from Kaggle

This project applies unsupervised machine learning techniques to analyze credit card usage patterns. It includes data preprocessing, dimensionality reduction with PCA, and clustering using KMeans, Hierarchical, and Spectral clustering. The project also evaluates clustering quality and visualizes the results.

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The purpose of analyzing this dataset is to cluster bank customers into several groups in order to understand their behavior and apply appropriate business strategies to each cluster.

This dataset is from kaggle

This Clustering project is based on Credit Card Dataset for Clustering from Kaggle. K-Means Clustering in R is used for this problem. Here, our goal is to understand customer segments of credit card usage for defining marketing strategy.

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This case requires to develop a customer segmentation to define marketing strategy. The sample Dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables.

Algoritmo de agrupamento para estratégias de marketing através de dados de cartão de crédito.

Objetivo: Realizar el análisis de un conjunto de datos, aplicando técnicas estadísticas, algoritmos de machine learning y técnicas heurísticas para explorar patrones y segmentar la información.

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unsupervised machine learning model using different techniques