Clustering_on_Customer_Credit_Card

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Clustering_on_Customer_Credit_Card

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# Clustering_on_Customer_Credit_Card

## DataSet used in this notebook :

Over 8900 customers are included in this dataset, with 17 different features related to their credit cards. The features are as follows:

Cust _ID: Credit card holder ID

BALANCE: Monthly average balance (based on daily balance averages)

BALANCE_FREQUENCY: Ratio of the last 12 months with balance

PURCHASES: Total purchase amount spent during the 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

## Libraries Used

pandas, numpy, matplotlib, seaborn, sklearn

## Clustering Method

Algorithm: KMeans

Finding Optimal K: Used Elbow Method to determine the best number of clusters

Visualization: 2D Cluster plots and also PCA applied for dimensionality reduction in visualization

## Key Insights

Customers are grouped into distinct behavioral clusters, which Can be used for targeted marketing, credit risk analysis, and personalized services

Clustering_on_Customer_Credit_Card

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