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Welcome to the Clus-Hierarchical-Credit-Card project! This project focuses on applying hierarchical clustering analysis to a Credit Card customers dataset to uncover valuable insights and customer segments based on their credit card usage patterns, spending behaviors, and demographics.

In summary, this project focuses on applying hierarchical clustering techniques to the Credit Card customers dataset to uncover customer segments with similar attributes and behaviors. By leveraging the insights gained from this analysis, businesses can make informed decisions to enhance their services, refine their marketing strategies, and provide a more personalized experience to their credit card customers.

In today's data-driven world, understanding customer behavior and preferences is essential for businesses to thrive. The Credit Card customers dataset, available on Kaggle, offers valuable insights into the characteristics, spending patterns, and credit card usage of customers. Analyzing this dataset using hierarchical clustering techniques can provide valuable segmentation and customer profiling, enabling businesses to tailor their strategies and services to specific customer segments.

The Credit Card customers dataset captures a wide range of features, including customer demographics, credit card attributes, and transactional data. By leveraging this rich dataset, we can uncover hidden structures and patterns within the customer base, leading to a deeper understanding of customer segments, preferences, and needs.

Hierarchical clustering is a powerful technique for grouping similar customers based on their attributes and behaviors. It constructs a hierarchical structure, known as a dendrogram, that reveals clusters at different levels of similarity. By utilizing a hierarchical approach, we can identify meaningful customer segments that share common characteristics, enabling businesses to develop targeted marketing campaigns, improve customer experiences, and optimize their business strategies.

Throughout this analysis, we will perform data preprocessing steps, such as handling missing values, encoding categorical variables, and scaling numerical features. Next, we will apply hierarchical clustering algorithms to the prepared dataset, enabling us to uncover distinct customer segments based on similarities in their credit card usage patterns, spending behaviors, and demographics. By conducting hierarchical clustering on the Credit Card customers dataset, we aim to provide actionable insights to businesses in the credit card industry. The identified customer segments can assist in customer retention, acquisition strategies, personalized marketing campaigns, and the development of tailored credit card offerings. Understanding the unique needs and preferences of different customer segments can help businesses enhance customer satisfaction, increase customer loyalty, and drive business growth.

Hierarchical clustering, specifically the agglomerative approach, is a type of clustering algorithm used to group similar objects or data points based on their pairwise similarities or distances. It is a bottom-up clustering technique that starts with each data point as an individual cluster and iteratively merges clusters until a termination condition is met. We will be looking at a clustering technique, which is Agglomerative Hierarchical Clustering. Remember that agglomerative is the bottom up approach.

• Hierarchical Clustering
• Credit Card
• Clustering
• Customer Segmentation
• Customer Patterns & Analysis

The Credit Card customers dataset, which is essential for conducting hierarchical clustering analysis, can be obtained from the Kaggle platform. Kaggle is a well-known online community and data science platform that provides a wide range of datasets for various analytical tasks. To access the Credit Card customers dataset, visit the Kaggle website and navigate to the dataset's page. The dataset, created by a user named sakshigoyal7, is publicly available for download.

• The Clus-Hierarchical-Credit-Card project utilizes hierarchical clustering techniques to analyze the Credit Card customers dataset.
• Hierarchical clustering is a powerful approach for grouping similar customers together based on their attributes and behaviors, helping businesses understand customer segmentation and tailor strategies accordingly.
• The Credit Card customers dataset provides a wealth of information on customer demographics, credit card attributes, and transactional data.
• By leveraging hierarchical clustering, we aim to identify distinct customer segments, uncover hidden patterns, and gain insights into customer behaviors in the credit card industry.

• Python 3.7 or higher
• Jupyter Notebook
• Required libraries: pandas, scikit-learn, matplotlib, seaborn (you can find all in requirements.txt)

• Clus-Hierarchical-Credit-Card.ipynb: This Jupyter Notebook contains the main code for the project, including data preprocessing, hierarchical clustering implementation, and visualization of results.
• BankChurners.csv: The dataset file used for the analysis. It is in the archive.zip file.
• clus_hierarchical_credit_card.py: Project codes with python extension.
• README.md: You are currently reading this file, which provides an overview of the project.

Feel free to explore the Clus-Hierarchical-Credit-Card.ipynb notebook to delve into the world of hierarchical clustering analysis and gain valuable insights from the Credit Card customers dataset.

If you have something to say to me please contact me:

• Twitter: Doguilmak
• Mail address: [email protected]