{"id":22141311,"url":"https://github.com/bjam24/bank_customer_segmentation_methods","last_synced_at":"2025-03-24T11:21:17.348Z","repository":{"id":252263773,"uuid":"839095120","full_name":"bjam24/bank_customer_segmentation_methods","owner":"bjam24","description":"The main goal of this project is to use various Clustering Methods for Bank Customer Segmentation.","archived":false,"fork":false,"pushed_at":"2024-11-11T13:47:17.000Z","size":13188,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-29T16:31:54.586Z","etag":null,"topics":["affinity-propagation","agglomerative-clustering","aic","bank-customer-analysis","bic","biplot","clustering","clustering-algorithm","clustering-methods","dbscan-clustering","dendrogram","finance","finances","gaussian-mixture-models","kmeans-clustering","pca","python","segmentation","silhouette-score","spectral-clustering"],"latest_commit_sha":null,"homepage":"https://www.kaggle.com/code/bartomiejjamiokowski/bank-customer-segmantation-methods-with-pca","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bjam24.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-08-07T00:50:50.000Z","updated_at":"2024-11-11T13:47:20.000Z","dependencies_parsed_at":"2025-01-29T16:41:32.162Z","dependency_job_id":null,"html_url":"https://github.com/bjam24/bank_customer_segmentation_methods","commit_stats":null,"previous_names":["bjam24/bank_customer_segmentation_methods"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bjam24%2Fbank_customer_segmentation_methods","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bjam24%2Fbank_customer_segmentation_methods/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bjam24%2Fbank_customer_segmentation_methods/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bjam24%2Fbank_customer_segmentation_methods/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bjam24","download_url":"https://codeload.github.com/bjam24/bank_customer_segmentation_methods/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245258215,"owners_count":20585977,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["affinity-propagation","agglomerative-clustering","aic","bank-customer-analysis","bic","biplot","clustering","clustering-algorithm","clustering-methods","dbscan-clustering","dendrogram","finance","finances","gaussian-mixture-models","kmeans-clustering","pca","python","segmentation","silhouette-score","spectral-clustering"],"created_at":"2024-12-01T21:12:29.887Z","updated_at":"2025-03-24T11:21:17.317Z","avatar_url":"https://github.com/bjam24.png","language":"Jupyter Notebook","readme":"# Bank Customer Segmentation Methods\n\n## Description\nThis project focuses on **Clustering Bank Customers** using various methods available in the **scikit-learn** library. My goal was to gain insights \ninto the nature of clustering methods and learn how to use **scikit-learn** to implement these techniques in practical problems. A relevant \nbusiness sector for this application is Finance and Banking, where understanding different customer segments is crucial for the survival \nof any institution providing financial services. This project was also created for a **Kaggle** challenge called **Credit Card Dataset for Clustering**.\nThe link to this event can be found in the repository description.\n\n## Topics \nThere are many techniques used in this notebook, but only a fraction of them are presented here. Please refer to the notebook to learn about all the \ntechniques used.\n\n**Principal Component Analysis (PCA)** \u003cbr\u003e\n\u003cp float=\"left\"\u003e\n\u003cimg src=\"images/3d_biplot.jpg\" width=\"400\"/\u003e\n\u003c/p\u003e\n\n**Clustering methods**\n- K-Means \u003cbr\u003e\n\u003cp float=\"left\"\u003e\n\u003cimg src=\"images/K-Means Clustering.jpg\" width=\"400\"/\u003e\n\u003c/p\u003e\n- DBSCAN \u003cbr\u003e\n\u003cp float=\"left\"\u003e\n\u003cimg src=\"images/DBSCAN Clustering.jpg\" width=\"400\"/\u003e\n\u003c/p\u003e\n- Agglomerative Clustering \u003cbr\u003e\n\u003cp float=\"left\"\u003e\n\u003cimg src=\"images/Agglomerative Clustering.jpg\" width=\"400\"/\u003e\n\u003c/p\u003e\n- Affinity Propagation \u003cbr\u003e\n\u003cp float=\"left\"\u003e\n\u003cimg src=\"images/Affinity Propagation Clustering.jpg\" width=\"400\"/\u003e\n\u003c/p\u003e\n- Spectral Clustering \u003cbr\u003e\n\u003cp float=\"left\"\u003e\n\u003cimg src=\"images/Spectral Clustering.jpg\" width=\"400\"/\u003e\n\u003c/p\u003e\n- Gaussian Mixture Model \u003cbr\u003e\n\u003cp float=\"left\"\u003e\n\u003cimg src=\"images/Gaussian Mixture Model Clustering.jpg\" width=\"400\"/\u003e\n\u003c/p\u003e\n\n## Installation\nTo run this notebook, you'll need to have Jupyter Notebook and an Anaconda environment set up on your system.\n\n#### 1. Clone the repository \u003cbr\u003e\nOpen your terminal or command prompt and run: \u003cbr\u003e\n```bash\ngit clone https://github.com/bjam24/bank_customer_segmentation_methods.git\ncd bank_customer_segmentation_methods\n```\n#### 2. Create and activate a new Anaconda environment \u003cbr\u003e\n```bash\nconda create --name myenv python=3.8\nconda activate myenv\n```\n#### 3. Install required packages \u003cbr\u003e\n```bash\npip install -r requirements.txt\n```\n#### 4. Launch Jupyter Notebook\n```bash\njupyter notebook\n```\n#### 5. Navigate to the notebook and run it\n\n## Technology stack\n- Python programming language\n- Jupyter Notebook\n\n## Data source\n- Kaggle: Your Machine Learning and Data Science Community https://www.kaggle.com/datasets/arjunbhasin2013/ccdata?resource=download\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjam24%2Fbank_customer_segmentation_methods","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbjam24%2Fbank_customer_segmentation_methods","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbjam24%2Fbank_customer_segmentation_methods/lists"}