{"id":22627749,"url":"https://github.com/annaanastasy/consumer-behavior-clustering","last_synced_at":"2026-04-28T13:34:03.930Z","repository":{"id":263015612,"uuid":"889066713","full_name":"AnnaAnastasy/Consumer-Behavior-Clustering","owner":"AnnaAnastasy","description":"Segmented customer data into clusters using KMeans to uncover actionable insights into consumer behavior for targeted marketing strategies.","archived":false,"fork":false,"pushed_at":"2024-11-22T14:51:34.000Z","size":846,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-07-01T03:03:43.354Z","etag":null,"topics":["cluster-analysis","clustering","data-science","exploratory-data-analysis","kmeans-clustering","machine-learning-algorithms","python","unsupervised-learning"],"latest_commit_sha":null,"homepage":"https://www.kaggle.com/code/annastasy/consumer-behavior-cluster-analysis-kmeans","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AnnaAnastasy.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2024-11-15T14:46:27.000Z","updated_at":"2025-04-27T09:49:00.000Z","dependencies_parsed_at":"2025-02-03T13:41:08.231Z","dependency_job_id":"cbc2745e-e76e-4477-a71c-faa8623fca75","html_url":"https://github.com/AnnaAnastasy/Consumer-Behavior-Clustering","commit_stats":null,"previous_names":["annaanastasy/consumer-behavior-clustering"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/AnnaAnastasy/Consumer-Behavior-Clustering","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnnaAnastasy%2FConsumer-Behavior-Clustering","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnnaAnastasy%2FConsumer-Behavior-Clustering/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnnaAnastasy%2FConsumer-Behavior-Clustering/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnnaAnastasy%2FConsumer-Behavior-Clustering/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AnnaAnastasy","download_url":"https://codeload.github.com/AnnaAnastasy/Consumer-Behavior-Clustering/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnnaAnastasy%2FConsumer-Behavior-Clustering/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":262887159,"owners_count":23379766,"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":["cluster-analysis","clustering","data-science","exploratory-data-analysis","kmeans-clustering","machine-learning-algorithms","python","unsupervised-learning"],"created_at":"2024-12-09T01:16:09.847Z","updated_at":"2026-04-28T13:34:03.856Z","avatar_url":"https://github.com/AnnaAnastasy.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Consumer Behavior Analysis: Uncovering Insights with KMeans Clustering\n\nThis project explores customer segmentation using the KMeans clustering algorithm to identify distinct behavioral patterns, enabling businesses to design targeted marketing and retention strategies.\n\n## Table of Contents\n1. [Project Overview](#project-overview)\n2. [Dataset](#dataset)\n3. [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda)\n4. [Clustering Analysis](#clustering-analysis)\n5. [Insights and Recommendations](#insights-and-recommendations)\n6. [How to Run the Notebook](#how-to-run-the-notebook)\n\n---\n\n## 1. Project Overview\n\nUnderstanding customer behavior is crucial for creating personalized marketing strategies. This project utilizes the KMeans clustering algorithm to segment customers into meaningful groups based on behavioral data.\n\n### Key Objectives:\n- Analyze customer data to identify distinct clusters.\n- Provide actionable insights into customer behavior for improved marketing strategies.\n\n---\n\n## 2. Dataset\n\nThe dataset contains customer data including demographics, purchase history, and other relevant metrics.\n\n### Key Information:\n- **Source:** [Kaggle Dataset](https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis)\n- **Size:** Several columns representing various behavioral and demographic attributes.\n- **Target Analysis:** Unsupervised clustering (no target variable).\n\n---\n\n## 3. Exploratory Data Analysis (EDA)\n\n- Addressed missing values and normalized the data for better clustering results.\n- Explored patterns in features such as spending habits and demographics.\n\n---\n\n## 4. Clustering Analysis\n\n### Methodology:\n- Applied KMeans clustering to segment customers based on their similarities.\n- Determined the optimal number of clusters using the Elbow Method and Silhouette Score.\n\n---\n\n## 5. Insights and Recommendations\n\n### Key Results:\n- Customers were segmented into **three distinct clusters** representing unique behavioral traits.\n- Each cluster provides valuable insights for designing targeted marketing campaigns and retention strategies.\n\n---\n\n## 6. How to Run the Notebook\n\n### Prerequisites\n- Python 3.8 or higher\n- Libraries: `numpy`, `pandas`, `matplotlib`, `seaborn`, `scikit-learn`.\n\n### Setup\n1. Install required libraries:\n   ```bash\n   pip install numpy pandas matplotlib seaborn scikit-learn\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fannaanastasy%2Fconsumer-behavior-clustering","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fannaanastasy%2Fconsumer-behavior-clustering","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fannaanastasy%2Fconsumer-behavior-clustering/lists"}