{"id":27121324,"url":"https://github.com/mehak-089/customer-segmentation-insights","last_synced_at":"2026-02-06T20:01:52.550Z","repository":{"id":286130626,"uuid":"960470579","full_name":"Mehak-089/Customer-Segmentation-Insights","owner":"Mehak-089","description":"This project analyzes real-world e-commerce sales data using RFM (Recency, Frequency, Monetary) analysis and K-Means Clustering to segment customers based on purchasing behavior.","archived":false,"fork":false,"pushed_at":"2025-04-04T16:35:44.000Z","size":14182,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-25T09:46:50.064Z","etag":null,"topics":["clustering","customer-segmentation","python","rfm","tableau","tableau-dashboards","tableau-public"],"latest_commit_sha":null,"homepage":"","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/Mehak-089.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":"2025-04-04T13:45:20.000Z","updated_at":"2025-04-04T16:37:43.000Z","dependencies_parsed_at":"2025-04-04T14:44:16.593Z","dependency_job_id":null,"html_url":"https://github.com/Mehak-089/Customer-Segmentation-Insights","commit_stats":null,"previous_names":["mehak-089/customer-segmentation-insights"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Mehak-089/Customer-Segmentation-Insights","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mehak-089%2FCustomer-Segmentation-Insights","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mehak-089%2FCustomer-Segmentation-Insights/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mehak-089%2FCustomer-Segmentation-Insights/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mehak-089%2FCustomer-Segmentation-Insights/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Mehak-089","download_url":"https://codeload.github.com/Mehak-089/Customer-Segmentation-Insights/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Mehak-089%2FCustomer-Segmentation-Insights/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29174325,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-06T19:56:27.068Z","status":"ssl_error","status_checked_at":"2026-02-06T19:56:18.934Z","response_time":59,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["clustering","customer-segmentation","python","rfm","tableau","tableau-dashboards","tableau-public"],"created_at":"2025-04-07T10:58:07.428Z","updated_at":"2026-02-06T20:01:52.527Z","avatar_url":"https://github.com/Mehak-089.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🛍️ Customer Segmentation \u0026 RFM Insights Dashboard (E-Commerce Analytics)\n\n[![Tableau Dashboard](https://img.shields.io/badge/View-Dashboard-blue)](https://public.tableau.com/views/CustomerSegmentationInsightsDashboard_17437831505140/Dashboard1)\n\n## 📌 Overview\n\nThis project analyzes an e-commerce dataset using RFM (Recency, Frequency, Monetary) analysis and K-Means Clustering to segment customers based on purchasing behavior. The final output is an interactive Tableau dashboard that offers business insights for targeting customer groups effectively.\n\n---\n\n## 📊 Dashboard Link\n\n🔗 **[Live Tableau Dashboard](https://public.tableau.com/views/CustomerSegmentationInsightsDashboard_17437831505140/Dashboard1)**  \n\u003e Explore cluster-wise customer behavior, purchasing patterns, and insights.\n\n---\n\n## 💼 Problem Statement\n\nBusinesses often struggle to understand customer behavior and personalize marketing efforts. This project solves this by segmenting customers into meaningful groups based on:\n- How recently they purchased\n- How frequently they purchase\n- How much money they spend\n\n---\n\n## 🧰 Tools \u0026 Technologies\n\n- **Python (Pandas, Sklearn, Matplotlib)**\n- **K-Means Clustering (Unsupervised Learning)**\n- **Tableau Public (Interactive Dashboard)**\n- **Jupyter/VS Code (Data Preprocessing)**\n\n---\n\n## 📁 Dataset\n\n- Source: Provided e-commerce sales data\n- Contains ~500,000 transactions\n- Features: `InvoiceNo`, `StockCode`, `Description`, `Quantity`, `InvoiceDate`, `UnitPrice`, `CustomerID`, `Country`\n\n---\n\n## 🔍 Steps Performed\n\n### 1. 📦 Data Cleaning \u0026 Feature Engineering\n- Removed missing values and duplicates\n- Filtered out canceled orders (negative quantities)\n- Added `TotalPurchaseAmount = Quantity * UnitPrice`\n\n### 2. 📈 RFM Analysis\n- **Recency**: Days since last purchase  \n- **Frequency**: Number of unique transactions  \n- **Monetary**: Total spending by customer\n\n### 3. 🤖 K-Means Clustering\n- Normalized RFM values\n- Used Elbow Method to find optimal number of clusters (K)\n- Labeled customers into clusters (0–3)\n\n### 4. 📊 Tableau Dashboard Design\n- Imported `rfm_clustered_customers.csv` into Tableau\n- Created extract and built visualizations:\n  - Cluster-wise RFM averages\n  - Customer count per cluster\n  - Geographic and behavioral insights\n- Added narrative insights using **Text Box**\n\n---\n\n## 📌 Key Insights\n\n- **Cluster 2**: High-value, loyal customers. Frequent buyers with high monetary value.\n- **Cluster 0**: Potentially valuable but need nurturing.\n- **Cluster 1**: Dormant customers; can be reactivated via targeted campaigns.\n- **Cluster 3**: Low activity; may be low-engagement customers.\n\n---\n\n## 🚀 Future Scope\n\n- Add demographic segmentation (e.g., region, country)\n- Use advanced models (e.g., DBSCAN, Hierarchical Clustering)\n- Deploy dashboard in a business analytics environment (Power BI or Streamlit)\n\n## 🧠 Author\n\n**Mehak Memon**  \n  \n📧 mehakm5555@gmail.com \n\n---\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmehak-089%2Fcustomer-segmentation-insights","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmehak-089%2Fcustomer-segmentation-insights","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmehak-089%2Fcustomer-segmentation-insights/lists"}