{"id":32862144,"url":"https://github.com/blladerunner/customer-churn-dashboard","last_synced_at":"2026-05-08T03:35:22.313Z","repository":{"id":321465939,"uuid":"1085879099","full_name":"BlladeRunner/customer-churn-dashboard","owner":"BlladeRunner","description":"Customer Churn Dashboard — SQL + Python analytics project exploring customer retention patterns, churn rate by demographics and services, and key insights for telecom business strategy.","archived":false,"fork":false,"pushed_at":"2025-10-29T18:46:43.000Z","size":4,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-10-29T20:50:12.425Z","etag":null,"topics":["business-intelligence","churn-analysis","customer-retention","dashboard","data-analysis","data-analytics","data-science","pandas","powerbi","python","sql","sqlite","telecom"],"latest_commit_sha":null,"homepage":"","language":"Python","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/BlladeRunner.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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-10-29T16:27:41.000Z","updated_at":"2025-10-29T18:46:46.000Z","dependencies_parsed_at":"2025-10-29T21:03:35.383Z","dependency_job_id":null,"html_url":"https://github.com/BlladeRunner/customer-churn-dashboard","commit_stats":null,"previous_names":["blladerunner/customer-churn-dashboard"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/BlladeRunner/customer-churn-dashboard","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BlladeRunner%2Fcustomer-churn-dashboard","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BlladeRunner%2Fcustomer-churn-dashboard/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BlladeRunner%2Fcustomer-churn-dashboard/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BlladeRunner%2Fcustomer-churn-dashboard/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BlladeRunner","download_url":"https://codeload.github.com/BlladeRunner/customer-churn-dashboard/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BlladeRunner%2Fcustomer-churn-dashboard/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":283424651,"owners_count":26833720,"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","status":"online","status_checked_at":"2025-11-08T02:00:06.281Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["business-intelligence","churn-analysis","customer-retention","dashboard","data-analysis","data-analytics","data-science","pandas","powerbi","python","sql","sqlite","telecom"],"created_at":"2025-11-08T22:01:27.979Z","updated_at":"2025-11-08T22:02:18.223Z","avatar_url":"https://github.com/BlladeRunner.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📊 Customer Churn Dashboard (SQL + Python)\n\n## 📘 Project Overview\n\nThis project analyzes customer churn patterns in a telecom company using **SQL (SQLite)** and **Python (pandas)**.  \nThe goal is to identify retention trends, churn drivers, and customer behavior based on demographic and service usage data.\n\n---\n\n## 🧱 Database Schema\n\n**Table:** `customers` (7,043 rows)\n\n| Column            | Type    | Description                          |\n| ----------------- | ------- | ------------------------------------ |\n| customerID        | TEXT    | Unique customer identifier           |\n| gender            | TEXT    | Male / Female                        |\n| senior_citizen    | INTEGER | 1 = Yes, 0 = No                      |\n| partner           | INTEGER | 1 = Has partner                      |\n| dependents        | INTEGER | 1 = Has dependents                   |\n| tenure            | INTEGER | Months as a customer                 |\n| phone_service     | INTEGER | 1 = Active phone service             |\n| multiple_lines    | TEXT    | Yes/No/No phone service              |\n| internet_service  | TEXT    | DSL / Fiber optic / None             |\n| online_security   | TEXT    | Yes/No/No internet service           |\n| online_backup     | TEXT    | Yes/No/No internet service           |\n| device_protection | TEXT    | Yes/No/No internet service           |\n| tech_support      | TEXT    | Yes/No/No internet service           |\n| streaming_tv      | TEXT    | Yes/No/No internet service           |\n| streaming_movies  | TEXT    | Yes/No/No internet service           |\n| contract          | TEXT    | Month-to-month / One year / Two year |\n| paperless_billing | INTEGER | 1 = Yes                              |\n| payment_method    | TEXT    | Payment method                       |\n| monthly_charges   | REAL    | Monthly payment                      |\n| total_charges     | REAL    | Total paid amount                    |\n| churn             | INTEGER | 1 = Customer left, 0 = Active        |\n\n---\n\n## ⚙️ Tools \u0026 Technologies\n\n- **Python 3.12+** → Data preprocessing (pandas, sqlite3)\n- **SQLite 3.50+** → Database management and SQL analysis\n- **VS Code + SQLTools** → Interactive SQL queries\n- _(Optional)_ Power BI or Tableau → Dashboard visualization\n\n---\n\n## 🧮 SQL Highlights\n\n- Customer churn distribution by demographics and contract type\n- Monthly revenue and churn segmentation\n- Average tenure and billing behavior by churn status\n- Correlation between internet service and churn\n- Churn by payment method and contract type\n\nExample:\n\n```sql\nSELECT\n  contract,\n  ROUND(AVG(monthly_charges), 2) AS avg_monthly,\n  ROUND(SUM(churn)*100.0/COUNT(*), 2) AS churn_rate\nFROM customers\nGROUP BY contract\nORDER BY churn_rate DESC;\n```\n\n📊 Key Insights\n\nShort-term contracts (month-to-month) show the highest churn rate.\nPaperless billing and electronic payments correlate with higher churn — likely due to low commitment.\nSenior citizens and fiber-optic internet users tend to churn more often.\nCustomers with online security and tech support are less likely to churn.\nLonger tenure (\u003e24 months) strongly correlates with customer retention.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblladerunner%2Fcustomer-churn-dashboard","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fblladerunner%2Fcustomer-churn-dashboard","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblladerunner%2Fcustomer-churn-dashboard/lists"}