{"id":15131151,"url":"https://github.com/rajull-agrawal/customer_analysis","last_synced_at":"2026-02-12T03:39:14.961Z","repository":{"id":255985884,"uuid":"854034627","full_name":"Rajull-Agrawal/customer_analysis","owner":"Rajull-Agrawal","description":"This project is for data analysis on customer-related datasets using PySpark and tablaeu.","archived":false,"fork":false,"pushed_at":"2024-09-08T09:54:10.000Z","size":9,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-05T20:43:14.700Z","etag":null,"topics":["csv","jupyter-notebook","pyspark","python","tableau"],"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/Rajull-Agrawal.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":"2024-09-08T08:20:27.000Z","updated_at":"2024-09-08T10:23:33.000Z","dependencies_parsed_at":"2024-09-08T10:35:34.367Z","dependency_job_id":"6bab0d19-8b9f-4cf3-9c3c-53d7e0a0f02c","html_url":"https://github.com/Rajull-Agrawal/customer_analysis","commit_stats":null,"previous_names":["rajull-agrawal/customer_analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Rajull-Agrawal/customer_analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rajull-Agrawal%2Fcustomer_analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rajull-Agrawal%2Fcustomer_analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rajull-Agrawal%2Fcustomer_analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rajull-Agrawal%2Fcustomer_analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Rajull-Agrawal","download_url":"https://codeload.github.com/Rajull-Agrawal/customer_analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Rajull-Agrawal%2Fcustomer_analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273868150,"owners_count":25182423,"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":"2025-09-06T02:00:13.247Z","response_time":2576,"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":["csv","jupyter-notebook","pyspark","python","tableau"],"created_at":"2024-09-26T03:24:12.994Z","updated_at":"2026-02-12T03:39:14.894Z","avatar_url":"https://github.com/Rajull-Agrawal.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\r\n# Customer Data Analysis\r\n\r\n## Author: Rajul Agrawal\r\n\r\n### Overview\r\nThis Jupyter notebook performs data analysis on customer-related datasets using PySpark. The analysis involves loading various datasets, cleaning data, transforming date formats, and displaying distinct demographic information.\r\n\r\n### Dataset\r\nThe notebook processes the following CSV datasets:\r\n- **Customer Product**: Contains product details held by customers.\r\n- **Customer Channel Activity**: Captures customer interactions through various channels.\r\n- **Customer Demographics**: Includes demographic details of customers.\r\n- **Customer Transaction History**: Tracks customers' transaction details.\r\n- **Product Lookup**: Provides details about different products.\r\n\r\n### Key Steps\r\n1. **Data Loading**: The datasets are loaded into PySpark DataFrames.\r\n2. **Data Transformation**: \r\n   - A UDF is applied to standardize various date formats across the datasets.\r\n   - The schema of each dataset is printed to understand the structure.\r\n   - Distinct values of demographic fields like `Marital_Status` are analyzed.\r\n3. **Data Export**: Commented-out sections allow exporting the DataFrames to CSV for further analysis.\r\n\r\n### Dependencies\r\n- PySpark\r\n- Pandas\r\n- Datetime\r\n- Pgeocode (for geographic data processing)\r\n\r\n### Tableau Dashboard\r\nThis analysis is complemented by a Tableau dashboard that visualizes key insights from the data. Screenshots of the dashboard can be added here.\r\n\r\n![Dashboard 1](https://github.com/user-attachments/assets/dae892a5-e2c4-4f1a-bfcc-dacb66f3fab7)\r\n![Dashboard 2](https://github.com/user-attachments/assets/3e13cad5-7777-4c96-bb5e-7158c6139d10)\r\n![Dashboard 3](https://github.com/user-attachments/assets/ba2ec6fb-0181-46c1-825d-3b3e01bffe44)\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frajull-agrawal%2Fcustomer_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frajull-agrawal%2Fcustomer_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frajull-agrawal%2Fcustomer_analysis/lists"}