{"id":25029191,"url":"https://github.com/okaditya84/ecommerce-transactions","last_synced_at":"2026-05-01T03:37:00.731Z","repository":{"id":274500809,"uuid":"923101706","full_name":"okaditya84/eCommerce-Transactions","owner":"okaditya84","description":"Outlined in this piece is an evaluation of core corporate metrics derived from various data files.","archived":false,"fork":false,"pushed_at":"2025-01-27T18:28:30.000Z","size":2001,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-05T21:05:24.304Z","etag":null,"topics":["analysis","data-science","eda","lookalike","python","segmentation"],"latest_commit_sha":null,"homepage":"","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/okaditya84.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2025-01-27T16:41:40.000Z","updated_at":"2025-01-27T18:28:34.000Z","dependencies_parsed_at":null,"dependency_job_id":"95c2c71c-be18-4a82-a2ff-534c33e04064","html_url":"https://github.com/okaditya84/eCommerce-Transactions","commit_stats":null,"previous_names":["okaditya84/ecommerce-transactions"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/okaditya84%2FeCommerce-Transactions","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/okaditya84%2FeCommerce-Transactions/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/okaditya84%2FeCommerce-Transactions/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/okaditya84%2FeCommerce-Transactions/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/okaditya84","download_url":"https://codeload.github.com/okaditya84/eCommerce-Transactions/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246365638,"owners_count":20765546,"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":["analysis","data-science","eda","lookalike","python","segmentation"],"created_at":"2025-02-05T21:00:24.791Z","updated_at":"2026-05-01T03:36:55.704Z","avatar_url":"https://github.com/okaditya84.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# eCommerce Transaction Analysis\n\nA project demonstrating an end-to-end workflow for exploring data, identifying lookalike customers, and segmenting customers using clustering techniques. Three Jupyter notebooks are included for each task.\n\n---\n\n## Table of Contents\n1. [Overview](#overview)  \n2. [File Structure](#file-structure)  \n3. [Task Descriptions](#task-descriptions)  \n4. [Dependencies](#dependencies)  \n5. [Usage](#usage)  \n6. [License](#license)\n\n---\n\n## Overview\nThis repository analyzes an e-commerce dataset to uncover insights into customer behavior, purchase patterns, and possible marketing strategies.\n\n---\n\n## File Structure\n- **Aditya_Jethani_EDA.ipynb** (Task 1): Exploratory Data Analysis  \n- **Aditya_Jethani_Lookalike.ipynb** (Task 2): Lookalike customer modeling  \n- **Aditya_Jethani_Clustering.ipynb** (Task 3): K-Means clustering for customer segmentation  \n- **Customers.csv, Products.csv, Transactions.csv**: Example input data files  \n- **README.md**: Project documentation  \n- **LICENSE**: MIT License  \n\n---\n\n## Task Descriptions\n\n### Task 1: Exploratory Data Analysis (EDA)\n- Merges multiple datasets to create a complete view of customers, products, and transactions.\n- Examines sales trends, category-wise revenue, and other key metrics.\n- Visualizes daily and monthly revenue, top-selling items, and customer segments.\n\n### Task 2: Lookalike Modeling\n- Computes features (e.g., total spend, frequency) for a set of target customers.\n- Uses cosine similarity to find the closest matches (lookalikes) based on transactional or demographic profiles.\n- Outputs top matches and saves them to a CSV for further use.\n\n### Task 3: Customer Segmentation (K-Means Clustering)\n- Scales relevant customer metrics with `StandardScaler`.\n- Determines the optimal number of clusters using metrics like Silhouette Score and Davies-Bouldin Index.\n- Assigns cluster labels, visualizes the results, and interprets cluster profiles.\n\n---\n\n## Dependencies\nInstall the following Python libraries:\n```bash\npandas\nnumpy\nmatplotlib\nseaborn\nscikit-learn","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fokaditya84%2Fecommerce-transactions","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fokaditya84%2Fecommerce-transactions","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fokaditya84%2Fecommerce-transactions/lists"}