{"id":23296640,"url":"https://github.com/ankit1598/av-jantahack-customer-segmentation","last_synced_at":"2025-04-06T19:50:24.944Z","repository":{"id":112175443,"uuid":"284533416","full_name":"Ankit1598/AV-Jantahack-Customer-Segmentation","owner":"Ankit1598","description":"Rank 89 Solution for Analytics Vidhya Jantahack Customer Segmentation","archived":false,"fork":false,"pushed_at":"2020-12-27T05:26:32.000Z","size":830,"stargazers_count":0,"open_issues_count":1,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-02-13T01:51:14.662Z","etag":null,"topics":["analytics-vidhya","analytics-vidhya-competition","jantahack","lgbmclassifier","prediction","problem-statement"],"latest_commit_sha":null,"homepage":"https://datahack.analyticsvidhya.com/contest/janatahack-customer-segmentation/#ProblemStatement","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/Ankit1598.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":"2020-08-02T20:10:04.000Z","updated_at":"2020-12-27T05:26:34.000Z","dependencies_parsed_at":"2023-05-11T02:15:47.362Z","dependency_job_id":null,"html_url":"https://github.com/Ankit1598/AV-Jantahack-Customer-Segmentation","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ankit1598%2FAV-Jantahack-Customer-Segmentation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ankit1598%2FAV-Jantahack-Customer-Segmentation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ankit1598%2FAV-Jantahack-Customer-Segmentation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ankit1598%2FAV-Jantahack-Customer-Segmentation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Ankit1598","download_url":"https://codeload.github.com/Ankit1598/AV-Jantahack-Customer-Segmentation/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247543611,"owners_count":20955865,"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":["analytics-vidhya","analytics-vidhya-competition","jantahack","lgbmclassifier","prediction","problem-statement"],"created_at":"2024-12-20T07:13:01.501Z","updated_at":"2025-04-06T19:50:24.915Z","avatar_url":"https://github.com/Ankit1598.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Analytics Vidhya Jantahack Customer Segmentation\n\nPublic Leaderboard Rank: 86\n \nPrivate Leaderboard Rank: 89\n\n## Problem Statement\n\u003cimg src=\"assets/Problem_statement.jpg\"\n     alt=\"Markdown Monster icon\"\n     style=\"float: left; margin-right: 10px;\" /\u003e\n\n## Data Description at a Glance:\n\u003cimg src=\"assets/Data_description.jpg\"\n     alt=\"Markdown Monster icon\"\n     style=\"float: left; margin-right: 10px;\" /\u003e\n\n## Data after Merging and Feature Engineering:\n\u003cimg src=\"assets/Data1.jpg\"\n     alt=\"Markdown Monster icon\"\n     style=\"float: left; margin-right: 10px;\" /\u003e\n\u003cimg src=\"assets/Data2.jpg\"\n     alt=\"Markdown Monster icon\"\n     style=\"float: left; margin-right: 10px;\" /\u003e\n\n## Description of Approach/Feature Engineering:\n1. Merged train dataset and test dataset to form a merged dataset\n2. Label Encoded the categorical columns\n3. Added dummy columns\n4. Split the merged dataset to train and test dataset\n5. Applied LGBMClassifier with the previously tested parameters and fitted it\n6. Created the final.csv with the prediction of the LGBMClassifier model\n\n## Tools used\n1. Python\n2. pandas and numpy libraries for data manipulation\n3. LGBMClassifier for prediction\n\n## Score\nThe score obtained using this solution is **0.9352380952**\n\n## Competition Result\n[Rank](https://datahack.analyticsvidhya.com/contest/janatahack-healthcare-analytics/#LeaderBoard): 86th on public LB and 89th on private LB\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fankit1598%2Fav-jantahack-customer-segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fankit1598%2Fav-jantahack-customer-segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fankit1598%2Fav-jantahack-customer-segmentation/lists"}