{"id":20380653,"url":"https://github.com/victorowinoke/data-science-challenge--dap-eldohub","last_synced_at":"2026-04-11T09:02:45.362Z","repository":{"id":178721361,"uuid":"321802365","full_name":"VictorOwinoKe/Data-Science-Challenge--DAP-EldoHub","owner":"VictorOwinoKe","description":"This is my solution for the EldoHub DAP data science challenge ","archived":false,"fork":false,"pushed_at":"2020-12-16T06:46:35.000Z","size":187,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-04T22:27:32.517Z","etag":null,"topics":["challenges-solved","data-science","data-structures","interview-questions","python"],"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/VictorOwinoKe.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":"2020-12-15T22:15:51.000Z","updated_at":"2024-01-03T19:05:30.000Z","dependencies_parsed_at":null,"dependency_job_id":"f2d45265-125e-4301-9cc2-a7df0d1d3259","html_url":"https://github.com/VictorOwinoKe/Data-Science-Challenge--DAP-EldoHub","commit_stats":null,"previous_names":["victorowinoke/data-science-challenge--dap-eldohub"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/VictorOwinoKe/Data-Science-Challenge--DAP-EldoHub","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VictorOwinoKe%2FData-Science-Challenge--DAP-EldoHub","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VictorOwinoKe%2FData-Science-Challenge--DAP-EldoHub/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VictorOwinoKe%2FData-Science-Challenge--DAP-EldoHub/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VictorOwinoKe%2FData-Science-Challenge--DAP-EldoHub/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/VictorOwinoKe","download_url":"https://codeload.github.com/VictorOwinoKe/Data-Science-Challenge--DAP-EldoHub/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/VictorOwinoKe%2FData-Science-Challenge--DAP-EldoHub/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31674624,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-11T08:18:19.405Z","status":"ssl_error","status_checked_at":"2026-04-11T08:17:08.892Z","response_time":54,"last_error":"SSL_read: 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":["challenges-solved","data-science","data-structures","interview-questions","python"],"created_at":"2024-11-15T02:08:18.487Z","updated_at":"2026-04-11T09:02:45.333Z","avatar_url":"https://github.com/VictorOwinoKe.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"### Data-Science-Challenge--DAP-EldoHub\nThis is my solution for the EldoHub DAP data science challenge \n\n## The Challenge\n\nConsider a dataset providing information on the functionality of infrastructure resources, for each water point it includes the name of the village it is in and its functional state. Implement a data processing module in python which takes a dataset URL as input and returns:\n -\tThe number of water points that are functional,\n - The number of water points per community,\n - The rank for each community by the percentage of broken water points.\n \n @Dataset URL\n \n Data: https://raw.githubusercontent.com/onaio/ona-tech/master/data/water_points.json\n \n ### MY SOLUTION\n \n # Introduction\n \n As a Data Scientist and AI programmer, you do most of the works on the JSON data. You usually fetch the JSON data from a particular URL and visualizes it. But traversing into a JSON data is always a challenging task especially for beginners.\n \n The full-form of JSON is JavaScript Object Notation. It means that a script (executable) file which is made of text in a programming language, is used to store and transfer the data. Python supports JSON through a built-in package called json. To use this feature, we import the json package in Python script. The text in JSON is done through quoted string which contains value in key-value mapping within . It is similar to the dictionary in Python. JSON shows an API similar to users of Standard Library marshal and pickle modules and Python natively supports JSON features.\n \n -Initially, I use pandas library which provides an inbuilt function (pd.read_json()) to read the json file\n -Then convert the json file into pandas data frame using the function DataFrame (At this point am ready to use my dataset to analysis)\n - The  water point dataset that contains many columns, the relevant ones are:\n         -communities villages \n         -water functioning\n         \n  # 1. Number of Functional Water Point\n  \n  ![4](https://github.com/Owinoh-victor/Data-Science-Challenge--DAP-EldoHub/blob/main/screenshots/4.JPG)\n  functioning           590\n  **************************\n  \n  broken                 40\n  \n  abandoned              36\n  \n  newly_constructed      28\n  \n  under_construction     16\n  \n  na_dn                   2\n\nNB: 590 Water point are functioning\n\n# 2. Number of Water Points per Community\n\n![1](https://github.com/Owinoh-victor/Data-Science-Challenge--DAP-EldoHub/blob/main/screenshots/1.JPG)\n\n![2](https://github.com/Owinoh-victor/Data-Science-Challenge--DAP-EldoHub/blob/main/screenshots/2.JPG)\n                      \n                      \n  # 3. Community Ranking     \n  \n\n![3](https://github.com/Owinoh-victor/Data-Science-Challenge--DAP-EldoHub/blob/main/screenshots/3.JPG)\n\n# 4. Conclusion\n\nHigh percentage of the water points are functional and few are brocken.\n\nA good number are either abandoned or under construction or newly_constructed\n\nGoogle Collab  Notebook:  https://colab.research.google.com/drive/1R47uK41R-oqj7dSemaw1baOHSfgov8jT?usp=sharing\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvictorowinoke%2Fdata-science-challenge--dap-eldohub","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvictorowinoke%2Fdata-science-challenge--dap-eldohub","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvictorowinoke%2Fdata-science-challenge--dap-eldohub/lists"}