{"id":16629396,"url":"https://github.com/imsanjoykb/data-processing","last_synced_at":"2025-10-30T03:31:39.826Z","repository":{"id":47253682,"uuid":"401760004","full_name":"imsanjoykb/Data-processing","owner":"imsanjoykb","description":"Scrape Job Portal Data","archived":false,"fork":false,"pushed_at":"2021-10-28T17:09:31.000Z","size":833,"stargazers_count":9,"open_issues_count":1,"forks_count":1,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-02-02T06:11:24.413Z","etag":null,"topics":["automation","glassdoor-scraper","indeed-scraping","job-porta","job-search-website","linkedin-scraper","monster","web-crawler","web-scraping"],"latest_commit_sha":null,"homepage":"https://imsanjoykb.github.io/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/imsanjoykb.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}},"created_at":"2021-08-31T15:49:32.000Z","updated_at":"2024-10-07T18:39:47.000Z","dependencies_parsed_at":"2022-09-22T21:40:45.977Z","dependency_job_id":null,"html_url":"https://github.com/imsanjoykb/Data-processing","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/imsanjoykb%2FData-processing","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/imsanjoykb%2FData-processing/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/imsanjoykb%2FData-processing/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/imsanjoykb%2FData-processing/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/imsanjoykb","download_url":"https://codeload.github.com/imsanjoykb/Data-processing/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":238930130,"owners_count":19554122,"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":["automation","glassdoor-scraper","indeed-scraping","job-porta","job-search-website","linkedin-scraper","monster","web-crawler","web-scraping"],"created_at":"2024-10-12T04:41:09.450Z","updated_at":"2025-10-30T03:31:34.382Z","avatar_url":"https://github.com/imsanjoykb.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"###Sanjoy Kumar Biswas\n\n###Scrape Job portal data\n\nStep 1 :\n\nFirst go through the problem statement that which domain data I need to collect and which type of columns and information gather by scrape the site.\n\n\nStep 2 :\n\nFind the bunch of particular URL from where I scrape the information. For the problem statement I will choose indeed.com , monster.com, linkedin.com such type of job portal website.\n\n\nStep 3 :\n\nInspect the web page: \nAs a lot of information store in webpage, I don’t need all the information. On basis of problem statement I inspect the webpage and highlighted the HTML to get particular information form webpage. And findout the data I want to extract.\n\n\nStep 4 : \n\nExtract the data from webpage :\nNow extracting data from those website. For extracting data by web scraping I will choose Python library BeautifulSoup.\n\n\nStep 5 :\n\nAfter scraping data I will do preprocess every dataset Like , \nDate-Time : May be different dataset have different date time format. I will do make a several format .\n\n\nDrop unnecessary columns . Drop null and duplicate rows. Scaling all the dataset . \n\n\nAs every datatsets don not have all the Columns . I will do rescale the columns for all scraping datasets that’s all dataset contain same columns and can merge easily\nLike : indeed.com [German] contain columns are company_name, job_title, city, years of experience, salary [Lets say missing columns- Skills]\n\nMonster.com [German] contain columns are company_name, job_title, city, year_of_experience,skills [Lets say missing columns- Salary]\n\nThis time I will do which columns are common for those two (any number) datasets. \n\nFor job portal scraping I get must having columns are Job_title \u0026 company_name\n\nNow I will make a model data columns after merge which information we need.\n\nModel columns : company_name, job_title, city, year_of_experience, skills,salary\n\nThen I will merge those two dataset by those columns.\n\n\nStep 6 : \n\nFill missing value [skills] for indeed.com:\nFor fill missing rows I will apply machine learning model here. Like we have dataset of monster.com where I get skills columns. I will take this dataset for train the model and for testing I will apply indeed.com dataset. \nFill missing value [salary] for monster.com:\nFor missing columns of salary at monster.com I will use any regression machine learning algorithm to fill those missing rows . And for train dataset I will take indeed.com data as its have salary columns.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimsanjoykb%2Fdata-processing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fimsanjoykb%2Fdata-processing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimsanjoykb%2Fdata-processing/lists"}