{"id":19553884,"url":"https://github.com/blankscreen-exe/linear-regression","last_synced_at":"2026-06-15T15:32:06.306Z","repository":{"id":109083056,"uuid":"296694058","full_name":"Blankscreen-exe/Linear-Regression","owner":"Blankscreen-exe","description":"Attempt to re-construct a linear regression algorithm with python.","archived":false,"fork":false,"pushed_at":"2021-10-15T19:41:42.000Z","size":88,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-02-26T07:32:51.117Z","etag":null,"topics":["matplotlib","python3","regression-algorithms"],"latest_commit_sha":null,"homepage":"","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/Blankscreen-exe.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":"2020-09-18T17:58:35.000Z","updated_at":"2021-12-14T00:28:24.000Z","dependencies_parsed_at":"2023-05-31T22:48:31.291Z","dependency_job_id":null,"html_url":"https://github.com/Blankscreen-exe/Linear-Regression","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Blankscreen-exe/Linear-Regression","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blankscreen-exe%2FLinear-Regression","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blankscreen-exe%2FLinear-Regression/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blankscreen-exe%2FLinear-Regression/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blankscreen-exe%2FLinear-Regression/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Blankscreen-exe","download_url":"https://codeload.github.com/Blankscreen-exe/Linear-Regression/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Blankscreen-exe%2FLinear-Regression/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34369840,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-15T02:00:07.085Z","response_time":63,"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":["matplotlib","python3","regression-algorithms"],"created_at":"2024-11-11T04:25:05.513Z","updated_at":"2026-06-15T15:32:06.284Z","avatar_url":"https://github.com/Blankscreen-exe.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\r\nCoded by:\r\n![# BLANK_SCREEN.exe](bs.png)\r\n      \r\n-------------------------------\r\n## AIM/Objective\r\n\r\nThis program aims to take relevant inputs in order to plot a Linear-regression using a matplotlib graph.\r\nThis program also misbehaves on wrong inputs.\r\nLike if you would enter alphabets instead of numbers when required\r\nThis program also shuts down itself on multiple instances if too many wrong inputs are provided.\r\nGood luck Having fun with this program!\r\n\r\n## Program Specs\r\nThis program is writen in Python 3.8.6\r\nDate finished: 5 Oct 2020\r\nModlues Imported:\r\n1. matplotlib\r\n2. time\r\n\r\nNOTE: Yeah! Those are the only ready made stuff I've used. Everything else is my doing.\r\nNo machine learning is used.\r\n\r\n## Basic algorithm followed by the program\r\n\r\n1. Input will be taken for observation range\r\n2. List will be made for all \"x\" inputs\r\n3. List will be made for all \"y\" inputs\r\n4. User will be presented with the final table of both \"x\" and \"y\" values (original plots)\r\n5. User will be presented with the final table of both \"x\" and \"y hat\" values (regression plots)\r\n6. Some calculations will be done and user will be presented with a Graph\\\r\n7. User will need to take a screen shot of the grapht if they like and close the Graph window to proceed further.\r\n8. User will be asked if they want to see additional information\r\n9. If yes, A menu will appear from which the user can select any inference they like.\r\n10. After user is done with observation, exiting seqence will follow.\r\n11. User will be humiliated if they have a high error count \r\n\r\n## Instructions to run\r\nDownload these things:\r\n1. python 3.8.6 from official site\r\n2. matplotlib library using pip\r\n3. pyinstaller for making .exe file\r\n\r\n## MODULE SPECS\r\n### Calculation.py   \r\n\tSxy(x,y)\t\t\r\n\tSxx(x)\t\t\t\r\n\ta1(Sxx,Sxy)\t\t--slope\r\n\ta0(x,y)\t\t--Y-intercept\r\n\ty_Hat(x,y)\t\t--regression plots for Y\r\n\taxb(lst1,lst2)\t\t--multiplies corresponding values of both list params\r\n\tlist_sum(lst)\t\t--summation of  alist\r\n\tlist_square(lst)\t--square of a list\r\n\tlist_mean(var)\t\t--mean of a list\r\n\t\r\n\tEXTRA FUNCTIONS:\r\n\tR(x,y)\t\t\t--Correlation coefficient\t\r\n\tSSE(x,y)\t\t--summation of squared error\r\n\tSSR(x,y)  \t\t--summation of squared regression\r\n\tSST(y)\t\t\t--summation of squared total\r\n### Tools.py   \r\n\tverify(inpt,typ=\"int\")\t--verifies input and return True or False\r\n\tnumOobs()\t\t--takes number of observations. Specific scenario only\r\n\tDTpt(Obs_num,varName)\t--makes a list of datapoints\r\n\tgraphCreds()\t\t--sets graph credentials. Specific scenario only\r\n\tMenuSelection()\t--gives a menu selection algorithm. Specific scenario only\r\n\tTotalErr_Tools()\t--calculates total error from the Tools module. Specific scenario only\r\n### Graphmaker.py   \r\n\tdaPlot(x,y,TITLE,XLABEL,YLABEL,TEXT)\t--plots graph with prespecified conditions\r\n\tOriginalplots(Bind)\t--displays original plots\r\n\tRegressionplots(Bind)\t--displays regression plots\r\n\tCoffR(X,Y)\t\t--displays Correlation coefficient\r\n\tCoffR2(X,Y)\t\t--displays Coefficient of determination\r\n\tSumSqErr(X,Y)\t\t--displays Error Analysis\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblankscreen-exe%2Flinear-regression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fblankscreen-exe%2Flinear-regression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fblankscreen-exe%2Flinear-regression/lists"}