{"id":27206209,"url":"https://github.com/grayoj/comex","last_synced_at":"2025-04-09T23:10:41.998Z","repository":{"id":37007366,"uuid":"481232000","full_name":"grayoj/comex","owner":"grayoj","description":"Linear regression to perform predictive analysis on individual's income.","archived":false,"fork":false,"pushed_at":"2023-01-29T22:16:10.000Z","size":120,"stargazers_count":12,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-09T23:10:37.861Z","etag":null,"topics":["linear-algebra","linear-regression","numpy","pandas","sklearn"],"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/grayoj.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":"2022-04-13T13:40:35.000Z","updated_at":"2023-01-13T19:30:44.000Z","dependencies_parsed_at":"2023-01-17T12:45:39.886Z","dependency_job_id":null,"html_url":"https://github.com/grayoj/comex","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/grayoj%2Fcomex","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/grayoj%2Fcomex/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/grayoj%2Fcomex/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/grayoj%2Fcomex/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/grayoj","download_url":"https://codeload.github.com/grayoj/comex/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248125607,"owners_count":21051770,"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":["linear-algebra","linear-regression","numpy","pandas","sklearn"],"created_at":"2025-04-09T23:10:41.570Z","updated_at":"2025-04-09T23:10:41.989Z","avatar_url":"https://github.com/grayoj.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Linear Regression Model\n***\n\nIn this project (jupyter notebook) we are using a \u003ca href=\"https://scikit-learn.org/\"\u003eSickitLearn\u003c/a\u003e model, to perform linear regression to make income predictions with the dataset made available in the repository.\n\nLinear regression, is a supervised machine learning model which implements a linear relationship between an independent and \ndependent variable. It is a statistical method of making predictive analysis.  We can illustrate linear regression by the below formula:\n\n\u003ci\u003e$y=a$\u003csub\u003e$0$\u003c/sub\u003e$+a$\u003csub\u003e$1$\u003c/sub\u003e$x+ ε$\u003c/i\u003e\n\nWhere\n- \u003ci\u003e$Y$\u003c/i\u003e represents  = The Dependent Variable (Target Variable)\n- $X$ represents = The Independent Variable (Predictor Variable)\n- $a0$ represents = The intercept of the line (Gives an additional degree of freedom)\n- $a1$ represents = The Linear regression coefficient (Scale factor for each input value)\n- $ε$ represents = The Random error\n\n## Preparing Data For Linear Regression\n***\n\n1. Linear Assumption. \n2. Eliminate Noise (Carry out data cleaning)\n3. Eliminate Collinearity.\n4. Rescale the provided inputs.\n\n## Requirements\n\nThese are the tools, environment variables and libraries used in the project.\n\n1. Jupyter Notebook.\n2. Python 3+\n3. SickitLearn.\n4. Pandas.\n5. Numpy.\n6. Data Set (Already Provided)\n\n## Installation\n\nTo use this project, you need to clone the repository.\n\n``\u003e git clone https://github.com/grayoj/income-prediction.git``\n\nAfter you finish cloning, install these modules using Pip\n\nInstall Sklearn\n\n``\u003e pip install sklearn``\n\nInstall Pandas to read datasets (CSV)\n\n``\u003e pip install pandas``\n\nInstall NumPy\n\n``\u003e pip install numpy``\n\nEnsure you have Jupyter Notebook installed, which you could easily set up if you use VsCode\n\n### Try out the notebook!\n\n\u003cimg src=\"LinearRegression.png\"\u003e\n\nAny suggestions, feedback and help, as well as improvements are all welcome and will be appreciated.\nContact!\n\u003ca href=\"mailto:mgeraldoj07@gmail.com\"\u003eMail me, here.\u003c/a\u003e\n\n- MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgrayoj%2Fcomex","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgrayoj%2Fcomex","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgrayoj%2Fcomex/lists"}