{"id":18075626,"url":"https://github.com/ngangawairimu/linear-regression-","last_synced_at":"2026-04-17T07:31:46.906Z","repository":{"id":260038961,"uuid":"880116194","full_name":"ngangawairimu/Linear-Regression-","owner":"ngangawairimu","description":"This project builds a linear regression model in Python to predict outcomes and derive insights from feature data. It covers data cleaning, feature analysis, and model evaluation, showcasing predictive modeling techniques using scikit-learn, pandas, and visualization libraries.","archived":false,"fork":false,"pushed_at":"2024-10-29T06:28:07.000Z","size":153,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-11T17:57:10.547Z","etag":null,"topics":["data-analysis","linear-regression","machine-learning","predictive-modeling","python","scikit-learn"],"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/ngangawairimu.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":"2024-10-29T06:26:04.000Z","updated_at":"2024-10-29T06:34:23.000Z","dependencies_parsed_at":"2024-10-29T07:35:04.458Z","dependency_job_id":null,"html_url":"https://github.com/ngangawairimu/Linear-Regression-","commit_stats":null,"previous_names":["ngangawairimu/linear-regression-"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ngangawairimu%2FLinear-Regression-","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ngangawairimu%2FLinear-Regression-/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ngangawairimu%2FLinear-Regression-/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ngangawairimu%2FLinear-Regression-/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ngangawairimu","download_url":"https://codeload.github.com/ngangawairimu/Linear-Regression-/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247393542,"owners_count":20931809,"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":["data-analysis","linear-regression","machine-learning","predictive-modeling","python","scikit-learn"],"created_at":"2024-10-31T11:06:40.561Z","updated_at":"2026-04-17T07:31:46.873Z","avatar_url":"https://github.com/ngangawairimu.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Project Overview\nThis project demonstrates the use of linear regression to predict target variables from structured data, focusing on identifying key factors that drive the predictions. The notebook provides a step-by-step approach, from data preparation to model evaluation, and emphasizes practical outcomes for data-driven decision-making.\n\n### Key Outcomes\nPredictive Insights: The linear regression model identifies and quantifies relationships between features and the target variable, enabling informed predictions and actionable insights.\nPerformance Metrics: Model performance is evaluated using key metrics such as:\nR-squared: For goodness-of-fit, measuring variance explained by the model.\nMean Absolute Error (MAE) and Mean Squared Error (MSE): To assess prediction accuracy.\nFeature Impact: Analysis of feature coefficients highlights the most influential variables, guiding focus on important predictors for further optimization or intervention\n\n### Technologies Used\nProgramming Language: Python \nLibraries:\npandas and numpy: Data handling and manipulation.\nmatplotlib and seaborn: Visualization of data trends and feature relationships.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fngangawairimu%2Flinear-regression-","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fngangawairimu%2Flinear-regression-","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fngangawairimu%2Flinear-regression-/lists"}