{"id":27399609,"url":"https://github.com/krithikahs/ml_visualizer","last_synced_at":"2026-05-09T09:05:38.004Z","repository":{"id":287352550,"uuid":"964448052","full_name":"KrithikaHS/ML_Visualizer","owner":"KrithikaHS","description":"Interactive Machine Learning Playground An interactive Streamlit-based tool to train, visualize, and interpret machine learning models in real time. ","archived":false,"fork":false,"pushed_at":"2025-04-11T08:42:22.000Z","size":4,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-15T02:40:27.172Z","etag":null,"topics":["matplotlib","ml","regression","sklearn","streamlit"],"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/KrithikaHS.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,"zenodo":null}},"created_at":"2025-04-11T08:23:38.000Z","updated_at":"2025-04-11T08:42:26.000Z","dependencies_parsed_at":"2025-04-11T10:47:13.748Z","dependency_job_id":"e19938b7-481a-4836-95a6-6be363342431","html_url":"https://github.com/KrithikaHS/ML_Visualizer","commit_stats":null,"previous_names":["krithikahs/ml_visualizer"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/KrithikaHS/ML_Visualizer","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KrithikaHS%2FML_Visualizer","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KrithikaHS%2FML_Visualizer/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KrithikaHS%2FML_Visualizer/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KrithikaHS%2FML_Visualizer/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/KrithikaHS","download_url":"https://codeload.github.com/KrithikaHS/ML_Visualizer/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KrithikaHS%2FML_Visualizer/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263400142,"owners_count":23460828,"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":["matplotlib","ml","regression","sklearn","streamlit"],"created_at":"2025-04-14T03:19:57.928Z","updated_at":"2026-05-09T09:05:37.908Z","avatar_url":"https://github.com/KrithikaHS.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Interactive ML Model Visualizer\n\nThis project is a **Streamlit-based interactive machine learning visualizer** that allows users to explore different regression models with custom datasets. It's designed for learning, experimentation, and demonstration of how ML models behave with various inputs.\n\n---\n\n## Features\n\n- **Interactive Data Input**\n  - Add (x, y) points manually\n  - Upload CSV file with 'x' and 'y' columns\n- **Regression Models Supported**\n  - Linear Regression\n  - Polynomial Regression\n  - Decision Tree Regression\n  - Random Forest Regression\n  - Support Vector Regression (SVR)\n  - K-Nearest Neighbors (KNN) Regression\n- **Auto Train/Test Splitting**\n  - Adjustable train/test ratio\n- **Evaluation Metrics**\n  - Mean Squared Error (MSE)\n  - R² Score\n- **Visual Output**\n  - Scatter plot of training and test data\n  - Model prediction curve\n  - Residual plot (to analyze error distribution)\n\n---\n\n## Technologies Used\n\n- **Python**\n- **Streamlit** for the frontend web interface\n- **scikit-learn** for ML models\n- **Matplotlib** for plotting\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkrithikahs%2Fml_visualizer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkrithikahs%2Fml_visualizer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkrithikahs%2Fml_visualizer/lists"}