{"id":28322915,"url":"https://github.com/yaswanth1702/anomaly-detection-dashboard","last_synced_at":"2026-02-16T19:41:11.110Z","repository":{"id":293747024,"uuid":"985008237","full_name":"Yaswanth1702/Anomaly-Detection-Dashboard","owner":"Yaswanth1702","description":null,"archived":false,"fork":false,"pushed_at":"2025-05-16T22:51:10.000Z","size":2293,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-01T22:49:57.656Z","etag":null,"topics":["business-intelligence","data-analytics","powerbi","sql"],"latest_commit_sha":null,"homepage":"","language":"TSQL","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/Yaswanth1702.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-05-16T22:34:13.000Z","updated_at":"2025-05-16T23:12:36.000Z","dependencies_parsed_at":"2025-05-16T23:36:51.729Z","dependency_job_id":null,"html_url":"https://github.com/Yaswanth1702/Anomaly-Detection-Dashboard","commit_stats":null,"previous_names":["yaswanth1702/yaswanth1702-anomaly-detection-dashboard"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Yaswanth1702/Anomaly-Detection-Dashboard","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Yaswanth1702%2FAnomaly-Detection-Dashboard","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Yaswanth1702%2FAnomaly-Detection-Dashboard/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Yaswanth1702%2FAnomaly-Detection-Dashboard/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Yaswanth1702%2FAnomaly-Detection-Dashboard/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Yaswanth1702","download_url":"https://codeload.github.com/Yaswanth1702/Anomaly-Detection-Dashboard/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Yaswanth1702%2FAnomaly-Detection-Dashboard/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":276678733,"owners_count":25684802,"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","status":"online","status_checked_at":"2025-09-23T02:00:09.130Z","response_time":73,"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":["business-intelligence","data-analytics","powerbi","sql"],"created_at":"2025-05-25T14:11:59.245Z","updated_at":"2025-09-24T01:28:10.882Z","avatar_url":"https://github.com/Yaswanth1702.png","language":"TSQL","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Anomaly Detection Dashboard\n\nThe **Anomaly Detection Dashboard** is an interactive web application built with Plotly Dash to enable real-time anomaly detection on time-series or tabular datasets. This project provides users with the ability to upload their own CSV data, preprocess it automatically, apply multiple machine learning-based anomaly detection algorithms, and visualize the detected anomalies through dynamic, intuitive graphs.\n\n## Features Implemented\n\n* **Dashboard Interface with Plotly Dash:**\n  Developed a responsive and user-friendly dashboard UI for data upload, algorithm selection, and visualization.\n\n* **Multiple Anomaly Detection Models:**\n  Implemented three popular unsupervised anomaly detection algorithms from Scikit-learn:\n\n  * Isolation Forest\n  * One-Class SVM\n  * Local Outlier Factor\n\n* **Interactive Visualization:**\n  Created line and scatter plots that dynamically highlight detected anomalies for easy interpretation.\n\n* **Data Upload and Preprocessing:**\n  Allowed users to upload custom CSV files. Built automated preprocessing including handling missing values and feature scaling to prepare the data for modeling.\n\n* **Result Export:**\n  Added functionality to download anomaly detection results for further offline analysis.\n\n\n## Technology Stack Used\n\n* **Python** for backend logic and machine learning.\n* **Plotly Dash** for building the interactive dashboard UI.\n* **Scikit-learn** for implementing anomaly detection algorithms.\n* **Pandas \u0026 NumPy** for data handling and preprocessing.\n* **Joblib** for model serialization and efficient loading.\n\n\n## Usage Instructions\n\n* Upload your time-series or tabular CSV dataset via the dashboard interface.\n* Choose one of the implemented anomaly detection algorithms.\n* View the interactive plots highlighting detected anomalies.\n* Download the processed results if needed.\n\n\n## Data Handling\n\n* Supported CSV data with time-series or tabular format.\n* Automatically preprocesses data by imputing missing values and scaling features to optimize model performance.\n\n\n## Future Work (Planned Enhancements)\n\n* Integrate performance metrics like precision, recall, and F1-score.\n* Add time-series anomaly trend visualizations.\n* Support real-time streaming data.\n* Implement alerting and notification features.\n\n\n## Project Structure (Key Components)\n\n* `app.py`: Main dashboard application script built with Dash.\n* `detect_anomalies.py`: Implementation of anomaly detection algorithms.\n* `preprocess.py`: Data preprocessing functions including missing data handling and scaling.\n* `requirements.txt`: List of dependencies.\n* Sample datasets and models organized under dedicated folders.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyaswanth1702%2Fanomaly-detection-dashboard","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyaswanth1702%2Fanomaly-detection-dashboard","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyaswanth1702%2Fanomaly-detection-dashboard/lists"}