{"id":25501762,"url":"https://github.com/singhxtushar/forest-fire-prediction-ridgeregression","last_synced_at":"2025-06-13T14:08:57.781Z","repository":{"id":197197314,"uuid":"698157115","full_name":"SINGHxTUSHAR/Forest-Fire-Prediction-RidgeRegression","owner":"SINGHxTUSHAR","description":"This model predicts the forest fire by using the Ridge-Regression algorithm.","archived":false,"fork":false,"pushed_at":"2023-09-30T05:03:09.000Z","size":484,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-19T04:58:20.804Z","etag":null,"topics":["algerian-forest-fire","flask-api","pickling","ridge-regression","standardscaler"],"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/SINGHxTUSHAR.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}},"created_at":"2023-09-29T09:32:59.000Z","updated_at":"2023-11-19T15:38:16.000Z","dependencies_parsed_at":"2023-10-02T21:50:40.230Z","dependency_job_id":null,"html_url":"https://github.com/SINGHxTUSHAR/Forest-Fire-Prediction-RidgeRegression","commit_stats":null,"previous_names":["singhxtushar/forest_fire_predictor","singhxtushar/forest-fire-prediction-ridgeregression"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SINGHxTUSHAR%2FForest-Fire-Prediction-RidgeRegression","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SINGHxTUSHAR%2FForest-Fire-Prediction-RidgeRegression/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SINGHxTUSHAR%2FForest-Fire-Prediction-RidgeRegression/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SINGHxTUSHAR%2FForest-Fire-Prediction-RidgeRegression/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SINGHxTUSHAR","download_url":"https://codeload.github.com/SINGHxTUSHAR/Forest-Fire-Prediction-RidgeRegression/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239599018,"owners_count":19665911,"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":["algerian-forest-fire","flask-api","pickling","ridge-regression","standardscaler"],"created_at":"2025-02-19T04:58:34.713Z","updated_at":"2025-02-19T04:58:36.786Z","avatar_url":"https://github.com/SINGHxTUSHAR.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# This project is made to predict the Algerian Forest Fires.\n\n## Algerian Forest Fires Dataset \nData Set Information:\n\nThe dataset includes 244 instances that regroup a data of two regions of Algeria,namely the Bejaia region located in the northeast of Algeria and the Sidi Bel-abbes region located in the northwest of Algeria.\n\n122 instances for each region.\n\nThe period from June 2012 to September 2012.\nThe dataset includes 11 attribues and 1 output attribue (class)\nThe 244 instances have been classified into fire(138 classes) and not fire (106 classes) classes.\n\n\n## Attribute Information:\n\n1. Date : (DD/MM/YYYY) Day, month ('june' to 'september'), year (2012)\nWeather data observations\n2. Temp : temperature noon (temperature max) in Celsius degrees: 22 to 42\n3. RH : Relative Humidity in %: 21 to 90\n4. Ws :Wind speed in km/h: 6 to 29\n5. Rain: total day in mm: 0 to 16.8\nFWI Components\n6. Fine Fuel Moisture Code (FFMC) index from the FWI system: 28.6 to 92.5\n7. Duff Moisture Code (DMC) index from the FWI system: 1.1 to 65.9\n8. Drought Code (DC) index from the FWI system: 7 to 220.4\n9. Initial Spread Index (ISI) index from the FWI system: 0 to 18.5\n10. Buildup Index (BUI) index from the FWI system: 1.1 to 68\n11. Fire Weather Index (FWI) Index: 0 to 31.1\n12. Classes: two classes, namely Fire and not Fire\n\n## FLOW OF THE PROJECT :\n - Data collection for the Algerian Forest Fires.\n - EDA on the Algerian Forest Fires Dataset.\n - FE on the Algerian Forest Fires Dataset.\n - Model training by using the Ridge Regression with an accuracy of 98.4%.\n - Web Application Design by using the FLASK.\n - Deployment of the Model on the AWS with Elastic Beanstalk and CodePipeline services.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsinghxtushar%2Fforest-fire-prediction-ridgeregression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsinghxtushar%2Fforest-fire-prediction-ridgeregression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsinghxtushar%2Fforest-fire-prediction-ridgeregression/lists"}