{"id":22549031,"url":"https://github.com/zenitsu272/fault-detection-ml","last_synced_at":"2026-05-05T21:37:47.992Z","repository":{"id":266833586,"uuid":"899497612","full_name":"Zenitsu272/Fault-detection-ML","owner":"Zenitsu272","description":"Machine Learning based Fault Detection in machines  using sensor data","archived":false,"fork":false,"pushed_at":"2024-12-15T05:16:13.000Z","size":1640,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-02T10:13:35.011Z","etag":null,"topics":["artificial-intelligence","decsion-tree","machine-learning","pandas","pandas-dataframe","pandas-python","scikit-learn"],"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/Zenitsu272.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":"2024-12-06T11:44:40.000Z","updated_at":"2025-01-18T02:29:16.000Z","dependencies_parsed_at":null,"dependency_job_id":"af896e49-448f-4854-ac53-b5e04f935d86","html_url":"https://github.com/Zenitsu272/Fault-detection-ML","commit_stats":null,"previous_names":["zenitsu272/fault-detection-ml"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Zenitsu272%2FFault-detection-ML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Zenitsu272%2FFault-detection-ML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Zenitsu272%2FFault-detection-ML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Zenitsu272%2FFault-detection-ML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Zenitsu272","download_url":"https://codeload.github.com/Zenitsu272/Fault-detection-ML/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246000748,"owners_count":20707783,"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":["artificial-intelligence","decsion-tree","machine-learning","pandas","pandas-dataframe","pandas-python","scikit-learn"],"created_at":"2024-12-07T16:07:37.399Z","updated_at":"2026-05-05T21:37:47.933Z","avatar_url":"https://github.com/Zenitsu272.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Fault-detection-ML\nSo here, i tried to create a machine learning model that uses the random forest algorithm to predict the chances of a machine becoming faulty using sensory data.\nBeing a beginner to ML concepts, this basic project classifies whether a Machine would get faulty very soon or the machine is safe (no fault will be there in the near future.\nThe output mainly consisted of 2 possibilities [0,1]\n1 being the machine would become faulty very soon or the chances of this machine being faulty is high\n0 being the machine is safe in the near future or chances of this machine being faulty is low\nKnowing that the possibilities of the output is only 2, I used the Desicion tree algorithm to evaluate this dataset.BUT, it turned out that my training set was overfitting and my testset accuracy was not great.\nConsidering all this, I used Random forest algorithm and evaluated this dataset.\nThis comes under supervised learning thus the dataset is splitted into 2 (I/O). \nAlso there is seperate allocation of the data for training and testing in the ratio of 7:3.\nAlso this model gave me an accuracy of more than 90% in almost all the cycles.\nSince this is just the beginning of me making projects, help me and correct me if there's something to be done.\nDataset link from kaggle:https://www.kaggle.com/datasets/umerrtx/machine-failure-prediction-using-sensor-data\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzenitsu272%2Ffault-detection-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzenitsu272%2Ffault-detection-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzenitsu272%2Ffault-detection-ml/lists"}