{"id":14067175,"url":"https://github.com/faizelkhan/Insurance-Claim-Using-Machine-Learning-","last_synced_at":"2025-07-30T00:32:39.832Z","repository":{"id":173386060,"uuid":"94457098","full_name":"faizelkhan/Insurance-Claim-Using-Machine-Learning-","owner":"faizelkhan","description":"Predicting property and casualty insurance claims: A Machine Learning Approach","archived":false,"fork":false,"pushed_at":"2017-06-15T17:14:56.000Z","size":9,"stargazers_count":5,"open_issues_count":0,"forks_count":4,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-12-04T07:36:45.665Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"R","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/faizelkhan.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":"2017-06-15T16:12:16.000Z","updated_at":"2024-11-02T16:44:31.000Z","dependencies_parsed_at":null,"dependency_job_id":"963cfb5c-bad7-4088-9e95-6743ccbd5681","html_url":"https://github.com/faizelkhan/Insurance-Claim-Using-Machine-Learning-","commit_stats":null,"previous_names":["faizelkhan/insurance-claim-using-machine-learning-"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/faizelkhan/Insurance-Claim-Using-Machine-Learning-","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/faizelkhan%2FInsurance-Claim-Using-Machine-Learning-","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/faizelkhan%2FInsurance-Claim-Using-Machine-Learning-/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/faizelkhan%2FInsurance-Claim-Using-Machine-Learning-/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/faizelkhan%2FInsurance-Claim-Using-Machine-Learning-/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/faizelkhan","download_url":"https://codeload.github.com/faizelkhan/Insurance-Claim-Using-Machine-Learning-/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/faizelkhan%2FInsurance-Claim-Using-Machine-Learning-/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267785784,"owners_count":24144121,"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":"2025-07-29T02:00:12.549Z","response_time":2574,"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":[],"created_at":"2024-08-13T07:05:28.134Z","updated_at":"2025-07-30T00:32:39.544Z","avatar_url":"https://github.com/faizelkhan.png","language":"R","funding_links":[],"categories":["R"],"sub_categories":[],"readme":"# Insurance Claim Using Machine Learning\n\n## Predicting property and casualty insurance claims: A Machine Learning Approach\n\n### Abstract:\n\nProperty and Casualty insurance companies often encounter problems in predicting the likelihood of a policyholder causing a claim. \nSome territories have few claim experiences, resulting in very sparse data. In addition, some data are highly dimensional in terms\nof the predictors of the likelihood of a claim. \n\nIn this research, simulated sparse claims data are used to identify the most appropriate predictive model for determining the likelihood\nof a policyholder causing a claim. Machine learning algorithms such as the logit model and the support vector machine will be used to \npredict whether the future policyholder will incur a claim.\n\n\n### Presentation Link:\nhttp://prezi.com/2smclr8un8vu/?utm_campaign=share\u0026utm_medium=copy\u0026rc=ex0share\n\n\n## Data Analysis\n\n### driver.R \nThis is the main file of this project. The algorithm in this file requires some external R libraries, which are already mentioned in the\ncomments in these files. The algorithm also gives us a choice to run either _Support Vector Machine(SVM)_ or _Logistic Regression(LR)_ \nand this can be done by commenting SVM  or LR source file code, respectively.\n\n\n### DataProcessing.R\nThis file is to process the generated data and split it into training and test data.\n \n### DataGen.R\nThis file generates the data of specific number of observations and feature ratios. \n\n### svm_linear.R\nThe file runs the SVM classification on the training data and then test the classifier on the test data to check the accuracy of the\nclassifier for the unknown data.\n\n### logit_reg.R\nThe file runs the LR classification on the training data and then test the classifier on the test data to check the accuracy of the\nclassifier for the unknown data.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffaizelkhan%2FInsurance-Claim-Using-Machine-Learning-","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffaizelkhan%2FInsurance-Claim-Using-Machine-Learning-","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffaizelkhan%2FInsurance-Claim-Using-Machine-Learning-/lists"}