{"id":23069819,"url":"https://github.com/patilni3/loan_approval","last_synced_at":"2025-06-24T21:33:37.481Z","repository":{"id":249495345,"uuid":"570954075","full_name":"PatilNi3/LOAN_APPROVAL","owner":"PatilNi3","description":"Loan Approval using Random Forest Algorithm","archived":false,"fork":false,"pushed_at":"2022-11-26T17:41:13.000Z","size":46,"stargazers_count":5,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-30T05:09:44.961Z","etag":null,"topics":["decision-tree","loan-approval","machine-learning-algorithms","matplotlib","pandas","random-forest","seaborn","sklearn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/PatilNi3.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":"2022-11-26T17:11:46.000Z","updated_at":"2025-03-08T21:10:23.000Z","dependencies_parsed_at":"2024-07-21T11:04:35.316Z","dependency_job_id":null,"html_url":"https://github.com/PatilNi3/LOAN_APPROVAL","commit_stats":null,"previous_names":["patilni3/loan_approval"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/PatilNi3/LOAN_APPROVAL","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PatilNi3%2FLOAN_APPROVAL","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PatilNi3%2FLOAN_APPROVAL/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PatilNi3%2FLOAN_APPROVAL/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PatilNi3%2FLOAN_APPROVAL/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PatilNi3","download_url":"https://codeload.github.com/PatilNi3/LOAN_APPROVAL/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PatilNi3%2FLOAN_APPROVAL/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261759469,"owners_count":23205555,"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":["decision-tree","loan-approval","machine-learning-algorithms","matplotlib","pandas","random-forest","seaborn","sklearn"],"created_at":"2024-12-16T06:17:30.223Z","updated_at":"2025-06-24T21:33:37.434Z","avatar_url":"https://github.com/PatilNi3.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# LOAN_APPROVAL\nLoan Approval using Random Forest Algorithm\n\n## About Project:\nIn this project we will check whether customer is applicable for loan or not. First it checks that if the our customer has a good credit history or not and based on that, it classifies the customer into two groups, Again it checks that the income of the customer and again classifies into two groups. And Finally, it checks the loan amount requested by the customer. Based on the outcomes from these three features, the decision tree and random forest decides if the customer’s loan should be approved or not.\n\n## Libraries Used:\n1. Pandas\n2. Matplotlib\n3. Seaborn\n4. SKlearn\n\n## Algorithms Used:\n1. Decision Tree\n2. Random Forest\n\n## Dataset  Used:\n• loan_data.csv (https://drive.google.com/file/d/157J3bT1torNBJaO9iwt_yKhdphPEZDTE/view)\n\n## Accuracy:\n1. Accuracy: 84 \n2. P5recision: 84 %\n3. Recall: 100 %\n4. F1-Score: 91 %\n\n# Thank You.☻\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpatilni3%2Floan_approval","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpatilni3%2Floan_approval","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpatilni3%2Floan_approval/lists"}