{"id":15132648,"url":"https://github.com/nirmalyabag20/loan-status-prediction-using-machine-learning","last_synced_at":"2026-01-19T12:33:32.734Z","repository":{"id":256897851,"uuid":"856754654","full_name":"nirmalyabag20/Loan-Status-Prediction-using-machine-learning","owner":"nirmalyabag20","description":"This project focuses on predicting the loan status (approved or not approved) based on various applicant details. The goal is to develop a machine learning model that accurately classifies whether a loan should be approved, helping financial institutions make informed lending decisions.","archived":false,"fork":false,"pushed_at":"2024-09-13T06:52:53.000Z","size":97,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-05T21:44:41.581Z","etag":null,"topics":["matplotlib","numpy","pandas","python","scikit-learn","seaborn","support-vector-machine"],"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/nirmalyabag20.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":"2024-09-13T06:28:11.000Z","updated_at":"2024-09-13T07:09:34.000Z","dependencies_parsed_at":"2024-09-13T18:33:36.662Z","dependency_job_id":null,"html_url":"https://github.com/nirmalyabag20/Loan-Status-Prediction-using-machine-learning","commit_stats":null,"previous_names":["nirmalyabag20/loan-status-prediction-using-machine-learning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nirmalyabag20%2FLoan-Status-Prediction-using-machine-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nirmalyabag20%2FLoan-Status-Prediction-using-machine-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nirmalyabag20%2FLoan-Status-Prediction-using-machine-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nirmalyabag20%2FLoan-Status-Prediction-using-machine-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nirmalyabag20","download_url":"https://codeload.github.com/nirmalyabag20/Loan-Status-Prediction-using-machine-learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247406069,"owners_count":20933802,"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":["matplotlib","numpy","pandas","python","scikit-learn","seaborn","support-vector-machine"],"created_at":"2024-09-26T04:22:06.548Z","updated_at":"2026-01-19T12:33:32.728Z","avatar_url":"https://github.com/nirmalyabag20.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Project Workflow~\n\n1.\tData Preprocessing:\n_______________________\n   \n  o\tHandled missing values through appropriate imputation techniques.\n  \n  o\tTransformed categorical variables using Label Encoder.\n  \n  o\tScaled numerical features for better model performance.\n  \n2.\tExploratory Data Analysis:\n_______________________________\n   \n  o\tVisualized the relationships between features and the loan status.\n  \n  o\tAnalyzed distribution patterns of key attributes like income, loan amount, and credit history.\n  \n3.\tModel Building:\n________________________\n   \n  o\tImplemented various machine learning algorithms including:\n  \n  •\t\tLogistic Regression\n\n  •\t\tRandom Forest\n\n  •\t\tSupport Vector Machine (SVM)\n\n  •\t\tKNeighborsClassifier\n\n  o\tPerformed hyperparameter tuning to optimize model performance.\n  \n4.\tModel Evaluation:\n_________________________\n   \n  o\tEvaluated models using accuracy_score\n  \n5.\tFinal Model:\n_________________________\n    \n  o\tSelected the best-performing model based on evaluation metrics.\n  \n  o\tProvided insights on feature importance to understand the key factors influencing loan approval.\n\nResults~\n   \n  •\tThe final model achieved an accuracy of 78%\n  \n  •\tThe most influential features in predicting loan status included credit history, applicant’s income, and loan amount.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnirmalyabag20%2Floan-status-prediction-using-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnirmalyabag20%2Floan-status-prediction-using-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnirmalyabag20%2Floan-status-prediction-using-machine-learning/lists"}