{"id":20472959,"url":"https://github.com/adityajn105/loan-prediction","last_synced_at":"2025-03-05T13:52:32.563Z","repository":{"id":141264267,"uuid":"157006423","full_name":"adityajn105/Loan-Prediction","owner":"adityajn105","description":"It is a Data Science competition by Analytics Vidhya. Learn to do preprocessing, hypertuning etc.","archived":false,"fork":false,"pushed_at":"2018-11-10T17:58:57.000Z","size":92,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-16T02:31:32.908Z","etag":null,"topics":["classification","hyperopt","machine-learning","preprocessing","xgboost"],"latest_commit_sha":null,"homepage":"https://datahack.analyticsvidhya.com/contest/practice-problem-loan-prediction-iii/","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/adityajn105.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":"2018-11-10T17:50:37.000Z","updated_at":"2018-11-13T04:38:25.000Z","dependencies_parsed_at":null,"dependency_job_id":"fa920425-2dd2-442f-8aa1-fc694ad5c51e","html_url":"https://github.com/adityajn105/Loan-Prediction","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/adityajn105%2FLoan-Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/adityajn105%2FLoan-Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/adityajn105%2FLoan-Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/adityajn105%2FLoan-Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/adityajn105","download_url":"https://codeload.github.com/adityajn105/Loan-Prediction/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242039688,"owners_count":20061925,"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":["classification","hyperopt","machine-learning","preprocessing","xgboost"],"created_at":"2024-11-15T14:22:49.959Z","updated_at":"2025-03-05T13:52:32.540Z","avatar_url":"https://github.com/adityajn105.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Loan Prediction\n\nCompany wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers. Here they have provided a partial data set.\n\n\nVariable\t|\tDescription\n------------|---------------\nLoan_ID\t|\tUnique Loan ID\nGender\t|\tMale/ Female\nMarried|\tApplicant married (Y/N)\nDependents|\tNumber of dependents\nEducation\t|\tApplicant Education (Graduate/ Under Graduate)\nSelf_Employed\t|\tSelf employed (Y/N)\nApplicantIncome\t|\tApplicant income\nCoapplicantIncome\t|\tCoapplicant income\nLoanAmount|\tLoan amount in thousands\nLoan_Amount_Term\t|\tTerm of loan in months\nCredit_History\t|\tcredit history meets guidelines\nProperty_Area\t|\tUrban/ Semi Urban/ Rural\nLoan_Status\t|\tLoan approved (Y/N)\n\n## Approach 1\n1. Replace Categorical NA values with mode, Non Categorical with mean\n2. Replace values above 95,96 percentile of ApplicantIncome, CoapplicantIncome with 95 and 96 percentile respectively.\n3. Replace NA Credit History with -1\n4. Perform MinMaxScaling on ApplicantIncome, CoApplicantIncome, LoanAmount and Loan Term.\n5. Perform LabelEncoding of all other categorical features\n6. Try LogisticRegression, SVM, RandomForest, XGBoost. \n7. Perform hypertuning with hyperopt on XGBoost\n8. XGBoost is performing with Accuracy = 0.7916666667","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadityajn105%2Floan-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadityajn105%2Floan-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadityajn105%2Floan-prediction/lists"}