· 5 years ago · Jan 19, 2021, 05:48 AM
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I dont have any intention for Plagiarism I just like to be clear myself. <br><br>\n\n\n <a src=\"https://en.wikipedia.org/wiki/Lending_Club\"> Lending Club Information </a>\n\n\n\n-------------------\n\nThe central idea and coding is abstract from Kevin mark ham youtube video series, Introduction to machine learning with scikit-learn video series. You can find link under resources section. \n\n\nData Description\n----------------\n\n- LoanStatNew\tDescription\n\n- addr_state\tThe state provided by the borrower in the loan application\n\n- annual_inc\tThe self-reported annual income provided by the borrower during registration.\n\n- annual_inc_joint\tThe combined self-reported annual income provided by the co-borrowers during registration\n\n- application_type\tIndicates whether the loan is an individual application or a joint application with two co-borrowers\n\n- collection_recovery_fee\tpost charge off collection fee\n\n- collections_12_mths_ex_med\tNumber of collections in 12 months excluding medical collections\n\n- delinq_2yrs\tThe number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years\n\n- desc\tLoan description provided by the borrower\n\n- dti\tA ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, - - - excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.\n\n- dti_joint\tA ratio calculated using the co-borrowers' total monthly payments on the total debt obligations, - excluding mortgages and the requested LC loan, divided by the co-borrowers' combined self-reported monthly income\n\n- earliest_cr_line\tThe month the borrower's earliest reported credit line was opened\n\n- emp_length\tEmployment length in years. Possible values are between 0 and 10 where 0 means less than one year \n\n- and 10 means ten or more years. \n\n- emp_title\tThe job title supplied by the Borrower when applying for the loan.*\n\n- fico_range_high\tThe upper boundary range the borrower’s FICO at loan origination belongs to.\n\n- fico_range_low\tThe lower boundary range the borrower’s FICO at loan origination belongs to.\n\n- funded_amnt\tThe total amount committed to that loan at that point in time.\n\n- funded_amnt_inv\tThe total amount committed by investors for that loan at that point in time.\n\n- grade\tLC assigned loan grade\n\n- home_ownership\tThe home ownership status provided by the borrower during registration. 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lending bank based in the ireland, in which bank provide funds for potential borrowers and bank earn a profit depending on the risk they take (the borrowers credit score). Irish Fake bank provides loan to their loyal customers. The complete data set is borrowed from Lending Club For more basic information about the company please check out the wikipedia article about the company. This dataset is copied and clean from kaggle but it has been changed. The any kind of similarity is just for learning purposes. I dont have any intention for Plagiarism I just like to be clear myself. \u003cbr\u003e\u003cbr\u003e\n\n\n \u003ca src=\u0022https://en.wikipedia.org/wiki/Lending_Club\u0022\u003e Lending Club Information \u003c/a\u003e\n\n\n\n-------------------\n\nThe central idea and coding is abstract from Kevin mark ham youtube video series, Introduction to machine learning with scikit-learn video series. You can find link under resources section. \n\n\nData Description\n----------------\n\n- LoanStatNew\tDescription\n\n- addr_state\tThe state provided by the borrower in the loan application\n\n- annual_inc\tThe self-reported annual income provided by the borrower during registration.\n\n- annual_inc_joint\tThe combined self-reported annual income provided by the co-borrowers during registration\n\n- application_type\tIndicates whether the loan is an individual application or a joint application with two co-borrowers\n\n- collection_recovery_fee\tpost charge off collection fee\n\n- collections_12_mths_ex_med\tNumber of collections in 12 months excluding medical collections\n\n- delinq_2yrs\tThe number of 30+ days past-due incidences of delinquency in the borrower\u0027s credit file for the past 2 years\n\n- desc\tLoan description provided by the borrower\n\n- dti\tA ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, - - - excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.\n\n- dti_joint\tA ratio calculated using the co-borrowers\u0027 total monthly payments on the total debt obligations, - excluding mortgages and the requested LC loan, divided by the co-borrowers\u0027 combined self-reported monthly income\n\n- earliest_cr_line\tThe month the borrower\u0027s earliest reported credit line was opened\n\n- emp_length\tEmployment length in years. Possible values are between 0 and 10 where 0 means less than one year \n\n- and 10 means ten or more years. \n\n- emp_title\tThe job title supplied by the Borrower when applying for the loan.*\n\n- fico_range_high\tThe upper boundary range the borrower’s FICO at loan origination belongs to.\n\n- fico_range_low\tThe lower boundary range the borrower’s FICO at loan origination belongs to.\n\n- funded_amnt\tThe total amount committed to that loan at that point in time.\n\n- funded_amnt_inv\tThe total amount committed by investors for that loan at that point in time.\n\n- grade\tLC assigned loan grade\n\n- home_ownership\tThe home ownership status provided by the borrower during registration. Our values are: RENT, OWN, MORTGAGE, OTHER.\n\n\n\n","actions":[],"flags":[],"title":"Loan Data for Dummy Bank ","slug":"loan-data-for-dummy-bank","licenseId":4,"data":{"id":68263,"databundleId":43270,"databundleVersionType":"fileset","databundleDiffType":"versioned","versionNumber":1,"zipBlobFileId":null,"totalCompressedSize":29512416,"totalSize":159331005,"ownerInfo":{"databundleVersionId":68263,"dataset":{"datasetId":40887,"datasetVersionId":65806,"dataviewToken":"eyJhbGciOiJkaXIiLCJlbmMiOiJBMTI4Q0JDLUhTMjU2In0..2Ljq9C6fTmt1xcfb7riIgg.KpKUFzThOKszL-VaIcwV3kvToX_tTJnlSpLVLV_zfB1Zg-8sB6UYPUluX39hAu7ey-RKAF1-ytUlMAWk0B1W1BGdbKqO-_PWAB_HF8ND6hUlXNuJlyj1NVpmTH5M2VLU3fhzslJJI-h4CZ8BvTgWN5Fhh9T4IWxTWLN6cuGHJBhktaUun6Cb85NGa8NiqrSDHe7hcPgK5ZY3brzvXYqWWgQZBHKR9gm1Q_D5YPf3ONuXA4r97hrScLKkuj_97T96bmPDjC8O7MrbxSEmPDIB5fH3ZMIazuijCvzWrtuM8OSz1UflYKn4HjX9QeGJr23XxUSie-Y6NizZpzpn2k0khMwpICdctognUmj9d3PpL9Z6-aRwOQbN1yuHoFk5MKUX1Gg2lP1MRvqxff4KGbkRemAle9uhhspCyR4X-f55YP58KIyE7vxDoBS1T_JzIZLsnGZRoGujJDJVAJGTTMahxw.tlIL2yk-DFdyeVZekpQ8yQ","scope":"mrferozi/loan-data-for-dummy-bank"},"competition":null,"kernel":null,"previewsDisabled":false},"usesArchiveV2":true,"hasBundleDownload":true,"type":"databundleVersion","collapsed":false,"info":{"metrics":null,"archiveInfo":null,"archiveInfoV2":null,"blobFileInfo":null,"convertCsvInfo":null,"kernelReference":null},"settings":{"csvSettings":null,"bigQuerySettings":null,"bigQueryMirrorSettings":null,"storageSettings":{"webDataLocation":{"bucket":"kaggle-data-sets","path":"40887/65806/upload"},"sdsDataLocation":{"bucket":"kagglesdsdata","path":"datasets/40887/65806"},"responseDataLocation":null},"remoteUrlSettings":null,"remoteGithubSettings":null},"render":null,"totalChildren":0,"children":[],"firestorePath":"CChGLxHs5R6Nzf1xbiBj/versions/FqVd8Xur3SNY99JoL9dL","firestoreKey":"FqVd8Xur3SNY99JoL9dL","name":"Loan Data for Dummy Bank ","description":"Company Information:\n--------------------------\n\nThe data set is based upon \u003ca src=\u0022https://www.kaggle.com/prateikmahendra/loan-data\u0022\u003e Lending Club Information \u003c/a\u003e. \n- TheIrish Dummy Banks is a peer to peer lending bank based in the ireland, in which bank provide funds for potential borrowers and bank earn a profit depending on the risk they take (the borrowers credit score). Irish Fake bank provides loan to their loyal customers. The complete data set is borrowed from Lending Club For more basic information about the company please check out the wikipedia article about the company. This dataset is copied and clean from kaggle but it has been changed. The any kind of similarity is just for learning purposes. I dont have any intention for Plagiarism I just like to be clear myself. \u003cbr\u003e\u003cbr\u003e\n\n\n \u003ca src=\u0022https://en.wikipedia.org/wiki/Lending_Club\u0022\u003e Lending Club Information \u003c/a\u003e\n\n\n\n-------------------\n\nThe central idea and coding is abstract from Kevin mark ham youtube video series, Introduction to machine learning with scikit-learn video series. You can find link under resources section. \n\n\nData Description\n----------------\n\n- LoanStatNew\tDescription\n\n- addr_state\tThe state provided by the borrower in the loan application\n\n- annual_inc\tThe self-reported annual income provided by the borrower during registration.\n\n- annual_inc_joint\tThe combined self-reported annual income provided by the co-borrowers during registration\n\n- application_type\tIndicates whether the loan is an individual application or a joint application with two co-borrowers\n\n- collection_recovery_fee\tpost charge off collection fee\n\n- collections_12_mths_ex_med\tNumber of collections in 12 months excluding medical collections\n\n- delinq_2yrs\tThe number of 30+ days past-due incidences of delinquency in the borrower\u0027s credit file for the past 2 years\n\n- desc\tLoan description provided by the borrower\n\n- dti\tA ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, - - - excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.\n\n- dti_joint\tA ratio calculated using the co-borrowers\u0027 total monthly payments on the total debt obligations, - excluding mortgages and the requested LC loan, divided by the co-borrowers\u0027 combined self-reported monthly income\n\n- earliest_cr_line\tThe month the borrower\u0027s earliest reported credit line was opened\n\n- emp_length\tEmployment length in years. 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Our values are: RENT, OWN, MORTGAGE, OTHER.\n\n\n\n"},"isPrivate":false,"owner":{"id":758380,"imageUrl":"https://storage.googleapis.com/kaggle-avatars/thumbnails/758380-gp.jpg","isOrganization":false,"name":"MuhammadNadeemFerozi","profileUrl":"/mrferozi","slug":"mrferozi","maxFileSizeBytes":null,"userTier":1,"organizationMemberCount":null}});performance && performance.mark && performance.mark("DatasetContainer.componentCouldBootstrap");</script><script nonce="TKeesMlr01aa1FlaUccfAw==" type="text/x-mathjax-config">
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