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November 23, 2024

excel work Homework 5 need to be worked. Provide me the excel file with calculation. Week5 load approve is the file need to add a new sheet and work.? Question. Using data in e

computer science

excel work

Homework 5 need to be worked. Provide me the excel file with calculation. Week5 load approve is the file need to add a new sheet and work. 

Question. Using data in excel sheet “data” in the attached Excel file “Week5-Loan_Approval”. Banks have to assess risk before approving loans by looking at many factors to deciding whether or not the applicant’s profile is relevant for granting with loan. 

Using Logistic Regression model, predict whether the candidate’s profile is relevant or not using key features like Gender, Married, Dependents, Education, Self_Employed, ApplicantIncome, CoapplicantIncome, LoanAmount, Loan_Amount_Term, Credit_History, and Property_Area. 

Based on the features below, predict the applicant’s approval acceptance: 

You can use Loan_Status as dependent variable and features as independent variables. Convert all textual categorical variables to numeric, and clean data if necessary. 

Gender: Male

Married: Yes

Dependents: 2

Education: Graduate 

Self_Employed: No 

ApplicantIncome: 5000 

CoapplicantIncome: 1000 

LoanAmount: 800 

Loan_Amount_Term: 240 

Credit_History: 1 

Property_Area: Urban

  • attachment

    Homework5.doc

  • attachment

    Week5-Loan_Approval.xlsx

  • attachment

    Week5-Real-Statistics-Examples-Regression-1-Linear2.xlsx

  • attachment

    Week5-LogisticRegression.pdf

data

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Please read the speech and write in 350-400 words what you understood from this lecture. ? More details are in the document.CHEM_402_project_Fall_2024.pdf Week 5 assignment: Complete the following assignment in ‘one MS Word document’:? Textbook: Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support Dursun

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Loan_ID Gender Married Dependents Education Self_Employed ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History Property_Area Loan_Status
LP001002 Male No 0 Graduate No 5849 0 360 1 Urban Y
LP001003 Male Yes 1 Graduate No 4583 1508 128 360 1 Rural N
LP001005 Male Yes 0 Graduate Yes 3000 0 66 360 1 Urban Y
LP001006 Male Yes 0 Not Graduate No 2583 2358 120 360 1 Urban Y
LP001008 Male No 0 Graduate No 6000 0 141 360 1 Urban Y
LP001011 Male Yes 2 Graduate Yes 5417 4196 267 360 1 Urban Y
LP001013 Male Yes 0 Not Graduate No 2333 1516 95 360 1 Urban Y
LP001014 Male Yes 3 Graduate No 3036 2504 158 360 0 Semiurban N
LP001018 Male Yes 2 Graduate No 4006 1526 168 360 1 Urban Y
LP001020 Male Yes 1 Graduate No 12841 10968 349 360 1 Semiurban N
LP001024 Male Yes 2 Graduate No 3200 700 70 360 1 Urban Y
LP001027 Male Yes 2 Graduate Yes 2500 1840 109 360 1 Urban Y
LP001028 Male Yes 2 Graduate No 3073 8106 200 360 1 Urban Y
LP001029 Male No 0 Graduate No 1853 2840 114 360 1 Rural N
LP001030 Male Yes 2 Graduate No 1299 1086 17 120 1 Urban Y
LP001032 Male No 0 Graduate No 4950 0 125 360 1 Urban Y
LP001034 Male No 1 Not Graduate No 3596 0 100 240 Urban Y
LP001036 Female No 0 Graduate No 3510 0 76 360 0 Urban N
LP001038 Male Yes 0 Not Graduate No 4887 0 133 360 1 Rural N
LP001041 Male Yes 0 Graduate Yes 2600 3500 115 1 Urban Y
LP001043 Male Yes 0 Not Graduate No 7660 0 104 360 0 Urban N
LP001046 Male Yes 1 Graduate No 5955 5625 315 360 1 Urban Y
LP001047 Male Yes 0 Not Graduate No 2600 1911 116 360 0 Semiurban N
LP001052 Male Yes 1 Graduate Yes 3717 2925 151 360 Semiurban N
LP001066 Male Yes 0 Graduate Yes 9560 0 191 360 1 Semiurban Y
LP001068 Male Yes 0 Graduate No 2799 2253 122 360 1 Semiurban Y
LP001073 Male Yes 2 Not Graduate No 4226 1040 110 360 1 Urban Y
LP001086 Male No 0 Not Graduate No 1442 0 35 360 1 Urban N
LP001087 Female No 2 Graduate Yes 3750 2083 120 360 1 Semiurban Y
LP001091 Male Yes 1 Graduate Yes 4166 3369 201 360 Urban N
LP001095 Male No 0 Graduate No 3167 0 74 360 1 Urban N
LP001097 Male No 1 Graduate Yes 4692 0 106 360 1 Rural N
LP001098 Male Yes 0 Graduate No 3500 1667 114 360 1 Semiurban Y
LP001100 Male No 3 Graduate No 12500 3000 320 360 1 Rural N
LP001106 Male Yes 0 Graduate No 2275 2067 360 1 Urban Y
LP001109 Male Yes 0 Graduate No 1828 1330 100 0 Urban N
LP001112 Female Yes 0 Graduate No 3667 1459 144 360 1 Semiurban Y
LP001114 Male No 0 Graduate No 4166 7210 184 360 1 Urban Y
LP001116 Male No 0 Not Graduate No 3748 1668 110 360 1 Semiurban Y
LP001119 Male No 0 Graduate No 3600 0 80 360 1 Urban N
LP001120 Male No 0 Graduate No 1800 1213 47 360 1 Urban Y
LP001123 Male Yes 0 Graduate No 2400 0 75 360 Urban Y
LP001131 Male Yes 0 Graduate No 3941 2336 134 360 1 Semiurban Y
LP001136 Male Yes 0 Not Graduate Yes 4695 0 96 1 Urban Y
LP001137 Female No 0 Graduate No 3410 0 88 1 Urban Y
LP001138 Male Yes 1 Graduate No 5649 0 44 360 1 Urban Y
LP001144 Male Yes 0 Graduate No 5821 0 144 360 1 Urban Y
LP001146 Female Yes 0 Graduate No 2645 3440 120 360 0 Urban N
LP001151 Female No 0 Graduate No 4000 2275 144 360 1 Semiurban Y
LP001155 Female Yes 0 Not Graduate No 1928 1644 100 360 1 Semiurban Y
LP001157 Female No 0 Graduate No 3086 0 120 360 1 Semiurban Y
LP001164 Female No 0 Graduate No 4230 0 112 360 1 Semiurban N
LP001179 Male Yes 2 Graduate No 4616 0 134 360 1 Urban N
LP001186 Female Yes 1 Graduate Yes 11500 0 286 360 0 Urban N
LP001194 Male Yes 2 Graduate No 2708 1167 97 360 1 Semiurban Y
LP001195 Male Yes 0 Graduate No 2132 1591 96 360 1 Semiurban Y
LP001197 Male Yes 0 Graduate No 3366 2200 135 360 1 Rural N
LP001198 Male Yes 1 Graduate No 8080 2250 180 360