Build any classification model in python using the giving data, split the training(80%) and testing data(20%)

Feature (X) is column value of Gender, Married, Dependents, Education, Self_Employed, ApplicantIncome, CoapplicantIncome, LoanAmount, Loan_Amount_Term, Credit_History, Property_Area

Label(y) is the Loan_Status: Y or N.

1. You need to have histogram plots to show the value before and after data cleaning.

2. You should provide the result of

A) confusion matrix

B) accuracy

C) precision

D) recall

E) F1 score in your testing data

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,,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,,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 LP001050,,Yes,2,Not Graduate,No,3365,1917,112,360,0,Rural,N LP001052,Male,Yes,1,Graduate,,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,,3750,2083,120,360,1,Semiurban,Y LP001091,Male,Yes,1,Graduate,,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