From 038e7ad05859d81250e4833b1f3554003520a372 Mon Sep 17 00:00:00 2001
From: Seersha Samikshya <106761122+Seersha9802@users.noreply.github.com>
Date: Sat, 25 May 2024 12:11:12 +0530
Subject: [PATCH 1/3] Create xyz

---
 Loan Status Prediction/xyz | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 Loan Status Prediction/xyz

diff --git a/Loan Status Prediction/xyz b/Loan Status Prediction/xyz
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/Loan Status Prediction/xyz	
@@ -0,0 +1 @@
+

From fd303e2741f03fb149b272be4e4458330578cb24 Mon Sep 17 00:00:00 2001
From: Seersha Samikshya <106761122+Seersha9802@users.noreply.github.com>
Date: Sat, 25 May 2024 12:17:49 +0530
Subject: [PATCH 2/3] Add files via upload

---
 Loan Status Prediction/Loan Status.csv        |  615 ++++
 .../Loan_Status_prediction.ipynb              | 2587 +++++++++++++++++
 2 files changed, 3202 insertions(+)
 create mode 100644 Loan Status Prediction/Loan Status.csv
 create mode 100644 Loan Status Prediction/Loan_Status_prediction.ipynb

diff --git a/Loan Status Prediction/Loan Status.csv b/Loan Status Prediction/Loan Status.csv
new file mode 100644
index 00000000..5dce6651
--- /dev/null
+++ b/Loan Status Prediction/Loan Status.csv	
@@ -0,0 +1,615 @@
+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 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,8080,2250,180,360,1,Urban,Y
+LP001199,Male,Yes,2,Not Graduate,No,3357,2859,144,360,1,Urban,Y
+LP001205,Male,Yes,0,Graduate,No,2500,3796,120,360,1,Urban,Y
+LP001206,Male,Yes,3+,Graduate,No,3029,0,99,360,1,Urban,Y
+LP001207,Male,Yes,0,Not Graduate,Yes,2609,3449,165,180,0,Rural,N
+LP001213,Male,Yes,1,Graduate,No,4945,0,,360,0,Rural,N
+LP001222,Female,No,0,Graduate,No,4166,0,116,360,0,Semiurban,N
+LP001225,Male,Yes,0,Graduate,No,5726,4595,258,360,1,Semiurban,N
+LP001228,Male,No,0,Not Graduate,No,3200,2254,126,180,0,Urban,N
+LP001233,Male,Yes,1,Graduate,No,10750,0,312,360,1,Urban,Y
+LP001238,Male,Yes,3+,Not Graduate,Yes,7100,0,125,60,1,Urban,Y
+LP001241,Female,No,0,Graduate,No,4300,0,136,360,0,Semiurban,N
+LP001243,Male,Yes,0,Graduate,No,3208,3066,172,360,1,Urban,Y
+LP001245,Male,Yes,2,Not Graduate,Yes,1875,1875,97,360,1,Semiurban,Y
+LP001248,Male,No,0,Graduate,No,3500,0,81,300,1,Semiurban,Y
+LP001250,Male,Yes,3+,Not Graduate,No,4755,0,95,,0,Semiurban,N
+LP001253,Male,Yes,3+,Graduate,Yes,5266,1774,187,360,1,Semiurban,Y
+LP001255,Male,No,0,Graduate,No,3750,0,113,480,1,Urban,N
+LP001256,Male,No,0,Graduate,No,3750,4750,176,360,1,Urban,N
+LP001259,Male,Yes,1,Graduate,Yes,1000,3022,110,360,1,Urban,N
+LP001263,Male,Yes,3+,Graduate,No,3167,4000,180,300,0,Semiurban,N
+LP001264,Male,Yes,3+,Not Graduate,Yes,3333,2166,130,360,,Semiurban,Y
+LP001265,Female,No,0,Graduate,No,3846,0,111,360,1,Semiurban,Y
+LP001266,Male,Yes,1,Graduate,Yes,2395,0,,360,1,Semiurban,Y
+LP001267,Female,Yes,2,Graduate,No,1378,1881,167,360,1,Urban,N
+LP001273,Male,Yes,0,Graduate,No,6000,2250,265,360,,Semiurban,N
+LP001275,Male,Yes,1,Graduate,No,3988,0,50,240,1,Urban,Y
+LP001279,Male,No,0,Graduate,No,2366,2531,136,360,1,Semiurban,Y
+LP001280,Male,Yes,2,Not Graduate,No,3333,2000,99,360,,Semiurban,Y
+LP001282,Male,Yes,0,Graduate,No,2500,2118,104,360,1,Semiurban,Y
+LP001289,Male,No,0,Graduate,No,8566,0,210,360,1,Urban,Y
+LP001310,Male,Yes,0,Graduate,No,5695,4167,175,360,1,Semiurban,Y
+LP001316,Male,Yes,0,Graduate,No,2958,2900,131,360,1,Semiurban,Y
+LP001318,Male,Yes,2,Graduate,No,6250,5654,188,180,1,Semiurban,Y
+LP001319,Male,Yes,2,Not Graduate,No,3273,1820,81,360,1,Urban,Y
+LP001322,Male,No,0,Graduate,No,4133,0,122,360,1,Semiurban,Y
+LP001325,Male,No,0,Not Graduate,No,3620,0,25,120,1,Semiurban,Y
+LP001326,Male,No,0,Graduate,,6782,0,,360,,Urban,N
+LP001327,Female,Yes,0,Graduate,No,2484,2302,137,360,1,Semiurban,Y
+LP001333,Male,Yes,0,Graduate,No,1977,997,50,360,1,Semiurban,Y
+LP001334,Male,Yes,0,Not Graduate,No,4188,0,115,180,1,Semiurban,Y
+LP001343,Male,Yes,0,Graduate,No,1759,3541,131,360,1,Semiurban,Y
+LP001345,Male,Yes,2,Not Graduate,No,4288,3263,133,180,1,Urban,Y
+LP001349,Male,No,0,Graduate,No,4843,3806,151,360,1,Semiurban,Y
+LP001350,Male,Yes,,Graduate,No,13650,0,,360,1,Urban,Y
+LP001356,Male,Yes,0,Graduate,No,4652,3583,,360,1,Semiurban,Y
+LP001357,Male,,,Graduate,No,3816,754,160,360,1,Urban,Y
+LP001367,Male,Yes,1,Graduate,No,3052,1030,100,360,1,Urban,Y
+LP001369,Male,Yes,2,Graduate,No,11417,1126,225,360,1,Urban,Y
+LP001370,Male,No,0,Not Graduate,,7333,0,120,360,1,Rural,N
+LP001379,Male,Yes,2,Graduate,No,3800,3600,216,360,0,Urban,N
+LP001384,Male,Yes,3+,Not Graduate,No,2071,754,94,480,1,Semiurban,Y
+LP001385,Male,No,0,Graduate,No,5316,0,136,360,1,Urban,Y
+LP001387,Female,Yes,0,Graduate,,2929,2333,139,360,1,Semiurban,Y
+LP001391,Male,Yes,0,Not Graduate,No,3572,4114,152,,0,Rural,N
+LP001392,Female,No,1,Graduate,Yes,7451,0,,360,1,Semiurban,Y
+LP001398,Male,No,0,Graduate,,5050,0,118,360,1,Semiurban,Y
+LP001401,Male,Yes,1,Graduate,No,14583,0,185,180,1,Rural,Y
+LP001404,Female,Yes,0,Graduate,No,3167,2283,154,360,1,Semiurban,Y
+LP001405,Male,Yes,1,Graduate,No,2214,1398,85,360,,Urban,Y
+LP001421,Male,Yes,0,Graduate,No,5568,2142,175,360,1,Rural,N
+LP001422,Female,No,0,Graduate,No,10408,0,259,360,1,Urban,Y
+LP001426,Male,Yes,,Graduate,No,5667,2667,180,360,1,Rural,Y
+LP001430,Female,No,0,Graduate,No,4166,0,44,360,1,Semiurban,Y
+LP001431,Female,No,0,Graduate,No,2137,8980,137,360,0,Semiurban,Y
+LP001432,Male,Yes,2,Graduate,No,2957,0,81,360,1,Semiurban,Y
+LP001439,Male,Yes,0,Not Graduate,No,4300,2014,194,360,1,Rural,Y
+LP001443,Female,No,0,Graduate,No,3692,0,93,360,,Rural,Y
+LP001448,,Yes,3+,Graduate,No,23803,0,370,360,1,Rural,Y
+LP001449,Male,No,0,Graduate,No,3865,1640,,360,1,Rural,Y
+LP001451,Male,Yes,1,Graduate,Yes,10513,3850,160,180,0,Urban,N
+LP001465,Male,Yes,0,Graduate,No,6080,2569,182,360,,Rural,N
+LP001469,Male,No,0,Graduate,Yes,20166,0,650,480,,Urban,Y
+LP001473,Male,No,0,Graduate,No,2014,1929,74,360,1,Urban,Y
+LP001478,Male,No,0,Graduate,No,2718,0,70,360,1,Semiurban,Y
+LP001482,Male,Yes,0,Graduate,Yes,3459,0,25,120,1,Semiurban,Y
+LP001487,Male,No,0,Graduate,No,4895,0,102,360,1,Semiurban,Y
+LP001488,Male,Yes,3+,Graduate,No,4000,7750,290,360,1,Semiurban,N
+LP001489,Female,Yes,0,Graduate,No,4583,0,84,360,1,Rural,N
+LP001491,Male,Yes,2,Graduate,Yes,3316,3500,88,360,1,Urban,Y
+LP001492,Male,No,0,Graduate,No,14999,0,242,360,0,Semiurban,N
+LP001493,Male,Yes,2,Not Graduate,No,4200,1430,129,360,1,Rural,N
+LP001497,Male,Yes,2,Graduate,No,5042,2083,185,360,1,Rural,N
+LP001498,Male,No,0,Graduate,No,5417,0,168,360,1,Urban,Y
+LP001504,Male,No,0,Graduate,Yes,6950,0,175,180,1,Semiurban,Y
+LP001507,Male,Yes,0,Graduate,No,2698,2034,122,360,1,Semiurban,Y
+LP001508,Male,Yes,2,Graduate,No,11757,0,187,180,1,Urban,Y
+LP001514,Female,Yes,0,Graduate,No,2330,4486,100,360,1,Semiurban,Y
+LP001516,Female,Yes,2,Graduate,No,14866,0,70,360,1,Urban,Y
+LP001518,Male,Yes,1,Graduate,No,1538,1425,30,360,1,Urban,Y
+LP001519,Female,No,0,Graduate,No,10000,1666,225,360,1,Rural,N
+LP001520,Male,Yes,0,Graduate,No,4860,830,125,360,1,Semiurban,Y
+LP001528,Male,No,0,Graduate,No,6277,0,118,360,0,Rural,N
+LP001529,Male,Yes,0,Graduate,Yes,2577,3750,152,360,1,Rural,Y
+LP001531,Male,No,0,Graduate,No,9166,0,244,360,1,Urban,N
+LP001532,Male,Yes,2,Not Graduate,No,2281,0,113,360,1,Rural,N
+LP001535,Male,No,0,Graduate,No,3254,0,50,360,1,Urban,Y
+LP001536,Male,Yes,3+,Graduate,No,39999,0,600,180,0,Semiurban,Y
+LP001541,Male,Yes,1,Graduate,No,6000,0,160,360,,Rural,Y
+LP001543,Male,Yes,1,Graduate,No,9538,0,187,360,1,Urban,Y
+LP001546,Male,No,0,Graduate,,2980,2083,120,360,1,Rural,Y
+LP001552,Male,Yes,0,Graduate,No,4583,5625,255,360,1,Semiurban,Y
+LP001560,Male,Yes,0,Not Graduate,No,1863,1041,98,360,1,Semiurban,Y
+LP001562,Male,Yes,0,Graduate,No,7933,0,275,360,1,Urban,N
+LP001565,Male,Yes,1,Graduate,No,3089,1280,121,360,0,Semiurban,N
+LP001570,Male,Yes,2,Graduate,No,4167,1447,158,360,1,Rural,Y
+LP001572,Male,Yes,0,Graduate,No,9323,0,75,180,1,Urban,Y
+LP001574,Male,Yes,0,Graduate,No,3707,3166,182,,1,Rural,Y
+LP001577,Female,Yes,0,Graduate,No,4583,0,112,360,1,Rural,N
+LP001578,Male,Yes,0,Graduate,No,2439,3333,129,360,1,Rural,Y
+LP001579,Male,No,0,Graduate,No,2237,0,63,480,0,Semiurban,N
+LP001580,Male,Yes,2,Graduate,No,8000,0,200,360,1,Semiurban,Y
+LP001581,Male,Yes,0,Not Graduate,,1820,1769,95,360,1,Rural,Y
+LP001585,,Yes,3+,Graduate,No,51763,0,700,300,1,Urban,Y
+LP001586,Male,Yes,3+,Not Graduate,No,3522,0,81,180,1,Rural,N
+LP001594,Male,Yes,0,Graduate,No,5708,5625,187,360,1,Semiurban,Y
+LP001603,Male,Yes,0,Not Graduate,Yes,4344,736,87,360,1,Semiurban,N
+LP001606,Male,Yes,0,Graduate,No,3497,1964,116,360,1,Rural,Y
+LP001608,Male,Yes,2,Graduate,No,2045,1619,101,360,1,Rural,Y
+LP001610,Male,Yes,3+,Graduate,No,5516,11300,495,360,0,Semiurban,N
+LP001616,Male,Yes,1,Graduate,No,3750,0,116,360,1,Semiurban,Y
+LP001630,Male,No,0,Not Graduate,No,2333,1451,102,480,0,Urban,N
+LP001633,Male,Yes,1,Graduate,No,6400,7250,180,360,0,Urban,N
+LP001634,Male,No,0,Graduate,No,1916,5063,67,360,,Rural,N
+LP001636,Male,Yes,0,Graduate,No,4600,0,73,180,1,Semiurban,Y
+LP001637,Male,Yes,1,Graduate,No,33846,0,260,360,1,Semiurban,N
+LP001639,Female,Yes,0,Graduate,No,3625,0,108,360,1,Semiurban,Y
+LP001640,Male,Yes,0,Graduate,Yes,39147,4750,120,360,1,Semiurban,Y
+LP001641,Male,Yes,1,Graduate,Yes,2178,0,66,300,0,Rural,N
+LP001643,Male,Yes,0,Graduate,No,2383,2138,58,360,,Rural,Y
+LP001644,,Yes,0,Graduate,Yes,674,5296,168,360,1,Rural,Y
+LP001647,Male,Yes,0,Graduate,No,9328,0,188,180,1,Rural,Y
+LP001653,Male,No,0,Not Graduate,No,4885,0,48,360,1,Rural,Y
+LP001656,Male,No,0,Graduate,No,12000,0,164,360,1,Semiurban,N
+LP001657,Male,Yes,0,Not Graduate,No,6033,0,160,360,1,Urban,N
+LP001658,Male,No,0,Graduate,No,3858,0,76,360,1,Semiurban,Y
+LP001664,Male,No,0,Graduate,No,4191,0,120,360,1,Rural,Y
+LP001665,Male,Yes,1,Graduate,No,3125,2583,170,360,1,Semiurban,N
+LP001666,Male,No,0,Graduate,No,8333,3750,187,360,1,Rural,Y
+LP001669,Female,No,0,Not Graduate,No,1907,2365,120,,1,Urban,Y
+LP001671,Female,Yes,0,Graduate,No,3416,2816,113,360,,Semiurban,Y
+LP001673,Male,No,0,Graduate,Yes,11000,0,83,360,1,Urban,N
+LP001674,Male,Yes,1,Not Graduate,No,2600,2500,90,360,1,Semiurban,Y
+LP001677,Male,No,2,Graduate,No,4923,0,166,360,0,Semiurban,Y
+LP001682,Male,Yes,3+,Not Graduate,No,3992,0,,180,1,Urban,N
+LP001688,Male,Yes,1,Not Graduate,No,3500,1083,135,360,1,Urban,Y
+LP001691,Male,Yes,2,Not Graduate,No,3917,0,124,360,1,Semiurban,Y
+LP001692,Female,No,0,Not Graduate,No,4408,0,120,360,1,Semiurban,Y
+LP001693,Female,No,0,Graduate,No,3244,0,80,360,1,Urban,Y
+LP001698,Male,No,0,Not Graduate,No,3975,2531,55,360,1,Rural,Y
+LP001699,Male,No,0,Graduate,No,2479,0,59,360,1,Urban,Y
+LP001702,Male,No,0,Graduate,No,3418,0,127,360,1,Semiurban,N
+LP001708,Female,No,0,Graduate,No,10000,0,214,360,1,Semiurban,N
+LP001711,Male,Yes,3+,Graduate,No,3430,1250,128,360,0,Semiurban,N
+LP001713,Male,Yes,1,Graduate,Yes,7787,0,240,360,1,Urban,Y
+LP001715,Male,Yes,3+,Not Graduate,Yes,5703,0,130,360,1,Rural,Y
+LP001716,Male,Yes,0,Graduate,No,3173,3021,137,360,1,Urban,Y
+LP001720,Male,Yes,3+,Not Graduate,No,3850,983,100,360,1,Semiurban,Y
+LP001722,Male,Yes,0,Graduate,No,150,1800,135,360,1,Rural,N
+LP001726,Male,Yes,0,Graduate,No,3727,1775,131,360,1,Semiurban,Y
+LP001732,Male,Yes,2,Graduate,,5000,0,72,360,0,Semiurban,N
+LP001734,Female,Yes,2,Graduate,No,4283,2383,127,360,,Semiurban,Y
+LP001736,Male,Yes,0,Graduate,No,2221,0,60,360,0,Urban,N
+LP001743,Male,Yes,2,Graduate,No,4009,1717,116,360,1,Semiurban,Y
+LP001744,Male,No,0,Graduate,No,2971,2791,144,360,1,Semiurban,Y
+LP001749,Male,Yes,0,Graduate,No,7578,1010,175,,1,Semiurban,Y
+LP001750,Male,Yes,0,Graduate,No,6250,0,128,360,1,Semiurban,Y
+LP001751,Male,Yes,0,Graduate,No,3250,0,170,360,1,Rural,N
+LP001754,Male,Yes,,Not Graduate,Yes,4735,0,138,360,1,Urban,N
+LP001758,Male,Yes,2,Graduate,No,6250,1695,210,360,1,Semiurban,Y
+LP001760,Male,,,Graduate,No,4758,0,158,480,1,Semiurban,Y
+LP001761,Male,No,0,Graduate,Yes,6400,0,200,360,1,Rural,Y
+LP001765,Male,Yes,1,Graduate,No,2491,2054,104,360,1,Semiurban,Y
+LP001768,Male,Yes,0,Graduate,,3716,0,42,180,1,Rural,Y
+LP001770,Male,No,0,Not Graduate,No,3189,2598,120,,1,Rural,Y
+LP001776,Female,No,0,Graduate,No,8333,0,280,360,1,Semiurban,Y
+LP001778,Male,Yes,1,Graduate,No,3155,1779,140,360,1,Semiurban,Y
+LP001784,Male,Yes,1,Graduate,No,5500,1260,170,360,1,Rural,Y
+LP001786,Male,Yes,0,Graduate,,5746,0,255,360,,Urban,N
+LP001788,Female,No,0,Graduate,Yes,3463,0,122,360,,Urban,Y
+LP001790,Female,No,1,Graduate,No,3812,0,112,360,1,Rural,Y
+LP001792,Male,Yes,1,Graduate,No,3315,0,96,360,1,Semiurban,Y
+LP001798,Male,Yes,2,Graduate,No,5819,5000,120,360,1,Rural,Y
+LP001800,Male,Yes,1,Not Graduate,No,2510,1983,140,180,1,Urban,N
+LP001806,Male,No,0,Graduate,No,2965,5701,155,60,1,Urban,Y
+LP001807,Male,Yes,2,Graduate,Yes,6250,1300,108,360,1,Rural,Y
+LP001811,Male,Yes,0,Not Graduate,No,3406,4417,123,360,1,Semiurban,Y
+LP001813,Male,No,0,Graduate,Yes,6050,4333,120,180,1,Urban,N
+LP001814,Male,Yes,2,Graduate,No,9703,0,112,360,1,Urban,Y
+LP001819,Male,Yes,1,Not Graduate,No,6608,0,137,180,1,Urban,Y
+LP001824,Male,Yes,1,Graduate,No,2882,1843,123,480,1,Semiurban,Y
+LP001825,Male,Yes,0,Graduate,No,1809,1868,90,360,1,Urban,Y
+LP001835,Male,Yes,0,Not Graduate,No,1668,3890,201,360,0,Semiurban,N
+LP001836,Female,No,2,Graduate,No,3427,0,138,360,1,Urban,N
+LP001841,Male,No,0,Not Graduate,Yes,2583,2167,104,360,1,Rural,Y
+LP001843,Male,Yes,1,Not Graduate,No,2661,7101,279,180,1,Semiurban,Y
+LP001844,Male,No,0,Graduate,Yes,16250,0,192,360,0,Urban,N
+LP001846,Female,No,3+,Graduate,No,3083,0,255,360,1,Rural,Y
+LP001849,Male,No,0,Not Graduate,No,6045,0,115,360,0,Rural,N
+LP001854,Male,Yes,3+,Graduate,No,5250,0,94,360,1,Urban,N
+LP001859,Male,Yes,0,Graduate,No,14683,2100,304,360,1,Rural,N
+LP001864,Male,Yes,3+,Not Graduate,No,4931,0,128,360,,Semiurban,N
+LP001865,Male,Yes,1,Graduate,No,6083,4250,330,360,,Urban,Y
+LP001868,Male,No,0,Graduate,No,2060,2209,134,360,1,Semiurban,Y
+LP001870,Female,No,1,Graduate,No,3481,0,155,36,1,Semiurban,N
+LP001871,Female,No,0,Graduate,No,7200,0,120,360,1,Rural,Y
+LP001872,Male,No,0,Graduate,Yes,5166,0,128,360,1,Semiurban,Y
+LP001875,Male,No,0,Graduate,No,4095,3447,151,360,1,Rural,Y
+LP001877,Male,Yes,2,Graduate,No,4708,1387,150,360,1,Semiurban,Y
+LP001882,Male,Yes,3+,Graduate,No,4333,1811,160,360,0,Urban,Y
+LP001883,Female,No,0,Graduate,,3418,0,135,360,1,Rural,N
+LP001884,Female,No,1,Graduate,No,2876,1560,90,360,1,Urban,Y
+LP001888,Female,No,0,Graduate,No,3237,0,30,360,1,Urban,Y
+LP001891,Male,Yes,0,Graduate,No,11146,0,136,360,1,Urban,Y
+LP001892,Male,No,0,Graduate,No,2833,1857,126,360,1,Rural,Y
+LP001894,Male,Yes,0,Graduate,No,2620,2223,150,360,1,Semiurban,Y
+LP001896,Male,Yes,2,Graduate,No,3900,0,90,360,1,Semiurban,Y
+LP001900,Male,Yes,1,Graduate,No,2750,1842,115,360,1,Semiurban,Y
+LP001903,Male,Yes,0,Graduate,No,3993,3274,207,360,1,Semiurban,Y
+LP001904,Male,Yes,0,Graduate,No,3103,1300,80,360,1,Urban,Y
+LP001907,Male,Yes,0,Graduate,No,14583,0,436,360,1,Semiurban,Y
+LP001908,Female,Yes,0,Not Graduate,No,4100,0,124,360,,Rural,Y
+LP001910,Male,No,1,Not Graduate,Yes,4053,2426,158,360,0,Urban,N
+LP001914,Male,Yes,0,Graduate,No,3927,800,112,360,1,Semiurban,Y
+LP001915,Male,Yes,2,Graduate,No,2301,985.7999878,78,180,1,Urban,Y
+LP001917,Female,No,0,Graduate,No,1811,1666,54,360,1,Urban,Y
+LP001922,Male,Yes,0,Graduate,No,20667,0,,360,1,Rural,N
+LP001924,Male,No,0,Graduate,No,3158,3053,89,360,1,Rural,Y
+LP001925,Female,No,0,Graduate,Yes,2600,1717,99,300,1,Semiurban,N
+LP001926,Male,Yes,0,Graduate,No,3704,2000,120,360,1,Rural,Y
+LP001931,Female,No,0,Graduate,No,4124,0,115,360,1,Semiurban,Y
+LP001935,Male,No,0,Graduate,No,9508,0,187,360,1,Rural,Y
+LP001936,Male,Yes,0,Graduate,No,3075,2416,139,360,1,Rural,Y
+LP001938,Male,Yes,2,Graduate,No,4400,0,127,360,0,Semiurban,N
+LP001940,Male,Yes,2,Graduate,No,3153,1560,134,360,1,Urban,Y
+LP001945,Female,No,,Graduate,No,5417,0,143,480,0,Urban,N
+LP001947,Male,Yes,0,Graduate,No,2383,3334,172,360,1,Semiurban,Y
+LP001949,Male,Yes,3+,Graduate,,4416,1250,110,360,1,Urban,Y
+LP001953,Male,Yes,1,Graduate,No,6875,0,200,360,1,Semiurban,Y
+LP001954,Female,Yes,1,Graduate,No,4666,0,135,360,1,Urban,Y
+LP001955,Female,No,0,Graduate,No,5000,2541,151,480,1,Rural,N
+LP001963,Male,Yes,1,Graduate,No,2014,2925,113,360,1,Urban,N
+LP001964,Male,Yes,0,Not Graduate,No,1800,2934,93,360,0,Urban,N
+LP001972,Male,Yes,,Not Graduate,No,2875,1750,105,360,1,Semiurban,Y
+LP001974,Female,No,0,Graduate,No,5000,0,132,360,1,Rural,Y
+LP001977,Male,Yes,1,Graduate,No,1625,1803,96,360,1,Urban,Y
+LP001978,Male,No,0,Graduate,No,4000,2500,140,360,1,Rural,Y
+LP001990,Male,No,0,Not Graduate,No,2000,0,,360,1,Urban,N
+LP001993,Female,No,0,Graduate,No,3762,1666,135,360,1,Rural,Y
+LP001994,Female,No,0,Graduate,No,2400,1863,104,360,0,Urban,N
+LP001996,Male,No,0,Graduate,No,20233,0,480,360,1,Rural,N
+LP001998,Male,Yes,2,Not Graduate,No,7667,0,185,360,,Rural,Y
+LP002002,Female,No,0,Graduate,No,2917,0,84,360,1,Semiurban,Y
+LP002004,Male,No,0,Not Graduate,No,2927,2405,111,360,1,Semiurban,Y
+LP002006,Female,No,0,Graduate,No,2507,0,56,360,1,Rural,Y
+LP002008,Male,Yes,2,Graduate,Yes,5746,0,144,84,,Rural,Y
+LP002024,,Yes,0,Graduate,No,2473,1843,159,360,1,Rural,N
+LP002031,Male,Yes,1,Not Graduate,No,3399,1640,111,180,1,Urban,Y
+LP002035,Male,Yes,2,Graduate,No,3717,0,120,360,1,Semiurban,Y
+LP002036,Male,Yes,0,Graduate,No,2058,2134,88,360,,Urban,Y
+LP002043,Female,No,1,Graduate,No,3541,0,112,360,,Semiurban,Y
+LP002050,Male,Yes,1,Graduate,Yes,10000,0,155,360,1,Rural,N
+LP002051,Male,Yes,0,Graduate,No,2400,2167,115,360,1,Semiurban,Y
+LP002053,Male,Yes,3+,Graduate,No,4342,189,124,360,1,Semiurban,Y
+LP002054,Male,Yes,2,Not Graduate,No,3601,1590,,360,1,Rural,Y
+LP002055,Female,No,0,Graduate,No,3166,2985,132,360,,Rural,Y
+LP002065,Male,Yes,3+,Graduate,No,15000,0,300,360,1,Rural,Y
+LP002067,Male,Yes,1,Graduate,Yes,8666,4983,376,360,0,Rural,N
+LP002068,Male,No,0,Graduate,No,4917,0,130,360,0,Rural,Y
+LP002082,Male,Yes,0,Graduate,Yes,5818,2160,184,360,1,Semiurban,Y
+LP002086,Female,Yes,0,Graduate,No,4333,2451,110,360,1,Urban,N
+LP002087,Female,No,0,Graduate,No,2500,0,67,360,1,Urban,Y
+LP002097,Male,No,1,Graduate,No,4384,1793,117,360,1,Urban,Y
+LP002098,Male,No,0,Graduate,No,2935,0,98,360,1,Semiurban,Y
+LP002100,Male,No,,Graduate,No,2833,0,71,360,1,Urban,Y
+LP002101,Male,Yes,0,Graduate,,63337,0,490,180,1,Urban,Y
+LP002103,,Yes,1,Graduate,Yes,9833,1833,182,180,1,Urban,Y
+LP002106,Male,Yes,,Graduate,Yes,5503,4490,70,,1,Semiurban,Y
+LP002110,Male,Yes,1,Graduate,,5250,688,160,360,1,Rural,Y
+LP002112,Male,Yes,2,Graduate,Yes,2500,4600,176,360,1,Rural,Y
+LP002113,Female,No,3+,Not Graduate,No,1830,0,,360,0,Urban,N
+LP002114,Female,No,0,Graduate,No,4160,0,71,360,1,Semiurban,Y
+LP002115,Male,Yes,3+,Not Graduate,No,2647,1587,173,360,1,Rural,N
+LP002116,Female,No,0,Graduate,No,2378,0,46,360,1,Rural,N
+LP002119,Male,Yes,1,Not Graduate,No,4554,1229,158,360,1,Urban,Y
+LP002126,Male,Yes,3+,Not Graduate,No,3173,0,74,360,1,Semiurban,Y
+LP002128,Male,Yes,2,Graduate,,2583,2330,125,360,1,Rural,Y
+LP002129,Male,Yes,0,Graduate,No,2499,2458,160,360,1,Semiurban,Y
+LP002130,Male,Yes,,Not Graduate,No,3523,3230,152,360,0,Rural,N
+LP002131,Male,Yes,2,Not Graduate,No,3083,2168,126,360,1,Urban,Y
+LP002137,Male,Yes,0,Graduate,No,6333,4583,259,360,,Semiurban,Y
+LP002138,Male,Yes,0,Graduate,No,2625,6250,187,360,1,Rural,Y
+LP002139,Male,Yes,0,Graduate,No,9083,0,228,360,1,Semiurban,Y
+LP002140,Male,No,0,Graduate,No,8750,4167,308,360,1,Rural,N
+LP002141,Male,Yes,3+,Graduate,No,2666,2083,95,360,1,Rural,Y
+LP002142,Female,Yes,0,Graduate,Yes,5500,0,105,360,0,Rural,N
+LP002143,Female,Yes,0,Graduate,No,2423,505,130,360,1,Semiurban,Y
+LP002144,Female,No,,Graduate,No,3813,0,116,180,1,Urban,Y
+LP002149,Male,Yes,2,Graduate,No,8333,3167,165,360,1,Rural,Y
+LP002151,Male,Yes,1,Graduate,No,3875,0,67,360,1,Urban,N
+LP002158,Male,Yes,0,Not Graduate,No,3000,1666,100,480,0,Urban,N
+LP002160,Male,Yes,3+,Graduate,No,5167,3167,200,360,1,Semiurban,Y
+LP002161,Female,No,1,Graduate,No,4723,0,81,360,1,Semiurban,N
+LP002170,Male,Yes,2,Graduate,No,5000,3667,236,360,1,Semiurban,Y
+LP002175,Male,Yes,0,Graduate,No,4750,2333,130,360,1,Urban,Y
+LP002178,Male,Yes,0,Graduate,No,3013,3033,95,300,,Urban,Y
+LP002180,Male,No,0,Graduate,Yes,6822,0,141,360,1,Rural,Y
+LP002181,Male,No,0,Not Graduate,No,6216,0,133,360,1,Rural,N
+LP002187,Male,No,0,Graduate,No,2500,0,96,480,1,Semiurban,N
+LP002188,Male,No,0,Graduate,No,5124,0,124,,0,Rural,N
+LP002190,Male,Yes,1,Graduate,No,6325,0,175,360,1,Semiurban,Y
+LP002191,Male,Yes,0,Graduate,No,19730,5266,570,360,1,Rural,N
+LP002194,Female,No,0,Graduate,Yes,15759,0,55,360,1,Semiurban,Y
+LP002197,Male,Yes,2,Graduate,No,5185,0,155,360,1,Semiurban,Y
+LP002201,Male,Yes,2,Graduate,Yes,9323,7873,380,300,1,Rural,Y
+LP002205,Male,No,1,Graduate,No,3062,1987,111,180,0,Urban,N
+LP002209,Female,No,0,Graduate,,2764,1459,110,360,1,Urban,Y
+LP002211,Male,Yes,0,Graduate,No,4817,923,120,180,1,Urban,Y
+LP002219,Male,Yes,3+,Graduate,No,8750,4996,130,360,1,Rural,Y
+LP002223,Male,Yes,0,Graduate,No,4310,0,130,360,,Semiurban,Y
+LP002224,Male,No,0,Graduate,No,3069,0,71,480,1,Urban,N
+LP002225,Male,Yes,2,Graduate,No,5391,0,130,360,1,Urban,Y
+LP002226,Male,Yes,0,Graduate,,3333,2500,128,360,1,Semiurban,Y
+LP002229,Male,No,0,Graduate,No,5941,4232,296,360,1,Semiurban,Y
+LP002231,Female,No,0,Graduate,No,6000,0,156,360,1,Urban,Y
+LP002234,Male,No,0,Graduate,Yes,7167,0,128,360,1,Urban,Y
+LP002236,Male,Yes,2,Graduate,No,4566,0,100,360,1,Urban,N
+LP002237,Male,No,1,Graduate,,3667,0,113,180,1,Urban,Y
+LP002239,Male,No,0,Not Graduate,No,2346,1600,132,360,1,Semiurban,Y
+LP002243,Male,Yes,0,Not Graduate,No,3010,3136,,360,0,Urban,N
+LP002244,Male,Yes,0,Graduate,No,2333,2417,136,360,1,Urban,Y
+LP002250,Male,Yes,0,Graduate,No,5488,0,125,360,1,Rural,Y
+LP002255,Male,No,3+,Graduate,No,9167,0,185,360,1,Rural,Y
+LP002262,Male,Yes,3+,Graduate,No,9504,0,275,360,1,Rural,Y
+LP002263,Male,Yes,0,Graduate,No,2583,2115,120,360,,Urban,Y
+LP002265,Male,Yes,2,Not Graduate,No,1993,1625,113,180,1,Semiurban,Y
+LP002266,Male,Yes,2,Graduate,No,3100,1400,113,360,1,Urban,Y
+LP002272,Male,Yes,2,Graduate,No,3276,484,135,360,,Semiurban,Y
+LP002277,Female,No,0,Graduate,No,3180,0,71,360,0,Urban,N
+LP002281,Male,Yes,0,Graduate,No,3033,1459,95,360,1,Urban,Y
+LP002284,Male,No,0,Not Graduate,No,3902,1666,109,360,1,Rural,Y
+LP002287,Female,No,0,Graduate,No,1500,1800,103,360,0,Semiurban,N
+LP002288,Male,Yes,2,Not Graduate,No,2889,0,45,180,0,Urban,N
+LP002296,Male,No,0,Not Graduate,No,2755,0,65,300,1,Rural,N
+LP002297,Male,No,0,Graduate,No,2500,20000,103,360,1,Semiurban,Y
+LP002300,Female,No,0,Not Graduate,No,1963,0,53,360,1,Semiurban,Y
+LP002301,Female,No,0,Graduate,Yes,7441,0,194,360,1,Rural,N
+LP002305,Female,No,0,Graduate,No,4547,0,115,360,1,Semiurban,Y
+LP002308,Male,Yes,0,Not Graduate,No,2167,2400,115,360,1,Urban,Y
+LP002314,Female,No,0,Not Graduate,No,2213,0,66,360,1,Rural,Y
+LP002315,Male,Yes,1,Graduate,No,8300,0,152,300,0,Semiurban,N
+LP002317,Male,Yes,3+,Graduate,No,81000,0,360,360,0,Rural,N
+LP002318,Female,No,1,Not Graduate,Yes,3867,0,62,360,1,Semiurban,N
+LP002319,Male,Yes,0,Graduate,,6256,0,160,360,,Urban,Y
+LP002328,Male,Yes,0,Not Graduate,No,6096,0,218,360,0,Rural,N
+LP002332,Male,Yes,0,Not Graduate,No,2253,2033,110,360,1,Rural,Y
+LP002335,Female,Yes,0,Not Graduate,No,2149,3237,178,360,0,Semiurban,N
+LP002337,Female,No,0,Graduate,No,2995,0,60,360,1,Urban,Y
+LP002341,Female,No,1,Graduate,No,2600,0,160,360,1,Urban,N
+LP002342,Male,Yes,2,Graduate,Yes,1600,20000,239,360,1,Urban,N
+LP002345,Male,Yes,0,Graduate,No,1025,2773,112,360,1,Rural,Y
+LP002347,Male,Yes,0,Graduate,No,3246,1417,138,360,1,Semiurban,Y
+LP002348,Male,Yes,0,Graduate,No,5829,0,138,360,1,Rural,Y
+LP002357,Female,No,0,Not Graduate,No,2720,0,80,,0,Urban,N
+LP002361,Male,Yes,0,Graduate,No,1820,1719,100,360,1,Urban,Y
+LP002362,Male,Yes,1,Graduate,No,7250,1667,110,,0,Urban,N
+LP002364,Male,Yes,0,Graduate,No,14880,0,96,360,1,Semiurban,Y
+LP002366,Male,Yes,0,Graduate,No,2666,4300,121,360,1,Rural,Y
+LP002367,Female,No,1,Not Graduate,No,4606,0,81,360,1,Rural,N
+LP002368,Male,Yes,2,Graduate,No,5935,0,133,360,1,Semiurban,Y
+LP002369,Male,Yes,0,Graduate,No,2920,16.12000084,87,360,1,Rural,Y
+LP002370,Male,No,0,Not Graduate,No,2717,0,60,180,1,Urban,Y
+LP002377,Female,No,1,Graduate,Yes,8624,0,150,360,1,Semiurban,Y
+LP002379,Male,No,0,Graduate,No,6500,0,105,360,0,Rural,N
+LP002386,Male,No,0,Graduate,,12876,0,405,360,1,Semiurban,Y
+LP002387,Male,Yes,0,Graduate,No,2425,2340,143,360,1,Semiurban,Y
+LP002390,Male,No,0,Graduate,No,3750,0,100,360,1,Urban,Y
+LP002393,Female,,,Graduate,No,10047,0,,240,1,Semiurban,Y
+LP002398,Male,No,0,Graduate,No,1926,1851,50,360,1,Semiurban,Y
+LP002401,Male,Yes,0,Graduate,No,2213,1125,,360,1,Urban,Y
+LP002403,Male,No,0,Graduate,Yes,10416,0,187,360,0,Urban,N
+LP002407,Female,Yes,0,Not Graduate,Yes,7142,0,138,360,1,Rural,Y
+LP002408,Male,No,0,Graduate,No,3660,5064,187,360,1,Semiurban,Y
+LP002409,Male,Yes,0,Graduate,No,7901,1833,180,360,1,Rural,Y
+LP002418,Male,No,3+,Not Graduate,No,4707,1993,148,360,1,Semiurban,Y
+LP002422,Male,No,1,Graduate,No,37719,0,152,360,1,Semiurban,Y
+LP002424,Male,Yes,0,Graduate,No,7333,8333,175,300,,Rural,Y
+LP002429,Male,Yes,1,Graduate,Yes,3466,1210,130,360,1,Rural,Y
+LP002434,Male,Yes,2,Not Graduate,No,4652,0,110,360,1,Rural,Y
+LP002435,Male,Yes,0,Graduate,,3539,1376,55,360,1,Rural,N
+LP002443,Male,Yes,2,Graduate,No,3340,1710,150,360,0,Rural,N
+LP002444,Male,No,1,Not Graduate,Yes,2769,1542,190,360,,Semiurban,N
+LP002446,Male,Yes,2,Not Graduate,No,2309,1255,125,360,0,Rural,N
+LP002447,Male,Yes,2,Not Graduate,No,1958,1456,60,300,,Urban,Y
+LP002448,Male,Yes,0,Graduate,No,3948,1733,149,360,0,Rural,N
+LP002449,Male,Yes,0,Graduate,No,2483,2466,90,180,0,Rural,Y
+LP002453,Male,No,0,Graduate,Yes,7085,0,84,360,1,Semiurban,Y
+LP002455,Male,Yes,2,Graduate,No,3859,0,96,360,1,Semiurban,Y
+LP002459,Male,Yes,0,Graduate,No,4301,0,118,360,1,Urban,Y
+LP002467,Male,Yes,0,Graduate,No,3708,2569,173,360,1,Urban,N
+LP002472,Male,No,2,Graduate,No,4354,0,136,360,1,Rural,Y
+LP002473,Male,Yes,0,Graduate,No,8334,0,160,360,1,Semiurban,N
+LP002478,,Yes,0,Graduate,Yes,2083,4083,160,360,,Semiurban,Y
+LP002484,Male,Yes,3+,Graduate,No,7740,0,128,180,1,Urban,Y
+LP002487,Male,Yes,0,Graduate,No,3015,2188,153,360,1,Rural,Y
+LP002489,Female,No,1,Not Graduate,,5191,0,132,360,1,Semiurban,Y
+LP002493,Male,No,0,Graduate,No,4166,0,98,360,0,Semiurban,N
+LP002494,Male,No,0,Graduate,No,6000,0,140,360,1,Rural,Y
+LP002500,Male,Yes,3+,Not Graduate,No,2947,1664,70,180,0,Urban,N
+LP002501,,Yes,0,Graduate,No,16692,0,110,360,1,Semiurban,Y
+LP002502,Female,Yes,2,Not Graduate,,210,2917,98,360,1,Semiurban,Y
+LP002505,Male,Yes,0,Graduate,No,4333,2451,110,360,1,Urban,N
+LP002515,Male,Yes,1,Graduate,Yes,3450,2079,162,360,1,Semiurban,Y
+LP002517,Male,Yes,1,Not Graduate,No,2653,1500,113,180,0,Rural,N
+LP002519,Male,Yes,3+,Graduate,No,4691,0,100,360,1,Semiurban,Y
+LP002522,Female,No,0,Graduate,Yes,2500,0,93,360,,Urban,Y
+LP002524,Male,No,2,Graduate,No,5532,4648,162,360,1,Rural,Y
+LP002527,Male,Yes,2,Graduate,Yes,16525,1014,150,360,1,Rural,Y
+LP002529,Male,Yes,2,Graduate,No,6700,1750,230,300,1,Semiurban,Y
+LP002530,,Yes,2,Graduate,No,2873,1872,132,360,0,Semiurban,N
+LP002531,Male,Yes,1,Graduate,Yes,16667,2250,86,360,1,Semiurban,Y
+LP002533,Male,Yes,2,Graduate,No,2947,1603,,360,1,Urban,N
+LP002534,Female,No,0,Not Graduate,No,4350,0,154,360,1,Rural,Y
+LP002536,Male,Yes,3+,Not Graduate,No,3095,0,113,360,1,Rural,Y
+LP002537,Male,Yes,0,Graduate,No,2083,3150,128,360,1,Semiurban,Y
+LP002541,Male,Yes,0,Graduate,No,10833,0,234,360,1,Semiurban,Y
+LP002543,Male,Yes,2,Graduate,No,8333,0,246,360,1,Semiurban,Y
+LP002544,Male,Yes,1,Not Graduate,No,1958,2436,131,360,1,Rural,Y
+LP002545,Male,No,2,Graduate,No,3547,0,80,360,0,Rural,N
+LP002547,Male,Yes,1,Graduate,No,18333,0,500,360,1,Urban,N
+LP002555,Male,Yes,2,Graduate,Yes,4583,2083,160,360,1,Semiurban,Y
+LP002556,Male,No,0,Graduate,No,2435,0,75,360,1,Urban,N
+LP002560,Male,No,0,Not Graduate,No,2699,2785,96,360,,Semiurban,Y
+LP002562,Male,Yes,1,Not Graduate,No,5333,1131,186,360,,Urban,Y
+LP002571,Male,No,0,Not Graduate,No,3691,0,110,360,1,Rural,Y
+LP002582,Female,No,0,Not Graduate,Yes,17263,0,225,360,1,Semiurban,Y
+LP002585,Male,Yes,0,Graduate,No,3597,2157,119,360,0,Rural,N
+LP002586,Female,Yes,1,Graduate,No,3326,913,105,84,1,Semiurban,Y
+LP002587,Male,Yes,0,Not Graduate,No,2600,1700,107,360,1,Rural,Y
+LP002588,Male,Yes,0,Graduate,No,4625,2857,111,12,,Urban,Y
+LP002600,Male,Yes,1,Graduate,Yes,2895,0,95,360,1,Semiurban,Y
+LP002602,Male,No,0,Graduate,No,6283,4416,209,360,0,Rural,N
+LP002603,Female,No,0,Graduate,No,645,3683,113,480,1,Rural,Y
+LP002606,Female,No,0,Graduate,No,3159,0,100,360,1,Semiurban,Y
+LP002615,Male,Yes,2,Graduate,No,4865,5624,208,360,1,Semiurban,Y
+LP002618,Male,Yes,1,Not Graduate,No,4050,5302,138,360,,Rural,N
+LP002619,Male,Yes,0,Not Graduate,No,3814,1483,124,300,1,Semiurban,Y
+LP002622,Male,Yes,2,Graduate,No,3510,4416,243,360,1,Rural,Y
+LP002624,Male,Yes,0,Graduate,No,20833,6667,480,360,,Urban,Y
+LP002625,,No,0,Graduate,No,3583,0,96,360,1,Urban,N
+LP002626,Male,Yes,0,Graduate,Yes,2479,3013,188,360,1,Urban,Y
+LP002634,Female,No,1,Graduate,No,13262,0,40,360,1,Urban,Y
+LP002637,Male,No,0,Not Graduate,No,3598,1287,100,360,1,Rural,N
+LP002640,Male,Yes,1,Graduate,No,6065,2004,250,360,1,Semiurban,Y
+LP002643,Male,Yes,2,Graduate,No,3283,2035,148,360,1,Urban,Y
+LP002648,Male,Yes,0,Graduate,No,2130,6666,70,180,1,Semiurban,N
+LP002652,Male,No,0,Graduate,No,5815,3666,311,360,1,Rural,N
+LP002659,Male,Yes,3+,Graduate,No,3466,3428,150,360,1,Rural,Y
+LP002670,Female,Yes,2,Graduate,No,2031,1632,113,480,1,Semiurban,Y
+LP002682,Male,Yes,,Not Graduate,No,3074,1800,123,360,0,Semiurban,N
+LP002683,Male,No,0,Graduate,No,4683,1915,185,360,1,Semiurban,N
+LP002684,Female,No,0,Not Graduate,No,3400,0,95,360,1,Rural,N
+LP002689,Male,Yes,2,Not Graduate,No,2192,1742,45,360,1,Semiurban,Y
+LP002690,Male,No,0,Graduate,No,2500,0,55,360,1,Semiurban,Y
+LP002692,Male,Yes,3+,Graduate,Yes,5677,1424,100,360,1,Rural,Y
+LP002693,Male,Yes,2,Graduate,Yes,7948,7166,480,360,1,Rural,Y
+LP002697,Male,No,0,Graduate,No,4680,2087,,360,1,Semiurban,N
+LP002699,Male,Yes,2,Graduate,Yes,17500,0,400,360,1,Rural,Y
+LP002705,Male,Yes,0,Graduate,No,3775,0,110,360,1,Semiurban,Y
+LP002706,Male,Yes,1,Not Graduate,No,5285,1430,161,360,0,Semiurban,Y
+LP002714,Male,No,1,Not Graduate,No,2679,1302,94,360,1,Semiurban,Y
+LP002716,Male,No,0,Not Graduate,No,6783,0,130,360,1,Semiurban,Y
+LP002717,Male,Yes,0,Graduate,No,1025,5500,216,360,,Rural,Y
+LP002720,Male,Yes,3+,Graduate,No,4281,0,100,360,1,Urban,Y
+LP002723,Male,No,2,Graduate,No,3588,0,110,360,0,Rural,N
+LP002729,Male,No,1,Graduate,No,11250,0,196,360,,Semiurban,N
+LP002731,Female,No,0,Not Graduate,Yes,18165,0,125,360,1,Urban,Y
+LP002732,Male,No,0,Not Graduate,,2550,2042,126,360,1,Rural,Y
+LP002734,Male,Yes,0,Graduate,No,6133,3906,324,360,1,Urban,Y
+LP002738,Male,No,2,Graduate,No,3617,0,107,360,1,Semiurban,Y
+LP002739,Male,Yes,0,Not Graduate,No,2917,536,66,360,1,Rural,N
+LP002740,Male,Yes,3+,Graduate,No,6417,0,157,180,1,Rural,Y
+LP002741,Female,Yes,1,Graduate,No,4608,2845,140,180,1,Semiurban,Y
+LP002743,Female,No,0,Graduate,No,2138,0,99,360,0,Semiurban,N
+LP002753,Female,No,1,Graduate,,3652,0,95,360,1,Semiurban,Y
+LP002755,Male,Yes,1,Not Graduate,No,2239,2524,128,360,1,Urban,Y
+LP002757,Female,Yes,0,Not Graduate,No,3017,663,102,360,,Semiurban,Y
+LP002767,Male,Yes,0,Graduate,No,2768,1950,155,360,1,Rural,Y
+LP002768,Male,No,0,Not Graduate,No,3358,0,80,36,1,Semiurban,N
+LP002772,Male,No,0,Graduate,No,2526,1783,145,360,1,Rural,Y
+LP002776,Female,No,0,Graduate,No,5000,0,103,360,0,Semiurban,N
+LP002777,Male,Yes,0,Graduate,No,2785,2016,110,360,1,Rural,Y
+LP002778,Male,Yes,2,Graduate,Yes,6633,0,,360,0,Rural,N
+LP002784,Male,Yes,1,Not Graduate,No,2492,2375,,360,1,Rural,Y
+LP002785,Male,Yes,1,Graduate,No,3333,3250,158,360,1,Urban,Y
+LP002788,Male,Yes,0,Not Graduate,No,2454,2333,181,360,0,Urban,N
+LP002789,Male,Yes,0,Graduate,No,3593,4266,132,180,0,Rural,N
+LP002792,Male,Yes,1,Graduate,No,5468,1032,26,360,1,Semiurban,Y
+LP002794,Female,No,0,Graduate,No,2667,1625,84,360,,Urban,Y
+LP002795,Male,Yes,3+,Graduate,Yes,10139,0,260,360,1,Semiurban,Y
+LP002798,Male,Yes,0,Graduate,No,3887,2669,162,360,1,Semiurban,Y
+LP002804,Female,Yes,0,Graduate,No,4180,2306,182,360,1,Semiurban,Y
+LP002807,Male,Yes,2,Not Graduate,No,3675,242,108,360,1,Semiurban,Y
+LP002813,Female,Yes,1,Graduate,Yes,19484,0,600,360,1,Semiurban,Y
+LP002820,Male,Yes,0,Graduate,No,5923,2054,211,360,1,Rural,Y
+LP002821,Male,No,0,Not Graduate,Yes,5800,0,132,360,1,Semiurban,Y
+LP002832,Male,Yes,2,Graduate,No,8799,0,258,360,0,Urban,N
+LP002833,Male,Yes,0,Not Graduate,No,4467,0,120,360,,Rural,Y
+LP002836,Male,No,0,Graduate,No,3333,0,70,360,1,Urban,Y
+LP002837,Male,Yes,3+,Graduate,No,3400,2500,123,360,0,Rural,N
+LP002840,Female,No,0,Graduate,No,2378,0,9,360,1,Urban,N
+LP002841,Male,Yes,0,Graduate,No,3166,2064,104,360,0,Urban,N
+LP002842,Male,Yes,1,Graduate,No,3417,1750,186,360,1,Urban,Y
+LP002847,Male,Yes,,Graduate,No,5116,1451,165,360,0,Urban,N
+LP002855,Male,Yes,2,Graduate,No,16666,0,275,360,1,Urban,Y
+LP002862,Male,Yes,2,Not Graduate,No,6125,1625,187,480,1,Semiurban,N
+LP002863,Male,Yes,3+,Graduate,No,6406,0,150,360,1,Semiurban,N
+LP002868,Male,Yes,2,Graduate,No,3159,461,108,84,1,Urban,Y
+LP002872,,Yes,0,Graduate,No,3087,2210,136,360,0,Semiurban,N
+LP002874,Male,No,0,Graduate,No,3229,2739,110,360,1,Urban,Y
+LP002877,Male,Yes,1,Graduate,No,1782,2232,107,360,1,Rural,Y
+LP002888,Male,No,0,Graduate,,3182,2917,161,360,1,Urban,Y
+LP002892,Male,Yes,2,Graduate,No,6540,0,205,360,1,Semiurban,Y
+LP002893,Male,No,0,Graduate,No,1836,33837,90,360,1,Urban,N
+LP002894,Female,Yes,0,Graduate,No,3166,0,36,360,1,Semiurban,Y
+LP002898,Male,Yes,1,Graduate,No,1880,0,61,360,,Rural,N
+LP002911,Male,Yes,1,Graduate,No,2787,1917,146,360,0,Rural,N
+LP002912,Male,Yes,1,Graduate,No,4283,3000,172,84,1,Rural,N
+LP002916,Male,Yes,0,Graduate,No,2297,1522,104,360,1,Urban,Y
+LP002917,Female,No,0,Not Graduate,No,2165,0,70,360,1,Semiurban,Y
+LP002925,,No,0,Graduate,No,4750,0,94,360,1,Semiurban,Y
+LP002926,Male,Yes,2,Graduate,Yes,2726,0,106,360,0,Semiurban,N
+LP002928,Male,Yes,0,Graduate,No,3000,3416,56,180,1,Semiurban,Y
+LP002931,Male,Yes,2,Graduate,Yes,6000,0,205,240,1,Semiurban,N
+LP002933,,No,3+,Graduate,Yes,9357,0,292,360,1,Semiurban,Y
+LP002936,Male,Yes,0,Graduate,No,3859,3300,142,180,1,Rural,Y
+LP002938,Male,Yes,0,Graduate,Yes,16120,0,260,360,1,Urban,Y
+LP002940,Male,No,0,Not Graduate,No,3833,0,110,360,1,Rural,Y
+LP002941,Male,Yes,2,Not Graduate,Yes,6383,1000,187,360,1,Rural,N
+LP002943,Male,No,,Graduate,No,2987,0,88,360,0,Semiurban,N
+LP002945,Male,Yes,0,Graduate,Yes,9963,0,180,360,1,Rural,Y
+LP002948,Male,Yes,2,Graduate,No,5780,0,192,360,1,Urban,Y
+LP002949,Female,No,3+,Graduate,,416,41667,350,180,,Urban,N
+LP002950,Male,Yes,0,Not Graduate,,2894,2792,155,360,1,Rural,Y
+LP002953,Male,Yes,3+,Graduate,No,5703,0,128,360,1,Urban,Y
+LP002958,Male,No,0,Graduate,No,3676,4301,172,360,1,Rural,Y
+LP002959,Female,Yes,1,Graduate,No,12000,0,496,360,1,Semiurban,Y
+LP002960,Male,Yes,0,Not Graduate,No,2400,3800,,180,1,Urban,N
+LP002961,Male,Yes,1,Graduate,No,3400,2500,173,360,1,Semiurban,Y
+LP002964,Male,Yes,2,Not Graduate,No,3987,1411,157,360,1,Rural,Y
+LP002974,Male,Yes,0,Graduate,No,3232,1950,108,360,1,Rural,Y
+LP002978,Female,No,0,Graduate,No,2900,0,71,360,1,Rural,Y
+LP002979,Male,Yes,3+,Graduate,No,4106,0,40,180,1,Rural,Y
+LP002983,Male,Yes,1,Graduate,No,8072,240,253,360,1,Urban,Y
+LP002984,Male,Yes,2,Graduate,No,7583,0,187,360,1,Urban,Y
+LP002990,Female,No,0,Graduate,Yes,4583,0,133,360,0,Semiurban,N
diff --git a/Loan Status Prediction/Loan_Status_prediction.ipynb b/Loan Status Prediction/Loan_Status_prediction.ipynb
new file mode 100644
index 00000000..49ef47e2
--- /dev/null
+++ b/Loan Status Prediction/Loan_Status_prediction.ipynb	
@@ -0,0 +1,2587 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qFgW43r3XWFE"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn import svm\n",
+        "from sklearn.metrics import accuracy_score"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df=pd.read_csv('/content/Loan.csv')"
+      ],
+      "metadata": {
+        "id": "klPs1bd-oe5E"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 461
+        },
+        "id": "F5Tac41Tyi8a",
+        "outputId": "b76b2cf6-23f6-4815-f496-db185e30bdb3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      Loan_ID  Gender Married Dependents     Education Self_Employed  \\\n",
+              "0    LP001002    Male      No          0      Graduate            No   \n",
+              "1    LP001003    Male     Yes          1      Graduate            No   \n",
+              "2    LP001005    Male     Yes          0      Graduate           Yes   \n",
+              "3    LP001006    Male     Yes          0  Not Graduate            No   \n",
+              "4    LP001008    Male      No          0      Graduate            No   \n",
+              "..        ...     ...     ...        ...           ...           ...   \n",
+              "609  LP002978  Female      No          0      Graduate            No   \n",
+              "610  LP002979    Male     Yes         3+      Graduate            No   \n",
+              "611  LP002983    Male     Yes          1      Graduate            No   \n",
+              "612  LP002984    Male     Yes          2      Graduate            No   \n",
+              "613  LP002990  Female      No          0      Graduate           Yes   \n",
+              "\n",
+              "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
+              "0               5849                0.0         NaN             360.0   \n",
+              "1               4583             1508.0       128.0             360.0   \n",
+              "2               3000                0.0        66.0             360.0   \n",
+              "3               2583             2358.0       120.0             360.0   \n",
+              "4               6000                0.0       141.0             360.0   \n",
+              "..               ...                ...         ...               ...   \n",
+              "609             2900                0.0        71.0             360.0   \n",
+              "610             4106                0.0        40.0             180.0   \n",
+              "611             8072              240.0       253.0             360.0   \n",
+              "612             7583                0.0       187.0             360.0   \n",
+              "613             4583                0.0       133.0             360.0   \n",
+              "\n",
+              "     Credit_History Property_Area Loan_Status  \n",
+              "0               1.0         Urban           Y  \n",
+              "1               1.0         Rural           N  \n",
+              "2               1.0         Urban           Y  \n",
+              "3               1.0         Urban           Y  \n",
+              "4               1.0         Urban           Y  \n",
+              "..              ...           ...         ...  \n",
+              "609             1.0         Rural           Y  \n",
+              "610             1.0         Rural           Y  \n",
+              "611             1.0         Urban           Y  \n",
+              "612             1.0         Urban           Y  \n",
+              "613             0.0     Semiurban           N  \n",
+              "\n",
+              "[614 rows x 13 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2790db90-3dc2-4a15-ba82-fa6e79ea908c\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Loan_ID</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Married</th>\n",
+              "      <th>Dependents</th>\n",
+              "      <th>Education</th>\n",
+              "      <th>Self_Employed</th>\n",
+              "      <th>ApplicantIncome</th>\n",
+              "      <th>CoapplicantIncome</th>\n",
+              "      <th>LoanAmount</th>\n",
+              "      <th>Loan_Amount_Term</th>\n",
+              "      <th>Credit_History</th>\n",
+              "      <th>Property_Area</th>\n",
+              "      <th>Loan_Status</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LP001002</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>5849</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>Y</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LP001003</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>4583</td>\n",
+              "      <td>1508.0</td>\n",
+              "      <td>128.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Rural</td>\n",
+              "      <td>N</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LP001005</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>3000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>Y</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>LP001006</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Not Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>2583</td>\n",
+              "      <td>2358.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>Y</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>LP001008</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>6000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>141.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>Y</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>609</th>\n",
+              "      <td>LP002978</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>2900</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Rural</td>\n",
+              "      <td>Y</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>610</th>\n",
+              "      <td>LP002979</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>3+</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>4106</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>180.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Rural</td>\n",
+              "      <td>Y</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>611</th>\n",
+              "      <td>LP002983</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>8072</td>\n",
+              "      <td>240.0</td>\n",
+              "      <td>253.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>Y</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>612</th>\n",
+              "      <td>LP002984</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>7583</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>187.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>Y</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>613</th>\n",
+              "      <td>LP002990</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>4583</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>133.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Semiurban</td>\n",
+              "      <td>N</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>614 rows × 13 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2790db90-3dc2-4a15-ba82-fa6e79ea908c')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2790db90-3dc2-4a15-ba82-fa6e79ea908c button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-2790db90-3dc2-4a15-ba82-fa6e79ea908c');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-800e1c08-3d6a-4a96-b261-95d519dcdc0b\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-800e1c08-3d6a-4a96-b261-95d519dcdc0b')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-800e1c08-3d6a-4a96-b261-95d519dcdc0b button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_a6cf3d86-54ad-4bff-8148-0137dd4c8c3f\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_a6cf3d86-54ad-4bff-8148-0137dd4c8c3f button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 614,\n  \"fields\": [\n    {\n      \"column\": \"Loan_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 614,\n        \"samples\": [\n          \"LP002139\",\n          \"LP002223\",\n          \"LP001570\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1\",\n          \"3+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Graduate\",\n          \"Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6109,\n        \"min\": 150,\n        \"max\": 81000,\n        \"num_unique_values\": 505,\n        \"samples\": [\n          8333,\n          4342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2926.2483692241885,\n        \"min\": 0.0,\n        \"max\": 41667.0,\n        \"num_unique_values\": 287,\n        \"samples\": [\n          1840.0,\n          2042.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.58732523570545,\n        \"min\": 9.0,\n        \"max\": 700.0,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          100.0,\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.12040985461256,\n        \"min\": 12.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          84.0,\n          120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3648783192364048,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Urban\",\n          \"Rural\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\",\n          \"Y\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "441oee6IykAY",
+        "outputId": "0893db3a-ab02-43f7-b77b-2b8a1cb97857"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(614, 13)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 243
+        },
+        "id": "96vdZk4YynBy",
+        "outputId": "b7381bd5-0f41-483e-ed6b-3915bf1284c3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
+              "0  LP001002   Male      No          0      Graduate            No   \n",
+              "1  LP001003   Male     Yes          1      Graduate            No   \n",
+              "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
+              "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
+              "4  LP001008   Male      No          0      Graduate            No   \n",
+              "\n",
+              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
+              "0             5849                0.0         NaN             360.0   \n",
+              "1             4583             1508.0       128.0             360.0   \n",
+              "2             3000                0.0        66.0             360.0   \n",
+              "3             2583             2358.0       120.0             360.0   \n",
+              "4             6000                0.0       141.0             360.0   \n",
+              "\n",
+              "   Credit_History Property_Area Loan_Status  \n",
+              "0             1.0         Urban           Y  \n",
+              "1             1.0         Rural           N  \n",
+              "2             1.0         Urban           Y  \n",
+              "3             1.0         Urban           Y  \n",
+              "4             1.0         Urban           Y  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b40ba6d7-2441-4599-8e58-763f7d92f24a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Loan_ID</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Married</th>\n",
+              "      <th>Dependents</th>\n",
+              "      <th>Education</th>\n",
+              "      <th>Self_Employed</th>\n",
+              "      <th>ApplicantIncome</th>\n",
+              "      <th>CoapplicantIncome</th>\n",
+              "      <th>LoanAmount</th>\n",
+              "      <th>Loan_Amount_Term</th>\n",
+              "      <th>Credit_History</th>\n",
+              "      <th>Property_Area</th>\n",
+              "      <th>Loan_Status</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>LP001002</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>5849</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>Y</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LP001003</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>4583</td>\n",
+              "      <td>1508.0</td>\n",
+              "      <td>128.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Rural</td>\n",
+              "      <td>N</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LP001005</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>3000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>Y</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>LP001006</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Not Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>2583</td>\n",
+              "      <td>2358.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>Y</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>LP001008</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>6000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>141.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>Y</td>\n",
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+              "    <div class=\"colab-df-buttons\">\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
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+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b40ba6d7-2441-4599-8e58-763f7d92f24a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b40ba6d7-2441-4599-8e58-763f7d92f24a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "    height: 32px;\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
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+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
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+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
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+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 614,\n  \"fields\": [\n    {\n      \"column\": \"Loan_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 614,\n        \"samples\": [\n          \"LP002139\",\n          \"LP002223\",\n          \"LP001570\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1\",\n          \"3+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Graduate\",\n          \"Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6109,\n        \"min\": 150,\n        \"max\": 81000,\n        \"num_unique_values\": 505,\n        \"samples\": [\n          8333,\n          4342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2926.2483692241885,\n        \"min\": 0.0,\n        \"max\": 41667.0,\n        \"num_unique_values\": 287,\n        \"samples\": [\n          1840.0,\n          2042.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.58732523570545,\n        \"min\": 9.0,\n        \"max\": 700.0,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          100.0,\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.12040985461256,\n        \"min\": 12.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          84.0,\n          120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3648783192364048,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Urban\",\n          \"Rural\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\",\n          \"Y\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "ej4jH9u7yv-P",
+        "outputId": "8a1bbf89-afcd-4ccc-be67-90134dd232ef"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
+              "count       614.000000         614.000000  592.000000         600.00000   \n",
+              "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
+              "std        6109.041673        2926.248369   85.587325          65.12041   \n",
+              "min         150.000000           0.000000    9.000000          12.00000   \n",
+              "25%        2877.500000           0.000000  100.000000         360.00000   \n",
+              "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
+              "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
+              "max       81000.000000       41667.000000  700.000000         480.00000   \n",
+              "\n",
+              "       Credit_History  \n",
+              "count      564.000000  \n",
+              "mean         0.842199  \n",
+              "std          0.364878  \n",
+              "min          0.000000  \n",
+              "25%          1.000000  \n",
+              "50%          1.000000  \n",
+              "75%          1.000000  \n",
+              "max          1.000000  "
+            ],
+            "text/html": [
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+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ApplicantIncome</th>\n",
+              "      <th>CoapplicantIncome</th>\n",
+              "      <th>LoanAmount</th>\n",
+              "      <th>Loan_Amount_Term</th>\n",
+              "      <th>Credit_History</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>614.000000</td>\n",
+              "      <td>614.000000</td>\n",
+              "      <td>592.000000</td>\n",
+              "      <td>600.00000</td>\n",
+              "      <td>564.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>5403.459283</td>\n",
+              "      <td>1621.245798</td>\n",
+              "      <td>146.412162</td>\n",
+              "      <td>342.00000</td>\n",
+              "      <td>0.842199</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>6109.041673</td>\n",
+              "      <td>2926.248369</td>\n",
+              "      <td>85.587325</td>\n",
+              "      <td>65.12041</td>\n",
+              "      <td>0.364878</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>150.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>12.00000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>2877.500000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>360.00000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>3812.500000</td>\n",
+              "      <td>1188.500000</td>\n",
+              "      <td>128.000000</td>\n",
+              "      <td>360.00000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>5795.000000</td>\n",
+              "      <td>2297.250000</td>\n",
+              "      <td>168.000000</td>\n",
+              "      <td>360.00000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>81000.000000</td>\n",
+              "      <td>41667.000000</td>\n",
+              "      <td>700.000000</td>\n",
+              "      <td>480.00000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b032cc44-ef19-45a0-9bc0-4402cc1249c6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b032cc44-ef19-45a0-9bc0-4402cc1249c6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b032cc44-ef19-45a0-9bc0-4402cc1249c6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-3aaf7f2a-d22f-40a9-bf4c-e2bd55e4b373\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3aaf7f2a-d22f-40a9-bf4c-e2bd55e4b373')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-3aaf7f2a-d22f-40a9-bf4c-e2bd55e4b373 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27480.19432327756,\n        \"min\": 150.0,\n        \"max\": 81000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5403.459283387622,\n          3812.5,\n          614.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14332.564053846478,\n        \"min\": 0.0,\n        \"max\": 41667.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          614.0,\n          1621.2457980271008,\n          2297.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 256.0091610169859,\n        \"min\": 9.0,\n        \"max\": 700.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          146.41216216216216,\n          128.0,\n          592.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 196.05608042946525,\n        \"min\": 12.0,\n        \"max\": 600.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          600.0,\n          342.0,\n          480.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 199.14146277938593,\n        \"min\": 0.0,\n        \"max\": 564.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.8421985815602837,\n          1.0,\n          0.3648783192364048\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1eLFMAD-y0k9",
+        "outputId": "a5b2a275-3bbd-418e-d1b2-a0beff35f65e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Loan_ID               0\n",
+              "Gender               13\n",
+              "Married               3\n",
+              "Dependents           15\n",
+              "Education             0\n",
+              "Self_Employed        32\n",
+              "ApplicantIncome       0\n",
+              "CoapplicantIncome     0\n",
+              "LoanAmount           22\n",
+              "Loan_Amount_Term     14\n",
+              "Credit_History       50\n",
+              "Property_Area         0\n",
+              "Loan_Status           0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df=df.dropna()"
+      ],
+      "metadata": {
+        "id": "mRTpcW3Vy9X1"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vnJJCNvD0CzM",
+        "outputId": "a02094d4-c2f6-4e47-df5e-9ad764fd5e0f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Loan_ID              0\n",
+              "Gender               0\n",
+              "Married              0\n",
+              "Dependents           0\n",
+              "Education            0\n",
+              "Self_Employed        0\n",
+              "ApplicantIncome      0\n",
+              "CoapplicantIncome    0\n",
+              "LoanAmount           0\n",
+              "Loan_Amount_Term     0\n",
+              "Credit_History       0\n",
+              "Property_Area        0\n",
+              "Loan_Status          0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.replace({\"Loan_Status\":{'N':0,'Y':1}},inplace=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kEZp4Z8V0Fc8",
+        "outputId": "ecdb1eb3-bc7f-4449-ad38-545ab5915347"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-10-fd5d5f81fe19>:1: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df.replace({\"Loan_Status\":{'N':0,'Y':1}},inplace=True)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 243
+        },
+        "id": "wekc07nU0ZAs",
+        "outputId": "ad92abc8-c409-4120-b370-e05773c608bf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
+              "1  LP001003   Male     Yes          1      Graduate            No   \n",
+              "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
+              "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
+              "4  LP001008   Male      No          0      Graduate            No   \n",
+              "5  LP001011   Male     Yes          2      Graduate           Yes   \n",
+              "\n",
+              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
+              "1             4583             1508.0       128.0             360.0   \n",
+              "2             3000                0.0        66.0             360.0   \n",
+              "3             2583             2358.0       120.0             360.0   \n",
+              "4             6000                0.0       141.0             360.0   \n",
+              "5             5417             4196.0       267.0             360.0   \n",
+              "\n",
+              "   Credit_History Property_Area  Loan_Status  \n",
+              "1             1.0         Rural            0  \n",
+              "2             1.0         Urban            1  \n",
+              "3             1.0         Urban            1  \n",
+              "4             1.0         Urban            1  \n",
+              "5             1.0         Urban            1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0868745f-372f-475f-84db-a5e9d8987338\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Loan_ID</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Married</th>\n",
+              "      <th>Dependents</th>\n",
+              "      <th>Education</th>\n",
+              "      <th>Self_Employed</th>\n",
+              "      <th>ApplicantIncome</th>\n",
+              "      <th>CoapplicantIncome</th>\n",
+              "      <th>LoanAmount</th>\n",
+              "      <th>Loan_Amount_Term</th>\n",
+              "      <th>Credit_History</th>\n",
+              "      <th>Property_Area</th>\n",
+              "      <th>Loan_Status</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LP001003</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>4583</td>\n",
+              "      <td>1508.0</td>\n",
+              "      <td>128.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Rural</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LP001005</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>3000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>LP001006</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Not Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>2583</td>\n",
+              "      <td>2358.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>LP001008</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>No</td>\n",
+              "      <td>6000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>141.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>LP001011</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Graduate</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>5417</td>\n",
+              "      <td>4196.0</td>\n",
+              "      <td>267.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Urban</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0868745f-372f-475f-84db-a5e9d8987338')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0868745f-372f-475f-84db-a5e9d8987338 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0868745f-372f-475f-84db-a5e9d8987338');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-fc9554f6-e2dc-4720-a6ec-7b0ce6b16911\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fc9554f6-e2dc-4720-a6ec-7b0ce6b16911')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-fc9554f6-e2dc-4720-a6ec-7b0ce6b16911 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 480,\n  \"fields\": [\n    {\n      \"column\": \"Loan_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 480,\n        \"samples\": [\n          \"LP001319\",\n          \"LP002716\",\n          \"LP002622\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"0\",\n          \"3+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Graduate\",\n          \"Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5668,\n        \"min\": 150,\n        \"max\": 81000,\n        \"num_unique_values\": 405,\n        \"samples\": [\n          2484,\n          3717\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2617.6922669225105,\n        \"min\": 0.0,\n        \"max\": 33837.0,\n        \"num_unique_values\": 232,\n        \"samples\": [\n          2064.0,\n          2083.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.50816421360777,\n        \"min\": 9.0,\n        \"max\": 600.0,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          192.0,\n          208.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.21240068043208,\n        \"min\": 36.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          36.0,\n          120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3533072691637982,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Rural\",\n          \"Urban\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['Dependents'].value_counts()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CPr1WpwV0yJT",
+        "outputId": "63df5031-0906-4b4f-ac91-27ce475d27d5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Dependents\n",
+              "0     274\n",
+              "2      85\n",
+              "1      80\n",
+              "3+     41\n",
+              "Name: count, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.replace(to_replace='3+',value=4,inplace=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mcbfVwaK5O4s",
+        "outputId": "db0a6a2a-3432-4c23-9c35-59c597285883"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-13-58532b24d642>:1: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df.replace(to_replace='3+',value=4,inplace=True)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['Dependents'].value_counts()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ONxcCmm_5b6s",
+        "outputId": "77e72b27-f0b1-4dc6-9c9a-3a9e096fad51"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Dependents\n",
+              "0    274\n",
+              "2     85\n",
+              "1     80\n",
+              "4     41\n",
+              "Name: count, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.countplot(x='Education',hue='Loan_Status',data=df)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "id": "8PWuTfN55iDF",
+        "outputId": "7fd1db05-0c2b-47e3-99df-89462b37f9b2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='Education', ylabel='count'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.countplot(x='Married',hue='Loan_Status',data=df)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "id": "AF_NVMLs58ZA",
+        "outputId": "a2d60213-7c36-4834-8508-d57472424737"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='Married', ylabel='count'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.replace({'Married':{'No':0,'Yes':1},'Gender':{'Male':1,'Female':0},'Self_Employed':{'No':0,'Yes':1},\n",
+        "                      'Property_Area':{'Rural':0,'Semiurban':1,'Urban':2},'Education':{'Graduate':1,'Not Graduate':0}},inplace=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TvowS78I6EqL",
+        "outputId": "432b9fc4-2f2b-47ae-e5ce-636375c30212"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-17-3bd62ffdcef9>:1: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df.replace({'Married':{'No':0,'Yes':1},'Gender':{'Male':1,'Female':0},'Self_Employed':{'No':0,'Yes':1},\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "i2Yvp9Jj6LQA",
+        "outputId": "65f172fe-b8fa-4cd3-a049-0afc8348dba6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Loan_ID  Gender  Married Dependents  Education  Self_Employed  \\\n",
+              "1  LP001003       1        1          1          1              0   \n",
+              "2  LP001005       1        1          0          1              1   \n",
+              "3  LP001006       1        1          0          0              0   \n",
+              "4  LP001008       1        0          0          1              0   \n",
+              "5  LP001011       1        1          2          1              1   \n",
+              "\n",
+              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
+              "1             4583             1508.0       128.0             360.0   \n",
+              "2             3000                0.0        66.0             360.0   \n",
+              "3             2583             2358.0       120.0             360.0   \n",
+              "4             6000                0.0       141.0             360.0   \n",
+              "5             5417             4196.0       267.0             360.0   \n",
+              "\n",
+              "   Credit_History  Property_Area  Loan_Status  \n",
+              "1             1.0              0            0  \n",
+              "2             1.0              2            1  \n",
+              "3             1.0              2            1  \n",
+              "4             1.0              2            1  \n",
+              "5             1.0              2            1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5d645ce6-63f9-4c5e-8954-067bb163e3a7\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Loan_ID</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Married</th>\n",
+              "      <th>Dependents</th>\n",
+              "      <th>Education</th>\n",
+              "      <th>Self_Employed</th>\n",
+              "      <th>ApplicantIncome</th>\n",
+              "      <th>CoapplicantIncome</th>\n",
+              "      <th>LoanAmount</th>\n",
+              "      <th>Loan_Amount_Term</th>\n",
+              "      <th>Credit_History</th>\n",
+              "      <th>Property_Area</th>\n",
+              "      <th>Loan_Status</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>LP001003</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4583</td>\n",
+              "      <td>1508.0</td>\n",
+              "      <td>128.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>LP001005</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>66.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>LP001006</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2583</td>\n",
+              "      <td>2358.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>LP001008</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>141.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>LP001011</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5417</td>\n",
+              "      <td>4196.0</td>\n",
+              "      <td>267.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d645ce6-63f9-4c5e-8954-067bb163e3a7')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-5d645ce6-63f9-4c5e-8954-067bb163e3a7 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-5d645ce6-63f9-4c5e-8954-067bb163e3a7');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-c6b94a28-fc21-4638-817c-b325ae690d4d\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c6b94a28-fc21-4638-817c-b325ae690d4d')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-c6b94a28-fc21-4638-817c-b325ae690d4d button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 480,\n  \"fields\": [\n    {\n      \"column\": \"Loan_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 480,\n        \"samples\": [\n          \"LP001319\",\n          \"LP002716\",\n          \"LP002622\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"0\",\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5668,\n        \"min\": 150,\n        \"max\": 81000,\n        \"num_unique_values\": 405,\n        \"samples\": [\n          2484,\n          3717\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2617.6922669225105,\n        \"min\": 0.0,\n        \"max\": 33837.0,\n        \"num_unique_values\": 232,\n        \"samples\": [\n          2064.0,\n          2083.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80.50816421360777,\n        \"min\": 9.0,\n        \"max\": 600.0,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          192.0,\n          208.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.21240068043208,\n        \"min\": 36.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          36.0,\n          120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3533072691637982,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X=df.drop(columns=['Loan_ID','Loan_Status'],axis=1)\n",
+        "y=df['Loan_Status']\n"
+      ],
+      "metadata": {
+        "id": "kBfmLZlh6Spy"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,stratify=y,random_state=2)\n"
+      ],
+      "metadata": {
+        "id": "xDizuEqX6wQv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model=svm.SVC(kernel='linear')"
+      ],
+      "metadata": {
+        "id": "8a7vdPP79h9d"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.fit(X_train,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "n0rJxiU29oOi",
+        "outputId": "5b1fe06d-0213-47f9-f13c-cd5985ba80f8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SVC(kernel='linear')"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "## Checking the accuracy"
+      ],
+      "metadata": {
+        "id": "pueK0CeF92-O"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train_prediction = model.predict(X_train)\n",
+        "training_data_accuray = accuracy_score(X_train_prediction,y_train)"
+      ],
+      "metadata": {
+        "id": "JKLG0fTv3pys"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Accuracy on training data : ', training_data_accuray)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7-bfkFeD32cC",
+        "outputId": "b19e384e-adeb-4c87-fce0-99806a536891"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy on training data :  0.7986111111111112\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# accuracy score on training data\n",
+        "X_test_prediction = model.predict(X_test)\n",
+        "test_data_accuray = accuracy_score(X_test_prediction,y_test)"
+      ],
+      "metadata": {
+        "id": "cUNfpqTY35_z"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('Accuracy on test data : ', test_data_accuray)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "j_CgCl3m4H2Q",
+        "outputId": "8dac868c-3ec0-4138-c784-65f635808645"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy on test data :  0.8333333333333334\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "yhxIAhY64KZE"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From f7b3190f6b27d2e1cf53d5430bd49de3e9443071 Mon Sep 17 00:00:00 2001
From: Seersha Samikshya <106761122+Seersha9802@users.noreply.github.com>
Date: Sat, 25 May 2024 12:18:18 +0530
Subject: [PATCH 3/3] Delete Loan Status Prediction/xyz

---
 Loan Status Prediction/xyz | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 Loan Status Prediction/xyz

diff --git a/Loan Status Prediction/xyz b/Loan Status Prediction/xyz
deleted file mode 100644
index 8b137891..00000000
--- a/Loan Status Prediction/xyz	
+++ /dev/null
@@ -1 +0,0 @@
-