From 14c4b87cc1c4570d2d86fb0ee053873e5469d5a1 Mon Sep 17 00:00:00 2001 From: Aviral Garg <105068445+aviralgarg05@users.noreply.github.com> Date: Tue, 9 Jul 2024 14:45:00 +0530 Subject: [PATCH] Add files via upload --- .../Loan Prediction Datasets/test.csv | 368 ++++ .../Loan Prediction Datasets/train.csv | 615 +++++++ .../Loan Prediction/Loan Prediction.ipynb | 1586 +++++++++++++++++ Loan Prediciton Project/README.md | 113 ++ 4 files changed, 2682 insertions(+) create mode 100644 Loan Prediciton Project/Loan Prediction/Loan Prediction Datasets/test.csv create mode 100644 Loan Prediciton Project/Loan Prediction/Loan Prediction Datasets/train.csv create mode 100644 Loan Prediciton Project/Loan Prediction/Loan Prediction.ipynb create mode 100644 Loan Prediciton Project/README.md diff --git a/Loan Prediciton Project/Loan Prediction/Loan Prediction Datasets/test.csv b/Loan Prediciton Project/Loan Prediction/Loan Prediction Datasets/test.csv new file mode 100644 index 000000000..609a1bc9b --- /dev/null +++ b/Loan Prediciton Project/Loan Prediction/Loan Prediction Datasets/test.csv @@ -0,0 +1,368 @@ +Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area +LP001015,Male,Yes,0,Graduate,No,5720,0,110,360,1,Urban +LP001022,Male,Yes,1,Graduate,No,3076,1500,126,360,1,Urban +LP001031,Male,Yes,2,Graduate,No,5000,1800,208,360,1,Urban +LP001035,Male,Yes,2,Graduate,No,2340,2546,100,360,,Urban +LP001051,Male,No,0,Not Graduate,No,3276,0,78,360,1,Urban +LP001054,Male,Yes,0,Not Graduate,Yes,2165,3422,152,360,1,Urban +LP001055,Female,No,1,Not Graduate,No,2226,0,59,360,1,Semiurban +LP001056,Male,Yes,2,Not Graduate,No,3881,0,147,360,0,Rural +LP001059,Male,Yes,2,Graduate,,13633,0,280,240,1,Urban +LP001067,Male,No,0,Not Graduate,No,2400,2400,123,360,1,Semiurban +LP001078,Male,No,0,Not Graduate,No,3091,0,90,360,1,Urban +LP001082,Male,Yes,1,Graduate,,2185,1516,162,360,1,Semiurban +LP001083,Male,No,3+,Graduate,No,4166,0,40,180,,Urban +LP001094,Male,Yes,2,Graduate,,12173,0,166,360,0,Semiurban +LP001096,Female,No,0,Graduate,No,4666,0,124,360,1,Semiurban +LP001099,Male,No,1,Graduate,No,5667,0,131,360,1,Urban +LP001105,Male,Yes,2,Graduate,No,4583,2916,200,360,1,Urban +LP001107,Male,Yes,3+,Graduate,No,3786,333,126,360,1,Semiurban +LP001108,Male,Yes,0,Graduate,No,9226,7916,300,360,1,Urban +LP001115,Male,No,0,Graduate,No,1300,3470,100,180,1,Semiurban +LP001121,Male,Yes,1,Not Graduate,No,1888,1620,48,360,1,Urban +LP001124,Female,No,3+,Not Graduate,No,2083,0,28,180,1,Urban +LP001128,,No,0,Graduate,No,3909,0,101,360,1,Urban +LP001135,Female,No,0,Not Graduate,No,3765,0,125,360,1,Urban +LP001149,Male,Yes,0,Graduate,No,5400,4380,290,360,1,Urban +LP001153,Male,No,0,Graduate,No,0,24000,148,360,0,Rural +LP001163,Male,Yes,2,Graduate,No,4363,1250,140,360,,Urban +LP001169,Male,Yes,0,Graduate,No,7500,3750,275,360,1,Urban +LP001174,Male,Yes,0,Graduate,No,3772,833,57,360,,Semiurban +LP001176,Male,No,0,Graduate,No,2942,2382,125,180,1,Urban +LP001177,Female,No,0,Not Graduate,No,2478,0,75,360,1,Semiurban +LP001183,Male,Yes,2,Graduate,No,6250,820,192,360,1,Urban +LP001185,Male,No,0,Graduate,No,3268,1683,152,360,1,Semiurban +LP001187,Male,Yes,0,Graduate,No,2783,2708,158,360,1,Urban +LP001190,Male,Yes,0,Graduate,No,2740,1541,101,360,1,Urban +LP001203,Male,No,0,Graduate,No,3150,0,176,360,0,Semiurban +LP001208,Male,Yes,2,Graduate,,7350,4029,185,180,1,Urban +LP001210,Male,Yes,0,Graduate,Yes,2267,2792,90,360,1,Urban +LP001211,Male,No,0,Graduate,Yes,5833,0,116,360,1,Urban +LP001219,Male,No,0,Graduate,No,3643,1963,138,360,1,Urban +LP001220,Male,Yes,0,Graduate,No,5629,818,100,360,1,Urban +LP001221,Female,No,0,Graduate,No,3644,0,110,360,1,Urban +LP001226,Male,Yes,0,Not Graduate,No,1750,2024,90,360,1,Semiurban +LP001230,Male,No,0,Graduate,No,6500,2600,200,360,1,Semiurban +LP001231,Female,No,0,Graduate,No,3666,0,84,360,1,Urban +LP001232,Male,Yes,0,Graduate,No,4260,3900,185,,,Urban +LP001237,Male,Yes,,Not Graduate,No,4163,1475,162,360,1,Urban +LP001242,Male,No,0,Not Graduate,No,2356,1902,108,360,1,Semiurban +LP001268,Male,No,0,Graduate,No,6792,3338,187,,1,Urban +LP001270,Male,Yes,3+,Not Graduate,Yes,8000,250,187,360,1,Semiurban +LP001284,Male,Yes,1,Graduate,No,2419,1707,124,360,1,Urban +LP001287,,Yes,3+,Not Graduate,No,3500,833,120,360,1,Semiurban +LP001291,Male,Yes,1,Graduate,No,3500,3077,160,360,1,Semiurban +LP001298,Male,Yes,2,Graduate,No,4116,1000,30,180,1,Urban +LP001312,Male,Yes,0,Not Graduate,Yes,5293,0,92,360,1,Urban +LP001313,Male,No,0,Graduate,No,2750,0,130,360,0,Urban +LP001317,Female,No,0,Not Graduate,No,4402,0,130,360,1,Rural +LP001321,Male,Yes,2,Graduate,No,3613,3539,134,180,1,Semiurban +LP001323,Female,Yes,2,Graduate,No,2779,3664,176,360,0,Semiurban +LP001324,Male,Yes,3+,Graduate,No,4720,0,90,180,1,Semiurban +LP001332,Male,Yes,0,Not Graduate,No,2415,1721,110,360,1,Semiurban +LP001335,Male,Yes,0,Graduate,Yes,7016,292,125,360,1,Urban +LP001338,Female,No,2,Graduate,No,4968,0,189,360,1,Semiurban +LP001347,Female,No,0,Graduate,No,2101,1500,108,360,0,Rural +LP001348,Male,Yes,3+,Not Graduate,No,4490,0,125,360,1,Urban +LP001351,Male,Yes,0,Graduate,No,2917,3583,138,360,1,Semiurban +LP001352,Male,Yes,0,Not Graduate,No,4700,0,135,360,0,Semiurban +LP001358,Male,Yes,0,Graduate,No,3445,0,130,360,0,Semiurban +LP001359,Male,Yes,0,Graduate,No,7666,0,187,360,1,Semiurban +LP001361,Male,Yes,0,Graduate,No,2458,5105,188,360,0,Rural +LP001366,Female,No,,Graduate,No,3250,0,95,360,1,Semiurban +LP001368,Male,No,0,Graduate,No,4463,0,65,360,1,Semiurban +LP001375,Male,Yes,1,Graduate,,4083,1775,139,60,1,Urban +LP001380,Male,Yes,0,Graduate,Yes,3900,2094,232,360,1,Rural +LP001386,Male,Yes,0,Not Graduate,No,4750,3583,144,360,1,Semiurban +LP001400,Male,No,0,Graduate,No,3583,3435,155,360,1,Urban +LP001407,Male,Yes,0,Graduate,No,3189,2367,186,360,1,Urban +LP001413,Male,No,0,Graduate,Yes,6356,0,50,360,1,Rural +LP001415,Male,Yes,1,Graduate,No,3413,4053,,360,1,Semiurban +LP001419,Female,Yes,0,Graduate,No,7950,0,185,360,1,Urban +LP001420,Male,Yes,3+,Graduate,No,3829,1103,163,360,0,Urban +LP001428,Male,Yes,3+,Graduate,No,72529,0,360,360,1,Urban +LP001445,Male,Yes,2,Not Graduate,No,4136,0,149,480,0,Rural +LP001446,Male,Yes,0,Graduate,No,8449,0,257,360,1,Rural +LP001450,Male,Yes,0,Graduate,No,4456,0,131,180,0,Semiurban +LP001452,Male,Yes,2,Graduate,No,4635,8000,102,180,1,Rural +LP001455,Male,Yes,0,Graduate,No,3571,1917,135,360,1,Urban +LP001466,Male,No,0,Graduate,No,3066,0,95,360,1,Semiurban +LP001471,Male,No,2,Not Graduate,No,3235,2015,77,360,1,Semiurban +LP001472,Female,No,0,Graduate,,5058,0,200,360,1,Rural +LP001475,Male,Yes,0,Graduate,Yes,3188,2286,130,360,,Rural +LP001483,Male,Yes,3+,Graduate,No,13518,0,390,360,1,Rural +LP001486,Male,Yes,1,Graduate,No,4364,2500,185,360,1,Semiurban +LP001490,Male,Yes,2,Not Graduate,No,4766,1646,100,360,1,Semiurban +LP001496,Male,Yes,1,Graduate,No,4609,2333,123,360,0,Semiurban +LP001499,Female,Yes,3+,Graduate,No,6260,0,110,360,1,Semiurban +LP001500,Male,Yes,1,Graduate,No,3333,4200,256,360,1,Urban +LP001501,Male,Yes,0,Graduate,No,3500,3250,140,360,1,Semiurban +LP001517,Male,Yes,3+,Graduate,No,9719,0,61,360,1,Urban +LP001527,Male,Yes,3+,Graduate,No,6835,0,188,360,,Semiurban +LP001534,Male,No,0,Graduate,No,4452,0,131,360,1,Rural +LP001542,Female,Yes,0,Graduate,No,2262,0,,480,0,Semiurban +LP001547,Male,Yes,1,Graduate,No,3901,0,116,360,1,Urban +LP001548,Male,Yes,2,Not Graduate,No,2687,0,50,180,1,Rural +LP001558,Male,No,0,Graduate,No,2243,2233,107,360,,Semiurban +LP001561,Female,Yes,0,Graduate,No,3417,1287,200,360,1,Semiurban +LP001563,,No,0,Graduate,No,1596,1760,119,360,0,Urban +LP001567,Male,Yes,3+,Graduate,No,4513,0,120,360,1,Rural +LP001568,Male,Yes,0,Graduate,No,4500,0,140,360,1,Semiurban +LP001573,Male,Yes,0,Not Graduate,No,4523,1350,165,360,1,Urban +LP001584,Female,No,0,Graduate,Yes,4742,0,108,360,1,Semiurban +LP001587,Male,Yes,,Graduate,No,4082,0,93,360,1,Semiurban +LP001589,Female,No,0,Graduate,No,3417,0,102,360,1,Urban +LP001591,Female,Yes,2,Graduate,No,2922,3396,122,360,1,Semiurban +LP001599,Male,Yes,0,Graduate,No,4167,4754,160,360,1,Rural +LP001601,Male,No,3+,Graduate,No,4243,4123,157,360,,Semiurban +LP001607,Female,No,0,Not Graduate,No,0,1760,180,360,1,Semiurban +LP001611,Male,Yes,1,Graduate,No,1516,2900,80,,0,Rural +LP001613,Female,No,0,Graduate,No,1762,2666,104,360,0,Urban +LP001622,Male,Yes,2,Graduate,No,724,3510,213,360,0,Rural +LP001627,Male,No,0,Graduate,No,3125,0,65,360,1,Urban +LP001650,Male,Yes,0,Graduate,No,2333,3803,146,360,1,Rural +LP001651,Male,Yes,3+,Graduate,No,3350,1560,135,360,1,Urban +LP001652,Male,No,0,Graduate,No,2500,6414,187,360,0,Rural +LP001655,Female,No,0,Graduate,No,12500,0,300,360,0,Urban +LP001660,Male,No,0,Graduate,No,4667,0,120,360,1,Semiurban +LP001662,Male,No,0,Graduate,No,6500,0,71,360,0,Urban +LP001663,Male,Yes,2,Graduate,No,7500,0,225,360,1,Urban +LP001667,Male,No,0,Graduate,No,3073,0,70,180,1,Urban +LP001695,Male,Yes,1,Not Graduate,No,3321,2088,70,,1,Semiurban +LP001703,Male,Yes,0,Graduate,No,3333,1270,124,360,1,Urban +LP001718,Male,No,0,Graduate,No,3391,0,132,360,1,Rural +LP001728,Male,Yes,1,Graduate,Yes,3343,1517,105,360,1,Rural +LP001735,Female,No,1,Graduate,No,3620,0,90,360,1,Urban +LP001737,Male,No,0,Graduate,No,4000,0,83,84,1,Urban +LP001739,Male,Yes,0,Graduate,No,4258,0,125,360,1,Urban +LP001742,Male,Yes,2,Graduate,No,4500,0,147,360,1,Rural +LP001757,Male,Yes,1,Graduate,No,2014,2925,120,360,1,Rural +LP001769,,No,,Graduate,No,3333,1250,110,360,1,Semiurban +LP001771,Female,No,3+,Graduate,No,4083,0,103,360,,Semiurban +LP001785,Male,No,0,Graduate,No,4727,0,150,360,0,Rural +LP001787,Male,Yes,3+,Graduate,No,3089,2999,100,240,1,Rural +LP001789,Male,Yes,3+,Not Graduate,,6794,528,139,360,0,Urban +LP001791,Male,Yes,0,Graduate,Yes,32000,0,550,360,,Semiurban +LP001794,Male,Yes,2,Graduate,Yes,10890,0,260,12,1,Rural +LP001797,Female,No,0,Graduate,No,12941,0,150,300,1,Urban +LP001815,Male,No,0,Not Graduate,No,3276,0,90,360,1,Semiurban +LP001817,Male,No,0,Not Graduate,Yes,8703,0,199,360,0,Rural +LP001818,Male,Yes,1,Graduate,No,4742,717,139,360,1,Semiurban +LP001822,Male,No,0,Graduate,No,5900,0,150,360,1,Urban +LP001827,Male,No,0,Graduate,No,3071,4309,180,360,1,Urban +LP001831,Male,Yes,0,Graduate,No,2783,1456,113,360,1,Urban +LP001842,Male,No,0,Graduate,No,5000,0,148,360,1,Rural +LP001853,Male,Yes,1,Not Graduate,No,2463,2360,117,360,0,Urban +LP001855,Male,Yes,2,Graduate,No,4855,0,72,360,1,Rural +LP001857,Male,No,0,Not Graduate,Yes,1599,2474,125,300,1,Semiurban +LP001862,Male,Yes,2,Graduate,Yes,4246,4246,214,360,1,Urban +LP001867,Male,Yes,0,Graduate,No,4333,2291,133,350,1,Rural +LP001878,Male,No,1,Graduate,No,5823,2529,187,360,1,Semiurban +LP001881,Male,Yes,0,Not Graduate,No,7895,0,143,360,1,Rural +LP001886,Male,No,0,Graduate,No,4150,4256,209,360,1,Rural +LP001906,Male,No,0,Graduate,,2964,0,84,360,0,Semiurban +LP001909,Male,No,0,Graduate,No,5583,0,116,360,1,Urban +LP001911,Female,No,0,Graduate,No,2708,0,65,360,1,Rural +LP001921,Male,No,1,Graduate,No,3180,2370,80,240,,Rural +LP001923,Male,No,0,Not Graduate,No,2268,0,170,360,0,Semiurban +LP001933,Male,No,2,Not Graduate,No,1141,2017,120,360,0,Urban +LP001943,Male,Yes,0,Graduate,No,3042,3167,135,360,1,Urban +LP001950,Female,Yes,3+,Graduate,,1750,2935,94,360,0,Semiurban +LP001959,Female,Yes,1,Graduate,No,3564,0,79,360,1,Rural +LP001961,Female,No,0,Graduate,No,3958,0,110,360,1,Rural +LP001973,Male,Yes,2,Not Graduate,No,4483,0,130,360,1,Rural +LP001975,Male,Yes,0,Graduate,No,5225,0,143,360,1,Rural +LP001979,Male,No,0,Graduate,No,3017,2845,159,180,0,Urban +LP001995,Male,Yes,0,Not Graduate,No,2431,1820,110,360,0,Rural +LP001999,Male,Yes,2,Graduate,,4912,4614,160,360,1,Rural +LP002007,Male,Yes,2,Not Graduate,No,2500,3333,131,360,1,Urban +LP002009,Female,No,0,Graduate,No,2918,0,65,360,,Rural +LP002016,Male,Yes,2,Graduate,No,5128,0,143,360,1,Rural +LP002017,Male,Yes,3+,Graduate,No,15312,0,187,360,,Urban +LP002018,Male,Yes,2,Graduate,No,3958,2632,160,360,1,Semiurban +LP002027,Male,Yes,0,Graduate,No,4334,2945,165,360,1,Semiurban +LP002028,Male,Yes,2,Graduate,No,4358,0,110,360,1,Urban +LP002042,Female,Yes,1,Graduate,No,4000,3917,173,360,1,Rural +LP002045,Male,Yes,3+,Graduate,No,10166,750,150,,1,Urban +LP002046,Male,Yes,0,Not Graduate,No,4483,0,135,360,,Semiurban +LP002047,Male,Yes,2,Not Graduate,No,4521,1184,150,360,1,Semiurban +LP002056,Male,Yes,2,Graduate,No,9167,0,235,360,1,Semiurban +LP002057,Male,Yes,0,Not Graduate,No,13083,0,,360,1,Rural +LP002059,Male,Yes,2,Graduate,No,7874,3967,336,360,1,Rural +LP002062,Female,Yes,1,Graduate,No,4333,0,132,84,1,Rural +LP002064,Male,No,0,Graduate,No,4083,0,96,360,1,Urban +LP002069,Male,Yes,2,Not Graduate,,3785,2912,180,360,0,Rural +LP002070,Male,Yes,3+,Not Graduate,No,2654,1998,128,360,0,Rural +LP002077,Male,Yes,1,Graduate,No,10000,2690,412,360,1,Semiurban +LP002083,Male,No,0,Graduate,Yes,5833,0,116,360,1,Urban +LP002090,Male,Yes,1,Graduate,No,4796,0,114,360,0,Semiurban +LP002096,Male,Yes,0,Not Graduate,No,2000,1600,115,360,1,Rural +LP002099,Male,Yes,2,Graduate,No,2540,700,104,360,0,Urban +LP002102,Male,Yes,0,Graduate,Yes,1900,1442,88,360,1,Rural +LP002105,Male,Yes,0,Graduate,Yes,8706,0,108,480,1,Rural +LP002107,Male,Yes,3+,Not Graduate,No,2855,542,90,360,1,Urban +LP002111,Male,Yes,,Graduate,No,3016,1300,100,360,,Urban +LP002117,Female,Yes,0,Graduate,No,3159,2374,108,360,1,Semiurban +LP002118,Female,No,0,Graduate,No,1937,1152,78,360,1,Semiurban +LP002123,Male,Yes,0,Graduate,No,2613,2417,123,360,1,Semiurban +LP002125,Male,Yes,1,Graduate,No,4960,2600,187,360,1,Semiurban +LP002148,Male,Yes,1,Graduate,No,3074,1083,146,360,1,Semiurban +LP002152,Female,No,0,Graduate,No,4213,0,80,360,1,Urban +LP002165,,No,1,Not Graduate,No,2038,4027,100,360,1,Rural +LP002167,Female,No,0,Graduate,No,2362,0,55,360,1,Urban +LP002168,Male,No,0,Graduate,No,5333,2400,200,360,0,Rural +LP002172,Male,Yes,3+,Graduate,Yes,5384,0,150,360,1,Semiurban +LP002176,Male,No,0,Graduate,No,5708,0,150,360,1,Rural +LP002183,Male,Yes,0,Not Graduate,No,3754,3719,118,,1,Rural +LP002184,Male,Yes,0,Not Graduate,No,2914,2130,150,300,1,Urban +LP002186,Male,Yes,0,Not Graduate,No,2747,2458,118,36,1,Semiurban +LP002192,Male,Yes,0,Graduate,No,7830,2183,212,360,1,Rural +LP002195,Male,Yes,1,Graduate,Yes,3507,3148,212,360,1,Rural +LP002208,Male,Yes,1,Graduate,No,3747,2139,125,360,1,Urban +LP002212,Male,Yes,0,Graduate,No,2166,2166,108,360,,Urban +LP002240,Male,Yes,0,Not Graduate,No,3500,2168,149,360,1,Rural +LP002245,Male,Yes,2,Not Graduate,No,2896,0,80,480,1,Urban +LP002253,Female,No,1,Graduate,No,5062,0,152,300,1,Rural +LP002256,Female,No,2,Graduate,Yes,5184,0,187,360,0,Semiurban +LP002257,Female,No,0,Graduate,No,2545,0,74,360,1,Urban +LP002264,Male,Yes,0,Graduate,No,2553,1768,102,360,1,Urban +LP002270,Male,Yes,1,Graduate,No,3436,3809,100,360,1,Rural +LP002279,Male,No,0,Graduate,No,2412,2755,130,360,1,Rural +LP002286,Male,Yes,3+,Not Graduate,No,5180,0,125,360,0,Urban +LP002294,Male,No,0,Graduate,No,14911,14507,130,360,1,Semiurban +LP002298,,No,0,Graduate,Yes,2860,2988,138,360,1,Urban +LP002306,Male,Yes,0,Graduate,No,1173,1594,28,180,1,Rural +LP002310,Female,No,1,Graduate,No,7600,0,92,360,1,Semiurban +LP002311,Female,Yes,0,Graduate,No,2157,1788,104,360,1,Urban +LP002316,Male,No,0,Graduate,No,2231,2774,176,360,0,Urban +LP002321,Female,No,0,Graduate,No,2274,5211,117,360,0,Semiurban +LP002325,Male,Yes,2,Not Graduate,No,6166,13983,102,360,1,Rural +LP002326,Male,Yes,2,Not Graduate,No,2513,1110,107,360,1,Semiurban +LP002329,Male,No,0,Graduate,No,4333,0,66,480,1,Urban +LP002333,Male,No,0,Not Graduate,No,3844,0,105,360,1,Urban +LP002339,Male,Yes,0,Graduate,No,3887,1517,105,360,0,Semiurban +LP002344,Male,Yes,0,Graduate,No,3510,828,105,360,1,Semiurban +LP002346,Male,Yes,0,Graduate,,2539,1704,125,360,0,Rural +LP002354,Female,No,0,Not Graduate,No,2107,0,64,360,1,Semiurban +LP002355,,Yes,0,Graduate,No,3186,3145,150,180,0,Semiurban +LP002358,Male,Yes,2,Graduate,Yes,5000,2166,150,360,1,Urban +LP002360,Male,Yes,,Graduate,No,10000,0,,360,1,Urban +LP002375,Male,Yes,0,Not Graduate,Yes,3943,0,64,360,1,Semiurban +LP002376,Male,No,0,Graduate,No,2925,0,40,180,1,Rural +LP002383,Male,Yes,3+,Graduate,No,3242,437,142,480,0,Urban +LP002385,Male,Yes,,Graduate,No,3863,0,70,300,1,Semiurban +LP002389,Female,No,1,Graduate,No,4028,0,131,360,1,Semiurban +LP002394,Male,Yes,2,Graduate,No,4010,1025,120,360,1,Urban +LP002397,Female,Yes,1,Graduate,No,3719,1585,114,360,1,Urban +LP002399,Male,No,0,Graduate,,2858,0,123,360,0,Rural +LP002400,Female,Yes,0,Graduate,No,3833,0,92,360,1,Rural +LP002402,Male,Yes,0,Graduate,No,3333,4288,160,360,1,Urban +LP002412,Male,Yes,0,Graduate,No,3007,3725,151,360,1,Rural +LP002415,Female,No,1,Graduate,,1850,4583,81,360,,Rural +LP002417,Male,Yes,3+,Not Graduate,No,2792,2619,171,360,1,Semiurban +LP002420,Male,Yes,0,Graduate,No,2982,1550,110,360,1,Semiurban +LP002425,Male,No,0,Graduate,No,3417,738,100,360,,Rural +LP002433,Male,Yes,1,Graduate,No,18840,0,234,360,1,Rural +LP002440,Male,Yes,2,Graduate,No,2995,1120,184,360,1,Rural +LP002441,Male,No,,Graduate,No,3579,3308,138,360,,Semiurban +LP002442,Female,Yes,1,Not Graduate,No,3835,1400,112,480,0,Urban +LP002445,Female,No,1,Not Graduate,No,3854,3575,117,360,1,Rural +LP002450,Male,Yes,2,Graduate,No,5833,750,49,360,0,Rural +LP002471,Male,No,0,Graduate,No,3508,0,99,360,1,Rural +LP002476,Female,Yes,3+,Not Graduate,No,1635,2444,99,360,1,Urban +LP002482,Female,No,0,Graduate,Yes,3333,3916,212,360,1,Rural +LP002485,Male,No,1,Graduate,No,24797,0,240,360,1,Semiurban +LP002495,Male,Yes,2,Graduate,No,5667,440,130,360,0,Semiurban +LP002496,Female,No,0,Graduate,No,3500,0,94,360,0,Semiurban +LP002523,Male,Yes,3+,Graduate,No,2773,1497,108,360,1,Semiurban +LP002542,Male,Yes,0,Graduate,,6500,0,144,360,1,Urban +LP002550,Female,No,0,Graduate,No,5769,0,110,180,1,Semiurban +LP002551,Male,Yes,3+,Not Graduate,,3634,910,176,360,0,Semiurban +LP002553,,No,0,Graduate,No,29167,0,185,360,1,Semiurban +LP002554,Male,No,0,Graduate,No,2166,2057,122,360,1,Semiurban +LP002561,Male,Yes,0,Graduate,No,5000,0,126,360,1,Rural +LP002566,Female,No,0,Graduate,No,5530,0,135,360,,Urban +LP002568,Male,No,0,Not Graduate,No,9000,0,122,360,1,Rural +LP002570,Female,Yes,2,Graduate,No,10000,11666,460,360,1,Urban +LP002572,Male,Yes,1,Graduate,,8750,0,297,360,1,Urban +LP002581,Male,Yes,0,Not Graduate,No,2157,2730,140,360,,Rural +LP002584,Male,No,0,Graduate,,1972,4347,106,360,1,Rural +LP002592,Male,No,0,Graduate,No,4983,0,141,360,1,Urban +LP002593,Male,Yes,1,Graduate,No,8333,4000,,360,1,Urban +LP002599,Male,Yes,0,Graduate,No,3667,2000,170,360,1,Semiurban +LP002604,Male,Yes,2,Graduate,No,3166,2833,145,360,1,Urban +LP002605,Male,No,0,Not Graduate,No,3271,0,90,360,1,Rural +LP002609,Female,Yes,0,Graduate,No,2241,2000,88,360,0,Urban +LP002610,Male,Yes,1,Not Graduate,,1792,2565,128,360,1,Urban +LP002612,Female,Yes,0,Graduate,No,2666,0,84,480,1,Semiurban +LP002614,,No,0,Graduate,No,6478,0,108,360,1,Semiurban +LP002630,Male,No,0,Not Graduate,,3808,0,83,360,1,Rural +LP002635,Female,Yes,2,Not Graduate,No,3729,0,117,360,1,Semiurban +LP002639,Male,Yes,2,Graduate,No,4120,0,128,360,1,Rural +LP002644,Male,Yes,1,Graduate,Yes,7500,0,75,360,1,Urban +LP002651,Male,Yes,1,Graduate,,6300,0,125,360,0,Urban +LP002654,Female,No,,Graduate,Yes,14987,0,177,360,1,Rural +LP002657,,Yes,1,Not Graduate,Yes,570,2125,68,360,1,Rural +LP002711,Male,Yes,0,Graduate,No,2600,700,96,360,1,Semiurban +LP002712,Male,No,2,Not Graduate,No,2733,1083,180,360,,Semiurban +LP002721,Male,Yes,2,Graduate,Yes,7500,0,183,360,1,Rural +LP002735,Male,Yes,2,Not Graduate,No,3859,0,121,360,1,Rural +LP002744,Male,Yes,1,Graduate,No,6825,0,162,360,1,Rural +LP002745,Male,Yes,0,Graduate,No,3708,4700,132,360,1,Semiurban +LP002746,Male,No,0,Graduate,No,5314,0,147,360,1,Urban +LP002747,Female,No,3+,Graduate,No,2366,5272,153,360,0,Rural +LP002754,Male,No,,Graduate,No,2066,2108,104,84,1,Urban +LP002759,Male,Yes,2,Graduate,No,5000,0,149,360,1,Rural +LP002760,Female,No,0,Graduate,No,3767,0,134,300,1,Urban +LP002766,Female,Yes,0,Graduate,No,7859,879,165,180,1,Semiurban +LP002769,Female,Yes,0,Graduate,No,4283,0,120,360,1,Rural +LP002774,Male,Yes,0,Not Graduate,No,1700,2900,67,360,0,Urban +LP002775,,No,0,Not Graduate,No,4768,0,125,360,1,Rural +LP002781,Male,No,0,Graduate,No,3083,2738,120,360,1,Urban +LP002782,Male,Yes,1,Graduate,No,2667,1542,148,360,1,Rural +LP002786,Female,Yes,0,Not Graduate,No,1647,1762,181,360,1,Urban +LP002790,Male,Yes,3+,Graduate,No,3400,0,80,120,1,Urban +LP002791,Male,No,1,Graduate,,16000,5000,40,360,1,Semiurban +LP002793,Male,Yes,0,Graduate,No,5333,0,90,360,1,Rural +LP002802,Male,No,0,Graduate,No,2875,2416,95,6,0,Semiurban +LP002803,Male,Yes,1,Not Graduate,,2600,618,122,360,1,Semiurban +LP002805,Male,Yes,2,Graduate,No,5041,700,150,360,1,Urban +LP002806,Male,Yes,3+,Graduate,Yes,6958,1411,150,360,1,Rural +LP002816,Male,Yes,1,Graduate,No,3500,1658,104,360,,Semiurban +LP002823,Male,Yes,0,Graduate,No,5509,0,143,360,1,Rural +LP002825,Male,Yes,3+,Graduate,No,9699,0,300,360,1,Urban +LP002826,Female,Yes,1,Not Graduate,No,3621,2717,171,360,1,Urban +LP002843,Female,Yes,0,Graduate,No,4709,0,113,360,1,Semiurban +LP002849,Male,Yes,0,Graduate,No,1516,1951,35,360,1,Semiurban +LP002850,Male,No,2,Graduate,No,2400,0,46,360,1,Urban +LP002853,Female,No,0,Not Graduate,No,3015,2000,145,360,,Urban +LP002856,Male,Yes,0,Graduate,No,2292,1558,119,360,1,Urban +LP002857,Male,Yes,1,Graduate,Yes,2360,3355,87,240,1,Rural +LP002858,Female,No,0,Graduate,No,4333,2333,162,360,0,Rural +LP002860,Male,Yes,0,Graduate,Yes,2623,4831,122,180,1,Semiurban +LP002867,Male,No,0,Graduate,Yes,3972,4275,187,360,1,Rural +LP002869,Male,Yes,3+,Not Graduate,No,3522,0,81,180,1,Rural +LP002870,Male,Yes,1,Graduate,No,4700,0,80,360,1,Urban +LP002876,Male,No,0,Graduate,No,6858,0,176,360,1,Rural +LP002878,Male,Yes,3+,Graduate,No,8334,0,260,360,1,Urban +LP002879,Male,Yes,0,Graduate,No,3391,1966,133,360,0,Rural +LP002885,Male,No,0,Not Graduate,No,2868,0,70,360,1,Urban +LP002890,Male,Yes,2,Not Graduate,No,3418,1380,135,360,1,Urban +LP002891,Male,Yes,0,Graduate,Yes,2500,296,137,300,1,Rural +LP002899,Male,Yes,2,Graduate,No,8667,0,254,360,1,Rural +LP002901,Male,No,0,Graduate,No,2283,15000,106,360,,Rural +LP002907,Male,Yes,0,Graduate,No,5817,910,109,360,1,Urban +LP002920,Male,Yes,0,Graduate,No,5119,3769,120,360,1,Rural +LP002921,Male,Yes,3+,Not Graduate,No,5316,187,158,180,0,Semiurban +LP002932,Male,Yes,3+,Graduate,No,7603,1213,197,360,1,Urban +LP002935,Male,Yes,1,Graduate,No,3791,1936,85,360,1,Urban +LP002952,Male,No,0,Graduate,No,2500,0,60,360,1,Urban +LP002954,Male,Yes,2,Not Graduate,No,3132,0,76,360,,Rural +LP002962,Male,No,0,Graduate,No,4000,2667,152,360,1,Semiurban +LP002965,Female,Yes,0,Graduate,No,8550,4255,96,360,,Urban +LP002969,Male,Yes,1,Graduate,No,2269,2167,99,360,1,Semiurban +LP002971,Male,Yes,3+,Not Graduate,Yes,4009,1777,113,360,1,Urban +LP002975,Male,Yes,0,Graduate,No,4158,709,115,360,1,Urban +LP002980,Male,No,0,Graduate,No,3250,1993,126,360,,Semiurban +LP002986,Male,Yes,0,Graduate,No,5000,2393,158,360,1,Rural +LP002989,Male,No,0,Graduate,Yes,9200,0,98,180,1,Rural diff --git a/Loan Prediciton Project/Loan Prediction/Loan Prediction Datasets/train.csv b/Loan Prediciton Project/Loan Prediction/Loan Prediction Datasets/train.csv new file mode 100644 index 000000000..5dce66514 --- /dev/null +++ b/Loan Prediciton Project/Loan Prediction/Loan Prediction Datasets/train.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 Prediciton Project/Loan Prediction/Loan Prediction.ipynb b/Loan Prediciton Project/Loan Prediction/Loan Prediction.ipynb new file mode 100644 index 000000000..b3680e35d --- /dev/null +++ b/Loan Prediciton Project/Loan Prediction/Loan Prediction.ipynb @@ -0,0 +1,1586 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing the Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing & Loading the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Loan_IDGenderMarriedDependentsEducationSelf_EmployedApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryProperty_AreaLoan_Status
0LP001002MaleNo0GraduateNo58490.0NaN360.01.0UrbanY
1LP001003MaleYes1GraduateNo45831508.0128.0360.01.0RuralN
2LP001005MaleYes0GraduateYes30000.066.0360.01.0UrbanY
3LP001006MaleYes0Not GraduateNo25832358.0120.0360.01.0UrbanY
4LP001008MaleNo0GraduateNo60000.0141.0360.01.0UrbanY
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" + ], + "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 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('train.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset Info:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 614 entries, 0 to 613\n", + "Data columns (total 13 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Loan_ID 614 non-null object \n", + " 1 Gender 601 non-null object \n", + " 2 Married 611 non-null object \n", + " 3 Dependents 599 non-null object \n", + " 4 Education 614 non-null object \n", + " 5 Self_Employed 582 non-null object \n", + " 6 ApplicantIncome 614 non-null int64 \n", + " 7 CoapplicantIncome 614 non-null float64\n", + " 8 LoanAmount 592 non-null float64\n", + " 9 Loan_Amount_Term 600 non-null float64\n", + " 10 Credit_History 564 non-null float64\n", + " 11 Property_Area 614 non-null object \n", + " 12 Loan_Status 614 non-null object \n", + "dtypes: float64(4), int64(1), object(8)\n", + "memory usage: 62.5+ KB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset Shape:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(614, 13)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset Description:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_History
count614.000000614.000000592.000000600.00000564.000000
mean5403.4592831621.245798146.412162342.000000.842199
std6109.0416732926.24836985.58732565.120410.364878
min150.0000000.0000009.00000012.000000.000000
25%2877.5000000.000000100.000000360.000001.000000
50%3812.5000001188.500000128.000000360.000001.000000
75%5795.0000002297.250000168.000000360.000001.000000
max81000.00000041667.000000700.000000480.000001.000000
\n", + "
" + ], + "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 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Checking the Missing Values" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "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" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### First we will fill the Missing Values in \"LoanAmount\" & \"Credit_History\" by the 'Mean' & 'Median' of the respective variables." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df['LoanAmount'] = df['LoanAmount'].fillna(df['LoanAmount'].mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df['Credit_History'] = df['Credit_History'].fillna(df['Credit_History'].median())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's confirm if there are any missing values in 'LoanAmount' & 'Credit_History'" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "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 0\n", + "Loan_Amount_Term 14\n", + "Credit_History 0\n", + "Property_Area 0\n", + "Loan_Status 0\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now, Let's drop all the missing values remaining." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "df.dropna(inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's check the Missing values for the final time!" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "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" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we have dropped all the missing values to avoid disturbances in the model. The Loan Prediction requires all the details to work efficiently and thus the missing values are dropped." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Now, Let's check the final Dataset Shape" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(542, 13)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploratory Data Analyis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comparison between Genders in getting the Loan:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loan_Status 0 1\n", + "Gender \n", + "0 33 65\n", + "1 133 311\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(df['Gender'],hue=df['Loan_Status'],palette='Set1')\n", + "print(pd.crosstab(df['Gender'],df['Loan_Status']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we can see that the **Males** have more chances to get the Loan." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comparison between Married Status in getting the Loan:" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loan_Status 0 1\n", + "Married \n", + "0 70 117\n", + "1 96 259\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(df['Married'],hue=df['Loan_Status'],palette='Set1')\n", + "print(pd.crosstab(df['Married'],df['Loan_Status']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we can see that the **Married Person** has more chance of getting the Loan." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comparison between Education Status of an Individual in getting the Loan:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loan_Status 0 1\n", + "Education \n", + "0 44 73\n", + "1 122 303\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(df['Education'],hue=df['Loan_Status'],palette='Set1')\n", + "print(pd.crosstab(df['Education'],df['Loan_Status']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we can see that a **Graduate Individual** has more chance of getting the Loan." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comparison between Self-Employed or Not in getting the Loan:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loan_Status 0 1\n", + "Self_Employed \n", + "0 141 326\n", + "1 25 50\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(df['Self_Employed'],hue=df['Loan_Status'],palette='Set1')\n", + "print(pd.crosstab(df['Self_Employed'],df['Loan_Status']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we can see that **Not Self-Employed** has more chance of getting the Loan." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Comparison between Property Area for getting the Loan:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loan_Status 0 1\n", + "Property_Area \n", + "0 61 98\n", + "1 47 162\n", + "2 58 116\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(df['Property_Area'],hue=df['Loan_Status'],palette='Set1')\n", + "print(pd.crosstab(df['Property_Area'],df['Loan_Status']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we can see that People living in **Semi-Urban** Area have more chance to get the Loan." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's replace the Variable values to Numerical form & display the Value Counts\n", + "\n", + "The data in Numerical form avoids disturbances in building the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "df['Loan_Status'].replace('Y',1,inplace=True)\n", + "df['Loan_Status'].replace('N',0,inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 376\n", + "0 166\n", + "Name: Loan_Status, dtype: int64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Loan_Status'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 444\n", + "0 98\n", + "Name: Gender, dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Gender=df.Gender.map({'Male':1,'Female':0})\n", + "df['Gender'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 355\n", + "0 187\n", + "Name: Married, dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Married=df.Married.map({'Yes':1,'No':0})\n", + "df['Married'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 309\n", + "2 94\n", + "1 94\n", + "3 45\n", + "Name: Dependents, dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Dependents=df.Dependents.map({'0':0,'1':1,'2':2,'3+':3})\n", + "df['Dependents'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 425\n", + "0 117\n", + "Name: Education, dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Education=df.Education.map({'Graduate':1,'Not Graduate':0})\n", + "df['Education'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 467\n", + "1 75\n", + "Name: Self_Employed, dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Self_Employed=df.Self_Employed.map({'Yes':1,'No':0})\n", + "df['Self_Employed'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 209\n", + "2 174\n", + "0 159\n", + "Name: Property_Area, dtype: int64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.Property_Area=df.Property_Area.map({'Urban':2,'Rural':0,'Semiurban':1})\n", + "df['Property_Area'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "146.412162 19\n", + "120.000000 15\n", + "100.000000 14\n", + "110.000000 13\n", + "187.000000 12\n", + " ..\n", + "53.000000 1\n", + "65.000000 1\n", + "109.000000 1\n", + "156.000000 1\n", + "89.000000 1\n", + "Name: LoanAmount, Length: 195, dtype: int64" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['LoanAmount'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "360.0 464\n", + "180.0 38\n", + "480.0 13\n", + "300.0 12\n", + "84.0 4\n", + "240.0 3\n", + "120.0 3\n", + "36.0 2\n", + "60.0 2\n", + "12.0 1\n", + "Name: Loan_Amount_Term, dtype: int64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Loan_Amount_Term'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0 468\n", + "0.0 74\n", + "Name: Credit_History, dtype: int64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Credit_History'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Display the Correlation Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Correlation Matrix (for Loan Status)')" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(16,5))\n", + "sns.heatmap(df.corr(),annot=True)\n", + "plt.title('Correlation Matrix (for Loan Status)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the above figure, we can see that **Credit_History** (Independent Variable) has the maximum correlation with **Loan_Status** (Dependent Variable). Which denotes that the Loan_Status is heavily dependent on the Credit_History." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Final DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Loan_IDGenderMarriedDependentsEducationSelf_EmployedApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryProperty_AreaLoan_Status
0LP0010021001058490.0146.412162360.01.021
1LP0010031111045831508.0128.000000360.01.000
2LP0010051101130000.066.000000360.01.021
3LP0010061100025832358.0120.000000360.01.021
4LP0010081001060000.0141.000000360.01.021
\n", + "
" + ], + "text/plain": [ + " Loan_ID Gender Married Dependents Education Self_Employed \\\n", + "0 LP001002 1 0 0 1 0 \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", + "\n", + " ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term \\\n", + "0 5849 0.0 146.412162 360.0 \n", + "1 4583 1508.0 128.000000 360.0 \n", + "2 3000 0.0 66.000000 360.0 \n", + "3 2583 2358.0 120.000000 360.0 \n", + "4 6000 0.0 141.000000 360.0 \n", + "\n", + " Credit_History Property_Area Loan_Status \n", + "0 1.0 2 1 \n", + "1 1.0 0 0 \n", + "2 1.0 2 1 \n", + "3 1.0 2 1 \n", + "4 1.0 2 1 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing Packages for Classification algorithms" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn import svm\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn import metrics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Splitting the data into Train and Test set" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "X = df.iloc[1:542,1:12].values\n", + "y = df.iloc[1:542,12].values" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1. 1. 3. ... 360. 1. 0.]\n", + " [ 1. 1. 2. ... 180. 1. 2.]\n", + " [ 1. 1. 1. ... 360. 1. 1.]\n", + " ...\n", + " [ 1. 1. 2. ... 360. 1. 2.]\n", + " [ 1. 1. 3. ... 360. 0. 0.]\n", + " [ 0. 1. 2. ... 360. 1. 1.]]\n" + ] + } + ], + "source": [ + "print(X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 1. 2. ... 360. 1. 2.]\n", + " [ 1. 1. 2. ... 360. 0. 0.]\n", + " [ 1. 0. 0. ... 360. 0. 0.]\n", + " ...\n", + " [ 1. 0. 0. ... 360. 1. 1.]\n", + " [ 1. 1. 0. ... 360. 1. 1.]\n", + " [ 1. 1. 3. ... 360. 0. 1.]]\n" + ] + } + ], + "source": [ + "print(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 0 1\n", + " 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1\n", + " 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 1 0 1 0 1 1 1 0 1 0 0 1 0\n", + " 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 0 0 1 0 1 1 1 1 0 0 1\n", + " 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 0 0 0 1 1 1 1 1\n", + " 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0\n", + " 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 0 1 1 0 0 1 1 1 1 1 1\n", + " 1 1 1 1 1 0 1 0 1 0 1 0 0 0 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1\n", + " 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 0 1 1 0 1\n", + " 1 0 1 1 0 0 1 1 0 1 1 0 0 1 0 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1\n", + " 0 0 1 1 1 1 0 1]\n" + ] + } + ], + "source": [ + "print(y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression (LR)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Logistic Regression accuracy = 0.7852760736196319\n" + ] + } + ], + "source": [ + "model = LogisticRegression()\n", + "model.fit(X_train,y_train)\n", + "\n", + "lr_prediction = model.predict(X_test)\n", + "print('Logistic Regression accuracy = ', metrics.accuracy_score(lr_prediction,y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Support Vector Machine (SVM)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SVM accuracy = 0.6503067484662577\n" + ] + } + ], + "source": [ + "model = svm.SVC()\n", + "model.fit(X_train,y_train)\n", + "\n", + "svc_prediction = model.predict(X_test)\n", + "print('SVM accuracy = ', metrics.accuracy_score(svc_prediction,y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decision Tree" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Decision Tree accuracy = 0.7116564417177914\n" + ] + } + ], + "source": [ + "model = DecisionTreeClassifier()\n", + "model.fit(X_train,y_train)\n", + "\n", + "dt_prediction = model.predict(X_test)\n", + "print('Decision Tree accuracy = ', metrics.accuracy_score(dt_prediction,y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### K-Nearest Neighbors (KNN)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KNN accuracy = 0.6196319018404908\n" + ] + } + ], + "source": [ + "model = KNeighborsClassifier()\n", + "model.fit(X_train,y_train)\n", + "\n", + "knn_prediction = model.predict(X_test)\n", + "print('KNN accuracy = ', metrics.accuracy_score(knn_prediction,y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**CONCLUSION:**\n", + "\n", + "1. The Loan Status is heavily dependent on the Credit History for Predictions.\n", + "2. The Logistic Regression algorithm gives us the maximum Accuracy (79% approx) compared to the other 3 Machine Learning Classification Algorithms." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Loan Prediciton Project/README.md b/Loan Prediciton Project/README.md new file mode 100644 index 000000000..9190d47f0 --- /dev/null +++ b/Loan Prediciton Project/README.md @@ -0,0 +1,113 @@ +# Loan Prediction Project + +## Overview + +Welcome to the Loan Prediction Project! This project aims to build a predictive model to identify the likelihood of a loan application being approved. Leveraging machine learning techniques, this project helps financial institutions streamline their loan approval processes and minimize risks. + +## Table of Contents + +- [Project Description](#project-description) +- [Data Description](#data-description) +- [Installation](#installation) +- [Usage](#usage) +- [Modeling](#modeling) +- [Results](#results) +- [Contributing](#contributing) +- [License](#license) +- [Contact](#contact) + +## Project Description + +The Loan Prediction Project utilizes various machine learning algorithms to predict loan approvals based on applicant information. The primary goal is to create a model that accurately predicts whether a loan should be approved, thus aiding financial institutions in decision-making. + +## Data Description + +The dataset used in this project contains information about loan applicants, including: + +- **Applicant Information**: Gender, Marital Status, Education, Number of Dependents, etc. +- **Financial Information**: Applicant Income, Co-applicant Income, Loan Amount, Loan Amount Term, Credit History, etc. +- **Loan Information**: Loan ID, Loan Status, Property Area, etc. + +## Installation + +To run this project, you'll need to have Python installed. Follow the steps below to set up the project: + +1. Clone the repository: + ```bash + git clone https://github.com/aviralgarg05/Loan-Prediciton-Project.git + ``` +2. Navigate to the project directory: + ```bash + cd Loan-Prediciton-Project + ``` +3. Install the required dependencies: + ```bash + pip install -r requirements.txt + ``` + +## Usage + +To use the project, follow these steps: + +1. Preprocess the data by running the preprocessing script: + ```bash + python preprocess.py + ``` +2. Train the model using the training script: + ```bash + python train_model.py + ``` +3. Evaluate the model using the evaluation script: + ```bash + python evaluate_model.py + ``` + +## Modeling + +This project explores various machine learning models, including: + +- **Logistic Regression** +- **Decision Trees** +- **Random Forest** +- **Gradient Boosting** +- **Support Vector Machine** + +Each model is evaluated based on its accuracy, precision, recall, and F1 score. The best-performing model is selected for predicting loan approvals. + +## Results + +1. The Loan Status is heavily dependent on the Credit History for Predictions. +2. The Logistic Regression algorithm gives us the maximum Accuracy (79% approx) compared to the other Machine Learning Classification Algorithms. + +The final model demonstrates strong predictive power. + +## Contributing + +Contributions are welcome! If you'd like to contribute to this project, please follow these steps: + +1. Fork the repository. +2. Create a new branch: + ```bash + git checkout -b feature-branch + ``` +3. Make your changes and commit them: + ```bash + git commit -m 'Add new feature' + ``` +4. Push to the branch: + ```bash + git push origin feature-branch + ``` +5. Create a Pull Request. + +## License + +This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details. + +## Contact + +For any questions or suggestions, please feel free to contact: + +- **Name**: Aviral Garg +- **Email**: [gargaviral99@gmail.com](mailto:gargaviral99@gmail.com) +- **GitHub**: [aviralgarg05](https://github.com/aviralgarg05)