From 046e1a98b8c039b3f08ad1dc0efcf2a6600089fc Mon Sep 17 00:00:00 2001 From: tanvideshmukh29 <70129990+tanvideshmukh29@users.noreply.github.com> Date: Sat, 23 Oct 2021 23:43:31 +0530 Subject: [PATCH 1/9] Create deleteme.html --- Projects/Loan Prediction/deleteme.html | 1 + 1 file changed, 1 insertion(+) create mode 100644 Projects/Loan Prediction/deleteme.html diff --git a/Projects/Loan Prediction/deleteme.html b/Projects/Loan Prediction/deleteme.html new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Projects/Loan Prediction/deleteme.html @@ -0,0 +1 @@ + From fb3d3f9a0e5a1f8a5ddef4b55ea1d0a0f8090ce5 Mon Sep 17 00:00:00 2001 From: tanvideshmukh29 <70129990+tanvideshmukh29@users.noreply.github.com> Date: Sat, 23 Oct 2021 23:44:21 +0530 Subject: [PATCH 2/9] Add files via upload --- Projects/data.csv | 615 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 615 insertions(+) create mode 100644 Projects/data.csv diff --git a/Projects/data.csv b/Projects/data.csv new file mode 100644 index 0000000..5dce665 --- /dev/null +++ b/Projects/data.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 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+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 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+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 From 2069a307a0004c21b5c48b72929a6e4158c6284b Mon Sep 17 00:00:00 2001 From: tanvideshmukh29 <70129990+tanvideshmukh29@users.noreply.github.com> Date: Sat, 23 Oct 2021 23:45:12 +0530 Subject: [PATCH 3/9] Add files via upload --- .../Loan Prediction/Loan _Prediction.ipynb | 1307 +++++++++++++++++ 1 file changed, 1307 insertions(+) create mode 100644 Projects/Loan Prediction/Loan _Prediction.ipynb diff --git a/Projects/Loan Prediction/Loan _Prediction.ipynb b/Projects/Loan Prediction/Loan _Prediction.ipynb new file mode 100644 index 0000000..7420dc5 --- /dev/null +++ b/Projects/Loan Prediction/Loan _Prediction.ipynb @@ -0,0 +1,1307 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0df672da", + "metadata": {}, + "source": [ + "# Loan Prediction\n" + ] + }, + { + "cell_type": "markdown", + "id": "df0d3360", + "metadata": {}, + "source": [ + "# Import modules" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "1cd2aa68", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "from matplotlib import pyplot as plt\n", + "import matplotlib" + ] + }, + { + "cell_type": "markdown", + "id": "69e1c076", + "metadata": {}, + "source": [ + "# Dataset:\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "d831a0ea", + "metadata": {}, + "source": [ + "https://github.com/tanvideshmukh29/Machine-learning/blob/tanvideshmukh29-patch-1/Projects/Loan%20_Prediction/loan.csv" + ] + }, + { + "cell_type": "markdown", + "id": "6c2f5dc4", + "metadata": {}, + "source": [ + "# Loading the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "4c273e9a", + "metadata": { + "scrolled": true + }, + "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
..........................................
609LP002978FemaleNo0GraduateNo29000.071.0360.01.0RuralY
610LP002979MaleYes3+GraduateNo41060.040.0180.01.0RuralY
611LP002983MaleYes1GraduateNo8072240.0253.0360.01.0UrbanY
612LP002984MaleYes2GraduateNo75830.0187.0360.01.0UrbanY
613LP002990FemaleNo0GraduateYes45830.0133.0360.00.0SemiurbanN
\n", + "

614 rows × 13 columns

\n", + "
" + ], + "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]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('C:/Users/deshm/Desktop/dataml/loan.csv')\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "3340f37c", + "metadata": {}, + "source": [ + "# Preprocessing the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c28c2a7f", + "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": "code", + "execution_count": 61, + "id": "9ca20f7d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "sns.heatmap(df.isnull())" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "6041f1c6", + "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", + "id": "20638be8", + "metadata": {}, + "source": [ + "# Numerical value" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9dc1a71e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n", + " 'Loan_Amount_Term', 'Credit_History'],\n", + " dtype='object')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "numerical_value=df.select_dtypes(include=['int64','float64'])\n", + "numerical_value.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "17f6b57d", + "metadata": {}, + "outputs": [], + "source": [ + "df['LoanAmount'] = df['LoanAmount'].fillna(df['LoanAmount'].mean())\n", + "df['Loan_Amount_Term'] = df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mean())\n", + "df['Credit_History'] = df['Credit_History'].fillna(df['Credit_History'].mean())" + ] + }, + { + "cell_type": "markdown", + "id": "5fe8c1a6", + "metadata": {}, + "source": [ + "# categorical value" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "abdfe443", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n", + " 'Self_Employed', 'Property_Area', 'Loan_Status'],\n", + " dtype='object')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "categorical_value=df.select_dtypes(include=['object'])\n", + "categorical_value.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "bf123915", + "metadata": {}, + "outputs": [], + "source": [ + "df['Gender'] = df[\"Gender\"].fillna(df['Gender'].mode()[0])\n", + "df['Married'] = df[\"Married\"].fillna(df['Married'].mode()[0])\n", + "df['Dependents'] = df[\"Dependents\"].fillna(df['Dependents'].mode()[0])\n", + "df['Self_Employed'] = df[\"Self_Employed\"].fillna(df['Self_Employed'].mode()[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "955ea61c", + "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": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "23545a68", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,10))\n", + "sns.heatmap(df.isnull())" + ] + }, + { + "cell_type": "markdown", + "id": "70f87d68", + "metadata": {}, + "source": [ + "# Exploratory Data Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "97987837", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for i ,val in enumerate(numerical_value):\n", + " plt.figure(figsize=(19,6))\n", + " \n", + " sns.distplot(df[val],bins=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "12169d13", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "categorical_val=['Gender', 'Married', 'Dependents', 'Education',\n", + " 'Self_Employed', 'Property_Area', 'Loan_Status']\n", + " \n", + "for i ,val in enumerate(categorical_val):\n", + " plt.figure(figsize=(19,6))\n", + " \n", + " sns.countplot(df[val])" + ] + }, + { + "cell_type": "markdown", + "id": "88b6aa1a", + "metadata": {}, + "source": [ + "# Label Encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "0f6df9f4", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "cols = ['Gender',\"Married\",\"Education\",'Self_Employed',\"Property_Area\",\"Loan_Status\",\"Dependents\"]\n", + "le = LabelEncoder()\n", + "for col in cols:\n", + " df[col] = le.fit_transform(df[col])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "0da4a701", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Loan_IDGenderMarriedDependentsEducationSelf_EmployedApplicantIncomeCoapplicantIncomeLoanAmountLoan_Amount_TermCredit_HistoryProperty_AreaLoan_Status
0LP0010021000058490.0146.412162360.01.021
1LP0010031110045831508.0128.000000360.01.000
2LP0010051100130000.066.000000360.01.021
3LP0010061101025832358.0120.000000360.01.021
4LP0010081000060000.0141.000000360.01.021
\n", + "
" + ], + "text/plain": [ + " Loan_ID Gender Married Dependents Education Self_Employed \\\n", + "0 LP001002 1 0 0 0 0 \n", + "1 LP001003 1 1 1 0 0 \n", + "2 LP001005 1 1 0 0 1 \n", + "3 LP001006 1 1 0 1 0 \n", + "4 LP001008 1 0 0 0 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": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "1ef4e9e0", + "metadata": {}, + "source": [ + "# Coorelation Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "e2100cf3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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8okzAY32eFn33DR3eaMr2Tet5+4NPnl3QTyAyIpyAwGTHxIjkx9TreCQ9pgYEpprf+jU/U75yVQAuX7yIp5c344YP4dP32jJh5DBu3bplx0xSSl4vfv4BqdRdOP5PWHcfdfmc+bOm8sEbzZg/cyrvffz83QYjUpe+vo2mDyWBgw9Y9iEQo7UuD5QHPlZK5bcsKwv0AEoABYCqSik34FugOVAdyJ5kWwOArZZt1QbGKaXuXYatDLyvta7zzLL6j0rtQqFO+RJ/h8Xw/drj/Br0N5VezAGYx1D7+2ThyOkIlv12koREE6+UdHwPVVJap8zmSS+OJiYmcOxAKGUq1XxGUf1Lj5FTanmjFAd2m+8BuHdfQFINmrdm2sL/MW7WYrx8fFk0O+VQoWcq1TxSJJJyvXtlHrB+avuvwrxO01avM3fZz0yfvwQfXz++mz7Jpty5s38xb9Y0uvbp/1gpPLFU00m+Qz747zJs0ixGz1hA3xET2Lj6J04cOQTAp5/3Z8Oa/9Gvcwdu3bpp/WKd1lL9vKEeowzEx97gwO5gJs5bwdTFP3Pn9m1Ctm6wlnnj/U+YsvAnqtRqwKY1Pz3z2J9Uqp8xHN8z+G+l/pF7jDpMViYkaAelXypjHT4ZsnMH3j4+FC1e4tkF+4T0Qz5bSUs9ukxK7330GfNX/kqt+o1Yu2rF04b4rzxOfo+z3x4+sI/f1vzMx527A2A0JnL61Emat36DWYuW4ZY5M8sXzUtlO/aTem6PXu9RdffbL//jo849mbdyLR917sG0sakPGc5olFJp9s9RpAH3CEqpGUqp35VS+4AGwHtKqcPAHsAXKGwpuldrHaa1NgGHgXxAMeCs1vq0Nh9Vvk+y6QZAX8u2tgNuwAuWZZu01tEPiKejUmq/Umr/Oc4/u0QzqLibCXhkud/T4JHFhfhbCQ8sfyk8jmxZXXFzNRB38y5xN+9yNeomAH+dv0aATxa7x/wkvHz8uR51/wrc9egIsnn7PtE2Th7eS678hcnq6f2sw3sqPsmuKkZHhuPja3vDvK9/AFFJrohHRYbj4+vHyWN/sD90J5+904pJIwZx9PB+po4237/p5e2LwWDAycmJek1acubP48889jU/raBLh3Z06dAOHz9/IsKvWJdFRlzFN1ke2by8iI+LxZiYaCkTbi3jFxCQ6vp+/gFEhl9N8no4Ppbhkd4+93Ns1PxVTp04dr9c+FW+7t+HXgOGkSOXfUaE+/j5ExVxP7boyIgUk3H4+AWkLONjLnOvnj29fChftQZnTp4AINcL+RgwagqjZsynSq36BObIZZf4H8XHL4DoyKT7ZgReKfLzJzrZ/uvt68fRw/vxD8xBNk9vnJ2dKV+lBqdPHEnxHlVq1Wffru12y+Fxefv62+R6LSoCL5/nc/jVTyuW0aFdGzq0a4Ofvz/hV+7vfxFXr+Lrb/u59PLyJi42lkTL5zIiPGWZLRvXUy/J8Mkjvx8mJGg7bzRvzNABX3Jw3z6+GtTPjlml5OcfQGSSz1ZUxNUUQ+Z8U5S5f/x4HDXrNmLXjpTDSe3llx+X88l7b/LJe2/i6+dP+NVkx8RksXt6eROX9JiarO7+PnOKiaO+4quxk6wTWvkHBOLvH0DxkqUBqFG7HqdPnbRzZraS111kKvXi6x9AxBPW3dYNv1K5hnl0TdVa9Th18tmf94RjSAMupWPAy/eeaK07A3UBf8yXcbpqrctY/uXXWt8b5ngnyTaM3J/hM7XLQVi29VqSbb2gtT5hWfbAaQ611nO01uW01uXyWtt74kGuRsXjldWVbO6ZcHJSFMnrzdmwGJsynh6u1sf+3pkxOClu3zFy83YicTcT8MpqXp47e7Z0N4lJnoJFibhykajwyyQmJnAodDulXnmyyTkO7trGy+lk+CRAoaLFuXzxAlcvXyIhIYGQ7ZsoV7m6TZlylauzY/M6tNacOn6ULO4eePv68faHnzF76Rq++f5neg74mlJlytGt7zAAm/us9obsII8dblJv3rqNdQKRytVrsWW9OcaTx47g7uGR4suUUooXy5YjeLt5MovN69dSqbq5J7Ri1Zqprl+kWAkuhV3gyqWLJCQkELRlI5Wq1QCwucduV9A28uYvCJhnWBvyRQ/af9KZki+WeeZ531OwaHGuXAwj/MolEhMS2LV9M69Usp0Y55VK1QjavB6tNadPHCVLFne8ff24ffsWt26aD323b9/ijwN7rXUUc918PctkMrFqyQLqNXPMPUYFihTjysUL1vx2B23m5YpVbcq8XLEawVvN+Z05ad43vXz88PUP5K8/j3Hn9m201hz7/QA58+QD4MrFC9b1D+4OJmfuvDha/sLFuHo5jIir5lz3Bm+hTIWqj14xHWrdpq114pHqtWqzft0acx0c+QMPDw/8kn0JVkpRtlx5tm/ZBMD6taupXvP+MTIuLpbDBw9QrWYt62ufdunOT+s2sXLNbwwdMYaXy5dn8NepT1xkL4WLluBS2HmuXLYcG7ZuokKVGjZlKlapwdYNv1qPK1ncPfB5xIynl8LuXyzesyuI3KlMEGUvLV9/k9mLljN70XKq1qjN5t/WorXm+NE/cHf3SNGAU0rx0svlCNq2GYCN69ZQpXotAMKvXGZY3958Ofhrcr9w/zPm4+uHf2B2Lpz7B4BD+/eSN40nMTHX3QVr3e3cupGKVWzPexWqVGfbhnVPVHc+vv4cPWweVPbHwX3kzJ3HbjmkJ/+Fe+DSxSQZ6cxWYKRSqpPWeqbltXvdLhuATkqprVrrBKVUEeDiQ7Z1EsivlCqotf4LSDq/+wagq1Kqq9ZaK6XKaq0PPetknoXeS/pQqlZpsvllY96FBSwd8gOb5m1ydFiPRWvYsf8CLeoUwkkpjv8VRXTMbUoVNh/0jp6OpOALXhTL74PJpEk0mlgffH9WuR37L9Cgaj4MTk7ciLvD5t3nHvRWDmEwGGjdvitzRvfFZDJRoVYjsufOx67NawCoUq85N65HM2ngZ9y+dROlFEHrf+LLsXNxy+LO3Tu3OXX0AG981MOxiSRhMDjzYZfejOjXHZPJRO2GzciTrwAbLcPKGjRvzcsVqnBozy66vv86mVzd6Nx74CO3u/jb6fzz12mUAv/AHHzSo69d8yhfuSr7dofwYdtWuLq50bPf/ZlcB/fpRvcvB+Hr50+HTl0ZM7Q/i76bScHCRWnYtOVD1zc4O9OpZx8G9uqKyWSkQdMW1oba3JlT+PvMKRSKwBw56Np7AABrflrOpYsXWLZwLssWzgVg+MTpeHn7PNOcDQZnOnT+nJH9e2IyGandwFx3m9aaZ4Gr3+xVylaowuF9oXTv8Aaurm582sscY8y1aCYMM/dYmIxGqtauT5nylQAI2bbJWv8VqtakVoOmzzTux2UwOPNep88ZN+hzTCYTNeo3JXfeAmyx3K9Xt0krXipfmcP7Q+n90ZtkcnXj457m4aqFipWkfNXaDOr+AU4GA/kKFKG2ZaKd5QtmcfnieZyUE74BgXTo3Mch+SVlMDjz9sc9mDSsNyajiWr1mpDrhfxsX2+eJbVWo5bEXIvi694duXUzHqWc2LzmR76etojMWdyZPWEYfx49RNyNGHp/+Bot23agev1mDs4KKletzu6QYNq2aoabmxv9hnxlXdanW2e+HDQEP/8AOnXtwdD+X/DdzBkULlqMpi3vXzQI2raV8hUrkzlz+hqRYXB25tPuXzCkTzdMJiP1GpuPDb/98j8AGrd8jXKVqrJ/Twgd334VV1c3un95f0bacV8N4MjhA9yIuU7715vSrkNHGjRtyYI507l4/hxOTk74B2an8+dp27N4T4Uq1dizK5j332iBq6sbvQcOtS7r/3kXPu83GD//AD7u3J0Rg/qyYPY3FCxSlEbNWwGweN4cbty4ztTx5oa1wWDgm/lLAOj8+ZeMGtqfxIREcuTKRe8Bw9I0N4OzM59078PQPt0wmUzUa9ycF1KpuwN7dvHJ261xdXWj25eDrOuP+2ogRy111+H1ZrzV4WMaNG1Jl979+Xb6RIzGRDJlcqVzL8fUnXj2VOrjhf/blFI5gElARSACc4/YLGAlMBzzPW3KsqwV5vvfemutm1nWnw7s11ovUEo1AiYDkUAwUEpr3UwpldnyehXLtv6xvN4eKKe17vKoOFuoZhm68up//9WjCz2nChRL+9/QSUt5/D0eXeg55u6Wsa993bj57GeoTE+Szjyb0dxNyLi5ARTJ7fnoQs+x63EZ+7Pn5pqxj5237jz4Fo2MoGgOz+fiRtgPXN9Ls+/H8+4scsjfJGN/kp6S1voy5p8OSE1/y7+ktlv+3Vu/S5LH6zHfC5f8PW4BKaZy0lovABY8WcRCCCGEEEKI/wJpwAkhhBBCCCEyBPUfmOIj42cohBBCCCGEEBmE9MAJIYQQQgghMgQnlfH7pzJ+hkIIIYQQQgiRQUgDTgghhBBCCCGeEzKEUgghhBBCCJEhKJ6LXzv4V6QHTgghhBBCCCGeE9IDJ4QQQgghhMgQZBITIYQQQgghhBDphvTACSGEEEIIITIEJ7kHTgghhBBCCCFEeiE9cEIIIYQQQogMQf0H+qcyfoZCCCGEEEIIkUFID5wQQgghhBAiQ3BScg+cEEIIIYQQQoh0QnrghBBCCCGEEBmC03+gfyrjZyiEEEIIIYQQGYT0wD3H6n//laNDsKtN7wx2dAh20/jHUY4Owa4K5/R0dAh2pbWjI7CvjH7/gMrAvxF0+26io0OwK2dDxr7u7OSUcfdNgLsJRkeHYFfXYu84OgT7yuHoAB6PyuDnMJAeOCGEEEIIIYR4bkgPnBBCCCGEECJDkHvghBBCCCGEEEKkG9KAE0IIIYQQQojnhAyhFEIIIYQQQmQIGX0iLpAeOCGEEEIIIYR4bkgPnBBCCCGEECJDUP+B/qmMn6EQQgghhBBCZBDSAyeEEEIIIYTIEOQeOCGEEEIIIYQQ6Yb0wAkhhBBCCCEyBLkHTgghhBBCCCFEuiE9cEIIIYQQQogMwUll/P6pjJ+hEEIIIYQQQmQQ0gMnhBBCCCGEyBCckFkohRBCCCGEEEKkE9IDJ4QQQgghhMgQ1H/gHjhpwAkAXsiRjRrlcqMUHD8TxYHjV22W58/tSaUXc6K1xqQ1Ow+EcTkiHoBMLgbqVnoBX8/MaGDL7nNciYx3QBZPp9vc7pRrVp6Y8Bi6lu7s6HCeWJ5AD6q9lBOl4MTZaxw6FWGzPF+OrFQoGYjWYNKakN8vcyXqJgYnRcuaBTA4KZycFH+HxbDvRLiDsrB1YM8uvp0+AZPRRP2mLXnj7fY2y7XWzJk2gQO7Q3B1c6N73yEUKlIMgCljvmJfaDCeXt7MWLDcus68mVPYu2snLi4uZM+Zm+5fDsYja9Y0yWf/nl3MmToek8lEg6ataPNOynxmTx3P/t0huLq60bPfUAoVLfZY6/5v6WLmzZzCktWb8fTy4urlS3z67hvkeiEvAMVKlKJL7/5pkSYAh/eFMn/mZEwmI3UbtaBV2/dslmutmf/NJA7t24Wrqxuf9R5EgcJFAej87qu4Zc6Ck5MBg8HA6BnzAVg8ZxoHdgfj7OJCYI5cfNZ7IO4eaVN3yf2+fzeL50zGZDJRq0FzWrR512a51prFsydzeH8orq5udOw5gPyFinIp7BzTRw+2lgu/conX3/mIRq3eBGDj6pVsXPs/DAYDZcpX4a0PHH8sOnZ4Lz/On47JZKJq3SY0aNXOZvmVi+f5/puxXDh7muZtP6BeizetywZ1fgs3tywoJycMBgNfjp6V1uE/Ea01k8aOZlfITtzc3Bg0bDhFi5dIUW7lsiUsX/I9Fy9c4LetQXh5ezsg2vvscWz5Yd5sNqz9mWxe5tze//gzyleuRkJCAtPHj+T0yeM4OTnRsVsvXixb7rnL757kx84/jx9l2viR9zZMuw4dqVKjtl3zS+qPA3v44dspmEwmatZvRrM33rFZrrXmhzlT+P3AbjK5uvJx9/7kK2Q+dm5cvZLtG9agtaZWw+Y0bNkGgL3B21i1ZB6Xw84xZMIc8hculmb5CPuSBlwSSikNfK+1ftfy3Bm4DOzRWjf7F9vNCUzVWr/+BOssANZqrX982vd9/PeCWuXz8PPW08TdTODNRkX5OyyGazduW8uEXYlladgJAHy9MtO4Wn6+X3scgBrlcnPu0g1+23kWJyeFs+H5uvKxZcFm1k5fS89Fnzs6lCemgOplcrIm+CzxNxN5rU5B/rl8g2uxd6xlwsLj+efyGQB8srnRoFIelm08jdGkWR10lkSjCScFrWoV5PzVWK5G33JQNmZGo5FZU8by9fjp+PoH8vmn71Oxag1eyFfAWubAnl1cCjvP7B9+4s/jR5k5aTQTZi4AoG6jZjR9tQ2TRg6x2W6ZchV5/+POGJydWTB7Gj8uWUD7T7qmST4zJ41h+MQZ+PkH0rPje1SqZpvP/t0hXAq7wLdLVvHn8aPMmDiKSbMXPnLdiKtXOLx/D/6B2W3eM0euXEyft8TuuSVnMhqZO30CA0dPwdcvgH5dP6Bc5erkzpvfWubQvlCuXLzA1PkrOX3yGN9NHcvIaXOty4eMm0E2Ty+b7b74cgXafdgJg8GZ77+bwapli3jno7Rv4JiMRhbOnEDf4ZPx8QtgcM+PeKVSNXK9cD+/3/eHcuVSGBO+Xc5ffx5jwYzxDJv0LTlz52Xk9IXW7XR9rxXlqtQE4PjvBziwO5hRMxbh4pKJmOvX0jy35EwmIyvmTqHrwHF4+foztl8nSperQo7c+axl3D2y8kaHLvy+LyTVbXQfMhGPbJ5pFPG/Exq8kwvnz7Hyl185duQPxo4cztzFKT9DL5YpS7UaNfnsow8cEKUtex5bWr7Rjtfesr04sWHNKgC+Wbic69eiGdynG5PnLMLJyT7n/LQ+duYtUIgpcxZhcHYmOjKSLh+8RcUq1TE42/+rssloZNGsiXzx9SR8fP0Z+vnHlK1Y1ebY8seB3Vy5FMbY2Uv568/jLJw5gSET5hB27m+2b1jDkAlzcHZxZvyQ3rxUvjLZc+Yhd978dOs/ggUzxtk9h/RE7oH774kHSimlMlue1wcuPskGLI0+m+da60tP0nhLa4G+7lyPvcONuLuYTJpT565RII/tSTch0WR97OLshE7yOGeAB8f/igLAZNLcTTCmVejPxLGdx4iLjnV0GE8lwCcLMfF3iY1PwKQ1Z8JiyJczm02ZRKNt3VkrL8kyJyeFk1JojcOdPnmMHLnykD1nblxcXKhRpz57QnbYlNkdsoM6DZuilKJYydLEx8USHRUJQKmXXiZr1mwptvty+UrWE3HREqWIjLiaoow9nDpxjJy58pDjXj51G7A7OFk+wTuo07CJbT6RkY9c99vpE+nQqRtKpY+T1Zk/j5M9Z24Cc+TC2cWFKjXrsW9XkE2Z/buCqFG/MUopihQvRXx8HNcsdfcgL5WriMFgrrsixUoSHeGYnuK/Tp0gMGduAiz5VapRlwO7d9qUObA7mGp1GqGUolCxUsTHx3It2ja/Y7/vJyBHLvwCzF8eN6/7meZvvIOLSyYAPL0c26sD8M+Zk/hnz4VfYE6cnV14pUod/ti3y6ZMVk9v8hYqhsFgcFCUz07Qjm00btYCpRSlXnyJuNhYIiMiUpQrWqw4OXLmckCEKdnz2JKa8/+c5aVXygPg5e2Dh0dWTp88/lzml9qx083NzXqOuHv3TpoeV/8+fYLAHLkIyJ4TZxcXKtaoy8E9wTZlDu4Opqr12FKSm/FxXI+O5NKFcxQsWgJXNzcMBmeKlSrDgVDzcTdnnnzkyP1CmuUh0o404FL6DWhqefwWsPTeAqVUBaXULqXUIcv/i1peb6+UWqmUWgNsTOV5PqXUUUtZg1JqnFJqn1LqD6XUJ5bXlVJqulLquFLqVyAgrRJ2z+xC3M271udxNxPwyOySolyB3J6806wEzWsVZMvucwB4ZnXl9u1E6lXKS9vGxahT8YXnrgfueeae2Zn4mwnW5/G3EnBPpe7y58xG2waFaVI1L9sO3L8moYA36haifbPihIXHEX7Nsb1vAFEREfj5B1qf+/oHEpXsi1TKMgFEPcGX+k3rVvNKhSr/PtjHEBUZjl/A/Vj9Uok1KjIC/4DsScoEEhUZ/tB1dwfvwNcvgAKFiqR4zyuXL9H1w3Z82bUjR38/9KxTeqDoyAh8/e8funz9A4iOsq276Khkdefnn6SMYkS/7nz5WXs2//pzqu+xdcNaypSv/KxDfyzXoiLw8bufn49fANeS5XctyvZvkFqZ0KAtVK5Zz/r8ysXz/Hnsd4b0/JjhX3bmr1Mn7JTB47seHYm37/08vHz9uB6dskHzIArF9BF9GP3lJwRvXmuPEJ+piPBwArPf/wz6BwYSEZ4+hpQ/iL2OLQBrV62gc/u2TB49jNjYGwDkL1SY3cE7MCYmcuXSRc6cOkFkuP0uhDni2Hny+FE6vdeGzh3a0rlXvzTpfYNUji2+/ikubF2LisA3lTK58+bnz2O/E3cjhju3b/P7/t1ER6bvfVf8ezKEMqVlwGCl1FrgRWAeUN2y7CRQQ2udqJSqB4wEXrMsqwy8qLWOVkq1T/Y8X5LtfwjEaK3LK6VcgRCl1EagLFAUKA0EAsct7213qV1kSq0j5u+wGP4OiyFngAeVXszBz1vP4KQU/j5Z2LH/AlejblL9ldy8UjKQPX9ctnvc4gFS6UY7e+kGZy/dIIdfFiqUDGTNzrPmosDKLWfI5OJEo0p58cnmSvSNOynWT0s6lb0v5ZXQxymTuuWL52EwOFOrfuOnCe+JpdqrmSxWnVqhB/WIKsXt27dZvngewyfMSLHYx9ePBSvXks3Ti9N/nmB4/97MXLScLO4eT5fAE3icuks1Vctwl68nz8bH15+Ya9EM79ednHnyUuLFstZyPy1ZgMFgoHrdhs828MeUaj3x6LpUScokJiRwcE8wb77/qfU1k8lIfFwsQyfO4e9TJ5g+ehAT5650bM9qank8QTyffz0VLx8/YmOuMW14HwJz5qFwiZeeZYTPVqr5OiCOJ2CPYwtAk1av0/b9j1BKsXjuTObOmESPvkNo0KQFF86dpXvH9wgIzE7xki/iZMfe17Q+doL5nuGZi1Zw/p+zTBo5hHIVq5DJ1fXJg39CD0gjealUC+XMk4+mr73N2EE9cc2chRfyF8LJ6fnvFf835Ie8/4O01n8A+TD3vq1LttgTWGnpTZsElEyybJPWOvohz+9pALynlDoM7AF8gcJADWCp1tqotb4EbE0tPqVUR6XUfqXU/pCtPz1xfqmJu5mAR5ZM1uceWVyIv5XwwPKXwuPIltUVN1cDcTfvEnfzLlejbgLw1/lrBPhkeSZxiUeLv5WIe5b7PW7umV2Iv534wPKXI2+SzT0TbplsD+53E0xciownT6BjJoZIys8/wGZ4Y1TEVXz8/GzK+KYoE46Pn/8jt71l/Vr2hQbTa+DXafbl2M8/wOYqdWREOL7JYvXzDyAi/EqSMlfx9fV/4LpXLoZx9fIlunzwFh3aNCcyIpzuH71NdFQkLpkyWe8hK1y0ODly5eLihfP2TdLC18/2CnlURDjePsnqzs/ftu4iI/D2NZfx8TX/XTy9fShfpSZn/rw/PGv7xl85sCeEbn2HOaxh4+MXYHNlOzoy3Bp70jJJ/wbRkeF4JSnz+/7d5CtYBE9vH+tr3r4BlKtSE6UUBYuWQClF7I3r9kvkMXj5+nMt6n4e16Mi8fT2e8gayda31HtWT29eKl+Nc2dOPvMY/60fly/lvTdf5703X8fPP4CrV+5/BiOuXsXPP80GwjwVexxbALx9fDEYDDg5OdGo2aucOnEMAIOzMx279mL6vCUMHjWRuLg4cuWx3/C8tD52JvVCvvy4Zs7MubN/2Sk7Wz5+/rbHlqgI62foHm/fAKKSlfH28QWgZoNmfDVlHgNGT8c9a1ay58yTJnELx5EGXOpWA+NJMnzS4mtgm9a6FNAccEuyLPm0iw+ahlEBXbXWZSz/8mutN1qWPfIOJK31HK11Oa11uap1Wj8ykcdxNSoer6yuZHPPhJOTokheb86GxdiU8fS4fwXK3zszBifF7TtGbt5OJO5mAl5ZzctzZ89GdMxtRNoIv3YTLw9XsmZxwUkpCuX25J9LN2zKZHO/3zj383LDyUlx+64Rt0wGMrmYDwEGJ0XuAA+uxzq29w2gcNESXAo7z5XLF0lISCBo6yYqVKlhU6ZilRps3fArWmtOHjtCFncPfHwf/uXywJ5d/G/pIgaNnICbm9tDyz5LRYqV4GLYBa5csuSzZSMVqybLp1pNtm5YZ83H3d0DHz+/B66br2AhlqzexPwVa5i/Yg1+/gFM+e4HfHz9iLl+DaPRfB/q5UthXAq7QPY0umenYNHiXL54gfDLl0hMSGDXjs2Uq1zdpky5ytUJ2vQbWmtOnThKFnd3vH39uH3rFrdumg+bt2/d4o+De6wTDhzeF8ovK77ny2FjcU3DukuuQJFiXLkYRvgVc367g7bwcsVqNmVerliN4K3r0Vpz5uRRsrh72DRiQ4M2UblmfZt1ylWuzvHfDwBw+eJ5EhMTyZrNy+75PEzegsUIv3yRyPDLJCYmcGDXVkqXe7yhq3du3+L2rZvWxyf+2E+OJJMxpBevv/kWi5b/yKLlP1Kjdh1+W7sarTVH//gddw8P/PwffVHIkexxbAGIjrzfmNm1cxt58xcE4Pbt29y+ZR5mf2jfbgwGg82EIs9Dfg87dl65dBFjovkCaPiVy1w8f46A7Dntll9S+QsX4+qlMCIsx5Y9QVsoW8H22FK2YlVCrMeWY2TO4mFt5N2wTHwUFX6VA7uCqJRkiPZ/kUrD/xxFhlCmbh7mYY5HlFK1krzuyf1JTdo/5bY3AJ2UUlu11glKqSKWbQYBnyilFmG+/602kCbTyGkNO/ZfoEWdQjgpxfG/ooiOuU2pwuYDw9HTkRR8wYti+X0wmTSJRhPrg89a19+x/wINqubD4OTEjbg7bLbcH/e86L2kD6VqlSabXzbmXVjA0iE/sGneJkeH9Vi0hp2HL9GsWn6UgpP/XONa7B1K5Ddf3T9+NpoCubJRNK+3pe40m/aYe2OyuLlQp3xunJT5YHcmLIZzVxw/mYvB2ZlPu3/BkD7dMJmM1Gvcgrz5C/LbL/8DoHHL1yhXqSr794TQ8e1XcXV1o/uX96dnH/fVAI4cPsCNmOu0f70p7Tp0pEHTlsyeMo6EhLsM6mWevbBoidJ07tUvTfLp1KMPg3p3xWQyUr+JOZ91v5gnmG3S8nXKV6rK/tAQPnqrlWUq7CEPXfdhjh4+yPfzZluvoHfu1Y+saTQToMHgzAddejGifw9MJhO1GzYjT74CbFxrHi3QoFlrylaowsG9u+jW/g0yubryWe+BAMRcj2b8sL6Aefa5arUbWO91mztjAol3E/i6b3cAChcvScfuX6ZJTsnze79TT8YO+hyTyUjN+s3InbcAW9aZZ+er2+RVypSvzO/7Q+n1URsyubrRsef9n3C4c/s2Rw/t44MuX9hst2b9ZsyZPJK+n72DwdmFTz4f6PCJaQwGA20+6MqMEV9iMhmpXLsxOfPkZ+fG1QBUb9CCmOvRjO37Kbdv3UQpxbZ1/2PgxPnEx8YwZ7z5M2k0GilfrS4ly1RwZDqPVKVadXYFB/FGiya4urkxcOhw67LPu3Si3+Bh+AcEsGLJD3y/cB7RUVG82+Y1KlerTv8hwxwSs72OLfNmTeHv06dQShGQPQddew8AIOZaNIN6d0EpJ3z9A+g98KvnMr8HOX7kMCt/WIjB2Rknpfjs8754ennZNcd7DAZn3v20J+OG9MJkMlGjXlNy583P1t9+BqBO41a8VK4yf+zfTZ+ObXF1deOj7vfPX9NGDSQuNsa8nU49rT+zsj80iO9nTyY25joTv/qCF/IXos9XE9MkJ2FfKvUx/f9NSqk4rbVHstdqAb211s2UUpWBhUAE5iGO72qt81nueSunte5iWSf583yYfxKglDL/uuBwzD14yrKtVsANYBpQBzhlefvvH/YzAtN+OJihK2/TO4MfXeg51fjHUY4Owa7qVsnr6BDsyskpnd8c8y/F337wEOqM4G6C6dGFnlMx8Y7vRbenlwun716xfys6HYyCEE8vMsbxE4HZU6UiAc/Fye8rn8Fp9v14cPRXDvmbSA9cEskbb5bXtgPbLY9DgaTTFg2yvL4AWJBkneTP/wFKWR6bgP6Wf8l1efrohRBCCCGEEBmdNOCEEEIIIYQQGUN6n0L2GZBJTIQQQgghhBDiOSE9cEIIIYQQQogMQWXw+9RBeuCEEEIIIYQQ4rkhPXBCCCGEEEKIjEHugRNCCCGEEEIIkV5ID5wQQgghhBAiY5B74IQQQgghhBBCpBfSAyeEEEIIIYTIGKQHTgghhBBCCCFEeiENOCGEEEIIIYR4TsgQSiGEEEIIIUSGoORnBIQQQgghhBBCpBfSAyeEEEIIIYTIGGQSEyGEEEIIIYQQ6YX0wAkhhBBCCCEyBrkHTgghhBBCCCHEk1JKNVJK/amUOqOU6pvKck+l1Bql1O9KqWNKqQ6Ps13pgXuOFSjm7+gQ7Krxj6McHYLd/PZ6P0eHYFeVzy5ydAh2pdGODsGuYuMTHB2CXfl6ujk6BLuJvHHb0SGIf8Etk8HRIdjVnQSjo0OwK99sGffY8lxJJ/fAKaUMwAygPhAG7FNKrdZaH09SrDNwXGvdXCnlD/yplPpBa333YduWHjghhBBCCCGEeLYqAGe01n9bGmTLgJbJymggqzL/9oEHEA0kPmrD0gMnhBBCCCGEyBhUuumfygVcSPI8DKiYrMx0YDVwCcgKvKm1Nj1qw+kmQyGEEEIIIYR4XiilOiql9if51zHp4lRWSX4PRkPgMJATKANMV0ple9T7Sg+cEEIIIYQQIkNQaXgPnNZ6DjDnAYvDgDxJnufG3NOWVAdgtNZaA2eUUmeBYsDeh72v9MAJIYQQQgghxLO1DyislMqvlMoEtMU8XDKp80BdAKVUIFAU+PtRG5YeOCGEEEIIIUTGkE5modRaJyqlugAbAAMwT2t9TCn1qWX5LOBrYIFS6gjmIZdfaq0jH7VtacAJIYQQQgghxDOmtV4HrEv22qwkjy8BDZ50u9KAE0IIIYQQQmQMKn30wNmT3AMnhBBCCCGEEM8J6YETQgghhBBCZAzp5B44e5IeOCGEEEIIIYR4TkgDTgghhBBCCCGeEzKEUgghhBBCCJEhKJnERAghhBBCCCFEeiE9cEIIIYQQQoiMQSYxEUIIIYQQQgiRXkgPnBBCCCGEECJj+A/cAycNOAHAid/38vOibzCZTFSq3Zi6Ld6yWX714nmWzR5H2D9naNKmA7WbtQEg/NIFFk0bbi0XFX6ZRq+/T83Gr6Vp/A+TJ9CDai/lRCk4cfYah05F2CzPlyMrFUoGojWYtCbk98tcibqJwUnRsmYBDE4KJyfF32Ex7DsR7qAsnl63ud0p16w8MeExdC3d2dHhPJbD+0JZMGsyJqOROo1b0OrN92yWa61ZMHMSh/buwtXNjU69BlGgcFEAurz3Km6Zs+DkZMBgMDBq+nzrer/9spINq3/E4GSgbMUqvPNRlzTN657D+3az0Jpfc1qmkt/CmZM4tDfUkt9A8lvza03mJPmNnD4PgMkjBnE57DwA8fGxuLtnZczMhWmbWCqOHtzDsnnTMJlMVK/XlMat37ZZfjnsHAumj+b836dp1e4jGrZqC0B0ZDjzpo4g5lo0ysmJGvWbU6/Z645I4YEO7g1l3oyJmEwm6jVpQeu33rdZrrVm7oyJHNyzC1dXN7p8MYiCRYpx9+4dBvb4lISEu5iMRirXqEPb9h0dlMWDnTi8l58WzUCbTFSq3YR6LVOeF5bMHkvY2TM0ffMD6ljOCwA34+NYPmc8l8P+ARRvfdKb/EVKpm0CT0BrzaSxo9kVshM3NzcGDRtO0eIlUpRbuWwJy5d8z8ULF/htaxBe3t4OiPbBtNbMmDiWPaEhuLq68cWgYRQpVjxFucuXLjJ8YF9ib8RQuGhx+g4djouLCyFB25g/eyZOTgqDwcBnPfpQukxZLpz7h68Hfnl//YsXad+xE6+1fTvFtp9FDrOnjGffbnMOn/cfSqGixVKUu3LpIqOH9icu9gYFixSj98CvcHFxeej6+/fsYvaU8ZhMJho2a0Wbd9oDMHfGFPbsCsLZ2YUcuXLTs98QPLJmtb5X+NUrfPruG7zdoSOvvfXuM885uQN7djFn2gRMJhMNmrbkjbfb2yzXWjNn6gT27zHn2KPfEAoVMec4efRX7AsNxtPbm28WLLd7rMIxnssGnFLKCBwBXIBEYCEwWWttclA8/wDltNaRT7FuK+CU1vr4s47rcZlMRn6aP41P+43B09efSQM7U/LlKmTPnddaJotHVl59vzNH9++yWTcgZx56j5pt3c6wzm0pXa5amsb/MAqoXiYna4LPEn8zkdfqFOSfyze4FnvHWiYsPJ5/Lp8BwCebGw0q5WHZxtMYTZrVQWdJNJpwUtCqVkHOX43lavQtB2XzdLYs2Mza6WvpuehzR4fyWExGI/NmTGDAqCn4+gXQr+sHlKtUndx581vLHN4XypWLF5gyfyWnTx5j7rSxjJg617p88NgZZPP0stnu0cMH2L8riHEzF+OSKRMx16PTKiUb5vzGW/Pr3/VDXkklv8sXw5g8fwVnTh7ju2njGDH1O+vyQWOnp8ivx4CvrY8Xz55KFncPu+fyKCajkSXfTqbnkAl4+/oz4otPeKl8VXLmyWct4+6RjbYfduPw3mCbdZ2cDLzxfmfyFizC7Vs3+br3x5R4qZzNuo5kNBr5duo4hoydhq9/AF981p7ylauTJ18Ba5mDe3dxOewCMxb9yKkTR5kzZSxjZszDxSUTwybMIHPmLCQmJjKge0fKVqhM0RKlHZiRLZPJyI/zp9Kp/1i8fP2ZOOAzSr1Smey581nLZPHIymvvd+HI/pAU669aOJ1iL5WnQ8+hJCYmcPfOnRRl0pPQ4J1cOH+Olb/8yrEjfzB25HDmLl6SotyLZcpSrUZNPvvoAwdE+Wh7Q4MJu3CeRSt/4cSxI0wZO5IZ8xanKPftjCm89tbb1KnfiEljhvPb6lW0eK0NL5erSJXqtVBK8dfpU3w98EsWLF9Fnrz5mLPY3BgwGo282bwh1WrWtksO+3eHcDHsAt8tXcWfx48yfcIoJs9JeTFq3qxpvNqmHTXrNWTa+JFsXPsLTV99/YHrG41Gvpk4hhGTZuDnH0iPj9+jUtUavJC/AGXLV6T9J50xODszb+ZUVnw/nw86dbO+15xpEyhXsYpd8k3OaDQyc/JYhk+Yjq9/ID0/eZ+KVWvwQpJjy/49u7gUdp45P/zEn8eP8s3E0UyctQCAeo2b0ax1GyaOHJIm8aZLcg9cunVLa11Ga10SqA80AZ7XPbUVkPIyXxo6f+ZP/AJz4huYE2dnF8pWrsXRA7Yn5Kye3rxQsBhOBsMDt3P66CF8A3Pi4x9o75AfW4BPFmLi7xIbn4BJa86ExZAvZzabMonG++1+F2cn0CmXOTkpnJRCa547x3YeIy461tFhPLYzfx4nMGduAnPkwtnFhSq16rEvNMimzL7QIGrUa4xSiiLFSxEfH8e1qIdfP9m09idavvkuLpkyAeDp5WO3HB7mzJ/HyZ4sv/2hO23K7A/dSY16jVBKUbh4KW4+Rn73aK0JDdpKldr17RH+Ezl75gT+OXLhnz0nzi4ulK9WJ0VDLZuXN/kLF8dgsL2e6OXjS96CRQBwy5yFHLnzcj3Ktvfckc6cPE6OXLnJnjMXLi4uVKtdn727bPfTvSFB1Gpg3k+LlihNfFws0VGRKKXInDkLAMbERBITE9PdtNfnzpzEL3su/KznhdocSXYB7/55wbbubt+M56+TR6hUuwkAzs4u6eKCwsME7dhG42YtUEpR6sWXiIuNJTIi5f5WtFhxcuTM5YAIH09I0A4aNGmGUooSpV4kLi6WqEjbPLTWHNq/j5q16wHQoElzQoK2A5A5Sxbrvnj79i0UKffLQ/v3kjNXbgJz5LRLDruDd1C3UROUUhQrafncRNoe/7TW/HFwH9Vq1QWgXqNmhO7c/tD1T504Rs5ceciRMzcuLi7UqNuA0OAdALxcoRIGZ/N+XKxkaSIj7o+22RW0nRw5cvNC/gKkhVMnjpEjVx6y34uzTn12W+K8Z0/wDuo0bGqbo+UcUeqll8maNVtqmxYZyPPagLPSWocDHYEuysyglBqnlNqnlPpDKfUJgFKqllIqSCm1Sil1XCk1SynlZFnWQCkVqpQ6qJRaqZTysLz+j1JqmOX1I0qpYpbXfZVSG5VSh5RSs+H+EU4p9Y5Saq9S6rBSarZSymB5PU4pNUIp9btSardSKlApVQVoAYyzlC+olOpmie8PpdSytPgbxlyLxMs3wPrcy8efmOioJ97OodBtlK1snytyT8s9szPxNxOsz+NvJeCe2SVFufw5s9G2QWGaVM3LtgMXra8r4I26hWjfrDhh4XGEX3u+et+eR9FREfj6398fff0CuJbsC8i1yAh8k1wo8PXzJ9r65V4xon93+nZuz+Z1P1vLXL54gZNHf2dAtw8Z2rsTZ/50TKe3Ob/7sfv4+ROdLL/oyFTKWPJTKEb270G/zh1s8rvn5NHDeHn7kCNXHvsk8ASuR0Xik+TY4u3rz/XoJx6oQGT4ZS6cPU3+Ig691mUjKjLcdh/0D0i1Hv0eUMZoNPJ5x3fo8FojXnqlAkWKl0qbwB9TzLVIvH39rc+9fP2JufZ4dRcZfhmPbJ4smTWWcX0/Ydmc8dy5nb6PnRHh4QRmz2597h8YSET48zdkPjIiHP+AJHkEBNo0RgBuxFzHI2tWa4MleZng7Vtp/+arDOjVjd4DU14b37ZpA3UaNLJTBhAZEWGTg59/IJGRyXOIwd3jfg5+/gFEWco8aP2oiHD8AgKTvH5/naQ2/rra2tt2+9YtflyykHYdPn52CT5CVGQE/jZxBqZohEdFRtjk4usfQFTE87e/2o2TSrt/jkrRYe/8DGmt/8acSwDwIRCjtS4PlAc+VkrdG5tUAegFlAYKAq2VUn7AQKCe1vplYD+QdKxZpOX1mUBvy2tDgGCtdVlgNfACgFKqOPAmUFVrXQYwAvcGiLsDu7XWLwFBwMda612W9ftYehT/AvoCZbXWLwKfPqu/0cPoVLqVnvRicGJiAscOhFKmUs1nFJUdpZLv2Us3WLbxNOtDz1Gh5P2DogZWbjnDonUnCfDOjE821zQM9L8ptf0x+Q6ZWkfovavGX02azZgZC+k3YiIbVv+P40cOAeYvzPFxsQyf8h3vfNSFySMGpv5e9pZqesk/cKl9Js1lhk2axegZC+g7YgIbV//ECUt+94Rs20yVWvWeVbT/ik61pp7M7Vs3mTl2MG9+0JXMWdyfQVR2lGI/ffCx1WAwMHHO93y7fA1nTh7j3Nm/0iLCx5fafppKb0xqTEYjYWdPU7V+C/qMnk0mVze2rE6T65FP7xmcB9OFVPNItl8+oky1WnVYsHwVX42ZyILZ39iUS0hIYNfOHdSoY8ce/sfIIdXhMPfKPGD9VM8byfbpZYvmYjAYqN2gMQDfz5tNqzbtyJwly2OF/kykFj9PVoci43su74F7gHt7bgPgRaXUvbvdPYHCwF1gr6Wxh1JqKVANuI15CGOIZefPBIQm2e5Plv8fAFpbHte491hr/atS6prl9brAK8A+y7YyA/cuidwF1ibZ1oOOfn8APyilfgZ+TpGkUh0x9zjSpf8oGrX+9zcQe/n4cz3q/pWb69ERZPP2faJtnDy8l1z5C5PVM33d0B1/KxH3LPd73NwzuxB/O/GB5S9H3iSbeybcMhm4fddoff1ugolLkfHkCcxK9I30fS/H887Xz/ZKYlRkON6+fjZlfPz8iYq4mqRMBN4+5jI+ll4DTy8fKlStyV8nj1OidFl8/fypUNV8b0ehYiVxcnIiNuY62bzSdp9NHnt0ZEQq+QWkLJNKfuWr1uDMyRMUL10WAKMxkX0h2xmZZOIWR/L29Sc6ybHlWlQEXj5+D1nDVmJiIjPHDaZijXq8XKmGPUJ8ar7J6igqIhyfZPXo6xdAZLIySXu1ANw9slKyzCsc2hdK3vwF7Rv0E/D08eNakiGr16Me/7zg5euPp48/+QqZJ894qWINtvyS/hpwPy5fyuqf/gdA8ZKluHrlinVZxNWr+CUZCZCe/fzjctb9Yv6qUrR4SSLCk+QRfhVfP9t9ztPLm7jYWIyJiRicnVMtA/Bi2Ve4dDGMmOvX8LQcJ/eGBlO4aDF8fJ/sO8KjrPlpBRvW/AxA4WIlbHKIjLiKb7LPTTYvL+Lj7ucQGRFuLeMXEJDq+okJCUSGX03yejg+SfLe/Nta9u4KZuTkmdbG0J/HjxK8fQvzZk4lPi4WpZzIlCkTzV9785nmn5SvfwARNnFexcfP9tji5x9gk0tUslz+6/4LjdkM0QOnlCqAubcrHHNDrqulR6uM1jq/1nqjpWjySxbaUn5TkvIltNYfJilz79u6EdsGb+oXc2Bhkm0V1VoPtSxL0PcvmSTfVlJNgRmYG4IHlFI25bTWc7TW5bTW5Z5F4w0gT8GiRFy5SFT4ZRITEzgUup1SrzzZzboHd23j5XQ2fBIg/NpNvDxcyZrFBSelKJTbk38u3bApk809k/Wxn5cbTk6K23eNuGUykMnF/BExOClyB3hwPVYab/ZWsGhxrly8QPiVSyQmJLBr+2bKVapuU6ZcpeoEbf4NrTWnThwlSxZ3vH39uH37FrduxgPm+zf+OLDHOqlE+So1OHZ4PwCXws6TmJBA1mQTgaQFc35hNvm9Usl24p9XKlUjaPN6tNacfmh+e20mzThycD858+S1GYLqSPkKFSP8chgRVy+TmJDAvuCtvFS+6mOtq7Vm4Ywx5MiVlwYt7Pdl6WkVKlacyxcvcPXyJRISEgjetonyVWwbmeWrVGf7RvN++ufxI2Rx98DH14+Y69eIjzPfl3rnzm3+OLCX3OlkcpZ7XihYjEib88K2xz4vZPPywdvXn6uXLgBw6ughApNMipVevP7mWyxa/iOLlv9Ijdp1+G3tarTWHP3jd9w9PPDzfz6+ELd6/U3mLF7OnMXLqVqzNhvXrUVrzfGjf+Du4ZGicaaUoswr5dixbTMAG9etoUr1WgBcvHDe2rtz6uQJEhITbCZM2rpxvV2GTzZv3Ybp85cwff4SKlevxZb169Bac/LYEdw9PFI0YJRSvFi2HMHbtwCwef1aKlU3jwCqWLVmqusXKVaCS2EXuHLpIgkJCQRt2UilaubP7P49u1j5w0KGjJqIm5ub9X3GzfiOBSvXsGDlGlq+8RZvvtvBro03wBLnea5ctsS5dRMVq9oeWypWrcHWDb9ac7x3bBH/Hc99D5xSyh+YBUzXWmul1Aagk1Jqq9Y6QSlVBLh3U1MFy3DKc5iHOs4BdgMzlFKFtNZnlFJZgNxa61MPedsgzEMjhyulGgP3LuFvAX5RSk3SWocrpXyArFrrcw/ZViyQ1ZKLE5BHa71NKRUMtAM8gOtP+Gd5IgaDgdbtuzJndF9MJhMVajUie+587Nq8BoAq9Zpz43o0kwZ+xu1bN1FKEbT+J74cOxe3LO7cvXObU0cP8MZHPewZ5lPRGnYevkSzavlRCk7+c41rsXcokd88gcXxs9EUyJWNonm9MZk0iUbNpj3mqdizuLlQp3xunJR5+MKZsBjOXXl+JgO5p/eSPpSqVZpsftmYd2EBS4f8wKZ5mxwd1gMZDM580LkXI/v3wGQyUatBM/LkK8CmteYrzPWbtaZshSoc2reL7h3eIJOrK516DQQg5lo044f1BczDuKrWbkCZ8pUBqN2wOTMnjqBXx7dxdnHmsz6DHHKVzmBwpkPnzxnZvycmk5Ha1vxWWfJ7lbIVqnB4XyjdO7yBq6sbn/YaYM1vwrB+SfKrT5nylazb3rVjM1VqOX7yknsMBmfafdSDyV/1RptMVK3bhFwv5Gf7hl8AqNWwJTHXohje5xNu34pHKSc2r/2Rr6YuJOzcX+zesZFceQsw7HPzNbXWb39M6VcqPewt04zB4MxHXXvz1ZfdMJlM1G3cnBfyFWDDGvN+2rB5a16pWJWDe3bx2buv4ermRpc+gwC4FhXJtLFfYTKaMGkTVWvWpVzl9DN7L5jPC6+178qsUV9iMpmoWKsxOfLkI2ST+bxQtb75vDBhQCfreWHHb/+j37h5uGVxp3X7rnw/fSSJiQn4Buag3SdfODijh6tSrTq7goN4o0UTXN3cGDj0/s/jfN6lE/0GD8M/IIAVS37g+4XziI6K4t02r1G5WnX6DxnmwMhtVaxSjT27gnn39Ra4ubnRZ+BQ67J+PbvQq/9g/PwD+Lhzd4YP6sv82d9QqEhRGrdoBUDQti1s+m0tzs7OZHJ1ZdDXY2wmNTmwdw89+w60aw7lK1dl3+4QPmzbClc3N3r2u38f3uA+3ej+5SB8/fzp0KkrY4b2Z9F3MylYuCgNm7Z86PoGZ2c69ezDwF5dMZmMNGjawtrrPXPSWBISEhjwufmndoqWLEXX3v3tmueDGJyd+bTHFwzu3Q2TyUj9JuY41/1i7i1u0vI1ylWqyv7dIXzc7lXzzwj0HWxdf+ywARw5fIAbMdd5//WmvN2hIw0sf5v/jP/ALJTKIfeA/Eup/IzAYmCi1tpkaQQNB5pj7hGLwDzTY1lgsOV5acyNsM8s69QBxgD3bnAaqLVenfTnAZRS5YDxWutaSilfYCngB+zAPJzyFUu5N4F+mHs3E4DOWuvdSqk4rfW9yVFeB5pprdsrpaoC32Lu6WsLzMU87FMB32utRz/o7/DrgQvPX+U9gfP/XHd0CHbz2+v9HB2CXX11dpGjQ7CrZ3FvV3oWG5/w6ELPMV9Pt0cXek6dD49zdAh2VbFY+uhdtpebdx48xD8juJNgfHSh55jJlLHPDYWzZ3suWkajXpmeZhXR70AXh/xNnsseOK31A+eyt/wWXH/LPyvLFaSbWusUfd9a662YJzxJ/nq+JI/3A7Usj6Mw32t3T88k5ZYDKX458V7jzfL4R+BHy+MQbH9GIH1dhhVCCCGEEOJ5IffACSGEEEIIIYRIL57LHrinobXeDmx3cBhCCCGEEEII8dT+Mw04IYQQQgghRAb3H5jERIZQCiGEEEIIIcRzQnrghBBCCCGEEBmC/JC3EEIIIYQQQoh0Q3rghBBCCCGEEBmD3AMnhBBCCCGEECK9kB44IYQQQgghRMYgPXBCCCGEEEIIIdIL6YETQgghhBBCZAwyC6UQQgghhBBCiPRCeuCEEEIIIYQQGYPcAyeEEEIIIYQQIr2QHjghhBBCCCFEhqDkHjghhBBCCCGEEOmF9MAJIYQQQgghMga5B04IIYQQQgghRHohPXDPsTz+Ho4Owa4K5/R0dAh2U/nsIkeHYFeD87/n6BDsakr4MkeHYFeFc3o5OgS7irud4OgQ7KZgjmyODsGubtzMuHUHkMnZ4OgQ7MrZkLH7DZz/Az0/In2QBpwQQgghhBAiY5BJTIQQQgghhBBCpBfSAyeEEEIIIYTIGP4DQ1mlB04IIYQQQgghnhPSAyeEEEIIIYTIGDJ+B5z0wAkhhBBCCCHE80J64IQQQgghhBAZg8xCKYQQQgghhBAivZAeOCGEEEIIIUSGoGQWSiGEEEIIIYQQ6YX0wAkhhBBCCCEyhozfASc9cEIIIYQQQgjxvJAeOCGEEEIIIUTGILNQCiGEEEIIIYRIL6QHTgghhBBCCJExyCyUQgghhBBCCCHSC2nACSGEEEIIIcRzQoZQ/kcd2hfK/G8mYTKZqNu4Ba+2fc9mudaa+d9M5ODeUFxdXencZxAFChezLjcajfTt3AEfP3/6DZ8AwIpF37J53WqyeXoB0O6DTrxcsUqa5ZTUgT27+Hb6BExGE/WbtuSNt9vbLNdaM2faBA7sDsHVzY3ufYdQqIg5vyljvmJfaDCeXt7MWLDcus68mVPYu2snLi4uZM+Zm+5fDsYja9a0TMvq8L5QFsyajMlopE7jFrR6M2X9LZg5iUN7d+Hq5kanXoMoULgoAF3eexW3zFlwcjJgMBgYNX2+db3fflnJhtU/YnAyULZiFd75qEua5vWkus3tTrlm5YkJj6Fr6c6ODuepaK2ZOWUc+0LN+2Kv/kMpXLR4inJXLl1k1JB+xMbeoFCRYvQZ9DUuLi5cOHeWCSOH8depk7z/8We83u69VN7FcbTWjBs9kpCdQbi5ZWbo8JEUL1EiRbnlS35gyfeLCLtwgc1BIXh7e9ssP3b0CO3ffotR4yZQr0HDtAo/Ba01UyeMYU9IMK5ubvQb8jVFiqWsr8sXwxg24Etu3LhBkaLFGPDVSFxcXAA4dGAf0yeMIzExAU8vb6bOmUf4lSuMGDqA6KgonJSi+auv8/pbb6d1eja01owZOZLgoCDcMrvx9ciRFC9RMkW5pT/8wA+LFnHhwnm2h+yy1t2CuXNZt3YtAInGRM7+/Tfbg0Pw9PJKyzRsaK2ZMn4Mu0N24urmRv+hX1O0WMr98dLFMIb2/4LYGzcoUqw4Ay31t2TRfDatXweAMTGRc/+cZc2mHVy/Fs2Q/l/YrP/hJ5/Rpt27aZYb2C+/bJ6eLP9hMWt/+QkFFChUmH5DvsbV1TVNc7PHZw/gzRaNyZwlCwYnAwZnA3MWLU2zvO7RWjN53BhCQ3bi5ubGgKFfU7R46nU3pN8X5vyKFWfw1+b84mJj+WpQP65euUKi0Ui7d9+naYtWAMTG3mD010P5+8wZlFL0H/IVpV58KY0zTEMZfwRlxu6BU0oZlVKHk/zrm0qZWkqptc/4fWsppaokef6pUirdfKsyGo3MnTaeASMnMem7pYRs28iFc2dtyhzaG8rlixeYtmAln/Tox7dTx9osX7dqObleyJdi281ea8v42YsZP3uxwxpvRqORWVPGMnTMFGYsXEHQ1o2c/+dvmzIH9uziUth5Zv/wE5179WfmpNHWZXUbNWPo2KkptlumXEVmzF/GtHlLyZXnBX5cssDeqaTKZDQyb8YE+g2fyMRvlxKybRNhyerv8L5Qrly8wJT5K/m4e1/mTrOtv8FjZzB25iKbxtvRwwfYvyuIcTMXM+HbJTR/vV2a5PNvbFmwmaGNhjg6jH9l3+4QLl24wLxlP9O9z0Cmjx+Varm5M6fy6ptvM2/Zz3hkzcaGtT8DkDWbJ5169OG1tmn7RfFxhewM4sK5c/z863oGDhnGqOHDUi33UtmyzPx2Hjly5kyxzGg0MnXSRCpXqWrvcB9pz65gws6f54ef1tC7/2Amjh6earlZ06fwRrt3WPLTGrJmy8avv6wCzF+kJo0ZyciJU1i4YhXDRo8DwOBsoHOP3ixe+TMz53/Pqh+X8c/ff6VZXqkJDgri/LlzrFm/nsHDhjF82FeplitTtiyz580jZ7K6a//hh6xYtYoVq1bRrefnvFK+vEMbbwC7Q4IJu3COpavW8sWAwUwY9YD6mzaZNu3eZemqtWTNmo21v/wEQLv3OjB/yUrmL1nJJ126U+blV8jm6ckL+fJbX/9u8TLc3NyoUbtuWqYG2C+/iPCr/G/5D3y3aCmLVqzCZDKxZeP6tEzNbp+9eybP+o65S1Y4pPEGEGqpu+U/r+WLgYMZ/4C6mzl1Mm++/S7Lf15L1mzZWPuzue7+t3IZ+QoUZOGyH5k+Zy7TJo0nISEBgMnjxlCxclWW/rSahct+JG/+/GmWl7CPDN2AA25prcsk+Tf60as8E7UAa+tFaz1La70ojd77kc78eZzsOXMTmCMXLi4uVK1Vn/27gmzK7AsNoma9JiilKFKiFPFxcVyLigQgKiKcg3t2UbdxC0eE/0inTx4jR648ZM+ZGxcXF2rUqc+ekB02ZXaH7KBOw6YopShWsjTxcbFEW/Ir9dLLZM2aLcV2Xy5fCYOzudO6aIlSREZctX8yqTjz53ECLfXn7OJClVr12Beasv5q1Gtsrr/ipYiPv19/D7Jp7U+0fPNdXDJlAsDTy8duOTwrx3YeIy461tFh/CuhO3dQt5F5XyxeqjRxcXFERUbYlNFa8/vBfVSvZf5CWK9xM3bt3A6Al7cPRYuXtO6b6c2ObVtp2qIlSilKv/QScbGxREREpChXrHgJcubKleo2li/5gbr16uPt42vvcB8peMc2GjZtjlKKkqVfJC42NtX6OrRvLzXr1AegYdMWBO/YCsDm9b9Ro3ZdArPnALDm5Ovnb+1NyOLuTt58BYiICE+rtFK1betWmrc0192LL5UhNvZGqjEVL1GCXA+ou3vWr/uVxk2a2CvUxxa8YxuNmtyrP/P+GJlK/R3ct5dadc3116hZC3Zu35ZiW5s3/Ebdho1TvH5g3x5y5spD9hwpL0bYmz3zMxqN3Llzh8TERG7fvo2fv799k0nGXp+99CJ4xzYaWfIrVfolYuNiiYxImd+BJHXXpFkLgix1p1DcjI9Ha82tmzfJls0Tg8FAfFwcvx86QPNWrQFwcXFJ9TtOhqJU2v1zkIzegEuVUqqRUuqkUioYaJ3k9aFKqd5Jnh9VSuWzPH5PKfWHUup3pdRiy2vNlVJ7lFKHlFKblVKBlvKfAj0tvX7Vk25XKVVGKbXbsq1VSilvy+vblVJjlFJ7lVKnlFLV7ZV/dGQEvv4B1uc+fgEpDoLRkRH4Btwv4+sXQLSlzPyZk3jn4y44pTLLz/pfVtKr49t8M344cbE37JTBw0VFRODnH2h97usfSFSyg2DKMgFEPcGXpU3rVvNKBcf0MEZH2dafr18A15LV37XICHyT5ufnT3TUvTKKEf2707dzezav+9la5vLFC5w8+jsDun3I0N6dOPPncXumISyiIsPxD7hfV/4BKT+PN2Ku4+6R1dpI8/cPSLFPp1fh4eEEZs9ufR4QGEhE+ONf/Ai/epVtWzbzWps37RHeE4uMCCcgMGl9BRIRbnvsiIm5jkfWrDhb6isgIJBIS5mw8+eIvXGD7p98yMfvtmX9r2tSvMflSxc5/edJSpQsbcdMHi08/KpN3QUGZif86pM3Km/dukXIzmDq1W/wLMN7KhER4QQkyck/8H7d3JO8/vwDAolMts/evn2LPaEh1LI0FJLasmE99VJp2KUFe+XnHxBI23fe5/VmDWjVqC4eHh5UqJS250C7fvYU9O7yKR+/25bVP/1o/2RSEREeTkBgkmNlQGCKCyYx11PWXYTlYvJrb77FP2fP0rJhXd578zV69P4SJycnLl4Mw8vbhxFDB9G+XRtGfTWEW7dupl1iwi4yegMuc7IhlG8qpdyAb4HmQHUg+8M3AUqpksAAoI7W+iWgu2VRMFBJa10WWAZ8obX+B5gFTLL0+u1MtrlFwJda6xeBI0DS8V/OWusKQI9krz9bWqd4KflFBJ1KGZTiwG7zvWEFixRLsbhB89ZMW/g/xs1ajJePL4tmpxyGmBY0qeWXvLH5OGVSt3zxPAwGZ2rVd8wJ+kF1Y1MmlfXu5ffVpNmMmbGQfiMmsmH1/zh+5BBgvroaHxfL8Cnf8c5HXZg8YmDq7yWeqdT+xirZAP7HqPJ063Hye5jxY0bRrWcvDAbDswzrqaVeFykOoKkVAsBoTOTUyeOMnjyNcdNmsmjuHC6c+8da7ObNmwz+shddP++Du4fHM4z8KaR6rnjyHW/H9m2Uebmsw4dPwgP2x+THz8coExK0g9IvlSGbp6fN6wkJCYQEbad2Pcc0Vu2VX+yNGwTv2Mby1b/x8/rN3Lp1iw3rnundJ49kz8/ejO8W8t33yxk7ZQY//7ic3w8eeMbRP9pj1V1q310sx9O9oSEULlqUXzZsYcHSlUwcO5L4uDiMRiOnTp7g1dfbsGDJCjJnzszi+fPsk0R64ZSG/xwkfY65eXZuaa3LJH1BKVUGOKu1Pm15/j3Q8RHbqQP8qLWOBNBaR1tezw0sV0rlADIBZx+w/r339gS8tNb3xvMtBFYmKfKT5f8HgHwP2EbHe/EOGjWR19u1f0ToKfkk622KjgzHx9d2KISvfwBRSa5sRUWG4+Prx+6dW9kfupNDe3dx9+5dbt2MZ+roIXTrOwwv7/vDEeo1acnoQb1xBD//AJvhjVERV/Hx87Mp45uiTDg+fo8eDrJl/Vr2hQYzfOI3T/VF5lnw9bOtv6jIcLx9bfPz8fMnKml+kRF4+5jL3KtrTy8fKlStyV8nj1OidFl8/fypULUWSikKFSuJk5MTsTHXyeZlO5mE+PdW/28F69eY78soUryETY9URHh4iv3V08uL+LhYjImJGJydiXjM/dVRVixdwqr/mQ9tJUqV5uqVK9Zl4Vev4pekd/9RThw/Rr8vegFw/do1QoKDMBgM1K5b79kG/RCrViyz3mdStERJwq8mra+rKYaSeXp5ExcbS2JiIs7OzoQnKeMfEIinlzeZM2chc+YsvFT2Zc6cPkWevPlITExg8JefU69RE2rUSbv8klq25Ad+WmnugShZupRN3V29egX/gCff79avW0fjJk2fWYxP6qcVy1jz8/8AKFaiJOFJcoq4ehXfZPXnlaz+IsKv2ox6ANiyMfVett0hwRQpVhwf37QbnpcW+e3fu5scOXPj7W0eWl+zdl2O/nGYhk2a2SstIO0+e36W/L19fKleqw4njh3lpZdfsWtuAP9bsYzVq8x1V7xEScKvJjlWhl/Fz+/RdXcv9l9X/8I7HT5AKUXuPC+QI2cuzv1zlsDsOfAPCKRk6RcBqFWvPt9n9Abcf0BG74F7kAd1KyRi+zdxs/xfPWCdacB0rXVp4JMk5Z/WHcv/jTygca21nqO1Lqe1Lvc0jTeAQkWLc/niBa5evmS+Wrh9E+Uq247YLFe5Ojs2r0NrzanjR8ni7oG3rx9vf/gZs5eu4Zvvf6bngK8pVaYc3fqaJyVIeo/V3pAd5MlX4Kni+7cKFy3BpbDzXLl8kYSEBIK2bqJClRo2ZSpWqcHWDb+itebksSNkcffAJ1kjKLkDe3bxv6WLGDRyAm5u/7aqn17BosW5cvEC4VcukZiQwK7tmylXKVn9VapO0ObfzPV34ihZsrjj7evH7du3uHUzHjAPkfnjwB5rPZWvUoNjh/cDcCnsPIkJCWS1zCgqnq0Wr7XhmwVL+WbBUipXr8WW9eZ98cTRI7h7eOCb7KStlOLFsuXYuX0LAJt/W0vlajUdEfpjafNWO5b+uIqlP66iVp26/Lr6F7TWHPn9dzw8suL/BPfOrFm/ibUbNrN2w2bq1m9I3wGD0rTxBvBqm7bMXbKCuUtWUL1WbTb8ugatNceO/PHA+ipTrjw7tm4CYMOvq6laozYAVWvW5o9DBy33Ed3ixNEj5M2X3zzj49dDyZuvAG++7bg5r9q2e9s68UjtunVZ84u57v74/TAeWbPi7//4jW+A2NhYDuzbT606dewU8aO1btPWOjFH9Vp1WL/uXv2Z98fkX5KVUpQtV57tW8z1t37taqrXrGVdHhcXy+GD+6lWs3aK93rQfXH2lBb5BWTPzrGjf3D79i3LfVh7yJsG5/i0+OzdunWTm/Hm8+KtWzfZtzuU/AUL2T03gNfatGXh0pUsXLqSGrXqsN6S39F7deefMr+Xk9TduiR1F5g9Owf27gEgOiqK8+fOkTNXbnz9/AgIDOTcP+Y+hgN795CvgGO+n6WZ/8A9cBm9By41J4H8SqmCWuu/gLeSLPsHaAaglHoZuDdNzxZglVJqktY6SinlY+mF8wQuWsq8n2Q7sUCKO0S11jFKqWtKqeqWoZXvAjuSl7M3g8GZD7v0ZkS/7phMJmo3bEaefAXYuMZ8latB89a8XKEKh/bsouv7r5PJ1Y3OvQc+cruLv53OP3+dRinwD8zBJz1STPqZJgzOznza/QuG9OmGyWSkXuMW5M1fkN9+MV/latzyNcpVqsr+PSF0fPtVXF3d6P7lYOv6474awJHDB7gRc532rzelXYeONGjaktlTxpGQcJdBvczT1RctUZrOvfqlfX4GZz7o3IuR/XtgMpmo1cBcf5vWmuuvfrPWlK1QhUP7dtG9wxtkcnWlUy9z/cVci2b8MHO9mIxGqtZuQJnylQGo3bA5MyeOoFfHt3F2ceazPoMc1sv4uHov6UOpWqXJ5peNeRcWsHTID2yat8nRYT2RCpWrsS80hA/ebImrmxuf9x9qXTaodzd69B2Er58/H3bqxqih/Vn47TcULFyUhs1aARAdFUm3j97lZnw8yknx88qlzP5+Je7uDh5+Z1Gteg1CgoJo2aQRbm5uDB0+wrqsW6dPGDTsa/wDAlj6w2IWzZtHVFQkbV9rRdXqNRg87GsHRp66SlWrszskmHavNsPVzY2+g+/PzPhF9858MXAIfv4BfNqlB8MGfMHcmTMoVLQYTVu+CkC+/AWoUKUqH7R7AyelaNqyNQUKFeaPwwfZuG4tBQoV5sN2bQD4uHNXKlW12+3Qj1S9Rk2Cg4Jo1qghbm5ufDVipHVZ5086MuTr4QQEBPDD4sUsmDeXqMhI3mjVkmo1ajD0a/MMels3b6Zy1SpkyZLFUWnYqFy1OrtDdtK2VVPcLFPR39On22d8OWgofv4BdOrak6H9v+C7mdMpXLQYTVtab5cnaNtWylesQubMtjndvn2L/XtD6TNgUJrlk5y98itZ6kVq1a3Hh2+/icFgoHDR4rRo/Xqa5mavz96lsDAGftETMP90Qr1GTajogBlvK1erTmjITtq0NNdd/6H3665Xt8/oO2go/v4BdOrWkyH9v2DON9MpUrQYzSyTk7T/+BNGDBnEu21ao9F81q0HXpaf9Oj5RT+GDexHYkICOXPlttm2eD6pjHyPi1LKiPk+s3vWa637KqUaAZOBSMz3sZXSWjdTSmUGfgECgH1ANaCx1vofpdT7QB/MvWOHtNbtlVItgUmYG3G7gfJa61pKqSLAj4AJ6ArUBeK01uMtQzhnAVmAv4EOWutrSqntQG+t9X6llB+wX2ud72H5/XH+WsatPMDNJX3c82IPN+8kOjoEuxqcP938aoZdTAlf5ugQ7MrfM7OjQ7CruNsJjg7BbrzcMzk6BLu6cTPj1t1/gSkDf+cEcE5lcreMxM/D9blIcFzrJWm2o/X5qZ1D/iYZugdOa51qC0BrvR5IMQuH1voWkOqdx1rrhZjvWUv62i+YG3zJy54CXkzy0s4kyw4DlVJZp1aSx5E84B44IYQQQgghxH9Xhm7ACSGEEEIIIf5Dnot+wn/nvzqJiRBCCCGEEEI8d6QHTgghhBBCCJExZPB7EUF64IQQQgghhBDiuSE9cEIIIYQQQoiMIZ3/BNKzID1wQgghhBBCCPGckAacEEIIIYQQQjwnZAilEEIIIYQQImPI+CMopQdOCCGEEEIIIZ4X0gMnhBBCCCGEyBjkZwSEEEIIIYQQQqQX0gMnhBBCCCGEyBgyfgec9MAJIYQQQgghxPNCeuCEEEIIIYQQGYP8kLcQQgghhBBCiPRCeuCEEEIIIYQQGYKSWSiFEEIIIYQQQqQX0gP3HHN3y9jVp7WjI7AfTQZODpgSvszRIdhV94C2jg7Brj4/8q2jQ7CrfNmzOToEu9kXFu7oEOwqI9cdwN1Eo6NDsKtMzgZHh2BXN40mR4dgV34ero4O4fFk/A446YETQgghhBBCiOeFNOCEEEIIIYQQGYNSaffvkaGoRkqpP5VSZ5RSfR9QppZS6rBS6phSasfjpJixx+AJIYQQQgghRBpTShmAGUB9IAzYp5RarbU+nqSMF/AN0EhrfV4pFfA425YGnBBCCCGEECJjSD+zUFYAzmit/wZQSi0DWgLHk5RpB/yktT4PoLV+rBuZZQilEEIIIYQQQjxbuYALSZ6HWV5LqgjgrZTarpQ6oJR673E2LD1wQgghhBBCCPGElFIdgY5JXpqjtZ5zb3EqqySfhtwZeAWoC2QGQpVSu7XWpx72vtKAE0IIIYQQQmQMaTiC0tJYm/OAxWFAniTPcwOXUikTqbWOB+KVUkHAS8BDG3AyhFIIIYQQQgghnq19QGGlVH6lVCagLbA6WZlfgOpKKWelVBagInDiURuWHjghhBBCCCFExvAY0/unBa11olKqC7ABMADztNbHlFKfWpbP0lqfUEqtB/4ATMB3Wuujj9q2NOCEEEIIIYQQ4hnTWq8D1iV7bVay5+OAcU+yXWnACSGEEEIIITKG/8ANYv+BFIUQQgghhBAiY5AeOCGEEEIIIUTGkE7ugbMn6YETQgghhBBCiOeE9MAJIYQQQgghMgQlPXBCCCGEEEIIIdIL6YETQgghhBBCZAz/ge4pacBlcFprZk8Zz77dIbi6uvF5/6EUKlosRbkrly4yemh/4mJvULBIMXoP/AoXF5eHrr9/zy5mTxmPyWSiYbNWtHmnPQDfz5vNhjU/4+nlDcD7HT+jfOVqHNy3mwWzppOQmICLswsffNadMq+Uf2a57t+zizlTzfE0aHo/Hpu/xdTx7Lfk0rOfbS4PW/d/Sxczb+YUlqzejKeXF1cvX+LTd98g1wt5AShWohRdevd/Zrk8yuF9u1k4azImo5E6jZvT8s33bJZrrVk4cxKH9obi6uZGp14DyV+4KABd3mtN5sxZcHIyYDAYGDl9HgCTRwzicth5AOLjY3F3z8qYmQvTLKcH0Vozc8o49oWG4OrmRq/+QylctHiKclcuXWTUkH7Ext6gUJFi9Bn0NS4uLlw4d5YJI4fx16mTvP/xZ7ze7r1U3iV96ja3O+WalScmPIaupTs7OpyncvTQHlbMm47JZKRa3aY0av22zfIrYedYMGMMF/4+Tct2H9KgZVsAEu7eYfyg7iQmJGA0Gnm5ck1atO3giBRsaK2ZOXkce0ODcXNzo9eAYQ/cH0cO6UfsjRgKFSnGF4OH4+LiwvlzZ5k4YihnTp3k/Y6deSPJ/hgXG8uk0V/xz99/oRR83n8IJUq9lJbp2Th6cA9L507DZDJRvV5TmrxmW3eXw84xf9pozv99mlff/oiGrcx1Fx0ZztwpI4i5Fo2TkxM16jenXvPXHZFCClprZkway95d5uPJF4NSr7/Lly4yYlBfc/0VLU7fIeb6CwnaxoI5M3FyUhgMBjr16EPpl8pa1zMajXzW4W38/AMYMWFqmuRjj3N8xNUrTBgxhGvRUSjlRKMWr9LqjbcAWPTdTHbv3IGTkxOe3t583n8ovn7+aZKrvepu3PCh7NkVhJe3D9/98KPdc0ma06xk57dCDziejE5yfuttOb89aP27d+7Qp8vHJNy9i9FopFrturz74acAjBrcl7Dz5wCIi4vFwyMrMxYsTbOcxbMhDbgMbv/uEC6GXeC7pav48/hRpk8YxeQ5Kb+Uz5s1jVfbtKNmvYZMGz+SjWt/oemrrz9wfaPRyDcTxzBi0gz8/APp8fF7VKpagxfyFwCgVZt2vPbWuzbv4enpxZAxk/D18+efv88wqFdXFq/67ZnkaTQamTlpDMMnmuPp2fE9KlWrwQv5Ctj8LS6FXeDbJeZcZkwcxaTZCx+5bsTVKxzevwf/wOw275kjVy6mz1vyTOJ/EiajkXkzxjNg1BR8/QLo3/VDXqlUndx581vLHN4XyuWLYUyev4IzJ4/x3bRxjJj6nXX5oLHTyebpZbPdHgO+tj5ePHsqWdw97J7L49i3O4RLFy4wb9nPnDx2lOnjRzHl20Upys2dOZVX33ybWvUaMnXcSDas/Zlmr75B1myedOrRh9Cg7Wkf/L+0ZcFm1k5fS89Fnzs6lKdiMhpZ+u0Uegwej7evP6O+/JQXy1clZ5581jJZsmaj7YfdOLwn2GZdZ5dM9Bw6EbfMWTAmJjJ2YFdKvVyBAkVKpnEWtvaFhnAx7Dzzl//CyWNHmDZ+FFNT2R+/mzmV1pb9ccrYEaxf+zPNX32DbNk86dTzC3YFbUuxzszJ4yhXsQqDRowjISGBO7dvp0VKqTIZjfwwZzKfD52At68/w7/4hDIVbOvO3SMbb33UjUPJ6s7JyUCb9p3JW7AIt2/d5OteH1OiTDmbdR1lb2gwFy+cZ+HKXzhx7AhTxo5k+tzFKcp9O2MKr7V9m9r1GzF5zHB+W7OKFq3b8HK5ilSpXgulFH+fOcXXA75k/vJV1vVWrVjCC/nyczM+Pk3ysdc53mBw5qPOPSlUtBg3b8bT7cN3eblcRV7IX4DX33qX9z7qBMAvPy5jyYJv6ZoGFzDtWXcNmzan1RtvMuarQXbPI6l757e5Sc5vk1M5nsybOZVWluPJtCTntwet75IpE6OnzCJzliwkJibQu9OHlKtYleKlStPvq9HW7X47bSJZPNLHuf6Zknvg0oZSaoBS6phS6g+l1GGlVMWHlF2glHrd8ri6Zb3DSqnMqZTNp5S6ZVl+798zufyulIp7Ftt5yPatef4bu4N3ULdRE5RSFCtZmvi4WKIjI23KaK354+A+qtWqC0C9Rs0I3bn9oeufOnGMnLnykCNnblxcXKhRtwGhwTseGkvBIsWsV+ny5i/I3bt3Sbh799+mCJBqPLuTxbM7eAd1Gj5eLknX/Xb6RDp06pZuboo98+dxsufMTWCOXDi7uFClVj32h+60KbM/dCc16jVCKUXh4qW4GR/HtajIB2zRltaa0KCtVKld3x7hP7HQnTuo26gpSimKlypNXFwcUZERNmW01vx+cB/V7+3DjZuxa+d2ALy8fShavCQG5+fvetWxnceIi451dBhP7eyZkwRkz4V/9pw4u7hQrlodft8XYlMmm6c3+QoVw+BssHldKYVb5iwAGI2JGBMTUTj+MxgavJ16jZpZ9scXiY+NTX1/PHB/f6zfpBmhlgbbvf3ROdn+GB8fx5HfD9KoeSsAXFxc8Mia1f4JPcDZ0ycIyHG/7ipUq8PhvbYNtWxe3uQvXDzFZ8vLx5e8BYsA4JY5Czly5+ValO3fyFF2Be2gfmNz/ZUo9SJxcanX3+ED+6hRux4ADZo0J8RyAShzlizWc8HtW7dszgsR4VfZExJMkxavpk0y2O8c7+PnZ+3Jy5LFnRfy5SMyMtz8PMnFvdu3bqXZ59Kedfdi2VfIms0zTfJIancq57foxzi/WevvAesrpcicxXz8TExMJNGYmKJNo7UmaNtmatVrZO80hR04/BuNUqoy0Ax4WWt9RynlB2R6zNXfBsZrrec/pMxfWusy/zLM51ZkRAT+Afd7jvz8A4mMDMfHz8/62o2YGNw9slpPwn7+AURZDtQPWj8qIhy/gMAkrwfw54mj1udrflrBlvW/UrhYcT7q0pOsWbPZxBWyfQsFCxfFJdPjVvXDRUWmEs/xo8nKpMwlKjL8oevuDt6Br18ABQoVSfGeVy5fouuH7ciSxYN3P+pEqSTDaOwpOioCX//78fr4+XPm5HHbMpEpy0RHReDt64dCMbJ/DxSKuk1bUq9JK5t1Tx49jJe3Dzly5bFrHo8rKjIc/yT14x8QQFRkhM2QnRsx1232YX//AKIi0scXxv+y69EReCepJ28ff86ePv6QNWyZjEZGfNGRiCsXqdnoVfIXKWGPMJ9IZITt/ugXYN7XUu6PHkmOqYFEPmJ/vHLxIp5e3kwYMZS/z5yicNHidOrRB7fMKa5Npolr0ZF4+wVYn3v7+vP3qRNPvJ3I8MucP3uaAumg7sBSf0lGU/j7BxIZEZ6i/jySnhMDAomKCLcuD96+lbkzp3H9WrTNMMlvJo/j4y7duXnzZhpkYmavc3zS9a9evsRfp/6kWIlS1tcWzpnBlg3rcHd3Z/SU2XbLLyl71p2jpPj+ERBAZGQEPg85v/klOb89bH2j0Ui3D9/h0sULNHu1DcVKlrZ576O/H8Lb24dceV6wZ4qOkU4uuNtTeuiBywFEaq3vAGitI7XWl5RSryildiilDiilNiilciRdSSn1EdAGGKyU+uFJ31QpFaeUGmPZ/malVAWl1Hal1N9KqRaWMu2VUr8opdYrpf5USg1JZTtKKTVOKXVUKXVEKfWm5fXFSqmWScr9oJRqoZQyWMrvs/Q4fpJkO9OVUseVUr8CAcnf66lonVrujyxj3fkfsH4qa1ivwjVt9Tpzl/3M9PlL8PH147vpk2zKnTv7F/NmTaNrn2c35CK1FJJ/gPUD8nzQurdv32b54nm8Yxk3npSPrx8LVq5l2twlfNSlJ+O+GsjNeLt2yt6XahrJD1YPrvdhk2YxesYC+o6YwMbVP3HiyCGbciHbNlOlVr1nFe2/llq9Jb/i+7BdWDjQY3wuH8bJYGDQhLmMnrOSf06f4OL5v59dbE/rMfa11PfHh+dtNBo5c+okzV59nW8WLMUtc2aWL37YtUk7S/XY/2SbuH3rJt+MGcybH3Qlcxb3ZxTYv6Mfcmy0lkn9pGB9VK1WHeYvX8WwMROZP+cbAHYHm++fKlIsjRuqdjrH33Pr5k1GDPyCjt162fS8vd+xM4v+9yu16jdmzU8rni72J2SvunOkf3t+e9j6BoOBGQuWsvin3zh14ij//H3Gptz2zeupWa/hU0YuHM3hPXDARsyNsFPAZmA5sAuYBrTUWkdYGkUjgA/uraS1/k4pVQ1Yq7V+2B2nBZVSh5M876q13gm4A9u11l8qpVYBw4H6QAlgIbDaUr4CUAq4CexTSv2qtd6fZHutgTLAS4CfpUwQ8B3QE/hFKeUJVAHeBz4EYrTW5ZVSrkCIUmojUBYoCpQGAoHjwLzkySilOgIdAYaPm0Lb91Le1L/mpxVsWPMzAIWLlSAi/Ip1WWTEVXx9bW82zublRXxcLMbERAzOzuYrWpYyfgEBqa6fmJBAZPjVJK+HW68Yefv4Wl9v1PxVhn7Z43658Kt83b8PvQYMI0eu3Clif1p+/gEp4kl+U7Wf/+Pn4uvnz5WLYVy9fIkuH7xlfb37R28zcfZCfHz9rL2HhYsWJ0euXFy8cJ7CaXDy9vHzJyrifrzRkeaeNdsyASnL+JjL+Fjq1tPLh/JVa3Dm5AmKlzb3HhqNiewL2c7I6Q784gis/t8K1q8x35tQpHgJIpLUT0S47dVhAM9k+3BEkv1ROI6Xrz/XkgwHuhYdgZeP30PWSF0W96wUKVWGY4f2kuuFAo9e4Rlb/b/l/Lb63v5Y0mZ/jAxPua+Z98e4JMfUq/j6PTxvv4AA/P0DrFfJq9Wqy4rvFzzbRJ6At68/1yLv91xci3qyuktMTGTm2MFUqlGPVyrXsEeIj+2XH5ezbvVPgKX+rt4/D0REXE1xrvD08iYu6Tkx/Cq+/imPJy+WfYXLF8OIuX6No38cJnTnDvbuCubu3bvcjI9n1NAB9Bs64pnnkxbneDDX4YiBX1CrfiOq1qyTaiy16jdi6BfdeefDT55lilZpUXf3JlxLK2uSnd8ikx1Pkh8rkp/fkn7f8vMPfOT6Hlmz8mLZcuzfvYt8BQoBYExMZNeObUyd+71dchT25/AeOK11HPAK5kZJBOYG3CeYG02bLI2vgcDTftv/S2tdJsm/ezcL3QXWWx4fAXZorRMsj/MlWX+T1jpKa30L+Amolmz71YClWmuj1voqsAMor7XeARRSSgUAbwH/01onAg2A9yx57QF8gcJAjSTbuQRsTS0ZrfUcrXU5rXW51BpvAM1bt2H6/CVMn7+EytVrsWX9OrTWnDx2BHcPjxRffpVSvFi2HMHbtwCwef1aKlWvCUDFqjVTXb9IsRJcCrvAlUsXSUhIIGjLRipVM5+kk46/3xW0jbz5CwLmGdaGfNGD9p90puSLZVKN/WkVKVaCi8niqVjV9ktDxWo12bohSS7u93NJbd18BQuxZPUm5q9Yw/wVa/DzD2DKdz/g4+tHzPVrGI1GAC5fCuNS2AWy58z1THN6kIJFi3PlYhjhVy6RmJDAru2beaWS7W75SqVqBG1ej9aa0yeOkiWLO96+fty+fYtbN80319++fYs/DuwlT5KJXo4c3E/OPHnx9X82HcBPq8VrbfhmwVK+WbDUsg//itaaE0fN+2Dyk/a9fXjnvX34t7VUrlbTEaGLJPIVKkr45TAir14mMSGB/cFbealclcdaNzbmOjfjzff/3b1zh5N/HCB7LscM9Wnx2pvMXLiMmQuXUaVGLTavX2vZH/8gywP2x5devr8/blq3lsrVaz30PXx8/fALCOTCuX8AOHxgLy/ky//QdewpX+FiXL0cRoSl7vYGb+Wl8lUfa12tNQtnjCFH7rw0aPmmnSN9tJavv8nsRcuZvWg5VWvUZtNv5vo7fvQP3N1Tr78yL5cjaNtmADauW0MVS/1dvHDe2utx+s8TJCQkkM3Ti48+68ay1Rv4YdU6Bnw9mjKvlLdL4w3S5hyvtWby6K/Iky8/rdu+Y7O9ixfOWx/vCd5B7hfy2SVPSJu6S2vNX2vDjAVLmfGA81vyC0IPO79VqlYj1fWvX7tGXKz5+Hnnzm0O7d9Dnrz5rNs8tH8vufPmsxkOnqE4peE/B0kPPXBorY3AdmC7UuoI0Bk4prWubMe3TdD3+55NwL0hnCalVNK/S/L+6eTPHzaoZDHm+/Tacr/3UGHuBdxgsxGlmqSy7X+tfOWq7NsdwodtW+Hq5kbPfvdHgQ7u043uXw7C18+fDp26MmZofxZ9N5OChYvSsGnLh65vcHamU88+DOzVFZPJSIOmLawNtbkzp/D3mVMoFIE5ctC19wAA1vy0nEsXL7Bs4VyWLZwLwPCJ0/Hy9vnXeRqcnenUow+Depvjqd/EHM+6X8yds01avk75SlXZHxrCR2+1svyMwJCHrvswRw8f5Pt5szEYDDg5OdG5V780uwHaYHCmQ+fPGdm/JyaTkdoNmpEnXwE2rTVf0avf7FXKVqjC4X2hdO/wBq6ubnzay1wHMdeimTCsH2C+v6hq7fqUKV/Juu1dOzZTpVb6mLzkngqVq7EvNIQP3myJq5t5mut7BvXuRo++5n34w07dGDW0Pwu//ca8DzdrBUB0VCTdPnqXm/HxKCfFzyuXMvv7lbink1k2H6b3kj6UqlWabH7ZmHdhAUuH/MCmeZscHdZjMxicaftRd6Z83QeTyUTVOo3J+UJ+dmz4BYCaDVsScy2KkV98wu1bN1FKsWXtjwydspCYa1EsmD4Kk9GE1iZeqVKbFx+z8WdP5v0xmA5tWlqn7b5nYK+u9Ow7GF9/8/44ckg/FsyZQaEixWz2x64fvnN/f1yxhDk//Ii7uwede37JmGEDSExMIHvO3DbbTmsGgzPtPu7B5GG9zXVXtwm5XsjP9vXmuqvVyFx3w/t8wq2b8SjlxOa1P/LV1IWE/fMXods3kitvAYb1/BCAV9/5mBdfqfSwt0wTFatUY++uYN57owWurm70GTjUuqz/5134vN9g/PwD+Khzd0YM6sv82d9QqEhRGlsml9m5fQubfluLs7MzmVxdGTh8jEMnuLLXOf74kd/ZumEd+QoUokuHdsD9nwSaP3saF8+fQyknArLnoEvvfmmSqz3rbsTgvvx+8AAx16/TtkVD3v/oUxqnwWQ05ZOc39zc3Oj5gPPbB526MXpofxZZzm8NLMeTB61/LSqS8SOGYDIZ0SZN9Tr1bC5q79iygVoyfPK5plIfL5yGAShVFDBprU9bng8HfDD3VL2rtQ5VSrkARbTWx5RSC7AMm0z6+AHbzmdZXiqVZXFaaw/L46FAnNZ6fNJlSqn2wEjMvYG3MPeYfaC13p+kTGvMPYZNLHHvBypqra8opQKBvcAVrXVFy7Y7Wsq+obVOUEoVAS4CDZNsJwDzEMqPHzY89K/wWMdWnp05eNe0q9hbz2b2zfTKy93V0SHYVfeAto4Owa4+P/Kto0Owq3zZsz260HPqQvjzO2vp48jIdQdwN9Ho6BDsKlOy2WYzmgSjydEh2FUBf4/n4u7yCd3WpNk3yF5Tmzvkb5IeeuA8gGlKKS8gETiDeTjlHGCq5f4xZ2AycOwptp/8Hrh5WusnmXooGHNPWiFgSbL73wBWAZWB3zH3oH2htb4CoLW+qpQ6AfycpPx3mIdoHlTmSz8RQCvLdupgHsJ5CvNQTCGEEEIIIYSwcngDTmt9APMEH8lFYr4vLHn59qk9fsC2/wFSnYP5Xu+b5fHQBy0DwrXWXR60vmUYZh/LPxtKqSyY729bmmQ9E9Df8i+5FO8jhBBCCCGEeEz/gWmoHT6JSUallKoHnASmaa1jHB2PEEIIIYQQ4vnn8B64Z0EpVRrzMMek7ty77+xpaa0XAAuect3NQAb8dUQhhBBCCCHSqf9A91SGaMBprY9g/i02IYQQQgghhMiwMkQDTgghhBBCCCHkHjghhBBCCCGEEOmG9MAJIYQQQgghMgbpgRNCCCGEEEIIkV5ID5wQQgghhBAiY/gPdE/9B1IUQgghhBBCiIxBeuCEEEIIIYQQGYPcAyeEEEIIIYQQIr2QBpwQQgghhBBCPCdkCKUQQgghhBAiY5AhlEIIIYQQQggh0gvpgRNCCCGEEEJkDP+B7qn/QIpCCCGEEEIIkTFID9xz7MbNu44Owa6cMvAY5tj4BEeHYFeFc3o5OgS7+vzIt44Owa4mlv7Y0SHY1aKbqxwdgt38b8ZeR4dgVwVG1Hd0CHZlcMrY19UTjSZHhyD+CzLw98d7MvaRQgghhBBCCCEyEOmBE0IIIYQQQmQMGb8DTnrghBBCCCGEEOJ5IT1wQgghhBBCiIzBKeN3wUkPnBBCCCGEEEI8J6QHTgghhBBCCJExyCyUQgghhBBCCCHSC+mBE0IIIYQQQmQMGb8DTnrghBBCCCGEEOJ5IT1wQgghhBBCiIxBZqEUQgghhBBCCJFeSANOCCGEEEIIIZ4TMoRSCCGEEEIIkTHIzwgIIYQQQgghhEgvpAdOCCGEEEIIkTFk/A446YETQgghhBBCiOeF9MAJIYQQQgghMob/wM8ISAPuP+rwvt0snDUZk9FIncbNafnmezbLtdYsnDmJQ3tDcXVzo1OvgeQvXBSALu+1JnPmLDg5GTAYDIycPg+Ac3+d5rtpY7l96xb+gTno8uVQsri7p3luAIf3hTJ/5mRMJiN1G7WgVduU+c3/ZhKH9u3C1dWNz3oPooAlv87vvopbkvxGz5gPwOI50ziwOxhnFxcCc+Tis94DcffImua5JXf04B6WzZuGyWSier2mNG79ts3yy2HnWDB9NOf/Pk2rdh/RsFVbAKIjw5k3dQQx16JRTk7UqN+ces1ed0QKD6W1ZtzokYTsDMLNLTNDh4+keIkSKcotX/IDS75fRNiFC2wOCsHb29tm+bGjR2j/9luMGjeBeg0aplX4j3T0/+zddXwURxvA8d9ESoAAcbS4BIdixYO1uJZi1ZeWCt7iLsXdi7vTQoEixUMSJLiVtlRwYkBIIJTIvH/ckVySSwg0F+P59sOnud2Zvee5ldvZmd07e4JNy+YSFRVJrQbNaBxn/d27dZ0V8yZx868/aNW5K++0Mqy/8Gf/MnV4byLCw4mMjOSt6nVp2fHT1EjhlfVa2pvKzasQ7B9Mz7LdUzucl6a1ZvrkCfh4HcXOzo7hY8bhXjL+trl5wzo2rF3NrZs32XvoKA7GbXPPzztZvWIpAJkzZ2HA0OEUL+GeojkkpmApV+q9VxZlpbjkfZ2T+67Fmp+vmDOtv6hKcNATAP44d5fju3/H0S0rzbtWji6XwzkLPj//xplDf6Vo/OZorZk7fTInfLyws7NjwPAxFHcvGa/c3Tu3GTtsICHBwRRzL8ngUeOwtbWNnn/1yiV6dP2I4d9Nom6DRgB0at2ELFmyYmVlhbW1Dd+vXJdieT2ntWbBzCmcPGbI79uhoylWIn5+9+7cZvzIwYQ8CqZocXcGjPgOW1tbblz/m+njRnHt96t83K077TvH/u6MjIykZ9cPcHZ1ZeyU2SmTz6wp+B7zJpOdHd8OGZVgPhNGDiYk5BFFi7vTf/hYbG1tE62/bdM6du/YhtaaJi3b0Ob9zgCsXrqQPTu2ksPBsJ9+8kV3qlavZbH8vo8TX9EE8ptokl8/k/wSqx8ZGUmvzz7ExdWV0ZNnATBhxCBu3bgOQGhoCPb22Zi3Yr1F8hOWY9EGnFKqDfAjUFJrffUVl7EC2Km13qKUWgJM11pfScYYh2itx5u8DtVa2yfX8tOiqMhIls2bytAJs3B2cWNIz65Uers2+QoUii5zzvcYd2/fYubyTVy7epklc6YwbvaS6PnDJ88lew6HWMtdOHMCH3zek1LlKnJo7052bFlLh4+7pVRa0aIiI1k6dxrDJhryG9zzf1SuHju/s77HuHf7JrOXb+aPq5dZMnsy4+csjZ4/csq8ePmVe6sqnbt+hbW1DWuWzGPrhlV88FnqnnRGRUaybvFM+o6chqOzK+MGfEH5KjXJ82bB6DJZ7bPTsWsvzp30ilXXysqa9h93p0CR4jwNe8LYfp9TqnzlWHXTAu+jnty8fp1tP+/h0oULTPhuNKvWbYxXrnzFitSu60G3/30cb15kZCSzZ0yneo2aKRFykkVFRrJ+8Sz6jJiKo7MrEwZ+Sbk46y9LNuP6OxF7/dnYvkHfUdOxy5yFyIgIJg/rSZm3qlK4eOkUzuLVHVixn51zd9J31TepHcor8fE6ys0bN9iyfReXLl5g8rixLFsT/0SoXIWK1Kxdl68/i93AzpM3LwuWriB79hz4eB1l4tjRZuunBqWgwfvl2DLnGCEPw+gyoA7XLt7j/r3QWOVuXQti2/cnY0174P+Y1ROORC/ni/Hv8Mf5uykWe2JO+Hhx++YNVm/Zzq+XLjJz8jjmL1sTr9yiuTN5r+MH1H+nMTMmfseu7Vtp1e59wHA8WTR3FpWrVY9Xb/r8xdEn/qnB95g3t2/dYPnGn7h6+SJzpk5g9uJV8cotWTCbth264NHwXWZNHseendto0aY92bPn4Ku+A/DxPGR2+ds2r+fNgoV48jjU7Pzk5nvcmzs3b7JswzauXr7E3KkTmGUmn6ULZtPGmM/sKePZu3Mbzdu0T7D+P39dY/eObcxavBJbG1uGftuTqtVrkffN/AC0eb8z78VpvFoyv6Um8c00k9+yBbNpbcxvjpn8Eqr/0+b15C9QkCdPHkdPGzxmYvTfi+dMJ4t9BjzlladQ/medAC+gY3IsTGv9WXI23oyGJPPy0rxrv10hV5585MydFxtbW2p4NOTUsaOxypw6dpQ6DRujlKJYyTI8eRzKg6DARJd799YNSpatAEDZilU46XXYQhkkLl5+dRvi6+MZq8wpH0/qNGqCUoriJcvwOAn5la9cDWtrwzWP4u6luR/gb7Eckurva7/imjsvrrnyYGNrS5Va9eM11LI7OFKoWMno2J9zcHKmQJHiANhlzkLufAV4GBSQYrEn1ZFDB2nWshVKKcqWL09oSAgBAfHjdC9Zijx585pdxsZ1a2nQsBGOTs6WDvel/H3tKm65YtZf5Vr1Oe/rHatM9hyOFCzqjrWNdazpSinsMmcBIDIygsiICFQ6u3P78tHLhN4PSe0wXpnn4UM0ad7SsG2WK09ISAiBZrbNEu4lzW6b5SpUJHv2HACUKVcOfz8/i8ecVLkKOvIw4DHBQU+IitT8dvo2Rcvleunl5C/hysOAJ4TcD7NAlC/Px/MwjZo0RylFqbLlCA0JISgw9jrTWnP2lC916zcE4J1mLfA+EtOg2bppPXXqNcDRySlFY0+KY16HadjYkF/JMuV4nEB+50/7UtujAQCNmjbnmLHB5uDoRImSpbGxiX99P8Dfj5M+R2nSorXF83ju2NEjNGjczJhPWUJDQ83ncyYmn4ZNmuNz9HCi9W/88zfupctgZ5cZaxsbylZ8K8FGqyUdNxPf/STkd+zo4RfWD/D34+QxL95NYH1prfE8tB+Pho0tlZ6wIIs14JRS9kBNoCvGBpxSykMp5amU2qqUuqKU+l4pZWWcF6qUmqaUOqOUOqCUcjWzzMNKqcrGvxsby55XSh0wTquqlPJRSp01/r+EcfonSqkflVJ7lFJ/KKUmG6dPBDIrpc4ppdbGeS8P4/ttUUpdVUqtVcrQpFdKVTEu/7xS6qRSKptSyk4ptVwpddH4/vVM3nubUmqHUupvpVQPpdQ3xjLHlVJOxnJFjPGdVkodVUpZbBzN/aAAnF1zRr92cnGNd8C4H2imjPHkXqEYP6QPg7t/yv5d26LL5CtQmNPGhuCJowcJSqUGjiF2t+jXzq5u0bFHlwkKwMUkP2eT/EAxbnBvBn79Cft/3mb2PQ7u3UmFKvGvvqa0h0GBODnH5Oro7MrD+4k3RM0J9L/Lzb//oFDx+MO/Upu/vz85c8WcOLrlzEmAf9JPdP39/Dh0YD/t3u9gifD+k4f3A3B0iTnUOTq5vlQjOioykrHfdqXf/1pTsnzlNLn+MrIAf7//tG2a2r71R6rXsswwrVdh72BHyIOYRlfIw6fYO2SOVy5PISc+HFyXtl9Xwzl3/CHl7pXzcvX0LYvG+jICA/xxyxmzzlzdchIY57vqUfBD7LNlw9rYiDEtE+Dvh9eRQ7Ro2z7eshWK/r2+4ouPOrFz6xYLZpGwwAB/XN1ivttc3NwIinNR4VHwQ7La20fn5+Ka0+yFh7i+nzWVz77ujfG0LUUEBcbOx9XNLV4DzpCPyfpyjck5ofoFCxfl0rmzPAp+yNOnYfge8461727/cRNfftyB6eNHE/LokUXzc4mzvgJfkJ9LnPwSqr9w9jS6ftUbqwTW16XzZ3F0dIrudcxQVAr+SyWWHELZGtijtf5dKXVfKfWWcXpVoBRwHdgDtAW2AFmBM1rrb5VSI4CRQA9zCzY27hYDdbTWfz9vBAFXjdMilFINgfFAO+O8CkBF4F/gN6XUHK31IKVUD611hQRyqAiUBu4A3kBNpdRJYCPQQWvtq5TKDoQBvQG01mWNja9flFLFjcspY1yWHXANGKi1rqiUmgF8BMwEFgFfaq3/UEpVA+YD9RP7gF+Zjj9Jxetujl/oeZnRM77HydmV4If3GTeoD3nfLEDJshX58pshrFgwgx/WLqdS9Vpmr+ClBJ1I7NFlzH0Gxj1x7MyFhvwe3Oe7wb3J82YBSpWrGF3ux3UrsLa2pnaD1L+PylyuL+tp2BMWTB5Bh//1JHOW1LlnMTHazMp6mZ6mqZMm0Kvvt1hbW7+4cEozt/peYuiHlbU1w6ct5cnjEBZMGs7tG3+RN3/h5ItPJMrctvkqQ3dO+Z5kx7YfWbR8dTJElTzMZhEnX/+bwSwesY/wfyMpVNqNVt2qsGz0wej5VtaKImVzcvSnXy0b7EtIyvHEbBnjep03Ywrduvc2ezyZvXgFLq5uPLh/n/49v+TNgoUoX7FSMkWeREnYJM1vtolvt8e9PXFwdKKYeynOnzn1HwJ8OUlbX/HrPU8nofr5Cxai/QcfM7jv12TOnIXCRYtHr9Pmbd6j8yefoZRi1eIFLJ47g2+GjPzvyZhhqfxOeHvi4OBIMfeSXEhgfR3ev4e6DVP/PEa8GkueYXfC0DAB2GB8/TNwUmv9F4BSaj1QC0MDLgpDwwhgDYZ75xLyNuCptf4bQGt93zg9B7BSKVUMw2HM1qTOAa11sPF9rwAFgJsvyOGk1vqWsc45oCAQDNzVWvsa3/uRcX4tYI5x2lWl1HXgeQPukNY6BAhRSgUDO4zTLwLljL2VNYDNJgfRTOYCUkp1A7oBDB03jXad49/v8yJOLq4EBcRcabofGICjs0ucMm7xyzgZyjg5G3oMcjg4UaVmHa5d/ZWSZSuSN39Bhk4w3CR759YNzp7weenYkoOzi1us3r+gAP/o2GPKuBJokl+QyWcQnZ+jE1Vq1OXab1eiG3CHf/mZ0ye8GTFpzgu/8FKCo7Mr94Nicn0QFIBDnFwTExERwYIpI6hWpyFvvV3HEiG+kk3r17H1h80AlCpTFr9796Ln+fv54eLmllDVeH69cpnBA74F4OGDB3h7eWJtbU29Bg2TN+hX4ODsygOTq60P7r/c+nsuS9ZsFC9TgctnT0oDzsI2b1jPTz8aeldKlS4Tb9t0dU36tgnwx++/MX70CGbO+54cDg7JGep/EvLwKdkcY3rcsjnYERr8NFaZZ08jov/++7I/DTpYkTnrG4Q9fgZAodI58bsZzJOQf1Mm6ARs27yBn38ynFKUKFUaf7+YdRbg74eza+wBPzkcHAkNCSEyIgJrGxtDGWNP+e+/XmHs8IEABD98yAkfL6xtrKlVtz4uxnXv6ORELY96XL18KUUacNt/2Mju7VsBKF6ydKyepEB/f5xc4ubnwOPQ0Oj8AgP8cHZJ/Lhz5cJ5jnsdwfeYF8+ePePJ48dMGj2UgSPHWSCfTezZ8TyfUrHyCfD3xylOrIZ8TNZXQEzOLq45E6zfuHlrGjdvDcDyhXNN1l/MUPvGLdswckCfZM1vR5z8AuOsr7jrIm5+gXHyM1ff6/B+jnt74nvcm/Bnz3jyOJTJY4YxYMR3AERGROBz5BCzl8a//zNDeA2eQmmRfnCllDOG3qMlSql/gP5ABwwX9eJeLkioCyGxrgVzywEYi6GxVAZogaHH6znTb5BIktZ4NVcnofdObGsxXU6Uyeso4zKtgIda6wom/+I/hgjQWi/SWlfWWld+lcYbQJESJbl3+xb+9+4QER6Oz+H9VHo79tCdSm/XwnP/HrTW/PHrJbJkyYqjswtPn4YRZrwZ9unTMC6cPsmbBQ0njMEPDe3oqKgotq5bQcPmbV4pvv+qSImS3L19E/+7xvyO7Kdy9dqxylSuXhvPfbvRWvP7r5fIktWYX5hJfmFhXDhzgvzG/M75HuOnTWsYOHoymezs4r1vaihY1B3/u7cI8LtLRHg4vl4HKV8laQ/q0Fqzct4kcuctwDst09bwwvc7dWb9lq2s37IVj/oN+Hn7T2ituXj+PPb22XB1jTfCOkE79uxj59797Ny7nwaN3mXQ0OFpovEGULBoCfzv3iLQuP5OeR2kfOUaSaobEvyQJ48N9489+/dfrl44Ta68GXAoTBrTvmMn1mz6gTWbfqBOvfrs3rndsG1eOI+9vT0uL7Ft3rt7l0Hf9mHUdxPIX6Cg5YJ+BfeuP8TBLSvZnbNgZa0oUSkvf16MPTw0S/aY64y5CjigFNGNNwD3Snm5eup2isWckNbtO7J4zSYWr9lErTr12Ld7J1prrly8QFZ7++jG2XNKKSpUqsyRg/sB+OXnHdSs4wHAum27WL9tN+u37aZu/Yb07j+EWnXrExYWxpPHhu+OsLAwTp04RqEiRVMkv5btOrBg5QYWrNxAjToe7N9jyO/XSxfIkkB+5d+qzNHDBwDYt2sn1Wt7JPoe//uqJ2u37WHVDz8zePQEyleqbJHGmyGf95m/Yj3zV6ynem0PDuz52ZjPxQTXV7mKMfns372T6rXqAvB2rToJ1n/4wHDO4n/vLt5HDkbfC2Y6RNPH8xAFCxdJ1vxatHufeSvWMy+B/OI2uF82PycXVz79sidrtu5m5ZadDBo1nvKVqkQ33gDOnjpJvgIFYw0vFemLpXrg3gNWaa2/eD5BKXUEQ29bVaVUIQxDKDtgGDoIhkbMexh66zpjePhJQo4B85RShZ4PoTT2wuUAnn9bfJLEWMOVUrZa6/Aklr8K5FFKVTEOocyGYQilJ9AFOGgcOpkf+A14K+FFGWitHxnvj2uvtd5svNeunNb6fBJjeinW1jZ82v0bxg/pS1RUJPXeac6bBQuzb6fhilCj5m2oWLUG53yP0fvT9mTKZMeX3w4FIPjBfaaNHgwY7r+pWa8RFaq8DYD3oX38ssNwlbNqzbp4vNPMEuG/kLW1Df/r8S3jhvQhKiqKeu8a8vtlpyG2d5q3pWLVGpw56UOvT9rzRqZMfN1vGGBohE4dPQgwPGmsVr13ou91WzpvGhHPwhk7qDcAxUqWplvvgamQYQxraxs6f9aHmWP6oaOiqNmgKXnzF+Lw3p8A8Hi3FcEPgviu/xc8DXuMUlbs37mFMbNXcuv6nxw/8gt5CxRm9DddAWjb5XPKVno7NVOKp1btOnh7etKqaWPs7OwY9V3MSUOvr75g+OixuLq5sX7talYtW0ZQUCAd27WmZu06jBg9NhUjfzFraxs6ftabWWP7ExUVRc36TciTvxBHjOuvrnH9jR/wBU/DnqCU4sDOLYyatZLgB0GsmDuBqMgotI6iUo16lEti4y+t6LeuP2U8ypLdJTvLbq5g/ci17Fu2L7XDSrKatevg43WUdi2aYGeXmeEm21uf7l8xdORoXN3c2LhuDatXLOd+UCBd3m9LjVq1GTpyDEsXLSD4YTCTxxtOrKxtrFm5blNqpROLjtIc3HSRdt3fxspKcenYDYLuhlCuVgEALnhdp3jF3JSvXZCoSE1EeCQ/LzsdXd/G1poC7q7sW2+Rr7FXVq1mbU74ePFBuxbGnxEYHT1vUJ/u9Bs6EhdXN7r16MPYYQNZtnAeRYuXoEnLxC9IPrgfxIgBhqepRkZG0ODdJlStnvJPva1avRa+x7z49P1W0Y+Vf27Ytz3pO2gEzq6udP2qF+NHDmbFonkULe7Ou8aeqPtBgfTs+gFPHj9GWSm2bVrHorVbyJo1dZ5UaMjHm/91MOTzjUk+w/v1os+g4Ti7GPKZMGoIKxfPp0ixEtH5JFZ/7ND+hDwKxtrahu7fDCJb9uyA4YmWf/3xGyhFzlx56NXfcs+6q2ISn52dHX0TyO9/X/Vi4qghrDLm944xv8TqJ+bIgb14ZOThk2lghJSlKbNj+P/rQpU6DEzUWu8xmdYL+Aq4CwQAZTE0er7WWkcppUKBGUBTDMMUO2itA1TsnxE4DPTTWp9SSjXBcI+bFeCvtW6klKoOrDQu/yDwoda6oFLqE6Cy1rqHMZadwFSt9WGl1CSgJYb777oo488IKKU8jO/V3FhnLnBKa71CKVUFw3DJzBgabw2BCOB7oJLx72+01ofMvPc/xteBpvOMjdoFQG4MQz83aK3HJPY5n/0nKPlXXhpilYF3wODQZy8ulI69VSzpvRDp0anfU/8JpJY0veznqR2CRa16sjW1Q7CYpf12p3YIFtVxXKPUDsGiwiOiUjsEi7LEOWdakrGzg8Ku9unixGza5CMptiq+HVA3VT4TizTgEnyzOI2iOPMy/O+vJTdpwKVf0oBL36QBl75JAy79kgZc+iYNuPQt3TTgpqZgA65f6jTgUu5ZsEIIIYQQQggh/pMUfc671vowcDiBedL7JoQQQgghhBCJSJ0f6hJCCCGEEEKI5JaBb8F5ToZQCiGEEEIIIUQ6IT1wQgghhBBCiIxBeuCEEEIIIYQQQqQV0gMnhBBCCCGEyBheg+6p1yBFIYQQQgghhMgYpAdOCCGEEEIIkTHIPXBCCCGEEEIIIdIK6YETQgghhBBCZAzSAyeEEEIIIYQQIq2QHjghhBBCCCFExvAadE+9BikKIYQQQgghRMYgPXBCCCGEEEKIjEHugRNCCCGEEEIIkVZID5wQQgghhBAiY5AeOCGEEEIIIYQQaYX0wKVj4RFRqR2CRSky7hUU5xx2qR2CRYU+DU/tECyqYK7sqR2CRa16sjW1Q7Coj7K0Se0QLOazE/NTOwSLssrgV9YjozL293pUlE7tECzKPvMbqR2CgNeie+o1SFEIIYQQQgghMgZpwAkhhBBCCCFEOiFDKIUQQgghhBAZgsrgQ61BeuCEEEIIIYQQItkppRorpX5TSl1TSg1KpFwVpVSkUuq9pCxXeuCEEEIIIYQQGUMa6YFTSlkD84BGwC3AVym1XWt9xUy5ScDepC5beuCEEEIIIYQQInlVBa5prf/SWj8DNgCtzJTrCfwA+Cd1wdIDJ4QQQgghhMgQ0kgHHEBe4KbJ61tANdMCSqm8QBugPlAlqQuWHjghhBBCCCGEeElKqW5KqVMm/7qZzjZTJe6PIc4EBmqtI1/mfaUHTgghhBBCCJEhpORTKLXWi4BFCcy+Bbxp8jofcCdOmcrABmPMLkBTpVSE1npbYu8rDTghhBBCCCGESF6+QDGlVCHgNtAR6GxaQGtd6PnfSqkVwM4XNd5AGnBCCCGEEEKIjCKN3CCmtY5QSvXA8HRJa2CZ1vqyUupL4/zvX3XZ0oATQgghhBBCiGSmtd4F7IozzWzDTWv9SVKXKw04IYQQQgghRIaQkvfApZY00skohBBCCCGEEOJFpAdOCCGEEEIIkTFID5wQQgghhBBCiLRCeuCEEEIIIYQQGcJr0AEnDbjX1YVTx1m9aBZRUVF4vNOcFu9/GGu+1prVC2dx/tQxMmWyo1vfIRQsWgKA3Vs3cuSXHaAUbxYozOd9h/DGG5nYsnoxZ457oZQiu4Mj3foOxdHZJTXS4/yp46xeNNOYXwtams1vJuei8xtKoaIluHPrOnMnjogu53/vDu998BmNW3cA4Jftm/ll5w9YW1tToUoNOv2ve4rmZc6Zk8dYNm86UVFRNGzakradPo41X2vN0nnTOXPCh0yZ7OgxYDhFirvz7Nm/DOvzJeHhz4iKjKR6nfp0/KRbKmURm9aa2dMmccLbi0x2dgweOZbi7iXjlbt7+xajhw7k0aNHFC/hztAx47G1tQXg7Glf5k6bQkREODkcHJm9aBn+9+4xbtRQ7gcFYaUULdq8x3uduqR0emitWTBzCiePeWFnZ8e3Q0dTrET8/O7duc34kYMJeRRM0eLuDBjxHba2tty4/jfTx43i2u9X+bhbd9p3/ii6TmhICDMmjuGfv/5EKfhmyEhKlSmfkunForVm+uQJ+Hgdxc7OjuFjxuFeslS8cps3rGPD2tXcunmTvYeO4uDoCMCen3eyesVSADJnzsKAocMpXsI9RXN4Vb2W9qZy8yoE+wfTs2zqHyte1tULvmxfPZ+oqCiqejShfouOseb737nBxsVTuf3PNRq/9ykezdpHzzu690dOHNoNaKp5NKV247YpHP2Laa2ZM20Sx30M++GgEQkfZ8YMiznODBkd5zgzfQqRxuPMrIXLUjyHhbOm4nvcm0yZ7PhmyCiKmtk/7t25zcRRQwgNeUSR4u70GzYGW1vbROvPmDCakz5eODg6smDVpuhlTRg5mNs3rgMQGhqCvX025i5fZ/FcT5/wYdGcaURFRfFOs1a07/JJrPlaaxbNnsapE4Zc+gweSdHihlxmThyD7zEvcjg6Mn/Fxug6Xof2s27FIm5e/4fp36+gmHv8Y1NK+a/b49nTvgzr14dcefICUKdefT7+7EsAtmxYy85tP4DWNGvdjvadPkjR3ETyS/IQSqVULqXUBqXUn0qpK0qpXUqp4pYMzvi+o5RS/Yx/j1FKNUzm5fdRSmUxef2PUip1Wh0pJCoykpULptN/9FQmLVjDMc/93L7xd6wy508dx+/OTaYu3sD/evZn+bypANwPDOCXHVsYM3MpE+evJioqiuNHDgDQrF1nxs9bybi5K6hQtQbb1i9P8dzgeX7TGDB6GpMXrOW42fyOce/OLaYt3kjXngNYYcwvT74CjJ+7kvFzV/LdrGVkymRH5Rp1Abhy/jSnj3sxYd4qJi1YS9O2neO9d0qLjIxk8ewpDJswk1nLNnD04C/c/OevWGXOnPTh7q2bzFu1hS+/GcSiWZMBsLV9g9HT5jFj8VqmLVrDWd/j/HblYmqkEc8JHy9u3bjB2h930G/ICKZP/M5sue/nzqJ95w9Y9+MOsmXPzs8/bQUgJOQRMyaNZ/z0WazctJXRE6cAYG1jTfc+/Vi9eRsLlq9h65YN/PPXnymW13O+x7y5fesGyzf+RO8Bw5gzdYLZcksWzKZthy4s3/gT9tmys2fnNgCyZ8/BV30H0K7Th/HqLJg5hcrVarB0/Y8sWLmR/AUKWzKVF/LxOsrNGzfYsn0Xg4aPYvK4sWbLlatQkTnfLyF37jyxpufJm5cFS1ewdvNW/tftSyaOHZ0SYSeLAyv2M6rxyNQO45VERUWydeUcuvYfT79JSzh37BB+t6/HKpMlazZaf9iduk3fizX93s2/OXFoN71Gz6HvuIVcOXecgHu3UjL8JDnh48WtmzdY+8MOvh08ghmTzB9nFs6dxXudPmDtDzuwz5adXSbHmZmTxzN+2ixWbNzKqAlTUjJ8AE4d9+b2rZssWb+VXgOGMnea+WPJsu/n0Ob9zixZvxX7bNn4ZedPL6zfsEkLxk6dE29Zg0dPYO7ydcxdvo6adetTo049yyRnIjIykgUzJzN68izmr9zEkQO/cCPOd92pEz7cuXWDRWt/pEe/IcyfPtEkl+aMnjI73nILFCrCkLGTKV2+osVzeJH/uj0ClK1QkaVrN7F07aboxttff/7Bzm0/8P2KtSxZu5ljXp7cunHd7LJF+pGkBpwyPI9zK3BYa11Ea10KGALktGRwcWmtR2it9yfzYvsAWV5UKCP58/dfyZknH26582Jja8vbdRpy+rhXrDJnjh+lVv3GKKUo6l6GJ49DeXg/EDA0kJ49+5fIyAie/ftvdC9b5ixZo+v/+/RpqvVhx8+vAaePH41V5vRxr1j5PX4cwgNjfs9dPn8Kt9x5cXHLBcD+Xdto0f4DbG3fACCHg2PKJJSIa1evkDtvPnLlyYutrS216jXipI9nrDInvT3xeKcJSilKlCrL49AQ7gcFopQic2bDph8ZEUFERESaefSu15FDvNusBUopSpctR2hICEGBAbHKaK0563uSuvUbAfBus5Z4HTkIwP49u6lTrwE5c+UGwNHJGQBnF9foK5pZsmalQMHCBAT4p1Ra0Y55HaZh4+YopShZphyPE8jv/Glfans0AKBR0+Yc8zwEgIOjEyVKlsbGJvYgisePQ7l4/gyNW7QGwNbWFvts2SyfUCI8Dx+iSfOWKKUoW648ISEhBAYExCtXwr0kefLmjTe9XIWKZM+eA4Ay5crh7+dn8ZiTy+Wjlwm9H5LaYbySG3/+hkvOPDi75cbGxpYKb3tw+bRPrDL2ORx5s3AJrK1jb4d+d25QoKg7b2Syw9ramsLu5bh0yjslw08Sb89DvNv0xceZM6dijjONTY4zB/buprZH/ONMSjrudYQGjZuilMK9tPH4Hhj7u0xrzYUzvtQyHksaNm7OsaOHX1i/bIW3yJY9e4LvrbXm6KH91G34rkVyM/X7r5fJnfdNcuXJh62tLXXqN+K415FYZU54HaH+u81i5xJkyKVM+bfIli1+Lm8WLES+/AUtHn9S/NftMSE3/v6bUmXKYWeXGRsbGyq8VYmjhxOvk+4plXL/UklSe+DqAeGmPzyntT4HeCmlpiilLimlLiqlOgAopeyVUgeUUmeM01sZpxdUSl1VSq1USl1QSm153vtl7PmapJQ6afxXNG4QSqkVSqn3jH9XUUr5KKXOG8tnMy7/qPF9zyilahjLeiilDhvf76pSaq0y6AXkAQ4ppQ7Fea+CSqlflVKLlVKXlVK/KKUyG+cVVUrtN773GaVUEePyzH0WHkqpI0qpTUqp35VSE5VSXYwxX1RKFTGWc1VK/aCU8jX+q5n01fhyHgQF4OTiFv3aycWVB0EBccoE4uRqWsaN+0GBOLm40rRtR/p80o6eH7Qmc9aslH2ranS5zSsX0vvjtvgc/oV2H3S1VAqJip+fm5n8AnB2TbzMMc8DVK8b0+F77/YNfrt8npF9P+e7gd358/dfLZRB0gUF+uPsGnMdxdnVjftxDvj3AwNwSaBMZGQk33T7gE/bNaZ8paoUL1kmZQJ/gcAAf9xyxsTs6paTAP/YDa3g4IfYZ8sW3Yhxc8tJoLHMrRvXCXn0iN5fdOXzDzuy5+cd8d7j7p3b/PHbVUqVLmvBTMwLDPDH1S0mPxc3N4LiNGoeBT8kq7091sb8XFxzmm34mLp3+zY5HByZNm4UX3/SiRkTxvA0LCz5E3gJAf5+5MyVK/q1W86cBPi/WiNs+9YfqV6rVnKFJhLx6EEgDk6u0a9zOLkQ/CAwkRoxcuUryF+/XeRxyCOe/fuUq+dPEnw/8W03NQT4++P6kscZ15w5oy/63LxxndCQR/T+sivdPurIXjPHGUsLDAjA1S1m/3JxzUlgYOwcHgUHk9U+m8mxxI0gY5mk1E/IpfNncXB0Iu+b+f9rGi8UFBgQ+5jpmjNe4yYoMAAXt9jfdUGpcIHuVf3X7RHgysULdO3cngG9v+bvP68BUKhIUS6cPU3ww4c8fRrGcW8v/P3upUBGwpKS2oArA5w2M70tUAEoDzQEpiilcgNPgTZa67cwNP6mqZhL+yWARVrrcsAj4GuT5T3SWlcF5gIzEwpGKfUGsBHorbV+/t5hgD/QyPi+HQDT/vKKGHrbSgGFgZpa69nAHaCe1trcGIBiwDytdWngIdDOOH2tcXp5oAZwN5HPAuO03kBZ4EOguDHPJUBPY5lZwAytdRXj+yxJKP//Smsdb5pCJaEMPA55xOnjXkxftonZq7fx79OneB/cG12m/cdfMGvlj9TweId9O35M9tiTwlzsJCm/mDIR4eGcOeFFtVr1o6dFRUXyODSEUdMX0el/3Zk7cXgC75XK4lwR0pjJ1VjE2tqa6YvWsHjjDq5dvcz1v1N+OKE55j7WeL2D5gsBEBkZwe9XrzBx5hymzFnAqqWGexyee/LkCSMGfkvPb/qT1d4+GSNPooRDjymSlM8gjsjISK79fpXmbd5j/or12GXOzMbVqTOU+Tmz+8grXLU85XuSHdt+pEfvb5IhKvEiZo+RSVxvOfMWoF6zDiyeNJAlU4aQJ39hrKyskztEi0jKcUaZHGd+u3qFiTPmMHn2AlYti32cSRFJWU+J7YP/YT0f2b8XjxTofQPMx5mU7/U0MqrkVb3M9li8REk2bN/D0nWbaft+J4YN6AtAgUKF6fTRp/Tr+QUDen1NkWLF4/WaZzTKSqXYv9TyX9dgLWC91joS8FNKHQGqALuB8UqpOkAUkJeY4ZY3tdbPx1KsAXoBU42v15v8f0Yi71sCuKu19gXQWj8CUEplBeYqpSoAkYDpPXontda3jOXOAQWB2OMG4/vb2NMIhgZsQaVUNiCv1nqr8b2fGpeZ0GfxCPDVWt81lvsT+MW4zIsYGrhgaPSVMtlZsyulsmmtY42/UUp1A7oBDBo7lTYdP+JlObm4cd/kCtv9wAAc4jxsxMnFlfsBpmX8cXR24dK5U7jmzE32HIbhg1Vq1OGPXy9Ss37sg3gNj0ZMHdU/VXrh4ufnH+9hKk4usa/M3Q/0j/UZnD91nIJFipPD0Sl6mqOzG5Vr1EUpRZESpVBKEfLoYfRnkRqcXdwICojpzQgK8McpTq7OLm4Exinj6Owaq0xW+2yUrlCJs77HKFCoiGWDTsDWTRvYuc3Q6C9RqnSsoXIB/n64uMaOOYeDI6EhIURERGBjY4O/SRlXt5zkcHAkc+YsZM6chfIV3+LaH7/zZoGCRESEM2LgNzRs3JQ69ZP1ltpEbf9hI7u3G+5VKF6ydKxeqEB/f5xc4ubnwOPQUCIjIrC2sSEwwA9nl8Rvz3Vxc8PV1Q13Y69iLY8GbFqzInkTSYLNG9bz049bAChVugx+92Ku9vr7+eFq0vudFH/8/hvjR49g5rzvyeHgkJyhigTkcHLloUmvWfD9QLI7JH2IYFWPJlT1aALA7k1LyeHk+oIaKWPr5pjjjHup0gS85HEmwM8PFxeT40wOk+NMhbf403icsaQdP25i745tABRzL0WAf8z+FRjgh3Oc43t2Bwceh4aYHEv8o8u4uLm9sL45kRER+HgeYvaS1cmQ0Ys5u7rFPmYG+OEU53jo4upGoH+c70OXtLHdJSQ5t0fTi5Fv16zNjMnjefjwAQ4OjjRr1ZZmrQwPElo8f3as3kyRPiW1B+4yUMnM9ISanl0AV6CS1roC4AfYGefFvXygk/C3ufc1N7+v8b3KA5WBN0zm/WvydyRJa7yaq5NQzok1w02XE2XyOsokDiuguta6gvFf3riNNwCt9SKtdWWtdeVXabwBFC7uzr3bN/G/d4eI8HCOe+7nrWqxR2y+Va0WXgf3oLXm2tVLZMlqj4OTC86uOfnzt8v8+/QpWmsunz9NnjcLAnDv9s3o+meOe5EnX4FXiu+/MuR3yyS/A7xVLfawK3P5OTrFfBkc89xH9bqNYtWpXL02V84bOqLv3r5BREQE2bI7WDyfxBR1L8nd2zfxu3uH8PBwvA7to0qNOrHKVKlRm8O/7EZrzW9XLpIlqz1Ozi4EP3zA41DDJvbvv0+5cPok+YzrMjW0eb8jS9dtYum6TdT2qMfen3cYtrGLF8hqb49znC9ipRQVKlfhyMF9AOz9eTs1jTfT16xbjwtnzxAREcHTp2H8eukiBQoWQmvNpLGjKFCwMB26vNr+86patuvAgpUbWLByAzXqeLB/z0601vx66QJZEsiv/FuVOXrY8JCgfbt2Ur22R6Lv4eTsgotbzuhegHOnT5K/YCFLpJOo9h07sWbTD6zZ9AN16tVn987taK25eOE89vb28U5KEnPv7l0GfduHUd9NIL+FT4xFjDcLlyDw3m3u+98lIiKcc8cPU+qt6kmuHxr8AIAHgf5cPOVNheqWf9BFUrRp3zH6IQ+16tZj764XH2cqVoo5zuz5eTs16xpyqVWnHhfPxRxnrly+SP5Clt/fWrR9P/ohItVre3Bgzy601ly9fJGs9vbxGjZKKcpVrIyX8Viyf89O3q5teDhXtZp1X1jfnLOnT5Ivf8FYQxYtqbh7Ke7cusG9u7cJDw/H8+A+qtWM/V1XrWYdDu79OTqX5991aVlybo9BgYHRvZC/Xr6IjooiRw4HAB7cDwLA795dPA8doME7TVIow1SiUvBfKklqD9xBDD1qn2utF4PhHjTgAdBBKbUScALqAP0xDF/011qHK6XqAaZn8vmVUtW11seATsTuBesATDT+/1gi8VwF8iilqmitfY29YmFADuCW1jpKKfUxkJQxGyFANiBJg/u11o+UUreUUq211tuUUpmM7+MJfGHms0jq865/AXoAUwCUUhVMev+SlbW1DR999Q1Thn9DVFQUdRo1I1+BwhzYtQ2ABk1bU75Kdc6dOka/zzrwRiY7Pu87BICi7qWpUrMew3v/DytrawoWLk69Ji0B2Ljie+7evoGVssLZLSefdu9vifCTlN/HX/Vl8vBviIqKpG6j5sb8thrza0OFKtU5f+oY3372Pm8YfybhuX+fPuXSWV/+12NArOXWbdScRTPHM+jrD7C2seWLb4al+vAMa2sbPuvZjzEDexEVFUWDJi3IX7Awe43DV99t0ZZK1Wpy5oQPX3/Yjkx2dvToPxww3Oc4Z/IYoiKjiNJR1KzbgMrV08b9RW/XrM1xby86t2lOJjs7Bo0YEz1vQO/uDBg2EhdXN77s0YfRQwewdME8ipZwp1mrNgAULFSYqjVq8r/O7bFSimat2lK4aDEunDvDL7t2UrhoMbp2fh+Az7v35O2atVM0v6rVa+F7zItP329FJjs7vh0yKnresG970nfQCJxdXen6VS/GjxzMikXzKFrcnXebtwbgflAgPbt+wJPHj1FWim2b1rFo7RayZrWne9+BTBo9lIiIcHLlyRdr2amhZu06+HgdpV2LJtjZZWb46JinUPbp/hVDR47G1c2NjevWsHrFcu4HBdLl/bbUqFWboSPHsHTRAoIfBjN5vOGJbNY21qxctymht0tT+q3rTxmPsmR3yc6ymytYP3It+5btS+2wksTa2prWH/Vg8ZTBhp8RqPMuufIV5NgBw31e1Ru04NHD+8we0Z2nYU9QVgqvvT/Sb9IS7DJnZdXsMTwOfYS1tQ1tPu5Blqyp+zAdc96uWZsTPl50aWs4zgwcHnOcGdinO/2HGo4zX/Tsw5ihA1j6/TyKFXenaUvDcaZAocJUrV6Trl3ao54fZ4oUS9EcqlSvie9xb7p2bE0mOzv6Do556umI/r3oPXA4zi6ufPpVTyaNGsKqJQsoUqwE7zZr9cL6k0YN4cLZ0zwKfsiHbZvywf+6RR+DPPf/Qt2G76RYntY2NnzZZwAj+vUiKiqSRk1bUqBQEXb99AMATVu1o/LbNTl13JvPO7cx/IzAoJifBJo8eigXzxly+fi9ZnT5tBvvNGuFj+chFs6eSvDDB4we1JdCRYubffJmSviv2+ORg/vY/sMmrK1teMMuEyPGTYo+Rxkx8FsePQrGxtqGPv2HJPpwGpE+qKTew6OUyoPhvrRKGO5x+wfDPWXdgCYYesS+01pvVIbH8O8AbIFzQE1jGYBdGBo7NYA/gA+11k+UUv8Ay4GmGHqjOmmtrymlRgGhWuupSqkVwE6t9RZjA3IOkBlD460hkBv4AXgCHAJ6aq3tlVIeQD+tdXNjLnOBU1rrFUqpnkB3DEMy6xnjqAzYG9+rjLFOP8Beaz1KKVUMWAi4AOFAe+BvYLKZzyLuex82vj5lOs/4mc0DSmJoWHtqrb9MbJ2cvBaQBm/ASj5xx7dnJFnsMvb4c+fsdi8ulI49fRaZ2iFYlEPWN15cKB37KEub1A7BYj47MT+1Q7CoKiVebvhtevPk3/DUDsGioqIy9GkL9pkz9rEzdw67dHFiNnfd2RTb0Hp0rpgqn0mSG3DJ8mZKFcSkURRn3j9AZa110h5zJaQBl45JAy59kwZc+iYNuPRLGnDpmzTg0jdpwMWXWg24jH0WKYQQQgghhHhtpPOHjyZJijbgtNb/YPhJAnPzCqZkLEIIIYQQQgiR3kgPnBBCCCGEECJjeA264JL6MwJCCCGEEEIIIVKZ9MAJIYQQQgghMoTU/omnlCA9cEIIIYQQQgiRTkgPnBBCCCGEECJjeA26p16DFIUQQgghhBAiY5AGnBBCCCGEEEKkEzKEUgghhBBCCJEhyENMhBBCCCGEEEKkGdIDJ4QQQgghhMgYpAdOCCGEEEIIIURaIT1wQgghhBBCiAzhNeiAkx44IYQQQgghhEgvpAdOCCGEEEIIkSHIUyiFEEIIIYQQQqQZ0gOXjj0Lj0rtECzq6bOI1A7BYgIfPU3tECyqSO7sqR2CRfne8k/tECzqh3knUzsEi/rsxPzUDsFillT7OrVDsKhCN1andggWZW9nm9ohWJSdXcY+7YyMytjnZenGa9A99RqkKIQQQgghhBAZQ8a+FCKEEEIIIYR4bcg9cEIIIYQQQggh0gzpgRNCCCGEEEJkDNIDJ4QQQgghhBAirZAeOCGEEEIIIUSG8Bp0wEkPnBBCCCGEEEKkF9KAE0IIIYQQQoh0QoZQCiGEEEIIITKG12AMpfTACSGEEEIIIUQ6IT1wQgghhBBCiAxBWUkPnBBCCCGEEEKINEJ64IQQQgghhBAZwmtwC5z0wAkhhBBCCCFEeiE9cEIIIYQQQoiM4TXogpMeOCGEEEIIIYRIJ6QHTgghhBBCCJEhqNegB04acAKAi2dOsH7JbHRUFLUbNaNpuw9izb976zrL5kzkxp+/0+aDz2jculP0vGVzJnLhlA/ZcjgydvbKlA79hS6fO8mW5XOJioqiZoOmvNO6c6z5927fYM38ydz8+w9adPwfDVt2iJ43vHsn7OyyoKyssLa2ZuDE71M6/Bf69dxJflw1Dx0Vxdv1mtKwVadY8/1u32Ddwsnc+vsazTr8j/rN34+e9+RxKBsXTeXurX8ARacv+lGoeOmUTeAFtNZMGj8eL09P7DLbMXb8eEqWih/j+rVrWbtqFTdv3uCwtw+Ojo4ArFi6lF07dwIQERnB33/9xWEvb3I4OKRkGgm6dOYE65fOISoqitoNm9G0XZdY8+/eus7yORO58dcftOnyGe+27gjA/UB/ls4aR/CD+1hZWVGnUQsatngvNVJIVMFSrtR7ryzKSnHJ+zon912LNT9fMWdaf1GV4KAnAPxx7i7Hd/+Oo1tWmnetHF0uh3MWfH7+jTOH/krR+BNz9YIv21fPJyoqiqoeTajfomOs+f53brBx8VRu/3ONxu99ikez9tHzju79kROHdgOaah5Nqd24bQpH/9/1Wtqbys2rEOwfTM+y3VM7nCQ5e/IYy+fPICoqigZNWtKm00ex5mutWTZvOmdPHuONTJnoMWA4hYu58+zZv4zo+xXh4c+IjIykep36dPj4cwD++fMPFs2cxNOwMFxz5aL34DFkyZo1RfLRWrNg1hR8j3mTyc6Ob4eMoliJkvHK3btzmwkjBxMS8oiixd3pP3wstra2Cda/eeMfJowYHKv+h599SZv3OzN+xCBu3bgOQGhoCPb22Zi/Yn2K5Dp3+mRO+HhhZ2fHgOFjKO4eP9e7d24zdthAQoKDKeZeksGjxmFraxs9/+qVS/To+hHDv5tE3QaNAOjUuglZsmTFysoKa2sbvl+5zuL5xKW1Zt70yZw45k2mTHYMGD46wfy+GzaIkEfBFCtRkkGjvsPW1hZvz0MsX7gAKyuFtbU1X/fpT9kKFQEIDQlh6vjR/PPXnygU/YaNpHTZ8imdokhG6aYBp5QK1VrbW/g9+gITgJxa62BLvtcL4hiitR6fUu8XFRnJ2oUz+Hb0dBydXRnbvxsVqtYiz5sFo8tktc9O5896cfaEV7z6Nes3pkHTNiyZlWIhJ1lUVCSbls6i57ApODi7MnnwV5StXIPc+QpGl8lqn432n/bgvK+32WX0Hjkd++w5UijilxMVFcmW5bP5ashkHJxdmT70a8pUqk4uk/yy2Gej3cc9uHgqfn5bV87FvXwVPu07ioiIcJ79+28KRp80Xp6e3Lh+nR179nDxwnm+Gz2GtRs3xitXoWJF6nh48NnHsU/IPunalU+6dgXg8KFDrFm1Ms003qIiI1m7aCbfjJqGo7Mr3w34ggpVa8bb9zqZ2fesrKx5/5PuFChSnKdhTxj77eeUqlA5Vt3UphQ0eL8cW+YcI+RhGF0G1OHaxXvcvxcaq9yta0Fs+/5krGkP/B+zesKR6OV8Mf4d/jh/N8Vif5GoqEi2rpxDt4GTyOHkwuwRPSj9VnVy5i0QXSZL1my0/rA7l07H3vfu3fybE4d202v0HKxtbFkyZTDuFarimitfSqfxnxxYsZ+dc3fSd9U3qR1KkkRGRrJkzlRGTJqNk6sbg7p/SuUatXmzQKHoMmdPHuPu7ZvMWbmZP369zKJZk5k4dxm2tm8wcupcMmfOQkREBMP6dKNileoUL1WGBdPG89EXPSld/i0O7N7BT5vW0OnTL1IkJ9/j3ty5eZNlG7Zx9fIl5k6dwKzFq+KVW7pgNm06dMGj4bvMnjKevTu30bxN+wTrv5m/YHSjLDIykg/aNKFGnXoADBkzMXq5i+ZMJ6u9RU/Nop3w8eL2zRus3rKdXy9dZObkccxftiZeuUVzZ/Jexw+o/05jZkz8jl3bt9Kq3fvRuSyaO4vK1arHqzd9/mJyODhaPI+EnDzmxa2bN1i1+Sd+vXyRWZPHM2/Z6njlFs+bRbtOXajfqDEzJn3H7u1badnufd6qXI0atT1QSvHnH78zdthAVmzcCsDcGZOp8nYNRk2YSnh4OP8+fZrS6aWsjN8BJ/fAxdEJ8AXapHIcQ1Lyzf7641fccufFNVcebGxtqVqrQbyTxewOjhQqVhJra+t49UuUrkBW++wpFe5L+efaVVxz5cUlZx5sbGypVKM+F3x9YpXJlsORAkXdzeaW1l2/dhUXk/wqVq/HxVPx88tfxB0r69jXa54+ecyfVy/ydr2mANjY2JIla8p8Eb+MQwcP0qJVK5RSlCtfgZCQRwQE+McrV7JUKfLmzZvosvbs+pkmTZtaKtSX9ne8fa8+504msO/ZxF5/Dk7OFChSHAC7zFnIna8AD4ICUiz2pMhV0JGHAY8JDnpCVKTmt9O3KVou10svJ38JVx4GPCHkfpgFonw1N/78DZeceXB2y42NjS0V3vbg8unY+559DkfeLFwC6zj7nt+dGxQo6s4bmeywtramsHs5Lpm5wJLWXT56mdD7IakdRpJd++0KufLkI2eevNja2lLToxG+3p6xyvj6eOLRqClKKYqXKsOT0FAeBAWilCJz5iwAREZEEBkREX2SeOfWdUqVM/R0lK9UlRNHD6VYTseOHqFB42YopShZpiyhoaEEBcY+DmitOX/Gl9oeDQBo2KQ5PkcPJ7n+udMnyZ03Hzlz5Y63XM9D+/Fo2Nhi+Zny8TxMoybNUUpRqmw5QkNCzOZ69pQvdes3BOCdZi3wPhKzPrZuWk+deg1wdHJKkZhfhrfnEd5pasyvTDlCQxPJr54xv6Yt8PY8DEDmLFmihw4+fRqGMm6gjx+HcvHsGZq2NJza2traYp8tWwplJSwlXTfglFIVlFLHlVIXlFJblVKOxumfK6V8lVLnlVI/KKWyGKevUErNVkr5KKX+Ukq9Z7KsIoA9MAxDQ+759E+UUtuUUjuUUn8rpXoopb5RSp01vrfTC2I5rJSqbPzbRSn1j8lyf1RK7VFK/aGUmmycPhHIrJQ6p5RamwIfIw/vB+Lk4hb92tHZlYf309aJ4Kt6eD8QR+eY3BycXV4qN4Vi7rj+TBz4BV77d1oixP8k+EEgjs6u0a8dnF0JfhCYpLqB/nexz56Ddd9PZsqgL9iwaCr/Pk07J8jP+fv7kTNXzEl/zpy58PeL34B7kbCwMLyPetGw0TvJGd5/8uB+II5x9r0HQUlbf6YC/e9y4+8/KFy8VHKG95/ZO9gR8iBmmwp5+BR7h8zxyuUp5MSHg+vS9utqOOeOf2LhXjkvV0/fsmisL+vRg0AcnGL2vRxOLkne93LlK8hfv13kccgjnv37lKvnTxKcQY65adn9wABc3GL2N2dXN+7HuegRFBiAs2tMGSdXt+iT6MjISPp98SFd32tCuUpVKV6yDABvFiyCr89RAI55HiDQzAUmSwkK9MfVLWf0a1c3t3gn/Y+CH5LVPlv0RSBXVzeCAgKSXP/I/l/waPhuvPe+dP4sjo5O5H0zf7Llk5jAAH/ccsZ8F7i65Yz3WT8Kfoh9NpNcTcoE+PvhdeQQLdq2Jy6Fon+vr/jio07s3LrFglkkLDDAH1e3V88PwOvwQT7p0Iah3/ai37CRANy9fZscjo5MHjuSLz7qyNRxowkLS3vf9clJWakU+5da0nUDDlgFDNRalwMuAiON03/UWlfRWpcHfgW6mtTJDdQCmgMTTaZ3AtYDR4ESSik3k3llgM5AVWAc8ERrXRE4Bjwfr5VQLImpAHQAygIdlFJvaq0HAWFa6wpa6y6J1k4mWmszUzNI/7OZ3F7m5tZvxs5m0KRFdB8yEc+92/jjyvnkjO6/M7PqVBLXXVRkJLf+/oOajVrSf+JC3shkx4HtG5I5wGTwH9fhc0cOH6LCWxXTzPBJIIHcXm4RT8OeMH/SCDr8ryeZs6TMfTdJZTaVODn73wxm8Yh9rJ5whLNH/qZVtyqx5ltZK4qUzcnvZ9LO8Ekwf9xM6naZM28B6jXrwOJJA1kyZQh58hfGyir9jQBIb8yus/iF4pcxrldra2umLlzNwg3buXb1Cjf+/hOA7v2Gsmf7FgZ89TFhT55gY5Nyd6eYz0nFKRO/3vNN9UX1w8PDOe59hNrGHh9Th/fvMduws5Sk5Zrw+ps3Ywrduvc2O9pm9uIVLFq1gYkz57FtyybOnz2dTFG/hCQcU1503KnlUZ8VG7cyZtJ0ViycD0BkZAR//HaVlm3bs3DVBuwyZ2bDqmXJHLxIaenmHri4lFI5AAet9RHjpJXAZuPfZZRS3wEOGHrV9ppU3aa1jgKuKKVymkzvCLTRWkcppX4E2gPzjPMOaa1DgBClVDCwwzj9IlDuBbEk5sDze+2UUleAAsDNF+TdDegG0H/UFFq+/2ES3iZxjs6u3A+MuYLzICgAByeX/7zctMDB2ZUHQTG5PQwKJIdj0nN7/jlky+FI+Sq1uH7tKsVKpZ0bf3M4ucQaNvcwKIDsjs5Jquvg7EoOJ1cKFjXcJF2+Wh0O/JQ2GnAb1q3lx82Gq6Cly5bB79696Hl+fvdwdXNNqGqC9uzaRZOmzZItxuTg6OzKg/+w70VERLBg8gjertOQStXrWCLE/yTk4VOyOcb0uGVzsCM0OPa9F8+eRkT//fdlfxp0sCJz1jcIe/wMgEKlc+J3M5gnIWnr/swcTrFHKgTfDyS7Q9L2PYCqHk2o6tEEgN2blpLD6eW3afFynF3dCPSP2d+CAvxjjWB4XibIpEfjfoA/Ts6x98ms9tkoXf4tzvoeJ3+hIuTNX5ARk2YDcOfWDc6ciD2UNrlt/2ETe3YY7m0qXrIUAf5+0fMC/P1xcokdbw4HBx6HhhAZEYG1jQ0BAf44uRjydnHNmWj9U8e9KVrcHUen2Nt2ZEQE3kcOMWdp/HvQktO2zRv4+acfAShRqjT+fjHfBQH+fji7xl5/ORwcCQ0xydXfD2djrr//eoWxwwcCEPzwISd8vLC2saZW3fq4GHtdHZ2cqOVRj6uXL1G+YiWL5gawbctGdj3Pr2RpAvzj5OeS9PxMlatYiTu3bxH88AGubjlxdXWjZJmyANSp35ANq5ZbMKvUl0G6IBKV3nvgErIC6KG1LguMBuxM5pmeBSgApVQ5oBiwzzjEsSMmwyjj1IkyeR3FixvBEcR8znZx5pkuNzIJy0JrvUhrXVlrXTk5Gm8AhYq543f3FgF+d4gID+ek1wEqVK2ZLMtObQWKuON/9zaB/neJiAjntM9BylaOf/OyOf8+DeNp2JPov3+9cIrc+Qu9oFbKyl/EncB7twky5nf22CHKVKqRpLrZHZxwdHbF747hmsHvl86SM1+BF9RKGR07d2HT1q1s2rqVeg0asOOnn9Bac+H8OeyzZcPV1e3FCzEREhLCad9TeNSvb6GIX03B6H3vrnHfO0j5Kknb97TWrJw3idz5CvBOqw4vrpAK7l1/iINbVrI7Z8HKWlGiUl7+vOgXq0yW7Jmi/85VwAGliG68AbhXysvVU7dTLOakerNwCQLv3ea+cd87d/wwpd5K2rEFIDT4AQAPAv25eMqbCtXrWSpUYVS0REnu3r6J3907hIeH4314H1Vq1I5VpnL12hzetwutNb9fuUSWrPY4OrsQ/PABj0MN9/v9++9TLpzxJW9+w/Ey+MF9AKKiotiyZjmNmlv2NvqW7d5n/or1zF+xnuq1PTiw52e01vx66SJZ7e3jndArpShXsTJHDx8AYP/unVSvVReAt2vVSbT+4f17zd7jdvbUSd4sUDDW8EtLaN2+I4vXbGLxmk3UqlOPfbt3orXmysULCeZaoVJljhzcD8AvP++gZh0PANZt28X6bbtZv203des3pHf/IdSqW5+wsDCePH4MGIbanzpxjEJFilo0r+j83uvAotUbWbR6IzXr1uOXXcb8Lr0gv0PG/HbtoEZtQ363b96I7qH7/eqvhEeEkz2HA07OLrjmzMXN6/8AcNb3JAUKFU6R/ITlpNseOK11sFLqgVKqttb6KPAh8LwHLBtwVyllC3QBXvTt3wkYpbWe8HyC8X63JJ3NviCWf4BKwEkgqc/4DldK2Wqtw5NY/j+xtrahy+d9mDG6H1GRUdRq2JS8+QtxeM9PAHg0bkXwgyDG9utG2JPHKGXF/h1bGDtnFZmzZGXhtNH8duksoY+C6de1Ha06fkrtRs1TIvQXsra25v3/9WTeuIFERUVSvV4T8rxZiKO/bAeg9jstCX54n8mDvuRp2BOUUhza9QPDpi/ncUgwi6aOAAz3PlSp1YDSFaqmZjrxWFtb0+6Tnnw/YSBRUVFU82hC7jcL4r3P0Elcs1ELHj28z7ShX0Xnd2T3Dwyesgy7LFlp+0lP1swdT0REOM45c9P5iwGpnFF8tevUxcvTk+aN38XOzo4x42Kedtr9i26MHPsdbm5urF29mhXLlhIUGEj71q2oVacOo8Z+B8DB/fupXrMGWbJkSa00zLK2tqHz532YObpf9M9cmNv3vuv/Rcy+t3MLY2av5NY/f3Ls8C/kLVCY0X0No8TbfPA55Sq9nZopxaKjNAc3XaRd97exslJcOnaDoLshlKtlOLRe8LpO8Yq5KV+7IFGRmojwSH5eFjN0ycbWmgLuruxbn8aGLmPY91p/1IPFUwYbfkagzrvkyleQYwcM+171BoZ9b/aI7oZ9z0rhtfdH+k1agl3mrKyaPYbHoY+wtrahzcc9yJI1/T1UoN+6/pTxKEt2l+wsu7mC9SPXsm/ZvtQOK0HW1jZ81rMf3w3qTVRUFPUbN+fNgoXZu8PQA/Jui7a8Va0GZ0760OOj98iUyY6v+w8DDPerzp00lqioSLTW1KjbgMpv1wLA69A+9vxkGDFQrZYH9Run3Pdf1eq18D3mzf86tCKTnR3fDBkVPW94v170GTQcZxdXun7ViwmjhrBy8XyKFCvBu81bv7D+06dhnPE9Qa/+8Z+rdvjA3hQdPglQrWZtTvh48UG7FsafERgdPW9Qn+70GzoSF1c3uvXow9hhA1m2cB5Fi5egScvEG9QP7gcxYoDhSaqRkRE0eLcJVaun/EXsajVqccLHiw/fa4mdnR39h42Knje4bw++HTICF1c3Pu/em++GD2L5wvnG/FoD4HnoAPt278TGxoY3MmVi+NhJ0cMre347kPEjhxAeHkHuvHkZMGy0mQhEeqLM3/+U9iilooA7JpOmAweB74EswF/Ap1rrB0qpr4ABwHUMwxyzaa0/UUqtAHZqrbcYlxmqtbZXSv0NNNFaXzV5v+mAn/FfZa11D+P0f4yvA5VSnzyfp5SqkEAs7sAmINQY7wda64KmdY3L3QlM1VofVkpNAloCZxK7D87rV7/0sfJe0dNnES8ulE6FR2boVUe98nlSOwSL8v0t5R5SkBpOzjv54kLpWLGPK6R2CBazpNrXqR2CRY27Ef+x6hmJvZ3tiwulY2/YZux7PdPLOfWryueYJV2MTlx58FqKrYiP6xdNlc8k3fTAaa0TGu4Z73Kz1noBsMDM9E/ivLY3/j/euDittekP26wwmV7Q5O8Vz+dprc8lEMtVoJzJpGFx6xpfNzf5eyAwMO6yhBBCCCGEEK+3dNOAE0IIIYQQQojEvMKDqtOdjPoQEyGEEEIIIYTIcKQHTgghhBBCCJEhSA+cEEIIIYQQQog0Q3rghBBCCCGEEBmCeg1+ylt64IQQQgghhBAinZAeOCGEEEIIIUSGIPfACSGEEEIIIYRIM6QHTgghhBBCCJEhSA+cEEIIIYQQQog0Q3rghBBCCCGEEBmCeg264KQHTgghhBBCCCHSCemBE0IIIYQQQmQIGb//TXrghBBCCCGEECLdkAacEEIIIYQQQqQTMoRSCCGEEEIIkSHIQ0yEEEIIIYQQQqQZSmud2jGIV+Qf8jRDrzwba7m+kF5FRGboTZN/wyNTOwSLsrLK2FcvrTLw1dnAR2GpHYJFDc3/YWqHYFEz/NandggWlS3zG6kdgkWFPYtI7RAsqoBz1nRx8Nzk/XeKnYS8X7NQqnwmcoYshBBCCCGEEOmE3AMnhBBCCCGEyBDSRTfhfyQ9cEIIIYQQQgiRTkgPnBBCCCGEECJDkKdQCiGEEEIIIYRIM6QHTgghhBBCCJEhvAYdcNIDJ4QQQgghhBDJTSnVWCn1m1LqmlJqkJn5XZRSF4z/fJRS5ZOyXOmBE0IIIYQQQmQIaeUeOKWUNTAPaATcAnyVUtu11ldMiv0N1NVaP1BKNQEWAdVetGzpgRNCCCGEEEKI5FUVuKa1/ktr/QzYALQyLaC19tFaPzC+PA7kS8qCpQEnhBBCCCGEyBBUCv57gbzATZPXt4zTEtIV2J2EFGUIpRBCCCGEEEK8LKVUN6CbyaRFWutFz2ebqaITWE49DA24Wkl5X2nACSGEEEIIITKElLwFzthYW5TA7FvAmyav8wF34hZSSpUDlgBNtNZBSXlfGUIphBBCCCGEEMnLFyimlCqklHoD6AhsNy2glMoP/Ah8qLX+PakLlh44IYQQQgghhEhGWusIpVQPYC9gDSzTWl9WSn1pnP89MAJwBuYbn54ZobWu/KJlSwNOCCGEEEIIkSGklZ8RANBa7wJ2xZn2vcnfnwGfvexyZQilEEIIIYQQQqQT0gMnhBBCCCGEyBDSTv+b5UgD7jWltWbW1Ekc9/Yik50dQ0aNpYR7yXjl7ty+xaghAwl59Iji7u4MGzMeW1tb1q1awb49hh7hyIgIrv/zNzv2HSZ7jhyGaZGRfP5hJ1zc3Jg8c26K5vYiWmtmTJ6Ij/dR7OzsGD76O0qULBWv3OYN69i4bg23b95k90FPHBwdUyHal5NRcovZPo+abJ/x8zBsnwOM22dJk+1zuZnt8wgPH9xn5JABsep3/eJr3u/8YYrlBob85s2YzEkfbzLZ2TFg+GiKlYi//929c5txwwcR8iiYoiVKMmjkd9ja2uLteYgVixZgZaWwtrbmqz79KVu+YnS9yMhIvv60Cy6uboybNjslU0Nrzdzpkznh44WdnR0Dho+huJljy907txk7bCAhwcEUcy/J4FHjsLW1jZ5/9colenT9iOHfTaJug0YAdGrdhCxZsmJlZYW1tQ3fr1yXYnmZo7VmzrRJHDfmOmjEWPO53r7FmGEDefToEcVLuDNk9PjoXM+e9mXu9ClERoSTw8GRWQuXpXQasZw9eYzl82cQFRVFgyYtadPpo1jztdYsmzedsyeP8UamTPQYMJzCxdx59uxfRvT9ivDwZ0RGRlK9Tn06fPw5AP/8+QeLZk7iaVgYrrly0XvwGLJkzZoa6SVZr6W9qdy8CsH+wfQs2z21w0nQqRM+LJw1laioKN5t3pr3P/gk1nytNQtnTcX3uDeZMtnxzZBRFC3hnmjdo4f2s3bZIm5e/5sZi1ZSPM6x19/vHl9+2J4un3ajXaeUO3Za6nshe44cbFy7mp0//YgCChctxuCRY8mUKVOK5fY8v/kzpuB7zHBe1m9Ywt8L40cMJuRRMMVKuDNghOF74cDeXWxaswKAzJmz0LP/EIoUKw7AtHGjOO59FAdHJxav3ZySaQkLkSGUr6nj3l7cunmD9Vt3MGDoCKZN+M5sue/nzOL9zh+wfusOsmXLzs6ftgLQ+aNPWL5uE8vXbeKLHr2o8Fal6MYbwOb1aylQqHCK5PKyjnkd5eaN62z+6WcGDRvJ5PHmcy9XoSJzvl9Mrtx5UjjCV5dRcjNsn9dZv3XnC7bPmbzf+UPWb91p3D5/BKDzR5+yfN1mlq/bzBc9ekdvn/kLFoqevmT1Buzs7KhTr0FKpgbAyWNe3L55g5Wbf6LvoGHMmjzebLnF82bRrmMXVm7eTrZs2di9w7D/vVW5GotWb2Thqo30GzqK6ePHxKq3ddM68hcsZPE8zDnhY8ht9ZbtfDNoODMnjzNbbtHcmbzX8QNW/2A4tuzavjV6XmRkJIvmzqJyterx6k2fv5jFazaleuMNDLneunmDtT/s4NvBI5gxyfx2unDuLN7r9AFrf9iBfbbs7DIeR0NCHjFz8njGT5vFio1bGTVhSkqGH09kZCRL5kxl6PgZzFi6Hq9Dv3Dz+t+xypw9eYy7t28yZ+Vmvuw7mEWzJgNga/sGI6fOZdqiNUxduJqzvsf4/colABZMG0+Xz75m+pK1VK3pwU+b1qR4bi/rwIr9jGo8MrXDSFRkZCTzp09izNTZfL96M0f27+XG33/FKnPquDe3b91kyfqt9BowlLnTJrywboFCRRg2bjJlTC4KmVo0ZxqVq9WwbHJmWOp7IcDfjx82rmXJqvWs2rSVqKgoDvyyJyVTA8D3mDe3b91g+aaf6DNwGLOnTDBbbun82bTt0IUVm37CPlt29uzYBkCuPHmZOm8JC1dvovOnnzPT5HjUqGkLxs9IWxfTLUmplPuXWl7YgFNKhaZEIGbet69S6qlSKseLS1s0jiGJzHNWSp0z/runlLpt8vqNlIzzZXkdOUTjpi1QSlG6bDlCQ0IIDAyIVUZrzRnfk3gYr343bt6So4cPxlvW/r17aPBuk+jX/n5+HPM+SvPWbSybxCvyPHKIJs1bopSiTLnyhtwDAuKVK+Fektx58qZChK8uo+QWe/ss/xLb56F4y9q/d3es7fO5074nyJP3zVRpxPp4HqFRk+YopShVphyhoSEEmcnv3Glf6tRrCMA7TVvg7XkYgMxZskTfpP00LCzWDdsB/n6c8PaiacvU2f98PA/H5GY8tpjL7ewpX+rWN+bWrAXeR2LW3dZN66lTrwGOTk4pGvvL8vY8xLtxjqPmcj1z6iR16xu302Yt8TpiOI4e2Lub2h4NyJkrNwCOTs4pm0Ac1367Qq48+ciZJy+2trbU9GiEr7dnrDK+Pp54NGqKUoripcrwJDSUB0GBKKXInDkLYOjdiIyIiB7HdOfWdUqVMzQGyleqyomj8ffTtOby0cuE3g9J7TAS9fuvl8mT901y58mHra0tdRq8wzGvI7HKHPc6QoPGhvXlXrosj0NDuB8YmGjd/AULkS9/QbPv6eN5mNy585E/FS7QWvJ7ITIykn///ZeIiAiePn2Ki6urZZMxw+foYRo1Nhw7S5Ypx+NEvxcMFx4bNWmOj6chv9Jly5Mte3YASpYuS6C/X3S9chUrkS17qp5Oi2SWlnvgOmH4/YTUbgUk2IDTWgdprStorSsA3wMznr/WWj9LbKFKqVQdvhoQ4I9brpzRr11z5iTQ3z9WmeDgh9hny4aNjSFUV7f4ZZ4+DePEMW88jCdiALOnTebrXn2xUmlz8wrw9ydnrlzRr11z5iQgTl7pVUbJzbB9xs4jadunX6wyMdtno3jvcWDvHhqaadilhMAAf1xzmuTnmpPAgNj5PQp+iL19NqyN+bm45STIpIzX4YN82qENQ7/tRb+hMT0F82dO4fMevVFWqbP/BQb442aam1sCuWWLyc20TIC/H15HDtGibft4y1Yo+vf6ii8+6sTOrVssmEXSBPj745rT5DjqFn9/i7ed5sxJgDHXmzeuExryiN5fdqXbRx3Z+/OOlAvejPuBAbi4uUW/dnZ1435Q7BPIoMAAnF1jyji5ukWfZEZGRtLviw/p+l4TylWqSvGSZQB4s2ARfH2OAnDM80C87UG8mqAAf1zcYrY/F1c3ggJjf7aBAQG4uuUyKZOTwED/JNWN62lYGFvWraTzp58nUwYvx1LfC65uOen4wce81/wdWjdugL29PVXfTvkexqCA2McTF1c3guJcgDV8L9jH+l4wd5F2z85tVKle07IBp2FKqRT7l1pe6RteKVVBKXVcKXVBKbVVKeVonP65UspXKXVeKfWDUiqLcfoKpdRspZSPUuovpdR7L1h+EcAeGIahIfd8+idKqW1KqR1Kqb+VUj2UUt8opc4a43F6QXyHlVKVjX+7KKX+MVnuj0qpPUqpP5RSk43TJwKZjT1qa1/i86mklDqilDqtlNqrlMpt8v7jlVJHgN7G1zOUUp5KqV+VUlWMcfyhlDI/NiCZaG027jhl4heKW8bb8whly1eIHj7pffQIjk5OZu+7SjPM5pUKcVhCBsktKdveq2yfz4WHh+PteZh6Dd9JhmhfnubV8jO9NbuWR32Wb9zK6EnTWb5oPgDHvTxxcHSKd89KSjK7Xkj6ups3YwrduvfG2to6XpnZi1ewaNUGJs6cx7Ytmzh/9nQyRZ184n2hJ5JrZGQEv129wsQZc5g8ewGrli3i5vV/UiBK88yvu3iF4pcx5mNtbc3UhatZuGE7165e4cbffwLQvd9Q9mzfwoCvPibsyZPok2vx35g/QiRt+0tS3TjWLFtI6/c7kzlLlqQHmYws9b0Q8ugRXkcOsXH7brbt2U9YWBh7d+1MxsiT5gWH/ATLxM3v3Glf9uzYxmdf90q+4ESa86pH0VVAT631EaXUGGAk0Af4UWu9GMDYAOkKzDHWyQ3UAtwx/Ap5YpdPOwHrgaNACaWUm9b6+WWWMkBFwA64BgzUWldUSs0APgJmJhJfYioYl/sv8JtSao7WepBSqoexhy1JlFK2xpxbaa0DlFIdgHHA/4xFHLTWdY1lWwDPtNZ1lFK9gZ+ASsB94E+l1AytdVCc5XcDugFMmTWXjz7tmtTQ+HHTBnZsM4wFdy9VGv97MVelAvz8cI4zZMDBwZHQkBAiIiKwsbEhwD9+mQO/xO7FuHj+HN6ehznu7cWzZ//yOPQxY4YPZsRY82O5U8qWjevZ/uMPAJQsXQa/e/ei5wX4+eFickU5vckouRm2T0Mehu0zdh5J2z5j5xp3+3zuuLcXxd1L4uScckPWftqykV3bDftf8ZKlCfAzyS/AD2eX2PnlcHAkNDSEyIgIrG1sCDSz/4FhaMzd27cIfviASxfOcezoEU76ePHs2TOePH7MhFFDGTzK/H1oyWXb5g38bLzPpESp0vib5mYm7hzGdfc8twD/mPx///UKY4cPBCD44UNO+HhhbWNNrbr1o7dlRycnannU4+rlS5SvWMmiucW1dfMGdpocRwP8TI6j/n7xhl7liLud+vnhYszV1S0nOXI4kjlzFjJnzkL5Cm/x5x+/82aBgimWjylnV7dYPRpBAf44OrvGK2PaE3w/wB8nZ5dYZbLaZ6N0+bc463uc/IWKkDd/QUZMMjxM586tG5w54WPBLF4fLq5usXqXAgP8cYpzHHFxcyPA/55JGT+cnV2JCA9/Yd24frtyCa/DB1i2YDaPQ0NQyoo33niDFu06JFNG8aXE98Kpk8fJnScfjo6GIdt16zXg0oVzvNu0uaXSirb9h43R9/+WcI99PAkM8DfzveBAaGho7O8Fl5j9769rvzNjwljGTZ9D9hwOFo8/rUqPF65f1kv3wCnDPWkOWuvnA61XAnWMf5dRSh1VSl0EugClTapu01pHaa2vADlJXEdgg9Y6CvgRMB1Lc0hrHaK1DgCCgedjTi4CBV8QX2IOaK2DtdZPgStAgSTUMacEhkbmPqXUOQy9iPlM5m+MU367SfyXtdZ3tdb/An8Bb8ZduNZ6kda6sta68ss03gDavt8x+sEjtT3qsWfXDrTWXL54AXt7++iTiueUUlSsXIXDB/YBsGfndmrXrRc9PzQ0hHNnTlOrrkf0tC979ObHXfvYvGM3o8ZN4q0qVVK98QbwXodOrNq4hVUbt1CnXn1279yO1ppLF86T1d4+Vca7J5eMkpth+zTcYF7bo77J9nkee/tsSdw+PaLnG7bPU9Qy2WafS+i+OEtq9V4HFq4yPHikZp167Nu9E601Vy5dIGtW+3hf1EopKrxVGc9D+wH4ZdcOatT2AOD2zRvRV5r/+O1XwsPDyZ7Dgc++7sWG7XtZu3UXQ8dOpEKlKhZvvAG0bt+RxWs2sXjNJmqZ5nbxAlntE8itUmWOHDTm9vMOatYx5LZu2y7Wb9vN+m27qVu/Ib37D6FW3fqEhYXx5PFjAMLCwjh14hiFihS1eG5xtWnfkaVrN7F07SZq1a3HXpPjaEK5VqxUhSMHjdvpz9upadwma9Wpx8VzZ4z33YRx5fJF8hdKnYfPABQtUZK7t2/id/eOoZf68D6q1Kgdq0zl6rU5vG8XWmt+v3KJLFntcXR2IfjhAx6HGu4Z+/ffp1w440ve/Iav0eAH9wGIiopiy5rlNGqe2ndGZAzF3Utx59ZN7t25TXh4OJ4HfuHtWrFPd6rVrMuBPYb1dfXyRbLa2+Pk4pKkunFNmbeEFZt3sGLzDlq170SHDz+1aOMNUuZ7wS1XLi5fusDTp2ForTnte4ICBVPmHr+W7Trw/coNfL9yAzXqeLBvj+HY+Wsi3wvl36qM56EDAOzbvZPqxu8F/3t3GTO4HwNGjiVf/lc9hRXpRXKPY1gBtNZan1dKfQJ4mMz71+TvBNvGSqlyQDEMDSCANzA0ZuaZWU6UyesoXpxPBDGNVrs480yXG5mEZSVEYWiIxX98msHjBN7XNJfnry02zqR6zdoc9/aiY+vm2NnZMXhkzFPs+vfqzsDhI3FxdeOrnn0YNWQASxbMo1gJd5q1ivni9Tx0kCrVqkffuJ5e1KhVGx8vT9q3bEomOzuGjYoZrfpNj68YPGI0rm5ubFq3ljUrl3E/KIgP329H9Vq1GTJydCpG/mIZJTfD9nmUjq2bGbfPsdHz+vf6moHDRxm3z77G7XOucftsG13OsH3WiLd9Pn0axqmTx+g/dHiK5RNXtRq1OOnjxUftW5Ipkx39h42Knjfkmx58M3gELq5ufNa9N+OGD2L5wvkULV6CJi1aA3D08AH27d6JjY0Nb2TKxLDvJqXqWHxT1WrW5oSPFx+0a2H8GYGY7WpQn+70G2o4tnTr0YexwwaybOE8Q24veOjKg/tBjBjwDWAYetjg3SZUTeV7PN425tqlbXMy2dkxcHjMcXRgn+70N+b6Rc8+jBk6gKXfz6NYcffoB8wUKFSYqtVr0rVLe5RSNGvVlsJFiqVWOlhb2/BZz358N6g3UVFR1G/cnDcLFmbvDkOP47st2vJWtRqcOelDj4/eI1MmO77uPwyAB/cDmTtpLFFRkWitqVG3AZXfrgWA16F97PnJMOimWi0P6je2fM/Gf9VvXX/KeJQlu0t2lt1cwfqRa9m3bF9qhxWLtY0NX/Xtz7BvexIVFck7zVpSoFARft5m+KybtX6PKtVr4nvcm64dW5PJzo6+g0cmWhfAx/MQC2ZOIfjhA0YN6EPhosX5bnrqP8HQUt8LpcuUw6NBQ7p26YC1tTXFSpSkZdtE7/SxiKo1anHymBeftG9l+BmBoaOi5w39tiffDBqBs6srn33di/EjBrNy0TyKFHensfF7Yc3yxTx6FMycqYaL5tbW1sxbZrj7Z/yIwVw4e5rghw/p3KoxH372ZfT3SUb0ouHAGYEyf5+FSQGlQrXW9nGmnQd6aK2PKqVGATm01n2VUoFAKeABsAu4rbX+RCm1Atiptd6S0DJNlj0BeKS1nmAy7W8MjcF6QGWtdQ/j9H+MrwONDcbKWuseicS3BDittV6glOoD9NFaFzSta1zuTmCq1vqwUuoB4Ka1Dn/B5zQKCAVmY+jB+1Brfcw4pLK41vqyUuow0E9rfcpYJ/q1UsrD+HfzuPMSek//kKeJr7x0zsY6bT4ERbxYRGSG3jT5NzwytUOwKCurjP3lZ5VGGruWEPgoLLVDsKih+VP2NxtT2gy/9akdgkVly5ymH9D9n4U9i0jtECyqgHPWdHHw3HX6VoqdhDStlC9VPpOk9PBkUUrdMnk9HfgY+N74kJK/gE+N84YDJ4DrGIYEZnuFmDoCccc2bTVO94tf3KyE4psKbFJKfQjEfx6+eYuAC0qpM1rrLi8qrLV+ZnxIy2zjcE4bDPflXU7i+wkhhBBCCCFeQQa+RhfthT1wIu2SHjiRVkkPXPomPXDpl/TApW/SA5e+SQ9c2rD7TMr1wDV5K+32wAkhhBBCCCFEmpeBr9FFS7UGnFKqLLA6zuR/tdbVUiOeF1FKOQMHzMxqEPdR/0IIIYQQQghhCanWgNNaX8Tw22vpgrGRViG14xBCCCGEEEK8vmQIpRBCCCGEECJDsHoNfkZAnhIhhBBCCCGEEOmE9MAJIYQQQgghMoTX4SEm0gMnhBBCCCGEEOmE9MAJIYQQQgghMgTpgRNCCCGEEEIIkWZID5wQQgghhBAiQ1CvQRec9MAJIYQQQgghRDohPXBCCCGEEEKIDCHj979JD5wQQgghhBBCpBvSAyeEEEIIIYTIEOQeOCGEEEIIIYQQaYb0wAkhhBBCCCEyhNegA04acOnZw9BnqR2CRVlZZdw90O4N69QOwaLesMnY+T2LiEztECzK2ipjD86IjIpK7RAsxt7ONrVDsKgZfutTOwSL6puzU2qHYFEZff1tm+iZ2iFY1LfTm6V2CMJIGnBCCCGEEEKIDOF16IHL2JdZhRBCCCGEECIDkQacEEIIIYQQQqQTMoRSCCGEEEIIkSGo1+CnvKUHTgghhBBCCCHSCemBE0IIIYQQQmQI8hATIYQQQgghhBBphvTACSGEEEIIITIE9Rp0wUkPnBBCCCGEEEKkE9IDJ4QQQgghhMgQXoMOOOmBE0IIIYQQQoj0QnrghBBCCCGEEBmC3AMnhBBCCCGEECLNkB44IYQQQgghRIaQ8fvfpAdOCCGEEEIIIdIN6YETQgghhBBCZAivwS1w0gMnhBBCCCGEEOmF9MAJIYQQQgghMoTX4SmU0oB7TZ0+4cPiudOIioyiUbNWtO/ySaz5WmsWzZnG6ePeZLKzo/egkRQt7g7ArElj8D3mRQ4HR+at2BhdZ83SBZzw9kQpRQ5HJ/oMGomzi2uK5XTqhA+LZk8lKiqKd5q15v0P4ue0cPZUTh33JlMmO/oOHkXREu6J1l27bCF7d24ju4MjAB9//jVVqtciPDycuVPH88fVK1hZWdGt17eUq1g5xXLVWjNv+mROHDPkMmD4aIq7l4xX7u6d23w3bBAhj4IpVqIkg0Z9h62tLd6eh1i+cAFWVgpra2u+7tOfshUqcvP6P4wdNjCm/u3bfNLtK9p17JJiuYEhv1lTJ3Hc+yiZ7OwYMmosJdxLxSt35/YtRg0ZQMijRxR3L8mwMeOxtbVl3arl7NuzC4DIiAiu//M3O/YdIXuOHGxcu5qdP/2IAgoXLcbgkWPJlCmTxfNZOGsqvsZt75shMdueqXt3bjNx1BBCQx5RpLg7/YaNwdbWNsH6AX73mDZuJA/uB6GUFY1btqF1+04ArFqygONHj2BlZUUOR0e+GTIqRfZHrTULZk7h5DEv7Ozs+HboaIqViL9t3rtzm/EjBxPyKJiixd0ZMMKwbd64/jfTx43i2u9X+bhbd9p3/ihWvcjISHp2/QBnV1fGTplt8Xye52SJ9QcwY8JoTvp44eDoyIJVm6KXNWHkYG7fuA5AaGgI9vbZmLt8ncXyWzBrCr7HDMf7b4eMSnCdTRg5mJCQRxQt7k7/4WOj8zNX/+aNf5gwYnCs+h9+9iVt3u/M+BGDuBUnv/kr1lskv1MnfFg4y3B8f7d5At8NCayfhOoePbSftcsWcfP638xYtJLicY5P/n73+PLD9nT5tBvtOn1okbz+i15Le1O5eRWC/YPpWbZ7aoeToNTY9/669jtzp04gLOwJOXPlYcCIsWTJap9iOQMUdHelXutSKCvFpeM3OXnwz1jz8xVxovX/KhN8/wkAf1y8x/FfrkXPVwo+6FuLkOCnbFt6KkVjFykjTQ2hVErlUkptUEr9qZS6opTapZQq/orLWqGUes/49xKlVCnj30OSUDc0zutPlFJzjX9/qZT6yHxNUEp5KKVqvErMKSUyMpLvZ01m1KRZzFu5Cc+Dv3Djn79ilTl9woc7t26wcO2PdP92CAtmTIye16Bxc0ZNjn/i1Lbjh8xZtp7ZS9dRpXotNqxcYvFcnouMjGTBjEmMnjKbBas243lgb7ycTh335s6tmyxet5We/Ycyb/qEJNVt1b4zc5etY+4yQ14Ae3dsBWD+yo18N30eS+bNJCoqKoWyhZPHvLh18warNv/EN4OHMWvyeLPlFs+bRbtOXVi1ZTv22bOxe7sh7rcqV2Pxmo0sWr2RfkNHMW3CGADeLFCQRasN0xesWEcmOztq1a2XYnk9d9zbi1s3r7N+604GDB3BtAnfmS33/ZyZvN/5Q9Zv3Um2bNnZ+dOPAHT+6FOWr9vM8nWb+aJHbyq8VYnsOXIQ4O/HDxvXsmTVelZt2kpUVBQHftlj8XxOHffm9q2bLFm/lV4DhjJ32gSz5ZZ9P4c273dmyfqt2GfLxi87f0q0vrW1DZ9178vCNVuYvnA5O3/czI2/Ddvue50+ZP7KDcxdvo6qNWqzbsVii+cJ4HvMm9u3brB840/0HjCMOVPN57pkwWzadujC8o0/YZ8tO3t2bgMge/YcfNV3QIInvds2r+fNgoUsFb5Zllp/AA2btGDs1DnxljV49ATmLl/H3OXrqFm3PjXqWG4/9D3uzZ2bN1m2YRu9+w9jbgLrbOmC2bTp0IVlG7Zhny07e43rLKH6b+YvyPwV65m/Yj1zlq4hk51ddB5DxkyMnlerbn1qWug4ExkZyfzpkxgzdTbfr97Mkf17o/eR5xJaP4nVLVCoCMPGTaZM+Ypm33fRnGlUrpZ2TwUOrNjPqMYjUzuMF0qNfW/WpO/49IseLFi5kRp1PNiyfrVlkkuAUtCgbWl+XHSSFZOOUOKtPDjljN+AvPXXfVZP82L1NK9YjTeAt+oUIsg/NF4dkXGkmQacMvR3bgUOa62LaK1LAUOAnCZlrF9l2Vrrz7TWV4wvX9iAe8Gyvtdar0qkiAfwUkdtpVSK9oT+cfUyufO+Sa48+bC1taVO/Uac8D4Sq8xx7yPUf7cZSincS5flcWgI94MCAShT/i2yZcseb7mmV6j+fRqWol3Yv/96mTx53yT385wavMNxrzg5eR2h/rtNY+cUGJikunHd+OdvyleqAoCDoxP29tn44+qVROskJ2/PI7zTtDlKKUqVKUdoaAhBgQGxymitOXvKl7r1GgLwTtMWeHseBiBzlizR6+fp0zCUmYfunj11kjx585Ezdx7LJmOG15FDNG7aAqUUpcuWJzQkhEAz+Z3xPYlHg0YANG7ekqOHD8Vb1v69u2nwbpPo15GRkfz7779ERETw9OlTXFwt3yt13OsIDRrH3/ZMaa25cMaXWh4NAGjYuDnHjh5OtL6Ti0v01eQsWbKSv2BBAgP9Da9N9senYebXsSUc8zpMw8aGbbNkmXI8DjG/bZ4/7UttY66NmjbnmKdh3Tk4OlGiZGlsbOIfFgP8/Tjpc5QmLVpbPA9Tllp/AGUrvEW27PGPp6bLPXpoP3UbvmuR3ACOHT1Cg8bNjOusLKGhoebX2ZmYddawSXN8jh5Ocv1zp0+SO28+cubKHW+5nof249GwsUVyM3d8P2bmu8Hc+kmsbv6ChciXv6DZ9/TxPEzu3PnIX6iwRXJKDpePXib0fkhqh/FCqbHv3bpxnTIV3gKgYuVqeB8+aLkEzciV34GHgU8Ivh9GVKTmt7N3KFom54srGtnnsKNQSTcuHr9pwSjTNqVS7l9qSTMNOKAeEK61/v75BK31OcBaKXVIKbUOuKiUslZKTVFK+SqlLiilvgBDA1ApNdfYc/cz4PZ8OUqpw0qpykqpiUBmpdQ5pdTaVwlSKTVKKdXP+Hcv4/tdMPYcFgS+BPoa36O2UqqAUuqAscwBpVR+Y90VSqnpSqlDwBSl1B9KKVfjPCul1DWllMurxPgiQQEBuLjGHAycXXMSFBDwgjJuBAX4v3DZq5bM59P2zTi8bw9d/vdF8gX9AkGB/ri4xcTrYibeoMAAXN1ymZTJSVCg/wvr7ty6ie6fdGTmxNGEhDwCoFDRYhz3OkJkRAT37tzm2u+/EujvZ6n04gkM8I+Vi6tbTgLj5Pso+CH22bJhbTwRjlvG6/BBPunQhqHf9qLfsPhXYg/t20v9dyxzUvUiAQH+uOUyyS9nTgL9Y+cXbMzPxjS/OOvg6dMwThzzxqN+o+gyHT/4mPeav0Prxg2wt7en6tuWv0oeGBB/23ve0HruUXAwWe1j1peLqxtBxjJJqe939w5//v4b7qXKRE9buWgeH7VrxuF9u/mw65fJnpc5hm3TZH9yc4t3fHkU/JCs9vYmueYkME4Zc76fNZXPvu6NUin71ZUS6y8hl86fxcHRibxv5v+vaSQoKDD2OnN1c4vXADOsM5PjiWvMek1K/SP7f8HDTCP00vmzOFowv6AAM8f3OJ99QusnKXXjehoWxpZ1K+n86efJlMHrLTX2vYKFi0RfxD16aH+KfreDoQEW8jAs+nXIw6fY57CLVy5PQUc+7Febtp9Xwdmkh65e61J47vwVrXWKxCtSR1pqwJUBTicwryow1Ngr1xUI1lpXAaoAnyulCgFtgBJAWeBzzPSCaa0HAWFa6wpa68Ru6nneyDunlDoHjEmg3CCgota6HPCl1vof4HtghvE9jgJzgVXGMmsB07GHxYGGWuu+wBrgeUwNgfNa69iXmZKJJv5OHb+3LCll4vvos69ZvvlnPBo1ZufWTS8sn1zMHqfixGv2YKZUonWbtn6PJeu3MWfZOhydXVg6bwYA7zRtiYurG727fcSiOdMoWbocVtav1EH8aswEHXf9mMvXtEwtj/qs2LiVMZOms2Lh/FjlwsPD8Tl6hDrGhk9Ke1HsSS3j7XmEsuUrkD1HDgBCHj3C68ghNm7fzbY9+wkLC2Pvrp3JGHkCkhCr2Q3xeZkX1A978oRxwwbQrde3sXrePu7WnVU//IxHoybs+DGF9sdE0oguYrZM4seX496eODg6UczMvZAWZ+H1l5gj+/eabfgkJ7P7EnH3t/j1YtJLvH54eDjHvY9Q2zgawNTh/Xssmp/Zw3vc3ugE1k+S6saxZtlCWr/fmcxZsiQ9SJGwVNj3+gwawc6tm+nV9QPCwp5gY2ub5HCTg9nw4qThf+sRi8ceZPXUo5z1+odW/zPcg1+4lBtPQp/hf+uR5QNNw1QK/pda0stDTE5qrf82/v0OUO75/W1ADqAYUAdYr7WOBO4opf5Ln3eY1rrC8xdKqU8Ac0+ouACsVUptA7YlsKzqQFvj36uBySbzNhvjBVgG/ATMBP4HLDe3MKVUN6AbwJjJM+nwwacvyiUeF1c3AgNirigFBfjh5BK7s885Xhl/nF7iAQh1GzRm9KA+dPk0ZXrhXFzdYl0lCwzwj/fABhdXNwL875mU8cPZ2ZWI8PAE6zo6OUdPb9y8DaMH9QHA2saGbj2/jZ737Vf/s+gVcoBtWzayy3iPV4mSpWPlEuDvFy/fHA6OhIaEEBkRgbWNjdkyAOUqVuLO7VsEP3xADuPDWk4e86JYCXecnJ3jlbeUHzdtYMe2HwBwL1Ua/3sm+fn54RxnqKODMb+IiAhsnufn6harzIFf9tDQZPjkqZPHyZ0nH46OTgDUrdeASxfO8W7T5smez44fN7F3xzYAirmXMrvtmcru4MDj0Jj1FRjgH13Gxc38tgsQERHBuGED8GjUmJp165uNxaNRY0YN6M0HXS2zP27/YWP0/ZXFS5YmwHR/8o9/7Mjh4MDj0FCTXP1wdkl8wMGVC+c57nUE32NePHv2jCePHzNp9FAGjhyX/AmRcusvMZEREfh4HmL2kuS/B2f7D5vYs+P5OisVa50F+PvH+07IESe/AJPvBBfXnInWP3Xcm6LF3WMdT5/n533kEHOWrkn2/J4z990Qd3tMaP2Y+2540ffgb1cu4XX4AMsWzOZxaAhKWfHGG2/Qol2HZMoo40vtfe/NAgUZN30eYBhO6XvMKznSSrKQh0/J5pA5+nU2BztCHz2NVebZvxHRf//9awAN2ikyZ7UlTyFHipR2o1DJetjYWPGGnS1NulRg99pzKRW+SCFpqQfuMlApgXmPTf5WQE9jD1cFrXUhrfUvxnkp3V/cDJiHIe7TSbyXzTTG6Ly01jcBP6VUfaAasNtsZa0Xaa0ra60rv0rjDaBYiVLcuXWDe3dvEx4ejufBfVStUSdWmWo16nBw789orbl6+SJZstrj5Jz4CdadWzei/z7h45ng/QGWUNy9FLdv3eTeHWNOB36hWs04OdWqy8G9u6JzyprVHicXl0Trmo619zl6iAKFigDw9OlTnoYZhjic9T2OtbU1+Qta9n6H1u91iH7ASM269fhl10601ly5dIGs9vbxGmdKKSpUqsyRQ/sB+GXXDmrU9gDg9s0b0VfNf7/6K+ER4WTP4RBd9+Ave1J8+GTb9ztGP3iktkd99uzagdaayxfPY2+fDRcz+VWsXIXDB/YBsGfndmrX9YieHxoawrkzp2I9hMUtVy4uX7rA06dhaK057XuCAhZaby3avh/9EIrqtT04sMdk27O3j3eCrJSiXMXKeB0+AMD+PTt5u3ZdAKrVrGu2vtaamRPH8GbBQrTt+EGs5d2+abI/eh2x6P7Ysl0HFqzcwIKVG6hRx4P9ewzb5q+XLpAlgW2z/FuVOWrMdd+unVQ3bpsJ+d9XPVm7bQ+rfviZwaMnUL5SZYs13iBl1t+LnD19knz5C8YaxpdcWrZ7P/ohIob8fjaus4sJHk/KVYxZZ/t376R6LUN+b9eqk2j9w/v3mr3H7eypk7xZoGCs4ZfJrbh7Ke7EOb6/XSvOd0MC6ycpdeOaMm8JKzbvYMXmHbRq34kOH34qjbeXlNr73sMH9wGIiopiw6qlNG3VzgJZJuzezWAcXLOS3SkzVtaKEhXz8Oel2MM4s2SLeXJyrvw5UEoR9jgcr59/Y9GYgyz57hA7V5/lxh+Br2Xj7XW4By4t9cAdBMYrpT7XWi8GUEpVAerGKbcX+EopdVBrHW58SuVtwBP4Qim1CsP9b/UAc89cDldK2Wqtw/9LsMpwE8abWutDSikvoDNgD4QApnfF+gAdMfS+dQESu5SzBMNQytUmPXPJztrGhi97D2Bk/15ERUXSsElLChQqwu6fDL0fTVq1o/LbNTl1wptuXdqQKZMdvQeOiK4/ZcxQLp47zaPgh3zyXjM6f9qNd5q1YsWiudy+cR0rKytcc+ai+zeDEwrBIjl91ac/w/v1JCoqkkZNDTnt+mkLAE1bvUeVt2ty6pg3n3VqbfwZgZGJ1gVY9v0s/vrjd5RSuOXKTc9+QwEIfnCf4f16oJQVzq5u9BuW0Chby6hWoxYnfLz48L2W2NnZ0X/YqOh5g/v24NshI3BxdePz7r35bvggli+cT9HiJWjSsjUAnocOsG/3TmxsbHgjUyaGj50U66Emp0+eoO+gYSmak6nqNWtz3PsoHVs3w87OjsEjx0bP69/rawYOH4WLqxtf9ezLqCEDWLJgLsVKuNOsVdvocp6HDlKlWg0yZ44ZylS6TDk8GjSka5cOWFtbU6xESVq2fQ9Lq1K9Jr7HvenasTWZ7GK2PYAR/XvRe+BwnF1c+fSrnkwaNYRVSxZQpFgJ3m3WKtH6Vy6e5+DeXRQsXJQen3YG4ONuhp+6WL5wDrdvXEcpK9xy5aZHv5TZH6tWr4XvMS8+fb9V9CPlnxv2bU/6DhqBs6srXb/qxfiRg1mxaB5Fi7vzbvPWANwPCqRn1w948vgxykqxbdM6Fq3dQtYUfoy3KUutP4BJo4Zw4azhePph26Z88L9u0Z+F5/5fqNvwHYvnZ1hn3vyvg2GdfWOyzob360WfQYb8un7ViwmjhrBy8XxDfsY4E6v/9GkYZ3xP0Kt//OeHHT5g+eGh1jY2fNW3P8O+NRzf32lmOL7/vM3w3dCs9XsJrp+E6gL4eB5iwcwpBD98wKgBfShctDjfTZ9r0VySU791/SnjUZbsLtlZdnMF60euZd+yfakdVjypse8d3r+XnT9uBqBm3Xo0atoyRXPWUZqDP16iXbeqWFkpLp28RZBfKOWqG0b5XDh2g+Llc1G+RgGiojQR4ZH8vPpsisYoUp9KSzc5KqXyYBhCWAl4CvyDYWhiK611c2MZK+A7oAWG3rgAoDXwCJgD1Ad+Ny5yjdZ6i1LqMNBPa31KKTUJaAmcSeg+OKVUqNba3uT1J0BlrXUPpdQoIBSYBRzCMIRTGd9rorFBuQWIAnoCNzEMj3Qxxvqp1vqGUmoFsFNrvcXkfWyBIKCq1vrqiz6v3+8+SjsrzwKsrDLuDzHavZGC98ulgjdsMnZ+IWHPUjsEi7K2SkuDM5JfZAr+5EdKs8rgP2AblYbOWSyhb85OqR2CRc3ws8xv/aUV2yZ6pnYIFvXt9Gbp4gBz+q+gFDtQVCrsnCqfSZpqwL3ulFKVMTwApXZSyksDLv2SBlz6Jg249E0acOmXNODSN2nApW/SgIsvtRpwaWkI5WtNKTUI+IqYJ1EKIYQQQgghXkIGv44FvMYNOKWUM3DAzKwGWuuglI5Haz0RmJjS7yuEEEIIIYRIP17bBpyxkVYhteMQQgghhBBCJI/XoQcuY9/oIIQQQgghhBAZyGvbAyeEEEIIIYTIWBQZvwtOeuCEEEIIIYQQIp2QHjghhBBCCCFEhiD3wAkhhBBCCCGESDOkB04IIYQQQgiRIajXoAtOeuCEEEIIIYQQIp2QBpwQQgghhBBCpBMyhFIIIYQQQgiRIbwGIyilB04IIYQQQggh0gvpgRNCCCGEEEJkCPJD3kIIIYQQQggh0gzpgRNCCCGEEEJkCHIPnBBCCCGEEEKINEN64NIxu0wZe/U9C49M7RAs5t8MnBuAjXXGvjb0ho11aodgURGRUakdgkVFRenUDsFi7Owy9veCbQY/tszwW5/aIVhU35ydUjsEi5pwc01qhyAAq9egCy5jHwmFEEIIIYQQIgPJ2JfqhBBCCCGEEK+N16ADTnrghBBCCCGEECK9kB44IYQQQgghRIYgPXBCCCGEEEIIIdIM6YETQgghhBBCZAiKjN8FJz1wQgghhBBCCJFOSA+cEEIIIYQQIkOQe+CEEEIIIYQQQrw0pVRjpdRvSqlrSqlBZuYrpdRs4/wLSqm3krJcacAJIYQQQgghRDJSSlkD84AmQCmgk1KqVJxiTYBixn/dgAVJWbYMoRRCCCGEEEJkCCrtjKGsClzTWv8FoJTaALQCrpiUaQWs0lpr4LhSykEplVtrfTexBUsPnBBCCCGEEEIkr7zATZPXt4zTXrZMPNIDJ4QQQgghhMgQUrIDTinVDcPQx+cWaa0XPZ9tpoqOu4gklIlHGnBCCCGEEEII8ZKMjbVFCcy+Bbxp8jofcOcVysQjQyiFEEIIIYQQGYJSKsX+vYAvUEwpVUgp9QbQEdgep8x24CPj0yjfBoJfdP8bSA+cEEIIIYQQQiQrrXWEUqoHsBewBpZprS8rpb40zv8e2AU0Ba4BT4BPk7JsacAJIYQQQgghMoQ08wxKQGu9C0MjzXTa9yZ/a6D7yy5XGnCvKa0182dM5qSPN5ns7Og/fDTFSpSMV+7unduMHz6IR4+CKVaiJANHfoetrS0H9u5i4+oVAGTOnJleA4ZQpFgJAEJDQpg+YTT//PknKEW/oSMpVba8xXM6EAsXhwAAXpxJREFUdcKHRbOnEhUVxTvNWvP+B5/Emq+1ZuHsqZw67k2mTHb0HTyKoiXck1T3h/WrWbZgFuu27yeHgwO/XbnEnKnjny+Yzp92o0adesmek9aahbOm4muM+ZshMTGbunfnNhNHDSE05BFFirvTb9gYbG1tE61/6oQPC2cZcn63eUzOS+fN4oSPJzY2tuTOm4++g0diny1b9Hv5+93jyw/b0+XTbrTr9GGy5/w879nTJnHC24tMdnYMHjmW4u5mts/btxg9dCCPHj2ieAl3ho4Zj62tLQBnT/syd9oUIiLCyeHgyOxFywDo0LIJmbNkwdrKGmsbaxatWm+RHBKjtWaeyf43IJH9b9zwQYQ8CqZoiZIMMu5/3p6HWLFoAVZWCmtra77q05+y5SsCMOW7UZzw8cTB0Ykla7ekWD4LZk3B95ghn2+HjDKbz707t5kwcjAhIY8oWtyd/sPHRm+nCdXftmkdu3dsQ2tNk5ZtaPN+ZwBWL13Inh1byeHgCMAnX3SnavVaFs/19AkfFs2ZZjxWtKJ9l09izddas2j2NE6dMOxzfQaPpGhxwz43c+IYfI95kcPRkfkrNkbX8Tq0n3UrFnHz+j9M/34Fxdzj/kxQytFaM3f6ZE74eGFnZ8eA4WPM73t3bjN22EBCgoMp5l6SwaPGRe97AFevXKJH148Y/t0k6jZoBECn1k3IkiUrVlZWWFvb8P3KdSmW13Naa2ZNncRx76NksrNjyKixlDDzed+5fYtRQwYQ8ugRxd1LMsx4bFm3ajn79hjOxSIjIrj+z9/s2HeE7DlysHHtanb+9CMKKFy0GINHjiVTpkwWz8dS3xEzJozmpI8XDo6OLFi1KXpZf137nblTJxAW9oScufIwYMRYsmS1t2ieL6vX0t5Ubl6FYP9gepZ96XPjVHHm5DGWzZtOVFQUDZu2pG2nj2PN11qzdN50zpzwIVMmO3oMGE6R4u48e/Yvw/p8SXj4M/7f3n3HR1FtARz/HRIkQOhJUEQEQUCaIKBSpFpQwYIFEHn28lQQFREQaRZsgEhRQKVLU0FAAZVq6FWKYnkWikoCSpUazvtjJmFTAdnNsJPz/XzyITM7szmXnZmdO/fec48nJVGnQRNa3+vk1Bg97G1WLY0nMjI3xUucT/vOL5A/ukBGf96EGRsDl0OtWBrP9q1bGDXlUzp26c7br7+S4XbvDRlIy9ZtGT1lOtEFCjB7xlQAzj2vBP2GvsfwcZNpe/9DvPXqSyn7DB3wOrWurMsHk6YybOwkSpW+KOTlSUpK4p0Br9H7jbd5Z8wUFs2dw5Zff061zapli/l921ZGfDiV9s8+z5D+fU9p38Qdf7Ju1XJii5+bsu7Ci8oxcPgYBn/wIX3eGMTgN18h6dixoJdr1bLFbN+2lfcmTKVD5+cZ3K9vhtt98O4gbr3zLt6bMJXoAgX4YuanWe6flJTE0P6v0efNt3l37BQWfjWHLb84Za5R+wreGT2JoaMncv4FpZg8bmSqvzV8UD9qXVE36GUNtHxJPNu2bGH8JzPo1K0H/QOOr0DvDh7IHXfdzYefzKBAwYJ89qlzfO7bt5cBr73CK/0HMnryVHq/+kaq/d569z3e/3CyJ5U3OHH+jZ7yKU916c7ATM6/EUMGcpt7/hUoUIBZ7vl3Wa0rGD52EsPGTKLT873o/0qflH2uu7EFfQcMyZZyJFu5bDG/b93KBxOn8eSz3Rn8ZsbH6fvvvM2trdrywcRpRBcoyJyZ07Lc/9eff2LWjGkMHDGad0ZNYPnir9m+dUvK+916510MHTWBoaMmZEvlLSkpiXfeep3erw9k6OjJLJz7RfrrzPIl/L5tC8PHf8ITnboxtP+rKa9dfX1zer/xdrr3vbBMWbq9+DqV3Uq4l5YvcY7NsR9N5+kuL/DW6y9nuN3wwW9xe+u7GfvxDAoUKMjn06emvJaUlMTwwQOpdUWddPv1HzqCEeMme1J5A1i2OJ5tW39jwtSZdH6+B/36ZnJtGfQWd97VjglTZ1KgQEFmfvoJAHf95z5GfjiFkR9O4ZEnnqT6ZTUpWKgQiQk7+HjSeN4bM4Exk6dy/Phx5n4xO+TlCdV3BMDV17fgxTcHpXuvga+9xH2PPME7oydRt0EjPpowNjSFOwNzR31Fr2Y9vQ7jlCUlJTHi7Tfo3vctBn4wka/nfcHWNNeWNSuW8Me2rQwZ8xGPPt2F4QNfByB37nPo3W8IA0aMp9/wcaxduYzvv90AwKU1L+et9z9kwHvjKVGyFB9/ODrby+aFs2gMXMicVRU4EUkSkXUislFEpohIvmz++x3P5G+KyK0ioiKS/vHXWWbpooVcfX1zRIRKVaqxf/8+du1MTLWNqrJu9UoaNL4agGtvaMHiRQsAqFytOgUKFgTgksrVSEzYAcCBA/vZsG4N17e4FYDcuXOnar0JlR++20SJ8y/gvBIlyZ07Nw2aXsuy+IWptlkWv5Am192AiFCxclUO7N/HXzt3nnTfEYP7c99/O6Q6UaOiooiIdBqwjxw5HLKTeFn8Qpo2Sx9zIFVl/ZqV1G/UFICrmzVn6dcLstw/ozIvdct82eVXppStYuWq7ExMSPlbSxYt4LzzSlKqTGgr5fEL53PdjS0QESpXrcb+fRkfn2tXrqBhE+fp/nU33kT8wnkAfDV7Fg0aN6X4uecBUKRosZDGe7qWLFrINWdw/uXNly/lmDt08GCq469ajZoUKFgoewriWvr1Qpo2uxER4ZIqVdm/f3+G5flmzUquSj5Or2/Okq8XZLn/ll9/oWLlKkRF5SUiMpKqNS5jyaL52Vq2QD98t4nzzr+Ac5PPmybXpLvOLI9fSJPrbkx9zu1yztkql15GgQIF073vBaXLULJU6ewowkktWbTgxLGZ1bm3aiUNm7jH5o0tWLzwxOcydfIEGjRuSpGiRbM19lMRv3A+zW5IvrZcyv59+9iZQfnWrFxBI7flsFnzm/h6Qfrj7qs5s2h63fUpy0lJSRw+fJhjx45x6NAhYmJjQ1sYQvcdAVC1+mUp3/OBtm35jSrVLwOgRq0rWLxgXugK+C9t+noT+//a53UYp+ynzd9y3vklObfE+eTOnZv6ja9hxZJFqbZZsXgRja69HhGhQqUT1xYRIW9e59Y16dgxjh07lvKdUL3WlUREON/n5StVYdfOBIw/nFUVOOCgqlZX1SrAEeDRwBdFJCJUf9h9747AmVQa2wDxOFlmMvsbZ4WdiQnEBbQoxcQWT3WjDrB3z26iowuk3MzHxBVnV2L6k3/2jGnUrlMPgD+2b6dQ4SK88VJPHv1Pa/q90puDBw+GsCSOXTsTiIkrnrIcExuXLtZdOxOJjUtd5l07E7Lcd1n8QorFxHFRufLp/ubmbzfy3//cyeP3tebxZ7qm/D8F087E9DHv3Jn2c9pD/sDPKTYu5SKd2f67EjMocwYX9i8+m57S2nbo4EE++nA0d933UPAKmAnn+DwRX2xccRITUse3Z89uogsUINItd1xccXa622zb8hv79u7lyUce4KF2rZn92YwTOwp0euJRHmrXmumfZE8Xw7R2JiakatGN/RfnX/yCedzX6laef6YDnZ739knzrp0JxMYFfl5x6W769+7Zneo4jY2NY1diYpb7l76oHBvXrWXvnt0cOnSQlUsXpzwsApj+yWQevacV/V/pzb69e0NZRDfOxFRxOteQxHTbBJ5bxTK4Fp3N0n43xMZlcmwWCPgsA7ZJTNhB/ML5tGh5R7r3FoRnO/yXR/7ThplTvTn3EhMTiDs3oHzFT1w3kqW9tsTGFWdnwHEHcOjQQZYvXUwj9wFSbFxxWt99D7c3v5ZbmjUlOjqay68MbU8FCN13RFZKX1Q25cHF1/O/Svd/Y07frp0JFItNfd34K8215a+dicRksk1SUhJPP3w3993WjEtrXk75S6qk+xvzZs3gstrpW8X9SCT7frxytlXgAn0NlBORRiIyX0Q+BDaISJSIjBSRDSKyVkQaA4jIvSLyqYjMFpHvRSTljkZE7haRFW7r3rDkipSI7BeRPiKyHHgeKAHMd//eAyIyIOA9HhKR/pkFKyLRQD3gAQIqcBnEHyEib4jIShFZLyKPJO8vInNFZI1btpuD+H+ZjmYwR2DaViRnXGW6rVItrVu9klkzpvHQ408CkJR0jB9/2EyLlnfw7piJROXNy6QxHwQt7sxkHOoplEck030PHTrEpLEfcPcDj2awAVSsVIV3xkxmwLAxTBk3kiOHD59+4CeTQXDpWvsyKVdW+2f8yaZ+34lj3iciIoLG1zpPmMd9MIxb7ryLvPlC3zCecZFOvdxJScf4YfO3vPrWIN4Y9A5j3nfGFwEMeW80742bxOsDhzDto0l8s2Z1kKM/uWCcf/UbNWHkpKn0fq0/I4cPDXaIpyWjWNMeT1kfphnvX6p0Ge64+x66PvUY3Z9pz0XlyhMR4TwHa37r7Yyc9ClDR06gaLEYRgwekO49gu6UynkK5+xZ7NQ+y8zLOGTAGzz8+JMpn1Ogt0eMYviYibz61hCmfTSZb9Z6cO6dwudzKtssXrSQqpdWp2Ahp7V73969xC+cz6Tps5g2+ysOHjzInM9nBjHyTIToOyIrHbv0YObUKXR44G4OHvyHyICxjyaI0h6XGX5vOP9GRETQf/g4RkyawU+bN/HbL/9Ltd1H40eSKyKCBlc3C1m4JnudlUlMRCQSuB5I7kB+OVBFVX8RkWcAVLWq21XxCxEpH7gdThrOlSLyGXAAaAXUU9WjIjIUaAuMAfIDG1W1h/t37wcaq+pOEckPrBeRzqp6FCet5yNZhH0LMFtVfxCRv0TkMlVdk0H8D+PM8VBbRPIAi0XkC2ArcKuq7hWRGGCZiEzXNN8kEjDje9/+g7jrnvtP+f/1048m8fl0px9/hUsqk7Djz5TXdibuoFhM6u4ehQoXYf/+fSQdO0ZEZCQ7E3ZQLKBLyM8//UD/vn14pf9gChYqDDhPIWNj47ikclUAGjS+moljU4+hCoWY2LhUTwF3JiakK09MbByJCWnKXCyWY0ePZrjvn9u3seOP33ni/jYp6598sC39h42maLGYlO1LlS5Dnrx5+e2X/wUl+cCMTyYzZ8Y0AC6uWCnDmAMVLFyYA4GfU2JCyjYxcade5qIB/19fzZrJiiXxvPLWOylf5t9/u5H4BXP54J23ObB/HyK5OOecc2hxW6szLjPA1MkTmTnNPT4rVSZhx4n4EhN2pOuOVKhwEfbv28exY8eIjIwkIWCb2LjiFCpchLx585E3bz4urXEZP/34AxdcWJqY2DjA6VZ5VaMmfLdpI5deVjMoZchK4PlX/pLKJAacf4n/4vxLVq1GTf7Yvo09u/9OSeiRHaZ/PDllTGz5SyqlahlLTEigaExMqu0LpTlOEwOOuZjY4pnu36z5LTRrfgsAI4cNTvX5JWt206307Nwx6GVMq1hsXKo4dybuSFfOtNeiXWnOrbPRtCkT+ezTwHMv4NjM4LhLPvdSPsuEE8fvD999y4svPAfAnt27Wb4knojICOo3bBLw2RWlfqPGbN60kUtrhP7c+2TyRGZM+xiAipUqk/BnQPl2pC9f4TTXFuf/IC7VNnO/mM3VAd0nV61YxnklSlKkiNNttGHjpmxcv47rbmge9PJkx3dEVi64sDQv93fG2W7b8hsrl8YHo1g5WrGYOHYlprluFItJt83ONNsUSfNZ5Y8uQOXqNVm7cikXlikLwPw5n7FqaTy93xwSVg+TzkROKOXZ1gKXV0TWAauALcD77voVqvqL+3t9YCyAqm4GfgOSK3BfquouVT0IfOJu2xSoiVOhW+cuJw/gSQI+zigQVT0AzAOauxXF3Kq6IYvY2wAT3d8nusvJAuO/FmfCvnXAcqAYcDHO8faKiKwHvgLOB4qThqoOV9VaqlrrdCpvADff3ophY5zEB/UaNOarWTNRVb7duJ78+aPT3UCKCJdeVotF878C4IvPZ1D3qkYAJPz5B727dOK5Hi9SstSFKfsULRZDbPFzU1o71q5awYXZkMSkfMVKbN+2lT9/387Ro0dZNPcLrqjXINU2V9RvyLw5n6OqbN60gfz5oykaE5PpvqXLluPD6V8ycvIMRk6eQUxsHAPfG0/RYjH8+fv2lKQlCX/+wfYtvxF3bomglKVFyzsZPPJDBo/8kDpXNWLu7ICYo6PT3TCKCNVq1CJ+wVwAvpo9kyuvauiUuV7DDPcvX7ESv6cp85X1nf+vVcuXMGX8aHr27U9UVFTK33ljyHuMmjKDUVNmcPMdbWjV7r6gVd4Abr2zNe9/OJn3P5zMVY0aM+ezGagqmzasJ390xsdn9Vq1WTjvSwDmfDadem4m0HoNG7N+7Rp3LMpBvtu4gQtLl+HgwX/458ABAA4e/IeVy5ZSpmy5oJUhK2nPvy9P4fyrnsn5t33rlpRWgh+//46jR4+mPETJLjfddmdKAhHnOP0MVeW7jRsy/byq1ajF18nH6ayZ1KnvHKdX1m+Q6f67//4LcM6zxQvn0ch9ghzYdXHJovmUvqhsyMvsnDdb+PMP97yZ92X660y9Bsyb81nKOZcvf3S6G7GzzS13tGbEuMmMGDeZ+oHHZlbnXs1aLJznHpufzaBeg0YAfDjtcyZMm8WEabNo2ORqnny2G/UbNuHgwYMB595BVi3PvnOv5Z2tUxKPXNWoCbM/T762fEN0dAFiMihfjVq1WTDXubbMnjmdqxo2Snl9//59rFuzivoNT2Qejjv3XDZtXM+hQwdRVVavXB6y777s+I7ISvI5efz4cSaOeZ8bbr4tBKXMWcpVvIQ/tm9lxx+/c/ToUeLnf0ntuqmvLbXrXsWCL2ahqnz/7Ylry57df3NgvzPe7/DhQ6xfvYKSF5QGnMyWUyeOoetLb5In4PvchL+zrQXuoKpWD1zhPi04ELgqi/3Tti+ru/1oVe2awfaHVDUpi/d7D+gGbAYybUYSkWJAE6CKiCjOZH0qIp3dTdLG315V56R5j3uBWKCm21L4KxCys+3yuvVZviSee+64iTx5oujUvVfKa92efoKnu/YgJjaOhx5/kpdf6MKoYUMpW74CzVrcAsDYD4azd+9u3nazxUVERDB0pJNV7PGnn6Nvr24cO3qM884/n07P9w5VMVJEREby347P8kKn9hw/nsQ1N9zEhWXK8vmnzjiLG26+ndpX1mPV0sU82OYWdxqBnlnum5VvN6xjyvjRRERGkkuEx57uQqHChYNertp16rFy2WIeaH0LeaJOxAzQ49kOPPncCxSLieW+/7bntV7dGPPeO5S9uALX3XhzlvtHREby36eepfszTpmvvfFEmd8Z8DpHjx7l+aed1MsVKlehfaduQS9bVq6sdxXLFsdz163NyRMVRZceJ7Isdn7ycTp370lMbByPPtGR3s935v13hlCuQkVuvNlJnlO6zEVcXrce9991B7lEuPHmllxU7mJ+37aN7p2fApzB3lc3u4Er6tbL1rIBXFG3PiuWxPMf9/x7NpPz70H3/Bs5bCjlylfgevf8+3rBXL6cNZPIyEjOyZOH7i+9lvJk9eUeXfhmzWr27N5N65uu454HH+X6m24NaXkur1OflUsXc3+rm8kT5aQiT/ZCpw507OIcpw/8twN9e3Vj9IihznHqtqxltf+Lzz/Lvr17iIiI5PGnu6QkVXj/nbf5+cfvQYTi55agw7OhP0YjIiN5tGNnenTqkOY64zwHvOHm26h1ZT1WLVvMQ3fd6kwj0KVHyv6v936eDetWs3fPbu65/Uba3vcw1954M0sWzWfY22+yZ/ff9O7yFGXKlc8w+192uKLeVSxfEs/dt7VwpxE4cf3u0vFxOj3vnHsPP9GRF7s/xwfDhjjH5kmOsb//2kWPzk8DThfnptddz+V1sv/cq1PvKpYt/prWt9xIlDtFSbJnOzzGcy/0IiY2jv+2f4pe3Trz3juDubhCRW68uWXKdovmz6P2FXVTEkcAVK5SjUZNr+aBtq2IiIjg4gqXcFPL20NenlB9RwC81qsb69c6x2u7ljdw9/0Pc13zW1jw1RxmfjIFcB6WXXPDTSEv5+nq9OGzVGlUlYIxBflg6ygm9BzPlx986XVYmYqIiOTB9p3o81wHjh8/TtPrW1Cq9EXMmeG0jF/XoiU1r6jHmuVLeKzdbeSJiuKJZ18A4O9dOxn0eh+OJx3nuB6nXsOm1HKz8r436E2OHj1C787tASh/SRUefaqLN4XMTjmgpVEyHmfhDRHZr6rRadY1AjqpanN3+Wmgsqo+4Had/BKnBa4N8ApOF8qDOK1b9+N0p/wUpwtlgogUBQqo6m9p/56IbABuCmgtQ0TW4FSsqqnq35nE/Qhwmao+ErBuIdAdpzIXGP/DODOu3+FW1MoD24EHgXKq2t4d1zcPKKOqv2b2/7Xlr3/Ong8vBI4czapuHd78fm3JH+XvMRFHjx33OoSQOpZk5QtX+Xx+7uWOONs6DgXXvoNHvA4hpJ4q3ubkG4WxvlvHeR1CSFUuWTgs7l6y8/64VNF8nvyfhOOVcCgQ4Va2JgH3qmpy9oh4nO6V64CPVXWVqn6LU5H6wu2e+CVwXibvPRyYJSKB+YInA4szq7y52gBT06z7GLgrg23fA74F1ojIRmAYTkvoeKCWiKzCGaO3OYu/Z4wxxhhjjMmBzqoWuDPhdkGspapPBPl9ZwIDVHVuMN83GKwFLnxZC1x4sxa48Obn8lkLXHizFrjwZi1wZ4et2Xh/fIG1wJ1dRKSwiPyAMy7vrKu8GWOMMcYYY3Kesy2Jyb+mqqOAUUF8v92cyG4JpCQryagy11RVdwXrbxtjjDHGGGNOn997OYGPKnDZwa2kVfc6DmOMMcYYY0zOZBU4Y4wxxhhjjC/kgAY4GwNnjDHGGGOMMeHCWuCMMcYYY4wx/pADBsFZC5wxxhhjjDHGhAlrgTPGGGOMMcb4gv/b36wFzhhjjDHGGGPChrXAGWOMMcYYY3whBwyBsxY4Y4wxxhhjjAkX1gJnjDHGGGOM8Qn/N8FZC5wxxhhjjDHGhAlrgTPGGGOMMcb4go2BM8YYY4wxxhhz1rAKnDHGGGOMMcaECetCGcYOHj7qdQgh9fe+w16HEDLFCkZ5HUJIRebyd/+Ff5KOex2COQPRec/xOoSQSTru72Pz4JFjXocQUtNeXeR1CCHVd+s4r0MIqa4X3O11CCE1XWd6HcIp8fcdiMNa4IwxxhhjjDEmTFgLnDHGGGOMMcYXLImJMcYYY4wxxpizhrXAGWOMMcYYY3zC/01w1gJnjDHGGGOMMWHCWuCMMcYYY4wxvmBj4IwxxhhjjDHGnDWsBc4YY4wxxhjjCzmgAc5a4IwxxhhjjDEmXFgLnDHGGGOMMcYfckATnLXAGWOMMcYYY0yYsBY4Y4wxxhhjjC9IDmiCsxY4Y4wxxhhjjAkT1gJnjDHGGGOM8QWbB84YY4wxxhhjzFnDKnDGGGOMMcYYEyasC6UxxhhjjDHGF3JAD0qrwOVUq5cv5b3B/UhKOs61N97M7W3vSfW6qjJiUD9WLVtCnqgoOnbpQdnyFQEY+NqLrFoaT6HCRRg8amLKPj//+AND+7/K0SOHiYiI4NGnnqP8JZWztVzJ1q9ezvgRAzl+/DgNr2lO8zvuTvW6qjJ++EC+Wb2Mc/Lk4aEnu1G6XAUAvpg+hQVzZqCqNLquBdfdfCcAK+LnM/XDD/hj22/07DecMhdXzPZyZWT18iUMH9SP48edz/KOtvemel1VGf52P1YtX0yePFF07NqTcu5n+darfVi5NJ5CRYowdNQkD6LPmKry1huvsXTx10RFRfF8rxepcEmldNv9vn0bPbt2Zu/evZSveAk9XnyF3Llzs3/fPvq80JUdf/7JsaQk7mp3DzfedAsA+/bt5dUXe/HzTz8hInTr2Ycq1S4NeXneHfgGK5cuJk9UFM9060W5Cpek2+7P37fzas+u7Nu3l3LlK9LphRfJnTt3pvsfOXyYZ594iKNHjpCUlET9xk1p98CjAPTt0YVtW34DYP/+fURHF2DIqAlhVb5kSUlJdHiwHTGxsfR+fWC2ly9tWQf1e41lS+KJioqiS48XKV8xfVn/2L6NPt2fc47NChXp1ts5NteuXkn3Th05t8T5ADRo3IR7HnQ+s48mjmfmtI9BlRtvuY072tyd7n1DTVUZ0v91li91rhedX+idcfl+385L3buwb+8eLq5wCV16vUTu3LlZvGg+I4e9Q65cQkREBI91fJaq1WsAsH/fPt58pTe//vw/BKFT955Urhracy+j8g0d8AYrl8aTJyqKTt17c3EGx+ofv2/nlR5d3fJVpHMPp3xz53zO5HGjAMibNx/tn+1G2YvLA9Dv5V4sW/w1hYsUZcT4KdlZrAyVrhhL41sqIbmEjcu2smLe/1K9XrJsUW65vxZ7/voHgB83/MmyL35KeV0E7n6qPvv2HGLa+6uyNfaMrFmxlA+G9Of48eNcfcNNtGyT/r7l/SH9WbN8CXnyRPFE5xcoW74iR44cpnvHRzl69AjHk5Ko06AJre99GIDRw95m1dJ4IiNzU7zE+bTv/AL5owt4UbzT0uH9J6nVvDZ7EvbQvurjXodjPGBdKHOgpKQkhg18nZ6vDWTI6EksmjeHLb/+nGqb1cuX8Pu2rQwb/zGPP9OVdwa8lvJa02Y30su9iQo0atgg2tz7IAPfH89d9z/CqHcHhbwsGTmelMSYd/vzTK836TtkLMsWfcX2Lb+k2mb96mX8+fs2Xh82gfse78zod/oBsO23n1kwZwY9+w3npUEjWbdyCX/+vhWAkheWoUO3l6lQOXtvOLKSlJTEO2+9Tu/XBzJ09GQWzv0i3We5avkSft+2heHjP+GJTt0Y2v/VlNeuvr45vd94O7vDPqmli+PZtvU3Jk2bSefuPXiz70sZbvfO22/Rqm07Jk2bSYGCBZk57RMAPp4ykdIXlWX0xI8YPPx9Bg14k6NHjwLw1huvcUWdekz4ZDqjJ37EhWXKhLw8K5ct5vetW3l/4jQ6PNudwW/2zXC7D955m1tateX9idOILlCQOTOnZbl/7nPO4dWB7zJ09ESGjPqQ1cuW8N3GDQB07fMqQ0ZNYMioCdRv2IS6DRuHXfmSfTplAqUuLJ1qXXaWL9DyJfFs27qF8R/P4JmuPRjwWsbH5rDBA7m9zd2M/3gG0QUK8vmnU1Neq1q9Bu+Pn8z74yenVN5+/t+PzJz2Me+OGs9746ewNH5RSgU1O61Y6pRvzJRPebprdwa+/kqG240YMpDb2rRlzEfTiS5YgFnTnfJdVusKRoybxPCxk+j0fC/69e2Tss/gAa9T+8q6jJo0leHjJnFh6YuypUyBVi5dzPZtWxg5+VM6Ptedt9/I+Fh9f+jbtGzVllGTPyW6QEFmz5gGwLklzufNIe8xbOxk7rrvId4K+PyvuaEFrwwYnB3FOCkRaNqyMp8MX8Go1xZS4bISFC0enW67bT//xdh+8YztF5+q8gZwWYMy7ErYn10hZykpKYkRb79B975vMfCDiXw97wu2pvmuW7NiCX9s28qQMR/x6NNdGD7wdQBy5z6H3v2GMGDEePoNH8falcv4/lvnOnlpzct56/0PGfDeeEqULMXHH47O9rL9G3NHfUWvZj29DuPsJZJ9Px4JywqciGTrFUVEconI2yKyUUQ2iMhKESnjvtbtFN/jlLbLDj9u3sR555fk3BLnkzt3bq5qci3LFy9Ktc3yxYtofN0NiAgVK1flwP59/LVrJwBVLr2M6AIF072vCPxz4AAABw7sp2hMTOgLk4Gff/yO4uedT9y5JYjMnZsrGjRlzfL4VNusWRZPvSbNEBHKVazMPwf2s/uvnfy+9TfKVqhEnqgoIiIiqVilOquXOv83JS4ozXklS3lRpEz98N0mzjv/As4tUZLcuXPToMk1LItfmGqb5fELaXLdjZl+lgUy+Cy9Fr9wPs1ubIGIUKXqpezbv4+diYmptlFVVq9cQaOm1wBwQ/ObWLRgPuDMAfPPgQOoKgf/+YeCBQsRERHBgf37+Wbtalrc0hKA3LlzZ0v5l329kKbNnM/gkipV2b9/P3/tTF+eb9as5KpGTQGncr306wVZ7i8i5M2XD4Bjx45xLOlYuu8TVWXR/K9odHWzsCsfQGLCDlYsjee6Frdk+Lezo3yBFi+az3U3OMdm5arV2L9vH7syKOuaVSto2MQ5NpvdeBPxC+dl+b5bfvmFSlWqERWVl8jISKpfVpOvF2S9TygsXrSQa29ojohQqUo19u/PuHxrV62kYeOrAbj2hhYsXrQAgLz58iHuQXjo0MGU+ZgOHNjPhrVruOGmWwHn3IsukP0tHUu+XsA1zZq7x1o1DmRSvnWrV9KgsXOsXnN9c5Yscq4tlateSoGCzjXjkspV2ZmwI2W/ajVqUqBgoewpyEmcW6owu3f+w56/DnI8Sfl+7e+Uq1L8lPePLhRFmUvi2LBsawijPHU/bf421X1L/cbXsGJJ6vuWFYsX0eja6xERKlQ68V0nIuTN61wnk44d49ixYynHaPVaVxIR4XRGK1+pCrt2JmRvwf6lTV9vYv9f+7wOw3goLCtwHmgFlACqqWpV4FZgt/vaqVbMzpoK3K7ERGJiT1zIY2Lj2JXm5nhXYgKxAdsUi41jV2LWF7YHn3iake++zf13NGfkO2/zn4e8adb/e1ciRWPiUpaLFovlb7fCErhNsQy2KXlhGb7f9A379+7h8KFDfLNqGX+dxRf0XTsTiY0L/CyLp7sZ2bUzkZi40/ssvZaYkEBc8XNTluPiipOYJuY9u3cTXaAAkZHOl29sXHESE52bqdtateHXX37h5uua8p9Wt9Gx03PkypWL7du3UbhIUV7u9QL33nUnffv05ODBf0Jenl07E1J9BjFxcexM8znt3bOb/NEFiHDLE3heZrV/UlISj9/bhjYtrqFGrSupWLlqqvfd+M1aihQpyvkXhO7hQyjLN+ztfjzw3yfJJRl/XWVH+QIlJiQQW/xErLFxxUlMSHNs7klzbBZPffx+u2E9D9x1B52ffIxf/ue0epQpW471a1ezZ/duDh06yLLF8STs+DMbSpTazsQEYuNOnHuxccXZmebc2+uWLyLg3AvcJn7BPO5tdSvPP9OBTt2dVoI/tm+nUJEivP5iTx75T2vefLk3Bw8ezIYSpbYrMfXnl9H33949u4mOjj5xrMYVT/cACWD2zGnUrlMvtAH/S9GFoti3+8T/777dh4guFJVuuxKli9Cu01W0fKg2xQJa6BrfUolFM79DVbMl3pPZtTOBYmnuSdI+JPprZ+p7m8BtkpKSePrhu7nvtmZcWvNyyl9SJd3fmDdrBpfVrhOiEpjsJNn44xXfVOBEpLqILBOR9SIyVUSKuOsfclvMvhGRj0Ukn7t+lNuqtkREfhaR27N4+/OAP1T1OICqblPVv0XkVSCviKwTkfHu+04TkdUisklEHnbXpdpOREqLyMaA2DuJSC/39w4i8q1bjolpAwkGJf0F+VRageUkG8369GMefPwpPpgykwcf78ig1zPuWhRqGX3fpA89441KXFCaG29ry+svPMWbvTpRqkw5cuWKCEWYwZFBYSXNJSWjL+CTfZZeO5WYMzyO3bKvWLqYiytU4NM5cxk1YQr9X3+FA/v3k5SUxA+bv+PW2+9k1IeTyZs3L2NHfhCaQgTGekqfU/r9kouc1f4REREMGTWBsZ/M4ofvNvLrz6m7QS34ajYNr77uX0Z+akJVvuWLF1G4cBEuzmAMVrLsKN/JpDufsjh+y1e4hInTZ/P+h1NoeWcbund+CoALy1xEm//cR6f2j9C5w2OUvbh8SstAtjqVc+8k29Rv1IRRk6bS57X+jBo2FICkpGP8+P1mbmp5B8PGTCQqb14mjgn9uZdWhvWRk3986f4P1q1eyewZ03jwsQ7BCy6IMrzEpylXwra9jHhxHmPf/Jq18b9y8/21ALioUhz/7D9Cwra9oQ/0TJzKd4K7SUREBP2Hj2PEpBn8tHkTv/2SejzgR+NHkisiggbZ1JJvzJnyTQUOGAM8p6rVgA1AcufgT1S1tqpeCnwHPBCwz3lAfaA58CqZmwy0cCtg/USkBoCqdgEOqmp1VW3rbnu/qtYEagEdRKRYJttlpgtQwy3Ho2lfFJGHRWSViKya5A6kPl0xsXHsTDzR7WNnYgJFY2JTbVMsNi6lNQOcp5Zpt0lr3pzPqNPAGYdSr9HV/LD5238V35kqGhObqtXsr12JFC6aujtnkWJxqbpK/LUrkSJFiwHQ8Nrm9Bn4Ac+/Opj8BQpwbokLsifwf6FYbByJCYGf5Y50XVdjYuNSdfM5lc/SCx9Pnsg9be7gnjZ3EBMbm6r1ISFhBzFpYi5cuAj79+3j2LFjgNPVLibWaVX9bPqnNGzSFBGh5AWlOK/E+fz26y/ExRUnNq44latWA6DR1dfww+bvQlKeGR9P5vF72/D4vW0oFhOb6jPYmZBAsTSfU6HChTmwfx9JbnkCz8uY2OIn3T+6QAGq1ajFqmVLUtYlHTvGkoXzadD02rAs37cbvmHZ4kXcc3tzXu3VjW9Wr+T1Pt2zpXyBpk6ZyANt7+SBtndSLCaWxB0nYnWOu9THZqG0x+aOE8dv/uho8rndXq+sdxXHjh1j9+6/Abjx5paMGDuJt4ePpGChQpQslT2titM+msTD7VrxcLtWTvkSTpx7iQk7KBaTcfmSAs69tNuA06Xw9+3b2LP7b2LjihMbG8clVZwW4gZNrubH7zeHsFQnTP94Eo/e05pH72md7vPbmZiQQfkKs3///hPHasKOVMfzzz/9wIC+L9L7tQEULFQ4W8pwuvbtPkSBwnlTlgsUjmL/3kOptjly+BhHjyQB8Mt3ieSKEPLmz02JMkUoWzmOB7s3pnm7GpS6OIbr21bPzvDTKRYTx6609yTFYtJtszPNNkWKpf5s80cXoHL1mqxduTRl3fw5n7FqaTxPdetz1j/cNKcmBwyB80cFTkQKAYVVNXnwz2iggft7FRH5WkQ2AG2BwLSI01T1uKp+C2TaOVxVtwEVgK7AcWCuiDTNZPMOIvINsAy4ALj4NIuzHhgvIncDxzKIZbiq1lLVWq3uvvc039pxcYVK/L5tK3/+sZ2jR4/y9bwvuKLuVam2ubzuVcyf8zmqyuZNG8iXPzrdxTKtosVi2bhujVOINSspUdKbik+Ziyuy4/dtJP75O8eOHmX5ornUuLx+qm1qXFGPxfNmo6r8tHkTefNFp1Ty9ro3U7sSdrB6ySKubHh1tpfhVJWvWInft21J+SwXzfuSK+o1SLXNFfUaMG/OZ6f1WXrhtjtbM3rCFEZPmEKDRk2Y/ZmTCXTjhm+Iji6Q7iZZRLisVm0WzP0SgM9nTueqho0AKH7uuaxesRyAv3btYstvv1Hi/JIUi4khrnhxfvvVSWqzesVySl8UmkQKLW67MyXJRp2rGjF3tvMZfLdxA/mjo9NVokWEajVq8fWCuQB8NWsmdeo3BODK+g0y3H/333+zf58zDuLw4UOsXbWcCwKSfaxdtYKSF5ZO1c02nMp336PtGTd1FqM/mkmXXq9wac3adO5xomU/lOULdOsdrVOSjtRv2Jg5nzvH5qYN68kfHZ2uAiAi1KhZm4XznGNz9mfTqecmWdm1c2dKC9Z3mzagx49TyK0E/P3XLgB2/PkHi+bPpem114e0XMluub0Vw8c6iUfqNWzMF5/PRFX5dmPm5atesxYL538FwBefz6DuVY0A2L51S0r5ftj8HUePHaVgocIULRZDbPFz2frbrwCsXbmCC8tkTxKTm25rxbujJ/Lu6InUbdCIL2fPdI+19eTPn3H5Lr2sFovmO8fql7NmUsctX8Kff9Cnayc693yRkqUuzJb4/40/t+6hcGx+ChbNS64IoUKNEvxv445U2+QrkCfl93NLFUJEOHjgKPGffc/wPvN476X5zBy7li0/7mTW+HXZXILUylW8hD+2b2XHH79z9OhR4ud/Se26qb/rate9igVfzEJV+f7bE991e3b/zYH9J66T61evoOQFpQEns+XUiWPo+tKb5IlK38XUmLNVTphGYBRwi6p+IyL3Ao0CXjsc8HuW9WhVPQzMAmaJyA7gFmBu4DYi0gi4Gqijqv+IyAIgoyvCMVJXngO3uRGn8nkT8IKIVFbVdBW5MxERGckjTz5Lr2c7OOl4r29BqTJlmfXpxwBcf/Nt1LqyHquXL+GRti3JkyeKDs+9kLL/G326s3Hdavbu2c19tzenzX0Pce2NN/NEp26MGNyfpKRjnHNOHh5/pmswwz718kVE0u7Rp3ij5zMcP36cBlffSMkLyzBv1jQAmlx/C5fWqsP6Vct49uHW5MkTxYNPnoh1UN/u7N+3x3mf/z6VklJ41dJFjBv2Fvv27KZ/n86UKlOOZ/v096KIKSIiI3m0Y2d6dOrA8eNJXHPDTVxYpiyfu5/lDe5nuWrZYh6661ZnGoEuPVL2f73382xwP8t7br+Rtvc9zLU33uxVcVLUqX8VSxd/zZ0330hUVBTder2Y8tozHR6jywu9iI2N478dnqJnt84MHzqY8hUq0txNTnLvQ4/wcs8XaHdnSxTlsQ4dKVykCABPde5K7+5dOXb0KCXOL5nqvUOldp36rFy6mPtb3UxUVBRPdeuV8toLnTrQscsLFIuJ5f7/duDVXt0YM2IoZS+uwLXNb8ly/7937eTNl3ty/HgSely5qsnVqSrwC+fOoVE2dC8MVflOJrvKF+jKelexfEk8bVs2J09UFM+9cCLL4nMdH+fZ53sSExvHI+070uf5zrz/7hAuLl8xJXnHwnlfMv3jyURERHJOVB56vPxaylP/Hs89w969e4iMiKTjs91SkmVkpyvq1mf5knja3X4TUVFRPNu9V8prXZ96gme69SAmNo6HHn+Sl17owshhQylXvgLXu9N0LJo/ly9nzSQyMpJz8uThhRdPlK/9M8/xSs9uHD16jPPOP5/O3Xtne/kur1ufFUvjufeOm51pBJ4/Ub7nn2nP0116UCw2lgcf68ArPboyevgQypavSDM3ic64kSPYu3cPg9xMqREREQz5YDwAr/TomjKO8a6bm9HuwUe5PpPkO6Gmx5V5n2zktocvJ1cuYeOKbezasZ9qdZxW3fVLt1D+0nO5tO6FHD+uHDuaxGdj13oS66mIiIjkwfad6POcc9/S9PoWlCp9EXNmOJmHr2vRkppX1GPN8iU81u428kRF8cSzzn3L37t2Muj1PhxPOs5xPU69hk2pVcd5qPveoDc5evQIvTu3B6D8JVV49Kku3hTyNHT68FmqNKpKwZiCfLB1FBN6jufLD770OqyzRk5oR5WzZYDq6RCR/aoanWbdN8ATqvq1O56skKo+JSI7gUrA38DnwHZVvVdERgEzVfWjzN4z4L0vA/5U1d9FJBdOpXC9qr4pIn8Dcap6VERuBh5U1RYiUhFYBzRT1QVptssN/IHTqrcfWAjMBvoApVT1V3ebbUAFVd2dUVzf/7En/D680/D3vsMn3yhMFSvo7yd9RaLznHyjMLb34FGvQzBnIO85/n12mXT8uNchhFTScV9/7fHRywu8DiGkmj19diZ9CZauF2T/3I3ZabrODIu60d5DR7PtQlEwKrcn/yfh+i2WT0S2BSz3B+4B3nWTlPwM3Oe+9gKwHPgNZ2zcv8lbHAeMEJHku9IVQPJkL8OB9SKyBrgfeFRE1gPf43SjJO12qtpWRPq4cf0CJA8EiADGuV1CBRiQWeXNGGOMMcYYk0YOGMsYli1wxmEtcOHLWuDCm7XAhTdrgQtf1gIX3qwFLryFTQvc4WPZ1wKXJ9Ja4IwxxhhjjDHm3wqLWuYZsgpcABGpCoxNs/qwql7hRTzGGGOMMcYYE8gqcAFUdQNQ3es4jDHGGGOMMacvBwyB88c8cMYYY4wxxhiTE1gFzhhjjDHGGGPChHWhNMYYY4wxxviE//tQWgucMcYYY4wxxoQJa4EzxhhjjDHG+IIlMTHGGGOMMcYYc9awFjhjjDHGGGOML+SABjhrgTPGGGOMMcaYcGEtcMYYY4wxxhhfsDFwxhhjjDHGGGPOGtYCZ4wxxhhjjPEJ/zfBWQucMcYYY4wxxoQJa4EzxhhjjDHG+IKNgTPGGGOMMcYYc9YQVfU6BhMmRORhVR3udRyhYuULb34un5/LBla+cGflC19+LhtY+Yx/WQucOR0Pex1AiFn5wpufy+fnsoGVL9xZ+cKXn8sGVj7jU1aBM8YYY4wxxpgwYRU4Y4wxxhhjjAkTVoEzp8Pv/aytfOHNz+Xzc9nAyhfurHzhy89lAyuf8SlLYmKMMcYYY4wxYcJa4IwxxhhjjDEmTFgFzhhjjDHGGGPChFXgjMkhRCSXiBT0Og5jcgoRye91DMakJSJFvY7BBId9r+dcNgbOZEpEIoA5qnq117EEm4i0zOp1Vf0ku2IJJRH5EHgUSAJWA4WA/qr6hqeBmUzlhGNTRMoD7wDFVbWKiFQDblLVlzwOLShEpC7wHhCtqqVE5FLgEVV9zOPQgkZExqpqu5OtC2ciUgS4AIhMXqeqa7yLKDhE5EdgHTASmKU+vhEUkTggKnlZVbd4GE5Q2Pe6AavAmZMQkelAO1Xd43UswSQiI91f44C6wDx3uTGwQFWzvIkOFyKyTlWri0hboCbwHLBaVat5HFpQiEg9oBdwIc5NlgCqqhd5GdeZyAnHpogsBJ4FhqlqDXfdRlWt4m1kwSEiy4Hbgel+LB+AiKxR1csCliOADapaycOwgkZEXgTuBf4HJN8oqao28SyoIBERAa4G7gcuByYBo1T1B08DCyIRuQnoB5QAEnC+I75T1cqeBhYEfv9eN6cm8uSbmBzuELBBRL4EDiSvVNUO3oV05lT1PgARmQlUUtU/3OXzgCFexhZkuUUkN3ALMFhVjzrf3b7xPvAUzlPIJI9jCYoccmzmU9UVaY7FY14FEwqqujVN+XxxfIpIV6AbkFdE9iavBo7gr5TmdwJlVfWI14EEm9vi9iXwpYg0BsYBj4nIN0AXVV3qaYDB8SJwJfCVqtZwy9nG45iCJaPvdWuNyWGsAmdO5jP3x69KJ98gu3YA5b0KJgSGAb8C3wCLRORCwE+tqXtUdZbXQYSIn4/NnSJSFrdlQ0RuB/7IepewstXtRqkicg7QAfjO45iCQlX7An1FpK+qdvU6nhDaCBTGab3xFREpBtwNtMO5rrQHpgPVgSlAGc+CC56jqrrLHSOWS1Xni8hrXgcVJBl9r+/Ncg/jO9aF0pyUiOQFSqnq917HEmwiMhi4GJiAczPZGvhJVdt7GliQiEgZVf0lYFmAcqr6o4dhBY2IvApEAJ8Ah5PX+2Scim+PTRG5CKe1pi7wN/ALcLeq/uplXMEiIjHAQJxuagJ8ATypqrs8DSzIROR8TnRfBkBVF3kXUfCISC3gU5yKXOC15SbPggoSEfkBGAuMVNVtaV57TlXDvqIjIl/htFC9ChTDqYjXVtW6XsYVKiISqaq+6sVgsmYVOJMlEWkBvAmco6plRKQ60McPX2LJRORWoIG7uEhVp3oZTzClHafirlutqjW9iimYRGR+Bqt9MU4F/H1sQkqWxlyqus/rWMzpcR+etAa+5UT3UPXLd4OIbMJp6dgAHE9er6oLPQsqCNyxim+o6tNexxJK7rXlIE629bY4iT7G++Ehioj0yGi9qvbJ7liMd6wLpTmZXjiDnBcAqOo6EfFD94pAa4B9qvqViOQTkQLhfkMpIhWBykChNFkNCxKQkSvcqWpjr2MIMd8dmwAiUhj4D1AaiEweKxbuY2uTudfI9rjlS17vl8qN61aggqoePumW4Wmnqr7tdRDBpqpJblZUX1PVA27XwotVdbSI5MPpreEHBwJ+jwKa45Mu2ubUWQXOnMwxVd2TZjC+b5ptReQh4GGgKFAWOB94F2jqZVxBUAHnol4YaBGwfh/wkBcBhYKIFAJ6cqKVaiFOC3HYj/Pz8bEJ8DmwjDStGz4yDSfBzgz8WT6An4HcBHQv9JnVItIXZ2yYr7pnA+vcDNNTSJ2cLOynKEnm5+unqvYLXBaRN3GOU5ODWAXOnMxGEbkLiBCRi3EG4y/xOKZgehynhXE5gKr+6M4bE9ZU9VPgUxGp45OMYpn5AGeMyp3ucjucuY3CPtU+Pj02XVE+78J1yI+tN2n8g1MRmEvqCo4vWlGBGu6/VwasU8AP3bOLArtIXRbFGUvsF36+fqaVDwjbqXPMv2MVOHMy7YHncb6gJwBzcNLz+sVhVT2S3MIoIpH4qIUR+ElEupG+K9f9nkUUXGVV9baA5d4iss6rYILMz8fmWPcJ+UxS3/z/5V1IQTVQRHriJC/xW+tNsun49Km/O05suqoO8DqWUEieqsTnfHv9FJENnChLBBCLv+7LzCmwCpzJkqr+g1OBe97rWEJkoVvBySsi1wCP4XR78otPga+Br/DJPFRpHBSR+qoaDykTex/0OKZg8fOxeQR4A+e6kjJJMv55ilwVpzW4CSe6UPql9QYAVR3tdQyh4o4TuwnwZQVOREoCg4B6OMdlPE6W1G1Z7hhe/Hz9bB7w+zFgh2WgzHksC6XJkIjMIIunVX4ZjC8iuYAHgGtx0n3PAd5Tn5wYIrJOVat7HUeouFlRR+NkGBPgL+BeVf3Gy7iCwc/Hpoj8D7hCVXd6HUsoiMhmoJofJ4FOJiK/kMF3hKr6ohIuIi/jXFcmkXqcWNi3oorIl8CHOFMJgDMnXFtVvca7qILLnTLnQfx5/Ryrqu1Ots74m1XgTIZEpKH7a0vgXGCcu9wG+FVVu3kSmDktIvISsERVP/c6llASkYIAquqbyUxFpDnwuar6LgmGm0ChtdvC7zsiMglor6q+mwQ6mTsZdLIo4A6gqKpmmOI83Ph5ipKMHuz56WGf+/BrvapW8TqWUEg7PZDbPXS9qlbyMCyTzawCZ7IkIotUtcHJ1oUbEZmsqnem6UueQlWreRBW0InIPiA/Tpe1IzhPIlVVC3oa2BkSkbtVdZyIZJgIQ1X7Z3dMwSYi44A6wMc4E+76Jk20iEzFmeZiPj5MgCEiC4BqwEp8Ngl0VkQkXlXrex2HyZo7yfUonHHt4DyYvU9Vwz5DYzIRGQ90VdUtXscSLCLSFegG5MVJIgTOd/oRYLiqdvUqNpP9bAycOZlYEblIVX+GlPmNYj2OKRiedP9tnuVWYU5VC3gdQ4jkd//NqHy+eCqlqne7LYttgJEiojgZNif4YC64ae6PX/X0OoBQE5HLAhZzAbXI+HwMSyJSHHgFKKGq14tIJaCOqr7vcWjBcD8wGGeMn+JklvZbYpPzgE0isoLUXWDD9iGKqvYF+opIX6usGWuBM1kSkWbAcJw5f8DJZviIqs7xLKggcTONzVHVq72OJVTccQBtgTKq+qKIXACcp6orPA4tKESknqouPtm6cCYiMThjVDriTNZaDnhbVQd5GdeZEpFzgPLu4veqetTLeILNrQDUdhdX+K07ZZouhseAX4E3VfV7byIKLhGZhfPA5HlVvdTtprZWVat6HNoZyyHXzYYZrVfVhdkdSyiISBHgYpzuywCo6iLvIjLZzSpw5qREJA9Q0V3crKq+mbjVHYvTzg8TP2dERN7ByYLXRFUvcS/6X6hq7ZPsGhbSjgXIbF04EpEWOE/Ky+IkGxitqgkikg/4TlUv9DTAMyAijXCSz/yK0wXoAuAev9yAiMidOFk2F+CU7yrgWVX9yMu4zKkTkZWqWltE1qpqDXedL8aJ+fm6mRk3Q/Fdqvq417GcKRF5EKcXUUlgHc5chUv9MD7TnDrrQmlORU1OzCN2qYigqmO8DSloDgEb3Kxcgd0sfDEWByfT32UishZAVf92Wz7CmojUAeridPENHAdXEGdeHD+4AxiQtlKjqv+ISLjP49cPuDa5tUZEyuOMx6npaVTB8zxQO7nVTURicaby8E0FTkQK4XQVTR4PvRDoE+4Pw0Qk0k3JfsBN1KLu+iuBcC9bTrhupnCzFN8F3An8gjOe2A+exGndX6aqjUWkItDb45hMNrMKnMmSiIzFaQFYx4l5xBTwSwXuM/fHr466XUWTb0JiOTEvVTg7B4jGuYYFjrvZC9zuSURBpqr/EZHibjZKCOiGp6pzPQwtGHIHdrVT1R9EJLeXAQVZrjRdJnfhjBPzkw+AjTg3x+DMezcSJ3NxOFsBXAY8gzNReVkRWYwz9jvcry2+v266D4Na44wd3oUzDYSoamNPAwuuQ6p6SEQQkTyqullEKngdlMle1oXSZElEvgMq+WHulLRyyBi4tkArnBuS0Thf0t1VdYqngQWJiFyoqr95HUcoiMgdwJv4sBueiHyA81AheR6qtkCkqvoikYKIvIGThTI5y18rYIOqdvYuquDyayr6NF0mI4EKOOefb8ZpBl433ZT70X6ZgkVEjgNfAw+o6k/uup/9Mj8hpGTxvQ9nXHQT4G+ch2I3eBmXyV5WgTNZEpEpQAdV/cPrWELB72PgANzuFU1xbkLm+iwdfSzQGSclfeBg7rAfCyAi3wDXpO2Gp6qXehvZmXPH1T4O1Mc5LhcBQ302vrYlAeVT1akehxRUIrIU54FCvLtcDyeJSR1vIzszIrINyHQaEp9MUfIh8ChOr5rVOBOW91fVNzwNLAhE5FacFri6wGxgIs4E3mU8DSxE3GQthYBZfnnAYE6NVeBMltxMY9VxupX4bj4jEZmMMwDYV2PgRKRoVq+r6l/ZFUsoicgXOF1kOuHckNwDJKrqc54GFgQisiEw4537pPwbn2TBy4/TDSjJXY4A8qhPJvZ2p1v5Q1UPuct5geKq+qungQWRO75oNM7NowB/Afeq6jdexnWmROQP4B2cMqWjqmE/1ii5pdTtoVETeA5YrT6Z/xRSrjG34HSlbIJzrE5V1S+8jCsYRGSsqrY72Trjb1aBM1nKAal478lovaqOzu5YgklEfsHpoiZAKZwuFgIUBrb45WmkiKxW1Zoisj755kNEFqpqhsdtOMmkG956n1ROlwFXq+p+dzkaJztqXW8jCw4RWQXUVdUj7vI5wGK/ZH8N5M5ViI+64Pk6GyOAiGzCeTD7ITBYVReKyDd+aN3PiPtA8w6gVXLvDBEpoqp/exvZv5P2GHUfgG1Q1UoehmWymSUxMVlyL+wXAher6lduCnPfZKsK94paZpIraCLyLjBdVT93l68H/DTmL7nLyB8iciPwO05q5bCnqs+KyG1APZzK93AfdcOLSq68Aajqfvfa4heRyZU3AFU94ofsr4FEpDDwH9wMxSJOg1W4914gk5a3dBuFcQUAGIYzhcc3wCL3O94XFfCMuD1Ohrk/yebijA0PGyLSFegG5BWR5M9LgCM48/WaHMRa4EyWROQh4GGgqKqWFZGLgXdVtanHoQWFW56+QCVSj6HyxYDn5BaqNOtWqWotr2IKJjdD49c484gNwkmH3VtVp3samMmSm9WvvaqucZdr4rQEhPX4qWTutCSDko9DEbkZZyyxL66bACKyBFgGbCAgs224PxQTkaKn0sXcby11AdMn5AiByWrCjYj0VdWuXsdhvGUtcOZkHgcuB5YDqOqPIhLnbUhBNRJnLqMBQGOczE6n9AQ2TOwUke7AOJwulXfjpFb2BVWd6f66B+fzC3sisg932oe0LwGqqgWzOaRQ6AhMEZHf3eXzcLqI+sWjwHgRGYzzuW3Faa3ykyhVffrkm4WX0xgfHHbfEyJyt6qOSzMHXKCwT9ByGsKu9cJtKd2dXHkTkcY44/x+BYYEtvob//PbvDQm+A4HXhTctMphd+HLQl53Ti1R1d9UtRfOgGe/aIMzf9FUYBoQ567zBREZ7XblSl4u4qaoD1uqWkBVC2bwU8AnlTdUdSVQEfgv8Bhwiaqu9jaq4FHV/6nqlTgt+5VUtW5ySnMfGSsiD4nIeSJSNPnH66CyUTh+D+Z3/y2QyY85u03G/QzdJEJTgC044xmHehaV8YS1wJmTWSgiyX2ur8G52ZrhcUzBdMjN7vejiDwBbMep5PiC+zT5Sa/jCKFqqro7eUFV/xaRsOwWkxERuQwnFb0C8aq61uOQgqk27vgpoIaIoKpjvA0pONxpEm4j/fiwPh6GFWxHgDeA5zlRmVHAF93P/UhVh7n/hn0mzSAIuxZUnAfOyb0W7gY+UNV+7j3MOu/CMl6wCpw5mS7AAzjjHB4GPlPV97wNKag6AvmADsCLOK1vGWamDEciUh4nxX5pAs53P8yT5soVmEzAbQHwxXVNRHrgZE77xF01SkSmqOpLHoYVFCIyFiiLc9OR5K5WwBcVOOBTnG69qwmYfsVnngbKqepOrwPxSNhVAETk7axe90ECmhQi8iYwUlU3ZbJJOI5HDTzmmgBdAVT1ePJDIpNz+OJGxwSfO+i+pKoOAUa4yUxigZoisltVP/I2wuBwu3IB7McZ/+Y3U4B3gfc4caPsJ/2AJSKSfDzeAbzsYTzB1AaoETCX2KvAGiDsK3BALZyuheHYDe1UlFTVZl4HEWKbAF/M25eRU5hrKxwrAIHdlHvjjP/2q83AcHfYx0hggqruSX4xTOdCnefOXfsHUASYByAi5+G0iJscxCpwJjOdgdYBy+fgTPgZjXMxDOsKnIhkmaXQLxOVA8dU9R2vgwgVVR3jzrnVBOfpZEtV/dbjsILlV5zMqIfc5TzA/zyLJrg2Aufi3Ij40RIRqaqqG7wOJISSgHUiMp+AVkYfteJUDlxw59pKyegbjhWAwAyhItIx3DOGZsXtKfSeiFTAeTi73s1+O0JV53sb3b/WESfZ03lAfVVNnkbnXJyuzCYHsQqcycw5qro1YDne/cL6S0TyZ7ZTGKmDkxluAk6GTb/2P5ghIo/hJDEJvMkKu5uPjIhIKZzW0+mB61R1i3dRBc1hYJObkl6Ba4D45G5QYX6jHAN8KyIrSH1c+uXBSX3gXhH5Bad8yRlEq3kbVlBNc38ChX2Lag6aayvsP6uTcSvdFd2fnTjz3j0tIo+oaussdz4LuT0WJmawPtXYaBFZ6pcpWUzmbB44kyER+UlVy2Xy2v9UtWx2xxRM7oX9GpxuatWAz3C6WGTWXz4suTeQaamP5rnbwIkbkbxAGeB7Va2c+V7hQUSyHIsZzk/PRaRhRutVdWF2xxIKbrrvdFT1t+yOJbuIyAVAa1V9w+tYgsHvc235bR67tESkP9ACp5vh+6q6IuC171W1gmfBhVg4z3FnTp1V4EyGRGQ8sEBVR6RZ/wjQSFX9lIo+D05F7g2gj6oO8jgk8y+5WRsfUdVHvI7F5DwnS6Pvl5bvZCISgzPutA1wPjBVVTt5G1XwiMj5wIWkTgC1yLuIzkyaOSbzcWIMo5/mmARARO4HJqpqunGaIlIocDyc3/i9cm4cVoEzGXIn656G0/1njbu6Js44nFtUdYdHoQWNW3G7EefmozRON7wPVHW7l3EFk4jkw8kWV0pVHxaRi4EKARNg+45fvrxEpDlOZtTkG8iwv8kKuIEUUnfhCvuyQUqLd3L50vJFy7eIFABuBe4CyuN0z26lqiU9DSzI3KRBrYFvCciU6qNuvpkKzOwbrkRkrqo2Pdk6P/LLd6DJmo2BMxlS1QSgrog04cRg7s9UdZ6HYQWNiIwGqgCzgN6qutHjkEJlJE7msbru8jaczJS+qMCJyNMBi7mAy4BEj8IJtreAlsAGv2RrVFVfTxasqmW8jiEbJAArgO44Y6NVRG71OKZQuBXnYZdfp4HIylyca2nYEZEonNbFGBEpwomHKQWBEp4Flr38OqbfBMjldQDm7Kaq81R1kPvji8qbqx3O0+MncTLG7XV/9gUMXPeDsqr6OnAUQFUP4q+Le4GAnzw4Yxlv9jSi4NkKbPRL5S2QOw/cSdeFKxGZeyrrwlQ3nOyo7wBdRSSsx0Nn4Wcgt9dBeCScvyMewXloWdH9N/nnU2CIh3Flp3Yn38SEO2uBMzmSquaUhxdHRCQvbnc192bLN0+UVbW31zGEUGfgcxFZSOpMjf29Cylo0qZojyQgRXu4cp/+58fHT/9VdQAwQEQuwul+Pg0oISLP4YyB+8HL+ILoH5xpEubiz2kSshK2D41UdaCIDAa6qeqLXscTCiLSEngNiMO5xqTqgu7jHkUmgFXgjPG3nsBs4AI3MU094F5PIwoCEZlBFjcZPhmn8jLOFAlROPMwhr0ckKL9EZy5mkrgPPVPrsDtxWdP/1X1Z5xj9GURqYozJm4W4JcWuekETE9iwoeqJonIDThjiP3odaCFqn7ndSDGO5bExBifE5FiwJU4N5PLVHWnxyGdsYA09C1xJjEd5y63AX5V1W6eBBZEIrJKVWt5HUco5IAU7e0tm60JV35IQy8ivYH1wCd+64YuIotVtZ7XcRhvWQXOGJ9zu1vUx2mxilfVqR6HFDQiskhVG5xsXThys+DNU9UvvI4lFPyWoj0tEamLk902sHxjPAsoyE7WjSvcBWQUTcUnmUTHqmq7zNaJSNFwn/LCzXibHyeDaPLYb18cnyIyEOfB5TRSd+/9xKuYTPazLpTG+JiIDAXKARPcVY+IyNWq+riHYQVTrIhc5HbnQkTKALEexxQsjwOdReQwThIaP92AZJiiHfBFBc5NyFIWWEfq8vmmAof/u3EFtn5H4cx3l+U8f2Ek7RjUCALGoIZ75Q18n/G2IM4YzWsD1ilgFbgcxFrgjPExEdkEVEnuQiIiuXDS0lfOes/wICLNcMZO/eyuKo0zkfccz4IyJyUi3wPV/JqiXUS+Ayr5retWoJzYjUtE4lW1vtdx/FuBY1BJPYn3EWC4n7o1i4gAbYEyqvqiiFwAnKeqKzwOzZigsBY4Y/zte6AU8Ju7fAHOuABfUNXZ7uTkFd1Vm8O9UiAid6vqOPf3eqq6OOC1J1R1sHfRBU1yivaw/qyysBGni9MfXgcSQqtEZBI+7cYlIoHzoOXCaZEL61YdVe0L9PX7GFTXUOA40AQnmcl+nERCtb0MKhjcbLcP4LSkRiWvV9X7PQvKZDurwBnjb8WA70Qk+aljbWCpiEyH8M3WKCKd3fntAG5S1SkBr70S5klMnuZEUpZBpJ5Q937ADxU4v6dojwG+dc+7wPKF5fmWCb934+oX8Psx4FfgTm9CCQ4Rqaiqm4EpaSqoAKjqGg/CCpUrVPUyEVkLoKp/i4gvsvkCY4HNwHVAH5yWRr92ZTaZsAqcMf7Ww+sAQqQ1zhgcgK7AlIDXmuF0EwpXksnvGS2HK7+naO/ldQChpqr3eR1DKKlqY69jCIFngIdIXTlNpjitVX5x1B3blzx8IBanRc4PyqnqHSJys6qOFpEPARs2kMNYBc4YH1PVhSJyIXCxqn7lTuodqar7vI7tDPm5kqOZ/J7RclhS1dFexxBKqrrQ6xhCze/duESkEM48mskZbRcCfVR1j3dRnRlVfcj914+V07TeBqYCxUXkZeB2oLu3IQXNUfff3SJSBfgTZ/y3yUGsAmeMj4nIQ8DDONnTygIlgXeBpl7GFQR+ruRUFJH1OBXRsu7vuMthn8IcwB232BeoROqbf7+U70qc7q+X4EzCHgEc8EMG0QB+78b1Ac5YxuRuk+2AkThzT4Yld+qHTPll/CKAqo4XkdWc+K67xUcZU4eLSBHgBZyeDNHu7yYHsQqcMf72OHA5sBxAVX8UkThvQwqKS0VkL06lJq/7O+5yVOa7hYVLvA4gG4zEad0YADQG7iP8W04DDcbp5jsFJ/nFf4CLPY0o+Pzejausqt4WsNxbRNZ5FUyQtHD/jQPqAvPc5cbAAvwzfjFZPpyHJ4qTedMXVPU999eF+OShnjl9ubwOwBgTUodV9UjygohEEv4tVKhqhKoWVNUCqhrp/p68nNvr+M6Eqv6W1U/ydiKy1Ms4z1BeVZ2LM5XNb6raC3+Nv0FVfwIiVDVJVUcCjTwOKdjSduMqhL+6cR0UkZQpA0SkHs6E0GFLVe9zxy4qzjQXt7mVVF9MKxNIRHoAo3F6n8QAI0XEF10oRaSQiAwQkVXuz5tul1+Tg1gLnDH+tlBEuuG0Ul0DPAbM8DgmExzh3NJ4yJ2T8EcReQLYjtMq4Bf/uBnv1onI6zjTCeT3OKZg83s3rv8Co90bYwH+Au71NKLgKa2qgVNc7ADKexVMiLQBaqjqIQAReRVYA7zkaVTB4bvuveb02UTexviYe5P8AE6qb8Hp4vSenycYzilEZI2qpksFHg5EpDbOeKnCOHM0FQTeUNVlXsYVLG7ioB0449+ewmmdGuq2ypkwIiIFAVR178m2DRciMhinS+8EnNa41sBPqtre08CCSERmAW1Udbe7XBgYp6rNvYwrGERknapWP9k6429WgTPG59z0yahqotexmOAJ5wqc34lIfuCgqh53lyOAPKr6j7eRBY/bMtULuMpdtQB4MZyzNAZyb/j/g9MtNKW3kl/mKhSRWzmRYXORqk71Mp5gE5FpOPOefolTSb0GiAcSILw/R7f7/LOqGu8u1wPeVNU63kZmspN1oTTGh0REcJJEPIHT8iYikgQMUtU+ngZnsiQieVT18Mm3DN+kHyLyJXBHwNPxIsBEVb3O08CCZy5wNbDfXc4LfIGTOMIv/N6N63NgGbAB/8wfFmgNsM+dXiafiBTwwfQygaa6P8kWeBRHKDwKjAkY9/Y3cI+H8RgPWAXOGH/qCNQDaqvqLwAichHwjog8paoDvAzOZGkpcJmIjFXVdllsl9VrZ7uY5MobgKr+7ZPsqMmiVDW58oaq7heRfF4GFAJ+zNIYKEpVn/Y6iFDIYHqZ8/HH9DIp3Myo53BibN/3qno0q33Chap+g5OJOaV7r4h0BNZnuaPxFctCaYw//Qen//8vyStU9Wfgbvc1c/Y6R0TuAeqKSMu0P8kbqepGD2M8U8dFpFTygjtmzE/9+Q+ISEr3VhGpSZhnMMyA77I0pjFWRB4SkfNEpGjyj9dBBcnjOA/49oIzvQz+SiKEiDQCfgSGAEOBH0SkQVb7hBtV3RswNtOXDxtM5qwFzhh/yq2qO9OuVNVEEQnrNPs5wKM4kyIX5sS8TckUf8zV9DwQLyIL3eUGOC0CftERmCIiv7vL5wGtvAsnJPzejesI8AbOsZr8cEHxx7xbh1X1iNPT3j/Ty6TRD7hWVb8HEJHyOElbanoaVeiEbZd68+9YBc4YfzryL18zHnMHpseLyCpVfd/reEJBVWe7LVRX4tx4PJXRA4dwpaorRaQiUAGnfJv90n0rWQ7oxvU0zmTlvjkuA+SE6WVyJ1feAFT1B58/vPRbBdychGWhNMaH3IQlBzJ6CWdsh5+/yMJaYDfJjKhq2LbAiUhFVd0c2L0wkKquye6YgklEmqjqvMw+w3D+7E6FiGxR1VIn3/LsJyLTgdZ+yhyazE1y9SA+nl5GREbiJJ8Z665qC0S6E5mHJRHZR8YVNQHyqqo1yuQg9mEb40OqGuF1DOZfS9ttMlC4d6F8BngIp3tTWgo0yd5wgq4hMI+MP8Nw/+xOhZ+6cSXhTMQ+H0jJChvO6echZW7Q9apaBRjhdTwh9CjOWL8OOMflIpyxcGFLVQt4HYM5e1gLnDHGGGPOmM9a4DIaz6eqOibbgwkyERkPdFXVLV7HEgppKqnG+JK1wBljzFlIRIoDrwAlVPV6EakE1AnncXF+7h4KICJZZoJT1f7ZFUuonKwbVzaHEzKqOjpwWUQuAFp7FE6wnQdsEpEVBHS1V9WbvAspeFT1uIh8IyKl/FpJNcYqcMYYc3YahTMx8vPu8g/AJCBsK3D4u3sogO+7OOWkblwiEgPcAbTBmSttatZ7nN1EpBxQHOid5qWGwPbsjyikfF1JNca6UBpjzFlIRFaqam0RWauqNdx161S1usehGeNbIlIAuBW4C2cS6KlAK1Ut6WlgQSAiM4Fuqro+zfpaQE9VzeoBS1gRkYYZrVfVhRmtNybcWAucMcacnQ6ISDHc7moiciWwx9uQgsMtV0+gPk754oE+qrrL08CCREQuAgbiTJOgwFKcqRJ+9jQwcyoSgBVAdyBeVVVEbvU4pmApnbbyBqCqq0SktAfxBJ2IROEkMCkHbADeV9Vj3kZlTPDl8joAY4wxGXoamA6UFZHFwBigvbchBc1EIBG4Dbjd/X2SpxEF14fAZJxuXCWAKTiTCJuzXzcgCngH6CoiZT2OJ5iisnjNL+MXRwO1cCpv15Nxxltjwp51oTTGmLOIiNQGtqrqnyISCTyCU9H5Fuihqn95GmAQiMhqVa2ZZt0qVa3lVUzBJCLLVfWKNOuWqeqVXsVkTo/bitoGJ3HJxTgtxlNV9QdPAzsDIjIBmKeqI9KsfwC4VlVbeRNZ8IjIBlWt6v4eCaxQ1QznnTQmnFkFzhhjziIisga4WlX/EpEGOK1V7YHqwCWqeruX8QWDiLwJrMJppQKnFa6yqvb0LqrgEZFXgd04n50CrYA8wBAAP1TCcxIRqYpTmWulqmHbIudmtp0KHAFWu6trAecAt6rqn17FFiwisiawwpZ22Ri/sAqcMcacRUTkG1W91P19CJCoqr3cZV8kMXFT0ecHjrurcnEiU5yqakFPAgsSEfkli5dVVS/KtmBMSIjIUlWt43Uc/4aINAaS50jbpKrzvIwnmEQkiRPXkuSpLf5xfw/7a4sxySyJiTHGnF0iRCTSHXjfFHg44DVfXLP9nopeVct4HYMJuazGk53VVHU+MN/rOEJBVSO8jsGY7OCLmwFjjPGRCcBCEdkJHAS+hpQ5nHyRhRJSJvVOzkL5tapO8zai4HEz4T1GQPmAd1X1kKeBmWCy7kvGGM9YF0pjjDnLuFMGnAd8oaoH3HXlgWhVXeNpcEEgIkNx0nwnZ2ZsBfxPVR/3LqrgEZHJwD5gnLuqDVBEVe/wLioTTDa2yhjjJavAGWOMyVYisgmoou4XkIjkAjaoamVvIwuOwHGMWa0z4UtE1qpqDa/jMMbkTDYPnDHGmOz2PVAqYPkCIN0Ew2FsrduKCoCIXAEs9jAeE3ztvA7AGJNzWQucMcaYbCUiC4HawAp3VW1gKU62OFT1Jo9CCwoR+Q6oAGxxV5UCvsPJuqmqWs2r2MypccdovgbE4WQwtCyGxpizhlXgjDHGZCsRaRi4iJPsow1O4g9UdaEXcQWLiFyY1euq+lt2xWL+HRH5CWihqt95HYsxxqRlFThjjDHZTkSqA3cBdwK/AJ+o6iBPgwoyEYkjIN28qm7JYnNzFhGRxapaz+s4jDEmIzaNgDHGmGzhZtJsjdPatguYhPMgsbGngQWZiNwE9ANKAAnAhThdKH2RpCWHWCUik4BpwOHklar6iWcRGWOMyypwxhhjsstmnDnRWqjqTwAi8pS3IYXEi8CVwFeqWkNEGuNUWk34KIgzJvPagHUKWAXOGOM5q8AZY4zJLrfhtMDNF5HZwEScMXB+c1RVd4lILhHJparzReQ1r4Myp05V7/M6BmOMyYxV4IwxxmQLVZ0KTBWR/MAtwFNAcRF5B5iqql94GV8Q7RaRaJzWxvEikgAc8zgmcxpEJAp4AKfba+A4xvs9C8oYY1w2D5wxxphspaoHVHW8qjYHSgLrgC7eRhVUNwMHgY7AbOB/QAsvAzKnbSxwLnAdsBDnON3naUTGGOOyLJTGGGNMkIlIcZz57QBWqGqCl/GY0yMia93xi+tVtZqI5AbmqGoTr2MzxhhrgTPGGGOCSETuxJmk/A6caRKWi8jt3kZlTtNR99/dIlIFKASU9i4cY4w5wcbAGWOMMcH1PFA7udVNRGKBr4CPPI3KnI7hIlIEeAGYDkS7vxtjjOesC6UxxhgTRCKyQVWrBiznAr4JXGeMMcb8W9YCZ4wxxgTXbBGZA0xwl1sBn3sYjzlNIlII6AVc5a5aALyoqnu8iskYY5JZC5wxxhgTBCJSDiiuqotFpCVQH2eeu7+B8ar6P08DNKdMRD4GNgKj3VXtgEtVtaV3URljjMMqcMYYY0wQiMhMoJuqrk+zvhbQU1VtKoEwISLrVLX6ydYZY4wXLAulMcYYExyl01beAFR1FZbBMNwcFJH6yQsiUg9nbj9jjPGcjYEzxhhjgiMqi9fyZlsUJhgeBca4Y+HA6QZ7j4fxGGNMCmuBM8YYY4JjpYg8lHaliDwArPYgHvMvqeo3qnopUA2opqo1AJvE2xhzVrAxcMYYY0wQiEhxYCpwhBMVtlrAOcCtqvqnV7GZMyciW1S1lNdxGGOMVeCMMcaYIBKRxkAVd3GTqs7zMh4THCKyVVUv8DoOY4yxCpwxxhhjzElYC5wx5mxhSUyMMcYYYwAR2Qdk9GRbsEQ0xpizhLXAGWOMMcYYY0yYsCyUxhhjjDHGGBMmrAJnjDHGGGOMMWHCKnDGGGOMMcYYEyasAmeMMcYYY4wxYcIqcMYYY4wxxhgTJv4PMmo4cWyh5x4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "corr = df.corr()\n", + "plt.figure(figsize=(15,10))\n", + "sns.heatmap(corr, annot = True, cmap=\"BuPu\")" + ] + }, + { + "cell_type": "markdown", + "id": "5436fd75", + "metadata": {}, + "source": [ + "# Train-Test Split" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "66f0fbcc", + "metadata": {}, + "outputs": [], + "source": [ + "X = df.drop(columns=['Loan_Status','Loan_ID'], axis=1)\n", + "y = df['Loan_Status']" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3b8f10a9", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "id": "a982a051", + "metadata": {}, + "source": [ + "# feature scalling" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "c78202d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.48637346, 0.76564149, 1.2636708 , ..., 0.30785991,\n", + " 0.44770618, 1.21879102],\n", + " [ 0.48637346, -1.30609431, -0.72024923, ..., 0.30785991,\n", + " 0.44770618, -1.33539518],\n", + " [ 0.48637346, 0.76564149, 2.25563082, ..., -4.12593972,\n", + " 0.44770618, 1.21879102],\n", + " ...,\n", + " [-2.05603325, -1.30609431, -0.72024923, ..., 0.30785991,\n", + " 0.44770618, 1.21879102],\n", + " [-2.05603325, 0.76564149, -0.72024923, ..., -1.46565994,\n", + " 0.44770618, -0.05830208],\n", + " [ 0.48637346, 0.76564149, -0.72024923, ..., 0.30785991,\n", + " 0.44770618, 1.21879102]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "sc = StandardScaler()\n", + "\n", + "x_train = sc.fit_transform(x_train)\n", + "x_test = sc.fit_transform(x_test)\n", + "x_train" + ] + }, + { + "cell_type": "markdown", + "id": "d0d18c8f", + "metadata": {}, + "source": [ + "# Model Training¶" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "d926d452", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from sklearn.model_selection import cross_val_score\n", + "def classify(model, x, y):\n", + " x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n", + " model.fit(x_train, y_train)\n", + " print(\"Accuracy is\", model.score(x_test, y_test)*100)\n", + " # cross validation - it is used for better validation of model\n", + " # eg: cv-5, train-4, test-1\n", + " score = cross_val_score(model, x, y, cv=5)\n", + " print(\"Cross validation is\",np.mean(score)*100)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "885518f5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy is 77.92207792207793\n", + "Cross validation is 80.29321604691458\n" + ] + } + ], + "source": [ + "\n", + "from sklearn.linear_model import LogisticRegression\n", + "model = LogisticRegression()\n", + "classify(model, X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "c5f7b476", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy is 66.88311688311688\n", + "Cross validation is 70.03598560575769\n" + ] + } + ], + "source": [ + "\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "model = DecisionTreeClassifier()\n", + "classify(model, X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "3c9e4e6e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy is 74.67532467532467\n", + "Cross validation is 78.01812608290018\n" + ] + } + ], + "source": [ + "\n", + "from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier\n", + "model = RandomForestClassifier()\n", + "classify(model, X, y)" + ] + }, + { + "cell_type": "markdown", + "id": "1cff89f0", + "metadata": {}, + "source": [ + "# Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "d06e5877", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestClassifier()" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = RandomForestClassifier()\n", + "model.fit(x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "f02f95c0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[23, 31],\n", + " [ 5, 95]], dtype=int64)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "y_pred = model.predict(x_test)\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "cm" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "7563fd75", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.heatmap(cm, annot=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96645eb1", + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 4a759b5e3eefc1c7d00d1046c8a380a0cb939b44 Mon Sep 17 00:00:00 2001 From: tanvideshmukh29 <70129990+tanvideshmukh29@users.noreply.github.com> Date: Sat, 23 Oct 2021 23:45:45 +0530 Subject: [PATCH 4/9] Add files via upload --- Projects/Loan Prediction/data.csv | 615 ++++++++++++++++++++++++++++++ 1 file changed, 615 insertions(+) create mode 100644 Projects/Loan Prediction/data.csv diff --git a/Projects/Loan Prediction/data.csv b/Projects/Loan Prediction/data.csv new file mode 100644 index 0000000..5dce665 --- /dev/null +++ b/Projects/Loan Prediction/data.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 From 6b41c74810a45fbcf4c9d6b90e4b5aeb5b65b1b0 Mon Sep 17 00:00:00 2001 From: tanvideshmukh29 <70129990+tanvideshmukh29@users.noreply.github.com> Date: Sat, 23 Oct 2021 23:46:09 +0530 Subject: [PATCH 5/9] Delete deleteme.html --- Projects/Loan Prediction/deleteme.html | 1 - 1 file changed, 1 deletion(-) delete mode 100644 Projects/Loan Prediction/deleteme.html diff --git a/Projects/Loan Prediction/deleteme.html b/Projects/Loan Prediction/deleteme.html deleted file mode 100644 index 8b13789..0000000 --- a/Projects/Loan Prediction/deleteme.html +++ /dev/null @@ -1 +0,0 @@ - From 5fbc5d27f95efd56e88680af93117ccd511e94e7 Mon Sep 17 00:00:00 2001 From: tanvideshmukh29 <70129990+tanvideshmukh29@users.noreply.github.com> Date: Sat, 23 Oct 2021 23:48:58 +0530 Subject: [PATCH 6/9] Delete data.csv --- Projects/data.csv | 615 ---------------------------------------------- 1 file changed, 615 deletions(-) delete mode 100644 Projects/data.csv diff --git a/Projects/data.csv b/Projects/data.csv deleted file mode 100644 index 5dce665..0000000 --- a/Projects/data.csv +++ /dev/null @@ -1,615 +0,0 @@ -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 From 0ec6dc256a8be4223b086c8115bbf03a7e799ae8 Mon Sep 17 00:00:00 2001 From: tanvideshmukh29 <70129990+tanvideshmukh29@users.noreply.github.com> Date: Sat, 23 Oct 2021 23:58:30 +0530 Subject: [PATCH 7/9] Add files via upload --- .../Loan Prediction/Loan _Prediction.ipynb | 93 +++++++++---------- 1 file changed, 43 insertions(+), 50 deletions(-) diff --git a/Projects/Loan Prediction/Loan _Prediction.ipynb b/Projects/Loan Prediction/Loan _Prediction.ipynb index 7420dc5..a28a223 100644 --- a/Projects/Loan Prediction/Loan _Prediction.ipynb +++ b/Projects/Loan Prediction/Loan _Prediction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "0df672da", + "id": "6a6d5d28", "metadata": {}, "source": [ "# Loan Prediction\n" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "df0d3360", + "id": "bd7ced3f", "metadata": {}, "source": [ "# Import modules" @@ -18,8 +18,8 @@ }, { "cell_type": "code", - "execution_count": 55, - "id": "1cd2aa68", + "execution_count": 64, + "id": "2ff226af", "metadata": {}, "outputs": [], "source": [ @@ -32,24 +32,17 @@ }, { "cell_type": "markdown", - "id": "69e1c076", + "id": "68ec3c84", "metadata": {}, "source": [ "# Dataset:\n", + "https://github.com/gdsc-gvp/Machine-learning/blob/5fbc5d27f95efd56e88680af93117ccd511e94e7/Projects/Loan%20Prediction/data.csv\n", " " ] }, { "cell_type": "markdown", - "id": "d831a0ea", - "metadata": {}, - "source": [ - "https://github.com/tanvideshmukh29/Machine-learning/blob/tanvideshmukh29-patch-1/Projects/Loan%20_Prediction/loan.csv" - ] - }, - { - "cell_type": "markdown", - "id": "6c2f5dc4", + "id": "7602e217", "metadata": {}, "source": [ "# Loading the dataset" @@ -58,7 +51,7 @@ { "cell_type": "code", "execution_count": 56, - "id": "4c273e9a", + "id": "3a8b1c71", "metadata": { "scrolled": true }, @@ -336,7 +329,7 @@ }, { "cell_type": "markdown", - "id": "3340f37c", + "id": "608846f3", "metadata": {}, "source": [ "# Preprocessing the dataset" @@ -345,7 +338,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "c28c2a7f", + "id": "673c855d", "metadata": {}, "outputs": [ { @@ -379,7 +372,7 @@ { "cell_type": "code", "execution_count": 61, - "id": "9ca20f7d", + "id": "2a32c5b7", "metadata": {}, "outputs": [ { @@ -413,7 +406,7 @@ { "cell_type": "code", "execution_count": 58, - "id": "6041f1c6", + "id": "758334fc", "metadata": {}, "outputs": [ { @@ -449,7 +442,7 @@ }, { "cell_type": "markdown", - "id": "20638be8", + "id": "eecbe8c9", "metadata": {}, "source": [ "# Numerical value" @@ -458,7 +451,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "9dc1a71e", + "id": "20c640e9", "metadata": {}, "outputs": [ { @@ -482,7 +475,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "17f6b57d", + "id": "afccf643", "metadata": {}, "outputs": [], "source": [ @@ -493,7 +486,7 @@ }, { "cell_type": "markdown", - "id": "5fe8c1a6", + "id": "7520a672", "metadata": {}, "source": [ "# categorical value" @@ -502,7 +495,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "abdfe443", + "id": "61c76525", "metadata": {}, "outputs": [ { @@ -526,7 +519,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "bf123915", + "id": "0c97c613", "metadata": {}, "outputs": [], "source": [ @@ -539,7 +532,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "955ea61c", + "id": "8a543e5f", "metadata": {}, "outputs": [ { @@ -573,7 +566,7 @@ { "cell_type": "code", "execution_count": 62, - "id": "23545a68", + "id": "c226d686", "metadata": {}, "outputs": [ { @@ -606,7 +599,7 @@ }, { "cell_type": "markdown", - "id": "70f87d68", + "id": "e2280296", "metadata": {}, "source": [ "# Exploratory Data Analysis" @@ -615,7 +608,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "97987837", + "id": "ac4a52e3", "metadata": {}, "outputs": [ { @@ -689,7 +682,7 @@ { "cell_type": "code", "execution_count": 60, - "id": "12169d13", + "id": "90ba60c4", "metadata": { "scrolled": false }, @@ -791,7 +784,7 @@ }, { "cell_type": "markdown", - "id": "88b6aa1a", + "id": "6ec6fb67", "metadata": {}, "source": [ "# Label Encoding" @@ -800,7 +793,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "0f6df9f4", + "id": "4482271c", "metadata": {}, "outputs": [], "source": [ @@ -814,7 +807,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "0da4a701", + "id": "23d24bea", "metadata": { "scrolled": false }, @@ -975,7 +968,7 @@ }, { "cell_type": "markdown", - "id": "1ef4e9e0", + "id": "1452b401", "metadata": {}, "source": [ "# Coorelation Matrix" @@ -984,7 +977,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "e2100cf3", + "id": "ba1f4e8e", "metadata": {}, "outputs": [ { @@ -1018,7 +1011,7 @@ }, { "cell_type": "markdown", - "id": "5436fd75", + "id": "787f0930", "metadata": {}, "source": [ "# Train-Test Split" @@ -1027,7 +1020,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "66f0fbcc", + "id": "8b72241f", "metadata": {}, "outputs": [], "source": [ @@ -1038,7 +1031,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "3b8f10a9", + "id": "64ac404a", "metadata": {}, "outputs": [], "source": [ @@ -1048,7 +1041,7 @@ }, { "cell_type": "markdown", - "id": "a982a051", + "id": "924a1ea3", "metadata": {}, "source": [ "# feature scalling" @@ -1057,7 +1050,7 @@ { "cell_type": "code", "execution_count": 40, - "id": "c78202d7", + "id": "24015fba", "metadata": {}, "outputs": [ { @@ -1095,7 +1088,7 @@ }, { "cell_type": "markdown", - "id": "d0d18c8f", + "id": "218e650b", "metadata": {}, "source": [ "# Model Training¶" @@ -1104,7 +1097,7 @@ { "cell_type": "code", "execution_count": 48, - "id": "d926d452", + "id": "7e96b1e1", "metadata": {}, "outputs": [], "source": [ @@ -1123,7 +1116,7 @@ { "cell_type": "code", "execution_count": 49, - "id": "885518f5", + "id": "c84c6392", "metadata": {}, "outputs": [ { @@ -1145,7 +1138,7 @@ { "cell_type": "code", "execution_count": 50, - "id": "c5f7b476", + "id": "433e6571", "metadata": {}, "outputs": [ { @@ -1167,7 +1160,7 @@ { "cell_type": "code", "execution_count": 51, - "id": "3c9e4e6e", + "id": "12754ced", "metadata": {}, "outputs": [ { @@ -1188,7 +1181,7 @@ }, { "cell_type": "markdown", - "id": "1cff89f0", + "id": "ba4fe585", "metadata": {}, "source": [ "# Confusion Matrix" @@ -1197,7 +1190,7 @@ { "cell_type": "code", "execution_count": 52, - "id": "d06e5877", + "id": "e5d0e9fb", "metadata": {}, "outputs": [ { @@ -1219,7 +1212,7 @@ { "cell_type": "code", "execution_count": 53, - "id": "f02f95c0", + "id": "7a93165d", "metadata": {}, "outputs": [ { @@ -1244,7 +1237,7 @@ { "cell_type": "code", "execution_count": 54, - "id": "7563fd75", + "id": "37310e76", "metadata": {}, "outputs": [ { @@ -1277,7 +1270,7 @@ { "cell_type": "code", "execution_count": null, - "id": "96645eb1", + "id": "df76473e", "metadata": {}, "outputs": [], "source": [] From 855c30e8155d8271c4ffebf8eacdfa0b8cd72a99 Mon Sep 17 00:00:00 2001 From: tanvideshmukh29 <70129990+tanvideshmukh29@users.noreply.github.com> Date: Sun, 24 Oct 2021 00:35:10 +0530 Subject: [PATCH 8/9] Add files via upload --- .../Loan Prediction/Loan _Prediction.ipynb | 158 ++++++++++-------- 1 file changed, 86 insertions(+), 72 deletions(-) diff --git a/Projects/Loan Prediction/Loan _Prediction.ipynb b/Projects/Loan Prediction/Loan _Prediction.ipynb index a28a223..451b66d 100644 --- a/Projects/Loan Prediction/Loan _Prediction.ipynb +++ b/Projects/Loan Prediction/Loan _Prediction.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 1, "id": "2ff226af", "metadata": {}, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 2, "id": "3a8b1c71", "metadata": { "scrolled": true @@ -317,13 +317,13 @@ "[614 rows x 13 columns]" ] }, - "execution_count": 56, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df = pd.read_csv('C:/Users/deshm/Desktop/dataml/loan.csv')\n", + "df = pd.read_csv('C:/Users/deshm/Desktop/dataml/data.csv')\n", "df" ] }, @@ -337,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "673c855d", "metadata": {}, "outputs": [ @@ -360,7 +360,7 @@ "dtype: int64" ] }, - "execution_count": 6, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -371,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 4, "id": "2a32c5b7", "metadata": {}, "outputs": [ @@ -381,7 +381,7 @@ "" ] }, - "execution_count": 61, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, @@ -405,7 +405,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 5, "id": "758334fc", "metadata": {}, "outputs": [ @@ -450,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "id": "20c640e9", "metadata": {}, "outputs": [ @@ -462,7 +462,7 @@ " dtype='object')" ] }, - "execution_count": 16, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -474,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "id": "afccf643", "metadata": {}, "outputs": [], @@ -494,7 +494,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "id": "61c76525", "metadata": {}, "outputs": [ @@ -506,7 +506,7 @@ " dtype='object')" ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -518,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "id": "0c97c613", "metadata": {}, "outputs": [], @@ -531,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "id": "8a543e5f", "metadata": {}, "outputs": [ @@ -554,7 +554,7 @@ "dtype: int64" ] }, - "execution_count": 23, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -565,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 11, "id": "c226d686", "metadata": {}, "outputs": [ @@ -575,13 +575,13 @@ "" ] }, - "execution_count": 62, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -607,10 +607,26 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 12, "id": "ac4a52e3", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\deshm\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\deshm\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\deshm\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\deshm\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n", + "C:\\Users\\deshm\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, { "data": { "image/png": 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\n", @@ -681,15 +697,35 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 13, "id": "90ba60c4", "metadata": { "scrolled": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\deshm\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + " warnings.warn(\n", + "C:\\Users\\deshm\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + " warnings.warn(\n", + "C:\\Users\\deshm\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + " warnings.warn(\n", + "C:\\Users\\deshm\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + " warnings.warn(\n", + "C:\\Users\\deshm\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + " warnings.warn(\n", + "C:\\Users\\deshm\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + " warnings.warn(\n", + "C:\\Users\\deshm\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n", + " warnings.warn(\n" + ] + }, { "data": { - "image/png": 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" ] @@ -701,7 +737,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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"text/plain": [ "
" ] @@ -792,7 +828,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 14, "id": "4482271c", "metadata": {}, "outputs": [], @@ -806,7 +842,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 15, "id": "23d24bea", "metadata": { "scrolled": false @@ -956,7 +992,7 @@ "4 1.0 2 1 " ] }, - "execution_count": 30, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -976,7 +1012,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 16, "id": "ba1f4e8e", "metadata": {}, "outputs": [ @@ -986,7 +1022,7 @@ "" ] }, - "execution_count": 33, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, @@ -1019,7 +1055,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 17, "id": "8b72241f", "metadata": {}, "outputs": [], @@ -1030,7 +1066,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 18, "id": "64ac404a", "metadata": {}, "outputs": [], @@ -1049,7 +1085,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 19, "id": "24015fba", "metadata": {}, "outputs": [ @@ -1071,7 +1107,7 @@ " 0.44770618, 1.21879102]])" ] }, - "execution_count": 40, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1096,7 +1132,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 20, "id": "7e96b1e1", "metadata": {}, "outputs": [], @@ -1115,29 +1151,7 @@ }, { "cell_type": "code", - "execution_count": 49, - "id": "c84c6392", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy is 77.92207792207793\n", - "Cross validation is 80.29321604691458\n" - ] - } - ], - "source": [ - "\n", - "from sklearn.linear_model import LogisticRegression\n", - "model = LogisticRegression()\n", - "classify(model, X, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, + "execution_count": 29, "id": "433e6571", "metadata": {}, "outputs": [ @@ -1145,8 +1159,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy is 66.88311688311688\n", - "Cross validation is 70.03598560575769\n" + "Accuracy is 67.53246753246754\n", + "Cross validation is 70.84766093562574\n" ] } ], @@ -1159,7 +1173,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 30, "id": "12754ced", "metadata": {}, "outputs": [ @@ -1167,8 +1181,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy is 74.67532467532467\n", - "Cross validation is 78.01812608290018\n" + "Accuracy is 77.92207792207793\n", + "Cross validation is 78.17939490870319\n" ] } ], @@ -1189,7 +1203,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 24, "id": "e5d0e9fb", "metadata": {}, "outputs": [ @@ -1199,7 +1213,7 @@ "RandomForestClassifier()" ] }, - "execution_count": 52, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1211,7 +1225,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 25, "id": "7a93165d", "metadata": {}, "outputs": [ @@ -1219,10 +1233,10 @@ "data": { "text/plain": [ "array([[23, 31],\n", - " [ 5, 95]], dtype=int64)" + " [ 4, 96]], dtype=int64)" ] }, - "execution_count": 53, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1236,7 +1250,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 26, "id": "37310e76", "metadata": {}, "outputs": [ @@ -1246,13 +1260,13 @@ "" ] }, - "execution_count": 54, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] From 6c66af6f605d745a72e6353c1aa977e212db64d6 Mon Sep 17 00:00:00 2001 From: tanvideshmukh29 <70129990+tanvideshmukh29@users.noreply.github.com> Date: Sun, 24 Oct 2021 00:38:44 +0530 Subject: [PATCH 9/9] Add files via upload --- .../Loan Prediction/Loan _Prediction.ipynb | 37 +++++++++---------- 1 file changed, 18 insertions(+), 19 deletions(-) diff --git a/Projects/Loan Prediction/Loan _Prediction.ipynb b/Projects/Loan Prediction/Loan _Prediction.ipynb index 451b66d..f7adeab 100644 --- a/Projects/Loan Prediction/Loan _Prediction.ipynb +++ b/Projects/Loan Prediction/Loan _Prediction.ipynb @@ -1127,12 +1127,12 @@ "id": "218e650b", "metadata": {}, "source": [ - "# Model Training¶" + "# Model Training" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 31, "id": "7e96b1e1", "metadata": {}, "outputs": [], @@ -1143,15 +1143,14 @@ " x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n", " model.fit(x_train, y_train)\n", " print(\"Accuracy is\", model.score(x_test, y_test)*100)\n", - " # cross validation - it is used for better validation of model\n", - " # eg: cv-5, train-4, test-1\n", + " \n", " score = cross_val_score(model, x, y, cv=5)\n", " print(\"Cross validation is\",np.mean(score)*100)" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 32, "id": "433e6571", "metadata": {}, "outputs": [ @@ -1159,8 +1158,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy is 67.53246753246754\n", - "Cross validation is 70.84766093562574\n" + "Accuracy is 69.48051948051948\n", + "Cross validation is 70.03598560575769\n" ] } ], @@ -1173,7 +1172,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 33, "id": "12754ced", "metadata": {}, "outputs": [ @@ -1181,8 +1180,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy is 77.92207792207793\n", - "Cross validation is 78.17939490870319\n" + "Accuracy is 76.62337662337663\n", + "Cross validation is 79.31627349060376\n" ] } ], @@ -1203,7 +1202,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 34, "id": "e5d0e9fb", "metadata": {}, "outputs": [ @@ -1213,7 +1212,7 @@ "RandomForestClassifier()" ] }, - "execution_count": 24, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1225,18 +1224,18 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 35, "id": "7a93165d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[23, 31],\n", - " [ 4, 96]], dtype=int64)" + "array([[24, 30],\n", + " [ 5, 95]], dtype=int64)" ] }, - "execution_count": 25, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1250,7 +1249,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 36, "id": "37310e76", "metadata": {}, "outputs": [ @@ -1260,13 +1259,13 @@ "" ] }, - "execution_count": 26, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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