diff --git a/.DS_Store b/.DS_Store
index 4e5ac95..2e44645 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/R/.DS_Store b/R/.DS_Store
new file mode 100644
index 0000000..c7a9639
Binary files /dev/null and b/R/.DS_Store differ
diff --git a/R/Credit Rating/Credit.csv b/R/Credit Rating/Credit.csv
new file mode 100755
index 0000000..9208862
--- /dev/null
+++ b/R/Credit Rating/Credit.csv	
@@ -0,0 +1,401 @@
+,Income,Limit,Rating,Cards,Age,Education,Gender,Student,Married,Ethnicity,Balance
+1,14.891,3606,283,2,34,11, Male,No,Yes,Caucasian,333
+2,106.025,6645,483,3,82,15,Female,Yes,Yes,Asian,903
+3,104.593,7075,514,4,71,11, Male,No,No,Asian,580
+4,148.924,9504,681,3,36,11,Female,No,No,Asian,964
+5,55.882,4897,357,2,68,16, Male,No,Yes,Caucasian,331
+6,80.18,8047,569,4,77,10, Male,No,No,Caucasian,1151
+7,20.996,3388,259,2,37,12,Female,No,No,African American,203
+8,71.408,7114,512,2,87,9, Male,No,No,Asian,872
+9,15.125,3300,266,5,66,13,Female,No,No,Caucasian,279
+10,71.061,6819,491,3,41,19,Female,Yes,Yes,African American,1350
+11,63.095,8117,589,4,30,14, Male,No,Yes,Caucasian,1407
+12,15.045,1311,138,3,64,16, Male,No,No,Caucasian,0
+13,80.616,5308,394,1,57,7,Female,No,Yes,Asian,204
+14,43.682,6922,511,1,49,9, Male,No,Yes,Caucasian,1081
+15,19.144,3291,269,2,75,13,Female,No,No,African American,148
+16,20.089,2525,200,3,57,15,Female,No,Yes,African American,0
+17,53.598,3714,286,3,73,17,Female,No,Yes,African American,0
+18,36.496,4378,339,3,69,15,Female,No,Yes,Asian,368
+19,49.57,6384,448,1,28,9,Female,No,Yes,Asian,891
+20,42.079,6626,479,2,44,9, Male,No,No,Asian,1048
+21,17.7,2860,235,4,63,16,Female,No,No,Asian,89
+22,37.348,6378,458,1,72,17,Female,No,No,Caucasian,968
+23,20.103,2631,213,3,61,10, Male,No,Yes,African American,0
+24,64.027,5179,398,5,48,8, Male,No,Yes,African American,411
+25,10.742,1757,156,3,57,15,Female,No,No,Caucasian,0
+26,14.09,4323,326,5,25,16,Female,No,Yes,African American,671
+27,42.471,3625,289,6,44,12,Female,Yes,No,Caucasian,654
+28,32.793,4534,333,2,44,16, Male,No,No,African American,467
+29,186.634,13414,949,2,41,14,Female,No,Yes,African American,1809
+30,26.813,5611,411,4,55,16,Female,No,No,Caucasian,915
+31,34.142,5666,413,4,47,5,Female,No,Yes,Caucasian,863
+32,28.941,2733,210,5,43,16, Male,No,Yes,Asian,0
+33,134.181,7838,563,2,48,13,Female,No,No,Caucasian,526
+34,31.367,1829,162,4,30,10, Male,No,Yes,Caucasian,0
+35,20.15,2646,199,2,25,14,Female,No,Yes,Asian,0
+36,23.35,2558,220,3,49,12,Female,Yes,No,Caucasian,419
+37,62.413,6457,455,2,71,11,Female,No,Yes,Caucasian,762
+38,30.007,6481,462,2,69,9,Female,No,Yes,Caucasian,1093
+39,11.795,3899,300,4,25,10,Female,No,No,Caucasian,531
+40,13.647,3461,264,4,47,14, Male,No,Yes,Caucasian,344
+41,34.95,3327,253,3,54,14,Female,No,No,African American,50
+42,113.659,7659,538,2,66,15, Male,Yes,Yes,African American,1155
+43,44.158,4763,351,2,66,13,Female,No,Yes,Asian,385
+44,36.929,6257,445,1,24,14,Female,No,Yes,Asian,976
+45,31.861,6375,469,3,25,16,Female,No,Yes,Caucasian,1120
+46,77.38,7569,564,3,50,12,Female,No,Yes,Caucasian,997
+47,19.531,5043,376,2,64,16,Female,Yes,Yes,Asian,1241
+48,44.646,4431,320,2,49,15, Male,Yes,Yes,Caucasian,797
+49,44.522,2252,205,6,72,15, Male,No,Yes,Asian,0
+50,43.479,4569,354,4,49,13, Male,Yes,Yes,African American,902
+51,36.362,5183,376,3,49,15, Male,No,Yes,African American,654
+52,39.705,3969,301,2,27,20, Male,No,Yes,African American,211
+53,44.205,5441,394,1,32,12, Male,No,Yes,Caucasian,607
+54,16.304,5466,413,4,66,10, Male,No,Yes,Asian,957
+55,15.333,1499,138,2,47,9,Female,No,Yes,Asian,0
+56,32.916,1786,154,2,60,8,Female,No,Yes,Asian,0
+57,57.1,4742,372,7,79,18,Female,No,Yes,Asian,379
+58,76.273,4779,367,4,65,14,Female,No,Yes,Caucasian,133
+59,10.354,3480,281,2,70,17, Male,No,Yes,Caucasian,333
+60,51.872,5294,390,4,81,17,Female,No,No,Caucasian,531
+61,35.51,5198,364,2,35,20,Female,No,No,Asian,631
+62,21.238,3089,254,3,59,10,Female,No,No,Caucasian,108
+63,30.682,1671,160,2,77,7,Female,No,No,Caucasian,0
+64,14.132,2998,251,4,75,17, Male,No,No,Caucasian,133
+65,32.164,2937,223,2,79,15,Female,No,Yes,African American,0
+66,12,4160,320,4,28,14,Female,No,Yes,Caucasian,602
+67,113.829,9704,694,4,38,13,Female,No,Yes,Asian,1388
+68,11.187,5099,380,4,69,16,Female,No,No,African American,889
+69,27.847,5619,418,2,78,15,Female,No,Yes,Caucasian,822
+70,49.502,6819,505,4,55,14, Male,No,Yes,Caucasian,1084
+71,24.889,3954,318,4,75,12, Male,No,Yes,Caucasian,357
+72,58.781,7402,538,2,81,12,Female,No,Yes,Asian,1103
+73,22.939,4923,355,1,47,18,Female,No,Yes,Asian,663
+74,23.989,4523,338,4,31,15, Male,No,No,Caucasian,601
+75,16.103,5390,418,4,45,10,Female,No,Yes,Caucasian,945
+76,33.017,3180,224,2,28,16, Male,No,Yes,African American,29
+77,30.622,3293,251,1,68,16, Male,Yes,No,Caucasian,532
+78,20.936,3254,253,1,30,15,Female,No,No,Asian,145
+79,110.968,6662,468,3,45,11,Female,No,Yes,Caucasian,391
+80,15.354,2101,171,2,65,14, Male,No,No,Asian,0
+81,27.369,3449,288,3,40,9,Female,No,Yes,Caucasian,162
+82,53.48,4263,317,1,83,15, Male,No,No,Caucasian,99
+83,23.672,4433,344,3,63,11, Male,No,No,Caucasian,503
+84,19.225,1433,122,3,38,14,Female,No,No,Caucasian,0
+85,43.54,2906,232,4,69,11, Male,No,No,Caucasian,0
+86,152.298,12066,828,4,41,12,Female,No,Yes,Asian,1779
+87,55.367,6340,448,1,33,15, Male,No,Yes,Caucasian,815
+88,11.741,2271,182,4,59,12,Female,No,No,Asian,0
+89,15.56,4307,352,4,57,8, Male,No,Yes,African American,579
+90,59.53,7518,543,3,52,9,Female,No,No,African American,1176
+91,20.191,5767,431,4,42,16, Male,No,Yes,African American,1023
+92,48.498,6040,456,3,47,16, Male,No,Yes,Caucasian,812
+93,30.733,2832,249,4,51,13, Male,No,No,Caucasian,0
+94,16.479,5435,388,2,26,16, Male,No,No,African American,937
+95,38.009,3075,245,3,45,15,Female,No,No,African American,0
+96,14.084,855,120,5,46,17,Female,No,Yes,African American,0
+97,14.312,5382,367,1,59,17, Male,Yes,No,Asian,1380
+98,26.067,3388,266,4,74,17,Female,No,Yes,African American,155
+99,36.295,2963,241,2,68,14,Female,Yes,No,African American,375
+100,83.851,8494,607,5,47,18, Male,No,No,Caucasian,1311
+101,21.153,3736,256,1,41,11, Male,No,No,Caucasian,298
+102,17.976,2433,190,3,70,16,Female,Yes,No,Caucasian,431
+103,68.713,7582,531,2,56,16, Male,Yes,No,Caucasian,1587
+104,146.183,9540,682,6,66,15, Male,No,No,Caucasian,1050
+105,15.846,4768,365,4,53,12,Female,No,No,Caucasian,745
+106,12.031,3182,259,2,58,18,Female,No,Yes,Caucasian,210
+107,16.819,1337,115,2,74,15, Male,No,Yes,Asian,0
+108,39.11,3189,263,3,72,12, Male,No,No,Asian,0
+109,107.986,6033,449,4,64,14, Male,No,Yes,Caucasian,227
+110,13.561,3261,279,5,37,19, Male,No,Yes,Asian,297
+111,34.537,3271,250,3,57,17,Female,No,Yes,Asian,47
+112,28.575,2959,231,2,60,11,Female,No,No,African American,0
+113,46.007,6637,491,4,42,14, Male,No,Yes,Caucasian,1046
+114,69.251,6386,474,4,30,12,Female,No,Yes,Asian,768
+115,16.482,3326,268,4,41,15, Male,No,No,Caucasian,271
+116,40.442,4828,369,5,81,8,Female,No,No,African American,510
+117,35.177,2117,186,3,62,16,Female,No,No,Caucasian,0
+118,91.362,9113,626,1,47,17, Male,No,Yes,Asian,1341
+119,27.039,2161,173,3,40,17,Female,No,No,Caucasian,0
+120,23.012,1410,137,3,81,16, Male,No,No,Caucasian,0
+121,27.241,1402,128,2,67,15,Female,No,Yes,Asian,0
+122,148.08,8157,599,2,83,13, Male,No,Yes,Caucasian,454
+123,62.602,7056,481,1,84,11,Female,No,No,Caucasian,904
+124,11.808,1300,117,3,77,14,Female,No,No,African American,0
+125,29.564,2529,192,1,30,12,Female,No,Yes,Caucasian,0
+126,27.578,2531,195,1,34,15,Female,No,Yes,Caucasian,0
+127,26.427,5533,433,5,50,15,Female,Yes,Yes,Asian,1404
+128,57.202,3411,259,3,72,11,Female,No,No,Caucasian,0
+129,123.299,8376,610,2,89,17, Male,Yes,No,African American,1259
+130,18.145,3461,279,3,56,15, Male,No,Yes,African American,255
+131,23.793,3821,281,4,56,12,Female,Yes,Yes,African American,868
+132,10.726,1568,162,5,46,19, Male,No,Yes,Asian,0
+133,23.283,5443,407,4,49,13, Male,No,Yes,African American,912
+134,21.455,5829,427,4,80,12,Female,No,Yes,African American,1018
+135,34.664,5835,452,3,77,15,Female,No,Yes,African American,835
+136,44.473,3500,257,3,81,16,Female,No,No,African American,8
+137,54.663,4116,314,2,70,8,Female,No,No,African American,75
+138,36.355,3613,278,4,35,9, Male,No,Yes,Asian,187
+139,21.374,2073,175,2,74,11,Female,No,Yes,Caucasian,0
+140,107.841,10384,728,3,87,7, Male,No,No,African American,1597
+141,39.831,6045,459,3,32,12,Female,Yes,Yes,African American,1425
+142,91.876,6754,483,2,33,10, Male,No,Yes,Caucasian,605
+143,103.893,7416,549,3,84,17, Male,No,No,Asian,669
+144,19.636,4896,387,3,64,10,Female,No,No,African American,710
+145,17.392,2748,228,3,32,14, Male,No,Yes,Caucasian,68
+146,19.529,4673,341,2,51,14, Male,No,No,Asian,642
+147,17.055,5110,371,3,55,15,Female,No,Yes,Caucasian,805
+148,23.857,1501,150,3,56,16, Male,No,Yes,Caucasian,0
+149,15.184,2420,192,2,69,11,Female,No,Yes,Caucasian,0
+150,13.444,886,121,5,44,10, Male,No,Yes,Asian,0
+151,63.931,5728,435,3,28,14,Female,No,Yes,African American,581
+152,35.864,4831,353,3,66,13,Female,No,Yes,Caucasian,534
+153,41.419,2120,184,4,24,11,Female,Yes,No,Caucasian,156
+154,92.112,4612,344,3,32,17, Male,No,No,Caucasian,0
+155,55.056,3155,235,2,31,16, Male,No,Yes,African American,0
+156,19.537,1362,143,4,34,9,Female,No,Yes,Asian,0
+157,31.811,4284,338,5,75,13,Female,No,Yes,Caucasian,429
+158,56.256,5521,406,2,72,16,Female,Yes,Yes,Caucasian,1020
+159,42.357,5550,406,2,83,12,Female,No,Yes,Asian,653
+160,53.319,3000,235,3,53,13, Male,No,No,Asian,0
+161,12.238,4865,381,5,67,11,Female,No,No,Caucasian,836
+162,31.353,1705,160,3,81,14, Male,No,Yes,Caucasian,0
+163,63.809,7530,515,1,56,12, Male,No,Yes,Caucasian,1086
+164,13.676,2330,203,5,80,16,Female,No,No,African American,0
+165,76.782,5977,429,4,44,12, Male,No,Yes,Asian,548
+166,25.383,4527,367,4,46,11, Male,No,Yes,Caucasian,570
+167,35.691,2880,214,2,35,15, Male,No,No,African American,0
+168,29.403,2327,178,1,37,14,Female,No,Yes,Caucasian,0
+169,27.47,2820,219,1,32,11,Female,No,Yes,Asian,0
+170,27.33,6179,459,4,36,12,Female,No,Yes,Caucasian,1099
+171,34.772,2021,167,3,57,9, Male,No,No,Asian,0
+172,36.934,4270,299,1,63,9,Female,No,Yes,Caucasian,283
+173,76.348,4697,344,4,60,18, Male,No,No,Asian,108
+174,14.887,4745,339,3,58,12, Male,No,Yes,African American,724
+175,121.834,10673,750,3,54,16, Male,No,No,African American,1573
+176,30.132,2168,206,3,52,17, Male,No,No,Caucasian,0
+177,24.05,2607,221,4,32,18, Male,No,Yes,Caucasian,0
+178,22.379,3965,292,2,34,14,Female,No,Yes,Asian,384
+179,28.316,4391,316,2,29,10,Female,No,No,Caucasian,453
+180,58.026,7499,560,5,67,11,Female,No,No,Caucasian,1237
+181,10.635,3584,294,5,69,16, Male,No,Yes,Asian,423
+182,46.102,5180,382,3,81,12, Male,No,Yes,African American,516
+183,58.929,6420,459,2,66,9,Female,No,Yes,African American,789
+184,80.861,4090,335,3,29,15,Female,No,Yes,Asian,0
+185,158.889,11589,805,1,62,17,Female,No,Yes,Caucasian,1448
+186,30.42,4442,316,1,30,14,Female,No,No,African American,450
+187,36.472,3806,309,2,52,13, Male,No,No,African American,188
+188,23.365,2179,167,2,75,15, Male,No,No,Asian,0
+189,83.869,7667,554,2,83,11, Male,No,No,African American,930
+190,58.351,4411,326,2,85,16,Female,No,Yes,Caucasian,126
+191,55.187,5352,385,4,50,17,Female,No,Yes,Caucasian,538
+192,124.29,9560,701,3,52,17,Female,Yes,No,Asian,1687
+193,28.508,3933,287,4,56,14, Male,No,Yes,Asian,336
+194,130.209,10088,730,7,39,19,Female,No,Yes,Caucasian,1426
+195,30.406,2120,181,2,79,14, Male,No,Yes,African American,0
+196,23.883,5384,398,2,73,16,Female,No,Yes,African American,802
+197,93.039,7398,517,1,67,12, Male,No,Yes,African American,749
+198,50.699,3977,304,2,84,17,Female,No,No,African American,69
+199,27.349,2000,169,4,51,16,Female,No,Yes,African American,0
+200,10.403,4159,310,3,43,7, Male,No,Yes,Asian,571
+201,23.949,5343,383,2,40,18, Male,No,Yes,African American,829
+202,73.914,7333,529,6,67,15,Female,No,Yes,Caucasian,1048
+203,21.038,1448,145,2,58,13,Female,No,Yes,Caucasian,0
+204,68.206,6784,499,5,40,16,Female,Yes,No,African American,1411
+205,57.337,5310,392,2,45,7,Female,No,No,Caucasian,456
+206,10.793,3878,321,8,29,13, Male,No,No,Caucasian,638
+207,23.45,2450,180,2,78,13, Male,No,No,Caucasian,0
+208,10.842,4391,358,5,37,10,Female,Yes,Yes,Caucasian,1216
+209,51.345,4327,320,3,46,15, Male,No,No,African American,230
+210,151.947,9156,642,2,91,11,Female,No,Yes,African American,732
+211,24.543,3206,243,2,62,12,Female,No,Yes,Caucasian,95
+212,29.567,5309,397,3,25,15, Male,No,No,Caucasian,799
+213,39.145,4351,323,2,66,13, Male,No,Yes,Caucasian,308
+214,39.422,5245,383,2,44,19, Male,No,No,African American,637
+215,34.909,5289,410,2,62,16,Female,No,Yes,Caucasian,681
+216,41.025,4229,337,3,79,19,Female,No,Yes,Caucasian,246
+217,15.476,2762,215,3,60,18, Male,No,No,Asian,52
+218,12.456,5395,392,3,65,14, Male,No,Yes,Caucasian,955
+219,10.627,1647,149,2,71,10,Female,Yes,Yes,Asian,195
+220,38.954,5222,370,4,76,13,Female,No,No,Caucasian,653
+221,44.847,5765,437,3,53,13,Female,Yes,No,Asian,1246
+222,98.515,8760,633,5,78,11,Female,No,No,African American,1230
+223,33.437,6207,451,4,44,9, Male,Yes,No,Caucasian,1549
+224,27.512,4613,344,5,72,17, Male,No,Yes,Asian,573
+225,121.709,7818,584,4,50,6, Male,No,Yes,Caucasian,701
+226,15.079,5673,411,4,28,15,Female,No,Yes,Asian,1075
+227,59.879,6906,527,6,78,15,Female,No,No,Caucasian,1032
+228,66.989,5614,430,3,47,14,Female,No,Yes,Caucasian,482
+229,69.165,4668,341,2,34,11,Female,No,No,African American,156
+230,69.943,7555,547,3,76,9, Male,No,Yes,Asian,1058
+231,33.214,5137,387,3,59,9, Male,No,No,African American,661
+232,25.124,4776,378,4,29,12, Male,No,Yes,Caucasian,657
+233,15.741,4788,360,1,39,14, Male,No,Yes,Asian,689
+234,11.603,2278,187,3,71,11, Male,No,Yes,Caucasian,0
+235,69.656,8244,579,3,41,14, Male,No,Yes,African American,1329
+236,10.503,2923,232,3,25,18,Female,No,Yes,African American,191
+237,42.529,4986,369,2,37,11, Male,No,Yes,Asian,489
+238,60.579,5149,388,5,38,15, Male,No,Yes,Asian,443
+239,26.532,2910,236,6,58,19,Female,No,Yes,Caucasian,52
+240,27.952,3557,263,1,35,13,Female,No,Yes,Asian,163
+241,29.705,3351,262,5,71,14,Female,No,Yes,Asian,148
+242,15.602,906,103,2,36,11, Male,No,Yes,African American,0
+243,20.918,1233,128,3,47,18,Female,Yes,Yes,Asian,16
+244,58.165,6617,460,1,56,12,Female,No,Yes,Caucasian,856
+245,22.561,1787,147,4,66,15,Female,No,No,Caucasian,0
+246,34.509,2001,189,5,80,18,Female,No,Yes,African American,0
+247,19.588,3211,265,4,59,14,Female,No,No,Asian,199
+248,36.364,2220,188,3,50,19, Male,No,No,Caucasian,0
+249,15.717,905,93,1,38,16, Male,Yes,Yes,Caucasian,0
+250,22.574,1551,134,3,43,13,Female,Yes,Yes,Caucasian,98
+251,10.363,2430,191,2,47,18,Female,No,Yes,Asian,0
+252,28.474,3202,267,5,66,12, Male,No,Yes,Caucasian,132
+253,72.945,8603,621,3,64,8,Female,No,No,Caucasian,1355
+254,85.425,5182,402,6,60,12, Male,No,Yes,African American,218
+255,36.508,6386,469,4,79,6,Female,No,Yes,Caucasian,1048
+256,58.063,4221,304,3,50,8, Male,No,No,African American,118
+257,25.936,1774,135,2,71,14,Female,No,No,Asian,0
+258,15.629,2493,186,1,60,14, Male,No,Yes,Asian,0
+259,41.4,2561,215,2,36,14, Male,No,Yes,Caucasian,0
+260,33.657,6196,450,6,55,9,Female,No,No,Caucasian,1092
+261,67.937,5184,383,4,63,12, Male,No,Yes,Asian,345
+262,180.379,9310,665,3,67,8,Female,Yes,Yes,Asian,1050
+263,10.588,4049,296,1,66,13,Female,No,Yes,Caucasian,465
+264,29.725,3536,270,2,52,15,Female,No,No,African American,133
+265,27.999,5107,380,1,55,10, Male,No,Yes,Caucasian,651
+266,40.885,5013,379,3,46,13,Female,No,Yes,African American,549
+267,88.83,4952,360,4,86,16,Female,No,Yes,Caucasian,15
+268,29.638,5833,433,3,29,15,Female,No,Yes,Asian,942
+269,25.988,1349,142,4,82,12, Male,No,No,Caucasian,0
+270,39.055,5565,410,4,48,18,Female,No,Yes,Caucasian,772
+271,15.866,3085,217,1,39,13, Male,No,No,Caucasian,136
+272,44.978,4866,347,1,30,10,Female,No,No,Caucasian,436
+273,30.413,3690,299,2,25,15,Female,Yes,No,Asian,728
+274,16.751,4706,353,6,48,14, Male,Yes,No,Asian,1255
+275,30.55,5869,439,5,81,9,Female,No,No,African American,967
+276,163.329,8732,636,3,50,14, Male,No,Yes,Caucasian,529
+277,23.106,3476,257,2,50,15,Female,No,No,Caucasian,209
+278,41.532,5000,353,2,50,12, Male,No,Yes,Caucasian,531
+279,128.04,6982,518,2,78,11,Female,No,Yes,Caucasian,250
+280,54.319,3063,248,3,59,8,Female,Yes,No,Caucasian,269
+281,53.401,5319,377,3,35,12,Female,No,No,African American,541
+282,36.142,1852,183,3,33,13,Female,No,No,African American,0
+283,63.534,8100,581,2,50,17,Female,No,Yes,Caucasian,1298
+284,49.927,6396,485,3,75,17,Female,No,Yes,Caucasian,890
+285,14.711,2047,167,2,67,6, Male,No,Yes,Caucasian,0
+286,18.967,1626,156,2,41,11,Female,No,Yes,Asian,0
+287,18.036,1552,142,2,48,15,Female,No,No,Caucasian,0
+288,60.449,3098,272,4,69,8, Male,No,Yes,Caucasian,0
+289,16.711,5274,387,3,42,16,Female,No,Yes,Asian,863
+290,10.852,3907,296,2,30,9, Male,No,No,Caucasian,485
+291,26.37,3235,268,5,78,11, Male,No,Yes,Asian,159
+292,24.088,3665,287,4,56,13,Female,No,Yes,Caucasian,309
+293,51.532,5096,380,2,31,15, Male,No,Yes,Caucasian,481
+294,140.672,11200,817,7,46,9, Male,No,Yes,African American,1677
+295,42.915,2532,205,4,42,13, Male,No,Yes,Asian,0
+296,27.272,1389,149,5,67,10,Female,No,Yes,Caucasian,0
+297,65.896,5140,370,1,49,17,Female,No,Yes,Caucasian,293
+298,55.054,4381,321,3,74,17, Male,No,Yes,Asian,188
+299,20.791,2672,204,1,70,18,Female,No,No,African American,0
+300,24.919,5051,372,3,76,11,Female,No,Yes,African American,711
+301,21.786,4632,355,1,50,17, Male,No,Yes,Caucasian,580
+302,31.335,3526,289,3,38,7,Female,No,No,Caucasian,172
+303,59.855,4964,365,1,46,13,Female,No,Yes,Caucasian,295
+304,44.061,4970,352,1,79,11, Male,No,Yes,African American,414
+305,82.706,7506,536,2,64,13,Female,No,Yes,Asian,905
+306,24.46,1924,165,2,50,14,Female,No,Yes,Asian,0
+307,45.12,3762,287,3,80,8, Male,No,Yes,Caucasian,70
+308,75.406,3874,298,3,41,14,Female,No,Yes,Asian,0
+309,14.956,4640,332,2,33,6, Male,No,No,Asian,681
+310,75.257,7010,494,3,34,18,Female,No,Yes,Caucasian,885
+311,33.694,4891,369,1,52,16, Male,Yes,No,African American,1036
+312,23.375,5429,396,3,57,15,Female,No,Yes,Caucasian,844
+313,27.825,5227,386,6,63,11, Male,No,Yes,Caucasian,823
+314,92.386,7685,534,2,75,18,Female,No,Yes,Asian,843
+315,115.52,9272,656,2,69,14, Male,No,No,African American,1140
+316,14.479,3907,296,3,43,16, Male,No,Yes,Caucasian,463
+317,52.179,7306,522,2,57,14, Male,No,No,Asian,1142
+318,68.462,4712,340,2,71,16, Male,No,Yes,Caucasian,136
+319,18.951,1485,129,3,82,13,Female,No,No,Caucasian,0
+320,27.59,2586,229,5,54,16, Male,No,Yes,African American,0
+321,16.279,1160,126,3,78,13, Male,Yes,Yes,African American,5
+322,25.078,3096,236,2,27,15,Female,No,Yes,Caucasian,81
+323,27.229,3484,282,6,51,11, Male,No,No,Caucasian,265
+324,182.728,13913,982,4,98,17, Male,No,Yes,Caucasian,1999
+325,31.029,2863,223,2,66,17, Male,Yes,Yes,Asian,415
+326,17.765,5072,364,1,66,12,Female,No,Yes,Caucasian,732
+327,125.48,10230,721,3,82,16, Male,No,Yes,Caucasian,1361
+328,49.166,6662,508,3,68,14,Female,No,No,Asian,984
+329,41.192,3673,297,3,54,16,Female,No,Yes,Caucasian,121
+330,94.193,7576,527,2,44,16,Female,No,Yes,Caucasian,846
+331,20.405,4543,329,2,72,17, Male,Yes,No,Asian,1054
+332,12.581,3976,291,2,48,16, Male,No,Yes,Caucasian,474
+333,62.328,5228,377,3,83,15, Male,No,No,Caucasian,380
+334,21.011,3402,261,2,68,17, Male,No,Yes,African American,182
+335,24.23,4756,351,2,64,15,Female,No,Yes,Caucasian,594
+336,24.314,3409,270,2,23,7,Female,No,Yes,Caucasian,194
+337,32.856,5884,438,4,68,13, Male,No,No,Caucasian,926
+338,12.414,855,119,3,32,12, Male,No,Yes,African American,0
+339,41.365,5303,377,1,45,14, Male,No,No,Caucasian,606
+340,149.316,10278,707,1,80,16, Male,No,No,African American,1107
+341,27.794,3807,301,4,35,8,Female,No,Yes,African American,320
+342,13.234,3922,299,2,77,17,Female,No,Yes,Caucasian,426
+343,14.595,2955,260,5,37,9, Male,No,Yes,African American,204
+344,10.735,3746,280,2,44,17,Female,No,Yes,Caucasian,410
+345,48.218,5199,401,7,39,10, Male,No,Yes,Asian,633
+346,30.012,1511,137,2,33,17, Male,No,Yes,Caucasian,0
+347,21.551,5380,420,5,51,18, Male,No,Yes,Asian,907
+348,160.231,10748,754,2,69,17, Male,No,No,Caucasian,1192
+349,13.433,1134,112,3,70,14, Male,No,Yes,Caucasian,0
+350,48.577,5145,389,3,71,13,Female,No,Yes,Asian,503
+351,30.002,1561,155,4,70,13,Female,No,Yes,Caucasian,0
+352,61.62,5140,374,1,71,9, Male,No,Yes,Caucasian,302
+353,104.483,7140,507,2,41,14, Male,No,Yes,African American,583
+354,41.868,4716,342,2,47,18, Male,No,No,Caucasian,425
+355,12.068,3873,292,1,44,18,Female,No,Yes,Asian,413
+356,180.682,11966,832,2,58,8,Female,No,Yes,African American,1405
+357,34.48,6090,442,3,36,14, Male,No,No,Caucasian,962
+358,39.609,2539,188,1,40,14, Male,No,Yes,Asian,0
+359,30.111,4336,339,1,81,18, Male,No,Yes,Caucasian,347
+360,12.335,4471,344,3,79,12, Male,No,Yes,African American,611
+361,53.566,5891,434,4,82,10,Female,No,No,Caucasian,712
+362,53.217,4943,362,2,46,16,Female,No,Yes,Asian,382
+363,26.162,5101,382,3,62,19,Female,No,No,African American,710
+364,64.173,6127,433,1,80,10, Male,No,Yes,Caucasian,578
+365,128.669,9824,685,3,67,16, Male,No,Yes,Asian,1243
+366,113.772,6442,489,4,69,15, Male,Yes,Yes,Caucasian,790
+367,61.069,7871,564,3,56,14, Male,No,Yes,Caucasian,1264
+368,23.793,3615,263,2,70,14, Male,No,No,African American,216
+369,89,5759,440,3,37,6,Female,No,No,Caucasian,345
+370,71.682,8028,599,3,57,16, Male,No,Yes,Caucasian,1208
+371,35.61,6135,466,4,40,12, Male,No,No,Caucasian,992
+372,39.116,2150,173,4,75,15, Male,No,No,Caucasian,0
+373,19.782,3782,293,2,46,16,Female,Yes,No,Caucasian,840
+374,55.412,5354,383,2,37,16,Female,Yes,Yes,Caucasian,1003
+375,29.4,4840,368,3,76,18,Female,No,Yes,Caucasian,588
+376,20.974,5673,413,5,44,16,Female,No,Yes,Caucasian,1000
+377,87.625,7167,515,2,46,10,Female,No,No,African American,767
+378,28.144,1567,142,3,51,10, Male,No,Yes,Caucasian,0
+379,19.349,4941,366,1,33,19, Male,No,Yes,Caucasian,717
+380,53.308,2860,214,1,84,10, Male,No,Yes,Caucasian,0
+381,115.123,7760,538,3,83,14,Female,No,No,African American,661
+382,101.788,8029,574,2,84,11, Male,No,Yes,Caucasian,849
+383,24.824,5495,409,1,33,9, Male,Yes,No,Caucasian,1352
+384,14.292,3274,282,9,64,9, Male,No,Yes,Caucasian,382
+385,20.088,1870,180,3,76,16, Male,No,No,African American,0
+386,26.4,5640,398,3,58,15,Female,No,No,Asian,905
+387,19.253,3683,287,4,57,10, Male,No,No,African American,371
+388,16.529,1357,126,3,62,9, Male,No,No,Asian,0
+389,37.878,6827,482,2,80,13,Female,No,No,Caucasian,1129
+390,83.948,7100,503,2,44,18, Male,No,No,Caucasian,806
+391,135.118,10578,747,3,81,15,Female,No,Yes,Asian,1393
+392,73.327,6555,472,2,43,15,Female,No,No,Caucasian,721
+393,25.974,2308,196,2,24,10, Male,No,No,Asian,0
+394,17.316,1335,138,2,65,13, Male,No,No,African American,0
+395,49.794,5758,410,4,40,8, Male,No,No,Caucasian,734
+396,12.096,4100,307,3,32,13, Male,No,Yes,Caucasian,560
+397,13.364,3838,296,5,65,17, Male,No,No,African American,480
+398,57.872,4171,321,5,67,12,Female,No,Yes,Caucasian,138
+399,37.728,2525,192,1,44,13, Male,No,Yes,Caucasian,0
+400,18.701,5524,415,5,64,7,Female,No,No,Asian,966
diff --git a/R/Credit Rating/credit_description.txt b/R/Credit Rating/credit_description.txt
new file mode 100755
index 0000000..50b00a7
--- /dev/null
+++ b/R/Credit Rating/credit_description.txt	
@@ -0,0 +1,15 @@
+"Credit.csv" data description
+
+Column	Variable name	Description
+A	"  "		Identifier
+B	Income		Income in $000
+C	Limit		Credit limit in $
+D	Rating		Credit rating score given at the issue of the most recent credit card
+E	Cards		Number of credit cards held
+F	Age		Age of the individual in years
+G	Education	Years of education
+H	Gender		Gender - recorded as Male or Female
+I	Student		Yes = student; No = non-student
+J	Married		Yes = married; No = not married
+K	Ethnicity	Recorded as Caucasian, Asian or African American
+L	Balance		Balance of credit account in $
diff --git a/R/Credit Rating/week1_credit.R b/R/Credit Rating/week1_credit.R
new file mode 100644
index 0000000..39dd7a7
--- /dev/null
+++ b/R/Credit Rating/week1_credit.R	
@@ -0,0 +1,158 @@
+#####################################################
+#Reading in the Credit data
+#####################################################
+data = read.csv("Credit.csv", header = TRUE, sep=",")
+
+#####################################################
+#Model #1: All numerical variables
+
+#####################################################
+fit1<-lm(Balance~Income+Limit+Rating+Cards+Age+Education,data=data)
+#look at the summary of the linear model fit
+summary(fit1)
+
+# creating a dummy variable for Gender
+gender_dummy <- ifelse(data$Gender == 'Female', 1, 0)
+
+# creating a dummy variable for Married
+married_dummy <- ifelse(data$Married == 'Yes', 1, 0)
+
+# creating a dummy variable for Student
+student_dummy <- ifelse(data$Student == 'Yes', 1, 0)
+
+# adding the dummy variables to the dataframe
+data[,13] = gender_dummy #V13
+data[,14] = married_dummy #V14
+data[,15] = student_dummy #V15
+
+#####################################################
+#Model #2: With dummy variables
+
+#####################################################
+fit2<-lm(Balance~Income+Limit+Rating+Cards+Age+Education+V13+V14+V15,data=data)
+#look at the summary of the linear model fit
+summary(fit2)
+
+# creating a dummy variable for Asian Ethinicity
+asian_dummy <- ifelse(data$Ethnicity == 'Asian', 1, 0)
+
+# creating a dummy variable for African Ethinicity
+african_dummy <- ifelse(data$Ethnicity == 'African American', 1, 0)
+
+data[,16] = asian_dummy #V16
+data[,17] = african_dummy #V17
+
+#####################################################
+#Model #3: With dummy variables (ethinicity)
+
+#####################################################
+fit3<-lm(Balance~Income+Limit+Rating+Cards+Age+Education+V13+V14+V15+V16+V17,data=data)
+#look at the summary of the linear model fit
+summary(fit3)
+
+#plot scatter of residuals vs Income
+plot(fit3$residuals~Income,data=data)
+abline(h=0)
+
+#plot scatter of residuals vs Limit
+plot(fit3$residuals~Limit,data=data)
+abline(h=0)
+
+#plot scatter of residuals vs Rating
+plot(fit3$residuals~Rating,data=data)
+abline(h=0)
+
+#plot scatter of residuals vs Cards
+plot(fit3$residuals~Cards,data=data)
+abline(h=0)
+
+#plot scatter of residuals vs V15
+plot(fit3$residuals~V15,data=data)
+abline(h=0)
+
+#plot scatter of residuals vs Age
+plot(fit3$residuals~Age,data=data)
+abline(h=0)
+
+#####################################################
+#Defining a function for the wald test - require MASS library
+#H0: RB=c vs Ha: RB!=c
+#R=the R matrix
+#B=the estimated coefficients
+#S=the covariance matrix of B
+#c=the vector of constants
+#####################################################
+library(MASS)
+wald<-function(R,B,S,c){
+  stats<-matrix(0,1,2)
+  dif=(R%*%B-c)
+  VV=R%*%(S)%*%t(R)
+  W=t(dif)%*%ginv(VV)%*%dif
+  stats[1]=W
+  stats[2]=pchisq(W,nrow(c),lower.tail=FALSE)
+  colnames(stats)<-c("Wald stat","p-value")
+  return(stats)
+}
+
+#define inputs to the wald test - joint significance of the three slope parameters
+RR=cbind(rbind(0,0,0,0,0,0,0,0,0,0,0),diag(11))
+cc=rbind(0,0,0,0,0,0,0,0,0,0,0)
+bhat=(fit3$coefficients)
+Shat=vcov(fit3)
+wald1=wald(RR,bhat,Shat,cc)
+wald1
+
+###########################################################
+#constructing quadratic terms for age, cards, limit
+###########################################################
+data[,18]=data$Age^2
+names(data)[names(data)=="V18"] <- "age2"
+
+data[,19]=data$Cards^2
+names(data)[names(data)=="V19"] <- "cards2"
+
+data[,20]=data$Limit^2
+names(data)[names(data)=="V20"] <- "limit2"
+
+#####################################################
+#Model #4: Updated with transformations and multicolineariy
+
+#####################################################
+fit4<-lm(Balance~Income+Limit+Cards+Age+Education+V13+V14+V15+age2+cards2+limit2,data=data)
+#look at the summary of the linear model fit
+summary(fit4)
+
+
+#####################################################
+#Analysis : Factors affecting credit balance
+ # Income | Limit | Cards | Age | Student | Limit^2
+#####################################################
+#Modelling interactions - context!
+
+#####################################################
+data[,21]=data$Limit*data$Cards
+names(data)[names(data)=="V21"] <- "LimitxCards"
+data[,22]=data$limit2*data$Cards
+names(data)[names(data)=="V22"] <- "Limit2xCards"
+
+# Step 1
+fit_step1<-lm(Balance~Income+Limit+Cards+Age+V15+limit2+LimitxCards+Limit2xCards,data=data)
+#look at the summary of the linear model fit
+summary(fit_step1)
+
+# Step 2
+fit_step2<-lm(Balance~Income+Limit+Age+V15+limit2+LimitxCards+Limit2xCards,data=data)
+#look at the summary of the linear model fit
+summary(fit_step2)
+
+# Step 3
+fit_step3<-lm(Balance~Income+Limit+Age+V15+limit2+LimitxCards,data=data)
+#look at the summary of the linear model fit
+summary(fit_step3)
+
+# Step 4
+fit_step4<-lm(Balance~Income+Limit+V15+limit2+LimitxCards,data=data)
+#look at the summary of the linear model fit
+summary(fit_step4)
+
+step(fit_step4)