From 22f7307187274633d077d7b71ca6e2cb4733b97d Mon Sep 17 00:00:00 2001
From: darsh8200 <46572301+darsh8200@users.noreply.github.com>
Date: Fri, 25 Oct 2019 16:38:57 +0530
Subject: [PATCH 01/11] Add files via upload
---
Cust_Segmentation.csv | 851 ++++++++++++++++++++++++++++
K_Means_Customer.ipynb | 1214 ++++++++++++++++++++++++++++++++++++++++
2 files changed, 2065 insertions(+)
create mode 100644 Cust_Segmentation.csv
create mode 100644 K_Means_Customer.ipynb
diff --git a/Cust_Segmentation.csv b/Cust_Segmentation.csv
new file mode 100644
index 0000000..17803f4
--- /dev/null
+++ b/Cust_Segmentation.csv
@@ -0,0 +1,851 @@
+Customer Id,Age,Edu,Years Employed,Income,Card Debt,Other Debt,Defaulted,Address,DebtIncomeRatio
+1,41,2,6,19,0.124,1.073,0,NBA001,6.3
+2,47,1,26,100,4.582,8.218,0,NBA021,12.8
+3,33,2,10,57,6.111,5.802,1,NBA013,20.9
+4,29,2,4,19,0.681,0.516,0,NBA009,6.3
+5,47,1,31,253,9.308,8.908,0,NBA008,7.2
+6,40,1,23,81,0.998,7.831,,NBA016,10.9
+7,38,2,4,56,0.442,0.454,0,NBA013,1.6
+8,42,3,0,64,0.279,3.945,0,NBA009,6.6
+9,26,1,5,18,0.575,2.215,,NBA006,15.5
+10,47,3,23,115,0.653,3.947,0,NBA011,4
+11,44,3,8,88,0.285,5.083,1,NBA010,6.1
+12,34,2,9,40,0.374,0.266,,NBA003,1.6
+13,24,1,7,18,0.526,0.643,0,NBA000,6.5
+14,46,1,6,30,1.415,3.865,,NBA019,17.6
+15,28,3,2,20,0.233,1.647,1,NBA000,9.4
+16,24,1,1,16,0.185,1.287,,NBA005,9.2
+17,29,1,1,17,0.132,0.293,0,NBA004,2.5
+18,43,4,1,26,1.519,1.237,0,NBA005,10.6
+19,44,1,18,61,2.806,3.782,,NBA000,10.8
+20,36,1,16,32,0.544,2.944,,NBA013,10.9
+21,29,2,6,25,0.585,0.465,0,NBA009,4.2
+22,36,3,10,43,0.961,4.629,0,NBA004,13
+23,28,3,6,47,5.574,3.732,1,NBA008,19.8
+24,45,1,19,77,2.303,4.165,0,NBA022,8.4
+25,37,4,10,123,3.022,18.257,0,NBA018,17.3
+26,43,1,9,66,2.341,3.467,,NBA008,8.8
+27,24,1,4,21,0.099,0.447,0,NBA002,2.6
+28,37,1,19,38,2.591,2.539,,NBA007,13.5
+29,38,2,13,59,0.408,1.008,0,NBA000,2.4
+30,34,2,9,45,1.118,3.427,0,NBA013,10.1
+31,40,3,18,100,1.485,1.015,0,NBA006,2.5
+32,42,2,12,55,2.533,5.717,0,NBA009,15
+33,23,2,0,42,1.019,0.619,1,NBA001,3.9
+34,40,3,5,28,1.263,1.537,,NBA018,10
+35,28,1,12,45,1.854,1.611,0,NBA007,7.7
+36,33,2,5,37,1.029,2.079,,NBA002,8.4
+37,35,3,5,37,0.581,1.417,1,NBA003,5.4
+38,37,1,0,18,1.584,0.738,1,NBA018,12.9
+39,22,1,1,18,0.27,0.522,0,NBA000,4.4
+40,39,3,16,126,1.405,7.163,,NBA010,6.8
+41,20,1,4,14,0.201,1.157,1,NBA000,9.7
+42,48,3,17,113,3.376,10.184,0,NBA026,12
+43,28,2,5,34,3.099,4.993,0,NBA009,23.8
+44,37,5,9,177,0.888,9.555,0,NBA016,5.9
+45,48,1,3,27,1.403,4.348,0,NBA011,21.3
+46,45,3,9,84,1.276,9.728,,NBA000,13.1
+47,22,1,4,14,0.225,2.225,0,NBA003,17.5
+48,30,1,4,21,0.229,0.506,,NBA002,3.5
+49,28,1,3,19,0.261,0.518,0,NBA007,4.1
+50,29,2,10,61,0.798,2.984,,NBA003,6.2
+51,47,1,22,81,1.506,2.949,0,NBA019,5.5
+52,36,1,11,33,1.266,9.459,0,NBA002,32.5
+53,24,1,3,19,1.358,3.278,1,NBA004,24.4
+54,56,1,19,66,0.847,1.331,,NBA026,3.3
+55,29,3,5,70,3.176,10.754,1,NBA006,19.9
+56,34,1,2,25,0.573,2.577,1,NBA011,12.6
+57,32,1,1,20,0.315,0.645,1,NBA000,4.8
+58,27,3,2,26,0.722,0.838,0,NBA007,6
+59,40,1,9,64,0.79,3.434,0,NBA004,6.6
+60,50,1,11,36,0.835,0.893,0,NBA005,4.8
+61,39,1,17,60,2.45,5.77,0,NBA020,13.7
+62,34,1,18,34,2.1,3.136,0,NBA010,15.4
+63,36,1,18,67,5.245,2.259,0,NBA009,11.2
+64,44,2,18,74,4.522,5.394,0,NBA004,13.4
+65,24,2,4,20,0.324,0.416,0,NBA004,3.7
+66,31,1,8,44,1.721,1.799,0,NBA004,8
+67,34,1,16,79,1.735,1.425,0,NBA009,4
+68,26,1,4,27,2.472,0.363,1,NBA003,10.5
+69,38,1,3,23,0.832,2.549,0,NBA019,14.7
+70,24,1,3,14,0.838,1.416,1,NBA000,16.1
+71,30,3,8,61,0.55,1.158,0,NBA007,2.8
+72,40,4,5,75,0.885,0.54,0,NBA006,1.9
+73,30,2,12,98,2.935,4.121,0,NBA009,7.2
+74,25,2,5,42,3.366,3.144,,NBA003,15.5
+75,29,1,9,33,0.694,0.89,0,NBA002,4.8
+76,33,1,5,18,0.985,1.337,0,NBA010,12.9
+77,43,1,8,45,0.677,0.808,0,NBA011,3.3
+78,35,2,1,21,0.37,1.289,0,NBA001,7.9
+79,41,2,21,145,3.237,14.453,0,NBA022,12.2
+80,46,2,18,55,0.419,2.001,,NBA018,4.4
+81,33,1,12,68,1.366,5.978,0,NBA012,10.8
+82,30,3,0,65,3.9,15.405,1,NBA008,29.7
+83,40,3,18,157,3.326,7.036,0,NBA014,6.6
+84,38,1,1,42,0.891,2.931,,NBA009,9.1
+85,49,2,11,51,0.543,7.107,0,NBA001,15
+86,26,1,0,15,0.386,0.424,,NBA002,5.4
+87,22,4,0,25,1.491,1.559,,NBA001,12.2
+88,29,2,2,16,0.433,1.247,,NBA004,10.5
+89,28,2,8,31,1.492,1.05,1,NBA003,8.2
+90,23,4,0,32,0.818,5.07,1,NBA004,18.4
+91,37,2,11,75,0.633,1.992,0,NBA004,3.5
+92,35,2,5,33,0.103,0.821,0,NBA001,2.8
+93,31,1,1,21,0.277,0.248,1,NBA006,2.5
+94,29,1,1,20,0.569,3.151,1,NBA002,18.6
+95,34,3,7,27,0.125,0.469,0,NBA000,2.2
+96,47,2,7,49,0.254,0.187,,NBA024,0.9
+97,37,1,5,27,0.419,2.119,,NBA011,9.4
+98,38,1,10,34,0.036,0.372,0,NBA008,1.2
+99,32,2,5,28,2.594,1.55,1,NBA000,14.8
+100,22,3,0,20,0.219,0.721,0,NBA002,4.7
+101,30,1,7,33,1.165,7.217,1,NBA002,25.4
+102,38,4,13,126,7.613,9.649,,NBA002,13.7
+103,36,1,20,60,1.056,2.184,0,NBA015,5.4
+104,44,1,5,48,1.97,2.35,1,NBA003,9
+105,27,2,4,22,1.086,1.818,1,NBA003,13.2
+106,47,1,19,50,3.176,11.874,0,NBA007,30.1
+107,41,1,7,32,0.072,0.856,0,NBA022,2.9
+108,25,1,8,27,1.019,2.869,0,NBA004,14.4
+109,33,2,10,54,0.12,1.5,,NBA002,3
+110,42,3,13,82,1.315,1.391,0,NBA000,3.3
+111,36,1,14,53,1.888,1.133,0,NBA017,5.7
+112,25,4,0,32,2.14,3.492,0,NBA002,17.6
+113,30,1,10,39,2.056,1.649,0,NBA011,9.5
+114,24,1,0,16,1.009,1.551,1,NBA002,16
+115,32,1,7,23,0.861,1.393,,NBA010,9.8
+116,39,2,9,32,1.43,0.682,0,NBA012,6.6
+117,30,1,4,18,0.227,1.699,0,NBA002,10.7
+118,33,1,16,46,2.4,1.326,,NBA012,8.1
+119,32,2,12,63,5.55,5.916,0,NBA003,18.2
+120,33,1,7,26,0.543,1.017,0,NBA006,6
+121,29,2,6,21,0.369,2.046,0,NBA004,11.5
+122,28,2,5,37,1.124,1.947,0,NBA002,8.3
+123,33,4,9,32,0.496,1.264,0,NBA008,5.5
+124,39,2,16,53,0.694,1.267,0,NBA012,3.7
+125,32,1,10,42,1.54,2.282,0,NBA011,9.1
+126,46,1,9,72,0.828,2.772,,NBA006,5
+127,32,2,3,47,0.429,0.464,0,NBA013,1.9
+128,28,2,5,25,0.339,0.386,0,NBA006,2.9
+129,26,1,2,21,1.023,3.135,0,NBA006,19.8
+130,27,1,0,16,0.183,0.089,0,NBA001,1.7
+131,35,1,16,36,0.179,1.045,0,NBA015,3.4
+132,35,1,16,57,1.143,4.842,0,NBA010,10.5
+133,31,2,6,25,2.332,0.543,0,NBA005,11.5
+134,32,3,5,23,0.326,0.709,0,NBA009,4.5
+135,29,1,9,25,1.07,2.405,0,NBA000,13.9
+136,35,4,4,29,1.844,1.346,0,NBA016,11
+137,48,2,21,86,0.146,0.886,,NBA014,1.2
+138,24,1,4,19,1.185,0.905,,NBA000,11
+139,44,1,12,31,0.358,2.649,0,NBA003,9.7
+140,40,2,5,35,1.584,4.156,1,NBA012,16.4
+141,36,2,13,41,2.918,3.806,1,NBA006,16.4
+142,23,2,0,21,0.455,1.372,0,NBA004,8.7
+143,26,2,8,40,0.444,4.276,0,NBA001,11.8
+144,30,1,8,23,0.762,1.998,0,NBA006,12
+145,34,1,12,68,7.817,9.251,1,NBA005,25.1
+146,28,3,2,30,1.522,4.448,0,NBA005,19.9
+147,37,1,18,58,2.868,3.106,,NBA009,10.3
+148,46,3,3,43,3.042,2.634,1,NBA013,13.2
+149,24,2,1,42,0.838,1.556,0,NBA002,5.7
+150,25,2,6,26,0.07,1.1,0,NBA002,4.5
+151,33,1,14,37,0.199,0.763,0,NBA002,2.6
+152,46,1,7,41,0.585,9.009,,NBA006,23.4
+153,38,3,4,31,1.332,1.551,1,NBA006,9.3
+154,47,1,3,21,0.068,3.166,,NBA001,15.4
+155,34,2,9,65,2.108,1.922,0,NBA009,6.2
+156,35,1,16,37,4.213,2.151,,NBA000,17.2
+157,39,1,22,113,0.987,1.951,0,NBA009,2.6
+158,44,1,24,101,2.992,2.664,0,NBA010,5.6
+159,28,2,1,24,1.338,2.766,1,NBA008,17.1
+160,31,3,9,59,3.11,5.504,1,NBA007,14.6
+161,24,1,1,20,0.325,0.495,,NBA000,4.1
+162,36,3,13,39,2.801,4.687,0,NBA003,19.2
+163,35,2,14,82,0.468,0.188,0,NBA003,0.8
+164,31,1,11,47,2.864,2.259,0,NBA012,10.9
+165,28,2,6,22,0.65,1.88,0,NBA009,11.5
+166,36,4,8,32,0.025,0.487,0,NBA000,1.6
+167,31,3,6,54,0.402,1.11,0,NBA008,2.8
+168,30,1,11,27,0.074,0.169,0,NBA009,0.9
+169,26,1,10,24,0.311,0.337,0,NBA006,2.7
+170,48,4,3,45,0.975,3.435,0,NBA023,9.8
+171,39,2,16,89,5.002,7.725,0,NBA009,14.3
+172,31,1,7,20,0.111,2.349,0,NBA002,12.3
+173,31,3,9,28,0.122,0.606,0,NBA002,2.6
+174,27,3,3,45,0.292,4.658,1,NBA004,11
+175,31,1,13,27,0.238,1.112,0,NBA012,5
+176,53,1,0,27,2.754,5.049,1,NBA026,28.9
+177,40,1,15,55,0.856,2.169,0,NBA014,5.5
+178,26,1,7,22,0.209,0.913,0,NBA003,5.1
+179,29,2,5,36,3.491,0.973,1,NBA004,12.4
+180,52,1,19,89,0.397,3.786,0,NBA025,4.7
+181,39,2,2,46,4.004,3.356,1,NBA012,16
+182,33,2,13,40,0.181,2.699,0,NBA002,7.2
+183,24,1,7,21,0.52,0.803,0,NBA002,6.3
+184,47,1,29,129,20.561,12.076,1,NBA018,25.3
+185,41,3,2,15,1.961,1.984,1,NBA000,26.3
+186,26,2,6,45,6.049,5.651,0,NBA007,26
+187,48,1,3,24,0.764,0.916,0,NBA007,7
+188,37,1,13,24,0.602,1.534,0,NBA005,8.9
+189,37,1,20,41,0.899,4.39,,NBA013,12.9
+190,46,4,7,73,0.556,0.247,,NBA016,1.1
+191,30,1,8,27,3.744,1.737,,NBA011,20.3
+192,47,1,15,30,2.212,5.138,0,NBA001,24.5
+193,37,3,16,75,0.022,0.053,,NBA008,0.1
+194,37,2,0,31,0.118,0.161,1,NBA013,0.9
+195,40,2,15,73,1.033,1.449,0,NBA016,3.4
+196,35,3,5,30,1.526,1.654,,NBA013,10.6
+197,21,2,0,16,0.15,0.938,0,NBA001,6.8
+198,52,1,24,64,3.93,2.47,0,NBA014,10
+199,47,3,16,221,15.792,23.104,1,NBA026,17.6
+200,41,3,0,26,0.099,0.343,0,NBA021,1.7
+201,47,1,27,113,2.381,8.015,0,NBA008,9.2
+202,38,2,0,21,0.612,0.354,,NBA018,4.6
+203,22,3,1,25,1.977,1.473,1,NBA003,13.8
+204,39,1,8,21,0.276,0.564,0,NBA000,4
+205,45,1,8,27,0.416,0.286,,NBA025,2.6
+206,51,1,10,44,4.96,0.848,1,NBA001,13.2
+207,33,2,10,26,0.031,0.775,0,NBA004,3.1
+208,43,1,25,242,1.636,4.656,0,NBA016,2.6
+209,40,1,22,95,0.633,2.787,0,NBA004,3.6
+210,27,4,2,23,0.32,0.991,1,NBA008,5.7
+211,31,1,11,25,0.141,2.134,0,NBA008,9.1
+212,34,1,10,48,0.264,2.184,,NBA012,5.1
+213,37,1,9,57,1.357,1.778,0,NBA008,5.5
+214,44,4,18,78,0.565,0.215,,NBA023,1
+215,42,1,3,24,0.667,1.877,0,NBA010,10.6
+216,25,1,3,16,1.011,1.837,1,NBA002,17.8
+217,39,2,7,68,1.017,7.823,1,NBA002,13
+218,26,4,1,64,7.754,7.158,,NBA006,23.3
+219,36,1,19,45,0.92,4.525,,NBA008,12.1
+220,48,1,15,60,0.321,0.939,0,NBA005,2.1
+221,21,2,0,21,0.488,2.137,1,NBA002,12.5
+222,40,3,17,116,2.561,4.399,0,NBA011,6
+223,31,2,9,30,0.602,1.138,0,NBA001,5.8
+224,23,4,0,23,0.468,1.073,0,NBA002,6.7
+225,31,1,6,21,0.729,1.014,0,NBA009,8.3
+226,31,1,7,23,0.225,0.902,0,NBA010,4.9
+227,50,3,18,102,5.782,8.6,0,NBA027,14.1
+228,29,1,1,31,0.156,2.324,0,NBA008,8
+229,37,3,5,29,1.405,5.729,0,NBA017,24.6
+230,46,1,16,52,3.032,3.676,1,NBA018,12.9
+231,39,1,9,35,0.233,1.517,0,NBA006,5
+232,34,1,14,28,1.138,3.622,0,NBA008,17
+233,27,1,11,56,4.637,4.827,0,NBA008,16.9
+234,23,1,6,15,1.375,0.53,,NBA002,12.7
+235,41,1,24,83,6.589,5.031,0,NBA012,14
+236,29,1,1,18,0.03,0.168,0,NBA001,1.1
+237,44,1,19,40,0.344,4.016,,NBA009,10.9
+238,24,2,0,15,0.321,2.094,1,NBA002,16.1
+239,32,3,9,51,2.686,9.044,0,NBA010,23
+240,23,2,3,34,0.262,1.234,0,NBA004,4.4
+241,31,1,4,28,0.291,2.873,0,NBA010,11.3
+242,36,1,15,39,0.686,0.562,0,NBA010,3.2
+243,25,1,3,31,0.881,1.63,,NBA005,8.1
+244,26,1,10,32,0.126,1.378,0,NBA002,4.7
+245,43,1,4,26,0.751,1.095,,NBA011,7.1
+246,47,1,29,169,0.349,3.369,,NBA020,2.2
+247,43,1,10,69,0.411,4.005,0,NBA009,6.4
+248,37,1,18,54,4.593,1.941,0,NBA008,12.1
+249,35,4,10,45,1.04,2.785,,NBA012,8.5
+250,28,4,1,26,0.377,2.847,0,NBA003,12.4
+251,28,1,8,30,0.613,1.547,0,NBA006,7.2
+252,26,3,2,37,0.205,5.049,0,NBA001,14.2
+253,39,1,10,31,0.185,1.303,0,NBA004,4.8
+254,34,2,3,21,1.402,3.449,0,NBA008,23.1
+255,51,3,16,82,2.605,1.003,0,NBA025,4.4
+256,28,2,3,41,5.897,4.394,1,NBA004,25.1
+257,39,1,20,39,0.774,2.424,0,NBA012,8.2
+258,23,1,3,19,0.279,1.317,1,NBA000,8.4
+259,23,1,3,13,0.046,0.357,,NBA004,3.1
+260,21,1,4,26,1.421,0.893,0,NBA000,8.9
+261,40,1,3,23,0.409,1.178,0,NBA013,6.9
+262,26,4,1,92,6.506,5.454,,NBA005,13
+263,32,2,12,54,3.196,4.58,0,NBA001,14.4
+264,29,3,3,50,0.259,0.941,1,NBA003,2.4
+265,43,1,25,64,0.951,9.737,0,NBA021,16.7
+266,55,1,3,40,0.563,2.637,1,NBA011,8
+267,30,1,12,40,1.249,1.551,,NBA004,7
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+541,39,1,20,116,2.24,2.516,,NBA008,4.1
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+555,28,1,10,42,1.047,2.565,,NBA001,8.6
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+584,39,1,12,46,0.561,1.739,0,NBA010,5
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+590,35,2,12,64,1.45,8.15,,NBA012,15
+591,29,2,3,32,1.071,1.809,,NBA009,9
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+596,29,1,3,23,1.104,1.035,,NBA010,9.3
+597,24,1,1,16,0.878,0.274,,NBA004,7.2
+598,25,1,9,18,0.066,0.15,0,NBA004,1.2
+599,43,2,16,83,0.259,3.144,0,NBA010,4.1
+600,45,1,12,37,1.343,2.172,0,NBA019,9.5
+601,35,1,13,35,0.432,1.143,0,NBA015,4.5
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+605,27,3,6,26,0.93,1.774,0,NBA004,10.4
+606,39,1,16,57,1.559,9.499,0,NBA020,19.4
+607,37,4,2,29,2.782,1.684,,NBA001,15.4
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+609,28,1,4,28,0.437,3.539,1,NBA002,14.2
+610,27,2,8,38,0.364,2.258,0,NBA004,6.9
+611,31,1,8,27,0.674,0.784,,NBA004,5.4
+612,31,1,9,26,0.31,0.86,0,NBA005,4.5
+613,32,1,0,20,0.263,0.337,1,NBA004,3
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+619,29,1,13,42,1.458,1.65,1,NBA001,7.4
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+622,30,1,1,27,1.275,2.046,,NBA001,12.3
+623,35,2,0,22,1.97,1.33,1,NBA016,15
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+629,42,2,18,66,1.641,4.695,0,NBA023,9.6
+630,45,3,8,140,4.184,15.276,,NBA001,13.9
+631,51,4,15,26,2.012,1.524,,NBA030,13.6
+632,36,1,10,28,0.815,2.321,1,NBA000,11.2
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+634,45,1,10,52,0.932,1.876,0,NBA014,5.4
+635,47,1,16,110,5.821,8.039,,NBA019,12.6
+636,26,1,6,22,0.721,1.545,0,NBA000,10.3
+637,35,1,12,30,1.899,2.721,,NBA003,15.4
+638,50,3,10,80,2.479,7.281,0,NBA020,12.2
+639,30,1,11,33,0.682,2.09,0,NBA001,8.4
+640,49,2,22,79,0.288,5.479,0,NBA004,7.3
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+646,39,2,9,56,4.765,5.707,0,NBA016,18.7
+647,40,2,15,138,1.222,4.436,,NBA019,4.1
+648,29,2,0,22,1.039,1.799,1,NBA008,12.9
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+655,30,1,0,20,0.622,0.458,,NBA002,5.4
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+663,35,1,11,77,1.1,2.519,,NBA012,4.7
+664,54,1,18,114,3.295,6.395,0,NBA034,8.5
+665,32,1,10,32,1.168,2.448,0,NBA013,11.3
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+678,41,1,14,52,0.926,2.87,0,NBA008,7.3
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+680,32,1,12,33,0.795,2.901,,NBA010,11.2
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+684,50,3,25,94,1.734,3.248,,NBA007,5.3
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+690,33,1,6,21,0.567,1.008,0,NBA014,7.5
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+694,56,1,11,59,4.673,4.177,0,NBA020,15
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+708,38,1,5,20,0.091,0.208,,NBA004,1.5
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+720,53,4,5,78,6.936,6.558,1,NBA029,17.3
+721,28,1,4,26,1.295,2.215,0,NBA007,13.5
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+723,42,1,7,25,1.629,0.921,0,NBA012,10.2
+724,33,1,11,35,0.506,5.059,,NBA006,15.9
+725,27,3,0,50,1.044,6.306,,NBA004,14.7
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+729,31,1,5,23,0.046,0.414,,NBA007,2
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+735,52,1,33,139,2.288,5.496,,NBA023,5.6
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+740,34,3,12,47,1.301,1.848,0,NBA008,6.7
+741,40,1,9,33,4.881,0.729,,NBA009,17
+742,45,3,16,80,0.912,1.488,0,NBA021,3
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+748,35,1,13,42,0.769,6.623,,NBA004,17.6
+749,22,2,0,14,0.242,2.152,,NBA000,17.1
+750,26,3,3,40,1.896,2.424,,NBA001,10.8
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+755,22,1,4,24,1.636,2.108,1,NBA002,15.6
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+763,30,1,0,17,0.304,1.736,0,NBA011,12
+764,39,2,16,69,1.061,0.595,0,NBA013,2.4
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+766,27,4,0,70,1.618,3.982,1,NBA006,8
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+768,36,1,9,40,1.976,1.944,0,NBA001,9.8
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+770,43,1,13,76,2.151,2.485,1,NBA023,6.1
+771,24,1,8,17,0.569,0.383,1,NBA000,5.6
+772,21,1,5,25,0.367,1.883,0,NBA001,9
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+775,36,3,11,44,0.446,6.022,0,NBA012,14.7
+776,43,1,15,62,0.694,5.134,,NBA021,9.4
+777,35,2,11,62,9.703,10.385,,NBA001,32.4
+778,34,2,10,33,0.574,2.561,0,NBA002,9.5
+779,47,2,17,41,0.456,3.439,0,NBA023,9.5
+780,48,1,0,30,2.663,4.027,0,NBA023,22.3
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+784,39,1,13,38,1.897,1.485,0,NBA001,8.9
+785,41,1,21,76,6.949,8.631,0,NBA002,20.5
+786,29,3,1,30,0.279,1.071,0,NBA010,4.5
+787,39,2,18,44,0.368,0.556,0,NBA009,2.1
+788,33,1,12,32,2.105,1.511,0,NBA011,11.3
+789,33,2,8,27,1.646,1,,NBA013,9.8
+790,39,1,0,39,1.066,2.015,0,NBA008,7.9
+791,40,2,8,57,0.878,2.314,0,NBA019,5.6
+792,53,1,33,324,7.053,15.627,,NBA025,7
+793,34,1,15,67,3.741,5.103,0,NBA000,13.2
+794,29,3,7,84,6.912,4.512,1,NBA002,13.6
+795,30,1,0,17,0.447,0.182,1,NBA011,3.7
+796,38,3,3,25,0.312,0.613,0,NBA018,3.7
+797,25,4,0,24,1.597,1.307,1,NBA006,12.1
+798,26,2,6,35,0.086,0.579,0,NBA000,1.9
+799,25,2,5,35,1.969,3.806,,NBA005,16.5
+800,29,1,8,24,0.47,1.138,0,NBA010,6.7
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+802,48,1,30,101,1.875,4.589,0,NBA008,6.4
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+804,36,1,4,23,1.327,2.422,0,NBA010,16.3
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+807,34,3,13,52,1.497,3.287,0,NBA007,9.2
+808,25,3,3,54,1.163,2.833,,NBA002,7.4
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+810,22,1,4,19,1.887,2.502,1,NBA003,23.1
+811,32,1,13,25,0.596,1.279,,NBA011,7.5
+812,21,2,1,17,0.555,1.23,,NBA000,10.5
+813,34,1,6,20,0.042,0.198,0,NBA001,1.2
+814,31,1,10,49,3.237,1.565,1,NBA012,9.8
+815,33,1,7,22,0.631,0.777,0,NBA000,6.4
+816,40,2,2,36,0.278,1.09,0,NBA001,3.8
+817,36,2,6,27,0.262,0.98,1,NBA015,4.6
+818,30,2,2,26,0.249,0.739,,NBA002,3.8
+819,35,2,0,35,2.383,1.957,,NBA006,12.4
+820,36,1,7,40,1.695,2.265,0,NBA017,9.9
+821,37,1,4,24,0.419,2.989,,NBA010,14.2
+822,32,1,16,38,0.694,7.286,0,NBA010,21
+823,45,1,3,20,0.105,0.315,0,NBA015,2.1
+824,27,4,0,25,1.419,1.756,1,NBA000,12.7
+825,41,2,4,26,1.473,3.519,1,NBA014,19.2
+826,32,2,12,116,4.027,2.585,,NBA011,5.7
+827,48,1,13,50,6.114,9.286,1,NBA020,30.8
+828,50,1,1,26,1.852,1.866,0,NBA026,14.3
+829,45,3,0,22,0.03,0.894,0,NBA019,4.2
+830,33,2,2,37,0.834,0.831,0,NBA009,4.5
+831,33,1,13,52,2.714,8.362,1,NBA003,21.3
+832,27,2,8,18,0.401,1.741,0,NBA007,11.9
+833,36,2,7,43,2.649,3.973,0,NBA016,15.4
+834,30,4,7,30,0.264,4.446,0,NBA010,15.7
+835,28,2,3,36,0.384,2.712,0,NBA001,8.6
+836,21,3,0,41,2.367,5.628,,NBA001,19.5
+837,23,2,3,24,0.552,0.96,0,NBA004,6.3
+838,23,1,7,22,0.849,2.319,0,NBA003,14.4
+839,26,1,10,25,1.306,0.469,0,NBA001,7.1
+840,31,1,8,22,0.37,1.104,0,NBA001,6.7
+841,38,3,13,25,0.343,1.082,0,NBA018,5.7
+842,29,3,7,63,0.572,2.893,0,NBA001,5.5
+843,32,1,14,36,0.273,0.591,0,NBA000,2.4
+844,32,2,8,45,0.982,0.683,0,NBA002,3.7
+845,41,1,7,43,0.694,1.198,0,NBA011,4.4
+846,27,1,5,26,0.548,1.22,,NBA007,6.8
+847,28,2,7,34,0.359,2.021,0,NBA002,7
+848,25,4,0,18,2.802,3.21,1,NBA001,33.4
+849,32,1,12,28,0.116,0.696,0,NBA012,2.9
+850,52,1,16,64,1.866,3.638,0,NBA025,8.6
\ No newline at end of file
diff --git a/K_Means_Customer.ipynb b/K_Means_Customer.ipynb
new file mode 100644
index 0000000..96b31a6
--- /dev/null
+++ b/K_Means_Customer.ipynb
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+ Machine_Learning-and-Deep_Learning/K_Means_Customer.ipynb at master · darsh8200/Machine_Learning-and-Deep_Learning
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From 950715324add55072bd3b6fa92a76dcdd4da8237 Mon Sep 17 00:00:00 2001
From: darsh8200 <46572301+darsh8200@users.noreply.github.com>
Date: Fri, 25 Oct 2019 16:39:35 +0530
Subject: [PATCH 02/11] Rename Cust_Segmentation.csv to
K_Means/Cust_Segmentation.csv
---
Cust_Segmentation.csv => K_Means/Cust_Segmentation.csv | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
rename Cust_Segmentation.csv => K_Means/Cust_Segmentation.csv (97%)
diff --git a/Cust_Segmentation.csv b/K_Means/Cust_Segmentation.csv
similarity index 97%
rename from Cust_Segmentation.csv
rename to K_Means/Cust_Segmentation.csv
index 17803f4..20d0287 100644
--- a/Cust_Segmentation.csv
+++ b/K_Means/Cust_Segmentation.csv
@@ -848,4 +848,4 @@ Customer Id,Age,Edu,Years Employed,Income,Card Debt,Other Debt,Defaulted,Address
847,28,2,7,34,0.359,2.021,0,NBA002,7
848,25,4,0,18,2.802,3.21,1,NBA001,33.4
849,32,1,12,28,0.116,0.696,0,NBA012,2.9
-850,52,1,16,64,1.866,3.638,0,NBA025,8.6
\ No newline at end of file
+850,52,1,16,64,1.866,3.638,0,NBA025,8.6
From 76067de77302796459ef9b3cd4322c7182c442ca Mon Sep 17 00:00:00 2001
From: darsh8200 <46572301+darsh8200@users.noreply.github.com>
Date: Fri, 25 Oct 2019 16:40:14 +0530
Subject: [PATCH 03/11] Rename K_Means_Customer.ipynb to
K_Means/K_Means_Customer.ipynb
---
K_Means_Customer.ipynb => K_Means/K_Means_Customer.ipynb | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename K_Means_Customer.ipynb => K_Means/K_Means_Customer.ipynb (100%)
diff --git a/K_Means_Customer.ipynb b/K_Means/K_Means_Customer.ipynb
similarity index 100%
rename from K_Means_Customer.ipynb
rename to K_Means/K_Means_Customer.ipynb
From 00f65cebf8c49f204f9e62309acd84517a100991 Mon Sep 17 00:00:00 2001
From: darsh8200 <46572301+darsh8200@users.noreply.github.com>
Date: Fri, 25 Oct 2019 16:48:26 +0530
Subject: [PATCH 04/11] Add files via upload
---
Vehicle_Hierarchical.ipynb | 1214 ++++++++++++++++++++++++++++++++++++
cars_clus.csv | 160 +++++
2 files changed, 1374 insertions(+)
create mode 100644 Vehicle_Hierarchical.ipynb
create mode 100644 cars_clus.csv
diff --git a/Vehicle_Hierarchical.ipynb b/Vehicle_Hierarchical.ipynb
new file mode 100644
index 0000000..7eb5db5
--- /dev/null
+++ b/Vehicle_Hierarchical.ipynb
@@ -0,0 +1,1214 @@
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+ Machine_Learning-and-Deep_Learning/Vehicle_Hierarchical.ipynb at master · darsh8200/Machine_Learning-and-Deep_Learning
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diff --git a/cars_clus.csv b/cars_clus.csv
new file mode 100644
index 0000000..9b9029e
--- /dev/null
+++ b/cars_clus.csv
@@ -0,0 +1,160 @@
+manufact,model,sales,resale,type,price,engine_s,horsepow,wheelbas,width,length,curb_wgt,fuel_cap,mpg,lnsales,partition
+Acura,Integra,16.919,16.360,0.000,21.500,1.800,140.000,101.200,67.300,172.400,2.639,13.200,28.000,2.828,0.000
+Acura,TL,39.384,19.875,0.000,28.400,3.200,225.000,108.100,70.300,192.900,3.517,17.200,25.000,3.673,0.000
+Acura,CL,14.114,18.225,0.000,$null$,3.200,225.000,106.900,70.600,192.000,3.470,17.200,26.000,2.647,0.000
+Acura,RL,8.588,29.725,0.000,42.000,3.500,210.000,114.600,71.400,196.600,3.850,18.000,22.000,2.150,0.000
+Audi,A4,20.397,22.255,0.000,23.990,1.800,150.000,102.600,68.200,178.000,2.998,16.400,27.000,3.015,0.000
+Audi,A6,18.780,23.555,0.000,33.950,2.800,200.000,108.700,76.100,192.000,3.561,18.500,22.000,2.933,0.000
+Audi,A8,1.380,39.000,0.000,62.000,4.200,310.000,113.000,74.000,198.200,3.902,23.700,21.000,0.322,0.000
+BMW,323i,19.747,$null$,0.000,26.990,2.500,170.000,107.300,68.400,176.000,3.179,16.600,26.100,2.983,0.000
+BMW,328i,9.231,28.675,0.000,33.400,2.800,193.000,107.300,68.500,176.000,3.197,16.600,24.000,2.223,0.000
+BMW,528i,17.527,36.125,0.000,38.900,2.800,193.000,111.400,70.900,188.000,3.472,18.500,24.800,2.864,0.000
+Buick,Century,91.561,12.475,0.000,21.975,3.100,175.000,109.000,72.700,194.600,3.368,17.500,25.000,4.517,0.000
+Buick,Regal,39.350,13.740,0.000,25.300,3.800,240.000,109.000,72.700,196.200,3.543,17.500,23.000,3.672,0.000
+Buick,Park Avenue,27.851,20.190,0.000,31.965,3.800,205.000,113.800,74.700,206.800,3.778,18.500,24.000,3.327,0.000
+Buick,LeSabre,83.257,13.360,0.000,27.885,3.800,205.000,112.200,73.500,200.000,3.591,17.500,25.000,4.422,0.000
+Cadillac,DeVille,63.729,22.525,0.000,39.895,4.600,275.000,115.300,74.500,207.200,3.978,18.500,22.000,4.155,0.000
+Cadillac,Seville,15.943,27.100,0.000,44.475,4.600,275.000,112.200,75.000,201.000,$null$,18.500,22.000,2.769,0.000
+Cadillac,Eldorado,6.536,25.725,0.000,39.665,4.600,275.000,108.000,75.500,200.600,3.843,19.000,22.000,1.877,0.000
+Cadillac,Catera,11.185,18.225,0.000,31.010,3.000,200.000,107.400,70.300,194.800,3.770,18.000,22.000,2.415,0.000
+Cadillac,Escalade,14.785,$null$,1.000,46.225,5.700,255.000,117.500,77.000,201.200,5.572,30.000,15.000,2.694,0.000
+Chevrolet,Cavalier,145.519,9.250,0.000,13.260,2.200,115.000,104.100,67.900,180.900,2.676,14.300,27.000,4.980,0.000
+Chevrolet,Malibu,135.126,11.225,0.000,16.535,3.100,170.000,107.000,69.400,190.400,3.051,15.000,25.000,4.906,0.000
+Chevrolet,Lumina,24.629,10.310,0.000,18.890,3.100,175.000,107.500,72.500,200.900,3.330,16.600,25.000,3.204,0.000
+Chevrolet,Monte Carlo,42.593,11.525,0.000,19.390,3.400,180.000,110.500,72.700,197.900,3.340,17.000,27.000,3.752,0.000
+Chevrolet,Camaro,26.402,13.025,0.000,24.340,3.800,200.000,101.100,74.100,193.200,3.500,16.800,25.000,3.273,0.000
+Chevrolet,Corvette,17.947,36.225,0.000,45.705,5.700,345.000,104.500,73.600,179.700,3.210,19.100,22.000,2.887,0.000
+Chevrolet,Prizm,32.299,9.125,0.000,13.960,1.800,120.000,97.100,66.700,174.300,2.398,13.200,33.000,3.475,0.000
+Chevrolet,Metro,21.855,5.160,0.000,9.235,1.000,55.000,93.100,62.600,149.400,1.895,10.300,45.000,3.084,0.000
+Chevrolet,Impala,107.995,$null$,0.000,18.890,3.400,180.000,110.500,73.000,200.000,3.389,17.000,27.000,4.682,0.000
+Chrysler,Sebring Coupe,7.854,12.360,0.000,19.840,2.500,163.000,103.700,69.700,190.900,2.967,15.900,24.000,2.061,0.000
+Chrysler,Sebring Conv.,32.775,14.180,0.000,24.495,2.500,168.000,106.000,69.200,193.000,3.332,16.000,24.000,3.490,0.000
+Chrysler,Concorde,31.148,13.725,0.000,22.245,2.700,200.000,113.000,74.400,209.100,3.452,17.000,26.000,3.439,0.000
+Chrysler,Cirrus,32.306,12.640,0.000,16.480,2.000,132.000,108.000,71.000,186.000,2.911,16.000,27.000,3.475,0.000
+Chrysler,LHS,13.462,17.325,0.000,28.340,3.500,253.000,113.000,74.400,207.700,3.564,17.000,23.000,2.600,0.000
+Chrysler,Town & Country,53.480,19.540,1.000,$null$,$null$,$null$,$null$,$null$,$null$,$null$,$null$,$null$,3.979,0.000
+Chrysler,300M,30.696,$null$,0.000,29.185,3.500,253.000,113.000,74.400,197.800,3.567,17.000,23.000,3.424,0.000
+Dodge,Neon,76.034,7.750,0.000,12.640,2.000,132.000,105.000,74.400,174.400,2.567,12.500,29.000,4.331,0.000
+Dodge,Avenger,4.734,12.545,0.000,19.045,2.500,163.000,103.700,69.100,190.200,2.879,15.900,24.000,1.555,0.000
+Dodge,Stratus,71.186,10.185,0.000,20.230,2.500,168.000,108.000,71.000,186.000,3.058,16.000,24.000,4.265,0.000
+Dodge,Intrepid,88.028,12.275,0.000,22.505,2.700,202.000,113.000,74.700,203.700,3.489,17.000,$null$,4.478,0.000
+Dodge,Viper,0.916,58.470,0.000,69.725,8.000,450.000,96.200,75.700,176.700,3.375,19.000,16.000,-0.088,0.000
+Dodge,Ram Pickup,227.061,15.060,1.000,19.460,5.200,230.000,138.700,79.300,224.200,4.470,26.000,17.000,5.425,0.000
+Dodge,Ram Wagon,16.767,15.510,1.000,21.315,3.900,175.000,109.600,78.800,192.600,4.245,32.000,15.000,2.819,0.000
+Dodge,Ram Van,31.038,13.425,1.000,18.575,3.900,175.000,127.200,78.800,208.500,4.298,32.000,16.000,3.435,0.000
+Dodge,Dakota,111.313,11.260,1.000,16.980,2.500,120.000,131.000,71.500,215.000,3.557,22.000,19.000,4.712,0.000
+Dodge,Durango,101.323,$null$,1.000,26.310,5.200,230.000,115.700,71.700,193.500,4.394,25.000,17.000,4.618,0.000
+Dodge,Caravan,181.749,12.025,1.000,19.565,2.400,150.000,113.300,76.800,186.300,3.533,20.000,24.000,5.203,0.000
+Ford,Escort,70.227,7.425,0.000,12.070,2.000,110.000,98.400,67.000,174.700,2.468,12.700,30.000,4.252,0.000
+Ford,Mustang,113.369,12.760,0.000,21.560,3.800,190.000,101.300,73.100,183.200,3.203,15.700,24.000,4.731,0.000
+Ford,Contour,35.068,8.835,0.000,17.035,2.500,170.000,106.500,69.100,184.600,2.769,15.000,25.000,3.557,0.000
+Ford,Taurus,245.815,10.055,0.000,17.885,3.000,155.000,108.500,73.000,197.600,3.368,16.000,24.000,5.505,0.000
+Ford,Focus,175.670,$null$,0.000,12.315,2.000,107.000,103.000,66.900,174.800,2.564,13.200,30.000,5.169,0.000
+Ford,Crown Victoria,63.403,14.210,0.000,22.195,4.600,200.000,114.700,78.200,212.000,3.908,19.000,21.000,4.150,0.000
+Ford,Explorer,276.747,16.640,1.000,31.930,4.000,210.000,111.600,70.200,190.700,3.876,21.000,19.000,5.623,0.000
+Ford,Windstar,155.787,13.175,1.000,21.410,3.000,150.000,120.700,76.600,200.900,3.761,26.000,21.000,5.048,0.000
+Ford,Expedition,125.338,23.575,1.000,36.135,4.600,240.000,119.000,78.700,204.600,4.808,26.000,16.000,4.831,0.000
+Ford,Ranger,220.650,7.850,1.000,12.050,2.500,119.000,117.500,69.400,200.700,3.086,20.000,23.000,5.397,0.000
+Ford,F-Series,540.561,15.075,1.000,26.935,4.600,220.000,138.500,79.100,224.500,4.241,25.100,18.000,6.293,0.000
+Honda,Civic,199.685,9.850,0.000,12.885,1.600,106.000,103.200,67.100,175.100,2.339,11.900,32.000,5.297,0.000
+Honda,Accord,230.902,13.210,0.000,15.350,2.300,135.000,106.900,70.300,188.800,2.932,17.100,27.000,5.442,0.000
+Honda,CR-V,73.203,17.710,1.000,20.550,2.000,146.000,103.200,68.900,177.600,3.219,15.300,24.000,4.293,0.000
+Honda,Passport,12.855,17.525,1.000,26.600,3.200,205.000,106.400,70.400,178.200,3.857,21.100,19.000,2.554,0.000
+Honda,Odyssey,76.029,19.490,1.000,26.000,3.500,210.000,118.100,75.600,201.200,4.288,20.000,23.000,4.331,0.000
+Hyundai,Accent,41.184,5.860,0.000,9.699,1.500,92.000,96.100,65.700,166.700,2.240,11.900,31.000,3.718,0.000
+Hyundai,Elantra,66.692,7.825,0.000,11.799,2.000,140.000,100.400,66.900,174.000,2.626,14.500,27.000,4.200,0.000
+Hyundai,Sonata,29.450,8.910,0.000,14.999,2.400,148.000,106.300,71.600,185.400,3.072,17.200,25.000,3.383,0.000
+Infiniti,I30,23.713,19.690,0.000,29.465,3.000,227.000,108.300,70.200,193.700,3.342,18.500,25.000,3.166,0.000
+Jaguar,S-Type,15.467,$null$,0.000,42.800,3.000,240.000,114.500,71.600,191.300,3.650,18.400,21.000,2.739,0.000
+Jeep,Wrangler,55.557,13.475,1.000,14.460,2.500,120.000,93.400,66.700,152.000,3.045,19.000,17.000,4.017,0.000
+Jeep,Cherokee,80.556,13.775,1.000,21.620,4.000,190.000,101.400,69.400,167.500,3.194,20.000,20.000,4.389,0.000
+Jeep,Grand Cherokee,157.040,18.810,1.000,26.895,4.000,195.000,105.900,72.300,181.500,3.880,20.500,19.000,5.057,0.000
+Lexus,ES300,24.072,26.975,0.000,31.505,3.000,210.000,105.100,70.500,190.200,3.373,18.500,23.000,3.181,0.000
+Lexus,GS300,12.698,32.075,0.000,37.805,3.000,225.000,110.200,70.900,189.200,3.638,19.800,23.000,2.541,0.000
+Lexus,GS400,3.334,$null$,0.000,46.305,4.000,300.000,110.200,70.900,189.200,3.693,19.800,21.000,1.204,0.000
+Lexus,LS400,6.375,40.375,0.000,54.005,4.000,290.000,112.200,72.000,196.700,3.890,22.500,22.000,1.852,0.000
+Lexus,LX470,9.126,$null$,1.000,60.105,4.700,230.000,112.200,76.400,192.500,5.401,25.400,15.000,2.211,0.000
+Lexus,RX300,51.238,$null$,1.000,34.605,3.000,220.000,103.000,71.500,180.100,3.900,17.200,21.000,3.936,0.000
+Lincoln,Continental,13.798,20.525,0.000,39.080,4.600,275.000,109.000,73.600,208.500,3.868,20.000,22.000,2.625,0.000
+Lincoln,Town car,48.911,21.725,0.000,43.330,4.600,215.000,117.700,78.200,215.300,4.121,19.000,21.000,3.890,0.000
+Lincoln,Navigator,22.925,$null$,1.000,42.660,5.400,300.000,119.000,79.900,204.800,5.393,30.000,15.000,3.132,0.000
+Mitsubishi,Mirage,26.232,8.325,0.000,13.987,1.800,113.000,98.400,66.500,173.600,2.250,13.200,30.000,3.267,0.000
+Mitsubishi,Eclipse,42.541,10.395,0.000,19.047,2.400,154.000,100.800,68.900,175.400,2.910,15.900,24.000,3.750,0.000
+Mitsubishi,Galant,55.616,10.595,0.000,17.357,2.400,145.000,103.700,68.500,187.800,2.945,16.300,25.000,4.018,0.000
+Mitsubishi,Diamante,5.711,16.575,0.000,24.997,3.500,210.000,107.100,70.300,194.100,3.443,19.000,22.000,1.742,0.000
+Mitsubishi,3000GT,0.110,20.940,0.000,25.450,3.000,161.000,97.200,72.400,180.300,3.131,19.800,21.000,-2.207,0.000
+Mitsubishi,Montero,11.337,19.125,1.000,31.807,3.500,200.000,107.300,69.900,186.600,4.520,24.300,18.000,2.428,0.000
+Mitsubishi,Montero Sport,39.348,13.880,1.000,22.527,3.000,173.000,107.300,66.700,178.300,3.510,19.500,20.000,3.672,0.000
+Mercury,Mystique,14.351,8.800,0.000,16.240,2.000,125.000,106.500,69.100,184.800,2.769,15.000,28.000,2.664,0.000
+Mercury,Cougar,26.529,13.890,0.000,16.540,2.000,125.000,106.400,69.600,185.000,2.892,16.000,30.000,3.278,0.000
+Mercury,Sable,67.956,11.030,0.000,19.035,3.000,153.000,108.500,73.000,199.700,3.379,16.000,24.000,4.219,0.000
+Mercury,Grand Marquis,81.174,14.875,0.000,22.605,4.600,200.000,114.700,78.200,212.000,3.958,19.000,21.000,4.397,0.000
+Mercury,Mountaineer,27.609,20.430,1.000,27.560,4.000,210.000,111.600,70.200,190.100,3.876,21.000,18.000,3.318,0.000
+Mercury,Villager,20.380,14.795,1.000,22.510,3.300,170.000,112.200,74.900,194.700,3.944,20.000,21.000,3.015,0.000
+Mercedes-Benz,C-Class,18.392,26.050,0.000,31.750,2.300,185.000,105.900,67.700,177.400,3.250,16.400,26.000,2.912,0.000
+Mercedes-Benz,E-Class,27.602,41.450,0.000,49.900,3.200,221.000,111.500,70.800,189.400,3.823,21.100,25.000,3.318,0.000
+Mercedes-Benz,S-Class,16.774,50.375,0.000,69.700,4.300,275.000,121.500,73.100,203.100,4.133,23.200,21.000,2.820,0.000
+Mercedes-Benz,SL-Class,3.311,58.600,0.000,82.600,5.000,302.000,99.000,71.300,177.100,4.125,21.100,20.000,1.197,0.000
+Mercedes-Benz,SLK,7.998,$null$,0.000,38.900,2.300,190.000,94.500,67.500,157.900,3.055,15.900,26.000,2.079,0.000
+Mercedes-Benz,SLK230,1.526,$null$,0.000,41.000,2.300,185.000,94.500,67.500,157.300,2.975,14.000,27.000,0.423,0.000
+Mercedes-Benz,CLK Coupe,11.592,$null$,0.000,41.600,3.200,215.000,105.900,67.800,180.300,3.213,16.400,26.000,2.450,0.000
+Mercedes-Benz,CL500,0.954,$null$,0.000,85.500,5.000,302.000,113.600,73.100,196.600,4.115,23.200,20.000,-0.047,0.000
+Mercedes-Benz,M-Class,28.976,$null$,1.000,35.300,3.200,215.000,111.000,72.200,180.600,4.387,19.000,20.000,3.366,0.000
+Nissan,Sentra,42.643,8.450,0.000,13.499,1.800,126.000,99.800,67.300,177.500,2.593,13.200,30.000,3.753,0.000
+Nissan,Altima,88.094,11.295,0.000,20.390,2.400,155.000,103.100,69.100,183.500,3.012,15.900,25.000,4.478,0.000
+Nissan,Maxima,79.853,15.125,0.000,26.249,3.000,222.000,108.300,70.300,190.500,3.294,18.500,25.000,4.380,0.000
+Nissan,Quest,27.308,15.380,1.000,26.399,3.300,170.000,112.200,74.900,194.800,3.991,20.000,21.000,3.307,0.000
+Nissan,Pathfinder,42.574,17.810,1.000,29.299,3.300,170.000,106.300,71.700,182.600,3.947,21.000,19.000,3.751,0.000
+Nissan,Xterra,54.158,$null$,1.000,22.799,3.300,170.000,104.300,70.400,178.000,3.821,19.400,18.000,3.992,0.000
+Nissan,Frontier,65.005,$null$,1.000,17.890,3.300,170.000,116.100,66.500,196.100,3.217,19.400,18.000,4.174,0.000
+Oldsmobile,Cutlass,1.112,11.240,0.000,18.145,3.100,150.000,107.000,69.400,192.000,3.102,15.200,25.000,0.106,0.000
+Oldsmobile,Intrigue,38.554,$null$,0.000,24.150,3.500,215.000,109.000,73.600,195.900,3.455,18.000,$null$,3.652,0.000
+Oldsmobile,Alero,80.255,$null$,0.000,18.270,2.400,150.000,107.000,70.100,186.700,2.958,15.000,27.000,4.385,0.000
+Oldsmobile,Aurora,14.690,19.890,0.000,36.229,4.000,250.000,113.800,74.400,205.400,3.967,18.500,22.000,2.687,0.000
+Oldsmobile,Bravada,20.017,19.925,1.000,31.598,4.300,190.000,107.000,67.800,181.200,4.068,17.500,19.000,2.997,0.000
+Oldsmobile,Silhouette,24.361,15.240,1.000,25.345,3.400,185.000,120.000,72.200,201.400,3.948,25.000,22.000,3.193,0.000
+Plymouth,Neon,32.734,7.750,0.000,12.640,2.000,132.000,105.000,74.400,174.400,2.559,12.500,29.000,3.488,0.000
+Plymouth,Breeze,5.240,9.800,0.000,16.080,2.000,132.000,108.000,71.000,186.300,2.942,16.000,27.000,1.656,0.000
+Plymouth,Voyager,24.155,12.025,1.000,18.850,2.400,150.000,113.300,76.800,186.300,3.528,20.000,24.000,3.184,0.000
+Plymouth,Prowler,1.872,$null$,0.000,43.000,3.500,253.000,113.300,76.300,165.400,2.850,12.000,21.000,0.627,0.000
+Pontiac,Sunfire,51.645,13.790,0.000,21.610,2.400,150.000,104.100,68.400,181.900,2.906,15.000,27.000,3.944,0.000
+Pontiac,Grand Am,131.097,10.290,0.000,19.720,3.400,175.000,107.000,70.400,186.300,3.091,15.200,25.000,4.876,0.000
+Pontiac,Firebird,19.911,17.805,0.000,25.310,3.800,200.000,101.100,74.500,193.400,3.492,16.800,25.000,2.991,0.000
+Pontiac,Grand Prix,92.364,14.010,0.000,21.665,3.800,195.000,110.500,72.700,196.500,3.396,18.000,25.000,4.526,0.000
+Pontiac,Bonneville,35.945,13.225,0.000,23.755,3.800,205.000,112.200,72.600,202.500,3.590,17.500,24.000,3.582,0.000
+Pontiac,Montana,39.572,$null$,1.000,25.635,3.400,185.000,120.000,72.700,201.300,3.942,25.000,23.000,3.678,0.000
+Porsche,Boxter,8.982,41.250,0.000,41.430,2.700,217.000,95.200,70.100,171.000,2.778,17.000,22.000,2.195,0.000
+Porsche,Carrera Coupe,1.280,60.625,0.000,71.020,3.400,300.000,92.600,69.500,174.500,3.032,17.000,21.000,0.247,0.000
+Porsche,Carrera Cabriolet,1.866,67.550,0.000,74.970,3.400,300.000,92.600,69.500,174.500,3.075,17.000,23.000,0.624,0.000
+Saab,9-5,9.191,$null$,0.000,33.120,2.300,170.000,106.400,70.600,189.200,3.280,18.500,23.000,2.218,0.000
+Saab,9-3,12.115,$null$,0.000,26.100,2.000,185.000,102.600,67.400,182.200,2.990,16.900,23.000,2.494,0.000
+Saturn,SL,80.620,9.200,0.000,10.685,1.900,100.000,102.400,66.400,176.900,2.332,12.100,33.000,4.390,0.000
+Saturn,SC,24.546,10.590,0.000,12.535,1.900,100.000,102.400,66.400,180.000,2.367,12.100,33.000,3.201,0.000
+Saturn,SW,5.223,10.790,0.000,14.290,1.900,124.000,102.400,66.400,176.900,2.452,12.100,31.000,1.653,0.000
+Saturn,LW,8.472,$null$,0.000,18.835,2.200,137.000,106.500,69.000,190.400,3.075,13.100,27.000,2.137,0.000
+Saturn,LS,49.989,$null$,0.000,15.010,2.200,137.000,106.500,69.000,190.400,2.910,13.100,28.000,3.912,0.000
+Subaru,Outback,47.107,$null$,0.000,22.695,2.500,165.000,103.500,67.500,185.800,3.415,16.900,25.000,3.852,0.000
+Subaru,Forester,33.028,$null$,1.000,20.095,2.500,165.000,99.400,68.300,175.200,3.125,15.900,24.000,3.497,0.000
+Toyota,Corolla,142.535,10.025,0.000,13.108,1.800,120.000,97.000,66.700,174.000,2.420,13.200,33.000,4.960,0.000
+Toyota,Camry,247.994,13.245,0.000,17.518,2.200,133.000,105.200,70.100,188.500,2.998,18.500,27.000,5.513,0.000
+Toyota,Avalon,63.849,18.140,0.000,25.545,3.000,210.000,107.100,71.700,191.900,3.417,18.500,26.000,4.157,0.000
+Toyota,Celica,33.269,15.445,0.000,16.875,1.800,140.000,102.400,68.300,170.500,2.425,14.500,31.000,3.505,0.000
+Toyota,Tacoma,84.087,9.575,1.000,11.528,2.400,142.000,103.300,66.500,178.700,2.580,15.100,23.000,4.432,0.000
+Toyota,Sienna,65.119,$null$,1.000,22.368,3.000,194.000,114.200,73.400,193.500,3.759,20.900,22.000,4.176,0.000
+Toyota,RAV4,25.106,13.325,1.000,16.888,2.000,127.000,94.900,66.700,163.800,2.668,15.300,27.000,3.223,0.000
+Toyota,4Runner,68.411,19.425,1.000,22.288,2.700,150.000,105.300,66.500,183.300,3.440,18.500,23.000,4.226,0.000
+Toyota,Land Cruiser,9.835,34.080,1.000,51.728,4.700,230.000,112.200,76.400,192.500,5.115,25.400,15.000,2.286,0.000
+Volkswagen,Golf,9.761,11.425,0.000,14.900,2.000,115.000,98.900,68.300,163.300,2.767,14.500,26.000,2.278,0.000
+Volkswagen,Jetta,83.721,13.240,0.000,16.700,2.000,115.000,98.900,68.300,172.300,2.853,14.500,26.000,4.427,0.000
+Volkswagen,Passat,51.102,16.725,0.000,21.200,1.800,150.000,106.400,68.500,184.100,3.043,16.400,27.000,3.934,0.000
+Volkswagen,Cabrio,9.569,16.575,0.000,19.990,2.000,115.000,97.400,66.700,160.400,3.079,13.700,26.000,2.259,0.000
+Volkswagen,GTI,5.596,13.760,0.000,17.500,2.000,115.000,98.900,68.300,163.300,2.762,14.600,26.000,1.722,0.000
+Volkswagen,Beetle,49.463,$null$,0.000,15.900,2.000,115.000,98.900,67.900,161.100,2.769,14.500,26.000,3.901,0.000
+Volvo,S40,16.957,$null$,0.000,23.400,1.900,160.000,100.500,67.600,176.600,2.998,15.800,25.000,2.831,0.000
+Volvo,V40,3.545,$null$,0.000,24.400,1.900,160.000,100.500,67.600,176.600,3.042,15.800,25.000,1.266,0.000
+Volvo,S70,15.245,$null$,0.000,27.500,2.400,168.000,104.900,69.300,185.900,3.208,17.900,25.000,2.724,0.000
+Volvo,V70,17.531,$null$,0.000,28.800,2.400,168.000,104.900,69.300,186.200,3.259,17.900,25.000,2.864,0.000
+Volvo,C70,3.493,$null$,0.000,45.500,2.300,236.000,104.900,71.500,185.700,3.601,18.500,23.000,1.251,0.000
+Volvo,S80,18.969,$null$,0.000,36.000,2.900,201.000,109.900,72.100,189.800,3.600,21.100,24.000,2.943,0.000
+,newCar,$null$,$null$,$null$,21.500,1.500,76.000,106.300,67.900,175.000,2.932,11.900,46.000,$null$,1.000
+,newTruck,$null$,$null$,$null$,34.200,3.500,167.000,109.800,75.200,188.400,4.508,17.200,26.000,$null$,1.000
From 4c5d1b2205faa64a2c011ca84f434bb07290240d Mon Sep 17 00:00:00 2001
From: darsh8200 <46572301+darsh8200@users.noreply.github.com>
Date: Fri, 25 Oct 2019 16:49:07 +0530
Subject: [PATCH 05/11] Rename Vehicle_Hierarchical.ipynb to
Hierarchical/Vehicle_Hierarchical.ipynb
---
.../Vehicle_Hierarchical.ipynb | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename Vehicle_Hierarchical.ipynb => Hierarchical/Vehicle_Hierarchical.ipynb (100%)
diff --git a/Vehicle_Hierarchical.ipynb b/Hierarchical/Vehicle_Hierarchical.ipynb
similarity index 100%
rename from Vehicle_Hierarchical.ipynb
rename to Hierarchical/Vehicle_Hierarchical.ipynb
From f430c4a364b63ad37bb0ab54a0012e5fec23cc8e Mon Sep 17 00:00:00 2001
From: darsh8200 <46572301+darsh8200@users.noreply.github.com>
Date: Fri, 25 Oct 2019 16:49:30 +0530
Subject: [PATCH 06/11] Rename cars_clus.csv to Hierarchical/cars_clus.csv
---
cars_clus.csv => Hierarchical/cars_clus.csv | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename cars_clus.csv => Hierarchical/cars_clus.csv (100%)
diff --git a/cars_clus.csv b/Hierarchical/cars_clus.csv
similarity index 100%
rename from cars_clus.csv
rename to Hierarchical/cars_clus.csv
From 75a43cd49103809b747a0edbfbfd77c7e846030b Mon Sep 17 00:00:00 2001
From: darsh8200 <46572301+darsh8200@users.noreply.github.com>
Date: Fri, 25 Oct 2019 17:01:51 +0530
Subject: [PATCH 07/11] Add files via upload
---
SVM.ipynb | 1214 ++++++++++++++++++++++++++++++++++++++++++++++
cell_samples.csv | 700 ++++++++++++++++++++++++++
2 files changed, 1914 insertions(+)
create mode 100644 SVM.ipynb
create mode 100644 cell_samples.csv
diff --git a/SVM.ipynb b/SVM.ipynb
new file mode 100644
index 0000000..239bf16
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diff --git a/cell_samples.csv b/cell_samples.csv
new file mode 100644
index 0000000..e6f7bb2
--- /dev/null
+++ b/cell_samples.csv
@@ -0,0 +1,700 @@
+ID,Clump,UnifSize,UnifShape,MargAdh,SingEpiSize,BareNuc,BlandChrom,NormNucl,Mit,Class
+1000025,5,1,1,1,2,1,3,1,1,2
+1002945,5,4,4,5,7,10,3,2,1,2
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+1016277,6,8,8,1,3,4,3,7,1,2
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+1033078,2,1,1,1,2,1,1,1,5,2
+1033078,4,2,1,1,2,1,2,1,1,2
+1035283,1,1,1,1,1,1,3,1,1,2
+1036172,2,1,1,1,2,1,2,1,1,2
+1041801,5,3,3,3,2,3,4,4,1,4
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+1044572,8,7,5,10,7,9,5,5,4,4
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+1056784,3,1,1,1,2,1,2,1,1,2
+1057013,8,4,5,1,2,?,7,3,1,4
+1059552,1,1,1,1,2,1,3,1,1,2
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+1096800,6,6,6,9,6,?,7,8,1,2
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+1197510,5,1,1,1,2,?,3,1,1,2
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From 3fb191df9305f6c563c65a5e8c382e974eda63df Mon Sep 17 00:00:00 2001
From: darsh8200 <46572301+darsh8200@users.noreply.github.com>
Date: Fri, 25 Oct 2019 17:02:43 +0530
Subject: [PATCH 08/11] Rename SVM.ipynb to Classification/SVM.ipynb
---
SVM.ipynb => Classification/SVM.ipynb | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename SVM.ipynb => Classification/SVM.ipynb (100%)
diff --git a/SVM.ipynb b/Classification/SVM.ipynb
similarity index 100%
rename from SVM.ipynb
rename to Classification/SVM.ipynb
From 53c0878337d8726eb737b3a7621b9d0764df39d8 Mon Sep 17 00:00:00 2001
From: darsh8200 <46572301+darsh8200@users.noreply.github.com>
Date: Fri, 25 Oct 2019 17:03:14 +0530
Subject: [PATCH 09/11] Rename cell_samples.csv to
Classification/cell_samples.csv
---
cell_samples.csv => Classification/cell_samples.csv | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename cell_samples.csv => Classification/cell_samples.csv (100%)
diff --git a/cell_samples.csv b/Classification/cell_samples.csv
similarity index 100%
rename from cell_samples.csv
rename to Classification/cell_samples.csv
From 16fe377e32878dd9417f9377def822eb11ae0fe5 Mon Sep 17 00:00:00 2001
From: darsh8200 <46572301+darsh8200@users.noreply.github.com>
Date: Fri, 25 Oct 2019 17:37:40 +0530
Subject: [PATCH 10/11] Create h-or-s_Prediction.py
it predicts whether the human in image is happy or sad
---
happy_sad_prediction/h-or-s_Prediction.py | 86 +++++++++++++++++++++++
1 file changed, 86 insertions(+)
create mode 100644 happy_sad_prediction/h-or-s_Prediction.py
diff --git a/happy_sad_prediction/h-or-s_Prediction.py b/happy_sad_prediction/h-or-s_Prediction.py
new file mode 100644
index 0000000..2759eb4
--- /dev/null
+++ b/happy_sad_prediction/h-or-s_Prediction.py
@@ -0,0 +1,86 @@
+import tensorflow as tf
+import os
+import zipfile
+from os import path, getcwd, chdir
+
+path = f"{getcwd()}/tmp2/happy-or-sad.zip"
+
+zip_ref = zipfile.ZipFile(path, 'r')
+zip_ref.extractall("/tmp/h-or-s")
+zip_ref.close()
+
+
+# GRADED FUNCTION: train_happy_sad_model
+def train_happy_sad_model():
+ DESIRED_ACCURACY = 0.999
+
+ class myCallback(tf.keras.callbacks.Callback):
+ def on_epoch_end(self, epoch, logs={}):
+ if(logs.get('acc')>0.999):
+ print("\nReached 99.9% accuracy so cancelling training!")
+ self.model.stop_training = True
+
+
+ # This Code Block should Define and Compile the Model. Please assume the images are 150 X 150 in your implementation.
+ model = tf.keras.models.Sequential([
+ # Your Code Here
+ # Note the input shape is the desired size of the image 300x300 with 3 bytes color
+ # This is the first convolution
+ tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)),
+ tf.keras.layers.MaxPooling2D(2, 2),
+ # The second convolution
+ tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
+ tf.keras.layers.MaxPooling2D(2,2),
+ # The third convolution
+ tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
+ tf.keras.layers.MaxPooling2D(2,2),
+ # The fourth convolution
+ tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
+ tf.keras.layers.MaxPooling2D(2,2),
+ # The fifth convolution
+ tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
+ tf.keras.layers.MaxPooling2D(2,2),
+ # Flatten the results to feed into a DNN
+ tf.keras.layers.Flatten(),
+ # 512 neuron hidden layer
+ tf.keras.layers.Dense(512, activation='relu'),
+ # Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('horses') and 1 for the other ('humans')
+ tf.keras.layers.Dense(1, activation='sigmoid')
+ ])
+ callbacks = myCallback()
+ from tensorflow.keras.optimizers import RMSprop
+
+ model.compile(
+ loss='binary_crossentropy',
+ optimizer=RMSprop(lr=0.001),
+ metrics=['acc'])
+
+
+ # This code block should create an instance of an ImageDataGenerator called train_datagen
+ # And a train_generator by calling train_datagen.flow_from_directory
+
+ from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+ train_datagen = ImageDataGenerator(rescale=1/255) # Your Code Here
+
+ # Please use a target_size of 150 X 150.
+ train_generator = train_datagen.flow_from_directory(
+ '/tmp/h-or-s/', # This is the source directory for training images
+ target_size=(150, 150), # All images will be resized to 150x150
+ batch_size=128,
+ # Since we use binary_crossentropy loss, we need binary labels
+ class_mode='binary')
+ # Expected output: 'Found 80 images belonging to 2 classes'
+
+ # This code block should call model.fit_generator and train for
+ # a number of epochs.
+ # model fitting
+ history = model.fit_generator(
+ train_generator,
+ steps_per_epoch=8,
+ epochs=15,
+ verbose=1)
+ # model fitting
+ return history.history['acc'][-1]
+
+train_happy_sad_model()
From 3083311b2d7a4f3b8a1a48532e50e92bf43aa086 Mon Sep 17 00:00:00 2001
From: darsh8200 <46572301+darsh8200@users.noreply.github.com>
Date: Fri, 25 Oct 2019 17:38:29 +0530
Subject: [PATCH 11/11] Add files via upload
---
happy_sad_prediction/happy-or-sad.zip | Bin 0 -> 2670333 bytes
1 file changed, 0 insertions(+), 0 deletions(-)
create mode 100644 happy_sad_prediction/happy-or-sad.zip
diff --git a/happy_sad_prediction/happy-or-sad.zip b/happy_sad_prediction/happy-or-sad.zip
new file mode 100644
index 0000000000000000000000000000000000000000..5452088175e62adf5ed0ce8282a7750dd6764af4
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