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Commit 029c339

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Implemented Hierarchical Clustering algorithm by Agglomerative model (taking help of Dendrogram for optimal number of clusters)
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‎Part 4 - Clustering/Section 24 - K-Means Clustering/kmeans.py‎

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plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')
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plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')
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plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')
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plt.title('Clusters of customers')
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plt.title('Clusters of customers')`
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plt.xlabel('Annual Income (k$)')
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plt.ylabel('Spending Score (1-100)')
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plt.legend()
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CustomerID,Genre,Age,Annual Income (k$),Spending Score (1-100)
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0001,Male,19,15,39
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0002,Male,21,15,81
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0003,Female,20,16,6
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0004,Female,23,16,77
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0005,Female,31,17,40
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0006,Female,22,17,76
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0007,Female,35,18,6
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0008,Female,23,18,94
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0009,Male,64,19,3
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0010,Female,30,19,72
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0011,Male,67,19,14
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0012,Female,35,19,99
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0013,Female,58,20,15
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0014,Female,24,20,77
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0015,Male,37,20,13
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0016,Male,22,20,79
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0017,Female,35,21,35
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0018,Male,20,21,66
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0019,Male,52,23,29
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0020,Female,35,23,98
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0021,Male,35,24,35
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0022,Male,25,24,73
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0023,Female,46,25,5
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0024,Male,31,25,73
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0025,Female,54,28,14
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0026,Male,29,28,82
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0027,Female,45,28,32
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0028,Male,35,28,61
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0029,Female,40,29,31
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0030,Female,23,29,87
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0031,Male,60,30,4
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0032,Female,21,30,73
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0033,Male,53,33,4
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0034,Male,18,33,92
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0035,Female,49,33,14
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0036,Female,21,33,81
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0037,Female,42,34,17
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0038,Female,30,34,73
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0039,Female,36,37,26
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0040,Female,20,37,75
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0041,Female,65,38,35
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0042,Male,24,38,92
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0043,Male,48,39,36
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0044,Female,31,39,61
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0045,Female,49,39,28
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0046,Female,24,39,65
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0047,Female,50,40,55
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0048,Female,27,40,47
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0049,Female,29,40,42
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0050,Female,31,40,42
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0051,Female,49,42,52
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0052,Male,33,42,60
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0053,Female,31,43,54
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0054,Male,59,43,60
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0055,Female,50,43,45
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0056,Male,47,43,41
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0057,Female,51,44,50
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0058,Male,69,44,46
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0059,Female,27,46,51
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0060,Male,53,46,46
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0061,Male,70,46,56
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0062,Male,19,46,55
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0063,Female,67,47,52
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0064,Female,54,47,59
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0065,Male,63,48,51
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0066,Male,18,48,59
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0067,Female,43,48,50
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0068,Female,68,48,48
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0069,Male,19,48,59
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0070,Female,32,48,47
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0071,Male,70,49,55
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0072,Female,47,49,42
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0073,Female,60,50,49
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0074,Female,60,50,56
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0075,Male,59,54,47
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0076,Male,26,54,54
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0077,Female,45,54,53
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0078,Male,40,54,48
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0079,Female,23,54,52
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0080,Female,49,54,42
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0081,Male,57,54,51
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0082,Male,38,54,55
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0083,Male,67,54,41
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0084,Female,46,54,44
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0085,Female,21,54,57
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0086,Male,48,54,46
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0087,Female,55,57,58
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0088,Female,22,57,55
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0089,Female,34,58,60
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0090,Female,50,58,46
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0091,Female,68,59,55
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0092,Male,18,59,41
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0093,Male,48,60,49
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0094,Female,40,60,40
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0095,Female,32,60,42
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0096,Male,24,60,52
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0097,Female,47,60,47
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0098,Female,27,60,50
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0099,Male,48,61,42
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0100,Male,20,61,49
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0101,Female,23,62,41
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0102,Female,49,62,48
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0103,Male,67,62,59
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0104,Male,26,62,55
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0105,Male,49,62,56
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0106,Female,21,62,42
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0107,Female,66,63,50
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0108,Male,54,63,46
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0109,Male,68,63,43
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0110,Male,66,63,48
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0111,Male,65,63,52
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0112,Female,19,63,54
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0113,Female,38,64,42
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0114,Male,19,64,46
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0115,Female,18,65,48
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0116,Female,19,65,50
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0117,Female,63,65,43
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0118,Female,49,65,59
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0119,Female,51,67,43
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0120,Female,50,67,57
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0121,Male,27,67,56
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0122,Female,38,67,40
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0123,Female,40,69,58
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0124,Male,39,69,91
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0125,Female,23,70,29
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0126,Female,31,70,77
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0127,Male,43,71,35
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0128,Male,40,71,95
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0129,Male,59,71,11
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0130,Male,38,71,75
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0131,Male,47,71,9
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0132,Male,39,71,75
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0133,Female,25,72,34
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0134,Female,31,72,71
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0135,Male,20,73,5
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0136,Female,29,73,88
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0137,Female,44,73,7
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0138,Male,32,73,73
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0139,Male,19,74,10
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0140,Female,35,74,72
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0141,Female,57,75,5
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0142,Male,32,75,93
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0143,Female,28,76,40
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0144,Female,32,76,87
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0145,Male,25,77,12
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0146,Male,28,77,97
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0147,Male,48,77,36
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0148,Female,32,77,74
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0149,Female,34,78,22
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0150,Male,34,78,90
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0151,Male,43,78,17
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0152,Male,39,78,88
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0153,Female,44,78,20
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0154,Female,38,78,76
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0155,Female,47,78,16
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0156,Female,27,78,89
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0157,Male,37,78,1
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0158,Female,30,78,78
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0159,Male,34,78,1
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0160,Female,30,78,73
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0161,Female,56,79,35
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0162,Female,29,79,83
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0163,Male,19,81,5
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0164,Female,31,81,93
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0165,Male,50,85,26
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0166,Female,36,85,75
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0167,Male,42,86,20
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0168,Female,33,86,95
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0169,Female,36,87,27
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0170,Male,32,87,63
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0171,Male,40,87,13
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0172,Male,28,87,75
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0173,Male,36,87,10
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0174,Male,36,87,92
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0175,Female,52,88,13
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0176,Female,30,88,86
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0177,Male,58,88,15
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0178,Male,27,88,69
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0179,Male,59,93,14
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0180,Male,35,93,90
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0181,Female,37,97,32
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0182,Female,32,97,86
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0183,Male,46,98,15
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0184,Female,29,98,88
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0185,Female,41,99,39
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0186,Male,30,99,97
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0187,Female,54,101,24
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0188,Male,28,101,68
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0189,Female,41,103,17
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0190,Female,36,103,85
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0191,Female,34,103,23
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0192,Female,32,103,69
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0193,Male,33,113,8
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0194,Female,38,113,91
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0195,Female,47,120,16
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0196,Female,35,120,79
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0197,Female,45,126,28
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0198,Male,32,126,74
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0199,Male,32,137,18
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0200,Male,30,137,83
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# Data Preprocessing Template
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# Importing the dataset
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dataset = read.csv('Data.csv')
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# Splitting the dataset into the Training set and Test set
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# install.packages('caTools')
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library(caTools)
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set.seed(123)
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split = sample.split(dataset$DependentVariable, SplitRatio = 0.8)
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training_set = subset(dataset, split == TRUE)
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test_set = subset(dataset, split == FALSE)
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# Feature Scaling
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# training_set = scale(training_set)
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# test_set = scale(test_set)
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# Data Preprocessing Template
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# Importing the libraries
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# Importing the dataset
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dataset = pd.read_csv('Data.csv')
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X = dataset.iloc[:, :-1].values
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y = dataset.iloc[:, 3].values
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# Splitting the dataset into the Training set and Test set
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from sklearn.cross_validation import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
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# Feature Scaling
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"""from sklearn.preprocessing import StandardScaler
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sc_X = StandardScaler()
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X_train = sc_X.fit_transform(X_train)
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X_test = sc_X.transform(X_test)
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sc_y = StandardScaler()
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y_train = sc_y.fit_transform(y_train)"""
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# Hierarchical Clustering
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# Importing the dataset
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dataset = read.csv('Mall_Customers.csv')
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dataset = dataset[4:5]
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# Splitting the dataset into the Training set and Test set
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# install.packages('caTools')
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# library(caTools)
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# set.seed(123)
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# split = sample.split(dataset$DependentVariable, SplitRatio = 0.8)
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# training_set = subset(dataset, split == TRUE)
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# test_set = subset(dataset, split == FALSE)
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# Feature Scaling
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# training_set = scale(training_set)
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# test_set = scale(test_set)
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# Using the dendrogram to find the optimal number of clusters
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dendrogram = hclust(d = dist(dataset, method = 'euclidean'), method = 'ward.D')
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plot(dendrogram,
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main = paste('Dendrogram'),
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xlab = 'Customers',
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ylab = 'Euclidean distances')
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# Fitting Hierarchical Clustering to the dataset
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hc = hclust(d = dist(dataset, method = 'euclidean'), method = 'ward.D')
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y_hc = cutree(hc, 5)
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# Visualising the clusters
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library(cluster)
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clusplot(dataset,
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y_hc,
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lines = 0,
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shade = TRUE,
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color = TRUE,
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labels= 2,
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plotchar = FALSE,
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span = TRUE,
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main = paste('Clusters of customers'),
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xlab = 'Annual Income',
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ylab = 'Spending Score')
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# Hierarchical Clustering
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# Importing the libraries
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import numpy as np
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import matplotlib.pyplot as plt
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import pandas as pd
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# Importing the dataset
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dataset = pd.read_csv('Mall_Customers.csv')
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X = dataset.iloc[:, [3, 4]].values
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# y = dataset.iloc[:, 3].values
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# Splitting the dataset into the Training set and Test set
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"""from sklearn.cross_validation import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"""
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# Feature Scaling
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"""from sklearn.preprocessing import StandardScaler
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sc_X = StandardScaler()
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X_train = sc_X.fit_transform(X_train)
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X_test = sc_X.transform(X_test)
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sc_y = StandardScaler()
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y_train = sc_y.fit_transform(y_train)"""
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# Using the dendrogram to find the optimal number of clusters
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import scipy.cluster.hierarchy as sch
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dendrogram = sch.dendrogram(sch.linkage(X, method = 'ward'))
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plt.title('Dendrogram')
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plt.xlabel('Customers')
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plt.ylabel('Euclidean distances')
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plt.show()
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# Fitting Hierarchical Clustering to the dataset
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from sklearn.cluster import AgglomerativeClustering
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hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')
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y_hc = hc.fit_predict(X)
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# Visualising the clusters
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plt.scatter(X[y_hc == 0, 0], X[y_hc == 0, 1], s = 100, c = 'red', label = 'Cluster 1')
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plt.scatter(X[y_hc == 1, 0], X[y_hc == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')
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plt.scatter(X[y_hc == 2, 0], X[y_hc == 2, 1], s = 100, c = 'green', label = 'Cluster 3')
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plt.scatter(X[y_hc == 3, 0], X[y_hc == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')
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plt.scatter(X[y_hc == 4, 0], X[y_hc == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')
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plt.title('Clusters of customers')
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plt.xlabel('Annual Income (k$)')
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plt.ylabel('Spending Score (1-100)')
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plt.legend()
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plt.show()
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‎README.md‎

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4. **Clustering**
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* K-Means Clustering
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* Hierarchical Clustering
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* Recommendation for ML Enthusiasts: [Machine Learning A-ZTM: Hands-On Python & R In Data Science](https://www.udemy.com/machinelearning/)
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