{"id":3580,"date":"2023-07-16T17:38:21","date_gmt":"2023-07-16T17:38:21","guid":{"rendered":"https:\/\/statorials.org\/my\/k-%e1%80%86%e1%80%ad%e1%80%af%e1%80%9e%e1%80%8a%e1%80%ba%e1%80%99%e1%80%be%e1%80%ac-python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-%e1%80%a1%e1%80%85%e1%80%af%e1%80%9c%e1%80%ad%e1%80%af%e1%80%80%e1%80%ba\/"},"modified":"2023-07-16T17:38:21","modified_gmt":"2023-07-16T17:38:21","slug":"k-%e1%80%86%e1%80%ad%e1%80%af%e1%80%9e%e1%80%8a%e1%80%ba%e1%80%99%e1%80%be%e1%80%ac-python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-%e1%80%a1%e1%80%85%e1%80%af%e1%80%9c%e1%80%ad%e1%80%af%e1%80%80%e1%80%ba","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/k-%e1%80%86%e1%80%ad%e1%80%af%e1%80%9e%e1%80%8a%e1%80%ba%e1%80%99%e1%80%be%e1%80%ac-python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-%e1%80%a1%e1%80%85%e1%80%af%e1%80%9c%e1%80%ad%e1%80%af%e1%80%80%e1%80%ba\/","title":{"rendered":"Python \u1010\u103d\u1004\u103a k-means clustering- step-by-step \u1025\u1015\u1019\u102c"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\" target=\"_blank\" rel=\"noopener\">machine learning<\/a> \u1010\u103d\u1004\u103a \u1021\u101e\u102f\u1036\u1038\u1021\u1019\u103b\u102c\u1038\u1006\u102f\u1036\u1038 \u1021\u1005\u102f\u101c\u102d\u102f\u1000\u103a\u1021\u1015\u103c\u102f\u1036\u101c\u102d\u102f\u1000\u103a \u1021\u101a\u103a\u101c\u1002\u102d\u102f\u101b\u102e\u101e\u1019\u103a\u1019\u103b\u102c\u1038\u1011\u1032\u1019\u103e \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f <strong>k-means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u1001\u103c\u1004\u103a\u1038<\/strong> \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">K \u1006\u102d\u102f\u101e\u100a\u103a\u1019\u103e\u102c \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f <em>K<\/em> \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u101e\u102d\u102f\u1037 \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u1005\u103a\u1001\u102f\u1019\u103e \u1005\u1030\u1038\u1005\u1019\u103a\u1038\u101c\u1031\u1037\u101c\u102c\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f \u1011\u102c\u1038\u101b\u103e\u102d\u1015\u1031\u1038\u101e\u100a\u1037\u103a \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1021\u1006\u102f\u1036\u1038\u1015\u1014\u103a\u1038\u1010\u102d\u102f\u1004\u103a\u1019\u103e\u102c \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u101b\u103e\u102d \u101b\u103e\u102f\u1019\u103c\u1004\u103a\u101e\u102f\u1036\u1038\u101e\u1015\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a\u1010\u1005\u103a\u1001\u102f \u1021\u101c\u103d\u1014\u103a\u1010\u1030\u100a\u102e\u1015\u103c\u102e\u1038 \u1000\u103d\u1032\u1015\u103c\u102c\u1038\u101e\u1031\u102c\u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u101c\u1031\u1037\u101c\u102c\u1019\u103e\u102f\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a\u1010\u1005\u103a\u1001\u102f \u1021\u101c\u103d\u1014\u103a\u1000\u103d\u102c\u1001\u103c\u102c\u1038\u101e\u1031\u102c\u103a\u101c\u100a\u103a\u1038 <em>K<\/em> \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u103b\u102c\u1038 \u101b\u103e\u102d\u101b\u1014\u103a\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1010\u103d\u1004\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a K-Means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1006\u1004\u1037\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a-<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. <em>K<\/em> \u1021\u1010\u103d\u1000\u103a \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u101b\u103d\u1031\u1038\u1015\u102b\u104b<\/strong><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u1015\u1011\u1019\u1026\u1038\u1005\u103d\u102c\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1012\u1031\u1010\u102c\u1010\u103d\u1004\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u1010\u103a\u1019\u103e\u1010\u103a\u101c\u102d\u102f\u101e\u100a\u1037\u103a \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u100a\u103a\u1019\u103b\u103e\u101b\u103e\u102d\u101e\u100a\u103a\u1000\u102d\u102f \u1006\u102f\u1036\u1038\u1016\u103c\u1010\u103a\u101b\u1014\u103a \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u100a\u103a\u104b \u1019\u1000\u103c\u102c\u1001\u100f\u1006\u102d\u102f\u101e\u101c\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a <em>K<\/em> \u1021\u1010\u103d\u1000\u103a \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1000\u102d\u102f \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1015\u103c\u102e\u1038 \u1015\u1031\u1038\u1011\u102c\u1038\u101e\u100a\u1037\u103a\u1015\u103c\u103f\u1014\u102c\u1021\u1010\u103d\u1000\u103a \u1021\u101e\u1004\u1037\u103a\u101c\u103b\u1031\u102c\u103a\u1006\u102f\u1036\u1038\u1016\u103c\u1005\u103a\u1015\u102f\u1036\u101b\u101e\u100a\u1037\u103a \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1000\u102d\u102f \u101e\u102d\u101b\u103e\u102d\u1014\u102d\u102f\u1004\u103a\u101b\u1014\u103a \u101b\u101c\u1012\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1001\u103d\u1032\u1001\u103c\u1019\u103a\u1038\u1005\u102d\u1010\u103a\u1016\u103c\u102c\u101b\u1014\u103a \u101c\u102d\u102f\u1021\u1015\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>2. \u1005\u1031\u102c\u1004\u1037\u103a\u1000\u103c\u100a\u1037\u103a\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f 1 \u1019\u103e <em>K<\/em> \u1019\u103e \u1000\u1014\u1026\u1038\u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u101e\u102d\u102f\u1037 \u1000\u103b\u1015\u1014\u103a\u1038\u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1015\u102b\u104b<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>3. \u1021\u1005\u102f\u1021\u1016\u103d\u1032\u1037\u1010\u102c\u101d\u1014\u103a\u1019\u103b\u102c\u1038 \u1019\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1019\u1001\u103b\u1004\u103a\u1038 \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u101c\u102f\u1015\u103a\u1004\u1014\u103a\u1038\u1005\u1009\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1015\u102b\u104b<\/strong><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><em>K<\/em> \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1021\u1010\u103d\u1000\u103a\u104a <em>\u1021\u1005\u102f\u1021\u101d\u1031\u1038\u104f \u1006\u103d\u1032\u1004\u1004\u103a\u1021\u102c\u1038\u1017\u101f\u102d\u102f\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1015\u102b\u104b<\/em> \u1024\u101e\u100a\u103a\u1019\u103e\u102c <em>kth<\/em> \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u104f \u101c\u1031\u1037\u101c\u102c\u1010\u103d\u1031\u1037\u101b\u103e\u102d\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1021\u1010\u103d\u1000\u103a <em>p-<\/em> mean features \u104f vector \u1019\u103b\u103e\u101e\u102c\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1005\u1031\u102c\u1004\u1037\u103a\u1000\u103c\u100a\u1037\u103a\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038 \u1021\u101c\u101a\u103a\u1017\u101f\u102d\u102f\u1016\u103c\u1004\u1037\u103a \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u101e\u102d\u102f\u1037 \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1015\u1031\u1038\u1015\u102b\u104b \u1024\u1010\u103d\u1004\u103a\u104a <em>\u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038<\/em> \u1000\u102d\u102f <a href=\"https:\/\/en.wikipedia.org\/wiki\/Euclidean_distance#Squared_Euclidean_distance\" target=\"_blank\" rel=\"noopener noreferrer\">Euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1000\u102d\u102f<\/a> \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u101e\u100a\u103a\u104b<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1015\u102b \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a \u1025\u1015\u1019\u102c\u101e\u100a\u103a <strong>sklearn<\/strong> module \u1019\u103e <strong>KMeans<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d Python \u1010\u103d\u1004\u103a k-means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 1: \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c module \u1019\u103b\u102c\u1038\u1000\u102d\u102f\u1010\u1004\u103a\u101e\u103d\u1004\u103a\u1038\u1015\u102b\u104b<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u1026\u1038\u1005\u103d\u102c\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a k-means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101b\u1014\u103a \u101c\u102d\u102f\u1021\u1015\u103a\u1019\u100a\u1037\u103a module \u1021\u102c\u1038\u101c\u102f\u1036\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u101e\u103d\u1004\u103a\u1038\u1015\u102b\u1019\u100a\u103a-<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">cluster<\/span> <span style=\"color: #008000;\">import<\/span> KMeans\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> StandardScaler<\/strong><\/span><\/pre>\n<h2> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 2: DataFrame \u1000\u102d\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u104b<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c \u1018\u1010\u103a\u1005\u1000\u1000\u103a\u1018\u1031\u102c\u1000\u1005\u102c\u1038\u101e\u1019\u102c\u1038 20 \u1021\u1010\u103d\u1000\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b variable \u101e\u102f\u1036\u1038\u1001\u102f\u1015\u102b\u101d\u1004\u103a\u101e\u1031\u102c DataFrame \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u1021\u1019\u103e\u1010\u103a\u1019\u103b\u102c\u1038<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1000\u1030\u100a\u102e\u1000\u103c\u1015\u102b<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1001\u102f\u1014\u103a\u1015\u1031\u102b\u1000\u103a\u101e\u100a\u103a\u104b<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1015\u102b \u1000\u102f\u1012\u103a\u101e\u100a\u103a \u1024\u1015\u1014\u103a\u1012\u102b DataFrame \u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102f\u1036\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a \u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">points<\/span> ': [18, np.nan, 19, 14, 14, 11, 20, 28, 30, 31,\n                              35, 33, 29, 25, 25, 27, 29, 30, 19, 23],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [3, 3, 4, 5, 4, 7, 8, 7, 6, 9, 12, 14,\n                               np.nan, 9, 4, 3, 4, 12, 15, 11],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [15, 14, 14, 10, 8, 14, 13, 9, 5, 4,\n                                11, 6, 5, 5, 3, 8, 12, 7, 6, 5]})\n\n<span style=\"color: #008080;\">#view first five rows of DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">df.head<\/span> ())\n\n   points assists rebounds\n0 18.0 3.0 15\n1 NaN 3.0 14\n2 19.0 4.0 14\n3 14.0 5.0 10\n4 14.0 4.0 8\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\u1024\u1019\u1000\u103a\u1011\u101b\u1005\u103a\u101e\u102f\u1036\u1038\u1001\u102f\u1021\u1015\u1031\u102b\u103a \u1021\u1001\u103c\u1031\u1001\u1036\u104d \u1021\u101c\u102c\u1038\u1010\u1030\u101e\u101b\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u102f\u1015\u103a\u1005\u102f\u1016\u103d\u1032\u1037\u101b\u1014\u103a k-Means \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 3- DataFrame \u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u101c\u1004\u103a\u1038\u1015\u103c\u102e\u1038 \u1015\u103c\u1004\u103a\u1006\u1004\u103a\u1015\u102b\u104b<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1006\u1004\u1037\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u1019\u100a\u103a\u101e\u100a\u1037\u103a\u1000\u1031\u102c\u103a\u101c\u1036\u1010\u103d\u1004\u103a\u1019\u1006\u102d\u102f NaN \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1015\u102b\u101e\u100a\u1037\u103a \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1001\u103b\u101b\u1014\u103a <strong>dropna() \u1000\u102d\u102f<\/strong> \u101e\u102f\u1036\u1038\u1015\u102b\u104b<\/span><\/li>\n<li> <span style=\"color: #000000;\">0 mean \u1014\u103e\u1004\u1037\u103a 1 \u104f \u1005\u1036\u101e\u103d\u1031\u1016\u100a\u103a\u1019\u103e\u102f\u101b\u103e\u102d\u101b\u1014\u103a variable \u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f <strong>StandardScaler()<\/strong> \u1000\u102d\u102f\u101e\u102f\u1036\u1038\u1015\u102b\u104b<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a \u104e\u1004\u103a\u1038\u1000\u102d\u102f\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#drop rows with NA values in any columns\n<\/span>df = df. <span style=\"color: #3366ff;\">dropna<\/span> ()\n\n<span style=\"color: #008080;\">#create scaled DataFrame where each variable has mean of 0 and standard dev of 1\n<\/span>scaled_df = StandardScaler(). <span style=\"color: #3366ff;\">fit_transform<\/span> (df)\n\n<span style=\"color: #008080;\">#view first five rows of scaled DataFrame<\/span>\n<span style=\"color: #008000;\">print<\/span> (scaled_df[:5])\n\n[[-0.86660275 -1.22683918 1.72722524]\n [-0.72081911 -0.96077767 1.45687694]\n [-1.44973731 -0.69471616 0.37548375]\n [-1.44973731 -0.96077767 -0.16521285]\n [-1.88708823 -0.16259314 1.45687694]]<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>\u1019\u103e\u1010\u103a\u1001\u103b\u1000\u103a<\/strong> &#8211; k-means algorithm \u1014\u103e\u1004\u1037\u103a \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101e\u1031\u102c\u1021\u1001\u102b\u1010\u103d\u1004\u103a \u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u101e\u100a\u103a \u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1021\u101b\u1031\u1038\u1015\u102b\u1019\u103e\u102f \u101b\u103e\u102d\u1005\u1031\u101b\u1014\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037 \u1021\u1010\u102d\u102f\u1004\u103a\u1038\u1021\u1010\u102c\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u101e\u100a\u103a\u104b \u1019\u101f\u102f\u1010\u103a\u1015\u102b\u1000\u104a \u1021\u1000\u103b\u101a\u103a\u1015\u103c\u1014\u1037\u103a\u1006\u102f\u1036\u1038\u101e\u1031\u102c \u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u101e\u103c\u1007\u102c\u101c\u103d\u103e\u1019\u103a\u1038\u1019\u102d\u102f\u1038\u1019\u103e\u102f \u101c\u103d\u1014\u103a\u1000\u1032\u1014\u1031\u1015\u1031\u101c\u102d\u1019\u1037\u103a\u1019\u100a\u103a\u104b<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 4- \u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038 \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1000\u102d\u102f \u101b\u103e\u102c\u1015\u102b\u104b<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Python \u1010\u103d\u1004\u103a k-means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1014\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a <strong>sklearn<\/strong> module \u1019\u103e <strong>KMeans<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u101e\u100a\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1001\u103c\u1031\u1001\u1036 syntax \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a-<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>KMeans(init=&#8217;random&#8217;\u104a n_clusters=8\u104a n_init=10\u104a random_state=None)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u101b\u103d\u103e\u1031-<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>init- \u1000<\/strong> \u1014\u1026\u1038\u1014\u100a\u103a\u1038\u1015\u100a\u102c\u1000\u102d\u102f \u1011\u102d\u1014\u103a\u1038\u1001\u103b\u102f\u1015\u103a\u101e\u100a\u103a\u104b<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>n_clusters<\/strong> : \u101c\u1031\u1037\u101c\u102c\u1010\u103d\u1031\u1037\u101b\u103e\u102d\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1014\u1031\u101b\u102c\u1001\u103b\u101b\u1014\u103a \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u104b<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>n_init<\/strong> : \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1014\u103a \u1000\u1014\u1026\u1038\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1019\u103e\u102f \u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u104b \u1019\u1030\u101b\u1004\u103a\u1038\u1019\u103e\u102c k-means algorithm \u1000\u102d\u102f 10 \u1000\u103c\u102d\u1019\u103a run \u1015\u103c\u102e\u1038 \u1021\u1014\u102d\u1019\u1037\u103a\u1006\u102f\u1036\u1038 SSE \u1016\u103c\u1004\u1037\u103a \u1015\u103c\u1014\u103a\u1015\u1031\u1038\u1001\u103c\u1004\u103a\u1038\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>Random_state<\/strong> : algorithm \u101b\u101c\u1012\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1015\u103c\u1014\u103a\u1011\u102f\u1010\u103a\u1015\u1031\u1038\u101b\u1014\u103a \u101e\u1004\u103a\u101b\u103d\u1031\u1038\u1001\u103b\u101a\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c \u1000\u102d\u1014\u103a\u1038\u1015\u103c\u100a\u1037\u103a\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u104b<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1021\u1010\u103d\u1000\u103a \u1021\u101b\u1031\u1038\u1000\u103c\u102e\u1038\u1006\u102f\u1036\u1038 \u1021\u1000\u103c\u1031\u102c\u1004\u103a\u1038\u1015\u103c\u1001\u103b\u1000\u103a\u1019\u103e\u102c n_clusters \u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104a \u104e\u1004\u103a\u1038\u101e\u100a\u103a \u1021\u1005\u102f\u101c\u102d\u102f\u1000\u103a\u1021\u1015\u103c\u102f\u1036\u101c\u102d\u102f\u1000\u103a\u1019\u100a\u103a\u1019\u103b\u103e\u101b\u103e\u102d\u101e\u100a\u103a\u1000\u102d\u102f \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1015\u1031\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u101e\u102d\u102f\u1037\u101b\u102c\u1010\u103d\u1004\u103a\u104a \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u100a\u103a\u1019\u103b\u103e\u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038\u1016\u103c\u1005\u103a\u1019\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1019\u101e\u102d\u101b\u101e\u1031\u102c\u1000\u103c\u1031\u102c\u1004\u1037\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u104f SSE (\u1014\u103e\u1005\u103a\u1011\u1015\u103a\u1000\u102d\u1014\u103a\u1038\u1021\u1019\u103e\u102c\u1038\u1019\u103b\u102c\u1038) \u1000\u102d\u102f\u1015\u103c\u101e\u101e\u100a\u1037\u103a \u1002\u101b\u1015\u103a\u1010\u1005\u103a\u1001\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u101b\u1014\u103a \u101c\u102d\u102f\u1021\u1015\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1015\u102f\u1036\u1019\u103e\u1014\u103a\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1024\u1007\u102c\u1010\u103a\u1000\u103d\u1000\u103a\u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u101e\u1031\u102c\u1021\u1001\u102b\u104a \u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1015\u1031\u102b\u1004\u103a\u1038\u101c\u1012\u103a\u101e\u100a\u103a \u201c\u1000\u103d\u1031\u1038\u101e\u100a\u103a\u201d \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a \u1021\u1006\u1004\u1037\u103a\u1019\u103e\u1005\u1010\u1004\u103a\u101e\u103d\u102c\u1038\u101e\u100a\u1037\u103a &#8220; \u1012\u1030\u1038&#8221;  \u1000\u102d\u102f \u101b\u103e\u102c\u101e\u100a\u103a\u104b \u104e\u1004\u103a\u1038\u101e\u100a\u103a \u101a\u1031\u1018\u102f\u101a\u103b\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a \u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038\u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u103b\u102c\u1038\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u1000\u102f\u1012\u103a\u101e\u100a\u103a x-axis \u1014\u103e\u1004\u1037\u103a y-axis \u1015\u1031\u102b\u103a\u101b\u103e\u102d \u1021\u1005\u102f\u1021\u1005\u100a\u103a\u1038\u1019\u103b\u102c\u1038\u104f \u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u1037\u103a \u1024\u1000\u103d\u1000\u103a\u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#initialize kmeans parameters\n<\/span>kmeans_kwargs = {\n\" <span style=\"color: #ff0000;\">init<\/span> \": \" <span style=\"color: #ff0000;\">random<\/span> \",\n\" <span style=\"color: #ff0000;\">n_init<\/span> \": 10,\n\" <span style=\"color: #ff0000;\">random_state<\/span> \": 1,\n}\n\n<span style=\"color: #008080;\">#create list to hold SSE values for each k\n<\/span>sse = []\n<span style=\"color: #008000;\">for<\/span> k <span style=\"color: #008000;\">in<\/span> range(1, 11):\n    kmeans = KMeans(n_clusters=k, <span style=\"color: #800080;\">**<\/span> kmeans_kwargs)\n    kmeans. <span style=\"color: #3366ff;\">fit<\/span> (scaled_df)\n    sse. <span style=\"color: #3366ff;\">append<\/span> (kmeans.inertia_)\n\n<span style=\"color: #008080;\">#visualize results\n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (range(1, 11), sse)\nplt. <span style=\"color: #3366ff;\">xticks<\/span> (range(1, 11))\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (\" <span style=\"color: #ff0000;\">Number of Clusters<\/span> \")\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (\u201c <span style=\"color: #ff0000;\">SSE<\/span> \u201d)\nplt. <span style=\"color: #3366ff;\">show<\/span> ()<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-29557 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/kmmoyenne1.jpg\" alt=\"\" width=\"531\" height=\"408\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u1002\u101b\u1015\u103a\u1010\u103d\u1004\u103a\u104a k = <strong>3 \u1015\u103c\u103d\u1010\u103a<\/strong> \u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u1021\u1000\u103d\u1031\u1038\u1021\u1000\u1031\u102c\u1000\u103a \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a &#8220; \u1012\u1030\u1038&#8221;  \u101b\u103e\u102d\u1014\u1031\u101e\u100a\u103a\u1000\u102d\u102f \u1010\u103d\u1031\u1037\u101b\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1011\u102d\u102f\u1037\u1000\u103c\u1031\u102c\u1004\u1037\u103a\u104a \u1014\u1031\u102c\u1000\u103a\u1010\u1006\u1004\u1037\u103a\u1010\u103d\u1004\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u104f k-Means \u1021\u1005\u102f\u101c\u102d\u102f\u1000\u103a\u1015\u102f\u1036\u1005\u1036\u1000\u102d\u102f \u1000\u102d\u102f\u1000\u103a\u100a\u102e\u101e\u1031\u102c\u1021\u1001\u102b\u1010\u103d\u1004\u103a \u1021\u1005\u102f\u1021\u1016\u103d\u1032\u1037 3 \u1001\u102f\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u1019\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u1019\u103e\u1010\u103a\u1001\u103b\u1000\u103a<\/strong> &#8211; \u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1000\u1019\u1039\u1018\u102c\u1010\u103d\u1004\u103a\u104a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1014\u103a \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1000\u102d\u102f \u101b\u103d\u1031\u1038\u1001\u103b\u101a\u103a\u101b\u1014\u103a \u1024\u1000\u103c\u1036\u1005\u100a\u103a\u1019\u103e\u102f\u1014\u103e\u1004\u1037\u103a \u1012\u102d\u102f\u1019\u102d\u1014\u103a\u1038\u1000\u103b\u103d\u1019\u103a\u1038\u1000\u103b\u1004\u103a\u1019\u103e\u102f\u1015\u1031\u102b\u1004\u103a\u1038\u1005\u1015\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1014\u103a \u1021\u1000\u103c\u1036\u1015\u103c\u102f\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u1021\u1006\u1004\u1037\u103a 5- Optimal <em>K<\/em> \u1016\u103c\u1004\u1037\u103a K-Means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1015\u102b\u104b<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a <em>k<\/em> -3 \u1021\u1010\u103d\u1000\u103a \u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1015\u1031\u102b\u103a\u1010\u103d\u1004\u103a k-\u1006\u102d\u102f\u101c\u102d\u102f\u101b\u1004\u103a\u1038 \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#instantiate the k-means class, using optimal number of clusters\n<\/span>kmeans = KMeans(init=\" <span style=\"color: #ff0000;\">random<\/span> \", n_clusters= <span style=\"color: #008000;\">3<\/span> , n_init= <span style=\"color: #008000;\">10<\/span> , random_state= <span style=\"color: #008000;\">1<\/span> )\n\n<span style=\"color: #008080;\">#fit k-means algorithm to data\n<\/span>kmeans. <span style=\"color: #3366ff;\">fit<\/span> (scaled_df)\n\n<span style=\"color: #008080;\">#view cluster assignments for each observation\n<\/span>kmeans. <span style=\"color: #3366ff;\">labels_\n\n<\/span>array([1, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0]) \n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u101b\u101c\u1012\u103a\u1007\u101a\u102c\u1038\u101e\u100a\u103a DataFrame \u101b\u103e\u102d \u1000\u103c\u100a\u1037\u103a\u101b\u103e\u102f\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1021\u1010\u103d\u1000\u103a \u1021\u1005\u102f\u101c\u102d\u102f\u1000\u103a\u1010\u102c\u101d\u1014\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1024\u101b\u101c\u1012\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u1013\u102d\u1015\u1039\u1015\u102c\u101a\u103a\u1016\u103d\u1004\u1037\u103a\u1006\u102d\u102f\u101b\u1014\u103a \u1015\u102d\u102f\u1019\u102d\u102f\u101c\u103d\u101a\u103a\u1000\u1030\u1005\u1031\u101b\u1014\u103a\u104a \u1000\u1005\u102c\u1038\u101e\u1019\u102c\u1038\u1010\u1005\u103a\u1026\u1038\u1005\u102e\u104f \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u102c\u101d\u1014\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u1037\u103a DataFrame \u1010\u103d\u1004\u103a \u1000\u1031\u102c\u103a\u101c\u1036\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1011\u100a\u1037\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#append cluster assingments to original DataFrame\n<\/span>df[' <span style=\"color: #ff0000;\">cluster<\/span> '] = kmeans. <span style=\"color: #3366ff;\">labels_<\/span>\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n<\/span><\/span>points assists rebounds cluster\n0 18.0 3.0 15 1\n2 19.0 4.0 14 1\n3 14.0 5.0 10 1\n4 14.0 4.0 8 1\n5 11.0 7.0 14 1\n6 20.0 8.0 13 1\n7 28.0 7.0 9 2\n8 30.0 6.0 5 2\n9 31.0 9.0 4 0\n10 35.0 12.0 11 0\n11 33.0 14.0 6 0\n13 25.0 9.0 5 0\n14 25.0 4.0 3 2\n15 27.0 3.0 8 2\n16 29.0 4.0 12 2\n17 30.0 12.0 7 0\n18 19.0 15.0 6 0\n19 23.0 11.0 5 0\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>\u1021\u1005\u102f\u1021\u101d\u1031\u1038<\/strong> \u1000\u1031\u102c\u103a\u101c\u1036\u1010\u103d\u1004\u103a \u1000\u1005\u102c\u1038\u101e\u1019\u102c\u1038\u1010\u1005\u103a\u1026\u1038\u1005\u102e\u1019\u103e \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1021\u1005\u102f\u1014\u1036\u1015\u102b\u1010\u103a (0\u104a 1 \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a 2) \u1015\u102b\u101b\u103e\u102d\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u1010\u1030\u100a\u102e\u101e\u1031\u102c \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u103e \u1015\u102d\u102f\u1004\u103a\u1006\u102d\u102f\u1004\u103a\u101e\u100a\u1037\u103a \u1000\u1005\u102c\u1038\u101e\u1019\u102c\u1038\u1019\u103b\u102c\u1038\u101e\u100a\u103a <strong>\u1021\u1019\u103e\u1010\u103a\u1019\u103b\u102c\u1038<\/strong> \u104a <strong>\u1000\u1030\u100a\u102e\u1015\u1031\u1038\u1001\u103c\u1004\u103a\u1038<\/strong> \u1014\u103e\u1004\u1037\u103a <strong>\u1015\u103c\u1014\u103a\u101c\u103e\u1014\u103a\u1001\u103c\u1004\u103a\u1038<\/strong> \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1021\u1010\u103d\u1000\u103a \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103c\u1031\u1010\u1030\u100a\u102e\u101e\u1031\u102c \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u101b\u103e\u102d\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u1019\u103e\u1010\u103a\u1001\u103b\u1000\u103a<\/strong> &#8211; <strong>sklearn<\/strong> \u104f <strong>KMeans<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1021\u1010\u103d\u1000\u103a \u1005\u102c\u101b\u103d\u1000\u103a\u1005\u102c\u1010\u1019\u103a\u1038\u1021\u1015\u103c\u100a\u1037\u103a\u1021\u1005\u102f\u1036\u1000\u102d\u102f <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.cluster.KMeans.html\" target=\"_blank\" rel=\"noopener\">\u1024\u1014\u1031\u101b\u102c\u1010\u103d\u1004\u103a<\/a> \u101b\u103e\u102c\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u1011\u1015\u103a\u101c\u1031\u102c\u1004\u103a\u1038\u1021\u101b\u1004\u103a\u1038\u1021\u1019\u103c\u1005\u103a\u1019\u103b\u102c\u1038<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1015\u102b \u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1019\u103b\u102c\u1038\u101e\u100a\u103a Python \u1010\u103d\u1004\u103a \u1021\u1001\u103c\u102c\u1038\u101e\u1031\u102c \u1021\u101e\u102f\u1036\u1038\u1019\u103b\u102c\u1038\u101e\u1031\u102c \u1021\u101c\u102f\u1015\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u101e\u100a\u103a-<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/my\/linear-regression-python\/\" target=\"_blank\" rel=\"noopener\">Python \u1010\u103d\u1004\u103a linear regression \u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038<\/a><br \/> <a href=\"https:\/\/statorials.org\/my\/logistic-regression-python\/\" target=\"_blank\" rel=\"noopener\">Python \u1010\u103d\u1004\u103a Logistic Regression \u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1019\u100a\u103a\u1014\u100a\u103a\u1038\u104b<\/a><br \/> <a href=\"https:\/\/statorials.org\/my\/k-python-\u1010\u103d\u1004\u103a-cross-validation-\u1000\u102d\u102f-\u1001\u1031\u102b\u1000\u103a\u1015\u102b\u104b\/\" target=\"_blank\" rel=\"noopener\">Python \u1010\u103d\u1004\u103a K-Fold \u1021\u1015\u103c\u1014\u103a\u1021\u101c\u103e\u1014\u103a validation \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1014\u100a\u103a\u1038<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>machine learning \u1010\u103d\u1004\u103a \u1021\u101e\u102f\u1036\u1038\u1021\u1019\u103b\u102c\u1038\u1006\u102f\u1036\u1038 \u1021\u1005\u102f\u101c\u102d\u102f\u1000\u103a\u1021\u1015\u103c\u102f\u1036\u101c\u102d\u102f\u1000\u103a \u1021\u101a\u103a\u101c\u1002\u102d\u102f\u101b\u102e\u101e\u1019\u103a\u1019\u103b\u102c\u1038\u1011\u1032\u1019\u103e \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f k-means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u1001\u103c\u1004\u103a\u1038 \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u100a\u103a\u104b K \u1006\u102d\u102f\u101e\u100a\u103a\u1019\u103e\u102c \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f K \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u101e\u102d\u102f\u1037 \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u1005\u103a\u1001\u102f\u1019\u103e \u1005\u1030\u1038\u1005\u1019\u103a\u1038\u101c\u1031\u1037\u101c\u102c\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f \u1011\u102c\u1038\u101b\u103e\u102d\u1015\u1031\u1038\u101e\u100a\u1037\u103a \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u1021\u1006\u102f\u1036\u1038\u1015\u1014\u103a\u1038\u1010\u102d\u102f\u1004\u103a\u1019\u103e\u102c \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u101b\u103e\u102d \u101b\u103e\u102f\u1019\u103c\u1004\u103a\u101e\u102f\u1036\u1038\u101e\u1015\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a\u1010\u1005\u103a\u1001\u102f \u1021\u101c\u103d\u1014\u103a\u1010\u1030\u100a\u102e\u1015\u103c\u102e\u1038 \u1000\u103d\u1032\u1015\u103c\u102c\u1038\u101e\u1031\u102c\u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u101c\u1031\u1037\u101c\u102c\u1019\u103e\u102f\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a\u1010\u1005\u103a\u1001\u102f \u1021\u101c\u103d\u1014\u103a\u1000\u103d\u102c\u1001\u103c\u102c\u1038\u101e\u1031\u102c\u103a\u101c\u100a\u103a\u1038 K \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1019\u103b\u102c\u1038 \u101b\u103e\u102d\u101b\u1014\u103a\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1010\u103d\u1004\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a K-Means \u1021\u1005\u102f\u1021\u101d\u1031\u1038\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1006\u1004\u1037\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a- 1. 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