{"id":3578,"date":"2023-07-16T17:38:21","date_gmt":"2023-07-16T17:38:21","guid":{"rendered":"https:\/\/statorials.org\/ko\/k%e1%84%82%e1%85%b3%e1%86%ab-python%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-%e1%84%8f%e1%85%b3%e1%86%af%e1%84%85%e1%85%a5%e1%84%89%e1%85%b3%e1%84%90%e1%85%a5%e1%84%85%e1%85%b5%e1%86%bc%e1%84%8b%e1%85%b3\/"},"modified":"2023-07-16T17:38:21","modified_gmt":"2023-07-16T17:38:21","slug":"k%e1%84%82%e1%85%b3%e1%86%ab-python%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-%e1%84%8f%e1%85%b3%e1%86%af%e1%84%85%e1%85%a5%e1%84%89%e1%85%b3%e1%84%90%e1%85%a5%e1%84%85%e1%85%b5%e1%86%bc%e1%84%8b%e1%85%b3","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/k%e1%84%82%e1%85%b3%e1%86%ab-python%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-%e1%84%8f%e1%85%b3%e1%86%af%e1%84%85%e1%85%a5%e1%84%89%e1%85%b3%e1%84%90%e1%85%a5%e1%84%85%e1%85%b5%e1%86%bc%e1%84%8b%e1%85%b3\/","title":{"rendered":"Python\uc758 k-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1: \ub2e8\uacc4\ubcc4 \uc608"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ko\/\u1110\u1169\u11bc\u1100\u1168\u1112\u1161\u11a8\u110b\u1173\u11ab-\u1100\u1161\u11ab\u1103\u1161\u11ab\u1112\u1161\u1100\u1169-\u110c\u1175\u11a8\u110c\u1165\u11b8\u110c\u1165\u11a8\u110b\u1175\u11ab-\u1107\u1161\u11bc\u1107\u1165\u11b8\u110b\u1173\u1105\u1169-\u1100\u1162\u1102\u1167\u11b7\u110b\u1173\u11af-\u1109\u1165\u11af\u1106\u1167\u11bc\u1112\u1161\u1106\u1173\u1105\u1169-\u1110\u1169\u11bc\u1100\u1168\u1105\u1173\u11af-\u1103\u1165-\u1109\u1171\u11b8\u1100\u1166-\u1107\u1162\u110b\u116e\u11af-\u1109\u116e-\u110b\u1175\u11bb\u1109\u1173\u11b8\u1102\u1175\u1103\u1161.\/\" target=\"_blank\" rel=\"noopener\">\uae30\uacc4 \ud559\uc2b5<\/a> \uc5d0\uc11c \uac00\uc7a5 \uc77c\ubc18\uc801\uc778 \ud074\ub7ec\uc2a4\ud130\ub9c1 \uc54c\uace0\ub9ac\uc998 \uc911 \ud558\ub098\ub294 <strong>k-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1<\/strong> \uc73c\ub85c \uc54c\ub824\uc838 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc740 \ub370\uc774\ud130 \uc138\ud2b8\uc758 \uac01 \uad00\uce21\uce58\ub97c <em>K<\/em> \ud074\ub7ec\uc2a4\ud130 \uc911 \ud558\ub098\uc5d0 \ubc30\uce58\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ucd5c\uc885 \ubaa9\ud45c\ub294 \uac01 \ud074\ub7ec\uc2a4\ud130 \ub0b4\uc758 \uad00\uce21\uce58\uac00 \uc11c\ub85c \ub9e4\uc6b0 \uc720\uc0ac\ud55c \ubc18\uba74 \ub2e4\ub978 \ud074\ub7ec\uc2a4\ud130\uc758 \uad00\uce21\uce58\ub294 \uc11c\ub85c \uc0c1\ub2f9\ud788 \ub2e4\ub978 <em>K<\/em> \uac1c\uc758 \ud074\ub7ec\uc2a4\ud130\ub97c \uac16\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc2e4\uc81c\ub85c K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \uc218\ud589\ud558\uae30 \uc704\ud574 \ub2e4\uc74c \ub2e8\uacc4\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. <em>K<\/em> \uac12\uc744 \uc120\ud0dd\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uba3c\uc800, \ub370\uc774\ud130\uc5d0\uc11c \uc2dd\ubcc4\ud558\ub824\ub294 \ud074\ub7ec\uc2a4\ud130 \uc218\ub97c \uacb0\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \uc885\uc885 \uc6b0\ub9ac\ub294 <em>K<\/em> \uc5d0 \ub300\ud574 \uc5ec\ub7ec \uac00\uc9c0 \ub2e4\ub978 \uac12\uc744 \ud14c\uc2a4\ud2b8\ud558\uace0 \uacb0\uacfc\ub97c \ubd84\uc11d\ud558\uc5ec \uc8fc\uc5b4\uc9c4 \ubb38\uc81c\uc5d0 \uac00\uc7a5 \uc801\ud569\ud55c \ud074\ub7ec\uc2a4\ud130 \uc218\ub97c \ud655\uc778\ud574\uc57c \ud569\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>2. \uac01 \uad00\uce21\uce58\ub97c 1\ubd80\ud130 <em>K<\/em> \uae4c\uc9c0 \ucd08\uae30 \ud074\ub7ec\uc2a4\ud130\uc5d0 \ubb34\uc791\uc704\ub85c \ud560\ub2f9\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>3. \ud074\ub7ec\uc2a4\ud130 \ud560\ub2f9 \ubcc0\uacbd\uc774 \uc911\uc9c0\ub420 \ub54c\uae4c\uc9c0 \ub2e4\uc74c \uc808\ucc28\ub97c \uc218\ud589\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><em>K<\/em> \uac1c\uc758 \ud074\ub7ec\uc2a4\ud130 \uac01\uac01\uc5d0 \ub300\ud574 <em>\ud074\ub7ec\uc2a4\ud130\uc758 \ubb34\uac8c \uc911\uc2ec\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/em> \uc774\uac83\uc740 \ub2e8\uc21c\ud788 <em>k\ubc88\uc9f8<\/em> \ud074\ub7ec\uc2a4\ud130\uc758 \uad00\uce21\uac12\uc5d0 \ub300\ud55c <em>p-<\/em> \ud3c9\uade0 \ud2b9\uc9d5\uc758 \ubca1\ud130\uc785\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc911\uc2ec\uc774 \uac00\uc7a5 \uac00\uae4c\uc6b4 \ud074\ub7ec\uc2a4\ud130\uc5d0 \uac01 \uad00\uce21\uce58\ub97c \ud560\ub2f9\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c <em>\uac00\uc7a5 \uac00\uae4c\uc6b4 \uac83\uc740<\/em> <a href=\"https:\/\/en.wikipedia.org\/wiki\/Euclidean_distance#Squared_Euclidean_distance\" target=\"_blank\" rel=\"noopener noreferrer\">\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac\ub97c<\/a> \uc0ac\uc6a9\ud558\uc5ec \uc815\uc758\ub429\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ub2e8\uacc4\ubcc4 \uc608\uc81c\uc5d0\uc11c\ub294 <strong>sklearn<\/strong> \ubaa8\ub4c8\uc758 <strong>KMeans<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec Python\uc5d0\uc11c k-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>1\ub2e8\uacc4: \ud544\uc694\ud55c \ubaa8\ub4c8 \uac00\uc838\uc624\uae30<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\uba3c\uc800 k-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \uc218\ud589\ud558\ub294 \ub370 \ud544\uc694\ud55c \ubaa8\ub4e0 \ubaa8\ub4c8\uc744 \uac00\uc838\uc635\ub2c8\ub2e4.<\/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>2\ub2e8\uacc4: DataFrame \uc0dd\uc131<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c, 20\uba85\uc758 \ub18d\uad6c \uc120\uc218\uc5d0 \ub300\ud574 \ub2e4\uc74c \uc138 \uac00\uc9c0 \ubcc0\uc218\ub97c \ud3ec\ud568\ud558\ub294 DataFrame\uc744 \ub9cc\ub4ed\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\ud3ec\uc778\ud2b8\ub4e4<\/span><\/li>\n<li> <span style=\"color: #000000;\">\ub3d5\ub2e4<\/span><\/li>\n<li> <span style=\"color: #000000;\">\ubc14\uc6b4\uc2a4<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 \uc774 Pandas DataFrame\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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;\">\uc6b0\ub9ac\ub294 k-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc774 \uc138 \uac00\uc9c0 \uc9c0\ud45c\ub97c \uae30\ubc18\uc73c\ub85c \uc720\uc0ac\ud55c \ud589\uc704\uc790\ub97c \uadf8\ub8f9\ud654\ud560 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>3\ub2e8\uacc4: DataFrame \uc815\ub9ac \ubc0f \uc900\ube44<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\uadf8\ub7f0 \ub2e4\uc74c \ub2e4\uc74c \ub2e8\uacc4\ub97c \uc218\ud589\ud569\ub2c8\ub2e4.<\/span><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>dropna()\ub97c<\/strong> \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub4e0 \uc5f4\uc5d0 NaN \uac12\uc774 \uc788\ub294 \ud589\uc744 \uc0ad\uc81c\ud569\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>StandardScaler()\ub97c<\/strong> \uc0ac\uc6a9\ud558\uc5ec \ud3c9\uade0\uc774 0\uc774\uace0 \ud45c\uc900\ud3b8\ucc28\uac00 1\uc774 \ub418\ub3c4\ub85d \uac01 \ubcc0\uc218\uc758 \ud06c\uae30\ub97c \uc870\uc815\ud569\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 \uc774\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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>\ucc38\uace0<\/strong> : k-\ud3c9\uade0 \uc54c\uace0\ub9ac\uc998\uc744 \uc801\uc6a9\ud560 \ub54c \uac01 \ubcc0\uc218\uac00 \ub3d9\uc77c\ud55c \uc911\uc694\uc131\uc744 \uac16\ub3c4\ub85d \uc2a4\ucf00\uc77c\ub9c1\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uadf8\ub807\uc9c0 \uc54a\uc73c\uba74 \ubc94\uc704\uac00 \uac00\uc7a5 \ub113\uc740 \ubcc0\uc218\uac00 \ub108\ubb34 \ub9ce\uc740 \uc601\ud5a5\uc744 \ubbf8\uce58\uac8c \ub429\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>4\ub2e8\uacc4: \ucd5c\uc801\uc758 \ud074\ub7ec\uc2a4\ud130 \uc218 \ucc3e\uae30<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Python\uc5d0\uc11c k-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \uc218\ud589\ud558\ub824\uba74 <strong>sklearn<\/strong> \ubaa8\ub4c8\uc758 <strong>KMeans<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \ud568\uc218\ub294 \ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>KMeans(init=&#8217;random&#8217;, n_clusters=8, n_init=10, random_state=None)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uae08:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>init<\/strong> : \ucd08\uae30\ud654 \uae30\uc220\uc744 \uc81c\uc5b4\ud569\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>n_clusters<\/strong> : \uad00\uce21\uce58\ub97c \ubc30\uce58\ud560 \ud074\ub7ec\uc2a4\ud130 \uc218\uc785\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>n_init<\/strong> : \uc218\ud589\ud560 \ucd08\uae30\ud654 \ud69f\uc218\uc785\ub2c8\ub2e4. \uae30\ubcf8\uac12\uc740 k-\ud3c9\uade0 \uc54c\uace0\ub9ac\uc998\uc744 10\ud68c \uc2e4\ud589\ud558\uace0 SSE\uac00 \uac00\uc7a5 \ub0ae\uc740 \uc54c\uace0\ub9ac\uc998\uc744 \ubc18\ud658\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>random_state<\/strong> : \uc54c\uace0\ub9ac\uc998 \uacb0\uacfc\ub97c \uc7ac\ud604 \uac00\ub2a5\ud558\uac8c \ub9cc\ub4e4\uae30 \uc704\ud574 \uc120\ud0dd\ud560 \uc218 \uc788\ub294 \uc815\uc218 \uac12\uc785\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\uc774 \ud568\uc218\uc758 \uac00\uc7a5 \uc911\uc694\ud55c \uc778\uc218\ub294 \uad00\ucc30\uc744 \ubc30\uce58\ud560 \ud074\ub7ec\uc2a4\ud130 \uc218\ub97c \uc9c0\uc815\ud558\ub294 n_clusters\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ud558\uc9c0\ub9cc \uba87 \uac1c\uc758 \ud074\ub7ec\uc2a4\ud130\uac00 \ucd5c\uc801\uc778\uc9c0 \ubbf8\ub9ac \uc54c \uc218 \uc5c6\uc73c\ubbc0\ub85c \ubaa8\ub378\uc758 SSE(\ud569\uacc4 \uc624\ucc28 \uc81c\uacf1\ud569)\uc640 \ud568\uaed8 \ud074\ub7ec\uc2a4\ud130 \uc218\ub97c \ud45c\uc2dc\ud558\ub294 \uadf8\ub798\ud504\ub97c \ub9cc\ub4e4\uc5b4\uc57c \ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc77c\ubc18\uc801\uc73c\ub85c \uc774\ub7ec\ud55c \uc720\ud615\uc758 \ud50c\ub86f\uc744 \uc0dd\uc131\ud560 \ub54c \uc81c\uacf1\ud569\uc774 &#8220;\uad6c\ubd80\ub7ec\uc9c0\uac70\ub098&#8221; \uc218\ud3c9\uc744 \uc774\ub8e8\uae30 \uc2dc\uc791\ud558\ub294 &#8220;\ubb34\ub98e&#8221;\uc744 \ucc3e\uc2b5\ub2c8\ub2e4. \uc774\ub294 \uc77c\ubc18\uc801\uc73c\ub85c \ucd5c\uc801\uc758 \ud074\ub7ec\uc2a4\ud130 \uc218\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 x\ucd95\uc5d0 \ud074\ub7ec\uc2a4\ud130 \uc218\ub97c \ud45c\uc2dc\ud558\uace0 y\ucd95\uc5d0 SSE\ub97c \ud45c\uc2dc\ud558\ub294 \uc774\ub7ec\ud55c \uc720\ud615\uc758 \ud50c\ub86f\uc744 \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \uadf8\ub798\ud504\uc5d0\uc11c\ub294 k = <strong>3\uac1c \ud074\ub7ec\uc2a4\ud130<\/strong> \uc5d0 \uaf2c\uc784 \ub610\ub294 &#8220;\ubb34\ub98e&#8221;\uc774 \uc788\ub294 \uac83\uc73c\ub85c \ub098\ud0c0\ub0a9\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub530\ub77c\uc11c \ub2e4\uc74c \ub2e8\uacc4\uc5d0\uc11c k-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1 \ubaa8\ub378\uc744 \ud53c\ud305\ud560 \ub54c 3\uac1c\uc758 \ud074\ub7ec\uc2a4\ud130\ub97c \uc0ac\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\ucc38\uace0<\/strong> : \uc2e4\uc81c \uc138\uacc4\uc5d0\uc11c\ub294 \uc0ac\uc6a9\ud560 \ud074\ub7ec\uc2a4\ud130 \uc218\ub97c \uc120\ud0dd\ud558\uae30 \uc704\ud574 \uc774 \ud50c\ub86f\uacfc \ub3c4\uba54\uc778 \uc804\ubb38 \uc9c0\uc2dd\uc744 \uc870\ud569\ud558\uc5ec \uc0ac\uc6a9\ud558\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>5\ub2e8\uacc4: \ucd5c\uc801 <em>K\ub97c<\/em> \uc0ac\uc6a9\ud558\uc5ec K-\ud3c9\uade0 \uad70\uc9d1\ud654 \uc218\ud589<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 3\uc758 <em>k<\/em> \uc5d0 \ub300\ud55c \ucd5c\uc801 \uac12\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \uc138\ud2b8\uc5d0\uc11c k-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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;\">\uacb0\uacfc \ud14c\uc774\ube14\uc5d0\ub294 DataFrame\uc758 \uac01 \uad00\ucc30\uc5d0 \ub300\ud55c \ud074\ub7ec\uc2a4\ud130 \ud560\ub2f9\uc774 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774\ub7ec\ud55c \uacb0\uacfc\ub97c \ub354 \uc27d\uac8c \ud574\uc11d\ud560 \uc218 \uc788\ub3c4\ub85d \uac01 \ud50c\ub808\uc774\uc5b4\uc758 \ud074\ub7ec\uc2a4\ud130 \ud560\ub2f9\uc744 \ud45c\uc2dc\ud558\ub294 \uc5f4\uc744 DataFrame\uc5d0 \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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>\ud074\ub7ec\uc2a4\ud130<\/strong> \uc5f4\uc5d0\ub294 \uac01 \ud50c\ub808\uc774\uc5b4\uac00 \ud560\ub2f9\ub41c \ud074\ub7ec\uc2a4\ud130 \ubc88\ud638(0, 1 \ub610\ub294 2)\uac00 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub3d9\uc77c\ud55c \ud074\ub7ec\uc2a4\ud130\uc5d0 \uc18d\ud55c \ud50c\ub808\uc774\uc5b4\ub294 <strong>\ud3ec\uc778\ud2b8<\/strong> , <strong>\uc5b4\uc2dc\uc2a4\ud2b8<\/strong> \ubc0f <strong>\ub9ac\ubc14\uc6b4\ub4dc<\/strong> \uc5f4\uc5d0 \ub300\ud574 \ub300\ub7b5 \uc720\uc0ac\ud55c \uac12\uc744 \uac16\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\ucc38\uace0<\/strong> : <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.cluster.KMeans.html\" target=\"_blank\" rel=\"noopener\">\uc5ec\uae30\uc5d0\uc11c<\/a> <strong>sklearn<\/strong> \uc758 <strong>KMeans<\/strong> \uae30\ub2a5\uc5d0 \ub300\ud55c \uc804\uccb4 \ubb38\uc11c\ub97c \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ko\/\u1109\u1165\u11ab\u1112\u1167\u11bc-\u1112\u116c\u1100\u1171-\u1111\u1161\u110b\u1175\u110a\u1165\u11ab\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1105\u1169\u110c\u1175\u1109\u1173\u1110\u1175\u11a8-\u1112\u116c\u1100\u1171-\u1111\u1161\u110b\u1175\u110a\u1165\u11ab\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/python\u110b\u1174-k-\u110c\u1165\u11b8\u1100\u1175-\u1100\u116d\u110e\u1161-\u1100\u1165\u11b7\u110c\u1173\u11bc\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c K-Fold \uad50\ucc28 \uac80\uc99d\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\uae30\uacc4 \ud559\uc2b5 \uc5d0\uc11c \uac00\uc7a5 \uc77c\ubc18\uc801\uc778 \ud074\ub7ec\uc2a4\ud130\ub9c1 \uc54c\uace0\ub9ac\uc998 \uc911 \ud558\ub098\ub294 k-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1 \uc73c\ub85c \uc54c\ub824\uc838 \uc788\uc2b5\ub2c8\ub2e4. K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc740 \ub370\uc774\ud130 \uc138\ud2b8\uc758 \uac01 \uad00\uce21\uce58\ub97c K \ud074\ub7ec\uc2a4\ud130 \uc911 \ud558\ub098\uc5d0 \ubc30\uce58\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \ucd5c\uc885 \ubaa9\ud45c\ub294 \uac01 \ud074\ub7ec\uc2a4\ud130 \ub0b4\uc758 \uad00\uce21\uce58\uac00 \uc11c\ub85c \ub9e4\uc6b0 \uc720\uc0ac\ud55c \ubc18\uba74 \ub2e4\ub978 \ud074\ub7ec\uc2a4\ud130\uc758 \uad00\uce21\uce58\ub294 \uc11c\ub85c \uc0c1\ub2f9\ud788 \ub2e4\ub978 K \uac1c\uc758 \ud074\ub7ec\uc2a4\ud130\ub97c \uac16\ub294 \uac83\uc785\ub2c8\ub2e4. \uc2e4\uc81c\ub85c K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \uc218\ud589\ud558\uae30 \uc704\ud574 \ub2e4\uc74c \ub2e8\uacc4\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-3578","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python\uc758 K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1: \ub2e8\uacc4\ubcc4 \uc608 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ub2e8\uacc4\ubcc4 \uc608\uc81c\ub97c \ud3ec\ud568\ud558\uc5ec Python\uc5d0\uc11c k-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/ko\/k\u1102\u1173\u11ab-python\u110b\u1166\u1109\u1165-\u110f\u1173\u11af\u1105\u1165\u1109\u1173\u1110\u1165\u1105\u1175\u11bc\u110b\u1173\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python\uc758 K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1: \ub2e8\uacc4\ubcc4 \uc608 - 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