{"id":9922,"date":"2020-07-06T19:19:03","date_gmt":"2020-07-06T10:19:03","guid":{"rendered":"http:\/\/www.gisdeveloper.co.kr\/?p=9922"},"modified":"2020-07-14T09:01:56","modified_gmt":"2020-07-14T00:01:56","slug":"%ed%9a%8c%ea%b7%80%eb%b6%84%ec%84%9d%ec%9d%98-%ec%84%b8%ea%b0%80%ec%a7%80-%eb%b0%a9%eb%b2%95-%ec%84%a0%ed%98%95%ed%9a%8c%ea%b7%80-%ec%9d%98%ec%82%ac%ea%b2%b0%ec%a0%95%ed%8a%b8%eb%a6%ac-%eb%9e%9c","status":"publish","type":"post","link":"http:\/\/www.gisdeveloper.co.kr\/?p=9922","title":{"rendered":"\ud68c\uadc0\ubd84\uc11d\uc758 \ub124\uac00\uc9c0 \ubc29\ubc95, \uc120\ud615\ud68c\uadc0\/\uc758\uc0ac\uacb0\uc815\ud2b8\ub9ac\/\ub79c\ub364\ud3ec\ub808\uc2a4\ud2b8\/SVM"},"content":{"rendered":"<p>\ud68c\uadc0\ubd84\uc11d\uc740 \ub2e4\uc218\uc758 \ud2b9\uc9d5\uac12\uc744 \uc785\ub825\uc73c\ub85c \ud558\ub098\uc758 \ud2b9\uc9d5\uac12(\uc2e4\uc218\uac12)\uc744 \uc0b0\ucd9c\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc138\uac00\uc9c0 \ubc29\ubc95\uc774 \uc788\ub294\ub370, \uc120\ud615\ud68c\uadc0(Linear Regression)\uacfc \uc758\uc0ac\uacb0\uc815\ud2b8\ub9ac(Decision Tree) \uadf8\ub9ac\uace0 \ub80c\ub364 \ud3ec\ub808\uc2a4\ud2b8(Random Forest)\uc785\ub2c8\ub2e4. \ud558\ub098\uc758 \uc8fc\uc81c\ub97c \uc815\ud558\uace0 \uc774 3\uac00\uc9c0 \ubc29\ubc95\uc744 \ud1b5\ud574 \ud68c\uadc0\ubd84\uc11d\uc744 \ud14c\uc2a4\ud2b8\ud574 \ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c \uc801\uc6a9\ud55c \ud68c\uadc0 \ubd84\uc11d \uc8fc\uc81c\ub294 \uc804\ubcf5\uc758 \ub098\uc774\ub97c \uc608\uce21\ud558\ub294 \uac83\uc73c\ub85c \uc804\ubcf5\uc758 &#8216;\uc131\ubcc4&#8217;, &#8216;\ud0a4&#8217;, &#8216;\uc9c0\ub984&#8217;, &#8216;\ub192\uc774&#8217;, &#8216;\uc804\uccb4\ubb34\uac8c&#8217;, &#8216;\ubab8\ud1b5\ubb34\uac8c&#8217;, &#8216;\ub0b4\uc7a5\ubb34\uac8c&#8217;, &#8216;\uaecd\uc9c8\ubb34\uac8c&#8217;\ub97c \uc785\ub825\ud558\uba74 &#8216;\uaecd\uc9c8\uc758 \uace0\ub9ac\uc218&#8217;\ub97c \uc608\uce21\ud55c \ub4a4 \uc608\uce21\ub41c &#8216;\uaecd\uc9c8\uc758 \uace0\ub9ac\uc218&#8217;\uc5d0 1.5\ub97c \ub354\ud558\uba74 \uc804\ubcf5\uc758 \ub098\uc774\uac00 \ub41c\ub2e4\uace0 \ud569\ub2c8\ub2e4.<\/p>\n<p>\uc774\uc5d0 \ub300\ud55c \uc804\ubcf5\uc758 \ub370\uc774\ud130\uc14b\uc740 <a href='https:\/\/www.kaggle.com\/maik3141\/abalone'>kaggle\uc5d0\uc11c \uc27d\uac8c \ub2e4\uc6b4\ub85c\ub4dc<\/a> \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ub2e8 \ub2e4\uc6b4\ub85c\ub4dc \ubc1b\uc740 \ub370\uc774\ud130\uc14b\uc744 \ub2e4\uc74c\uacfc \uac19\uc740 \ucf54\ub4dc\ub97c \ud1b5\ud574 \uc804\ucc98\ub9ac\ud558\uc5ec \ud2b9\uc9d5\uacfc \ub808\uc774\ube14\ub85c \uad6c\ubd84\ud558\uace0, \ud559\uc2b5\uc6a9\uacfc \ud14c\uc2a4\ud2b8\uc6a9\uc73c\ub85c \uad6c\ubd84\ud569\ub2c8\ub2e4.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">\r\n# \ub370\uc774\ud130 \ud30c\uc77c \ub85c\ub529\r\nimport pandas as pd\r\nraw_data = pd.read_csv('.\/datasets\/datasets_1495_2672_abalone.data.csv', \r\n        names=['sex', 'tall', 'radius', 'height', 'weg1', 'weg2', 'weg3', 'weg4', 'ring_cnt'])\r\n    #names=['\uc131\ubcc4', '\ud0a4', '\uc9c0\ub984', '\ub192\uc774', '\uc804\uccb4\ubb34\uac8c', '\ubab8\ud1b5\ubb34\uac8c', '\ub0b4\uc7a5\ubb34\uac8c', '\uaecd\uc9c8\ubb34\uac8c', '\uaecd\uc9c8\uc758\uace0\ub9ac\uc218']\r\nprint(raw_data[:7])\r\n\r\n# \ub808\uc774\ube14 \ubd84\ub9ac\r\ndata_ring_cnt = raw_data[[\"ring_cnt\"]]\r\ndata = raw_data.drop(\"ring_cnt\", axis=1)\r\n\r\n# \ubc94\uc8fc\ud615 \ud2b9\uc9d5(sex)\uc5d0 \ub300\ud55c \uc6d0\ud56b \uc778\ucf54\ub529\r\nfrom sklearn.preprocessing import OneHotEncoder\r\ndata_cat = data[[\"sex\"]]\r\nonehot_encoder = OneHotEncoder()\r\ndata_cat_onehot = onehot_encoder.fit_transform(data_cat)\r\nprint(onehot_encoder.categories_)\r\n\r\n# \ubc94\uc8fc\ud615 \ud2b9\uc9d5 \uc81c\uac70\r\ndata = data.drop(\"sex\", axis=1)\r\n\r\n# \ubc94\uc8fc\ud615 \ud544\ub4dc\uac00 \uc81c\uac70\ub418\uc5b4 \uc218\uce58\ud615 \ud2b9\uc9d5\ub4e4\uc5d0 \ub300\ud574 0~1 \uad6c\uac04\uc758 \ud06c\uae30\ub85c \uc870\uc815\r\n#from sklearn.preprocessing import StandardScaler \r\n#minmax_scaler = StandardScaler()\r\n#data = minmax_scaler.fit_transform(data)\r\n\r\n# \uc6d0\ud56b\uc778\ucf54\ub529\ub41c \ubc94\uc8fc\ud615 \ud2b9\uc9d5\uacfc \uc2a4\ucf00\uc77c\ub9c1\ub41c \uc218\uce58\ud615 \ud2b9\uc9d5 \ubc0f \ub808\uc774\ube14 \uacb0\ud569\r\nimport numpy as np\r\ndata = np.c_[data_cat_onehot.toarray(), data, data_ring_cnt]\r\n\r\ndata = pd.DataFrame(data, columns=['sex_F', 'sex_I', 'sex_M', 'sex_''tall', 'radius', 'height', 'weg1', 'weg2', 'weg3', 'weg4', 'ring_cnt'])\r\nprint(data[:7])\r\n\r\n# \ud559\uc2b5 \ub370\uc774\ud130\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \ubd84\ub9ac \r\nfrom sklearn.model_selection import train_test_split\r\ntrain_set, test_set = train_test_split(data, test_size=0.1, random_state=47)\r\n\r\n# \uc785\ub825 \ud2b9\uc9d5\uacfc \ub808\uc774\ube14\uc758 \ubd84\ub9ac\r\ntrain_data = train_set.drop(\"ring_cnt\", axis=1)\r\ntrain_data_label = train_set[\"ring_cnt\"].copy()\r\ntest_data = test_set.drop(\"ring_cnt\", axis=1)\r\ntest_data_label = test_set[\"ring_cnt\"].copy()\r\n<\/pre>\n<p>\uc704\uc758 \ucf54\ub4dc\ub294 \uc544\ub798\uc758 \uae00\ub4e4\uc744 \ucc38\uc870\ud558\uc5ec \ud30c\uc545\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<blockquote class=\"wp-embedded-content\" data-secret=\"4BvPqhT0Zk\"><p><a href=\"http:\/\/www.gisdeveloper.co.kr\/?p=9871\">\ubd84\uc11d\uac00 \uad00\uc810\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uac1c\ub7b5\uc801\uc73c\ub85c \uc0b4\ud3b4\ubcf4\uae30<\/a><\/p><\/blockquote>\n<p><iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; clip: rect(1px, 1px, 1px, 1px);\" title=\"&#8220;\ubd84\uc11d\uac00 \uad00\uc810\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uac1c\ub7b5\uc801\uc73c\ub85c \uc0b4\ud3b4\ubcf4\uae30&#8221; &#8212; GIS Developer\" src=\"http:\/\/www.gisdeveloper.co.kr\/?p=9871&#038;embed=true#?secret=WtkrcyMPU4#?secret=4BvPqhT0Zk\" data-secret=\"4BvPqhT0Zk\" width=\"525\" height=\"296\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe><\/p>\n<blockquote class=\"wp-embedded-content\" data-secret=\"Ro9JEDyJC1\"><p><a href=\"http:\/\/www.gisdeveloper.co.kr\/?p=9891\">\uacc4\uce35\uc801 \uc0d8\ud50c\ub9c1(Stratified Sampling)<\/a><\/p><\/blockquote>\n<p><iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; clip: rect(1px, 1px, 1px, 1px);\" title=\"&#8220;\uacc4\uce35\uc801 \uc0d8\ud50c\ub9c1(Stratified Sampling)&#8221; &#8212; GIS Developer\" src=\"http:\/\/www.gisdeveloper.co.kr\/?p=9891&#038;embed=true#?secret=84y58qqlEe#?secret=Ro9JEDyJC1\" data-secret=\"Ro9JEDyJC1\" width=\"525\" height=\"296\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe><\/p>\n<blockquote class=\"wp-embedded-content\" data-secret=\"0i718D1ywb\"><p><a href=\"http:\/\/www.gisdeveloper.co.kr\/?p=9907\">OneHot \uc778\ucf54\ub529(Encoding) \ubc0f \uc2a4\ucf00\uc77c\ub9c1(Scaling)<\/a><\/p><\/blockquote>\n<p><iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; clip: rect(1px, 1px, 1px, 1px);\" title=\"&#8220;OneHot \uc778\ucf54\ub529(Encoding) \ubc0f \uc2a4\ucf00\uc77c\ub9c1(Scaling)&#8221; &#8212; GIS Developer\" src=\"http:\/\/www.gisdeveloper.co.kr\/?p=9907&#038;embed=true#?secret=s5GLPIeIR6#?secret=0i718D1ywb\" data-secret=\"0i718D1ywb\" width=\"525\" height=\"296\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe><\/p>\n<p>\ub2e4\uc18c \ud5d8\ub09c\ud55c \ub370\uc774\ud130\uc14b\uc758 \uc900\ube44\uac00 \ub05d\ub0ac\uc73c\ub2c8, \uc774\uc81c \uc774 \uae00\uc758 \ubcf8\uc9c8\uc778 \uc138\uac00\uc9c0 \ud68c\uadc0\ubd84\uc11d \ubc29\uc2dd\uc744 \ud558\ub098\uc529 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uba3c\uc800 \uc120\ud615\ud68c\uadc0\uc785\ub2c8\ub2e4. \ucc38\uace0\ub85c \uae30\uacc4\ud559\uc2b5 \uacfc\uc815\uc5d0 \ub300\ud55c \uc2dc\uac04 \ub300\ubd80\ubd84\uc744 \ucc28\uc9c0 \ud558\ub294 \uc791\uc5c5\uc740 \uc774\ub7ec\ud55c \ub370\uc774\ud130\uc758 \uc218\uc9d1\uacfc \uac00\uacf5 \ubc0f \uc815\uc81c\uc774\uba70, \uadf8 \ub2e4\uc74c\uc73c\ub85c \ucef4\ud4e8\ud130(GPU)\ub97c \ud1b5\ud55c \ubaa8\ub378\uc758 \ud559\uc2b5\uc785\ub2c8\ub2e4. \uc0ac\ub78c\uc774 \uad00\uc5ec\ud558\ub294 \uc2dc\uac04\uc740 \uc0c1\ub300\uc801\uc73c\ub85c \ub9e4\uc6b0 \uc801\uc2b5\ub2c8\ub2e4. \ubb3c\ub860 \ubaa8\ub378\uc744 \uc9c1\uc811 \uc124\uacc4\ud558\ub294 \uacbd\uc6b0\ub77c\uba74 \ub2ec\ub77c\uc9c8 \uc218 \uc788\uaca0\uc73c\ub098, \uc5ed\uc2dc \ub370\uc774\ud130 \uc791\uc5c5\uacfc GPU\uc758 \ud559\uc2b5 \uc2dc\uac04\uc740 \uc0c1\ub300\uc801\uc73c\ub85c \ub9ce\uc774 \uc18c\uc694\ub429\ub2c8\ub2e4.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">\r\nfrom sklearn.linear_model import LinearRegression\r\nmodel = LinearRegression()\r\nmodel.fit(train_data, train_data_label)\r\n\r\nfrom sklearn.metrics import mean_squared_error\r\nsome_predicted = model.predict(test_data)\r\nmse = np.sqrt(mean_squared_error(some_predicted, test_data_label))\r\nprint('\ud3c9\uade0\uc81c\uacf1\uadfc\uc624\ucc28', mse)\r\n<\/pre>\n<p>\ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \uc14b\uc73c\ub85c \ud3c9\uac00\ud55c \uc624\ucc28\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n<pre class='code'>\r\n\ud3c9\uade0\uc81c\uacf1\uadfc\uc624\ucc28 1.9166262592968584\r\n<\/pre>\n<p>\ub2e4\uc74c\uc740 \uc758\uc0ac\uacb0\uc815\ud2b8\ub9ac \ubc29\uc2dd\uc785\ub2c8\ub2e4.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">\r\nfrom sklearn.tree import DecisionTreeRegressor\r\nmodel = DecisionTreeRegressor()\r\nmodel.fit(train_data, train_data_label)\r\n\r\nfrom sklearn.metrics import mean_squared_error\r\nsome_predicted = model.predict(test_data)\r\nmse = np.sqrt(mean_squared_error(some_predicted, test_data_label))\r\nprint('\ud3c9\uade0\uc81c\uacf1\uadfc\uc624\ucc28', mse)\r\n<\/pre>\n<pre class='code'>\r\n\ud3c9\uade0\uc81c\uacf1\uadfc\uc624\ucc28 2.8716565747410656\r\n<\/pre>\n<p>\uc138\ubc88\uc9f8\ub85c \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8 \ubc29\uc2dd\uc785\ub2c8\ub2e4.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">\r\nfrom sklearn.ensemble import RandomForestRegressor\r\nmodel = RandomForestRegressor()\r\nmodel.fit(train_data, train_data_label)\r\n\r\nfrom sklearn.metrics import mean_squared_error\r\nsome_predicted = model.predict(test_data)\r\nmse = np.sqrt(mean_squared_error(some_predicted, test_data_label))\r\nprint('\ud3c9\uade0\uc81c\uacf1\uadfc\uc624\ucc28', mse)\r\n<\/pre>\n<pre class='code'>\r\n\ud3c9\uade0\uc81c\uacf1\uadfc\uc624\ucc28 2.0828589571564127\r\n<\/pre>\n<p>\ub9c8\uc9c0\ub9c9\uc73c\ub85c SVM(Support Vector Manchine) \ubc29\uc2dd\uc785\ub2c8\ub2e4.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">\r\nfrom sklearn import svm\r\nmodel = svm.SVC()\r\nmodel.fit(train_data, train_data_label)\r\n<\/pre>\n<pre class='code'>\r\n\ud3c9\uade0\uc81c\uacf1\uadfc\uc624\ucc28 2.537753216441624\r\n<\/pre>\n<p>\uc774 \uae00\uc758 \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud574\uc11c\ub294 \uc120\ud615\ud68c\uadc0 \ubc29\uc2dd\uc774 \uac00\uc7a5 \uc624\ucc28\uac00 \uc791\uc740 \uac83\uc740 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc774 \uae00\uc5d0\uc11c \ud14c\uc2a4\ud2b8\ud55c 3\uac00\uc9c0 \ubaa8\ub378\uc758 \ud558\uc774\ud37c\ud30c\ub77c\uba54\ud130\ub294 \uae30\ubcf8\uac12\uc744 \uc0ac\uc6a9\ud558\uc600\uc2b5\ub2c8\ub2e4. \ud558\uc774\ud37c\ud30c\ub77c\uba54\ud130\uc758 \uc138\ubd80 \ud29c\ud305\uc744 \uc218\ud589\ud558\uba74 \uacb0\uacfc\uac00 \ub2ec\ub77c\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ud68c\uadc0\ubd84\uc11d\uc740 \ub2e4\uc218\uc758 \ud2b9\uc9d5\uac12\uc744 \uc785\ub825\uc73c\ub85c \ud558\ub098\uc758 \ud2b9\uc9d5\uac12(\uc2e4\uc218\uac12)\uc744 \uc0b0\ucd9c\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc138\uac00\uc9c0 \ubc29\ubc95\uc774 \uc788\ub294\ub370, \uc120\ud615\ud68c\uadc0(Linear Regression)\uacfc \uc758\uc0ac\uacb0\uc815\ud2b8\ub9ac(Decision Tree) \uadf8\ub9ac\uace0 \ub80c\ub364 \ud3ec\ub808\uc2a4\ud2b8(Random Forest)\uc785\ub2c8\ub2e4. \ud558\ub098\uc758 \uc8fc\uc81c\ub97c \uc815\ud558\uace0 \uc774 3\uac00\uc9c0 \ubc29\ubc95\uc744 \ud1b5\ud574 \ud68c\uadc0\ubd84\uc11d\uc744 \ud14c\uc2a4\ud2b8\ud574 \ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c \uc801\uc6a9\ud55c \ud68c\uadc0 \ubd84\uc11d \uc8fc\uc81c\ub294 \uc804\ubcf5\uc758 \ub098\uc774\ub97c \uc608\uce21\ud558\ub294 \uac83\uc73c\ub85c \uc804\ubcf5\uc758 &#8216;\uc131\ubcc4&#8217;, &#8216;\ud0a4&#8217;, &#8216;\uc9c0\ub984&#8217;, &#8216;\ub192\uc774&#8217;, &#8216;\uc804\uccb4\ubb34\uac8c&#8217;, &#8216;\ubab8\ud1b5\ubb34\uac8c&#8217;, &#8216;\ub0b4\uc7a5\ubb34\uac8c&#8217;, &#8216;\uaecd\uc9c8\ubb34\uac8c&#8217;\ub97c \uc785\ub825\ud558\uba74 &#8216;\uaecd\uc9c8\uc758 \uace0\ub9ac\uc218&#8217;\ub97c \uc608\uce21\ud55c \ub4a4 \uc608\uce21\ub41c &hellip; <\/p>\n<p class=\"link-more\"><a href=\"http:\/\/www.gisdeveloper.co.kr\/?p=9922\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ud68c\uadc0\ubd84\uc11d\uc758 \ub124\uac00\uc9c0 \ubc29\ubc95, \uc120\ud615\ud68c\uadc0\/\uc758\uc0ac\uacb0\uc815\ud2b8\ub9ac\/\ub79c\ub364\ud3ec\ub808\uc2a4\ud2b8\/SVM&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[132],"tags":[],"class_list":["post-9922","post","type-post","status-publish","format-standard","hentry","category-deep-machine-learning"],"_links":{"self":[{"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/posts\/9922","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=9922"}],"version-history":[{"count":10,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/posts\/9922\/revisions"}],"predecessor-version":[{"id":9927,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/posts\/9922\/revisions\/9927"}],"wp:attachment":[{"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9922"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9922"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9922"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}