\ud604\uc7ac \uae08\uc735 \uc2dc\uc7a5\uc740 \uae09\uaca9\ud55c \ubcc0\ud654\uc640 \ub2e4\uc591\ud55c \ub370\uc774\ud130\uc758 \ud750\ub984 \uc18d\uc5d0\uc11c \ud601\uc2e0\uc801\uc778 \uae30\uc220\uc744 \ud544\uc694\ub85c \ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc0c1\ud669\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd(Machine Learning)\uacfc \ub525\ub7ec\ub2dd(Deep Learning)\uc740 \uc2e0\ub8b0\uc131 \ub192\uc740 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c\uc758 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ud65c\uc6a9\ubc95, \ud2b9\ud788 \uc120\ud615 \ucc28\uc6d0 \ucd95\uc18c(linear dimensionality reduction)\uc758 \ud544\uc694\uc131\uacfc \ubc29\uc2dd\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ud2b9\uc815\ud55c \uc218\ud559\uc801 \uc218\uc2dd\uc774\ub098 \uaddc\uce59\uc5d0 \uae30\ucd08\ud558\uc5ec \uc790\ub3d9\ud654\ub41c \ubc29\ubc95\uc73c\ub85c \uae08\uc735 \uc790\uc0b0\uc744 \uac70\ub798\ud558\ub294 \uc2dc\uc2a4\ud15c\uc785\ub2c8\ub2e4. \uae30\uc220\uc801 \ubd84\uc11d, \uae30\ubcf8\uc801 \ubd84\uc11d, \ub370\uc774\ud130 \uae30\ubc18\uc758 \uc54c\uace0\ub9ac\uc998\uc744 \ud1b5\ud574 \ud2b8\ub808\uc774\ub529 \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7f0 \uc2dc\uc2a4\ud15c\uc740 \uc778\uac04\uc758 \uac10\uc815\uc744 \ubc30\uc81c\ud558\uace0 \ubcf4\ub2e4 \ube60\ub974\uace0 \ud6a8\uc728\uc801\uc73c\ub85c \uac70\ub798\ub97c \ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc90d\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \uacfc\uac70\uc758 \ub370\uc774\ud130\ub97c \ubc14\ud0d5\uc73c\ub85c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \ubbf8\ub798\uc758 \uacb0\uacfc\ub97c \uc608\uce21\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uc774\ub294 \uac00\uaca9 \uc6c0\uc9c1\uc784 \uc608\uce21, \uc804\ub7b5 \ucd5c\uc801\ud654, \ub9ac\uc2a4\ud06c \uad00\ub9ac \ub4f1 \ub2e4\uc591\ud55c \ubc29\uc2dd\uc73c\ub85c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \ubc18\ubcf5\uc801\uc778 \ud559\uc2b5 \uacfc\uc815\uc744 \ud1b5\ud574 \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub97c \uc9c0\uc18d\uc801\uc73c\ub85c \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd(Artificial Neural Network)\uc744 \ud65c\uc6a9\ud558\uc5ec \ubcf5\uc7a1\ud55c \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uace0 \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud558\ub294 \ub370 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. \ud2b9\ud788 \ube44\uc815\ud615 \ub370\uc774\ud130(\uc608: \ub274\uc2a4 \uae30\uc0ac, \uc18c\uc15c \ubbf8\ub514\uc5b4 \ub370\uc774\ud130 \ub4f1)\uc5d0\uc11c \ub354 \ud070 \ud6a8\uacfc\ub97c \ubc1c\ud718\ud569\ub2c8\ub2e4.<\/p>\n
\uae08\uc735 \ub370\uc774\ud130\ub294 \uc885\uc885 \uace0\ucc28\uc6d0(high-dimensional)\uc785\ub2c8\ub2e4. \uace0\ucc28\uc6d0 \ub370\uc774\ud130\ub294 \uacc4\uc0b0 \ubcf5\uc7a1\uc131 \ubc0f \uc624\ubc84\ud53c\ud305(overfitting) \ubb38\uc81c\ub97c \uc77c\uc73c\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub54c \uc0ac\uc6a9\ud558\ub294 \uae30\ubc95\uc774 \ubc14\ub85c \ucc28\uc6d0 \ucd95\uc18c(dimensionality reduction)\uc785\ub2c8\ub2e4. \ud2b9\ud788, \uc120\ud615 \ucc28\uc6d0 \ucd95\uc18c \ubc29\ubc95\uc740 \ub370\uc774\ud130\ub97c \uc800\ucc28\uc6d0 \uacf5\uac04\uc73c\ub85c \ud6a8\uacfc\uc801\uc73c\ub85c \ubcc0\ud658\ud558\uc5ec \ubd84\uc11d \ubc0f \uc2dc\uac01\ud654\ub97c \uc6a9\uc774\ud558\uac8c \ub9cc\ub4ed\ub2c8\ub2e4.<\/p>\n
\uc8fc\uc694 \uc120\ud615 \ucc28\uc6d0 \ucd95\uc18c \uae30\ubc95\uc5d0\ub294 \uc8fc\uc131\ubd84 \ubd84\uc11d(Principal Component Analysis, PCA), \ud2b9\uc774\uac12 \ubd84\ud574(Singular Value Decomposition, SVD), \uc120\ud615 \ud310\ubcc4 \ubd84\uc11d(Linear Discriminant Analysis, LDA)\uac00 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
PCA\ub294 \uace0\ucc28\uc6d0 \ub370\uc774\ud130\ub97c \ub354 \ub0ae\uc740 \ucc28\uc6d0\uc73c\ub85c \ucd95\uc18c\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \ub370\uc774\ud130\uc758 \ubcc0\ub3d9\uc131\uc744 \ucd5c\ub300\ud55c \ubcf4\uc874\ud558\uba74\uc11c \uc11c\ub85c \uc9c1\uad50\ud558\ub294 \uc0c8\ub85c\uc6b4 \ucd95\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. PCA\uc758 \ud575\uc2ec \uc544\uc774\ub514\uc5b4\ub294 \ub370\uc774\ud130\uc758 \ubd84\uc0b0\uc744 \ucd5c\ub300\ud654\ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \ucc28\uc6d0\uc744 \ucd95\uc18c\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
import numpy as np\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler\n\n# \ub370\uc774\ud130 \uc900\ube44\ndata = np.random.rand(100, 10) # 100x10 \ub79c\ub364 \ub370\uc774\ud130\nscaler = StandardScaler()\ndata_scaled = scaler.fit_transform(data)\n\n# PCA \uc801\uc6a9\npca = PCA(n_components=2) # 2\ucc28\uc6d0\uc73c\ub85c \ucd95\uc18c\ndata_pca = pca.fit_transform(data_scaled)\n\nprint(data_pca.shape) # (100, 2)<\/code><\/pre>\n2.2.2 \ud2b9\uc774\uac12 \ubd84\ud574 (SVD)<\/h4>\n
SVD\ub294 \ud589\ub82c \ubd84\ud574 \uae30\ubc95\uc73c\ub85c, \uc8fc\ub85c \ucd94\ucc9c \uc2dc\uc2a4\ud15c \ubc0f \ub370\uc774\ud130 \uc555\ucd95\uc5d0 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ub370\uc774\ud130 \ud589\ub82c\uc744 \uc138 \uac1c\uc758 \ud589\ub82c \uacf1\uc73c\ub85c \ubd84\ud574\ud558\uc5ec \ud575\uc2ec \uc815\ubcf4\ub9cc \ucd94\ucd9c\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ud2b8\ub808\uc774\ub529\uc5d0\uc11c\ub294 \uc2dc\uac04\uc5d0 \ub530\ub978 \ud328\ud134\uc744 \ubd84\uc11d\ud558\ub294 \ub370 \uc720\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
2.2.3 \uc120\ud615 \ud310\ubcc4 \ubd84\uc11d (LDA)<\/h4>\n
LDA\ub294 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8 \uc0ac\uc774\uc758 \uc120\ud615 \ubd84\ub9ac\ub97c \uadf9\ub300\ud654\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc8fc\ub85c \ubd84\ub958 \ubb38\uc81c\uc5d0 \ud6a8\uacfc\uc801\uc774\uba70, \ud074\ub798\uc2a4 \uac04\uc758 \ubd84\uc0b0\uc744 \ucd5c\ub300\ud654\ud558\uace0 \ud074\ub798\uc2a4 \ub0b4\uc758 \ubd84\uc0b0\uc744 \ucd5c\uc18c\ud654\ud558\uc5ec \ub370\uc774\ud130\uc758 \ucc28\uc6d0\uc744 \uc904\uc785\ub2c8\ub2e4. \uae08\uc735 \ub370\uc774\ud130\uc5d0\uc11c \uc2e0\uc6a9 \ub9ac\uc2a4\ud06c \ubd84\uc11d\uc774\ub098 \uc0ac\uae30 \ud0d0\uc9c0\uc5d0 \uc720\uc6a9\ud558\uac8c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
3. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c\uc758 \uc2e4\uc81c \uc801\uc6a9<\/h2>\n
\uc774\uc81c \uc120\ud615 \ucc28\uc6d0 \ucd95\uc18c \uae30\ubc95\uc744 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \uc2e4\uc81c\ub85c \uc5b4\ub5bb\uac8c \uc801\uc6a9\ud560 \uc218 \uc788\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 PCA\ub97c \uc608\uc81c\ub85c \ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
\uc608\ub97c \ub4e4\uc5b4, \uacfc\uac70\uc758 \uc8fc\uac00 \ub370\uc774\ud130\uc640 \uad00\ub828\ub41c \ub2e4\uc591\ud55c \uae30\uc220\uc801 \uc9c0\ud45c\ub4e4\uc744 \ubaa8\uc740 \ub370\uc774\ud130\uc14b\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\uc14b\uc744 PCA\ub97c \ud1b5\ud574 \ucc28\uc6d0 \ucd95\uc18c\ub97c \uc9c4\ud589\ud55c \ud6c4, \ud2b8\ub808\uc774\ub529 \ubaa8\ub378\uc5d0 \uc785\ub825\ud569\ub2c8\ub2e4.<\/p>\n
3.2 PCA\ub97c \ud1b5\ud55c \ucc28\uc6d0 \ucd95\uc18c<\/h3>\nimport pandas as pd\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler\n\n# \uc8fc\uac00 \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('stock_data.csv')\nfeatures = data[['feature1', 'feature2', 'feature3', ...]] # \ud2b9\uc9d5 \uc120\ud0dd\n\n# \ub370\uc774\ud130 \ud45c\uc900\ud654\nscaler = StandardScaler()\ndata_scaled = scaler.fit_transform(features)\n\n# PCA \uc801\uc6a9\npca = PCA(n_components=5) # 5\ucc28\uc6d0\uc73c\ub85c \ucd95\uc18c\ndata_pca = pca.fit_transform(data_scaled)\n\n# \ucd95\uc18c\ub41c \ub370\uc774\ud130\uc640 \uc885\uac00 \ub808\uc774\ube14\uc744 \uacb0\ud569\ndf_pca = pd.DataFrame(data_pca, columns=[f'PC{i}' for i in range(1, 6)])\ndf_pca['target'] = data['target'] # \uc885\uac00 \ub808\uc774\ube14\n<\/code><\/pre>\n3.3 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \ud6c8\ub828<\/h3>\n
PCA\ub85c \ucc28\uc6d0 \ucd95\uc18c\ud55c \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8(Random Forest) \ub098 XGBoost \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\n\n# \ub370\uc774\ud130 \ubd84\ud560\nX = df_pca.drop('target', axis=1)\ny = df_pca['target']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n\n# \ubaa8\ub378 \ud6c8\ub828\nmodel = RandomForestClassifier()\nmodel.fit(X_train, y_train)\n\n# \ubaa8\ub378 \ud3c9\uac00\naccuracy = model.score(X_test, y_test)\nprint(f'Accuracy: {accuracy:.2f}')\n<\/code><\/pre>\n3.4 \uac70\ub798 \uc2e0\ud638 \uc0dd\uc131<\/h3>\n
\ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uae30\ubc18\uc73c\ub85c \uac70\ub798 \uc2e0\ud638\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \ubc14\ud0d5\uc73c\ub85c \ub9e4\uc218 \ubc0f \ub9e4\ub3c4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
predictions = model.predict(X_test)\n\n# \ub9e4\uc218\uc2e0\ud638\nbuy_signals = [1 if pred == 1 else 0 for pred in predictions]\nsell_signals = [1 if pred == 0 else 0 for pred in predictions]\n<\/code><\/pre>\n4. \uacb0\ub860<\/h2>\n
\ubcf8 \uae00\uc5d0\uc11c\ub294 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\uc220\uc758 \ud544\uc694\uc131\uacfc \uc120\ud615 \ucc28\uc6d0 \ucd95\uc18c \uae30\ubc95\uc758 \uc911\uc694\uc131\uc5d0 \ub300\ud574 \uc124\uba85\ud588\uc2b5\ub2c8\ub2e4. \ud6a8\uc728\uc801\uc778 \ucc28\uc6d0 \ucd95\uc18c\ub294 \ub370\uc774\ud130 \ubd84\uc11d\uacfc \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uba70, \uc774\ub294 \uad81\uadf9\uc801\uc73c\ub85c \uc790\ub3d9\ud654\ub41c \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc758 \uc131\uacf5\uc5d0 \uae30\uc5ec\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub2e4\uc591\ud55c \ucc28\uc6d0 \ucd95\uc18c \uae30\ubc95\uacfc \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \uc0c1\ud669\uc5d0 \ub9de\uac8c \uc801\uc808\ud788 \uc120\ud0dd\ud558\uc5ec \ud65c\uc6a9\ud568\uc73c\ub85c\uc368 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uacbd\uc7c1\ub825\uc744 \uac16\ucd9c \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\ucd94\uac00\uc801\uc778 \uc2ec\ud654 \ud559\uc2b5\uc744 \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc0c8\ub85c\uc6b4 \uac00\ub2a5\uc131\uc5d0 \ub3c4\uc804\ud574 \ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"