{"id":29227,"date":"2024-10-27T16:17:24","date_gmt":"2024-10-27T16:17:24","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29227"},"modified":"2024-11-26T06:53:32","modified_gmt":"2024-11-26T06:53:32","slug":"%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d-%eb%b0%8f-%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%95%8c%ea%b3%a0%eb%a6%ac%ec%a6%98-%ed%8a%b8%eb%a0%88%ec%9d%b4%eb%94%a9-%ec%9d%80%eb%8b%89%ec%b8%b5","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29227\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc740\ub2c9\uce35"},"content":{"rendered":"

<\/p>\n

1. \uc11c\ub860<\/h2>\n

\n \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uac70\ub798\ub294 \ubcf5\uc7a1\ud558\uace0 \uc608\uce21\ud558\uae30 \uc5b4\ub824\uc6b4 \ub370\uc774\ud130\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc5b4\ub824\uc6c0\uc744 \uadf9\ubcf5\ud558\uae30 \uc704\ud574 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\ubc95\uc774 \ub110\ub9ac \uc0ac\uc6a9\ub418\uace0 \uc788\uc73c\uba70, \ud2b9\ud788 \uc740\ub2c9\uce35\uc758 \uc124\uacc4\uc640 \ud65c\uc6a9\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\ub294 \ub370 \uc911\uc694\ud55c \uc694\uc18c\ub85c \uc791\uc6a9\ud569\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150\ubd80\ud130 \uc2dc\uc791\ud558\uc5ec, \uc740\ub2c9\uce35\uc758 \ub3d9\uc791 \uc6d0\ub9ac, \uc124\uacc4 \ubc29\ubc95, \uadf8\ub9ac\uace0 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c\uc758 \uc801\uc6a9 \uc0ac\ub840\ub97c \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n

2. \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ucd08<\/h2>\n

2.1 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uc815\uc758<\/h3>\n

\n \uba38\uc2e0\ub7ec\ub2dd\uc740 \ucef4\ud4e8\ud130\uac00 \uba85\uc2dc\uc801\uc778 \ud504\ub85c\uadf8\ub798\ubc0d \uc5c6\uc774\ub3c4 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uba38\uc2e0\ub7ec\ub2dd\uc740 \uc9c0\ub3c4 \ud559\uc2b5, \ube44\uc9c0\ub3c4 \ud559\uc2b5, \uac15\ud654 \ud559\uc2b5\uc73c\ub85c \ubd84\ub958\ub429\ub2c8\ub2e4.\n <\/p>\n

2.2 \ub525\ub7ec\ub2dd\uc758 \uc815\uc758<\/h3>\n

\n \ub525\ub7ec\ub2dd\uc740 \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \ub2e4\uce35 \ub124\ud2b8\uc6cc\ud06c\ub97c \ud1b5\ud574 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ud45c\ud604\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4. \ud2b9\ud788, \uc774\ubbf8\uc9c0, \uc74c\uc131 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ub180\ub77c\uc6b4 \uc131\uacfc\ub97c \ubcf4\uc5ec\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

2.3 \uae30\ubcf8 \uac1c\ub150\uc758 \uc774\ud574<\/h3>\n

\n \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8\uc801\uc778 \uad6c\uc131 \uc694\uc18c\ub97c \uc774\ud574\ud558\uae30 \uc704\ud574, \ub370\uc774\ud130 \uc138\ud2b8, \ud53c\ucc98, \ub808\uc774\ube14, \ud6c8\ub828 \ubc0f \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc758 \uac1c\ub150\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n

3. \uc740\ub2c9\uce35\uc758 \uac1c\ub150<\/h2>\n

3.1 \ub124\ud2b8\uc6cc\ud06c \uad6c\uc870<\/h3>\n

\n \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc740 \uc785\ub825\uce35, \uc740\ub2c9\uce35, \ucd9c\ub825\uce35\uc73c\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \uc785\ub825\uce35\uc740 \ub370\uc774\ud130\uac00 \uc785\ub825\ub418\ub294 \uce35\uc774\uba70, \ucd9c\ub825\uce35\uc740 \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uc81c\uacf5\ud558\ub294 \uce35\uc785\ub2c8\ub2e4. \uc740\ub2c9\uce35\uc740 \uc785\ub825 \ub370\uc774\ud130\uc758 \uc911\uc694 \ud2b9\uc131\uc744 \ud559\uc2b5\ud558\uace0 \ubcc0\ud658\ud558\ub294 \uc5ed\ud560\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.\n <\/p>\n

3.2 \uc740\ub2c9\uce35\uc758 \uc5ed\ud560<\/h3>\n

\n \uc740\ub2c9\uce35\uc740 \uc5ec\ub7ec \uac1c\uc758 \ub274\ub7f0\uc73c\ub85c \uad6c\uc131\ub418\uba70, \uac01 \ub274\ub7f0\uc740 \uac00\uc911\uce58\uc640 \ud3b8\ud5a5\uc744 \uac00\uc9d1\ub2c8\ub2e4. \uc774 \uce35\ub4e4\uc740 \ube44\uc120\ud615 \ubcc0\ud658\uc744 \ud1b5\ud574 \uc785\ub825 \ub370\uc774\ud130\ub97c \ub354\uc6b1 \ucd94\uc0c1\ud654\ub41c \ud615\ud0dc\ub85c \ubcc0\ud658\ud558\uc5ec \ucd5c\uc885 \ucd9c\ub825 \uacb0\uacfc\uc758 \ud488\uc9c8\uc744 \ud5a5\uc0c1\uc2dc\ud0b5\ub2c8\ub2e4.\n <\/p>\n

4. \uc740\ub2c9\uce35\uc758 \uc124\uacc4<\/h2>\n

4.1 \uc740\ub2c9\uce35\uc758 \uc218<\/h3>\n

\n \uc740\ub2c9\uce35\uc758 \uc218\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc5d0 \uacb0\uc815\uc801\uc778 \uc601\ud5a5\uc744 \ubbf8\uce69\ub2c8\ub2e4. \ub450 \uce35 \uc774\uc0c1\uc744 \uac00\uc9c4 \ub124\ud2b8\uc6cc\ud06c\ub294 \ub354\uc6b1 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ud328\ud134\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\uc9c0\ub9cc, \uacfc\uc801\ud569(overfitting)\uc758 \uc704\ud5d8 \ub610\ud55c \uc99d\uac00\ud569\ub2c8\ub2e4. \ub530\ub77c\uc11c \uc801\uc808\ud55c \uc218\uc758 \uc740\ub2c9\uce35\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.\n <\/p>\n

4.2 \uc740\ub2c9\uce35\uc758 \ub178\ub4dc \uc218<\/h3>\n

\n \uac01 \uc740\ub2c9\uce35\uc758 \ub178\ub4dc \uc218(\ub274\ub7f0 \uc218)\ub294 \ud559\uc2b5\ud574\uc57c \ud560 \ub370\uc774\ud130 \ud2b9\uc131\uacfc \uadf8 \ubcf5\uc7a1\ub3c4\uc5d0 \ub530\ub77c \ub2ec\ub77c\uc9d1\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ub370\uc774\ud130\uc758 \ucc28\uc6d0\uc774 \ub192\uc744\uc218\ub85d \ub354 \ub9ce\uc740 \ub178\ub4dc\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \ucd5c\uc801\uc758 \ub178\ub4dc \uc218\ub97c \ucc3e\uae30 \uc704\ud574\uc11c\ub294 \uc5ec\ub7ec \ubc88\uc758 \uc2e4\ud5d8\uacfc \uac80\uc99d\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.\n <\/p>\n

5. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc758 \uc801\uc6a9<\/h2>\n

5.1 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n

\n \uace0\ud488\uc9c8\uc758 \ub370\uc774\ud130\ub97c \uc900\ube44\ud558\ub294 \uac83\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc131\uacf5\uc5d0 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \uacfc\uac70\uc758 \uac00\uaca9 \ub370\uc774\ud130, \uac70\ub798\ub7c9, \uc7ac\ubb34\uc81c\ud45c \ub370\uc774\ud130 \ub4f1\uc744 \uc218\uc9d1\ud558\uace0 \uc804\ucc98\ub9ac \uacfc\uc815\uc5d0\uc11c \ud53c\ucc98 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1\uc744 \uc218\ud589\ud574\uc57c \ud569\ub2c8\ub2e4.\n <\/p>\n

5.2 \ubaa8\ub378 \ud6c8\ub828<\/h3>\n

\n \uc900\ube44\ub41c \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uace0, \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \ud559\uc2b5\ud558\uac8c \ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 \uc190\uc2e4 \ud568\uc218\uc640 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uc9c0\uc18d\uc801\uc73c\ub85c \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

5.3 \uc218\uc775\ub960 \uc608\uce21<\/h3>\n

\n \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \ud1b5\ud574 \ud5a5\ud6c4\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc744 \uc608\uce21\ud558\uace0, \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c \ub9e4\ub9e4 \uacb0\uc815\uc744 \ub0b4\ub9ac\uac8c \ub429\ub2c8\ub2e4. \uc608\uce21\ub41c \uc218\uc775\ub960\uc744 \uae30\ubc18\uc73c\ub85c \ud3ec\ud2b8\ud3f4\ub9ac\uc624\ub97c \uad6c\uc131\ud558\uace0 \ub9ac\uc2a4\ud06c \uad00\ub9ac \uc804\ub7b5\uc744 \uc218\ub9bd\ud574\uc57c \ud569\ub2c8\ub2e4.\n <\/p>\n

6. \uc2e4\uc2b5: \uac04\ub2e8\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ucd95<\/h2>\n

6.1 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h3>\n
!pip install pandas numpy matplotlib tensorflow<\/code><\/pre>\n
6.2 \ub370\uc774\ud130 \uc218\uc9d1 \ubc0f \uc804\ucc98\ub9ac<\/h3>\n
import pandas as pd\nfrom sklearn.model_selection import train_test_split\n# CSV \ud30c\uc77c\ub85c\ubd80\ud130 \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('stock_data.csv')\nX = data[['feature1', 'feature2']].values\ny = data['target'].values\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/code><\/pre>\n
6.3 \ubaa8\ub378 \uad6c\ucd95 \ubc0f \ud6c8\ub828<\/h3>\n
from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\n\n# \ubaa8\ub378 \uc815\uc758\nmodel = Sequential()\nmodel.add(Dense(64, activation='relu', input_dim=X_train.shape[1]))\nmodel.add(Dense(32, activation='relu'))\nmodel.add(Dense(1, activation='linear'))\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nmodel.compile(optimizer='adam', loss='mean_squared_error')\n\n# \ubaa8\ub378 \ud6c8\ub828\nmodel.fit(X_train, y_train, epochs=100, batch_size=10)<\/code><\/pre>\n
6.4 \uc608\uce21 \ubc0f \ud3c9\uac00<\/h3>\n
y_pred = model.predict(X_test)\n# \uc131\ub2a5 \ud3c9\uac00\nfrom sklearn.metrics import mean_squared_error\nmse = mean_squared_error(y_test, y_pred)\nprint(f'Mean Squared Error: {mse}') <\/code><\/pre>\n
7. \uacb0\ub860<\/h2>\n
\n        \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uac15\ub825\ud55c \ub3c4\uad6c\uac00 \ub420 \uc218 \uc788\uc73c\uba70, \uc740\ub2c9\uce35\uc758 \uc801\uc808\ud55c \uc124\uacc4\uac00 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uacb0\uc815\uc9d3\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\ub97c \ud1b5\ud574 \uc740\ub2c9\uce35\uc758 \uc911\uc694\uc131\uacfc \ub525\ub7ec\ub2dd \ubaa8\ub378 \uc124\uacc4\uc5d0 \ub300\ud55c \uc774\ud574\ub97c \ub192\uc774\uace0, \uc774\ub97c \ud1b5\ud574 \uc2e4\uc81c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\ub294 \uae30\ucd08\ub97c \ub9c8\ub828\ud558\uae38 \ubc14\ub78d\ub2c8\ub2e4.\n    <\/p>\n
8. \ucc38\uace0\ubb38\ud5cc<\/h2>\n