{"id":29071,"date":"2024-10-27T16:16:37","date_gmt":"2024-10-27T16:16:37","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29071"},"modified":"2024-11-26T06:54:45","modified_gmt":"2024-11-26T06:54:45","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%82%ac%ec%9d%b4%ed%82%b7%eb%9f%b0%ec%9d%84","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29071\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc0ac\uc774\ud0b7\ub7f0\uc744 \uc774\uc6a9\ud55c \ub9ac\uc9c0 \ud68c\uadc0 \ubd84\uc11d"},"content":{"rendered":"<p><body><\/p>\n<p>\uc774\ubc88 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc911\uc694\ud55c \uae30\ubc95 \uc911 \ud558\ub098\uc778 \ub9ac\uc9c0 \ud68c\uadc0 \ubd84\uc11d\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \uc0ac\uc774\ud0b7\ub7f0(Scikit-learn)\uc744 \uc774\uc6a9\ud55c \uc2e4\uc81c \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub9ac\uc9c0 \ud68c\uadc0\ub294 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc758 \ubcc0\ud615 \uc911 \ud558\ub098\ub85c, \uacfc\uc801\ud569(overfitting)\uc744 \ubc29\uc9c0\ud558\uae30 \uc704\ud574 \uc815\uaddc\ud654(regularization)\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc8fc\uc2dd \ubc0f \uae08\uc735 \ub370\uc774\ud130\uc5d0\uc11c\uc758 \uc608\uce21 \uc815\ud655\ub3c4\ub97c \ud5a5\uc0c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2 class=\"section-title\">1. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uac1c\uc694<\/h2>\n<p>\uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud559\uc2b5\ud558\uc5ec \ud328\ud134\uc774\ub098 \uaddc\uce59\uc744 \ucc3e\ub294 \uc54c\uace0\ub9ac\uc998\uc758 \uc9d1\ud569\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud558\uc704 \ubd84\uc57c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uace0 \uc608\uce21\ud569\ub2c8\ub2e4. \ub450 \uae30\uc220 \ubaa8\ub450 \uae08\uc735 \uc2dc\uc7a5\uc758 \uc608\uce21 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ub370 \uac15\ub825\ud55c \ub3c4\uad6c\ub85c \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>1.1 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uc885\ub958<\/h3>\n<ul>\n<li><strong>\uc9c0\ub3c4 \ud559\uc2b5(Supervised Learning)<\/strong>: \ub808\uc774\ube14\uc774 \uc788\ub294 \ub370\uc774\ud130\uc14b\uc744 \uae30\ubc18\uc73c\ub85c \ud559\uc2b5\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc8fc\uc2dd \uac00\uaca9 \uc608\uce21 \ubaa8\ub378\uc744 \ub9cc\ub4e4\uae30 \uc704\ud574 \uacfc\uac70 \uc8fc\uc2dd \uac00\uaca9 \ub370\uc774\ud130\ub97c \ud559\uc2b5\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\ube44\uc9c0\ub3c4 \ud559\uc2b5(Unsupervised Learning)<\/strong>: \ub808\uc774\ube14\uc774 \uc5c6\ub294 \ub370\uc774\ud130\uc14b\uc5d0\uc11c \ud328\ud134\uc744 \ucc3e\uc2b5\ub2c8\ub2e4. \ud074\ub7ec\uc2a4\ud130\ub9c1\uc774\ub098 \ucc28\uc6d0 \ucd95\uc18c \uae30\ubc95\uc774 \uc774\uc5d0 \ud574\ub2f9\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uac15\ud654 \ud559\uc2b5(Reinforcement Learning)<\/strong>: \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba74\uc11c \ucd5c\uc801\uc758 \ud589\ub3d9\uc744 \ud559\uc2b5\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \uc8fc\uc2dd \uac70\ub798 \uc804\ub7b5\uc744 \ucd5c\uc801\ud654\ud558\ub294 \ub370 \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h3>1.2 \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804<\/h3>\n<p>\ub525\ub7ec\ub2dd\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\uc640 \uace0\uc131\ub2a5 \ucef4\ud4e8\ud305 \ud30c\uc6cc\uc758 \ubc1c\uc804\uc5d0 \ud798\uc785\uc5b4 \uae09\uaca9\ud788 \ubc1c\uc804\ud558\uc600\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, CNN(Convolutional Neural Networks)\uacfc RNN(Recurrent Neural Networks)\uacfc \uac19\uc740 \ub2e4\uc591\ud55c \uc544\ud0a4\ud14d\ucc98\uac00 \uac1c\ubc1c\ub418\uba74\uc11c \uc774\ubbf8\uc9c0\uc640 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \uac15\ud55c \uc131\ub2a5\uc744 \ubc1c\ud718\ud558\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n<h2 class=\"section-title\">2. \ub9ac\uc9c0 \ud68c\uadc0 \ubd84\uc11d<\/h2>\n<p>\ub9ac\uc9c0 \ud68c\uadc0(Ridge Regression)\ub294 \uc120\ud615 \ud68c\uadc0\uc758 \ud55c \ud615\ud0dc\ub85c, \ub2e4\uc911\uacf5\uc120\uc131(multi-collinearity) \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ub9ac\uc9c0 \ud68c\uadc0\ub294 \uc190\uc2e4\ud568\uc218\uc5d0 L2 \uc815\uaddc\ud654 \ud56d\uc744 \ucd94\uac00\ud558\uc5ec \ubaa8\ub378 \ubcf5\uc7a1\uc131\uc744 \uc81c\uc5b4\ud569\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc744 \ud1b5\ud574 \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\uace0 \uc77c\ubc18\ud654 \ub2a5\ub825\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>2.1 \ub9ac\uc9c0 \ud68c\uadc0\uc758 \uc218\ud559\uc801 \ubc30\uacbd<\/h3>\n<p>\ub9ac\uc9c0 \ud68c\uadc0\uc758 \uae30\ubcf8 \uc218\uc2dd\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<pre>\nY = \u03b2\u2080 + \u03b2\u2081X\u2081 + \u03b2\u2082X\u2082 + ... + \u03b2\u2096X\u2096 + \u03b5\n<\/pre>\n<p>\uc5ec\uae30\uc11c <code>Y<\/code>\ub294 \uc608\uce21\ud558\uace0\uc790 \ud558\ub294 \uc885\uc18d \ubcc0\uc218, <code>X\u2081, X\u2082, ..., X\u2096<\/code>\ub294 \ub3c5\ub9bd \ubcc0\uc218, <code>\u03b2\u2080, \u03b2\u2081, ..., \u03b2\u2096<\/code>\ub294 \ud68c\uadc0 \uacc4\uc218, <code>\u03b5<\/code>\ub294 \uc624\ub958 \ud56d\uc785\ub2c8\ub2e4. \ub9ac\uc9c0 \ud68c\uadc0\ub294 \ud68c\uadc0 \uacc4\uc218\uc758 \uc81c\uacf1\ud569\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \ud559\uc2b5\ud569\ub2c8\ub2e4:<\/p>\n<pre>\nL(\u03b2) = \u03a3(yi - (\u03b2\u2080 + \u03a3(\u03b2j * xij))^2) + \u03bb\u03a3(\u03b2j^2)\n<\/pre>\n<p>\uc5ec\uae30\uc11c <code>\u03bb<\/code>\ub294 \uc815\uaddc\ud654 \uac15\ub3c4\ub97c \uc870\uc808\ud558\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\uc785\ub2c8\ub2e4.<\/p>\n<h2 class=\"section-title\">3. \uc0ac\uc774\ud0b7\ub7f0\uc744 \uc774\uc6a9\ud55c \ub9ac\uc9c0 \ud68c\uadc0 \ubd84\uc11d<\/h2>\n<p>\uc0ac\uc774\ud0b7\ub7f0\uc740 \ud30c\uc774\uc36c\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc8fc\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \ub9ac\uc9c0 \ud68c\uadc0\ub97c \ubd84\uc11d\ud558\uae30 \uc704\ud574 \uc0ac\uc774\ud0b7\ub7f0\uc744 \uc0ac\uc6a9\ud558\ub294 \uc608\uc81c\ub97c \ud1b5\ud574 \uadf8 \uacfc\uc815\uacfc \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>3.1 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n<p>\uc8fc\uc2dd \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \ub2e4\uc6b4\ub85c\ub4dc\ud558\uc138\uc694. Yahoo Finance \ub610\ub294 Quandl\uacfc \uac19\uc740 API\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub2e4\uc74c\uacfc \uac19\uc740 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n<pre>\nDate, Open, High, Low, Close, Volume\n2021-01-01, 150, 155, 149, 153, 100000\n2021-01-02, 153, 158, 152, 157, 120000\n...\n<\/pre>\n<p>\uc704 \ub370\uc774\ud130\ub97c pandas DataFrame\uc73c\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4:<\/p>\n<pre>\nimport pandas as pd\n\ndata = pd.read_csv('stock_data.csv')\ndata['Date'] = pd.to_datetime(data['Date'])\ndata.set_index('Date', inplace=True)\n<\/pre>\n<h3>3.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n<p>\uc8fc\uc2dd \uac00\uaca9\uc744 \uc608\uce21\ud558\uae30 \uc704\ud574 \uc785\ub825 \ubcc0\uc218\uc640 \ud0c0\uac9f \ubcc0\uc218\ub97c \uc815\uc758\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc8fc\uc2dd\uc758 \uc885\uac00\uac00 \ud0c0\uac9f \ubcc0\uc218\ub85c \uc0ac\uc6a9\ub418\uba70, \uc774\uc640 \uad00\ub828\ub41c \ub2e4\ub978 \ubcc0\uc218\ub4e4\uc740 \uc785\ub825 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n<pre>\nX = data[['Open', 'High', 'Low', 'Volume']]\ny = data['Close']\n<\/pre>\n<h3>3.3 \ub370\uc774\ud130 \ubd84\ud560<\/h3>\n<p>\ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ub370\uc774\ud130\ub97c \ubd84\ud560\ud569\ub2c8\ub2e4. \ubaa8\ub378\uc758 \uc77c\ubc18\ud654 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130\ub97c \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ub098\ub204\uc5b4\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<pre>\nfrom sklearn.model_selection import train_test_split\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n<\/pre>\n<h3>3.4 \ub9ac\uc9c0 \ud68c\uadc0 \ubaa8\ub378 \ud6c8\ub828<\/h3>\n<p>\uc0ac\uc774\ud0b7\ub7f0\uc758 <code>Ridge<\/code> \ud074\ub798\uc2a4\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub9ac\uc9c0 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0dd\uc131\ud558\uace0 \ud6c8\ub828\ud569\ub2c8\ub2e4.<\/p>\n<pre>\nfrom sklearn.linear_model import Ridge\n\nmodel = Ridge(alpha=1.0)  # alpha\ub294 \uc815\uaddc\ud654 \uac15\ub3c4\nmodel.fit(X_train, y_train)\n<\/pre>\n<h3>3.5 \ubaa8\ub378 \ud3c9\uac00<\/h3>\n<p>\ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 \ud3c9\uac00 \uc9c0\ud45c\ub85c\ub294 \ud3c9\uade0 \uc81c\uacf1 \uc624\ucc28(MSE)\uc640 \uacb0\uc815 \uacc4\uc218(R\u00b2)\uac00 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre>\nfrom sklearn.metrics import mean_squared_error, r2_score\n\ny_pred = model.predict(X_test)\nmse = mean_squared_error(y_test, y_pred)\nr2 = r2_score(y_test, y_pred)\n\nprint(f'Mean Squared Error: {mse}')\nprint(f'R\u00b2 Score: {r2}')\n<\/pre>\n<h3>3.6 \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n<p>\ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \uc131\ub2a5\uc744 \uc9c1\uad00\uc801\uc73c\ub85c \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/p>\n<pre>\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(14, 7))\nplt.plot(y_test.index, y_test, label='Actual', color='blue')\nplt.plot(y_test.index, y_pred, label='Predicted', color='red')\nplt.title('Stock Price Prediction using Ridge Regression')\nplt.xlabel('Date')\nplt.ylabel('Price')\nplt.legend()\nplt.show()\n<\/pre>\n<h2 class=\"section-title\">4. \uacb0\ub860<\/h2>\n<p>\uc774\ubc88 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08 \uac1c\ub150\uc744 \uc18c\uac1c\ud558\uace0, \ub9ac\uc9c0 \ud68c\uadc0 \ubd84\uc11d\uc758 \uc6d0\ub9ac\uc640 \uc0ac\uc774\ud0b7\ub7f0\uc744 \ud65c\uc6a9\ud55c \uc2e4\uc81c \uad6c\ud604 \uc608\uc81c\ub97c \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub9ac\uc9c0 \ud68c\uadc0\ub294 \ub2e8\uc21c\ud55c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uac1c\uc120\ud558\ub294 \uac15\ub825\ud55c \ub3c4\uad6c\ub85c, \uae08\uc735 \ub370\uc774\ud130 \ubd84\uc11d\uc5d0\uc11c\ub3c4 \ud6a8\uacfc\uc801\uc778 \uc131\ub2a5\uc744 \ubc1c\ud718\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\uc758 \uc804\ucc98\ub9ac \ubc0f \ubaa8\ub378 \ud6c8\ub828 \uacfc\uc815\uc5d0\uc11c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uba74\uc11c, \ub354 \ub098\uc740 \uc608\uce21 \ubaa8\ub378\uc744 \uac1c\ubc1c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<p>\ub9c8\uc9c0\ub9c9\uc73c\ub85c, \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc740 \uacc4\uc18d\ud574\uc11c \ube60\ub974\uac8c \ubc1c\uc804\ud558\uace0 \uc788\uc73c\uba70, \uc774\ub97c \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc55e\uc73c\ub85c\ub3c4 \ub9ce\uc740 \uac00\ub2a5\uc131\uc744 \uc9c0\ub2c8\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uacc4\uc18d\ud574\uc11c \uc0c8\ub85c\uc6b4 \uae30\ubc95 \ubc0f \uc54c\uace0\ub9ac\uc998\uc744 \ud559\uc2b5\ud558\uace0 \uc801\uc6a9\ud558\uc5ec \uc2a4\ud0ac\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\uc774\ubc88 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc911\uc694\ud55c \uae30\ubc95 \uc911 \ud558\ub098\uc778 \ub9ac\uc9c0 \ud68c\uadc0 \ubd84\uc11d\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \uc0ac\uc774\ud0b7\ub7f0(Scikit-learn)\uc744 \uc774\uc6a9\ud55c \uc2e4\uc81c \uad6c\ud604 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub9ac\uc9c0 \ud68c\uadc0\ub294 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc758 \ubcc0\ud615 \uc911 \ud558\ub098\ub85c, \uacfc\uc801\ud569(overfitting)\uc744 \ubc29\uc9c0\ud558\uae30 \uc704\ud574 \uc815\uaddc\ud654(regularization)\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc8fc\uc2dd \ubc0f \uae08\uc735 \ub370\uc774\ud130\uc5d0\uc11c\uc758 \uc608\uce21 \uc815\ud655\ub3c4\ub97c \ud5a5\uc0c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. 1. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uac1c\uc694 \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud559\uc2b5\ud558\uc5ec \ud328\ud134\uc774\ub098 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/29071\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc0ac\uc774\ud0b7\ub7f0\uc744 \uc774\uc6a9\ud55c \ub9ac\uc9c0 \ud68c\uadc0 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