\ud000\ud2b8 \ub9e4\ub9e4\ub294 \uc774\uc81c \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911\uc5d0\uc11c\ub3c4 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc810\uc810 \ub354 \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc73c\uba70, \uc774\ub97c \ud1b5\ud574 \uac70\ub798 \uc804\ub7b5\uc744 \uac1c\uc120\ud558\uace0 \uc218\uc775\uc131\uc744 \ub192\uc77c \uc218 \uc788\ub294 \uac00\ub2a5\uc131\uc744 \uc5ff\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc774\ub7ec\ud55c \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud574\uc11c\ub294 \uc5ec\ub7ec \uac00\uc9c0 \ucd5c\uc801\ud654 \uae30\ubc95\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
\uba3c\uc800 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150\uc744 \uc774\ud574\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub97c \uc774\uc6a9\ud558\uc5ec \ud328\ud134\uc744 \ucc3e\uc544\ub0b4\uace0, \uc774 \ud328\ud134\uc744 \ud1b5\ud574 \uc608\uce21 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uc5ec\ub7ec \uce35\uc758 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc5d0\uc11c \ud2b9\uc9d5\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \ud06c\uac8c \uc138 \uac00\uc9c0\ub85c \ub098\ub20c \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\ub525\ub7ec\ub2dd\uc740 \ud2b9\ud788 \ub300\ub7c9\uc758 \ub370\uc774\ud130 \ucc98\ub9ac\uc5d0 \uac15\uc810\uc744 \uac00\uc9c0\uba70, \uace0\ucc28\uc6d0 \ub370\uc774\ud130\uc5d0\uc11c\ub3c4 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub098 \uc774\ubbf8\uc9c0 \uc778\uc2dd\uacfc \uac19\uc740 \ubd84\uc57c\uc5d0\uc11c \ube60\ub974\uac8c \ubc1c\uc804\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc740 \uc77c\ubc18\uc801\uc73c\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \uc694\uc18c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4:<\/p>\n
\ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\uacf5\uc740 \ub370\uc774\ud130 \uc900\ube44\uc5d0 \ud06c\uac8c \uc758\uc874\ud569\ub2c8\ub2e4. \ub370\uc774\ud130\uc758 \ud488\uc9c8\uc774 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc90d\ub2c8\ub2e4.<\/p>\n
\uc2e0\ub8b0\ud560 \uc218 \uc788\ub294 \ucd9c\ucc98\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud574\uc57c \ud569\ub2c8\ub2e4. \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud560 \ub54c\ub294 Yahoo Finance, Alpha Vantage, Quandl \ub4f1\uc744 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uae30 \uc704\ud574\uc11c\ub294 \uba3c\uc800 \ubd88\ud544\uc694\ud55c \ub370\uc774\ud130\ub97c \uc81c\uac70\ud558\uace0 \uacb0\uce21\uce58\ub97c \ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. Pandas\uc640 \uac19\uc740 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc27d\uac8c \uc774\ub97c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport pandas as pd\n\n# \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\ndata = pd.read_csv('stock_data.csv')\n\n# \uacb0\uce21\uce58 \ud655\uc778\nprint(data.isnull().sum())\n\n# \uacb0\uce21\uce58 \uc81c\uac70\ndata.dropna(inplace=True)\n<\/pre>\n<\/div>\n2.3 \ub370\uc774\ud130 \ubcc0\ud658<\/h3>\n
\ubaa8\ub378\uc774 \ud559\uc2b5\ud558\uae30 \uc88b\ub3c4\ub85d \ub370\uc774\ud130\ub97c \uc2a4\ucf00\uc77c\ub9c1\ud558\uac70\ub098 \uc815\uaddc\ud654\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc694\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Min-Max \uc2a4\ucf00\uc77c\ub9c1\uc774\ub098 \ud45c\uc900\ud654\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\n\nfrom sklearn.preprocessing import MinMaxScaler\n\nscaler = MinMaxScaler()\nscaled_data = scaler.fit_transform(data[['Close']])\n<\/pre>\n<\/div>\n3. \ubaa8\ub378 \uc120\ud0dd \ubc0f \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd<\/h2>\n
\ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc124\uacc4\ud560 \ub54c\ub294 \uc5ec\ub7ec \uac00\uc9c0 \uc544\ud0a4\ud14d\ucc98 \uc911\uc5d0\uc11c \uc120\ud0dd\ud574\uc57c \ud558\uba70, \uadf8\uc5d0 \ub9de\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uc774 \uc5ed\uc2dc \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
3.1 \uc2e0\uacbd\ub9dd \uc544\ud0a4\ud14d\ucc98 \uc120\ud0dd<\/h3>\n
\ub2e4\uc591\ud55c \uc544\ud0a4\ud14d\ucc98\uac00 \uc874\uc7ac\ud569\ub2c8\ub2e4. \uc8fc\uac00 \uc608\uce21\uc744 \uc704\ud55c \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc5d0\ub294 LSTM(Long Short-Term Memory) \ubaa8\ub378\uc774 \uc720\uc6a9\ud569\ub2c8\ub2e4. CNN(Convolutional Neural Network)\uc740 \uc774\ubbf8\uc9c0 \ub370\uc774\ud130 \ucc98\ub9ac\uc5d0 \uc8fc\ub85c \uc0ac\uc6a9\ub418\uc9c0\ub9cc, \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud560 \ub54c\ub3c4 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.2 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ucd5c\uc801\ud654<\/h3>\n
\ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub294 \ubaa8\ub378 \ud6c8\ub828\uc2dc \uc785\ub825\ud558\ub294 \uac12\uc73c\ub85c, \ubaa8\ub378\uc758 \uc131\ub2a5\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\uce69\ub2c8\ub2e4. \uba87 \uac00\uc9c0 \uc8fc\uc694 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \ud559\uc2b5\ub960(Learning Rate)<\/li>\n
- \ubc30\uce58 \uc0ac\uc774\uc988(Batch Size)<\/li>\n
- \uc5d0\ud3ed \uc218(Epochs)<\/li>\n
- \ub4dc\ub86d\uc544\uc6c3 \ube44\uc728(Dropout Rate)<\/li>\n<\/ul>\n
\ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uc744 \uc704\ud574 Grid Search \ub610\ub294 Random Search \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, \ucd5c\uadfc\uc5d0\ub294 Bayesian Optimization \uae30\ubc95\ub3c4 \ub9ce\uc774 \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4. \ud6c8\ub828 \ud6a8\uc728\uc131 \ud5a5\uc0c1 \uae30\uc220<\/h2>\n
\ub525\ub7ec\ub2dd \ubaa8\ub378 \ud6c8\ub828\uc744 \ubcf4\ub2e4 \ud6a8\uc728\uc801\uc73c\ub85c \ub9cc\ub4e4\uae30 \uc704\ud574 \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \uae30\uc220\ub4e4\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 \ub370\uc774\ud130 \uc99d\uac15(Data Augmentation)<\/h3>\n
\ud6c8\ub828 \ub370\uc774\ud130\uac00 \ubd80\uc871\ud560 \uacbd\uc6b0, \ub370\uc774\ud130 \uc99d\uac15 \uae30\ubc95\uc744 \ud1b5\ud574 \uae30\uc874 \ub370\uc774\ud130\ub97c \ubcc0\ud615\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc77c\ubc18\ud654 \uc131\ub2a5\uc744 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.2 \uc870\uae30 \uc885\ub8cc(Early Stopping)<\/h3>\n
\ud6c8\ub828 \uc911 \uac80\uc99d \uc190\uc2e4\uc774 \uc99d\uac00\ud558\ub294 \uacbd\uc6b0, \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\uae30 \uc704\ud574 \ud6c8\ub828\uc744 \uc870\uae30\uc5d0 \uc885\ub8cc\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. TensorFlow \ubc0f Keras\uc5d0\uc11c\ub294 `EarlyStopping` \ucf5c\ubc31\uc744 \uc81c\uacf5\ud558\uc5ec \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\n\nfrom keras.callbacks import EarlyStopping\n\nearly_stopping = EarlyStopping(monitor='val_loss', patience=5)\nmodel.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=100, callbacks=[early_stopping])\n<\/pre>\n<\/div>\n4.3 \ubc30\uce58 \uc815\uaddc\ud654(Batch Normalization)<\/h3>\n
\ud6c8\ub828 \uc18d\ub3c4\ub97c \ub192\uc774\uace0 \uc548\uc815\uc131\uc744 \uc904 \uc218 \uc788\ub294 \uae30\ubc95\uc73c\ub85c, \uac01 \ubc30\uce58\uc758 \ud3c9\uade0\uacfc \ubd84\uc0b0\uc744 \uc815\uaddc\ud654\ud558\uc5ec \ud559\uc2b5 \uc18d\ub3c4\ub97c \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.4 \uc804\uc774 \ud559\uc2b5(Transfer Learning)<\/h3>\n
\uc0ac\uc804\uc5d0 \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uae30\ubcf8 \ubaa8\ub378\ub85c \ud558\uc5ec \uc0c8\ub85c\uc6b4 \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ub370\uc774\ud130\uac00 \ubd80\uc871\ud55c \uc0c1\ud669\uc5d0\uc11c\ub3c4 \uc6b0\uc218\ud55c \uacb0\uacfc\ub97c \uc774\ub04c\uc5b4\ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5. \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00<\/h2>\n
\ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a8 \ud6c4, \uadf8 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294 \uac83\uc774 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. \ud3c9\uac00 \ubc29\ubc95\uc5d0\ub294 \uc5ec\ub7ec \uac00\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
5.1 \uc131\ub2a5 \uc9c0\ud45c \uc120\ud0dd<\/h3>\n
\uc8fc\uac00 \uc608\uce21 \ubb38\uc81c\uc5d0 \uc801\ud569\ud55c \uc131\ub2a5 \uc9c0\ud45c\ub97c \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc544\ub798\uc640 \uac19\uc740 \uc9c0\ud45c\ub4e4\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4:<\/p>\n
\n- RMSE(Root Mean Squared Error)<\/li>\n
- MSE(Mean Squared Error)<\/li>\n
- MAE(Mean Absolute Error)<\/li>\n
- R\u00b2 Score<\/li>\n<\/ul>\n
5.2 \uad50\ucc28 \uac80\uc99d(Cross Validation)<\/h3>\n
\ubaa8\ub378\uc758 \uc77c\ubc18\ud654 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\uae30 \uc704\ud55c \uae30\uc220\uc785\ub2c8\ub2e4. K-Fold \uad50\ucc28 \uac80\uc99d\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c K\uac1c\uc758 \ud3f4\ub4dc\ub85c \ub098\ub204\uc5b4 \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uace0 \ud3c9\uade0 \uc131\ub2a5\uc744 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
6. \uacb0\ub860 \ubc0f \ub2e4\uc74c \ub2e8\uacc4<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ud000\ud2b8 \ub9e4\ub9e4 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \ud6c8\ub828 \uc18d\ub3c4\ub97c \ub192\uc774\uae30 \uc704\ud55c \ub2e4\uc591\ud55c \ucd5c\uc801\ud654 \uae30\ubc95\ub4e4\uc744 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc704\uc5d0\uc11c \uc18c\uac1c\ud55c \ubc29\ubc95\ub4e4\uc744 \ud1b5\ud574 \ub354 \ub098\uc740 \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uace0, \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uc131\uacf5\uc801\uc778 \uac70\ub798 \uc804\ub7b5\uc744 \ub9c8\ub828\ud560 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\uc55e\uc73c\ub85c\uc758 \uc5f0\uad6c \ubc29\ud5a5\uc73c\ub85c\ub294 \uac15\ud654 \ud559\uc2b5\uae30\ubc18\uc758 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \ubc1c\uc804, \ucd5c\uc2e0 \ub525\ub7ec\ub2dd \uae30\ubc95\uc758 \uc801\uc6a9, \uadf8\ub9ac\uace0 \uae08\uc735 \ub370\uc774\ud130\uc758 \ube44\uc815\ud615\uc801\uc778 \ud2b9\uc131\uc744 \ubc18\uc601\ud55c \ubaa8\ub378\ub4e4\uc774 \uae30\ub300\ub429\ub2c8\ub2e4.<\/p>\n
\ubd80\ub85d<\/h3>\n
\ub2e4\uc74c\uacfc \uac19\uc740 \ub9ac\uc18c\uc2a4\ub97c \ucc38\uace0\ud558\uc5ec \ud559\uc2b5\uc744 \uacc4\uc18d \uc9c4\ud589\ud558\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- TensorFlow \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n
- Pandas \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n
- Scikit-Learn \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n
- Towards Data Science \ube14\ub85c\uadf8<\/a><\/li>\n<\/ul>\n
\ud000\ud2b8 \ub9e4\ub9e4\uc758 \uc138\uacc4\ub294 \uae4a\uace0 \ub113\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uae30\ubc95\uacfc \uc54c\uace0\ub9ac\uc998\uc744 \uc5f0\uad6c\ud558\uace0 \uc801\uc6a9\ud574\ubcf4\uba70 \uc790\uc2e0\ub9cc\uc758 \ub9e4\ub9e4 \uc804\ub7b5\uc744 \uad6c\ucd95\ud574 \ub098\uac00\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"