\n1. \uc11c\ub860<\/h2>\n
\uc2a4\ub9c8\ud2b8 \uac70\ub798\uac00 \uc774\ubbf8 \uae08\uc735 \uc2dc\uc7a5\uc758 \ud328\ub7ec\ub2e4\uc784\uc744 \ubcc0\ud654\uc2dc\ud0a4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc778\uacf5\uc9c0\ub2a5\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4\ub294 \ub354 \uc774\uc0c1 \ubbf8\ub798\uc758 \uae30\uc220\uc774 \uc544\ub2cc \ud604\uc7ac\uc758 \uae30\uc220\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08\ubd80\ud130 \uace0\uae09 \uac1c\ub150\uae4c\uc9c0 \uccb4\uacc4\uc801\uc73c\ub85c \uc124\uba85\ud569\ub2c8\ub2e4. \uc218\uc791\uc5c5 \ucf54\ub529\uc758 \uae30\ucd08\ub97c \ubc30\uc6b0\uace0, \ub2e4\uc591\ud55c \ub370\uc774\ud130 \ud544\ud130\ub9c1 \uae30\ubc95\uc744 \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f9\ub2c8\ub2e4.<\/p>\n<\/section>\n\n2. \uba38\uc2e0\ub7ec\ub2dd \uae30\ubcf8 \uac1c\ub150<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\ub85c, \ucef4\ud4e8\ud130\uac00 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uae30\ubcf8\uc801\uc73c\ub85c \ud070 \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ud1b5\ud574 \uc54c\uace0\ub9ac\uc998\uc774 \uc0c1\uad00\uad00\uacc4\ub97c \ud559\uc2b5\ud558\uba70, \uc774\ub97c \ud1b5\ud574 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc2dc\ub3c4\ud569\ub2c8\ub2e4.<\/p>\n
2.1 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uc885\ub958<\/h3>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \ud06c\uac8c \uc138 \uac00\uc9c0 \uc885\ub958\ub85c \ub098\ub20c \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \uc9c0\ub3c4 \ud559\uc2b5 (Supervised Learning)<\/strong>: \ub808\uc774\ube14\uc774 \uc788\ub294 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21 \ubaa8\ub378\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n
- \ube44\uc9c0\ub3c4 \ud559\uc2b5 (Unsupervised Learning)<\/strong>: \ub808\uc774\ube14\uc774 \uc5c6\ub294 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud328\ud134\uc774\ub098 \uad6c\uc870\ub97c \ucc3e\uc2b5\ub2c8\ub2e4.<\/li>\n
- \uac15\ud654 \ud559\uc2b5 (Reinforcement Learning)<\/strong>: \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba74\uc11c \ubcf4\uc0c1\uc744 \ucd5c\ub300\ud654\ud558\ub3c4\ub85d \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<\/section>\n\n
3. \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804<\/h2>\n
\ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud558\uc704 \ubd84\uc57c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd(Artificial Neural Networks)\uc744 \ud65c\uc6a9\ud558\uc5ec \ub354 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4. \ucd5c\uadfc\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \ub300\ub7c9\uc758 \ub370\uc774\ud130\uc640 \ub192\uc740 \ucef4\ud4e8\ud305 \ud30c\uc6cc\ub97c \ud65c\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ub4f1\uc758 \ubd84\uc57c\uc5d0\uc11c \ud68d\uae30\uc801\uc778 \uc131\uacfc\ub97c \uc774\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \ub525\ub7ec\ub2dd\uc758 \uc8fc\uc694 \uad6c\uc870<\/h3>\n
\ub525\ub7ec\ub2dd\uc740 \uc5ec\ub7ec \uce35\uc758 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uac01 \uce35\uc740 \uc785\ub825 \ubc1b\uc740 \ub370\uc774\ud130\ub97c \ubcc0\ud658\ud558\uace0, \ub2e4\uc74c \uce35\uc5d0 \uc804\ub2ec\ud569\ub2c8\ub2e4. \ub808\uc774\uc5b4\uc758 \uac1c\uc218\uac00 \ub9ce\uc544\uc9c8\uc218\ub85d \ud544\ub4dc\uc758 \ubcf5\uc7a1\ud55c \uae30\ub2a5\uc744 \ubc30\uc6b0\ub294 \ub2a5\ub825\uc774 \ud5a5\uc0c1\ub429\ub2c8\ub2e4.<\/p>\n<\/section>\n\n4. \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uba38\uc2e0\ub7ec\ub2dd \uc801\uc6a9<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\uc220\uc740 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\ub3c4 \ub2e4\uc591\ud55c \ubc29\uc2dd\uc73c\ub85c \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc8fc\uc2dd \uac00\uaca9 \uc608\uce21, \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc704\ud5d8 \uad00\ub9ac \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 \uc8fc\uc2dd \uac00\uaca9 \uc608\uce21<\/h3>\n
\uae30\uacc4 \ud559\uc2b5 \ubaa8\ub378\uc740 \uc5ed\uc0ac\uc801 \uac00\uaca9 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uc5ec \ubbf8\ub798 \uac00\uaca9 \ubcc0\ub3d9\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud22c\uc790\uc790\ub4e4\uc5d0\uac8c \uadc0\uc911\ud55c \uc815\ubcf4\ub97c \uc81c\uacf5\ud558\uace0, \ubcf4\ub2e4 \ub098\uc740 \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4.<\/p>\n
4.2 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529<\/h3>\n
\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ucef4\ud4e8\ud130 \ud504\ub85c\uadf8\ub7a8\uc744 \ud65c\uc6a9\ud558\uc5ec \uc2dc\uc7a5\uc5d0\uc11c \uc790\ub3d9\uc73c\ub85c \uac70\ub798\ub97c \uc218\ud589\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc774\ub294 \uc2e4\uc2dc\uac04\uc73c\ub85c \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uc5ec \uc2dc\uc7a5 \uae30\ud68c\ub97c \ud3ec\ucc29\ud558\uace0, \uc778\uac04\uc758 \uac10\uc815\uc744 \ubc30\uc81c\ud55c \uac1d\uad00\uc801\uc778 \uac70\ub798\ub97c \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.<\/p>\n<\/section>\n\n5. \uc218\uc791\uc5c5 \ucf54\ub529\uc758 \uae30\ucd08<\/h2>\n
\uc790\ub3d9\ud654\ub41c \ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574\uc11c\ub294 \uae30\ubcf8\uc801\uc778 \ud504\ub85c\uadf8\ub798\ubc0d \uc9c0\uc2dd\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \ud30c\uc774\uc36c\uc740 \uae08\uc735 \ub370\uc774\ud130 \ubd84\uc11d \ubc0f \uba38\uc2e0\ub7ec\ub2dd\uc5d0 \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 \uc5b8\uc5b4\uc785\ub2c8\ub2e4.<\/p>\n
5.1 \ud30c\uc774\uc36c \uc124\uce58 \ubc0f \ud658\uacbd \uc124\uc815<\/h3>\n
\ud30c\uc774\uc36c\uc740 \ubb34\ub8cc\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, Anaconda\uc640 \uac19\uc740 \ubc30\ud3ec\ud310\uc744 \ud1b5\ud574 \uc190\uc27d\uac8c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac(\uc608: NumPy, pandas, scikit-learn, TensorFlow, Keras \ub4f1)\ub97c \uc124\uce58\ud558\uc5ec \uac1c\ubc1c \ud658\uacbd\uc744 \uc900\ube44\ud569\ub2c8\ub2e4.<\/p>\n<\/section>\n\n6. \ub370\uc774\ud130 \uc218\uc9d1 \ubc0f \uc804\ucc98\ub9ac<\/h2>\n
\ubaa8\ub378 \ud6c8\ub828\uc744 \uc704\ud574\uc11c\ub294 \uc2e0\ub8b0\ud560 \uc218 \uc788\ub294 \ub370\uc774\ud130 \uc218\uc9d1\uc774 \ud544\uc218\uc785\ub2c8\ub2e4. Yahoo Finance, Alpha Vantage\uc640 \uac19\uc740 API\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc27d\uac8c \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
6.1 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n
\uc608\ub97c \ub4e4\uc5b4, Yahoo Finance API\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud2b9\uc815 \uc8fc\uc2dd\uc758 \uc5ed\uc0ac\uc801 \ub370\uc774\ud130\ub97c \uac00\uc838\uc624\ub294 \ucf54\ub4dc\ub97c \uc791\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport pandas as pd\nimport yfinance as yf\n\ndata = yf.download('AAPL', start='2010-01-01', end='2023-01-01')\nprint(data.head())\n <\/pre>\n6.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub294 \uacb0\uce21\uce58 \ucc98\ub9ac, \uc815\uaddc\ud654 \ubc0f \ubcc0\ud658 \ub4f1\uc758 \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uac70\uccd0\uc57c \ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uacfc\uc815\uc740 \ubaa8\ub378\uc758 \uc131\ub2a5\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\uce60 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\n# \uacb0\uce21\uce58 \ucc98\ub9ac\ndata.fillna(method='ffill', inplace=True)\n\n# \uc815\uaddc\ud654\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler()\ndata[['Open', 'High', 'Low', 'Close']] = scaler.fit_transform(data[['Open', 'High', 'Low', 'Close']])\n <\/pre>\n<\/section>\n\n7. \ubaa8\ub378 \ud6c8\ub828 \ubc0f \uac80\uc99d<\/h2>\n
\ub370\uc774\ud130\uac00 \uc900\ube44\ub418\uba74 \uba38\uc2e0\ub7ec\ub2dd \ub610\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc120\ud0dd\ud558\uc5ec \ud6c8\ub828\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 \ubaa8\ub378\ub85c\ub294 \uc120\ud615 \ud68c\uadc0, \uacb0\uc815 \ud2b8\ub9ac, \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8, LSTM \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
7.1 \ubaa8\ub378 \uc120\ud0dd<\/h3>\n
\uc8fc\uc2dd \uac00\uaca9 \uc608\uce21\uc744 \uc704\ud55c LSTM \ubaa8\ub378\uc774 \uc608\uc2dc\uc785\ub2c8\ub2e4. LSTM\uc740 \uc21c\ud658 \uc2e0\uacbd\ub9dd\uc758 \ud55c \ud615\ud0dc\ub85c, \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc5d0 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4.<\/p>\n
\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Dense\n\nmodel = Sequential()\nmodel.add(LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], 1)))\nmodel.add(LSTM(50))\nmodel.add(Dense(1))\nmodel.compile(loss='mean_squared_error', optimizer='adam')\nmodel.fit(X_train, y_train, epochs=100, batch_size=32)\n <\/pre>\n7.2 \ubaa8\ub378 \uac80\uc99d<\/h3>\n
\ubaa8\ub378 \uac80\uc99d\uc740 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. RMSE, MAE \ubc0f R\u00b2\uc640 \uac19\uc740 \ud3c9\uac00 \uc9c0\ud45c\ub97c \ud65c\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/section>\n\n8. \ud559\uc2b5 \ud544\ud130\ub9c1 \uae30\ubc95<\/h2>\n
\ubaa8\ub378 \ud6c8\ub828\uacfc \uac80\uc99d\uc774 \uc644\ub8cc\ub41c \ud6c4, \ud559\uc2b5\ud544\ud130\ub9c1(Feature Selection) \uae30\ubc95\uc744 \ud1b5\ud574 \uc131\ub2a5\uc744 \ub354\uc6b1 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud544\ud130\ub9c1 \uae30\ubc95\uc740 \ub2e4\uc591\ud55c \ud1b5\uacc4\uc801 \ubc29\ubc95 \ub610\ub294 \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \ud1b5\ud574 \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4.<\/p>\n
8.1 \ud1b5\uacc4\uc801 \ubc29\ubc95<\/h3>\n
\uc0c1\uad00\uacc4\uc218 \ubd84\uc11d, ANOVA\uc640 \uac19\uc740 \ud1b5\uacc4\uc801 \uc811\uadfc\uc744 \ud1b5\ud574 \uc720\uc758\ubbf8\ud55c \ud53c\uccd0\ub97c \uc120\ud0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
8.2 \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95<\/h3>\n
\ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8 \uae30\ubc18\uc758 \ud53c\uccd0 \uc911\uc694\ub3c4 \ubd84\uc11d\uc744 \ud1b5\ud574 \uc601\ud5a5\ub825\uc774 \ud070 \ud53c\uccd0\ub97c \uc2dd\ubcc4\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nfrom sklearn.ensemble import RandomForestRegressor\n\nmodel = RandomForestRegressor()\nmodel.fit(X_train, y_train)\nimportance = model.feature_importances_\n <\/pre>\n<\/section>\n\n9. \uacb0\uacfc \ubd84\uc11d \ubc0f \uc2dc\uac01\ud654<\/h2>\n
\ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \ubd84\uc11d\ud558\uace0, \uc774\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \ud1b5\ucc30\ub825\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Matplotlib\uc640 Seaborn\uacfc \uac19\uc740 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc131\uacfc\ub97c \uc2dc\uac01\uc801\uc73c\ub85c \ud45c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nimport matplotlib.pyplot as plt\n\nplt.plot(y_test, label='Actual Prices')\nplt.plot(predicted_prices, label='Predicted Prices')\nplt.legend()\nplt.show()\n <\/pre>\n<\/section>\n\n10. \uacb0\ub860<\/h2>\n
\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud1b5\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08\ubd80\ud130 \uc751\uc6a9\uae4c\uc9c0 \ud3ed\ub113\uc740 \ub0b4\uc6a9\uc744 \ub2e4\ub918\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\uc740 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \ub354\uc6b1 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc73c\uba70, \uc9c0\uc18d\uc801\uc778 \ud559\uc2b5\uacfc \uc5f0\uad6c\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc744 \ud1b5\ud574 \ub354 \ub098\uc740 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uad6c\ud604\ud560 \uc218 \uc788\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
\uc55e\uc73c\ub85c\ub3c4 \uc0c8\ub85c\uc6b4 \uae30\ubc95\uacfc \ud2b8\ub80c\ub4dc\ub97c \uc5f0\uad6c\ud558\uace0, \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc138\uacc4\uc5d0\uc11c \uc131\uacf5\uc801\uc778 \uc131\uacfc\ub97c \uc774\ub8e8\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n<\/section>\n