{"id":31389,"date":"2024-10-28T12:01:41","date_gmt":"2024-10-28T12:01:41","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=31389"},"modified":"2024-11-26T06:45:19","modified_gmt":"2024-11-26T06:45:19","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d%ea%b3%bc-%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d%ec%9d%84-%ed%99%9c%ec%9a%a9%ed%95%9c-%ec%9e%90%eb%8f%99%eb%a7%a4%eb%a7%a4-%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d%ec%9d%84","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/31389\/","title":{"rendered":"\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, \uba38\uc2e0\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uac00\uaca9 \uc608\uce21 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778\uc758 \ub2e8\uae30 \uac00\uaca9 \uc6c0\uc9c1\uc784 \uc608\uce21\ud558\uae30."},"content":{"rendered":"

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\uba38\uc2e0\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc608\uce21<\/h2>\n

\n \ube44\ud2b8\ucf54\uc778\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uac00\uc7a5 \uc778\uae30 \uc788\ub294 \uc790\uc0b0 \uc911 \ud558\ub098\ub85c \uc790\ub9ac \uc7a1\uc558\uc2b5\ub2c8\ub2e4.
\n \ub9ce\uc740 \ud22c\uc790\uc790\ub4e4\uc774 \ube44\ud2b8\ucf54\uc778\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc131\uc744 \ud65c\uc6a9\ud558\uc5ec \uc218\uc775\uc744 \ucc3d\ucd9c\ud558\uace0\uc790 \ud569\ub2c8\ub2e4.
\n \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778\uc758 \ub2e8\uae30 \uac00\uaca9 \uc6c0\uc9c1\uc784\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6cc\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.
\n \ud2b9\ud788, \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778 \uac00\uaca9\uc744 \uc608\uce21\ud558\ub294 \uacfc\uc815\uc5d0 \uc9d1\uc911\ud560 \uac83\uc785\ub2c8\ub2e4.\n<\/p>\n

1. \ub370\uc774\ud130 \uc900\ube44<\/h3>\n

\n \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc608\uce21\uc5d0 \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\uc14b\uc740 \uc8fc\ub85c \ube44\ud2b8\ucf54\uc778\uc758 \uac00\uaca9\uacfc \uac70\ub798\ub7c9, \uace0\uac00 \ubc0f \uc800\uac00 \ub4f1\uc758 \uc815\ubcf4\ub97c \ud3ec\ud568\ud569\ub2c8\ub2e4.
\n \uc77c\ubc18\uc801\uc73c\ub85c \ucf54\uc778\ub9c8\ucf13\ucea1\uc774\ub098 \ubc14\uc774\ub0b8\uc2a4\uc640 \uac19\uc740 \uc554\ud638\ud654\ud3d0 \uac70\ub798\uc18c\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 API\ub97c \ud1b5\ud574 \uc2e4\uc2dc\uac04 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uc608\uc81c\ub97c \uc704\ud574 \uc5ed\uc0ac\uc801 \uac00\uaca9 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n<\/p>\n

import pandas as pd\n\n# Binance API\uc5d0\uc11c CSV \ud30c\uc77c\ub85c \ub370\uc774\ud130\ub97c \ub2e4\uc6b4\ub85c\ub4dc\ud558\uace0 \uc77d\uc5b4\uc635\ub2c8\ub2e4.\ndf = pd.read_csv('bitcoin_price.csv')\ndf['Date'] = pd.to_datetime(df['Date'])\ndf.set_index('Date', inplace=True)\n\n# \ud544\uc694\ud55c \uceec\ub7fc\ub9cc \uc120\ud0dd\ud558\uc5ec \ub370\uc774\ud130\ud504\ub808\uc784\uc744 \ub9cc\ub4ed\ub2c8\ub2e4.\ndata = df[['Open', 'High', 'Low', 'Close', 'Volume']]\ndata.head()\n<\/code><\/pre>\n
2. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\n    \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ub192\uc774\uae30 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uac00 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4.
\n    \uacb0\uce21\uce58 \ucc98\ub9ac, \uc2a4\ucf00\uc77c\ub9c1, \ubcd1\ud569 \ub4f1 \uc5ec\ub7ec \uc804\ucc98\ub9ac\uac00 \ud544\uc694\ud569\ub2c8\ub2e4.
\n    \ub610\ud55c, \uac00\uaca9\uc758 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \ud2b9\uc131\uc0c1, \uacfc\uac70 \uac00\uaca9 \uc815\ubcf4\uac00 \ubbf8\ub798 \uac00\uaca9\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce60 \uc218 \uc788\ub2e4\ub294 \uc810\uc744 \ud65c\uc6a9\ud569\ub2c8\ub2e4.\n<\/p>\n
# \uacb0\uce21\uce58 \ucc98\ub9ac\ndata = data.fillna(method='ffill')\n\n# \ub370\uc774\ud130 \uc815\uaddc\ud654\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler(feature_range=(0, 1))\nscaled_data = scaler.fit_transform(data)\n\n# \uc2dc\ud000\uc2a4 \ub370\uc774\ud130 \uc0dd\uc131\ndef create_dataset(dataset, time_step=1):\n    X, y = [], []\n    for i in range(len(dataset) - time_step - 1):\n        X.append(dataset[i:(i + time_step), 0:dataset.shape[1]])\n        y.append(dataset[i + time_step, 3])  # Close price\n    return np.array(X), np.array(y)\n\n# \uc2dc\uac04 \ub2e8\uacc4 \uc124\uc815\ntime_step = 10\nX, y = create_dataset(scaled_data, time_step)\n\n# \ud6c8\ub828\uc6a9\uacfc \ud14c\uc2a4\ud2b8\uc6a9 \ub370\uc774\ud130\uc14b\uc73c\ub85c \ubd84\ub9ac\ud569\ub2c8\ub2e4.\ntrain_size = int(len(X) * 0.8)\nX_train, X_test = X[0:train_size], X[train_size:len(X)]\ny_train, y_test = y[0:train_size], y[train_size:len(y)]\n<\/code><\/pre>\n
3. \ubaa8\ub378 \uad6c\ucd95<\/h3>\n
\n    \uc6b0\ub9ac\ub294 LSTM(Long Short-Term Memory) \ub124\ud2b8\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ud559\uc2b5\ud560 \uac83\uc785\ub2c8\ub2e4.
\n    LSTM\uc740 RNN(Recurrent Neural Network)\uc758 \uc77c\uc885\uc73c\ub85c, \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc758 \ud328\ud134\uc744 \uc798 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
from keras.models import Sequential\nfrom keras.layers import LSTM, Dense, Dropout\n\nmodel = Sequential()\nmodel.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))\nmodel.add(Dropout(0.2))\n\nmodel.add(LSTM(units=50, return_sequences=True))\nmodel.add(Dropout(0.2))\n\nmodel.add(LSTM(units=50))\nmodel.add(Dropout(0.2))\n\nmodel.add(Dense(units=1))  # \uac00\uaca9 \uc608\uce21\nmodel.compile(optimizer='adam', loss='mean_squared_error')\n<\/code><\/pre>\n
4. \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\n    \uc774\uc81c \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ucf1c \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
\n    \ucda9\ubd84\ud55c \uc5d0\ud3ec\ud06c(Epoch) \uc218\uc5d0 \uac78\uccd0 \ud559\uc2b5\ud558\uc5ec \ubaa8\ub378\uc774 \ub370\uc774\ud130 \ud328\ud134\uc744 \uc798 \ud559\uc2b5\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.\n<\/p>\n
# \ubaa8\ub378 \ud559\uc2b5\nmodel.fit(X_train, y_train, epochs=100, batch_size=32)\n<\/code><\/pre>\n
5. \ubaa8\ub378 \ud3c9\uac00<\/h3>\n
\n    \ud559\uc2b5\uc774 \ub05d\ub09c \ubaa8\ub378\uc744 \uac80\uc99d \ub370\uc774\ud130\uc14b\uc73c\ub85c \ud3c9\uac00\ud569\ub2c8\ub2e4.
\n    \ubaa8\ub378\uc758 \uc608\uce21 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 RMSE(Root Mean Square Error)\ub97c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.\n<\/p>\n
import numpy as np\n\n# \uc608\uce21\ntrain_predict = model.predict(X_train)\ntest_predict = model.predict(X_test)\n\n# \uc5ed\uc2a4\ucf00\uc77c\ub9c1\ntrain_predict = scaler.inverse_transform(np.concatenate((np.zeros((train_predict.shape[0], 4)), train_predict), axis=1))[:, 4]\ntest_predict = scaler.inverse_transform(np.concatenate((np.zeros((test_predict.shape[0], 4)), test_predict), axis=1))[:, 4]\n\n# RMSE \uacc4\uc0b0\ntrain_rmse = np.sqrt(np.mean((train_predict - y_train) ** 2))\ntest_rmse = np.sqrt(np.mean((test_predict - y_test) ** 2))\n\nprint(f'Train RMSE: {train_rmse}')\nprint(f'Test RMSE: {test_rmse}')\n<\/code><\/pre>\n
6. \uc608\uce21 \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\n    \ub9c8\uc9c0\ub9c9\uc73c\ub85c, \uc608\uce21 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uaca0\uc2b5\ub2c8\ub2e4.
\n    \uc2e4\uc81c \uac00\uaca9\uacfc \ubaa8\ub378\uc774 \uc608\uce21\ud55c \uac00\uaca9\uc744 \uc2dc\uac01\uc801\uc73c\ub85c \ube44\uad50\ud558\uc5ec \ubaa8\ub378\uc758 \uc608\uce21 \uc131\ub2a5\uc744 \ud30c\uc545\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
import matplotlib.pyplot as plt\n\n# \uc2dc\uac01\ud654\nplt.figure(figsize=(14, 5))\nplt.plot(df.index[:len(y_train)], y_train, label='Actual Price (Train)', color='blue')\nplt.plot(df.index[len(y_train):len(y_train)+len(y_test)], y_test, label='Actual Price (Test)', color='green')\nplt.plot(df.index[:len(y_train)], train_predict, label='Predicted Price (Train)', color='red')\nplt.plot(df.index[len(y_train):len(y_train)+len(y_test)], test_predict, label='Predicted Price (Test)', color='orange')\nplt.title('Bitcoin Price Prediction')\nplt.xlabel('Date')\nplt.ylabel('Price')\nplt.legend()\nplt.show()\n<\/code><\/pre>\n
\uacb0\ub860<\/h3>\n
\n    \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc608\uce21 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6e0\uc2b5\ub2c8\ub2e4.
\n    LSTM \ubaa8\ub378\uc744 \ud1b5\ud574 \uacfc\uac70 \uac00\uaca9 \ub370\uc774\ud130\uc758 \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \ubbf8\ub798 \uac00\uaca9\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4.
\n    \uc774\uc640 \uac19\uc740 \ubc29\uc2dd\uc73c\ub85c \uc5ec\ub7ec \uac00\uc9c0 \ubaa8\ub378\uc744 \uc2dc\ub3c4\ud574\ubcf4\uba74\uc11c, \ub354 \ub098\uc740 \uc608\uce21 \uc131\ub2a5\uc744 \ub2ec\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n    \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud560 \ub54c, \uac00\uaca9 \uc608\uce21\uc740 \uc911\uc694\ud55c \uc694\uc18c \uc911 \ud558\ub098\uc774\uba70, \uc774 \uacfc\uc815\uc740 \ud22c\uc790 \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub420 \uac83\uc785\ub2c8\ub2e4.\n<\/p>\n
\ucc38\uace0 \uc790\ub8cc<\/h3>\n