\ub370\uc774\ud130 \uc81c\uacf5 \uc5c5\uccb4:<\/strong> \ud2b9\uc815 \ub370\uc774\ud130\ub97c \uc804\ubb38\uc73c\ub85c \uc81c\uacf5\ud558\ub294 \uc5c5\uccb4\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uad6c\ub9e4\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n2.1 API \ud65c\uc6a9 \uc608\uc81c<\/h3>\n
\uc608\ub97c \ub4e4\uc5b4, Alpha Vantage API\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uac00 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
import requests\n\napi_key = 'YOUR_API_KEY'\nsymbol = 'AAPL'\nurl = f'https:\/\/www.alphavantage.co\/query?function=TIME_SERIES_DAILY&symbol={symbol}&apikey={api_key}'\n\nresponse = requests.get(url)\ndata = response.json()\n<\/code><\/pre>\n\uc704 \ucf54\ub4dc\ub294 Apple Inc. (AAPL)\uc758 \uc77c\uc77c \uc8fc\uac00 \ub370\uc774\ud130\ub97c \uc694\uccad\ud558\uc5ec JSON \ud615\uc2dd\uc73c\ub85c \uc751\ub2f5\uc744 \ubc1b\uc2b5\ub2c8\ub2e4.<\/p>\n
2.2 \uc6f9 \ud06c\ub864\ub9c1 \uc608\uc81c<\/h3>\n
\uc6f9 \ud06c\ub864\ub9c1\uc744 \ud1b5\ud55c \ub370\uc774\ud130 \uc218\uc9d1\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ubc29\ubc95\uc73c\ub85c \uc9c4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
from bs4 import BeautifulSoup\nimport requests\n\nurl = 'https:\/\/finance.yahoo.com\/quote\/AAPL'\nresponse = requests.get(url)\nsoup = BeautifulSoup(response.text, 'html.parser')\n\nprice = soup.find('fin-streamer', {'data-field': 'regularMarketPrice'}).text\n<\/code><\/pre>\n\uc704 \ucf54\ub4dc\ub294 Yahoo Finance\uc5d0\uc11c Apple\uc758 \ud604\uc7ac \uc8fc\uac00\ub97c \ud06c\ub864\ub9c1\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n
3. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub294 \ubaa8\ub378 \ud6c8\ub828 \uc804\uc5d0 \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uac70\uccd0\uc57c \ud569\ub2c8\ub2e4. \uc804\ucc98\ub9ac\ub97c \ud1b5\ud574 \ub370\uc774\ud130\uc758 \ud488\uc9c8\uc744 \ub192\uc774\uace0, \uc54c\uace0\ub9ac\uc998\uc774 \ub354 \ud6a8\uacfc\uc801\uc73c\ub85c \ud559\uc2b5\ud560 \uc218 \uc788\ub3c4\ub85d \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \uacb0\uce21\uce58 \ucc98\ub9ac<\/h3>\n
\uacb0\uce21\uce58\ub294 \ub370\uc774\ud130 \ubd84\uc11d \uacfc\uc815\uc5d0\uc11c \ube48 \uc790\ub9ac\ub97c \ub098\ud0c0\ub0b4\uba70, \uc774\ub97c \ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\uc740 \uc5ec\ub7ec \uac00\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \uacb0\uce21\uce58\ub97c \uc0ad\uc81c\ud55c\ub2e4.<\/li>\n
- \uacb0\uce21\uce58\ub97c \ud3c9\uade0 \ub610\ub294 \uc911\uc704\uc218 \ub4f1\uc73c\ub85c \ub300\uccb4\ud55c\ub2e4.<\/li>\n
- \ub2e4\ub978 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uc608\uce21\ud558\uc5ec \ub300\uccb4\ud55c\ub2e4.<\/li>\n<\/ul>\n
import pandas as pd\n\ndata = pd.read_csv('data.csv')\ndata.fillna(data.mean(), inplace=True)\n<\/code><\/pre>\n3.2 \ub370\uc774\ud130 \uc815\uaddc\ud654<\/h3>\n
\ub370\uc774\ud130\uc758 \uc2a4\ucf00\uc77c\uc744 \ud1b5\uc77c\ud558\ub824\uba74 \uc815\uaddc\ud654\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc54c\uace0\ub9ac\uc998\uc774 \ub354 \ube60\ub974\uac8c \uc218\ub834\ud558\ub3c4\ub85d \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.preprocessing import MinMaxScaler\n\nscaler = MinMaxScaler()\ndata[['feature1', 'feature2']] = scaler.fit_transform(data[['feature1', 'feature2']])\n<\/code><\/pre>\n3.3 \ud53c\uccd0 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1<\/h3>\n
\ubaa8\ub378\uc5d0 \uc720\uc6a9\ud55c \ud53c\uccd0\ub97c \uc0dd\uc131\ud558\ub294 \uacfc\uc815\uc740 \ubaa8\ub378\uc758 \uc131\ub2a5\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\uce69\ub2c8\ub2e4. \uc774\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ubc29\ubc95\uc73c\ub85c \uc9c4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \uacfc\uac70 \uac00\uaca9 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uae30\uc220\uc801 \uc9c0\ud45c \uc0dd\uc131<\/li>\n
- \uc8fc\uac00\uc640 \ub2e4\ub978 \ubcc0\uc218\uc758 \uc0c1\uad00\uad00\uacc4 \ubd84\uc11d<\/li>\n<\/ul>\n
data['SMA'] = data['close'].rolling(window=20).mean() # 20\uc77c \uc774\ub3d9 \ud3c9\uade0\n<\/code><\/pre>\n4. \uc54c\uace0\ub9ac\uc998 \ud6c8\ub828 \uc900\ube44<\/h2>\n
\ub370\uc774\ud130 \uc804\ucc98\ub9ac\uac00 \ub05d\ub098\uba74 \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \ud6c8\ub828\ud560 \uc900\ube44\uac00 \uc644\ub8cc\ub41c \uac83\uc785\ub2c8\ub2e4. \ud6c8\ub828 \ub370\uc774\ud130\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \ubd84\ub9ac\ud558\uc5ec \ubaa8\ub378 \ud3c9\uac00\uac00 \uac00\ub2a5\ud558\ub3c4\ub85d \ud569\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import train_test_split\n\nX = data[['feature1', 'feature2']] # \ud53c\uccd0\ny = data['target'] # \ud0c0\uac9f\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n<\/code><\/pre>\n5. \uacb0\ub860<\/h2>\n
\ub370\uc774\ud130\uc758 \uc218\uc9d1\uacfc \uc900\ube44\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \uac00\uc7a5 \uc911\uc694\ud55c \ub2e8\uacc4\uc785\ub2c8\ub2e4. \uc62c\ubc14\ub978 \ub370\uc774\ud130 \uc218\uc9d1 \ubc29\ubc95\uacfc \ucca0\uc800\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ud6c4, \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \ud1b5\ud574 \uc2e4\uc81c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\uace0, \ubc31\ud14c\uc2a4\ud2b8\ub97c \ud1b5\ud574 \uc804\ub7b5\uc758 \uc720\ud6a8\uc131\uc744 \uac80\uc99d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \ub370\uc774\ud130 \uc218\uc9d1\uc5d0\uc11c\ubd80\ud130 \uc804\ucc98\ub9ac\uae4c\uc9c0\uc758 \uc804 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \ub2e8\uacc4\ub85c\ub294 \uc774\ub7ec\ud55c \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f0 \uc608\uc815\uc785\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc758 \uc131\uacf5\uc801\uc778 \ud000\ud2b8 \ub9e4\ub9e4\ub97c \uae30\uc6d0\ud569\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ud000\ud2b8 \ub9e4\ub9e4\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud558\ub294 \uac83\uc740 \ud6a8\uacfc\uc801\uc778 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\ub294 \ub370 \uc788\uc5b4 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \uc811\uadfc\ubc95\uc785\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uac00\uc7a5 \uc911\uc694\ud55c \uccab \ub2e8\uacc4\ub294 ‘\ub370\uc774\ud130\uc758 \uc218\uc9d1\uacfc \uc900\ube44’\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub370\uc774\ud130\uc758 \uc911\uc694\uc131, \uc218\uc9d1 \ubc29\ubc95, \uc804\ucc98\ub9ac \uacfc\uc815, \uadf8\ub9ac\uace0 \uc900\ube44\ub41c \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud558\uc5ec \uc54c\uace0\ub9ac\uc998\uc744 \ud6c8\ub828\uc2dc\ud0a4\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ub4dc\ub9ac\uaca0\uc2b5\ub2c8\ub2e4. 1. \ub370\uc774\ud130\uc758 \uc911\uc694\uc131 \ub370\uc774\ud130\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08\uac00 \ub429\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998\uc758 \uc131\ub2a5\uc740 \uc0ac\uc6a9\ub418\ub294 \ub370\uc774\ud130\uc758 \ud488\uc9c8\uc5d0 \ud06c\uac8c … \ub354 \ubcf4\uae30 “\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ub370\uc774\ud130\uc758 \uc218\uc9d1\uacfc \uc900\ube44”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29],"tags":[],"class_list":["post-28932","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"\n\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ub370\uc774\ud130\uc758 \uc218\uc9d1\uacfc \uc900\ube44 - 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