{"id":455,"date":"2020-04-04T18:29:39","date_gmt":"2020-04-04T09:29:39","guid":{"rendered":"https:\/\/www.skyer9.pe.kr\/wordpress\/?p=455"},"modified":"2020-04-04T18:29:39","modified_gmt":"2020-04-04T09:29:39","slug":"%eb%8b%a4%ec%a4%91-%ec%84%a0%ed%98%95-%ed%9a%8c%ea%b7%80multiple-linear-regression","status":"publish","type":"post","link":"https:\/\/www.skyer9.pe.kr\/wordpress\/?p=455","title":{"rendered":"\ub2e4\uc911 \uc120\ud615 \ud68c\uadc0(multiple linear regression)"},"content":{"rendered":"

\ub2e4\uc911 \uc120\ud615 \ud68c\uadc0(multiple linear regression)<\/h1>\n

\uc544\ud30c\ud2b8\uc758 \ud3c9\uc218\ub97c \uc774\uc6a9\ud574 \uc544\ud30c\ud2b8\uc758 \ub9e4\ub9e4\uac00\ub97c \uc608\uce21\ud574 \ubcf4\uc558\ub294\ub370\uc694. \uc544\ud30c\ud2b8\ub294 \ub2e8\uc21c\ud788 \ud3c9\uc218\ub9cc\uc73c\ub85c \uac00\uaca9\uc774 \uc0b0\uc815\ub418\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \uc9c0\ud558\ucca0\uacfc\uc758 \uac70\ub9ac, \uc778\uadfc \ud559\uad70 \ub4f1, \uc5ec\ub7ec \ud2b9\uc131(feature)<\/code> \uc744 \uace0\ub824\ud574\uc57c\ub9cc \uc801\uc815\ud55c \uc9d1\uac12\uc744 \uc0b0\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

\uc5ec\ub7ec \uac1c\uc758 \ud2b9\uc131(feature) \uc744 \uc785\ub825\ubc1b\uc544 \uacc4\uc0b0\ud558\uac8c \ub418\uba74 \uc218\uc2dd\uc740 \uc544\ub798\uc640 \uac19\uc774 \uc5ec\ub7ec\uac1c\uc758 \uac00\uc911\uce58(weight)<\/code> \ub3c4 \uc788\uc5b4\uc57c \ud569\ub2c8\ub2e4.<\/p>\n

H(x_1, x_2, x_3, ..., x_n) = W_1x_1 + W_2x_2 + W_3x_3 + ... + W_nx_n + b<\/code><\/pre>\n
\uc544\ub798\uc5d0\uc11c, \ub9e8\ud558\ud134\uc758 \uc9d1\uc815\ubcf4 \ub370\uc774\ud0c0\ub97c \uc774\uc6a9\ud574 \uc784\ub300\ub8cc\ub97c \uc0b0\uc815\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubd05\ub2c8\ub2e4.<\/p>\n
\ub370\uc774\ud0c0\uc14b \uad6c\ud558\uae30<\/h2>\n
\ub370\uc774\ud0c0\uc14b\uc740 \uc5ec\uae30<\/a> \uc5d0\uc11c \uad6c\ud569\ub2c8\ub2e4.<\/p>\n
git clone https:\/\/github.com\/Codecademy\/datasets.git\nhead \/home\/ubuntu\/workspace\/datasets\/streeteasy\/manhattan.csv<\/code><\/pre>\n
\ub370\uc774\ud0c0\ub294 \uc544\ub798\uc640 \uac19\uc774 \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\ud5e4\ub354<\/th>\n\uc124\uba85<\/th>\n<\/tr>\n<\/thead>\n
rental_id<\/code><\/td>\nrental ID<\/td>\n<\/tr>\n
rent<\/code><\/td>\n\uc784\ub300\ub8cc ($)<\/td>\n<\/tr>\n
bedrooms<\/code><\/td>\n\uce68\uc2e4\uc218<\/td>\n<\/tr>\n
bathrooms<\/code><\/td>\n\ud654\uc7a5\uc2e4\uc218<\/td>\n<\/tr>\n
size_sqft<\/code><\/td>\n\ud3c9\uc218(feet)<\/td>\n<\/tr>\n
min_to_subway<\/code><\/td>\n\uc9c0\ud558\ucca0\uacfc\uc758 \uac70\ub9ac (minutes)<\/td>\n<\/tr>\n
floor<\/code><\/td>\n\uce35\uc218<\/td>\n<\/tr>\n
building_age_yrs<\/code><\/td>\n\uac74\ubb3c \uc5f0\ub839<\/td>\n<\/tr>\n
no_fee<\/code><\/td>\n\uc911\uacc4\uc218\uc218\ub8cc (0 for fee, 1 for no fee)<\/td>\n<\/tr>\n
has_roofdeck<\/code><\/td>\n\uc625\uc0c1 (o for no, 1 for yes)<\/td>\n<\/tr>\n
has_washer_dryer<\/code><\/td>\n\uc138\ud0c1\uae30\/\uac74\uc870\uae30 (0\/1)<\/td>\n<\/tr>\n
has_doorman<\/code><\/td>\n\ub3c4\uc5b4\ub9e8 (0\/1)<\/td>\n<\/tr>\n
has_elevator<\/code><\/td>\n\uc5d8\ub9ac\ubca0\uc774\ud130 (0\/1)<\/td>\n<\/tr>\n
has_dishwasher<\/code><\/td>\n\uc2dd\uae30\uc138\ucc99\uae30 (0\/1)<\/td>\n<\/tr>\n
has_patio<\/code><\/td>\n\uc548\ub9c8\ub2f9(patio) (0\/1)<\/td>\n<\/tr>\n
has_gym<\/code><\/td>\n\ud5ec\uc2a4\uc7a5(gym)  (0\/1)<\/td>\n<\/tr>\n
neighborhood<\/code><\/td>\n\uc774\uc6c3 (ex: \ud55c\uc778\ud0c0\uc6b4)<\/td>\n<\/tr>\n
submarket<\/code><\/td>\nsubmarket (ex: North Brooklyn)<\/td>\n<\/tr>\n
borough<\/code><\/td>\nborough (ex: Brooklyn)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\ud30c\uc774\uc36c\uc73c\ub85c \uad6c\ud604\ud558\uae30<\/h2>\n
sklearn<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud574 \uad6c\ud604\ud574 \ubd05\ub2c8\ub2e4.<\/p>\n
from sklearn.linear_model import LinearRegression\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\ndf = pd.read_csv("\/home\/ubuntu\/workspace\/datasets\/streeteasy\/manhattan.csv")\n# df.head()\n\nX = df[['bedrooms',\n        'bathrooms',\n        'size_sqft',\n        'min_to_subway',\n        'floor',\n        'building_age_yrs',\n        'no_fee',\n        'has_roofdeck',\n        'has_washer_dryer',\n        'has_doorman',\n        'has_elevator',\n        'has_dishwasher',\n        'has_patio',\n        'has_gym']]\ny = df[['rent']]\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2)\n\nmlr = LinearRegression()\nmlr.fit(X_train, y_train)\n\nmy_apartment = [[1, 1, 620, 16, 1, 98, 1, 0, 1, 0, 0, 1, 1, 0]]\nmlr.predict(my_apartment)\n# array([[2400.66691039]])\n\nmlr.score(X_test, y_test)\n# 0.7799479629374102<\/code><\/pre>\n
train_test_split<\/code> \uc744 \uc774\uc6a9\ud574 \ud559\uc2b5\uc14b(80%)\uacfc \ud14c\uc2a4\ud2b8\uc14b(20%)\uc744 \ubd84\ub9ac\ud569\ub2c8\ub2e4.<\/p>\n
predict<\/code> \ub97c \uc774\uc6a9\ud574 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud0c0\ub97c \uc608\uce21\ud574 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
score<\/code> \ub97c \uc774\uc6a9\ud574 \uc704 \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub97c \uce21\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc704\uc5d0\uc11c\ub294 78%\uc758 \uc815\ud655\ub3c4\ub97c \uac00\uc9d1\ub2c8\ub2e4.<\/p>\n","protected":false},"excerpt":{"rendered":"
\ub2e4\uc911 \uc120\ud615 \ud68c\uadc0(multiple linear regression) \uc544\ud30c\ud2b8\uc758 \ud3c9\uc218\ub97c \uc774\uc6a9\ud574 \uc544\ud30c\ud2b8\uc758 \ub9e4\ub9e4\uac00\ub97c \uc608\uce21\ud574 \ubcf4\uc558\ub294\ub370\uc694. \uc544\ud30c\ud2b8\ub294 \ub2e8\uc21c\ud788 \ud3c9\uc218\ub9cc\uc73c\ub85c \uac00\uaca9\uc774 \uc0b0\uc815\ub418\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \uc9c0\ud558\ucca0\uacfc\uc758 \uac70\ub9ac, \uc778\uadfc \ud559\uad70 \ub4f1, \uc5ec\ub7ec \ud2b9\uc131(feature) \uc744 \uace0\ub824\ud574\uc57c\ub9cc \uc801\uc815\ud55c \uc9d1\uac12\uc744 \uc0b0\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\ub7ec \uac1c\uc758 \ud2b9\uc131(feature) \uc744 \uc785\ub825\ubc1b\uc544 \uacc4\uc0b0\ud558\uac8c \ub418\uba74 \uc218\uc2dd\uc740 \uc544\ub798\uc640 \uac19\uc774 \uc5ec\ub7ec\uac1c\uc758 \uac00\uc911\uce58(weight) \ub3c4 \uc788\uc5b4\uc57c \ud569\ub2c8\ub2e4. H(x_1, x_2, x_3, …, x_n) = W_1x_1 +\u2026 Read More »<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14],"tags":[],"class_list":["post-455","post","type-post","status-publish","format-standard","hentry","category-ml"],"_links":{"self":[{"href":"https:\/\/www.skyer9.pe.kr\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/455","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.skyer9.pe.kr\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.skyer9.pe.kr\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.skyer9.pe.kr\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.skyer9.pe.kr\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=455"}],"version-history":[{"count":1,"href":"https:\/\/www.skyer9.pe.kr\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/455\/revisions"}],"predecessor-version":[{"id":456,"href":"https:\/\/www.skyer9.pe.kr\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/455\/revisions\/456"}],"wp:attachment":[{"href":"https:\/\/www.skyer9.pe.kr\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=455"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.skyer9.pe.kr\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=455"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.skyer9.pe.kr\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=455"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}