[Kaggle] ๊ฐ„๋‹จํ•œ HousePrices ์˜ˆ์ธกํ•ด๋ณด๊ธฐ
ยท
Artificial_Intelligence๐Ÿค–/Prediction
2์ฃผ ์ „์— ์บ๊ธ€๋ฌธ์ œ ํ•˜๋‚˜ ํ’€๊ณ ์‹ถ์–ด์„œ ์ฃผํƒ๊ฐ€๊ฒฉ์˜ˆ์ธก ๋Œ€ํšŒ์— ๋“ค์–ด๊ฐ”๋‹ค. ๋“ค์–ด๊ฐ€์„œ ๊ทธ๋ƒฅ ํ‰๊ท ๊ฐ’์œผ๋กœ๋งŒ ์ „๋ถ€ ๋•Œ๋ ค๋ฐ•์œผ๋ฉด ๋ช‡์ ๋‚˜์˜ฌ๊นŒ ๊ถ๊ธˆํ•ด์„œ ํ•ด๋ดค๋”๋‹ˆ 0.4์ ๋‚˜์˜ค๊ธธ๋ž˜, 1์ ์ด ๋งŒ์ ์ด ์•„๋‹Œ๊ฐ€ ๋ดค๋”๋‹ˆ 0์ ์— ๊ทผ์ ‘ํ•  ์ˆ˜๋ก ๋†’์€ ์ ์ˆ˜์˜€๋‹ค. ์•„ ๊ทธ๋ ‡๊ตฌ๋‚˜ ํ•˜๊ณ  ์ข…๋ฃŒํ–ˆ์—ˆ๋Š”๋ฐ, ์›๋ž˜ ํ•˜๋˜๊ฑฐ ๋๋‚œ ๊ธฐ๋…์œผ๋กœ 3์‹œ๊ฐ„๋™์•ˆ ๋…ธ๋ž˜๋“ค์œผ๋ฉด์„œ ๋„์ ์—ฌ๋ดค๋‹ค. import numpy as np import pandas as pd import os for dirname, _, filenames in os.walk('./house_prices'): for filename in filenames: print(os.path.join(dirname, filename)) train_data = pd.read_csv('./house_prices/train.csv'..
[AI] Boston_housing :(Linear regression)
ยท
Artificial_Intelligence๐Ÿค–/Prediction
from keras.datasets import boston_housing import numpy print(numpy.shape(boston_housing.load_data())) (train_data, train_labels), (test_data, test_labels) = boston_housing.load_data() print(len(train_data)) print(len(train_labels)) print(len(test_data)) print(len(test_labels)) print(numpy.shape(train_data)) print(numpy.shape(train_labels)) print(numpy.shape(test_data)) print(numpy.shape(test_lab..
[RNN] ์ „์—ผ๋ณ‘(covid-19) ์˜ˆ์ธก ์ธ๊ณต์ง€๋Šฅ ์ œ์ž‘
ยท
Artificial_Intelligence๐Ÿค–/Prediction
colab.research.google.com/drive/1925aJnKBtplTrvAv0TfYbdjumcEnAPON?usp=sharing Google Colaboratory colab.research.google.com ์ฝ”๋žฉ์œผ๋กœ ์ž‘์„ฑํ•œ ์›๋ฌธ์ž…๋‹ˆ๋‹ค. # ๊ฐœ๋ฐœ ์‹œ์ž‘ ์ด์ „ ๋ฉฐ์น ๊ฐ„์˜ ํ™•์ง„์ž ์ˆ˜๋ฅผ ์ž…๋ ฅ๋ฐ›์•„ ๋‹ค์Œ ๋‚ ์˜ ํ™•์ง„์ž ์ˆ˜๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ฐฉ๋ฒ•. 3์ผ ๋™์•ˆ์˜ ํ™•์ง„์ž ์ˆ˜ ๋ณด๊ณ , ๊ทธ ๋‹ค์Œ๋‚  ํ™•์ง„์ž ์ˆ˜๊ฐ€ ์–ด๋–ป๊ฒŒ ๋ ์ง€ ํ•™์Šตํ•จ. ex) 1, 2, 3 ์ผ ์ฐจ ํ™•์ง„์ž -> 4์ผ์ฐจ ํ™•์ง„์ž ์˜ˆ์ธก. ์—ฐ์†๋œ ๋ฐ์ดํ„ฐ์˜ ํ˜•ํƒœ์—์„œ ๊ทธ ํŒจํ„ด์„ ์ฐพ์•„๋ƒ„. ์ˆœํ™˜ ์‹ ๊ฒฝ๋ง ๋ฐฉ์‹ (RNN). (RNN = Recurrent Neural Network, ์ˆœํ™˜์  ๊ตฌ์กฐ, ์ž…์ถœ๋ ฅ์„ ์‹œํ€€์Šค ๋‹จ์œ„๋กœ ์ฒ˜๋ฆฌ.) from keras.models import Seque..
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