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241112미니 프로젝트 4차 day2In [ ]:# 8. bar차트 그리기df['SeniorCitizen'].value_counts().plot(kinde = 'bar')In [ ]:# 15. 딥러닝 모델 (이중분류)from tensorflow.keras.models import Sequential, load_modelfrom tensorflow.keras.layers import Input, Dense, Activation, Dropout, BatchNormalizationfrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpointfrom tensorflow.keras.utils import to_categoricalnfeatures =..

241111미니 프로젝트 4차 day1In [ ]:# 4-2. df에서 '_'이 절반 이상 되는 col 제거# 방법1for col in df.columns: if (df[col]== '_').mean() >= 0.5: df.drop(columns = col, inplace = True)# 방법2: hard codingIn [ ]:# 4-4. '_' 값을 결측치로 변경df.replace({'_': np.nan})In [ ]:# 4-8. 결측치가 있는 col에서 중앙값으로 결측치 채우기# 결측치가 있을 경우에는 .median() 사용 불가m = df.loc[df['age_itg_cd'].notnull(), 'age_itg_cd'].median()df['age_itg_cd'].fillna(m..

2411051. 언어모델의 이해APIAPI: 데이터를 주고받는 상호작용할 수 있게 해주는 인터페이스클라이언트에게 요청받아 서버로 전달, 결과 데이터를 클라이언트로 전달할 때 API를 거침request & responseNLP(Natural Language Processing)자연어 처리(NLP): 컴퓨터가 인간의 언어를 이해가소 생성할 수 있도록 하는 기술, 데이터를 분석하여 의미 파악(맥락 이해)RNN(Recurrent Neural Network)시퀀스 길이에 따라 순차적 정보처리, 시퀀스가 길어질 경우 연산속도 저하장기 의존성: 멀리 떨어진 위치의 정보 연결하기 어려움TransformerRNN 모델 단점 보완구조 복잡문맥 파악에 유리함Transformer의 사용문장의 긍정, 부정 분류학습되지 않은 클..

241104In [ ]:import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsimport random as rdimport cv2, os # cv2 : OpenCVfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import *from keras.models import Sequentialfrom keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Inputfrom keras.backend import clear_sessionfrom keras.optimizers im..

241101In [1]:import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import *from sklearn.preprocessing import StandardScaler, MinMaxScalerfrom keras.models import Sequentialfrom keras.layers import Dense, Flatten, Inputfrom keras.backend import clear_sessionfrom keras.optimizers import Adam..

2410311. 회귀In [1]:import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import *from sklearn.preprocessing import MinMaxScalerfrom keras.models import Sequentialfrom keras.layers import Dense, Inputfrom keras.backend import clear_sessionfrom keras.optimizers import AdamIn [2]:path = 'https..

241030딥러닝In [1]:import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import *from sklearn.preprocessing import StandardScaler, MinMaxScalerfrom keras.models import Sequentialfrom keras.layers import Dense, Inputfrom keras.backend import clear_sessionfrom keras.optimizers import AdamIn [2]..

241029In [8]:import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom tqdm.auto import tqdmimport warningswarnings.filterwarnings('ignore')import joblibfrom sklearn.ensemble import *from sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import MinMaxScalerfrom sklearn.metrics import *from sklearn.neighbors import KNeighborsClassifierfr..

241021 ~ 241022비지도 학습차원축소클러스터링이상탐지In [177]:import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsfrom sklearn.model_selection import train_test_splitfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.metrics import *from sklearn.datasets import load_breast_cancer, load_digits, load_iris, make_swiss_rollfrom sklearn.preprocessing import MinMaxScalerf..

241018In [149]:import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport warningswarnings.filterwarnings(action='ignore')%config InlineBackend.figure_format = 'retina'EnsembleIn [151]:path = 'https://raw.githubusercontent.com/jangrae/csv/master/admission_simple.csv'data = pd.read_csv(path)data.tail()Out[151]:GRETOEFLRANKSOPLORGPARESEARCHADMIT49549649749849933210854.54.09.0211337..