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코딩으로 익히는 Python/모델링

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[Python] 24. 시계열 예측 from statsmodels.tsa.ar_model import AR from statsmodels.tsa.arima_model import ARIMA from statsmodels.graphics.tsaplots import plot_acf, acf from statsmodels.tsa.stattools import adfuller from statsmodels.graphics.tsaplots import plot_pacf from statsmodels.tsa.stattools import adfuller import pandas as pd import matplotlib.pyplot as plt import numpy as np from warnings import filterwarnings fil..
[Python] 23. PCA(차원축소),T-SNE from sklearn.datasets import load_iris, load_wine from mpl_toolkits.mplot3d import Axes3D # 3차원 시각화 가능 import matplotlib.pyplot as plt import pandas as pd import numpy as np from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler from sklearn.pipeline import make_pipeline import matplotlib.pyplot as plt import seaborn as sns PCA(차원축소) 3차원까지 시각화 가능하나 4차원 이상은 무리 -> 차..
[Python] 22. Kmeans from sklearn.cluster import KMeans import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import mglearn from sklearn.datasets import load_iris from sklearn.model_selection import GridSearchCV import warnings warnings.simplefilter('ignore') Kmeans mglearn.plots.plot_kmeans_algorithm() # 랜덤하게 점 찍은 후 가까운 점들 분류하고 센터로 이동 분류 이동 ... 데이터 불러오기 (pd.read_csv) python 파..
[Python] 21. SVM(서포트벡터머신) from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.model_selection import cross_val_score, cross_validate import multiprocessing from sklearn.svm import SVC, SVR from sklearn.manifold import TSNE from sklearn.linear_model import SGDRegressor, SGDClassifier from sklear..
[Python] 20. 나이브베이즈 from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.model_selection import cross_val_score, cross_validate import multiprocessing from sklearn.svm import SVC, SVR from sklearn.manifold import TSNE from sklearn.linear_model import SGDRegressor, SGDClassifier from sklear..
[Python] 19. MLP : pima-indians 예제 from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.model_selection import validation_curve from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.metrics import confusion_matrix import sklearn.metrics as metrics from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, AdaBoostClassifier from sklearn.ens..
[Python] 18. Ensemble(앙상블) : breast_cancer,wine 예제 import pandas as pd import matplotlib.pyplot as plt from sklearn.ensemble import VotingClassifier from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.preprocessing import StandardScaler from sklearn.ensemble import BaggingClassifier, RandomForestClassifier from sklearn.model_selection ..
[Python] 17. Decision Tree(의사결정나무) : iris, breast_cancer from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.model_selection import cross_val_score, cross_validate import multiprocessing import os from sklearn.datasets import load_iris from sklearn.pipeline import make_pipeline, Pipeline from sklearn.preprocessing import Sta..
[Python] 16. 강아지&고양이 이미지 분류 실습 import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import mglearn from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier from sklearn.linear_model import LogisticRegression import sklearn.metrics as m # from tensorflow.keras.preprocessing.image import img_to_array from tensorflow.keras.preprocessing import i..
[Python] 15. 이미지분류 : mnist, MLPClassifier import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import mglearn from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier import sklearn.metrics as m from sklearn.datasets import fetch_openml import warnings warnings.simplefilter('ignore') from sklearn.datasets import fetch_openml mnist = fetch_openml('mnist_..
[Python] 14. NN : XOR문제, MLPClassifier import numpy as np import pandas as pd import seaborn as sb import matplotlib.pyplot as plt import mglearn from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier from sklearn.linear_model import LogisticRegression import warnings warnings.simplefilter('ignore') x_data = np.array( [[0,0],[0,1],[1,0],[1,1]]) y_data = np.array( [[0],[1],[1],[0]]) Logis..
[Python] 13. KNN분류 from sklearn.datasets import make_classification, load_iris from sklearn.cluster import KMeans import numpy as np import pandas as pd import seaborn as sb import matplotlib.pyplot as plt import mglearn from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import GridSearchCV import warnings warnings.simplefilter('igno..
[Python] 12. 다중분류 from sklearn.datasets import make_classification, load_breast_cancer,load_iris from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.model_selection import GridSearchCV import pandas as pd i..
[Python] 11. softmax import numpy as np def fn(x): print(x/x.sum()) a = np.array([2.0,1.0,0.1]) fn(a) [OUT]: [0.64516129 0.32258065 0.03225806] -> 전체 합에서 차지하는 비율 Softmax def softmax(x): e = np.exp(x) print(e) print( e/np.sum(e)) a = np.array([2.0,1.0,0.1]) softmax(a) [OUT]: [7.3890561 2.71828183 1.10517092] [0.65900114 0.24243297 0.09856589] -> e^x를 하여 확률이 높은곳에 가중치를 더 주는 형식 review - 다중분류 시 사용되는 softmax
[Python] 10. confusion matrix : precision,recall,f1,ROC from sklearn.datasets import make_classification, load_breast_cancer from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split import pandas as pd import matplotlib.pyplot as plt import numpy as np import seaborn as sns from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import OneHo..
[Python] 9. Logistic Regression (로지스틱 회귀) from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score import sklearn.metrics as metrics from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler import pandas as pd import numpy as np import matplotlib.pyplot as..
[Python] 8. Sigmoid & Logistic import numpy as np import pandas as pd import matplotlib.pyplot as plt import math x = np.array( [1., 2., 3., 4., 5., 6.] ) y = np.array( [ 5., 7., 9., 11., 13., 15.] ) w = 0 b = 0 n = len(x) epochs = 5000 learning_rate = 0.01 for i in range(epochs): hy = w*x + b # w=2, b=3 cost = np.sum((hy-y)**2)/n gradientW = np.sum(2*x*(w*x+b-y))/n gradientB = np.sum(2*1*(w*x+b-y))/n w=w-learning_rate*gradie..
[Python] 7. Sigmoid 함수 import numpy as np import pandas as pd import matplotlib.pyplot as plt import math print(2**2) print(2**3) print(2**(-2)) print(2**(-3)) [OUT]: 4 8 0.25 0.125 math.e [OUT]: 2.718281828459045 Sigmoid (시그모이드 함수) x=0: 0.5이상의 값을 뱉음 def sigmoid(z): return 1/(1+math.e**(-z)) print(sigmoid(-100)) print(sigmoid(-10)) print(sigmoid(-1)) print(sigmoid(0)) print(sigmoid(1)) print(sigmoid(10)) print(sigmoid..
[Python] 6. L1 norm, L2 norm import pandas as pd import numpy as np from numpy import linalg import matplotlib.pyplot as plt import warnings import math warnings.simplefilter('ignore') p = 1 L1 Norm p = 2 L2 Norm 출처 : en.wikipedia.org/wiki/Norm_(mathematics) Norm (mathematics) - Wikipedia Length in a vector space In mathematics, a norm is a function from a real or complex vector space to the nonnegative real numbers that be..
[Python] 5. 문자열encoding : LabelEncoder, OneHotEncoder, get_dummies(), make_column_transformer 예제 import pandas as pd import numpy as np import seaborn as sns from sklearn.datasets import load_boston, load_iris from sklearn.linear_model import Ridge,Lasso,ElasticNet,LinearRegression from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.neural_network import MLPRegressor from sklearn.model_..
[Python] 4. 다중선형회귀 : 릿지L2규제, 라쏘L1규제, 엘라스틱넷 import pandas as pd import numpy as np import seaborn as sns from sklearn.datasets import load_boston, load_iris from sklearn.linear_model import Ridge,Lasso,ElasticNet,LinearRegression from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.neural_network import MLPRegressor from sklearn.model_..
[Python] 3. 다중선형회귀 import pandas as pd import numpy as np import seaborn as sns from sklearn.datasets import load_boston, load_iris from sklearn.datasets import fetch_california_housing from sklearn.linear_model import LinearRegression,Ridge, SGDRegressor from sklearn.neural_network import MLPRegressor from sklearn.model_selection import train_test_split from sklearn.model_selection import GridSearchCV from sklear..
[Python] 2. 정규화 : 상관관계, 다중공선성 import pandas as pd import numpy as np import seaborn as sns from sklearn.datasets import load_boston, load_iris from sklearn.linear_model import LinearRegression,Ridge, SGDRegressor from sklearn.neural_network import MLPRegressor from sklearn.model_selection import train_test_split from sklearn.model_selection import GridSearchCV from sklearn.model_selection import cross_val_score from sklearn...
[Python] 1. 선형회귀분석 import pandas as pd import numpy as np import seaborn as sns from sklearn.datasets import load_boston, load_iris from sklearn.linear_model import LinearRegression,Ridge, SGDRegressor # SGDRegressor은 학습 나머지는 공식 from sklearn.neural_network import MLPRegressor # MLPRegressor은 딥러닝 학습 from sklearn.metrics import r2_score # 선형 모델(Linear Models) from sklearn.model_selection import train_test_split impo..
[Python] 0. sklearn 구조 scikit-learn 특징다양한 머신러닝 알고리즘을 구현한 파이썬 라이브러리심플하고 일관성 있는 API, 유용한 온라인 문서, 풍부한 예제머신러닝을 위한 쉽고 효율적인 개발 라이브러리 제공다양한 머신러닝 관련 알고리즘과 개발을 위한 프레임워크와 API 제공많은 사람들이 사용하며 다양한 환경에서 검증된 라이브러리scikit-learn 주요 모듈 estimator API일관성: 모든 객체는 일관된 문서를 갖춘 제한된 메서드 집합에서 비롯된 공통 인터페이스 공유검사(inspection): 모든 지정된 파라미터 값은 공개 속성으로 노출구성: 많은 머신러닝 작업은 기본 알고리즘의 시퀀스로 나타낼 수 있으며, Scikit-Learn은 가능한 곳이라면 어디서든 이 방식을 사용합리적인 기본값: 모델이 사용자 지정 파..

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