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Computer Science/Machine Learning43

Linear Regression : Logistic Regression [수면시간에 따른 우울증 예측] (6) # 필요한 라이브러리를 임포트 import random import os import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, confusion_matrix # 랜덤시드 고정 seed = 42 random.seed(seed) np.random.seed(seed) os.environ["PYTHONHASHSEED"] = str(seed) # 데이터 폴더(Input) 내 경로 확인 for dirname, _, filenames in.. 2024. 3. 24.
Linear Regression : Logistic Regression [은하계 종류 예측] (5) # 필요한 라이브러리를 임포트 import random import os import numpy as np import pandas as pd from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, confusion_matrix import warnings warnings.filterwarnings(action='ignore') # 랜덤시드 고정 seed = 42 random.seed(seed) np.random... 2024. 3. 24.
Linear Regression : Logistic Regression [Wine] (4) 0. Dataset # 데이터 불러오기. y값은 이미 범주형으로 되어있음. import pandas as pd import numpy as np dat_wine=pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/' 'wine/wine.data',header=None) dat_wine.head() dat_wine.columns = ['class label', 'alchohol', 'malic acid', 'ash', 'alcalinity of ash', 'magnesium', 'total phenols', 'flavanoids', 'nonflavanoid phenols', 'proanthocyanins', 'color intensi.. 2024. 3. 24.
Linear Regression : Logistic Regression [Iris] (3) 0. Dataset # 데이터 불러오기 import seaborn as sns # seaborn을 불러오고 SNS로 축약 iris = sns.load_dataset('iris') # iris라는 변수명으로 Iris data를 download X = iris.drop('species', axis=1) # 'species'열을 drop하고 input X를 정의 y = iris['species'] # y data를 범주형으로 변환 from sklearn.preprocessing import LabelEncoder # LabelEncoder() method를 불러옴 classle = LabelEncoder() y = classle.fit_transform(iris['species'].values) # speci.. 2024. 3. 24.

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