데이터 공부를 기록하는 공간

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from datetime import datetime import statsmodels.api as sm from statsmodels.tsa.stattools import adfuller from statsmodels.tsa.stattools import acf, pacf from statsmodels.tsa.seasonal import seasonal_decompose from statsmodels.tsa.arima_model import ARIMA from statsmodels.tsa.statespace.sarimax import SA..

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns plt.style.use('seaborn-whitegrid') from datetime import datetime import statsmodels.api as sm from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.tsa.arima_model import ARIMA from statsmodels.tsa.statespace.sarimax import SARIMAX import itertools path= './smp/smp.xlsx' df = pd...

import numpy as np from sklearn import datasets from sklearn.linear_model import LogisticRegression from sklearn.pipeline import Pipeline, FeatureUnion from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler from sklearn.model_selection import GridSearchCV np.random.seed(0) iris = datasets.load_iris() features = iris.data target = iris.target 1. PCA with FeatureUni..

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings('ignore') df = pd.read_csv('./Mall_Customers/Mall_Customers.csv') print(df.shape) df.head(3) df = df.rename(columns = {"Annual Income (k$)": "income", "Spending Score (1-100)":"score", "Gender":"gender", "Age":"age"}) sns.pairplot(df, hue='gender') df.drop('Custome..