데이터 공부를 기록하는 공간

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..

1. 데이터 전처리 import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns df = pd.read_csv("./mobile_cust_churn/mobile_cust_churn.csv") df.drop(columns=['Unnamed: 0','id'], axis=1, inplace=True) target = 'CHURN' features = df.columns.tolist()[:-1] numeric_features = df.select_dtypes(include=['int64']).columns.tolist() category_features= [] for col in features: if co..