데이터 공부를 기록하는 공간

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..

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings('ignore') train = pd.read_csv('./titanic/train.csv') test = pd.read_csv('./titanic/test.csv') 1. 데이터 전처리 # check null data train.isnull().sum() test.isnull().sum() # category, numeric feature seperation target = 'Survived' train[target].value_counts() features = tr..

1. EDA 및 데이터 전처리 2. 다중공선성 처리 3. Train/Valid/Test set으로 분리하기 4. 교호작용 고려 모델링 및 평가 # library import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # data train = pd.read_csv("./house-prices-advanced-regression-techniques/train.csv") test = pd.read_csv("./house-prices-advanced-regression-techniques/test.csv") # data shape print(train.shape, test.shape) # data s..