목록데이터분석 (5)

데이터 공부를 기록하는 공간

[ARIMA] airplane

1. library import 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 from matplotlib.pyplot import ..
STUDY/ADP, 빅데이터분석기사 2021. 3. 20. 13:16
[classification] titanic

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..
STUDY/ADP, 빅데이터분석기사 2021. 3. 20. 10:51
[regression] restaurant revenue prediction

kaggle > restaurant revenue 1. EDA import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings('ignore') pd.options.display.max_columns=None train_df = pd.read_csv("./restaurant-revenue-prediction/train.csv") test_df = pd.read_csv("./restaurant-revenue-prediction/test.csv") train_df['part'] = 'train' test_df['part'] = 'test..
STUDY/ADP, 빅데이터분석기사 2021. 3. 20. 00:12
[multi-classification] forest_cover_type

kaggle > forest cover type 1. 데이터 임포트 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("./forest-cover-type-prediction/train.csv") test = pd.read_csv("./forest-cover-type-prediction/test.csv") print(train.shape, test.shape) train = pd.read_csv("./forest-cover-type-prediction/train.cs..
STUDY/ADP, 빅데이터분석기사 2021. 3. 19. 23:17
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