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다양한 모델 적용 1. AirQualityUCI 데이터셋¶In [155]:import numpy as npimport pandas as pdimport seaborn as snsimport matplotlib.pyplot as pltIn [156]:air_df = pd.read_csv('/content/drive/MyDrive/KDT/6. 머신러닝과 딥러닝/Data/AirQualityUCI.csv')In [157]:air_dfOut[157]:DateTimeCO(GT)PT08.S1(CO)NMHC(GT)C6H6(GT)PT08.S2(NMHC)NOx(GT)PT08.S3(NOx)NO2(GT)PT08.S4(NO2)PT08.S5(O3)TRHAHUnnamed: 15Unnamed: 16010-03-200418:00:.. 2024. 7. 17.
LightGBM 1. credit 데이터셋¶ In [169]:import numpy as npimport pandas as pdimport seaborn as snsimport matplotlib.pyplot as plt In [170]:credit_df = pd.read_csv('/content/drive/MyDrive/KDT/6. 머신러닝과 딥러닝/Data/credit.csv')credit_df Out[170]: IDCustomer_IDNameAgeSSNOccupationAnnual_IncomeNum_Bank_AccountsNum_Credit_CardInterest_Rate...Num_Credit_InquiriesOutstanding_DebtCredit_Utilization_RatioCredit.. 2024. 7. 17.
랜덤 포레스트 1. hotel 데이터셋import numpy as npimport pandas as pdimport seaborn as snsimport matplotlib.pyplot as plthotel_df = pd.read_csv('/content/drive/MyDrive/KDT/6. 머신러닝과 딥러닝/Data/hotel.csv')hotel_dfhotel_df.info()출력:RangeIndex: 119390 entries, 0 to 119389Data columns (total 32 columns): # Column Non-Null Count Dtype --- ------ ---------.. 2024. 6. 13.
서포트 벡터 머신 1. 손글씨 데이터셋from sklearn.datasets import load_digitsdigits = load_digits()digits.keys()# dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])data = digits['data']data.shape# (1797, 64)target = digits['target']target.shape# (1797,)target# array([0, 1, 2, ..., 8, 9, 8])import matplotlib.pyplot as plt_, axes = plt.subplots(2, 5, figsize=(14, 8))for i , ax in enu.. 2024. 6. 12.

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