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코딩/머신러닝과 딥러닝20

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.
의사 결정 나무 1. bike 데이터셋import numpy as npimport pandas as pdimport seaborn as snsimport matplotlib.pyplot as pltbike_df = pd.read_csv('/content/drive/MyDrive/KDT/6. 머신러닝과 딥러닝/Data/bike.csv')bike_dfbike_df.info()datetime: 날짜count: 대여 개수holiday: 휴일workingday: 근무일temp: 기온feels_like: 체감온도temp_min: 최저온도temp_max: 최고온도pressure: 기압humidity: 습도wind_speed: 풍속wind_deg: 풍향rain_1h: 1시간당 내리는 비의 양snow_1h: 1시간.. 2024. 6. 11.

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