06단계 · 실전으로 넘어가기
세 유형을 한 번에 — 그리고 실전 문항으로
한 번의 실행 안에서 1·2·3유형을 각각 끝내 보고, 기존 실기 문항으로 넘어간다.
먼저 알아볼게요
코드로 이동 ↓여기까지 배운 것이 실기 세 유형의 뼈대다. 1유형은 전처리 후 요구된 값 계산, 2유형은 예측 후 제출 파일, 3유형은 검정 결과와 해석이다.
이 수업의 과제는 세 가지를 한 번에 하는 것이다. 문제마다 새로 배울 것은 없다 — 앞에서 한 줄씩 써 본 것을 이어 붙이면 된다.
다음 단계는 기존 실기 문항 21개와 180분 모의고사다. 그쪽은 데이터도 발문도 더 길고, 답안 칸에 값을 직접 입력해 채점을 받는다. 이 수업의 연습 화면과 달리 정답을 바로 보여 주지 않는다.
넘어가기 전 체크리스트: 파일을 읽고 info() 로 확인했는가 · 발문의 조건을 순서대로 옮겼는가 · 자리수를 지정한 대로 맞췄는가 · 2유형이면 index=False 로 한 열만 저장했는가 · 3유형이면 "채택" 이라고 쓰지 않았는가.
여기 있는 데이터와 발문은 전부 연습용으로 만든 것이다. 실제 시험 문제를 옮겨 온 것이 아니므로, 같은 값이 나오는지가 아니라 같은 순서로 손이 움직이는지를 확인하는 자리로 쓰면 된다.
이렇게 써요
import pandas as pd
h = pd.read_csv("health.csv")
print(h.info())기억할 한 가지
세 유형은 서로 다른 기술이 아니라 같은 도구의 다른 조합이다. 다음은 기존 21문항과 모의고사다.
직접 해보기한 번의 실행으로 ①
한 번의 실행으로 ① health.csv 콜레스테롤의 IQR 이상치 개수 ② churn_train.csv 전체로 학습해 churn_test.csv 를 예측하고 result.csv 로 저장한 뒤 되읽은 shape ③ 운동 여부에 따른 콜레스테롤 차이의 p-value(소수 4) ④ 그 p-value 가 0.05 미만인지를 출력하세요.
제공 파일 3개 · 내용 보기
파일은 준비되어 있어요. 코드에서 이름으로 불러오세요.
health.csv
성별,나이,키,몸무게,콜레스테롤,식단,운동,진단 여,27,167,79,203.3,C,예,0 남,73,176,77,235.1,A,아니오,0 남,74,168,57,229.0,B,아니오,1 여,50,159,60,251.0,C,아니오,0 여,71,161,46,201.6,C,예,0 남,70,182,82,189.1,C,예,0 여,47,161,59,240.0,C,아니오,1 남,52,179,73,197.5,A,예,0 남,57,176,74,207.0,A,예,0 남,65,167,51,185.8,A,예,0 여,25,165,49,202.1,B,아니오,0 남,59,169,59,205.3,B,아니오,0 여,26,164,58,201.2,B,아니오,0 남,38,166,65,193.3,C,아니오,0 남,58,170,60,199.0,A,아니오,1 여,55,160,66,191.8,C,아니오,0 여,37,160,69,234.6,C,아니오,0 여,50,161,65,204.2,B,예,0 남,59,172,50,228.9,C,예,0 남,64,168,61,196.7,A,아니오,1 남,60,178,73,208.7,B,예,0 남,38,168,71,183.0,A,예,0 여,42,159,53,204.0,A,예,0 여,45,164,55,183.2,A,예,0 여,37,157,72,212.6,B,아니오,0 남,38,171,66,200.9,A,아니오,0 남,25,164,55,213.1,C,아니오,0 여,37,166,64,178.7,A,아니오,0 남,37,173,79,197.4,C,예,0 남,42,177,75,262.3,C,아니오,1 남,49,173,64,161.1,C,예,0 여,39,158,64,187.9,A,아니오,0 남,58,171,68,225.6,A,아니오,0 여,29,167,71,176.7,A,예,0 남,46,179,71,222.7,B,아니오,0 여,39,161,48,158.9,C,예,0 남,60,168,60,175.7,B,아니오,0 여,42,169,58,181.6,B,예,0 여,49,155,50,221.8,C,예,0 여,60,156,51,252.5,A,아니오,0 남,29,174,74,213.5,C,아니오,0 여,34,164,57,172.4,A,아니오,0 여,69,155,40,188.8,A,아니오,0 여,59,165,56,211.3,B,아니오,0 여,71,161,46,188.6,A,아니오,1 남,25,174,66,168.0,A,예,0 여,35,147,31,180.7,C,예,0 여,73,155,54,211.2,C,아니오,0 여,48,159,63,191.7,A,예,0 남,30,167,69,166.0,A,아니오,0 여,36,168,58,158.9,A,아니오,0 남,25,162,68,202.5,B,예,0 여,52,166,60,155.4,C,예,0 여,51,161,62,196.7,A,예,0 여,33,163,57,169.2,A,아니오,0 남,52,177,68,217.5,A,아니오,0 남,51,172,65,204.7,C,예,0 남,38,172,79,201.2,B,예,0 남,67,181,79,238.9,C,아니오,0 남,50,170,62,186.9,C,예,0 여,55,155,67,214.7,B,예,0 여,38,166,63,188.7,B,예,0 남,35,177,80,149.7,A,예,0 여,73,167,60,220.7,C,예,0 남,37,173,57,157.1,B,아니오,0 여,42,157,51,190.2,A,예,0 여,60,160,61,180.4,A,아니오,0 남,49,176,71,208.3,C,예,0 남,27,168,64,198.4,B,예,0 여,47,165,53,183.3,A,예,0 여,38,147,37,210.2,B,예,0 남,34,171,63,207.8,C,예,0 여,74,166,59,190.5,C,예,1 여,61,160,52,190.7,C,아니오,0 여,57,162,70,195.5,C,아니오,0 여,37,166,63,164.6,A,예,0 여,32,153,53,202.9,B,아니오,0 남,74,169,66,219.7,C,아니오,0 남,62,178,67,207.1,A,아니오,0 남,56,175,75,177.0,A,아니오,0 남,46,170,57,139.4,B,예,0 남,32,180,84,191.4,B,아니오,0 남,26,172,61,177.9,A,예,0 남,53,171,76,195.3,A,예,0 남,51,167,53,188.8,C,예,0 남,39,178,75,204.0,A,예,0 여,73,158,53,189.1,B,아니오,0 남,59,171,53,193.8,C,예,0 남,46,167,77,200.4,C,예,0 여,42,159,63,205.1,C,아니오,0 남,53,175,62,138.0,C,예,0 남,71,176,66,210.3,B,예,1 남,35,175,73,196.1,A,아니오,0 남,32,158,48,199.7,B,예,0 여,73,155,61,182.1,B,예,0 남,69,180,72,192.8,B,예,0 남,56,161,59,168.3,A,예,0 남,57,190,75,193.9,C,예,0 여,63,162,56,192.5,B,아니오,0 남,74,175,76,211.0,A,예,0 남,39,178,89,210.9,A,아니오,0 여,67,160,55,202.4,C,예,0 남,64,172,55,206.3,B,예,0 여,52,165,75,209.5,B,아니오,0 남,66,172,70,215.0,C,아니오,0 남,67,177,84,187.1,A,예,0 남,39,183,75,180.3,C,예,0 여,71,162,61,199.0,B,예,0 남,56,168,72,210.1,A,아니오,0 여,25,158,42,172.7,C,예,0 여,64,147,50,177.1,B,예,0 남,26,180,74,201.2,A,아니오,0 여,36,162,55,215.4,C,아니오,0 남,70,175,73,212.7,B,아니오,1 여,73,164,53,197.3,A,예,0 여,31,154,45,192.5,B,아니오,0 여,29,159,51,147.9,A,아니오,0 여,66,162,46,202.9,C,아니오,0 남,51,181,95,179.2,A,예,0 남,50,174,59,181.2,A,아니오,0 남,50,160,65,187.8,B,예,0 여,72,175,65,216.2,A,아니오,1 남,69,171,68,192.1,B,아니오,0 남,52,177,78,185.5,C,예,0 남,70,169,83,206.2,A,예,0 여,37,159,50,177.4,C,예,0 남,67,162,60,235.3,C,아니오,0 여,42,158,56,224.3,B,예,0 여,59,154,36,214.2,B,예,0 남,30,170,68,163.8,B,예,0 남,73,168,61,218.5,C,예,0 남,57,157,57,191.5,B,예,0 남,52,166,59,225.2,C,아니오,0 여,26,159,63,181.1,A,아니오,1 남,35,177,75,200.6,A,아니오,0 여,29,155,41,132.1,A,예,0 여,60,162,64,244.1,C,예,0 여,55,159,55,222.1,B,아니오,1 남,62,181,67,214.9,C,예,0 남,64,171,64,215.4,B,아니오,0 남,56,176,72,227.0,C,아니오,0 여,36,163,46,183.5,C,아니오,0 여,48,155,46,201.9,C,예,0 여,67,163,44,156.1,A,예,0 남,27,176,71,167.5,A,예,0 여,64,158,48,198.1,C,아니오,0 남,46,167,66,170.4,B,아니오,0 여,39,159,65,208.6,C,아니오,0 남,52,167,55,191.7,B,예,0 여,45,155,60,205.7,B,예,0
churn_train.csv
고객ID,성별,요금제,가입개월,월요금,부가서비스,이탈 C1001,남,standard,22,44300,3,1 C1002,여,premium,1,62000,1,1 C1003,남,premium,45,64000,0,1 C1004,남,basic,10,32200,1,1 C1005,남,premium,5,69500,0,1 C1006,여,basic,56,29500,0,0 C1007,여,premium,48,62800,2,0 C1008,여,standard,5,56700,1,1 C1009,여,basic,49,30600,3,0 C1010,여,premium,52,74800,2,1 C1011,여,basic,19,32800,2,1 C1012,여,standard,18,42200,3,0 C1013,남,standard,37,47900,1,0 C1014,남,basic,1,30500,4,1 C1015,여,standard,52,43200,0,0 C1016,남,premium,31,75300,3,1 C1017,여,basic,22,29100,1,1 C1018,남,standard,39,48400,1,1 C1019,남,premium,35,82500,0,0 C1020,남,standard,19,42600,3,0 C1021,남,basic,22,27300,4,1 C1022,여,basic,38,27800,1,1 C1023,남,standard,5,54200,0,1 C1024,여,premium,36,77200,3,0 C1025,여,standard,14,46500,1,1 C1026,남,premium,15,64500,2,1 C1027,남,basic,45,28100,4,0 C1028,남,standard,49,40600,1,1 C1029,남,standard,59,50800,4,0 C1030,여,basic,16,27800,3,1 C1031,남,basic,36,24900,0,1 C1032,남,basic,17,30200,4,0 C1033,여,basic,36,29900,3,1 C1034,남,basic,36,23800,2,0 C1035,여,standard,9,50200,1,1 C1036,여,premium,45,60800,2,0 C1037,여,standard,37,42900,3,0 C1038,여,basic,37,24800,2,0 C1039,남,standard,42,46300,0,0 C1040,여,basic,34,28400,2,1 C1041,남,premium,11,84200,3,1 C1042,남,standard,9,57000,4,1 C1043,여,standard,11,43500,3,1 C1044,여,basic,42,29200,2,1 C1045,여,premium,10,66800,4,1 C1046,남,premium,56,62700,2,0 C1047,남,standard,19,44100,3,1 C1048,여,basic,14,33700,2,0 C1049,여,basic,44,28800,0,1 C1050,여,premium,32,69800,0,1 C1051,남,basic,52,24100,3,0 C1052,남,standard,48,35900,1,0 C1053,여,basic,41,26300,0,0 C1054,여,basic,36,25500,1,1 C1055,여,basic,7,29000,3,0 C1056,여,basic,34,32700,3,1 C1057,남,standard,42,48900,4,1 C1058,여,basic,32,25800,1,1 C1059,남,standard,56,50100,3,0 C1060,여,standard,28,41500,1,0 C1061,여,premium,47,73900,0,0 C1062,남,premium,4,53900,3,1 C1063,여,premium,4,72600,0,1 C1064,여,basic,42,29100,4,1 C1065,남,basic,59,29800,4,0 C1066,남,premium,28,75100,1,0 C1067,남,basic,27,27200,0,0 C1068,남,basic,60,30100,2,1 C1069,여,premium,4,67400,1,1 C1070,여,basic,4,34100,0,1 C1071,남,standard,42,40800,4,0 C1072,여,premium,53,72900,4,0 C1073,남,standard,36,37500,1,0 C1074,남,standard,44,49800,0,0 C1075,남,basic,5,29700,3,1 C1076,남,premium,1,75600,3,1 C1077,남,standard,44,40600,1,1 C1078,여,basic,7,23800,1,0 C1079,여,premium,47,66100,4,1 C1080,남,standard,32,41500,1,0 C1081,남,premium,37,78500,4,1 C1082,남,premium,52,68500,1,0 C1083,남,premium,51,61900,3,1 C1084,남,basic,38,30000,2,1 C1085,남,basic,12,30900,0,0 C1086,남,premium,25,74900,1,1 C1087,여,standard,40,47300,1,0 C1088,남,standard,6,41600,1,1 C1089,남,premium,57,64200,4,0 C1090,여,basic,42,28700,0,1 C1091,여,standard,27,37300,0,1 C1092,남,basic,27,25900,2,1 C1093,여,basic,41,27900,0,0 C1094,남,standard,60,44200,0,0 C1095,남,premium,12,67100,2,1 C1096,남,standard,46,45700,1,0 C1097,여,standard,23,48800,1,1 C1098,남,premium,57,75300,4,0 C1099,남,premium,13,53900,0,0 C1100,여,premium,29,61600,3,1 C1101,남,standard,2,47300,3,1 C1102,남,standard,10,55900,1,1 C1103,남,premium,3,71800,1,1 C1104,남,premium,53,70000,1,0 C1105,남,premium,32,71300,2,1 C1106,남,premium,9,66000,0,1 C1107,여,premium,30,66100,2,1 C1108,남,standard,43,53500,3,0 C1109,남,basic,26,29400,4,0 C1110,여,standard,21,48500,4,0 C1111,남,standard,51,42400,0,1 C1112,남,premium,21,74100,4,0 C1113,남,premium,56,56600,0,0 C1114,남,standard,46,45300,4,0 C1115,여,standard,48,42300,4,0 C1116,여,basic,35,24700,4,1 C1117,여,premium,22,42700,1,0 C1118,남,standard,9,39900,4,1 C1119,남,premium,36,73100,1,0 C1120,여,premium,38,73800,0,1 C1121,여,standard,13,49300,4,0 C1122,남,basic,48,30300,0,0 C1123,남,standard,16,43800,0,1 C1124,남,premium,15,74000,2,1 C1125,남,basic,25,30200,0,1 C1126,남,basic,44,33000,2,1 C1127,남,basic,56,21800,3,0 C1128,남,standard,48,38600,1,0 C1129,여,premium,15,68200,0,1 C1130,여,basic,28,27400,3,0 C1131,남,standard,54,40800,4,0 C1132,남,basic,45,26900,3,0 C1133,여,premium,21,66200,0,1 C1134,여,premium,39,58900,0,0 C1135,남,basic,43,29000,3,0 C1136,남,standard,9,48600,4,1 C1137,남,basic,28,30300,1,0 C1138,남,premium,30,78600,4,0 C1139,남,basic,18,23900,3,0 C1140,남,standard,46,40900,2,1 C1141,남,premium,6,70300,0,1 C1142,남,basic,46,29100,1,0 C1143,남,standard,18,41600,3,0 C1144,여,basic,53,27800,3,1 C1145,여,basic,7,25400,0,1 C1146,남,basic,27,28800,0,1 C1147,여,premium,51,64300,4,0 C1148,남,premium,25,53100,2,0 C1149,남,standard,54,43800,1,1 C1150,남,premium,40,57700,0,1 C1151,여,standard,24,42900,3,1 C1152,남,premium,21,63300,1,1 C1153,여,standard,44,34800,3,0 C1154,여,basic,29,27400,2,1 C1155,여,premium,38,61500,1,0 C1156,여,premium,48,62600,4,0 C1157,남,basic,27,26900,1,1 C1158,남,standard,20,38000,4,0 C1159,여,premium,22,63700,2,1 C1160,여,standard,20,42800,1,0
churn_test.csv
고객ID,성별,요금제,가입개월,월요금,부가서비스 C2001,남,basic,32,30800,2 C2002,남,premium,31,63600,0 C2003,여,premium,51,56500,1 C2004,남,basic,15,31600,0 C2005,남,basic,8,35800,1 C2006,남,premium,4,67100,1 C2007,여,basic,13,21200,1 C2008,남,premium,57,67200,4 C2009,남,standard,55,45400,0 C2010,남,standard,30,46700,3 C2011,남,standard,43,38400,4 C2012,남,basic,33,31700,0 C2013,여,basic,20,29900,1 C2014,남,standard,47,43100,2 C2015,남,basic,4,28100,3 C2016,여,basic,31,31000,4 C2017,여,basic,7,25000,1 C2018,남,standard,10,49900,3 C2019,여,premium,2,73800,4 C2020,여,standard,51,40200,0 C2021,여,premium,48,63300,0 C2022,남,premium,43,60000,0 C2023,여,standard,12,48400,1 C2024,남,premium,8,68400,0 C2025,여,standard,47,42900,4 C2026,여,premium,1,66400,4 C2027,여,basic,18,34800,0 C2028,남,basic,27,27400,0 C2029,남,standard,2,57500,4 C2030,남,premium,8,74400,4 C2031,남,premium,44,74600,0 C2032,남,basic,7,35500,4 C2033,남,premium,39,76400,0 C2034,여,basic,40,29700,1 C2035,여,standard,18,42100,1 C2036,여,standard,25,47700,4 C2037,여,premium,46,52700,4 C2038,남,premium,46,64700,2 C2039,여,premium,47,65700,1 C2040,여,basic,15,29500,2
내 코드 Python
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import pandas as pd
from scipy import stats
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OrdinalEncoder
h = pd.read_csv("health.csv")
chol = h["콜레스테롤"]
q1, q3 = chol.quantile(0.25), chol.quantile(0.75)
iqr = q3 - q1
print(int(((chol < q1 - 1.5 * iqr) | (chol > q3 + 1.5 * iqr)).sum()))
train = pd.read_csv("churn_train.csv")
test = pd.read_csv("churn_test.csv")
X = train.drop(columns=["고객ID", "이탈"])
X_test = test.drop(columns=["고객ID"])
enc = OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1)
X[["성별", "요금제"]] = enc.fit_transform(X[["성별", "요금제"]])
X_test[["성별", "요금제"]] = enc.transform(X_test[["성별", "요금제"]])
model = RandomForestClassifier(n_estimators=100, random_state=42).fit(X, train["이탈"])
pd.DataFrame({"pred": model.predict_proba(X_test)[:, 1]}).to_csv("result.csv", index=False)
print(pd.read_csv("result.csv").shape)
a = h[h["운동"] == "예"]["콜레스테롤"]
b = h[h["운동"] == "아니오"]["콜레스테롤"]
pvalue = float(stats.ttest_ind(a, b, equal_var=True).pvalue)
print(round(pvalue, 4))
print(pvalue < 0.05)