├── 제16장 딥러닝 ├── Movie Review.xlsx ├── Zoo.txt └── Optical Recognition of Handwritten Digits.txt ├── 제06장 로지스틱 회귀 분석 ├── KidCreative.txt └── 연소득과주택소유.xlsx ├── 제05장 회귀 분석 ├── Baseball.txt ├── 자동차보험회사.txt └── Auto MPG.txt ├── 제12장 랜덤 포레스트 ├── Telco Customer Churn.txt └── RFexample.csv ├── 제13장 새로운 회귀 분석 기법들 ├── Baseball.txt └── Auto MPG.txt ├── 제08장 베이즈 분류기 └── Spambase.txt ├── 제11장 의사결정 트리 └── German Credit.txt ├── 제14장 서포트 벡터 머신 └── Breast Cancer Wisconsin.txt ├── 제09장 군집 분석 └── User Knowledge Modeling.txt ├── 제07장 선형 판별 분석 └── Loan.txt ├── 제15장 인공 신경망 └── Heart Disease.txt └── 제10장 연관 분석 ├── Assocs2.csv └── Assocs2Seq.csv /제16장 딥러닝/Movie Review.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wikibook/data-analytics/master/제16장 딥러닝/Movie Review.xlsx -------------------------------------------------------------------------------- /제06장 로지스틱 회귀 분석/KidCreative.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | http://logisticregressionanalysis.com/MiscPages/KidCreative.csv 4 | -------------------------------------------------------------------------------- /제06장 로지스틱 회귀 분석/연소득과주택소유.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wikibook/data-analytics/master/제06장 로지스틱 회귀 분석/연소득과주택소유.xlsx -------------------------------------------------------------------------------- /제05장 회귀 분석/Baseball.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | http://college.cengage.com/mathematics/brase/understandable_statistics/7e/students/datasets/mlr/frames/mlr08.html -------------------------------------------------------------------------------- /제05장 회귀 분석/자동차보험회사.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | http://college.cengage.com/mathematics/brase/understandable_statistics/7e/students/datasets/slr/frames/slr06.html -------------------------------------------------------------------------------- /제12장 랜덤 포레스트/Telco Customer Churn.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | https://www.kaggle.com/blastchar/telco-customer-churn 4 | 5 | Explorer를 사용하면 접속이 안될 수 있으므로 Chrome을 사용해야 함. -------------------------------------------------------------------------------- /제13장 새로운 회귀 분석 기법들/Baseball.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | http://college.cengage.com/mathematics/brase/understandable_statistics/7e/students/datasets/mlr/frames/mlr08.html -------------------------------------------------------------------------------- /제16장 딥러닝/Zoo.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. 4 | Irvine, CA: University of California, School of Information and Computer Science. 5 | 6 | https://archive.ics.uci.edu/ml/datasets/zoo -------------------------------------------------------------------------------- /제05장 회귀 분석/Auto MPG.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. 4 | Irvine, CA: University of California, School of Information and Computer Science. 5 | 6 | https://archive.ics.uci.edu/ml/datasets/auto+mpg -------------------------------------------------------------------------------- /제08장 베이즈 분류기/Spambase.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. 4 | Irvine, CA: University of California, School of Information and Computer Science. 5 | 6 | https://archive.ics.uci.edu/ml/datasets/spambase -------------------------------------------------------------------------------- /제13장 새로운 회귀 분석 기법들/Auto MPG.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. 4 | Irvine, CA: University of California, School of Information and Computer Science. 5 | 6 | https://archive.ics.uci.edu/ml/datasets/auto+mpg -------------------------------------------------------------------------------- /제11장 의사결정 트리/German Credit.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. 4 | Irvine, CA: University of California, School of Information and Computer Science. 5 | 6 | https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data) -------------------------------------------------------------------------------- /제14장 서포트 벡터 머신/Breast Cancer Wisconsin.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. 4 | Irvine, CA: University of California, School of Information and Computer Science. 5 | 6 | https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original) -------------------------------------------------------------------------------- /제16장 딥러닝/Optical Recognition of Handwritten Digits.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. 4 | Irvine, CA: University of California, School of Information and Computer Science. 5 | 6 | https://archive.ics.uci.edu/ml/datasets/optical+recognition+of+handwritten+digits -------------------------------------------------------------------------------- /제09장 군집 분석/User Knowledge Modeling.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. 4 | Irvine, CA: University of California, School of Information and Computer Science. 5 | 6 | https://archive.ics.uci.edu/ml/datasets/User+Knowledge+Modeling 7 | 8 | Training_Data와 Test_Data를 합쳐서 사용함. -------------------------------------------------------------------------------- /제07장 선형 판별 분석/Loan.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | http://calcnet.mth.cmich.edu/org/spss/Prj_loan_data.htm 4 | 5 | 데이터가 sav format으로 되어있기 때문에 변환을 해야 한다. 6 | 7 | https://pspp.benpfaff.org/ 에 접속하여 8 | Output Format을 으로 설정하면 9 | 데이터를 볼 수 있다. 10 | 11 | Output Format을 으로 설정하면 12 | 데이터에 대한 설명을 볼 수 있다. -------------------------------------------------------------------------------- /제12장 랜덤 포레스트/RFexample.csv: -------------------------------------------------------------------------------- 1 | Record,X,Y,Class 2 | 1,47,21,W 3 | 2,37,38,B 4 | 3,17,12,B 5 | 4,39,12,W 6 | 5,31,37,B 7 | 6,41,21,W 8 | 7,25,9,W 9 | 8,26,38,B 10 | 9,42,29,W 11 | 10,6,9,B 12 | 11,11,43,B 13 | 12,20,32,B 14 | 13,19,10,W 15 | 14,47,10,W 16 | 15,6,34,B 17 | 16,36,22,W 18 | 17,4,48,B 19 | 18,30,27,B 20 | 19,33,28,W 21 | 20,11,5,W 22 | 21,17,37,B 23 | 22,38,20,W 24 | 23,4,36,B 25 | 24,9,15,B 26 | 25,44,48,B 27 | 26,26,33,B 28 | 27,41,26,W 29 | 28,31,4,W 30 | 29,46,33,W 31 | 30,31,25,W 32 | 31,22,46,B 33 | 32,29,48,B 34 | 33,48,45,W 35 | 34,15,19,B 36 | 35,20,44,B 37 | 36,8,36,B 38 | 37,25,15,W 39 | 38,14,32,B 40 | 39,19,21,B 41 | 40,5,25,B 42 | 41,30,23,W 43 | 42,18,25,B 44 | 43,26,19,W 45 | 44,32,33,W 46 | 45,20,40,B 47 | 46,11,31,B 48 | 47,44,2,W 49 | 48,2,47,B 50 | 49,33,12,W 51 | 50,12,19,B 52 | -------------------------------------------------------------------------------- /제15장 인공 신경망/Heart Disease.txt: -------------------------------------------------------------------------------- 1 | Data 출처 2 | 3 | Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. 4 | Irvine, CA: University of California, School of Information and Computer Science. 5 | 6 | https://archive.ics.uci.edu/ml/datasets/Heart+Disease 7 | 8 | Data 출처에 아래와 같이 기술되어 있음. 9 | 10 | This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. 11 | In particular, the Cleveland database is the only one that has been used by ML researchers to this date. 12 | The "goal" field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4. 13 | Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) 14 | from absence (value 0). 15 | 16 | 그러므로 입력속성은 14개만 사용하였고, 목표속성 disease의 값이 2,3,4인 것은 모두 1로 변환하였음. -------------------------------------------------------------------------------- /제10장 연관 분석/Assocs2.csv: -------------------------------------------------------------------------------- 1 | TransactionID,Item1,Item2,Item3,Item4,Item5 2 | 1,pork,peppers,chicken,, 3 | 2,chicken,beer,ice_cream,olives, 4 | 3,pork,peppers,,, 5 | 4,baguette,fish,wine,ham, 6 | 5,soda,olives,beer,potato,peppers 7 | 6,baguette,fish,,, 8 | 7,soda,chicken,beer,, 9 | 8,fish,pork,,, 10 | 9,beef,peppers,chicken,fish,apples 11 | 10,wine,lobster,,, 12 | 11,fish,peppers,beef,ham,ice_cream 13 | 12,fish,pork,turkey,chicken,coke 14 | 13,avocado,cracker,,, 15 | 14,baguette,fish,beer,, 16 | 15,chicken,beer,,, 17 | 16,baguette,soda,,, 18 | 17,wine,lobster,,, 19 | 18,pork,peppers,,, 20 | 19,fish,beef,,, 21 | 20,olives,whiskey,ice_cream,, 22 | 21,pork,peppers,chicken,, 23 | 22,avocado,cracker,ham,fish,baguette 24 | 23,chicken,beer,ice_cream,beef, 25 | 24,olives,whiskey,,, 26 | 25,olives,whiskey,ice_cream,, 27 | 26,fish,pork,,, 28 | 27,olives,whiskey,,, 29 | 28,baguette,fish,,, 30 | 29,chicken,beer,ice_cream,avocado, 31 | 30,avocado,cracker,ham,fish, 32 | 31,wine,lobster,,, 33 | 32,fish,pork,turkey,coke, 34 | 33,soda,olives,beer,, 35 | 34,baguette,chicken,beer,, 36 | 35,soda,olives,beer,beef,fish 37 | 36,soda,olives,,, 38 | 37,soda,peppers,beer,potato, 39 | 38,chicken,beer,ice_cream,pork, 40 | 39,pork,peppers,chicken,, 41 | 40,olives,whiskey,ice_cream,potato, 42 | 41,avocado,cracker,ham,, 43 | 42,fish,beef,,, 44 | 43,olives,whiskey,,, 45 | 44,fish,pork,turkey,cracker, 46 | 45,fish,beef,chicken,whiskey,beer 47 | 46,baguette,soda,,, 48 | 47,olives,whiskey,ice_cream,apples, 49 | 48,soda,chicken,beer,, 50 | 49,avocado,cracker,,, 51 | 50,fish,pork,,, 52 | 51,fish,pork,beef,baguette, 53 | 52,fish,pork,beef,, 54 | 53,baguette,fish,beer,, 55 | 54,soda,peppers,beer,pork,fish 56 | 55,baguette,fish,wine,, 57 | 56,fish,beef,,, 58 | 57,soda,chicken,beer,, 59 | 58,fish,pork,,, 60 | 59,soda,olives,,, 61 | 60,baguette,soda,,, 62 | 61,baguette,lobster,avocado,, 63 | 62,pork,peppers,,, 64 | 63,fish,peppers,chicken,wine, 65 | 64,baguette,lobster,avocado,, 66 | 65,avocado,cracker,ham,pork,lobster 67 | 66,baguette,soda,beer,pork, 68 | 67,baguette,soda,beer,, 69 | 68,avocado,cracker,ham,, 70 | 69,soda,chicken,beer,, 71 | 70,fish,pork,beef,coke, 72 | 71,fish,pork,turkey,ice_cream,potato 73 | 72,fish,pork,turkey,, 74 | 73,wine,lobster,,, 75 | 74,olives,whiskey,,, 76 | 75,fish,beef,turkey,peppers, 77 | 76,soda,olives,cracker,, 78 | 77,fish,pork,beef,peppers, 79 | 78,baguette,fish,,, 80 | 79,chicken,beer,ice_cream,pork, 81 | 80,chicken,beer,,, 82 | 81,baguette,soda,beer,lobster, 83 | 82,baguette,lobster,avocado,turkey, 84 | 83,fish,pork,,, 85 | 84,olives,whiskey,,, 86 | 85,soda,chicken,beer,, 87 | 86,baguette,soda,cracker,olives,avocado 88 | 87,pork,peppers,,, 89 | 88,olives,whiskey,ice_cream,, 90 | 89,fish,pork,beef,baguette,chicken 91 | 90,beer,peppers,chicken,ice_cream, 92 | 91,chicken,beer,ice_cream,potato,beef 93 | 92,soda,olives,beer,, 94 | 93,baguette,lobster,avocado,cracker, 95 | 94,baguette,soda,,, 96 | 95,avocado,cracker,ham,beef,olives 97 | 96,baguette,soda,beer,ice_cream, 98 | 97,chicken,beer,,, 99 | 98,baguette,lobster,avocado,beef, 100 | 99,pork,peppers,,, 101 | 100,baguette,fish,,, 102 | -------------------------------------------------------------------------------- /제10장 연관 분석/Assocs2Seq.csv: -------------------------------------------------------------------------------- 1 | Customer,Time,Event,Item1,Item2,Item3,Item4,Item5 2 | 1,1,3,pork,peppers,chicken,, 3 | 1,2,4,chicken,beer,ice_cream,olives, 4 | 1,3,2,pork,peppers,,, 5 | 1,4,4,baguette,fish,wine,ham, 6 | 2,1,5,soda,olives,beer,potato,peppers 7 | 2,2,2,baguette,fish,,, 8 | 3,1,3,soda,chicken,beer,, 9 | 3,2,2,fish,pork,,, 10 | 3,3,5,beef,peppers,chicken,fish,apples 11 | 4,1,2,wine,lobster,,, 12 | 4,2,5,fish,peppers,beef,ham,ice_cream 13 | 4,3,5,fish,pork,turkey,chicken,coke 14 | 5,1,2,avocado,cracker,,, 15 | 5,2,3,baguette,fish,beer,, 16 | 5,3,2,chicken,beer,,, 17 | 5,4,2,baguette,soda,,, 18 | 6,1,2,wine,lobster,,, 19 | 6,2,2,pork,peppers,,, 20 | 6,3,2,fish,beef,,, 21 | 7,1,3,olives,whiskey,ice_cream,, 22 | 7,2,3,pork,peppers,chicken,, 23 | 7,3,5,avocado,cracker,ham,fish,baguette 24 | 7,4,4,chicken,beer,ice_cream,beef, 25 | 8,1,2,olives,whiskey,,, 26 | 9,1,3,olives,whiskey,ice_cream,, 27 | 9,2,2,fish,pork,,, 28 | 9,3,2,olives,whiskey,,, 29 | 10,1,2,baguette,fish,,, 30 | 11,1,4,chicken,beer,ice_cream,avocado, 31 | 12,1,4,avocado,cracker,ham,fish, 32 | 12,2,2,wine,lobster,,, 33 | 12,3,4,fish,pork,turkey,coke, 34 | 13,1,3,soda,olives,beer,, 35 | 14,1,3,baguette,chicken,beer,, 36 | 14,2,5,soda,olives,beer,beef,fish 37 | 15,1,2,soda,olives,,, 38 | 16,1,4,soda,peppers,beer,potato, 39 | 16,2,4,chicken,beer,ice_cream,pork, 40 | 17,1,3,pork,peppers,chicken,, 41 | 17,2,4,olives,whiskey,ice_cream,potato, 42 | 17,3,3,avocado,cracker,ham,, 43 | 17,4,2,fish,beef,,, 44 | 18,1,2,olives,whiskey,,, 45 | 18,2,4,fish,pork,turkey,cracker, 46 | 18,3,5,fish,beef,chicken,whiskey,beer 47 | 19,1,2,baguette,soda,,, 48 | 20,1,4,olives,whiskey,ice_cream,apples, 49 | 20,2,3,soda,chicken,beer,, 50 | 21,1,2,avocado,cracker,,, 51 | 21,2,2,fish,pork,,, 52 | 22,1,4,fish,pork,beef,baguette, 53 | 23,1,3,fish,pork,beef,, 54 | 23,2,3,baguette,fish,beer,, 55 | 24,1,5,soda,peppers,beer,pork,fish 56 | 24,2,3,baguette,fish,wine,, 57 | 24,3,2,fish,beef,,, 58 | 24,4,3,soda,chicken,beer,, 59 | 25,1,2,fish,pork,,, 60 | 25,2,2,soda,olives,,, 61 | 26,1,2,baguette,soda,,, 62 | 27,1,3,baguette,lobster,avocado,, 63 | 27,2,2,pork,peppers,,, 64 | 28,1,4,fish,peppers,chicken,wine, 65 | 28,2,3,baguette,lobster,avocado,, 66 | 28,3,5,avocado,cracker,ham,pork,lobster 67 | 28,4,4,baguette,soda,beer,pork, 68 | 29,1,3,baguette,soda,beer,, 69 | 29,2,3,avocado,cracker,ham,, 70 | 30,1,3,soda,chicken,beer,, 71 | 30,2,4,fish,pork,beef,coke, 72 | 30,3,5,fish,pork,turkey,ice_cream,potato 73 | 31,1,3,fish,pork,turkey,, 74 | 31,2,2,wine,lobster,,, 75 | 31,3,2,olives,whiskey,,, 76 | 31,4,4,fish,beef,turkey,peppers, 77 | 31,5,3,soda,olives,cracker,, 78 | 32,1,4,fish,pork,beef,peppers, 79 | 32,2,2,baguette,fish,,, 80 | 33,1,4,chicken,beer,ice_cream,pork, 81 | 33,2,2,chicken,beer,,, 82 | 33,3,4,baguette,soda,beer,lobster, 83 | 33,4,4,baguette,lobster,avocado,turkey, 84 | 34,1,2,fish,pork,,, 85 | 34,2,2,olives,whiskey,,, 86 | 34,3,3,soda,chicken,beer,, 87 | 34,4,5,baguette,soda,cracker,olives,avocado 88 | 35,1,2,pork,peppers,,, 89 | 35,2,3,olives,whiskey,ice_cream,, 90 | 36,1,5,fish,pork,beef,baguette,chicken 91 | 37,1,4,beer,peppers,chicken,ice_cream, 92 | 38,1,5,chicken,beer,ice_cream,potato,beef 93 | 38,2,3,soda,olives,beer,, 94 | 38,3,4,baguette,lobster,avocado,cracker, 95 | 39,1,2,baguette,soda,,, 96 | 39,2,5,avocado,cracker,ham,beef,olives 97 | 39,3,4,baguette,soda,beer,ice_cream, 98 | 40,1,2,chicken,beer,,, 99 | 40,2,4,baguette,lobster,avocado,beef, 100 | 40,3,2,pork,peppers,,, 101 | 40,4,2,baguette,fish,,, 102 | --------------------------------------------------------------------------------