├── .gitignore ├── data ├── rda │ ├── OJ.rda │ ├── Auto.rda │ ├── Khan.rda │ ├── NCI60.rda │ ├── Wage.rda │ ├── Boston.rda │ ├── Caravan.rda │ ├── College.rda │ ├── Credit.rda │ ├── Default.rda │ ├── Hitters.rda │ ├── Smarket.rda │ ├── Weekly.rda │ ├── Carseats.rda │ └── Portfolio.rda ├── datalist └── csv │ ├── USArrests.csv │ ├── Portfolio.csv │ ├── Carseats.csv │ ├── Auto.csv │ ├── Credit.csv │ ├── Hitters.csv │ └── Boston.csv ├── requirements.txt ├── LICENSE.md ├── README.md └── labs ├── chapter8 └── decision_trees.ipynb └── chapter4 └── KNN.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | .ipynb_checkpoints 3 | -------------------------------------------------------------------------------- /data/rda/OJ.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dsnair/ISLR/HEAD/data/rda/OJ.rda 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scipy==1.0.0 4 | seaborn==0.8.1 5 | scikit-learn==0.19.1 6 | statsmodels==0.8.0 7 | -------------------------------------------------------------------------------- /data/datalist: -------------------------------------------------------------------------------- 1 | Auto 2 | Boston 3 | Caravan 4 | Carseats 5 | College 6 | Credit 7 | Default 8 | Hitters 9 | Khan 10 | NCI60 11 | OJ 12 | Portfolio 13 | Smarket 14 | USArrests 15 | Wage 16 | Weekly 17 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Jordi Warmenhoven 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /data/csv/USArrests.csv: -------------------------------------------------------------------------------- 1 | "State","Murder","Assault","UrbanPop","Rape" 2 | "Alabama",13.2,236,58,21.2 3 | "Alaska",10,263,48,44.5 4 | "Arizona",8.1,294,80,31 5 | "Arkansas",8.8,190,50,19.5 6 | "California",9,276,91,40.6 7 | "Colorado",7.9,204,78,38.7 8 | "Connecticut",3.3,110,77,11.1 9 | "Delaware",5.9,238,72,15.8 10 | "Florida",15.4,335,80,31.9 11 | "Georgia",17.4,211,60,25.8 12 | "Hawaii",5.3,46,83,20.2 13 | "Idaho",2.6,120,54,14.2 14 | "Illinois",10.4,249,83,24 15 | "Indiana",7.2,113,65,21 16 | "Iowa",2.2,56,57,11.3 17 | "Kansas",6,115,66,18 18 | "Kentucky",9.7,109,52,16.3 19 | "Louisiana",15.4,249,66,22.2 20 | "Maine",2.1,83,51,7.8 21 | "Maryland",11.3,300,67,27.8 22 | "Massachusetts",4.4,149,85,16.3 23 | "Michigan",12.1,255,74,35.1 24 | "Minnesota",2.7,72,66,14.9 25 | "Mississippi",16.1,259,44,17.1 26 | "Missouri",9,178,70,28.2 27 | "Montana",6,109,53,16.4 28 | "Nebraska",4.3,102,62,16.5 29 | "Nevada",12.2,252,81,46 30 | "New Hampshire",2.1,57,56,9.5 31 | "New Jersey",7.4,159,89,18.8 32 | "New Mexico",11.4,285,70,32.1 33 | "New York",11.1,254,86,26.1 34 | "North Carolina",13,337,45,16.1 35 | "North Dakota",0.8,45,44,7.3 36 | "Ohio",7.3,120,75,21.4 37 | "Oklahoma",6.6,151,68,20 38 | "Oregon",4.9,159,67,29.3 39 | "Pennsylvania",6.3,106,72,14.9 40 | "Rhode Island",3.4,174,87,8.3 41 | "South Carolina",14.4,279,48,22.5 42 | "South Dakota",3.8,86,45,12.8 43 | "Tennessee",13.2,188,59,26.9 44 | "Texas",12.7,201,80,25.5 45 | "Utah",3.2,120,80,22.9 46 | "Vermont",2.2,48,32,11.2 47 | "Virginia",8.5,156,63,20.7 48 | "Washington",4,145,73,26.2 49 | "West Virginia",5.7,81,39,9.3 50 | "Wisconsin",2.6,53,66,10.8 51 | "Wyoming",6.8,161,60,15.6 52 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # README 2 | 3 | ## Project Summary 4 | 5 | Do the labs from '[An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/index.html)' book, in Python. (The labs are done in R in the book.) The book is available for free download on the author's [website](http://www-bcf.usc.edu/~gareth/ISL/index.html) along with slides, video tutorials, and some datasets. 6 | 7 | ## Data 8 | 9 | The labs require the datasets listed below. These datasets are available on the [CRAN GitHub repo](https://github.com/cran/ISLR/tree/master/data). 10 | 11 | * `Auto`: Gas mileage, horsepower, and other information for cars. 12 | * `Boston`: Housing values and other information about Boston suburbs. 13 | * `Caravan`: Information about individuals offered caravan insurance. 14 | * `Carseats`: Information about car seat sales in 400 stores. 15 | * `College`: Demographic characteristics, tuition, and more for USA colleges. 16 | * `Default`: Customer default records for a credit card company. 17 | * `Hitters`: Records and salaries for baseball players. 18 | * `Khan`: Gene expression measurements for four cancer types. 19 | * `NCI60`: Gene expression measurements for 64 cancer cell lines. 20 | * `OJ`: Sales information for Citrus Hill and Minute Maid orange juice. 21 | * `Portfolio`: Past values of financial assets, for use in portfolio allocation. 22 | * `Smarket`: Daily percentage returns for S&P 500 over a 5-year period. 23 | * `USArrests`: Crime statistics per 100,000 residents in 50 states of USA. 24 | * `Wage`: Income survey data for males in central Atlantic region of USA. 25 | * `Weekly`: 1,089 weekly stock market returns for 21 years. 26 | 27 | ## Requirements 28 | 29 | ```shell 30 | $ pip install -r requirements.txt 31 | ``` 32 | 33 | ## Work Summary 34 | 35 | 3 Regression: 36 | * Simple Linear Regression: 37 | * Coefficient of Determination 38 | * Residual Plot 39 | * Multiple Linear Regression: 40 | * Non-linearity 41 | * Heteroscedasticity 42 | * Leverage Statistic 43 | * Studentized Residuals 44 | * Correlation Heatmap 45 | * Variance Inflation Factor (VIF) 46 | 47 | 4 Classification: 48 | * Logistic Regression: 49 | * Confusion Matrix 50 | * Sensitivity 51 | * Precision 52 | * F1 score 53 | * K-Nearest Neighbors (KNN) 54 | ~~* Linear Discriminant Analysis~~ 55 | ~~* Quadratic Discriminant Analysis~~ 56 | 57 | 5 Resampling Methods: 58 | ~~* Validation Set~~ 59 | ~~* Leave-One-Out Cross-Validation~~ 60 | ~~* k-fold Cross-Validation~~ 61 | ~~* Bootstrap~~ 62 | 63 | 6 Linear Model Selection and Regularization: 64 | 7 Moving Beyond Linearity: 65 | 66 | 8 Tree-Based Methods: 67 | * Decision trees 68 | ~~* Bagging~~ 69 | ~~* Random Forests~~ 70 | ~~* Boosting~~ 71 | 72 | 9 Support Vector Machines: 73 | 74 | 10 Unsupervised Learning: 75 | * Principal Component Analysis (PCA) 76 | * K-Means Clustering 77 | * Hierarchical Clustering 78 | ~~* Gaussian Mixture Models (GMM) Clustering~~ 79 | ~~* Spectral Clustering~~ 80 | ~~* Mean-Shift Clustering~~ 81 | ~~* DBSCAN~~ 82 | -------------------------------------------------------------------------------- /data/csv/Portfolio.csv: -------------------------------------------------------------------------------- 1 | X,Y 2 | -0.895250889142,-0.234923525765 3 | -1.56245432748,-0.885175993045 4 | -0.417089883126,0.27188801805 5 | 1.04435572527,-0.734197504068 6 | -0.315568406681,0.841983429961 7 | -1.73712384902,-2.03719104075 8 | 1.96641315717,1.45295666192 9 | 2.15286789801,-0.43413862818 10 | -0.0812080267603,1.45080850219 11 | -0.891781794029,0.82101623454 12 | -0.29320170201,-1.04239112184 13 | 0.505779171069,0.608477825847 14 | 0.526751254093,-0.222493343283 15 | 1.06646932095,1.23135667526 16 | 0.294015895064,0.628589480036 17 | 0.0425492997634,-1.26757361755 18 | 1.83096958062,-0.572751605499 19 | -0.326937498878,-0.487472465046 20 | 0.521480415807,2.56598528732 21 | 1.39986834733,-0.357836127482 22 | -0.645447596469,-1.4124313895 23 | -0.90435187845,-0.568304791042 24 | -1.76458606962,-0.746272562068 25 | -1.81048463819,0.493747359351 26 | -1.16989891378,-2.72528149494 27 | -0.685375735369,-0.457615734339 28 | 1.09091803184,0.0144945075275 29 | -0.432340114041,-0.399831023509 30 | 0.268814775371,-0.201608350198 31 | -0.851840753541,-1.74182928585 32 | -1.49708417204,-0.826033329438 33 | 0.0887747459974,-0.887360712724 34 | -1.60172430963,-0.695299045953 35 | -1.24685724026,-1.52958488449 36 | -1.06298912831,-0.110637447364 37 | -0.26628305531,0.0451634696289 38 | 1.67658383263,2.52005288263 39 | 0.119572571441,0.535542781034 40 | -0.0860079872691,1.36359582806 41 | 0.368080289749,1.72937250997 42 | -0.27149420694,1.37926732742 43 | -0.0859264618788,-0.127662573751 44 | -0.190750153683,-0.461333357788 45 | -0.781679768391,1.02239787731 46 | 0.792436346461,-0.814298088655 47 | -0.282869886234,-1.03846880699 48 | -0.236625531903,0.928450553143 49 | 1.17183009101,1.72983145003 50 | 0.496942768505,-0.925139825949 51 | -0.887370979477,-2.28349795939 52 | -1.30695315836,-2.38160058115 53 | -2.4327641204,-2.02554558512 54 | -0.40718896096,-0.335098643325 55 | -0.285665299455,-1.30878131267 56 | 1.5222148831,1.20100315335 57 | -0.998106907438,-0.946268900068 58 | -0.289973726127,0.206256579941 59 | -1.236839243,-0.675447507317 60 | -0.359506962064,-2.70015447022 61 | 0.543559153033,0.422547552093 62 | -0.403647282895,-0.0543899228706 63 | 1.30330893266,1.32896747385 64 | -0.717117243406,1.33137979804 65 | -1.01270788406,-0.924769230819 66 | 0.831992902159,2.24774586895 67 | 1.33764359604,0.868256457488 68 | 0.601693509867,-0.198217563055 69 | 1.30285098047,1.10466637602 70 | -0.881700578927,-1.54068478518 71 | -0.824529071305,-1.3370078772 72 | -0.984356518466,-1.13916026592 73 | -1.38499150721,0.702699932949 74 | -0.358842560436,-1.69451276978 75 | -0.226618229456,0.801938547571 76 | -0.941077436691,-0.733188708932 77 | 2.46033594813,-0.0483728170022 78 | 0.716797281413,0.602336759898 79 | -0.248087023209,-1.01849037379 80 | 1.01077288944,0.0529780222229 81 | 2.31304863448,1.75235887916 82 | 0.835179797449,0.985714875658 83 | -1.07190333914,-1.24729787324 84 | -1.65052614385,0.215464529577 85 | -0.600485690305,-0.420940526974 86 | -0.0585293830471,0.127620874053 87 | 0.0757267446339,-0.522149221026 88 | -1.15783156137,0.590893742239 89 | 1.67360608794,0.114623316085 90 | -1.04398823978,-0.418944284341 91 | 0.014687476592,-0.558746620673 92 | 0.675321970429,1.48262978763 93 | 1.77834230986,0.942774111448 94 | -1.29576363941,-1.0852038131 95 | 0.0796020218475,-0.539100814054 96 | 2.26085771442,0.673224840267 97 | 0.479090923234,1.45477446091 98 | -0.535019997433,-0.399174811276 99 | -0.773129330645,-0.957174849521 100 | 0.403634339015,1.39603816899 101 | -0.588496438718,-0.497285090818 102 | -------------------------------------------------------------------------------- /data/csv/Carseats.csv: -------------------------------------------------------------------------------- 1 | Sales,CompPrice,Income,Advertising,Population,Price,ShelveLoc,Age,Education,Urban,US 2 | 9.5,138,73,11,276,120,Bad,42,17,Yes,Yes 3 | 11.22,111,48,16,260,83,Good,65,10,Yes,Yes 4 | 10.06,113,35,10,269,80,Medium,59,12,Yes,Yes 5 | 7.4,117,100,4,466,97,Medium,55,14,Yes,Yes 6 | 4.15,141,64,3,340,128,Bad,38,13,Yes,No 7 | 10.81,124,113,13,501,72,Bad,78,16,No,Yes 8 | 6.63,115,105,0,45,108,Medium,71,15,Yes,No 9 | 11.85,136,81,15,425,120,Good,67,10,Yes,Yes 10 | 6.54,132,110,0,108,124,Medium,76,10,No,No 11 | 4.69,132,113,0,131,124,Medium,76,17,No,Yes 12 | 9.01,121,78,9,150,100,Bad,26,10,No,Yes 13 | 11.96,117,94,4,503,94,Good,50,13,Yes,Yes 14 | 3.98,122,35,2,393,136,Medium,62,18,Yes,No 15 | 10.96,115,28,11,29,86,Good,53,18,Yes,Yes 16 | 11.17,107,117,11,148,118,Good,52,18,Yes,Yes 17 | 8.71,149,95,5,400,144,Medium,76,18,No,No 18 | 7.58,118,32,0,284,110,Good,63,13,Yes,No 19 | 12.29,147,74,13,251,131,Good,52,10,Yes,Yes 20 | 13.91,110,110,0,408,68,Good,46,17,No,Yes 21 | 8.73,129,76,16,58,121,Medium,69,12,Yes,Yes 22 | 6.41,125,90,2,367,131,Medium,35,18,Yes,Yes 23 | 12.13,134,29,12,239,109,Good,62,18,No,Yes 24 | 5.08,128,46,6,497,138,Medium,42,13,Yes,No 25 | 5.87,121,31,0,292,109,Medium,79,10,Yes,No 26 | 10.14,145,119,16,294,113,Bad,42,12,Yes,Yes 27 | 14.9,139,32,0,176,82,Good,54,11,No,No 28 | 8.33,107,115,11,496,131,Good,50,11,No,Yes 29 | 5.27,98,118,0,19,107,Medium,64,17,Yes,No 30 | 2.99,103,74,0,359,97,Bad,55,11,Yes,Yes 31 | 7.81,104,99,15,226,102,Bad,58,17,Yes,Yes 32 | 13.55,125,94,0,447,89,Good,30,12,Yes,No 33 | 8.25,136,58,16,241,131,Medium,44,18,Yes,Yes 34 | 6.2,107,32,12,236,137,Good,64,10,No,Yes 35 | 8.77,114,38,13,317,128,Good,50,16,Yes,Yes 36 | 2.67,115,54,0,406,128,Medium,42,17,Yes,Yes 37 | 11.07,131,84,11,29,96,Medium,44,17,No,Yes 38 | 8.89,122,76,0,270,100,Good,60,18,No,No 39 | 4.95,121,41,5,412,110,Medium,54,10,Yes,Yes 40 | 6.59,109,73,0,454,102,Medium,65,15,Yes,No 41 | 3.24,130,60,0,144,138,Bad,38,10,No,No 42 | 2.07,119,98,0,18,126,Bad,73,17,No,No 43 | 7.96,157,53,0,403,124,Bad,58,16,Yes,No 44 | 10.43,77,69,0,25,24,Medium,50,18,Yes,No 45 | 4.12,123,42,11,16,134,Medium,59,13,Yes,Yes 46 | 4.16,85,79,6,325,95,Medium,69,13,Yes,Yes 47 | 4.56,141,63,0,168,135,Bad,44,12,Yes,Yes 48 | 12.44,127,90,14,16,70,Medium,48,15,No,Yes 49 | 4.38,126,98,0,173,108,Bad,55,16,Yes,No 50 | 3.91,116,52,0,349,98,Bad,69,18,Yes,No 51 | 10.61,157,93,0,51,149,Good,32,17,Yes,No 52 | 1.42,99,32,18,341,108,Bad,80,16,Yes,Yes 53 | 4.42,121,90,0,150,108,Bad,75,16,Yes,No 54 | 7.91,153,40,3,112,129,Bad,39,18,Yes,Yes 55 | 6.92,109,64,13,39,119,Medium,61,17,Yes,Yes 56 | 4.9,134,103,13,25,144,Medium,76,17,No,Yes 57 | 6.85,143,81,5,60,154,Medium,61,18,Yes,Yes 58 | 11.91,133,82,0,54,84,Medium,50,17,Yes,No 59 | 0.91,93,91,0,22,117,Bad,75,11,Yes,No 60 | 5.42,103,93,15,188,103,Bad,74,16,Yes,Yes 61 | 5.21,118,71,4,148,114,Medium,80,13,Yes,No 62 | 8.32,122,102,19,469,123,Bad,29,13,Yes,Yes 63 | 7.32,105,32,0,358,107,Medium,26,13,No,No 64 | 1.82,139,45,0,146,133,Bad,77,17,Yes,Yes 65 | 8.47,119,88,10,170,101,Medium,61,13,Yes,Yes 66 | 7.8,100,67,12,184,104,Medium,32,16,No,Yes 67 | 4.9,122,26,0,197,128,Medium,55,13,No,No 68 | 8.85,127,92,0,508,91,Medium,56,18,Yes,No 69 | 9.01,126,61,14,152,115,Medium,47,16,Yes,Yes 70 | 13.39,149,69,20,366,134,Good,60,13,Yes,Yes 71 | 7.99,127,59,0,339,99,Medium,65,12,Yes,No 72 | 9.46,89,81,15,237,99,Good,74,12,Yes,Yes 73 | 6.5,148,51,16,148,150,Medium,58,17,No,Yes 74 | 5.52,115,45,0,432,116,Medium,25,15,Yes,No 75 | 12.61,118,90,10,54,104,Good,31,11,No,Yes 76 | 6.2,150,68,5,125,136,Medium,64,13,No,Yes 77 | 8.55,88,111,23,480,92,Bad,36,16,No,Yes 78 | 10.64,102,87,10,346,70,Medium,64,15,Yes,Yes 79 | 7.7,118,71,12,44,89,Medium,67,18,No,Yes 80 | 4.43,134,48,1,139,145,Medium,65,12,Yes,Yes 81 | 9.14,134,67,0,286,90,Bad,41,13,Yes,No 82 | 8.01,113,100,16,353,79,Bad,68,11,Yes,Yes 83 | 7.52,116,72,0,237,128,Good,70,13,Yes,No 84 | 11.62,151,83,4,325,139,Good,28,17,Yes,Yes 85 | 4.42,109,36,7,468,94,Bad,56,11,Yes,Yes 86 | 2.23,111,25,0,52,121,Bad,43,18,No,No 87 | 8.47,125,103,0,304,112,Medium,49,13,No,No 88 | 8.7,150,84,9,432,134,Medium,64,15,Yes,No 89 | 11.7,131,67,7,272,126,Good,54,16,No,Yes 90 | 6.56,117,42,7,144,111,Medium,62,10,Yes,Yes 91 | 7.95,128,66,3,493,119,Medium,45,16,No,No 92 | 5.33,115,22,0,491,103,Medium,64,11,No,No 93 | 4.81,97,46,11,267,107,Medium,80,15,Yes,Yes 94 | 4.53,114,113,0,97,125,Medium,29,12,Yes,No 95 | 8.86,145,30,0,67,104,Medium,55,17,Yes,No 96 | 8.39,115,97,5,134,84,Bad,55,11,Yes,Yes 97 | 5.58,134,25,10,237,148,Medium,59,13,Yes,Yes 98 | 9.48,147,42,10,407,132,Good,73,16,No,Yes 99 | 7.45,161,82,5,287,129,Bad,33,16,Yes,Yes 100 | 12.49,122,77,24,382,127,Good,36,16,No,Yes 101 | 4.88,121,47,3,220,107,Bad,56,16,No,Yes 102 | 4.11,113,69,11,94,106,Medium,76,12,No,Yes 103 | 6.2,128,93,0,89,118,Medium,34,18,Yes,No 104 | 5.3,113,22,0,57,97,Medium,65,16,No,No 105 | 5.07,123,91,0,334,96,Bad,78,17,Yes,Yes 106 | 4.62,121,96,0,472,138,Medium,51,12,Yes,No 107 | 5.55,104,100,8,398,97,Medium,61,11,Yes,Yes 108 | 0.16,102,33,0,217,139,Medium,70,18,No,No 109 | 8.55,134,107,0,104,108,Medium,60,12,Yes,No 110 | 3.47,107,79,2,488,103,Bad,65,16,Yes,No 111 | 8.98,115,65,0,217,90,Medium,60,17,No,No 112 | 9.0,128,62,7,125,116,Medium,43,14,Yes,Yes 113 | 6.62,132,118,12,272,151,Medium,43,14,Yes,Yes 114 | 6.67,116,99,5,298,125,Good,62,12,Yes,Yes 115 | 6.01,131,29,11,335,127,Bad,33,12,Yes,Yes 116 | 9.31,122,87,9,17,106,Medium,65,13,Yes,Yes 117 | 8.54,139,35,0,95,129,Medium,42,13,Yes,No 118 | 5.08,135,75,0,202,128,Medium,80,10,No,No 119 | 8.8,145,53,0,507,119,Medium,41,12,Yes,No 120 | 7.57,112,88,2,243,99,Medium,62,11,Yes,Yes 121 | 7.37,130,94,8,137,128,Medium,64,12,Yes,Yes 122 | 6.87,128,105,11,249,131,Medium,63,13,Yes,Yes 123 | 11.67,125,89,10,380,87,Bad,28,10,Yes,Yes 124 | 6.88,119,100,5,45,108,Medium,75,10,Yes,Yes 125 | 8.19,127,103,0,125,155,Good,29,15,No,Yes 126 | 8.87,131,113,0,181,120,Good,63,14,Yes,No 127 | 9.34,89,78,0,181,49,Medium,43,15,No,No 128 | 11.27,153,68,2,60,133,Good,59,16,Yes,Yes 129 | 6.52,125,48,3,192,116,Medium,51,14,Yes,Yes 130 | 4.96,133,100,3,350,126,Bad,55,13,Yes,Yes 131 | 4.47,143,120,7,279,147,Bad,40,10,No,Yes 132 | 8.41,94,84,13,497,77,Medium,51,12,Yes,Yes 133 | 6.5,108,69,3,208,94,Medium,77,16,Yes,No 134 | 9.54,125,87,9,232,136,Good,72,10,Yes,Yes 135 | 7.62,132,98,2,265,97,Bad,62,12,Yes,Yes 136 | 3.67,132,31,0,327,131,Medium,76,16,Yes,No 137 | 6.44,96,94,14,384,120,Medium,36,18,No,Yes 138 | 5.17,131,75,0,10,120,Bad,31,18,No,No 139 | 6.52,128,42,0,436,118,Medium,80,11,Yes,No 140 | 10.27,125,103,12,371,109,Medium,44,10,Yes,Yes 141 | 12.3,146,62,10,310,94,Medium,30,13,No,Yes 142 | 6.03,133,60,10,277,129,Medium,45,18,Yes,Yes 143 | 6.53,140,42,0,331,131,Bad,28,15,Yes,No 144 | 7.44,124,84,0,300,104,Medium,77,15,Yes,No 145 | 0.53,122,88,7,36,159,Bad,28,17,Yes,Yes 146 | 9.09,132,68,0,264,123,Good,34,11,No,No 147 | 8.77,144,63,11,27,117,Medium,47,17,Yes,Yes 148 | 3.9,114,83,0,412,131,Bad,39,14,Yes,No 149 | 10.51,140,54,9,402,119,Good,41,16,No,Yes 150 | 7.56,110,119,0,384,97,Medium,72,14,No,Yes 151 | 11.48,121,120,13,140,87,Medium,56,11,Yes,Yes 152 | 10.49,122,84,8,176,114,Good,57,10,No,Yes 153 | 10.77,111,58,17,407,103,Good,75,17,No,Yes 154 | 7.64,128,78,0,341,128,Good,45,13,No,No 155 | 5.93,150,36,7,488,150,Medium,25,17,No,Yes 156 | 6.89,129,69,10,289,110,Medium,50,16,No,Yes 157 | 7.71,98,72,0,59,69,Medium,65,16,Yes,No 158 | 7.49,146,34,0,220,157,Good,51,16,Yes,No 159 | 10.21,121,58,8,249,90,Medium,48,13,No,Yes 160 | 12.53,142,90,1,189,112,Good,39,10,No,Yes 161 | 9.32,119,60,0,372,70,Bad,30,18,No,No 162 | 4.67,111,28,0,486,111,Medium,29,12,No,No 163 | 2.93,143,21,5,81,160,Medium,67,12,No,Yes 164 | 3.63,122,74,0,424,149,Medium,51,13,Yes,No 165 | 5.68,130,64,0,40,106,Bad,39,17,No,No 166 | 8.22,148,64,0,58,141,Medium,27,13,No,Yes 167 | 0.37,147,58,7,100,191,Bad,27,15,Yes,Yes 168 | 6.71,119,67,17,151,137,Medium,55,11,Yes,Yes 169 | 6.71,106,73,0,216,93,Medium,60,13,Yes,No 170 | 7.3,129,89,0,425,117,Medium,45,10,Yes,No 171 | 11.48,104,41,15,492,77,Good,73,18,Yes,Yes 172 | 8.01,128,39,12,356,118,Medium,71,10,Yes,Yes 173 | 12.49,93,106,12,416,55,Medium,75,15,Yes,Yes 174 | 9.03,104,102,13,123,110,Good,35,16,Yes,Yes 175 | 6.38,135,91,5,207,128,Medium,66,18,Yes,Yes 176 | 0.0,139,24,0,358,185,Medium,79,15,No,No 177 | 7.54,115,89,0,38,122,Medium,25,12,Yes,No 178 | 5.61,138,107,9,480,154,Medium,47,11,No,Yes 179 | 10.48,138,72,0,148,94,Medium,27,17,Yes,Yes 180 | 10.66,104,71,14,89,81,Medium,25,14,No,Yes 181 | 7.78,144,25,3,70,116,Medium,77,18,Yes,Yes 182 | 4.94,137,112,15,434,149,Bad,66,13,Yes,Yes 183 | 7.43,121,83,0,79,91,Medium,68,11,Yes,No 184 | 4.74,137,60,4,230,140,Bad,25,13,Yes,No 185 | 5.32,118,74,6,426,102,Medium,80,18,Yes,Yes 186 | 9.95,132,33,7,35,97,Medium,60,11,No,Yes 187 | 10.07,130,100,11,449,107,Medium,64,10,Yes,Yes 188 | 8.68,120,51,0,93,86,Medium,46,17,No,No 189 | 6.03,117,32,0,142,96,Bad,62,17,Yes,No 190 | 8.07,116,37,0,426,90,Medium,76,15,Yes,No 191 | 12.11,118,117,18,509,104,Medium,26,15,No,Yes 192 | 8.79,130,37,13,297,101,Medium,37,13,No,Yes 193 | 6.67,156,42,13,170,173,Good,74,14,Yes,Yes 194 | 7.56,108,26,0,408,93,Medium,56,14,No,No 195 | 13.28,139,70,7,71,96,Good,61,10,Yes,Yes 196 | 7.23,112,98,18,481,128,Medium,45,11,Yes,Yes 197 | 4.19,117,93,4,420,112,Bad,66,11,Yes,Yes 198 | 4.1,130,28,6,410,133,Bad,72,16,Yes,Yes 199 | 2.52,124,61,0,333,138,Medium,76,16,Yes,No 200 | 3.62,112,80,5,500,128,Medium,69,10,Yes,Yes 201 | 6.42,122,88,5,335,126,Medium,64,14,Yes,Yes 202 | 5.56,144,92,0,349,146,Medium,62,12,No,No 203 | 5.94,138,83,0,139,134,Medium,54,18,Yes,No 204 | 4.1,121,78,4,413,130,Bad,46,10,No,Yes 205 | 2.05,131,82,0,132,157,Bad,25,14,Yes,No 206 | 8.74,155,80,0,237,124,Medium,37,14,Yes,No 207 | 5.68,113,22,1,317,132,Medium,28,12,Yes,No 208 | 4.97,162,67,0,27,160,Medium,77,17,Yes,Yes 209 | 8.19,111,105,0,466,97,Bad,61,10,No,No 210 | 7.78,86,54,0,497,64,Bad,33,12,Yes,No 211 | 3.02,98,21,11,326,90,Bad,76,11,No,Yes 212 | 4.36,125,41,2,357,123,Bad,47,14,No,Yes 213 | 9.39,117,118,14,445,120,Medium,32,15,Yes,Yes 214 | 12.04,145,69,19,501,105,Medium,45,11,Yes,Yes 215 | 8.23,149,84,5,220,139,Medium,33,10,Yes,Yes 216 | 4.83,115,115,3,48,107,Medium,73,18,Yes,Yes 217 | 2.34,116,83,15,170,144,Bad,71,11,Yes,Yes 218 | 5.73,141,33,0,243,144,Medium,34,17,Yes,No 219 | 4.34,106,44,0,481,111,Medium,70,14,No,No 220 | 9.7,138,61,12,156,120,Medium,25,14,Yes,Yes 221 | 10.62,116,79,19,359,116,Good,58,17,Yes,Yes 222 | 10.59,131,120,15,262,124,Medium,30,10,Yes,Yes 223 | 6.43,124,44,0,125,107,Medium,80,11,Yes,No 224 | 7.49,136,119,6,178,145,Medium,35,13,Yes,Yes 225 | 3.45,110,45,9,276,125,Medium,62,14,Yes,Yes 226 | 4.1,134,82,0,464,141,Medium,48,13,No,No 227 | 6.68,107,25,0,412,82,Bad,36,14,Yes,No 228 | 7.8,119,33,0,245,122,Good,56,14,Yes,No 229 | 8.69,113,64,10,68,101,Medium,57,16,Yes,Yes 230 | 5.4,149,73,13,381,163,Bad,26,11,No,Yes 231 | 11.19,98,104,0,404,72,Medium,27,18,No,No 232 | 5.16,115,60,0,119,114,Bad,38,14,No,No 233 | 8.09,132,69,0,123,122,Medium,27,11,No,No 234 | 13.14,137,80,10,24,105,Good,61,15,Yes,Yes 235 | 8.65,123,76,18,218,120,Medium,29,14,No,Yes 236 | 9.43,115,62,11,289,129,Good,56,16,No,Yes 237 | 5.53,126,32,8,95,132,Medium,50,17,Yes,Yes 238 | 9.32,141,34,16,361,108,Medium,69,10,Yes,Yes 239 | 9.62,151,28,8,499,135,Medium,48,10,Yes,Yes 240 | 7.36,121,24,0,200,133,Good,73,13,Yes,No 241 | 3.89,123,105,0,149,118,Bad,62,16,Yes,Yes 242 | 10.31,159,80,0,362,121,Medium,26,18,Yes,No 243 | 12.01,136,63,0,160,94,Medium,38,12,Yes,No 244 | 4.68,124,46,0,199,135,Medium,52,14,No,No 245 | 7.82,124,25,13,87,110,Medium,57,10,Yes,Yes 246 | 8.78,130,30,0,391,100,Medium,26,18,Yes,No 247 | 10.0,114,43,0,199,88,Good,57,10,No,Yes 248 | 6.9,120,56,20,266,90,Bad,78,18,Yes,Yes 249 | 5.04,123,114,0,298,151,Bad,34,16,Yes,No 250 | 5.36,111,52,0,12,101,Medium,61,11,Yes,Yes 251 | 5.05,125,67,0,86,117,Bad,65,11,Yes,No 252 | 9.16,137,105,10,435,156,Good,72,14,Yes,Yes 253 | 3.72,139,111,5,310,132,Bad,62,13,Yes,Yes 254 | 8.31,133,97,0,70,117,Medium,32,16,Yes,No 255 | 5.64,124,24,5,288,122,Medium,57,12,No,Yes 256 | 9.58,108,104,23,353,129,Good,37,17,Yes,Yes 257 | 7.71,123,81,8,198,81,Bad,80,15,Yes,Yes 258 | 4.2,147,40,0,277,144,Medium,73,10,Yes,No 259 | 8.67,125,62,14,477,112,Medium,80,13,Yes,Yes 260 | 3.47,108,38,0,251,81,Bad,72,14,No,No 261 | 5.12,123,36,10,467,100,Bad,74,11,No,Yes 262 | 7.67,129,117,8,400,101,Bad,36,10,Yes,Yes 263 | 5.71,121,42,4,188,118,Medium,54,15,Yes,Yes 264 | 6.37,120,77,15,86,132,Medium,48,18,Yes,Yes 265 | 7.77,116,26,6,434,115,Medium,25,17,Yes,Yes 266 | 6.95,128,29,5,324,159,Good,31,15,Yes,Yes 267 | 5.31,130,35,10,402,129,Bad,39,17,Yes,Yes 268 | 9.1,128,93,12,343,112,Good,73,17,No,Yes 269 | 5.83,134,82,7,473,112,Bad,51,12,No,Yes 270 | 6.53,123,57,0,66,105,Medium,39,11,Yes,No 271 | 5.01,159,69,0,438,166,Medium,46,17,Yes,No 272 | 11.99,119,26,0,284,89,Good,26,10,Yes,No 273 | 4.55,111,56,0,504,110,Medium,62,16,Yes,No 274 | 12.98,113,33,0,14,63,Good,38,12,Yes,No 275 | 10.04,116,106,8,244,86,Medium,58,12,Yes,Yes 276 | 7.22,135,93,2,67,119,Medium,34,11,Yes,Yes 277 | 6.67,107,119,11,210,132,Medium,53,11,Yes,Yes 278 | 6.93,135,69,14,296,130,Medium,73,15,Yes,Yes 279 | 7.8,136,48,12,326,125,Medium,36,16,Yes,Yes 280 | 7.22,114,113,2,129,151,Good,40,15,No,Yes 281 | 3.42,141,57,13,376,158,Medium,64,18,Yes,Yes 282 | 2.86,121,86,10,496,145,Bad,51,10,Yes,Yes 283 | 11.19,122,69,7,303,105,Good,45,16,No,Yes 284 | 7.74,150,96,0,80,154,Good,61,11,Yes,No 285 | 5.36,135,110,0,112,117,Medium,80,16,No,No 286 | 6.97,106,46,11,414,96,Bad,79,17,No,No 287 | 7.6,146,26,11,261,131,Medium,39,10,Yes,Yes 288 | 7.53,117,118,11,429,113,Medium,67,18,No,Yes 289 | 6.88,95,44,4,208,72,Bad,44,17,Yes,Yes 290 | 6.98,116,40,0,74,97,Medium,76,15,No,No 291 | 8.75,143,77,25,448,156,Medium,43,17,Yes,Yes 292 | 9.49,107,111,14,400,103,Medium,41,11,No,Yes 293 | 6.64,118,70,0,106,89,Bad,39,17,Yes,No 294 | 11.82,113,66,16,322,74,Good,76,15,Yes,Yes 295 | 11.28,123,84,0,74,89,Good,59,10,Yes,No 296 | 12.66,148,76,3,126,99,Good,60,11,Yes,Yes 297 | 4.21,118,35,14,502,137,Medium,79,10,No,Yes 298 | 8.21,127,44,13,160,123,Good,63,18,Yes,Yes 299 | 3.07,118,83,13,276,104,Bad,75,10,Yes,Yes 300 | 10.98,148,63,0,312,130,Good,63,15,Yes,No 301 | 9.4,135,40,17,497,96,Medium,54,17,No,Yes 302 | 8.57,116,78,1,158,99,Medium,45,11,Yes,Yes 303 | 7.41,99,93,0,198,87,Medium,57,16,Yes,Yes 304 | 5.28,108,77,13,388,110,Bad,74,14,Yes,Yes 305 | 10.01,133,52,16,290,99,Medium,43,11,Yes,Yes 306 | 11.93,123,98,12,408,134,Good,29,10,Yes,Yes 307 | 8.03,115,29,26,394,132,Medium,33,13,Yes,Yes 308 | 4.78,131,32,1,85,133,Medium,48,12,Yes,Yes 309 | 5.9,138,92,0,13,120,Bad,61,12,Yes,No 310 | 9.24,126,80,19,436,126,Medium,52,10,Yes,Yes 311 | 11.18,131,111,13,33,80,Bad,68,18,Yes,Yes 312 | 9.53,175,65,29,419,166,Medium,53,12,Yes,Yes 313 | 6.15,146,68,12,328,132,Bad,51,14,Yes,Yes 314 | 6.8,137,117,5,337,135,Bad,38,10,Yes,Yes 315 | 9.33,103,81,3,491,54,Medium,66,13,Yes,No 316 | 7.72,133,33,10,333,129,Good,71,14,Yes,Yes 317 | 6.39,131,21,8,220,171,Good,29,14,Yes,Yes 318 | 15.63,122,36,5,369,72,Good,35,10,Yes,Yes 319 | 6.41,142,30,0,472,136,Good,80,15,No,No 320 | 10.08,116,72,10,456,130,Good,41,14,No,Yes 321 | 6.97,127,45,19,459,129,Medium,57,11,No,Yes 322 | 5.86,136,70,12,171,152,Medium,44,18,Yes,Yes 323 | 7.52,123,39,5,499,98,Medium,34,15,Yes,No 324 | 9.16,140,50,10,300,139,Good,60,15,Yes,Yes 325 | 10.36,107,105,18,428,103,Medium,34,12,Yes,Yes 326 | 2.66,136,65,4,133,150,Bad,53,13,Yes,Yes 327 | 11.7,144,69,11,131,104,Medium,47,11,Yes,Yes 328 | 4.69,133,30,0,152,122,Medium,53,17,Yes,No 329 | 6.23,112,38,17,316,104,Medium,80,16,Yes,Yes 330 | 3.15,117,66,1,65,111,Bad,55,11,Yes,Yes 331 | 11.27,100,54,9,433,89,Good,45,12,Yes,Yes 332 | 4.99,122,59,0,501,112,Bad,32,14,No,No 333 | 10.1,135,63,15,213,134,Medium,32,10,Yes,Yes 334 | 5.74,106,33,20,354,104,Medium,61,12,Yes,Yes 335 | 5.87,136,60,7,303,147,Medium,41,10,Yes,Yes 336 | 7.63,93,117,9,489,83,Bad,42,13,Yes,Yes 337 | 6.18,120,70,15,464,110,Medium,72,15,Yes,Yes 338 | 5.17,138,35,6,60,143,Bad,28,18,Yes,No 339 | 8.61,130,38,0,283,102,Medium,80,15,Yes,No 340 | 5.97,112,24,0,164,101,Medium,45,11,Yes,No 341 | 11.54,134,44,4,219,126,Good,44,15,Yes,Yes 342 | 7.5,140,29,0,105,91,Bad,43,16,Yes,No 343 | 7.38,98,120,0,268,93,Medium,72,10,No,No 344 | 7.81,137,102,13,422,118,Medium,71,10,No,Yes 345 | 5.99,117,42,10,371,121,Bad,26,14,Yes,Yes 346 | 8.43,138,80,0,108,126,Good,70,13,No,Yes 347 | 4.81,121,68,0,279,149,Good,79,12,Yes,No 348 | 8.97,132,107,0,144,125,Medium,33,13,No,No 349 | 6.88,96,39,0,161,112,Good,27,14,No,No 350 | 12.57,132,102,20,459,107,Good,49,11,Yes,Yes 351 | 9.32,134,27,18,467,96,Medium,49,14,No,Yes 352 | 8.64,111,101,17,266,91,Medium,63,17,No,Yes 353 | 10.44,124,115,16,458,105,Medium,62,16,No,Yes 354 | 13.44,133,103,14,288,122,Good,61,17,Yes,Yes 355 | 9.45,107,67,12,430,92,Medium,35,12,No,Yes 356 | 5.3,133,31,1,80,145,Medium,42,18,Yes,Yes 357 | 7.02,130,100,0,306,146,Good,42,11,Yes,No 358 | 3.58,142,109,0,111,164,Good,72,12,Yes,No 359 | 13.36,103,73,3,276,72,Medium,34,15,Yes,Yes 360 | 4.17,123,96,10,71,118,Bad,69,11,Yes,Yes 361 | 3.13,130,62,11,396,130,Bad,66,14,Yes,Yes 362 | 8.77,118,86,7,265,114,Good,52,15,No,Yes 363 | 8.68,131,25,10,183,104,Medium,56,15,No,Yes 364 | 5.25,131,55,0,26,110,Bad,79,12,Yes,Yes 365 | 10.26,111,75,1,377,108,Good,25,12,Yes,No 366 | 10.5,122,21,16,488,131,Good,30,14,Yes,Yes 367 | 6.53,154,30,0,122,162,Medium,57,17,No,No 368 | 5.98,124,56,11,447,134,Medium,53,12,No,Yes 369 | 14.37,95,106,0,256,53,Good,52,17,Yes,No 370 | 10.71,109,22,10,348,79,Good,74,14,No,Yes 371 | 10.26,135,100,22,463,122,Medium,36,14,Yes,Yes 372 | 7.68,126,41,22,403,119,Bad,42,12,Yes,Yes 373 | 9.08,152,81,0,191,126,Medium,54,16,Yes,No 374 | 7.8,121,50,0,508,98,Medium,65,11,No,No 375 | 5.58,137,71,0,402,116,Medium,78,17,Yes,No 376 | 9.44,131,47,7,90,118,Medium,47,12,Yes,Yes 377 | 7.9,132,46,4,206,124,Medium,73,11,Yes,No 378 | 16.27,141,60,19,319,92,Good,44,11,Yes,Yes 379 | 6.81,132,61,0,263,125,Medium,41,12,No,No 380 | 6.11,133,88,3,105,119,Medium,79,12,Yes,Yes 381 | 5.81,125,111,0,404,107,Bad,54,15,Yes,No 382 | 9.64,106,64,10,17,89,Medium,68,17,Yes,Yes 383 | 3.9,124,65,21,496,151,Bad,77,13,Yes,Yes 384 | 4.95,121,28,19,315,121,Medium,66,14,Yes,Yes 385 | 9.35,98,117,0,76,68,Medium,63,10,Yes,No 386 | 12.85,123,37,15,348,112,Good,28,12,Yes,Yes 387 | 5.87,131,73,13,455,132,Medium,62,17,Yes,Yes 388 | 5.32,152,116,0,170,160,Medium,39,16,Yes,No 389 | 8.67,142,73,14,238,115,Medium,73,14,No,Yes 390 | 8.14,135,89,11,245,78,Bad,79,16,Yes,Yes 391 | 8.44,128,42,8,328,107,Medium,35,12,Yes,Yes 392 | 5.47,108,75,9,61,111,Medium,67,12,Yes,Yes 393 | 6.1,153,63,0,49,124,Bad,56,16,Yes,No 394 | 4.53,129,42,13,315,130,Bad,34,13,Yes,Yes 395 | 5.57,109,51,10,26,120,Medium,30,17,No,Yes 396 | 5.35,130,58,19,366,139,Bad,33,16,Yes,Yes 397 | 12.57,138,108,17,203,128,Good,33,14,Yes,Yes 398 | 6.14,139,23,3,37,120,Medium,55,11,No,Yes 399 | 7.41,162,26,12,368,159,Medium,40,18,Yes,Yes 400 | 5.94,100,79,7,284,95,Bad,50,12,Yes,Yes 401 | 9.71,134,37,0,27,120,Good,49,16,Yes,Yes 402 | -------------------------------------------------------------------------------- /data/csv/Auto.csv: -------------------------------------------------------------------------------- 1 | mpg,cylinders,displacement,horsepower,weight,acceleration,year,origin,name 2 | 18.0,8,307.0,130,3504,12.0,70,1,chevrolet chevelle malibu 3 | 15.0,8,350.0,165,3693,11.5,70,1,buick skylark 320 4 | 18.0,8,318.0,150,3436,11.0,70,1,plymouth satellite 5 | 16.0,8,304.0,150,3433,12.0,70,1,amc rebel sst 6 | 17.0,8,302.0,140,3449,10.5,70,1,ford torino 7 | 15.0,8,429.0,198,4341,10.0,70,1,ford galaxie 500 8 | 14.0,8,454.0,220,4354,9.0,70,1,chevrolet impala 9 | 14.0,8,440.0,215,4312,8.5,70,1,plymouth fury iii 10 | 14.0,8,455.0,225,4425,10.0,70,1,pontiac catalina 11 | 15.0,8,390.0,190,3850,8.5,70,1,amc ambassador dpl 12 | 15.0,8,383.0,170,3563,10.0,70,1,dodge challenger se 13 | 14.0,8,340.0,160,3609,8.0,70,1,plymouth 'cuda 340 14 | 15.0,8,400.0,150,3761,9.5,70,1,chevrolet monte carlo 15 | 14.0,8,455.0,225,3086,10.0,70,1,buick estate wagon (sw) 16 | 24.0,4,113.0,95,2372,15.0,70,3,toyota corona mark ii 17 | 22.0,6,198.0,95,2833,15.5,70,1,plymouth duster 18 | 18.0,6,199.0,97,2774,15.5,70,1,amc hornet 19 | 21.0,6,200.0,85,2587,16.0,70,1,ford maverick 20 | 27.0,4,97.0,88,2130,14.5,70,3,datsun pl510 21 | 26.0,4,97.0,46,1835,20.5,70,2,volkswagen 1131 deluxe sedan 22 | 25.0,4,110.0,87,2672,17.5,70,2,peugeot 504 23 | 24.0,4,107.0,90,2430,14.5,70,2,audi 100 ls 24 | 25.0,4,104.0,95,2375,17.5,70,2,saab 99e 25 | 26.0,4,121.0,113,2234,12.5,70,2,bmw 2002 26 | 21.0,6,199.0,90,2648,15.0,70,1,amc gremlin 27 | 10.0,8,360.0,215,4615,14.0,70,1,ford f250 28 | 10.0,8,307.0,200,4376,15.0,70,1,chevy c20 29 | 11.0,8,318.0,210,4382,13.5,70,1,dodge d200 30 | 9.0,8,304.0,193,4732,18.5,70,1,hi 1200d 31 | 27.0,4,97.0,88,2130,14.5,71,3,datsun pl510 32 | 28.0,4,140.0,90,2264,15.5,71,1,chevrolet vega 2300 33 | 25.0,4,113.0,95,2228,14.0,71,3,toyota corona 34 | 19.0,6,232.0,100,2634,13.0,71,1,amc gremlin 35 | 16.0,6,225.0,105,3439,15.5,71,1,plymouth satellite custom 36 | 17.0,6,250.0,100,3329,15.5,71,1,chevrolet chevelle malibu 37 | 19.0,6,250.0,88,3302,15.5,71,1,ford torino 500 38 | 18.0,6,232.0,100,3288,15.5,71,1,amc matador 39 | 14.0,8,350.0,165,4209,12.0,71,1,chevrolet impala 40 | 14.0,8,400.0,175,4464,11.5,71,1,pontiac catalina brougham 41 | 14.0,8,351.0,153,4154,13.5,71,1,ford galaxie 500 42 | 14.0,8,318.0,150,4096,13.0,71,1,plymouth fury iii 43 | 12.0,8,383.0,180,4955,11.5,71,1,dodge monaco (sw) 44 | 13.0,8,400.0,170,4746,12.0,71,1,ford country squire (sw) 45 | 13.0,8,400.0,175,5140,12.0,71,1,pontiac safari (sw) 46 | 18.0,6,258.0,110,2962,13.5,71,1,amc hornet sportabout (sw) 47 | 22.0,4,140.0,72,2408,19.0,71,1,chevrolet vega (sw) 48 | 19.0,6,250.0,100,3282,15.0,71,1,pontiac firebird 49 | 18.0,6,250.0,88,3139,14.5,71,1,ford mustang 50 | 23.0,4,122.0,86,2220,14.0,71,1,mercury capri 2000 51 | 28.0,4,116.0,90,2123,14.0,71,2,opel 1900 52 | 30.0,4,79.0,70,2074,19.5,71,2,peugeot 304 53 | 30.0,4,88.0,76,2065,14.5,71,2,fiat 124b 54 | 31.0,4,71.0,65,1773,19.0,71,3,toyota corolla 1200 55 | 35.0,4,72.0,69,1613,18.0,71,3,datsun 1200 56 | 27.0,4,97.0,60,1834,19.0,71,2,volkswagen model 111 57 | 26.0,4,91.0,70,1955,20.5,71,1,plymouth cricket 58 | 24.0,4,113.0,95,2278,15.5,72,3,toyota corona hardtop 59 | 25.0,4,97.5,80,2126,17.0,72,1,dodge colt hardtop 60 | 23.0,4,97.0,54,2254,23.5,72,2,volkswagen type 3 61 | 20.0,4,140.0,90,2408,19.5,72,1,chevrolet vega 62 | 21.0,4,122.0,86,2226,16.5,72,1,ford pinto runabout 63 | 13.0,8,350.0,165,4274,12.0,72,1,chevrolet impala 64 | 14.0,8,400.0,175,4385,12.0,72,1,pontiac catalina 65 | 15.0,8,318.0,150,4135,13.5,72,1,plymouth fury iii 66 | 14.0,8,351.0,153,4129,13.0,72,1,ford galaxie 500 67 | 17.0,8,304.0,150,3672,11.5,72,1,amc ambassador sst 68 | 11.0,8,429.0,208,4633,11.0,72,1,mercury marquis 69 | 13.0,8,350.0,155,4502,13.5,72,1,buick lesabre custom 70 | 12.0,8,350.0,160,4456,13.5,72,1,oldsmobile delta 88 royale 71 | 13.0,8,400.0,190,4422,12.5,72,1,chrysler newport royal 72 | 19.0,3,70.0,97,2330,13.5,72,3,mazda rx2 coupe 73 | 15.0,8,304.0,150,3892,12.5,72,1,amc matador (sw) 74 | 13.0,8,307.0,130,4098,14.0,72,1,chevrolet chevelle concours (sw) 75 | 13.0,8,302.0,140,4294,16.0,72,1,ford gran torino (sw) 76 | 14.0,8,318.0,150,4077,14.0,72,1,plymouth satellite custom (sw) 77 | 18.0,4,121.0,112,2933,14.5,72,2,volvo 145e (sw) 78 | 22.0,4,121.0,76,2511,18.0,72,2,volkswagen 411 (sw) 79 | 21.0,4,120.0,87,2979,19.5,72,2,peugeot 504 (sw) 80 | 26.0,4,96.0,69,2189,18.0,72,2,renault 12 (sw) 81 | 22.0,4,122.0,86,2395,16.0,72,1,ford pinto (sw) 82 | 28.0,4,97.0,92,2288,17.0,72,3,datsun 510 (sw) 83 | 23.0,4,120.0,97,2506,14.5,72,3,toyouta corona mark ii (sw) 84 | 28.0,4,98.0,80,2164,15.0,72,1,dodge colt (sw) 85 | 27.0,4,97.0,88,2100,16.5,72,3,toyota corolla 1600 (sw) 86 | 13.0,8,350.0,175,4100,13.0,73,1,buick century 350 87 | 14.0,8,304.0,150,3672,11.5,73,1,amc matador 88 | 13.0,8,350.0,145,3988,13.0,73,1,chevrolet malibu 89 | 14.0,8,302.0,137,4042,14.5,73,1,ford gran torino 90 | 15.0,8,318.0,150,3777,12.5,73,1,dodge coronet custom 91 | 12.0,8,429.0,198,4952,11.5,73,1,mercury marquis brougham 92 | 13.0,8,400.0,150,4464,12.0,73,1,chevrolet caprice classic 93 | 13.0,8,351.0,158,4363,13.0,73,1,ford ltd 94 | 14.0,8,318.0,150,4237,14.5,73,1,plymouth fury gran sedan 95 | 13.0,8,440.0,215,4735,11.0,73,1,chrysler new yorker brougham 96 | 12.0,8,455.0,225,4951,11.0,73,1,buick electra 225 custom 97 | 13.0,8,360.0,175,3821,11.0,73,1,amc ambassador brougham 98 | 18.0,6,225.0,105,3121,16.5,73,1,plymouth valiant 99 | 16.0,6,250.0,100,3278,18.0,73,1,chevrolet nova custom 100 | 18.0,6,232.0,100,2945,16.0,73,1,amc hornet 101 | 18.0,6,250.0,88,3021,16.5,73,1,ford maverick 102 | 23.0,6,198.0,95,2904,16.0,73,1,plymouth duster 103 | 26.0,4,97.0,46,1950,21.0,73,2,volkswagen super beetle 104 | 11.0,8,400.0,150,4997,14.0,73,1,chevrolet impala 105 | 12.0,8,400.0,167,4906,12.5,73,1,ford country 106 | 13.0,8,360.0,170,4654,13.0,73,1,plymouth custom suburb 107 | 12.0,8,350.0,180,4499,12.5,73,1,oldsmobile vista cruiser 108 | 18.0,6,232.0,100,2789,15.0,73,1,amc gremlin 109 | 20.0,4,97.0,88,2279,19.0,73,3,toyota carina 110 | 21.0,4,140.0,72,2401,19.5,73,1,chevrolet vega 111 | 22.0,4,108.0,94,2379,16.5,73,3,datsun 610 112 | 18.0,3,70.0,90,2124,13.5,73,3,maxda rx3 113 | 19.0,4,122.0,85,2310,18.5,73,1,ford pinto 114 | 21.0,6,155.0,107,2472,14.0,73,1,mercury capri v6 115 | 26.0,4,98.0,90,2265,15.5,73,2,fiat 124 sport coupe 116 | 15.0,8,350.0,145,4082,13.0,73,1,chevrolet monte carlo s 117 | 16.0,8,400.0,230,4278,9.5,73,1,pontiac grand prix 118 | 29.0,4,68.0,49,1867,19.5,73,2,fiat 128 119 | 24.0,4,116.0,75,2158,15.5,73,2,opel manta 120 | 20.0,4,114.0,91,2582,14.0,73,2,audi 100ls 121 | 19.0,4,121.0,112,2868,15.5,73,2,volvo 144ea 122 | 15.0,8,318.0,150,3399,11.0,73,1,dodge dart custom 123 | 24.0,4,121.0,110,2660,14.0,73,2,saab 99le 124 | 20.0,6,156.0,122,2807,13.5,73,3,toyota mark ii 125 | 11.0,8,350.0,180,3664,11.0,73,1,oldsmobile omega 126 | 20.0,6,198.0,95,3102,16.5,74,1,plymouth duster 127 | 19.0,6,232.0,100,2901,16.0,74,1,amc hornet 128 | 15.0,6,250.0,100,3336,17.0,74,1,chevrolet nova 129 | 31.0,4,79.0,67,1950,19.0,74,3,datsun b210 130 | 26.0,4,122.0,80,2451,16.5,74,1,ford pinto 131 | 32.0,4,71.0,65,1836,21.0,74,3,toyota corolla 1200 132 | 25.0,4,140.0,75,2542,17.0,74,1,chevrolet vega 133 | 16.0,6,250.0,100,3781,17.0,74,1,chevrolet chevelle malibu classic 134 | 16.0,6,258.0,110,3632,18.0,74,1,amc matador 135 | 18.0,6,225.0,105,3613,16.5,74,1,plymouth satellite sebring 136 | 16.0,8,302.0,140,4141,14.0,74,1,ford gran torino 137 | 13.0,8,350.0,150,4699,14.5,74,1,buick century luxus (sw) 138 | 14.0,8,318.0,150,4457,13.5,74,1,dodge coronet custom (sw) 139 | 14.0,8,302.0,140,4638,16.0,74,1,ford gran torino (sw) 140 | 14.0,8,304.0,150,4257,15.5,74,1,amc matador (sw) 141 | 29.0,4,98.0,83,2219,16.5,74,2,audi fox 142 | 26.0,4,79.0,67,1963,15.5,74,2,volkswagen dasher 143 | 26.0,4,97.0,78,2300,14.5,74,2,opel manta 144 | 31.0,4,76.0,52,1649,16.5,74,3,toyota corona 145 | 32.0,4,83.0,61,2003,19.0,74,3,datsun 710 146 | 28.0,4,90.0,75,2125,14.5,74,1,dodge colt 147 | 24.0,4,90.0,75,2108,15.5,74,2,fiat 128 148 | 26.0,4,116.0,75,2246,14.0,74,2,fiat 124 tc 149 | 24.0,4,120.0,97,2489,15.0,74,3,honda civic 150 | 26.0,4,108.0,93,2391,15.5,74,3,subaru 151 | 31.0,4,79.0,67,2000,16.0,74,2,fiat x1.9 152 | 19.0,6,225.0,95,3264,16.0,75,1,plymouth valiant custom 153 | 18.0,6,250.0,105,3459,16.0,75,1,chevrolet nova 154 | 15.0,6,250.0,72,3432,21.0,75,1,mercury monarch 155 | 15.0,6,250.0,72,3158,19.5,75,1,ford maverick 156 | 16.0,8,400.0,170,4668,11.5,75,1,pontiac catalina 157 | 15.0,8,350.0,145,4440,14.0,75,1,chevrolet bel air 158 | 16.0,8,318.0,150,4498,14.5,75,1,plymouth grand fury 159 | 14.0,8,351.0,148,4657,13.5,75,1,ford ltd 160 | 17.0,6,231.0,110,3907,21.0,75,1,buick century 161 | 16.0,6,250.0,105,3897,18.5,75,1,chevroelt chevelle malibu 162 | 15.0,6,258.0,110,3730,19.0,75,1,amc matador 163 | 18.0,6,225.0,95,3785,19.0,75,1,plymouth fury 164 | 21.0,6,231.0,110,3039,15.0,75,1,buick skyhawk 165 | 20.0,8,262.0,110,3221,13.5,75,1,chevrolet monza 2+2 166 | 13.0,8,302.0,129,3169,12.0,75,1,ford mustang ii 167 | 29.0,4,97.0,75,2171,16.0,75,3,toyota corolla 168 | 23.0,4,140.0,83,2639,17.0,75,1,ford pinto 169 | 20.0,6,232.0,100,2914,16.0,75,1,amc gremlin 170 | 23.0,4,140.0,78,2592,18.5,75,1,pontiac astro 171 | 24.0,4,134.0,96,2702,13.5,75,3,toyota corona 172 | 25.0,4,90.0,71,2223,16.5,75,2,volkswagen dasher 173 | 24.0,4,119.0,97,2545,17.0,75,3,datsun 710 174 | 18.0,6,171.0,97,2984,14.5,75,1,ford pinto 175 | 29.0,4,90.0,70,1937,14.0,75,2,volkswagen rabbit 176 | 19.0,6,232.0,90,3211,17.0,75,1,amc pacer 177 | 23.0,4,115.0,95,2694,15.0,75,2,audi 100ls 178 | 23.0,4,120.0,88,2957,17.0,75,2,peugeot 504 179 | 22.0,4,121.0,98,2945,14.5,75,2,volvo 244dl 180 | 25.0,4,121.0,115,2671,13.5,75,2,saab 99le 181 | 33.0,4,91.0,53,1795,17.5,75,3,honda civic cvcc 182 | 28.0,4,107.0,86,2464,15.5,76,2,fiat 131 183 | 25.0,4,116.0,81,2220,16.9,76,2,opel 1900 184 | 25.0,4,140.0,92,2572,14.9,76,1,capri ii 185 | 26.0,4,98.0,79,2255,17.7,76,1,dodge colt 186 | 27.0,4,101.0,83,2202,15.3,76,2,renault 12tl 187 | 17.5,8,305.0,140,4215,13.0,76,1,chevrolet chevelle malibu classic 188 | 16.0,8,318.0,150,4190,13.0,76,1,dodge coronet brougham 189 | 15.5,8,304.0,120,3962,13.9,76,1,amc matador 190 | 14.5,8,351.0,152,4215,12.8,76,1,ford gran torino 191 | 22.0,6,225.0,100,3233,15.4,76,1,plymouth valiant 192 | 22.0,6,250.0,105,3353,14.5,76,1,chevrolet nova 193 | 24.0,6,200.0,81,3012,17.6,76,1,ford maverick 194 | 22.5,6,232.0,90,3085,17.6,76,1,amc hornet 195 | 29.0,4,85.0,52,2035,22.2,76,1,chevrolet chevette 196 | 24.5,4,98.0,60,2164,22.1,76,1,chevrolet woody 197 | 29.0,4,90.0,70,1937,14.2,76,2,vw rabbit 198 | 33.0,4,91.0,53,1795,17.4,76,3,honda civic 199 | 20.0,6,225.0,100,3651,17.7,76,1,dodge aspen se 200 | 18.0,6,250.0,78,3574,21.0,76,1,ford granada ghia 201 | 18.5,6,250.0,110,3645,16.2,76,1,pontiac ventura sj 202 | 17.5,6,258.0,95,3193,17.8,76,1,amc pacer d/l 203 | 29.5,4,97.0,71,1825,12.2,76,2,volkswagen rabbit 204 | 32.0,4,85.0,70,1990,17.0,76,3,datsun b-210 205 | 28.0,4,97.0,75,2155,16.4,76,3,toyota corolla 206 | 26.5,4,140.0,72,2565,13.6,76,1,ford pinto 207 | 20.0,4,130.0,102,3150,15.7,76,2,volvo 245 208 | 13.0,8,318.0,150,3940,13.2,76,1,plymouth volare premier v8 209 | 19.0,4,120.0,88,3270,21.9,76,2,peugeot 504 210 | 19.0,6,156.0,108,2930,15.5,76,3,toyota mark ii 211 | 16.5,6,168.0,120,3820,16.7,76,2,mercedes-benz 280s 212 | 16.5,8,350.0,180,4380,12.1,76,1,cadillac seville 213 | 13.0,8,350.0,145,4055,12.0,76,1,chevy c10 214 | 13.0,8,302.0,130,3870,15.0,76,1,ford f108 215 | 13.0,8,318.0,150,3755,14.0,76,1,dodge d100 216 | 31.5,4,98.0,68,2045,18.5,77,3,honda accord cvcc 217 | 30.0,4,111.0,80,2155,14.8,77,1,buick opel isuzu deluxe 218 | 36.0,4,79.0,58,1825,18.6,77,2,renault 5 gtl 219 | 25.5,4,122.0,96,2300,15.5,77,1,plymouth arrow gs 220 | 33.5,4,85.0,70,1945,16.8,77,3,datsun f-10 hatchback 221 | 17.5,8,305.0,145,3880,12.5,77,1,chevrolet caprice classic 222 | 17.0,8,260.0,110,4060,19.0,77,1,oldsmobile cutlass supreme 223 | 15.5,8,318.0,145,4140,13.7,77,1,dodge monaco brougham 224 | 15.0,8,302.0,130,4295,14.9,77,1,mercury cougar brougham 225 | 17.5,6,250.0,110,3520,16.4,77,1,chevrolet concours 226 | 20.5,6,231.0,105,3425,16.9,77,1,buick skylark 227 | 19.0,6,225.0,100,3630,17.7,77,1,plymouth volare custom 228 | 18.5,6,250.0,98,3525,19.0,77,1,ford granada 229 | 16.0,8,400.0,180,4220,11.1,77,1,pontiac grand prix lj 230 | 15.5,8,350.0,170,4165,11.4,77,1,chevrolet monte carlo landau 231 | 15.5,8,400.0,190,4325,12.2,77,1,chrysler cordoba 232 | 16.0,8,351.0,149,4335,14.5,77,1,ford thunderbird 233 | 29.0,4,97.0,78,1940,14.5,77,2,volkswagen rabbit custom 234 | 24.5,4,151.0,88,2740,16.0,77,1,pontiac sunbird coupe 235 | 26.0,4,97.0,75,2265,18.2,77,3,toyota corolla liftback 236 | 25.5,4,140.0,89,2755,15.8,77,1,ford mustang ii 2+2 237 | 30.5,4,98.0,63,2051,17.0,77,1,chevrolet chevette 238 | 33.5,4,98.0,83,2075,15.9,77,1,dodge colt m/m 239 | 30.0,4,97.0,67,1985,16.4,77,3,subaru dl 240 | 30.5,4,97.0,78,2190,14.1,77,2,volkswagen dasher 241 | 22.0,6,146.0,97,2815,14.5,77,3,datsun 810 242 | 21.5,4,121.0,110,2600,12.8,77,2,bmw 320i 243 | 21.5,3,80.0,110,2720,13.5,77,3,mazda rx-4 244 | 43.1,4,90.0,48,1985,21.5,78,2,volkswagen rabbit custom diesel 245 | 36.1,4,98.0,66,1800,14.4,78,1,ford fiesta 246 | 32.8,4,78.0,52,1985,19.4,78,3,mazda glc deluxe 247 | 39.4,4,85.0,70,2070,18.6,78,3,datsun b210 gx 248 | 36.1,4,91.0,60,1800,16.4,78,3,honda civic cvcc 249 | 19.9,8,260.0,110,3365,15.5,78,1,oldsmobile cutlass salon brougham 250 | 19.4,8,318.0,140,3735,13.2,78,1,dodge diplomat 251 | 20.2,8,302.0,139,3570,12.8,78,1,mercury monarch ghia 252 | 19.2,6,231.0,105,3535,19.2,78,1,pontiac phoenix lj 253 | 20.5,6,200.0,95,3155,18.2,78,1,chevrolet malibu 254 | 20.2,6,200.0,85,2965,15.8,78,1,ford fairmont (auto) 255 | 25.1,4,140.0,88,2720,15.4,78,1,ford fairmont (man) 256 | 20.5,6,225.0,100,3430,17.2,78,1,plymouth volare 257 | 19.4,6,232.0,90,3210,17.2,78,1,amc concord 258 | 20.6,6,231.0,105,3380,15.8,78,1,buick century special 259 | 20.8,6,200.0,85,3070,16.7,78,1,mercury zephyr 260 | 18.6,6,225.0,110,3620,18.7,78,1,dodge aspen 261 | 18.1,6,258.0,120,3410,15.1,78,1,amc concord d/l 262 | 19.2,8,305.0,145,3425,13.2,78,1,chevrolet monte carlo landau 263 | 17.7,6,231.0,165,3445,13.4,78,1,buick regal sport coupe (turbo) 264 | 18.1,8,302.0,139,3205,11.2,78,1,ford futura 265 | 17.5,8,318.0,140,4080,13.7,78,1,dodge magnum xe 266 | 30.0,4,98.0,68,2155,16.5,78,1,chevrolet chevette 267 | 27.5,4,134.0,95,2560,14.2,78,3,toyota corona 268 | 27.2,4,119.0,97,2300,14.7,78,3,datsun 510 269 | 30.9,4,105.0,75,2230,14.5,78,1,dodge omni 270 | 21.1,4,134.0,95,2515,14.8,78,3,toyota celica gt liftback 271 | 23.2,4,156.0,105,2745,16.7,78,1,plymouth sapporo 272 | 23.8,4,151.0,85,2855,17.6,78,1,oldsmobile starfire sx 273 | 23.9,4,119.0,97,2405,14.9,78,3,datsun 200-sx 274 | 20.3,5,131.0,103,2830,15.9,78,2,audi 5000 275 | 17.0,6,163.0,125,3140,13.6,78,2,volvo 264gl 276 | 21.6,4,121.0,115,2795,15.7,78,2,saab 99gle 277 | 16.2,6,163.0,133,3410,15.8,78,2,peugeot 604sl 278 | 31.5,4,89.0,71,1990,14.9,78,2,volkswagen scirocco 279 | 29.5,4,98.0,68,2135,16.6,78,3,honda accord lx 280 | 21.5,6,231.0,115,3245,15.4,79,1,pontiac lemans v6 281 | 19.8,6,200.0,85,2990,18.2,79,1,mercury zephyr 6 282 | 22.3,4,140.0,88,2890,17.3,79,1,ford fairmont 4 283 | 20.2,6,232.0,90,3265,18.2,79,1,amc concord dl 6 284 | 20.6,6,225.0,110,3360,16.6,79,1,dodge aspen 6 285 | 17.0,8,305.0,130,3840,15.4,79,1,chevrolet caprice classic 286 | 17.6,8,302.0,129,3725,13.4,79,1,ford ltd landau 287 | 16.5,8,351.0,138,3955,13.2,79,1,mercury grand marquis 288 | 18.2,8,318.0,135,3830,15.2,79,1,dodge st. regis 289 | 16.9,8,350.0,155,4360,14.9,79,1,buick estate wagon (sw) 290 | 15.5,8,351.0,142,4054,14.3,79,1,ford country squire (sw) 291 | 19.2,8,267.0,125,3605,15.0,79,1,chevrolet malibu classic (sw) 292 | 18.5,8,360.0,150,3940,13.0,79,1,chrysler lebaron town @ country (sw) 293 | 31.9,4,89.0,71,1925,14.0,79,2,vw rabbit custom 294 | 34.1,4,86.0,65,1975,15.2,79,3,maxda glc deluxe 295 | 35.7,4,98.0,80,1915,14.4,79,1,dodge colt hatchback custom 296 | 27.4,4,121.0,80,2670,15.0,79,1,amc spirit dl 297 | 25.4,5,183.0,77,3530,20.1,79,2,mercedes benz 300d 298 | 23.0,8,350.0,125,3900,17.4,79,1,cadillac eldorado 299 | 27.2,4,141.0,71,3190,24.8,79,2,peugeot 504 300 | 23.9,8,260.0,90,3420,22.2,79,1,oldsmobile cutlass salon brougham 301 | 34.2,4,105.0,70,2200,13.2,79,1,plymouth horizon 302 | 34.5,4,105.0,70,2150,14.9,79,1,plymouth horizon tc3 303 | 31.8,4,85.0,65,2020,19.2,79,3,datsun 210 304 | 37.3,4,91.0,69,2130,14.7,79,2,fiat strada custom 305 | 28.4,4,151.0,90,2670,16.0,79,1,buick skylark limited 306 | 28.8,6,173.0,115,2595,11.3,79,1,chevrolet citation 307 | 26.8,6,173.0,115,2700,12.9,79,1,oldsmobile omega brougham 308 | 33.5,4,151.0,90,2556,13.2,79,1,pontiac phoenix 309 | 41.5,4,98.0,76,2144,14.7,80,2,vw rabbit 310 | 38.1,4,89.0,60,1968,18.8,80,3,toyota corolla tercel 311 | 32.1,4,98.0,70,2120,15.5,80,1,chevrolet chevette 312 | 37.2,4,86.0,65,2019,16.4,80,3,datsun 310 313 | 28.0,4,151.0,90,2678,16.5,80,1,chevrolet citation 314 | 26.4,4,140.0,88,2870,18.1,80,1,ford fairmont 315 | 24.3,4,151.0,90,3003,20.1,80,1,amc concord 316 | 19.1,6,225.0,90,3381,18.7,80,1,dodge aspen 317 | 34.3,4,97.0,78,2188,15.8,80,2,audi 4000 318 | 29.8,4,134.0,90,2711,15.5,80,3,toyota corona liftback 319 | 31.3,4,120.0,75,2542,17.5,80,3,mazda 626 320 | 37.0,4,119.0,92,2434,15.0,80,3,datsun 510 hatchback 321 | 32.2,4,108.0,75,2265,15.2,80,3,toyota corolla 322 | 46.6,4,86.0,65,2110,17.9,80,3,mazda glc 323 | 27.9,4,156.0,105,2800,14.4,80,1,dodge colt 324 | 40.8,4,85.0,65,2110,19.2,80,3,datsun 210 325 | 44.3,4,90.0,48,2085,21.7,80,2,vw rabbit c (diesel) 326 | 43.4,4,90.0,48,2335,23.7,80,2,vw dasher (diesel) 327 | 36.4,5,121.0,67,2950,19.9,80,2,audi 5000s (diesel) 328 | 30.0,4,146.0,67,3250,21.8,80,2,mercedes-benz 240d 329 | 44.6,4,91.0,67,1850,13.8,80,3,honda civic 1500 gl 330 | 33.8,4,97.0,67,2145,18.0,80,3,subaru dl 331 | 29.8,4,89.0,62,1845,15.3,80,2,vokswagen rabbit 332 | 32.7,6,168.0,132,2910,11.4,80,3,datsun 280-zx 333 | 23.7,3,70.0,100,2420,12.5,80,3,mazda rx-7 gs 334 | 35.0,4,122.0,88,2500,15.1,80,2,triumph tr7 coupe 335 | 32.4,4,107.0,72,2290,17.0,80,3,honda accord 336 | 27.2,4,135.0,84,2490,15.7,81,1,plymouth reliant 337 | 26.6,4,151.0,84,2635,16.4,81,1,buick skylark 338 | 25.8,4,156.0,92,2620,14.4,81,1,dodge aries wagon (sw) 339 | 23.5,6,173.0,110,2725,12.6,81,1,chevrolet citation 340 | 30.0,4,135.0,84,2385,12.9,81,1,plymouth reliant 341 | 39.1,4,79.0,58,1755,16.9,81,3,toyota starlet 342 | 39.0,4,86.0,64,1875,16.4,81,1,plymouth champ 343 | 35.1,4,81.0,60,1760,16.1,81,3,honda civic 1300 344 | 32.3,4,97.0,67,2065,17.8,81,3,subaru 345 | 37.0,4,85.0,65,1975,19.4,81,3,datsun 210 mpg 346 | 37.7,4,89.0,62,2050,17.3,81,3,toyota tercel 347 | 34.1,4,91.0,68,1985,16.0,81,3,mazda glc 4 348 | 34.7,4,105.0,63,2215,14.9,81,1,plymouth horizon 4 349 | 34.4,4,98.0,65,2045,16.2,81,1,ford escort 4w 350 | 29.9,4,98.0,65,2380,20.7,81,1,ford escort 2h 351 | 33.0,4,105.0,74,2190,14.2,81,2,volkswagen jetta 352 | 33.7,4,107.0,75,2210,14.4,81,3,honda prelude 353 | 32.4,4,108.0,75,2350,16.8,81,3,toyota corolla 354 | 32.9,4,119.0,100,2615,14.8,81,3,datsun 200sx 355 | 31.6,4,120.0,74,2635,18.3,81,3,mazda 626 356 | 28.1,4,141.0,80,3230,20.4,81,2,peugeot 505s turbo diesel 357 | 30.7,6,145.0,76,3160,19.6,81,2,volvo diesel 358 | 25.4,6,168.0,116,2900,12.6,81,3,toyota cressida 359 | 24.2,6,146.0,120,2930,13.8,81,3,datsun 810 maxima 360 | 22.4,6,231.0,110,3415,15.8,81,1,buick century 361 | 26.6,8,350.0,105,3725,19.0,81,1,oldsmobile cutlass ls 362 | 20.2,6,200.0,88,3060,17.1,81,1,ford granada gl 363 | 17.6,6,225.0,85,3465,16.6,81,1,chrysler lebaron salon 364 | 28.0,4,112.0,88,2605,19.6,82,1,chevrolet cavalier 365 | 27.0,4,112.0,88,2640,18.6,82,1,chevrolet cavalier wagon 366 | 34.0,4,112.0,88,2395,18.0,82,1,chevrolet cavalier 2-door 367 | 31.0,4,112.0,85,2575,16.2,82,1,pontiac j2000 se hatchback 368 | 29.0,4,135.0,84,2525,16.0,82,1,dodge aries se 369 | 27.0,4,151.0,90,2735,18.0,82,1,pontiac phoenix 370 | 24.0,4,140.0,92,2865,16.4,82,1,ford fairmont futura 371 | 36.0,4,105.0,74,1980,15.3,82,2,volkswagen rabbit l 372 | 37.0,4,91.0,68,2025,18.2,82,3,mazda glc custom l 373 | 31.0,4,91.0,68,1970,17.6,82,3,mazda glc custom 374 | 38.0,4,105.0,63,2125,14.7,82,1,plymouth horizon miser 375 | 36.0,4,98.0,70,2125,17.3,82,1,mercury lynx l 376 | 36.0,4,120.0,88,2160,14.5,82,3,nissan stanza xe 377 | 36.0,4,107.0,75,2205,14.5,82,3,honda accord 378 | 34.0,4,108.0,70,2245,16.9,82,3,toyota corolla 379 | 38.0,4,91.0,67,1965,15.0,82,3,honda civic 380 | 32.0,4,91.0,67,1965,15.7,82,3,honda civic (auto) 381 | 38.0,4,91.0,67,1995,16.2,82,3,datsun 310 gx 382 | 25.0,6,181.0,110,2945,16.4,82,1,buick century limited 383 | 38.0,6,262.0,85,3015,17.0,82,1,oldsmobile cutlass ciera (diesel) 384 | 26.0,4,156.0,92,2585,14.5,82,1,chrysler lebaron medallion 385 | 22.0,6,232.0,112,2835,14.7,82,1,ford granada l 386 | 32.0,4,144.0,96,2665,13.9,82,3,toyota celica gt 387 | 36.0,4,135.0,84,2370,13.0,82,1,dodge charger 2.2 388 | 27.0,4,151.0,90,2950,17.3,82,1,chevrolet camaro 389 | 27.0,4,140.0,86,2790,15.6,82,1,ford mustang gl 390 | 44.0,4,97.0,52,2130,24.6,82,2,vw pickup 391 | 32.0,4,135.0,84,2295,11.6,82,1,dodge rampage 392 | 28.0,4,120.0,79,2625,18.6,82,1,ford ranger 393 | 31.0,4,119.0,82,2720,19.4,82,1,chevy s-10 394 | -------------------------------------------------------------------------------- /data/csv/Credit.csv: -------------------------------------------------------------------------------- 1 | ID,Income,Limit,Rating,Cards,Age,Education,Gender,Student,Married,Ethnicity,Balance 2 | 1,14.891,3606,283,2,34,11, Male,No,Yes,Caucasian,333 3 | 2,106.025,6645,483,3,82,15,Female,Yes,Yes,Asian,903 4 | 3,104.593,7075,514,4,71,11, Male,No,No,Asian,580 5 | 4,148.924,9504,681,3,36,11,Female,No,No,Asian,964 6 | 5,55.882,4897,357,2,68,16, Male,No,Yes,Caucasian,331 7 | 6,80.18,8047,569,4,77,10, Male,No,No,Caucasian,1151 8 | 7,20.996,3388,259,2,37,12,Female,No,No,African American,203 9 | 8,71.408,7114,512,2,87,9, Male,No,No,Asian,872 10 | 9,15.125,3300,266,5,66,13,Female,No,No,Caucasian,279 11 | 10,71.061,6819,491,3,41,19,Female,Yes,Yes,African American,1350 12 | 11,63.095,8117,589,4,30,14, Male,No,Yes,Caucasian,1407 13 | 12,15.045,1311,138,3,64,16, Male,No,No,Caucasian,0 14 | 13,80.616,5308,394,1,57,7,Female,No,Yes,Asian,204 15 | 14,43.682,6922,511,1,49,9, Male,No,Yes,Caucasian,1081 16 | 15,19.144,3291,269,2,75,13,Female,No,No,African American,148 17 | 16,20.089,2525,200,3,57,15,Female,No,Yes,African American,0 18 | 17,53.598,3714,286,3,73,17,Female,No,Yes,African American,0 19 | 18,36.496,4378,339,3,69,15,Female,No,Yes,Asian,368 20 | 19,49.57,6384,448,1,28,9,Female,No,Yes,Asian,891 21 | 20,42.079,6626,479,2,44,9, Male,No,No,Asian,1048 22 | 21,17.7,2860,235,4,63,16,Female,No,No,Asian,89 23 | 22,37.348,6378,458,1,72,17,Female,No,No,Caucasian,968 24 | 23,20.103,2631,213,3,61,10, Male,No,Yes,African American,0 25 | 24,64.027,5179,398,5,48,8, Male,No,Yes,African American,411 26 | 25,10.742,1757,156,3,57,15,Female,No,No,Caucasian,0 27 | 26,14.09,4323,326,5,25,16,Female,No,Yes,African American,671 28 | 27,42.471,3625,289,6,44,12,Female,Yes,No,Caucasian,654 29 | 28,32.793,4534,333,2,44,16, Male,No,No,African American,467 30 | 29,186.634,13414,949,2,41,14,Female,No,Yes,African American,1809 31 | 30,26.813,5611,411,4,55,16,Female,No,No,Caucasian,915 32 | 31,34.142,5666,413,4,47,5,Female,No,Yes,Caucasian,863 33 | 32,28.941,2733,210,5,43,16, Male,No,Yes,Asian,0 34 | 33,134.181,7838,563,2,48,13,Female,No,No,Caucasian,526 35 | 34,31.367,1829,162,4,30,10, Male,No,Yes,Caucasian,0 36 | 35,20.15,2646,199,2,25,14,Female,No,Yes,Asian,0 37 | 36,23.35,2558,220,3,49,12,Female,Yes,No,Caucasian,419 38 | 37,62.413,6457,455,2,71,11,Female,No,Yes,Caucasian,762 39 | 38,30.007,6481,462,2,69,9,Female,No,Yes,Caucasian,1093 40 | 39,11.795,3899,300,4,25,10,Female,No,No,Caucasian,531 41 | 40,13.647,3461,264,4,47,14, Male,No,Yes,Caucasian,344 42 | 41,34.95,3327,253,3,54,14,Female,No,No,African American,50 43 | 42,113.659,7659,538,2,66,15, Male,Yes,Yes,African American,1155 44 | 43,44.158,4763,351,2,66,13,Female,No,Yes,Asian,385 45 | 44,36.929,6257,445,1,24,14,Female,No,Yes,Asian,976 46 | 45,31.861,6375,469,3,25,16,Female,No,Yes,Caucasian,1120 47 | 46,77.38,7569,564,3,50,12,Female,No,Yes,Caucasian,997 48 | 47,19.531,5043,376,2,64,16,Female,Yes,Yes,Asian,1241 49 | 48,44.646,4431,320,2,49,15, Male,Yes,Yes,Caucasian,797 50 | 49,44.522,2252,205,6,72,15, Male,No,Yes,Asian,0 51 | 50,43.479,4569,354,4,49,13, Male,Yes,Yes,African American,902 52 | 51,36.362,5183,376,3,49,15, Male,No,Yes,African American,654 53 | 52,39.705,3969,301,2,27,20, Male,No,Yes,African American,211 54 | 53,44.205,5441,394,1,32,12, Male,No,Yes,Caucasian,607 55 | 54,16.304,5466,413,4,66,10, Male,No,Yes,Asian,957 56 | 55,15.333,1499,138,2,47,9,Female,No,Yes,Asian,0 57 | 56,32.916,1786,154,2,60,8,Female,No,Yes,Asian,0 58 | 57,57.1,4742,372,7,79,18,Female,No,Yes,Asian,379 59 | 58,76.273,4779,367,4,65,14,Female,No,Yes,Caucasian,133 60 | 59,10.354,3480,281,2,70,17, Male,No,Yes,Caucasian,333 61 | 60,51.872,5294,390,4,81,17,Female,No,No,Caucasian,531 62 | 61,35.51,5198,364,2,35,20,Female,No,No,Asian,631 63 | 62,21.238,3089,254,3,59,10,Female,No,No,Caucasian,108 64 | 63,30.682,1671,160,2,77,7,Female,No,No,Caucasian,0 65 | 64,14.132,2998,251,4,75,17, Male,No,No,Caucasian,133 66 | 65,32.164,2937,223,2,79,15,Female,No,Yes,African American,0 67 | 66,12.0,4160,320,4,28,14,Female,No,Yes,Caucasian,602 68 | 67,113.829,9704,694,4,38,13,Female,No,Yes,Asian,1388 69 | 68,11.187,5099,380,4,69,16,Female,No,No,African American,889 70 | 69,27.847,5619,418,2,78,15,Female,No,Yes,Caucasian,822 71 | 70,49.502,6819,505,4,55,14, Male,No,Yes,Caucasian,1084 72 | 71,24.889,3954,318,4,75,12, Male,No,Yes,Caucasian,357 73 | 72,58.781,7402,538,2,81,12,Female,No,Yes,Asian,1103 74 | 73,22.939,4923,355,1,47,18,Female,No,Yes,Asian,663 75 | 74,23.989,4523,338,4,31,15, Male,No,No,Caucasian,601 76 | 75,16.103,5390,418,4,45,10,Female,No,Yes,Caucasian,945 77 | 76,33.017,3180,224,2,28,16, Male,No,Yes,African American,29 78 | 77,30.622,3293,251,1,68,16, Male,Yes,No,Caucasian,532 79 | 78,20.936,3254,253,1,30,15,Female,No,No,Asian,145 80 | 79,110.968,6662,468,3,45,11,Female,No,Yes,Caucasian,391 81 | 80,15.354,2101,171,2,65,14, Male,No,No,Asian,0 82 | 81,27.369,3449,288,3,40,9,Female,No,Yes,Caucasian,162 83 | 82,53.48,4263,317,1,83,15, Male,No,No,Caucasian,99 84 | 83,23.672,4433,344,3,63,11, Male,No,No,Caucasian,503 85 | 84,19.225,1433,122,3,38,14,Female,No,No,Caucasian,0 86 | 85,43.54,2906,232,4,69,11, Male,No,No,Caucasian,0 87 | 86,152.298,12066,828,4,41,12,Female,No,Yes,Asian,1779 88 | 87,55.367,6340,448,1,33,15, Male,No,Yes,Caucasian,815 89 | 88,11.741,2271,182,4,59,12,Female,No,No,Asian,0 90 | 89,15.56,4307,352,4,57,8, Male,No,Yes,African American,579 91 | 90,59.53,7518,543,3,52,9,Female,No,No,African American,1176 92 | 91,20.191,5767,431,4,42,16, Male,No,Yes,African American,1023 93 | 92,48.498,6040,456,3,47,16, Male,No,Yes,Caucasian,812 94 | 93,30.733,2832,249,4,51,13, Male,No,No,Caucasian,0 95 | 94,16.479,5435,388,2,26,16, Male,No,No,African American,937 96 | 95,38.009,3075,245,3,45,15,Female,No,No,African American,0 97 | 96,14.084,855,120,5,46,17,Female,No,Yes,African American,0 98 | 97,14.312,5382,367,1,59,17, Male,Yes,No,Asian,1380 99 | 98,26.067,3388,266,4,74,17,Female,No,Yes,African American,155 100 | 99,36.295,2963,241,2,68,14,Female,Yes,No,African American,375 101 | 100,83.851,8494,607,5,47,18, Male,No,No,Caucasian,1311 102 | 101,21.153,3736,256,1,41,11, Male,No,No,Caucasian,298 103 | 102,17.976,2433,190,3,70,16,Female,Yes,No,Caucasian,431 104 | 103,68.713,7582,531,2,56,16, Male,Yes,No,Caucasian,1587 105 | 104,146.183,9540,682,6,66,15, Male,No,No,Caucasian,1050 106 | 105,15.846,4768,365,4,53,12,Female,No,No,Caucasian,745 107 | 106,12.031,3182,259,2,58,18,Female,No,Yes,Caucasian,210 108 | 107,16.819,1337,115,2,74,15, Male,No,Yes,Asian,0 109 | 108,39.11,3189,263,3,72,12, Male,No,No,Asian,0 110 | 109,107.986,6033,449,4,64,14, Male,No,Yes,Caucasian,227 111 | 110,13.561,3261,279,5,37,19, Male,No,Yes,Asian,297 112 | 111,34.537,3271,250,3,57,17,Female,No,Yes,Asian,47 113 | 112,28.575,2959,231,2,60,11,Female,No,No,African American,0 114 | 113,46.007,6637,491,4,42,14, Male,No,Yes,Caucasian,1046 115 | 114,69.251,6386,474,4,30,12,Female,No,Yes,Asian,768 116 | 115,16.482,3326,268,4,41,15, Male,No,No,Caucasian,271 117 | 116,40.442,4828,369,5,81,8,Female,No,No,African American,510 118 | 117,35.177,2117,186,3,62,16,Female,No,No,Caucasian,0 119 | 118,91.362,9113,626,1,47,17, Male,No,Yes,Asian,1341 120 | 119,27.039,2161,173,3,40,17,Female,No,No,Caucasian,0 121 | 120,23.012,1410,137,3,81,16, Male,No,No,Caucasian,0 122 | 121,27.241,1402,128,2,67,15,Female,No,Yes,Asian,0 123 | 122,148.08,8157,599,2,83,13, Male,No,Yes,Caucasian,454 124 | 123,62.602,7056,481,1,84,11,Female,No,No,Caucasian,904 125 | 124,11.808,1300,117,3,77,14,Female,No,No,African American,0 126 | 125,29.564,2529,192,1,30,12,Female,No,Yes,Caucasian,0 127 | 126,27.578,2531,195,1,34,15,Female,No,Yes,Caucasian,0 128 | 127,26.427,5533,433,5,50,15,Female,Yes,Yes,Asian,1404 129 | 128,57.202,3411,259,3,72,11,Female,No,No,Caucasian,0 130 | 129,123.299,8376,610,2,89,17, Male,Yes,No,African American,1259 131 | 130,18.145,3461,279,3,56,15, Male,No,Yes,African American,255 132 | 131,23.793,3821,281,4,56,12,Female,Yes,Yes,African American,868 133 | 132,10.726,1568,162,5,46,19, Male,No,Yes,Asian,0 134 | 133,23.283,5443,407,4,49,13, Male,No,Yes,African American,912 135 | 134,21.455,5829,427,4,80,12,Female,No,Yes,African American,1018 136 | 135,34.664,5835,452,3,77,15,Female,No,Yes,African American,835 137 | 136,44.473,3500,257,3,81,16,Female,No,No,African American,8 138 | 137,54.663,4116,314,2,70,8,Female,No,No,African American,75 139 | 138,36.355,3613,278,4,35,9, Male,No,Yes,Asian,187 140 | 139,21.374,2073,175,2,74,11,Female,No,Yes,Caucasian,0 141 | 140,107.841,10384,728,3,87,7, Male,No,No,African American,1597 142 | 141,39.831,6045,459,3,32,12,Female,Yes,Yes,African American,1425 143 | 142,91.876,6754,483,2,33,10, Male,No,Yes,Caucasian,605 144 | 143,103.893,7416,549,3,84,17, Male,No,No,Asian,669 145 | 144,19.636,4896,387,3,64,10,Female,No,No,African American,710 146 | 145,17.392,2748,228,3,32,14, Male,No,Yes,Caucasian,68 147 | 146,19.529,4673,341,2,51,14, Male,No,No,Asian,642 148 | 147,17.055,5110,371,3,55,15,Female,No,Yes,Caucasian,805 149 | 148,23.857,1501,150,3,56,16, Male,No,Yes,Caucasian,0 150 | 149,15.184,2420,192,2,69,11,Female,No,Yes,Caucasian,0 151 | 150,13.444,886,121,5,44,10, Male,No,Yes,Asian,0 152 | 151,63.931,5728,435,3,28,14,Female,No,Yes,African American,581 153 | 152,35.864,4831,353,3,66,13,Female,No,Yes,Caucasian,534 154 | 153,41.419,2120,184,4,24,11,Female,Yes,No,Caucasian,156 155 | 154,92.112,4612,344,3,32,17, Male,No,No,Caucasian,0 156 | 155,55.056,3155,235,2,31,16, Male,No,Yes,African American,0 157 | 156,19.537,1362,143,4,34,9,Female,No,Yes,Asian,0 158 | 157,31.811,4284,338,5,75,13,Female,No,Yes,Caucasian,429 159 | 158,56.256,5521,406,2,72,16,Female,Yes,Yes,Caucasian,1020 160 | 159,42.357,5550,406,2,83,12,Female,No,Yes,Asian,653 161 | 160,53.319,3000,235,3,53,13, Male,No,No,Asian,0 162 | 161,12.238,4865,381,5,67,11,Female,No,No,Caucasian,836 163 | 162,31.353,1705,160,3,81,14, Male,No,Yes,Caucasian,0 164 | 163,63.809,7530,515,1,56,12, Male,No,Yes,Caucasian,1086 165 | 164,13.676,2330,203,5,80,16,Female,No,No,African American,0 166 | 165,76.782,5977,429,4,44,12, Male,No,Yes,Asian,548 167 | 166,25.383,4527,367,4,46,11, Male,No,Yes,Caucasian,570 168 | 167,35.691,2880,214,2,35,15, Male,No,No,African American,0 169 | 168,29.403,2327,178,1,37,14,Female,No,Yes,Caucasian,0 170 | 169,27.47,2820,219,1,32,11,Female,No,Yes,Asian,0 171 | 170,27.33,6179,459,4,36,12,Female,No,Yes,Caucasian,1099 172 | 171,34.772,2021,167,3,57,9, Male,No,No,Asian,0 173 | 172,36.934,4270,299,1,63,9,Female,No,Yes,Caucasian,283 174 | 173,76.348,4697,344,4,60,18, Male,No,No,Asian,108 175 | 174,14.887,4745,339,3,58,12, Male,No,Yes,African American,724 176 | 175,121.834,10673,750,3,54,16, Male,No,No,African American,1573 177 | 176,30.132,2168,206,3,52,17, Male,No,No,Caucasian,0 178 | 177,24.05,2607,221,4,32,18, Male,No,Yes,Caucasian,0 179 | 178,22.379,3965,292,2,34,14,Female,No,Yes,Asian,384 180 | 179,28.316,4391,316,2,29,10,Female,No,No,Caucasian,453 181 | 180,58.026,7499,560,5,67,11,Female,No,No,Caucasian,1237 182 | 181,10.635,3584,294,5,69,16, Male,No,Yes,Asian,423 183 | 182,46.102,5180,382,3,81,12, Male,No,Yes,African American,516 184 | 183,58.929,6420,459,2,66,9,Female,No,Yes,African American,789 185 | 184,80.861,4090,335,3,29,15,Female,No,Yes,Asian,0 186 | 185,158.889,11589,805,1,62,17,Female,No,Yes,Caucasian,1448 187 | 186,30.42,4442,316,1,30,14,Female,No,No,African American,450 188 | 187,36.472,3806,309,2,52,13, Male,No,No,African American,188 189 | 188,23.365,2179,167,2,75,15, Male,No,No,Asian,0 190 | 189,83.869,7667,554,2,83,11, Male,No,No,African American,930 191 | 190,58.351,4411,326,2,85,16,Female,No,Yes,Caucasian,126 192 | 191,55.187,5352,385,4,50,17,Female,No,Yes,Caucasian,538 193 | 192,124.29,9560,701,3,52,17,Female,Yes,No,Asian,1687 194 | 193,28.508,3933,287,4,56,14, Male,No,Yes,Asian,336 195 | 194,130.209,10088,730,7,39,19,Female,No,Yes,Caucasian,1426 196 | 195,30.406,2120,181,2,79,14, Male,No,Yes,African American,0 197 | 196,23.883,5384,398,2,73,16,Female,No,Yes,African American,802 198 | 197,93.039,7398,517,1,67,12, Male,No,Yes,African American,749 199 | 198,50.699,3977,304,2,84,17,Female,No,No,African American,69 200 | 199,27.349,2000,169,4,51,16,Female,No,Yes,African American,0 201 | 200,10.403,4159,310,3,43,7, Male,No,Yes,Asian,571 202 | 201,23.949,5343,383,2,40,18, Male,No,Yes,African American,829 203 | 202,73.914,7333,529,6,67,15,Female,No,Yes,Caucasian,1048 204 | 203,21.038,1448,145,2,58,13,Female,No,Yes,Caucasian,0 205 | 204,68.206,6784,499,5,40,16,Female,Yes,No,African American,1411 206 | 205,57.337,5310,392,2,45,7,Female,No,No,Caucasian,456 207 | 206,10.793,3878,321,8,29,13, Male,No,No,Caucasian,638 208 | 207,23.45,2450,180,2,78,13, Male,No,No,Caucasian,0 209 | 208,10.842,4391,358,5,37,10,Female,Yes,Yes,Caucasian,1216 210 | 209,51.345,4327,320,3,46,15, Male,No,No,African American,230 211 | 210,151.947,9156,642,2,91,11,Female,No,Yes,African American,732 212 | 211,24.543,3206,243,2,62,12,Female,No,Yes,Caucasian,95 213 | 212,29.567,5309,397,3,25,15, Male,No,No,Caucasian,799 214 | 213,39.145,4351,323,2,66,13, Male,No,Yes,Caucasian,308 215 | 214,39.422,5245,383,2,44,19, Male,No,No,African American,637 216 | 215,34.909,5289,410,2,62,16,Female,No,Yes,Caucasian,681 217 | 216,41.025,4229,337,3,79,19,Female,No,Yes,Caucasian,246 218 | 217,15.476,2762,215,3,60,18, Male,No,No,Asian,52 219 | 218,12.456,5395,392,3,65,14, Male,No,Yes,Caucasian,955 220 | 219,10.627,1647,149,2,71,10,Female,Yes,Yes,Asian,195 221 | 220,38.954,5222,370,4,76,13,Female,No,No,Caucasian,653 222 | 221,44.847,5765,437,3,53,13,Female,Yes,No,Asian,1246 223 | 222,98.515,8760,633,5,78,11,Female,No,No,African American,1230 224 | 223,33.437,6207,451,4,44,9, Male,Yes,No,Caucasian,1549 225 | 224,27.512,4613,344,5,72,17, Male,No,Yes,Asian,573 226 | 225,121.709,7818,584,4,50,6, Male,No,Yes,Caucasian,701 227 | 226,15.079,5673,411,4,28,15,Female,No,Yes,Asian,1075 228 | 227,59.879,6906,527,6,78,15,Female,No,No,Caucasian,1032 229 | 228,66.989,5614,430,3,47,14,Female,No,Yes,Caucasian,482 230 | 229,69.165,4668,341,2,34,11,Female,No,No,African American,156 231 | 230,69.943,7555,547,3,76,9, Male,No,Yes,Asian,1058 232 | 231,33.214,5137,387,3,59,9, Male,No,No,African American,661 233 | 232,25.124,4776,378,4,29,12, Male,No,Yes,Caucasian,657 234 | 233,15.741,4788,360,1,39,14, Male,No,Yes,Asian,689 235 | 234,11.603,2278,187,3,71,11, Male,No,Yes,Caucasian,0 236 | 235,69.656,8244,579,3,41,14, Male,No,Yes,African American,1329 237 | 236,10.503,2923,232,3,25,18,Female,No,Yes,African American,191 238 | 237,42.529,4986,369,2,37,11, Male,No,Yes,Asian,489 239 | 238,60.579,5149,388,5,38,15, Male,No,Yes,Asian,443 240 | 239,26.532,2910,236,6,58,19,Female,No,Yes,Caucasian,52 241 | 240,27.952,3557,263,1,35,13,Female,No,Yes,Asian,163 242 | 241,29.705,3351,262,5,71,14,Female,No,Yes,Asian,148 243 | 242,15.602,906,103,2,36,11, Male,No,Yes,African American,0 244 | 243,20.918,1233,128,3,47,18,Female,Yes,Yes,Asian,16 245 | 244,58.165,6617,460,1,56,12,Female,No,Yes,Caucasian,856 246 | 245,22.561,1787,147,4,66,15,Female,No,No,Caucasian,0 247 | 246,34.509,2001,189,5,80,18,Female,No,Yes,African American,0 248 | 247,19.588,3211,265,4,59,14,Female,No,No,Asian,199 249 | 248,36.364,2220,188,3,50,19, Male,No,No,Caucasian,0 250 | 249,15.717,905,93,1,38,16, Male,Yes,Yes,Caucasian,0 251 | 250,22.574,1551,134,3,43,13,Female,Yes,Yes,Caucasian,98 252 | 251,10.363,2430,191,2,47,18,Female,No,Yes,Asian,0 253 | 252,28.474,3202,267,5,66,12, Male,No,Yes,Caucasian,132 254 | 253,72.945,8603,621,3,64,8,Female,No,No,Caucasian,1355 255 | 254,85.425,5182,402,6,60,12, Male,No,Yes,African American,218 256 | 255,36.508,6386,469,4,79,6,Female,No,Yes,Caucasian,1048 257 | 256,58.063,4221,304,3,50,8, Male,No,No,African American,118 258 | 257,25.936,1774,135,2,71,14,Female,No,No,Asian,0 259 | 258,15.629,2493,186,1,60,14, Male,No,Yes,Asian,0 260 | 259,41.4,2561,215,2,36,14, Male,No,Yes,Caucasian,0 261 | 260,33.657,6196,450,6,55,9,Female,No,No,Caucasian,1092 262 | 261,67.937,5184,383,4,63,12, Male,No,Yes,Asian,345 263 | 262,180.379,9310,665,3,67,8,Female,Yes,Yes,Asian,1050 264 | 263,10.588,4049,296,1,66,13,Female,No,Yes,Caucasian,465 265 | 264,29.725,3536,270,2,52,15,Female,No,No,African American,133 266 | 265,27.999,5107,380,1,55,10, Male,No,Yes,Caucasian,651 267 | 266,40.885,5013,379,3,46,13,Female,No,Yes,African American,549 268 | 267,88.83,4952,360,4,86,16,Female,No,Yes,Caucasian,15 269 | 268,29.638,5833,433,3,29,15,Female,No,Yes,Asian,942 270 | 269,25.988,1349,142,4,82,12, Male,No,No,Caucasian,0 271 | 270,39.055,5565,410,4,48,18,Female,No,Yes,Caucasian,772 272 | 271,15.866,3085,217,1,39,13, Male,No,No,Caucasian,136 273 | 272,44.978,4866,347,1,30,10,Female,No,No,Caucasian,436 274 | 273,30.413,3690,299,2,25,15,Female,Yes,No,Asian,728 275 | 274,16.751,4706,353,6,48,14, Male,Yes,No,Asian,1255 276 | 275,30.55,5869,439,5,81,9,Female,No,No,African American,967 277 | 276,163.329,8732,636,3,50,14, Male,No,Yes,Caucasian,529 278 | 277,23.106,3476,257,2,50,15,Female,No,No,Caucasian,209 279 | 278,41.532,5000,353,2,50,12, Male,No,Yes,Caucasian,531 280 | 279,128.04,6982,518,2,78,11,Female,No,Yes,Caucasian,250 281 | 280,54.319,3063,248,3,59,8,Female,Yes,No,Caucasian,269 282 | 281,53.401,5319,377,3,35,12,Female,No,No,African American,541 283 | 282,36.142,1852,183,3,33,13,Female,No,No,African American,0 284 | 283,63.534,8100,581,2,50,17,Female,No,Yes,Caucasian,1298 285 | 284,49.927,6396,485,3,75,17,Female,No,Yes,Caucasian,890 286 | 285,14.711,2047,167,2,67,6, Male,No,Yes,Caucasian,0 287 | 286,18.967,1626,156,2,41,11,Female,No,Yes,Asian,0 288 | 287,18.036,1552,142,2,48,15,Female,No,No,Caucasian,0 289 | 288,60.449,3098,272,4,69,8, Male,No,Yes,Caucasian,0 290 | 289,16.711,5274,387,3,42,16,Female,No,Yes,Asian,863 291 | 290,10.852,3907,296,2,30,9, Male,No,No,Caucasian,485 292 | 291,26.37,3235,268,5,78,11, Male,No,Yes,Asian,159 293 | 292,24.088,3665,287,4,56,13,Female,No,Yes,Caucasian,309 294 | 293,51.532,5096,380,2,31,15, Male,No,Yes,Caucasian,481 295 | 294,140.672,11200,817,7,46,9, Male,No,Yes,African American,1677 296 | 295,42.915,2532,205,4,42,13, Male,No,Yes,Asian,0 297 | 296,27.272,1389,149,5,67,10,Female,No,Yes,Caucasian,0 298 | 297,65.896,5140,370,1,49,17,Female,No,Yes,Caucasian,293 299 | 298,55.054,4381,321,3,74,17, Male,No,Yes,Asian,188 300 | 299,20.791,2672,204,1,70,18,Female,No,No,African American,0 301 | 300,24.919,5051,372,3,76,11,Female,No,Yes,African American,711 302 | 301,21.786,4632,355,1,50,17, Male,No,Yes,Caucasian,580 303 | 302,31.335,3526,289,3,38,7,Female,No,No,Caucasian,172 304 | 303,59.855,4964,365,1,46,13,Female,No,Yes,Caucasian,295 305 | 304,44.061,4970,352,1,79,11, Male,No,Yes,African American,414 306 | 305,82.706,7506,536,2,64,13,Female,No,Yes,Asian,905 307 | 306,24.46,1924,165,2,50,14,Female,No,Yes,Asian,0 308 | 307,45.12,3762,287,3,80,8, Male,No,Yes,Caucasian,70 309 | 308,75.406,3874,298,3,41,14,Female,No,Yes,Asian,0 310 | 309,14.956,4640,332,2,33,6, Male,No,No,Asian,681 311 | 310,75.257,7010,494,3,34,18,Female,No,Yes,Caucasian,885 312 | 311,33.694,4891,369,1,52,16, Male,Yes,No,African American,1036 313 | 312,23.375,5429,396,3,57,15,Female,No,Yes,Caucasian,844 314 | 313,27.825,5227,386,6,63,11, Male,No,Yes,Caucasian,823 315 | 314,92.386,7685,534,2,75,18,Female,No,Yes,Asian,843 316 | 315,115.52,9272,656,2,69,14, Male,No,No,African American,1140 317 | 316,14.479,3907,296,3,43,16, Male,No,Yes,Caucasian,463 318 | 317,52.179,7306,522,2,57,14, Male,No,No,Asian,1142 319 | 318,68.462,4712,340,2,71,16, Male,No,Yes,Caucasian,136 320 | 319,18.951,1485,129,3,82,13,Female,No,No,Caucasian,0 321 | 320,27.59,2586,229,5,54,16, Male,No,Yes,African American,0 322 | 321,16.279,1160,126,3,78,13, Male,Yes,Yes,African American,5 323 | 322,25.078,3096,236,2,27,15,Female,No,Yes,Caucasian,81 324 | 323,27.229,3484,282,6,51,11, Male,No,No,Caucasian,265 325 | 324,182.728,13913,982,4,98,17, Male,No,Yes,Caucasian,1999 326 | 325,31.029,2863,223,2,66,17, Male,Yes,Yes,Asian,415 327 | 326,17.765,5072,364,1,66,12,Female,No,Yes,Caucasian,732 328 | 327,125.48,10230,721,3,82,16, Male,No,Yes,Caucasian,1361 329 | 328,49.166,6662,508,3,68,14,Female,No,No,Asian,984 330 | 329,41.192,3673,297,3,54,16,Female,No,Yes,Caucasian,121 331 | 330,94.193,7576,527,2,44,16,Female,No,Yes,Caucasian,846 332 | 331,20.405,4543,329,2,72,17, Male,Yes,No,Asian,1054 333 | 332,12.581,3976,291,2,48,16, Male,No,Yes,Caucasian,474 334 | 333,62.328,5228,377,3,83,15, Male,No,No,Caucasian,380 335 | 334,21.011,3402,261,2,68,17, Male,No,Yes,African American,182 336 | 335,24.23,4756,351,2,64,15,Female,No,Yes,Caucasian,594 337 | 336,24.314,3409,270,2,23,7,Female,No,Yes,Caucasian,194 338 | 337,32.856,5884,438,4,68,13, Male,No,No,Caucasian,926 339 | 338,12.414,855,119,3,32,12, Male,No,Yes,African American,0 340 | 339,41.365,5303,377,1,45,14, Male,No,No,Caucasian,606 341 | 340,149.316,10278,707,1,80,16, Male,No,No,African American,1107 342 | 341,27.794,3807,301,4,35,8,Female,No,Yes,African American,320 343 | 342,13.234,3922,299,2,77,17,Female,No,Yes,Caucasian,426 344 | 343,14.595,2955,260,5,37,9, Male,No,Yes,African American,204 345 | 344,10.735,3746,280,2,44,17,Female,No,Yes,Caucasian,410 346 | 345,48.218,5199,401,7,39,10, Male,No,Yes,Asian,633 347 | 346,30.012,1511,137,2,33,17, Male,No,Yes,Caucasian,0 348 | 347,21.551,5380,420,5,51,18, Male,No,Yes,Asian,907 349 | 348,160.231,10748,754,2,69,17, Male,No,No,Caucasian,1192 350 | 349,13.433,1134,112,3,70,14, Male,No,Yes,Caucasian,0 351 | 350,48.577,5145,389,3,71,13,Female,No,Yes,Asian,503 352 | 351,30.002,1561,155,4,70,13,Female,No,Yes,Caucasian,0 353 | 352,61.62,5140,374,1,71,9, Male,No,Yes,Caucasian,302 354 | 353,104.483,7140,507,2,41,14, Male,No,Yes,African American,583 355 | 354,41.868,4716,342,2,47,18, Male,No,No,Caucasian,425 356 | 355,12.068,3873,292,1,44,18,Female,No,Yes,Asian,413 357 | 356,180.682,11966,832,2,58,8,Female,No,Yes,African American,1405 358 | 357,34.48,6090,442,3,36,14, Male,No,No,Caucasian,962 359 | 358,39.609,2539,188,1,40,14, Male,No,Yes,Asian,0 360 | 359,30.111,4336,339,1,81,18, Male,No,Yes,Caucasian,347 361 | 360,12.335,4471,344,3,79,12, Male,No,Yes,African American,611 362 | 361,53.566,5891,434,4,82,10,Female,No,No,Caucasian,712 363 | 362,53.217,4943,362,2,46,16,Female,No,Yes,Asian,382 364 | 363,26.162,5101,382,3,62,19,Female,No,No,African American,710 365 | 364,64.173,6127,433,1,80,10, Male,No,Yes,Caucasian,578 366 | 365,128.669,9824,685,3,67,16, Male,No,Yes,Asian,1243 367 | 366,113.772,6442,489,4,69,15, Male,Yes,Yes,Caucasian,790 368 | 367,61.069,7871,564,3,56,14, Male,No,Yes,Caucasian,1264 369 | 368,23.793,3615,263,2,70,14, Male,No,No,African American,216 370 | 369,89.0,5759,440,3,37,6,Female,No,No,Caucasian,345 371 | 370,71.682,8028,599,3,57,16, Male,No,Yes,Caucasian,1208 372 | 371,35.61,6135,466,4,40,12, Male,No,No,Caucasian,992 373 | 372,39.116,2150,173,4,75,15, Male,No,No,Caucasian,0 374 | 373,19.782,3782,293,2,46,16,Female,Yes,No,Caucasian,840 375 | 374,55.412,5354,383,2,37,16,Female,Yes,Yes,Caucasian,1003 376 | 375,29.4,4840,368,3,76,18,Female,No,Yes,Caucasian,588 377 | 376,20.974,5673,413,5,44,16,Female,No,Yes,Caucasian,1000 378 | 377,87.625,7167,515,2,46,10,Female,No,No,African American,767 379 | 378,28.144,1567,142,3,51,10, Male,No,Yes,Caucasian,0 380 | 379,19.349,4941,366,1,33,19, Male,No,Yes,Caucasian,717 381 | 380,53.308,2860,214,1,84,10, Male,No,Yes,Caucasian,0 382 | 381,115.123,7760,538,3,83,14,Female,No,No,African American,661 383 | 382,101.788,8029,574,2,84,11, Male,No,Yes,Caucasian,849 384 | 383,24.824,5495,409,1,33,9, Male,Yes,No,Caucasian,1352 385 | 384,14.292,3274,282,9,64,9, Male,No,Yes,Caucasian,382 386 | 385,20.088,1870,180,3,76,16, Male,No,No,African American,0 387 | 386,26.4,5640,398,3,58,15,Female,No,No,Asian,905 388 | 387,19.253,3683,287,4,57,10, Male,No,No,African American,371 389 | 388,16.529,1357,126,3,62,9, Male,No,No,Asian,0 390 | 389,37.878,6827,482,2,80,13,Female,No,No,Caucasian,1129 391 | 390,83.948,7100,503,2,44,18, Male,No,No,Caucasian,806 392 | 391,135.118,10578,747,3,81,15,Female,No,Yes,Asian,1393 393 | 392,73.327,6555,472,2,43,15,Female,No,No,Caucasian,721 394 | 393,25.974,2308,196,2,24,10, Male,No,No,Asian,0 395 | 394,17.316,1335,138,2,65,13, Male,No,No,African American,0 396 | 395,49.794,5758,410,4,40,8, Male,No,No,Caucasian,734 397 | 396,12.096,4100,307,3,32,13, Male,No,Yes,Caucasian,560 398 | 397,13.364,3838,296,5,65,17, Male,No,No,African American,480 399 | 398,57.872,4171,321,5,67,12,Female,No,Yes,Caucasian,138 400 | 399,37.728,2525,192,1,44,13, Male,No,Yes,Caucasian,0 401 | 400,18.701,5524,415,5,64,7,Female,No,No,Asian,966 402 | -------------------------------------------------------------------------------- /labs/chapter8/decision_trees.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "from sklearn.preprocessing import LabelEncoder\n", 11 | "from sklearn.model_selection import train_test_split\n", 12 | "from sklearn.tree import DecisionTreeClassifier\n", 13 | "from sklearn.metrics import classification_report" 14 | ] 15 | }, 16 | { 17 | "cell_type": "markdown", 18 | "metadata": {}, 19 | "source": [ 20 | "**Summary** \n", 21 | "\n", 22 | "Decision trees can be used for both regression and classification problems. Think of decision trees as playing a [game of twenty questions](https://en.wikipedia.org/wiki/Twenty_Questions), where you have to arrive at an answer by asking no more than twenty questions and your next question depends on the answers to your previous questions. \n", 23 | "\n", 24 | "Mathematically, they split the predictor space into rectangular regions (refer Figure 8.3, page 308, PDF page 322), of possibly different sizes, such that the RSS in regression problems and the classification error rate in classification problems is minimized. That is, regression trees predict the mean response of the training observations that belong to the same region, and classification trees predict that each observation belongs to the most commonly occurring (mode) class of training observations in the region to which it belongs. (The variables used for splits are called *internal nodes*.)\n", 25 | "\n", 26 | "* RSS is defined as $\\sum \\left(y_i - \\hat{y}_{R_1} \\right)+\\sum \\left(y_i - \\hat{y}_{R_2} \\right)+\\cdots$, where $\\hat{y}$ is the mean response of observations in each of the regions $R_1, R_2, \\dots$.\n", 27 | "* Classification error rate is the fraction of observations in a region that do not belong to the most common class. Gini index and entropy serve as sophisticated classification error rate, where they measure the total variance across the classes.\n", 28 | "\n", 29 | "**Algorithm Biases** \n", 30 | "\n", 31 | "* Since decision trees make top-down decisions, they produce trees with good data splits at the top as opposed to bad splits at the top, even though both trees represent the same model.\n", 32 | "* This results to the trees being shorter since the trees ask good questions at the beginning. Note that larger trees are indicative of over-fitting the data, since they result from asking too many questions about the data.\n", 33 | "* Decision trees prefer correct models over incorrect models. That is, it will prefer a tree with not-so-good splits at the top but gives correct answers over a tree that has good splits at the top but produces incorrect answers." 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 2, 39 | "metadata": {}, 40 | "outputs": [ 41 | { 42 | "data": { 43 | "text/html": [ 44 | "
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SalesCompPriceIncomeAdvertisingPopulationPriceShelveLocAgeEducationUrbanUS
09.501387311276120Bad4217YesYes
111.22111481626083Good6510YesYes
210.06113351026980Medium5912YesYes
37.40117100446697Medium5514YesYes
44.15141643340128Bad3813YesNo
\n", 148 | "
" 149 | ], 150 | "text/plain": [ 151 | " Sales CompPrice Income Advertising Population Price ShelveLoc Age \\\n", 152 | "0 9.50 138 73 11 276 120 Bad 42 \n", 153 | "1 11.22 111 48 16 260 83 Good 65 \n", 154 | "2 10.06 113 35 10 269 80 Medium 59 \n", 155 | "3 7.40 117 100 4 466 97 Medium 55 \n", 156 | "4 4.15 141 64 3 340 128 Bad 38 \n", 157 | "\n", 158 | " Education Urban US \n", 159 | "0 17 Yes Yes \n", 160 | "1 10 Yes Yes \n", 161 | "2 12 Yes Yes \n", 162 | "3 14 Yes Yes \n", 163 | "4 13 Yes No " 164 | ] 165 | }, 166 | "execution_count": 2, 167 | "metadata": {}, 168 | "output_type": "execute_result" 169 | } 170 | ], 171 | "source": [ 172 | "data = pd.read_csv(\"../../data/csv/Carseats.csv\")\n", 173 | "data.head()" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 3, 179 | "metadata": {}, 180 | "outputs": [ 181 | { 182 | "data": { 183 | "text/plain": [ 184 | "Sales 0\n", 185 | "CompPrice 0\n", 186 | "Income 0\n", 187 | "Advertising 0\n", 188 | "Population 0\n", 189 | "Price 0\n", 190 | "ShelveLoc 0\n", 191 | "Age 0\n", 192 | "Education 0\n", 193 | "Urban 0\n", 194 | "US 0\n", 195 | "dtype: int64" 196 | ] 197 | }, 198 | "execution_count": 3, 199 | "metadata": {}, 200 | "output_type": "execute_result" 201 | } 202 | ], 203 | "source": [ 204 | "data.isnull().sum()" 205 | ] 206 | }, 207 | { 208 | "cell_type": "code", 209 | "execution_count": 4, 210 | "metadata": {}, 211 | "outputs": [ 212 | { 213 | "data": { 214 | "text/plain": [ 215 | "array(['Bad', 'Good', 'Medium'], dtype=object)" 216 | ] 217 | }, 218 | "execution_count": 4, 219 | "metadata": {}, 220 | "output_type": "execute_result" 221 | } 222 | ], 223 | "source": [ 224 | "data['ShelveLoc'].unique()" 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": 5, 230 | "metadata": {}, 231 | "outputs": [ 232 | { 233 | "data": { 234 | "text/plain": [ 235 | "array(['Yes', 'No'], dtype=object)" 236 | ] 237 | }, 238 | "execution_count": 5, 239 | "metadata": {}, 240 | "output_type": "execute_result" 241 | } 242 | ], 243 | "source": [ 244 | "data['Urban'].unique()" 245 | ] 246 | }, 247 | { 248 | "cell_type": "code", 249 | "execution_count": 6, 250 | "metadata": {}, 251 | "outputs": [ 252 | { 253 | "data": { 254 | "text/plain": [ 255 | "array(['Yes', 'No'], dtype=object)" 256 | ] 257 | }, 258 | "execution_count": 6, 259 | "metadata": {}, 260 | "output_type": "execute_result" 261 | } 262 | ], 263 | "source": [ 264 | "data['US'].unique()" 265 | ] 266 | }, 267 | { 268 | "cell_type": "markdown", 269 | "metadata": {}, 270 | "source": [ 271 | "LabelEncoder() assigns numbers to alphabetically sorted data. Eg: [C, A, B] $\\to$ [3, 1, 2]. This implies a natural ordering of the values and it is appropriate to order Bad, Good, Medium in an increasing manner." 272 | ] 273 | }, 274 | { 275 | "cell_type": "code", 276 | "execution_count": 7, 277 | "metadata": {}, 278 | "outputs": [ 279 | { 280 | "data": { 281 | "text/html": [ 282 | "
\n", 283 | "\n", 296 | "\n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | " \n", 323 | " \n", 324 | " \n", 325 | " \n", 326 | " \n", 327 | " \n", 328 | " \n", 329 | " \n", 330 | " \n", 331 | " \n", 332 | " \n", 333 | " \n", 334 | " \n", 335 | " \n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | " \n", 343 | " \n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | " \n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | "
SalesCompPriceIncomeAdvertisingPopulationPriceShelveLocAgeEducationUrbanUS
025549511114154017711
129722271612918140011
226724141013815234211
31582877424931230411
4375242317862013310
\n", 386 | "
" 387 | ], 388 | "text/plain": [ 389 | " Sales CompPrice Income Advertising Population Price ShelveLoc Age \\\n", 390 | "0 255 49 51 11 141 54 0 17 \n", 391 | "1 297 22 27 16 129 18 1 40 \n", 392 | "2 267 24 14 10 138 15 2 34 \n", 393 | "3 158 28 77 4 249 31 2 30 \n", 394 | "4 37 52 42 3 178 62 0 13 \n", 395 | "\n", 396 | " Education Urban US \n", 397 | "0 7 1 1 \n", 398 | "1 0 1 1 \n", 399 | "2 2 1 1 \n", 400 | "3 4 1 1 \n", 401 | "4 3 1 0 " 402 | ] 403 | }, 404 | "execution_count": 7, 405 | "metadata": {}, 406 | "output_type": "execute_result" 407 | } 408 | ], 409 | "source": [ 410 | "data = data.apply(LabelEncoder().fit_transform)\n", 411 | "data.head()" 412 | ] 413 | }, 414 | { 415 | "cell_type": "markdown", 416 | "metadata": {}, 417 | "source": [ 418 | "Create a binary variable High (sales), where High$=1$ if Sales$>8$; High$=0$ if Sales$\\le8$." 419 | ] 420 | }, 421 | { 422 | "cell_type": "code", 423 | "execution_count": 8, 424 | "metadata": {}, 425 | "outputs": [ 426 | { 427 | "data": { 428 | "text/html": [ 429 | "
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SalesCompPriceIncomeAdvertisingPopulationPriceShelveLocAgeEducationUrbanUSHigh
0255495111141540177111
1297222716129181400111
2267241410138152342111
315828774249312304111
43752423178620133101
\n", 539 | "
" 540 | ], 541 | "text/plain": [ 542 | " Sales CompPrice Income Advertising Population Price ShelveLoc Age \\\n", 543 | "0 255 49 51 11 141 54 0 17 \n", 544 | "1 297 22 27 16 129 18 1 40 \n", 545 | "2 267 24 14 10 138 15 2 34 \n", 546 | "3 158 28 77 4 249 31 2 30 \n", 547 | "4 37 52 42 3 178 62 0 13 \n", 548 | "\n", 549 | " Education Urban US High \n", 550 | "0 7 1 1 1 \n", 551 | "1 0 1 1 1 \n", 552 | "2 2 1 1 1 \n", 553 | "3 4 1 1 1 \n", 554 | "4 3 1 0 1 " 555 | ] 556 | }, 557 | "execution_count": 8, 558 | "metadata": {}, 559 | "output_type": "execute_result" 560 | } 561 | ], 562 | "source": [ 563 | "data['High'] = data['Sales'].map(lambda x: 1 if x>8 else 0)\n", 564 | "data.head()" 565 | ] 566 | }, 567 | { 568 | "cell_type": "code", 569 | "execution_count": 9, 570 | "metadata": {}, 571 | "outputs": [ 572 | { 573 | "data": { 574 | "text/plain": [ 575 | "1 391\n", 576 | "0 9\n", 577 | "Name: High, dtype: int64" 578 | ] 579 | }, 580 | "execution_count": 9, 581 | "metadata": {}, 582 | "output_type": "execute_result" 583 | } 584 | ], 585 | "source": [ 586 | "data['High'].value_counts()" 587 | ] 588 | }, 589 | { 590 | "cell_type": "code", 591 | "execution_count": 10, 592 | "metadata": {}, 593 | "outputs": [], 594 | "source": [ 595 | "y = data['High']\n", 596 | "x = data.loc[:, ~data.columns.isin(['Sales', 'High'])]\n", 597 | "xTrainVal, xTestVal, yTrainVal, yTestVal = train_test_split(x, y, test_size=0.5)" 598 | ] 599 | }, 600 | { 601 | "cell_type": "code", 602 | "execution_count": 11, 603 | "metadata": {}, 604 | "outputs": [ 605 | { 606 | "data": { 607 | "text/plain": [ 608 | "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", 609 | " max_features=None, max_leaf_nodes=None,\n", 610 | " min_impurity_decrease=0.0, min_impurity_split=None,\n", 611 | " min_samples_leaf=1, min_samples_split=2,\n", 612 | " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", 613 | " splitter='best')" 614 | ] 615 | }, 616 | "execution_count": 11, 617 | "metadata": {}, 618 | "output_type": "execute_result" 619 | } 620 | ], 621 | "source": [ 622 | "model = DecisionTreeClassifier()\n", 623 | "model.fit(xTrainVal, yTrainVal)" 624 | ] 625 | }, 626 | { 627 | "cell_type": "markdown", 628 | "metadata": {}, 629 | "source": [ 630 | "The true positive rate (Recall) is 100% in the training data and 97% in the testing data." 631 | ] 632 | }, 633 | { 634 | "cell_type": "code", 635 | "execution_count": 12, 636 | "metadata": {}, 637 | "outputs": [ 638 | { 639 | "name": "stdout", 640 | "output_type": "stream", 641 | "text": [ 642 | " precision recall f1-score support\n", 643 | "\n", 644 | " 0 1.00 1.00 1.00 5\n", 645 | " 1 1.00 1.00 1.00 195\n", 646 | "\n", 647 | "avg / total 1.00 1.00 1.00 200\n", 648 | "\n" 649 | ] 650 | } 651 | ], 652 | "source": [ 653 | "# Training error\n", 654 | "print(classification_report(yTrainVal, model.predict(xTrainVal)))" 655 | ] 656 | }, 657 | { 658 | "cell_type": "code", 659 | "execution_count": 13, 660 | "metadata": {}, 661 | "outputs": [ 662 | { 663 | "data": { 664 | "text/plain": [ 665 | "1.0" 666 | ] 667 | }, 668 | "execution_count": 13, 669 | "metadata": {}, 670 | "output_type": "execute_result" 671 | } 672 | ], 673 | "source": [ 674 | "model.score(xTrainVal, yTrainVal)" 675 | ] 676 | }, 677 | { 678 | "cell_type": "code", 679 | "execution_count": 14, 680 | "metadata": {}, 681 | "outputs": [ 682 | { 683 | "name": "stdout", 684 | "output_type": "stream", 685 | "text": [ 686 | " precision recall f1-score support\n", 687 | "\n", 688 | " 0 0.00 0.00 0.00 4\n", 689 | " 1 0.98 0.99 0.98 196\n", 690 | "\n", 691 | "avg / total 0.96 0.97 0.97 200\n", 692 | "\n" 693 | ] 694 | } 695 | ], 696 | "source": [ 697 | "# Testing error\n", 698 | "print(classification_report(yTestVal, model.predict(xTestVal)))" 699 | ] 700 | }, 701 | { 702 | "cell_type": "code", 703 | "execution_count": 15, 704 | "metadata": {}, 705 | "outputs": [ 706 | { 707 | "data": { 708 | "text/plain": [ 709 | "0.96999999999999997" 710 | ] 711 | }, 712 | "execution_count": 15, 713 | "metadata": {}, 714 | "output_type": "execute_result" 715 | } 716 | ], 717 | "source": [ 718 | "model.score(xTestVal, yTestVal)" 719 | ] 720 | }, 721 | { 722 | "cell_type": "code", 723 | "execution_count": 16, 724 | "metadata": {}, 725 | "outputs": [ 726 | { 727 | "data": { 728 | "text/plain": [ 729 | "[(0.5, 'Price'),\n", 730 | " (0.32, 'Age'),\n", 731 | " (0.09, 'Population'),\n", 732 | " (0.09, 'CompPrice'),\n", 733 | " (0.0, 'Urban'),\n", 734 | " (0.0, 'US'),\n", 735 | " (0.0, 'ShelveLoc'),\n", 736 | " (0.0, 'Income'),\n", 737 | " (0.0, 'Education'),\n", 738 | " (0.0, 'Advertising')]" 739 | ] 740 | }, 741 | "execution_count": 16, 742 | "metadata": {}, 743 | "output_type": "execute_result" 744 | } 745 | ], 746 | "source": [ 747 | "# Feature importance\n", 748 | "sorted(zip(map(lambda x: round(x, 2), model.feature_importances_), x.columns), reverse=True)" 749 | ] 750 | }, 751 | { 752 | "cell_type": "markdown", 753 | "metadata": {}, 754 | "source": [ 755 | "Notice that decision trees have high variance. That is, running the algorithm multiple times to randomly split training and validation data returns different predictions and feature importance score." 756 | ] 757 | }, 758 | { 759 | "cell_type": "code", 760 | "execution_count": 17, 761 | "metadata": {}, 762 | "outputs": [ 763 | { 764 | "name": "stdout", 765 | "output_type": "stream", 766 | "text": [ 767 | "Training error: 1.0\n", 768 | "Testing error: 0.97\n", 769 | "[(0.42, 'Age'), (0.25, 'Price'), (0.23, 'Advertising'), (0.1, 'Population'), (0.0, 'Urban'), (0.0, 'US'), (0.0, 'ShelveLoc'), (0.0, 'Income'), (0.0, 'Education'), (0.0, 'CompPrice')]\n", 770 | "\n", 771 | "Training error: 1.0\n", 772 | "Testing error: 0.96\n", 773 | "[(0.31, 'CompPrice'), (0.29, 'Income'), (0.16, 'Price'), (0.13, 'Advertising'), (0.11, 'Population'), (0.0, 'Urban'), (0.0, 'US'), (0.0, 'ShelveLoc'), (0.0, 'Education'), (0.0, 'Age')]\n", 774 | "\n", 775 | "Training error: 1.0\n", 776 | "Testing error: 0.98\n", 777 | "[(0.49, 'Price'), (0.28, 'Age'), (0.14, 'ShelveLoc'), (0.09, 'Population'), (0.01, 'CompPrice'), (0.0, 'Urban'), (0.0, 'US'), (0.0, 'Income'), (0.0, 'Education'), (0.0, 'Advertising')]\n", 778 | "\n", 779 | "Training error: 1.0\n", 780 | "Testing error: 0.97\n", 781 | "[(0.43, 'Age'), (0.36, 'Income'), (0.14, 'CompPrice'), (0.08, 'Price'), (0.0, 'Urban'), (0.0, 'US'), (0.0, 'ShelveLoc'), (0.0, 'Population'), (0.0, 'Education'), (0.0, 'Advertising')]\n", 782 | "\n" 783 | ] 784 | } 785 | ], 786 | "source": [ 787 | "for k in range(1,5):\n", 788 | " xTrainVal, xTestVal, yTrainVal, yTestVal = train_test_split(x, y, test_size=0.5)\n", 789 | " model = DecisionTreeClassifier()\n", 790 | " model.fit(xTrainVal, yTrainVal)\n", 791 | " print \"Training error: {}\".format(model.score(xTrainVal, yTrainVal))\n", 792 | " print \"Testing error: {}\".format(model.score(xTestVal, yTestVal))\n", 793 | " print sorted(zip(map(lambda x: round(x, 2), model.feature_importances_), x.columns), reverse=True)\n", 794 | " print " 795 | ] 796 | } 797 | ], 798 | "metadata": { 799 | "kernelspec": { 800 | "display_name": "Python 2", 801 | "language": "python", 802 | "name": "python2" 803 | }, 804 | "language_info": { 805 | "codemirror_mode": { 806 | "name": "ipython", 807 | "version": 2 808 | }, 809 | "file_extension": ".py", 810 | "mimetype": "text/x-python", 811 | "name": "python", 812 | "nbconvert_exporter": "python", 813 | "pygments_lexer": "ipython2", 814 | "version": "2.7.14" 815 | } 816 | }, 817 | "nbformat": 4, 818 | "nbformat_minor": 2 819 | } 820 | -------------------------------------------------------------------------------- /data/csv/Hitters.csv: -------------------------------------------------------------------------------- 1 | "","AtBat","Hits","HmRun","Runs","RBI","Walks","Years","CAtBat","CHits","CHmRun","CRuns","CRBI","CWalks","League","Division","PutOuts","Assists","Errors","Salary","NewLeague" 2 | "-Andy Allanson",293,66,1,30,29,14,1,293,66,1,30,29,14,"A","E",446,33,20,NA,"A" 3 | "-Alan Ashby",315,81,7,24,38,39,14,3449,835,69,321,414,375,"N","W",632,43,10,475,"N" 4 | "-Alvin Davis",479,130,18,66,72,76,3,1624,457,63,224,266,263,"A","W",880,82,14,480,"A" 5 | "-Andre Dawson",496,141,20,65,78,37,11,5628,1575,225,828,838,354,"N","E",200,11,3,500,"N" 6 | "-Andres Galarraga",321,87,10,39,42,30,2,396,101,12,48,46,33,"N","E",805,40,4,91.5,"N" 7 | "-Alfredo Griffin",594,169,4,74,51,35,11,4408,1133,19,501,336,194,"A","W",282,421,25,750,"A" 8 | "-Al Newman",185,37,1,23,8,21,2,214,42,1,30,9,24,"N","E",76,127,7,70,"A" 9 | "-Argenis Salazar",298,73,0,24,24,7,3,509,108,0,41,37,12,"A","W",121,283,9,100,"A" 10 | "-Andres Thomas",323,81,6,26,32,8,2,341,86,6,32,34,8,"N","W",143,290,19,75,"N" 11 | "-Andre Thornton",401,92,17,49,66,65,13,5206,1332,253,784,890,866,"A","E",0,0,0,1100,"A" 12 | "-Alan Trammell",574,159,21,107,75,59,10,4631,1300,90,702,504,488,"A","E",238,445,22,517.143,"A" 13 | "-Alex Trevino",202,53,4,31,26,27,9,1876,467,15,192,186,161,"N","W",304,45,11,512.5,"N" 14 | "-Andy VanSlyke",418,113,13,48,61,47,4,1512,392,41,205,204,203,"N","E",211,11,7,550,"N" 15 | "-Alan Wiggins",239,60,0,30,11,22,6,1941,510,4,309,103,207,"A","E",121,151,6,700,"A" 16 | "-Bill Almon",196,43,7,29,27,30,13,3231,825,36,376,290,238,"N","E",80,45,8,240,"N" 17 | "-Billy Beane",183,39,3,20,15,11,3,201,42,3,20,16,11,"A","W",118,0,0,NA,"A" 18 | "-Buddy Bell",568,158,20,89,75,73,15,8068,2273,177,1045,993,732,"N","W",105,290,10,775,"N" 19 | "-Buddy Biancalana",190,46,2,24,8,15,5,479,102,5,65,23,39,"A","W",102,177,16,175,"A" 20 | "-Bruce Bochte",407,104,6,57,43,65,12,5233,1478,100,643,658,653,"A","W",912,88,9,NA,"A" 21 | "-Bruce Bochy",127,32,8,16,22,14,8,727,180,24,67,82,56,"N","W",202,22,2,135,"N" 22 | "-Barry Bonds",413,92,16,72,48,65,1,413,92,16,72,48,65,"N","E",280,9,5,100,"N" 23 | "-Bobby Bonilla",426,109,3,55,43,62,1,426,109,3,55,43,62,"A","W",361,22,2,115,"N" 24 | "-Bob Boone",22,10,1,4,2,1,6,84,26,2,9,9,3,"A","W",812,84,11,NA,"A" 25 | "-Bob Brenly",472,116,16,60,62,74,6,1924,489,67,242,251,240,"N","W",518,55,3,600,"N" 26 | "-Bill Buckner",629,168,18,73,102,40,18,8424,2464,164,1008,1072,402,"A","E",1067,157,14,776.667,"A" 27 | "-Brett Butler",587,163,4,92,51,70,6,2695,747,17,442,198,317,"A","E",434,9,3,765,"A" 28 | "-Bob Dernier",324,73,4,32,18,22,7,1931,491,13,291,108,180,"N","E",222,3,3,708.333,"N" 29 | "-Bo Diaz",474,129,10,50,56,40,10,2331,604,61,246,327,166,"N","W",732,83,13,750,"N" 30 | "-Bill Doran",550,152,6,92,37,81,5,2308,633,32,349,182,308,"N","W",262,329,16,625,"N" 31 | "-Brian Downing",513,137,20,90,95,90,14,5201,1382,166,763,734,784,"A","W",267,5,3,900,"A" 32 | "-Bobby Grich",313,84,9,42,30,39,17,6890,1833,224,1033,864,1087,"A","W",127,221,7,NA,"A" 33 | "-Billy Hatcher",419,108,6,55,36,22,3,591,149,8,80,46,31,"N","W",226,7,4,110,"N" 34 | "-Bob Horner",517,141,27,70,87,52,9,3571,994,215,545,652,337,"N","W",1378,102,8,NA,"N" 35 | "-Brook Jacoby",583,168,17,83,80,56,5,1646,452,44,219,208,136,"A","E",109,292,25,612.5,"A" 36 | "-Bob Kearney",204,49,6,23,25,12,7,1309,308,27,126,132,66,"A","W",419,46,5,300,"A" 37 | "-Bill Madlock",379,106,10,38,60,30,14,6207,1906,146,859,803,571,"N","W",72,170,24,850,"N" 38 | "-Bobby Meacham",161,36,0,19,10,17,4,1053,244,3,156,86,107,"A","E",70,149,12,NA,"A" 39 | "-Bob Melvin",268,60,5,24,25,15,2,350,78,5,34,29,18,"N","W",442,59,6,90,"N" 40 | "-Ben Oglivie",346,98,5,31,53,30,16,5913,1615,235,784,901,560,"A","E",0,0,0,NA,"A" 41 | "-Bip Roberts",241,61,1,34,12,14,1,241,61,1,34,12,14,"N","W",166,172,10,NA,"N" 42 | "-BillyJo Robidoux",181,41,1,15,21,33,2,232,50,4,20,29,45,"A","E",326,29,5,67.5,"A" 43 | "-Bill Russell",216,54,0,21,18,15,18,7318,1926,46,796,627,483,"N","W",103,84,5,NA,"N" 44 | "-Billy Sample",200,57,6,23,14,14,9,2516,684,46,371,230,195,"N","W",69,1,1,NA,"N" 45 | "-Bill Schroeder",217,46,7,32,19,9,4,694,160,32,86,76,32,"A","E",307,25,1,180,"A" 46 | "-Butch Wynegar",194,40,7,19,29,30,11,4183,1069,64,486,493,608,"A","E",325,22,2,NA,"A" 47 | "-Chris Bando",254,68,2,28,26,22,6,999,236,21,108,117,118,"A","E",359,30,4,305,"A" 48 | "-Chris Brown",416,132,7,57,49,33,3,932,273,24,113,121,80,"N","W",73,177,18,215,"N" 49 | "-Carmen Castillo",205,57,8,34,32,9,5,756,192,32,117,107,51,"A","E",58,4,4,247.5,"A" 50 | "-Cecil Cooper",542,140,12,46,75,41,16,7099,2130,235,987,1089,431,"A","E",697,61,9,NA,"A" 51 | "-Chili Davis",526,146,13,71,70,84,6,2648,715,77,352,342,289,"N","W",303,9,9,815,"N" 52 | "-Carlton Fisk",457,101,14,42,63,22,17,6521,1767,281,1003,977,619,"A","W",389,39,4,875,"A" 53 | "-Curt Ford",214,53,2,30,29,23,2,226,59,2,32,32,27,"N","E",109,7,3,70,"N" 54 | "-Cliff Johnson",19,7,0,1,2,1,4,41,13,1,3,4,4,"A","E",0,0,0,NA,"A" 55 | "-Carney Lansford",591,168,19,80,72,39,9,4478,1307,113,634,563,319,"A","W",67,147,4,1200,"A" 56 | "-Chet Lemon",403,101,12,45,53,39,12,5150,1429,166,747,666,526,"A","E",316,6,5,675,"A" 57 | "-Candy Maldonado",405,102,18,49,85,20,6,950,231,29,99,138,64,"N","W",161,10,3,415,"N" 58 | "-Carmelo Martinez",244,58,9,28,25,35,4,1335,333,49,164,179,194,"N","W",142,14,2,340,"N" 59 | "-Charlie Moore",235,61,3,24,39,21,14,3926,1029,35,441,401,333,"A","E",425,43,4,NA,"A" 60 | "-Craig Reynolds",313,78,6,32,41,12,12,3742,968,35,409,321,170,"N","W",106,206,7,416.667,"N" 61 | "-Cal Ripken",627,177,25,98,81,70,6,3210,927,133,529,472,313,"A","E",240,482,13,1350,"A" 62 | "-Cory Snyder",416,113,24,58,69,16,1,416,113,24,58,69,16,"A","E",203,70,10,90,"A" 63 | "-Chris Speier",155,44,6,21,23,15,16,6631,1634,98,698,661,777,"N","E",53,88,3,275,"N" 64 | "-Curt Wilkerson",236,56,0,27,15,11,4,1115,270,1,116,64,57,"A","W",125,199,13,230,"A" 65 | "-Dave Anderson",216,53,1,31,15,22,4,926,210,9,118,69,114,"N","W",73,152,11,225,"N" 66 | "-Doug Baker",24,3,0,1,0,2,3,159,28,0,20,12,9,"A","W",80,4,0,NA,"A" 67 | "-Don Baylor",585,139,31,93,94,62,17,7546,1982,315,1141,1179,727,"A","E",0,0,0,950,"A" 68 | "-Dann Bilardello",191,37,4,12,17,14,4,773,163,16,61,74,52,"N","E",391,38,8,NA,"N" 69 | "-Daryl Boston",199,53,5,29,22,21,3,514,120,8,57,40,39,"A","W",152,3,5,75,"A" 70 | "-Darnell Coles",521,142,20,67,86,45,4,815,205,22,99,103,78,"A","E",107,242,23,105,"A" 71 | "-Dave Collins",419,113,1,44,27,44,12,4484,1231,32,612,344,422,"A","E",211,2,1,NA,"A" 72 | "-Dave Concepcion",311,81,3,42,30,26,17,8247,2198,100,950,909,690,"N","W",153,223,10,320,"N" 73 | "-Darren Daulton",138,31,8,18,21,38,3,244,53,12,33,32,55,"N","E",244,21,4,NA,"N" 74 | "-Doug DeCinces",512,131,26,69,96,52,14,5347,1397,221,712,815,548,"A","W",119,216,12,850,"A" 75 | "-Darrell Evans",507,122,29,78,85,91,18,7761,1947,347,1175,1152,1380,"A","E",808,108,2,535,"A" 76 | "-Dwight Evans",529,137,26,86,97,97,15,6661,1785,291,1082,949,989,"A","E",280,10,5,933.333,"A" 77 | "-Damaso Garcia",424,119,6,57,46,13,9,3651,1046,32,461,301,112,"A","E",224,286,8,850,"N" 78 | "-Dan Gladden",351,97,4,55,29,39,4,1258,353,16,196,110,117,"N","W",226,7,3,210,"A" 79 | "-Danny Heep",195,55,5,24,33,30,8,1313,338,25,144,149,153,"N","E",83,2,1,NA,"N" 80 | "-Dave Henderson",388,103,15,59,47,39,6,2174,555,80,285,274,186,"A","W",182,9,4,325,"A" 81 | "-Donnie Hill",339,96,4,37,29,23,4,1064,290,11,123,108,55,"A","W",104,213,9,275,"A" 82 | "-Dave Kingman",561,118,35,70,94,33,16,6677,1575,442,901,1210,608,"A","W",463,32,8,NA,"A" 83 | "-Davey Lopes",255,70,7,49,35,43,15,6311,1661,154,1019,608,820,"N","E",51,54,8,450,"N" 84 | "-Don Mattingly",677,238,31,117,113,53,5,2223,737,93,349,401,171,"A","E",1377,100,6,1975,"A" 85 | "-Darryl Motley",227,46,7,23,20,12,5,1325,324,44,156,158,67,"A","W",92,2,2,NA,"A" 86 | "-Dale Murphy",614,163,29,89,83,75,11,5017,1388,266,813,822,617,"N","W",303,6,6,1900,"N" 87 | "-Dwayne Murphy",329,83,9,50,39,56,9,3828,948,145,575,528,635,"A","W",276,6,2,600,"A" 88 | "-Dave Parker",637,174,31,89,116,56,14,6727,2024,247,978,1093,495,"N","W",278,9,9,1041.667,"N" 89 | "-Dan Pasqua",280,82,16,44,45,47,2,428,113,25,61,70,63,"A","E",148,4,2,110,"A" 90 | "-Darrell Porter",155,41,12,21,29,22,16,5409,1338,181,746,805,875,"A","W",165,9,1,260,"A" 91 | "-Dick Schofield",458,114,13,67,57,48,4,1350,298,28,160,123,122,"A","W",246,389,18,475,"A" 92 | "-Don Slaught",314,83,13,39,46,16,5,1457,405,28,156,159,76,"A","W",533,40,4,431.5,"A" 93 | "-Darryl Strawberry",475,123,27,76,93,72,4,1810,471,108,292,343,267,"N","E",226,10,6,1220,"N" 94 | "-Dale Sveum",317,78,7,35,35,32,1,317,78,7,35,35,32,"A","E",45,122,26,70,"A" 95 | "-Danny Tartabull",511,138,25,76,96,61,3,592,164,28,87,110,71,"A","W",157,7,8,145,"A" 96 | "-Dickie Thon",278,69,3,24,21,29,8,2079,565,32,258,192,162,"N","W",142,210,10,NA,"N" 97 | "-Denny Walling",382,119,13,54,58,36,12,2133,594,41,287,294,227,"N","W",59,156,9,595,"N" 98 | "-Dave Winfield",565,148,24,90,104,77,14,7287,2083,305,1135,1234,791,"A","E",292,9,5,1861.46,"A" 99 | "-Enos Cabell",277,71,2,27,29,14,15,5952,1647,60,753,596,259,"N","W",360,32,5,NA,"N" 100 | "-Eric Davis",415,115,27,97,71,68,3,711,184,45,156,119,99,"N","W",274,2,7,300,"N" 101 | "-Eddie Milner",424,110,15,70,47,36,7,2130,544,38,335,174,258,"N","W",292,6,3,490,"N" 102 | "-Eddie Murray",495,151,17,61,84,78,10,5624,1679,275,884,1015,709,"A","E",1045,88,13,2460,"A" 103 | "-Ernest Riles",524,132,9,69,47,54,2,972,260,14,123,92,90,"A","E",212,327,20,NA,"A" 104 | "-Ed Romero",233,49,2,41,23,18,8,1350,336,7,166,122,106,"A","E",102,132,10,375,"A" 105 | "-Ernie Whitt",395,106,16,48,56,35,10,2303,571,86,266,323,248,"A","E",709,41,7,NA,"A" 106 | "-Fred Lynn",397,114,23,67,67,53,13,5589,1632,241,906,926,716,"A","E",244,2,4,NA,"A" 107 | "-Floyd Rayford",210,37,8,15,19,15,6,994,244,36,107,114,53,"A","E",40,115,15,NA,"A" 108 | "-Franklin Stubbs",420,95,23,55,58,37,3,646,139,31,77,77,61,"N","W",206,10,7,NA,"N" 109 | "-Frank White",566,154,22,76,84,43,14,6100,1583,131,743,693,300,"A","W",316,439,10,750,"A" 110 | "-George Bell",641,198,31,101,108,41,5,2129,610,92,297,319,117,"A","E",269,17,10,1175,"A" 111 | "-Glenn Braggs",215,51,4,19,18,11,1,215,51,4,19,18,11,"A","E",116,5,12,70,"A" 112 | "-George Brett",441,128,16,70,73,80,14,6675,2095,209,1072,1050,695,"A","W",97,218,16,1500,"A" 113 | "-Greg Brock",325,76,16,33,52,37,5,1506,351,71,195,219,214,"N","W",726,87,3,385,"A" 114 | "-Gary Carter",490,125,24,81,105,62,13,6063,1646,271,847,999,680,"N","E",869,62,8,1925.571,"N" 115 | "-Glenn Davis",574,152,31,91,101,64,3,985,260,53,148,173,95,"N","W",1253,111,11,215,"N" 116 | "-George Foster",284,64,14,30,42,24,18,7023,1925,348,986,1239,666,"N","E",96,4,4,NA,"N" 117 | "-Gary Gaetti",596,171,34,91,108,52,6,2862,728,107,361,401,224,"A","W",118,334,21,900,"A" 118 | "-Greg Gagne",472,118,12,63,54,30,4,793,187,14,102,80,50,"A","W",228,377,26,155,"A" 119 | "-George Hendrick",283,77,14,45,47,26,16,6840,1910,259,915,1067,546,"A","W",144,6,5,700,"A" 120 | "-Glenn Hubbard",408,94,4,42,36,66,9,3573,866,59,429,365,410,"N","W",282,487,19,535,"N" 121 | "-Garth Iorg",327,85,3,30,44,20,8,2140,568,16,216,208,93,"A","E",91,185,12,362.5,"A" 122 | "-Gary Matthews",370,96,21,49,46,60,15,6986,1972,231,1070,955,921,"N","E",137,5,9,733.333,"N" 123 | "-Graig Nettles",354,77,16,36,55,41,20,8716,2172,384,1172,1267,1057,"N","W",83,174,16,200,"N" 124 | "-Gary Pettis",539,139,5,93,58,69,5,1469,369,12,247,126,198,"A","W",462,9,7,400,"A" 125 | "-Gary Redus",340,84,11,62,33,47,5,1516,376,42,284,141,219,"N","E",185,8,4,400,"A" 126 | "-Garry Templeton",510,126,2,42,44,35,11,5562,1578,44,703,519,256,"N","W",207,358,20,737.5,"N" 127 | "-Gorman Thomas",315,59,16,45,36,58,13,4677,1051,268,681,782,697,"A","W",0,0,0,NA,"A" 128 | "-Greg Walker",282,78,13,37,51,29,5,1649,453,73,211,280,138,"A","W",670,57,5,500,"A" 129 | "-Gary Ward",380,120,5,54,51,31,8,3118,900,92,444,419,240,"A","W",237,8,1,600,"A" 130 | "-Glenn Wilson",584,158,15,70,84,42,5,2358,636,58,265,316,134,"N","E",331,20,4,662.5,"N" 131 | "-Harold Baines",570,169,21,72,88,38,7,3754,1077,140,492,589,263,"A","W",295,15,5,950,"A" 132 | "-Hubie Brooks",306,104,14,50,58,25,7,2954,822,55,313,377,187,"N","E",116,222,15,750,"N" 133 | "-Howard Johnson",220,54,10,30,39,31,5,1185,299,40,145,154,128,"N","E",50,136,20,297.5,"N" 134 | "-Hal McRae",278,70,7,22,37,18,18,7186,2081,190,935,1088,643,"A","W",0,0,0,325,"A" 135 | "-Harold Reynolds",445,99,1,46,24,29,4,618,129,1,72,31,48,"A","W",278,415,16,87.5,"A" 136 | "-Harry Spilman",143,39,5,18,30,15,9,639,151,16,80,97,61,"N","W",138,15,1,175,"N" 137 | "-Herm Winningham",185,40,4,23,11,18,3,524,125,7,58,37,47,"N","E",97,2,2,90,"N" 138 | "-Jesse Barfield",589,170,40,107,108,69,6,2325,634,128,371,376,238,"A","E",368,20,3,1237.5,"A" 139 | "-Juan Beniquez",343,103,6,48,36,40,15,4338,1193,70,581,421,325,"A","E",211,56,13,430,"A" 140 | "-Juan Bonilla",284,69,1,33,18,25,5,1407,361,6,139,98,111,"A","E",122,140,5,NA,"N" 141 | "-John Cangelosi",438,103,2,65,32,71,2,440,103,2,67,32,71,"A","W",276,7,9,100,"N" 142 | "-Jose Canseco",600,144,33,85,117,65,2,696,173,38,101,130,69,"A","W",319,4,14,165,"A" 143 | "-Joe Carter",663,200,29,108,121,32,4,1447,404,57,210,222,68,"A","E",241,8,6,250,"A" 144 | "-Jack Clark",232,55,9,34,23,45,12,4405,1213,194,702,705,625,"N","E",623,35,3,1300,"N" 145 | "-Jose Cruz",479,133,10,48,72,55,17,7472,2147,153,980,1032,854,"N","W",237,5,4,773.333,"N" 146 | "-Julio Cruz",209,45,0,38,19,42,10,3859,916,23,557,279,478,"A","W",132,205,5,NA,"A" 147 | "-Jody Davis",528,132,21,61,74,41,6,2641,671,97,273,383,226,"N","E",885,105,8,1008.333,"N" 148 | "-Jim Dwyer",160,39,8,18,31,22,14,2128,543,56,304,268,298,"A","E",33,3,0,275,"A" 149 | "-Julio Franco",599,183,10,80,74,32,5,2482,715,27,330,326,158,"A","E",231,374,18,775,"A" 150 | "-Jim Gantner",497,136,7,58,38,26,11,3871,1066,40,450,367,241,"A","E",304,347,10,850,"A" 151 | "-Johnny Grubb",210,70,13,32,51,28,15,4040,1130,97,544,462,551,"A","E",0,0,0,365,"A" 152 | "-Jerry Hairston",225,61,5,32,26,26,11,1568,408,25,202,185,257,"A","W",132,9,0,NA,"A" 153 | "-Jack Howell",151,41,4,26,21,19,2,288,68,9,45,39,35,"A","W",28,56,2,95,"A" 154 | "-John Kruk",278,86,4,33,38,45,1,278,86,4,33,38,45,"N","W",102,4,2,110,"N" 155 | "-Jeffrey Leonard",341,95,6,48,42,20,10,2964,808,81,379,428,221,"N","W",158,4,5,100,"N" 156 | "-Jim Morrison",537,147,23,58,88,47,10,2744,730,97,302,351,174,"N","E",92,257,20,277.5,"N" 157 | "-John Moses",399,102,3,56,34,34,5,670,167,4,89,48,54,"A","W",211,9,3,80,"A" 158 | "-Jerry Mumphrey",309,94,5,37,32,26,13,4618,1330,57,616,522,436,"N","E",161,3,3,600,"N" 159 | "-Joe Orsulak",401,100,2,60,19,28,4,876,238,2,126,44,55,"N","E",193,11,4,NA,"N" 160 | "-Jorge Orta",336,93,9,35,46,23,15,5779,1610,128,730,741,497,"A","W",0,0,0,NA,"A" 161 | "-Jim Presley",616,163,27,83,107,32,3,1437,377,65,181,227,82,"A","W",110,308,15,200,"A" 162 | "-Jamie Quirk",219,47,8,24,26,17,12,1188,286,23,100,125,63,"A","W",260,58,4,NA,"A" 163 | "-Johnny Ray",579,174,7,67,78,58,6,3053,880,32,366,337,218,"N","E",280,479,5,657,"N" 164 | "-Jeff Reed",165,39,2,13,9,16,3,196,44,2,18,10,18,"A","W",332,19,2,75,"N" 165 | "-Jim Rice",618,200,20,98,110,62,13,7127,2163,351,1104,1289,564,"A","E",330,16,8,2412.5,"A" 166 | "-Jerry Royster",257,66,5,31,26,32,14,3910,979,33,518,324,382,"N","W",87,166,14,250,"A" 167 | "-John Russell",315,76,13,35,60,25,3,630,151,24,68,94,55,"N","E",498,39,13,155,"N" 168 | "-Juan Samuel",591,157,16,90,78,26,4,2020,541,52,310,226,91,"N","E",290,440,25,640,"N" 169 | "-John Shelby",404,92,11,54,49,18,6,1354,325,30,188,135,63,"A","E",222,5,5,300,"A" 170 | "-Joel Skinner",315,73,5,23,37,16,4,450,108,6,38,46,28,"A","W",227,15,3,110,"A" 171 | "-Jeff Stone",249,69,6,32,19,20,4,702,209,10,97,48,44,"N","E",103,8,2,NA,"N" 172 | "-Jim Sundberg",429,91,12,41,42,57,13,5590,1397,83,578,579,644,"A","W",686,46,4,825,"N" 173 | "-Jim Traber",212,54,13,28,44,18,2,233,59,13,31,46,20,"A","E",243,23,5,NA,"A" 174 | "-Jose Uribe",453,101,3,46,43,61,3,948,218,6,96,72,91,"N","W",249,444,16,195,"N" 175 | "-Jerry Willard",161,43,4,17,26,22,3,707,179,21,77,99,76,"A","W",300,12,2,NA,"A" 176 | "-Joel Youngblood",184,47,5,20,28,18,11,3327,890,74,419,382,304,"N","W",49,2,0,450,"N" 177 | "-Kevin Bass",591,184,20,83,79,38,5,1689,462,40,219,195,82,"N","W",303,12,5,630,"N" 178 | "-Kal Daniels",181,58,6,34,23,22,1,181,58,6,34,23,22,"N","W",88,0,3,86.5,"N" 179 | "-Kirk Gibson",441,118,28,84,86,68,8,2723,750,126,433,420,309,"A","E",190,2,2,1300,"A" 180 | "-Ken Griffey",490,150,21,69,58,35,14,6126,1839,121,983,707,600,"A","E",96,5,3,1000,"N" 181 | "-Keith Hernandez",551,171,13,94,83,94,13,6090,1840,128,969,900,917,"N","E",1199,149,5,1800,"N" 182 | "-Kent Hrbek",550,147,29,85,91,71,6,2816,815,117,405,474,319,"A","W",1218,104,10,1310,"A" 183 | "-Ken Landreaux",283,74,4,34,29,22,10,3919,1062,85,505,456,283,"N","W",145,5,7,737.5,"N" 184 | "-Kevin McReynolds",560,161,26,89,96,66,4,1789,470,65,233,260,155,"N","W",332,9,8,625,"N" 185 | "-Kevin Mitchell",328,91,12,51,43,33,2,342,94,12,51,44,33,"N","E",145,59,8,125,"N" 186 | "-Keith Moreland",586,159,12,72,79,53,9,3082,880,83,363,477,295,"N","E",181,13,4,1043.333,"N" 187 | "-Ken Oberkfell",503,136,5,62,48,83,10,3423,970,20,408,303,414,"N","W",65,258,8,725,"N" 188 | "-Ken Phelps",344,85,24,69,64,88,7,911,214,64,150,156,187,"A","W",0,0,0,300,"A" 189 | "-Kirby Puckett",680,223,31,119,96,34,3,1928,587,35,262,201,91,"A","W",429,8,6,365,"A" 190 | "-Kurt Stillwell",279,64,0,31,26,30,1,279,64,0,31,26,30,"N","W",107,205,16,75,"N" 191 | "-Leon Durham",484,127,20,66,65,67,7,3006,844,116,436,458,377,"N","E",1231,80,7,1183.333,"N" 192 | "-Len Dykstra",431,127,8,77,45,58,2,667,187,9,117,64,88,"N","E",283,8,3,202.5,"N" 193 | "-Larry Herndon",283,70,8,33,37,27,12,4479,1222,94,557,483,307,"A","E",156,2,2,225,"A" 194 | "-Lee Lacy",491,141,11,77,47,37,15,4291,1240,84,615,430,340,"A","E",239,8,2,525,"A" 195 | "-Len Matuszek",199,52,9,26,28,21,6,805,191,30,113,119,87,"N","W",235,22,5,265,"N" 196 | "-Lloyd Moseby",589,149,21,89,86,64,7,3558,928,102,513,471,351,"A","E",371,6,6,787.5,"A" 197 | "-Lance Parrish",327,84,22,53,62,38,10,4273,1123,212,577,700,334,"A","E",483,48,6,800,"N" 198 | "-Larry Parrish",464,128,28,67,94,52,13,5829,1552,210,740,840,452,"A","W",0,0,0,587.5,"A" 199 | "-Luis Rivera",166,34,0,20,13,17,1,166,34,0,20,13,17,"N","E",64,119,9,NA,"N" 200 | "-Larry Sheets",338,92,18,42,60,21,3,682,185,36,88,112,50,"A","E",0,0,0,145,"A" 201 | "-Lonnie Smith",508,146,8,80,44,46,9,3148,915,41,571,289,326,"A","W",245,5,9,NA,"A" 202 | "-Lou Whitaker",584,157,20,95,73,63,10,4704,1320,93,724,522,576,"A","E",276,421,11,420,"A" 203 | "-Mike Aldrete",216,54,2,27,25,33,1,216,54,2,27,25,33,"N","W",317,36,1,75,"N" 204 | "-Marty Barrett",625,179,4,94,60,65,5,1696,476,12,216,163,166,"A","E",303,450,14,575,"A" 205 | "-Mike Brown",243,53,4,18,26,27,4,853,228,23,101,110,76,"N","E",107,3,3,NA,"N" 206 | "-Mike Davis",489,131,19,77,55,34,7,2051,549,62,300,263,153,"A","W",310,9,9,780,"A" 207 | "-Mike Diaz",209,56,12,22,36,19,2,216,58,12,24,37,19,"N","E",201,6,3,90,"N" 208 | "-Mariano Duncan",407,93,8,47,30,30,2,969,230,14,121,69,68,"N","W",172,317,25,150,"N" 209 | "-Mike Easler",490,148,14,64,78,49,13,3400,1000,113,445,491,301,"A","E",0,0,0,700,"N" 210 | "-Mike Fitzgerald",209,59,6,20,37,27,4,884,209,14,66,106,92,"N","E",415,35,3,NA,"N" 211 | "-Mel Hall",442,131,18,68,77,33,6,1416,398,47,210,203,136,"A","E",233,7,7,550,"A" 212 | "-Mickey Hatcher",317,88,3,40,32,19,8,2543,715,28,269,270,118,"A","W",220,16,4,NA,"A" 213 | "-Mike Heath",288,65,8,30,36,27,9,2815,698,55,315,325,189,"N","E",259,30,10,650,"A" 214 | "-Mike Kingery",209,54,3,25,14,12,1,209,54,3,25,14,12,"A","W",102,6,3,68,"A" 215 | "-Mike LaValliere",303,71,3,18,30,36,3,344,76,3,20,36,45,"N","E",468,47,6,100,"N" 216 | "-Mike Marshall",330,77,19,47,53,27,6,1928,516,90,247,288,161,"N","W",149,8,6,670,"N" 217 | "-Mike Pagliarulo",504,120,28,71,71,54,3,1085,259,54,150,167,114,"A","E",103,283,19,175,"A" 218 | "-Mark Salas",258,60,8,28,33,18,3,638,170,17,80,75,36,"A","W",358,32,8,137,"A" 219 | "-Mike Schmidt",20,1,0,0,0,0,2,41,9,2,6,7,4,"N","E",78,220,6,2127.333,"N" 220 | "-Mike Scioscia",374,94,5,36,26,62,7,1968,519,26,181,199,288,"N","W",756,64,15,875,"N" 221 | "-Mickey Tettleton",211,43,10,26,35,39,3,498,116,14,59,55,78,"A","W",463,32,8,120,"A" 222 | "-Milt Thompson",299,75,6,38,23,26,3,580,160,8,71,33,44,"N","E",212,1,2,140,"N" 223 | "-Mitch Webster",576,167,8,89,49,57,4,822,232,19,132,83,79,"N","E",325,12,8,210,"N" 224 | "-Mookie Wilson",381,110,9,61,45,32,7,3015,834,40,451,249,168,"N","E",228,7,5,800,"N" 225 | "-Marvell Wynne",288,76,7,34,37,15,4,1644,408,16,198,120,113,"N","W",203,3,3,240,"N" 226 | "-Mike Young",369,93,9,43,42,49,5,1258,323,54,181,177,157,"A","E",149,1,6,350,"A" 227 | "-Nick Esasky",330,76,12,35,41,47,4,1367,326,55,167,198,167,"N","W",512,30,5,NA,"N" 228 | "-Ozzie Guillen",547,137,2,58,47,12,2,1038,271,3,129,80,24,"A","W",261,459,22,175,"A" 229 | "-Oddibe McDowell",572,152,18,105,49,65,2,978,249,36,168,91,101,"A","W",325,13,3,200,"A" 230 | "-Omar Moreno",359,84,4,46,27,21,12,4992,1257,37,699,386,387,"N","W",151,8,5,NA,"N" 231 | "-Ozzie Smith",514,144,0,67,54,79,9,4739,1169,13,583,374,528,"N","E",229,453,15,1940,"N" 232 | "-Ozzie Virgil",359,80,15,45,48,63,7,1493,359,61,176,202,175,"N","W",682,93,13,700,"N" 233 | "-Phil Bradley",526,163,12,88,50,77,4,1556,470,38,245,167,174,"A","W",250,11,1,750,"A" 234 | "-Phil Garner",313,83,9,43,41,30,14,5885,1543,104,751,714,535,"N","W",58,141,23,450,"N" 235 | "-Pete Incaviglia",540,135,30,82,88,55,1,540,135,30,82,88,55,"A","W",157,6,14,172,"A" 236 | "-Paul Molitor",437,123,9,62,55,40,9,4139,1203,79,676,390,364,"A","E",82,170,15,1260,"A" 237 | "-Pete O'Brien",551,160,23,86,90,87,5,2235,602,75,278,328,273,"A","W",1224,115,11,NA,"A" 238 | "-Pete Rose",237,52,0,15,25,30,24,14053,4256,160,2165,1314,1566,"N","W",523,43,6,750,"N" 239 | "-Pat Sheridan",236,56,6,41,19,21,5,1257,329,24,166,125,105,"A","E",172,1,4,190,"A" 240 | "-Pat Tabler",473,154,6,61,48,29,6,1966,566,29,250,252,178,"A","E",846,84,9,580,"A" 241 | "-Rafael Belliard",309,72,0,33,31,26,5,354,82,0,41,32,26,"N","E",117,269,12,130,"N" 242 | "-Rick Burleson",271,77,5,35,29,33,12,4933,1358,48,630,435,403,"A","W",62,90,3,450,"A" 243 | "-Randy Bush",357,96,7,50,45,39,5,1394,344,43,178,192,136,"A","W",167,2,4,300,"A" 244 | "-Rick Cerone",216,56,4,22,18,15,12,2796,665,43,266,304,198,"A","E",391,44,4,250,"A" 245 | "-Ron Cey",256,70,13,42,36,44,16,7058,1845,312,965,1128,990,"N","E",41,118,8,1050,"A" 246 | "-Rob Deer",466,108,33,75,86,72,3,652,142,44,102,109,102,"A","E",286,8,8,215,"A" 247 | "-Rick Dempsey",327,68,13,42,29,45,18,3949,939,78,438,380,466,"A","E",659,53,7,400,"A" 248 | "-Rich Gedman",462,119,16,49,65,37,7,2131,583,69,244,288,150,"A","E",866,65,6,NA,"A" 249 | "-Ron Hassey",341,110,9,45,49,46,9,2331,658,50,249,322,274,"A","E",251,9,4,560,"A" 250 | "-Rickey Henderson",608,160,28,130,74,89,8,4071,1182,103,862,417,708,"A","E",426,4,6,1670,"A" 251 | "-Reggie Jackson",419,101,18,65,58,92,20,9528,2510,548,1509,1659,1342,"A","W",0,0,0,487.5,"A" 252 | "-Ricky Jones",33,6,0,2,4,7,1,33,6,0,2,4,7,"A","W",205,5,4,NA,"A" 253 | "-Ron Kittle",376,82,21,42,60,35,5,1770,408,115,238,299,157,"A","W",0,0,0,425,"A" 254 | "-Ray Knight",486,145,11,51,76,40,11,3967,1102,67,410,497,284,"N","E",88,204,16,500,"A" 255 | "-Randy Kutcher",186,44,7,28,16,11,1,186,44,7,28,16,11,"N","W",99,3,1,NA,"N" 256 | "-Rudy Law",307,80,1,42,36,29,7,2421,656,18,379,198,184,"A","W",145,2,2,NA,"A" 257 | "-Rick Leach",246,76,5,35,39,13,6,912,234,12,102,96,80,"A","E",44,0,1,250,"A" 258 | "-Rick Manning",205,52,8,31,27,17,12,5134,1323,56,643,445,459,"A","E",155,3,2,400,"A" 259 | "-Rance Mulliniks",348,90,11,50,45,43,10,2288,614,43,295,273,269,"A","E",60,176,6,450,"A" 260 | "-Ron Oester",523,135,8,52,44,52,9,3368,895,39,377,284,296,"N","W",367,475,19,750,"N" 261 | "-Rey Quinones",312,68,2,32,22,24,1,312,68,2,32,22,24,"A","E",86,150,15,70,"A" 262 | "-Rafael Ramirez",496,119,8,57,33,21,7,3358,882,36,365,280,165,"N","W",155,371,29,875,"N" 263 | "-Ronn Reynolds",126,27,3,8,10,5,4,239,49,3,16,13,14,"N","E",190,2,9,190,"N" 264 | "-Ron Roenicke",275,68,5,42,42,61,6,961,238,16,128,104,172,"N","E",181,3,2,191,"N" 265 | "-Ryne Sandberg",627,178,14,68,76,46,6,3146,902,74,494,345,242,"N","E",309,492,5,740,"N" 266 | "-Rafael Santana",394,86,1,38,28,36,4,1089,267,3,94,71,76,"N","E",203,369,16,250,"N" 267 | "-Rick Schu",208,57,8,32,25,18,3,653,170,17,98,54,62,"N","E",42,94,13,140,"N" 268 | "-Ruben Sierra",382,101,16,50,55,22,1,382,101,16,50,55,22,"A","W",200,7,6,97.5,"A" 269 | "-Roy Smalley",459,113,20,59,57,68,12,5348,1369,155,713,660,735,"A","W",0,0,0,740,"A" 270 | "-Robby Thompson",549,149,7,73,47,42,1,549,149,7,73,47,42,"N","W",255,450,17,140,"N" 271 | "-Rob Wilfong",288,63,3,25,33,16,10,2682,667,38,315,259,204,"A","W",135,257,7,341.667,"A" 272 | "-Reggie Williams",303,84,4,35,32,23,2,312,87,4,39,32,23,"N","W",179,5,3,NA,"N" 273 | "-Robin Yount",522,163,9,82,46,62,13,7037,2019,153,1043,827,535,"A","E",352,9,1,1000,"A" 274 | "-Steve Balboni",512,117,29,54,88,43,6,1750,412,100,204,276,155,"A","W",1236,98,18,100,"A" 275 | "-Scott Bradley",220,66,5,20,28,13,3,290,80,5,27,31,15,"A","W",281,21,3,90,"A" 276 | "-Sid Bream",522,140,16,73,77,60,4,730,185,22,93,106,86,"N","E",1320,166,17,200,"N" 277 | "-Steve Buechele",461,112,18,54,54,35,2,680,160,24,76,75,49,"A","W",111,226,11,135,"A" 278 | "-Shawon Dunston",581,145,17,66,68,21,2,831,210,21,106,86,40,"N","E",320,465,32,155,"N" 279 | "-Scott Fletcher",530,159,3,82,50,47,6,1619,426,11,218,149,163,"A","W",196,354,15,475,"A" 280 | "-Steve Garvey",557,142,21,58,81,23,18,8759,2583,271,1138,1299,478,"N","W",1160,53,7,1450,"N" 281 | "-Steve Jeltz",439,96,0,44,36,65,4,711,148,1,68,56,99,"N","E",229,406,22,150,"N" 282 | "-Steve Lombardozzi",453,103,8,53,33,52,2,507,123,8,63,39,58,"A","W",289,407,6,105,"A" 283 | "-Spike Owen",528,122,1,67,45,51,4,1716,403,12,211,146,155,"A","W",209,372,17,350,"A" 284 | "-Steve Sax",633,210,6,91,56,59,6,3070,872,19,420,230,274,"N","W",367,432,16,90,"N" 285 | "-Tony Armas",16,2,0,1,0,0,2,28,4,0,1,0,0,"A","E",247,4,8,NA,"A" 286 | "-Tony Bernazard",562,169,17,88,73,53,8,3181,841,61,450,342,373,"A","E",351,442,17,530,"A" 287 | "-Tom Brookens",281,76,3,42,25,20,8,2658,657,48,324,300,179,"A","E",106,144,7,341.667,"A" 288 | "-Tom Brunansky",593,152,23,69,75,53,6,2765,686,133,369,384,321,"A","W",315,10,6,940,"A" 289 | "-Tony Fernandez",687,213,10,91,65,27,4,1518,448,15,196,137,89,"A","E",294,445,13,350,"A" 290 | "-Tim Flannery",368,103,3,48,28,54,8,1897,493,9,207,162,198,"N","W",209,246,3,326.667,"N" 291 | "-Tom Foley",263,70,1,26,23,30,4,888,220,9,83,82,86,"N","E",81,147,4,250,"N" 292 | "-Tony Gwynn",642,211,14,107,59,52,5,2364,770,27,352,230,193,"N","W",337,19,4,740,"N" 293 | "-Terry Harper",265,68,8,26,30,29,7,1337,339,32,135,163,128,"N","W",92,5,3,425,"A" 294 | "-Toby Harrah",289,63,7,36,41,44,17,7402,1954,195,1115,919,1153,"A","W",166,211,7,NA,"A" 295 | "-Tommy Herr",559,141,2,48,61,73,8,3162,874,16,421,349,359,"N","E",352,414,9,925,"N" 296 | "-Tim Hulett",520,120,17,53,44,21,4,927,227,22,106,80,52,"A","W",70,144,11,185,"A" 297 | "-Terry Kennedy",19,4,1,2,3,1,1,19,4,1,2,3,1,"N","W",692,70,8,920,"A" 298 | "-Tito Landrum",205,43,2,24,17,20,7,854,219,12,105,99,71,"N","E",131,6,1,286.667,"N" 299 | "-Tim Laudner",193,47,10,21,29,24,6,1136,256,42,129,139,106,"A","W",299,13,5,245,"A" 300 | "-Tom O'Malley",181,46,1,19,18,17,5,937,238,9,88,95,104,"A","E",37,98,9,NA,"A" 301 | "-Tom Paciorek",213,61,4,17,22,3,17,4061,1145,83,488,491,244,"A","W",178,45,4,235,"A" 302 | "-Tony Pena",510,147,10,56,52,53,7,2872,821,63,307,340,174,"N","E",810,99,18,1150,"N" 303 | "-Terry Pendleton",578,138,1,56,59,34,3,1399,357,7,149,161,87,"N","E",133,371,20,160,"N" 304 | "-Tony Perez",200,51,2,14,29,25,23,9778,2732,379,1272,1652,925,"N","W",398,29,7,NA,"N" 305 | "-Tony Phillips",441,113,5,76,52,76,5,1546,397,17,226,149,191,"A","W",160,290,11,425,"A" 306 | "-Terry Puhl",172,42,3,17,14,15,10,4086,1150,57,579,363,406,"N","W",65,0,0,900,"N" 307 | "-Tim Raines",580,194,9,91,62,78,8,3372,1028,48,604,314,469,"N","E",270,13,6,NA,"N" 308 | "-Ted Simmons",127,32,4,14,25,12,19,8396,2402,242,1048,1348,819,"N","W",167,18,6,500,"N" 309 | "-Tim Teufel",279,69,4,35,31,32,4,1359,355,31,180,148,158,"N","E",133,173,9,277.5,"N" 310 | "-Tim Wallach",480,112,18,50,71,44,7,3031,771,110,338,406,239,"N","E",94,270,16,750,"N" 311 | "-Vince Coleman",600,139,0,94,29,60,2,1236,309,1,201,69,110,"N","E",300,12,9,160,"N" 312 | "-Von Hayes",610,186,19,107,98,74,6,2728,753,69,399,366,286,"N","E",1182,96,13,1300,"N" 313 | "-Vance Law",360,81,5,37,44,37,7,2268,566,41,279,257,246,"N","E",170,284,3,525,"N" 314 | "-Wally Backman",387,124,1,67,27,36,7,1775,506,6,272,125,194,"N","E",186,290,17,550,"N" 315 | "-Wade Boggs",580,207,8,107,71,105,5,2778,978,32,474,322,417,"A","E",121,267,19,1600,"A" 316 | "-Will Clark",408,117,11,66,41,34,1,408,117,11,66,41,34,"N","W",942,72,11,120,"N" 317 | "-Wally Joyner",593,172,22,82,100,57,1,593,172,22,82,100,57,"A","W",1222,139,15,165,"A" 318 | "-Wayne Krenchicki",221,53,2,21,23,22,8,1063,283,15,107,124,106,"N","E",325,58,6,NA,"N" 319 | "-Willie McGee",497,127,7,65,48,37,5,2703,806,32,379,311,138,"N","E",325,9,3,700,"N" 320 | "-Willie Randolph",492,136,5,76,50,94,12,5511,1511,39,897,451,875,"A","E",313,381,20,875,"A" 321 | "-Wayne Tolleson",475,126,3,61,43,52,6,1700,433,7,217,93,146,"A","W",37,113,7,385,"A" 322 | "-Willie Upshaw",573,144,9,85,60,78,8,3198,857,97,470,420,332,"A","E",1314,131,12,960,"A" 323 | "-Willie Wilson",631,170,9,77,44,31,11,4908,1457,30,775,357,249,"A","W",408,4,3,1000,"A" 324 | -------------------------------------------------------------------------------- /data/csv/Boston.csv: -------------------------------------------------------------------------------- 1 | "crim","zn","indus","chas","nox","rm","age","dis","rad","tax","ptratio","black","lstat","medv" 2 | 0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24 3 | 0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6 4 | 0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7 5 | 0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4 6 | 0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2 7 | 0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7 8 | 0.08829,12.5,7.87,0,0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43,22.9 9 | 0.14455,12.5,7.87,0,0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15,27.1 10 | 0.21124,12.5,7.87,0,0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93,16.5 11 | 0.17004,12.5,7.87,0,0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1,18.9 12 | 0.22489,12.5,7.87,0,0.524,6.377,94.3,6.3467,5,311,15.2,392.52,20.45,15 13 | 0.11747,12.5,7.87,0,0.524,6.009,82.9,6.2267,5,311,15.2,396.9,13.27,18.9 14 | 0.09378,12.5,7.87,0,0.524,5.889,39,5.4509,5,311,15.2,390.5,15.71,21.7 15 | 0.62976,0,8.14,0,0.538,5.949,61.8,4.7075,4,307,21,396.9,8.26,20.4 16 | 0.63796,0,8.14,0,0.538,6.096,84.5,4.4619,4,307,21,380.02,10.26,18.2 17 | 0.62739,0,8.14,0,0.538,5.834,56.5,4.4986,4,307,21,395.62,8.47,19.9 18 | 1.05393,0,8.14,0,0.538,5.935,29.3,4.4986,4,307,21,386.85,6.58,23.1 19 | 0.7842,0,8.14,0,0.538,5.99,81.7,4.2579,4,307,21,386.75,14.67,17.5 20 | 0.80271,0,8.14,0,0.538,5.456,36.6,3.7965,4,307,21,288.99,11.69,20.2 21 | 0.7258,0,8.14,0,0.538,5.727,69.5,3.7965,4,307,21,390.95,11.28,18.2 22 | 1.25179,0,8.14,0,0.538,5.57,98.1,3.7979,4,307,21,376.57,21.02,13.6 23 | 0.85204,0,8.14,0,0.538,5.965,89.2,4.0123,4,307,21,392.53,13.83,19.6 24 | 1.23247,0,8.14,0,0.538,6.142,91.7,3.9769,4,307,21,396.9,18.72,15.2 25 | 0.98843,0,8.14,0,0.538,5.813,100,4.0952,4,307,21,394.54,19.88,14.5 26 | 0.75026,0,8.14,0,0.538,5.924,94.1,4.3996,4,307,21,394.33,16.3,15.6 27 | 0.84054,0,8.14,0,0.538,5.599,85.7,4.4546,4,307,21,303.42,16.51,13.9 28 | 0.67191,0,8.14,0,0.538,5.813,90.3,4.682,4,307,21,376.88,14.81,16.6 29 | 0.95577,0,8.14,0,0.538,6.047,88.8,4.4534,4,307,21,306.38,17.28,14.8 30 | 0.77299,0,8.14,0,0.538,6.495,94.4,4.4547,4,307,21,387.94,12.8,18.4 31 | 1.00245,0,8.14,0,0.538,6.674,87.3,4.239,4,307,21,380.23,11.98,21 32 | 1.13081,0,8.14,0,0.538,5.713,94.1,4.233,4,307,21,360.17,22.6,12.7 33 | 1.35472,0,8.14,0,0.538,6.072,100,4.175,4,307,21,376.73,13.04,14.5 34 | 1.38799,0,8.14,0,0.538,5.95,82,3.99,4,307,21,232.6,27.71,13.2 35 | 1.15172,0,8.14,0,0.538,5.701,95,3.7872,4,307,21,358.77,18.35,13.1 36 | 1.61282,0,8.14,0,0.538,6.096,96.9,3.7598,4,307,21,248.31,20.34,13.5 37 | 0.06417,0,5.96,0,0.499,5.933,68.2,3.3603,5,279,19.2,396.9,9.68,18.9 38 | 0.09744,0,5.96,0,0.499,5.841,61.4,3.3779,5,279,19.2,377.56,11.41,20 39 | 0.08014,0,5.96,0,0.499,5.85,41.5,3.9342,5,279,19.2,396.9,8.77,21 40 | 0.17505,0,5.96,0,0.499,5.966,30.2,3.8473,5,279,19.2,393.43,10.13,24.7 41 | 0.02763,75,2.95,0,0.428,6.595,21.8,5.4011,3,252,18.3,395.63,4.32,30.8 42 | 0.03359,75,2.95,0,0.428,7.024,15.8,5.4011,3,252,18.3,395.62,1.98,34.9 43 | 0.12744,0,6.91,0,0.448,6.77,2.9,5.7209,3,233,17.9,385.41,4.84,26.6 44 | 0.1415,0,6.91,0,0.448,6.169,6.6,5.7209,3,233,17.9,383.37,5.81,25.3 45 | 0.15936,0,6.91,0,0.448,6.211,6.5,5.7209,3,233,17.9,394.46,7.44,24.7 46 | 0.12269,0,6.91,0,0.448,6.069,40,5.7209,3,233,17.9,389.39,9.55,21.2 47 | 0.17142,0,6.91,0,0.448,5.682,33.8,5.1004,3,233,17.9,396.9,10.21,19.3 48 | 0.18836,0,6.91,0,0.448,5.786,33.3,5.1004,3,233,17.9,396.9,14.15,20 49 | 0.22927,0,6.91,0,0.448,6.03,85.5,5.6894,3,233,17.9,392.74,18.8,16.6 50 | 0.25387,0,6.91,0,0.448,5.399,95.3,5.87,3,233,17.9,396.9,30.81,14.4 51 | 0.21977,0,6.91,0,0.448,5.602,62,6.0877,3,233,17.9,396.9,16.2,19.4 52 | 0.08873,21,5.64,0,0.439,5.963,45.7,6.8147,4,243,16.8,395.56,13.45,19.7 53 | 0.04337,21,5.64,0,0.439,6.115,63,6.8147,4,243,16.8,393.97,9.43,20.5 54 | 0.0536,21,5.64,0,0.439,6.511,21.1,6.8147,4,243,16.8,396.9,5.28,25 55 | 0.04981,21,5.64,0,0.439,5.998,21.4,6.8147,4,243,16.8,396.9,8.43,23.4 56 | 0.0136,75,4,0,0.41,5.888,47.6,7.3197,3,469,21.1,396.9,14.8,18.9 57 | 0.01311,90,1.22,0,0.403,7.249,21.9,8.6966,5,226,17.9,395.93,4.81,35.4 58 | 0.02055,85,0.74,0,0.41,6.383,35.7,9.1876,2,313,17.3,396.9,5.77,24.7 59 | 0.01432,100,1.32,0,0.411,6.816,40.5,8.3248,5,256,15.1,392.9,3.95,31.6 60 | 0.15445,25,5.13,0,0.453,6.145,29.2,7.8148,8,284,19.7,390.68,6.86,23.3 61 | 0.10328,25,5.13,0,0.453,5.927,47.2,6.932,8,284,19.7,396.9,9.22,19.6 62 | 0.14932,25,5.13,0,0.453,5.741,66.2,7.2254,8,284,19.7,395.11,13.15,18.7 63 | 0.17171,25,5.13,0,0.453,5.966,93.4,6.8185,8,284,19.7,378.08,14.44,16 64 | 0.11027,25,5.13,0,0.453,6.456,67.8,7.2255,8,284,19.7,396.9,6.73,22.2 65 | 0.1265,25,5.13,0,0.453,6.762,43.4,7.9809,8,284,19.7,395.58,9.5,25 66 | 0.01951,17.5,1.38,0,0.4161,7.104,59.5,9.2229,3,216,18.6,393.24,8.05,33 67 | 0.03584,80,3.37,0,0.398,6.29,17.8,6.6115,4,337,16.1,396.9,4.67,23.5 68 | 0.04379,80,3.37,0,0.398,5.787,31.1,6.6115,4,337,16.1,396.9,10.24,19.4 69 | 0.05789,12.5,6.07,0,0.409,5.878,21.4,6.498,4,345,18.9,396.21,8.1,22 70 | 0.13554,12.5,6.07,0,0.409,5.594,36.8,6.498,4,345,18.9,396.9,13.09,17.4 71 | 0.12816,12.5,6.07,0,0.409,5.885,33,6.498,4,345,18.9,396.9,8.79,20.9 72 | 0.08826,0,10.81,0,0.413,6.417,6.6,5.2873,4,305,19.2,383.73,6.72,24.2 73 | 0.15876,0,10.81,0,0.413,5.961,17.5,5.2873,4,305,19.2,376.94,9.88,21.7 74 | 0.09164,0,10.81,0,0.413,6.065,7.8,5.2873,4,305,19.2,390.91,5.52,22.8 75 | 0.19539,0,10.81,0,0.413,6.245,6.2,5.2873,4,305,19.2,377.17,7.54,23.4 76 | 0.07896,0,12.83,0,0.437,6.273,6,4.2515,5,398,18.7,394.92,6.78,24.1 77 | 0.09512,0,12.83,0,0.437,6.286,45,4.5026,5,398,18.7,383.23,8.94,21.4 78 | 0.10153,0,12.83,0,0.437,6.279,74.5,4.0522,5,398,18.7,373.66,11.97,20 79 | 0.08707,0,12.83,0,0.437,6.14,45.8,4.0905,5,398,18.7,386.96,10.27,20.8 80 | 0.05646,0,12.83,0,0.437,6.232,53.7,5.0141,5,398,18.7,386.4,12.34,21.2 81 | 0.08387,0,12.83,0,0.437,5.874,36.6,4.5026,5,398,18.7,396.06,9.1,20.3 82 | 0.04113,25,4.86,0,0.426,6.727,33.5,5.4007,4,281,19,396.9,5.29,28 83 | 0.04462,25,4.86,0,0.426,6.619,70.4,5.4007,4,281,19,395.63,7.22,23.9 84 | 0.03659,25,4.86,0,0.426,6.302,32.2,5.4007,4,281,19,396.9,6.72,24.8 85 | 0.03551,25,4.86,0,0.426,6.167,46.7,5.4007,4,281,19,390.64,7.51,22.9 86 | 0.05059,0,4.49,0,0.449,6.389,48,4.7794,3,247,18.5,396.9,9.62,23.9 87 | 0.05735,0,4.49,0,0.449,6.63,56.1,4.4377,3,247,18.5,392.3,6.53,26.6 88 | 0.05188,0,4.49,0,0.449,6.015,45.1,4.4272,3,247,18.5,395.99,12.86,22.5 89 | 0.07151,0,4.49,0,0.449,6.121,56.8,3.7476,3,247,18.5,395.15,8.44,22.2 90 | 0.0566,0,3.41,0,0.489,7.007,86.3,3.4217,2,270,17.8,396.9,5.5,23.6 91 | 0.05302,0,3.41,0,0.489,7.079,63.1,3.4145,2,270,17.8,396.06,5.7,28.7 92 | 0.04684,0,3.41,0,0.489,6.417,66.1,3.0923,2,270,17.8,392.18,8.81,22.6 93 | 0.03932,0,3.41,0,0.489,6.405,73.9,3.0921,2,270,17.8,393.55,8.2,22 94 | 0.04203,28,15.04,0,0.464,6.442,53.6,3.6659,4,270,18.2,395.01,8.16,22.9 95 | 0.02875,28,15.04,0,0.464,6.211,28.9,3.6659,4,270,18.2,396.33,6.21,25 96 | 0.04294,28,15.04,0,0.464,6.249,77.3,3.615,4,270,18.2,396.9,10.59,20.6 97 | 0.12204,0,2.89,0,0.445,6.625,57.8,3.4952,2,276,18,357.98,6.65,28.4 98 | 0.11504,0,2.89,0,0.445,6.163,69.6,3.4952,2,276,18,391.83,11.34,21.4 99 | 0.12083,0,2.89,0,0.445,8.069,76,3.4952,2,276,18,396.9,4.21,38.7 100 | 0.08187,0,2.89,0,0.445,7.82,36.9,3.4952,2,276,18,393.53,3.57,43.8 101 | 0.0686,0,2.89,0,0.445,7.416,62.5,3.4952,2,276,18,396.9,6.19,33.2 102 | 0.14866,0,8.56,0,0.52,6.727,79.9,2.7778,5,384,20.9,394.76,9.42,27.5 103 | 0.11432,0,8.56,0,0.52,6.781,71.3,2.8561,5,384,20.9,395.58,7.67,26.5 104 | 0.22876,0,8.56,0,0.52,6.405,85.4,2.7147,5,384,20.9,70.8,10.63,18.6 105 | 0.21161,0,8.56,0,0.52,6.137,87.4,2.7147,5,384,20.9,394.47,13.44,19.3 106 | 0.1396,0,8.56,0,0.52,6.167,90,2.421,5,384,20.9,392.69,12.33,20.1 107 | 0.13262,0,8.56,0,0.52,5.851,96.7,2.1069,5,384,20.9,394.05,16.47,19.5 108 | 0.1712,0,8.56,0,0.52,5.836,91.9,2.211,5,384,20.9,395.67,18.66,19.5 109 | 0.13117,0,8.56,0,0.52,6.127,85.2,2.1224,5,384,20.9,387.69,14.09,20.4 110 | 0.12802,0,8.56,0,0.52,6.474,97.1,2.4329,5,384,20.9,395.24,12.27,19.8 111 | 0.26363,0,8.56,0,0.52,6.229,91.2,2.5451,5,384,20.9,391.23,15.55,19.4 112 | 0.10793,0,8.56,0,0.52,6.195,54.4,2.7778,5,384,20.9,393.49,13,21.7 113 | 0.10084,0,10.01,0,0.547,6.715,81.6,2.6775,6,432,17.8,395.59,10.16,22.8 114 | 0.12329,0,10.01,0,0.547,5.913,92.9,2.3534,6,432,17.8,394.95,16.21,18.8 115 | 0.22212,0,10.01,0,0.547,6.092,95.4,2.548,6,432,17.8,396.9,17.09,18.7 116 | 0.14231,0,10.01,0,0.547,6.254,84.2,2.2565,6,432,17.8,388.74,10.45,18.5 117 | 0.17134,0,10.01,0,0.547,5.928,88.2,2.4631,6,432,17.8,344.91,15.76,18.3 118 | 0.13158,0,10.01,0,0.547,6.176,72.5,2.7301,6,432,17.8,393.3,12.04,21.2 119 | 0.15098,0,10.01,0,0.547,6.021,82.6,2.7474,6,432,17.8,394.51,10.3,19.2 120 | 0.13058,0,10.01,0,0.547,5.872,73.1,2.4775,6,432,17.8,338.63,15.37,20.4 121 | 0.14476,0,10.01,0,0.547,5.731,65.2,2.7592,6,432,17.8,391.5,13.61,19.3 122 | 0.06899,0,25.65,0,0.581,5.87,69.7,2.2577,2,188,19.1,389.15,14.37,22 123 | 0.07165,0,25.65,0,0.581,6.004,84.1,2.1974,2,188,19.1,377.67,14.27,20.3 124 | 0.09299,0,25.65,0,0.581,5.961,92.9,2.0869,2,188,19.1,378.09,17.93,20.5 125 | 0.15038,0,25.65,0,0.581,5.856,97,1.9444,2,188,19.1,370.31,25.41,17.3 126 | 0.09849,0,25.65,0,0.581,5.879,95.8,2.0063,2,188,19.1,379.38,17.58,18.8 127 | 0.16902,0,25.65,0,0.581,5.986,88.4,1.9929,2,188,19.1,385.02,14.81,21.4 128 | 0.38735,0,25.65,0,0.581,5.613,95.6,1.7572,2,188,19.1,359.29,27.26,15.7 129 | 0.25915,0,21.89,0,0.624,5.693,96,1.7883,4,437,21.2,392.11,17.19,16.2 130 | 0.32543,0,21.89,0,0.624,6.431,98.8,1.8125,4,437,21.2,396.9,15.39,18 131 | 0.88125,0,21.89,0,0.624,5.637,94.7,1.9799,4,437,21.2,396.9,18.34,14.3 132 | 0.34006,0,21.89,0,0.624,6.458,98.9,2.1185,4,437,21.2,395.04,12.6,19.2 133 | 1.19294,0,21.89,0,0.624,6.326,97.7,2.271,4,437,21.2,396.9,12.26,19.6 134 | 0.59005,0,21.89,0,0.624,6.372,97.9,2.3274,4,437,21.2,385.76,11.12,23 135 | 0.32982,0,21.89,0,0.624,5.822,95.4,2.4699,4,437,21.2,388.69,15.03,18.4 136 | 0.97617,0,21.89,0,0.624,5.757,98.4,2.346,4,437,21.2,262.76,17.31,15.6 137 | 0.55778,0,21.89,0,0.624,6.335,98.2,2.1107,4,437,21.2,394.67,16.96,18.1 138 | 0.32264,0,21.89,0,0.624,5.942,93.5,1.9669,4,437,21.2,378.25,16.9,17.4 139 | 0.35233,0,21.89,0,0.624,6.454,98.4,1.8498,4,437,21.2,394.08,14.59,17.1 140 | 0.2498,0,21.89,0,0.624,5.857,98.2,1.6686,4,437,21.2,392.04,21.32,13.3 141 | 0.54452,0,21.89,0,0.624,6.151,97.9,1.6687,4,437,21.2,396.9,18.46,17.8 142 | 0.2909,0,21.89,0,0.624,6.174,93.6,1.6119,4,437,21.2,388.08,24.16,14 143 | 1.62864,0,21.89,0,0.624,5.019,100,1.4394,4,437,21.2,396.9,34.41,14.4 144 | 3.32105,0,19.58,1,0.871,5.403,100,1.3216,5,403,14.7,396.9,26.82,13.4 145 | 4.0974,0,19.58,0,0.871,5.468,100,1.4118,5,403,14.7,396.9,26.42,15.6 146 | 2.77974,0,19.58,0,0.871,4.903,97.8,1.3459,5,403,14.7,396.9,29.29,11.8 147 | 2.37934,0,19.58,0,0.871,6.13,100,1.4191,5,403,14.7,172.91,27.8,13.8 148 | 2.15505,0,19.58,0,0.871,5.628,100,1.5166,5,403,14.7,169.27,16.65,15.6 149 | 2.36862,0,19.58,0,0.871,4.926,95.7,1.4608,5,403,14.7,391.71,29.53,14.6 150 | 2.33099,0,19.58,0,0.871,5.186,93.8,1.5296,5,403,14.7,356.99,28.32,17.8 151 | 2.73397,0,19.58,0,0.871,5.597,94.9,1.5257,5,403,14.7,351.85,21.45,15.4 152 | 1.6566,0,19.58,0,0.871,6.122,97.3,1.618,5,403,14.7,372.8,14.1,21.5 153 | 1.49632,0,19.58,0,0.871,5.404,100,1.5916,5,403,14.7,341.6,13.28,19.6 154 | 1.12658,0,19.58,1,0.871,5.012,88,1.6102,5,403,14.7,343.28,12.12,15.3 155 | 2.14918,0,19.58,0,0.871,5.709,98.5,1.6232,5,403,14.7,261.95,15.79,19.4 156 | 1.41385,0,19.58,1,0.871,6.129,96,1.7494,5,403,14.7,321.02,15.12,17 157 | 3.53501,0,19.58,1,0.871,6.152,82.6,1.7455,5,403,14.7,88.01,15.02,15.6 158 | 2.44668,0,19.58,0,0.871,5.272,94,1.7364,5,403,14.7,88.63,16.14,13.1 159 | 1.22358,0,19.58,0,0.605,6.943,97.4,1.8773,5,403,14.7,363.43,4.59,41.3 160 | 1.34284,0,19.58,0,0.605,6.066,100,1.7573,5,403,14.7,353.89,6.43,24.3 161 | 1.42502,0,19.58,0,0.871,6.51,100,1.7659,5,403,14.7,364.31,7.39,23.3 162 | 1.27346,0,19.58,1,0.605,6.25,92.6,1.7984,5,403,14.7,338.92,5.5,27 163 | 1.46336,0,19.58,0,0.605,7.489,90.8,1.9709,5,403,14.7,374.43,1.73,50 164 | 1.83377,0,19.58,1,0.605,7.802,98.2,2.0407,5,403,14.7,389.61,1.92,50 165 | 1.51902,0,19.58,1,0.605,8.375,93.9,2.162,5,403,14.7,388.45,3.32,50 166 | 2.24236,0,19.58,0,0.605,5.854,91.8,2.422,5,403,14.7,395.11,11.64,22.7 167 | 2.924,0,19.58,0,0.605,6.101,93,2.2834,5,403,14.7,240.16,9.81,25 168 | 2.01019,0,19.58,0,0.605,7.929,96.2,2.0459,5,403,14.7,369.3,3.7,50 169 | 1.80028,0,19.58,0,0.605,5.877,79.2,2.4259,5,403,14.7,227.61,12.14,23.8 170 | 2.3004,0,19.58,0,0.605,6.319,96.1,2.1,5,403,14.7,297.09,11.1,23.8 171 | 2.44953,0,19.58,0,0.605,6.402,95.2,2.2625,5,403,14.7,330.04,11.32,22.3 172 | 1.20742,0,19.58,0,0.605,5.875,94.6,2.4259,5,403,14.7,292.29,14.43,17.4 173 | 2.3139,0,19.58,0,0.605,5.88,97.3,2.3887,5,403,14.7,348.13,12.03,19.1 174 | 0.13914,0,4.05,0,0.51,5.572,88.5,2.5961,5,296,16.6,396.9,14.69,23.1 175 | 0.09178,0,4.05,0,0.51,6.416,84.1,2.6463,5,296,16.6,395.5,9.04,23.6 176 | 0.08447,0,4.05,0,0.51,5.859,68.7,2.7019,5,296,16.6,393.23,9.64,22.6 177 | 0.06664,0,4.05,0,0.51,6.546,33.1,3.1323,5,296,16.6,390.96,5.33,29.4 178 | 0.07022,0,4.05,0,0.51,6.02,47.2,3.5549,5,296,16.6,393.23,10.11,23.2 179 | 0.05425,0,4.05,0,0.51,6.315,73.4,3.3175,5,296,16.6,395.6,6.29,24.6 180 | 0.06642,0,4.05,0,0.51,6.86,74.4,2.9153,5,296,16.6,391.27,6.92,29.9 181 | 0.0578,0,2.46,0,0.488,6.98,58.4,2.829,3,193,17.8,396.9,5.04,37.2 182 | 0.06588,0,2.46,0,0.488,7.765,83.3,2.741,3,193,17.8,395.56,7.56,39.8 183 | 0.06888,0,2.46,0,0.488,6.144,62.2,2.5979,3,193,17.8,396.9,9.45,36.2 184 | 0.09103,0,2.46,0,0.488,7.155,92.2,2.7006,3,193,17.8,394.12,4.82,37.9 185 | 0.10008,0,2.46,0,0.488,6.563,95.6,2.847,3,193,17.8,396.9,5.68,32.5 186 | 0.08308,0,2.46,0,0.488,5.604,89.8,2.9879,3,193,17.8,391,13.98,26.4 187 | 0.06047,0,2.46,0,0.488,6.153,68.8,3.2797,3,193,17.8,387.11,13.15,29.6 188 | 0.05602,0,2.46,0,0.488,7.831,53.6,3.1992,3,193,17.8,392.63,4.45,50 189 | 0.07875,45,3.44,0,0.437,6.782,41.1,3.7886,5,398,15.2,393.87,6.68,32 190 | 0.12579,45,3.44,0,0.437,6.556,29.1,4.5667,5,398,15.2,382.84,4.56,29.8 191 | 0.0837,45,3.44,0,0.437,7.185,38.9,4.5667,5,398,15.2,396.9,5.39,34.9 192 | 0.09068,45,3.44,0,0.437,6.951,21.5,6.4798,5,398,15.2,377.68,5.1,37 193 | 0.06911,45,3.44,0,0.437,6.739,30.8,6.4798,5,398,15.2,389.71,4.69,30.5 194 | 0.08664,45,3.44,0,0.437,7.178,26.3,6.4798,5,398,15.2,390.49,2.87,36.4 195 | 0.02187,60,2.93,0,0.401,6.8,9.9,6.2196,1,265,15.6,393.37,5.03,31.1 196 | 0.01439,60,2.93,0,0.401,6.604,18.8,6.2196,1,265,15.6,376.7,4.38,29.1 197 | 0.01381,80,0.46,0,0.422,7.875,32,5.6484,4,255,14.4,394.23,2.97,50 198 | 0.04011,80,1.52,0,0.404,7.287,34.1,7.309,2,329,12.6,396.9,4.08,33.3 199 | 0.04666,80,1.52,0,0.404,7.107,36.6,7.309,2,329,12.6,354.31,8.61,30.3 200 | 0.03768,80,1.52,0,0.404,7.274,38.3,7.309,2,329,12.6,392.2,6.62,34.6 201 | 0.0315,95,1.47,0,0.403,6.975,15.3,7.6534,3,402,17,396.9,4.56,34.9 202 | 0.01778,95,1.47,0,0.403,7.135,13.9,7.6534,3,402,17,384.3,4.45,32.9 203 | 0.03445,82.5,2.03,0,0.415,6.162,38.4,6.27,2,348,14.7,393.77,7.43,24.1 204 | 0.02177,82.5,2.03,0,0.415,7.61,15.7,6.27,2,348,14.7,395.38,3.11,42.3 205 | 0.0351,95,2.68,0,0.4161,7.853,33.2,5.118,4,224,14.7,392.78,3.81,48.5 206 | 0.02009,95,2.68,0,0.4161,8.034,31.9,5.118,4,224,14.7,390.55,2.88,50 207 | 0.13642,0,10.59,0,0.489,5.891,22.3,3.9454,4,277,18.6,396.9,10.87,22.6 208 | 0.22969,0,10.59,0,0.489,6.326,52.5,4.3549,4,277,18.6,394.87,10.97,24.4 209 | 0.25199,0,10.59,0,0.489,5.783,72.7,4.3549,4,277,18.6,389.43,18.06,22.5 210 | 0.13587,0,10.59,1,0.489,6.064,59.1,4.2392,4,277,18.6,381.32,14.66,24.4 211 | 0.43571,0,10.59,1,0.489,5.344,100,3.875,4,277,18.6,396.9,23.09,20 212 | 0.17446,0,10.59,1,0.489,5.96,92.1,3.8771,4,277,18.6,393.25,17.27,21.7 213 | 0.37578,0,10.59,1,0.489,5.404,88.6,3.665,4,277,18.6,395.24,23.98,19.3 214 | 0.21719,0,10.59,1,0.489,5.807,53.8,3.6526,4,277,18.6,390.94,16.03,22.4 215 | 0.14052,0,10.59,0,0.489,6.375,32.3,3.9454,4,277,18.6,385.81,9.38,28.1 216 | 0.28955,0,10.59,0,0.489,5.412,9.8,3.5875,4,277,18.6,348.93,29.55,23.7 217 | 0.19802,0,10.59,0,0.489,6.182,42.4,3.9454,4,277,18.6,393.63,9.47,25 218 | 0.0456,0,13.89,1,0.55,5.888,56,3.1121,5,276,16.4,392.8,13.51,23.3 219 | 0.07013,0,13.89,0,0.55,6.642,85.1,3.4211,5,276,16.4,392.78,9.69,28.7 220 | 0.11069,0,13.89,1,0.55,5.951,93.8,2.8893,5,276,16.4,396.9,17.92,21.5 221 | 0.11425,0,13.89,1,0.55,6.373,92.4,3.3633,5,276,16.4,393.74,10.5,23 222 | 0.35809,0,6.2,1,0.507,6.951,88.5,2.8617,8,307,17.4,391.7,9.71,26.7 223 | 0.40771,0,6.2,1,0.507,6.164,91.3,3.048,8,307,17.4,395.24,21.46,21.7 224 | 0.62356,0,6.2,1,0.507,6.879,77.7,3.2721,8,307,17.4,390.39,9.93,27.5 225 | 0.6147,0,6.2,0,0.507,6.618,80.8,3.2721,8,307,17.4,396.9,7.6,30.1 226 | 0.31533,0,6.2,0,0.504,8.266,78.3,2.8944,8,307,17.4,385.05,4.14,44.8 227 | 0.52693,0,6.2,0,0.504,8.725,83,2.8944,8,307,17.4,382,4.63,50 228 | 0.38214,0,6.2,0,0.504,8.04,86.5,3.2157,8,307,17.4,387.38,3.13,37.6 229 | 0.41238,0,6.2,0,0.504,7.163,79.9,3.2157,8,307,17.4,372.08,6.36,31.6 230 | 0.29819,0,6.2,0,0.504,7.686,17,3.3751,8,307,17.4,377.51,3.92,46.7 231 | 0.44178,0,6.2,0,0.504,6.552,21.4,3.3751,8,307,17.4,380.34,3.76,31.5 232 | 0.537,0,6.2,0,0.504,5.981,68.1,3.6715,8,307,17.4,378.35,11.65,24.3 233 | 0.46296,0,6.2,0,0.504,7.412,76.9,3.6715,8,307,17.4,376.14,5.25,31.7 234 | 0.57529,0,6.2,0,0.507,8.337,73.3,3.8384,8,307,17.4,385.91,2.47,41.7 235 | 0.33147,0,6.2,0,0.507,8.247,70.4,3.6519,8,307,17.4,378.95,3.95,48.3 236 | 0.44791,0,6.2,1,0.507,6.726,66.5,3.6519,8,307,17.4,360.2,8.05,29 237 | 0.33045,0,6.2,0,0.507,6.086,61.5,3.6519,8,307,17.4,376.75,10.88,24 238 | 0.52058,0,6.2,1,0.507,6.631,76.5,4.148,8,307,17.4,388.45,9.54,25.1 239 | 0.51183,0,6.2,0,0.507,7.358,71.6,4.148,8,307,17.4,390.07,4.73,31.5 240 | 0.08244,30,4.93,0,0.428,6.481,18.5,6.1899,6,300,16.6,379.41,6.36,23.7 241 | 0.09252,30,4.93,0,0.428,6.606,42.2,6.1899,6,300,16.6,383.78,7.37,23.3 242 | 0.11329,30,4.93,0,0.428,6.897,54.3,6.3361,6,300,16.6,391.25,11.38,22 243 | 0.10612,30,4.93,0,0.428,6.095,65.1,6.3361,6,300,16.6,394.62,12.4,20.1 244 | 0.1029,30,4.93,0,0.428,6.358,52.9,7.0355,6,300,16.6,372.75,11.22,22.2 245 | 0.12757,30,4.93,0,0.428,6.393,7.8,7.0355,6,300,16.6,374.71,5.19,23.7 246 | 0.20608,22,5.86,0,0.431,5.593,76.5,7.9549,7,330,19.1,372.49,12.5,17.6 247 | 0.19133,22,5.86,0,0.431,5.605,70.2,7.9549,7,330,19.1,389.13,18.46,18.5 248 | 0.33983,22,5.86,0,0.431,6.108,34.9,8.0555,7,330,19.1,390.18,9.16,24.3 249 | 0.19657,22,5.86,0,0.431,6.226,79.2,8.0555,7,330,19.1,376.14,10.15,20.5 250 | 0.16439,22,5.86,0,0.431,6.433,49.1,7.8265,7,330,19.1,374.71,9.52,24.5 251 | 0.19073,22,5.86,0,0.431,6.718,17.5,7.8265,7,330,19.1,393.74,6.56,26.2 252 | 0.1403,22,5.86,0,0.431,6.487,13,7.3967,7,330,19.1,396.28,5.9,24.4 253 | 0.21409,22,5.86,0,0.431,6.438,8.9,7.3967,7,330,19.1,377.07,3.59,24.8 254 | 0.08221,22,5.86,0,0.431,6.957,6.8,8.9067,7,330,19.1,386.09,3.53,29.6 255 | 0.36894,22,5.86,0,0.431,8.259,8.4,8.9067,7,330,19.1,396.9,3.54,42.8 256 | 0.04819,80,3.64,0,0.392,6.108,32,9.2203,1,315,16.4,392.89,6.57,21.9 257 | 0.03548,80,3.64,0,0.392,5.876,19.1,9.2203,1,315,16.4,395.18,9.25,20.9 258 | 0.01538,90,3.75,0,0.394,7.454,34.2,6.3361,3,244,15.9,386.34,3.11,44 259 | 0.61154,20,3.97,0,0.647,8.704,86.9,1.801,5,264,13,389.7,5.12,50 260 | 0.66351,20,3.97,0,0.647,7.333,100,1.8946,5,264,13,383.29,7.79,36 261 | 0.65665,20,3.97,0,0.647,6.842,100,2.0107,5,264,13,391.93,6.9,30.1 262 | 0.54011,20,3.97,0,0.647,7.203,81.8,2.1121,5,264,13,392.8,9.59,33.8 263 | 0.53412,20,3.97,0,0.647,7.52,89.4,2.1398,5,264,13,388.37,7.26,43.1 264 | 0.52014,20,3.97,0,0.647,8.398,91.5,2.2885,5,264,13,386.86,5.91,48.8 265 | 0.82526,20,3.97,0,0.647,7.327,94.5,2.0788,5,264,13,393.42,11.25,31 266 | 0.55007,20,3.97,0,0.647,7.206,91.6,1.9301,5,264,13,387.89,8.1,36.5 267 | 0.76162,20,3.97,0,0.647,5.56,62.8,1.9865,5,264,13,392.4,10.45,22.8 268 | 0.7857,20,3.97,0,0.647,7.014,84.6,2.1329,5,264,13,384.07,14.79,30.7 269 | 0.57834,20,3.97,0,0.575,8.297,67,2.4216,5,264,13,384.54,7.44,50 270 | 0.5405,20,3.97,0,0.575,7.47,52.6,2.872,5,264,13,390.3,3.16,43.5 271 | 0.09065,20,6.96,1,0.464,5.92,61.5,3.9175,3,223,18.6,391.34,13.65,20.7 272 | 0.29916,20,6.96,0,0.464,5.856,42.1,4.429,3,223,18.6,388.65,13,21.1 273 | 0.16211,20,6.96,0,0.464,6.24,16.3,4.429,3,223,18.6,396.9,6.59,25.2 274 | 0.1146,20,6.96,0,0.464,6.538,58.7,3.9175,3,223,18.6,394.96,7.73,24.4 275 | 0.22188,20,6.96,1,0.464,7.691,51.8,4.3665,3,223,18.6,390.77,6.58,35.2 276 | 0.05644,40,6.41,1,0.447,6.758,32.9,4.0776,4,254,17.6,396.9,3.53,32.4 277 | 0.09604,40,6.41,0,0.447,6.854,42.8,4.2673,4,254,17.6,396.9,2.98,32 278 | 0.10469,40,6.41,1,0.447,7.267,49,4.7872,4,254,17.6,389.25,6.05,33.2 279 | 0.06127,40,6.41,1,0.447,6.826,27.6,4.8628,4,254,17.6,393.45,4.16,33.1 280 | 0.07978,40,6.41,0,0.447,6.482,32.1,4.1403,4,254,17.6,396.9,7.19,29.1 281 | 0.21038,20,3.33,0,0.4429,6.812,32.2,4.1007,5,216,14.9,396.9,4.85,35.1 282 | 0.03578,20,3.33,0,0.4429,7.82,64.5,4.6947,5,216,14.9,387.31,3.76,45.4 283 | 0.03705,20,3.33,0,0.4429,6.968,37.2,5.2447,5,216,14.9,392.23,4.59,35.4 284 | 0.06129,20,3.33,1,0.4429,7.645,49.7,5.2119,5,216,14.9,377.07,3.01,46 285 | 0.01501,90,1.21,1,0.401,7.923,24.8,5.885,1,198,13.6,395.52,3.16,50 286 | 0.00906,90,2.97,0,0.4,7.088,20.8,7.3073,1,285,15.3,394.72,7.85,32.2 287 | 0.01096,55,2.25,0,0.389,6.453,31.9,7.3073,1,300,15.3,394.72,8.23,22 288 | 0.01965,80,1.76,0,0.385,6.23,31.5,9.0892,1,241,18.2,341.6,12.93,20.1 289 | 0.03871,52.5,5.32,0,0.405,6.209,31.3,7.3172,6,293,16.6,396.9,7.14,23.2 290 | 0.0459,52.5,5.32,0,0.405,6.315,45.6,7.3172,6,293,16.6,396.9,7.6,22.3 291 | 0.04297,52.5,5.32,0,0.405,6.565,22.9,7.3172,6,293,16.6,371.72,9.51,24.8 292 | 0.03502,80,4.95,0,0.411,6.861,27.9,5.1167,4,245,19.2,396.9,3.33,28.5 293 | 0.07886,80,4.95,0,0.411,7.148,27.7,5.1167,4,245,19.2,396.9,3.56,37.3 294 | 0.03615,80,4.95,0,0.411,6.63,23.4,5.1167,4,245,19.2,396.9,4.7,27.9 295 | 0.08265,0,13.92,0,0.437,6.127,18.4,5.5027,4,289,16,396.9,8.58,23.9 296 | 0.08199,0,13.92,0,0.437,6.009,42.3,5.5027,4,289,16,396.9,10.4,21.7 297 | 0.12932,0,13.92,0,0.437,6.678,31.1,5.9604,4,289,16,396.9,6.27,28.6 298 | 0.05372,0,13.92,0,0.437,6.549,51,5.9604,4,289,16,392.85,7.39,27.1 299 | 0.14103,0,13.92,0,0.437,5.79,58,6.32,4,289,16,396.9,15.84,20.3 300 | 0.06466,70,2.24,0,0.4,6.345,20.1,7.8278,5,358,14.8,368.24,4.97,22.5 301 | 0.05561,70,2.24,0,0.4,7.041,10,7.8278,5,358,14.8,371.58,4.74,29 302 | 0.04417,70,2.24,0,0.4,6.871,47.4,7.8278,5,358,14.8,390.86,6.07,24.8 303 | 0.03537,34,6.09,0,0.433,6.59,40.4,5.4917,7,329,16.1,395.75,9.5,22 304 | 0.09266,34,6.09,0,0.433,6.495,18.4,5.4917,7,329,16.1,383.61,8.67,26.4 305 | 0.1,34,6.09,0,0.433,6.982,17.7,5.4917,7,329,16.1,390.43,4.86,33.1 306 | 0.05515,33,2.18,0,0.472,7.236,41.1,4.022,7,222,18.4,393.68,6.93,36.1 307 | 0.05479,33,2.18,0,0.472,6.616,58.1,3.37,7,222,18.4,393.36,8.93,28.4 308 | 0.07503,33,2.18,0,0.472,7.42,71.9,3.0992,7,222,18.4,396.9,6.47,33.4 309 | 0.04932,33,2.18,0,0.472,6.849,70.3,3.1827,7,222,18.4,396.9,7.53,28.2 310 | 0.49298,0,9.9,0,0.544,6.635,82.5,3.3175,4,304,18.4,396.9,4.54,22.8 311 | 0.3494,0,9.9,0,0.544,5.972,76.7,3.1025,4,304,18.4,396.24,9.97,20.3 312 | 2.63548,0,9.9,0,0.544,4.973,37.8,2.5194,4,304,18.4,350.45,12.64,16.1 313 | 0.79041,0,9.9,0,0.544,6.122,52.8,2.6403,4,304,18.4,396.9,5.98,22.1 314 | 0.26169,0,9.9,0,0.544,6.023,90.4,2.834,4,304,18.4,396.3,11.72,19.4 315 | 0.26938,0,9.9,0,0.544,6.266,82.8,3.2628,4,304,18.4,393.39,7.9,21.6 316 | 0.3692,0,9.9,0,0.544,6.567,87.3,3.6023,4,304,18.4,395.69,9.28,23.8 317 | 0.25356,0,9.9,0,0.544,5.705,77.7,3.945,4,304,18.4,396.42,11.5,16.2 318 | 0.31827,0,9.9,0,0.544,5.914,83.2,3.9986,4,304,18.4,390.7,18.33,17.8 319 | 0.24522,0,9.9,0,0.544,5.782,71.7,4.0317,4,304,18.4,396.9,15.94,19.8 320 | 0.40202,0,9.9,0,0.544,6.382,67.2,3.5325,4,304,18.4,395.21,10.36,23.1 321 | 0.47547,0,9.9,0,0.544,6.113,58.8,4.0019,4,304,18.4,396.23,12.73,21 322 | 0.1676,0,7.38,0,0.493,6.426,52.3,4.5404,5,287,19.6,396.9,7.2,23.8 323 | 0.18159,0,7.38,0,0.493,6.376,54.3,4.5404,5,287,19.6,396.9,6.87,23.1 324 | 0.35114,0,7.38,0,0.493,6.041,49.9,4.7211,5,287,19.6,396.9,7.7,20.4 325 | 0.28392,0,7.38,0,0.493,5.708,74.3,4.7211,5,287,19.6,391.13,11.74,18.5 326 | 0.34109,0,7.38,0,0.493,6.415,40.1,4.7211,5,287,19.6,396.9,6.12,25 327 | 0.19186,0,7.38,0,0.493,6.431,14.7,5.4159,5,287,19.6,393.68,5.08,24.6 328 | 0.30347,0,7.38,0,0.493,6.312,28.9,5.4159,5,287,19.6,396.9,6.15,23 329 | 0.24103,0,7.38,0,0.493,6.083,43.7,5.4159,5,287,19.6,396.9,12.79,22.2 330 | 0.06617,0,3.24,0,0.46,5.868,25.8,5.2146,4,430,16.9,382.44,9.97,19.3 331 | 0.06724,0,3.24,0,0.46,6.333,17.2,5.2146,4,430,16.9,375.21,7.34,22.6 332 | 0.04544,0,3.24,0,0.46,6.144,32.2,5.8736,4,430,16.9,368.57,9.09,19.8 333 | 0.05023,35,6.06,0,0.4379,5.706,28.4,6.6407,1,304,16.9,394.02,12.43,17.1 334 | 0.03466,35,6.06,0,0.4379,6.031,23.3,6.6407,1,304,16.9,362.25,7.83,19.4 335 | 0.05083,0,5.19,0,0.515,6.316,38.1,6.4584,5,224,20.2,389.71,5.68,22.2 336 | 0.03738,0,5.19,0,0.515,6.31,38.5,6.4584,5,224,20.2,389.4,6.75,20.7 337 | 0.03961,0,5.19,0,0.515,6.037,34.5,5.9853,5,224,20.2,396.9,8.01,21.1 338 | 0.03427,0,5.19,0,0.515,5.869,46.3,5.2311,5,224,20.2,396.9,9.8,19.5 339 | 0.03041,0,5.19,0,0.515,5.895,59.6,5.615,5,224,20.2,394.81,10.56,18.5 340 | 0.03306,0,5.19,0,0.515,6.059,37.3,4.8122,5,224,20.2,396.14,8.51,20.6 341 | 0.05497,0,5.19,0,0.515,5.985,45.4,4.8122,5,224,20.2,396.9,9.74,19 342 | 0.06151,0,5.19,0,0.515,5.968,58.5,4.8122,5,224,20.2,396.9,9.29,18.7 343 | 0.01301,35,1.52,0,0.442,7.241,49.3,7.0379,1,284,15.5,394.74,5.49,32.7 344 | 0.02498,0,1.89,0,0.518,6.54,59.7,6.2669,1,422,15.9,389.96,8.65,16.5 345 | 0.02543,55,3.78,0,0.484,6.696,56.4,5.7321,5,370,17.6,396.9,7.18,23.9 346 | 0.03049,55,3.78,0,0.484,6.874,28.1,6.4654,5,370,17.6,387.97,4.61,31.2 347 | 0.03113,0,4.39,0,0.442,6.014,48.5,8.0136,3,352,18.8,385.64,10.53,17.5 348 | 0.06162,0,4.39,0,0.442,5.898,52.3,8.0136,3,352,18.8,364.61,12.67,17.2 349 | 0.0187,85,4.15,0,0.429,6.516,27.7,8.5353,4,351,17.9,392.43,6.36,23.1 350 | 0.01501,80,2.01,0,0.435,6.635,29.7,8.344,4,280,17,390.94,5.99,24.5 351 | 0.02899,40,1.25,0,0.429,6.939,34.5,8.7921,1,335,19.7,389.85,5.89,26.6 352 | 0.06211,40,1.25,0,0.429,6.49,44.4,8.7921,1,335,19.7,396.9,5.98,22.9 353 | 0.0795,60,1.69,0,0.411,6.579,35.9,10.7103,4,411,18.3,370.78,5.49,24.1 354 | 0.07244,60,1.69,0,0.411,5.884,18.5,10.7103,4,411,18.3,392.33,7.79,18.6 355 | 0.01709,90,2.02,0,0.41,6.728,36.1,12.1265,5,187,17,384.46,4.5,30.1 356 | 0.04301,80,1.91,0,0.413,5.663,21.9,10.5857,4,334,22,382.8,8.05,18.2 357 | 0.10659,80,1.91,0,0.413,5.936,19.5,10.5857,4,334,22,376.04,5.57,20.6 358 | 8.98296,0,18.1,1,0.77,6.212,97.4,2.1222,24,666,20.2,377.73,17.6,17.8 359 | 3.8497,0,18.1,1,0.77,6.395,91,2.5052,24,666,20.2,391.34,13.27,21.7 360 | 5.20177,0,18.1,1,0.77,6.127,83.4,2.7227,24,666,20.2,395.43,11.48,22.7 361 | 4.26131,0,18.1,0,0.77,6.112,81.3,2.5091,24,666,20.2,390.74,12.67,22.6 362 | 4.54192,0,18.1,0,0.77,6.398,88,2.5182,24,666,20.2,374.56,7.79,25 363 | 3.83684,0,18.1,0,0.77,6.251,91.1,2.2955,24,666,20.2,350.65,14.19,19.9 364 | 3.67822,0,18.1,0,0.77,5.362,96.2,2.1036,24,666,20.2,380.79,10.19,20.8 365 | 4.22239,0,18.1,1,0.77,5.803,89,1.9047,24,666,20.2,353.04,14.64,16.8 366 | 3.47428,0,18.1,1,0.718,8.78,82.9,1.9047,24,666,20.2,354.55,5.29,21.9 367 | 4.55587,0,18.1,0,0.718,3.561,87.9,1.6132,24,666,20.2,354.7,7.12,27.5 368 | 3.69695,0,18.1,0,0.718,4.963,91.4,1.7523,24,666,20.2,316.03,14,21.9 369 | 13.5222,0,18.1,0,0.631,3.863,100,1.5106,24,666,20.2,131.42,13.33,23.1 370 | 4.89822,0,18.1,0,0.631,4.97,100,1.3325,24,666,20.2,375.52,3.26,50 371 | 5.66998,0,18.1,1,0.631,6.683,96.8,1.3567,24,666,20.2,375.33,3.73,50 372 | 6.53876,0,18.1,1,0.631,7.016,97.5,1.2024,24,666,20.2,392.05,2.96,50 373 | 9.2323,0,18.1,0,0.631,6.216,100,1.1691,24,666,20.2,366.15,9.53,50 374 | 8.26725,0,18.1,1,0.668,5.875,89.6,1.1296,24,666,20.2,347.88,8.88,50 375 | 11.1081,0,18.1,0,0.668,4.906,100,1.1742,24,666,20.2,396.9,34.77,13.8 376 | 18.4982,0,18.1,0,0.668,4.138,100,1.137,24,666,20.2,396.9,37.97,13.8 377 | 19.6091,0,18.1,0,0.671,7.313,97.9,1.3163,24,666,20.2,396.9,13.44,15 378 | 15.288,0,18.1,0,0.671,6.649,93.3,1.3449,24,666,20.2,363.02,23.24,13.9 379 | 9.82349,0,18.1,0,0.671,6.794,98.8,1.358,24,666,20.2,396.9,21.24,13.3 380 | 23.6482,0,18.1,0,0.671,6.38,96.2,1.3861,24,666,20.2,396.9,23.69,13.1 381 | 17.8667,0,18.1,0,0.671,6.223,100,1.3861,24,666,20.2,393.74,21.78,10.2 382 | 88.9762,0,18.1,0,0.671,6.968,91.9,1.4165,24,666,20.2,396.9,17.21,10.4 383 | 15.8744,0,18.1,0,0.671,6.545,99.1,1.5192,24,666,20.2,396.9,21.08,10.9 384 | 9.18702,0,18.1,0,0.7,5.536,100,1.5804,24,666,20.2,396.9,23.6,11.3 385 | 7.99248,0,18.1,0,0.7,5.52,100,1.5331,24,666,20.2,396.9,24.56,12.3 386 | 20.0849,0,18.1,0,0.7,4.368,91.2,1.4395,24,666,20.2,285.83,30.63,8.8 387 | 16.8118,0,18.1,0,0.7,5.277,98.1,1.4261,24,666,20.2,396.9,30.81,7.2 388 | 24.3938,0,18.1,0,0.7,4.652,100,1.4672,24,666,20.2,396.9,28.28,10.5 389 | 22.5971,0,18.1,0,0.7,5,89.5,1.5184,24,666,20.2,396.9,31.99,7.4 390 | 14.3337,0,18.1,0,0.7,4.88,100,1.5895,24,666,20.2,372.92,30.62,10.2 391 | 8.15174,0,18.1,0,0.7,5.39,98.9,1.7281,24,666,20.2,396.9,20.85,11.5 392 | 6.96215,0,18.1,0,0.7,5.713,97,1.9265,24,666,20.2,394.43,17.11,15.1 393 | 5.29305,0,18.1,0,0.7,6.051,82.5,2.1678,24,666,20.2,378.38,18.76,23.2 394 | 11.5779,0,18.1,0,0.7,5.036,97,1.77,24,666,20.2,396.9,25.68,9.7 395 | 8.64476,0,18.1,0,0.693,6.193,92.6,1.7912,24,666,20.2,396.9,15.17,13.8 396 | 13.3598,0,18.1,0,0.693,5.887,94.7,1.7821,24,666,20.2,396.9,16.35,12.7 397 | 8.71675,0,18.1,0,0.693,6.471,98.8,1.7257,24,666,20.2,391.98,17.12,13.1 398 | 5.87205,0,18.1,0,0.693,6.405,96,1.6768,24,666,20.2,396.9,19.37,12.5 399 | 7.67202,0,18.1,0,0.693,5.747,98.9,1.6334,24,666,20.2,393.1,19.92,8.5 400 | 38.3518,0,18.1,0,0.693,5.453,100,1.4896,24,666,20.2,396.9,30.59,5 401 | 9.91655,0,18.1,0,0.693,5.852,77.8,1.5004,24,666,20.2,338.16,29.97,6.3 402 | 25.0461,0,18.1,0,0.693,5.987,100,1.5888,24,666,20.2,396.9,26.77,5.6 403 | 14.2362,0,18.1,0,0.693,6.343,100,1.5741,24,666,20.2,396.9,20.32,7.2 404 | 9.59571,0,18.1,0,0.693,6.404,100,1.639,24,666,20.2,376.11,20.31,12.1 405 | 24.8017,0,18.1,0,0.693,5.349,96,1.7028,24,666,20.2,396.9,19.77,8.3 406 | 41.5292,0,18.1,0,0.693,5.531,85.4,1.6074,24,666,20.2,329.46,27.38,8.5 407 | 67.9208,0,18.1,0,0.693,5.683,100,1.4254,24,666,20.2,384.97,22.98,5 408 | 20.7162,0,18.1,0,0.659,4.138,100,1.1781,24,666,20.2,370.22,23.34,11.9 409 | 11.9511,0,18.1,0,0.659,5.608,100,1.2852,24,666,20.2,332.09,12.13,27.9 410 | 7.40389,0,18.1,0,0.597,5.617,97.9,1.4547,24,666,20.2,314.64,26.4,17.2 411 | 14.4383,0,18.1,0,0.597,6.852,100,1.4655,24,666,20.2,179.36,19.78,27.5 412 | 51.1358,0,18.1,0,0.597,5.757,100,1.413,24,666,20.2,2.6,10.11,15 413 | 14.0507,0,18.1,0,0.597,6.657,100,1.5275,24,666,20.2,35.05,21.22,17.2 414 | 18.811,0,18.1,0,0.597,4.628,100,1.5539,24,666,20.2,28.79,34.37,17.9 415 | 28.6558,0,18.1,0,0.597,5.155,100,1.5894,24,666,20.2,210.97,20.08,16.3 416 | 45.7461,0,18.1,0,0.693,4.519,100,1.6582,24,666,20.2,88.27,36.98,7 417 | 18.0846,0,18.1,0,0.679,6.434,100,1.8347,24,666,20.2,27.25,29.05,7.2 418 | 10.8342,0,18.1,0,0.679,6.782,90.8,1.8195,24,666,20.2,21.57,25.79,7.5 419 | 25.9406,0,18.1,0,0.679,5.304,89.1,1.6475,24,666,20.2,127.36,26.64,10.4 420 | 73.5341,0,18.1,0,0.679,5.957,100,1.8026,24,666,20.2,16.45,20.62,8.8 421 | 11.8123,0,18.1,0,0.718,6.824,76.5,1.794,24,666,20.2,48.45,22.74,8.4 422 | 11.0874,0,18.1,0,0.718,6.411,100,1.8589,24,666,20.2,318.75,15.02,16.7 423 | 7.02259,0,18.1,0,0.718,6.006,95.3,1.8746,24,666,20.2,319.98,15.7,14.2 424 | 12.0482,0,18.1,0,0.614,5.648,87.6,1.9512,24,666,20.2,291.55,14.1,20.8 425 | 7.05042,0,18.1,0,0.614,6.103,85.1,2.0218,24,666,20.2,2.52,23.29,13.4 426 | 8.79212,0,18.1,0,0.584,5.565,70.6,2.0635,24,666,20.2,3.65,17.16,11.7 427 | 15.8603,0,18.1,0,0.679,5.896,95.4,1.9096,24,666,20.2,7.68,24.39,8.3 428 | 12.2472,0,18.1,0,0.584,5.837,59.7,1.9976,24,666,20.2,24.65,15.69,10.2 429 | 37.6619,0,18.1,0,0.679,6.202,78.7,1.8629,24,666,20.2,18.82,14.52,10.9 430 | 7.36711,0,18.1,0,0.679,6.193,78.1,1.9356,24,666,20.2,96.73,21.52,11 431 | 9.33889,0,18.1,0,0.679,6.38,95.6,1.9682,24,666,20.2,60.72,24.08,9.5 432 | 8.49213,0,18.1,0,0.584,6.348,86.1,2.0527,24,666,20.2,83.45,17.64,14.5 433 | 10.0623,0,18.1,0,0.584,6.833,94.3,2.0882,24,666,20.2,81.33,19.69,14.1 434 | 6.44405,0,18.1,0,0.584,6.425,74.8,2.2004,24,666,20.2,97.95,12.03,16.1 435 | 5.58107,0,18.1,0,0.713,6.436,87.9,2.3158,24,666,20.2,100.19,16.22,14.3 436 | 13.9134,0,18.1,0,0.713,6.208,95,2.2222,24,666,20.2,100.63,15.17,11.7 437 | 11.1604,0,18.1,0,0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27,13.4 438 | 14.4208,0,18.1,0,0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05,9.6 439 | 15.1772,0,18.1,0,0.74,6.152,100,1.9142,24,666,20.2,9.32,26.45,8.7 440 | 13.6781,0,18.1,0,0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02,8.4 441 | 9.39063,0,18.1,0,0.74,5.627,93.9,1.8172,24,666,20.2,396.9,22.88,12.8 442 | 22.0511,0,18.1,0,0.74,5.818,92.4,1.8662,24,666,20.2,391.45,22.11,10.5 443 | 9.72418,0,18.1,0,0.74,6.406,97.2,2.0651,24,666,20.2,385.96,19.52,17.1 444 | 5.66637,0,18.1,0,0.74,6.219,100,2.0048,24,666,20.2,395.69,16.59,18.4 445 | 9.96654,0,18.1,0,0.74,6.485,100,1.9784,24,666,20.2,386.73,18.85,15.4 446 | 12.8023,0,18.1,0,0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79,10.8 447 | 10.6718,0,18.1,0,0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98,11.8 448 | 6.28807,0,18.1,0,0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79,14.9 449 | 9.92485,0,18.1,0,0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44,12.6 450 | 9.32909,0,18.1,0,0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13,14.1 451 | 7.52601,0,18.1,0,0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31,13 452 | 6.71772,0,18.1,0,0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44,13.4 453 | 5.44114,0,18.1,0,0.713,6.655,98.2,2.3552,24,666,20.2,355.29,17.73,15.2 454 | 5.09017,0,18.1,0,0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27,16.1 455 | 8.24809,0,18.1,0,0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74,17.8 456 | 9.51363,0,18.1,0,0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71,14.9 457 | 4.75237,0,18.1,0,0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13,14.1 458 | 4.66883,0,18.1,0,0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01,12.7 459 | 8.20058,0,18.1,0,0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94,13.5 460 | 7.75223,0,18.1,0,0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23,14.9 461 | 6.80117,0,18.1,0,0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7,20 462 | 4.81213,0,18.1,0,0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42,16.4 463 | 3.69311,0,18.1,0,0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65,17.7 464 | 6.65492,0,18.1,0,0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99,19.5 465 | 5.82115,0,18.1,0,0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29,20.2 466 | 7.83932,0,18.1,0,0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22,21.4 467 | 3.1636,0,18.1,0,0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13,19.9 468 | 3.77498,0,18.1,0,0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15,19 469 | 4.42228,0,18.1,0,0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32,19.1 470 | 15.5757,0,18.1,0,0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13,19.1 471 | 13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76,20.1 472 | 4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29,19.9 473 | 4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6 474 | 3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2 475 | 4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66,29.8 476 | 8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8 477 | 6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3 478 | 4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7 479 | 15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12 480 | 10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6 481 | 14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4 482 | 5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23 483 | 5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7 484 | 5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25 485 | 2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8 486 | 2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6 487 | 3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2 488 | 5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1 489 | 4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6 490 | 0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2 491 | 0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7 492 | 0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1 493 | 0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6 494 | 0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1 495 | 0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8 496 | 0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5 497 | 0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1 498 | 0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7 499 | 0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3 500 | 0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2 501 | 0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5 502 | 0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8 503 | 0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4 504 | 0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6 505 | 0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9 506 | 0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22 507 | 0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9 508 | -------------------------------------------------------------------------------- /labs/chapter4/KNN.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np\n", 11 | "\n", 12 | "import seaborn as sns\n", 13 | "%matplotlib inline\n", 14 | "sns.set(rc={\"figure.figsize\":(12,8)})\n", 15 | "\n", 16 | "from sklearn.neighbors import KNeighborsClassifier\n", 17 | "from sklearn.metrics import confusion_matrix" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 2, 23 | 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YearLag1Lag2Lag3Lag4Lag5VolumeTodayDirection
020010.381-0.192-2.624-1.0555.0101.19130.959Up
120010.9590.381-0.192-2.624-1.0551.29651.032Up
220011.0320.9590.381-0.192-2.6241.4112-0.623Down
32001-0.6231.0320.9590.381-0.1921.27600.614Up
420010.614-0.6231.0320.9590.3811.20570.213Up
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" 121 | ], 122 | "text/plain": [ 123 | " Year Lag1 Lag2 Lag3 Lag4 Lag5 Volume Today Direction\n", 124 | "0 2001 0.381 -0.192 -2.624 -1.055 5.010 1.1913 0.959 Up\n", 125 | "1 2001 0.959 0.381 -0.192 -2.624 -1.055 1.2965 1.032 Up\n", 126 | "2 2001 1.032 0.959 0.381 -0.192 -2.624 1.4112 -0.623 Down\n", 127 | "3 2001 -0.623 1.032 0.959 0.381 -0.192 1.2760 0.614 Up\n", 128 | "4 2001 0.614 -0.623 1.032 0.959 0.381 1.2057 0.213 Up" 129 | ] 130 | }, 131 | "execution_count": 2, 132 | "metadata": {}, 133 | "output_type": "execute_result" 134 | } 135 | ], 136 | "source": [ 137 | "df = pd.read_csv(\"../../data/csv/Smarket.csv\")\n", 138 | "df.head()" 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "### The Stock Market Data" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": 3, 151 | "metadata": {}, 152 | "outputs": [ 153 | { 154 | "data": { 155 | "text/plain": [ 156 | "['Year',\n", 157 | " 'Lag1',\n", 158 | " 'Lag2',\n", 159 | " 'Lag3',\n", 160 | " 'Lag4',\n", 161 | " 'Lag5',\n", 162 | " 'Volume',\n", 163 | " 'Today',\n", 164 | " 'Direction']" 165 | ] 166 | }, 167 | "execution_count": 3, 168 | "metadata": {}, 169 | "output_type": "execute_result" 170 | } 171 | ], 172 | "source": [ 173 | "df.columns.tolist()" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 4, 179 | "metadata": {}, 180 | "outputs": [ 181 | { 182 | "data": { 183 | "text/plain": [ 184 | "(1250, 9)" 185 | ] 186 | }, 187 | "execution_count": 4, 188 | "metadata": {}, 189 | "output_type": "execute_result" 190 | } 191 | ], 192 | "source": [ 193 | "df.shape" 194 | ] 195 | }, 196 | { 197 | "cell_type": "code", 198 | "execution_count": 5, 199 | "metadata": {}, 200 | "outputs": [ 201 | { 202 | "data": { 203 | "text/html": [ 204 | "
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YearLag1Lag2Lag3Lag4Lag5VolumeToday
count1250.0000001250.0000001250.0000001250.0000001250.0000001250.000001250.0000001250.000000
mean2003.0160000.0038340.0039190.0017160.0016360.005611.4783050.003138
std1.4090181.1362991.1362801.1387031.1387741.147550.3603571.136334
min2001.000000-4.922000-4.922000-4.922000-4.922000-4.922000.356070-4.922000
25%2002.000000-0.639500-0.639500-0.640000-0.640000-0.640001.257400-0.639500
50%2003.0000000.0390000.0390000.0385000.0385000.038501.4229500.038500
75%2004.0000000.5967500.5967500.5967500.5967500.597001.6416750.596750
max2005.0000005.7330005.7330005.7330005.7330005.733003.1524705.733000
\n", 323 | "
" 324 | ], 325 | "text/plain": [ 326 | " Year Lag1 Lag2 Lag3 Lag4 \\\n", 327 | "count 1250.000000 1250.000000 1250.000000 1250.000000 1250.000000 \n", 328 | "mean 2003.016000 0.003834 0.003919 0.001716 0.001636 \n", 329 | "std 1.409018 1.136299 1.136280 1.138703 1.138774 \n", 330 | "min 2001.000000 -4.922000 -4.922000 -4.922000 -4.922000 \n", 331 | "25% 2002.000000 -0.639500 -0.639500 -0.640000 -0.640000 \n", 332 | "50% 2003.000000 0.039000 0.039000 0.038500 0.038500 \n", 333 | "75% 2004.000000 0.596750 0.596750 0.596750 0.596750 \n", 334 | "max 2005.000000 5.733000 5.733000 5.733000 5.733000 \n", 335 | "\n", 336 | " Lag5 Volume Today \n", 337 | "count 1250.00000 1250.000000 1250.000000 \n", 338 | "mean 0.00561 1.478305 0.003138 \n", 339 | "std 1.14755 0.360357 1.136334 \n", 340 | "min -4.92200 0.356070 -4.922000 \n", 341 | "25% -0.64000 1.257400 -0.639500 \n", 342 | "50% 0.03850 1.422950 0.038500 \n", 343 | "75% 0.59700 1.641675 0.596750 \n", 344 | "max 5.73300 3.152470 5.733000 " 345 | ] 346 | }, 347 | "execution_count": 5, 348 | "metadata": {}, 349 | "output_type": "execute_result" 350 | } 351 | ], 352 | "source": [ 353 | "df.describe()" 354 | ] 355 | }, 356 | { 357 | "cell_type": "code", 358 | "execution_count": 6, 359 | "metadata": {}, 360 | "outputs": [ 361 | { 362 | "data": { 363 | "text/plain": [ 364 | "count 1250\n", 365 | "unique 2\n", 366 | "top Up\n", 367 | "freq 648\n", 368 | "Name: Direction, dtype: object" 369 | ] 370 | }, 371 | "execution_count": 6, 372 | "metadata": {}, 373 | "output_type": "execute_result" 374 | } 375 | ], 376 | "source": [ 377 | "df['Direction'].describe()" 378 | ] 379 | }, 380 | { 381 | "cell_type": "code", 382 | "execution_count": 7, 383 | "metadata": {}, 384 | "outputs": [ 385 | { 386 | "data": { 387 | "text/html": [ 388 | "
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YearLag1Lag2Lag3Lag4Lag5VolumeToday
Year1.0000000.0297000.0305960.0331950.0356890.0297880.5390060.030095
Lag10.0297001.000000-0.026294-0.010803-0.002986-0.0056750.040910-0.026155
Lag20.030596-0.0262941.000000-0.025897-0.010854-0.003558-0.043383-0.010250
Lag30.033195-0.010803-0.0258971.000000-0.024051-0.018808-0.041824-0.002448
Lag40.035689-0.002986-0.010854-0.0240511.000000-0.027084-0.048414-0.006900
Lag50.029788-0.005675-0.003558-0.018808-0.0270841.000000-0.022002-0.034860
Volume0.5390060.040910-0.043383-0.041824-0.048414-0.0220021.0000000.014592
Today0.030095-0.026155-0.010250-0.002448-0.006900-0.0348600.0145921.000000
\n", 507 | "
" 508 | ], 509 | "text/plain": [ 510 | " Year Lag1 Lag2 Lag3 Lag4 Lag5 Volume \\\n", 511 | "Year 1.000000 0.029700 0.030596 0.033195 0.035689 0.029788 0.539006 \n", 512 | "Lag1 0.029700 1.000000 -0.026294 -0.010803 -0.002986 -0.005675 0.040910 \n", 513 | "Lag2 0.030596 -0.026294 1.000000 -0.025897 -0.010854 -0.003558 -0.043383 \n", 514 | "Lag3 0.033195 -0.010803 -0.025897 1.000000 -0.024051 -0.018808 -0.041824 \n", 515 | "Lag4 0.035689 -0.002986 -0.010854 -0.024051 1.000000 -0.027084 -0.048414 \n", 516 | "Lag5 0.029788 -0.005675 -0.003558 -0.018808 -0.027084 1.000000 -0.022002 \n", 517 | "Volume 0.539006 0.040910 -0.043383 -0.041824 -0.048414 -0.022002 1.000000 \n", 518 | "Today 0.030095 -0.026155 -0.010250 -0.002448 -0.006900 -0.034860 0.014592 \n", 519 | "\n", 520 | " Today \n", 521 | "Year 0.030095 \n", 522 | "Lag1 -0.026155 \n", 523 | "Lag2 -0.010250 \n", 524 | "Lag3 -0.002448 \n", 525 | "Lag4 -0.006900 \n", 526 | "Lag5 -0.034860 \n", 527 | "Volume 0.014592 \n", 528 | "Today 1.000000 " 529 | ] 530 | }, 531 | "execution_count": 7, 532 | "metadata": {}, 533 | "output_type": "execute_result" 534 | } 535 | ], 536 | "source": [ 537 | "# pairwise correlations\n", 538 | "df.corr()" 539 | ] 540 | }, 541 | { 542 | "cell_type": "markdown", 543 | "metadata": {}, 544 | "source": [ 545 | "The correlations between the lag variables (previous days’) and today’s returns are close to zero." 546 | ] 547 | }, 548 | { 549 | "cell_type": "code", 550 | "execution_count": 8, 551 | "metadata": { 552 | "scrolled": true 553 | }, 554 | "outputs": [ 555 | { 556 | "data": { 557 | "image/png": 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558 | "text/plain": [ 559 | "" 560 | ] 561 | }, 562 | "metadata": {}, 563 | "output_type": "display_data" 564 | } 565 | ], 566 | "source": [ 567 | "sns.boxplot(x=df['Year'], y=df['Volume'], palette=sns.color_palette(\"Blues\", 1));" 568 | ] 569 | }, 570 | { 571 | "cell_type": "markdown", 572 | "metadata": {}, 573 | "source": [ 574 | "The average number of shares traded daily increased from 2001 to 2005." 575 | ] 576 | }, 577 | { 578 | "cell_type": "markdown", 579 | "metadata": {}, 580 | "source": [ 581 | "## K-Nearest Neighbor\n", 582 | "\n", 583 | "Direction = whether the market was Up or Down \n", 584 | "Lag1, Lag2 = percentage returns for the two previous trading days \n", 585 | "\n", 586 | "Predict Direction from Lag1 and Lag2:" 587 | ] 588 | }, 589 | { 590 | "cell_type": "code", 591 | "execution_count": 9, 592 | "metadata": {}, 593 | "outputs": [], 594 | "source": [ 595 | "model = KNeighborsClassifier(n_neighbors=3)" 596 | ] 597 | }, 598 | { 599 | "cell_type": "markdown", 600 | "metadata": {}, 601 | "source": [ 602 | "### Model Fit\n", 603 | "\n", 604 | "Use the observations from 2001 to 2004 as training data and the observations from 2005 as testing data." 605 | ] 606 | }, 607 | { 608 | "cell_type": "code", 609 | "execution_count": 10, 610 | "metadata": {}, 611 | "outputs": [ 612 | { 613 | "name": "stdout", 614 | "output_type": "stream", 615 | "text": [ 616 | "(998, 2)\n", 617 | "(252, 2)\n" 618 | ] 619 | } 620 | ], 621 | "source": [ 622 | "xtrain = df[df['Year'] != 2005][['Lag1', 'Lag2']]\n", 623 | "xtest = df[df['Year'] == 2005][['Lag1', 'Lag2']]\n", 624 | "\n", 625 | "ytrain = df[df['Year'] != 2005][['Direction']]\n", 626 | "ytest = df[df['Year'] == 2005][['Direction']]\n", 627 | "\n", 628 | "print xtrain.shape\n", 629 | "print xtest.shape" 630 | ] 631 | }, 632 | { 633 | "cell_type": "code", 634 | "execution_count": 11, 635 | "metadata": {}, 636 | "outputs": [ 637 | { 638 | "name": "stderr", 639 | "output_type": "stream", 640 | "text": [ 641 | "/Users/divyanair/.pyenv/versions/2.7.14/envs/interview_env/lib/python2.7/site-packages/ipykernel_launcher.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", 642 | " \"\"\"Entry point for launching an IPython kernel.\n" 643 | ] 644 | }, 645 | { 646 | "data": { 647 | "text/plain": [ 648 | "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", 649 | " metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n", 650 | " weights='uniform')" 651 | ] 652 | }, 653 | "execution_count": 11, 654 | "metadata": {}, 655 | "output_type": "execute_result" 656 | } 657 | ], 658 | "source": [ 659 | "model.fit(X=xtrain, y=ytrain)" 660 | ] 661 | }, 662 | { 663 | "cell_type": "markdown", 664 | "metadata": {}, 665 | "source": [ 666 | "### Model Accuracy\n", 667 | "\n", 668 | "#### 1. Training Error" 669 | ] 670 | }, 671 | { 672 | "cell_type": "code", 673 | "execution_count": 12, 674 | "metadata": {}, 675 | "outputs": [ 676 | { 677 | "data": { 678 | "text/plain": [ 679 | "array(['Up', 'Up', 'Down', 'Up', 'Up'], dtype=object)" 680 | ] 681 | }, 682 | "execution_count": 12, 683 | "metadata": {}, 684 | "output_type": "execute_result" 685 | } 686 | ], 687 | "source": [ 688 | "ypredTrain = model.predict(X=xtrain)\n", 689 | "ypredTrain[:5]" 690 | ] 691 | }, 692 | { 693 | "cell_type": "code", 694 | "execution_count": 13, 695 | "metadata": {}, 696 | "outputs": [ 697 | { 698 | "data": { 699 | "text/plain": [ 700 | "0.75450901803607218" 701 | ] 702 | }, 703 | "execution_count": 13, 704 | "metadata": {}, 705 | "output_type": "execute_result" 706 | } 707 | ], 708 | "source": [ 709 | "np.mean(np.equal(ypredTrain, ytrain['Direction'].tolist()))" 710 | ] 711 | }, 712 | { 713 | "cell_type": "markdown", 714 | "metadata": {}, 715 | "source": [ 716 | "#### 2. Testing Error" 717 | ] 718 | }, 719 | { 720 | "cell_type": "code", 721 | "execution_count": 14, 722 | "metadata": {}, 723 | "outputs": [ 724 | { 725 | "data": { 726 | "text/plain": [ 727 | "array(['Down', 'Down', 'Down', 'Up', 'Up'], dtype=object)" 728 | ] 729 | }, 730 | "execution_count": 14, 731 | "metadata": {}, 732 | "output_type": "execute_result" 733 | } 734 | ], 735 | "source": [ 736 | "ypred = model.predict(X=xtest)\n", 737 | "ypred[:5]" 738 | ] 739 | }, 740 | { 741 | "cell_type": "code", 742 | "execution_count": 15, 743 | "metadata": {}, 744 | "outputs": [ 745 | { 746 | "data": { 747 | "text/plain": [ 748 | "array([[48, 63],\n", 749 | " [55, 86]])" 750 | ] 751 | }, 752 | "execution_count": 15, 753 | "metadata": {}, 754 | "output_type": "execute_result" 755 | } 756 | ], 757 | "source": [ 758 | "confusion_matrix(ytest, ypred, labels=[\"Down\", \"Up\"])" 759 | ] 760 | }, 761 | { 762 | "cell_type": "code", 763 | "execution_count": 16, 764 | "metadata": {}, 765 | "outputs": [ 766 | { 767 | "data": { 768 | "text/plain": [ 769 | "0.53174603174603174" 770 | ] 771 | }, 772 | "execution_count": 16, 773 | "metadata": {}, 774 | "output_type": "execute_result" 775 | } 776 | ], 777 | "source": [ 778 | "np.mean(np.equal(ypred, ytest['Direction'].tolist()))" 779 | ] 780 | } 781 | ], 782 | "metadata": { 783 | "kernelspec": { 784 | "display_name": "Python 2", 785 | "language": "python", 786 | "name": "python2" 787 | }, 788 | "language_info": { 789 | "codemirror_mode": { 790 | "name": "ipython", 791 | "version": 2 792 | }, 793 | "file_extension": ".py", 794 | "mimetype": "text/x-python", 795 | "name": "python", 796 | "nbconvert_exporter": "python", 797 | "pygments_lexer": "ipython2", 798 | "version": "2.7.14" 799 | } 800 | }, 801 | "nbformat": 4, 802 | "nbformat_minor": 2 803 | } 804 | --------------------------------------------------------------------------------