├── .gitattributes
├── .gitignore
├── Chapter10_Unsupervised_Learning.ipynb
├── Chapter3_Linear_regression.ipynb
├── Chapter4_classification.ipynb
├── Chapter5_Resampling_Methods.ipynb
├── Chapter6_Linear_Model_Selection_and_Regularization.ipynb
├── Chapter7_Moving_Beyond_Linearity.ipynb
├── Chapter8_Tree_Based_Methods.ipynb
├── Chapter9_Support_Vector_Machines.ipynb
├── Data
    ├── .Rhistory
    ├── Advertising.csv
    ├── Auto.csv
    ├── Boston.csv
    ├── Caravan.csv
    ├── Carseats.csv
    ├── College.csv
    ├── Credit.csv
    ├── Default.csv
    ├── Default.xlsx
    ├── Heart.csv
    ├── Hitters.csv
    ├── Hitters_X_test.csv
    ├── Hitters_X_train.csv
    ├── Hitters_y_test.csv
    ├── Hitters_y_train.csv
    ├── Khan.json
    ├── Khan.rda
    ├── Khan_xtest.csv
    ├── Khan_xtrain.csv
    ├── Khan_ytest.csv
    ├── Khan_ytrain.csv
    ├── NCI60.json
    ├── NCI60.rda
    ├── NCI60_X.csv
    ├── NCI60_y.csv
    ├── OJ.csv
    ├── Portfolio.csv
    ├── Smarket.csv
    ├── USArrests.csv
    ├── Wage.csv
    ├── Weekly.csv
    └── datalist
├── README.md
├── classification_helper.py
├── local_regressor.py
├── plot_lm.py
├── requirements.txt
├── splines.py
└── util.py


/.gitattributes:
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 1 | # Auto detect text files and perform LF normalization
 2 | * text=auto
 3 | 
 4 | # Custom for Visual Studio
 5 | *.cs     diff=csharp
 6 | 
 7 | # Standard to msysgit
 8 | *.doc	 diff=astextplain
 9 | *.DOC	 diff=astextplain
10 | *.docx diff=astextplain
11 | *.DOCX diff=astextplain
12 | *.dot  diff=astextplain
13 | *.DOT  diff=astextplain
14 | *.pdf  diff=astextplain
15 | *.PDF	 diff=astextplain
16 | *.rtf	 diff=astextplain
17 | *.RTF	 diff=astextplain
18 | 


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/.gitignore:
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 1 | # Windows thumbnail cache files
 2 | Thumbs.db
 3 | ehthumbs.db
 4 | ehthumbs_vista.db
 5 | 
 6 | # Folder config file
 7 | Desktop.ini
 8 | 
 9 | # Recycle Bin used on file shares
10 | $RECYCLE.BIN/
11 | 
12 | # Windows Installer files
13 | *.cab
14 | *.msi
15 | *.msm
16 | *.msp
17 | 
18 | # Windows shortcuts
19 | *.lnk
20 | 
21 | # =========================
22 | # Operating System Files
23 | # =========================
24 | 
25 | .ipynb_checkpoints/
26 | __pycache__/
27 | *.pyc
28 | 


--------------------------------------------------------------------------------
/Data/.Rhistory:
--------------------------------------------------------------------------------
 1 | load("~/Desktop/ISL_python/data/Khan.rda")
 2 | cat(capture.output(print(Khan), file="test.txt")
 3 | setwd("~/Desktop/ISL_python/data")
 4 | setwd("~/Desktop/ISL_python/data")
 5 | cat(capture.output(print(Khan), file="test.txt")
 6 | setwd("~/Desktop/ISL_python/data")
 7 | print(head(Khan,2))
 8 | lapply(Khan, write, "test.txt", append=TRUE, ncolumns=1000)
 9 | sink("mylist.txt")
10 | print(Khan)
11 | sink()
12 | install.packages('rjson')
13 | library(rjson)
14 | install.packages('RJSONIO')
15 | library(RJSONIO)
16 | exportJSON <- toJSON(Khan)
17 | write(exportJSON,"Khan.json")
18 | load("~/Desktop/ISL_python/data/NCI60.rda")
19 | exportJSON <- toJSON(NCI60)
20 | write(exportJSON,"NCI60.json")
21 | test = Khan$xtrain
22 | View(test)
23 | names(Khan)
24 | 


--------------------------------------------------------------------------------
/Data/Advertising.csv:
--------------------------------------------------------------------------------
  1 | "","TV","Radio","Newspaper","Sales"
  2 | "1",230.1,37.8,69.2,22.1
  3 | "2",44.5,39.3,45.1,10.4
  4 | "3",17.2,45.9,69.3,9.3
  5 | "4",151.5,41.3,58.5,18.5
  6 | "5",180.8,10.8,58.4,12.9
  7 | "6",8.7,48.9,75,7.2
  8 | "7",57.5,32.8,23.5,11.8
  9 | "8",120.2,19.6,11.6,13.2
 10 | "9",8.6,2.1,1,4.8
 11 | "10",199.8,2.6,21.2,10.6
 12 | "11",66.1,5.8,24.2,8.6
 13 | "12",214.7,24,4,17.4
 14 | "13",23.8,35.1,65.9,9.2
 15 | "14",97.5,7.6,7.2,9.7
 16 | "15",204.1,32.9,46,19
 17 | "16",195.4,47.7,52.9,22.4
 18 | "17",67.8,36.6,114,12.5
 19 | "18",281.4,39.6,55.8,24.4
 20 | "19",69.2,20.5,18.3,11.3
 21 | "20",147.3,23.9,19.1,14.6
 22 | "21",218.4,27.7,53.4,18
 23 | "22",237.4,5.1,23.5,12.5
 24 | "23",13.2,15.9,49.6,5.6
 25 | "24",228.3,16.9,26.2,15.5
 26 | "25",62.3,12.6,18.3,9.7
 27 | "26",262.9,3.5,19.5,12
 28 | "27",142.9,29.3,12.6,15
 29 | "28",240.1,16.7,22.9,15.9
 30 | "29",248.8,27.1,22.9,18.9
 31 | "30",70.6,16,40.8,10.5
 32 | "31",292.9,28.3,43.2,21.4
 33 | "32",112.9,17.4,38.6,11.9
 34 | "33",97.2,1.5,30,9.6
 35 | "34",265.6,20,0.3,17.4
 36 | "35",95.7,1.4,7.4,9.5
 37 | "36",290.7,4.1,8.5,12.8
 38 | "37",266.9,43.8,5,25.4
 39 | "38",74.7,49.4,45.7,14.7
 40 | "39",43.1,26.7,35.1,10.1
 41 | "40",228,37.7,32,21.5
 42 | "41",202.5,22.3,31.6,16.6
 43 | "42",177,33.4,38.7,17.1
 44 | "43",293.6,27.7,1.8,20.7
 45 | "44",206.9,8.4,26.4,12.9
 46 | "45",25.1,25.7,43.3,8.5
 47 | "46",175.1,22.5,31.5,14.9
 48 | "47",89.7,9.9,35.7,10.6
 49 | "48",239.9,41.5,18.5,23.2
 50 | "49",227.2,15.8,49.9,14.8
 51 | "50",66.9,11.7,36.8,9.7
 52 | "51",199.8,3.1,34.6,11.4
 53 | "52",100.4,9.6,3.6,10.7
 54 | "53",216.4,41.7,39.6,22.6
 55 | "54",182.6,46.2,58.7,21.2
 56 | "55",262.7,28.8,15.9,20.2
 57 | "56",198.9,49.4,60,23.7
 58 | "57",7.3,28.1,41.4,5.5
 59 | "58",136.2,19.2,16.6,13.2
 60 | "59",210.8,49.6,37.7,23.8
 61 | "60",210.7,29.5,9.3,18.4
 62 | "61",53.5,2,21.4,8.1
 63 | "62",261.3,42.7,54.7,24.2
 64 | "63",239.3,15.5,27.3,15.7
 65 | "64",102.7,29.6,8.4,14
 66 | "65",131.1,42.8,28.9,18
 67 | "66",69,9.3,0.9,9.3
 68 | "67",31.5,24.6,2.2,9.5
 69 | "68",139.3,14.5,10.2,13.4
 70 | "69",237.4,27.5,11,18.9
 71 | "70",216.8,43.9,27.2,22.3
 72 | "71",199.1,30.6,38.7,18.3
 73 | "72",109.8,14.3,31.7,12.4
 74 | "73",26.8,33,19.3,8.8
 75 | "74",129.4,5.7,31.3,11
 76 | "75",213.4,24.6,13.1,17
 77 | "76",16.9,43.7,89.4,8.7
 78 | "77",27.5,1.6,20.7,6.9
 79 | "78",120.5,28.5,14.2,14.2
 80 | "79",5.4,29.9,9.4,5.3
 81 | "80",116,7.7,23.1,11
 82 | "81",76.4,26.7,22.3,11.8
 83 | "82",239.8,4.1,36.9,12.3
 84 | "83",75.3,20.3,32.5,11.3
 85 | "84",68.4,44.5,35.6,13.6
 86 | "85",213.5,43,33.8,21.7
 87 | "86",193.2,18.4,65.7,15.2
 88 | "87",76.3,27.5,16,12
 89 | "88",110.7,40.6,63.2,16
 90 | "89",88.3,25.5,73.4,12.9
 91 | "90",109.8,47.8,51.4,16.7
 92 | "91",134.3,4.9,9.3,11.2
 93 | "92",28.6,1.5,33,7.3
 94 | "93",217.7,33.5,59,19.4
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 97 | "96",163.3,31.6,52.9,16.9
 98 | "97",197.6,3.5,5.9,11.7
 99 | "98",184.9,21,22,15.5
100 | "99",289.7,42.3,51.2,25.4
101 | "100",135.2,41.7,45.9,17.2
102 | "101",222.4,4.3,49.8,11.7
103 | "102",296.4,36.3,100.9,23.8
104 | "103",280.2,10.1,21.4,14.8
105 | "104",187.9,17.2,17.9,14.7
106 | "105",238.2,34.3,5.3,20.7
107 | "106",137.9,46.4,59,19.2
108 | "107",25,11,29.7,7.2
109 | "108",90.4,0.3,23.2,8.7
110 | "109",13.1,0.4,25.6,5.3
111 | "110",255.4,26.9,5.5,19.8
112 | "111",225.8,8.2,56.5,13.4
113 | "112",241.7,38,23.2,21.8
114 | "113",175.7,15.4,2.4,14.1
115 | "114",209.6,20.6,10.7,15.9
116 | "115",78.2,46.8,34.5,14.6
117 | "116",75.1,35,52.7,12.6
118 | "117",139.2,14.3,25.6,12.2
119 | "118",76.4,0.8,14.8,9.4
120 | "119",125.7,36.9,79.2,15.9
121 | "120",19.4,16,22.3,6.6
122 | "121",141.3,26.8,46.2,15.5
123 | "122",18.8,21.7,50.4,7
124 | "123",224,2.4,15.6,11.6
125 | "124",123.1,34.6,12.4,15.2
126 | "125",229.5,32.3,74.2,19.7
127 | "126",87.2,11.8,25.9,10.6
128 | "127",7.8,38.9,50.6,6.6
129 | "128",80.2,0,9.2,8.8
130 | "129",220.3,49,3.2,24.7
131 | "130",59.6,12,43.1,9.7
132 | "131",0.7,39.6,8.7,1.6
133 | "132",265.2,2.9,43,12.7
134 | "133",8.4,27.2,2.1,5.7
135 | "134",219.8,33.5,45.1,19.6
136 | "135",36.9,38.6,65.6,10.8
137 | "136",48.3,47,8.5,11.6
138 | "137",25.6,39,9.3,9.5
139 | "138",273.7,28.9,59.7,20.8
140 | "139",43,25.9,20.5,9.6
141 | "140",184.9,43.9,1.7,20.7
142 | "141",73.4,17,12.9,10.9
143 | "142",193.7,35.4,75.6,19.2
144 | "143",220.5,33.2,37.9,20.1
145 | "144",104.6,5.7,34.4,10.4
146 | "145",96.2,14.8,38.9,11.4
147 | "146",140.3,1.9,9,10.3
148 | "147",240.1,7.3,8.7,13.2
149 | "148",243.2,49,44.3,25.4
150 | "149",38,40.3,11.9,10.9
151 | "150",44.7,25.8,20.6,10.1
152 | "151",280.7,13.9,37,16.1
153 | "152",121,8.4,48.7,11.6
154 | "153",197.6,23.3,14.2,16.6
155 | "154",171.3,39.7,37.7,19
156 | "155",187.8,21.1,9.5,15.6
157 | "156",4.1,11.6,5.7,3.2
158 | "157",93.9,43.5,50.5,15.3
159 | "158",149.8,1.3,24.3,10.1
160 | "159",11.7,36.9,45.2,7.3
161 | "160",131.7,18.4,34.6,12.9
162 | "161",172.5,18.1,30.7,14.4
163 | "162",85.7,35.8,49.3,13.3
164 | "163",188.4,18.1,25.6,14.9
165 | "164",163.5,36.8,7.4,18
166 | "165",117.2,14.7,5.4,11.9
167 | "166",234.5,3.4,84.8,11.9
168 | "167",17.9,37.6,21.6,8
169 | "168",206.8,5.2,19.4,12.2
170 | "169",215.4,23.6,57.6,17.1
171 | "170",284.3,10.6,6.4,15
172 | "171",50,11.6,18.4,8.4
173 | "172",164.5,20.9,47.4,14.5
174 | "173",19.6,20.1,17,7.6
175 | "174",168.4,7.1,12.8,11.7
176 | "175",222.4,3.4,13.1,11.5
177 | "176",276.9,48.9,41.8,27
178 | "177",248.4,30.2,20.3,20.2
179 | "178",170.2,7.8,35.2,11.7
180 | "179",276.7,2.3,23.7,11.8
181 | "180",165.6,10,17.6,12.6
182 | "181",156.6,2.6,8.3,10.5
183 | "182",218.5,5.4,27.4,12.2
184 | "183",56.2,5.7,29.7,8.7
185 | "184",287.6,43,71.8,26.2
186 | "185",253.8,21.3,30,17.6
187 | "186",205,45.1,19.6,22.6
188 | "187",139.5,2.1,26.6,10.3
189 | "188",191.1,28.7,18.2,17.3
190 | "189",286,13.9,3.7,15.9
191 | "190",18.7,12.1,23.4,6.7
192 | "191",39.5,41.1,5.8,10.8
193 | "192",75.5,10.8,6,9.9
194 | "193",17.2,4.1,31.6,5.9
195 | "194",166.8,42,3.6,19.6
196 | "195",149.7,35.6,6,17.3
197 | "196",38.2,3.7,13.8,7.6
198 | "197",94.2,4.9,8.1,9.7
199 | "198",177,9.3,6.4,12.8
200 | "199",283.6,42,66.2,25.5
201 | "200",232.1,8.6,8.7,13.4
202 | 


--------------------------------------------------------------------------------
/Data/Auto.csv:
--------------------------------------------------------------------------------
  1 | mpg,cylinders,displacement,horsepower,weight,acceleration,year,origin,name
  2 | 18,8,307,130,3504,12,70,1,chevrolet chevelle malibu
  3 | 15,8,350,165,3693,11.5,70,1,buick skylark 320
  4 | 18,8,318,150,3436,11,70,1,plymouth satellite
  5 | 16,8,304,150,3433,12,70,1,amc rebel sst
  6 | 17,8,302,140,3449,10.5,70,1,ford torino
  7 | 15,8,429,198,4341,10,70,1,ford galaxie 500
  8 | 14,8,454,220,4354,9,70,1,chevrolet impala
  9 | 14,8,440,215,4312,8.5,70,1,plymouth fury iii
 10 | 14,8,455,225,4425,10,70,1,pontiac catalina
 11 | 15,8,390,190,3850,8.5,70,1,amc ambassador dpl
 12 | 15,8,383,170,3563,10,70,1,dodge challenger se
 13 | 14,8,340,160,3609,8,70,1,plymouth 'cuda 340
 14 | 15,8,400,150,3761,9.5,70,1,chevrolet monte carlo
 15 | 14,8,455,225,3086,10,70,1,buick estate wagon (sw)
 16 | 24,4,113,95,2372,15,70,3,toyota corona mark ii
 17 | 22,6,198,95,2833,15.5,70,1,plymouth duster
 18 | 18,6,199,97,2774,15.5,70,1,amc hornet
 19 | 21,6,200,85,2587,16,70,1,ford maverick
 20 | 27,4,97,88,2130,14.5,70,3,datsun pl510
 21 | 26,4,97,46,1835,20.5,70,2,volkswagen 1131 deluxe sedan
 22 | 25,4,110,87,2672,17.5,70,2,peugeot 504
 23 | 24,4,107,90,2430,14.5,70,2,audi 100 ls
 24 | 25,4,104,95,2375,17.5,70,2,saab 99e
 25 | 26,4,121,113,2234,12.5,70,2,bmw 2002
 26 | 21,6,199,90,2648,15,70,1,amc gremlin
 27 | 10,8,360,215,4615,14,70,1,ford f250
 28 | 10,8,307,200,4376,15,70,1,chevy c20
 29 | 11,8,318,210,4382,13.5,70,1,dodge d200
 30 | 9,8,304,193,4732,18.5,70,1,hi 1200d
 31 | 27,4,97,88,2130,14.5,71,3,datsun pl510
 32 | 28,4,140,90,2264,15.5,71,1,chevrolet vega 2300
 33 | 25,4,113,95,2228,14,71,3,toyota corona
 34 | 25,4,98,?,2046,19,71,1,ford pinto
 35 | 19,6,232,100,2634,13,71,1,amc gremlin
 36 | 16,6,225,105,3439,15.5,71,1,plymouth satellite custom
 37 | 17,6,250,100,3329,15.5,71,1,chevrolet chevelle malibu
 38 | 19,6,250,88,3302,15.5,71,1,ford torino 500
 39 | 18,6,232,100,3288,15.5,71,1,amc matador
 40 | 14,8,350,165,4209,12,71,1,chevrolet impala
 41 | 14,8,400,175,4464,11.5,71,1,pontiac catalina brougham
 42 | 14,8,351,153,4154,13.5,71,1,ford galaxie 500
 43 | 14,8,318,150,4096,13,71,1,plymouth fury iii
 44 | 12,8,383,180,4955,11.5,71,1,dodge monaco (sw)
 45 | 13,8,400,170,4746,12,71,1,ford country squire (sw)
 46 | 13,8,400,175,5140,12,71,1,pontiac safari (sw)
 47 | 18,6,258,110,2962,13.5,71,1,amc hornet sportabout (sw)
 48 | 22,4,140,72,2408,19,71,1,chevrolet vega (sw)
 49 | 19,6,250,100,3282,15,71,1,pontiac firebird
 50 | 18,6,250,88,3139,14.5,71,1,ford mustang
 51 | 23,4,122,86,2220,14,71,1,mercury capri 2000
 52 | 28,4,116,90,2123,14,71,2,opel 1900
 53 | 30,4,79,70,2074,19.5,71,2,peugeot 304
 54 | 30,4,88,76,2065,14.5,71,2,fiat 124b
 55 | 31,4,71,65,1773,19,71,3,toyota corolla 1200
 56 | 35,4,72,69,1613,18,71,3,datsun 1200
 57 | 27,4,97,60,1834,19,71,2,volkswagen model 111
 58 | 26,4,91,70,1955,20.5,71,1,plymouth cricket
 59 | 24,4,113,95,2278,15.5,72,3,toyota corona hardtop
 60 | 25,4,97.5,80,2126,17,72,1,dodge colt hardtop
 61 | 23,4,97,54,2254,23.5,72,2,volkswagen type 3
 62 | 20,4,140,90,2408,19.5,72,1,chevrolet vega
 63 | 21,4,122,86,2226,16.5,72,1,ford pinto runabout
 64 | 13,8,350,165,4274,12,72,1,chevrolet impala
 65 | 14,8,400,175,4385,12,72,1,pontiac catalina
 66 | 15,8,318,150,4135,13.5,72,1,plymouth fury iii
 67 | 14,8,351,153,4129,13,72,1,ford galaxie 500
 68 | 17,8,304,150,3672,11.5,72,1,amc ambassador sst
 69 | 11,8,429,208,4633,11,72,1,mercury marquis
 70 | 13,8,350,155,4502,13.5,72,1,buick lesabre custom
 71 | 12,8,350,160,4456,13.5,72,1,oldsmobile delta 88 royale
 72 | 13,8,400,190,4422,12.5,72,1,chrysler newport royal
 73 | 19,3,70,97,2330,13.5,72,3,mazda rx2 coupe
 74 | 15,8,304,150,3892,12.5,72,1,amc matador (sw)
 75 | 13,8,307,130,4098,14,72,1,chevrolet chevelle concours (sw)
 76 | 13,8,302,140,4294,16,72,1,ford gran torino (sw)
 77 | 14,8,318,150,4077,14,72,1,plymouth satellite custom (sw)
 78 | 18,4,121,112,2933,14.5,72,2,volvo 145e (sw)
 79 | 22,4,121,76,2511,18,72,2,volkswagen 411 (sw)
 80 | 21,4,120,87,2979,19.5,72,2,peugeot 504 (sw)
 81 | 26,4,96,69,2189,18,72,2,renault 12 (sw)
 82 | 22,4,122,86,2395,16,72,1,ford pinto (sw)
 83 | 28,4,97,92,2288,17,72,3,datsun 510 (sw)
 84 | 23,4,120,97,2506,14.5,72,3,toyouta corona mark ii (sw)
 85 | 28,4,98,80,2164,15,72,1,dodge colt (sw)
 86 | 27,4,97,88,2100,16.5,72,3,toyota corolla 1600 (sw)
 87 | 13,8,350,175,4100,13,73,1,buick century 350
 88 | 14,8,304,150,3672,11.5,73,1,amc matador
 89 | 13,8,350,145,3988,13,73,1,chevrolet malibu
 90 | 14,8,302,137,4042,14.5,73,1,ford gran torino
 91 | 15,8,318,150,3777,12.5,73,1,dodge coronet custom
 92 | 12,8,429,198,4952,11.5,73,1,mercury marquis brougham
 93 | 13,8,400,150,4464,12,73,1,chevrolet caprice classic
 94 | 13,8,351,158,4363,13,73,1,ford ltd
 95 | 14,8,318,150,4237,14.5,73,1,plymouth fury gran sedan
 96 | 13,8,440,215,4735,11,73,1,chrysler new yorker brougham
 97 | 12,8,455,225,4951,11,73,1,buick electra 225 custom
 98 | 13,8,360,175,3821,11,73,1,amc ambassador brougham
 99 | 18,6,225,105,3121,16.5,73,1,plymouth valiant
100 | 16,6,250,100,3278,18,73,1,chevrolet nova custom
101 | 18,6,232,100,2945,16,73,1,amc hornet
102 | 18,6,250,88,3021,16.5,73,1,ford maverick
103 | 23,6,198,95,2904,16,73,1,plymouth duster
104 | 26,4,97,46,1950,21,73,2,volkswagen super beetle
105 | 11,8,400,150,4997,14,73,1,chevrolet impala
106 | 12,8,400,167,4906,12.5,73,1,ford country
107 | 13,8,360,170,4654,13,73,1,plymouth custom suburb
108 | 12,8,350,180,4499,12.5,73,1,oldsmobile vista cruiser
109 | 18,6,232,100,2789,15,73,1,amc gremlin
110 | 20,4,97,88,2279,19,73,3,toyota carina
111 | 21,4,140,72,2401,19.5,73,1,chevrolet vega
112 | 22,4,108,94,2379,16.5,73,3,datsun 610
113 | 18,3,70,90,2124,13.5,73,3,maxda rx3
114 | 19,4,122,85,2310,18.5,73,1,ford pinto
115 | 21,6,155,107,2472,14,73,1,mercury capri v6
116 | 26,4,98,90,2265,15.5,73,2,fiat 124 sport coupe
117 | 15,8,350,145,4082,13,73,1,chevrolet monte carlo s
118 | 16,8,400,230,4278,9.5,73,1,pontiac grand prix
119 | 29,4,68,49,1867,19.5,73,2,fiat 128
120 | 24,4,116,75,2158,15.5,73,2,opel manta
121 | 20,4,114,91,2582,14,73,2,audi 100ls
122 | 19,4,121,112,2868,15.5,73,2,volvo 144ea
123 | 15,8,318,150,3399,11,73,1,dodge dart custom
124 | 24,4,121,110,2660,14,73,2,saab 99le
125 | 20,6,156,122,2807,13.5,73,3,toyota mark ii
126 | 11,8,350,180,3664,11,73,1,oldsmobile omega
127 | 20,6,198,95,3102,16.5,74,1,plymouth duster
128 | 21,6,200,?,2875,17,74,1,ford maverick
129 | 19,6,232,100,2901,16,74,1,amc hornet
130 | 15,6,250,100,3336,17,74,1,chevrolet nova
131 | 31,4,79,67,1950,19,74,3,datsun b210
132 | 26,4,122,80,2451,16.5,74,1,ford pinto
133 | 32,4,71,65,1836,21,74,3,toyota corolla 1200
134 | 25,4,140,75,2542,17,74,1,chevrolet vega
135 | 16,6,250,100,3781,17,74,1,chevrolet chevelle malibu classic
136 | 16,6,258,110,3632,18,74,1,amc matador
137 | 18,6,225,105,3613,16.5,74,1,plymouth satellite sebring
138 | 16,8,302,140,4141,14,74,1,ford gran torino
139 | 13,8,350,150,4699,14.5,74,1,buick century luxus (sw)
140 | 14,8,318,150,4457,13.5,74,1,dodge coronet custom (sw)
141 | 14,8,302,140,4638,16,74,1,ford gran torino (sw)
142 | 14,8,304,150,4257,15.5,74,1,amc matador (sw)
143 | 29,4,98,83,2219,16.5,74,2,audi fox
144 | 26,4,79,67,1963,15.5,74,2,volkswagen dasher
145 | 26,4,97,78,2300,14.5,74,2,opel manta
146 | 31,4,76,52,1649,16.5,74,3,toyota corona
147 | 32,4,83,61,2003,19,74,3,datsun 710
148 | 28,4,90,75,2125,14.5,74,1,dodge colt
149 | 24,4,90,75,2108,15.5,74,2,fiat 128
150 | 26,4,116,75,2246,14,74,2,fiat 124 tc
151 | 24,4,120,97,2489,15,74,3,honda civic
152 | 26,4,108,93,2391,15.5,74,3,subaru
153 | 31,4,79,67,2000,16,74,2,fiat x1.9
154 | 19,6,225,95,3264,16,75,1,plymouth valiant custom
155 | 18,6,250,105,3459,16,75,1,chevrolet nova
156 | 15,6,250,72,3432,21,75,1,mercury monarch
157 | 15,6,250,72,3158,19.5,75,1,ford maverick
158 | 16,8,400,170,4668,11.5,75,1,pontiac catalina
159 | 15,8,350,145,4440,14,75,1,chevrolet bel air
160 | 16,8,318,150,4498,14.5,75,1,plymouth grand fury
161 | 14,8,351,148,4657,13.5,75,1,ford ltd
162 | 17,6,231,110,3907,21,75,1,buick century
163 | 16,6,250,105,3897,18.5,75,1,chevroelt chevelle malibu
164 | 15,6,258,110,3730,19,75,1,amc matador
165 | 18,6,225,95,3785,19,75,1,plymouth fury
166 | 21,6,231,110,3039,15,75,1,buick skyhawk
167 | 20,8,262,110,3221,13.5,75,1,chevrolet monza 2+2
168 | 13,8,302,129,3169,12,75,1,ford mustang ii
169 | 29,4,97,75,2171,16,75,3,toyota corolla
170 | 23,4,140,83,2639,17,75,1,ford pinto
171 | 20,6,232,100,2914,16,75,1,amc gremlin
172 | 23,4,140,78,2592,18.5,75,1,pontiac astro
173 | 24,4,134,96,2702,13.5,75,3,toyota corona
174 | 25,4,90,71,2223,16.5,75,2,volkswagen dasher
175 | 24,4,119,97,2545,17,75,3,datsun 710
176 | 18,6,171,97,2984,14.5,75,1,ford pinto
177 | 29,4,90,70,1937,14,75,2,volkswagen rabbit
178 | 19,6,232,90,3211,17,75,1,amc pacer
179 | 23,4,115,95,2694,15,75,2,audi 100ls
180 | 23,4,120,88,2957,17,75,2,peugeot 504
181 | 22,4,121,98,2945,14.5,75,2,volvo 244dl
182 | 25,4,121,115,2671,13.5,75,2,saab 99le
183 | 33,4,91,53,1795,17.5,75,3,honda civic cvcc
184 | 28,4,107,86,2464,15.5,76,2,fiat 131
185 | 25,4,116,81,2220,16.9,76,2,opel 1900
186 | 25,4,140,92,2572,14.9,76,1,capri ii
187 | 26,4,98,79,2255,17.7,76,1,dodge colt
188 | 27,4,101,83,2202,15.3,76,2,renault 12tl
189 | 17.5,8,305,140,4215,13,76,1,chevrolet chevelle malibu classic
190 | 16,8,318,150,4190,13,76,1,dodge coronet brougham
191 | 15.5,8,304,120,3962,13.9,76,1,amc matador
192 | 14.5,8,351,152,4215,12.8,76,1,ford gran torino
193 | 22,6,225,100,3233,15.4,76,1,plymouth valiant
194 | 22,6,250,105,3353,14.5,76,1,chevrolet nova
195 | 24,6,200,81,3012,17.6,76,1,ford maverick
196 | 22.5,6,232,90,3085,17.6,76,1,amc hornet
197 | 29,4,85,52,2035,22.2,76,1,chevrolet chevette
198 | 24.5,4,98,60,2164,22.1,76,1,chevrolet woody
199 | 29,4,90,70,1937,14.2,76,2,vw rabbit
200 | 33,4,91,53,1795,17.4,76,3,honda civic
201 | 20,6,225,100,3651,17.7,76,1,dodge aspen se
202 | 18,6,250,78,3574,21,76,1,ford granada ghia
203 | 18.5,6,250,110,3645,16.2,76,1,pontiac ventura sj
204 | 17.5,6,258,95,3193,17.8,76,1,amc pacer d/l
205 | 29.5,4,97,71,1825,12.2,76,2,volkswagen rabbit
206 | 32,4,85,70,1990,17,76,3,datsun b-210
207 | 28,4,97,75,2155,16.4,76,3,toyota corolla
208 | 26.5,4,140,72,2565,13.6,76,1,ford pinto
209 | 20,4,130,102,3150,15.7,76,2,volvo 245
210 | 13,8,318,150,3940,13.2,76,1,plymouth volare premier v8
211 | 19,4,120,88,3270,21.9,76,2,peugeot 504
212 | 19,6,156,108,2930,15.5,76,3,toyota mark ii
213 | 16.5,6,168,120,3820,16.7,76,2,mercedes-benz 280s
214 | 16.5,8,350,180,4380,12.1,76,1,cadillac seville
215 | 13,8,350,145,4055,12,76,1,chevy c10
216 | 13,8,302,130,3870,15,76,1,ford f108
217 | 13,8,318,150,3755,14,76,1,dodge d100
218 | 31.5,4,98,68,2045,18.5,77,3,honda accord cvcc
219 | 30,4,111,80,2155,14.8,77,1,buick opel isuzu deluxe
220 | 36,4,79,58,1825,18.6,77,2,renault 5 gtl
221 | 25.5,4,122,96,2300,15.5,77,1,plymouth arrow gs
222 | 33.5,4,85,70,1945,16.8,77,3,datsun f-10 hatchback
223 | 17.5,8,305,145,3880,12.5,77,1,chevrolet caprice classic
224 | 17,8,260,110,4060,19,77,1,oldsmobile cutlass supreme
225 | 15.5,8,318,145,4140,13.7,77,1,dodge monaco brougham
226 | 15,8,302,130,4295,14.9,77,1,mercury cougar brougham
227 | 17.5,6,250,110,3520,16.4,77,1,chevrolet concours
228 | 20.5,6,231,105,3425,16.9,77,1,buick skylark
229 | 19,6,225,100,3630,17.7,77,1,plymouth volare custom
230 | 18.5,6,250,98,3525,19,77,1,ford granada
231 | 16,8,400,180,4220,11.1,77,1,pontiac grand prix lj
232 | 15.5,8,350,170,4165,11.4,77,1,chevrolet monte carlo landau
233 | 15.5,8,400,190,4325,12.2,77,1,chrysler cordoba
234 | 16,8,351,149,4335,14.5,77,1,ford thunderbird
235 | 29,4,97,78,1940,14.5,77,2,volkswagen rabbit custom
236 | 24.5,4,151,88,2740,16,77,1,pontiac sunbird coupe
237 | 26,4,97,75,2265,18.2,77,3,toyota corolla liftback
238 | 25.5,4,140,89,2755,15.8,77,1,ford mustang ii 2+2
239 | 30.5,4,98,63,2051,17,77,1,chevrolet chevette
240 | 33.5,4,98,83,2075,15.9,77,1,dodge colt m/m
241 | 30,4,97,67,1985,16.4,77,3,subaru dl
242 | 30.5,4,97,78,2190,14.1,77,2,volkswagen dasher
243 | 22,6,146,97,2815,14.5,77,3,datsun 810
244 | 21.5,4,121,110,2600,12.8,77,2,bmw 320i
245 | 21.5,3,80,110,2720,13.5,77,3,mazda rx-4
246 | 43.1,4,90,48,1985,21.5,78,2,volkswagen rabbit custom diesel
247 | 36.1,4,98,66,1800,14.4,78,1,ford fiesta
248 | 32.8,4,78,52,1985,19.4,78,3,mazda glc deluxe
249 | 39.4,4,85,70,2070,18.6,78,3,datsun b210 gx
250 | 36.1,4,91,60,1800,16.4,78,3,honda civic cvcc
251 | 19.9,8,260,110,3365,15.5,78,1,oldsmobile cutlass salon brougham
252 | 19.4,8,318,140,3735,13.2,78,1,dodge diplomat
253 | 20.2,8,302,139,3570,12.8,78,1,mercury monarch ghia
254 | 19.2,6,231,105,3535,19.2,78,1,pontiac phoenix lj
255 | 20.5,6,200,95,3155,18.2,78,1,chevrolet malibu
256 | 20.2,6,200,85,2965,15.8,78,1,ford fairmont (auto)
257 | 25.1,4,140,88,2720,15.4,78,1,ford fairmont (man)
258 | 20.5,6,225,100,3430,17.2,78,1,plymouth volare
259 | 19.4,6,232,90,3210,17.2,78,1,amc concord
260 | 20.6,6,231,105,3380,15.8,78,1,buick century special
261 | 20.8,6,200,85,3070,16.7,78,1,mercury zephyr
262 | 18.6,6,225,110,3620,18.7,78,1,dodge aspen
263 | 18.1,6,258,120,3410,15.1,78,1,amc concord d/l
264 | 19.2,8,305,145,3425,13.2,78,1,chevrolet monte carlo landau
265 | 17.7,6,231,165,3445,13.4,78,1,buick regal sport coupe (turbo)
266 | 18.1,8,302,139,3205,11.2,78,1,ford futura
267 | 17.5,8,318,140,4080,13.7,78,1,dodge magnum xe
268 | 30,4,98,68,2155,16.5,78,1,chevrolet chevette
269 | 27.5,4,134,95,2560,14.2,78,3,toyota corona
270 | 27.2,4,119,97,2300,14.7,78,3,datsun 510
271 | 30.9,4,105,75,2230,14.5,78,1,dodge omni
272 | 21.1,4,134,95,2515,14.8,78,3,toyota celica gt liftback
273 | 23.2,4,156,105,2745,16.7,78,1,plymouth sapporo
274 | 23.8,4,151,85,2855,17.6,78,1,oldsmobile starfire sx
275 | 23.9,4,119,97,2405,14.9,78,3,datsun 200-sx
276 | 20.3,5,131,103,2830,15.9,78,2,audi 5000
277 | 17,6,163,125,3140,13.6,78,2,volvo 264gl
278 | 21.6,4,121,115,2795,15.7,78,2,saab 99gle
279 | 16.2,6,163,133,3410,15.8,78,2,peugeot 604sl
280 | 31.5,4,89,71,1990,14.9,78,2,volkswagen scirocco
281 | 29.5,4,98,68,2135,16.6,78,3,honda accord lx
282 | 21.5,6,231,115,3245,15.4,79,1,pontiac lemans v6
283 | 19.8,6,200,85,2990,18.2,79,1,mercury zephyr 6
284 | 22.3,4,140,88,2890,17.3,79,1,ford fairmont 4
285 | 20.2,6,232,90,3265,18.2,79,1,amc concord dl 6
286 | 20.6,6,225,110,3360,16.6,79,1,dodge aspen 6
287 | 17,8,305,130,3840,15.4,79,1,chevrolet caprice classic
288 | 17.6,8,302,129,3725,13.4,79,1,ford ltd landau
289 | 16.5,8,351,138,3955,13.2,79,1,mercury grand marquis
290 | 18.2,8,318,135,3830,15.2,79,1,dodge st. regis
291 | 16.9,8,350,155,4360,14.9,79,1,buick estate wagon (sw)
292 | 15.5,8,351,142,4054,14.3,79,1,ford country squire (sw)
293 | 19.2,8,267,125,3605,15,79,1,chevrolet malibu classic (sw)
294 | 18.5,8,360,150,3940,13,79,1,chrysler lebaron town @ country (sw)
295 | 31.9,4,89,71,1925,14,79,2,vw rabbit custom
296 | 34.1,4,86,65,1975,15.2,79,3,maxda glc deluxe
297 | 35.7,4,98,80,1915,14.4,79,1,dodge colt hatchback custom
298 | 27.4,4,121,80,2670,15,79,1,amc spirit dl
299 | 25.4,5,183,77,3530,20.1,79,2,mercedes benz 300d
300 | 23,8,350,125,3900,17.4,79,1,cadillac eldorado
301 | 27.2,4,141,71,3190,24.8,79,2,peugeot 504
302 | 23.9,8,260,90,3420,22.2,79,1,oldsmobile cutlass salon brougham
303 | 34.2,4,105,70,2200,13.2,79,1,plymouth horizon
304 | 34.5,4,105,70,2150,14.9,79,1,plymouth horizon tc3
305 | 31.8,4,85,65,2020,19.2,79,3,datsun 210
306 | 37.3,4,91,69,2130,14.7,79,2,fiat strada custom
307 | 28.4,4,151,90,2670,16,79,1,buick skylark limited
308 | 28.8,6,173,115,2595,11.3,79,1,chevrolet citation
309 | 26.8,6,173,115,2700,12.9,79,1,oldsmobile omega brougham
310 | 33.5,4,151,90,2556,13.2,79,1,pontiac phoenix
311 | 41.5,4,98,76,2144,14.7,80,2,vw rabbit
312 | 38.1,4,89,60,1968,18.8,80,3,toyota corolla tercel
313 | 32.1,4,98,70,2120,15.5,80,1,chevrolet chevette
314 | 37.2,4,86,65,2019,16.4,80,3,datsun 310
315 | 28,4,151,90,2678,16.5,80,1,chevrolet citation
316 | 26.4,4,140,88,2870,18.1,80,1,ford fairmont
317 | 24.3,4,151,90,3003,20.1,80,1,amc concord
318 | 19.1,6,225,90,3381,18.7,80,1,dodge aspen
319 | 34.3,4,97,78,2188,15.8,80,2,audi 4000
320 | 29.8,4,134,90,2711,15.5,80,3,toyota corona liftback
321 | 31.3,4,120,75,2542,17.5,80,3,mazda 626
322 | 37,4,119,92,2434,15,80,3,datsun 510 hatchback
323 | 32.2,4,108,75,2265,15.2,80,3,toyota corolla
324 | 46.6,4,86,65,2110,17.9,80,3,mazda glc
325 | 27.9,4,156,105,2800,14.4,80,1,dodge colt
326 | 40.8,4,85,65,2110,19.2,80,3,datsun 210
327 | 44.3,4,90,48,2085,21.7,80,2,vw rabbit c (diesel)
328 | 43.4,4,90,48,2335,23.7,80,2,vw dasher (diesel)
329 | 36.4,5,121,67,2950,19.9,80,2,audi 5000s (diesel)
330 | 30,4,146,67,3250,21.8,80,2,mercedes-benz 240d
331 | 44.6,4,91,67,1850,13.8,80,3,honda civic 1500 gl
332 | 40.9,4,85,?,1835,17.3,80,2,renault lecar deluxe
333 | 33.8,4,97,67,2145,18,80,3,subaru dl
334 | 29.8,4,89,62,1845,15.3,80,2,vokswagen rabbit
335 | 32.7,6,168,132,2910,11.4,80,3,datsun 280-zx
336 | 23.7,3,70,100,2420,12.5,80,3,mazda rx-7 gs
337 | 35,4,122,88,2500,15.1,80,2,triumph tr7 coupe
338 | 23.6,4,140,?,2905,14.3,80,1,ford mustang cobra
339 | 32.4,4,107,72,2290,17,80,3,honda accord
340 | 27.2,4,135,84,2490,15.7,81,1,plymouth reliant
341 | 26.6,4,151,84,2635,16.4,81,1,buick skylark
342 | 25.8,4,156,92,2620,14.4,81,1,dodge aries wagon (sw)
343 | 23.5,6,173,110,2725,12.6,81,1,chevrolet citation
344 | 30,4,135,84,2385,12.9,81,1,plymouth reliant
345 | 39.1,4,79,58,1755,16.9,81,3,toyota starlet
346 | 39,4,86,64,1875,16.4,81,1,plymouth champ
347 | 35.1,4,81,60,1760,16.1,81,3,honda civic 1300
348 | 32.3,4,97,67,2065,17.8,81,3,subaru
349 | 37,4,85,65,1975,19.4,81,3,datsun 210 mpg
350 | 37.7,4,89,62,2050,17.3,81,3,toyota tercel
351 | 34.1,4,91,68,1985,16,81,3,mazda glc 4
352 | 34.7,4,105,63,2215,14.9,81,1,plymouth horizon 4
353 | 34.4,4,98,65,2045,16.2,81,1,ford escort 4w
354 | 29.9,4,98,65,2380,20.7,81,1,ford escort 2h
355 | 33,4,105,74,2190,14.2,81,2,volkswagen jetta
356 | 34.5,4,100,?,2320,15.8,81,2,renault 18i
357 | 33.7,4,107,75,2210,14.4,81,3,honda prelude
358 | 32.4,4,108,75,2350,16.8,81,3,toyota corolla
359 | 32.9,4,119,100,2615,14.8,81,3,datsun 200sx
360 | 31.6,4,120,74,2635,18.3,81,3,mazda 626
361 | 28.1,4,141,80,3230,20.4,81,2,peugeot 505s turbo diesel
362 | 30.7,6,145,76,3160,19.6,81,2,volvo diesel
363 | 25.4,6,168,116,2900,12.6,81,3,toyota cressida
364 | 24.2,6,146,120,2930,13.8,81,3,datsun 810 maxima
365 | 22.4,6,231,110,3415,15.8,81,1,buick century
366 | 26.6,8,350,105,3725,19,81,1,oldsmobile cutlass ls
367 | 20.2,6,200,88,3060,17.1,81,1,ford granada gl
368 | 17.6,6,225,85,3465,16.6,81,1,chrysler lebaron salon
369 | 28,4,112,88,2605,19.6,82,1,chevrolet cavalier
370 | 27,4,112,88,2640,18.6,82,1,chevrolet cavalier wagon
371 | 34,4,112,88,2395,18,82,1,chevrolet cavalier 2-door
372 | 31,4,112,85,2575,16.2,82,1,pontiac j2000 se hatchback
373 | 29,4,135,84,2525,16,82,1,dodge aries se
374 | 27,4,151,90,2735,18,82,1,pontiac phoenix
375 | 24,4,140,92,2865,16.4,82,1,ford fairmont futura
376 | 36,4,105,74,1980,15.3,82,2,volkswagen rabbit l
377 | 37,4,91,68,2025,18.2,82,3,mazda glc custom l
378 | 31,4,91,68,1970,17.6,82,3,mazda glc custom
379 | 38,4,105,63,2125,14.7,82,1,plymouth horizon miser
380 | 36,4,98,70,2125,17.3,82,1,mercury lynx l
381 | 36,4,120,88,2160,14.5,82,3,nissan stanza xe
382 | 36,4,107,75,2205,14.5,82,3,honda accord
383 | 34,4,108,70,2245,16.9,82,3,toyota corolla
384 | 38,4,91,67,1965,15,82,3,honda civic
385 | 32,4,91,67,1965,15.7,82,3,honda civic (auto)
386 | 38,4,91,67,1995,16.2,82,3,datsun 310 gx
387 | 25,6,181,110,2945,16.4,82,1,buick century limited
388 | 38,6,262,85,3015,17,82,1,oldsmobile cutlass ciera (diesel)
389 | 26,4,156,92,2585,14.5,82,1,chrysler lebaron medallion
390 | 22,6,232,112,2835,14.7,82,1,ford granada l
391 | 32,4,144,96,2665,13.9,82,3,toyota celica gt
392 | 36,4,135,84,2370,13,82,1,dodge charger 2.2
393 | 27,4,151,90,2950,17.3,82,1,chevrolet camaro
394 | 27,4,140,86,2790,15.6,82,1,ford mustang gl
395 | 44,4,97,52,2130,24.6,82,2,vw pickup
396 | 32,4,135,84,2295,11.6,82,1,dodge rampage
397 | 28,4,120,79,2625,18.6,82,1,ford ranger
398 | 31,4,119,82,2720,19.4,82,1,chevy s-10
399 | 


--------------------------------------------------------------------------------
/Data/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
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  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
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 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
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 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
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 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
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 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
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 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
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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
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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
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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 | 


--------------------------------------------------------------------------------
/Data/Carseats.csv:
--------------------------------------------------------------------------------
  1 | "","Sales","CompPrice","Income","Advertising","Population","Price","ShelveLoc","Age","Education","Urban","US"
  2 | "1",9.5,138,73,11,276,120,"Bad",42,17,"Yes","Yes"
  3 | "2",11.22,111,48,16,260,83,"Good",65,10,"Yes","Yes"
  4 | "3",10.06,113,35,10,269,80,"Medium",59,12,"Yes","Yes"
  5 | "4",7.4,117,100,4,466,97,"Medium",55,14,"Yes","Yes"
  6 | "5",4.15,141,64,3,340,128,"Bad",38,13,"Yes","No"
  7 | "6",10.81,124,113,13,501,72,"Bad",78,16,"No","Yes"
  8 | "7",6.63,115,105,0,45,108,"Medium",71,15,"Yes","No"
  9 | "8",11.85,136,81,15,425,120,"Good",67,10,"Yes","Yes"
 10 | "9",6.54,132,110,0,108,124,"Medium",76,10,"No","No"
 11 | "10",4.69,132,113,0,131,124,"Medium",76,17,"No","Yes"
 12 | "11",9.01,121,78,9,150,100,"Bad",26,10,"No","Yes"
 13 | "12",11.96,117,94,4,503,94,"Good",50,13,"Yes","Yes"
 14 | "13",3.98,122,35,2,393,136,"Medium",62,18,"Yes","No"
 15 | "14",10.96,115,28,11,29,86,"Good",53,18,"Yes","Yes"
 16 | "15",11.17,107,117,11,148,118,"Good",52,18,"Yes","Yes"
 17 | "16",8.71,149,95,5,400,144,"Medium",76,18,"No","No"
 18 | "17",7.58,118,32,0,284,110,"Good",63,13,"Yes","No"
 19 | "18",12.29,147,74,13,251,131,"Good",52,10,"Yes","Yes"
 20 | "19",13.91,110,110,0,408,68,"Good",46,17,"No","Yes"
 21 | "20",8.73,129,76,16,58,121,"Medium",69,12,"Yes","Yes"
 22 | "21",6.41,125,90,2,367,131,"Medium",35,18,"Yes","Yes"
 23 | "22",12.13,134,29,12,239,109,"Good",62,18,"No","Yes"
 24 | "23",5.08,128,46,6,497,138,"Medium",42,13,"Yes","No"
 25 | "24",5.87,121,31,0,292,109,"Medium",79,10,"Yes","No"
 26 | "25",10.14,145,119,16,294,113,"Bad",42,12,"Yes","Yes"
 27 | "26",14.9,139,32,0,176,82,"Good",54,11,"No","No"
 28 | "27",8.33,107,115,11,496,131,"Good",50,11,"No","Yes"
 29 | "28",5.27,98,118,0,19,107,"Medium",64,17,"Yes","No"
 30 | "29",2.99,103,74,0,359,97,"Bad",55,11,"Yes","Yes"
 31 | "30",7.81,104,99,15,226,102,"Bad",58,17,"Yes","Yes"
 32 | "31",13.55,125,94,0,447,89,"Good",30,12,"Yes","No"
 33 | "32",8.25,136,58,16,241,131,"Medium",44,18,"Yes","Yes"
 34 | "33",6.2,107,32,12,236,137,"Good",64,10,"No","Yes"
 35 | "34",8.77,114,38,13,317,128,"Good",50,16,"Yes","Yes"
 36 | "35",2.67,115,54,0,406,128,"Medium",42,17,"Yes","Yes"
 37 | "36",11.07,131,84,11,29,96,"Medium",44,17,"No","Yes"
 38 | "37",8.89,122,76,0,270,100,"Good",60,18,"No","No"
 39 | "38",4.95,121,41,5,412,110,"Medium",54,10,"Yes","Yes"
 40 | "39",6.59,109,73,0,454,102,"Medium",65,15,"Yes","No"
 41 | "40",3.24,130,60,0,144,138,"Bad",38,10,"No","No"
 42 | "41",2.07,119,98,0,18,126,"Bad",73,17,"No","No"
 43 | "42",7.96,157,53,0,403,124,"Bad",58,16,"Yes","No"
 44 | "43",10.43,77,69,0,25,24,"Medium",50,18,"Yes","No"
 45 | "44",4.12,123,42,11,16,134,"Medium",59,13,"Yes","Yes"
 46 | "45",4.16,85,79,6,325,95,"Medium",69,13,"Yes","Yes"
 47 | "46",4.56,141,63,0,168,135,"Bad",44,12,"Yes","Yes"
 48 | "47",12.44,127,90,14,16,70,"Medium",48,15,"No","Yes"
 49 | "48",4.38,126,98,0,173,108,"Bad",55,16,"Yes","No"
 50 | "49",3.91,116,52,0,349,98,"Bad",69,18,"Yes","No"
 51 | "50",10.61,157,93,0,51,149,"Good",32,17,"Yes","No"
 52 | "51",1.42,99,32,18,341,108,"Bad",80,16,"Yes","Yes"
 53 | "52",4.42,121,90,0,150,108,"Bad",75,16,"Yes","No"
 54 | "53",7.91,153,40,3,112,129,"Bad",39,18,"Yes","Yes"
 55 | "54",6.92,109,64,13,39,119,"Medium",61,17,"Yes","Yes"
 56 | "55",4.9,134,103,13,25,144,"Medium",76,17,"No","Yes"
 57 | "56",6.85,143,81,5,60,154,"Medium",61,18,"Yes","Yes"
 58 | "57",11.91,133,82,0,54,84,"Medium",50,17,"Yes","No"
 59 | "58",0.91,93,91,0,22,117,"Bad",75,11,"Yes","No"
 60 | "59",5.42,103,93,15,188,103,"Bad",74,16,"Yes","Yes"
 61 | "60",5.21,118,71,4,148,114,"Medium",80,13,"Yes","No"
 62 | "61",8.32,122,102,19,469,123,"Bad",29,13,"Yes","Yes"
 63 | "62",7.32,105,32,0,358,107,"Medium",26,13,"No","No"
 64 | "63",1.82,139,45,0,146,133,"Bad",77,17,"Yes","Yes"
 65 | "64",8.47,119,88,10,170,101,"Medium",61,13,"Yes","Yes"
 66 | "65",7.8,100,67,12,184,104,"Medium",32,16,"No","Yes"
 67 | "66",4.9,122,26,0,197,128,"Medium",55,13,"No","No"
 68 | "67",8.85,127,92,0,508,91,"Medium",56,18,"Yes","No"
 69 | "68",9.01,126,61,14,152,115,"Medium",47,16,"Yes","Yes"
 70 | "69",13.39,149,69,20,366,134,"Good",60,13,"Yes","Yes"
 71 | "70",7.99,127,59,0,339,99,"Medium",65,12,"Yes","No"
 72 | "71",9.46,89,81,15,237,99,"Good",74,12,"Yes","Yes"
 73 | "72",6.5,148,51,16,148,150,"Medium",58,17,"No","Yes"
 74 | "73",5.52,115,45,0,432,116,"Medium",25,15,"Yes","No"
 75 | "74",12.61,118,90,10,54,104,"Good",31,11,"No","Yes"
 76 | "75",6.2,150,68,5,125,136,"Medium",64,13,"No","Yes"
 77 | "76",8.55,88,111,23,480,92,"Bad",36,16,"No","Yes"
 78 | "77",10.64,102,87,10,346,70,"Medium",64,15,"Yes","Yes"
 79 | "78",7.7,118,71,12,44,89,"Medium",67,18,"No","Yes"
 80 | "79",4.43,134,48,1,139,145,"Medium",65,12,"Yes","Yes"
 81 | "80",9.14,134,67,0,286,90,"Bad",41,13,"Yes","No"
 82 | "81",8.01,113,100,16,353,79,"Bad",68,11,"Yes","Yes"
 83 | "82",7.52,116,72,0,237,128,"Good",70,13,"Yes","No"
 84 | "83",11.62,151,83,4,325,139,"Good",28,17,"Yes","Yes"
 85 | "84",4.42,109,36,7,468,94,"Bad",56,11,"Yes","Yes"
 86 | "85",2.23,111,25,0,52,121,"Bad",43,18,"No","No"
 87 | "86",8.47,125,103,0,304,112,"Medium",49,13,"No","No"
 88 | "87",8.7,150,84,9,432,134,"Medium",64,15,"Yes","No"
 89 | "88",11.7,131,67,7,272,126,"Good",54,16,"No","Yes"
 90 | "89",6.56,117,42,7,144,111,"Medium",62,10,"Yes","Yes"
 91 | "90",7.95,128,66,3,493,119,"Medium",45,16,"No","No"
 92 | "91",5.33,115,22,0,491,103,"Medium",64,11,"No","No"
 93 | "92",4.81,97,46,11,267,107,"Medium",80,15,"Yes","Yes"
 94 | "93",4.53,114,113,0,97,125,"Medium",29,12,"Yes","No"
 95 | "94",8.86,145,30,0,67,104,"Medium",55,17,"Yes","No"
 96 | "95",8.39,115,97,5,134,84,"Bad",55,11,"Yes","Yes"
 97 | "96",5.58,134,25,10,237,148,"Medium",59,13,"Yes","Yes"
 98 | "97",9.48,147,42,10,407,132,"Good",73,16,"No","Yes"
 99 | "98",7.45,161,82,5,287,129,"Bad",33,16,"Yes","Yes"
100 | "99",12.49,122,77,24,382,127,"Good",36,16,"No","Yes"
101 | "100",4.88,121,47,3,220,107,"Bad",56,16,"No","Yes"
102 | "101",4.11,113,69,11,94,106,"Medium",76,12,"No","Yes"
103 | "102",6.2,128,93,0,89,118,"Medium",34,18,"Yes","No"
104 | "103",5.3,113,22,0,57,97,"Medium",65,16,"No","No"
105 | "104",5.07,123,91,0,334,96,"Bad",78,17,"Yes","Yes"
106 | "105",4.62,121,96,0,472,138,"Medium",51,12,"Yes","No"
107 | "106",5.55,104,100,8,398,97,"Medium",61,11,"Yes","Yes"
108 | "107",0.16,102,33,0,217,139,"Medium",70,18,"No","No"
109 | "108",8.55,134,107,0,104,108,"Medium",60,12,"Yes","No"
110 | "109",3.47,107,79,2,488,103,"Bad",65,16,"Yes","No"
111 | "110",8.98,115,65,0,217,90,"Medium",60,17,"No","No"
112 | "111",9,128,62,7,125,116,"Medium",43,14,"Yes","Yes"
113 | "112",6.62,132,118,12,272,151,"Medium",43,14,"Yes","Yes"
114 | "113",6.67,116,99,5,298,125,"Good",62,12,"Yes","Yes"
115 | "114",6.01,131,29,11,335,127,"Bad",33,12,"Yes","Yes"
116 | "115",9.31,122,87,9,17,106,"Medium",65,13,"Yes","Yes"
117 | "116",8.54,139,35,0,95,129,"Medium",42,13,"Yes","No"
118 | "117",5.08,135,75,0,202,128,"Medium",80,10,"No","No"
119 | "118",8.8,145,53,0,507,119,"Medium",41,12,"Yes","No"
120 | "119",7.57,112,88,2,243,99,"Medium",62,11,"Yes","Yes"
121 | "120",7.37,130,94,8,137,128,"Medium",64,12,"Yes","Yes"
122 | "121",6.87,128,105,11,249,131,"Medium",63,13,"Yes","Yes"
123 | "122",11.67,125,89,10,380,87,"Bad",28,10,"Yes","Yes"
124 | "123",6.88,119,100,5,45,108,"Medium",75,10,"Yes","Yes"
125 | "124",8.19,127,103,0,125,155,"Good",29,15,"No","Yes"
126 | "125",8.87,131,113,0,181,120,"Good",63,14,"Yes","No"
127 | "126",9.34,89,78,0,181,49,"Medium",43,15,"No","No"
128 | "127",11.27,153,68,2,60,133,"Good",59,16,"Yes","Yes"
129 | "128",6.52,125,48,3,192,116,"Medium",51,14,"Yes","Yes"
130 | "129",4.96,133,100,3,350,126,"Bad",55,13,"Yes","Yes"
131 | "130",4.47,143,120,7,279,147,"Bad",40,10,"No","Yes"
132 | "131",8.41,94,84,13,497,77,"Medium",51,12,"Yes","Yes"
133 | "132",6.5,108,69,3,208,94,"Medium",77,16,"Yes","No"
134 | "133",9.54,125,87,9,232,136,"Good",72,10,"Yes","Yes"
135 | "134",7.62,132,98,2,265,97,"Bad",62,12,"Yes","Yes"
136 | "135",3.67,132,31,0,327,131,"Medium",76,16,"Yes","No"
137 | "136",6.44,96,94,14,384,120,"Medium",36,18,"No","Yes"
138 | "137",5.17,131,75,0,10,120,"Bad",31,18,"No","No"
139 | "138",6.52,128,42,0,436,118,"Medium",80,11,"Yes","No"
140 | "139",10.27,125,103,12,371,109,"Medium",44,10,"Yes","Yes"
141 | "140",12.3,146,62,10,310,94,"Medium",30,13,"No","Yes"
142 | "141",6.03,133,60,10,277,129,"Medium",45,18,"Yes","Yes"
143 | "142",6.53,140,42,0,331,131,"Bad",28,15,"Yes","No"
144 | "143",7.44,124,84,0,300,104,"Medium",77,15,"Yes","No"
145 | "144",0.53,122,88,7,36,159,"Bad",28,17,"Yes","Yes"
146 | "145",9.09,132,68,0,264,123,"Good",34,11,"No","No"
147 | "146",8.77,144,63,11,27,117,"Medium",47,17,"Yes","Yes"
148 | "147",3.9,114,83,0,412,131,"Bad",39,14,"Yes","No"
149 | "148",10.51,140,54,9,402,119,"Good",41,16,"No","Yes"
150 | "149",7.56,110,119,0,384,97,"Medium",72,14,"No","Yes"
151 | "150",11.48,121,120,13,140,87,"Medium",56,11,"Yes","Yes"
152 | "151",10.49,122,84,8,176,114,"Good",57,10,"No","Yes"
153 | "152",10.77,111,58,17,407,103,"Good",75,17,"No","Yes"
154 | "153",7.64,128,78,0,341,128,"Good",45,13,"No","No"
155 | "154",5.93,150,36,7,488,150,"Medium",25,17,"No","Yes"
156 | "155",6.89,129,69,10,289,110,"Medium",50,16,"No","Yes"
157 | "156",7.71,98,72,0,59,69,"Medium",65,16,"Yes","No"
158 | "157",7.49,146,34,0,220,157,"Good",51,16,"Yes","No"
159 | "158",10.21,121,58,8,249,90,"Medium",48,13,"No","Yes"
160 | "159",12.53,142,90,1,189,112,"Good",39,10,"No","Yes"
161 | "160",9.32,119,60,0,372,70,"Bad",30,18,"No","No"
162 | "161",4.67,111,28,0,486,111,"Medium",29,12,"No","No"
163 | "162",2.93,143,21,5,81,160,"Medium",67,12,"No","Yes"
164 | "163",3.63,122,74,0,424,149,"Medium",51,13,"Yes","No"
165 | "164",5.68,130,64,0,40,106,"Bad",39,17,"No","No"
166 | "165",8.22,148,64,0,58,141,"Medium",27,13,"No","Yes"
167 | "166",0.37,147,58,7,100,191,"Bad",27,15,"Yes","Yes"
168 | "167",6.71,119,67,17,151,137,"Medium",55,11,"Yes","Yes"
169 | "168",6.71,106,73,0,216,93,"Medium",60,13,"Yes","No"
170 | "169",7.3,129,89,0,425,117,"Medium",45,10,"Yes","No"
171 | "170",11.48,104,41,15,492,77,"Good",73,18,"Yes","Yes"
172 | "171",8.01,128,39,12,356,118,"Medium",71,10,"Yes","Yes"
173 | "172",12.49,93,106,12,416,55,"Medium",75,15,"Yes","Yes"
174 | "173",9.03,104,102,13,123,110,"Good",35,16,"Yes","Yes"
175 | "174",6.38,135,91,5,207,128,"Medium",66,18,"Yes","Yes"
176 | "175",0,139,24,0,358,185,"Medium",79,15,"No","No"
177 | "176",7.54,115,89,0,38,122,"Medium",25,12,"Yes","No"
178 | "177",5.61,138,107,9,480,154,"Medium",47,11,"No","Yes"
179 | "178",10.48,138,72,0,148,94,"Medium",27,17,"Yes","Yes"
180 | "179",10.66,104,71,14,89,81,"Medium",25,14,"No","Yes"
181 | "180",7.78,144,25,3,70,116,"Medium",77,18,"Yes","Yes"
182 | "181",4.94,137,112,15,434,149,"Bad",66,13,"Yes","Yes"
183 | "182",7.43,121,83,0,79,91,"Medium",68,11,"Yes","No"
184 | "183",4.74,137,60,4,230,140,"Bad",25,13,"Yes","No"
185 | "184",5.32,118,74,6,426,102,"Medium",80,18,"Yes","Yes"
186 | "185",9.95,132,33,7,35,97,"Medium",60,11,"No","Yes"
187 | "186",10.07,130,100,11,449,107,"Medium",64,10,"Yes","Yes"
188 | "187",8.68,120,51,0,93,86,"Medium",46,17,"No","No"
189 | "188",6.03,117,32,0,142,96,"Bad",62,17,"Yes","No"
190 | "189",8.07,116,37,0,426,90,"Medium",76,15,"Yes","No"
191 | "190",12.11,118,117,18,509,104,"Medium",26,15,"No","Yes"
192 | "191",8.79,130,37,13,297,101,"Medium",37,13,"No","Yes"
193 | "192",6.67,156,42,13,170,173,"Good",74,14,"Yes","Yes"
194 | "193",7.56,108,26,0,408,93,"Medium",56,14,"No","No"
195 | "194",13.28,139,70,7,71,96,"Good",61,10,"Yes","Yes"
196 | "195",7.23,112,98,18,481,128,"Medium",45,11,"Yes","Yes"
197 | "196",4.19,117,93,4,420,112,"Bad",66,11,"Yes","Yes"
198 | "197",4.1,130,28,6,410,133,"Bad",72,16,"Yes","Yes"
199 | "198",2.52,124,61,0,333,138,"Medium",76,16,"Yes","No"
200 | "199",3.62,112,80,5,500,128,"Medium",69,10,"Yes","Yes"
201 | "200",6.42,122,88,5,335,126,"Medium",64,14,"Yes","Yes"
202 | "201",5.56,144,92,0,349,146,"Medium",62,12,"No","No"
203 | "202",5.94,138,83,0,139,134,"Medium",54,18,"Yes","No"
204 | "203",4.1,121,78,4,413,130,"Bad",46,10,"No","Yes"
205 | "204",2.05,131,82,0,132,157,"Bad",25,14,"Yes","No"
206 | "205",8.74,155,80,0,237,124,"Medium",37,14,"Yes","No"
207 | "206",5.68,113,22,1,317,132,"Medium",28,12,"Yes","No"
208 | "207",4.97,162,67,0,27,160,"Medium",77,17,"Yes","Yes"
209 | "208",8.19,111,105,0,466,97,"Bad",61,10,"No","No"
210 | "209",7.78,86,54,0,497,64,"Bad",33,12,"Yes","No"
211 | "210",3.02,98,21,11,326,90,"Bad",76,11,"No","Yes"
212 | "211",4.36,125,41,2,357,123,"Bad",47,14,"No","Yes"
213 | "212",9.39,117,118,14,445,120,"Medium",32,15,"Yes","Yes"
214 | "213",12.04,145,69,19,501,105,"Medium",45,11,"Yes","Yes"
215 | "214",8.23,149,84,5,220,139,"Medium",33,10,"Yes","Yes"
216 | "215",4.83,115,115,3,48,107,"Medium",73,18,"Yes","Yes"
217 | "216",2.34,116,83,15,170,144,"Bad",71,11,"Yes","Yes"
218 | "217",5.73,141,33,0,243,144,"Medium",34,17,"Yes","No"
219 | "218",4.34,106,44,0,481,111,"Medium",70,14,"No","No"
220 | "219",9.7,138,61,12,156,120,"Medium",25,14,"Yes","Yes"
221 | "220",10.62,116,79,19,359,116,"Good",58,17,"Yes","Yes"
222 | "221",10.59,131,120,15,262,124,"Medium",30,10,"Yes","Yes"
223 | "222",6.43,124,44,0,125,107,"Medium",80,11,"Yes","No"
224 | "223",7.49,136,119,6,178,145,"Medium",35,13,"Yes","Yes"
225 | "224",3.45,110,45,9,276,125,"Medium",62,14,"Yes","Yes"
226 | "225",4.1,134,82,0,464,141,"Medium",48,13,"No","No"
227 | "226",6.68,107,25,0,412,82,"Bad",36,14,"Yes","No"
228 | "227",7.8,119,33,0,245,122,"Good",56,14,"Yes","No"
229 | "228",8.69,113,64,10,68,101,"Medium",57,16,"Yes","Yes"
230 | "229",5.4,149,73,13,381,163,"Bad",26,11,"No","Yes"
231 | "230",11.19,98,104,0,404,72,"Medium",27,18,"No","No"
232 | "231",5.16,115,60,0,119,114,"Bad",38,14,"No","No"
233 | "232",8.09,132,69,0,123,122,"Medium",27,11,"No","No"
234 | "233",13.14,137,80,10,24,105,"Good",61,15,"Yes","Yes"
235 | "234",8.65,123,76,18,218,120,"Medium",29,14,"No","Yes"
236 | "235",9.43,115,62,11,289,129,"Good",56,16,"No","Yes"
237 | "236",5.53,126,32,8,95,132,"Medium",50,17,"Yes","Yes"
238 | "237",9.32,141,34,16,361,108,"Medium",69,10,"Yes","Yes"
239 | "238",9.62,151,28,8,499,135,"Medium",48,10,"Yes","Yes"
240 | "239",7.36,121,24,0,200,133,"Good",73,13,"Yes","No"
241 | "240",3.89,123,105,0,149,118,"Bad",62,16,"Yes","Yes"
242 | "241",10.31,159,80,0,362,121,"Medium",26,18,"Yes","No"
243 | "242",12.01,136,63,0,160,94,"Medium",38,12,"Yes","No"
244 | "243",4.68,124,46,0,199,135,"Medium",52,14,"No","No"
245 | "244",7.82,124,25,13,87,110,"Medium",57,10,"Yes","Yes"
246 | "245",8.78,130,30,0,391,100,"Medium",26,18,"Yes","No"
247 | "246",10,114,43,0,199,88,"Good",57,10,"No","Yes"
248 | "247",6.9,120,56,20,266,90,"Bad",78,18,"Yes","Yes"
249 | "248",5.04,123,114,0,298,151,"Bad",34,16,"Yes","No"
250 | "249",5.36,111,52,0,12,101,"Medium",61,11,"Yes","Yes"
251 | "250",5.05,125,67,0,86,117,"Bad",65,11,"Yes","No"
252 | "251",9.16,137,105,10,435,156,"Good",72,14,"Yes","Yes"
253 | "252",3.72,139,111,5,310,132,"Bad",62,13,"Yes","Yes"
254 | "253",8.31,133,97,0,70,117,"Medium",32,16,"Yes","No"
255 | "254",5.64,124,24,5,288,122,"Medium",57,12,"No","Yes"
256 | "255",9.58,108,104,23,353,129,"Good",37,17,"Yes","Yes"
257 | "256",7.71,123,81,8,198,81,"Bad",80,15,"Yes","Yes"
258 | "257",4.2,147,40,0,277,144,"Medium",73,10,"Yes","No"
259 | "258",8.67,125,62,14,477,112,"Medium",80,13,"Yes","Yes"
260 | "259",3.47,108,38,0,251,81,"Bad",72,14,"No","No"
261 | "260",5.12,123,36,10,467,100,"Bad",74,11,"No","Yes"
262 | "261",7.67,129,117,8,400,101,"Bad",36,10,"Yes","Yes"
263 | "262",5.71,121,42,4,188,118,"Medium",54,15,"Yes","Yes"
264 | "263",6.37,120,77,15,86,132,"Medium",48,18,"Yes","Yes"
265 | "264",7.77,116,26,6,434,115,"Medium",25,17,"Yes","Yes"
266 | "265",6.95,128,29,5,324,159,"Good",31,15,"Yes","Yes"
267 | "266",5.31,130,35,10,402,129,"Bad",39,17,"Yes","Yes"
268 | "267",9.1,128,93,12,343,112,"Good",73,17,"No","Yes"
269 | "268",5.83,134,82,7,473,112,"Bad",51,12,"No","Yes"
270 | "269",6.53,123,57,0,66,105,"Medium",39,11,"Yes","No"
271 | "270",5.01,159,69,0,438,166,"Medium",46,17,"Yes","No"
272 | "271",11.99,119,26,0,284,89,"Good",26,10,"Yes","No"
273 | "272",4.55,111,56,0,504,110,"Medium",62,16,"Yes","No"
274 | "273",12.98,113,33,0,14,63,"Good",38,12,"Yes","No"
275 | "274",10.04,116,106,8,244,86,"Medium",58,12,"Yes","Yes"
276 | "275",7.22,135,93,2,67,119,"Medium",34,11,"Yes","Yes"
277 | "276",6.67,107,119,11,210,132,"Medium",53,11,"Yes","Yes"
278 | "277",6.93,135,69,14,296,130,"Medium",73,15,"Yes","Yes"
279 | "278",7.8,136,48,12,326,125,"Medium",36,16,"Yes","Yes"
280 | "279",7.22,114,113,2,129,151,"Good",40,15,"No","Yes"
281 | "280",3.42,141,57,13,376,158,"Medium",64,18,"Yes","Yes"
282 | "281",2.86,121,86,10,496,145,"Bad",51,10,"Yes","Yes"
283 | "282",11.19,122,69,7,303,105,"Good",45,16,"No","Yes"
284 | "283",7.74,150,96,0,80,154,"Good",61,11,"Yes","No"
285 | "284",5.36,135,110,0,112,117,"Medium",80,16,"No","No"
286 | "285",6.97,106,46,11,414,96,"Bad",79,17,"No","No"
287 | "286",7.6,146,26,11,261,131,"Medium",39,10,"Yes","Yes"
288 | "287",7.53,117,118,11,429,113,"Medium",67,18,"No","Yes"
289 | "288",6.88,95,44,4,208,72,"Bad",44,17,"Yes","Yes"
290 | "289",6.98,116,40,0,74,97,"Medium",76,15,"No","No"
291 | "290",8.75,143,77,25,448,156,"Medium",43,17,"Yes","Yes"
292 | "291",9.49,107,111,14,400,103,"Medium",41,11,"No","Yes"
293 | "292",6.64,118,70,0,106,89,"Bad",39,17,"Yes","No"
294 | "293",11.82,113,66,16,322,74,"Good",76,15,"Yes","Yes"
295 | "294",11.28,123,84,0,74,89,"Good",59,10,"Yes","No"
296 | "295",12.66,148,76,3,126,99,"Good",60,11,"Yes","Yes"
297 | "296",4.21,118,35,14,502,137,"Medium",79,10,"No","Yes"
298 | "297",8.21,127,44,13,160,123,"Good",63,18,"Yes","Yes"
299 | "298",3.07,118,83,13,276,104,"Bad",75,10,"Yes","Yes"
300 | "299",10.98,148,63,0,312,130,"Good",63,15,"Yes","No"
301 | "300",9.4,135,40,17,497,96,"Medium",54,17,"No","Yes"
302 | "301",8.57,116,78,1,158,99,"Medium",45,11,"Yes","Yes"
303 | "302",7.41,99,93,0,198,87,"Medium",57,16,"Yes","Yes"
304 | "303",5.28,108,77,13,388,110,"Bad",74,14,"Yes","Yes"
305 | "304",10.01,133,52,16,290,99,"Medium",43,11,"Yes","Yes"
306 | "305",11.93,123,98,12,408,134,"Good",29,10,"Yes","Yes"
307 | "306",8.03,115,29,26,394,132,"Medium",33,13,"Yes","Yes"
308 | "307",4.78,131,32,1,85,133,"Medium",48,12,"Yes","Yes"
309 | "308",5.9,138,92,0,13,120,"Bad",61,12,"Yes","No"
310 | "309",9.24,126,80,19,436,126,"Medium",52,10,"Yes","Yes"
311 | "310",11.18,131,111,13,33,80,"Bad",68,18,"Yes","Yes"
312 | "311",9.53,175,65,29,419,166,"Medium",53,12,"Yes","Yes"
313 | "312",6.15,146,68,12,328,132,"Bad",51,14,"Yes","Yes"
314 | "313",6.8,137,117,5,337,135,"Bad",38,10,"Yes","Yes"
315 | "314",9.33,103,81,3,491,54,"Medium",66,13,"Yes","No"
316 | "315",7.72,133,33,10,333,129,"Good",71,14,"Yes","Yes"
317 | "316",6.39,131,21,8,220,171,"Good",29,14,"Yes","Yes"
318 | "317",15.63,122,36,5,369,72,"Good",35,10,"Yes","Yes"
319 | "318",6.41,142,30,0,472,136,"Good",80,15,"No","No"
320 | "319",10.08,116,72,10,456,130,"Good",41,14,"No","Yes"
321 | "320",6.97,127,45,19,459,129,"Medium",57,11,"No","Yes"
322 | "321",5.86,136,70,12,171,152,"Medium",44,18,"Yes","Yes"
323 | "322",7.52,123,39,5,499,98,"Medium",34,15,"Yes","No"
324 | "323",9.16,140,50,10,300,139,"Good",60,15,"Yes","Yes"
325 | "324",10.36,107,105,18,428,103,"Medium",34,12,"Yes","Yes"
326 | "325",2.66,136,65,4,133,150,"Bad",53,13,"Yes","Yes"
327 | "326",11.7,144,69,11,131,104,"Medium",47,11,"Yes","Yes"
328 | "327",4.69,133,30,0,152,122,"Medium",53,17,"Yes","No"
329 | "328",6.23,112,38,17,316,104,"Medium",80,16,"Yes","Yes"
330 | "329",3.15,117,66,1,65,111,"Bad",55,11,"Yes","Yes"
331 | "330",11.27,100,54,9,433,89,"Good",45,12,"Yes","Yes"
332 | "331",4.99,122,59,0,501,112,"Bad",32,14,"No","No"
333 | "332",10.1,135,63,15,213,134,"Medium",32,10,"Yes","Yes"
334 | "333",5.74,106,33,20,354,104,"Medium",61,12,"Yes","Yes"
335 | "334",5.87,136,60,7,303,147,"Medium",41,10,"Yes","Yes"
336 | "335",7.63,93,117,9,489,83,"Bad",42,13,"Yes","Yes"
337 | "336",6.18,120,70,15,464,110,"Medium",72,15,"Yes","Yes"
338 | "337",5.17,138,35,6,60,143,"Bad",28,18,"Yes","No"
339 | "338",8.61,130,38,0,283,102,"Medium",80,15,"Yes","No"
340 | "339",5.97,112,24,0,164,101,"Medium",45,11,"Yes","No"
341 | "340",11.54,134,44,4,219,126,"Good",44,15,"Yes","Yes"
342 | "341",7.5,140,29,0,105,91,"Bad",43,16,"Yes","No"
343 | "342",7.38,98,120,0,268,93,"Medium",72,10,"No","No"
344 | "343",7.81,137,102,13,422,118,"Medium",71,10,"No","Yes"
345 | "344",5.99,117,42,10,371,121,"Bad",26,14,"Yes","Yes"
346 | "345",8.43,138,80,0,108,126,"Good",70,13,"No","Yes"
347 | "346",4.81,121,68,0,279,149,"Good",79,12,"Yes","No"
348 | "347",8.97,132,107,0,144,125,"Medium",33,13,"No","No"
349 | "348",6.88,96,39,0,161,112,"Good",27,14,"No","No"
350 | "349",12.57,132,102,20,459,107,"Good",49,11,"Yes","Yes"
351 | "350",9.32,134,27,18,467,96,"Medium",49,14,"No","Yes"
352 | "351",8.64,111,101,17,266,91,"Medium",63,17,"No","Yes"
353 | "352",10.44,124,115,16,458,105,"Medium",62,16,"No","Yes"
354 | "353",13.44,133,103,14,288,122,"Good",61,17,"Yes","Yes"
355 | "354",9.45,107,67,12,430,92,"Medium",35,12,"No","Yes"
356 | "355",5.3,133,31,1,80,145,"Medium",42,18,"Yes","Yes"
357 | "356",7.02,130,100,0,306,146,"Good",42,11,"Yes","No"
358 | "357",3.58,142,109,0,111,164,"Good",72,12,"Yes","No"
359 | "358",13.36,103,73,3,276,72,"Medium",34,15,"Yes","Yes"
360 | "359",4.17,123,96,10,71,118,"Bad",69,11,"Yes","Yes"
361 | "360",3.13,130,62,11,396,130,"Bad",66,14,"Yes","Yes"
362 | "361",8.77,118,86,7,265,114,"Good",52,15,"No","Yes"
363 | "362",8.68,131,25,10,183,104,"Medium",56,15,"No","Yes"
364 | "363",5.25,131,55,0,26,110,"Bad",79,12,"Yes","Yes"
365 | "364",10.26,111,75,1,377,108,"Good",25,12,"Yes","No"
366 | "365",10.5,122,21,16,488,131,"Good",30,14,"Yes","Yes"
367 | "366",6.53,154,30,0,122,162,"Medium",57,17,"No","No"
368 | "367",5.98,124,56,11,447,134,"Medium",53,12,"No","Yes"
369 | "368",14.37,95,106,0,256,53,"Good",52,17,"Yes","No"
370 | "369",10.71,109,22,10,348,79,"Good",74,14,"No","Yes"
371 | "370",10.26,135,100,22,463,122,"Medium",36,14,"Yes","Yes"
372 | "371",7.68,126,41,22,403,119,"Bad",42,12,"Yes","Yes"
373 | "372",9.08,152,81,0,191,126,"Medium",54,16,"Yes","No"
374 | "373",7.8,121,50,0,508,98,"Medium",65,11,"No","No"
375 | "374",5.58,137,71,0,402,116,"Medium",78,17,"Yes","No"
376 | "375",9.44,131,47,7,90,118,"Medium",47,12,"Yes","Yes"
377 | "376",7.9,132,46,4,206,124,"Medium",73,11,"Yes","No"
378 | "377",16.27,141,60,19,319,92,"Good",44,11,"Yes","Yes"
379 | "378",6.81,132,61,0,263,125,"Medium",41,12,"No","No"
380 | "379",6.11,133,88,3,105,119,"Medium",79,12,"Yes","Yes"
381 | "380",5.81,125,111,0,404,107,"Bad",54,15,"Yes","No"
382 | "381",9.64,106,64,10,17,89,"Medium",68,17,"Yes","Yes"
383 | "382",3.9,124,65,21,496,151,"Bad",77,13,"Yes","Yes"
384 | "383",4.95,121,28,19,315,121,"Medium",66,14,"Yes","Yes"
385 | "384",9.35,98,117,0,76,68,"Medium",63,10,"Yes","No"
386 | "385",12.85,123,37,15,348,112,"Good",28,12,"Yes","Yes"
387 | "386",5.87,131,73,13,455,132,"Medium",62,17,"Yes","Yes"
388 | "387",5.32,152,116,0,170,160,"Medium",39,16,"Yes","No"
389 | "388",8.67,142,73,14,238,115,"Medium",73,14,"No","Yes"
390 | "389",8.14,135,89,11,245,78,"Bad",79,16,"Yes","Yes"
391 | "390",8.44,128,42,8,328,107,"Medium",35,12,"Yes","Yes"
392 | "391",5.47,108,75,9,61,111,"Medium",67,12,"Yes","Yes"
393 | "392",6.1,153,63,0,49,124,"Bad",56,16,"Yes","No"
394 | "393",4.53,129,42,13,315,130,"Bad",34,13,"Yes","Yes"
395 | "394",5.57,109,51,10,26,120,"Medium",30,17,"No","Yes"
396 | "395",5.35,130,58,19,366,139,"Bad",33,16,"Yes","Yes"
397 | "396",12.57,138,108,17,203,128,"Good",33,14,"Yes","Yes"
398 | "397",6.14,139,23,3,37,120,"Medium",55,11,"No","Yes"
399 | "398",7.41,162,26,12,368,159,"Medium",40,18,"Yes","Yes"
400 | "399",5.94,100,79,7,284,95,"Bad",50,12,"Yes","Yes"
401 | "400",9.71,134,37,0,27,120,"Good",49,16,"Yes","Yes"
402 | 


--------------------------------------------------------------------------------
/Data/Credit.csv:
--------------------------------------------------------------------------------
  1 | "","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",12,4160,320,4,28,14,"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",89,5759,440,3,37,6,"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 | 


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/Data/Default.xlsx:
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https://raw.githubusercontent.com/pedvide/ISLR_Python/HEAD/Data/Default.xlsx


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/Data/Heart.csv:
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  1 | "","Age","Sex","ChestPain","RestBP","Chol","Fbs","RestECG","MaxHR","ExAng","Oldpeak","Slope","Ca","Thal","AHD"
  2 | "1",63,1,"typical",145,233,1,2,150,0,2.3,3,0,"fixed","No"
  3 | "2",67,1,"asymptomatic",160,286,0,2,108,1,1.5,2,3,"normal","Yes"
  4 | "3",67,1,"asymptomatic",120,229,0,2,129,1,2.6,2,2,"reversable","Yes"
  5 | "4",37,1,"nonanginal",130,250,0,0,187,0,3.5,3,0,"normal","No"
  6 | "5",41,0,"nontypical",130,204,0,2,172,0,1.4,1,0,"normal","No"
  7 | "6",56,1,"nontypical",120,236,0,0,178,0,0.8,1,0,"normal","No"
  8 | "7",62,0,"asymptomatic",140,268,0,2,160,0,3.6,3,2,"normal","Yes"
  9 | "8",57,0,"asymptomatic",120,354,0,0,163,1,0.6,1,0,"normal","No"
 10 | "9",63,1,"asymptomatic",130,254,0,2,147,0,1.4,2,1,"reversable","Yes"
 11 | "10",53,1,"asymptomatic",140,203,1,2,155,1,3.1,3,0,"reversable","Yes"
 12 | "11",57,1,"asymptomatic",140,192,0,0,148,0,0.4,2,0,"fixed","No"
 13 | "12",56,0,"nontypical",140,294,0,2,153,0,1.3,2,0,"normal","No"
 14 | "13",56,1,"nonanginal",130,256,1,2,142,1,0.6,2,1,"fixed","Yes"
 15 | "14",44,1,"nontypical",120,263,0,0,173,0,0,1,0,"reversable","No"
 16 | "15",52,1,"nonanginal",172,199,1,0,162,0,0.5,1,0,"reversable","No"
 17 | "16",57,1,"nonanginal",150,168,0,0,174,0,1.6,1,0,"normal","No"
 18 | "17",48,1,"nontypical",110,229,0,0,168,0,1,3,0,"reversable","Yes"
 19 | "18",54,1,"asymptomatic",140,239,0,0,160,0,1.2,1,0,"normal","No"
 20 | "19",48,0,"nonanginal",130,275,0,0,139,0,0.2,1,0,"normal","No"
 21 | "20",49,1,"nontypical",130,266,0,0,171,0,0.6,1,0,"normal","No"
 22 | "21",64,1,"typical",110,211,0,2,144,1,1.8,2,0,"normal","No"
 23 | "22",58,0,"typical",150,283,1,2,162,0,1,1,0,"normal","No"
 24 | "23",58,1,"nontypical",120,284,0,2,160,0,1.8,2,0,"normal","Yes"
 25 | "24",58,1,"nonanginal",132,224,0,2,173,0,3.2,1,2,"reversable","Yes"
 26 | "25",60,1,"asymptomatic",130,206,0,2,132,1,2.4,2,2,"reversable","Yes"
 27 | "26",50,0,"nonanginal",120,219,0,0,158,0,1.6,2,0,"normal","No"
 28 | "27",58,0,"nonanginal",120,340,0,0,172,0,0,1,0,"normal","No"
 29 | "28",66,0,"typical",150,226,0,0,114,0,2.6,3,0,"normal","No"
 30 | "29",43,1,"asymptomatic",150,247,0,0,171,0,1.5,1,0,"normal","No"
 31 | "30",40,1,"asymptomatic",110,167,0,2,114,1,2,2,0,"reversable","Yes"
 32 | "31",69,0,"typical",140,239,0,0,151,0,1.8,1,2,"normal","No"
 33 | "32",60,1,"asymptomatic",117,230,1,0,160,1,1.4,1,2,"reversable","Yes"
 34 | "33",64,1,"nonanginal",140,335,0,0,158,0,0,1,0,"normal","Yes"
 35 | "34",59,1,"asymptomatic",135,234,0,0,161,0,0.5,2,0,"reversable","No"
 36 | "35",44,1,"nonanginal",130,233,0,0,179,1,0.4,1,0,"normal","No"
 37 | "36",42,1,"asymptomatic",140,226,0,0,178,0,0,1,0,"normal","No"
 38 | "37",43,1,"asymptomatic",120,177,0,2,120,1,2.5,2,0,"reversable","Yes"
 39 | "38",57,1,"asymptomatic",150,276,0,2,112,1,0.6,2,1,"fixed","Yes"
 40 | "39",55,1,"asymptomatic",132,353,0,0,132,1,1.2,2,1,"reversable","Yes"
 41 | "40",61,1,"nonanginal",150,243,1,0,137,1,1,2,0,"normal","No"
 42 | "41",65,0,"asymptomatic",150,225,0,2,114,0,1,2,3,"reversable","Yes"
 43 | "42",40,1,"typical",140,199,0,0,178,1,1.4,1,0,"reversable","No"
 44 | "43",71,0,"nontypical",160,302,0,0,162,0,0.4,1,2,"normal","No"
 45 | "44",59,1,"nonanginal",150,212,1,0,157,0,1.6,1,0,"normal","No"
 46 | "45",61,0,"asymptomatic",130,330,0,2,169,0,0,1,0,"normal","Yes"
 47 | "46",58,1,"nonanginal",112,230,0,2,165,0,2.5,2,1,"reversable","Yes"
 48 | "47",51,1,"nonanginal",110,175,0,0,123,0,0.6,1,0,"normal","No"
 49 | "48",50,1,"asymptomatic",150,243,0,2,128,0,2.6,2,0,"reversable","Yes"
 50 | "49",65,0,"nonanginal",140,417,1,2,157,0,0.8,1,1,"normal","No"
 51 | "50",53,1,"nonanginal",130,197,1,2,152,0,1.2,3,0,"normal","No"
 52 | "51",41,0,"nontypical",105,198,0,0,168,0,0,1,1,"normal","No"
 53 | "52",65,1,"asymptomatic",120,177,0,0,140,0,0.4,1,0,"reversable","No"
 54 | "53",44,1,"asymptomatic",112,290,0,2,153,0,0,1,1,"normal","Yes"
 55 | "54",44,1,"nontypical",130,219,0,2,188,0,0,1,0,"normal","No"
 56 | "55",60,1,"asymptomatic",130,253,0,0,144,1,1.4,1,1,"reversable","Yes"
 57 | "56",54,1,"asymptomatic",124,266,0,2,109,1,2.2,2,1,"reversable","Yes"
 58 | "57",50,1,"nonanginal",140,233,0,0,163,0,0.6,2,1,"reversable","Yes"
 59 | "58",41,1,"asymptomatic",110,172,0,2,158,0,0,1,0,"reversable","Yes"
 60 | "59",54,1,"nonanginal",125,273,0,2,152,0,0.5,3,1,"normal","No"
 61 | "60",51,1,"typical",125,213,0,2,125,1,1.4,1,1,"normal","No"
 62 | "61",51,0,"asymptomatic",130,305,0,0,142,1,1.2,2,0,"reversable","Yes"
 63 | "62",46,0,"nonanginal",142,177,0,2,160,1,1.4,3,0,"normal","No"
 64 | "63",58,1,"asymptomatic",128,216,0,2,131,1,2.2,2,3,"reversable","Yes"
 65 | "64",54,0,"nonanginal",135,304,1,0,170,0,0,1,0,"normal","No"
 66 | "65",54,1,"asymptomatic",120,188,0,0,113,0,1.4,2,1,"reversable","Yes"
 67 | "66",60,1,"asymptomatic",145,282,0,2,142,1,2.8,2,2,"reversable","Yes"
 68 | "67",60,1,"nonanginal",140,185,0,2,155,0,3,2,0,"normal","Yes"
 69 | "68",54,1,"nonanginal",150,232,0,2,165,0,1.6,1,0,"reversable","No"
 70 | "69",59,1,"asymptomatic",170,326,0,2,140,1,3.4,3,0,"reversable","Yes"
 71 | "70",46,1,"nonanginal",150,231,0,0,147,0,3.6,2,0,"normal","Yes"
 72 | "71",65,0,"nonanginal",155,269,0,0,148,0,0.8,1,0,"normal","No"
 73 | "72",67,1,"asymptomatic",125,254,1,0,163,0,0.2,2,2,"reversable","Yes"
 74 | "73",62,1,"asymptomatic",120,267,0,0,99,1,1.8,2,2,"reversable","Yes"
 75 | "74",65,1,"asymptomatic",110,248,0,2,158,0,0.6,1,2,"fixed","Yes"
 76 | "75",44,1,"asymptomatic",110,197,0,2,177,0,0,1,1,"normal","Yes"
 77 | "76",65,0,"nonanginal",160,360,0,2,151,0,0.8,1,0,"normal","No"
 78 | "77",60,1,"asymptomatic",125,258,0,2,141,1,2.8,2,1,"reversable","Yes"
 79 | "78",51,0,"nonanginal",140,308,0,2,142,0,1.5,1,1,"normal","No"
 80 | "79",48,1,"nontypical",130,245,0,2,180,0,0.2,2,0,"normal","No"
 81 | "80",58,1,"asymptomatic",150,270,0,2,111,1,0.8,1,0,"reversable","Yes"
 82 | "81",45,1,"asymptomatic",104,208,0,2,148,1,3,2,0,"normal","No"
 83 | "82",53,0,"asymptomatic",130,264,0,2,143,0,0.4,2,0,"normal","No"
 84 | "83",39,1,"nonanginal",140,321,0,2,182,0,0,1,0,"normal","No"
 85 | "84",68,1,"nonanginal",180,274,1,2,150,1,1.6,2,0,"reversable","Yes"
 86 | "85",52,1,"nontypical",120,325,0,0,172,0,0.2,1,0,"normal","No"
 87 | "86",44,1,"nonanginal",140,235,0,2,180,0,0,1,0,"normal","No"
 88 | "87",47,1,"nonanginal",138,257,0,2,156,0,0,1,0,"normal","No"
 89 | "88",53,0,"nonanginal",128,216,0,2,115,0,0,1,0,NA,"No"
 90 | "89",53,0,"asymptomatic",138,234,0,2,160,0,0,1,0,"normal","No"
 91 | "90",51,0,"nonanginal",130,256,0,2,149,0,0.5,1,0,"normal","No"
 92 | "91",66,1,"asymptomatic",120,302,0,2,151,0,0.4,2,0,"normal","No"
 93 | "92",62,0,"asymptomatic",160,164,0,2,145,0,6.2,3,3,"reversable","Yes"
 94 | "93",62,1,"nonanginal",130,231,0,0,146,0,1.8,2,3,"reversable","No"
 95 | "94",44,0,"nonanginal",108,141,0,0,175,0,0.6,2,0,"normal","No"
 96 | "95",63,0,"nonanginal",135,252,0,2,172,0,0,1,0,"normal","No"
 97 | "96",52,1,"asymptomatic",128,255,0,0,161,1,0,1,1,"reversable","Yes"
 98 | "97",59,1,"asymptomatic",110,239,0,2,142,1,1.2,2,1,"reversable","Yes"
 99 | "98",60,0,"asymptomatic",150,258,0,2,157,0,2.6,2,2,"reversable","Yes"
100 | "99",52,1,"nontypical",134,201,0,0,158,0,0.8,1,1,"normal","No"
101 | "100",48,1,"asymptomatic",122,222,0,2,186,0,0,1,0,"normal","No"
102 | "101",45,1,"asymptomatic",115,260,0,2,185,0,0,1,0,"normal","No"
103 | "102",34,1,"typical",118,182,0,2,174,0,0,1,0,"normal","No"
104 | "103",57,0,"asymptomatic",128,303,0,2,159,0,0,1,1,"normal","No"
105 | "104",71,0,"nonanginal",110,265,1,2,130,0,0,1,1,"normal","No"
106 | "105",49,1,"nonanginal",120,188,0,0,139,0,2,2,3,"reversable","Yes"
107 | "106",54,1,"nontypical",108,309,0,0,156,0,0,1,0,"reversable","No"
108 | "107",59,1,"asymptomatic",140,177,0,0,162,1,0,1,1,"reversable","Yes"
109 | "108",57,1,"nonanginal",128,229,0,2,150,0,0.4,2,1,"reversable","Yes"
110 | "109",61,1,"asymptomatic",120,260,0,0,140,1,3.6,2,1,"reversable","Yes"
111 | "110",39,1,"asymptomatic",118,219,0,0,140,0,1.2,2,0,"reversable","Yes"
112 | "111",61,0,"asymptomatic",145,307,0,2,146,1,1,2,0,"reversable","Yes"
113 | "112",56,1,"asymptomatic",125,249,1,2,144,1,1.2,2,1,"normal","Yes"
114 | "113",52,1,"typical",118,186,0,2,190,0,0,2,0,"fixed","No"
115 | "114",43,0,"asymptomatic",132,341,1,2,136,1,3,2,0,"reversable","Yes"
116 | "115",62,0,"nonanginal",130,263,0,0,97,0,1.2,2,1,"reversable","Yes"
117 | "116",41,1,"nontypical",135,203,0,0,132,0,0,2,0,"fixed","No"
118 | "117",58,1,"nonanginal",140,211,1,2,165,0,0,1,0,"normal","No"
119 | "118",35,0,"asymptomatic",138,183,0,0,182,0,1.4,1,0,"normal","No"
120 | "119",63,1,"asymptomatic",130,330,1,2,132,1,1.8,1,3,"reversable","Yes"
121 | "120",65,1,"asymptomatic",135,254,0,2,127,0,2.8,2,1,"reversable","Yes"
122 | "121",48,1,"asymptomatic",130,256,1,2,150,1,0,1,2,"reversable","Yes"
123 | "122",63,0,"asymptomatic",150,407,0,2,154,0,4,2,3,"reversable","Yes"
124 | "123",51,1,"nonanginal",100,222,0,0,143,1,1.2,2,0,"normal","No"
125 | "124",55,1,"asymptomatic",140,217,0,0,111,1,5.6,3,0,"reversable","Yes"
126 | "125",65,1,"typical",138,282,1,2,174,0,1.4,2,1,"normal","Yes"
127 | "126",45,0,"nontypical",130,234,0,2,175,0,0.6,2,0,"normal","No"
128 | "127",56,0,"asymptomatic",200,288,1,2,133,1,4,3,2,"reversable","Yes"
129 | "128",54,1,"asymptomatic",110,239,0,0,126,1,2.8,2,1,"reversable","Yes"
130 | "129",44,1,"nontypical",120,220,0,0,170,0,0,1,0,"normal","No"
131 | "130",62,0,"asymptomatic",124,209,0,0,163,0,0,1,0,"normal","No"
132 | "131",54,1,"nonanginal",120,258,0,2,147,0,0.4,2,0,"reversable","No"
133 | "132",51,1,"nonanginal",94,227,0,0,154,1,0,1,1,"reversable","No"
134 | "133",29,1,"nontypical",130,204,0,2,202,0,0,1,0,"normal","No"
135 | "134",51,1,"asymptomatic",140,261,0,2,186,1,0,1,0,"normal","No"
136 | "135",43,0,"nonanginal",122,213,0,0,165,0,0.2,2,0,"normal","No"
137 | "136",55,0,"nontypical",135,250,0,2,161,0,1.4,2,0,"normal","No"
138 | "137",70,1,"asymptomatic",145,174,0,0,125,1,2.6,3,0,"reversable","Yes"
139 | "138",62,1,"nontypical",120,281,0,2,103,0,1.4,2,1,"reversable","Yes"
140 | "139",35,1,"asymptomatic",120,198,0,0,130,1,1.6,2,0,"reversable","Yes"
141 | "140",51,1,"nonanginal",125,245,1,2,166,0,2.4,2,0,"normal","No"
142 | "141",59,1,"nontypical",140,221,0,0,164,1,0,1,0,"normal","No"
143 | "142",59,1,"typical",170,288,0,2,159,0,0.2,2,0,"reversable","Yes"
144 | "143",52,1,"nontypical",128,205,1,0,184,0,0,1,0,"normal","No"
145 | "144",64,1,"nonanginal",125,309,0,0,131,1,1.8,2,0,"reversable","Yes"
146 | "145",58,1,"nonanginal",105,240,0,2,154,1,0.6,2,0,"reversable","No"
147 | "146",47,1,"nonanginal",108,243,0,0,152,0,0,1,0,"normal","Yes"
148 | "147",57,1,"asymptomatic",165,289,1,2,124,0,1,2,3,"reversable","Yes"
149 | "148",41,1,"nonanginal",112,250,0,0,179,0,0,1,0,"normal","No"
150 | "149",45,1,"nontypical",128,308,0,2,170,0,0,1,0,"normal","No"
151 | "150",60,0,"nonanginal",102,318,0,0,160,0,0,1,1,"normal","No"
152 | "151",52,1,"typical",152,298,1,0,178,0,1.2,2,0,"reversable","No"
153 | "152",42,0,"asymptomatic",102,265,0,2,122,0,0.6,2,0,"normal","No"
154 | "153",67,0,"nonanginal",115,564,0,2,160,0,1.6,2,0,"reversable","No"
155 | "154",55,1,"asymptomatic",160,289,0,2,145,1,0.8,2,1,"reversable","Yes"
156 | "155",64,1,"asymptomatic",120,246,0,2,96,1,2.2,3,1,"normal","Yes"
157 | "156",70,1,"asymptomatic",130,322,0,2,109,0,2.4,2,3,"normal","Yes"
158 | "157",51,1,"asymptomatic",140,299,0,0,173,1,1.6,1,0,"reversable","Yes"
159 | "158",58,1,"asymptomatic",125,300,0,2,171,0,0,1,2,"reversable","Yes"
160 | "159",60,1,"asymptomatic",140,293,0,2,170,0,1.2,2,2,"reversable","Yes"
161 | "160",68,1,"nonanginal",118,277,0,0,151,0,1,1,1,"reversable","No"
162 | "161",46,1,"nontypical",101,197,1,0,156,0,0,1,0,"reversable","No"
163 | "162",77,1,"asymptomatic",125,304,0,2,162,1,0,1,3,"normal","Yes"
164 | "163",54,0,"nonanginal",110,214,0,0,158,0,1.6,2,0,"normal","No"
165 | "164",58,0,"asymptomatic",100,248,0,2,122,0,1,2,0,"normal","No"
166 | "165",48,1,"nonanginal",124,255,1,0,175,0,0,1,2,"normal","No"
167 | "166",57,1,"asymptomatic",132,207,0,0,168,1,0,1,0,"reversable","No"
168 | "167",52,1,"nonanginal",138,223,0,0,169,0,0,1,NA,"normal","No"
169 | "168",54,0,"nontypical",132,288,1,2,159,1,0,1,1,"normal","No"
170 | "169",35,1,"asymptomatic",126,282,0,2,156,1,0,1,0,"reversable","Yes"
171 | "170",45,0,"nontypical",112,160,0,0,138,0,0,2,0,"normal","No"
172 | "171",70,1,"nonanginal",160,269,0,0,112,1,2.9,2,1,"reversable","Yes"
173 | "172",53,1,"asymptomatic",142,226,0,2,111,1,0,1,0,"reversable","No"
174 | "173",59,0,"asymptomatic",174,249,0,0,143,1,0,2,0,"normal","Yes"
175 | "174",62,0,"asymptomatic",140,394,0,2,157,0,1.2,2,0,"normal","No"
176 | "175",64,1,"asymptomatic",145,212,0,2,132,0,2,2,2,"fixed","Yes"
177 | "176",57,1,"asymptomatic",152,274,0,0,88,1,1.2,2,1,"reversable","Yes"
178 | "177",52,1,"asymptomatic",108,233,1,0,147,0,0.1,1,3,"reversable","No"
179 | "178",56,1,"asymptomatic",132,184,0,2,105,1,2.1,2,1,"fixed","Yes"
180 | "179",43,1,"nonanginal",130,315,0,0,162,0,1.9,1,1,"normal","No"
181 | "180",53,1,"nonanginal",130,246,1,2,173,0,0,1,3,"normal","No"
182 | "181",48,1,"asymptomatic",124,274,0,2,166,0,0.5,2,0,"reversable","Yes"
183 | "182",56,0,"asymptomatic",134,409,0,2,150,1,1.9,2,2,"reversable","Yes"
184 | "183",42,1,"typical",148,244,0,2,178,0,0.8,1,2,"normal","No"
185 | "184",59,1,"typical",178,270,0,2,145,0,4.2,3,0,"reversable","No"
186 | "185",60,0,"asymptomatic",158,305,0,2,161,0,0,1,0,"normal","Yes"
187 | "186",63,0,"nontypical",140,195,0,0,179,0,0,1,2,"normal","No"
188 | "187",42,1,"nonanginal",120,240,1,0,194,0,0.8,3,0,"reversable","No"
189 | "188",66,1,"nontypical",160,246,0,0,120,1,0,2,3,"fixed","Yes"
190 | "189",54,1,"nontypical",192,283,0,2,195,0,0,1,1,"reversable","Yes"
191 | "190",69,1,"nonanginal",140,254,0,2,146,0,2,2,3,"reversable","Yes"
192 | "191",50,1,"nonanginal",129,196,0,0,163,0,0,1,0,"normal","No"
193 | "192",51,1,"asymptomatic",140,298,0,0,122,1,4.2,2,3,"reversable","Yes"
194 | "193",43,1,"asymptomatic",132,247,1,2,143,1,0.1,2,NA,"reversable","Yes"
195 | "194",62,0,"asymptomatic",138,294,1,0,106,0,1.9,2,3,"normal","Yes"
196 | "195",68,0,"nonanginal",120,211,0,2,115,0,1.5,2,0,"normal","No"
197 | "196",67,1,"asymptomatic",100,299,0,2,125,1,0.9,2,2,"normal","Yes"
198 | "197",69,1,"typical",160,234,1,2,131,0,0.1,2,1,"normal","No"
199 | "198",45,0,"asymptomatic",138,236,0,2,152,1,0.2,2,0,"normal","No"
200 | "199",50,0,"nontypical",120,244,0,0,162,0,1.1,1,0,"normal","No"
201 | "200",59,1,"typical",160,273,0,2,125,0,0,1,0,"normal","Yes"
202 | "201",50,0,"asymptomatic",110,254,0,2,159,0,0,1,0,"normal","No"
203 | "202",64,0,"asymptomatic",180,325,0,0,154,1,0,1,0,"normal","No"
204 | "203",57,1,"nonanginal",150,126,1,0,173,0,0.2,1,1,"reversable","No"
205 | "204",64,0,"nonanginal",140,313,0,0,133,0,0.2,1,0,"reversable","No"
206 | "205",43,1,"asymptomatic",110,211,0,0,161,0,0,1,0,"reversable","No"
207 | "206",45,1,"asymptomatic",142,309,0,2,147,1,0,2,3,"reversable","Yes"
208 | "207",58,1,"asymptomatic",128,259,0,2,130,1,3,2,2,"reversable","Yes"
209 | "208",50,1,"asymptomatic",144,200,0,2,126,1,0.9,2,0,"reversable","Yes"
210 | "209",55,1,"nontypical",130,262,0,0,155,0,0,1,0,"normal","No"
211 | "210",62,0,"asymptomatic",150,244,0,0,154,1,1.4,2,0,"normal","Yes"
212 | "211",37,0,"nonanginal",120,215,0,0,170,0,0,1,0,"normal","No"
213 | "212",38,1,"typical",120,231,0,0,182,1,3.8,2,0,"reversable","Yes"
214 | "213",41,1,"nonanginal",130,214,0,2,168,0,2,2,0,"normal","No"
215 | "214",66,0,"asymptomatic",178,228,1,0,165,1,1,2,2,"reversable","Yes"
216 | "215",52,1,"asymptomatic",112,230,0,0,160,0,0,1,1,"normal","Yes"
217 | "216",56,1,"typical",120,193,0,2,162,0,1.9,2,0,"reversable","No"
218 | "217",46,0,"nontypical",105,204,0,0,172,0,0,1,0,"normal","No"
219 | "218",46,0,"asymptomatic",138,243,0,2,152,1,0,2,0,"normal","No"
220 | "219",64,0,"asymptomatic",130,303,0,0,122,0,2,2,2,"normal","No"
221 | "220",59,1,"asymptomatic",138,271,0,2,182,0,0,1,0,"normal","No"
222 | "221",41,0,"nonanginal",112,268,0,2,172,1,0,1,0,"normal","No"
223 | "222",54,0,"nonanginal",108,267,0,2,167,0,0,1,0,"normal","No"
224 | "223",39,0,"nonanginal",94,199,0,0,179,0,0,1,0,"normal","No"
225 | "224",53,1,"asymptomatic",123,282,0,0,95,1,2,2,2,"reversable","Yes"
226 | "225",63,0,"asymptomatic",108,269,0,0,169,1,1.8,2,2,"normal","Yes"
227 | "226",34,0,"nontypical",118,210,0,0,192,0,0.7,1,0,"normal","No"
228 | "227",47,1,"asymptomatic",112,204,0,0,143,0,0.1,1,0,"normal","No"
229 | "228",67,0,"nonanginal",152,277,0,0,172,0,0,1,1,"normal","No"
230 | "229",54,1,"asymptomatic",110,206,0,2,108,1,0,2,1,"normal","Yes"
231 | "230",66,1,"asymptomatic",112,212,0,2,132,1,0.1,1,1,"normal","Yes"
232 | "231",52,0,"nonanginal",136,196,0,2,169,0,0.1,2,0,"normal","No"
233 | "232",55,0,"asymptomatic",180,327,0,1,117,1,3.4,2,0,"normal","Yes"
234 | "233",49,1,"nonanginal",118,149,0,2,126,0,0.8,1,3,"normal","Yes"
235 | "234",74,0,"nontypical",120,269,0,2,121,1,0.2,1,1,"normal","No"
236 | "235",54,0,"nonanginal",160,201,0,0,163,0,0,1,1,"normal","No"
237 | "236",54,1,"asymptomatic",122,286,0,2,116,1,3.2,2,2,"normal","Yes"
238 | "237",56,1,"asymptomatic",130,283,1,2,103,1,1.6,3,0,"reversable","Yes"
239 | "238",46,1,"asymptomatic",120,249,0,2,144,0,0.8,1,0,"reversable","Yes"
240 | "239",49,0,"nontypical",134,271,0,0,162,0,0,2,0,"normal","No"
241 | "240",42,1,"nontypical",120,295,0,0,162,0,0,1,0,"normal","No"
242 | "241",41,1,"nontypical",110,235,0,0,153,0,0,1,0,"normal","No"
243 | "242",41,0,"nontypical",126,306,0,0,163,0,0,1,0,"normal","No"
244 | "243",49,0,"asymptomatic",130,269,0,0,163,0,0,1,0,"normal","No"
245 | "244",61,1,"typical",134,234,0,0,145,0,2.6,2,2,"normal","Yes"
246 | "245",60,0,"nonanginal",120,178,1,0,96,0,0,1,0,"normal","No"
247 | "246",67,1,"asymptomatic",120,237,0,0,71,0,1,2,0,"normal","Yes"
248 | "247",58,1,"asymptomatic",100,234,0,0,156,0,0.1,1,1,"reversable","Yes"
249 | "248",47,1,"asymptomatic",110,275,0,2,118,1,1,2,1,"normal","Yes"
250 | "249",52,1,"asymptomatic",125,212,0,0,168,0,1,1,2,"reversable","Yes"
251 | "250",62,1,"nontypical",128,208,1,2,140,0,0,1,0,"normal","No"
252 | "251",57,1,"asymptomatic",110,201,0,0,126,1,1.5,2,0,"fixed","No"
253 | "252",58,1,"asymptomatic",146,218,0,0,105,0,2,2,1,"reversable","Yes"
254 | "253",64,1,"asymptomatic",128,263,0,0,105,1,0.2,2,1,"reversable","No"
255 | "254",51,0,"nonanginal",120,295,0,2,157,0,0.6,1,0,"normal","No"
256 | "255",43,1,"asymptomatic",115,303,0,0,181,0,1.2,2,0,"normal","No"
257 | "256",42,0,"nonanginal",120,209,0,0,173,0,0,2,0,"normal","No"
258 | "257",67,0,"asymptomatic",106,223,0,0,142,0,0.3,1,2,"normal","No"
259 | "258",76,0,"nonanginal",140,197,0,1,116,0,1.1,2,0,"normal","No"
260 | "259",70,1,"nontypical",156,245,0,2,143,0,0,1,0,"normal","No"
261 | "260",57,1,"nontypical",124,261,0,0,141,0,0.3,1,0,"reversable","Yes"
262 | "261",44,0,"nonanginal",118,242,0,0,149,0,0.3,2,1,"normal","No"
263 | "262",58,0,"nontypical",136,319,1,2,152,0,0,1,2,"normal","Yes"
264 | "263",60,0,"typical",150,240,0,0,171,0,0.9,1,0,"normal","No"
265 | "264",44,1,"nonanginal",120,226,0,0,169,0,0,1,0,"normal","No"
266 | "265",61,1,"asymptomatic",138,166,0,2,125,1,3.6,2,1,"normal","Yes"
267 | "266",42,1,"asymptomatic",136,315,0,0,125,1,1.8,2,0,"fixed","Yes"
268 | "267",52,1,"asymptomatic",128,204,1,0,156,1,1,2,0,NA,"Yes"
269 | "268",59,1,"nonanginal",126,218,1,0,134,0,2.2,2,1,"fixed","Yes"
270 | "269",40,1,"asymptomatic",152,223,0,0,181,0,0,1,0,"reversable","Yes"
271 | "270",42,1,"nonanginal",130,180,0,0,150,0,0,1,0,"normal","No"
272 | "271",61,1,"asymptomatic",140,207,0,2,138,1,1.9,1,1,"reversable","Yes"
273 | "272",66,1,"asymptomatic",160,228,0,2,138,0,2.3,1,0,"fixed","No"
274 | "273",46,1,"asymptomatic",140,311,0,0,120,1,1.8,2,2,"reversable","Yes"
275 | "274",71,0,"asymptomatic",112,149,0,0,125,0,1.6,2,0,"normal","No"
276 | "275",59,1,"typical",134,204,0,0,162,0,0.8,1,2,"normal","Yes"
277 | "276",64,1,"typical",170,227,0,2,155,0,0.6,2,0,"reversable","No"
278 | "277",66,0,"nonanginal",146,278,0,2,152,0,0,2,1,"normal","No"
279 | "278",39,0,"nonanginal",138,220,0,0,152,0,0,2,0,"normal","No"
280 | "279",57,1,"nontypical",154,232,0,2,164,0,0,1,1,"normal","Yes"
281 | "280",58,0,"asymptomatic",130,197,0,0,131,0,0.6,2,0,"normal","No"
282 | "281",57,1,"asymptomatic",110,335,0,0,143,1,3,2,1,"reversable","Yes"
283 | "282",47,1,"nonanginal",130,253,0,0,179,0,0,1,0,"normal","No"
284 | "283",55,0,"asymptomatic",128,205,0,1,130,1,2,2,1,"reversable","Yes"
285 | "284",35,1,"nontypical",122,192,0,0,174,0,0,1,0,"normal","No"
286 | "285",61,1,"asymptomatic",148,203,0,0,161,0,0,1,1,"reversable","Yes"
287 | "286",58,1,"asymptomatic",114,318,0,1,140,0,4.4,3,3,"fixed","Yes"
288 | "287",58,0,"asymptomatic",170,225,1,2,146,1,2.8,2,2,"fixed","Yes"
289 | "288",58,1,"nontypical",125,220,0,0,144,0,0.4,2,NA,"reversable","No"
290 | "289",56,1,"nontypical",130,221,0,2,163,0,0,1,0,"reversable","No"
291 | "290",56,1,"nontypical",120,240,0,0,169,0,0,3,0,"normal","No"
292 | "291",67,1,"nonanginal",152,212,0,2,150,0,0.8,2,0,"reversable","Yes"
293 | "292",55,0,"nontypical",132,342,0,0,166,0,1.2,1,0,"normal","No"
294 | "293",44,1,"asymptomatic",120,169,0,0,144,1,2.8,3,0,"fixed","Yes"
295 | "294",63,1,"asymptomatic",140,187,0,2,144,1,4,1,2,"reversable","Yes"
296 | "295",63,0,"asymptomatic",124,197,0,0,136,1,0,2,0,"normal","Yes"
297 | "296",41,1,"nontypical",120,157,0,0,182,0,0,1,0,"normal","No"
298 | "297",59,1,"asymptomatic",164,176,1,2,90,0,1,2,2,"fixed","Yes"
299 | "298",57,0,"asymptomatic",140,241,0,0,123,1,0.2,2,0,"reversable","Yes"
300 | "299",45,1,"typical",110,264,0,0,132,0,1.2,2,0,"reversable","Yes"
301 | "300",68,1,"asymptomatic",144,193,1,0,141,0,3.4,2,2,"reversable","Yes"
302 | "301",57,1,"asymptomatic",130,131,0,0,115,1,1.2,2,1,"reversable","Yes"
303 | "302",57,0,"nontypical",130,236,0,2,174,0,0,2,1,"normal","Yes"
304 | "303",38,1,"nonanginal",138,175,0,0,173,0,0,1,NA,"normal","No"
305 | 


--------------------------------------------------------------------------------
/Data/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/Hitters_X_test.csv:
--------------------------------------------------------------------------------
  1 | "","AtBat","Hits","HmRun","Runs","RBI","Walks","Years","CAtBat","CHits","CHmRun","CRuns","CRBI","CWalks","LeagueN","DivisionW","PutOuts","Assists","Errors","NewLeagueN"
  2 | "-Alan Ashby",315,81,7,24,38,39,14,3449,835,69,321,414,375,1,1,632,43,10,1
  3 | "-Andre Dawson",496,141,20,65,78,37,11,5628,1575,225,828,838,354,1,0,200,11,3,1
  4 | "-Alfredo Griffin",594,169,4,74,51,35,11,4408,1133,19,501,336,194,0,1,282,421,25,0
  5 | "-Argenis Salazar",298,73,0,24,24,7,3,509,108,0,41,37,12,0,1,121,283,9,0
  6 | "-Andres Thomas",323,81,6,26,32,8,2,341,86,6,32,34,8,1,1,143,290,19,1
  7 | "-Andre Thornton",401,92,17,49,66,65,13,5206,1332,253,784,890,866,0,0,0,0,0,0
  8 | "-Alan Trammell",574,159,21,107,75,59,10,4631,1300,90,702,504,488,0,0,238,445,22,0
  9 | "-Andy VanSlyke",418,113,13,48,61,47,4,1512,392,41,205,204,203,1,0,211,11,7,1
 10 | "-Alan Wiggins",239,60,0,30,11,22,6,1941,510,4,309,103,207,0,0,121,151,6,0
 11 | "-Bill Almon",196,43,7,29,27,30,13,3231,825,36,376,290,238,1,0,80,45,8,1
 12 | "-Barry Bonds",413,92,16,72,48,65,1,413,92,16,72,48,65,1,0,280,9,5,1
 13 | "-Bobby Bonilla",426,109,3,55,43,62,1,426,109,3,55,43,62,0,1,361,22,2,1
 14 | "-Bob Brenly",472,116,16,60,62,74,6,1924,489,67,242,251,240,1,1,518,55,3,1
 15 | "-Bo Diaz",474,129,10,50,56,40,10,2331,604,61,246,327,166,1,1,732,83,13,1
 16 | "-Brian Downing",513,137,20,90,95,90,14,5201,1382,166,763,734,784,0,1,267,5,3,0
 17 | "-Billy Hatcher",419,108,6,55,36,22,3,591,149,8,80,46,31,1,1,226,7,4,1
 18 | "-Brook Jacoby",583,168,17,83,80,56,5,1646,452,44,219,208,136,0,0,109,292,25,0
 19 | "-Bill Madlock",379,106,10,38,60,30,14,6207,1906,146,859,803,571,1,1,72,170,24,1
 20 | "-BillyJo Robidoux",181,41,1,15,21,33,2,232,50,4,20,29,45,0,0,326,29,5,0
 21 | "-Bill Schroeder",217,46,7,32,19,9,4,694,160,32,86,76,32,0,0,307,25,1,0
 22 | "-Chris Brown",416,132,7,57,49,33,3,932,273,24,113,121,80,1,1,73,177,18,1
 23 | "-Carmen Castillo",205,57,8,34,32,9,5,756,192,32,117,107,51,0,0,58,4,4,0
 24 | "-Carlton Fisk",457,101,14,42,63,22,17,6521,1767,281,1003,977,619,0,1,389,39,4,0
 25 | "-Curt Ford",214,53,2,30,29,23,2,226,59,2,32,32,27,1,0,109,7,3,1
 26 | "-Carney Lansford",591,168,19,80,72,39,9,4478,1307,113,634,563,319,0,1,67,147,4,0
 27 | "-Chet Lemon",403,101,12,45,53,39,12,5150,1429,166,747,666,526,0,0,316,6,5,0
 28 | "-Cory Snyder",416,113,24,58,69,16,1,416,113,24,58,69,16,0,0,203,70,10,0
 29 | "-Chris Speier",155,44,6,21,23,15,16,6631,1634,98,698,661,777,1,0,53,88,3,1
 30 | "-Curt Wilkerson",236,56,0,27,15,11,4,1115,270,1,116,64,57,0,1,125,199,13,0
 31 | "-Dave Anderson",216,53,1,31,15,22,4,926,210,9,118,69,114,1,1,73,152,11,1
 32 | "-Don Baylor",585,139,31,93,94,62,17,7546,1982,315,1141,1179,727,0,0,0,0,0,0
 33 | "-Daryl Boston",199,53,5,29,22,21,3,514,120,8,57,40,39,0,1,152,3,5,0
 34 | "-Darrell Evans",507,122,29,78,85,91,18,7761,1947,347,1175,1152,1380,0,0,808,108,2,0
 35 | "-Dwight Evans",529,137,26,86,97,97,15,6661,1785,291,1082,949,989,0,0,280,10,5,0
 36 | "-Damaso Garcia",424,119,6,57,46,13,9,3651,1046,32,461,301,112,0,0,224,286,8,1
 37 | "-Davey Lopes",255,70,7,49,35,43,15,6311,1661,154,1019,608,820,1,0,51,54,8,1
 38 | "-Don Mattingly",677,238,31,117,113,53,5,2223,737,93,349,401,171,0,0,1377,100,6,0
 39 | "-Dick Schofield",458,114,13,67,57,48,4,1350,298,28,160,123,122,0,1,246,389,18,0
 40 | "-Danny Tartabull",511,138,25,76,96,61,3,592,164,28,87,110,71,0,1,157,7,8,0
 41 | "-Dave Winfield",565,148,24,90,104,77,14,7287,2083,305,1135,1234,791,0,0,292,9,5,0
 42 | "-Eddie Milner",424,110,15,70,47,36,7,2130,544,38,335,174,258,1,1,292,6,3,1
 43 | "-Ed Romero",233,49,2,41,23,18,8,1350,336,7,166,122,106,0,0,102,132,10,0
 44 | "-Frank White",566,154,22,76,84,43,14,6100,1583,131,743,693,300,0,1,316,439,10,0
 45 | "-Glenn Braggs",215,51,4,19,18,11,1,215,51,4,19,18,11,0,0,116,5,12,0
 46 | "-George Brett",441,128,16,70,73,80,14,6675,2095,209,1072,1050,695,0,1,97,218,16,0
 47 | "-Gary Carter",490,125,24,81,105,62,13,6063,1646,271,847,999,680,1,0,869,62,8,1
 48 | "-Glenn Davis",574,152,31,91,101,64,3,985,260,53,148,173,95,1,1,1253,111,11,1
 49 | "-Gary Gaetti",596,171,34,91,108,52,6,2862,728,107,361,401,224,0,1,118,334,21,0
 50 | "-Greg Gagne",472,118,12,63,54,30,4,793,187,14,102,80,50,0,1,228,377,26,0
 51 | "-George Hendrick",283,77,14,45,47,26,16,6840,1910,259,915,1067,546,0,1,144,6,5,0
 52 | "-Glenn Hubbard",408,94,4,42,36,66,9,3573,866,59,429,365,410,1,1,282,487,19,1
 53 | "-Garth Iorg",327,85,3,30,44,20,8,2140,568,16,216,208,93,0,0,91,185,12,0
 54 | "-Greg Walker",282,78,13,37,51,29,5,1649,453,73,211,280,138,0,1,670,57,5,0
 55 | "-Harold Baines",570,169,21,72,88,38,7,3754,1077,140,492,589,263,0,1,295,15,5,0
 56 | "-Harold Reynolds",445,99,1,46,24,29,4,618,129,1,72,31,48,0,1,278,415,16,0
 57 | "-John Cangelosi",438,103,2,65,32,71,2,440,103,2,67,32,71,0,1,276,7,9,1
 58 | "-Jose Canseco",600,144,33,85,117,65,2,696,173,38,101,130,69,0,1,319,4,14,0
 59 | "-Jack Clark",232,55,9,34,23,45,12,4405,1213,194,702,705,625,1,0,623,35,3,1
 60 | "-Jim Dwyer",160,39,8,18,31,22,14,2128,543,56,304,268,298,0,0,33,3,0,0
 61 | "-Jim Gantner",497,136,7,58,38,26,11,3871,1066,40,450,367,241,0,0,304,347,10,0
 62 | "-Jack Howell",151,41,4,26,21,19,2,288,68,9,45,39,35,0,1,28,56,2,0
 63 | "-John Kruk",278,86,4,33,38,45,1,278,86,4,33,38,45,1,1,102,4,2,1
 64 | "-Jeffrey Leonard",341,95,6,48,42,20,10,2964,808,81,379,428,221,1,1,158,4,5,1
 65 | "-Jim Morrison",537,147,23,58,88,47,10,2744,730,97,302,351,174,1,0,92,257,20,1
 66 | "-Jerry Mumphrey",309,94,5,37,32,26,13,4618,1330,57,616,522,436,1,0,161,3,3,1
 67 | "-Johnny Ray",579,174,7,67,78,58,6,3053,880,32,366,337,218,1,0,280,479,5,1
 68 | "-Jim Rice",618,200,20,98,110,62,13,7127,2163,351,1104,1289,564,0,0,330,16,8,0
 69 | "-Jerry Royster",257,66,5,31,26,32,14,3910,979,33,518,324,382,1,1,87,166,14,0
 70 | "-John Russell",315,76,13,35,60,25,3,630,151,24,68,94,55,1,0,498,39,13,1
 71 | "-John Shelby",404,92,11,54,49,18,6,1354,325,30,188,135,63,0,0,222,5,5,0
 72 | "-Jim Sundberg",429,91,12,41,42,57,13,5590,1397,83,578,579,644,0,1,686,46,4,1
 73 | "-Joel Youngblood",184,47,5,20,28,18,11,3327,890,74,419,382,304,1,1,49,2,0,1
 74 | "-Kal Daniels",181,58,6,34,23,22,1,181,58,6,34,23,22,1,1,88,0,3,1
 75 | "-Kirk Gibson",441,118,28,84,86,68,8,2723,750,126,433,420,309,0,0,190,2,2,0
 76 | "-Ken Griffey",490,150,21,69,58,35,14,6126,1839,121,983,707,600,0,0,96,5,3,1
 77 | "-Kent Hrbek",550,147,29,85,91,71,6,2816,815,117,405,474,319,0,1,1218,104,10,0
 78 | "-Keith Moreland",586,159,12,72,79,53,9,3082,880,83,363,477,295,1,0,181,13,4,1
 79 | "-Ken Oberkfell",503,136,5,62,48,83,10,3423,970,20,408,303,414,1,1,65,258,8,1
 80 | "-Ken Phelps",344,85,24,69,64,88,7,911,214,64,150,156,187,0,1,0,0,0,0
 81 | "-Kirby Puckett",680,223,31,119,96,34,3,1928,587,35,262,201,91,0,1,429,8,6,0
 82 | "-Larry Herndon",283,70,8,33,37,27,12,4479,1222,94,557,483,307,0,0,156,2,2,0
 83 | "-Lloyd Moseby",589,149,21,89,86,64,7,3558,928,102,513,471,351,0,0,371,6,6,0
 84 | "-Lance Parrish",327,84,22,53,62,38,10,4273,1123,212,577,700,334,0,0,483,48,6,1
 85 | "-Larry Parrish",464,128,28,67,94,52,13,5829,1552,210,740,840,452,0,1,0,0,0,0
 86 | "-Mike Aldrete",216,54,2,27,25,33,1,216,54,2,27,25,33,1,1,317,36,1,1
 87 | "-Mariano Duncan",407,93,8,47,30,30,2,969,230,14,121,69,68,1,1,172,317,25,1
 88 | "-Mike Easler",490,148,14,64,78,49,13,3400,1000,113,445,491,301,0,0,0,0,0,1
 89 | "-Mike LaValliere",303,71,3,18,30,36,3,344,76,3,20,36,45,1,0,468,47,6,1
 90 | "-Mike Pagliarulo",504,120,28,71,71,54,3,1085,259,54,150,167,114,0,0,103,283,19,0
 91 | "-Mark Salas",258,60,8,28,33,18,3,638,170,17,80,75,36,0,1,358,32,8,0
 92 | "-Mickey Tettleton",211,43,10,26,35,39,3,498,116,14,59,55,78,0,1,463,32,8,0
 93 | "-Milt Thompson",299,75,6,38,23,26,3,580,160,8,71,33,44,1,0,212,1,2,1
 94 | "-Marvell Wynne",288,76,7,34,37,15,4,1644,408,16,198,120,113,1,1,203,3,3,1
 95 | "-Mike Young",369,93,9,43,42,49,5,1258,323,54,181,177,157,0,0,149,1,6,0
 96 | "-Oddibe McDowell",572,152,18,105,49,65,2,978,249,36,168,91,101,0,1,325,13,3,0
 97 | "-Pete Incaviglia",540,135,30,82,88,55,1,540,135,30,82,88,55,0,1,157,6,14,0
 98 | "-Pete Rose",237,52,0,15,25,30,24,14053,4256,160,2165,1314,1566,1,1,523,43,6,1
 99 | "-Pat Tabler",473,154,6,61,48,29,6,1966,566,29,250,252,178,0,0,846,84,9,0
100 | "-Rick Burleson",271,77,5,35,29,33,12,4933,1358,48,630,435,403,0,1,62,90,3,0
101 | "-Randy Bush",357,96,7,50,45,39,5,1394,344,43,178,192,136,0,1,167,2,4,0
102 | "-Rick Cerone",216,56,4,22,18,15,12,2796,665,43,266,304,198,0,0,391,44,4,0
103 | "-Ron Cey",256,70,13,42,36,44,16,7058,1845,312,965,1128,990,1,0,41,118,8,0
104 | "-Rob Deer",466,108,33,75,86,72,3,652,142,44,102,109,102,0,0,286,8,8,0
105 | "-Rick Dempsey",327,68,13,42,29,45,18,3949,939,78,438,380,466,0,0,659,53,7,0
106 | "-Ron Kittle",376,82,21,42,60,35,5,1770,408,115,238,299,157,0,1,0,0,0,0
107 | "-Rick Leach",246,76,5,35,39,13,6,912,234,12,102,96,80,0,0,44,0,1,0
108 | "-Rafael Ramirez",496,119,8,57,33,21,7,3358,882,36,365,280,165,1,1,155,371,29,1
109 | "-Rafael Santana",394,86,1,38,28,36,4,1089,267,3,94,71,76,1,0,203,369,16,1
110 | "-Ruben Sierra",382,101,16,50,55,22,1,382,101,16,50,55,22,0,1,200,7,6,0
111 | "-Roy Smalley",459,113,20,59,57,68,12,5348,1369,155,713,660,735,0,1,0,0,0,0
112 | "-Robby Thompson",549,149,7,73,47,42,1,549,149,7,73,47,42,1,1,255,450,17,1
113 | "-Rob Wilfong",288,63,3,25,33,16,10,2682,667,38,315,259,204,0,1,135,257,7,0
114 | "-Robin Yount",522,163,9,82,46,62,13,7037,2019,153,1043,827,535,0,0,352,9,1,0
115 | "-Steve Balboni",512,117,29,54,88,43,6,1750,412,100,204,276,155,0,1,1236,98,18,0
116 | "-Steve Buechele",461,112,18,54,54,35,2,680,160,24,76,75,49,0,1,111,226,11,0
117 | "-Scott Fletcher",530,159,3,82,50,47,6,1619,426,11,218,149,163,0,1,196,354,15,0
118 | "-Steve Jeltz",439,96,0,44,36,65,4,711,148,1,68,56,99,1,0,229,406,22,1
119 | "-Spike Owen",528,122,1,67,45,51,4,1716,403,12,211,146,155,0,1,209,372,17,0
120 | "-Tony Bernazard",562,169,17,88,73,53,8,3181,841,61,450,342,373,0,0,351,442,17,0
121 | "-Tom Brunansky",593,152,23,69,75,53,6,2765,686,133,369,384,321,0,1,315,10,6,0
122 | "-Terry Harper",265,68,8,26,30,29,7,1337,339,32,135,163,128,1,1,92,5,3,0
123 | "-Terry Pendleton",578,138,1,56,59,34,3,1399,357,7,149,161,87,1,0,133,371,20,1
124 | "-Tony Phillips",441,113,5,76,52,76,5,1546,397,17,226,149,191,0,1,160,290,11,0
125 | "-Terry Puhl",172,42,3,17,14,15,10,4086,1150,57,579,363,406,1,1,65,0,0,1
126 | "-Tim Teufel",279,69,4,35,31,32,4,1359,355,31,180,148,158,1,0,133,173,9,1
127 | "-Vince Coleman",600,139,0,94,29,60,2,1236,309,1,201,69,110,1,0,300,12,9,1
128 | "-Von Hayes",610,186,19,107,98,74,6,2728,753,69,399,366,286,1,0,1182,96,13,1
129 | "-Wally Backman",387,124,1,67,27,36,7,1775,506,6,272,125,194,1,0,186,290,17,1
130 | "-Wally Joyner",593,172,22,82,100,57,1,593,172,22,82,100,57,0,1,1222,139,15,0
131 | "-Willie Randolph",492,136,5,76,50,94,12,5511,1511,39,897,451,875,0,0,313,381,20,0
132 | "-Willie Upshaw",573,144,9,85,60,78,8,3198,857,97,470,420,332,0,0,1314,131,12,0
133 | "-Willie Wilson",631,170,9,77,44,31,11,4908,1457,30,775,357,249,0,1,408,4,3,0
134 | 


--------------------------------------------------------------------------------
/Data/Hitters_X_train.csv:
--------------------------------------------------------------------------------
  1 | "","AtBat","Hits","HmRun","Runs","RBI","Walks","Years","CAtBat","CHits","CHmRun","CRuns","CRBI","CWalks","LeagueN","DivisionW","PutOuts","Assists","Errors","NewLeagueN"
  2 | "-Darryl Strawberry",475,123,27,76,93,72,4,1810,471,108,292,343,267,1,0,226,10,6,1
  3 | "-Glenn Wilson",584,158,15,70,84,42,5,2358,636,58,265,316,134,1,0,331,20,4,1
  4 | "-Leon Durham",484,127,20,66,65,67,7,3006,844,116,436,458,377,1,0,1231,80,7,1
  5 | "-Tony Gwynn",642,211,14,107,59,52,5,2364,770,27,352,230,193,1,1,337,19,4,1
  6 | "-Dave Concepcion",311,81,3,42,30,26,17,8247,2198,100,950,909,690,1,1,153,223,10,1
  7 | "-Tom Brookens",281,76,3,42,25,20,8,2658,657,48,324,300,179,0,0,106,144,7,0
  8 | "-Tim Laudner",193,47,10,21,29,24,6,1136,256,42,129,139,106,0,1,299,13,5,0
  9 | "-Mike Marshall",330,77,19,47,53,27,6,1928,516,90,247,288,161,1,1,149,8,6,1
 10 | "-Marty Barrett",625,179,4,94,60,65,5,1696,476,12,216,163,166,0,0,303,450,14,0
 11 | "-Buddy Biancalana",190,46,2,24,8,15,5,479,102,5,65,23,39,0,1,102,177,16,0
 12 | "-Willie McGee",497,127,7,65,48,37,5,2703,806,32,379,311,138,1,0,325,9,3,1
 13 | "-Cal Ripken",627,177,25,98,81,70,6,3210,927,133,529,472,313,0,0,240,482,13,0
 14 | "-Mike Schmidt",20,1,0,0,0,0,2,41,9,2,6,7,4,1,0,78,220,6,1
 15 | "-Gary Ward",380,120,5,54,51,31,8,3118,900,92,444,419,240,0,1,237,8,1,0
 16 | "-Rafael Belliard",309,72,0,33,31,26,5,354,82,0,41,32,26,1,0,117,269,12,1
 17 | "-Jim Presley",616,163,27,83,107,32,3,1437,377,65,181,227,82,0,1,110,308,15,0
 18 | "-Mookie Wilson",381,110,9,61,45,32,7,3015,834,40,451,249,168,1,0,228,7,5,1
 19 | "-Tony Pena",510,147,10,56,52,53,7,2872,821,63,307,340,174,1,0,810,99,18,1
 20 | "-Gary Redus",340,84,11,62,33,47,5,1516,376,42,284,141,219,1,0,185,8,4,0
 21 | "-Pat Sheridan",236,56,6,41,19,21,5,1257,329,24,166,125,105,0,0,172,1,4,0
 22 | "-Steve Lombardozzi",453,103,8,53,33,52,2,507,123,8,63,39,58,0,1,289,407,6,0
 23 | "-Darnell Coles",521,142,20,67,86,45,4,815,205,22,99,103,78,0,0,107,242,23,0
 24 | "-Larry Sheets",338,92,18,42,60,21,3,682,185,36,88,112,50,0,0,0,0,0,0
 25 | "-Bob Melvin",268,60,5,24,25,15,2,350,78,5,34,29,18,1,1,442,59,6,1
 26 | "-Dwayne Murphy",329,83,9,50,39,56,9,3828,948,145,575,528,635,0,1,276,6,2,0
 27 | "-Graig Nettles",354,77,16,36,55,41,20,8716,2172,384,1172,1267,1057,1,1,83,174,16,1
 28 | "-Andres Galarraga",321,87,10,39,42,30,2,396,101,12,48,46,33,1,0,805,40,4,1
 29 | "-Gary Matthews",370,96,21,49,46,60,15,6986,1972,231,1070,955,921,1,0,137,5,9,1
 30 | "-Rick Manning",205,52,8,31,27,17,12,5134,1323,56,643,445,459,0,0,155,3,2,0
 31 | "-George Bell",641,198,31,101,108,41,5,2129,610,92,297,319,117,0,0,269,17,10,0
 32 | "-Jody Davis",528,132,21,61,74,41,6,2641,671,97,273,383,226,1,0,885,105,8,1
 33 | "-Keith Hernandez",551,171,13,94,83,94,13,6090,1840,128,969,900,917,1,0,1199,149,5,1
 34 | "-Julio Franco",599,183,10,80,74,32,5,2482,715,27,330,326,158,0,0,231,374,18,0
 35 | "-Carmelo Martinez",244,58,9,28,25,35,4,1335,333,49,164,179,194,1,1,142,14,2,1
 36 | "-Tom Paciorek",213,61,4,17,22,3,17,4061,1145,83,488,491,244,0,1,178,45,4,0
 37 | "-Lee Lacy",491,141,11,77,47,37,15,4291,1240,84,615,430,340,0,0,239,8,2,0
 38 | "-Ozzie Guillen",547,137,2,58,47,12,2,1038,271,3,129,80,24,0,1,261,459,22,0
 39 | "-Bill Doran",550,152,6,92,37,81,5,2308,633,32,349,182,308,1,1,262,329,16,1
 40 | "-Mike Diaz",209,56,12,22,36,19,2,216,58,12,24,37,19,1,0,201,6,3,1
 41 | "-Gary Pettis",539,139,5,93,58,69,5,1469,369,12,247,126,198,0,1,462,9,7,0
 42 | "-Ozzie Virgil",359,80,15,45,48,63,7,1493,359,61,176,202,175,1,1,682,93,13,1
 43 | "-Kevin Mitchell",328,91,12,51,43,33,2,342,94,12,51,44,33,1,0,145,59,8,1
 44 | "-Mike Scioscia",374,94,5,36,26,62,7,1968,519,26,181,199,288,1,1,756,64,15,1
 45 | "-John Moses",399,102,3,56,34,34,5,670,167,4,89,48,54,0,1,211,9,3,0
 46 | "-Johnny Grubb",210,70,13,32,51,28,15,4040,1130,97,544,462,551,0,0,0,0,0,0
 47 | "-Tim Wallach",480,112,18,50,71,44,7,3031,771,110,338,406,239,1,0,94,270,16,1
 48 | "-Al Newman",185,37,1,23,8,21,2,214,42,1,30,9,24,1,0,76,127,7,0
 49 | "-Harry Spilman",143,39,5,18,30,15,9,639,151,16,80,97,61,1,1,138,15,1,1
 50 | "-Terry Kennedy",19,4,1,2,3,1,1,19,4,1,2,3,1,1,1,692,70,8,0
 51 | "-Kurt Stillwell",279,64,0,31,26,30,1,279,64,0,31,26,30,1,1,107,205,16,1
 52 | "-Hal McRae",278,70,7,22,37,18,18,7186,2081,190,935,1088,643,0,1,0,0,0,0
 53 | "-Ozzie Smith",514,144,0,67,54,79,9,4739,1169,13,583,374,528,1,0,229,453,15,1
 54 | "-Shawon Dunston",581,145,17,66,68,21,2,831,210,21,106,86,40,1,0,320,465,32,1
 55 | "-Tito Landrum",205,43,2,24,17,20,7,854,219,12,105,99,71,1,0,131,6,1,1
 56 | "-Buddy Bell",568,158,20,89,75,73,15,8068,2273,177,1045,993,732,1,1,105,290,10,1
 57 | "-Bill Buckner",629,168,18,73,102,40,18,8424,2464,164,1008,1072,402,0,0,1067,157,14,0
 58 | "-Dan Pasqua",280,82,16,44,45,47,2,428,113,25,61,70,63,0,0,148,4,2,0
 59 | "-Juan Beniquez",343,103,6,48,36,40,15,4338,1193,70,581,421,325,0,0,211,56,13,0
 60 | "-Kevin Bass",591,184,20,83,79,38,5,1689,462,40,219,195,82,1,1,303,12,5,1
 61 | "-Greg Brock",325,76,16,33,52,37,5,1506,351,71,195,219,214,1,1,726,87,3,0
 62 | "-Phil Garner",313,83,9,43,41,30,14,5885,1543,104,751,714,535,1,1,58,141,23,1
 63 | "-Donnie Hill",339,96,4,37,29,23,4,1064,290,11,123,108,55,0,1,104,213,9,0
 64 | "-Ron Roenicke",275,68,5,42,42,61,6,961,238,16,128,104,172,1,0,181,3,2,1
 65 | "-Darrell Porter",155,41,12,21,29,22,16,5409,1338,181,746,805,875,0,1,165,9,1,0
 66 | "-Juan Samuel",591,157,16,90,78,26,4,2020,541,52,310,226,91,1,0,290,440,25,1
 67 | "-Ronn Reynolds",126,27,3,8,10,5,4,239,49,3,16,13,14,1,0,190,2,9,1
 68 | "-Garry Templeton",510,126,2,42,44,35,11,5562,1578,44,703,519,256,1,1,207,358,20,1
 69 | "-Len Dykstra",431,127,8,77,45,58,2,667,187,9,117,64,88,1,0,283,8,3,1
 70 | "-Bruce Bochy",127,32,8,16,22,14,8,727,180,24,67,82,56,1,1,202,22,2,1
 71 | "-Wade Boggs",580,207,8,107,71,105,5,2778,978,32,474,322,417,0,0,121,267,19,0
 72 | "-Ron Oester",523,135,8,52,44,52,9,3368,895,39,377,284,296,1,1,367,475,19,1
 73 | "-Mike Davis",489,131,19,77,55,34,7,2051,549,62,300,263,153,0,1,310,9,9,0
 74 | "-Rickey Henderson",608,160,28,130,74,89,8,4071,1182,103,862,417,708,0,0,426,4,6,0
 75 | "-Tommy Herr",559,141,2,48,61,73,8,3162,874,16,421,349,359,1,0,352,414,9,1
 76 | "-Tom Foley",263,70,1,26,23,30,4,888,220,9,83,82,86,1,0,81,147,4,1
 77 | "-Mike Kingery",209,54,3,25,14,12,1,209,54,3,25,14,12,0,1,102,6,3,0
 78 | "-Ted Simmons",127,32,4,14,25,12,19,8396,2402,242,1048,1348,819,1,1,167,18,6,1
 79 | "-Denny Walling",382,119,13,54,58,36,12,2133,594,41,287,294,227,1,1,59,156,9,1
 80 | "-Sid Bream",522,140,16,73,77,60,4,730,185,22,93,106,86,1,0,1320,166,17,1
 81 | "-Mitch Webster",576,167,8,89,49,57,4,822,232,19,132,83,79,1,0,325,12,8,1
 82 | "-Tony Fernandez",687,213,10,91,65,27,4,1518,448,15,196,137,89,0,0,294,445,13,0
 83 | "-Ron Hassey",341,110,9,45,49,46,9,2331,658,50,249,322,274,0,0,251,9,4,0
 84 | "-Ray Knight",486,145,11,51,76,40,11,3967,1102,67,410,497,284,1,0,88,204,16,0
 85 | "-Dave Henderson",388,103,15,59,47,39,6,2174,555,80,285,274,186,0,1,182,9,4,0
 86 | "-Tim Flannery",368,103,3,48,28,54,8,1897,493,9,207,162,198,1,1,209,246,3,1
 87 | "-Chili Davis",526,146,13,71,70,84,6,2648,715,77,352,342,289,1,1,303,9,9,1
 88 | "-Jeff Reed",165,39,2,13,9,16,3,196,44,2,18,10,18,0,1,332,19,2,1
 89 | "-Brett Butler",587,163,4,92,51,70,6,2695,747,17,442,198,317,0,0,434,9,3,0
 90 | "-Steve Sax",633,210,6,91,56,59,6,3070,872,19,420,230,274,1,1,367,432,16,1
 91 | "-Steve Garvey",557,142,21,58,81,23,18,8759,2583,271,1138,1299,478,1,1,1160,53,7,1
 92 | "-Candy Maldonado",405,102,18,49,85,20,6,950,231,29,99,138,64,1,1,161,10,3,1
 93 | "-Alex Trevino",202,53,4,31,26,27,9,1876,467,15,192,186,161,1,1,304,45,11,1
 94 | "-Joe Carter",663,200,29,108,121,32,4,1447,404,57,210,222,68,0,0,241,8,6,0
 95 | "-Rick Schu",208,57,8,32,25,18,3,653,170,17,98,54,62,1,0,42,94,13,1
 96 | "-Joel Skinner",315,73,5,23,37,16,4,450,108,6,38,46,28,0,1,227,15,3,0
 97 | "-Jose Uribe",453,101,3,46,43,61,3,948,218,6,96,72,91,1,1,249,444,16,1
 98 | "-Eddie Murray",495,151,17,61,84,78,10,5624,1679,275,884,1015,709,0,0,1045,88,13,0
 99 | "-Don Slaught",314,83,13,39,46,16,5,1457,405,28,156,159,76,0,1,533,40,4,0
100 | "-Paul Molitor",437,123,9,62,55,40,9,4139,1203,79,676,390,364,0,0,82,170,15,0
101 | "-Hubie Brooks",306,104,14,50,58,25,7,2954,822,55,313,377,187,1,0,116,222,15,1
102 | "-Rance Mulliniks",348,90,11,50,45,43,10,2288,614,43,295,273,269,0,0,60,176,6,0
103 | "-Dan Gladden",351,97,4,55,29,39,4,1258,353,16,196,110,117,1,1,226,7,3,0
104 | "-Craig Reynolds",313,78,6,32,41,12,12,3742,968,35,409,321,170,1,1,106,206,7,1
105 | "-Lou Whitaker",584,157,20,95,73,63,10,4704,1320,93,724,522,576,0,0,276,421,11,0
106 | "-Howard Johnson",220,54,10,30,39,31,5,1185,299,40,145,154,128,1,0,50,136,20,1
107 | "-Chris Bando",254,68,2,28,26,22,6,999,236,21,108,117,118,0,0,359,30,4,0
108 | "-Rey Quinones",312,68,2,32,22,24,1,312,68,2,32,22,24,0,0,86,150,15,0
109 | "-Eric Davis",415,115,27,97,71,68,3,711,184,45,156,119,99,1,1,274,2,7,1
110 | "-Phil Bradley",526,163,12,88,50,77,4,1556,470,38,245,167,174,0,1,250,11,1,0
111 | "-Reggie Jackson",419,101,18,65,58,92,20,9528,2510,548,1509,1659,1342,0,1,0,0,0,0
112 | "-Wayne Tolleson",475,126,3,61,43,52,6,1700,433,7,217,93,146,0,1,37,113,7,0
113 | "-Jose Cruz",479,133,10,48,72,55,17,7472,2147,153,980,1032,854,1,1,237,5,4,1
114 | "-Doug DeCinces",512,131,26,69,96,52,14,5347,1397,221,712,815,548,0,1,119,216,12,0
115 | "-Dave Parker",637,174,31,89,116,56,14,6727,2024,247,978,1093,495,1,1,278,9,9,1
116 | "-Bob Dernier",324,73,4,32,18,22,7,1931,491,13,291,108,180,1,0,222,3,3,1
117 | "-Alvin Davis",479,130,18,66,72,76,3,1624,457,63,224,266,263,0,1,880,82,14,0
118 | "-Jesse Barfield",589,170,40,107,108,69,6,2325,634,128,371,376,238,0,0,368,20,3,0
119 | "-Vance Law",360,81,5,37,44,37,7,2268,566,41,279,257,246,1,0,170,284,3,1
120 | "-Will Clark",408,117,11,66,41,34,1,408,117,11,66,41,34,1,1,942,72,11,1
121 | "-Len Matuszek",199,52,9,26,28,21,6,805,191,30,113,119,87,1,1,235,22,5,1
122 | "-Ken Landreaux",283,74,4,34,29,22,10,3919,1062,85,505,456,283,1,1,145,5,7,1
123 | "-Dale Sveum",317,78,7,35,35,32,1,317,78,7,35,35,32,0,0,45,122,26,0
124 | "-Mel Hall",442,131,18,68,77,33,6,1416,398,47,210,203,136,0,0,233,7,7,0
125 | "-Scott Bradley",220,66,5,20,28,13,3,290,80,5,27,31,15,0,1,281,21,3,0
126 | "-Herm Winningham",185,40,4,23,11,18,3,524,125,7,58,37,47,1,0,97,2,2,1
127 | "-Dale Murphy",614,163,29,89,83,75,11,5017,1388,266,813,822,617,1,1,303,6,6,1
128 | "-Kevin McReynolds",560,161,26,89,96,66,4,1789,470,65,233,260,155,1,1,332,9,8,1
129 | "-Bob Kearney",204,49,6,23,25,12,7,1309,308,27,126,132,66,0,1,419,46,5,0
130 | "-Tim Hulett",520,120,17,53,44,21,4,927,227,22,106,80,52,0,1,70,144,11,0
131 | "-Ryne Sandberg",627,178,14,68,76,46,6,3146,902,74,494,345,242,1,0,309,492,5,1
132 | "-Mike Heath",288,65,8,30,36,27,9,2815,698,55,315,325,189,1,0,259,30,10,0
133 | 


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65 | 


--------------------------------------------------------------------------------
/Data/NCI60.rda:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/pedvide/ISLR_Python/HEAD/Data/NCI60.rda


--------------------------------------------------------------------------------
/Data/NCI60_y.csv:
--------------------------------------------------------------------------------
 1 | "","x"
 2 | "1","CNS"
 3 | "2","CNS"
 4 | "3","CNS"
 5 | "4","RENAL"
 6 | "5","BREAST"
 7 | "6","CNS"
 8 | "7","CNS"
 9 | "8","BREAST"
10 | "9","NSCLC"
11 | "10","NSCLC"
12 | "11","RENAL"
13 | "12","RENAL"
14 | "13","RENAL"
15 | "14","RENAL"
16 | "15","RENAL"
17 | "16","RENAL"
18 | "17","RENAL"
19 | "18","BREAST"
20 | "19","NSCLC"
21 | "20","RENAL"
22 | "21","UNKNOWN"
23 | "22","OVARIAN"
24 | "23","MELANOMA"
25 | "24","PROSTATE"
26 | "25","OVARIAN"
27 | "26","OVARIAN"
28 | "27","OVARIAN"
29 | "28","OVARIAN"
30 | "29","OVARIAN"
31 | "30","PROSTATE"
32 | "31","NSCLC"
33 | "32","NSCLC"
34 | "33","NSCLC"
35 | "34","LEUKEMIA"
36 | "35","K562B-repro"
37 | "36","K562A-repro"
38 | "37","LEUKEMIA"
39 | "38","LEUKEMIA"
40 | "39","LEUKEMIA"
41 | "40","LEUKEMIA"
42 | "41","LEUKEMIA"
43 | "42","COLON"
44 | "43","COLON"
45 | "44","COLON"
46 | "45","COLON"
47 | "46","COLON"
48 | "47","COLON"
49 | "48","COLON"
50 | "49","MCF7A-repro"
51 | "50","BREAST"
52 | "51","MCF7D-repro"
53 | "52","BREAST"
54 | "53","NSCLC"
55 | "54","NSCLC"
56 | "55","NSCLC"
57 | "56","MELANOMA"
58 | "57","BREAST"
59 | "58","BREAST"
60 | "59","MELANOMA"
61 | "60","MELANOMA"
62 | "61","MELANOMA"
63 | "62","MELANOMA"
64 | "63","MELANOMA"
65 | "64","MELANOMA"
66 | 


--------------------------------------------------------------------------------
/Data/Portfolio.csv:
--------------------------------------------------------------------------------
  1 | "X","Y"
  2 | -0.895250889141557,-0.234923525765402
  3 | -1.5624543274753,-0.885175993044695
  4 | -0.417089883126492,0.271888018049829
  5 | 1.04435572526951,-0.734197504067649
  6 | -0.315568406681027,0.841983429961188
  7 | -1.73712384902476,-2.03719104074984
  8 | 1.96641315717111,1.45295666192369
  9 | 2.1528678980109,-0.434138628179502
 10 | -0.0812080267602958,1.45080850218963
 11 | -0.891781794029037,0.821016234539977
 12 | -0.293201702010266,-1.04239112183501
 13 | 0.50577917106943,0.608477825846609
 14 | 0.52675125409276,-0.222493343282789
 15 | 1.06646932095091,1.2313566752569
 16 | 0.294015895063748,0.628589480036184
 17 | 0.0425492997633765,-1.26757361755317
 18 | 1.83096958062302,-0.572751605498511
 19 | -0.32693749887808,-0.487472465045569
 20 | 0.521480415807099,2.56598528732423
 21 | 1.39986834733422,-0.35783612748179
 22 | -0.645447596468841,-1.41243138949505
 23 | -0.904351878449744,-0.568304791041892
 24 | -1.76458606961956,-0.746272562068363
 25 | -1.81048463818975,0.493747359351401
 26 | -1.16989891378141,-2.72528149494243
 27 | -0.685375735369436,-0.457615734339251
 28 | 1.09091803183517,0.0144945075275287
 29 | -0.432340114040807,-0.399831023509433
 30 | 0.268814775370724,-0.201608350198064
 31 | -0.851840753541132,-1.74182928585454
 32 | -1.49708417203583,-0.826033329437587
 33 | 0.0887747459974043,-0.887360712723633
 34 | -1.60172430963135,-0.695299045952921
 35 | -1.24685724025742,-1.52958488449121
 36 | -1.06298912830654,-0.110637447363915
 37 | -0.26628305530967,0.0451634696288592
 38 | 1.67658383263088,2.5200528826286
 39 | 0.119572571440877,0.535542781034257
 40 | -0.0860079872690871,1.36359582805839
 41 | 0.368080289748909,1.72937250996995
 42 | -0.271494206939639,1.37926732742329
 43 | -0.0859264618788124,-0.127662573750838
 44 | -0.190750153683344,-0.461333357787814
 45 | -0.781679768391051,1.02239787730549
 46 | 0.792436346460761,-0.814298088654853
 47 | -0.28286988623389,-1.03846880699277
 48 | -0.236625531902745,0.928450553143057
 49 | 1.17183009101022,1.72983145002732
 50 | 0.496942768505321,-0.925139825948684
 51 | -0.887370979477135,-2.2834979593885
 52 | -1.30695315836496,-2.38160058115405
 53 | -2.43276412040427,-2.02554558512495
 54 | -0.407188960959958,-0.335098643325459
 55 | -0.285665299455223,-1.30878131266949
 56 | 1.52221488310337,1.20100315334525
 57 | -0.998106907437742,-0.946268900068486
 58 | -0.289973726127379,0.206256579940999
 59 | -1.23683924300474,-0.675447507316727
 60 | -0.359506962064002,-2.70015447021752
 61 | 0.543559153033075,0.42254755209331
 62 | -0.403647282894893,-0.0543899228706378
 63 | 1.30330893265591,1.32896747385231
 64 | -0.717117243405944,1.33137979803966
 65 | -1.01270788405516,-0.92476923081864
 66 | 0.831992902158869,2.24774586894653
 67 | 1.33764359604195,0.868256457487716
 68 | 0.601693509867379,-0.198217563055149
 69 | 1.30285098047145,1.10466637601686
 70 | -0.881700578927026,-1.54068478518396
 71 | -0.824529071304578,-1.33700787719544
 72 | -0.984356518466055,-1.1391602659206
 73 | -1.38499150721135,0.70269993294853
 74 | -0.358842560435759,-1.69451276977832
 75 | -0.226618229456359,0.801938547570983
 76 | -0.941077436691343,-0.733188708932247
 77 | 2.4603359481276,-0.048372817002224
 78 | 0.716797281412897,0.602336759898045
 79 | -0.248087023209405,-1.01849037378952
 80 | 1.01077288944297,0.0529780222228798
 81 | 2.31304863448491,1.75235887915611
 82 | 0.835179797449368,0.98571487565829
 83 | -1.07190333913753,-1.24729787324372
 84 | -1.6505261438491,0.215464529577012
 85 | -0.60048569030458,-0.420940526974254
 86 | -0.0585293830470613,0.127620874053089
 87 | 0.0757267446338611,-0.522149221026395
 88 | -1.15783156137448,0.590893742238611
 89 | 1.67360608794112,0.114623316085095
 90 | -1.04398823978305,-0.418944284341397
 91 | 0.0146874765920298,-0.558746620672602
 92 | 0.675321970429067,1.48262978763307
 93 | 1.77834230986132,0.942774111448264
 94 | -1.29576363940663,-1.0852038131022
 95 | 0.0796020218474959,-0.539100814053817
 96 | 2.2608577144194,0.673224840266669
 97 | 0.479090923233913,1.45477446090542
 98 | -0.535019997432816,-0.399174811276031
 99 | -0.773129330645406,-0.957174849520677
100 | 0.403634339015336,1.39603816898688
101 | -0.58849643871802,-0.497285090817856
102 | 


--------------------------------------------------------------------------------
/Data/USArrests.csv:
--------------------------------------------------------------------------------
 1 | "","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 | 


--------------------------------------------------------------------------------
/Data/datalist:
--------------------------------------------------------------------------------
 1 | Auto
 2 | Caravan
 3 | Carseats
 4 | College
 5 | Default
 6 | Hitters
 7 | Khan (json file)
 8 | NCI60 (json file)
 9 | OJ
10 | Portfolio
11 | Smarket
12 | Wage
13 | Weekly
14 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # ISLR_Python
 2 | 
 3 | Figures, Tables and Problems from the book <A target="_blank" href='http://www-bcf.usc.edu/%7Egareth/ISL/index.html'>'An Introduction to Statistical Learning with Applications in R'</A> by James, Witten, Hastie, Tibshirani (2013).
 4 | Using Python 3.x.
 5 | 
 6 | List of Chapters:
 7 | 
 8 | - [x] <A href='http://nbviewer.jupyter.org/github/pedvide/ISLR_Python/blob/master/Chapter3_Linear_regression.ipynb'>Chapter 3 - Linear Regression</A>
 9 | - [x] <A href='http://nbviewer.jupyter.org/github/pedvide/ISLR_Python/blob/master/Chapter4_classification.ipynb'>Chapter 4 - Classification</A>
10 | - [x] <A href='http://nbviewer.jupyter.org/github/pedvide/ISLR_Python/blob/master/Chapter5_Resampling_Methods.ipynb'>Chapter 5 - Resampling Methods</A>
11 | - [x] <A href='http://nbviewer.jupyter.org/github/pedvide/ISLR_Python/blob/master/Chapter6_Linear_Model_Selection_and_Regularization.ipynb'>Chapter 6 - Linear Model Selection and Regularization</A>
12 | - [x] <A href='http://nbviewer.jupyter.org/github/pedvide/ISLR_Python/blob/master/Chapter7_Moving_Beyond_Linearity.ipynb'>Chapter 7 - Moving Beyond Linearity</A>
13 | - [x] <A href='http://nbviewer.jupyter.org/github/pedvide/ISLR_Python/blob/master/Chapter8_Tree_Based_Methods.ipynb'>Chapter 8 - Tree-Based Methods</A>
14 | - [x] <A href='http://nbviewer.jupyter.org/github/pedvide/ISLR_Python/blob/master/Chapter9_Support_Vector_Machines.ipynb'>Chapter 9 - Support Vector Machines</A>
15 | - [x] <A href='http://nbviewer.jupyter.org/github/pedvide/ISLR_Python/blob/master/Chapter10_Unsupervised_Learning.ipynb'>Chapter 10 - Unsupervised Learning</A>
16 | 
17 | Dependencies:
18 | 
19 |  - pandas
20 |  - numpy
21 |  - scipy
22 |  - scikit-learn
23 |  - statsmodels
24 |  - patsy
25 |  - matplotlib
26 |  - seaborn
27 |  - pyGAM
28 |  - pydot and graphviz (to plot decission trees)
29 |  - scikit-plot (to plot ROC for classification)
30 |  
31 | I obtained the data from https://github.com/JWarmenhoven/ISLR-python.
32 | 
33 | 
34 | #### References: 
35 | James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). <I>An Introduction to Statistical Learning with Applications in  R</I>,  Springer Science+Business Media, New York.
36 | http://www-bcf.usc.edu/~gareth/ISL/index.html
37 | 
38 | Hastie, T., Tibshirani, R., Friedman, J. (2009). <I>Elements of Statistical Learning</I>, Second Edition, Springer Science+Business Media, New York.
39 | https://web.stanford.edu/~hastie/ElemStatLearn/


--------------------------------------------------------------------------------
/classification_helper.py:
--------------------------------------------------------------------------------
  1 | import numpy as np
  2 | import scikitplot as skplt
  3 | import sklearn.linear_model as skl_lm
  4 | from sklearn.metrics import confusion_matrix, classification_report, precision_score, roc_curve, auc, log_loss
  5 | import pandas as pd
  6 | import matplotlib.pyplot as plt
  7 | 
  8 | from scipy import stats
  9 | 
 10 | # Helper functions to print classification diagnostics
 11 | def print_classification_statistics(model, X_test, y_test, labels=None):
 12 |     y_pred = model.predict(X_test)
 13 |     print('Classification Report:')
 14 |     print(classification_report(y_test, y_pred, target_names=labels, digits=3))
 15 |     cm = confusion_matrix(y_test, y_pred)
 16 |     cm = cm.astype('float')/cm.sum(axis=1)[:, np.newaxis]
 17 |     num_class = len(cm)
 18 |     df_cm = pd.DataFrame(cm, columns=pd.MultiIndex.from_product([['Predicted'], np.arange(0, num_class)]),
 19 |                          index=pd.MultiIndex.from_product([['Real'], np.arange(0, num_class)]))
 20 |     print('Confusion Matrix:')
 21 |     print(df_cm)
 22 |     print()
 23 |     
 24 | def plot_ROC(model, X_test, y_test, label=''):
 25 |     y_prob = model.predict_proba(X_test)
 26 |     ax = skplt.metrics.plot_roc(y_test, y_prob, plot_micro=False, plot_macro=False)
 27 |     ax.set_xlim([-0.01, 1.0])
 28 |     ax.set_ylim([0.0, 1.05])
 29 |     
 30 |     handles, labels = ax.get_legend_handles_labels()
 31 |     if len(labels) > 0:
 32 |         labels[0] = labels[0].replace('ROC curve of class 1', label)
 33 |     ax.legend(handles, labels, loc="lower right")
 34 |     
 35 | def print_OLS_error_table(model, X_train, y_train):
 36 |     params = np.append(model.intercept_, model.coef_)
 37 |     predictions = model.predict(X_train)
 38 |     
 39 |     if isinstance(X_train, pd.DataFrame):
 40 |         X_cols = X_train.columns
 41 |         X_train = X_train.values
 42 |     else:
 43 |         X_cols = [f'Feature {num}' for num in range(1, len(X_train))]
 44 | 
 45 |     newX = pd.DataFrame({"Constant": np.ones(len(X_train))}).join(pd.DataFrame(X_train))
 46 |     
 47 |     if isinstance(model, skl_lm.LinearRegression):
 48 |         # for linear regression:
 49 |         MSE = (sum((y_train-predictions)**2))/(len(newX)-len(newX.columns))
 50 |         var_b = MSE*(np.linalg.inv(np.dot(newX.T, newX)).diagonal())
 51 |     elif isinstance(model, skl_lm.LogisticRegression):
 52 |         # for logistic regression:
 53 |         pred_prob = model.predict_proba(X_train)
 54 |         W = np.diagflat(pred_prob[:, 1]*(1-pred_prob[:, 1]))
 55 |         cov = np.dot(newX.T, np.dot(W, newX))
 56 |         var_b = np.linalg.inv(cov).diagonal()
 57 |         
 58 |     std_errs = np.sqrt(var_b)
 59 |     t_values = params/std_errs
 60 |     p_values =[2*(1-stats.t.cdf(np.abs(i),(len(newX)-1))) for i in t_values]
 61 | 
 62 |     std_errs = np.round(std_errs,3)
 63 |     t_values = np.round(t_values,3)
 64 |     p_values = np.round(p_values,3)
 65 |     params = np.round(params,4)
 66 |     
 67 |     # log likelihood and AIC
 68 |     y_pred = model.predict_proba(X_train)
 69 |     LLK = -log_loss(y_train, y_pred, normalize=False) 
 70 |     df_model = len(*model.coef_)
 71 |     aic = 2*(df_model+1) - 2*LLK
 72 | 
 73 |     model_stats = pd.DataFrame(np.array([params, std_errs, t_values, p_values]).T, 
 74 |                                index=['Intercept', *X_cols], 
 75 |                                columns=['Coefficients', 'Standard Errors', 't values', 'p values'])
 76 |     
 77 |     print(f'No. Observations: {len(y_train)}')
 78 |     print(f'Df Residuals: {len(y_train)-df_model-1}')
 79 |     print(f'Df Model: {df_model}')
 80 |     print(f'Log-Likelihood: {LLK:.2f}')
 81 |     print(f'AIC: {aic:.2f}')
 82 |     print(model_stats)
 83 |     print()
 84 |     
 85 | def plot_classification(model, X_test, y_test):   
 86 |     fig = plt.figure(figsize=(8,8))
 87 |     ax = plt.subplot(1,1,1)
 88 |     
 89 |     if isinstance(X_test, pd.DataFrame):
 90 |         X_cols = X_test.columns
 91 |         X_test = X_test.values
 92 |     else:
 93 |         X_cols = ['', '']
 94 |         
 95 |     if isinstance(y_test, pd.DataFrame):
 96 |         y_test = y_test.values
 97 |         
 98 |     # Data
 99 |     ax.scatter(X_test[:,0], X_test[:,1], c=y_test, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
100 |     ax.set_xlabel(X_cols[0]);
101 |     ax.set_ylabel(X_cols[1]);
102 |     
103 |     maxX1, maxX2 = np.max(X_test, axis=0)
104 |     minX1, minX2 = np.min(X_test, axis=0)
105 |     N_points_grid = 200
106 |     xx, yy = np.meshgrid(np.linspace(minX1, maxX1, N_points_grid), np.linspace(minX2, maxX2, N_points_grid))
107 |     X = np.c_[xx.ravel(), yy.ravel()]
108 |     est_region = model.predict(X)
109 | 
110 |     # regions
111 |     plt.contourf(xx, yy, est_region.reshape(xx.shape), cmap=plt.cm.coolwarm, alpha=0.5);
112 |     
113 |     return ax


--------------------------------------------------------------------------------
/local_regressor.py:
--------------------------------------------------------------------------------
 1 | from sklearn import neighbors
 2 | import sklearn.linear_model as skl_lm
 3 | from sklearn.utils.validation import check_array
 4 | from sklearn.neighbors.base import _get_weights, _check_weights
 5 | import numpy as np
 6 | 
 7 | class LocalRegressor(neighbors.KNeighborsRegressor):
 8 |     
 9 |     def predict(self, X):
10 |         """Predict the target for the provided data
11 |         It fits a weighted least squares linear model locally for the nearest neighbors
12 |         Parameters
13 |         ----------
14 |         X : array-like, shape (n_query, n_features), \
15 |                 or (n_query, n_indexed) if metric == 'precomputed'
16 |             Test samples.
17 |         Returns
18 |         -------
19 |         y : array of int, shape = [n_samples] or [n_samples, n_outputs]
20 |             Target values
21 |         """
22 |         X = check_array(X, accept_sparse='csr')
23 | 
24 |         # NN of X with respect to the train data (_fit_X)
25 |         neigh_dist, neigh_ind = self.kneighbors(X)
26 | 
27 |         weights = _get_weights(neigh_dist, self.weights)
28 |         
29 |         _y = self._y
30 |         _fit_X = self._fit_X
31 |         if _y.ndim == 1:
32 |             _y = _y.reshape((-1, 1))
33 | 
34 |         y_pred = np.empty((X.shape[0], 1), dtype=np.float64)
35 | 
36 |         # NN in the train data
37 |         X_nn = np.squeeze(_fit_X[neigh_ind])
38 |         y_nn = np.squeeze(_y[neigh_ind])
39 |         
40 |         # loop over the samples, not ideal from a speed point of view
41 |         for i in range(X_nn.shape[0]):
42 |             linear = skl_lm.LinearRegression()
43 |             if weights is not None:
44 |                 linear.fit(X_nn[i].reshape(-1, 1), y_nn[i], sample_weight=weights[i])
45 |             else:
46 |                 linear.fit(X_nn[i].reshape(-1, 1), y_nn[i])
47 |             y_pred[i] = linear.predict(X[i].reshape(-1, 1))
48 | 
49 |         if self._y.ndim == 1:
50 |             y_pred = y_pred.ravel()
51 | 
52 |         return y_pred
53 |     


--------------------------------------------------------------------------------
/plot_lm.py:
--------------------------------------------------------------------------------
  1 | def plot_lm(model_fit, data, key, n=3):
  2 |     import numpy as np
  3 |     import pandas as pd
  4 |     import seaborn as sns
  5 |     import matplotlib.pyplot as plt
  6 |     import statsmodels.formula.api as smf
  7 | 
  8 |     from statsmodels.graphics.gofplots import ProbPlot
  9 |     
 10 |     # fitted values (need a constant term for intercept)
 11 |     model_fitted_y = model_fit.fittedvalues
 12 |     # model residuals
 13 |     model_residuals = model_fit.resid
 14 |     # normalized residuals
 15 |     model_norm_residuals = model_fit.get_influence().resid_studentized_internal
 16 |     # absolute squared normalized residuals
 17 |     model_norm_residuals_abs_sqrt = np.sqrt(np.abs(model_norm_residuals))
 18 |     # absolute residuals
 19 |     model_abs_resid = np.abs(model_residuals)
 20 |     # leverage, from statsmodels internals
 21 |     model_leverage = model_fit.get_influence().hat_matrix_diag
 22 |     # cook's distance, from statsmodels internals
 23 |     model_cooks = model_fit.get_influence().cooks_distance[0]
 24 |     
 25 |     fig = plt.figure()
 26 |     fig.set_figheight(8)
 27 |     fig.set_figwidth(12)
 28 |     
 29 |     # first plot
 30 |     plot_lm_1 = plt.subplot(2, 2, 1)
 31 | 
 32 |     temp_plot = sns.residplot(model_fitted_y, key, data=data, 
 33 |                               lowess=True, 
 34 |                               scatter_kws={'alpha': 0.5}, 
 35 |                               line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8})
 36 | 
 37 |     plot_lm_1.set_title('Residuals vs Fitted')
 38 |     plot_lm_1.set_xlabel('Fitted values')
 39 |     plot_lm_1.set_ylabel('Residuals')
 40 | 
 41 |     # annotations
 42 |     abs_resid = model_abs_resid.sort_values(ascending=False)
 43 |     abs_resid_top_3 = abs_resid[:3]
 44 | 
 45 |     for i in abs_resid_top_3.index:
 46 |         plot_lm_1.annotate(i, xy=(model_fitted_y[i], model_residuals[i]));
 47 |         
 48 |     # second plot
 49 |     plot_lm_2 = plt.subplot(2, 2, 2)
 50 |     QQ = ProbPlot(model_norm_residuals)
 51 |     temp_plot = QQ.qqplot(line='45', alpha=0.5, color='#4C72B0', lw=1, ax=plot_lm_2)
 52 | 
 53 |     plot_lm_2.set_title('Normal Q-Q')
 54 |     plot_lm_2.set_xlabel('Theoretical Quantiles')
 55 |     plot_lm_2.set_ylabel('Standardized Residuals');
 56 | 
 57 |     # annotations
 58 |     abs_norm_resid = np.flip(np.argsort(np.abs(model_norm_residuals)), 0)
 59 |     abs_norm_resid_top_3 = abs_norm_resid[:3]
 60 | 
 61 |     for r, i in enumerate(abs_norm_resid_top_3):
 62 |         plot_lm_2.annotate(i, xy=(np.flip(QQ.theoretical_quantiles, 0)[r],model_norm_residuals[i]));
 63 |         
 64 |     # third plot
 65 |     plot_lm_3 = plt.subplot(2, 2, 3)
 66 | 
 67 |     plt.scatter(model_fitted_y, model_norm_residuals_abs_sqrt, alpha=0.5)
 68 |     sns.regplot(model_fitted_y, model_norm_residuals_abs_sqrt, 
 69 |                 scatter=False, 
 70 |                 ci=False, 
 71 |                 lowess=True,
 72 |                 line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8})
 73 | 
 74 |     plot_lm_3.set_title('Scale-Location')
 75 |     plot_lm_3.set_xlabel('Fitted values')
 76 |     plot_lm_3.set_ylabel('$\sqrt{|Standardized Residuals|}$');
 77 | 
 78 |     # annotations
 79 |     abs_sq_norm_resid = np.flip(np.argsort(model_norm_residuals_abs_sqrt), 0)
 80 |     abs_sq_norm_resid_top_3 = abs_sq_norm_resid[:3]
 81 | 
 82 |     for i in abs_norm_resid_top_3:
 83 |         try:
 84 |             plot_lm_3.annotate(i, xy=(model_fitted_y[i], model_norm_residuals_abs_sqrt[i]));
 85 |         except KeyError as err:
 86 |             continue
 87 |         
 88 |     # fourth plot
 89 |     plot_lm_4 = plt.subplot(2, 2, 4)
 90 | 
 91 |     plt.scatter(model_leverage, model_norm_residuals, alpha=0.5)
 92 |     sns.regplot(model_leverage, model_norm_residuals, 
 93 |                 scatter=False, 
 94 |                 ci=False, 
 95 |                 lowess=True,
 96 |                 line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8})
 97 | 
 98 |     plot_lm_4.set_xlim(0, 0.20)
 99 |     plot_lm_4.set_ylim(-3, 5)
100 |     plot_lm_4.set_title('Residuals vs Leverage')
101 |     plot_lm_4.set_xlabel('Leverage')
102 |     plot_lm_4.set_ylabel('Standardized Residuals')
103 | 
104 |     # annotations
105 |     leverage_top_3 = np.flip(np.argsort(model_cooks), 0)[:3]
106 | 
107 |     for i in leverage_top_3:
108 |         plot_lm_4.annotate(i, xy=(model_leverage[i], model_norm_residuals[i]))
109 | 
110 |     # shenanigans for cook's distance contours
111 |     def graph(formula, x_range, label=None):
112 |         x = x_range
113 |         y = formula(x)
114 |         plt.plot(x, y, label=label, lw=1, ls='--', color='red')
115 | 
116 |     p = len(model_fit.params) # number of model parameters
117 | 
118 |     graph(lambda x: np.sqrt((0.5 * p * (1 - x)) / x), 
119 |           np.linspace(0.001, 0.200, 50), 
120 |           'Cook\'s distance') # 0.5 line
121 |     graph(lambda x: np.sqrt((1 * p * (1 - x)) / x), 
122 |           np.linspace(0.001, 0.200, 50)) # 1 line
123 |     plt.legend(loc='upper right');
124 |     plt.xlim(xmax=np.max(model_cooks))
125 |     
126 |     plt.tight_layout()


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/requirements.txt:
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1 | pandas
2 | numpy
3 | scipy
4 | scikit-learn
5 | statsmodels
6 | patsy
7 | matplotlib
8 | seaborn
9 | 


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/splines.py:
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  1 | # get spline basis and fit it to data using scipy
  2 | # from https://gist.github.com/MMesch/35d7833a3daa4a9e8ca9c6953cbe21d4
  3 | 
  4 | import numpy as np
  5 | import scipy.interpolate as si
  6 | from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin
  7 | from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
  8 | from patsy import dmatrix
  9 | 
 10 | 
 11 | class BSplineFeatures(TransformerMixin):
 12 |     '''Cubic splines using scipy'''
 13 |     def __init__(self, knots, degree=3, periodic=False):
 14 |         self.bsplines = self.get_bspline_basis(knots, degree, periodic=periodic)
 15 |         self.nsplines = len(self.bsplines)
 16 | 
 17 |     def fit(self, X, y=None):
 18 |         return self
 19 | 
 20 |     def transform(self, X):
 21 |         nsamples, nfeatures = X.shape
 22 |         features = np.zeros((nsamples, nfeatures * self.nsplines))
 23 |         for ispline, spline in enumerate(self.bsplines):
 24 |             istart = ispline * nfeatures
 25 |             iend = (ispline + 1) * nfeatures
 26 |             features[:, istart:iend] = si.splev(X, spline)
 27 |         return features
 28 | 
 29 |     def get_bspline_basis(self, knots, degree=3, periodic=False):
 30 |         """Get spline coefficients for each basis spline."""
 31 |         nknots = len(knots)
 32 |         y_dummy = np.zeros(nknots)
 33 | 
 34 |         knots, coeffs, degree = si.splrep(knots, y_dummy, k=degree, s=0,
 35 |                                           per=periodic)
 36 |         ncoeffs = len(coeffs)
 37 |         bsplines = []
 38 |         for ispline in range(nknots):
 39 |             coeffs = [1.0 if ispl == ispline else 0.0 for ispl in range(ncoeffs)]
 40 |             bsplines.append((knots, coeffs, degree))
 41 |         return bsplines
 42 | 
 43 | 
 44 | class NaturalSplineFeatures(TransformerMixin):
 45 |     '''Cubic natural splines using scipy. Not really natural splines yet'''
 46 |     def __init__(self, knots, degree=3, periodic=False):
 47 |         self.natsplines = self.get_natural_spline_basis(knots, degree, periodic=periodic)
 48 |         self.nsplines = len(self.natsplines)
 49 | 
 50 |     def fit(self, X, y=None):
 51 |         return self
 52 | 
 53 |     def transform(self, X):
 54 |         nsamples, nfeatures = X.shape
 55 |         features = np.zeros((nsamples, nfeatures * self.nsplines))
 56 |         for ispline, spline in enumerate(self.natsplines):
 57 |             istart = ispline * nfeatures
 58 |             iend = (ispline + 1) * nfeatures
 59 |             features[:, istart:iend] = si.splev(X, spline)
 60 |         return features
 61 | 
 62 |     def get_natural_spline_basis(self, knots, degree=3, periodic=False):
 63 |         """Get spline coefficients for each basis spline."""
 64 |         nknots = len(knots)
 65 |         X_dummy = np.linspace(knots[0], knots[-1], 20*nknots)
 66 |         y_dummy = np.zeros_like(X_dummy)
 67 | 
 68 |         new_knots, coeffs, degree = si.splrep(X_dummy, y_dummy, k=degree, s=0, t=knots[1:-1], per=periodic)
 69 |         ncoeffs = len(coeffs)
 70 |         natsplines = []
 71 |         for ispline in range(nknots):
 72 |             new_coeffs = [1.0 if ispl == ispline else 0.0 for ispl in range(ncoeffs)]
 73 |             natsplines.append((new_knots, new_coeffs, degree))
 74 |         return natsplines
 75 | 
 76 | 
 77 | class PatsySplineFeatures(TransformerMixin):
 78 |     '''Cubic splines (natural or bspline) using patsy'''
 79 |     def __init__(self, knots=None, df=None, spline_type=None):
 80 |         '''Either knots or df is required'''
 81 |         self.knots = knots
 82 |         self.df = df
 83 |         if spline_type == 'natural' or spline_type is None:
 84 |             self.function = 'cr'
 85 |         elif spline_type == 'bspline':
 86 |             self.function = 'bs'
 87 |         elif spline_type == 'cyclic':
 88 |             self.function = 'cc'
 89 |         else:
 90 |             raise AttributeError('Wrong spline type')
 91 | 
 92 |     def fit(self, X, y=None):
 93 |         return self
 94 | 
 95 |     def transform(self, X):
 96 |         options = 'df=df' if self.df else 'knots=knots'
 97 |         features = dmatrix(f"{self.function}(x, {options})", {"x": X, 'knots': self.knots, 
 98 |                                                               'df': self.df}, return_type='dataframe')
 99 |         return features.values
100 | 
101 |     
102 | class SmoothingSpline(BaseEstimator, RegressorMixin):
103 |     '''Smoothing cubic splines estimator using scipy'''
104 |     def __init__(self, s=0):
105 |         self.s = s
106 |     
107 |     def fit(self, X, y):
108 |         # Check that X and y have correct shape
109 |         X, y = check_X_y(X, y, y_numeric=True)
110 |         
111 |         self.X_ = X
112 |         self.y_ = y
113 |         
114 |         X_unique, unq_idx, unq_inv, unq_cnt = np.unique(X, return_index=True, return_inverse=True, return_counts=True)
115 |         y_unique_mean = np.bincount(unq_inv, weights=y) / unq_cnt
116 |         self.smoothing_spline_ = si.UnivariateSpline(X_unique, y_unique_mean, s=self.s)
117 |         
118 |         # this should give the effective df of the model
119 |         # but doesn't work
120 |         #pred = smoothing_spline(X_unique).reshape((-1,1))
121 |         #np.trace(np.multiply(pred, y_unique_mean.reshape((-1,1))))
122 |         self.df_ = len(self.smoothing_spline_.get_knots())
123 |         return self
124 |     
125 |     def predict(self, X, y=None):
126 |         # Check is fit had been called
127 |         check_is_fitted(self, ['X_', 'y_'])
128 | 
129 |         # Input validation
130 |         X = check_array(X)
131 | 
132 |         return self.smoothing_spline_(X)
133 |     


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/util.py:
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1 | def vif(model):
2 |     return 1/(1-model.rsquared)


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