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Commit af4fe336 by Tooba Mukhtar
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adding datasets

parent 81199142
Showing with 1155 additions and 58 deletions
Datasets/AdultCensusIncome.csv 0 → 100644
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Datasets/Dwell_Time/Dwell_Time_VersionA.csv 0 → 100644
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Datasets/Dwell_Time/Dwell_Time_VersionB.csv 0 → 100644
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Datasets/HiveSampleData.csv 0 → 100644
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Datasets/Iris_Data.csv 0 → 100644
"Sepal.Length","Sepal.Width","Petal.Length","Petal.Width","Species"
5.1,3.5,1.4,0.2,"setosa"
4.9,3,1.4,0.2,"setosa"
4.7,3.2,1.3,0.2,"setosa"
4.6,3.1,1.5,0.2,"setosa"
5,3.6,1.4,0.2,"setosa"
5.4,3.9,1.7,0.4,"setosa"
4.6,3.4,1.4,0.3,"setosa"
5,3.4,1.5,0.2,"setosa"
4.4,2.9,1.4,0.2,"setosa"
4.9,3.1,1.5,0.1,"setosa"
5.4,3.7,1.5,0.2,"setosa"
4.8,3.4,1.6,0.2,"setosa"
4.8,3,1.4,0.1,"setosa"
4.3,3,1.1,0.1,"setosa"
5.8,4,1.2,0.2,"setosa"
5.7,4.4,1.5,0.4,"setosa"
5.4,3.9,1.3,0.4,"setosa"
5.1,3.5,1.4,0.3,"setosa"
5.7,3.8,1.7,0.3,"setosa"
5.1,3.8,1.5,0.3,"setosa"
5.4,3.4,1.7,0.2,"setosa"
5.1,3.7,1.5,0.4,"setosa"
4.6,3.6,1,0.2,"setosa"
5.1,3.3,1.7,0.5,"setosa"
4.8,3.4,1.9,0.2,"setosa"
5,3,1.6,0.2,"setosa"
5,3.4,1.6,0.4,"setosa"
5.2,3.5,1.5,0.2,"setosa"
5.2,3.4,1.4,0.2,"setosa"
4.7,3.2,1.6,0.2,"setosa"
4.8,3.1,1.6,0.2,"setosa"
5.4,3.4,1.5,0.4,"setosa"
5.2,4.1,1.5,0.1,"setosa"
5.5,4.2,1.4,0.2,"setosa"
4.9,3.1,1.5,0.2,"setosa"
5,3.2,1.2,0.2,"setosa"
5.5,3.5,1.3,0.2,"setosa"
4.9,3.6,1.4,0.1,"setosa"
4.4,3,1.3,0.2,"setosa"
5.1,3.4,1.5,0.2,"setosa"
5,3.5,1.3,0.3,"setosa"
4.5,2.3,1.3,0.3,"setosa"
4.4,3.2,1.3,0.2,"setosa"
5,3.5,1.6,0.6,"setosa"
5.1,3.8,1.9,0.4,"setosa"
4.8,3,1.4,0.3,"setosa"
5.1,3.8,1.6,0.2,"setosa"
4.6,3.2,1.4,0.2,"setosa"
5.3,3.7,1.5,0.2,"setosa"
5,3.3,1.4,0.2,"setosa"
7,3.2,4.7,1.4,"versicolor"
6.4,3.2,4.5,1.5,"versicolor"
6.9,3.1,4.9,1.5,"versicolor"
5.5,2.3,4,1.3,"versicolor"
6.5,2.8,4.6,1.5,"versicolor"
5.7,2.8,4.5,1.3,"versicolor"
6.3,3.3,4.7,1.6,"versicolor"
4.9,2.4,3.3,1,"versicolor"
6.6,2.9,4.6,1.3,"versicolor"
5.2,2.7,3.9,1.4,"versicolor"
5,2,3.5,1,"versicolor"
5.9,3,4.2,1.5,"versicolor"
6,2.2,4,1,"versicolor"
6.1,2.9,4.7,1.4,"versicolor"
5.6,2.9,3.6,1.3,"versicolor"
6.7,3.1,4.4,1.4,"versicolor"
5.6,3,4.5,1.5,"versicolor"
5.8,2.7,4.1,1,"versicolor"
6.2,2.2,4.5,1.5,"versicolor"
5.6,2.5,3.9,1.1,"versicolor"
5.9,3.2,4.8,1.8,"versicolor"
6.1,2.8,4,1.3,"versicolor"
6.3,2.5,4.9,1.5,"versicolor"
6.1,2.8,4.7,1.2,"versicolor"
6.4,2.9,4.3,1.3,"versicolor"
6.6,3,4.4,1.4,"versicolor"
6.8,2.8,4.8,1.4,"versicolor"
6.7,3,5,1.7,"versicolor"
6,2.9,4.5,1.5,"versicolor"
5.7,2.6,3.5,1,"versicolor"
5.5,2.4,3.8,1.1,"versicolor"
5.5,2.4,3.7,1,"versicolor"
5.8,2.7,3.9,1.2,"versicolor"
6,2.7,5.1,1.6,"versicolor"
5.4,3,4.5,1.5,"versicolor"
6,3.4,4.5,1.6,"versicolor"
6.7,3.1,4.7,1.5,"versicolor"
6.3,2.3,4.4,1.3,"versicolor"
5.6,3,4.1,1.3,"versicolor"
5.5,2.5,4,1.3,"versicolor"
5.5,2.6,4.4,1.2,"versicolor"
6.1,3,4.6,1.4,"versicolor"
5.8,2.6,4,1.2,"versicolor"
5,2.3,3.3,1,"versicolor"
5.6,2.7,4.2,1.3,"versicolor"
5.7,3,4.2,1.2,"versicolor"
5.7,2.9,4.2,1.3,"versicolor"
6.2,2.9,4.3,1.3,"versicolor"
5.1,2.5,3,1.1,"versicolor"
5.7,2.8,4.1,1.3,"versicolor"
6.3,3.3,6,2.5,"virginica"
5.8,2.7,5.1,1.9,"virginica"
7.1,3,5.9,2.1,"virginica"
6.3,2.9,5.6,1.8,"virginica"
6.5,3,5.8,2.2,"virginica"
7.6,3,6.6,2.1,"virginica"
4.9,2.5,4.5,1.7,"virginica"
7.3,2.9,6.3,1.8,"virginica"
6.7,2.5,5.8,1.8,"virginica"
7.2,3.6,6.1,2.5,"virginica"
6.5,3.2,5.1,2,"virginica"
6.4,2.7,5.3,1.9,"virginica"
6.8,3,5.5,2.1,"virginica"
5.7,2.5,5,2,"virginica"
5.8,2.8,5.1,2.4,"virginica"
6.4,3.2,5.3,2.3,"virginica"
6.5,3,5.5,1.8,"virginica"
7.7,3.8,6.7,2.2,"virginica"
7.7,2.6,6.9,2.3,"virginica"
6,2.2,5,1.5,"virginica"
6.9,3.2,5.7,2.3,"virginica"
5.6,2.8,4.9,2,"virginica"
7.7,2.8,6.7,2,"virginica"
6.3,2.7,4.9,1.8,"virginica"
6.7,3.3,5.7,2.1,"virginica"
7.2,3.2,6,1.8,"virginica"
6.2,2.8,4.8,1.8,"virginica"
6.1,3,4.9,1.8,"virginica"
6.4,2.8,5.6,2.1,"virginica"
7.2,3,5.8,1.6,"virginica"
7.4,2.8,6.1,1.9,"virginica"
7.9,3.8,6.4,2,"virginica"
6.4,2.8,5.6,2.2,"virginica"
6.3,2.8,5.1,1.5,"virginica"
6.1,2.6,5.6,1.4,"virginica"
7.7,3,6.1,2.3,"virginica"
6.3,3.4,5.6,2.4,"virginica"
6.4,3.1,5.5,1.8,"virginica"
6,3,4.8,1.8,"virginica"
6.9,3.1,5.4,2.1,"virginica"
6.7,3.1,5.6,2.4,"virginica"
6.9,3.1,5.1,2.3,"virginica"
5.8,2.7,5.1,1.9,"virginica"
6.8,3.2,5.9,2.3,"virginica"
6.7,3.3,5.7,2.5,"virginica"
6.7,3,5.2,2.3,"virginica"
6.3,2.5,5,1.9,"virginica"
6.5,3,5.2,2,"virginica"
6.2,3.4,5.4,2.3,"virginica"
5.9,3,5.1,1.8,"virginica"
Datasets/SLID.csv 0 → 100644
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Datasets/USAccDeaths.csv 0 → 100644
"","x"
"1",9007
"2",8106
"3",8928
"4",9137
"5",10017
"6",10826
"7",11317
"8",10744
"9",9713
"10",9938
"11",9161
"12",8927
"13",7750
"14",6981
"15",8038
"16",8422
"17",8714
"18",9512
"19",10120
"20",9823
"21",8743
"22",9129
"23",8710
"24",8680
"25",8162
"26",7306
"27",8124
"28",7870
"29",9387
"30",9556
"31",10093
"32",9620
"33",8285
"34",8466
"35",8160
"36",8034
"37",7717
"38",7461
"39",7767
"40",7925
"41",8623
"42",8945
"43",10078
"44",9179
"45",8037
"46",8488
"47",7874
"48",8647
"49",7792
"50",6957
"51",7726
"52",8106
"53",8890
"54",9299
"55",10625
"56",9302
"57",8314
"58",8850
"59",8265
"60",8796
"61",7836
"62",6892
"63",7791
"64",8192
"65",9115
"66",9434
"67",10484
"68",9827
"69",9110
"70",9070
"71",8633
"72",9240
Datasets/Wine_Quality/whitewine.csv 0 → 100644
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Datasets/Wine_Quality/winequality.names 0 → 100644
Citation Request:
This dataset is public available for research. The details are described in [Cortez et al., 2009].
Please include this citation if you plan to use this database:
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
Modeling wine preferences by data mining from physicochemical properties.
In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.
Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016
[Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf
[bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib
1. Title: Wine Quality
2. Sources
Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009
3. Past Usage:
P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
Modeling wine preferences by data mining from physicochemical properties.
In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.
In the above reference, two datasets were created, using red and white wine samples.
The inputs include objective tests (e.g. PH values) and the output is based on sensory data
(median of at least 3 evaluations made by wine experts). Each expert graded the wine quality
between 0 (very bad) and 10 (very excellent). Several data mining methods were applied to model
these datasets under a regression approach. The support vector machine model achieved the
best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T),
etc. Also, we plot the relative importances of the input variables (as measured by a sensitivity
analysis procedure).
4. Relevant Information:
The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine.
For more details, consult: http://www.vinhoverde.pt/en/ or the reference [Cortez et al., 2009].
Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables
are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).
These datasets can be viewed as classification or regression tasks.
The classes are ordered and not balanced (e.g. there are munch more normal wines than
excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent
or poor wines. Also, we are not sure if all input variables are relevant. So
it could be interesting to test feature selection methods.
5. Number of Instances: red wine - 1599; white wine - 4898.
6. Number of Attributes: 11 + output attribute
Note: several of the attributes may be correlated, thus it makes sense to apply some sort of
feature selection.
7. Attribute information:
For more information, read [Cortez et al., 2009].
Input variables (based on physicochemical tests):
1 - fixed acidity
2 - volatile acidity
3 - citric acid
4 - residual sugar
5 - chlorides
6 - free sulfur dioxide
7 - total sulfur dioxide
8 - density
9 - pH
10 - sulphates
11 - alcohol
Output variable (based on sensory data):
12 - quality (score between 0 and 10)
8. Missing Attribute Values: None
Datasets/Zip/Digits/train.0 0 → 100644
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Datasets/Zip/Digits/train.1 0 → 100644
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Datasets/Zip/Digits/train.2 0 → 100644
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Datasets/Zip/Digits/train.3 0 → 100644
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Datasets/Zip/Digits/train.4 0 → 100644
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Datasets/Zip/Digits/train.5 0 → 100644
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Datasets/Zip/Digits/train.6 0 → 100644
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Datasets/Zip/Digits/train.7 0 → 100644
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Datasets/Zip/Digits/train.8 0 → 100644
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Datasets/Zip/Digits/train.9 0 → 100644
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Datasets/Zip/zip.info.txt 0 → 100644
Normalized handwritten digits, automatically
scanned from envelopes by the U.S. Postal Service. The original
scanned digits are binary and of different sizes and orientations; the
images here have been deslanted and size normalized, resulting
in 16 x 16 grayscale images (Le Cun et al., 1990).
The data are in two gzipped files, and each line consists of the digit
id (0-9) followed by the 256 grayscale values.
There are 7291 training observations and 2007 test observations,
distributed as follows:
0 1 2 3 4 5 6 7 8 9 Total
Train 1194 1005 731 658 652 556 664 645 542 644 7291
Test 359 264 198 166 200 160 170 147 166 177 2007
or as proportions:
0 1 2 3 4 5 6 7 8 9
Train 0.16 0.14 0.1 0.09 0.09 0.08 0.09 0.09 0.07 0.09
Test 0.18 0.13 0.1 0.08 0.10 0.08 0.08 0.07 0.08 0.09
Alternatively, the training data are available as separate files per
digit (and hence without the digit identifier in each row)
The test set is notoriously "difficult", and a 2.5% error rate is
excellent. These data were kindly made available by the neural network
group at AT&T research labs (thanks to Yann Le Cunn).
Datasets/Zip/zip.test.csv 0 → 100644
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Datasets/Zip/zip.train.csv 0 → 100644
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Datasets/airquality.csv 0 → 100644
"","Ozone","Solar.R","Wind","Temp","Month","Day"
"1",41,190,7.4,67,5,1
"2",36,118,8,72,5,2
"3",12,149,12.6,74,5,3
"4",18,313,11.5,62,5,4
"5",NA,NA,14.3,56,5,5
"6",28,NA,14.9,66,5,6
"7",23,299,8.6,65,5,7
"8",19,99,13.8,59,5,8
"9",8,19,20.1,61,5,9
"10",NA,194,8.6,69,5,10
"11",7,NA,6.9,74,5,11
"12",16,256,9.7,69,5,12
"13",11,290,9.2,66,5,13
"14",14,274,10.9,68,5,14
"15",18,65,13.2,58,5,15
"16",14,334,11.5,64,5,16
"17",34,307,12,66,5,17
"18",6,78,18.4,57,5,18
"19",30,322,11.5,68,5,19
"20",11,44,9.7,62,5,20
"21",1,8,9.7,59,5,21
"22",11,320,16.6,73,5,22
"23",4,25,9.7,61,5,23
"24",32,92,12,61,5,24
"25",NA,66,16.6,57,5,25
"26",NA,266,14.9,58,5,26
"27",NA,NA,8,57,5,27
"28",23,13,12,67,5,28
"29",45,252,14.9,81,5,29
"30",115,223,5.7,79,5,30
"31",37,279,7.4,76,5,31
"32",NA,286,8.6,78,6,1
"33",NA,287,9.7,74,6,2
"34",NA,242,16.1,67,6,3
"35",NA,186,9.2,84,6,4
"36",NA,220,8.6,85,6,5
"37",NA,264,14.3,79,6,6
"38",29,127,9.7,82,6,7
"39",NA,273,6.9,87,6,8
"40",71,291,13.8,90,6,9
"41",39,323,11.5,87,6,10
"42",NA,259,10.9,93,6,11
"43",NA,250,9.2,92,6,12
"44",23,148,8,82,6,13
"45",NA,332,13.8,80,6,14
"46",NA,322,11.5,79,6,15
"47",21,191,14.9,77,6,16
"48",37,284,20.7,72,6,17
"49",20,37,9.2,65,6,18
"50",12,120,11.5,73,6,19
"51",13,137,10.3,76,6,20
"52",NA,150,6.3,77,6,21
"53",NA,59,1.7,76,6,22
"54",NA,91,4.6,76,6,23
"55",NA,250,6.3,76,6,24
"56",NA,135,8,75,6,25
"57",NA,127,8,78,6,26
"58",NA,47,10.3,73,6,27
"59",NA,98,11.5,80,6,28
"60",NA,31,14.9,77,6,29
"61",NA,138,8,83,6,30
"62",135,269,4.1,84,7,1
"63",49,248,9.2,85,7,2
"64",32,236,9.2,81,7,3
"65",NA,101,10.9,84,7,4
"66",64,175,4.6,83,7,5
"67",40,314,10.9,83,7,6
"68",77,276,5.1,88,7,7
"69",97,267,6.3,92,7,8
"70",97,272,5.7,92,7,9
"71",85,175,7.4,89,7,10
"72",NA,139,8.6,82,7,11
"73",10,264,14.3,73,7,12
"74",27,175,14.9,81,7,13
"75",NA,291,14.9,91,7,14
"76",7,48,14.3,80,7,15
"77",48,260,6.9,81,7,16
"78",35,274,10.3,82,7,17
"79",61,285,6.3,84,7,18
"80",79,187,5.1,87,7,19
"81",63,220,11.5,85,7,20
"82",16,7,6.9,74,7,21
"83",NA,258,9.7,81,7,22
"84",NA,295,11.5,82,7,23
"85",80,294,8.6,86,7,24
"86",108,223,8,85,7,25
"87",20,81,8.6,82,7,26
"88",52,82,12,86,7,27
"89",82,213,7.4,88,7,28
"90",50,275,7.4,86,7,29
"91",64,253,7.4,83,7,30
"92",59,254,9.2,81,7,31
"93",39,83,6.9,81,8,1
"94",9,24,13.8,81,8,2
"95",16,77,7.4,82,8,3
"96",78,NA,6.9,86,8,4
"97",35,NA,7.4,85,8,5
"98",66,NA,4.6,87,8,6
"99",122,255,4,89,8,7
"100",89,229,10.3,90,8,8
"101",110,207,8,90,8,9
"102",NA,222,8.6,92,8,10
"103",NA,137,11.5,86,8,11
"104",44,192,11.5,86,8,12
"105",28,273,11.5,82,8,13
"106",65,157,9.7,80,8,14
"107",NA,64,11.5,79,8,15
"108",22,71,10.3,77,8,16
"109",59,51,6.3,79,8,17
"110",23,115,7.4,76,8,18
"111",31,244,10.9,78,8,19
"112",44,190,10.3,78,8,20
"113",21,259,15.5,77,8,21
"114",9,36,14.3,72,8,22
"115",NA,255,12.6,75,8,23
"116",45,212,9.7,79,8,24
"117",168,238,3.4,81,8,25
"118",73,215,8,86,8,26
"119",NA,153,5.7,88,8,27
"120",76,203,9.7,97,8,28
"121",118,225,2.3,94,8,29
"122",84,237,6.3,96,8,30
"123",85,188,6.3,94,8,31
"124",96,167,6.9,91,9,1
"125",78,197,5.1,92,9,2
"126",73,183,2.8,93,9,3
"127",91,189,4.6,93,9,4
"128",47,95,7.4,87,9,5
"129",32,92,15.5,84,9,6
"130",20,252,10.9,80,9,7
"131",23,220,10.3,78,9,8
"132",21,230,10.9,75,9,9
"133",24,259,9.7,73,9,10
"134",44,236,14.9,81,9,11
"135",21,259,15.5,76,9,12
"136",28,238,6.3,77,9,13
"137",9,24,10.9,71,9,14
"138",13,112,11.5,71,9,15
"139",46,237,6.9,78,9,16
"140",18,224,13.8,67,9,17
"141",13,27,10.3,76,9,18
"142",24,238,10.3,68,9,19
"143",16,201,8,82,9,20
"144",13,238,12.6,64,9,21
"145",23,14,9.2,71,9,22
"146",36,139,10.3,81,9,23
"147",7,49,10.3,69,9,24
"148",14,20,16.6,63,9,25
"149",30,193,6.9,70,9,26
"150",NA,145,13.2,77,9,27
"151",14,191,14.3,75,9,28
"152",18,131,8,76,9,29
"153",20,223,11.5,68,9,30
Datasets/diamonds.csv 0 → 100644
This source diff could not be displayed because it is too large. You can view the blob instead.
Datasets/gtemp_land.csv 0 → 100644
"","x"
"1",-0.62
"2",-0.45
"3",-0.47
"4",-0.62
"5",-0.82
"6",-0.7
"7",-0.7
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"34",-0.25
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"36",0.01
"37",-0.32
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"41",-0.22
"42",0.03
"43",-0.16
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"46",0
"47",0.16
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"84",0.19
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"89",-0.13
"90",-0.1
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"96",0.12
"97",-0.28
"98",0.21
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"103",0.04
"104",0.42
"105",0.1
"106",0.05
"107",0.24
"108",0.4
"109",0.62
"110",0.38
"111",0.63
"112",0.59
"113",0.24
"114",0.24
"115",0.45
"116",0.74
"117",0.42
"118",0.62
"119",0.95
"120",0.73
"121",0.63
"122",0.81
"123",1
"124",0.9
"125",0.77
"126",1.08
"127",0.91
"128",1.15
"129",0.82
"130",0.91
"131",1.07
"132",0.92
"133",0.92
"134",1.01
"135",1.03
"136",1.26
"137",1.44
"138",1.35
Datasets/gtemp_ocean.csv 0 → 100644
"","x"
"1",-0.05
"2",0.01
"3",0
"4",-0.06
"5",-0.15
"6",-0.21
"7",-0.21
"8",-0.24
"9",-0.05
"10",-0.04
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"16",-0.14
"17",0
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"22",-0.18
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"24",-0.41
"25",-0.51
"26",-0.28
"27",-0.26
"28",-0.32
"29",-0.47
"30",-0.52
"31",-0.49
"32",-0.47
"33",-0.31
"34",-0.37
"35",-0.21
"36",-0.14
"37",-0.33
"38",-0.38
"39",-0.22
"40",-0.27
"41",-0.26
"42",-0.24
"43",-0.3
"44",-0.3
"45",-0.3
"46",-0.26
"47",-0.17
"48",-0.23
"49",-0.28
"50",-0.33
"51",-0.19
"52",-0.16
"53",-0.24
"54",-0.29
"55",-0.22
"56",-0.23
"57",-0.19
"58",-0.09
"59",-0.17
"60",-0.09
"61",0.11
"62",0.25
"63",0.05
"64",0.03
"65",0.21
"66",0.19
"67",-0.1
"68",-0.13
"69",-0.17
"70",-0.13
"71",-0.14
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"73",0.02
"74",0.01
"75",-0.17
"76",-0.19
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"78",0.05
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"80",0.01
"81",0
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"83",0.02
"84",0.03
"85",-0.13
"86",-0.09
"87",-0.04
"88",-0.06
"89",-0.04
"90",0.14
"91",0.02
"92",-0.12
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"94",0.1
"95",-0.07
"96",-0.09
"97",-0.03
"98",0.14
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"100",0.21
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"115",0.24
"116",0.28
"117",0.24
"118",0.39
"119",0.44
"120",0.23
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"122",0.38
"123",0.42
"124",0.44
"125",0.43
"126",0.43
"127",0.43
"128",0.34
"129",0.33
"130",0.47
"131",0.47
"132",0.35
"133",0.42
"134",0.46
"135",0.55
"136",0.68
"137",0.7
"138",0.64
Datasets/kyphosis.csv 0 → 100644
"","Kyphosis","Age","Number","Start"
"1","absent",71,3,5
"2","absent",158,3,14
"3","present",128,4,5
"4","absent",2,5,1
"5","absent",1,4,15
"6","absent",1,2,16
"7","absent",61,2,17
"8","absent",37,3,16
"9","absent",113,2,16
"10","present",59,6,12
"11","present",82,5,14
"12","absent",148,3,16
"13","absent",18,5,2
"14","absent",1,4,12
"15","absent",168,3,18
"16","absent",1,3,16
"17","absent",78,6,15
"18","absent",175,5,13
"19","absent",80,5,16
"20","absent",27,4,9
"21","absent",22,2,16
"22","present",105,6,5
"23","present",96,3,12
"24","absent",131,2,3
"25","present",15,7,2
"26","absent",9,5,13
"27","absent",8,3,6
"28","absent",100,3,14
"29","absent",4,3,16
"30","absent",151,2,16
"31","absent",31,3,16
"32","absent",125,2,11
"33","absent",130,5,13
"34","absent",112,3,16
"35","absent",140,5,11
"36","absent",93,3,16
"37","absent",1,3,9
"38","present",52,5,6
"39","absent",20,6,9
"40","present",91,5,12
"41","present",73,5,1
"42","absent",35,3,13
"43","absent",143,9,3
"44","absent",61,4,1
"45","absent",97,3,16
"46","present",139,3,10
"47","absent",136,4,15
"48","absent",131,5,13
"49","present",121,3,3
"50","absent",177,2,14
"51","absent",68,5,10
"52","absent",9,2,17
"53","present",139,10,6
"54","absent",2,2,17
"55","absent",140,4,15
"56","absent",72,5,15
"57","absent",2,3,13
"58","present",120,5,8
"59","absent",51,7,9
"60","absent",102,3,13
"61","present",130,4,1
"62","present",114,7,8
"63","absent",81,4,1
"64","absent",118,3,16
"65","absent",118,4,16
"66","absent",17,4,10
"67","absent",195,2,17
"68","absent",159,4,13
"69","absent",18,4,11
"70","absent",15,5,16
"71","absent",158,5,14
"72","absent",127,4,12
"73","absent",87,4,16
"74","absent",206,4,10
"75","absent",11,3,15
"76","absent",178,4,15
"77","present",157,3,13
"78","absent",26,7,13
"79","absent",120,2,13
"80","present",42,7,6
"81","absent",36,4,13
Datasets/mtcars.csv 0 → 100644
"","mpg","cyl","disp","hp","drat","wt","qsec","vs","am","gear","carb"
"Mazda RX4",21,6,160,110,3.9,2.62,16.46,0,1,4,4
"Mazda RX4 Wag",21,6,160,110,3.9,2.875,17.02,0,1,4,4
"Datsun 710",22.8,4,108,93,3.85,2.32,18.61,1,1,4,1
"Hornet 4 Drive",21.4,6,258,110,3.08,3.215,19.44,1,0,3,1
"Hornet Sportabout",18.7,8,360,175,3.15,3.44,17.02,0,0,3,2
"Valiant",18.1,6,225,105,2.76,3.46,20.22,1,0,3,1
"Duster 360",14.3,8,360,245,3.21,3.57,15.84,0,0,3,4
"Merc 240D",24.4,4,146.7,62,3.69,3.19,20,1,0,4,2
"Merc 230",22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2
"Merc 280",19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4
"Merc 280C",17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4
"Merc 450SE",16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3
"Merc 450SL",17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3
"Merc 450SLC",15.2,8,275.8,180,3.07,3.78,18,0,0,3,3
"Cadillac Fleetwood",10.4,8,472,205,2.93,5.25,17.98,0,0,3,4
"Lincoln Continental",10.4,8,460,215,3,5.424,17.82,0,0,3,4
"Chrysler Imperial",14.7,8,440,230,3.23,5.345,17.42,0,0,3,4
"Fiat 128",32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1
"Honda Civic",30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2
"Toyota Corolla",33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1
"Toyota Corona",21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1
"Dodge Challenger",15.5,8,318,150,2.76,3.52,16.87,0,0,3,2
"AMC Javelin",15.2,8,304,150,3.15,3.435,17.3,0,0,3,2
"Camaro Z28",13.3,8,350,245,3.73,3.84,15.41,0,0,3,4
"Pontiac Firebird",19.2,8,400,175,3.08,3.845,17.05,0,0,3,2
"Fiat X1-9",27.3,4,79,66,4.08,1.935,18.9,1,1,4,1
"Porsche 914-2",26,4,120.3,91,4.43,2.14,16.7,0,1,5,2
"Lotus Europa",30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2
"Ford Pantera L",15.8,8,351,264,4.22,3.17,14.5,0,1,5,4
"Ferrari Dino",19.7,6,145,175,3.62,2.77,15.5,0,1,5,6
"Maserati Bora",15,8,301,335,3.54,3.57,14.6,0,1,5,8
"Volvo 142E",21.4,4,121,109,4.11,2.78,18.6,1,1,4,2
Datasets/multiTimeline.csv 0 → 100644
Category: All categories
Month,diet: (Worldwide),gym: (Worldwide),finance: (Worldwide)
2004-01,100,31,48
2004-02,75,26,49
2004-03,67,24,47
2004-04,70,22,48
2004-05,72,22,43
2004-06,64,24,45
2004-07,60,23,44
2004-08,59,28,44
2004-09,53,25,44
2004-10,52,24,45
2004-11,50,23,43
2004-12,42,24,41
2005-01,64,32,44
2005-02,54,28,48
2005-03,56,27,46
2005-04,56,25,44
2005-05,59,24,42
2005-06,53,25,44
2005-07,53,25,44
2005-08,51,28,44
2005-09,47,28,44
2005-10,46,27,43
2005-11,44,25,42
2005-12,40,24,38
2006-01,64,34,44
2006-02,51,29,44
2006-03,51,28,46
2006-04,50,27,47
2006-05,50,26,45
2006-06,52,25,44
2006-07,51,27,42
2006-08,51,30,44
2006-09,45,30,46
2006-10,42,27,45
2006-11,43,26,45
2006-12,37,26,41
2007-01,57,35,46
2007-02,49,33,47
2007-03,51,32,48
2007-04,51,32,48
2007-05,49,32,47
2007-06,47,31,46
2007-07,49,30,50
2007-08,44,31,54
2007-09,46,32,52
2007-10,43,28,52
2007-11,40,27,50
2007-12,34,26,43
2008-01,52,35,53
2008-02,47,30,50
2008-03,46,29,53
2008-04,47,28,52
2008-05,45,27,48
2008-06,43,27,49
2008-07,44,28,52
2008-08,43,31,48
2008-09,42,33,61
2008-10,43,28,73
2008-11,39,28,58
2008-12,38,27,50
2009-01,52,35,54
2009-02,46,30,58
2009-03,48,28,58
2009-04,49,29,57
2009-05,48,28,53
2009-06,47,28,55
2009-07,47,28,57
2009-08,48,30,57
2009-09,44,31,60
2009-10,44,28,57
2009-11,41,27,52
2009-12,39,27,47
2010-01,57,35,51
2010-02,50,31,53
2010-03,51,30,53
2010-04,51,29,56
2010-05,49,28,55
2010-06,47,28,52
2010-07,48,29,50
2010-08,48,31,51
2010-09,48,32,54
2010-10,45,30,51
2010-11,43,28,49
2010-12,39,28,46
2011-01,61,39,51
2011-02,53,34,50
2011-03,54,33,51
2011-04,59,31,49
2011-05,57,31,50
2011-06,52,32,48
2011-07,52,30,48
2011-08,52,34,56
2011-09,50,36,52
2011-10,48,33,50
2011-11,49,33,47
2011-12,44,32,42
2012-01,64,42,44
2012-02,57,37,47
2012-03,57,35,47
2012-04,56,34,45
2012-05,55,33,47
2012-06,52,35,43
2012-07,55,37,44
2012-08,55,37,45
2012-09,51,39,46
2012-10,46,33,45
2012-11,44,32,42
2012-12,42,32,38
2013-01,65,43,46
2013-02,58,37,46
2013-03,59,37,46
2013-04,58,37,47
2013-05,55,36,46
2013-06,55,37,43
2013-07,55,37,46
2013-08,51,39,46
2013-09,52,41,47
2013-10,46,38,47
2013-11,46,37,44
2013-12,42,36,40
2014-01,61,47,46
2014-02,53,44,47
2014-03,54,43,47
2014-04,53,40,46
2014-05,50,39,44
2014-06,49,39,44
2014-07,48,41,45
2014-08,47,40,44
2014-09,46,40,48
2014-10,43,38,47
2014-11,42,37,42
2014-12,38,38,42
2015-01,54,48,46
2015-02,48,43,47
2015-03,51,43,46
2015-04,49,42,46
2015-05,48,41,44
2015-06,48,42,45
2015-07,47,42,46
2015-08,46,43,48
2015-09,43,45,48
2015-10,42,41,46
2015-11,39,42,42
2015-12,38,42,40
2016-01,51,52,44
2016-02,48,46,46
2016-03,48,47,44
2016-04,48,44,43
2016-05,47,46,42
2016-06,44,46,45
2016-07,43,58,41
2016-08,45,53,41
2016-09,43,51,44
2016-10,40,45,41
2016-11,39,44,43
2016-12,36,44,39
2017-01,55,56,43
2017-02,56,51,44
2017-03,50,51,44
2017-04,49,48,42
2017-05,48,48,43
2017-06,48,49,41
2017-07,52,52,43
2017-08,46,52,43
2017-09,44,50,47
2017-10,44,47,45
2017-11,41,47,47
2017-12,39,45,56
Datasets/ozone.data 0 → 100644
ozone radiation temperature wind
41 190 67 7.4
36 118 72 8
12 149 74 12.6
18 313 62 11.5
23 299 65 8.6
19 99 59 13.8
8 19 61 20.1
16 256 69 9.7
11 290 66 9.2
14 274 68 10.9
18 65 58 13.2
14 334 64 11.5
34 307 66 12
6 78 57 18.4
30 322 68 11.5
11 44 62 9.7
1 8 59 9.7
11 320 73 16.6
4 25 61 9.7
32 92 61 12
23 13 67 12
45 252 81 14.9
115 223 79 5.7
37 279 76 7.4
29 127 82 9.7
71 291 90 13.8
39 323 87 11.5
23 148 82 8
21 191 77 14.9
37 284 72 20.7
20 37 65 9.2
12 120 73 11.5
13 137 76 10.3
135 269 84 4
49 248 85 9.2
32 236 81 9.2
64 175 83 4.6
40 314 83 10.9
77 276 88 5.1
97 267 92 6.3
97 272 92 5.7
85 175 89 7.4
10 264 73 14.3
27 175 81 14.9
7 48 80 14.3
48 260 81 6.9
35 274 82 10.3
61 285 84 6.3
79 187 87 5.1
63 220 85 11.5
16 7 74 6.9
80 294 86 8.6
108 223 85 8
20 81 82 8.6
52 82 86 12
82 213 88 7.4
50 275 86 7.4
64 253 83 7.4
59 254 81 9.2
39 83 81 6.9
9 24 81 13.8
16 77 82 7.4
122 255 89 4
89 229 90 10.3
110 207 90 8
44 192 86 11.5
28 273 82 11.5
65 157 80 9.7
22 71 77 10.3
59 51 79 6.3
23 115 76 7.4
31 244 78 10.9
44 190 78 10.3
21 259 77 15.5
9 36 72 14.3
45 212 79 9.7
168 238 81 3.4
73 215 86 8
76 203 97 9.7
118 225 94 2.3
84 237 96 6.3
85 188 94 6.3
95.9999999999999 167 91 6.9
78 197 92 5.1
73 183 93 2.8
91 189 93 4.6
47 95 87 7.4
32 92 84 15.5
20 252 80 10.9
23 220 78 10.3
21 230 75 10.9
24 259 73 9.7
44 236 81 14.9
21 259 76 15.5
28 238 77 6.3
9 24 71 10.9
13 112 71 11.5
46 237 78 6.9
18 224 67 13.8
13 27 76 10.3
24 238 68 10.3
16 201 82 8
13 238 64 12.6
23 14 71 9.2
36 139 81 10.3
7 49 69 10.3
14 20 63 16.6
30 193 70 6.9
14 191 75 14.3
18 131 76 8
20 223 68 11.5
Datasets/rmarkdownd_template.Rmd deleted 100644 → 0
---
title: "Work and fun in Data Science Dojo"
author: your name
date:
output:
pdf_document:
toc: true
---
[linked phrase](http://datasciencedojo.com/)
# My story of Titanic tragedy
## Obtain the data
<!-- You may want to load data here -->
## Overview of the data
<!-- You may want to do the preliminary exploration of the data, using str(), summary(), head(), class(), etc. -->
<!-- Also write down your feelings of the data -->
## Modification of the original data
<!-- You can revise the data you got. -->
<!-- For example: if you feel the feature Survived should better to be a factor, you can do something like: titanic$Survived = factor(titanic$Survived, labels=c("died", "survived")) -->
## First plot of Titanic data
<!-- Make your first plot of Titanic data, and write down what you see from the plot. -->
<!-- Feel free to revise the headers to make this storybook nicer. -->
## Second plot of Titanic data
<!-- Make the 2nd, 3rd, 4th plots from here. Doesn't need to be a lot, but try to make every single one telling. -->
## Your summary of the Titanic data (story of Titanic tragedy)
* First...
* Second...
* Third...
* Fourth...
# Another course in Data Science Dojo
<!-- Keep adding your note, code and thoughts during the bootcamp! -->
# Another course in Data Science Dojo
# Important contacts in DSD bootcamp
* Raja Iqbal (Instructor)
[email protected]
* Jasmine Wilkerson (Instructor)
[email protected]
* Phuc Duong (Instructor)
[email protected]
* Yuhui Zhang (Instructor)
[email protected]
* Lisa Nicholson
[email protected]
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