├── 00_00_Introducción_a_Jupyter.pdf ├── 00_01_Introduccion-a-la-Programacion-en-Python.pdf ├── 01_00_Intro_DataScience_Numpy.pdf ├── 01_01_Intro_DataScience_Matplotlib.pdf ├── 01_02_Intro_DataScience_Pandas.pdf ├── 01_03_Introduccion_a_Data_Science_Proceso.pdf ├── 02_Limpieza_de_Datos.pdf ├── 03_Data_Wrangling.pdf ├── 04_Conceptos_básicos_de_estadísticas.pdf ├── 05_Regresión_Lineal.pdf ├── Instalar ambiente de Desarrollo Python_Anaconda.pdf ├── Introducción a Jupyter Notebook.pdf ├── README.md ├── datasets ├── Advertising.csv ├── Athelete_Country_Map.csv ├── Athelete_Sports_Map.csv ├── Bank data dictionary.txt ├── Boston.csv ├── Car Classification.txt ├── Cereal Data Description.txt ├── Cereal data columns.xlsx ├── Cereal data.txt ├── Customer Churn Columns.csv ├── Customer Churn Model.csv ├── Customer Churn Model.txt ├── Description.txt ├── Ecom Expense.csv ├── Ecom Expense.xlsx ├── Medals.csv ├── Tab Customer Churn Model.txt ├── Titanic Description.txt ├── alumnos.csv ├── auto-mpg.csv ├── bank.csv ├── breast-cancer-wisconsin.data.txt ├── breast-cancer-wisconsin.names.txt ├── chopstick-effectiveness.csv ├── downloaded_medals.csv ├── downloaded_medals.xls ├── iris.csv ├── movies.csv ├── test.txt ├── titanic3.csv ├── titanic3.xls ├── titanic3.xlsx ├── titanic_custom.csv ├── titanic_custom.json ├── titanic_custom.xls ├── winequality-red.csv └── winequality-white.csv ├── imagenes ├── CRISP-DM_Process_Diagram.png ├── Casos_hipotesis.PNG ├── Cnotraste_hipotesis.PNG ├── Conclusion_hipotesis.PNG ├── Corr_Pearson.PNG ├── Distribución_Normal.PNG ├── Estadisticas_bases.PNG ├── FilmDialogueFigure1.png ├── HADOOP-ECOSYSTEM-Edureka.png ├── Intervalos_confianza.PNG ├── MonteCarlo.PNG ├── Rechazo_hipotesis.PNG ├── Teorema_Central_Limite.PNG ├── bullet_gauge.png ├── calidad_datos_ejemplo.png ├── change_cell_type.png ├── circular_gauge.png ├── content_jupyternotebook1.gif ├── data_disciplines.jpg ├── data_science_applications.png ├── data_scientist.png ├── ggplot2_cheatsheet1.png ├── ggplot2_cheatsheet2.png ├── imagen.txt ├── muestreo.PNG ├── nbextensions_example.gif └── p_valor.PNG ├── mimodulo.py └── notebooks ├── 00_00_Introducción a Jupyter.ipynb ├── 00_01_Introduccion-a-la-Programacion-en-Python.ipynb ├── 01_00_Intro_DataScience_Numpy.ipynb ├── 01_01_Intro_DataScience_Matplotlib.ipynb ├── 01_02_Intro_DataScience_Pandas.ipynb ├── 01_03_Introduccion a Data Science_Proceso y repaso.ipynb ├── 02_Limpieza_de_Datos.ipynb ├── 03_Data_Wrangling.ipynb ├── 04_Conceptos_básicos_de_estadísticas.ipynb └── 05_Regresión_Lineal.ipynb /00_00_Introducción_a_Jupyter.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/felipesanma/Intro_to_DataScience/8d4b1381a534a2dc4ce48b8a2a666464b1d2d492/00_00_Introducción_a_Jupyter.pdf -------------------------------------------------------------------------------- /00_01_Introduccion-a-la-Programacion-en-Python.pdf: 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-------------------------------------------------------------------------------- 1 | # Repositorio con notebooks para Data Science 2 | 3 | Es normal tener dudas cuando uno comienza, es por esto que te voy a dejar una pequeña guía para que inicies hoy mismo, te aclaro que el repositorio está pensado para hacerlo de manera secuencial. 4 | 5 | De todas formas, dejo una pequeña guía para saber donde partir si ya tienes ciertos conocimientos: 6 | 7 | 8 | 1. ¿Sabes utilizar Jupyter Notebooks? Si no lo sabes, te dejo una guía con lo esencial En el repositorio tienes un Notebook con una introducción a [Jupyter Notebooks](00_00_Introducción_a_Jupyter.pdf) 9 | 10 | 2. ¿No sabes programar?, o bien sabes pero ¿no sabes Python? Entonces comienza [aquí](00_01_Introduccion-a-la-Programacion-en-Python.pdf) 11 | 12 | 3. Sabes Python, pero ¿no conoces las librerías esenciales para data science: numpy / pandas ? Entonces comienza [aquí](01_00_Intro_DataScience_Numpy.pdf) 13 | 14 | 4. Sabes programar en python y has utilizado las librerías numpy/pandas y algunas de visualización, pero ¿no sabes como hacer data science? Entonces comienza [aquí](01_03_Introduccion_a_Data_Science_Proceso.pdf) 15 | 16 | En cualquier otro caso, sientete en libertad de revisar los módulos como quieras :) 17 | -------------------------------------------------------------------------------- /datasets/Advertising.csv: -------------------------------------------------------------------------------- 1 | TV,Radio,Newspaper,Sales 2 | 230.1,37.8,69.2,22.1 3 | 44.5,39.3,45.1,10.4 4 | 17.2,45.9,69.3,9.3 5 | 151.5,41.3,58.5,18.5 6 | 180.8,10.8,58.4,12.9 7 | 8.7,48.9,75,7.2 8 | 57.5,32.8,23.5,11.8 9 | 120.2,19.6,11.6,13.2 10 | 8.6,2.1,1,4.8 11 | 199.8,2.6,21.2,10.6 12 | 66.1,5.8,24.2,8.6 13 | 214.7,24,4,17.4 14 | 23.8,35.1,65.9,9.2 15 | 97.5,7.6,7.2,9.7 16 | 204.1,32.9,46,19 17 | 195.4,47.7,52.9,22.4 18 | 67.8,36.6,114,12.5 19 | 281.4,39.6,55.8,24.4 20 | 69.2,20.5,18.3,11.3 21 | 147.3,23.9,19.1,14.6 22 | 218.4,27.7,53.4,18 23 | 237.4,5.1,23.5,12.5 24 | 13.2,15.9,49.6,5.6 25 | 228.3,16.9,26.2,15.5 26 | 62.3,12.6,18.3,9.7 27 | 262.9,3.5,19.5,12 28 | 142.9,29.3,12.6,15 29 | 240.1,16.7,22.9,15.9 30 | 248.8,27.1,22.9,18.9 31 | 70.6,16,40.8,10.5 32 | 292.9,28.3,43.2,21.4 33 | 112.9,17.4,38.6,11.9 34 | 97.2,1.5,30,9.6 35 | 265.6,20,0.3,17.4 36 | 95.7,1.4,7.4,9.5 37 | 290.7,4.1,8.5,12.8 38 | 266.9,43.8,5,25.4 39 | 74.7,49.4,45.7,14.7 40 | 43.1,26.7,35.1,10.1 41 | 228,37.7,32,21.5 42 | 202.5,22.3,31.6,16.6 43 | 177,33.4,38.7,17.1 44 | 293.6,27.7,1.8,20.7 45 | 206.9,8.4,26.4,12.9 46 | 25.1,25.7,43.3,8.5 47 | 175.1,22.5,31.5,14.9 48 | 89.7,9.9,35.7,10.6 49 | 239.9,41.5,18.5,23.2 50 | 227.2,15.8,49.9,14.8 51 | 66.9,11.7,36.8,9.7 52 | 199.8,3.1,34.6,11.4 53 | 100.4,9.6,3.6,10.7 54 | 216.4,41.7,39.6,22.6 55 | 182.6,46.2,58.7,21.2 56 | 262.7,28.8,15.9,20.2 57 | 198.9,49.4,60,23.7 58 | 7.3,28.1,41.4,5.5 59 | 136.2,19.2,16.6,13.2 60 | 210.8,49.6,37.7,23.8 61 | 210.7,29.5,9.3,18.4 62 | 53.5,2,21.4,8.1 63 | 261.3,42.7,54.7,24.2 64 | 239.3,15.5,27.3,15.7 65 | 102.7,29.6,8.4,14 66 | 131.1,42.8,28.9,18 67 | 69,9.3,0.9,9.3 68 | 31.5,24.6,2.2,9.5 69 | 139.3,14.5,10.2,13.4 70 | 237.4,27.5,11,18.9 71 | 216.8,43.9,27.2,22.3 72 | 199.1,30.6,38.7,18.3 73 | 109.8,14.3,31.7,12.4 74 | 26.8,33,19.3,8.8 75 | 129.4,5.7,31.3,11 76 | 213.4,24.6,13.1,17 77 | 16.9,43.7,89.4,8.7 78 | 27.5,1.6,20.7,6.9 79 | 120.5,28.5,14.2,14.2 80 | 5.4,29.9,9.4,5.3 81 | 116,7.7,23.1,11 82 | 76.4,26.7,22.3,11.8 83 | 239.8,4.1,36.9,12.3 84 | 75.3,20.3,32.5,11.3 85 | 68.4,44.5,35.6,13.6 86 | 213.5,43,33.8,21.7 87 | 193.2,18.4,65.7,15.2 88 | 76.3,27.5,16,12 89 | 110.7,40.6,63.2,16 90 | 88.3,25.5,73.4,12.9 91 | 109.8,47.8,51.4,16.7 92 | 134.3,4.9,9.3,11.2 93 | 28.6,1.5,33,7.3 94 | 217.7,33.5,59,19.4 95 | 250.9,36.5,72.3,22.2 96 | 107.4,14,10.9,11.5 97 | 163.3,31.6,52.9,16.9 98 | 197.6,3.5,5.9,11.7 99 | 184.9,21,22,15.5 100 | 289.7,42.3,51.2,25.4 101 | 135.2,41.7,45.9,17.2 102 | 222.4,4.3,49.8,11.7 103 | 296.4,36.3,100.9,23.8 104 | 280.2,10.1,21.4,14.8 105 | 187.9,17.2,17.9,14.7 106 | 238.2,34.3,5.3,20.7 107 | 137.9,46.4,59,19.2 108 | 25,11,29.7,7.2 109 | 90.4,0.3,23.2,8.7 110 | 13.1,0.4,25.6,5.3 111 | 255.4,26.9,5.5,19.8 112 | 225.8,8.2,56.5,13.4 113 | 241.7,38,23.2,21.8 114 | 175.7,15.4,2.4,14.1 115 | 209.6,20.6,10.7,15.9 116 | 78.2,46.8,34.5,14.6 117 | 75.1,35,52.7,12.6 118 | 139.2,14.3,25.6,12.2 119 | 76.4,0.8,14.8,9.4 120 | 125.7,36.9,79.2,15.9 121 | 19.4,16,22.3,6.6 122 | 141.3,26.8,46.2,15.5 123 | 18.8,21.7,50.4,7 124 | 224,2.4,15.6,11.6 125 | 123.1,34.6,12.4,15.2 126 | 229.5,32.3,74.2,19.7 127 | 87.2,11.8,25.9,10.6 128 | 7.8,38.9,50.6,6.6 129 | 80.2,0,9.2,8.8 130 | 220.3,49,3.2,24.7 131 | 59.6,12,43.1,9.7 132 | 0.7,39.6,8.7,1.6 133 | 265.2,2.9,43,12.7 134 | 8.4,27.2,2.1,5.7 135 | 219.8,33.5,45.1,19.6 136 | 36.9,38.6,65.6,10.8 137 | 48.3,47,8.5,11.6 138 | 25.6,39,9.3,9.5 139 | 273.7,28.9,59.7,20.8 140 | 43,25.9,20.5,9.6 141 | 184.9,43.9,1.7,20.7 142 | 73.4,17,12.9,10.9 143 | 193.7,35.4,75.6,19.2 144 | 220.5,33.2,37.9,20.1 145 | 104.6,5.7,34.4,10.4 146 | 96.2,14.8,38.9,11.4 147 | 140.3,1.9,9,10.3 148 | 240.1,7.3,8.7,13.2 149 | 243.2,49,44.3,25.4 150 | 38,40.3,11.9,10.9 151 | 44.7,25.8,20.6,10.1 152 | 280.7,13.9,37,16.1 153 | 121,8.4,48.7,11.6 154 | 197.6,23.3,14.2,16.6 155 | 171.3,39.7,37.7,19 156 | 187.8,21.1,9.5,15.6 157 | 4.1,11.6,5.7,3.2 158 | 93.9,43.5,50.5,15.3 159 | 149.8,1.3,24.3,10.1 160 | 11.7,36.9,45.2,7.3 161 | 131.7,18.4,34.6,12.9 162 | 172.5,18.1,30.7,14.4 163 | 85.7,35.8,49.3,13.3 164 | 188.4,18.1,25.6,14.9 165 | 163.5,36.8,7.4,18 166 | 117.2,14.7,5.4,11.9 167 | 234.5,3.4,84.8,11.9 168 | 17.9,37.6,21.6,8 169 | 206.8,5.2,19.4,12.2 170 | 215.4,23.6,57.6,17.1 171 | 284.3,10.6,6.4,15 172 | 50,11.6,18.4,8.4 173 | 164.5,20.9,47.4,14.5 174 | 19.6,20.1,17,7.6 175 | 168.4,7.1,12.8,11.7 176 | 222.4,3.4,13.1,11.5 177 | 276.9,48.9,41.8,27 178 | 248.4,30.2,20.3,20.2 179 | 170.2,7.8,35.2,11.7 180 | 276.7,2.3,23.7,11.8 181 | 165.6,10,17.6,12.6 182 | 156.6,2.6,8.3,10.5 183 | 218.5,5.4,27.4,12.2 184 | 56.2,5.7,29.7,8.7 185 | 287.6,43,71.8,26.2 186 | 253.8,21.3,30,17.6 187 | 205,45.1,19.6,22.6 188 | 139.5,2.1,26.6,10.3 189 | 191.1,28.7,18.2,17.3 190 | 286,13.9,3.7,15.9 191 | 18.7,12.1,23.4,6.7 192 | 39.5,41.1,5.8,10.8 193 | 75.5,10.8,6,9.9 194 | 17.2,4.1,31.6,5.9 195 | 166.8,42,3.6,19.6 196 | 149.7,35.6,6,17.3 197 | 38.2,3.7,13.8,7.6 198 | 94.2,4.9,8.1,9.7 199 | 177,9.3,6.4,12.8 200 | 283.6,42,66.2,25.5 201 | 232.1,8.6,8.7,13.4 202 | -------------------------------------------------------------------------------- /datasets/Athelete_Country_Map.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/felipesanma/Intro_to_DataScience/8d4b1381a534a2dc4ce48b8a2a666464b1d2d492/datasets/Athelete_Country_Map.csv -------------------------------------------------------------------------------- /datasets/Athelete_Sports_Map.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/felipesanma/Intro_to_DataScience/8d4b1381a534a2dc4ce48b8a2a666464b1d2d492/datasets/Athelete_Sports_Map.csv -------------------------------------------------------------------------------- /datasets/Bank data dictionary.txt: -------------------------------------------------------------------------------- 1 | 1 - age (numeric) 2 | 2 - job : type of job (categorical: "admin.","blue-collar","entrepreneur","housemaid","management","retired","self-employed","services","student","technician","unemployed","unknown") 3 | 3 - marital : marital status (categorical: "divorced","married","single","unknown"; note: "divorced" means divorced or widowed) 4 | 4 - education (categorical: "basic.4y","basic.6y","basic.9y","high.school","illiterate","professional.course","university.degree","unknown") 5 | 5 - default: has credit in default? (categorical: "no","yes","unknown") 6 | 6 - housing: has housing loan? (categorical: "no","yes","unknown") 7 | 7 - loan: has personal loan? (categorical: "no","yes","unknown") 8 | # related with the last contact of the current campaign: 9 | 8 - contact: contact communication type (categorical: "cellular","telephone") 10 | 9 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 11 | 10 - day_of_week: last contact day of the week (categorical: "mon","tue","wed","thu","fri") 12 | 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y="no"). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. 13 | # other attributes: 14 | 12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 15 | 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 16 | 14 - previous: number of contacts performed before this campaign and for this client (numeric) 17 | 15 - poutcome: outcome of the previous marketing campaign (categorical: "failure","nonexistent","success") 18 | # social and economic context attributes 19 | 16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 20 | 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 21 | 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 22 | 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 23 | 20 - nr.employed: number of employees - quarterly indicator (numeric) 24 | 25 | Output variable (desired target): 26 | 21 - y - has the client subscribed a term deposit? (binary: "yes","no") 27 | -------------------------------------------------------------------------------- /datasets/Boston.csv: -------------------------------------------------------------------------------- 1 | crim,zn,indus,chas,nox,rm,age,dis,rad,tax,ptratio,black,lstat,medv 2 | 0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24 3 | 0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6 4 | 0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7 5 | 0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4 6 | 0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2 7 | 0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7 8 | 0.08829,12.5,7.87,0,0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43,22.9 9 | 0.14455,12.5,7.87,0,0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15,27.1 10 | 0.21124,12.5,7.87,0,0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93,16.5 11 | 0.17004,12.5,7.87,0,0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1,18.9 12 | 0.22489,12.5,7.87,0,0.524,6.377,94.3,6.3467,5,311,15.2,392.52,20.45,15 13 | 0.11747,12.5,7.87,0,0.524,6.009,82.9,6.2267,5,311,15.2,396.9,13.27,18.9 14 | 0.09378,12.5,7.87,0,0.524,5.889,39,5.4509,5,311,15.2,390.5,15.71,21.7 15 | 0.62976,0,8.14,0,0.538,5.949,61.8,4.7075,4,307,21,396.9,8.26,20.4 16 | 0.63796,0,8.14,0,0.538,6.096,84.5,4.4619,4,307,21,380.02,10.26,18.2 17 | 0.62739,0,8.14,0,0.538,5.834,56.5,4.4986,4,307,21,395.62,8.47,19.9 18 | 1.05393,0,8.14,0,0.538,5.935,29.3,4.4986,4,307,21,386.85,6.58,23.1 19 | 0.7842,0,8.14,0,0.538,5.99,81.7,4.2579,4,307,21,386.75,14.67,17.5 20 | 0.80271,0,8.14,0,0.538,5.456,36.6,3.7965,4,307,21,288.99,11.69,20.2 21 | 0.7258,0,8.14,0,0.538,5.727,69.5,3.7965,4,307,21,390.95,11.28,18.2 22 | 1.25179,0,8.14,0,0.538,5.57,98.1,3.7979,4,307,21,376.57,21.02,13.6 23 | 0.85204,0,8.14,0,0.538,5.965,89.2,4.0123,4,307,21,392.53,13.83,19.6 24 | 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0.25915,0,21.89,0,0.624,5.693,96,1.7883,4,437,21.2,392.11,17.19,16.2 130 | 0.32543,0,21.89,0,0.624,6.431,98.8,1.8125,4,437,21.2,396.9,15.39,18 131 | 0.88125,0,21.89,0,0.624,5.637,94.7,1.9799,4,437,21.2,396.9,18.34,14.3 132 | 0.34006,0,21.89,0,0.624,6.458,98.9,2.1185,4,437,21.2,395.04,12.6,19.2 133 | 1.19294,0,21.89,0,0.624,6.326,97.7,2.271,4,437,21.2,396.9,12.26,19.6 134 | 0.59005,0,21.89,0,0.624,6.372,97.9,2.3274,4,437,21.2,385.76,11.12,23 135 | 0.32982,0,21.89,0,0.624,5.822,95.4,2.4699,4,437,21.2,388.69,15.03,18.4 136 | 0.97617,0,21.89,0,0.624,5.757,98.4,2.346,4,437,21.2,262.76,17.31,15.6 137 | 0.55778,0,21.89,0,0.624,6.335,98.2,2.1107,4,437,21.2,394.67,16.96,18.1 138 | 0.32264,0,21.89,0,0.624,5.942,93.5,1.9669,4,437,21.2,378.25,16.9,17.4 139 | 0.35233,0,21.89,0,0.624,6.454,98.4,1.8498,4,437,21.2,394.08,14.59,17.1 140 | 0.2498,0,21.89,0,0.624,5.857,98.2,1.6686,4,437,21.2,392.04,21.32,13.3 141 | 0.54452,0,21.89,0,0.624,6.151,97.9,1.6687,4,437,21.2,396.9,18.46,17.8 142 | 0.2909,0,21.89,0,0.624,6.174,93.6,1.6119,4,437,21.2,388.08,24.16,14 143 | 1.62864,0,21.89,0,0.624,5.019,100,1.4394,4,437,21.2,396.9,34.41,14.4 144 | 3.32105,0,19.58,1,0.871,5.403,100,1.3216,5,403,14.7,396.9,26.82,13.4 145 | 4.0974,0,19.58,0,0.871,5.468,100,1.4118,5,403,14.7,396.9,26.42,15.6 146 | 2.77974,0,19.58,0,0.871,4.903,97.8,1.3459,5,403,14.7,396.9,29.29,11.8 147 | 2.37934,0,19.58,0,0.871,6.13,100,1.4191,5,403,14.7,172.91,27.8,13.8 148 | 2.15505,0,19.58,0,0.871,5.628,100,1.5166,5,403,14.7,169.27,16.65,15.6 149 | 2.36862,0,19.58,0,0.871,4.926,95.7,1.4608,5,403,14.7,391.71,29.53,14.6 150 | 2.33099,0,19.58,0,0.871,5.186,93.8,1.5296,5,403,14.7,356.99,28.32,17.8 151 | 2.73397,0,19.58,0,0.871,5.597,94.9,1.5257,5,403,14.7,351.85,21.45,15.4 152 | 1.6566,0,19.58,0,0.871,6.122,97.3,1.618,5,403,14.7,372.8,14.1,21.5 153 | 1.49632,0,19.58,0,0.871,5.404,100,1.5916,5,403,14.7,341.6,13.28,19.6 154 | 1.12658,0,19.58,1,0.871,5.012,88,1.6102,5,403,14.7,343.28,12.12,15.3 155 | 2.14918,0,19.58,0,0.871,5.709,98.5,1.6232,5,403,14.7,261.95,15.79,19.4 156 | 1.41385,0,19.58,1,0.871,6.129,96,1.7494,5,403,14.7,321.02,15.12,17 157 | 3.53501,0,19.58,1,0.871,6.152,82.6,1.7455,5,403,14.7,88.01,15.02,15.6 158 | 2.44668,0,19.58,0,0.871,5.272,94,1.7364,5,403,14.7,88.63,16.14,13.1 159 | 1.22358,0,19.58,0,0.605,6.943,97.4,1.8773,5,403,14.7,363.43,4.59,41.3 160 | 1.34284,0,19.58,0,0.605,6.066,100,1.7573,5,403,14.7,353.89,6.43,24.3 161 | 1.42502,0,19.58,0,0.871,6.51,100,1.7659,5,403,14.7,364.31,7.39,23.3 162 | 1.27346,0,19.58,1,0.605,6.25,92.6,1.7984,5,403,14.7,338.92,5.5,27 163 | 1.46336,0,19.58,0,0.605,7.489,90.8,1.9709,5,403,14.7,374.43,1.73,50 164 | 1.83377,0,19.58,1,0.605,7.802,98.2,2.0407,5,403,14.7,389.61,1.92,50 165 | 1.51902,0,19.58,1,0.605,8.375,93.9,2.162,5,403,14.7,388.45,3.32,50 166 | 2.24236,0,19.58,0,0.605,5.854,91.8,2.422,5,403,14.7,395.11,11.64,22.7 167 | 2.924,0,19.58,0,0.605,6.101,93,2.2834,5,403,14.7,240.16,9.81,25 168 | 2.01019,0,19.58,0,0.605,7.929,96.2,2.0459,5,403,14.7,369.3,3.7,50 169 | 1.80028,0,19.58,0,0.605,5.877,79.2,2.4259,5,403,14.7,227.61,12.14,23.8 170 | 2.3004,0,19.58,0,0.605,6.319,96.1,2.1,5,403,14.7,297.09,11.1,23.8 171 | 2.44953,0,19.58,0,0.605,6.402,95.2,2.2625,5,403,14.7,330.04,11.32,22.3 172 | 1.20742,0,19.58,0,0.605,5.875,94.6,2.4259,5,403,14.7,292.29,14.43,17.4 173 | 2.3139,0,19.58,0,0.605,5.88,97.3,2.3887,5,403,14.7,348.13,12.03,19.1 174 | 0.13914,0,4.05,0,0.51,5.572,88.5,2.5961,5,296,16.6,396.9,14.69,23.1 175 | 0.09178,0,4.05,0,0.51,6.416,84.1,2.6463,5,296,16.6,395.5,9.04,23.6 176 | 0.08447,0,4.05,0,0.51,5.859,68.7,2.7019,5,296,16.6,393.23,9.64,22.6 177 | 0.06664,0,4.05,0,0.51,6.546,33.1,3.1323,5,296,16.6,390.96,5.33,29.4 178 | 0.07022,0,4.05,0,0.51,6.02,47.2,3.5549,5,296,16.6,393.23,10.11,23.2 179 | 0.05425,0,4.05,0,0.51,6.315,73.4,3.3175,5,296,16.6,395.6,6.29,24.6 180 | 0.06642,0,4.05,0,0.51,6.86,74.4,2.9153,5,296,16.6,391.27,6.92,29.9 181 | 0.0578,0,2.46,0,0.488,6.98,58.4,2.829,3,193,17.8,396.9,5.04,37.2 182 | 0.06588,0,2.46,0,0.488,7.765,83.3,2.741,3,193,17.8,395.56,7.56,39.8 183 | 0.06888,0,2.46,0,0.488,6.144,62.2,2.5979,3,193,17.8,396.9,9.45,36.2 184 | 0.09103,0,2.46,0,0.488,7.155,92.2,2.7006,3,193,17.8,394.12,4.82,37.9 185 | 0.10008,0,2.46,0,0.488,6.563,95.6,2.847,3,193,17.8,396.9,5.68,32.5 186 | 0.08308,0,2.46,0,0.488,5.604,89.8,2.9879,3,193,17.8,391,13.98,26.4 187 | 0.06047,0,2.46,0,0.488,6.153,68.8,3.2797,3,193,17.8,387.11,13.15,29.6 188 | 0.05602,0,2.46,0,0.488,7.831,53.6,3.1992,3,193,17.8,392.63,4.45,50 189 | 0.07875,45,3.44,0,0.437,6.782,41.1,3.7886,5,398,15.2,393.87,6.68,32 190 | 0.12579,45,3.44,0,0.437,6.556,29.1,4.5667,5,398,15.2,382.84,4.56,29.8 191 | 0.0837,45,3.44,0,0.437,7.185,38.9,4.5667,5,398,15.2,396.9,5.39,34.9 192 | 0.09068,45,3.44,0,0.437,6.951,21.5,6.4798,5,398,15.2,377.68,5.1,37 193 | 0.06911,45,3.44,0,0.437,6.739,30.8,6.4798,5,398,15.2,389.71,4.69,30.5 194 | 0.08664,45,3.44,0,0.437,7.178,26.3,6.4798,5,398,15.2,390.49,2.87,36.4 195 | 0.02187,60,2.93,0,0.401,6.8,9.9,6.2196,1,265,15.6,393.37,5.03,31.1 196 | 0.01439,60,2.93,0,0.401,6.604,18.8,6.2196,1,265,15.6,376.7,4.38,29.1 197 | 0.01381,80,0.46,0,0.422,7.875,32,5.6484,4,255,14.4,394.23,2.97,50 198 | 0.04011,80,1.52,0,0.404,7.287,34.1,7.309,2,329,12.6,396.9,4.08,33.3 199 | 0.04666,80,1.52,0,0.404,7.107,36.6,7.309,2,329,12.6,354.31,8.61,30.3 200 | 0.03768,80,1.52,0,0.404,7.274,38.3,7.309,2,329,12.6,392.2,6.62,34.6 201 | 0.0315,95,1.47,0,0.403,6.975,15.3,7.6534,3,402,17,396.9,4.56,34.9 202 | 0.01778,95,1.47,0,0.403,7.135,13.9,7.6534,3,402,17,384.3,4.45,32.9 203 | 0.03445,82.5,2.03,0,0.415,6.162,38.4,6.27,2,348,14.7,393.77,7.43,24.1 204 | 0.02177,82.5,2.03,0,0.415,7.61,15.7,6.27,2,348,14.7,395.38,3.11,42.3 205 | 0.0351,95,2.68,0,0.4161,7.853,33.2,5.118,4,224,14.7,392.78,3.81,48.5 206 | 0.02009,95,2.68,0,0.4161,8.034,31.9,5.118,4,224,14.7,390.55,2.88,50 207 | 0.13642,0,10.59,0,0.489,5.891,22.3,3.9454,4,277,18.6,396.9,10.87,22.6 208 | 0.22969,0,10.59,0,0.489,6.326,52.5,4.3549,4,277,18.6,394.87,10.97,24.4 209 | 0.25199,0,10.59,0,0.489,5.783,72.7,4.3549,4,277,18.6,389.43,18.06,22.5 210 | 0.13587,0,10.59,1,0.489,6.064,59.1,4.2392,4,277,18.6,381.32,14.66,24.4 211 | 0.43571,0,10.59,1,0.489,5.344,100,3.875,4,277,18.6,396.9,23.09,20 212 | 0.17446,0,10.59,1,0.489,5.96,92.1,3.8771,4,277,18.6,393.25,17.27,21.7 213 | 0.37578,0,10.59,1,0.489,5.404,88.6,3.665,4,277,18.6,395.24,23.98,19.3 214 | 0.21719,0,10.59,1,0.489,5.807,53.8,3.6526,4,277,18.6,390.94,16.03,22.4 215 | 0.14052,0,10.59,0,0.489,6.375,32.3,3.9454,4,277,18.6,385.81,9.38,28.1 216 | 0.28955,0,10.59,0,0.489,5.412,9.8,3.5875,4,277,18.6,348.93,29.55,23.7 217 | 0.19802,0,10.59,0,0.489,6.182,42.4,3.9454,4,277,18.6,393.63,9.47,25 218 | 0.0456,0,13.89,1,0.55,5.888,56,3.1121,5,276,16.4,392.8,13.51,23.3 219 | 0.07013,0,13.89,0,0.55,6.642,85.1,3.4211,5,276,16.4,392.78,9.69,28.7 220 | 0.11069,0,13.89,1,0.55,5.951,93.8,2.8893,5,276,16.4,396.9,17.92,21.5 221 | 0.11425,0,13.89,1,0.55,6.373,92.4,3.3633,5,276,16.4,393.74,10.5,23 222 | 0.35809,0,6.2,1,0.507,6.951,88.5,2.8617,8,307,17.4,391.7,9.71,26.7 223 | 0.40771,0,6.2,1,0.507,6.164,91.3,3.048,8,307,17.4,395.24,21.46,21.7 224 | 0.62356,0,6.2,1,0.507,6.879,77.7,3.2721,8,307,17.4,390.39,9.93,27.5 225 | 0.6147,0,6.2,0,0.507,6.618,80.8,3.2721,8,307,17.4,396.9,7.6,30.1 226 | 0.31533,0,6.2,0,0.504,8.266,78.3,2.8944,8,307,17.4,385.05,4.14,44.8 227 | 0.52693,0,6.2,0,0.504,8.725,83,2.8944,8,307,17.4,382,4.63,50 228 | 0.38214,0,6.2,0,0.504,8.04,86.5,3.2157,8,307,17.4,387.38,3.13,37.6 229 | 0.41238,0,6.2,0,0.504,7.163,79.9,3.2157,8,307,17.4,372.08,6.36,31.6 230 | 0.29819,0,6.2,0,0.504,7.686,17,3.3751,8,307,17.4,377.51,3.92,46.7 231 | 0.44178,0,6.2,0,0.504,6.552,21.4,3.3751,8,307,17.4,380.34,3.76,31.5 232 | 0.537,0,6.2,0,0.504,5.981,68.1,3.6715,8,307,17.4,378.35,11.65,24.3 233 | 0.46296,0,6.2,0,0.504,7.412,76.9,3.6715,8,307,17.4,376.14,5.25,31.7 234 | 0.57529,0,6.2,0,0.507,8.337,73.3,3.8384,8,307,17.4,385.91,2.47,41.7 235 | 0.33147,0,6.2,0,0.507,8.247,70.4,3.6519,8,307,17.4,378.95,3.95,48.3 236 | 0.44791,0,6.2,1,0.507,6.726,66.5,3.6519,8,307,17.4,360.2,8.05,29 237 | 0.33045,0,6.2,0,0.507,6.086,61.5,3.6519,8,307,17.4,376.75,10.88,24 238 | 0.52058,0,6.2,1,0.507,6.631,76.5,4.148,8,307,17.4,388.45,9.54,25.1 239 | 0.51183,0,6.2,0,0.507,7.358,71.6,4.148,8,307,17.4,390.07,4.73,31.5 240 | 0.08244,30,4.93,0,0.428,6.481,18.5,6.1899,6,300,16.6,379.41,6.36,23.7 241 | 0.09252,30,4.93,0,0.428,6.606,42.2,6.1899,6,300,16.6,383.78,7.37,23.3 242 | 0.11329,30,4.93,0,0.428,6.897,54.3,6.3361,6,300,16.6,391.25,11.38,22 243 | 0.10612,30,4.93,0,0.428,6.095,65.1,6.3361,6,300,16.6,394.62,12.4,20.1 244 | 0.1029,30,4.93,0,0.428,6.358,52.9,7.0355,6,300,16.6,372.75,11.22,22.2 245 | 0.12757,30,4.93,0,0.428,6.393,7.8,7.0355,6,300,16.6,374.71,5.19,23.7 246 | 0.20608,22,5.86,0,0.431,5.593,76.5,7.9549,7,330,19.1,372.49,12.5,17.6 247 | 0.19133,22,5.86,0,0.431,5.605,70.2,7.9549,7,330,19.1,389.13,18.46,18.5 248 | 0.33983,22,5.86,0,0.431,6.108,34.9,8.0555,7,330,19.1,390.18,9.16,24.3 249 | 0.19657,22,5.86,0,0.431,6.226,79.2,8.0555,7,330,19.1,376.14,10.15,20.5 250 | 0.16439,22,5.86,0,0.431,6.433,49.1,7.8265,7,330,19.1,374.71,9.52,24.5 251 | 0.19073,22,5.86,0,0.431,6.718,17.5,7.8265,7,330,19.1,393.74,6.56,26.2 252 | 0.1403,22,5.86,0,0.431,6.487,13,7.3967,7,330,19.1,396.28,5.9,24.4 253 | 0.21409,22,5.86,0,0.431,6.438,8.9,7.3967,7,330,19.1,377.07,3.59,24.8 254 | 0.08221,22,5.86,0,0.431,6.957,6.8,8.9067,7,330,19.1,386.09,3.53,29.6 255 | 0.36894,22,5.86,0,0.431,8.259,8.4,8.9067,7,330,19.1,396.9,3.54,42.8 256 | 0.04819,80,3.64,0,0.392,6.108,32,9.2203,1,315,16.4,392.89,6.57,21.9 257 | 0.03548,80,3.64,0,0.392,5.876,19.1,9.2203,1,315,16.4,395.18,9.25,20.9 258 | 0.01538,90,3.75,0,0.394,7.454,34.2,6.3361,3,244,15.9,386.34,3.11,44 259 | 0.61154,20,3.97,0,0.647,8.704,86.9,1.801,5,264,13,389.7,5.12,50 260 | 0.66351,20,3.97,0,0.647,7.333,100,1.8946,5,264,13,383.29,7.79,36 261 | 0.65665,20,3.97,0,0.647,6.842,100,2.0107,5,264,13,391.93,6.9,30.1 262 | 0.54011,20,3.97,0,0.647,7.203,81.8,2.1121,5,264,13,392.8,9.59,33.8 263 | 0.53412,20,3.97,0,0.647,7.52,89.4,2.1398,5,264,13,388.37,7.26,43.1 264 | 0.52014,20,3.97,0,0.647,8.398,91.5,2.2885,5,264,13,386.86,5.91,48.8 265 | 0.82526,20,3.97,0,0.647,7.327,94.5,2.0788,5,264,13,393.42,11.25,31 266 | 0.55007,20,3.97,0,0.647,7.206,91.6,1.9301,5,264,13,387.89,8.1,36.5 267 | 0.76162,20,3.97,0,0.647,5.56,62.8,1.9865,5,264,13,392.4,10.45,22.8 268 | 0.7857,20,3.97,0,0.647,7.014,84.6,2.1329,5,264,13,384.07,14.79,30.7 269 | 0.57834,20,3.97,0,0.575,8.297,67,2.4216,5,264,13,384.54,7.44,50 270 | 0.5405,20,3.97,0,0.575,7.47,52.6,2.872,5,264,13,390.3,3.16,43.5 271 | 0.09065,20,6.96,1,0.464,5.92,61.5,3.9175,3,223,18.6,391.34,13.65,20.7 272 | 0.29916,20,6.96,0,0.464,5.856,42.1,4.429,3,223,18.6,388.65,13,21.1 273 | 0.16211,20,6.96,0,0.464,6.24,16.3,4.429,3,223,18.6,396.9,6.59,25.2 274 | 0.1146,20,6.96,0,0.464,6.538,58.7,3.9175,3,223,18.6,394.96,7.73,24.4 275 | 0.22188,20,6.96,1,0.464,7.691,51.8,4.3665,3,223,18.6,390.77,6.58,35.2 276 | 0.05644,40,6.41,1,0.447,6.758,32.9,4.0776,4,254,17.6,396.9,3.53,32.4 277 | 0.09604,40,6.41,0,0.447,6.854,42.8,4.2673,4,254,17.6,396.9,2.98,32 278 | 0.10469,40,6.41,1,0.447,7.267,49,4.7872,4,254,17.6,389.25,6.05,33.2 279 | 0.06127,40,6.41,1,0.447,6.826,27.6,4.8628,4,254,17.6,393.45,4.16,33.1 280 | 0.07978,40,6.41,0,0.447,6.482,32.1,4.1403,4,254,17.6,396.9,7.19,29.1 281 | 0.21038,20,3.33,0,0.4429,6.812,32.2,4.1007,5,216,14.9,396.9,4.85,35.1 282 | 0.03578,20,3.33,0,0.4429,7.82,64.5,4.6947,5,216,14.9,387.31,3.76,45.4 283 | 0.03705,20,3.33,0,0.4429,6.968,37.2,5.2447,5,216,14.9,392.23,4.59,35.4 284 | 0.06129,20,3.33,1,0.4429,7.645,49.7,5.2119,5,216,14.9,377.07,3.01,46 285 | 0.01501,90,1.21,1,0.401,7.923,24.8,5.885,1,198,13.6,395.52,3.16,50 286 | 0.00906,90,2.97,0,0.4,7.088,20.8,7.3073,1,285,15.3,394.72,7.85,32.2 287 | 0.01096,55,2.25,0,0.389,6.453,31.9,7.3073,1,300,15.3,394.72,8.23,22 288 | 0.01965,80,1.76,0,0.385,6.23,31.5,9.0892,1,241,18.2,341.6,12.93,20.1 289 | 0.03871,52.5,5.32,0,0.405,6.209,31.3,7.3172,6,293,16.6,396.9,7.14,23.2 290 | 0.0459,52.5,5.32,0,0.405,6.315,45.6,7.3172,6,293,16.6,396.9,7.6,22.3 291 | 0.04297,52.5,5.32,0,0.405,6.565,22.9,7.3172,6,293,16.6,371.72,9.51,24.8 292 | 0.03502,80,4.95,0,0.411,6.861,27.9,5.1167,4,245,19.2,396.9,3.33,28.5 293 | 0.07886,80,4.95,0,0.411,7.148,27.7,5.1167,4,245,19.2,396.9,3.56,37.3 294 | 0.03615,80,4.95,0,0.411,6.63,23.4,5.1167,4,245,19.2,396.9,4.7,27.9 295 | 0.08265,0,13.92,0,0.437,6.127,18.4,5.5027,4,289,16,396.9,8.58,23.9 296 | 0.08199,0,13.92,0,0.437,6.009,42.3,5.5027,4,289,16,396.9,10.4,21.7 297 | 0.12932,0,13.92,0,0.437,6.678,31.1,5.9604,4,289,16,396.9,6.27,28.6 298 | 0.05372,0,13.92,0,0.437,6.549,51,5.9604,4,289,16,392.85,7.39,27.1 299 | 0.14103,0,13.92,0,0.437,5.79,58,6.32,4,289,16,396.9,15.84,20.3 300 | 0.06466,70,2.24,0,0.4,6.345,20.1,7.8278,5,358,14.8,368.24,4.97,22.5 301 | 0.05561,70,2.24,0,0.4,7.041,10,7.8278,5,358,14.8,371.58,4.74,29 302 | 0.04417,70,2.24,0,0.4,6.871,47.4,7.8278,5,358,14.8,390.86,6.07,24.8 303 | 0.03537,34,6.09,0,0.433,6.59,40.4,5.4917,7,329,16.1,395.75,9.5,22 304 | 0.09266,34,6.09,0,0.433,6.495,18.4,5.4917,7,329,16.1,383.61,8.67,26.4 305 | 0.1,34,6.09,0,0.433,6.982,17.7,5.4917,7,329,16.1,390.43,4.86,33.1 306 | 0.05515,33,2.18,0,0.472,7.236,41.1,4.022,7,222,18.4,393.68,6.93,36.1 307 | 0.05479,33,2.18,0,0.472,6.616,58.1,3.37,7,222,18.4,393.36,8.93,28.4 308 | 0.07503,33,2.18,0,0.472,7.42,71.9,3.0992,7,222,18.4,396.9,6.47,33.4 309 | 0.04932,33,2.18,0,0.472,6.849,70.3,3.1827,7,222,18.4,396.9,7.53,28.2 310 | 0.49298,0,9.9,0,0.544,6.635,82.5,3.3175,4,304,18.4,396.9,4.54,22.8 311 | 0.3494,0,9.9,0,0.544,5.972,76.7,3.1025,4,304,18.4,396.24,9.97,20.3 312 | 2.63548,0,9.9,0,0.544,4.973,37.8,2.5194,4,304,18.4,350.45,12.64,16.1 313 | 0.79041,0,9.9,0,0.544,6.122,52.8,2.6403,4,304,18.4,396.9,5.98,22.1 314 | 0.26169,0,9.9,0,0.544,6.023,90.4,2.834,4,304,18.4,396.3,11.72,19.4 315 | 0.26938,0,9.9,0,0.544,6.266,82.8,3.2628,4,304,18.4,393.39,7.9,21.6 316 | 0.3692,0,9.9,0,0.544,6.567,87.3,3.6023,4,304,18.4,395.69,9.28,23.8 317 | 0.25356,0,9.9,0,0.544,5.705,77.7,3.945,4,304,18.4,396.42,11.5,16.2 318 | 0.31827,0,9.9,0,0.544,5.914,83.2,3.9986,4,304,18.4,390.7,18.33,17.8 319 | 0.24522,0,9.9,0,0.544,5.782,71.7,4.0317,4,304,18.4,396.9,15.94,19.8 320 | 0.40202,0,9.9,0,0.544,6.382,67.2,3.5325,4,304,18.4,395.21,10.36,23.1 321 | 0.47547,0,9.9,0,0.544,6.113,58.8,4.0019,4,304,18.4,396.23,12.73,21 322 | 0.1676,0,7.38,0,0.493,6.426,52.3,4.5404,5,287,19.6,396.9,7.2,23.8 323 | 0.18159,0,7.38,0,0.493,6.376,54.3,4.5404,5,287,19.6,396.9,6.87,23.1 324 | 0.35114,0,7.38,0,0.493,6.041,49.9,4.7211,5,287,19.6,396.9,7.7,20.4 325 | 0.28392,0,7.38,0,0.493,5.708,74.3,4.7211,5,287,19.6,391.13,11.74,18.5 326 | 0.34109,0,7.38,0,0.493,6.415,40.1,4.7211,5,287,19.6,396.9,6.12,25 327 | 0.19186,0,7.38,0,0.493,6.431,14.7,5.4159,5,287,19.6,393.68,5.08,24.6 328 | 0.30347,0,7.38,0,0.493,6.312,28.9,5.4159,5,287,19.6,396.9,6.15,23 329 | 0.24103,0,7.38,0,0.493,6.083,43.7,5.4159,5,287,19.6,396.9,12.79,22.2 330 | 0.06617,0,3.24,0,0.46,5.868,25.8,5.2146,4,430,16.9,382.44,9.97,19.3 331 | 0.06724,0,3.24,0,0.46,6.333,17.2,5.2146,4,430,16.9,375.21,7.34,22.6 332 | 0.04544,0,3.24,0,0.46,6.144,32.2,5.8736,4,430,16.9,368.57,9.09,19.8 333 | 0.05023,35,6.06,0,0.4379,5.706,28.4,6.6407,1,304,16.9,394.02,12.43,17.1 334 | 0.03466,35,6.06,0,0.4379,6.031,23.3,6.6407,1,304,16.9,362.25,7.83,19.4 335 | 0.05083,0,5.19,0,0.515,6.316,38.1,6.4584,5,224,20.2,389.71,5.68,22.2 336 | 0.03738,0,5.19,0,0.515,6.31,38.5,6.4584,5,224,20.2,389.4,6.75,20.7 337 | 0.03961,0,5.19,0,0.515,6.037,34.5,5.9853,5,224,20.2,396.9,8.01,21.1 338 | 0.03427,0,5.19,0,0.515,5.869,46.3,5.2311,5,224,20.2,396.9,9.8,19.5 339 | 0.03041,0,5.19,0,0.515,5.895,59.6,5.615,5,224,20.2,394.81,10.56,18.5 340 | 0.03306,0,5.19,0,0.515,6.059,37.3,4.8122,5,224,20.2,396.14,8.51,20.6 341 | 0.05497,0,5.19,0,0.515,5.985,45.4,4.8122,5,224,20.2,396.9,9.74,19 342 | 0.06151,0,5.19,0,0.515,5.968,58.5,4.8122,5,224,20.2,396.9,9.29,18.7 343 | 0.01301,35,1.52,0,0.442,7.241,49.3,7.0379,1,284,15.5,394.74,5.49,32.7 344 | 0.02498,0,1.89,0,0.518,6.54,59.7,6.2669,1,422,15.9,389.96,8.65,16.5 345 | 0.02543,55,3.78,0,0.484,6.696,56.4,5.7321,5,370,17.6,396.9,7.18,23.9 346 | 0.03049,55,3.78,0,0.484,6.874,28.1,6.4654,5,370,17.6,387.97,4.61,31.2 347 | 0.03113,0,4.39,0,0.442,6.014,48.5,8.0136,3,352,18.8,385.64,10.53,17.5 348 | 0.06162,0,4.39,0,0.442,5.898,52.3,8.0136,3,352,18.8,364.61,12.67,17.2 349 | 0.0187,85,4.15,0,0.429,6.516,27.7,8.5353,4,351,17.9,392.43,6.36,23.1 350 | 0.01501,80,2.01,0,0.435,6.635,29.7,8.344,4,280,17,390.94,5.99,24.5 351 | 0.02899,40,1.25,0,0.429,6.939,34.5,8.7921,1,335,19.7,389.85,5.89,26.6 352 | 0.06211,40,1.25,0,0.429,6.49,44.4,8.7921,1,335,19.7,396.9,5.98,22.9 353 | 0.0795,60,1.69,0,0.411,6.579,35.9,10.7103,4,411,18.3,370.78,5.49,24.1 354 | 0.07244,60,1.69,0,0.411,5.884,18.5,10.7103,4,411,18.3,392.33,7.79,18.6 355 | 0.01709,90,2.02,0,0.41,6.728,36.1,12.1265,5,187,17,384.46,4.5,30.1 356 | 0.04301,80,1.91,0,0.413,5.663,21.9,10.5857,4,334,22,382.8,8.05,18.2 357 | 0.10659,80,1.91,0,0.413,5.936,19.5,10.5857,4,334,22,376.04,5.57,20.6 358 | 8.98296,0,18.1,1,0.77,6.212,97.4,2.1222,24,666,20.2,377.73,17.6,17.8 359 | 3.8497,0,18.1,1,0.77,6.395,91,2.5052,24,666,20.2,391.34,13.27,21.7 360 | 5.20177,0,18.1,1,0.77,6.127,83.4,2.7227,24,666,20.2,395.43,11.48,22.7 361 | 4.26131,0,18.1,0,0.77,6.112,81.3,2.5091,24,666,20.2,390.74,12.67,22.6 362 | 4.54192,0,18.1,0,0.77,6.398,88,2.5182,24,666,20.2,374.56,7.79,25 363 | 3.83684,0,18.1,0,0.77,6.251,91.1,2.2955,24,666,20.2,350.65,14.19,19.9 364 | 3.67822,0,18.1,0,0.77,5.362,96.2,2.1036,24,666,20.2,380.79,10.19,20.8 365 | 4.22239,0,18.1,1,0.77,5.803,89,1.9047,24,666,20.2,353.04,14.64,16.8 366 | 3.47428,0,18.1,1,0.718,8.78,82.9,1.9047,24,666,20.2,354.55,5.29,21.9 367 | 4.55587,0,18.1,0,0.718,3.561,87.9,1.6132,24,666,20.2,354.7,7.12,27.5 368 | 3.69695,0,18.1,0,0.718,4.963,91.4,1.7523,24,666,20.2,316.03,14,21.9 369 | 13.5222,0,18.1,0,0.631,3.863,100,1.5106,24,666,20.2,131.42,13.33,23.1 370 | 4.89822,0,18.1,0,0.631,4.97,100,1.3325,24,666,20.2,375.52,3.26,50 371 | 5.66998,0,18.1,1,0.631,6.683,96.8,1.3567,24,666,20.2,375.33,3.73,50 372 | 6.53876,0,18.1,1,0.631,7.016,97.5,1.2024,24,666,20.2,392.05,2.96,50 373 | 9.2323,0,18.1,0,0.631,6.216,100,1.1691,24,666,20.2,366.15,9.53,50 374 | 8.26725,0,18.1,1,0.668,5.875,89.6,1.1296,24,666,20.2,347.88,8.88,50 375 | 11.1081,0,18.1,0,0.668,4.906,100,1.1742,24,666,20.2,396.9,34.77,13.8 376 | 18.4982,0,18.1,0,0.668,4.138,100,1.137,24,666,20.2,396.9,37.97,13.8 377 | 19.6091,0,18.1,0,0.671,7.313,97.9,1.3163,24,666,20.2,396.9,13.44,15 378 | 15.288,0,18.1,0,0.671,6.649,93.3,1.3449,24,666,20.2,363.02,23.24,13.9 379 | 9.82349,0,18.1,0,0.671,6.794,98.8,1.358,24,666,20.2,396.9,21.24,13.3 380 | 23.6482,0,18.1,0,0.671,6.38,96.2,1.3861,24,666,20.2,396.9,23.69,13.1 381 | 17.8667,0,18.1,0,0.671,6.223,100,1.3861,24,666,20.2,393.74,21.78,10.2 382 | 88.9762,0,18.1,0,0.671,6.968,91.9,1.4165,24,666,20.2,396.9,17.21,10.4 383 | 15.8744,0,18.1,0,0.671,6.545,99.1,1.5192,24,666,20.2,396.9,21.08,10.9 384 | 9.18702,0,18.1,0,0.7,5.536,100,1.5804,24,666,20.2,396.9,23.6,11.3 385 | 7.99248,0,18.1,0,0.7,5.52,100,1.5331,24,666,20.2,396.9,24.56,12.3 386 | 20.0849,0,18.1,0,0.7,4.368,91.2,1.4395,24,666,20.2,285.83,30.63,8.8 387 | 16.8118,0,18.1,0,0.7,5.277,98.1,1.4261,24,666,20.2,396.9,30.81,7.2 388 | 24.3938,0,18.1,0,0.7,4.652,100,1.4672,24,666,20.2,396.9,28.28,10.5 389 | 22.5971,0,18.1,0,0.7,5,89.5,1.5184,24,666,20.2,396.9,31.99,7.4 390 | 14.3337,0,18.1,0,0.7,4.88,100,1.5895,24,666,20.2,372.92,30.62,10.2 391 | 8.15174,0,18.1,0,0.7,5.39,98.9,1.7281,24,666,20.2,396.9,20.85,11.5 392 | 6.96215,0,18.1,0,0.7,5.713,97,1.9265,24,666,20.2,394.43,17.11,15.1 393 | 5.29305,0,18.1,0,0.7,6.051,82.5,2.1678,24,666,20.2,378.38,18.76,23.2 394 | 11.5779,0,18.1,0,0.7,5.036,97,1.77,24,666,20.2,396.9,25.68,9.7 395 | 8.64476,0,18.1,0,0.693,6.193,92.6,1.7912,24,666,20.2,396.9,15.17,13.8 396 | 13.3598,0,18.1,0,0.693,5.887,94.7,1.7821,24,666,20.2,396.9,16.35,12.7 397 | 8.71675,0,18.1,0,0.693,6.471,98.8,1.7257,24,666,20.2,391.98,17.12,13.1 398 | 5.87205,0,18.1,0,0.693,6.405,96,1.6768,24,666,20.2,396.9,19.37,12.5 399 | 7.67202,0,18.1,0,0.693,5.747,98.9,1.6334,24,666,20.2,393.1,19.92,8.5 400 | 38.3518,0,18.1,0,0.693,5.453,100,1.4896,24,666,20.2,396.9,30.59,5 401 | 9.91655,0,18.1,0,0.693,5.852,77.8,1.5004,24,666,20.2,338.16,29.97,6.3 402 | 25.0461,0,18.1,0,0.693,5.987,100,1.5888,24,666,20.2,396.9,26.77,5.6 403 | 14.2362,0,18.1,0,0.693,6.343,100,1.5741,24,666,20.2,396.9,20.32,7.2 404 | 9.59571,0,18.1,0,0.693,6.404,100,1.639,24,666,20.2,376.11,20.31,12.1 405 | 24.8017,0,18.1,0,0.693,5.349,96,1.7028,24,666,20.2,396.9,19.77,8.3 406 | 41.5292,0,18.1,0,0.693,5.531,85.4,1.6074,24,666,20.2,329.46,27.38,8.5 407 | 67.9208,0,18.1,0,0.693,5.683,100,1.4254,24,666,20.2,384.97,22.98,5 408 | 20.7162,0,18.1,0,0.659,4.138,100,1.1781,24,666,20.2,370.22,23.34,11.9 409 | 11.9511,0,18.1,0,0.659,5.608,100,1.2852,24,666,20.2,332.09,12.13,27.9 410 | 7.40389,0,18.1,0,0.597,5.617,97.9,1.4547,24,666,20.2,314.64,26.4,17.2 411 | 14.4383,0,18.1,0,0.597,6.852,100,1.4655,24,666,20.2,179.36,19.78,27.5 412 | 51.1358,0,18.1,0,0.597,5.757,100,1.413,24,666,20.2,2.6,10.11,15 413 | 14.0507,0,18.1,0,0.597,6.657,100,1.5275,24,666,20.2,35.05,21.22,17.2 414 | 18.811,0,18.1,0,0.597,4.628,100,1.5539,24,666,20.2,28.79,34.37,17.9 415 | 28.6558,0,18.1,0,0.597,5.155,100,1.5894,24,666,20.2,210.97,20.08,16.3 416 | 45.7461,0,18.1,0,0.693,4.519,100,1.6582,24,666,20.2,88.27,36.98,7 417 | 18.0846,0,18.1,0,0.679,6.434,100,1.8347,24,666,20.2,27.25,29.05,7.2 418 | 10.8342,0,18.1,0,0.679,6.782,90.8,1.8195,24,666,20.2,21.57,25.79,7.5 419 | 25.9406,0,18.1,0,0.679,5.304,89.1,1.6475,24,666,20.2,127.36,26.64,10.4 420 | 73.5341,0,18.1,0,0.679,5.957,100,1.8026,24,666,20.2,16.45,20.62,8.8 421 | 11.8123,0,18.1,0,0.718,6.824,76.5,1.794,24,666,20.2,48.45,22.74,8.4 422 | 11.0874,0,18.1,0,0.718,6.411,100,1.8589,24,666,20.2,318.75,15.02,16.7 423 | 7.02259,0,18.1,0,0.718,6.006,95.3,1.8746,24,666,20.2,319.98,15.7,14.2 424 | 12.0482,0,18.1,0,0.614,5.648,87.6,1.9512,24,666,20.2,291.55,14.1,20.8 425 | 7.05042,0,18.1,0,0.614,6.103,85.1,2.0218,24,666,20.2,2.52,23.29,13.4 426 | 8.79212,0,18.1,0,0.584,5.565,70.6,2.0635,24,666,20.2,3.65,17.16,11.7 427 | 15.8603,0,18.1,0,0.679,5.896,95.4,1.9096,24,666,20.2,7.68,24.39,8.3 428 | 12.2472,0,18.1,0,0.584,5.837,59.7,1.9976,24,666,20.2,24.65,15.69,10.2 429 | 37.6619,0,18.1,0,0.679,6.202,78.7,1.8629,24,666,20.2,18.82,14.52,10.9 430 | 7.36711,0,18.1,0,0.679,6.193,78.1,1.9356,24,666,20.2,96.73,21.52,11 431 | 9.33889,0,18.1,0,0.679,6.38,95.6,1.9682,24,666,20.2,60.72,24.08,9.5 432 | 8.49213,0,18.1,0,0.584,6.348,86.1,2.0527,24,666,20.2,83.45,17.64,14.5 433 | 10.0623,0,18.1,0,0.584,6.833,94.3,2.0882,24,666,20.2,81.33,19.69,14.1 434 | 6.44405,0,18.1,0,0.584,6.425,74.8,2.2004,24,666,20.2,97.95,12.03,16.1 435 | 5.58107,0,18.1,0,0.713,6.436,87.9,2.3158,24,666,20.2,100.19,16.22,14.3 436 | 13.9134,0,18.1,0,0.713,6.208,95,2.2222,24,666,20.2,100.63,15.17,11.7 437 | 11.1604,0,18.1,0,0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27,13.4 438 | 14.4208,0,18.1,0,0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05,9.6 439 | 15.1772,0,18.1,0,0.74,6.152,100,1.9142,24,666,20.2,9.32,26.45,8.7 440 | 13.6781,0,18.1,0,0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02,8.4 441 | 9.39063,0,18.1,0,0.74,5.627,93.9,1.8172,24,666,20.2,396.9,22.88,12.8 442 | 22.0511,0,18.1,0,0.74,5.818,92.4,1.8662,24,666,20.2,391.45,22.11,10.5 443 | 9.72418,0,18.1,0,0.74,6.406,97.2,2.0651,24,666,20.2,385.96,19.52,17.1 444 | 5.66637,0,18.1,0,0.74,6.219,100,2.0048,24,666,20.2,395.69,16.59,18.4 445 | 9.96654,0,18.1,0,0.74,6.485,100,1.9784,24,666,20.2,386.73,18.85,15.4 446 | 12.8023,0,18.1,0,0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79,10.8 447 | 10.6718,0,18.1,0,0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98,11.8 448 | 6.28807,0,18.1,0,0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79,14.9 449 | 9.92485,0,18.1,0,0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44,12.6 450 | 9.32909,0,18.1,0,0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13,14.1 451 | 7.52601,0,18.1,0,0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31,13 452 | 6.71772,0,18.1,0,0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44,13.4 453 | 5.44114,0,18.1,0,0.713,6.655,98.2,2.3552,24,666,20.2,355.29,17.73,15.2 454 | 5.09017,0,18.1,0,0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27,16.1 455 | 8.24809,0,18.1,0,0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74,17.8 456 | 9.51363,0,18.1,0,0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71,14.9 457 | 4.75237,0,18.1,0,0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13,14.1 458 | 4.66883,0,18.1,0,0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01,12.7 459 | 8.20058,0,18.1,0,0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94,13.5 460 | 7.75223,0,18.1,0,0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23,14.9 461 | 6.80117,0,18.1,0,0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7,20 462 | 4.81213,0,18.1,0,0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42,16.4 463 | 3.69311,0,18.1,0,0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65,17.7 464 | 6.65492,0,18.1,0,0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99,19.5 465 | 5.82115,0,18.1,0,0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29,20.2 466 | 7.83932,0,18.1,0,0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22,21.4 467 | 3.1636,0,18.1,0,0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13,19.9 468 | 3.77498,0,18.1,0,0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15,19 469 | 4.42228,0,18.1,0,0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32,19.1 470 | 15.5757,0,18.1,0,0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13,19.1 471 | 13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76,20.1 472 | 4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29,19.9 473 | 4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6 474 | 3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2 475 | 4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66,29.8 476 | 8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8 477 | 6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3 478 | 4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7 479 | 15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12 480 | 10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6 481 | 14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4 482 | 5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23 483 | 5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7 484 | 5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25 485 | 2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8 486 | 2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6 487 | 3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2 488 | 5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1 489 | 4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6 490 | 0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2 491 | 0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7 492 | 0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1 493 | 0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6 494 | 0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1 495 | 0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8 496 | 0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5 497 | 0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1 498 | 0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7 499 | 0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3 500 | 0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2 501 | 0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5 502 | 0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8 503 | 0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4 504 | 0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6 505 | 0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9 506 | 0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22 507 | 0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9 508 | -------------------------------------------------------------------------------- /datasets/Cereal Data Description.txt: -------------------------------------------------------------------------------- 1 | StatLib---1993 Graphics Exposition 2 | 3 | "Serial Correlation or Cereal Correlation ??" 4 | 5 | Call for Poster Presentations for the 1993 Statistical Graphics Exposition 6 | REVISED README FILE 7 | 8 | (new breakfast cereal data and new information about the data) 9 | Every two years the Section on Statistical Graphics sponsors a special exposition where one or more data sets are made available, analyzed by anyone interested and presented in a special poster session at the Annual Meeting. 10 | 11 | For the 1993 Statistical Graphics Exposition, there are two datasets to analyze, one synthesized, one real: 12 | 13 | OSCILLATOR TIME SERIES - a synthesized univariate time series with 1024 observations. These data are similar to those which might be found in a university or industrial laboratory setting, or possibly from a process monitor on a plant floor. They show obvious structure, but there is more than one feature present, and good graphics are key to uncovering the features. The objective is to find ALL the features. At the Exposition next year, the algorithm and coefficients by which the dataset was constructed will be presented, along with the stages of analysis which would uncover the features. Some questions to consider: 14 | 15 | What graphics are helpful in selecting the right analytical tools? 16 | What combinations of graphics are essential to finding all the features? 17 | For what features are the traditional graphics and analytical tools weak? 18 | Are there graphics that you can retrospectively develop which more clearly reveal the features which were hard to uncover? 19 | The oscillator data are available in an ASCII file, one observation per record. To obtain the data, send an email message to statlib@lib.stat.cmu.edu containing the single line: 20 | send oscillator from 1993.expo 21 | BREAKFAST CEREAL DATA (REVISED)- a multivariate dataset describing seventy-seven commonly available breakfast cereals, based on the information now available on the newly-mandated F&DA food label. What are you getting when you eat a bowl of cereal? Can you get a lot of fiber without a lot of calories? Can you describe what cereals are displayed on high, low, and middle shelves? The good news is that none of the cereals for which we collected data had any cholesterol, and manufacturers rarely use artificial sweeteners and colors, nowadays. However, there is still a lot of data for the consumer to understand while choosing a good breakfast cereal. 22 | Two new variables have been added to the data (end of each record): 23 | 24 | weight (in ounces) of one serving (serving size) [weight] 25 | cups per serving [cups] 26 | Otherwise, the data are the same, except for minor typo corrections. The addition of these variables (suggested by Abbe Herzig of Consumers Union. Cereals vary considerably in their densities and listed serving sizes. Thus, the serving sizes listed on cereal labels (in weight units) translate into different amounts of nutrients in your bowl. Most people simply fill a cereal bowl (resulting in constant volume, but not weight). The new variables help standardize other ways, which provides other ways to differentiate and group cereals. 27 | Here are some facts about nutrition that might help you in your analysis. Nutritional recommendations are drawn from the references at the end of this document: 28 | 29 | Adults should consume between 20 and 35 grams of dietary fiber per day. 30 | The recommended daily intake (RDI) for calories is 2200 for women and 2900 for men. 31 | Calories come in three food components. There are 9 calories per gram of fat, and 4 calories per gram of carbohydrate and protein. 32 | Overall, in your diet, no more than 10% of your calories should be consumed from simple carbohydrates (sugars), and no more than 30% should come from fat. The RDI of protein is 50 grams for women and 63 grams for men. The balance of calories should be consumed in the form of complex carbohydrates (starches). 33 | The average adult with no defined risk factors or other dietary restrictions should consume between 1800 and 2400 mg of sodium per day. 34 | The type and amount of milk added to cereal can make a significant difference in the fat and protein content of your breakfast. 35 | One possible task is to develop a graphic that would allow the consumer to quickly compare a particular cereal to other possible choices. Some additional questions to consider, and try to answer with effective graphics: 36 | Can you find the correlations you might expect? Are there any surprising correlations? 37 | What is the true "dimensionality" of the data? 38 | Are there any cereals which are virtually identical? 39 | Is there any way to discriminate among the major manufacturers by cereal characteristics, or do they each have a "balanced portfolio" of cereals? 40 | Do the nutritional claims made in cereal advertisements stand the scrutiny of data analysis? 41 | Are there cereals which are clearly nutritionally superior, or inferior? Are there clusters of cereals? 42 | Is a ranking or scoring scheme possible or reasonable, and if so, are there cereals which are nutritionally superior or inferior under all reasonable weighting schemes? 43 | The variables of the dataset are listed below, in order. For convenience, we suggest that you use the variable name supplied in square brackets. 44 | Breakfast cereal variables: 45 | cereal name [name] 46 | manufacturer (e.g., Kellogg's) [mfr] 47 | type (cold/hot) [type] 48 | calories (number) [calories] 49 | protein(g) [protein] 50 | fat(g) [fat] 51 | sodium(mg) [sodium] 52 | dietary fiber(g) [fiber] 53 | complex carbohydrates(g) [carbo] 54 | sugars(g) [sugars] 55 | display shelf (1, 2, or 3, counting from the floor) [shelf] 56 | potassium(mg) [potass] 57 | vitamins & minerals (0, 25, or 100, respectively indicating 58 | 'none added'; 'enriched, often to 25% FDA recommended'; '100% of 59 | FDA recommended') [vitamins] 60 | weight (in ounces) of one serving (serving size) [weight] 61 | cups per serving [cups] 62 | Manufacturers are represented by their first initial: A=American Home Food Products, G=General Mills, K=Kelloggs, N=Nabisco, P=Post, Q=Quaker Oats, R=Ralston Purina) 63 | The breakfast cereal data are available in an ASCII file, one cereal per record, with underscores in place of the spaces in the cereal name, and spaces separating the different variables. The value -1 indicates missing data. To obtain the data, send an email message to: statlib@lib.stat.cmu.edu containing the single line: 64 | 65 | send cereal from 1993.expo 66 | Work alone or put together a team of data analysts to look at one or both of these two data sets! Try to answer the questions posed here or conduct an exploratory analysis to find and answer your own questions. 67 | To participate in the Exposition, you must submit a contributed paper abstract for inclusion in the formal ASA Contributed Paper Program. This reserves a poster session slot for you. Your abstract, on the official ASA abstract form, is due by the contributed paper deadline, February 1, 1993. 68 | 69 | If you do not have electronic mail access, try to get the data files from someone who already has them. If you cannot obtain the data via electronic mail, contact David Coleman, AMCT-D, Alcoa Technology Center, Alcoa Center, PA 15069, or e-mail COLEMAN1@ncf.al.alcoa.com 70 | 71 | References: 72 | 73 | National Research Council, 1989a. "Diet and Health: Implications for Reducing Chronic Disease Risk". National Academy Press, Washington, D.C. 74 | National Research Council, 1989b. "Recommended Dietary Allowances, 10th Ed." National Academy Press, Washington, D.C. 75 | 76 | National Cancer Institute, 1987. "Diet, Nutrition, and Cancer Prevention: A Guide to Food Choices," NIH Publ. No. 87-2878. National Institutes of Health, Public Health Service, U.S. Department of Health and Human Service, U.S. Government Printing Office, Washington, D.C. -------------------------------------------------------------------------------- /datasets/Cereal data columns.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/felipesanma/Intro_to_DataScience/8d4b1381a534a2dc4ce48b8a2a666464b1d2d492/datasets/Cereal data columns.xlsx -------------------------------------------------------------------------------- /datasets/Cereal data.txt: -------------------------------------------------------------------------------- 1 | 100%_Bran N C 70 4 1 130 10 5 6 3 280 25 1 0.33 2 | 100%_Natural_Bran Q C 120 3 5 15 2 8 8 3 135 0 1 -1 3 | All-Bran K C 70 4 1 260 9 7 5 3 320 25 1 0.33 4 | All-Bran_with_Extra_Fiber K C 50 4 0 140 14 8 0 3 330 25 1 0.5 5 | Almond_Delight R C 110 2 2 200 1 14 8 3 -1 25 1 0.75 6 | Apple_Cinnamon_Cheerios G C 110 2 2 180 1.5 10.5 10 1 70 25 1 0.75 7 | Apple_Jacks K C 110 2 0 125 1 11 14 2 30 25 1 1 8 | Basic_4 G C 130 3 2 210 2 18 8 3 100 25 1.33 0.75 9 | Bran_Chex R C 90 2 1 200 4 15 6 1 125 25 1 0.67 10 | Bran_Flakes P C 90 3 0 210 5 13 5 3 190 25 1 0.67 11 | Cap'n'Crunch Q C 120 1 2 220 0 12 12 2 35 25 1 0.75 12 | Cheerios G C 110 6 2 290 2 17 1 1 105 25 1 1.25 13 | Cinnamon_Toast_Crunch G C 120 1 3 210 0 13 9 2 45 25 1 0.75 14 | Clusters G C 110 3 2 140 2 13 7 3 105 25 1 0.5 15 | Cocoa_Puffs G C 110 1 1 180 0 12 13 2 55 25 1 1 16 | Corn_Chex R C 110 2 0 280 0 22 3 1 25 25 1 1 17 | Corn_Flakes K C 100 2 0 290 1 21 2 1 35 25 1 1 18 | Corn_Pops K C 110 1 0 90 1 13 12 2 20 25 1 1 19 | Count_Chocula G C 110 1 1 180 0 12 13 2 65 25 1 1 20 | Cracklin'_Oat_Bran K C 110 3 3 140 4 10 7 3 160 25 1 0.5 21 | Cream_of_Wheat_(Quick) N H 100 3 0 80 1 21 0 2 -1 0 1 1 22 | Crispix K C 110 2 0 220 1 21 3 3 30 25 1 1 23 | Crispy_Wheat_&_Raisins G C 100 2 1 140 2 11 10 3 120 25 1 0.75 24 | Double_Chex R C 100 2 0 190 1 18 5 3 80 25 1 0.75 25 | Froot_Loops K C 110 2 1 125 1 11 13 2 30 25 1 1 26 | Frosted_Flakes K C 110 1 0 200 1 14 11 1 25 25 1 0.75 27 | Frosted_Mini-Wheats K C 100 3 0 0 3 14 7 2 100 25 1 0.8 28 | Fruit_&_Fibre_Dates,_Walnuts,_and_Oats P C 120 3 2 160 5 12 10 3 200 25 1.25 0.67 29 | Fruitful_Bran K C 120 3 0 240 5 14 12 3 190 25 1.33 0.67 30 | Fruity_Pebbles P C 110 1 1 135 0 13 12 2 25 25 1 0.75 31 | Golden_Crisp P C 100 2 0 45 0 11 15 1 40 25 1 0.88 32 | Golden_Grahams G C 110 1 1 280 0 15 9 2 45 25 1 0.75 33 | Grape_Nuts_Flakes P C 100 3 1 140 3 15 5 3 85 25 1 0.88 34 | Grape-Nuts P C 110 3 0 170 3 17 3 3 90 25 1 0.25 35 | Great_Grains_Pecan P C 120 3 3 75 3 13 4 3 100 25 1 0.33 36 | Honey_Graham_Ohs Q C 120 1 2 220 1 12 11 2 45 25 1 1 37 | Honey_Nut_Cheerios G C 110 3 1 250 1.5 11.5 10 1 90 25 1 0.75 38 | Honey-comb P C 110 1 0 180 0 14 11 1 35 25 1 1.33 39 | Just_Right_Crunchy__Nuggets K C 110 2 1 170 1 17 6 3 60 100 1 -1 40 | Just_Right_Fruit_&_Nut K C 140 3 1 170 2 20 9 3 95 100 1.3 0.75 41 | Kix G C 110 2 1 260 0 21 3 2 40 25 1 1.5 42 | Life Q C 100 4 2 150 2 12 6 2 95 25 1 0.67 43 | Lucky_Charms G C 110 2 1 180 0 12 12 2 55 25 1 1 44 | Maypo A H 100 4 1 0 0 16 3 2 95 25 1 -1 45 | Muesli_Raisins,_Dates,_&_Almonds R C 150 4 3 95 3 16 11 3 170 25 -1 -1 46 | Muesli_Raisins,_Peaches,_&_Pecans R C 150 4 3 150 3 16 11 3 170 25 -1 -1 47 | Mueslix_Crispy_Blend K C 160 3 2 150 3 17 13 3 160 25 1.5 0.67 48 | Multi-Grain_Cheerios G C 100 2 1 220 2 15 6 1 90 25 1 1 49 | Nut&Honey_Crunch K C 120 2 1 190 0 15 9 2 40 25 1 0.67 50 | Nutri-Grain_Almond-Raisin K C 140 3 2 220 3 21 7 3 130 25 1.33 0.67 51 | Nutri-grain_Wheat K C 90 3 0 170 3 18 2 3 90 25 1 -1 52 | Oatmeal_Raisin_Crisp G C 130 3 2 170 1.5 13.5 10 3 120 25 1.25 0.5 53 | Post_Nat._Raisin_Bran P C 120 3 1 200 6 11 14 3 260 25 1.33 0.67 54 | Product_19 K C 100 3 0 320 1 20 3 3 45 100 1 1 55 | Puffed_Rice Q C 50 1 0 0 0 13 0 3 15 0 0.5 1 56 | Puffed_Wheat Q C 50 2 0 0 1 10 0 3 50 0 0.5 -1 57 | Quaker_Oat_Squares Q C 100 4 1 135 2 14 6 3 110 25 1 0.5 58 | Quaker_Oatmeal Q H 100 5 2 0 2.7 -1 -1 1 110 0 1 0.67 59 | Raisin_Bran K C 120 3 1 210 5 14 12 2 240 25 1.33 0.75 60 | Raisin_Nut_Bran G C 100 3 2 140 2.5 10.5 8 3 140 25 1 0.5 61 | Raisin_Squares K C 90 2 0 0 2 15 6 3 110 25 1 0.5 62 | Rice_Chex R C 110 1 0 240 0 23 2 1 30 25 1 1.13 63 | Rice_Krispies K C 110 2 0 290 0 22 3 1 35 25 1 1 64 | Shredded_Wheat N C 80 2 0 0 3 16 0 1 95 0 0.83 -1 65 | Shredded_Wheat_'n'Bran N C 90 3 0 0 4 19 0 1 140 0 1 0.67 66 | Shredded_Wheat_spoon_size N C 90 3 0 0 3 20 0 1 120 0 1 0.67 67 | Smacks K C 110 2 1 70 1 9 15 2 40 25 1 0.75 68 | Special_K K C 110 6 0 230 1 16 3 1 55 25 1 1 69 | Strawberry_Fruit_Wheats N C 90 2 0 15 3 15 5 2 90 25 1 -1 70 | Total_Corn_Flakes G C 110 2 1 200 0 21 3 3 35 100 1 1 71 | Total_Raisin_Bran G C 140 3 1 190 4 15 14 3 230 100 1.5 1 72 | Total_Whole_Grain G C 100 3 1 200 3 16 3 3 110 100 1 1 73 | Triples G C 110 2 1 250 0 21 3 3 60 25 1 0.75 74 | Trix G C 110 1 1 140 0 13 12 2 25 25 1 1 75 | Wheat_Chex R C 100 3 1 230 3 17 3 1 115 25 1 0.67 76 | Wheaties G C 100 3 1 200 3 17 3 1 110 25 1 1 77 | Wheaties_Honey_Gold G C 110 2 1 200 1 16 8 1 60 25 1 0.75 -------------------------------------------------------------------------------- /datasets/Customer Churn Columns.csv: -------------------------------------------------------------------------------- 1 | Column_Names 2 | A 3 | B 4 | C 5 | D 6 | E 7 | F 8 | G 9 | H 10 | I 11 | J 12 | K 13 | L 14 | M 15 | N 16 | O 17 | P 18 | Q 19 | R 20 | S 21 | T 22 | U 23 | -------------------------------------------------------------------------------- /datasets/Description.txt: -------------------------------------------------------------------------------- 1 | Data Set Information: 2 | 3 | These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. 4 | 5 | I think that the initial data set had around 30 variables, but for some reason I only have the 13 dimensional version. I had a list of what the 30 or so variables were, but a.) I lost it, and b.), I would not know which 13 variables are included in the set. 6 | 7 | The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it ) 8 | 1) Alcohol 9 | 2) Malic acid 10 | 3) Ash 11 | 4) Alcalinity of ash 12 | 5) Magnesium 13 | 6) Total phenols 14 | 7) Flavanoids 15 | 8) Nonflavanoid phenols 16 | 9) Proanthocyanins 17 | 10)Color intensity 18 | 11)Hue 19 | 12)OD280/OD315 of diluted wines 20 | 13)Proline 21 | 22 | In a classification context, this is a well posed problem with "well behaved" class structures. A good data set for first testing of a new classifier, but not very challenging. 23 | 24 | 25 | Attribute Information: 26 | 27 | All attributes are continuous 28 | 29 | No statistics available, but suggest to standardise variables for certain uses (e.g. for us with classifiers which are NOT scale invariant) 30 | 31 | NOTE: 1st attribute is class identifier (1-3) -------------------------------------------------------------------------------- /datasets/Ecom Expense.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/felipesanma/Intro_to_DataScience/8d4b1381a534a2dc4ce48b8a2a666464b1d2d492/datasets/Ecom Expense.xlsx -------------------------------------------------------------------------------- /datasets/Medals.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/felipesanma/Intro_to_DataScience/8d4b1381a534a2dc4ce48b8a2a666464b1d2d492/datasets/Medals.csv -------------------------------------------------------------------------------- /datasets/Titanic Description.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/felipesanma/Intro_to_DataScience/8d4b1381a534a2dc4ce48b8a2a666464b1d2d492/datasets/Titanic Description.txt -------------------------------------------------------------------------------- /datasets/alumnos.csv: -------------------------------------------------------------------------------- 1 | nombre,altura,peso,edad,sexo 2 | Hugo,1.67,60,23,h 3 | Paco,1.73,83,25,h 4 | Luis,1.62,70,28,h 5 | Diana,1.58,58,21,m 6 | Francisco,1.86,98,28,h 7 | Felipe,1.79,100,26,h 8 | Jacinta,1.69,62,20,m 9 | Bernardo,1.6,83,31,h 10 | Marisol,1.6,56,30,m 11 | Facundo,1.98,112,36,h 12 | Trinidad,1.72,72,21,m 13 | Camila,1.63,57,26,m 14 | Macarena,1.73,68,27,m 15 | Diego,1.62,78,23,h 16 | Gonzalo,1.58,67,22,h 17 | Alejandra,1.86,74,21,m 18 | Fernando,1.79,93,27,h 19 | Carolina,1.6,63,28,m 20 | Vicente,1.98,102,31,h 21 | Benjamín,1.72,78,36,h 22 | Gloria,1.58,65,23,m 23 | -------------------------------------------------------------------------------- /datasets/auto-mpg.csv: -------------------------------------------------------------------------------- 1 | mpg,cylinders,displacement,horsepower,weight,acceleration,model year,origin,car 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 | NA,4,133,115,3090,17.5,70,2,citroen ds-21 pallas 13 | NA,8,350,165,4142,11.5,70,1,chevrolet chevelle concours (sw) 14 | NA,8,351,153,4034,11,70,1,ford torino (sw) 15 | NA,8,383,175,4166,10.5,70,1,plymouth satellite (sw) 16 | NA,8,360,175,3850,11,70,1,amc rebel sst (sw) 17 | 15,8,383,170,3563,10,70,1,dodge challenger se 18 | 14,8,340,160,3609,8,70,1,plymouth 'cuda 340 19 | NA,8,302,140,3353,8,70,1,ford mustang boss 302 20 | 15,8,400,150,3761,9.5,70,1,chevrolet monte carlo 21 | 14,8,455,225,3086,10,70,1,buick estate wagon (sw) 22 | 24,4,113,95,2372,15,70,3,toyota corona mark ii 23 | 22,6,198,95,2833,15.5,70,1,plymouth duster 24 | 18,6,199,97,2774,15.5,70,1,amc hornet 25 | 21,6,200,85,2587,16,70,1,ford maverick 26 | 27,4,97,88,2130,14.5,70,3,datsun pl510 27 | 26,4,97,46,1835,20.5,70,2,volkswagen 1131 deluxe sedan 28 | 25,4,110,87,2672,17.5,70,2,peugeot 504 29 | 24,4,107,90,2430,14.5,70,2,audi 100 ls 30 | 25,4,104,95,2375,17.5,70,2,saab 99e 31 | 26,4,121,113,2234,12.5,70,2,bmw 2002 32 | 21,6,199,90,2648,15,70,1,amc gremlin 33 | 10,8,360,215,4615,14,70,1,ford f250 34 | 10,8,307,200,4376,15,70,1,chevy c20 35 | 11,8,318,210,4382,13.5,70,1,dodge d200 36 | 9,8,304,193,4732,18.5,70,1,hi 1200d 37 | 27,4,97,88,2130,14.5,71,3,datsun pl510 38 | 28,4,140,90,2264,15.5,71,1,chevrolet vega 2300 39 | 25,4,113,95,2228,14,71,3,toyota corona 40 | 25,4,98,NA,2046,19,71,1,ford pinto 41 | NA,4,97,48,1978,20,71,2,volkswagen super beetle 117 42 | 19,6,232,100,2634,13,71,1,amc gremlin 43 | 16,6,225,105,3439,15.5,71,1,plymouth satellite custom 44 | 17,6,250,100,3329,15.5,71,1,chevrolet chevelle malibu 45 | 19,6,250,88,3302,15.5,71,1,ford torino 500 46 | 18,6,232,100,3288,15.5,71,1,amc matador 47 | 14,8,350,165,4209,12,71,1,chevrolet impala 48 | 14,8,400,175,4464,11.5,71,1,pontiac catalina brougham 49 | 14,8,351,153,4154,13.5,71,1,ford galaxie 500 50 | 14,8,318,150,4096,13,71,1,plymouth fury iii 51 | 12,8,383,180,4955,11.5,71,1,dodge monaco (sw) 52 | 13,8,400,170,4746,12,71,1,ford country squire (sw) 53 | 13,8,400,175,5140,12,71,1,pontiac safari (sw) 54 | 18,6,258,110,2962,13.5,71,1,amc hornet sportabout (sw) 55 | 22,4,140,72,2408,19,71,1,chevrolet vega (sw) 56 | 19,6,250,100,3282,15,71,1,pontiac firebird 57 | 18,6,250,88,3139,14.5,71,1,ford mustang 58 | 23,4,122,86,2220,14,71,1,mercury capri 2000 59 | 28,4,116,90,2123,14,71,2,opel 1900 60 | 30,4,79,70,2074,19.5,71,2,peugeot 304 61 | 30,4,88,76,2065,14.5,71,2,fiat 124b 62 | 31,4,71,65,1773,19,71,3,toyota corolla 1200 63 | 35,4,72,69,1613,18,71,3,datsun 1200 64 | 27,4,97,60,1834,19,71,2,volkswagen model 111 65 | 26,4,91,70,1955,20.5,71,1,plymouth cricket 66 | 24,4,113,95,2278,15.5,72,3,toyota corona hardtop 67 | 25,4,97.5,80,2126,17,72,1,dodge colt hardtop 68 | 23,4,97,54,2254,23.5,72,2,volkswagen type 3 69 | 20,4,140,90,2408,19.5,72,1,chevrolet vega 70 | 21,4,122,86,2226,16.5,72,1,ford pinto runabout 71 | 13,8,350,165,4274,12,72,1,chevrolet impala 72 | 14,8,400,175,4385,12,72,1,pontiac catalina 73 | 15,8,318,150,4135,13.5,72,1,plymouth fury iii 74 | 14,8,351,153,4129,13,72,1,ford galaxie 500 75 | 17,8,304,150,3672,11.5,72,1,amc ambassador sst 76 | 11,8,429,208,4633,11,72,1,mercury marquis 77 | 13,8,350,155,4502,13.5,72,1,buick lesabre custom 78 | 12,8,350,160,4456,13.5,72,1,oldsmobile delta 88 royale 79 | 13,8,400,190,4422,12.5,72,1,chrysler newport royal 80 | 19,3,70,97,2330,13.5,72,3,mazda rx2 coupe 81 | 15,8,304,150,3892,12.5,72,1,amc matador (sw) 82 | 13,8,307,130,4098,14,72,1,chevrolet chevelle concours (sw) 83 | 13,8,302,140,4294,16,72,1,ford gran torino (sw) 84 | 14,8,318,150,4077,14,72,1,plymouth satellite custom (sw) 85 | 18,4,121,112,2933,14.5,72,2,volvo 145e (sw) 86 | 22,4,121,76,2511,18,72,2,volkswagen 411 (sw) 87 | 21,4,120,87,2979,19.5,72,2,peugeot 504 (sw) 88 | 26,4,96,69,2189,18,72,2,renault 12 (sw) 89 | 22,4,122,86,2395,16,72,1,ford pinto (sw) 90 | 28,4,97,92,2288,17,72,3,datsun 510 (sw) 91 | 23,4,120,97,2506,14.5,72,3,toyouta corona mark ii (sw) 92 | 28,4,98,80,2164,15,72,1,dodge colt (sw) 93 | 27,4,97,88,2100,16.5,72,3,toyota corolla 1600 (sw) 94 | 13,8,350,175,4100,13,73,1,buick century 350 95 | 14,8,304,150,3672,11.5,73,1,amc matador 96 | 13,8,350,145,3988,13,73,1,chevrolet malibu 97 | 14,8,302,137,4042,14.5,73,1,ford gran torino 98 | 15,8,318,150,3777,12.5,73,1,dodge coronet custom 99 | 12,8,429,198,4952,11.5,73,1,mercury marquis brougham 100 | 13,8,400,150,4464,12,73,1,chevrolet caprice classic 101 | 13,8,351,158,4363,13,73,1,ford ltd 102 | 14,8,318,150,4237,14.5,73,1,plymouth fury gran sedan 103 | 13,8,440,215,4735,11,73,1,chrysler new yorker brougham 104 | 12,8,455,225,4951,11,73,1,buick electra 225 custom 105 | 13,8,360,175,3821,11,73,1,amc ambassador brougham 106 | 18,6,225,105,3121,16.5,73,1,plymouth valiant 107 | 16,6,250,100,3278,18,73,1,chevrolet nova custom 108 | 18,6,232,100,2945,16,73,1,amc hornet 109 | 18,6,250,88,3021,16.5,73,1,ford maverick 110 | 23,6,198,95,2904,16,73,1,plymouth duster 111 | 26,4,97,46,1950,21,73,2,volkswagen super beetle 112 | 11,8,400,150,4997,14,73,1,chevrolet impala 113 | 12,8,400,167,4906,12.5,73,1,ford country 114 | 13,8,360,170,4654,13,73,1,plymouth custom suburb 115 | 12,8,350,180,4499,12.5,73,1,oldsmobile vista cruiser 116 | 18,6,232,100,2789,15,73,1,amc gremlin 117 | 20,4,97,88,2279,19,73,3,toyota carina 118 | 21,4,140,72,2401,19.5,73,1,chevrolet vega 119 | 22,4,108,94,2379,16.5,73,3,datsun 610 120 | 18,3,70,90,2124,13.5,73,3,maxda rx3 121 | 19,4,122,85,2310,18.5,73,1,ford pinto 122 | 21,6,155,107,2472,14,73,1,mercury capri v6 123 | 26,4,98,90,2265,15.5,73,2,fiat 124 sport coupe 124 | 15,8,350,145,4082,13,73,1,chevrolet monte carlo s 125 | 16,8,400,230,4278,9.5,73,1,pontiac grand prix 126 | 29,4,68,49,1867,19.5,73,2,fiat 128 127 | 24,4,116,75,2158,15.5,73,2,opel manta 128 | 20,4,114,91,2582,14,73,2,audi 100ls 129 | 19,4,121,112,2868,15.5,73,2,volvo 144ea 130 | 15,8,318,150,3399,11,73,1,dodge dart custom 131 | 24,4,121,110,2660,14,73,2,saab 99le 132 | 20,6,156,122,2807,13.5,73,3,toyota mark ii 133 | 11,8,350,180,3664,11,73,1,oldsmobile omega 134 | 20,6,198,95,3102,16.5,74,1,plymouth duster 135 | 21,6,200,NA,2875,17,74,1,ford maverick 136 | 19,6,232,100,2901,16,74,1,amc hornet 137 | 15,6,250,100,3336,17,74,1,chevrolet nova 138 | 31,4,79,67,1950,19,74,3,datsun b210 139 | 26,4,122,80,2451,16.5,74,1,ford pinto 140 | 32,4,71,65,1836,21,74,3,toyota corolla 1200 141 | 25,4,140,75,2542,17,74,1,chevrolet vega 142 | 16,6,250,100,3781,17,74,1,chevrolet chevelle malibu classic 143 | 16,6,258,110,3632,18,74,1,amc matador 144 | 18,6,225,105,3613,16.5,74,1,plymouth satellite sebring 145 | 16,8,302,140,4141,14,74,1,ford gran torino 146 | 13,8,350,150,4699,14.5,74,1,buick century luxus (sw) 147 | 14,8,318,150,4457,13.5,74,1,dodge coronet custom (sw) 148 | 14,8,302,140,4638,16,74,1,ford gran torino (sw) 149 | 14,8,304,150,4257,15.5,74,1,amc matador (sw) 150 | 29,4,98,83,2219,16.5,74,2,audi fox 151 | 26,4,79,67,1963,15.5,74,2,volkswagen dasher 152 | 26,4,97,78,2300,14.5,74,2,opel manta 153 | 31,4,76,52,1649,16.5,74,3,toyota corona 154 | 32,4,83,61,2003,19,74,3,datsun 710 155 | 28,4,90,75,2125,14.5,74,1,dodge colt 156 | 24,4,90,75,2108,15.5,74,2,fiat 128 157 | 26,4,116,75,2246,14,74,2,fiat 124 tc 158 | 24,4,120,97,2489,15,74,3,honda civic 159 | 26,4,108,93,2391,15.5,74,3,subaru 160 | 31,4,79,67,2000,16,74,2,fiat x1.9 161 | 19,6,225,95,3264,16,75,1,plymouth valiant custom 162 | 18,6,250,105,3459,16,75,1,chevrolet nova 163 | 15,6,250,72,3432,21,75,1,mercury monarch 164 | 15,6,250,72,3158,19.5,75,1,ford maverick 165 | 16,8,400,170,4668,11.5,75,1,pontiac catalina 166 | 15,8,350,145,4440,14,75,1,chevrolet bel air 167 | 16,8,318,150,4498,14.5,75,1,plymouth grand fury 168 | 14,8,351,148,4657,13.5,75,1,ford ltd 169 | 17,6,231,110,3907,21,75,1,buick century 170 | 16,6,250,105,3897,18.5,75,1,chevroelt chevelle malibu 171 | 15,6,258,110,3730,19,75,1,amc matador 172 | 18,6,225,95,3785,19,75,1,plymouth fury 173 | 21,6,231,110,3039,15,75,1,buick skyhawk 174 | 20,8,262,110,3221,13.5,75,1,chevrolet monza 2+2 175 | 13,8,302,129,3169,12,75,1,ford mustang ii 176 | 29,4,97,75,2171,16,75,3,toyota corolla 177 | 23,4,140,83,2639,17,75,1,ford pinto 178 | 20,6,232,100,2914,16,75,1,amc gremlin 179 | 23,4,140,78,2592,18.5,75,1,pontiac astro 180 | 24,4,134,96,2702,13.5,75,3,toyota corona 181 | 25,4,90,71,2223,16.5,75,2,volkswagen dasher 182 | 24,4,119,97,2545,17,75,3,datsun 710 183 | 18,6,171,97,2984,14.5,75,1,ford pinto 184 | 29,4,90,70,1937,14,75,2,volkswagen rabbit 185 | 19,6,232,90,3211,17,75,1,amc pacer 186 | 23,4,115,95,2694,15,75,2,audi 100ls 187 | 23,4,120,88,2957,17,75,2,peugeot 504 188 | 22,4,121,98,2945,14.5,75,2,volvo 244dl 189 | 25,4,121,115,2671,13.5,75,2,saab 99le 190 | 33,4,91,53,1795,17.5,75,3,honda civic cvcc 191 | 28,4,107,86,2464,15.5,76,2,fiat 131 192 | 25,4,116,81,2220,16.9,76,2,opel 1900 193 | 25,4,140,92,2572,14.9,76,1,capri ii 194 | 26,4,98,79,2255,17.7,76,1,dodge colt 195 | 27,4,101,83,2202,15.3,76,2,renault 12tl 196 | 17.5,8,305,140,4215,13,76,1,chevrolet chevelle malibu classic 197 | 16,8,318,150,4190,13,76,1,dodge coronet brougham 198 | 15.5,8,304,120,3962,13.9,76,1,amc matador 199 | 14.5,8,351,152,4215,12.8,76,1,ford gran torino 200 | 22,6,225,100,3233,15.4,76,1,plymouth valiant 201 | 22,6,250,105,3353,14.5,76,1,chevrolet nova 202 | 24,6,200,81,3012,17.6,76,1,ford maverick 203 | 22.5,6,232,90,3085,17.6,76,1,amc hornet 204 | 29,4,85,52,2035,22.2,76,1,chevrolet chevette 205 | 24.5,4,98,60,2164,22.1,76,1,chevrolet woody 206 | 29,4,90,70,1937,14.2,76,2,vw rabbit 207 | 33,4,91,53,1795,17.4,76,3,honda civic 208 | 20,6,225,100,3651,17.7,76,1,dodge aspen se 209 | 18,6,250,78,3574,21,76,1,ford granada ghia 210 | 18.5,6,250,110,3645,16.2,76,1,pontiac ventura sj 211 | 17.5,6,258,95,3193,17.8,76,1,amc pacer d/l 212 | 29.5,4,97,71,1825,12.2,76,2,volkswagen rabbit 213 | 32,4,85,70,1990,17,76,3,datsun b-210 214 | 28,4,97,75,2155,16.4,76,3,toyota corolla 215 | 26.5,4,140,72,2565,13.6,76,1,ford pinto 216 | 20,4,130,102,3150,15.7,76,2,volvo 245 217 | 13,8,318,150,3940,13.2,76,1,plymouth volare premier v8 218 | 19,4,120,88,3270,21.9,76,2,peugeot 504 219 | 19,6,156,108,2930,15.5,76,3,toyota mark ii 220 | 16.5,6,168,120,3820,16.7,76,2,mercedes-benz 280s 221 | 16.5,8,350,180,4380,12.1,76,1,cadillac seville 222 | 13,8,350,145,4055,12,76,1,chevy c10 223 | 13,8,302,130,3870,15,76,1,ford f108 224 | 13,8,318,150,3755,14,76,1,dodge d100 225 | 31.5,4,98,68,2045,18.5,77,3,honda accord cvcc 226 | 30,4,111,80,2155,14.8,77,1,buick opel isuzu deluxe 227 | 36,4,79,58,1825,18.6,77,2,renault 5 gtl 228 | 25.5,4,122,96,2300,15.5,77,1,plymouth arrow gs 229 | 33.5,4,85,70,1945,16.8,77,3,datsun f-10 hatchback 230 | 17.5,8,305,145,3880,12.5,77,1,chevrolet caprice classic 231 | 17,8,260,110,4060,19,77,1,oldsmobile cutlass supreme 232 | 15.5,8,318,145,4140,13.7,77,1,dodge monaco brougham 233 | 15,8,302,130,4295,14.9,77,1,mercury cougar brougham 234 | 17.5,6,250,110,3520,16.4,77,1,chevrolet concours 235 | 20.5,6,231,105,3425,16.9,77,1,buick skylark 236 | 19,6,225,100,3630,17.7,77,1,plymouth volare custom 237 | 18.5,6,250,98,3525,19,77,1,ford granada 238 | 16,8,400,180,4220,11.1,77,1,pontiac grand prix lj 239 | 15.5,8,350,170,4165,11.4,77,1,chevrolet monte carlo landau 240 | 15.5,8,400,190,4325,12.2,77,1,chrysler cordoba 241 | 16,8,351,149,4335,14.5,77,1,ford thunderbird 242 | 29,4,97,78,1940,14.5,77,2,volkswagen rabbit custom 243 | 24.5,4,151,88,2740,16,77,1,pontiac sunbird coupe 244 | 26,4,97,75,2265,18.2,77,3,toyota corolla liftback 245 | 25.5,4,140,89,2755,15.8,77,1,ford mustang ii 2+2 246 | 30.5,4,98,63,2051,17,77,1,chevrolet chevette 247 | 33.5,4,98,83,2075,15.9,77,1,dodge colt m/m 248 | 30,4,97,67,1985,16.4,77,3,subaru dl 249 | 30.5,4,97,78,2190,14.1,77,2,volkswagen dasher 250 | 22,6,146,97,2815,14.5,77,3,datsun 810 251 | 21.5,4,121,110,2600,12.8,77,2,bmw 320i 252 | 21.5,3,80,110,2720,13.5,77,3,mazda rx-4 253 | 43.1,4,90,48,1985,21.5,78,2,volkswagen rabbit custom diesel 254 | 36.1,4,98,66,1800,14.4,78,1,ford fiesta 255 | 32.8,4,78,52,1985,19.4,78,3,mazda glc deluxe 256 | 39.4,4,85,70,2070,18.6,78,3,datsun b210 gx 257 | 36.1,4,91,60,1800,16.4,78,3,honda civic cvcc 258 | 19.9,8,260,110,3365,15.5,78,1,oldsmobile cutlass salon brougham 259 | 19.4,8,318,140,3735,13.2,78,1,dodge diplomat 260 | 20.2,8,302,139,3570,12.8,78,1,mercury monarch ghia 261 | 19.2,6,231,105,3535,19.2,78,1,pontiac phoenix lj 262 | 20.5,6,200,95,3155,18.2,78,1,chevrolet malibu 263 | 20.2,6,200,85,2965,15.8,78,1,ford fairmont (auto) 264 | 25.1,4,140,88,2720,15.4,78,1,ford fairmont (man) 265 | 20.5,6,225,100,3430,17.2,78,1,plymouth volare 266 | 19.4,6,232,90,3210,17.2,78,1,amc concord 267 | 20.6,6,231,105,3380,15.8,78,1,buick century special 268 | 20.8,6,200,85,3070,16.7,78,1,mercury zephyr 269 | 18.6,6,225,110,3620,18.7,78,1,dodge aspen 270 | 18.1,6,258,120,3410,15.1,78,1,amc concord d/l 271 | 19.2,8,305,145,3425,13.2,78,1,chevrolet monte carlo landau 272 | 17.7,6,231,165,3445,13.4,78,1,buick regal sport coupe (turbo) 273 | 18.1,8,302,139,3205,11.2,78,1,ford futura 274 | 17.5,8,318,140,4080,13.7,78,1,dodge magnum xe 275 | 30,4,98,68,2155,16.5,78,1,chevrolet chevette 276 | 27.5,4,134,95,2560,14.2,78,3,toyota corona 277 | 27.2,4,119,97,2300,14.7,78,3,datsun 510 278 | 30.9,4,105,75,2230,14.5,78,1,dodge omni 279 | 21.1,4,134,95,2515,14.8,78,3,toyota celica gt liftback 280 | 23.2,4,156,105,2745,16.7,78,1,plymouth sapporo 281 | 23.8,4,151,85,2855,17.6,78,1,oldsmobile starfire sx 282 | 23.9,4,119,97,2405,14.9,78,3,datsun 200-sx 283 | 20.3,5,131,103,2830,15.9,78,2,audi 5000 284 | 17,6,163,125,3140,13.6,78,2,volvo 264gl 285 | 21.6,4,121,115,2795,15.7,78,2,saab 99gle 286 | 16.2,6,163,133,3410,15.8,78,2,peugeot 604sl 287 | 31.5,4,89,71,1990,14.9,78,2,volkswagen scirocco 288 | 29.5,4,98,68,2135,16.6,78,3,honda accord lx 289 | 21.5,6,231,115,3245,15.4,79,1,pontiac lemans v6 290 | 19.8,6,200,85,2990,18.2,79,1,mercury zephyr 6 291 | 22.3,4,140,88,2890,17.3,79,1,ford fairmont 4 292 | 20.2,6,232,90,3265,18.2,79,1,amc concord dl 6 293 | 20.6,6,225,110,3360,16.6,79,1,dodge aspen 6 294 | 17,8,305,130,3840,15.4,79,1,chevrolet caprice classic 295 | 17.6,8,302,129,3725,13.4,79,1,ford ltd landau 296 | 16.5,8,351,138,3955,13.2,79,1,mercury grand marquis 297 | 18.2,8,318,135,3830,15.2,79,1,dodge st. regis 298 | 16.9,8,350,155,4360,14.9,79,1,buick estate wagon (sw) 299 | 15.5,8,351,142,4054,14.3,79,1,ford country squire (sw) 300 | 19.2,8,267,125,3605,15,79,1,chevrolet malibu classic (sw) 301 | 18.5,8,360,150,3940,13,79,1,chrysler lebaron town @ country (sw) 302 | 31.9,4,89,71,1925,14,79,2,vw rabbit custom 303 | 34.1,4,86,65,1975,15.2,79,3,maxda glc deluxe 304 | 35.7,4,98,80,1915,14.4,79,1,dodge colt hatchback custom 305 | 27.4,4,121,80,2670,15,79,1,amc spirit dl 306 | 25.4,5,183,77,3530,20.1,79,2,mercedes benz 300d 307 | 23,8,350,125,3900,17.4,79,1,cadillac eldorado 308 | 27.2,4,141,71,3190,24.8,79,2,peugeot 504 309 | 23.9,8,260,90,3420,22.2,79,1,oldsmobile cutlass salon brougham 310 | 34.2,4,105,70,2200,13.2,79,1,plymouth horizon 311 | 34.5,4,105,70,2150,14.9,79,1,plymouth horizon tc3 312 | 31.8,4,85,65,2020,19.2,79,3,datsun 210 313 | 37.3,4,91,69,2130,14.7,79,2,fiat strada custom 314 | 28.4,4,151,90,2670,16,79,1,buick skylark limited 315 | 28.8,6,173,115,2595,11.3,79,1,chevrolet citation 316 | 26.8,6,173,115,2700,12.9,79,1,oldsmobile omega brougham 317 | 33.5,4,151,90,2556,13.2,79,1,pontiac phoenix 318 | 41.5,4,98,76,2144,14.7,80,2,vw rabbit 319 | 38.1,4,89,60,1968,18.8,80,3,toyota corolla tercel 320 | 32.1,4,98,70,2120,15.5,80,1,chevrolet chevette 321 | 37.2,4,86,65,2019,16.4,80,3,datsun 310 322 | 28,4,151,90,2678,16.5,80,1,chevrolet citation 323 | 26.4,4,140,88,2870,18.1,80,1,ford fairmont 324 | 24.3,4,151,90,3003,20.1,80,1,amc concord 325 | 19.1,6,225,90,3381,18.7,80,1,dodge aspen 326 | 34.3,4,97,78,2188,15.8,80,2,audi 4000 327 | 29.8,4,134,90,2711,15.5,80,3,toyota corona liftback 328 | 31.3,4,120,75,2542,17.5,80,3,mazda 626 329 | 37,4,119,92,2434,15,80,3,datsun 510 hatchback 330 | 32.2,4,108,75,2265,15.2,80,3,toyota corolla 331 | 46.6,4,86,65,2110,17.9,80,3,mazda glc 332 | 27.9,4,156,105,2800,14.4,80,1,dodge colt 333 | 40.8,4,85,65,2110,19.2,80,3,datsun 210 334 | 44.3,4,90,48,2085,21.7,80,2,vw rabbit c (diesel) 335 | 43.4,4,90,48,2335,23.7,80,2,vw dasher (diesel) 336 | 36.4,5,121,67,2950,19.9,80,2,audi 5000s (diesel) 337 | 30,4,146,67,3250,21.8,80,2,mercedes-benz 240d 338 | 44.6,4,91,67,1850,13.8,80,3,honda civic 1500 gl 339 | 40.9,4,85,NA,1835,17.3,80,2,renault lecar deluxe 340 | 33.8,4,97,67,2145,18,80,3,subaru dl 341 | 29.8,4,89,62,1845,15.3,80,2,vokswagen rabbit 342 | 32.7,6,168,132,2910,11.4,80,3,datsun 280-zx 343 | 23.7,3,70,100,2420,12.5,80,3,mazda rx-7 gs 344 | 35,4,122,88,2500,15.1,80,2,triumph tr7 coupe 345 | 23.6,4,140,NA,2905,14.3,80,1,ford mustang cobra 346 | 32.4,4,107,72,2290,17,80,3,honda accord 347 | 27.2,4,135,84,2490,15.7,81,1,plymouth reliant 348 | 26.6,4,151,84,2635,16.4,81,1,buick skylark 349 | 25.8,4,156,92,2620,14.4,81,1,dodge aries wagon (sw) 350 | 23.5,6,173,110,2725,12.6,81,1,chevrolet citation 351 | 30,4,135,84,2385,12.9,81,1,plymouth reliant 352 | 39.1,4,79,58,1755,16.9,81,3,toyota starlet 353 | 39,4,86,64,1875,16.4,81,1,plymouth champ 354 | 35.1,4,81,60,1760,16.1,81,3,honda civic 1300 355 | 32.3,4,97,67,2065,17.8,81,3,subaru 356 | 37,4,85,65,1975,19.4,81,3,datsun 210 mpg 357 | 37.7,4,89,62,2050,17.3,81,3,toyota tercel 358 | 34.1,4,91,68,1985,16,81,3,mazda glc 4 359 | 34.7,4,105,63,2215,14.9,81,1,plymouth horizon 4 360 | 34.4,4,98,65,2045,16.2,81,1,ford escort 4w 361 | 29.9,4,98,65,2380,20.7,81,1,ford escort 2h 362 | 33,4,105,74,2190,14.2,81,2,volkswagen jetta 363 | 34.5,4,100,NA,2320,15.8,81,2,renault 18i 364 | 33.7,4,107,75,2210,14.4,81,3,honda prelude 365 | 32.4,4,108,75,2350,16.8,81,3,toyota corolla 366 | 32.9,4,119,100,2615,14.8,81,3,datsun 200sx 367 | 31.6,4,120,74,2635,18.3,81,3,mazda 626 368 | 28.1,4,141,80,3230,20.4,81,2,peugeot 505s turbo diesel 369 | NA,4,121,110,2800,15.4,81,2,saab 900s 370 | 30.7,6,145,76,3160,19.6,81,2,volvo diesel 371 | 25.4,6,168,116,2900,12.6,81,3,toyota cressida 372 | 24.2,6,146,120,2930,13.8,81,3,datsun 810 maxima 373 | 22.4,6,231,110,3415,15.8,81,1,buick century 374 | 26.6,8,350,105,3725,19,81,1,oldsmobile cutlass ls 375 | 20.2,6,200,88,3060,17.1,81,1,ford granada gl 376 | 17.6,6,225,85,3465,16.6,81,1,chrysler lebaron salon 377 | 28,4,112,88,2605,19.6,82,1,chevrolet cavalier 378 | 27,4,112,88,2640,18.6,82,1,chevrolet cavalier wagon 379 | 34,4,112,88,2395,18,82,1,chevrolet cavalier 2-door 380 | 31,4,112,85,2575,16.2,82,1,pontiac j2000 se hatchback 381 | 29,4,135,84,2525,16,82,1,dodge aries se 382 | 27,4,151,90,2735,18,82,1,pontiac phoenix 383 | 24,4,140,92,2865,16.4,82,1,ford fairmont futura 384 | 23,4,151,NA,3035,20.5,82,1,amc concord dl 385 | 36,4,105,74,1980,15.3,82,2,volkswagen rabbit l 386 | 37,4,91,68,2025,18.2,82,3,mazda glc custom l 387 | 31,4,91,68,1970,17.6,82,3,mazda glc custom 388 | 38,4,105,63,2125,14.7,82,1,plymouth horizon miser 389 | 36,4,98,70,2125,17.3,82,1,mercury lynx l 390 | 36,4,120,88,2160,14.5,82,3,nissan stanza xe 391 | 36,4,107,75,2205,14.5,82,3,honda accord 392 | 34,4,108,70,2245,16.9,82,3,toyota corolla 393 | 38,4,91,67,1965,15,82,3,honda civic 394 | 32,4,91,67,1965,15.7,82,3,honda civic (auto) 395 | 38,4,91,67,1995,16.2,82,3,datsun 310 gx 396 | 25,6,181,110,2945,16.4,82,1,buick century limited 397 | 38,6,262,85,3015,17,82,1,oldsmobile cutlass ciera (diesel) 398 | 26,4,156,92,2585,14.5,82,1,chrysler lebaron medallion 399 | 22,6,232,112,2835,14.7,82,1,ford granada l 400 | 32,4,144,96,2665,13.9,82,3,toyota celica gt 401 | 36,4,135,84,2370,13,82,1,dodge charger 2.2 402 | 27,4,151,90,2950,17.3,82,1,chevrolet camaro 403 | 27,4,140,86,2790,15.6,82,1,ford mustang gl 404 | 44,4,97,52,2130,24.6,82,2,vw pickup 405 | 32,4,135,84,2295,11.6,82,1,dodge rampage 406 | 28,4,120,79,2625,18.6,82,1,ford ranger 407 | 31,4,119,82,2720,19.4,82,1,chevy s-10 408 | -------------------------------------------------------------------------------- /datasets/breast-cancer-wisconsin.data.txt: -------------------------------------------------------------------------------- 1 | 1000025,5,1,1,1,2,1,3,1,1,2 2 | 1002945,5,4,4,5,7,10,3,2,1,2 3 | 1015425,3,1,1,1,2,2,3,1,1,2 4 | 1016277,6,8,8,1,3,4,3,7,1,2 5 | 1017023,4,1,1,3,2,1,3,1,1,2 6 | 1017122,8,10,10,8,7,10,9,7,1,4 7 | 1018099,1,1,1,1,2,10,3,1,1,2 8 | 1018561,2,1,2,1,2,1,3,1,1,2 9 | 1033078,2,1,1,1,2,1,1,1,5,2 10 | 1033078,4,2,1,1,2,1,2,1,1,2 11 | 1035283,1,1,1,1,1,1,3,1,1,2 12 | 1036172,2,1,1,1,2,1,2,1,1,2 13 | 1041801,5,3,3,3,2,3,4,4,1,4 14 | 1043999,1,1,1,1,2,3,3,1,1,2 15 | 1044572,8,7,5,10,7,9,5,5,4,4 16 | 1047630,7,4,6,4,6,1,4,3,1,4 17 | 1048672,4,1,1,1,2,1,2,1,1,2 18 | 1049815,4,1,1,1,2,1,3,1,1,2 19 | 1050670,10,7,7,6,4,10,4,1,2,4 20 | 1050718,6,1,1,1,2,1,3,1,1,2 21 | 1054590,7,3,2,10,5,10,5,4,4,4 22 | 1054593,10,5,5,3,6,7,7,10,1,4 23 | 1056784,3,1,1,1,2,1,2,1,1,2 24 | 1057013,8,4,5,1,2,?,7,3,1,4 25 | 1059552,1,1,1,1,2,1,3,1,1,2 26 | 1065726,5,2,3,4,2,7,3,6,1,4 27 | 1066373,3,2,1,1,1,1,2,1,1,2 28 | 1066979,5,1,1,1,2,1,2,1,1,2 29 | 1067444,2,1,1,1,2,1,2,1,1,2 30 | 1070935,1,1,3,1,2,1,1,1,1,2 31 | 1070935,3,1,1,1,1,1,2,1,1,2 32 | 1071760,2,1,1,1,2,1,3,1,1,2 33 | 1072179,10,7,7,3,8,5,7,4,3,4 34 | 1074610,2,1,1,2,2,1,3,1,1,2 35 | 1075123,3,1,2,1,2,1,2,1,1,2 36 | 1079304,2,1,1,1,2,1,2,1,1,2 37 | 1080185,10,10,10,8,6,1,8,9,1,4 38 | 1081791,6,2,1,1,1,1,7,1,1,2 39 | 1084584,5,4,4,9,2,10,5,6,1,4 40 | 1091262,2,5,3,3,6,7,7,5,1,4 41 | 1096800,6,6,6,9,6,?,7,8,1,2 42 | 1099510,10,4,3,1,3,3,6,5,2,4 43 | 1100524,6,10,10,2,8,10,7,3,3,4 44 | 1102573,5,6,5,6,10,1,3,1,1,4 45 | 1103608,10,10,10,4,8,1,8,10,1,4 46 | 1103722,1,1,1,1,2,1,2,1,2,2 47 | 1105257,3,7,7,4,4,9,4,8,1,4 48 | 1105524,1,1,1,1,2,1,2,1,1,2 49 | 1106095,4,1,1,3,2,1,3,1,1,2 50 | 1106829,7,8,7,2,4,8,3,8,2,4 51 | 1108370,9,5,8,1,2,3,2,1,5,4 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673637,3,1,1,1,2,5,5,1,1,2 311 | 684955,2,1,1,1,3,1,2,1,1,2 312 | 688033,1,1,1,1,2,1,1,1,1,2 313 | 691628,8,6,4,10,10,1,3,5,1,4 314 | 693702,1,1,1,1,2,1,1,1,1,2 315 | 704097,1,1,1,1,1,1,2,1,1,2 316 | 704168,4,6,5,6,7,?,4,9,1,2 317 | 706426,5,5,5,2,5,10,4,3,1,4 318 | 709287,6,8,7,8,6,8,8,9,1,4 319 | 718641,1,1,1,1,5,1,3,1,1,2 320 | 721482,4,4,4,4,6,5,7,3,1,2 321 | 730881,7,6,3,2,5,10,7,4,6,4 322 | 733639,3,1,1,1,2,?,3,1,1,2 323 | 733639,3,1,1,1,2,1,3,1,1,2 324 | 733823,5,4,6,10,2,10,4,1,1,4 325 | 740492,1,1,1,1,2,1,3,1,1,2 326 | 743348,3,2,2,1,2,1,2,3,1,2 327 | 752904,10,1,1,1,2,10,5,4,1,4 328 | 756136,1,1,1,1,2,1,2,1,1,2 329 | 760001,8,10,3,2,6,4,3,10,1,4 330 | 760239,10,4,6,4,5,10,7,1,1,4 331 | 76389,10,4,7,2,2,8,6,1,1,4 332 | 764974,5,1,1,1,2,1,3,1,2,2 333 | 770066,5,2,2,2,2,1,2,2,1,2 334 | 785208,5,4,6,6,4,10,4,3,1,4 335 | 785615,8,6,7,3,3,10,3,4,2,4 336 | 792744,1,1,1,1,2,1,1,1,1,2 337 | 797327,6,5,5,8,4,10,3,4,1,4 338 | 798429,1,1,1,1,2,1,3,1,1,2 339 | 704097,1,1,1,1,1,1,2,1,1,2 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1196263,4,1,1,1,2,1,1,1,1,2 399 | 1196475,3,2,1,1,2,1,2,2,1,2 400 | 1206314,1,2,3,1,2,1,1,1,1,2 401 | 1211265,3,10,8,7,6,9,9,3,8,4 402 | 1213784,3,1,1,1,2,1,1,1,1,2 403 | 1223003,5,3,3,1,2,1,2,1,1,2 404 | 1223306,3,1,1,1,2,4,1,1,1,2 405 | 1223543,1,2,1,3,2,1,1,2,1,2 406 | 1229929,1,1,1,1,2,1,2,1,1,2 407 | 1231853,4,2,2,1,2,1,2,1,1,2 408 | 1234554,1,1,1,1,2,1,2,1,1,2 409 | 1236837,2,3,2,2,2,2,3,1,1,2 410 | 1237674,3,1,2,1,2,1,2,1,1,2 411 | 1238021,1,1,1,1,2,1,2,1,1,2 412 | 1238464,1,1,1,1,1,?,2,1,1,2 413 | 1238633,10,10,10,6,8,4,8,5,1,4 414 | 1238915,5,1,2,1,2,1,3,1,1,2 415 | 1238948,8,5,6,2,3,10,6,6,1,4 416 | 1239232,3,3,2,6,3,3,3,5,1,2 417 | 1239347,8,7,8,5,10,10,7,2,1,4 418 | 1239967,1,1,1,1,2,1,2,1,1,2 419 | 1240337,5,2,2,2,2,2,3,2,2,2 420 | 1253505,2,3,1,1,5,1,1,1,1,2 421 | 1255384,3,2,2,3,2,3,3,1,1,2 422 | 1257200,10,10,10,7,10,10,8,2,1,4 423 | 1257648,4,3,3,1,2,1,3,3,1,2 424 | 1257815,5,1,3,1,2,1,2,1,1,2 425 | 1257938,3,1,1,1,2,1,1,1,1,2 426 | 1258549,9,10,10,10,10,10,10,10,1,4 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1246562,10,2,2,1,2,6,1,1,2,4 457 | 1257470,10,6,5,8,5,10,8,6,1,4 458 | 1259008,8,8,9,6,6,3,10,10,1,4 459 | 1266124,5,1,2,1,2,1,1,1,1,2 460 | 1267898,5,1,3,1,2,1,1,1,1,2 461 | 1268313,5,1,1,3,2,1,1,1,1,2 462 | 1268804,3,1,1,1,2,5,1,1,1,2 463 | 1276091,6,1,1,3,2,1,1,1,1,2 464 | 1280258,4,1,1,1,2,1,1,2,1,2 465 | 1293966,4,1,1,1,2,1,1,1,1,2 466 | 1296572,10,9,8,7,6,4,7,10,3,4 467 | 1298416,10,6,6,2,4,10,9,7,1,4 468 | 1299596,6,6,6,5,4,10,7,6,2,4 469 | 1105524,4,1,1,1,2,1,1,1,1,2 470 | 1181685,1,1,2,1,2,1,2,1,1,2 471 | 1211594,3,1,1,1,1,1,2,1,1,2 472 | 1238777,6,1,1,3,2,1,1,1,1,2 473 | 1257608,6,1,1,1,1,1,1,1,1,2 474 | 1269574,4,1,1,1,2,1,1,1,1,2 475 | 1277145,5,1,1,1,2,1,1,1,1,2 476 | 1287282,3,1,1,1,2,1,1,1,1,2 477 | 1296025,4,1,2,1,2,1,1,1,1,2 478 | 1296263,4,1,1,1,2,1,1,1,1,2 479 | 1296593,5,2,1,1,2,1,1,1,1,2 480 | 1299161,4,8,7,10,4,10,7,5,1,4 481 | 1301945,5,1,1,1,1,1,1,1,1,2 482 | 1302428,5,3,2,4,2,1,1,1,1,2 483 | 1318169,9,10,10,10,10,5,10,10,10,4 484 | 474162,8,7,8,5,5,10,9,10,1,4 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1174841,5,3,1,1,2,1,1,1,1,2 544 | 1184586,4,1,1,1,2,1,2,1,1,2 545 | 1186936,2,1,3,2,2,1,2,1,1,2 546 | 1197527,5,1,1,1,2,1,2,1,1,2 547 | 1222464,6,10,10,10,4,10,7,10,1,4 548 | 1240603,2,1,1,1,1,1,1,1,1,2 549 | 1240603,3,1,1,1,1,1,1,1,1,2 550 | 1241035,7,8,3,7,4,5,7,8,2,4 551 | 1287971,3,1,1,1,2,1,2,1,1,2 552 | 1289391,1,1,1,1,2,1,3,1,1,2 553 | 1299924,3,2,2,2,2,1,4,2,1,2 554 | 1306339,4,4,2,1,2,5,2,1,2,2 555 | 1313658,3,1,1,1,2,1,1,1,1,2 556 | 1313982,4,3,1,1,2,1,4,8,1,2 557 | 1321264,5,2,2,2,1,1,2,1,1,2 558 | 1321321,5,1,1,3,2,1,1,1,1,2 559 | 1321348,2,1,1,1,2,1,2,1,1,2 560 | 1321931,5,1,1,1,2,1,2,1,1,2 561 | 1321942,5,1,1,1,2,1,3,1,1,2 562 | 1321942,5,1,1,1,2,1,3,1,1,2 563 | 1328331,1,1,1,1,2,1,3,1,1,2 564 | 1328755,3,1,1,1,2,1,2,1,1,2 565 | 1331405,4,1,1,1,2,1,3,2,1,2 566 | 1331412,5,7,10,10,5,10,10,10,1,4 567 | 1333104,3,1,2,1,2,1,3,1,1,2 568 | 1334071,4,1,1,1,2,3,2,1,1,2 569 | 1343068,8,4,4,1,6,10,2,5,2,4 570 | 1343374,10,10,8,10,6,5,10,3,1,4 571 | 1344121,8,10,4,4,8,10,8,2,1,4 572 | 142932,7,6,10,5,3,10,9,10,2,4 573 | 183936,3,1,1,1,2,1,2,1,1,2 574 | 324382,1,1,1,1,2,1,2,1,1,2 575 | 378275,10,9,7,3,4,2,7,7,1,4 576 | 385103,5,1,2,1,2,1,3,1,1,2 577 | 690557,5,1,1,1,2,1,2,1,1,2 578 | 695091,1,1,1,1,2,1,2,1,1,2 579 | 695219,1,1,1,1,2,1,2,1,1,2 580 | 824249,1,1,1,1,2,1,3,1,1,2 581 | 871549,5,1,2,1,2,1,2,1,1,2 582 | 878358,5,7,10,6,5,10,7,5,1,4 583 | 1107684,6,10,5,5,4,10,6,10,1,4 584 | 1115762,3,1,1,1,2,1,1,1,1,2 585 | 1217717,5,1,1,6,3,1,1,1,1,2 586 | 1239420,1,1,1,1,2,1,1,1,1,2 587 | 1254538,8,10,10,10,6,10,10,10,1,4 588 | 1261751,5,1,1,1,2,1,2,2,1,2 589 | 1268275,9,8,8,9,6,3,4,1,1,4 590 | 1272166,5,1,1,1,2,1,1,1,1,2 591 | 1294261,4,10,8,5,4,1,10,1,1,4 592 | 1295529,2,5,7,6,4,10,7,6,1,4 593 | 1298484,10,3,4,5,3,10,4,1,1,4 594 | 1311875,5,1,2,1,2,1,1,1,1,2 595 | 1315506,4,8,6,3,4,10,7,1,1,4 596 | 1320141,5,1,1,1,2,1,2,1,1,2 597 | 1325309,4,1,2,1,2,1,2,1,1,2 598 | 1333063,5,1,3,1,2,1,3,1,1,2 599 | 1333495,3,1,1,1,2,1,2,1,1,2 600 | 1334659,5,2,4,1,1,1,1,1,1,2 601 | 1336798,3,1,1,1,2,1,2,1,1,2 602 | 1344449,1,1,1,1,1,1,2,1,1,2 603 | 1350568,4,1,1,1,2,1,2,1,1,2 604 | 1352663,5,4,6,8,4,1,8,10,1,4 605 | 188336,5,3,2,8,5,10,8,1,2,4 606 | 352431,10,5,10,3,5,8,7,8,3,4 607 | 353098,4,1,1,2,2,1,1,1,1,2 608 | 411453,1,1,1,1,2,1,1,1,1,2 609 | 557583,5,10,10,10,10,10,10,1,1,4 610 | 636375,5,1,1,1,2,1,1,1,1,2 611 | 736150,10,4,3,10,3,10,7,1,2,4 612 | 803531,5,10,10,10,5,2,8,5,1,4 613 | 822829,8,10,10,10,6,10,10,10,10,4 614 | 1016634,2,3,1,1,2,1,2,1,1,2 615 | 1031608,2,1,1,1,1,1,2,1,1,2 616 | 1041043,4,1,3,1,2,1,2,1,1,2 617 | 1042252,3,1,1,1,2,1,2,1,1,2 618 | 1057067,1,1,1,1,1,?,1,1,1,2 619 | 1061990,4,1,1,1,2,1,2,1,1,2 620 | 1073836,5,1,1,1,2,1,2,1,1,2 621 | 1083817,3,1,1,1,2,1,2,1,1,2 622 | 1096352,6,3,3,3,3,2,6,1,1,2 623 | 1140597,7,1,2,3,2,1,2,1,1,2 624 | 1149548,1,1,1,1,2,1,1,1,1,2 625 | 1174009,5,1,1,2,1,1,2,1,1,2 626 | 1183596,3,1,3,1,3,4,1,1,1,2 627 | 1190386,4,6,6,5,7,6,7,7,3,4 628 | 1190546,2,1,1,1,2,5,1,1,1,2 629 | 1213273,2,1,1,1,2,1,1,1,1,2 630 | 1218982,4,1,1,1,2,1,1,1,1,2 631 | 1225382,6,2,3,1,2,1,1,1,1,2 632 | 1235807,5,1,1,1,2,1,2,1,1,2 633 | 1238777,1,1,1,1,2,1,1,1,1,2 634 | 1253955,8,7,4,4,5,3,5,10,1,4 635 | 1257366,3,1,1,1,2,1,1,1,1,2 636 | 1260659,3,1,4,1,2,1,1,1,1,2 637 | 1268952,10,10,7,8,7,1,10,10,3,4 638 | 1275807,4,2,4,3,2,2,2,1,1,2 639 | 1277792,4,1,1,1,2,1,1,1,1,2 640 | 1277792,5,1,1,3,2,1,1,1,1,2 641 | 1285722,4,1,1,3,2,1,1,1,1,2 642 | 1288608,3,1,1,1,2,1,2,1,1,2 643 | 1290203,3,1,1,1,2,1,2,1,1,2 644 | 1294413,1,1,1,1,2,1,1,1,1,2 645 | 1299596,2,1,1,1,2,1,1,1,1,2 646 | 1303489,3,1,1,1,2,1,2,1,1,2 647 | 1311033,1,2,2,1,2,1,1,1,1,2 648 | 1311108,1,1,1,3,2,1,1,1,1,2 649 | 1315807,5,10,10,10,10,2,10,10,10,4 650 | 1318671,3,1,1,1,2,1,2,1,1,2 651 | 1319609,3,1,1,2,3,4,1,1,1,2 652 | 1323477,1,2,1,3,2,1,2,1,1,2 653 | 1324572,5,1,1,1,2,1,2,2,1,2 654 | 1324681,4,1,1,1,2,1,2,1,1,2 655 | 1325159,3,1,1,1,2,1,3,1,1,2 656 | 1326892,3,1,1,1,2,1,2,1,1,2 657 | 1330361,5,1,1,1,2,1,2,1,1,2 658 | 1333877,5,4,5,1,8,1,3,6,1,2 659 | 1334015,7,8,8,7,3,10,7,2,3,4 660 | 1334667,1,1,1,1,2,1,1,1,1,2 661 | 1339781,1,1,1,1,2,1,2,1,1,2 662 | 1339781,4,1,1,1,2,1,3,1,1,2 663 | 13454352,1,1,3,1,2,1,2,1,1,2 664 | 1345452,1,1,3,1,2,1,2,1,1,2 665 | 1345593,3,1,1,3,2,1,2,1,1,2 666 | 1347749,1,1,1,1,2,1,1,1,1,2 667 | 1347943,5,2,2,2,2,1,1,1,2,2 668 | 1348851,3,1,1,1,2,1,3,1,1,2 669 | 1350319,5,7,4,1,6,1,7,10,3,4 670 | 1350423,5,10,10,8,5,5,7,10,1,4 671 | 1352848,3,10,7,8,5,8,7,4,1,4 672 | 1353092,3,2,1,2,2,1,3,1,1,2 673 | 1354840,2,1,1,1,2,1,3,1,1,2 674 | 1354840,5,3,2,1,3,1,1,1,1,2 675 | 1355260,1,1,1,1,2,1,2,1,1,2 676 | 1365075,4,1,4,1,2,1,1,1,1,2 677 | 1365328,1,1,2,1,2,1,2,1,1,2 678 | 1368267,5,1,1,1,2,1,1,1,1,2 679 | 1368273,1,1,1,1,2,1,1,1,1,2 680 | 1368882,2,1,1,1,2,1,1,1,1,2 681 | 1369821,10,10,10,10,5,10,10,10,7,4 682 | 1371026,5,10,10,10,4,10,5,6,3,4 683 | 1371920,5,1,1,1,2,1,3,2,1,2 684 | 466906,1,1,1,1,2,1,1,1,1,2 685 | 466906,1,1,1,1,2,1,1,1,1,2 686 | 534555,1,1,1,1,2,1,1,1,1,2 687 | 536708,1,1,1,1,2,1,1,1,1,2 688 | 566346,3,1,1,1,2,1,2,3,1,2 689 | 603148,4,1,1,1,2,1,1,1,1,2 690 | 654546,1,1,1,1,2,1,1,1,8,2 691 | 654546,1,1,1,3,2,1,1,1,1,2 692 | 695091,5,10,10,5,4,5,4,4,1,4 693 | 714039,3,1,1,1,2,1,1,1,1,2 694 | 763235,3,1,1,1,2,1,2,1,2,2 695 | 776715,3,1,1,1,3,2,1,1,1,2 696 | 841769,2,1,1,1,2,1,1,1,1,2 697 | 888820,5,10,10,3,7,3,8,10,2,4 698 | 897471,4,8,6,4,3,4,10,6,1,4 699 | 897471,4,8,8,5,4,5,10,4,1,4 700 | -------------------------------------------------------------------------------- /datasets/breast-cancer-wisconsin.names.txt: -------------------------------------------------------------------------------- 1 | Citation Request: 2 | This breast cancer databases was obtained from the University of Wisconsin 3 | Hospitals, Madison from Dr. William H. Wolberg. If you publish results 4 | when using this database, then please include this information in your 5 | acknowledgements. Also, please cite one or more of: 6 | 7 | 1. O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear 8 | programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18. 9 | 10 | 2. William H. Wolberg and O.L. Mangasarian: "Multisurface method of 11 | pattern separation for medical diagnosis applied to breast cytology", 12 | Proceedings of the National Academy of Sciences, U.S.A., Volume 87, 13 | December 1990, pp 9193-9196. 14 | 15 | 3. O. L. Mangasarian, R. Setiono, and W.H. Wolberg: "Pattern recognition 16 | via linear programming: Theory and application to medical diagnosis", 17 | in: "Large-scale numerical optimization", Thomas F. Coleman and Yuying 18 | Li, editors, SIAM Publications, Philadelphia 1990, pp 22-30. 19 | 20 | 4. K. P. Bennett & O. L. Mangasarian: "Robust linear programming 21 | discrimination of two linearly inseparable sets", Optimization Methods 22 | and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers). 23 | 24 | 1. Title: Wisconsin Breast Cancer Database (January 8, 1991) 25 | 26 | 2. Sources: 27 | -- Dr. WIlliam H. Wolberg (physician) 28 | University of Wisconsin Hospitals 29 | Madison, Wisconsin 30 | USA 31 | -- Donor: Olvi Mangasarian (mangasarian@cs.wisc.edu) 32 | Received by David W. Aha (aha@cs.jhu.edu) 33 | -- Date: 15 July 1992 34 | 35 | 3. Past Usage: 36 | 37 | Attributes 2 through 10 have been used to represent instances. 38 | Each instance has one of 2 possible classes: benign or malignant. 39 | 40 | 1. Wolberg,~W.~H., \& Mangasarian,~O.~L. (1990). Multisurface method of 41 | pattern separation for medical diagnosis applied to breast cytology. In 42 | {\it Proceedings of the National Academy of Sciences}, {\it 87}, 43 | 9193--9196. 44 | -- Size of data set: only 369 instances (at that point in time) 45 | -- Collected classification results: 1 trial only 46 | -- Two pairs of parallel hyperplanes were found to be consistent with 47 | 50% of the data 48 | -- Accuracy on remaining 50% of dataset: 93.5% 49 | -- Three pairs of parallel hyperplanes were found to be consistent with 50 | 67% of data 51 | -- Accuracy on remaining 33% of dataset: 95.9% 52 | 53 | 2. Zhang,~J. (1992). Selecting typical instances in instance-based 54 | learning. In {\it Proceedings of the Ninth International Machine 55 | Learning Conference} (pp. 470--479). Aberdeen, Scotland: Morgan 56 | Kaufmann. 57 | -- Size of data set: only 369 instances (at that point in time) 58 | -- Applied 4 instance-based learning algorithms 59 | -- Collected classification results averaged over 10 trials 60 | -- Best accuracy result: 61 | -- 1-nearest neighbor: 93.7% 62 | -- trained on 200 instances, tested on the other 169 63 | -- Also of interest: 64 | -- Using only typical instances: 92.2% (storing only 23.1 instances) 65 | -- trained on 200 instances, tested on the other 169 66 | 67 | 4. Relevant Information: 68 | 69 | Samples arrive periodically as Dr. Wolberg reports his clinical cases. 70 | The database therefore reflects this chronological grouping of the data. 71 | This grouping information appears immediately below, having been removed 72 | from the data itself: 73 | 74 | Group 1: 367 instances (January 1989) 75 | Group 2: 70 instances (October 1989) 76 | Group 3: 31 instances (February 1990) 77 | Group 4: 17 instances (April 1990) 78 | Group 5: 48 instances (August 1990) 79 | Group 6: 49 instances (Updated January 1991) 80 | Group 7: 31 instances (June 1991) 81 | Group 8: 86 instances (November 1991) 82 | ----------------------------------------- 83 | Total: 699 points (as of the donated datbase on 15 July 1992) 84 | 85 | Note that the results summarized above in Past Usage refer to a dataset 86 | of size 369, while Group 1 has only 367 instances. This is because it 87 | originally contained 369 instances; 2 were removed. The following 88 | statements summarizes changes to the original Group 1's set of data: 89 | 90 | ##### Group 1 : 367 points: 200B 167M (January 1989) 91 | ##### Revised Jan 10, 1991: Replaced zero bare nuclei in 1080185 & 1187805 92 | ##### Revised Nov 22,1991: Removed 765878,4,5,9,7,10,10,10,3,8,1 no record 93 | ##### : Removed 484201,2,7,8,8,4,3,10,3,4,1 zero epithelial 94 | ##### : Changed 0 to 1 in field 6 of sample 1219406 95 | ##### : Changed 0 to 1 in field 8 of following sample: 96 | ##### : 1182404,2,3,1,1,1,2,0,1,1,1 97 | 98 | 5. Number of Instances: 699 (as of 15 July 1992) 99 | 100 | 6. Number of Attributes: 10 plus the class attribute 101 | 102 | 7. Attribute Information: (class attribute has been moved to last column) 103 | 104 | # Attribute Domain 105 | -- ----------------------------------------- 106 | 1. Sample code number id number 107 | 2. Clump Thickness 1 - 10 108 | 3. Uniformity of Cell Size 1 - 10 109 | 4. Uniformity of Cell Shape 1 - 10 110 | 5. Marginal Adhesion 1 - 10 111 | 6. Single Epithelial Cell Size 1 - 10 112 | 7. Bare Nuclei 1 - 10 113 | 8. Bland Chromatin 1 - 10 114 | 9. Normal Nucleoli 1 - 10 115 | 10. Mitoses 1 - 10 116 | 11. Class: (2 for benign, 4 for malignant) 117 | 118 | 8. Missing attribute values: 16 119 | 120 | There are 16 instances in Groups 1 to 6 that contain a single missing 121 | (i.e., unavailable) attribute value, now denoted by "?". 122 | 123 | 9. Class distribution: 124 | 125 | Benign: 458 (65.5%) 126 | Malignant: 241 (34.5%) 127 | -------------------------------------------------------------------------------- /datasets/chopstick-effectiveness.csv: -------------------------------------------------------------------------------- 1 | "Food.Pinching.Effeciency","Individual","Chopstick.Length" 2 | 19.55,1,180 3 | 27.24,2,180 4 | 28.76,3,180 5 | 31.19,4,180 6 | 21.91,5,180 7 | 27.62,6,180 8 | 29.46,7,180 9 | 26.35,8,180 10 | 26.69,9,180 11 | 30.22,10,180 12 | 27.81,11,180 13 | 23.46,12,180 14 | 23.64,13,180 15 | 27.85,14,180 16 | 20.62,15,180 17 | 25.35,16,180 18 | 28,17,180 19 | 23.49,18,180 20 | 27.77,19,180 21 | 18.48,20,180 22 | 23.01,21,180 23 | 22.66,22,180 24 | 23.24,23,180 25 | 22.82,24,180 26 | 17.94,25,180 27 | 26.67,26,180 28 | 28.98,27,180 29 | 21.48,28,180 30 | 14.47,29,180 31 | 28.29,30,180 32 | 27.97,31,180 33 | 23.53,1,210 34 | 26.39,2,210 35 | 30.9,3,210 36 | 26.05,4,210 37 | 23.27,5,210 38 | 29.17,6,210 39 | 30.93,7,210 40 | 17.55,8,210 41 | 32.55,9,210 42 | 28.87,10,210 43 | 26.53,11,210 44 | 25.26,12,210 45 | 25.65,13,210 46 | 29.39,14,210 47 | 23.26,15,210 48 | 24.77,16,210 49 | 25.42,17,210 50 | 23.65,18,210 51 | 32.22,19,210 52 | 18.86,20,210 53 | 21.75,21,210 54 | 23.07,22,210 55 | 22.3,23,210 56 | 27.04,24,210 57 | 22.24,25,210 58 | 24.87,26,210 59 | 30.85,27,210 60 | 21.15,28,210 61 | 16.47,29,210 62 | 29.05,30,210 63 | 26.99,31,210 64 | 21.34,1,240 65 | 29.94,2,240 66 | 32.95,3,240 67 | 29.4,4,240 68 | 22.32,5,240 69 | 28.36,6,240 70 | 28.49,7,240 71 | 22.24,8,240 72 | 36.15,9,240 73 | 30.62,10,240 74 | 26.53,11,240 75 | 27.95,12,240 76 | 31.49,13,240 77 | 30.24,14,240 78 | 24.8,15,240 79 | 26.43,16,240 80 | 29.35,17,240 81 | 21.15,18,240 82 | 29.18,19,240 83 | 21.6,20,240 84 | 25.39,21,240 85 | 22.26,22,240 86 | 24.85,23,240 87 | 24.56,24,240 88 | 16.35,25,240 89 | 22.96,26,240 90 | 25.82,27,240 91 | 19.46,28,240 92 | 23.6,29,240 93 | 33.1,30,240 94 | 27.13,31,240 95 | 24.4,1,270 96 | 25.88,2,270 97 | 27.97,3,270 98 | 24.54,4,270 99 | 22.66,5,270 100 | 28.94,6,270 101 | 30.72,7,270 102 | 16.7,8,270 103 | 30.27,9,270 104 | 26.29,10,270 105 | 22.33,11,270 106 | 24.85,12,270 107 | 24.33,13,270 108 | 24.5,14,270 109 | 22.67,15,270 110 | 22.28,16,270 111 | 23.8,17,270 112 | 25.36,18,270 113 | 29.5,19,270 114 | 20.19,20,270 115 | 20.14,21,270 116 | 21.09,22,270 117 | 24.78,23,270 118 | 24.74,24,270 119 | 22.73,25,270 120 | 21.08,26,270 121 | 25.7,27,270 122 | 19.79,28,270 123 | 16.82,29,270 124 | 31.15,30,270 125 | 27.84,31,270 126 | 22.5,1,300 127 | 23.1,2,300 128 | 28.26,3,300 129 | 25.55,4,300 130 | 16.71,5,300 131 | 27.88,6,300 132 | 31.07,7,300 133 | 23.44,8,300 134 | 28.82,9,300 135 | 27.77,10,300 136 | 24.54,11,300 137 | 24.55,12,300 138 | 27.78,13,300 139 | 26.14,14,300 140 | 23.44,15,300 141 | 26.44,16,300 142 | 27.47,17,300 143 | 24.94,18,300 144 | 29.68,19,300 145 | 24.33,20,300 146 | 25.42,21,300 147 | 24.64,22,300 148 | 22.78,23,300 149 | 26.5,24,300 150 | 18.71,25,300 151 | 22.86,26,300 152 | 25.09,27,300 153 | 19.72,28,300 154 | 17.05,29,300 155 | 30.91,30,300 156 | 25.92,31,300 157 | 21.32,1,330 158 | 26.18,2,330 159 | 25.93,3,330 160 | 28.61,4,330 161 | 20.54,5,330 162 | 26.44,6,330 163 | 29.36,7,330 164 | 19.77,8,330 165 | 31.69,9,330 166 | 24.64,10,330 167 | 22.09,11,330 168 | 23.42,12,330 169 | 28.63,13,330 170 | 26.3,14,330 171 | 22.89,15,330 172 | 22.68,16,330 173 | 30.92,17,330 174 | 20.74,18,330 175 | 27.24,19,330 176 | 17.12,20,330 177 | 23.63,21,330 178 | 20.91,22,330 179 | 23.49,23,330 180 | 24.86,24,330 181 | 16.28,25,330 182 | 21.52,26,330 183 | 27.22,27,330 184 | 17.41,28,330 185 | 16.42,29,330 186 | 28.22,30,330 187 | 27.52,31,330 188 | -------------------------------------------------------------------------------- /datasets/downloaded_medals.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/felipesanma/Intro_to_DataScience/8d4b1381a534a2dc4ce48b8a2a666464b1d2d492/datasets/downloaded_medals.xls -------------------------------------------------------------------------------- /datasets/iris.csv: -------------------------------------------------------------------------------- 1 | Sepal.Length,Sepal.Width,Petal.Length,Petal.Width,Species 2 | 5.1,3.5,1.4,0.2,setosa 3 | 4.9,3,1.4,0.2,setosa 4 | 4.7,3.2,1.3,0.2,setosa 5 | 4.6,3.1,1.5,0.2,setosa 6 | 5,3.6,1.4,0.2,setosa 7 | 5.4,3.9,1.7,0.4,setosa 8 | 4.6,3.4,1.4,0.3,setosa 9 | 5,3.4,1.5,0.2,setosa 10 | 4.4,2.9,1.4,0.2,setosa 11 | 4.9,3.1,1.5,0.1,setosa 12 | 5.4,3.7,1.5,0.2,setosa 13 | 4.8,3.4,1.6,0.2,setosa 14 | 4.8,3,1.4,0.1,setosa 15 | 4.3,3,1.1,0.1,setosa 16 | 5.8,4,1.2,0.2,setosa 17 | 5.7,4.4,1.5,0.4,setosa 18 | 5.4,3.9,1.3,0.4,setosa 19 | 5.1,3.5,1.4,0.3,setosa 20 | 5.7,3.8,1.7,0.3,setosa 21 | 5.1,3.8,1.5,0.3,setosa 22 | 5.4,3.4,1.7,0.2,setosa 23 | 5.1,3.7,1.5,0.4,setosa 24 | 4.6,3.6,1,0.2,setosa 25 | 5.1,3.3,1.7,0.5,setosa 26 | 4.8,3.4,1.9,0.2,setosa 27 | 5,3,1.6,0.2,setosa 28 | 5,3.4,1.6,0.4,setosa 29 | 5.2,3.5,1.5,0.2,setosa 30 | 5.2,3.4,1.4,0.2,setosa 31 | 4.7,3.2,1.6,0.2,setosa 32 | 4.8,3.1,1.6,0.2,setosa 33 | 5.4,3.4,1.5,0.4,setosa 34 | 5.2,4.1,1.5,0.1,setosa 35 | 5.5,4.2,1.4,0.2,setosa 36 | 4.9,3.1,1.5,0.2,setosa 37 | 5,3.2,1.2,0.2,setosa 38 | 5.5,3.5,1.3,0.2,setosa 39 | 4.9,3.6,1.4,0.1,setosa 40 | 4.4,3,1.3,0.2,setosa 41 | 5.1,3.4,1.5,0.2,setosa 42 | 5,3.5,1.3,0.3,setosa 43 | 4.5,2.3,1.3,0.3,setosa 44 | 4.4,3.2,1.3,0.2,setosa 45 | 5,3.5,1.6,0.6,setosa 46 | 5.1,3.8,1.9,0.4,setosa 47 | 4.8,3,1.4,0.3,setosa 48 | 5.1,3.8,1.6,0.2,setosa 49 | 4.6,3.2,1.4,0.2,setosa 50 | 5.3,3.7,1.5,0.2,setosa 51 | 5,3.3,1.4,0.2,setosa 52 | 7,3.2,4.7,1.4,versicolor 53 | 6.4,3.2,4.5,1.5,versicolor 54 | 6.9,3.1,4.9,1.5,versicolor 55 | 5.5,2.3,4,1.3,versicolor 56 | 6.5,2.8,4.6,1.5,versicolor 57 | 5.7,2.8,4.5,1.3,versicolor 58 | 6.3,3.3,4.7,1.6,versicolor 59 | 4.9,2.4,3.3,1,versicolor 60 | 6.6,2.9,4.6,1.3,versicolor 61 | 5.2,2.7,3.9,1.4,versicolor 62 | 5,2,3.5,1,versicolor 63 | 5.9,3,4.2,1.5,versicolor 64 | 6,2.2,4,1,versicolor 65 | 6.1,2.9,4.7,1.4,versicolor 66 | 5.6,2.9,3.6,1.3,versicolor 67 | 6.7,3.1,4.4,1.4,versicolor 68 | 5.6,3,4.5,1.5,versicolor 69 | 5.8,2.7,4.1,1,versicolor 70 | 6.2,2.2,4.5,1.5,versicolor 71 | 5.6,2.5,3.9,1.1,versicolor 72 | 5.9,3.2,4.8,1.8,versicolor 73 | 6.1,2.8,4,1.3,versicolor 74 | 6.3,2.5,4.9,1.5,versicolor 75 | 6.1,2.8,4.7,1.2,versicolor 76 | 6.4,2.9,4.3,1.3,versicolor 77 | 6.6,3,4.4,1.4,versicolor 78 | 6.8,2.8,4.8,1.4,versicolor 79 | 6.7,3,5,1.7,versicolor 80 | 6,2.9,4.5,1.5,versicolor 81 | 5.7,2.6,3.5,1,versicolor 82 | 5.5,2.4,3.8,1.1,versicolor 83 | 5.5,2.4,3.7,1,versicolor 84 | 5.8,2.7,3.9,1.2,versicolor 85 | 6,2.7,5.1,1.6,versicolor 86 | 5.4,3,4.5,1.5,versicolor 87 | 6,3.4,4.5,1.6,versicolor 88 | 6.7,3.1,4.7,1.5,versicolor 89 | 6.3,2.3,4.4,1.3,versicolor 90 | 5.6,3,4.1,1.3,versicolor 91 | 5.5,2.5,4,1.3,versicolor 92 | 5.5,2.6,4.4,1.2,versicolor 93 | 6.1,3,4.6,1.4,versicolor 94 | 5.8,2.6,4,1.2,versicolor 95 | 5,2.3,3.3,1,versicolor 96 | 5.6,2.7,4.2,1.3,versicolor 97 | 5.7,3,4.2,1.2,versicolor 98 | 5.7,2.9,4.2,1.3,versicolor 99 | 6.2,2.9,4.3,1.3,versicolor 100 | 5.1,2.5,3,1.1,versicolor 101 | 5.7,2.8,4.1,1.3,versicolor 102 | 6.3,3.3,6,2.5,virginica 103 | 5.8,2.7,5.1,1.9,virginica 104 | 7.1,3,5.9,2.1,virginica 105 | 6.3,2.9,5.6,1.8,virginica 106 | 6.5,3,5.8,2.2,virginica 107 | 7.6,3,6.6,2.1,virginica 108 | 4.9,2.5,4.5,1.7,virginica 109 | 7.3,2.9,6.3,1.8,virginica 110 | 6.7,2.5,5.8,1.8,virginica 111 | 7.2,3.6,6.1,2.5,virginica 112 | 6.5,3.2,5.1,2,virginica 113 | 6.4,2.7,5.3,1.9,virginica 114 | 6.8,3,5.5,2.1,virginica 115 | 5.7,2.5,5,2,virginica 116 | 5.8,2.8,5.1,2.4,virginica 117 | 6.4,3.2,5.3,2.3,virginica 118 | 6.5,3,5.5,1.8,virginica 119 | 7.7,3.8,6.7,2.2,virginica 120 | 7.7,2.6,6.9,2.3,virginica 121 | 6,2.2,5,1.5,virginica 122 | 6.9,3.2,5.7,2.3,virginica 123 | 5.6,2.8,4.9,2,virginica 124 | 7.7,2.8,6.7,2,virginica 125 | 6.3,2.7,4.9,1.8,virginica 126 | 6.7,3.3,5.7,2.1,virginica 127 | 7.2,3.2,6,1.8,virginica 128 | 6.2,2.8,4.8,1.8,virginica 129 | 6.1,3,4.9,1.8,virginica 130 | 6.4,2.8,5.6,2.1,virginica 131 | 7.2,3,5.8,1.6,virginica 132 | 7.4,2.8,6.1,1.9,virginica 133 | 7.9,3.8,6.4,2,virginica 134 | 6.4,2.8,5.6,2.2,virginica 135 | 6.3,2.8,5.1,1.5,virginica 136 | 6.1,2.6,5.6,1.4,virginica 137 | 7.7,3,6.1,2.3,virginica 138 | 6.3,3.4,5.6,2.4,virginica 139 | 6.4,3.1,5.5,1.8,virginica 140 | 6,3,4.8,1.8,virginica 141 | 6.9,3.1,5.4,2.1,virginica 142 | 6.7,3.1,5.6,2.4,virginica 143 | 6.9,3.1,5.1,2.3,virginica 144 | 5.8,2.7,5.1,1.9,virginica 145 | 6.8,3.2,5.9,2.3,virginica 146 | 6.7,3.3,5.7,2.5,virginica 147 | 6.7,3,5.2,2.3,virginica 148 | 6.3,2.5,5,1.9,virginica 149 | 6.5,3,5.2,2,virginica 150 | 6.2,3.4,5.4,2.3,virginica 151 | 5.9,3,5.1,1.8,virginica 152 | -------------------------------------------------------------------------------- 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Ejemplo de un módulo Python. Contiene una variable llamada mi_variable, 4 | una función llamada mi_function, y una clase llamada MiClase. 5 | """ 6 | 7 | mi_variable = 0 8 | 9 | def mi_function(): 10 | """ 11 | Función ejemplo 12 | """ 13 | return mi_variable 14 | 15 | class MiClase: 16 | """ 17 | Clase ejemplo. 18 | """ 19 | 20 | def __init__(self): 21 | self.variable = mi_variable 22 | 23 | def set_variable(self, nuevo_valor): 24 | """ 25 | Asigna self.variable a un nuevo valor 26 | """ 27 | self.variable = nuevo_valor 28 | 29 | def get_variable(self): 30 | return self.variable 31 | -------------------------------------------------------------------------------- /notebooks/00_00_Introducción a Jupyter.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 0 - Introducción a Jupyter" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## 0.1 - Corriendo código de Python\n", 15 | "En su versión mas sencilla, cada una de las celdas se puede pensar como un **snippet de codigo que se puede ejecutar independientemente** de las demas.\n", 16 | "Para ejecutar la siguiente celda, selccionarla y apretar el boton ubicado en la barra superior, o equivalentemente, presionar Shift + Enter" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 2, 22 | "metadata": { 23 | "scrolled": false 24 | }, 25 | "outputs": [ 26 | { 27 | "name": "stdout", 28 | "output_type": "stream", 29 | "text": [ 30 | "Hola notebook!\n" 31 | ] 32 | } 33 | ], 34 | "source": [ 35 | "print('Hola notebook!')" 36 | ] 37 | }, 38 | { 39 | "cell_type": "markdown", 40 | "metadata": {}, 41 | "source": [ 42 | "Pueden ver una lista completa de todos los shortcuts disponibles en el menu Help Keyboard Shortcuts o con el shortcut H\n", 43 | "
\n", 44 | "Shortcuts importantes:\n", 45 | "- Click para selccionar una celda\n", 46 | "- Shift + Enter para ejecutar una celda y seleccionar la celda siguiente" 47 | ] 48 | }, 49 | { 50 | "cell_type": "markdown", 51 | "metadata": {}, 52 | "source": [ 53 | "Cada celda es independiente de las demás, pero **comparten el mismo contexto**. Definiciones de variables, funciones y clases, e importaciones de módulos dentro de una celda, permiten que las siguientes celdas tengan acceso a éstas.\n", 54 | "\n", 55 | "Si no sabes que son las variables, funciones y clases, tranquilo sólo quedate con que: *lo que ejecutes desde la primera celda, estará disponible para usar en las siguientes celdas*" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 3, 61 | "metadata": {}, 62 | "outputs": [], 63 | "source": [ 64 | "import getpass\n", 65 | "# defino variable en una celda\n", 66 | "user = getpass.getuser()" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": 4, 72 | "metadata": { 73 | "scrolled": true 74 | }, 75 | "outputs": [ 76 | { 77 | "name": "stdout", 78 | "output_type": "stream", 79 | "text": [ 80 | "Hola fsanmartin!\n" 81 | ] 82 | } 83 | ], 84 | "source": [ 85 | "# accedo a la variable definida en la celda anterior\n", 86 | "print (\"Hola {}!\".format(user))" 87 | ] 88 | }, 89 | { 90 | "cell_type": "markdown", 91 | "metadata": {}, 92 | "source": [ 93 | "Una diferencia notable con un script.py es que **no estamos obligados a ejecutar celdas en un orden preestablecido**. De todas maneras, es recomendable ordenar un notebook según el orden de lectura estandar para evitar confusiones." 94 | ] 95 | }, 96 | { 97 | "cell_type": "markdown", 98 | "metadata": {}, 99 | "source": [ 100 | "## 0.2 - Más que solo Python\n", 101 | "El contenido de las celdas no está limitado a solo código de Python. Una alternativa posible es **Markdown**, que es muy útil para explicar las ideas detras de cada snippet de código y para guiar al lector en la ejecución del notebook (como venimos haciendo hasta ahora).\n", 102 | "\n", 103 | "Y extendiendolo aún mas, es posible insertar código html para insertar imágenes, audios, videos y todas las alternativas que esto provee. Esto es especialmente útil para visualizar información y generar gráficos con datos generados por los snippets de código del notebook." 104 | ] 105 | }, 106 | { 107 | "cell_type": "markdown", 108 | "metadata": {}, 109 | "source": [ 110 | "En modo Markdown está habilitada la interpretación de código html, con algunas excepciones por temas de seguridad y consistencia del notebook (ej: interpretación de <script>, <style> y atributos relacionados). " 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "metadata": {}, 116 | "source": [ 117 | "## 0.3 - Integración con bash\n", 118 | "\n", 119 | "De forma nativa jupyter tiene integración con comandos de bash. Estos son reconocidos mediante el prefijo !. Por ej, si quisiera ver qué versión de python estoy corriendo, podría correr:" 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 6, 125 | "metadata": {}, 126 | "outputs": [ 127 | { 128 | "name": "stdout", 129 | "output_type": "stream", 130 | "text": [ 131 | "Python 3.7.3\n" 132 | ] 133 | } 134 | ], 135 | "source": [ 136 | "!python --version" 137 | ] 138 | }, 139 | { 140 | "cell_type": "markdown", 141 | "metadata": {}, 142 | "source": [ 143 | "Notar que la celda anterior es interpretada como una celda de código al igual que las celdas de que contiene código python. Esto permite que dentro de una celda conviva código de python y de bash sin problemas. \n", 144 | "\n", 145 | "Para ejemplificar esto, usemos el comando **ls** que lista los archivos de la carpeta en la cual estemos parados:" 146 | ] 147 | }, 148 | { 149 | "cell_type": "code", 150 | "execution_count": 8, 151 | "metadata": {}, 152 | "outputs": [ 153 | { 154 | "name": "stdout", 155 | "output_type": "stream", 156 | "text": [ 157 | "0 - Introducción a Jupyter.ipynb\n", 158 | "01_Cargar un Zip en Jupyter.ipynb\n", 159 | "02_Limpieza_de_Datos.ipynb\n", 160 | "03_Data_Wrangling.ipynb\n", 161 | "04_Conceptos_básicos_de_estadísticas.ipynb\n", 162 | "05_Regresión_Lineal.ipynb\n", 163 | "06_Regresión_Logística.ipynb\n", 164 | "07_Clustering_y_clasificación.ipynb\n" 165 | ] 166 | } 167 | ], 168 | "source": [ 169 | "files_in_this_folder = !ls\n", 170 | "for filename in files_in_this_folder:\n", 171 | " if filename[-6:] == '.ipynb':\n", 172 | " print(filename)" 173 | ] 174 | }, 175 | { 176 | "cell_type": "markdown", 177 | "metadata": {}, 178 | "source": [ 179 | "## 0.4 - Jupyter Magic\n", 180 | "\n", 181 | "Extendiendo la capacidad de jupyter de interpretar Python, Bash y Markdown, existen comandos especiales que son parte de la *magia de jupyter*. Estos comandos empiezan con % - para evaluaciones en una sola linea - y con %% para evaluaciones multi linea. Un ejemplo de esto es la posibilidad de insertar html excediendo las capacidad de Markdown y tener la posibilidad de insertar scripts de javascript:" 182 | ] 183 | }, 184 | { 185 | "cell_type": "code", 186 | "execution_count": 9, 187 | "metadata": {}, 188 | "outputs": [ 189 | { 190 | "data": { 191 | "text/html": [ 192 | "\n", 193 | "No me has clickeado todavía.\n", 194 | "\n" 204 | ], 205 | "text/plain": [ 206 | "" 207 | ] 208 | }, 209 | "metadata": {}, 210 | "output_type": "display_data" 211 | } 212 | ], 213 | "source": [ 214 | "%%html\n", 215 | "\n", 216 | "No me has clickeado todavía.\n", 217 | "" 227 | ] 228 | }, 229 | { 230 | "cell_type": "markdown", 231 | "metadata": {}, 232 | "source": [ 233 | "Para listar por completo todas las magias con las que viene jupyter, existe el comando % lsmagic" 234 | ] 235 | }, 236 | { 237 | "cell_type": "code", 238 | "execution_count": null, 239 | "metadata": {}, 240 | "outputs": [], 241 | "source": [ 242 | "% lsmagic" 243 | ] 244 | }, 245 | { 246 | "cell_type": "markdown", 247 | "metadata": {}, 248 | "source": [ 249 | "## 0.5 - Servidor remoto\n", 250 | "\n", 251 | "La principal interfaz de Jupyter involucra un navegador para proveer la interfaz al usuario, pero la ejecución de código se realiza del lado del servidor. Esto permite que, habiendo hecho las configuraciones pertinentes, el usuario acceda de forma remota al servidor. Por ej, en la necesidad de procesar una gran cantidad de información, se puede dejar corriendo un servidor de jupyter en una computadora con mejores recursos de hardware y acceder a éste desde cualquier computadora que cuente con un navegador y una conexión a internet.\n", 252 | "\n", 253 | "Para mas información, leer la [documentación oficial de jupyter](http://jupyter-notebook.readthedocs.io/en/latest/public_server.html) sobre este tema" 254 | ] 255 | }, 256 | { 257 | "cell_type": "markdown", 258 | "metadata": {}, 259 | "source": [ 260 | "## 0.6 - Extensiones de la comunidad\n", 261 | "\n", 262 | "Con la rápida adopción y la creciente comunidad de usuarios de Jupyter, han aparecido una gran cantidad de extensiones que le agregan funcionalidades. Éstas se encuentran en el [repositorio de extensiones](https://github.com/ipython-contrib/jupyter_contrib_nbextensions) de Jupyter.\n", 263 | "\n", 264 | "Las extensiones disponibles ofrecen un amplio rango de funcionalidades agregadas. Desde correctores ortográficos hasta generadores automaticos de tablas de contenidos:\n", 265 | "\n", 266 | "" 267 | ] 268 | }, 269 | { 270 | "cell_type": "markdown", 271 | "metadata": {}, 272 | "source": [ 273 | "# Referencias\n", 274 | "\n", 275 | "* https://github.com/datosgobar/taller-analisis-datos-101" 276 | ] 277 | }, 278 | { 279 | "cell_type": "code", 280 | "execution_count": null, 281 | "metadata": {}, 282 | "outputs": [], 283 | "source": [] 284 | } 285 | ], 286 | "metadata": { 287 | "kernelspec": { 288 | "display_name": "Python 3", 289 | "language": "python", 290 | "name": "python3" 291 | }, 292 | "language_info": { 293 | "codemirror_mode": { 294 | "name": "ipython", 295 | "version": 3 296 | }, 297 | "file_extension": ".py", 298 | "mimetype": "text/x-python", 299 | "name": "python", 300 | "nbconvert_exporter": "python", 301 | "pygments_lexer": "ipython3", 302 | "version": "3.7.3" 303 | } 304 | }, 305 | "nbformat": 4, 306 | "nbformat_minor": 1 307 | } 308 | -------------------------------------------------------------------------------- /notebooks/01_02_Intro_DataScience_Pandas.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Pandas \n", 8 | "\n", 9 | "Pandas (acrónimo para Panel Data) es un módulo orientado a la manipulación y limpieza de estructuras de datos.\n", 10 | "\n", 11 | "Se suele utilizar en conjunto a otros módulos como numpy , scipy y matplotlib para el análisis de datos.\n", 12 | "\n", 13 | "### ¿Por qué pandas ?\n", 14 | "\n", 15 | "1. Es una colección de funciones y algoritmos que implementan las principales convenciones sobre el análisis de datos.\n", 16 | "\n", 17 | "2. Permite importar distintos archivos de datos con procedimientos robustos permiten centrar al investigador en lo substancial y no en procesar archivos.\n", 18 | "3. Genera un objeto DataFrame con una estructura de matriz (Filas y Columnas) que resulta intuitiva para desarrollar el análisis orientado en variables y segmentado por casos.\n", 19 | "4. Presenta una amplia gama de funciones y procedimientos comunes generadas en los objetos.\n", 20 | "\n", 21 | "\n", 22 | "\n", 23 | "## Importando archivos con pandas\n", 24 | "\n", 25 | "Usualmente trabajaremos con datos en un formato de texto plano, llámese csv o xlsx .\n", 26 | "\n", 27 | "Ahora generaremos nuestra primera interacción con pandas :\n", 28 | "\n", 29 | "* La convención indica que pandas se abrevia de forma pd .\n" 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 1, 35 | "metadata": {}, 36 | "outputs": [], 37 | "source": [ 38 | "import pandas as pd" 39 | ] 40 | }, 41 | { 42 | "cell_type": "markdown", 43 | "metadata": {}, 44 | "source": [ 45 | "* Dentro de nuestro directorio de trabajo encontraremos el archivo alumnos.csv .\n", 46 | "* Un archivo csv (comma separated value) presenta el nombre de los atributos en la primera fila. Después de ella, le siguen las observaciones ingresadas.\n", 47 | "\n", 48 | "* Cada observación está separada mediante comas (de ahí el nombre archivo separado por comas).\n" 49 | ] 50 | }, 51 | { 52 | "cell_type": "code", 53 | "execution_count": 2, 54 | "metadata": {}, 55 | "outputs": [ 56 | { 57 | "name": "stdout", 58 | "output_type": "stream", 59 | "text": [ 60 | "nombre,altura,peso,edad,sexo\n", 61 | "Hugo,1.67,60,23,h\n", 62 | "Paco,1.73,83,25,h\n", 63 | "Luis,1.62,70,28,h\n", 64 | "Diana,1.58,58,21,m\n", 65 | "Francisco,1.86,98,28,h\n", 66 | "Felipe,1.79,100,26,h\n", 67 | "Jacinta,1.69,62,20,m\n", 68 | "Bernardo,1.6,83,31,h\n", 69 | "Marisol,1.6,56,30,m\n" 70 | ] 71 | } 72 | ], 73 | "source": [ 74 | "# solicitemos las primeras filas del archivo alumnos (desde bash)\n", 75 | "!head alumnos.csv" 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": {}, 81 | "source": [ 82 | "Para ingresar éste archivo a nuestro notebook, utilizaremos la función read_csv de pandas.\n", 83 | "\n", 84 | "## Digresión: ¿Cómo llamar funciones dentro de un módulo?\n", 85 | "\n", 86 | "Para llamar una función y/u objeto específico dentro de nuestro módulo importado, utilizamos la siguiente sintáxis:\n", 87 | "\n", 88 | "$$ modulo.funcion $$\n", 89 | "\n", 90 | "Vamos a generar un objeto llamado df (contracción de dataframe) mediante read_csv" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 3, 96 | "metadata": {}, 97 | "outputs": [], 98 | "source": [ 99 | "df = pd.read_csv('alumnos.csv')" 100 | ] 101 | }, 102 | { 103 | "cell_type": "markdown", 104 | "metadata": {}, 105 | "source": [ 106 | "Si todo resulta bien, podemos ver las primeras 5 observaciones de nuestro nuevo objeto con df.head().\n" 107 | ] 108 | }, 109 | { 110 | "cell_type": "code", 111 | "execution_count": 4, 112 | "metadata": {}, 113 | "outputs": [ 114 | { 115 | "data": { 116 | "text/html": [ 117 | "
\n", 118 | "\n", 131 | "\n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | "
nombrealturapesoedadsexo
0Hugo1.676023h
1Paco1.738325h
2Luis1.627028h
3Diana1.585821m
4Francisco1.869828h
\n", 185 | "
" 186 | ], 187 | "text/plain": [ 188 | " nombre altura peso edad sexo\n", 189 | "0 Hugo 1.67 60 23 h\n", 190 | "1 Paco 1.73 83 25 h\n", 191 | "2 Luis 1.62 70 28 h\n", 192 | "3 Diana 1.58 58 21 m\n", 193 | "4 Francisco 1.86 98 28 h" 194 | ] 195 | }, 196 | "execution_count": 4, 197 | "metadata": {}, 198 | "output_type": "execute_result" 199 | } 200 | ], 201 | "source": [ 202 | "df.head()" 203 | ] 204 | }, 205 | { 206 | "cell_type": "markdown", 207 | "metadata": {}, 208 | "source": [ 209 | "Los resultados son idénticos que el archivo alumnos.csv , con la salvedad que están mejor presentados.\n", 210 | "\n", 211 | "El resultado de head es la representación en filas y columnas.\n", 212 | "\n", 213 | "Hay algunas salvedades a destacar:\n", 214 | "\n", 215 | " 1. En Python, los índices comienzan en 0.\n", 216 | " 2. La primera columna de nuestra tabla corresponde a la posición de la fila respecto al DataFrame. Esta información nos facilitará segmentación de archivos.\n", 217 | "\n", 218 | "\n", 219 | "## DataFrame\n", 220 | "\n", 221 | "El objeto df que creamos recientemente es un objeto DataFrame, una de las estructuras elementales de pandas .\n", 222 | "\n", 223 | "Un objeto DataFrame representa una tabla rectangular de datos compuestas por filas (observaciones registradas en el archivo) y una serie de columnas (atributos medibles que pueden ser integer, float, string, boolean, etc...).\n", 224 | "\n", 225 | "Las observaciones registradas son insertadas en bloques bidimensionales que responden a la notación de matrices.\n", 226 | "\n", 227 | "Podemos inspeccionar las dimensiones de la tabla mediante .shape. Éste nos informará de la cantidad de filas y columnas.\n" 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": 5, 233 | "metadata": {}, 234 | "outputs": [ 235 | { 236 | "data": { 237 | "text/plain": [ 238 | "(21, 5)" 239 | ] 240 | }, 241 | "execution_count": 5, 242 | "metadata": {}, 243 | "output_type": "execute_result" 244 | } 245 | ], 246 | "source": [ 247 | "df.shape" 248 | ] 249 | }, 250 | { 251 | "cell_type": "markdown", 252 | "metadata": {}, 253 | "source": [ 254 | "Esta tabla es manipulable y segmentable. Imaginemos que ahora desamos extraer sólo las primeras 3 observaciones de nuestra tabla. La operación se realiza de la siguiente manera" 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": 7, 260 | "metadata": {}, 261 | "outputs": [ 262 | { 263 | "data": { 264 | "text/html": [ 265 | "
\n", 266 | "\n", 279 | "\n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | "
nombrealturapesoedadsexo
0Hugo1.676023h
1Paco1.738325h
2Luis1.627028h
\n", 317 | "
" 318 | ], 319 | "text/plain": [ 320 | " nombre altura peso edad sexo\n", 321 | "0 Hugo 1.67 60 23 h\n", 322 | "1 Paco 1.73 83 25 h\n", 323 | "2 Luis 1.62 70 28 h" 324 | ] 325 | }, 326 | "execution_count": 7, 327 | "metadata": {}, 328 | "output_type": "execute_result" 329 | } 330 | ], 331 | "source": [ 332 | "df[:3]" 333 | ] 334 | }, 335 | { 336 | "cell_type": "markdown", 337 | "metadata": {}, 338 | "source": [ 339 | "Python interpretó esta instrucción como \"dentro de la tabla df , muéstrame las observaciones hasta la 5\".\n", 340 | "\n", 341 | "Eso se generó dentro de los brackets [] , donde pasamos un operador: que se llama slice y permite instruír hasta dónde se puede cortar un elemento.\n", 342 | "\n", 343 | "En el caso anterior utilizamos slice para generar una submuestra hasta cierta condición (que se evalúa por el índice de la tabla; la primera columna).\n", 344 | "\n", 345 | "¿Qué pasa si deseamos generar una segmentación desde un valor en específico? Utilizamos la siguiente sintáxis:" 346 | ] 347 | }, 348 | { 349 | "cell_type": "code", 350 | "execution_count": 8, 351 | "metadata": {}, 352 | "outputs": [ 353 | { 354 | "data": { 355 | "text/html": [ 356 | "
\n", 357 | "\n", 370 | "\n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | " \n", 391 | " \n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | "
nombrealturapesoedadsexo
13Diego1.627823h
14Gonzalo1.586722h
15Alejandra1.867421m
16Fernando1.799327h
17Carolina1.606328m
18Vicente1.9810231h
19Benjamín1.727836h
20Gloria1.586523m
\n", 448 | "
" 449 | ], 450 | "text/plain": [ 451 | " nombre altura peso edad sexo\n", 452 | "13 Diego 1.62 78 23 h\n", 453 | "14 Gonzalo 1.58 67 22 h\n", 454 | "15 Alejandra 1.86 74 21 m\n", 455 | "16 Fernando 1.79 93 27 h\n", 456 | "17 Carolina 1.60 63 28 m\n", 457 | "18 Vicente 1.98 102 31 h\n", 458 | "19 Benjamín 1.72 78 36 h\n", 459 | "20 Gloria 1.58 65 23 m" 460 | ] 461 | }, 462 | "execution_count": 8, 463 | "metadata": {}, 464 | "output_type": "execute_result" 465 | } 466 | ], 467 | "source": [ 468 | "df[13:]" 469 | ] 470 | }, 471 | { 472 | "cell_type": "markdown", 473 | "metadata": {}, 474 | "source": [ 475 | "Acá instruímos a la tabla que entregue los resultados desde la fila 13 hasta el final de las observaciones.\n", 476 | "\n", 477 | "¿Y si queremos seleccionar entre dos valores? Utilizamos la siguiente sintáxis:" 478 | ] 479 | }, 480 | { 481 | "cell_type": "code", 482 | "execution_count": 9, 483 | "metadata": {}, 484 | "outputs": [ 485 | { 486 | "data": { 487 | "text/html": [ 488 | "
\n", 489 | "\n", 502 | "\n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | " \n", 523 | " \n", 524 | " \n", 525 | " \n", 526 | " \n", 527 | " \n", 528 | " \n", 529 | " \n", 530 | " \n", 531 | " \n", 532 | " \n", 533 | " \n", 534 | " \n", 535 | " \n", 536 | " \n", 537 | " \n", 538 | " \n", 539 | " \n", 540 | " \n", 541 | " \n", 542 | " \n", 543 | " \n", 544 | " \n", 545 | " \n", 546 | " \n", 547 | " \n", 548 | " \n", 549 | " \n", 550 | " \n", 551 | " \n", 552 | " \n", 553 | " \n", 554 | " \n", 555 | " \n", 556 | " \n", 557 | " \n", 558 | " \n", 559 | " \n", 560 | " \n", 561 | " \n", 562 | " \n", 563 | " \n", 564 | " \n", 565 | " \n", 566 | " \n", 567 | " \n", 568 | " \n", 569 | " \n", 570 | " \n", 571 | " \n", 572 | " \n", 573 | " \n", 574 | " \n", 575 | " \n", 576 | " \n", 577 | " \n", 578 | " \n", 579 | " \n", 580 | " \n", 581 | " \n", 582 | " \n", 583 | " \n", 584 | " \n", 585 | " \n", 586 | " \n", 587 | " \n", 588 | " \n", 589 | " \n", 590 | " \n", 591 | " \n", 592 | " \n", 593 | " \n", 594 | " \n", 595 | "
nombrealturapesoedadsexo
3Diana1.585821m
4Francisco1.869828h
5Felipe1.7910026h
6Jacinta1.696220m
7Bernardo1.608331h
8Marisol1.605630m
9Facundo1.9811236h
10Trinidad1.727221m
11Camila1.635726m
12Macarena1.736827m
\n", 596 | "
" 597 | ], 598 | "text/plain": [ 599 | " nombre altura peso edad sexo\n", 600 | "3 Diana 1.58 58 21 m\n", 601 | "4 Francisco 1.86 98 28 h\n", 602 | "5 Felipe 1.79 100 26 h\n", 603 | "6 Jacinta 1.69 62 20 m\n", 604 | "7 Bernardo 1.60 83 31 h\n", 605 | "8 Marisol 1.60 56 30 m\n", 606 | "9 Facundo 1.98 112 36 h\n", 607 | "10 Trinidad 1.72 72 21 m\n", 608 | "11 Camila 1.63 57 26 m\n", 609 | "12 Macarena 1.73 68 27 m" 610 | ] 611 | }, 612 | "execution_count": 9, 613 | "metadata": {}, 614 | "output_type": "execute_result" 615 | } 616 | ], 617 | "source": [ 618 | "df[3:13]" 619 | ] 620 | }, 621 | { 622 | "cell_type": "markdown", 623 | "metadata": {}, 624 | "source": [ 625 | "## Series\n", 626 | "Las segmentaciones realizadas anteriormiente fueron orientadas a las filas de una tabla. Esto también se puede realizar a las columnas de la tabla.\n", 627 | "\n", 628 | "Para ello utilizamos una forma similar. Lo que vamos a separar la columna peso.\n" 629 | ] 630 | }, 631 | { 632 | "cell_type": "code", 633 | "execution_count": 12, 634 | "metadata": {}, 635 | "outputs": [ 636 | { 637 | "data": { 638 | "text/plain": [ 639 | "0 60\n", 640 | "1 83\n", 641 | "2 70\n", 642 | "3 58\n", 643 | "4 98\n", 644 | "5 100\n", 645 | "6 62\n", 646 | "7 83\n", 647 | "8 56\n", 648 | "9 112\n", 649 | "10 72\n", 650 | "11 57\n", 651 | "12 68\n", 652 | "13 78\n", 653 | "14 67\n", 654 | "15 74\n", 655 | "16 93\n", 656 | "17 63\n", 657 | "18 102\n", 658 | "19 78\n", 659 | "20 65\n", 660 | "Name: peso, dtype: int64" 661 | ] 662 | }, 663 | "execution_count": 12, 664 | "metadata": {}, 665 | "output_type": "execute_result" 666 | } 667 | ], 668 | "source": [ 669 | "df['peso']" 670 | ] 671 | }, 672 | { 673 | "cell_type": "markdown", 674 | "metadata": {}, 675 | "source": [ 676 | "Entre los brackets pasamos el nombre exacto de la columna que deseamos analizar. Ya que trabajaremos con ésta columna, guardémosla en un nuevo objeto" 677 | ] 678 | }, 679 | { 680 | "cell_type": "code", 681 | "execution_count": 13, 682 | "metadata": {}, 683 | "outputs": [ 684 | { 685 | "data": { 686 | "text/plain": [ 687 | "pandas.core.series.Series" 688 | ] 689 | }, 690 | "execution_count": 13, 691 | "metadata": {}, 692 | "output_type": "execute_result" 693 | } 694 | ], 695 | "source": [ 696 | "peso = df['peso']\n", 697 | "type(peso)\n" 698 | ] 699 | }, 700 | { 701 | "cell_type": "markdown", 702 | "metadata": {}, 703 | "source": [ 704 | "Cuando separamos ésta columna y preguntamos por su tipo, Python nos entrega que es un objeto pandas.core.series.Series , este elemento que separamos se conoce como Series.\n", 705 | "\n", 706 | "En pandas , las series son listas unidimensionales que contienen una secuencia de valores.\n", 707 | "\n", 708 | "Todo objeto pd.Series tiene asociado una lista de etiquetas de datos denominada index. De manera similar a su comportamiento en DataFrame , nos permite realizar segmentaciones.\n" 709 | ] 710 | }, 711 | { 712 | "cell_type": "code", 713 | "execution_count": 14, 714 | "metadata": {}, 715 | "outputs": [ 716 | { 717 | "data": { 718 | "text/plain": [ 719 | "15 74\n", 720 | "16 93\n", 721 | "17 63\n", 722 | "18 102\n", 723 | "19 78\n", 724 | "20 65\n", 725 | "Name: peso, dtype: int64" 726 | ] 727 | }, 728 | "execution_count": 14, 729 | "metadata": {}, 730 | "output_type": "execute_result" 731 | } 732 | ], 733 | "source": [ 734 | "peso[15:]" 735 | ] 736 | }, 737 | { 738 | "cell_type": "code", 739 | "execution_count": null, 740 | "metadata": {}, 741 | "outputs": [], 742 | "source": [] 743 | } 744 | ], 745 | "metadata": { 746 | "kernelspec": { 747 | "display_name": "Python 3", 748 | "language": "python", 749 | "name": "python3" 750 | }, 751 | "language_info": { 752 | "codemirror_mode": { 753 | "name": "ipython", 754 | "version": 3 755 | }, 756 | "file_extension": ".py", 757 | "mimetype": "text/x-python", 758 | "name": "python", 759 | "nbconvert_exporter": "python", 760 | "pygments_lexer": "ipython3", 761 | "version": "3.7.3" 762 | } 763 | }, 764 | "nbformat": 4, 765 | "nbformat_minor": 2 766 | } 767 | -------------------------------------------------------------------------------- /notebooks/01_03_Introduccion a Data Science_Proceso y repaso.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/efviodo/idatha-data-science-course/blob/master/notebooks/02%20-%20DS%20-%20Introduccion%20a%20Data%20Science%20-%20Python.ipynb)" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "# Introducción a la Ciencia de Datos" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "## Objetivos\n", 29 | "\n", 30 | "- Conocer conceptos básicos de Data Sience\n", 31 | "- Entender qué problemas se resuelven en Data Sience\n", 32 | "- Introducir una metodología de trabajo para Data Science" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": {}, 38 | "source": [ 39 | "\n", 40 | "## Índice\n", 41 | "[Inicio ▲](#Indice)\n", 42 | "\n", 43 | "1. [Data Science](#Data-Sience)\n", 44 | "1. [Data Scientist](#Data-Scientist)\n", 45 | "1. [Conceptos Básicos](#Conceptos-basicos)\n", 46 | "1. [Aplicaciones de Data Science](#Aplicaciones-Data-Science)\n", 47 | "1. [Metodologias](#Metodologias)\n", 48 | " 1. [Modelo CRISP-DM](#Modelo-CRISP-DM)\n", 49 | " 1. [Comprensión del Negocio](#Comprension-Negocio)\n", 50 | " 1. [Comprensión de los Datos](#Comprension-Datos)\n", 51 | " 1. [Preparación de los Datos](#Preparacion-Datos)\n", 52 | " 1. [Evaluación](#Evaluacion)\n", 53 | " 1. [Despliegue](#Despliegue)\n", 54 | "1. [Herramientas](#Herramientas)\n", 55 | "1. [Bibliografía](#Bibliografia)" 56 | ] 57 | }, 58 | { 59 | "cell_type": "markdown", 60 | "metadata": {}, 61 | "source": [ 62 | "\n", 63 | "## Data Sience\n", 64 | "[Inicio ▲](#Indice)\n", 65 | "\n", 66 | "La ciencia de datos es un campo interdisciplinario que involucra métodos científicos, procesos y sistemas para extraer conocimiento o alcanzar un mejor entendimiento de datos en sus diferentes formas (estructurados y no estructurados). Para ello se basa en algunos campos del análisis de datos como la estadística, la minería de datos, el aprendizaje automático y la analítica predictiva.\n", 67 | "\n", 68 | "En otras palabras, puede imaginarse como un área de conocimiento que se basa en otras áreas ya desarrolladas, como la Minería de Datos, Análisis Estadístico y utiliza técnicas de Aprendizaje Automático y BigData para descubrir nueva información.\n", 69 | "\n", 70 | "
\n", 71 | "\n", 72 | "![figure1](https://github.com/efviodo/data-science/raw/master/courses/utec/figures/data_disciplines.jpg)" 73 | ] 74 | }, 75 | { 76 | "cell_type": "markdown", 77 | "metadata": {}, 78 | "source": [ 79 | "___" 80 | ] 81 | }, 82 | { 83 | "cell_type": "markdown", 84 | "metadata": {}, 85 | "source": [ 86 | "\n", 87 | "## Data Scientist\n", 88 | "[Inicio ▲](#Indice)\n", 89 | "\n", 90 | "El Data Scientist o Científico de Datos, es una persona capaz de analizar e interpretar datos complejos, así como utilizar tecnicas de estadística y aprendizaje automático para comprender mejor estos datos y extraer conslusiones que permitan resolver un problema de la realidad.\n", 91 | "\n", 92 | "Combina una sólida formación teórica y práctica en las materias fundamentales asociadas al análisis avanzado de datos: pensamiento analítico, comprensión de problemas de la realidad, estadística, programación, tratamiento de bases de datos, trabajo con algoritmos y comunicación efectiva, preparado para encarar problemas de la realidad y convertirlos en soluciones utilizando datos. \n", 93 | "\n", 94 | "Es muy común colcoar a un data scientist en la intersección de las siguientes áreas de conocimento: (i) Ciencias de la Computación, (ii) Matemáticas y Estadística y (iii) Conocimiento de un dominio específico\n", 95 | "\n", 96 | "![figure1](https://github.com/efviodo/data-science/raw/master/courses/utec/figures/data_scientist.png)" 97 | ] 98 | }, 99 | { 100 | "cell_type": "markdown", 101 | "metadata": {}, 102 | "source": [ 103 | "\n", 104 | "## Conceptos Básicos\n", 105 | "[Inicio ▲](#Indice)\n", 106 | "\n", 107 | "\n", 108 | "### Modelo \n", 109 | "Representación matemática de un proceso del mundo real; un modelo predictivo pronostica el resultado del futuro basado en comportamientos del pasado.\n", 110 | "\n", 111 | "*Ejemplo*: Modelo de probabilidad de fuga de clientes.\n", 112 | "\n", 113 | "### Algoritmo \n", 114 | "Conjunto ordenado de operaciones sistemáticas que permite hacer un cálculo y hallar la solución a un problema.\n", 115 | "\n", 116 | "*Ejemplo*: Algoritmo de ordenamiento [QuickSort](https://es.wikipedia.org/wiki/Quicksort).\n", 117 | "\n", 118 | "### Entrenamiento\n", 119 | "El proceso de crear un modelo a partir de los datos de entrenamiento. Los datos alimentan un algoritmo de entrenamiento que aprende la representación del problema y produce un modelo. Comúnmente llamado “aprendizaje”.\n", 120 | "\n", 121 | "### Regresión\n", 122 | "Método de predicción cuyo resultado es un número real (un valor que representa una cantidad en una recta). Por ejemplo: predecir la temperatura de un motor o la ganancia de una empresa.\n", 123 | "\n", 124 | "### Clasificación\n", 125 | "Método de predicción que asigna una categoría predefinida a cada dato de entrada, por ejemplo, categoría de cliente según sus compras.\n", 126 | "\n", 127 | "### Target \n", 128 | "En estadística se le llama variable dependiente. Es la salida del modelo o la variable que se quiere predecir.\n", 129 | "\n", 130 | "### Conjunto de Entrenamiento \n", 131 | "Comunmente llamado *Training dataset*, se utiliza para encontrar relaciones potencialmente predictivas que serán utilizadas para crear un modelo. También puede encontrarse como *Corpus de entrenamiento*.\n", 132 | "\n", 133 | "### Conjunto de Verificación\n", 134 | "Comunmente llamado *Test dataset*, es un conjunto de datos diferente al de entrenamiento, pero con la misma estructura. Se utiliza para evaluar la performance de los modelos predictivos. También puede encontrarse como *Corpus de pruebas*.\n", 135 | "\n", 136 | "### Feature \n", 137 | "También conocida como variable independiente o variable predictora, una feature es una cantidad observable, utilizada por un modelo predictivo. También se puede hacer ingeniería de features, creando nuevas variables a partir de la combinación de las mismas, pudiendo agregar información." 138 | ] 139 | }, 140 | { 141 | "cell_type": "markdown", 142 | "metadata": {}, 143 | "source": [ 144 | "\n", 145 | "## Aplicaciónes de Data Science\n", 146 | "[Inicio ▲](#Indice)\n", 147 | "\n", 148 | "Ejemplos más comúnes:\n", 149 | "- Mantenimiento predictivo\n", 150 | "- Análisis de sentimiento\n", 151 | "- Detección de intereses\n", 152 | "- Segmentación de clientes\n", 153 | "- Riesgo de fuga\n", 154 | "- Detección de spam\n", 155 | "- Predicción de demanda\n", 156 | "- Detección de fraude\n", 157 | "\n", 158 | "### Ejemplos de aplicación por industria y vertical\n", 159 | "\n", 160 | "
\n", 161 | "\n", 162 | "![figure2](https://github.com/efviodo/data-science/raw/master/courses/utec/figures/data_science_applications.png)" 163 | ] 164 | }, 165 | { 166 | "cell_type": "markdown", 167 | "metadata": {}, 168 | "source": [ 169 | "\n", 170 | "## Metodologías\n", 171 | "[Inicio ▲](#Indice)\n", 172 | "\n", 173 | "### Motivación\n", 174 | "Necesito contar con una metodología o marco de trabajo que me permita sistematizar ciertas etapas como recolectar datos, limpiarlos, generar un modelo predictivo y determinar acciones. Un proceso estándar que traduzca un problema de la vida real en tareas abordables por un equipo de cientificos de datos.\n", 175 | "\n", 176 | "### Alternativas\n", 177 | "\n", 178 | "Existen varias propuestas de modelos o marcos de trabajo, que ayudan a un cientifico de datos a abordar un problema de forma ordenada. En este taller vamos a trabajar con un modelo bastante conocido, que se llama CRISP-DM y es bien conocido por ser uno de los modelos más adoptados para problemas en Minería de Datos.\n", 179 | "\n", 180 | "Cabe destcar, que existen otros modelos e incluso algunas empresas tecnológicas muy fuertes como Facebook y Uber, implementaron sus propias herramientas o plataformas para data science, basadas en sus propios modelos de trabajo.\n", 181 | "\n", 182 | "- [Microsoft TDSP](https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/), es una metodología o flujo de rabajo alternativa.\n", 183 | "- [FBLearner Flow](https://code.fb.com/core-data/introducing-fblearner-flow-facebook-s-ai-backbone/), es una plataforma de Machine Learning de Facebook.\n", 184 | "\n", 185 | "\n", 186 | "Queda a cargo del lector profundizar en cualquiera de las alternativas propuestas o buscar nuevas." 187 | ] 188 | }, 189 | { 190 | "cell_type": "markdown", 191 | "metadata": {}, 192 | "source": [ 193 | "\n", 194 | "### Modelo CRISP-DM\n", 195 | "[Inicio ▲](#Indice)\n", 196 | "\n", 197 | "\n", 198 | "CRISP-DM es la sigla para *CRoss Industry Standard Process for Data Mining* (algo así como *Proceso Estándar Multi Industria para Minería de Datos*). Es un modelo de proceso, propuesto inicialmente para proyectos de minería de datos y que puede ser adaptado para proyectos en ciencia de datos. El proceso es independiente del sector de la industira del cual proviene el problema que queremos resolver o de las tecnologías utilizadas. \n", 199 | "\n", 200 | "Fue presentado por primera vez en el año 2000, a través del trabajo *[CRISP-DM: Towards a standard process model for data mining](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.198.5133&rep=rep1&type=pdf)* [1]. En caso de estar interesado en el trabajo podes encontrar la cita en la sección de Biblografía.\n", 201 | "\n", 202 | "
\n", 203 | "\n", 204 | "\"figure3\"\n", 205 | "\n", 206 | "En este taller veremos solamente las primeras tres fases del modelo, quedando fuera de alcance las otras tres.\n", 207 | "\n", 208 | "\n", 209 | "#### Fases del modelo CRISP-DM\n", 210 | "\n", 211 | "El modelo CRISP-DM se divide en 6 fases o etapas, que inicialmente en un proyecto de ciencia de datos se ejecutan en un orden determinado. Luego el proceso puede retro-alimentarse, volviendo a la etapa inicial o cualquier etapa intermedia, formando un circulo de retroalimentación.\n", 212 | "\n", 213 | "\n", 214 | "#### I. Comprensión del negocio\n", 215 | "\n", 216 | "Comprensión de los objetivos y requisitos del proyecto desde una perspectiva empresarial, y luego convertir este conocimiento en una definición del problema de minería de datos, y un plan preliminar diseñado para alcanzar los objetivos.\n", 217 | "\n", 218 | "Etapas:\n", 219 | "\n", 220 | "- Determinar objetivos empresariales.\n", 221 | "- Analizar la situación actual.\n", 222 | "- Determinar objetivos de minería de datos.\n", 223 | "- Planificación con duración, recursos y riesgos.\n", 224 | "\n", 225 | "\n", 226 | "#### II. Comprensión de los datos\n", 227 | "\n", 228 | "Recolección inicial de datos y procesos con actividades con el objetivo de familiarizarse con los mismos, identificar problemas en la calidad de los datos, descubrir primeros insights en los datos, o detectar subconjuntos de datos para formular hipótesis sobre datos ocultos. Hay un vinculo muy cercano entre las etapas de *Comprensión del negocio* y *Comprensión de los datos*\n", 229 | "\n", 230 | "Etapas:\n", 231 | "\n", 232 | "- Recopilar datos disponibles\n", 233 | "- Explorar y describir los datos con tablas y gráficos.\n", 234 | "- Verificar calidad de los datos.\n", 235 | "\n", 236 | "\n", 237 | "#### III. Preparación de datos\n", 238 | "\n", 239 | "Actividades para construir el conjunto de datos de entrenamiento. Estas tareas son ejecutadas en múltiples oportunidades y sin orden. Las tareas incluyen selección y transformación de tablas, registros y atributos, y limpieza de datos para las herramientas de modelado.\n", 240 | "\n", 241 | "Etapas:\n", 242 | "- Selección de subconjunto de datos.\n", 243 | "- Limpieza de datos.\n", 244 | "- Creación de nuevos atributos (ingeniería de atributos).\n", 245 | "- Fusión y agregado de conjuntos y registros.\n", 246 | "- Verificación de formato de datos para el modelado.\n", 247 | "- División en conjuntos de datos de prueba y entrenamiento.\n", 248 | "\n", 249 | "\n", 250 | "#### IV. Modelado\n", 251 | "\n", 252 | "Se seleccionan y aplican varias técnicas de modelado y se calibran los parámetros para mejorar los resultados. Hay varias técnicas que tienen requerimientos específicos sobre la forma de los datos, por lo que puede ser necesario volver a la fase de preparación de datos.\n", 253 | "\n", 254 | "\n", 255 | "#### V. Evaluación\n", 256 | "\n", 257 | "Evaluación del modelo (o modelos) construidos, que parecen tener gran calidad desde una perspectiva del análisis de datos.\n", 258 | "\n", 259 | "\n", 260 | "#### VI. Despliegue\n", 261 | "\n", 262 | "Esta fase depende de los requerimientos, pudiendo ser simple como la generación de un reporte o compleja como la implementación de un proceso de explotación de información que atraviese a toda la organización." 263 | ] 264 | }, 265 | { 266 | "cell_type": "markdown", 267 | "metadata": {}, 268 | "source": [ 269 | "\n", 270 | "## Herramientas\n", 271 | "[Inicio ▲](#Indice)\n", 272 | "\n", 273 | "### Jupyter Notebooks\n", 274 | "- Proyecto open-source basado en IPython.\n", 275 | "- **Entorno interactivo** para la ejecución de código:\n", 276 | " - Versionado de notebooks\n", 277 | " - Celda como unidad de trabajo con un único formato (tipo de celda)\n", 278 | " - Edición (cortar, copiar, pegar), merge, split y desplazamiento de celdas\n", 279 | " - Visualización de celdas de diferentes tipos\n", 280 | " - Inserción de celdas arriba y abajo (shortcut: 'A' para insertar arriba y 'B' abajo)\n", 281 | " - Manejo de visualización de resultado (esconder, permitir scroll y borrar)\n", 282 | " - Administración del Kernel (interrupción, reinicio, cambio)\n", 283 | "- Puede mostrar:\n", 284 | " - `Código`\n", 285 | " - Gráficas generadas a partir de código\n", 286 | " - Texto enriqucido\n", 287 | " - Expresiones matemáticas: $e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$\n", 288 | " - Dibujos y *rich media* (HTML, LaTeX, PNG, SVG, etc.)\n", 289 | "- **Lenguajes soportados**: Python, R, Scala y muchos más (ver [lista de kernels soportados](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels)).\n", 290 | "\n", 291 | "### Lenguaje R\n", 292 | "- Lenguaje de programación con fuerte foco en el análisis estadístico.\n", 293 | "- Ofrece una amplia variedad de herramientas y librerias para trabajar con datos\n", 294 | "\n", 295 | " - **dplyr**: Libreria **R** para manipular data frames de forma simple: ```select```, ```filter```, etc. [https://dplyr.tidyverse.org/](https://dplyr.tidyverse.org/)\n", 296 | "\n", 297 | " - Herramientas para data profiling: [DataExplorer](https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html), [HMisc](https://cran.r-project.org/web/packages/Hmisc/index.html), entre otros.\n", 298 | " - Herramientas para data visualization: [ggplot2](https://ggplot2.tidyverse.org/)\n", 299 | " \n", 300 | "### Lenguaje Python\n", 301 | "- Es otro de los lenguajes de programación elegido por la comunidad de data scientists.\n", 302 | "- Interpretado, de fácil aprendizaje y muy ligero.\n", 303 | "- Tiene una oferta de herramientas muy buena para data scientists:\n", 304 | " - [Pandas](https://pandas.pydata.org/): Librería para manipulación de data frames.\n", 305 | " - [Scikit-Learn](https://scikit-learn.org/stable/): Librería para mineria y análisis de datos, implementa algorítmos para los problemas clásicos de Machine Learning: Classification, Regression, Clustering.\n", 306 | " - Herramientas para data visualization: [matplotlib](https://matplotlib.org/), [seaborn](https://seaborn.pydata.org/), [bokeh](https://bokeh.pydata.org/en/latest/).\n", 307 | " - Librerías para profiling de datos: [Pandas Profiling](https://pypi.org/project/pandas-profiling/)\n", 308 | " - Libreraías matemáticas: [NumPy](http://www.numpy.org/), [SciPy](https://www.scipy.org/), " 309 | ] 310 | }, 311 | { 312 | "cell_type": "markdown", 313 | "metadata": {}, 314 | "source": [ 315 | "\n", 316 | "## Bibliografía\n", 317 | "[Inicio ▲](#Indice)\n", 318 | "\n", 319 | "
    \n", 320 | "
  1. Wirth, R., & Hipp, J. (2000, April). CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining (pp. 29-39). Citeseer.\n", 321 | "
    2. \n", 322 | "
" 323 | ] 324 | } 325 | ], 326 | "metadata": { 327 | "kernelspec": { 328 | "display_name": "Python 3", 329 | "language": "python", 330 | "name": "python3" 331 | }, 332 | "language_info": { 333 | "codemirror_mode": { 334 | "name": "ipython", 335 | "version": 3 336 | }, 337 | "file_extension": ".py", 338 | "mimetype": "text/x-python", 339 | "name": "python", 340 | "nbconvert_exporter": "python", 341 | "pygments_lexer": "ipython3", 342 | "version": "3.7.3" 343 | } 344 | }, 345 | "nbformat": 4, 346 | "nbformat_minor": 2 347 | } 348 | --------------------------------------------------------------------------------