├── .gitignore ├── .idea ├── Machine-Learning-for-Economics.iml ├── inspectionProfiles │ └── profiles_settings.xml ├── modules.xml ├── vcs.xml └── workspace.xml ├── LICENSE ├── README.md ├── convert.sh ├── custom.css ├── figures ├── cnn1.gif ├── cnn1.png ├── impurity.png ├── nn1.jpeg ├── nn2.jpeg ├── nn3.jpeg ├── nonlinearities.jpg ├── ridgelasso.png ├── rnn1.png ├── rnn2.png └── rnn3.png ├── img ├── 01_regression_114_0.png ├── 01_regression_120_0.png ├── 01_regression_128_0.png ├── 01_regression_131_0.png ├── 01_regression_141_0.png ├── 01_regression_150_0.png ├── 01_regression_156_0.png ├── 01_regression_164_0.png ├── 01_regression_167_0.png ├── 01_regression_170_0.png ├── 01_regression_178_0.png ├── 01_regression_23_0.png ├── 01_regression_29_0.png ├── 01_regression_34_0.png ├── 01_regression_43_0.png ├── 01_regression_48_0.png ├── 01_regression_50_0.png ├── 01_regression_52_0.png ├── 01_regression_79_0.png ├── 02_iv_28_1.png ├── 02_iv_43_0.png ├── 02_iv_9_0.png ├── 03_nonparametric_100_0.png ├── 03_nonparametric_106_0.png ├── 03_nonparametric_108_0.png ├── 03_nonparametric_10_0.png ├── 03_nonparametric_112_0.png ├── 03_nonparametric_115_0.png ├── 03_nonparametric_120_0.png ├── 03_nonparametric_122_0.png ├── 03_nonparametric_125_0.png ├── 03_nonparametric_129_0.png ├── 03_nonparametric_12_0.png ├── 03_nonparametric_130_0.png ├── 03_nonparametric_135_0.png ├── 03_nonparametric_140_0.png ├── 03_nonparametric_147_0.png ├── 03_nonparametric_39_0.png ├── 03_nonparametric_44_0.png ├── 03_nonparametric_60_0.png ├── 03_nonparametric_69_0.png ├── 03_nonparametric_71_0.png ├── 03_nonparametric_78_0.png ├── 03_nonparametric_85_0.png ├── 03_nonparametric_90_0.png ├── 03_nonparametric_93_0.png ├── 03_nonparametric_98_0.png ├── 04_crossvalidation_15_0.png ├── 04_crossvalidation_26_0.png ├── 04_crossvalidation_37_0.png ├── 04_crossvalidation_52_0.png ├── 05_regularization_100_0.png ├── 05_regularization_108_0.png ├── 05_regularization_117_0.png ├── 05_regularization_131_0.png ├── 05_regularization_19_0.png ├── 05_regularization_31_0.png ├── 05_regularization_40_0.png ├── 05_regularization_44_0.png ├── 05_regularization_52_0.png ├── 05_regularization_62_0.png ├── 05_regularization_76_0.png ├── 05_regularization_86_0.png ├── 06_convexity_101_0.png ├── 06_convexity_18_0.png ├── 06_convexity_24_0.png ├── 06_convexity_27_0.png ├── 06_convexity_35_0.png ├── 06_convexity_37_0.png ├── 06_convexity_41_0.png ├── 06_convexity_43_0.png ├── 06_convexity_54_0.png ├── 06_convexity_57_0.png ├── 06_convexity_60_0.png ├── 06_convexity_66_0.png ├── 06_convexity_67_0.png ├── 06_convexity_73_0.png ├── 06_convexity_75_0.png ├── 06_convexity_77_0.png ├── 06_convexity_82_0.png ├── 06_convexity_91_0.png ├── 06_convexity_99_0.png ├── 07_trees_101_0.png ├── 07_trees_103_0.png ├── 07_trees_108_0.png ├── 07_trees_10_0.png ├── 07_trees_110_0.png ├── 07_trees_111_0.png ├── 07_trees_113_0.png ├── 07_trees_120_0.png ├── 07_trees_122_0.png ├── 07_trees_12_0.png ├── 07_trees_131_0.png ├── 07_trees_133_0.png ├── 07_trees_14_0.png ├── 07_trees_16_0.png ├── 07_trees_19_0.png ├── 07_trees_21_0.png ├── 07_trees_45_0.png ├── 07_trees_47_0.png ├── 07_trees_48_0.png ├── 07_trees_50_0.png ├── 07_trees_55_0.png ├── 07_trees_57_0.png ├── 07_trees_72_0.png ├── 07_trees_74_0.png ├── 07_trees_78_0.png ├── 07_trees_80_0.png ├── 07_trees_94_0.png ├── 07_trees_96_0.png ├── 08_neuralnets_103_0.png ├── 08_neuralnets_113_0.png ├── 08_neuralnets_119_0.png ├── 08_neuralnets_124_0.png ├── 08_neuralnets_127_0.png ├── 08_neuralnets_135_0.png ├── 08_neuralnets_141_0.png ├── 08_neuralnets_147_0.png ├── 08_neuralnets_56_0.png ├── 08_neuralnets_66_0.png ├── 08_neuralnets_76_0.png ├── 08_neuralnets_82_0.png ├── 08_neuralnets_93_0.png ├── 09_postdoubleselection_36_0.png ├── 09_postdoubleselection_41_0.png ├── 09_postdoubleselection_44_0.png ├── 09_postdoubleselection_48_0.png ├── 09_postdoubleselection_62_0.png ├── 09_postdoubleselection_65_0.png ├── 09_postdoubleselection_69_0.png ├── 09_postdoubleselection_73_0.png ├── 10_unsupervised_102_0.png ├── 10_unsupervised_103_0.png ├── 10_unsupervised_106_0.png ├── 10_unsupervised_107_0.png ├── 10_unsupervised_112_0.png ├── 10_unsupervised_113_0.png ├── 10_unsupervised_114_0.png ├── 10_unsupervised_125_0.png ├── 10_unsupervised_126_0.png ├── 10_unsupervised_134_0.png ├── 10_unsupervised_137_0.png ├── 10_unsupervised_138_0.png ├── 10_unsupervised_144_0.png ├── 10_unsupervised_145_0.png ├── 10_unsupervised_154_0.png ├── 10_unsupervised_155_0.png ├── 10_unsupervised_161_0.png ├── 10_unsupervised_162_0.png ├── 10_unsupervised_166_0.png ├── 10_unsupervised_167_0.png ├── 10_unsupervised_168_0.png ├── 10_unsupervised_169_0.png ├── 10_unsupervised_171_0.png ├── 10_unsupervised_172_0.png ├── 10_unsupervised_19_0.png ├── 10_unsupervised_26_0.png ├── 10_unsupervised_30_0.png ├── 10_unsupervised_38_0.png ├── 10_unsupervised_46_0.png ├── 10_unsupervised_49_0.png ├── 10_unsupervised_56_0.png ├── 10_unsupervised_58_0.png ├── 10_unsupervised_61_0.png ├── 10_unsupervised_65_0.png ├── 10_unsupervised_68_0.png ├── 10_unsupervised_70_0.png ├── 10_unsupervised_71_0.png ├── 10_unsupervised_72_0.png ├── 10_unsupervised_74_0.png ├── 10_unsupervised_75_0.png ├── 10_unsupervised_78_0.png ├── 10_unsupervised_81_0.png ├── 10_unsupervised_82_0.png ├── 10_unsupervised_85_0.png ├── 10_unsupervised_87_0.png ├── 10_unsupervised_88_0.png ├── 10_unsupervised_92_0.png ├── 10_unsupervised_93_0.png ├── 10_unsupervised_95_0.png ├── 10_unsupervised_96_0.png └── 10_unsupervised_97_0.png └── notebooks ├── 01_regression.ipynb ├── 02_iv.ipynb ├── 03_nonparametric.ipynb ├── 04_crossvalidation.ipynb ├── 05_regularization.ipynb ├── 06_convexity.ipynb ├── 07_trees.ipynb ├── 08_neuralnets.ipynb ├── 09_postdoubleselection.ipynb ├── 10_unsupervised.ipynb ├── __pycache__ └── utils.cpython-39.pyc ├── data ├── AJR02.csv ├── Advertising.csv ├── Auto.csv ├── Boston.csv ├── Caravan.csv ├── Carseats.csv ├── College.csv ├── Credit.csv ├── Default.csv ├── Heart.csv ├── Hitters.csv ├── Hitters_X_test.csv ├── Hitters_X_train.csv ├── Hitters_y_test.csv ├── Hitters_y_train.csv ├── Khan.csv ├── NCI60_X.csv ├── NCI60_y.csv ├── OJ.csv ├── Portfolio.csv ├── Smarket.csv ├── USArrests.csv ├── Wage.csv └── Weekly.csv └── utils ├── __pycache__ ├── lecture03.cpython-39.pyc └── lecture07.cpython-39.pyc ├── lecture03.py ├── lecture07.py └── lecture10.py /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints 2 | makeslides.sh 3 | rename.py 4 | -------------------------------------------------------------------------------- /.idea/Machine-Learning-for-Economics.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /.idea/workspace.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | 144 | 145 | 146 | 147 | 148 | 149 | 150 | 151 | 152 | 153 | 154 | 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 192 | 193 | 195 | 196 | 198 | 199 | 200 | 201 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 1642854050585 214 | 219 | 220 | 221 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Matteo Courthoud 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning for Economic Analysis 2 | 3 | ⚠️ **WORK IN PROGRESS!** ⚠️ 4 | 5 | Welcome to my notes for the Machine Learning for Economic Analysis course by [Damian Kozbur](https://www.econ.uzh.ch/en/people/faculty/kozbur.html) @UZH! 6 | 7 | The exercise sessions are entirely coded in [Python](https://www.python.org/downloads/) on Jupyter Notebooks. The examples heavily borrow from [*An Introduction to Statistical Learning*](https://hastie.su.domains/ISLR2/ISLRv2_website.pdf) by James, Witten, Tibshirani, Friedman and its advanced version [*Elements of Statistical Learning*](https://hastie.su.domains/Papers/ESLII.pdf) by Hastie, Tibshirani, Friedman. Other recommended free resources are the documentation of the Python library [scikit-learn](https://scikit-learn.org/) and Bruce Hansen's [*Econometrics*](https://www.ssc.wisc.edu/~bhansen/econometrics/) book. 8 | 9 | Please, if you find any typos or mistakes, [open a new issue](https://help.github.com/articles/creating-an-issue/). Or even better, fork the repo and [submit a pull request](https://help.github.com/articles/creating-a-pull-request-from-a-fork/). I am happy to share my work and I am even happier if it can be useful. 10 | 11 | 12 | 13 | ## Content 14 | 15 | 1. [OLS Regression](https://matteocourthoud.github.io/course/ml-econ/01_regression/) 16 | - ISLR, chapter 3 17 | - ESL, chapter 3 18 | - Econometrics, chapters 3 and 4 19 | 20 | 2. [Instrumental Variables](https://matteocourthoud.github.io/course/ml-econ/02_iv/) 21 | - Econometrics, chapter 12.1-12.12 22 | 23 | 3. [Nonparametric Regression](https://matteocourthoud.github.io/course/ml-econ/03_nonparametric/) 24 | - ISLR, chapter 7 25 | - ESL, chapter 5 26 | - Econometrics, chapters 19 and 20 27 | 28 | 4. [Cross-Validation](https://matteocourthoud.github.io/course/ml-econ/04_crossvalidation/) 29 | - ISLR, chapter 5 30 | - ESL, chapter 7 31 | 32 | 5. [Lasso and Forward Regression](https://matteocourthoud.github.io/course/ml-econ/05_regularization/) 33 | - ISLR, chapter 6 34 | - ESL, chapters 3 and 18 35 | - Econometrics, chapter 29.2-29.5 36 | 37 | 6. [Convexity and Optimization](https://matteocourthoud.github.io/course/ml-econ/06_convexity/) 38 | 39 | 7. [Trees and Forests](https://matteocourthoud.github.io/course/ml-econ/07_trees/) 40 | - ISLR, chapter 8 41 | - ESL, chapters 9, 10, 15, 16 42 | - Econometrics, chapter 29.6-29.9 43 | 44 | 8. [Neural Networks](https://matteocourthoud.github.io/course/ml-econ/08_neuralnets/) 45 | - ESL, chapter 11 46 | 47 | 9. [Post-Double Selection](https://matteocourthoud.github.io/course/ml-econ/09_postdoubleselection/) 48 | 49 | - Econometrics, chapter 3.18 50 | - Belloni, Chen, Chernozhukov, Hansen (2012) 51 | - Belloni, Chernozhukov, Hansen (2014) 52 | - Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, Robins (2018) 53 | 54 | 10. [Unsupervised Learning](https://matteocourthoud.github.io/course/ml-econ/10-unsupervised/) 55 | 56 | - ISLR, chapter 10 57 | - ESL, chapter 14 58 | 59 | 60 | 61 | ## Pre-requisites 62 | 63 | Students should be familiar with the following concepts: 64 | 65 | - Matrix Algebra 66 | - Econometrics, appendix A.1-A.10 67 | - Conditional Expectation and Projection 68 | - Econometrics, chapter 2.1-2.25 69 | - Large Sample Asymptotics 70 | - Econometrics, chapter 6.1-6.5 71 | - Python basics 72 | - [Quant-Econ Tutorial](https://python.quantecon.org/index_learning_python.html) 73 | 74 | 75 | 76 | 77 | ## Readings 78 | 79 | - Athey, S., & Imbens, G. W. (n.d.). *Machine Learning Methods Economists Should Know About*. 62. 80 | - Belloni, A., Chen, H., Chernozhukov, V., & Hansen, C. B. (2012). Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain. *Econometrica*, *80*(6), 2369–2429. https://doi.org/10.3982/ECTA9626 81 | - Belloni, A., Chernozhukov, V., & Hansen, C. (2014). Inference on Treatment Effects after Selection among High-Dimensional Controls. *The Review of Economic Studies*, *81*(2), 608–650. https://doi.org/10.1093/restud/rdt044 82 | - Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. *The Econometrics Journal*, *21*(1), C1–C68. https://doi.org/10.1111/ectj.12097 83 | - Franks, A., Miller, A., Bornn, L., & Goldsberry, K. (2015). Characterizing the spatial structure of defensive skill in professional basketball. *The Annals of Applied Statistics*, *9*(1), 94–121. https://doi.org/10.1214/14-AOAS799 84 | - Gentzkow, M., Shapiro, J. M., & Taddy, M. (2019). Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech. *Econometrica*, *87*(4), 1307–1340. https://doi.org/10.3982/ECTA16566 85 | - Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017). Human Decisions and Machine Predictions. *The Quarterly Journal of Economics*. https://doi.org/10.1093/qje/qjx032 86 | - Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction Policy Problems. *American Economic Review*, *105*(5), 491–495. https://doi.org/10.1257/aer.p20151023 87 | - Mullainathan, S., & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. *Journal of Economic Perspectives*, *31*(2), 87–106. https://doi.org/10.1257/jep.31.2.87 88 | - Wager, S., & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. *Journal of the American Statistical Association*, *113*(523), 1228–1242. https://doi.org/10.1080/01621459.2017.1319839 89 | 90 | 91 | 92 | ## Thanks 93 | 94 | These exercise sessions heavily borrow from 95 | 96 | - [Jordi Warmenhoven's](https://github.com/JWarmenhoven) git repo [ISLR-python](https://github.com/JWarmenhoven/ISLR-python) 97 | - [Quant-Econ](https://quantecon.org/python-lectures/) website 98 | - Prof. [Damian Kozbur](https://www.econ.uzh.ch/en/people/faculty/kozbur.html) past UZH [PhD Econometrics Class](https://matteocourthoud.github.io/econometrics/) 99 | - Clark Science Center [Machine Learning couse](http://www.science.smith.edu/~jcrouser/SDS293/) 100 | - UC Berkeley [Convex Optimization and Approximation class](https://ee227c.github.io/) by [Moritz Hardt](http://mrtz.org/) 101 | - [Morvan Zhou](https://github.com/MorvanZhou/) and [Yunjey Choi](https://github.com/yunjey/) [pytorch](https://github.com/MorvanZhou/PyTorch-Tutorial) [tutorials](https://github.com/yunjey/pytorch-tutorial) 102 | - [Daniel Godoy](https://medium.com/@dvgodoy) excellent article on Pytorch in [Medium's towardsdatascience](https://towardsdatascience.com/understanding-pytorch-with-an-example-a-step-by-step-tutorial-81fc5f8c4e8e) 103 | 104 | 105 | 106 | ## Contacts 107 | 108 | If you have any issue or suggestion for the course, please feel free to [pull edits](https://github.com/matteocourthoud/Machine-Learning-for-Economic-Analysis-2020/pulls) or contact me [via mail](mailto:matteo.courthoud@uzh.ch). All feedback is greatly appreciated! -------------------------------------------------------------------------------- /convert.sh: -------------------------------------------------------------------------------- 1 | # Script that copies selected output into final figures 2 | 3 | # To make script executable: chmod a+x makesplides.sh 4 | 5 | printf "\nMaking slides...\n\n" 6 | 7 | # Save directories 8 | indir="Dropbox/Projects/Machine-Learning-for-Economics" 9 | outdir="Dropbox/Code/website/content/course/ml-econ" 10 | 11 | # List files 12 | cd ${indir}/notebooks/ 13 | FILES=$(ls *.ipynb) 14 | cd 15 | 16 | # Convert slides 17 | for FILE in $FILES; do 18 | file=$(echo $FILE | cut -d'.' -f 1) 19 | jupyter nbconvert ${indir}/notebooks/${file}.ipynb --to markdown --NbConvertApp.output_files_dir=../img 20 | #jupyter nbconvert ${indir}/notebooks/${file}.ipynb --to slides 21 | #mv ${indir}/notebooks/${file}.slides.html ${indir}/slides/${file}.slides.html 22 | #cp ${indir}/slides/${file}.slides.html ${outdir}/${file}_slides/index.html 23 | 24 | # Move to website 25 | mv ${indir}/notebooks/${file}.md ${outdir}/${file}.md 26 | cp -R ${indir}/img ${outdir} 27 | python3 ${indir}/rename.py "${outdir}/" "${file}.md" 28 | done 29 | 30 | # Terminate 31 | exit 32 | -------------------------------------------------------------------------------- /custom.css: -------------------------------------------------------------------------------- 1 | /** 2 | * A simple theme for reveal.js presentations, derived from serif.css 3 | * It's in the spirit of the Metropolis theme for beamer https://github.com/matze/mtheme 4 | * 5 | * This theme is Copyright (C) 2016 Vince Hodges, http://sourdoughlabs.com - it is MIT licensed. 6 | */ 7 | 8 | @import url('https://fonts.googleapis.com/css?family=Fira+Sans'); 9 | 10 | .reveal a { 11 | line-height: 1.3em; } 12 | 13 | /********************************************* 14 | * GLOBAL STYLES 15 | *********************************************/ 16 | body { 17 | background: #f1f1f1; 18 | background-color: #f1f1f1; } 19 | 20 | body.dark { 21 | background: #33474b; 22 | background-color: #33474b; } 23 | 24 | body.dark .reveal { 25 | color:#f1f1f1; 26 | } 27 | 28 | body.dark .reveal h1, 29 | body.dark .reveal h2, 30 | body.dark .reveal h3, 31 | body.dark .reveal h4, 32 | body.dark .reveal h5, 33 | body.dark .reveal h6 { 34 | color:#f1f1f1; 35 | } 36 | 37 | .reveal { 38 | font-family: "Fira Sans"; 39 | font-size: 32px; 40 | font-weight: normal; 41 | color: #33474b; } 42 | 43 | ::selection { 44 | color: #fff; 45 | background: #26351C; 46 | text-shadow: none; } 47 | 48 | .reveal .slides { 49 | text-align:left; 50 | } 51 | 52 | .reveal .slides > section, 53 | .reveal .slides > section > section { 54 | line-height: 1.2; 55 | font-weight: inherit; } 56 | 57 | /********************************************* 58 | * HEADERS 59 | *********************************************/ 60 | .reveal h1, 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| "","TV","Radio","Newspaper","Sales" 2 | "1",230.1,37.8,69.2,22.1 3 | "2",44.5,39.3,45.1,10.4 4 | "3",17.2,45.9,69.3,9.3 5 | "4",151.5,41.3,58.5,18.5 6 | "5",180.8,10.8,58.4,12.9 7 | "6",8.7,48.9,75,7.2 8 | "7",57.5,32.8,23.5,11.8 9 | "8",120.2,19.6,11.6,13.2 10 | "9",8.6,2.1,1,4.8 11 | "10",199.8,2.6,21.2,10.6 12 | "11",66.1,5.8,24.2,8.6 13 | "12",214.7,24,4,17.4 14 | "13",23.8,35.1,65.9,9.2 15 | "14",97.5,7.6,7.2,9.7 16 | "15",204.1,32.9,46,19 17 | "16",195.4,47.7,52.9,22.4 18 | "17",67.8,36.6,114,12.5 19 | "18",281.4,39.6,55.8,24.4 20 | "19",69.2,20.5,18.3,11.3 21 | "20",147.3,23.9,19.1,14.6 22 | "21",218.4,27.7,53.4,18 23 | "22",237.4,5.1,23.5,12.5 24 | "23",13.2,15.9,49.6,5.6 25 | "24",228.3,16.9,26.2,15.5 26 | "25",62.3,12.6,18.3,9.7 27 | "26",262.9,3.5,19.5,12 28 | "27",142.9,29.3,12.6,15 29 | "28",240.1,16.7,22.9,15.9 30 | "29",248.8,27.1,22.9,18.9 31 | "30",70.6,16,40.8,10.5 32 | "31",292.9,28.3,43.2,21.4 33 | "32",112.9,17.4,38.6,11.9 34 | "33",97.2,1.5,30,9.6 35 | 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"68",139.3,14.5,10.2,13.4 70 | "69",237.4,27.5,11,18.9 71 | "70",216.8,43.9,27.2,22.3 72 | "71",199.1,30.6,38.7,18.3 73 | "72",109.8,14.3,31.7,12.4 74 | "73",26.8,33,19.3,8.8 75 | "74",129.4,5.7,31.3,11 76 | "75",213.4,24.6,13.1,17 77 | "76",16.9,43.7,89.4,8.7 78 | "77",27.5,1.6,20.7,6.9 79 | "78",120.5,28.5,14.2,14.2 80 | "79",5.4,29.9,9.4,5.3 81 | "80",116,7.7,23.1,11 82 | "81",76.4,26.7,22.3,11.8 83 | "82",239.8,4.1,36.9,12.3 84 | "83",75.3,20.3,32.5,11.3 85 | "84",68.4,44.5,35.6,13.6 86 | "85",213.5,43,33.8,21.7 87 | "86",193.2,18.4,65.7,15.2 88 | "87",76.3,27.5,16,12 89 | "88",110.7,40.6,63.2,16 90 | "89",88.3,25.5,73.4,12.9 91 | "90",109.8,47.8,51.4,16.7 92 | "91",134.3,4.9,9.3,11.2 93 | "92",28.6,1.5,33,7.3 94 | "93",217.7,33.5,59,19.4 95 | "94",250.9,36.5,72.3,22.2 96 | "95",107.4,14,10.9,11.5 97 | "96",163.3,31.6,52.9,16.9 98 | "97",197.6,3.5,5.9,11.7 99 | "98",184.9,21,22,15.5 100 | "99",289.7,42.3,51.2,25.4 101 | "100",135.2,41.7,45.9,17.2 102 | "101",222.4,4.3,49.8,11.7 103 | "102",296.4,36.3,100.9,23.8 104 | "103",280.2,10.1,21.4,14.8 105 | "104",187.9,17.2,17.9,14.7 106 | "105",238.2,34.3,5.3,20.7 107 | "106",137.9,46.4,59,19.2 108 | "107",25,11,29.7,7.2 109 | "108",90.4,0.3,23.2,8.7 110 | "109",13.1,0.4,25.6,5.3 111 | "110",255.4,26.9,5.5,19.8 112 | "111",225.8,8.2,56.5,13.4 113 | "112",241.7,38,23.2,21.8 114 | "113",175.7,15.4,2.4,14.1 115 | "114",209.6,20.6,10.7,15.9 116 | "115",78.2,46.8,34.5,14.6 117 | "116",75.1,35,52.7,12.6 118 | "117",139.2,14.3,25.6,12.2 119 | "118",76.4,0.8,14.8,9.4 120 | "119",125.7,36.9,79.2,15.9 121 | "120",19.4,16,22.3,6.6 122 | "121",141.3,26.8,46.2,15.5 123 | "122",18.8,21.7,50.4,7 124 | "123",224,2.4,15.6,11.6 125 | "124",123.1,34.6,12.4,15.2 126 | "125",229.5,32.3,74.2,19.7 127 | "126",87.2,11.8,25.9,10.6 128 | "127",7.8,38.9,50.6,6.6 129 | "128",80.2,0,9.2,8.8 130 | "129",220.3,49,3.2,24.7 131 | "130",59.6,12,43.1,9.7 132 | "131",0.7,39.6,8.7,1.6 133 | "132",265.2,2.9,43,12.7 134 | "133",8.4,27.2,2.1,5.7 135 | "134",219.8,33.5,45.1,19.6 136 | "135",36.9,38.6,65.6,10.8 137 | "136",48.3,47,8.5,11.6 138 | "137",25.6,39,9.3,9.5 139 | "138",273.7,28.9,59.7,20.8 140 | "139",43,25.9,20.5,9.6 141 | "140",184.9,43.9,1.7,20.7 142 | "141",73.4,17,12.9,10.9 143 | "142",193.7,35.4,75.6,19.2 144 | "143",220.5,33.2,37.9,20.1 145 | "144",104.6,5.7,34.4,10.4 146 | "145",96.2,14.8,38.9,11.4 147 | "146",140.3,1.9,9,10.3 148 | "147",240.1,7.3,8.7,13.2 149 | "148",243.2,49,44.3,25.4 150 | "149",38,40.3,11.9,10.9 151 | "150",44.7,25.8,20.6,10.1 152 | "151",280.7,13.9,37,16.1 153 | "152",121,8.4,48.7,11.6 154 | "153",197.6,23.3,14.2,16.6 155 | "154",171.3,39.7,37.7,19 156 | "155",187.8,21.1,9.5,15.6 157 | "156",4.1,11.6,5.7,3.2 158 | "157",93.9,43.5,50.5,15.3 159 | "158",149.8,1.3,24.3,10.1 160 | "159",11.7,36.9,45.2,7.3 161 | "160",131.7,18.4,34.6,12.9 162 | "161",172.5,18.1,30.7,14.4 163 | "162",85.7,35.8,49.3,13.3 164 | "163",188.4,18.1,25.6,14.9 165 | "164",163.5,36.8,7.4,18 166 | "165",117.2,14.7,5.4,11.9 167 | "166",234.5,3.4,84.8,11.9 168 | "167",17.9,37.6,21.6,8 169 | "168",206.8,5.2,19.4,12.2 170 | "169",215.4,23.6,57.6,17.1 171 | "170",284.3,10.6,6.4,15 172 | "171",50,11.6,18.4,8.4 173 | "172",164.5,20.9,47.4,14.5 174 | "173",19.6,20.1,17,7.6 175 | "174",168.4,7.1,12.8,11.7 176 | "175",222.4,3.4,13.1,11.5 177 | "176",276.9,48.9,41.8,27 178 | "177",248.4,30.2,20.3,20.2 179 | "178",170.2,7.8,35.2,11.7 180 | "179",276.7,2.3,23.7,11.8 181 | "180",165.6,10,17.6,12.6 182 | "181",156.6,2.6,8.3,10.5 183 | "182",218.5,5.4,27.4,12.2 184 | "183",56.2,5.7,29.7,8.7 185 | "184",287.6,43,71.8,26.2 186 | "185",253.8,21.3,30,17.6 187 | "186",205,45.1,19.6,22.6 188 | "187",139.5,2.1,26.6,10.3 189 | "188",191.1,28.7,18.2,17.3 190 | "189",286,13.9,3.7,15.9 191 | "190",18.7,12.1,23.4,6.7 192 | "191",39.5,41.1,5.8,10.8 193 | "192",75.5,10.8,6,9.9 194 | "193",17.2,4.1,31.6,5.9 195 | "194",166.8,42,3.6,19.6 196 | "195",149.7,35.6,6,17.3 197 | "196",38.2,3.7,13.8,7.6 198 | "197",94.2,4.9,8.1,9.7 199 | "198",177,9.3,6.4,12.8 200 | "199",283.6,42,66.2,25.5 201 | "200",232.1,8.6,8.7,13.4 202 | -------------------------------------------------------------------------------- /notebooks/data/Auto.csv: -------------------------------------------------------------------------------- 1 | mpg,cylinders,displacement,horsepower,weight,acceleration,year,origin,name 2 | 18.0,8,307.0,130,3504,12.0,70,1,chevrolet chevelle malibu 3 | 15.0,8,350.0,165,3693,11.5,70,1,buick skylark 320 4 | 18.0,8,318.0,150,3436,11.0,70,1,plymouth satellite 5 | 16.0,8,304.0,150,3433,12.0,70,1,amc rebel sst 6 | 17.0,8,302.0,140,3449,10.5,70,1,ford torino 7 | 15.0,8,429.0,198,4341,10.0,70,1,ford galaxie 500 8 | 14.0,8,454.0,220,4354,9.0,70,1,chevrolet impala 9 | 14.0,8,440.0,215,4312,8.5,70,1,plymouth fury iii 10 | 14.0,8,455.0,225,4425,10.0,70,1,pontiac catalina 11 | 15.0,8,390.0,190,3850,8.5,70,1,amc ambassador dpl 12 | 15.0,8,383.0,170,3563,10.0,70,1,dodge challenger se 13 | 14.0,8,340.0,160,3609,8.0,70,1,plymouth 'cuda 340 14 | 15.0,8,400.0,150,3761,9.5,70,1,chevrolet monte carlo 15 | 14.0,8,455.0,225,3086,10.0,70,1,buick estate wagon (sw) 16 | 24.0,4,113.0,95,2372,15.0,70,3,toyota corona mark ii 17 | 22.0,6,198.0,95,2833,15.5,70,1,plymouth duster 18 | 18.0,6,199.0,97,2774,15.5,70,1,amc hornet 19 | 21.0,6,200.0,85,2587,16.0,70,1,ford maverick 20 | 27.0,4,97.0,88,2130,14.5,70,3,datsun pl510 21 | 26.0,4,97.0,46,1835,20.5,70,2,volkswagen 1131 deluxe sedan 22 | 25.0,4,110.0,87,2672,17.5,70,2,peugeot 504 23 | 24.0,4,107.0,90,2430,14.5,70,2,audi 100 ls 24 | 25.0,4,104.0,95,2375,17.5,70,2,saab 99e 25 | 26.0,4,121.0,113,2234,12.5,70,2,bmw 2002 26 | 21.0,6,199.0,90,2648,15.0,70,1,amc gremlin 27 | 10.0,8,360.0,215,4615,14.0,70,1,ford f250 28 | 10.0,8,307.0,200,4376,15.0,70,1,chevy c20 29 | 11.0,8,318.0,210,4382,13.5,70,1,dodge d200 30 | 9.0,8,304.0,193,4732,18.5,70,1,hi 1200d 31 | 27.0,4,97.0,88,2130,14.5,71,3,datsun pl510 32 | 28.0,4,140.0,90,2264,15.5,71,1,chevrolet vega 2300 33 | 25.0,4,113.0,95,2228,14.0,71,3,toyota corona 34 | 19.0,6,232.0,100,2634,13.0,71,1,amc gremlin 35 | 16.0,6,225.0,105,3439,15.5,71,1,plymouth satellite custom 36 | 17.0,6,250.0,100,3329,15.5,71,1,chevrolet chevelle malibu 37 | 19.0,6,250.0,88,3302,15.5,71,1,ford torino 500 38 | 18.0,6,232.0,100,3288,15.5,71,1,amc matador 39 | 14.0,8,350.0,165,4209,12.0,71,1,chevrolet impala 40 | 14.0,8,400.0,175,4464,11.5,71,1,pontiac catalina brougham 41 | 14.0,8,351.0,153,4154,13.5,71,1,ford galaxie 500 42 | 14.0,8,318.0,150,4096,13.0,71,1,plymouth fury iii 43 | 12.0,8,383.0,180,4955,11.5,71,1,dodge monaco (sw) 44 | 13.0,8,400.0,170,4746,12.0,71,1,ford country squire (sw) 45 | 13.0,8,400.0,175,5140,12.0,71,1,pontiac safari (sw) 46 | 18.0,6,258.0,110,2962,13.5,71,1,amc hornet sportabout (sw) 47 | 22.0,4,140.0,72,2408,19.0,71,1,chevrolet vega (sw) 48 | 19.0,6,250.0,100,3282,15.0,71,1,pontiac firebird 49 | 18.0,6,250.0,88,3139,14.5,71,1,ford mustang 50 | 23.0,4,122.0,86,2220,14.0,71,1,mercury capri 2000 51 | 28.0,4,116.0,90,2123,14.0,71,2,opel 1900 52 | 30.0,4,79.0,70,2074,19.5,71,2,peugeot 304 53 | 30.0,4,88.0,76,2065,14.5,71,2,fiat 124b 54 | 31.0,4,71.0,65,1773,19.0,71,3,toyota corolla 1200 55 | 35.0,4,72.0,69,1613,18.0,71,3,datsun 1200 56 | 27.0,4,97.0,60,1834,19.0,71,2,volkswagen model 111 57 | 26.0,4,91.0,70,1955,20.5,71,1,plymouth cricket 58 | 24.0,4,113.0,95,2278,15.5,72,3,toyota corona hardtop 59 | 25.0,4,97.5,80,2126,17.0,72,1,dodge colt hardtop 60 | 23.0,4,97.0,54,2254,23.5,72,2,volkswagen type 3 61 | 20.0,4,140.0,90,2408,19.5,72,1,chevrolet vega 62 | 21.0,4,122.0,86,2226,16.5,72,1,ford pinto runabout 63 | 13.0,8,350.0,165,4274,12.0,72,1,chevrolet impala 64 | 14.0,8,400.0,175,4385,12.0,72,1,pontiac catalina 65 | 15.0,8,318.0,150,4135,13.5,72,1,plymouth fury iii 66 | 14.0,8,351.0,153,4129,13.0,72,1,ford galaxie 500 67 | 17.0,8,304.0,150,3672,11.5,72,1,amc ambassador sst 68 | 11.0,8,429.0,208,4633,11.0,72,1,mercury marquis 69 | 13.0,8,350.0,155,4502,13.5,72,1,buick lesabre custom 70 | 12.0,8,350.0,160,4456,13.5,72,1,oldsmobile delta 88 royale 71 | 13.0,8,400.0,190,4422,12.5,72,1,chrysler newport royal 72 | 19.0,3,70.0,97,2330,13.5,72,3,mazda rx2 coupe 73 | 15.0,8,304.0,150,3892,12.5,72,1,amc matador (sw) 74 | 13.0,8,307.0,130,4098,14.0,72,1,chevrolet chevelle concours (sw) 75 | 13.0,8,302.0,140,4294,16.0,72,1,ford gran torino (sw) 76 | 14.0,8,318.0,150,4077,14.0,72,1,plymouth satellite custom (sw) 77 | 18.0,4,121.0,112,2933,14.5,72,2,volvo 145e (sw) 78 | 22.0,4,121.0,76,2511,18.0,72,2,volkswagen 411 (sw) 79 | 21.0,4,120.0,87,2979,19.5,72,2,peugeot 504 (sw) 80 | 26.0,4,96.0,69,2189,18.0,72,2,renault 12 (sw) 81 | 22.0,4,122.0,86,2395,16.0,72,1,ford pinto (sw) 82 | 28.0,4,97.0,92,2288,17.0,72,3,datsun 510 (sw) 83 | 23.0,4,120.0,97,2506,14.5,72,3,toyouta corona mark ii (sw) 84 | 28.0,4,98.0,80,2164,15.0,72,1,dodge colt (sw) 85 | 27.0,4,97.0,88,2100,16.5,72,3,toyota corolla 1600 (sw) 86 | 13.0,8,350.0,175,4100,13.0,73,1,buick century 350 87 | 14.0,8,304.0,150,3672,11.5,73,1,amc matador 88 | 13.0,8,350.0,145,3988,13.0,73,1,chevrolet malibu 89 | 14.0,8,302.0,137,4042,14.5,73,1,ford gran torino 90 | 15.0,8,318.0,150,3777,12.5,73,1,dodge coronet custom 91 | 12.0,8,429.0,198,4952,11.5,73,1,mercury marquis brougham 92 | 13.0,8,400.0,150,4464,12.0,73,1,chevrolet caprice classic 93 | 13.0,8,351.0,158,4363,13.0,73,1,ford ltd 94 | 14.0,8,318.0,150,4237,14.5,73,1,plymouth fury gran sedan 95 | 13.0,8,440.0,215,4735,11.0,73,1,chrysler new yorker brougham 96 | 12.0,8,455.0,225,4951,11.0,73,1,buick electra 225 custom 97 | 13.0,8,360.0,175,3821,11.0,73,1,amc ambassador brougham 98 | 18.0,6,225.0,105,3121,16.5,73,1,plymouth valiant 99 | 16.0,6,250.0,100,3278,18.0,73,1,chevrolet nova custom 100 | 18.0,6,232.0,100,2945,16.0,73,1,amc hornet 101 | 18.0,6,250.0,88,3021,16.5,73,1,ford maverick 102 | 23.0,6,198.0,95,2904,16.0,73,1,plymouth duster 103 | 26.0,4,97.0,46,1950,21.0,73,2,volkswagen super beetle 104 | 11.0,8,400.0,150,4997,14.0,73,1,chevrolet impala 105 | 12.0,8,400.0,167,4906,12.5,73,1,ford country 106 | 13.0,8,360.0,170,4654,13.0,73,1,plymouth custom suburb 107 | 12.0,8,350.0,180,4499,12.5,73,1,oldsmobile vista cruiser 108 | 18.0,6,232.0,100,2789,15.0,73,1,amc gremlin 109 | 20.0,4,97.0,88,2279,19.0,73,3,toyota carina 110 | 21.0,4,140.0,72,2401,19.5,73,1,chevrolet vega 111 | 22.0,4,108.0,94,2379,16.5,73,3,datsun 610 112 | 18.0,3,70.0,90,2124,13.5,73,3,maxda rx3 113 | 19.0,4,122.0,85,2310,18.5,73,1,ford pinto 114 | 21.0,6,155.0,107,2472,14.0,73,1,mercury capri v6 115 | 26.0,4,98.0,90,2265,15.5,73,2,fiat 124 sport coupe 116 | 15.0,8,350.0,145,4082,13.0,73,1,chevrolet monte carlo s 117 | 16.0,8,400.0,230,4278,9.5,73,1,pontiac grand prix 118 | 29.0,4,68.0,49,1867,19.5,73,2,fiat 128 119 | 24.0,4,116.0,75,2158,15.5,73,2,opel manta 120 | 20.0,4,114.0,91,2582,14.0,73,2,audi 100ls 121 | 19.0,4,121.0,112,2868,15.5,73,2,volvo 144ea 122 | 15.0,8,318.0,150,3399,11.0,73,1,dodge dart custom 123 | 24.0,4,121.0,110,2660,14.0,73,2,saab 99le 124 | 20.0,6,156.0,122,2807,13.5,73,3,toyota mark ii 125 | 11.0,8,350.0,180,3664,11.0,73,1,oldsmobile omega 126 | 20.0,6,198.0,95,3102,16.5,74,1,plymouth duster 127 | 19.0,6,232.0,100,2901,16.0,74,1,amc hornet 128 | 15.0,6,250.0,100,3336,17.0,74,1,chevrolet nova 129 | 31.0,4,79.0,67,1950,19.0,74,3,datsun b210 130 | 26.0,4,122.0,80,2451,16.5,74,1,ford pinto 131 | 32.0,4,71.0,65,1836,21.0,74,3,toyota corolla 1200 132 | 25.0,4,140.0,75,2542,17.0,74,1,chevrolet vega 133 | 16.0,6,250.0,100,3781,17.0,74,1,chevrolet chevelle malibu classic 134 | 16.0,6,258.0,110,3632,18.0,74,1,amc matador 135 | 18.0,6,225.0,105,3613,16.5,74,1,plymouth satellite sebring 136 | 16.0,8,302.0,140,4141,14.0,74,1,ford gran torino 137 | 13.0,8,350.0,150,4699,14.5,74,1,buick century luxus (sw) 138 | 14.0,8,318.0,150,4457,13.5,74,1,dodge coronet custom (sw) 139 | 14.0,8,302.0,140,4638,16.0,74,1,ford gran torino (sw) 140 | 14.0,8,304.0,150,4257,15.5,74,1,amc matador (sw) 141 | 29.0,4,98.0,83,2219,16.5,74,2,audi fox 142 | 26.0,4,79.0,67,1963,15.5,74,2,volkswagen dasher 143 | 26.0,4,97.0,78,2300,14.5,74,2,opel manta 144 | 31.0,4,76.0,52,1649,16.5,74,3,toyota corona 145 | 32.0,4,83.0,61,2003,19.0,74,3,datsun 710 146 | 28.0,4,90.0,75,2125,14.5,74,1,dodge colt 147 | 24.0,4,90.0,75,2108,15.5,74,2,fiat 128 148 | 26.0,4,116.0,75,2246,14.0,74,2,fiat 124 tc 149 | 24.0,4,120.0,97,2489,15.0,74,3,honda civic 150 | 26.0,4,108.0,93,2391,15.5,74,3,subaru 151 | 31.0,4,79.0,67,2000,16.0,74,2,fiat x1.9 152 | 19.0,6,225.0,95,3264,16.0,75,1,plymouth valiant custom 153 | 18.0,6,250.0,105,3459,16.0,75,1,chevrolet nova 154 | 15.0,6,250.0,72,3432,21.0,75,1,mercury monarch 155 | 15.0,6,250.0,72,3158,19.5,75,1,ford maverick 156 | 16.0,8,400.0,170,4668,11.5,75,1,pontiac catalina 157 | 15.0,8,350.0,145,4440,14.0,75,1,chevrolet bel air 158 | 16.0,8,318.0,150,4498,14.5,75,1,plymouth grand fury 159 | 14.0,8,351.0,148,4657,13.5,75,1,ford ltd 160 | 17.0,6,231.0,110,3907,21.0,75,1,buick century 161 | 16.0,6,250.0,105,3897,18.5,75,1,chevroelt chevelle malibu 162 | 15.0,6,258.0,110,3730,19.0,75,1,amc matador 163 | 18.0,6,225.0,95,3785,19.0,75,1,plymouth fury 164 | 21.0,6,231.0,110,3039,15.0,75,1,buick skyhawk 165 | 20.0,8,262.0,110,3221,13.5,75,1,chevrolet monza 2+2 166 | 13.0,8,302.0,129,3169,12.0,75,1,ford mustang ii 167 | 29.0,4,97.0,75,2171,16.0,75,3,toyota corolla 168 | 23.0,4,140.0,83,2639,17.0,75,1,ford pinto 169 | 20.0,6,232.0,100,2914,16.0,75,1,amc gremlin 170 | 23.0,4,140.0,78,2592,18.5,75,1,pontiac astro 171 | 24.0,4,134.0,96,2702,13.5,75,3,toyota corona 172 | 25.0,4,90.0,71,2223,16.5,75,2,volkswagen dasher 173 | 24.0,4,119.0,97,2545,17.0,75,3,datsun 710 174 | 18.0,6,171.0,97,2984,14.5,75,1,ford pinto 175 | 29.0,4,90.0,70,1937,14.0,75,2,volkswagen rabbit 176 | 19.0,6,232.0,90,3211,17.0,75,1,amc pacer 177 | 23.0,4,115.0,95,2694,15.0,75,2,audi 100ls 178 | 23.0,4,120.0,88,2957,17.0,75,2,peugeot 504 179 | 22.0,4,121.0,98,2945,14.5,75,2,volvo 244dl 180 | 25.0,4,121.0,115,2671,13.5,75,2,saab 99le 181 | 33.0,4,91.0,53,1795,17.5,75,3,honda civic cvcc 182 | 28.0,4,107.0,86,2464,15.5,76,2,fiat 131 183 | 25.0,4,116.0,81,2220,16.9,76,2,opel 1900 184 | 25.0,4,140.0,92,2572,14.9,76,1,capri ii 185 | 26.0,4,98.0,79,2255,17.7,76,1,dodge colt 186 | 27.0,4,101.0,83,2202,15.3,76,2,renault 12tl 187 | 17.5,8,305.0,140,4215,13.0,76,1,chevrolet chevelle malibu classic 188 | 16.0,8,318.0,150,4190,13.0,76,1,dodge coronet brougham 189 | 15.5,8,304.0,120,3962,13.9,76,1,amc matador 190 | 14.5,8,351.0,152,4215,12.8,76,1,ford gran torino 191 | 22.0,6,225.0,100,3233,15.4,76,1,plymouth valiant 192 | 22.0,6,250.0,105,3353,14.5,76,1,chevrolet nova 193 | 24.0,6,200.0,81,3012,17.6,76,1,ford maverick 194 | 22.5,6,232.0,90,3085,17.6,76,1,amc hornet 195 | 29.0,4,85.0,52,2035,22.2,76,1,chevrolet chevette 196 | 24.5,4,98.0,60,2164,22.1,76,1,chevrolet woody 197 | 29.0,4,90.0,70,1937,14.2,76,2,vw rabbit 198 | 33.0,4,91.0,53,1795,17.4,76,3,honda civic 199 | 20.0,6,225.0,100,3651,17.7,76,1,dodge aspen se 200 | 18.0,6,250.0,78,3574,21.0,76,1,ford granada ghia 201 | 18.5,6,250.0,110,3645,16.2,76,1,pontiac ventura sj 202 | 17.5,6,258.0,95,3193,17.8,76,1,amc pacer d/l 203 | 29.5,4,97.0,71,1825,12.2,76,2,volkswagen rabbit 204 | 32.0,4,85.0,70,1990,17.0,76,3,datsun b-210 205 | 28.0,4,97.0,75,2155,16.4,76,3,toyota corolla 206 | 26.5,4,140.0,72,2565,13.6,76,1,ford pinto 207 | 20.0,4,130.0,102,3150,15.7,76,2,volvo 245 208 | 13.0,8,318.0,150,3940,13.2,76,1,plymouth volare premier v8 209 | 19.0,4,120.0,88,3270,21.9,76,2,peugeot 504 210 | 19.0,6,156.0,108,2930,15.5,76,3,toyota mark ii 211 | 16.5,6,168.0,120,3820,16.7,76,2,mercedes-benz 280s 212 | 16.5,8,350.0,180,4380,12.1,76,1,cadillac seville 213 | 13.0,8,350.0,145,4055,12.0,76,1,chevy c10 214 | 13.0,8,302.0,130,3870,15.0,76,1,ford f108 215 | 13.0,8,318.0,150,3755,14.0,76,1,dodge d100 216 | 31.5,4,98.0,68,2045,18.5,77,3,honda accord cvcc 217 | 30.0,4,111.0,80,2155,14.8,77,1,buick opel isuzu deluxe 218 | 36.0,4,79.0,58,1825,18.6,77,2,renault 5 gtl 219 | 25.5,4,122.0,96,2300,15.5,77,1,plymouth arrow gs 220 | 33.5,4,85.0,70,1945,16.8,77,3,datsun f-10 hatchback 221 | 17.5,8,305.0,145,3880,12.5,77,1,chevrolet caprice classic 222 | 17.0,8,260.0,110,4060,19.0,77,1,oldsmobile cutlass supreme 223 | 15.5,8,318.0,145,4140,13.7,77,1,dodge monaco brougham 224 | 15.0,8,302.0,130,4295,14.9,77,1,mercury cougar brougham 225 | 17.5,6,250.0,110,3520,16.4,77,1,chevrolet concours 226 | 20.5,6,231.0,105,3425,16.9,77,1,buick skylark 227 | 19.0,6,225.0,100,3630,17.7,77,1,plymouth volare custom 228 | 18.5,6,250.0,98,3525,19.0,77,1,ford granada 229 | 16.0,8,400.0,180,4220,11.1,77,1,pontiac grand prix lj 230 | 15.5,8,350.0,170,4165,11.4,77,1,chevrolet monte carlo landau 231 | 15.5,8,400.0,190,4325,12.2,77,1,chrysler cordoba 232 | 16.0,8,351.0,149,4335,14.5,77,1,ford thunderbird 233 | 29.0,4,97.0,78,1940,14.5,77,2,volkswagen rabbit custom 234 | 24.5,4,151.0,88,2740,16.0,77,1,pontiac sunbird coupe 235 | 26.0,4,97.0,75,2265,18.2,77,3,toyota corolla liftback 236 | 25.5,4,140.0,89,2755,15.8,77,1,ford mustang ii 2+2 237 | 30.5,4,98.0,63,2051,17.0,77,1,chevrolet chevette 238 | 33.5,4,98.0,83,2075,15.9,77,1,dodge colt m/m 239 | 30.0,4,97.0,67,1985,16.4,77,3,subaru dl 240 | 30.5,4,97.0,78,2190,14.1,77,2,volkswagen dasher 241 | 22.0,6,146.0,97,2815,14.5,77,3,datsun 810 242 | 21.5,4,121.0,110,2600,12.8,77,2,bmw 320i 243 | 21.5,3,80.0,110,2720,13.5,77,3,mazda rx-4 244 | 43.1,4,90.0,48,1985,21.5,78,2,volkswagen rabbit custom diesel 245 | 36.1,4,98.0,66,1800,14.4,78,1,ford fiesta 246 | 32.8,4,78.0,52,1985,19.4,78,3,mazda glc deluxe 247 | 39.4,4,85.0,70,2070,18.6,78,3,datsun b210 gx 248 | 36.1,4,91.0,60,1800,16.4,78,3,honda civic cvcc 249 | 19.9,8,260.0,110,3365,15.5,78,1,oldsmobile cutlass salon brougham 250 | 19.4,8,318.0,140,3735,13.2,78,1,dodge diplomat 251 | 20.2,8,302.0,139,3570,12.8,78,1,mercury monarch ghia 252 | 19.2,6,231.0,105,3535,19.2,78,1,pontiac phoenix lj 253 | 20.5,6,200.0,95,3155,18.2,78,1,chevrolet malibu 254 | 20.2,6,200.0,85,2965,15.8,78,1,ford fairmont (auto) 255 | 25.1,4,140.0,88,2720,15.4,78,1,ford fairmont (man) 256 | 20.5,6,225.0,100,3430,17.2,78,1,plymouth volare 257 | 19.4,6,232.0,90,3210,17.2,78,1,amc concord 258 | 20.6,6,231.0,105,3380,15.8,78,1,buick century special 259 | 20.8,6,200.0,85,3070,16.7,78,1,mercury zephyr 260 | 18.6,6,225.0,110,3620,18.7,78,1,dodge aspen 261 | 18.1,6,258.0,120,3410,15.1,78,1,amc concord d/l 262 | 19.2,8,305.0,145,3425,13.2,78,1,chevrolet monte carlo landau 263 | 17.7,6,231.0,165,3445,13.4,78,1,buick regal sport coupe (turbo) 264 | 18.1,8,302.0,139,3205,11.2,78,1,ford futura 265 | 17.5,8,318.0,140,4080,13.7,78,1,dodge magnum xe 266 | 30.0,4,98.0,68,2155,16.5,78,1,chevrolet chevette 267 | 27.5,4,134.0,95,2560,14.2,78,3,toyota corona 268 | 27.2,4,119.0,97,2300,14.7,78,3,datsun 510 269 | 30.9,4,105.0,75,2230,14.5,78,1,dodge omni 270 | 21.1,4,134.0,95,2515,14.8,78,3,toyota celica gt liftback 271 | 23.2,4,156.0,105,2745,16.7,78,1,plymouth sapporo 272 | 23.8,4,151.0,85,2855,17.6,78,1,oldsmobile starfire sx 273 | 23.9,4,119.0,97,2405,14.9,78,3,datsun 200-sx 274 | 20.3,5,131.0,103,2830,15.9,78,2,audi 5000 275 | 17.0,6,163.0,125,3140,13.6,78,2,volvo 264gl 276 | 21.6,4,121.0,115,2795,15.7,78,2,saab 99gle 277 | 16.2,6,163.0,133,3410,15.8,78,2,peugeot 604sl 278 | 31.5,4,89.0,71,1990,14.9,78,2,volkswagen scirocco 279 | 29.5,4,98.0,68,2135,16.6,78,3,honda accord lx 280 | 21.5,6,231.0,115,3245,15.4,79,1,pontiac lemans v6 281 | 19.8,6,200.0,85,2990,18.2,79,1,mercury zephyr 6 282 | 22.3,4,140.0,88,2890,17.3,79,1,ford fairmont 4 283 | 20.2,6,232.0,90,3265,18.2,79,1,amc concord dl 6 284 | 20.6,6,225.0,110,3360,16.6,79,1,dodge aspen 6 285 | 17.0,8,305.0,130,3840,15.4,79,1,chevrolet caprice classic 286 | 17.6,8,302.0,129,3725,13.4,79,1,ford ltd landau 287 | 16.5,8,351.0,138,3955,13.2,79,1,mercury grand marquis 288 | 18.2,8,318.0,135,3830,15.2,79,1,dodge st. regis 289 | 16.9,8,350.0,155,4360,14.9,79,1,buick estate wagon (sw) 290 | 15.5,8,351.0,142,4054,14.3,79,1,ford country squire (sw) 291 | 19.2,8,267.0,125,3605,15.0,79,1,chevrolet malibu classic (sw) 292 | 18.5,8,360.0,150,3940,13.0,79,1,chrysler lebaron town @ country (sw) 293 | 31.9,4,89.0,71,1925,14.0,79,2,vw rabbit custom 294 | 34.1,4,86.0,65,1975,15.2,79,3,maxda glc deluxe 295 | 35.7,4,98.0,80,1915,14.4,79,1,dodge colt hatchback custom 296 | 27.4,4,121.0,80,2670,15.0,79,1,amc spirit dl 297 | 25.4,5,183.0,77,3530,20.1,79,2,mercedes benz 300d 298 | 23.0,8,350.0,125,3900,17.4,79,1,cadillac eldorado 299 | 27.2,4,141.0,71,3190,24.8,79,2,peugeot 504 300 | 23.9,8,260.0,90,3420,22.2,79,1,oldsmobile cutlass salon brougham 301 | 34.2,4,105.0,70,2200,13.2,79,1,plymouth horizon 302 | 34.5,4,105.0,70,2150,14.9,79,1,plymouth horizon tc3 303 | 31.8,4,85.0,65,2020,19.2,79,3,datsun 210 304 | 37.3,4,91.0,69,2130,14.7,79,2,fiat strada custom 305 | 28.4,4,151.0,90,2670,16.0,79,1,buick skylark limited 306 | 28.8,6,173.0,115,2595,11.3,79,1,chevrolet citation 307 | 26.8,6,173.0,115,2700,12.9,79,1,oldsmobile omega brougham 308 | 33.5,4,151.0,90,2556,13.2,79,1,pontiac phoenix 309 | 41.5,4,98.0,76,2144,14.7,80,2,vw rabbit 310 | 38.1,4,89.0,60,1968,18.8,80,3,toyota corolla tercel 311 | 32.1,4,98.0,70,2120,15.5,80,1,chevrolet chevette 312 | 37.2,4,86.0,65,2019,16.4,80,3,datsun 310 313 | 28.0,4,151.0,90,2678,16.5,80,1,chevrolet citation 314 | 26.4,4,140.0,88,2870,18.1,80,1,ford fairmont 315 | 24.3,4,151.0,90,3003,20.1,80,1,amc concord 316 | 19.1,6,225.0,90,3381,18.7,80,1,dodge aspen 317 | 34.3,4,97.0,78,2188,15.8,80,2,audi 4000 318 | 29.8,4,134.0,90,2711,15.5,80,3,toyota corona liftback 319 | 31.3,4,120.0,75,2542,17.5,80,3,mazda 626 320 | 37.0,4,119.0,92,2434,15.0,80,3,datsun 510 hatchback 321 | 32.2,4,108.0,75,2265,15.2,80,3,toyota corolla 322 | 46.6,4,86.0,65,2110,17.9,80,3,mazda glc 323 | 27.9,4,156.0,105,2800,14.4,80,1,dodge colt 324 | 40.8,4,85.0,65,2110,19.2,80,3,datsun 210 325 | 44.3,4,90.0,48,2085,21.7,80,2,vw rabbit c (diesel) 326 | 43.4,4,90.0,48,2335,23.7,80,2,vw dasher (diesel) 327 | 36.4,5,121.0,67,2950,19.9,80,2,audi 5000s (diesel) 328 | 30.0,4,146.0,67,3250,21.8,80,2,mercedes-benz 240d 329 | 44.6,4,91.0,67,1850,13.8,80,3,honda civic 1500 gl 330 | 33.8,4,97.0,67,2145,18.0,80,3,subaru dl 331 | 29.8,4,89.0,62,1845,15.3,80,2,vokswagen rabbit 332 | 32.7,6,168.0,132,2910,11.4,80,3,datsun 280-zx 333 | 23.7,3,70.0,100,2420,12.5,80,3,mazda rx-7 gs 334 | 35.0,4,122.0,88,2500,15.1,80,2,triumph tr7 coupe 335 | 32.4,4,107.0,72,2290,17.0,80,3,honda accord 336 | 27.2,4,135.0,84,2490,15.7,81,1,plymouth reliant 337 | 26.6,4,151.0,84,2635,16.4,81,1,buick skylark 338 | 25.8,4,156.0,92,2620,14.4,81,1,dodge aries wagon (sw) 339 | 23.5,6,173.0,110,2725,12.6,81,1,chevrolet citation 340 | 30.0,4,135.0,84,2385,12.9,81,1,plymouth reliant 341 | 39.1,4,79.0,58,1755,16.9,81,3,toyota starlet 342 | 39.0,4,86.0,64,1875,16.4,81,1,plymouth champ 343 | 35.1,4,81.0,60,1760,16.1,81,3,honda civic 1300 344 | 32.3,4,97.0,67,2065,17.8,81,3,subaru 345 | 37.0,4,85.0,65,1975,19.4,81,3,datsun 210 mpg 346 | 37.7,4,89.0,62,2050,17.3,81,3,toyota tercel 347 | 34.1,4,91.0,68,1985,16.0,81,3,mazda glc 4 348 | 34.7,4,105.0,63,2215,14.9,81,1,plymouth horizon 4 349 | 34.4,4,98.0,65,2045,16.2,81,1,ford escort 4w 350 | 29.9,4,98.0,65,2380,20.7,81,1,ford escort 2h 351 | 33.0,4,105.0,74,2190,14.2,81,2,volkswagen jetta 352 | 33.7,4,107.0,75,2210,14.4,81,3,honda prelude 353 | 32.4,4,108.0,75,2350,16.8,81,3,toyota corolla 354 | 32.9,4,119.0,100,2615,14.8,81,3,datsun 200sx 355 | 31.6,4,120.0,74,2635,18.3,81,3,mazda 626 356 | 28.1,4,141.0,80,3230,20.4,81,2,peugeot 505s turbo diesel 357 | 30.7,6,145.0,76,3160,19.6,81,2,volvo diesel 358 | 25.4,6,168.0,116,2900,12.6,81,3,toyota cressida 359 | 24.2,6,146.0,120,2930,13.8,81,3,datsun 810 maxima 360 | 22.4,6,231.0,110,3415,15.8,81,1,buick century 361 | 26.6,8,350.0,105,3725,19.0,81,1,oldsmobile cutlass ls 362 | 20.2,6,200.0,88,3060,17.1,81,1,ford granada gl 363 | 17.6,6,225.0,85,3465,16.6,81,1,chrysler lebaron salon 364 | 28.0,4,112.0,88,2605,19.6,82,1,chevrolet cavalier 365 | 27.0,4,112.0,88,2640,18.6,82,1,chevrolet cavalier wagon 366 | 34.0,4,112.0,88,2395,18.0,82,1,chevrolet cavalier 2-door 367 | 31.0,4,112.0,85,2575,16.2,82,1,pontiac j2000 se hatchback 368 | 29.0,4,135.0,84,2525,16.0,82,1,dodge aries se 369 | 27.0,4,151.0,90,2735,18.0,82,1,pontiac phoenix 370 | 24.0,4,140.0,92,2865,16.4,82,1,ford fairmont futura 371 | 36.0,4,105.0,74,1980,15.3,82,2,volkswagen rabbit l 372 | 37.0,4,91.0,68,2025,18.2,82,3,mazda glc custom l 373 | 31.0,4,91.0,68,1970,17.6,82,3,mazda glc custom 374 | 38.0,4,105.0,63,2125,14.7,82,1,plymouth horizon miser 375 | 36.0,4,98.0,70,2125,17.3,82,1,mercury lynx l 376 | 36.0,4,120.0,88,2160,14.5,82,3,nissan stanza xe 377 | 36.0,4,107.0,75,2205,14.5,82,3,honda accord 378 | 34.0,4,108.0,70,2245,16.9,82,3,toyota corolla 379 | 38.0,4,91.0,67,1965,15.0,82,3,honda civic 380 | 32.0,4,91.0,67,1965,15.7,82,3,honda civic (auto) 381 | 38.0,4,91.0,67,1995,16.2,82,3,datsun 310 gx 382 | 25.0,6,181.0,110,2945,16.4,82,1,buick century limited 383 | 38.0,6,262.0,85,3015,17.0,82,1,oldsmobile cutlass ciera (diesel) 384 | 26.0,4,156.0,92,2585,14.5,82,1,chrysler lebaron medallion 385 | 22.0,6,232.0,112,2835,14.7,82,1,ford granada l 386 | 32.0,4,144.0,96,2665,13.9,82,3,toyota celica gt 387 | 36.0,4,135.0,84,2370,13.0,82,1,dodge charger 2.2 388 | 27.0,4,151.0,90,2950,17.3,82,1,chevrolet camaro 389 | 27.0,4,140.0,86,2790,15.6,82,1,ford mustang gl 390 | 44.0,4,97.0,52,2130,24.6,82,2,vw pickup 391 | 32.0,4,135.0,84,2295,11.6,82,1,dodge rampage 392 | 28.0,4,120.0,79,2625,18.6,82,1,ford ranger 393 | 31.0,4,119.0,82,2720,19.4,82,1,chevy s-10 394 | -------------------------------------------------------------------------------- /notebooks/data/Carseats.csv: -------------------------------------------------------------------------------- 1 | Sales,CompPrice,Income,Advertising,Population,Price,ShelveLoc,Age,Education,Urban,US 2 | 9.5,138,73,11,276,120,Bad,42,17,Yes,Yes 3 | 11.22,111,48,16,260,83,Good,65,10,Yes,Yes 4 | 10.06,113,35,10,269,80,Medium,59,12,Yes,Yes 5 | 7.4,117,100,4,466,97,Medium,55,14,Yes,Yes 6 | 4.15,141,64,3,340,128,Bad,38,13,Yes,No 7 | 10.81,124,113,13,501,72,Bad,78,16,No,Yes 8 | 6.63,115,105,0,45,108,Medium,71,15,Yes,No 9 | 11.85,136,81,15,425,120,Good,67,10,Yes,Yes 10 | 6.54,132,110,0,108,124,Medium,76,10,No,No 11 | 4.69,132,113,0,131,124,Medium,76,17,No,Yes 12 | 9.01,121,78,9,150,100,Bad,26,10,No,Yes 13 | 11.96,117,94,4,503,94,Good,50,13,Yes,Yes 14 | 3.98,122,35,2,393,136,Medium,62,18,Yes,No 15 | 10.96,115,28,11,29,86,Good,53,18,Yes,Yes 16 | 11.17,107,117,11,148,118,Good,52,18,Yes,Yes 17 | 8.71,149,95,5,400,144,Medium,76,18,No,No 18 | 7.58,118,32,0,284,110,Good,63,13,Yes,No 19 | 12.29,147,74,13,251,131,Good,52,10,Yes,Yes 20 | 13.91,110,110,0,408,68,Good,46,17,No,Yes 21 | 8.73,129,76,16,58,121,Medium,69,12,Yes,Yes 22 | 6.41,125,90,2,367,131,Medium,35,18,Yes,Yes 23 | 12.13,134,29,12,239,109,Good,62,18,No,Yes 24 | 5.08,128,46,6,497,138,Medium,42,13,Yes,No 25 | 5.87,121,31,0,292,109,Medium,79,10,Yes,No 26 | 10.14,145,119,16,294,113,Bad,42,12,Yes,Yes 27 | 14.9,139,32,0,176,82,Good,54,11,No,No 28 | 8.33,107,115,11,496,131,Good,50,11,No,Yes 29 | 5.27,98,118,0,19,107,Medium,64,17,Yes,No 30 | 2.99,103,74,0,359,97,Bad,55,11,Yes,Yes 31 | 7.81,104,99,15,226,102,Bad,58,17,Yes,Yes 32 | 13.55,125,94,0,447,89,Good,30,12,Yes,No 33 | 8.25,136,58,16,241,131,Medium,44,18,Yes,Yes 34 | 6.2,107,32,12,236,137,Good,64,10,No,Yes 35 | 8.77,114,38,13,317,128,Good,50,16,Yes,Yes 36 | 2.67,115,54,0,406,128,Medium,42,17,Yes,Yes 37 | 11.07,131,84,11,29,96,Medium,44,17,No,Yes 38 | 8.89,122,76,0,270,100,Good,60,18,No,No 39 | 4.95,121,41,5,412,110,Medium,54,10,Yes,Yes 40 | 6.59,109,73,0,454,102,Medium,65,15,Yes,No 41 | 3.24,130,60,0,144,138,Bad,38,10,No,No 42 | 2.07,119,98,0,18,126,Bad,73,17,No,No 43 | 7.96,157,53,0,403,124,Bad,58,16,Yes,No 44 | 10.43,77,69,0,25,24,Medium,50,18,Yes,No 45 | 4.12,123,42,11,16,134,Medium,59,13,Yes,Yes 46 | 4.16,85,79,6,325,95,Medium,69,13,Yes,Yes 47 | 4.56,141,63,0,168,135,Bad,44,12,Yes,Yes 48 | 12.44,127,90,14,16,70,Medium,48,15,No,Yes 49 | 4.38,126,98,0,173,108,Bad,55,16,Yes,No 50 | 3.91,116,52,0,349,98,Bad,69,18,Yes,No 51 | 10.61,157,93,0,51,149,Good,32,17,Yes,No 52 | 1.42,99,32,18,341,108,Bad,80,16,Yes,Yes 53 | 4.42,121,90,0,150,108,Bad,75,16,Yes,No 54 | 7.91,153,40,3,112,129,Bad,39,18,Yes,Yes 55 | 6.92,109,64,13,39,119,Medium,61,17,Yes,Yes 56 | 4.9,134,103,13,25,144,Medium,76,17,No,Yes 57 | 6.85,143,81,5,60,154,Medium,61,18,Yes,Yes 58 | 11.91,133,82,0,54,84,Medium,50,17,Yes,No 59 | 0.91,93,91,0,22,117,Bad,75,11,Yes,No 60 | 5.42,103,93,15,188,103,Bad,74,16,Yes,Yes 61 | 5.21,118,71,4,148,114,Medium,80,13,Yes,No 62 | 8.32,122,102,19,469,123,Bad,29,13,Yes,Yes 63 | 7.32,105,32,0,358,107,Medium,26,13,No,No 64 | 1.82,139,45,0,146,133,Bad,77,17,Yes,Yes 65 | 8.47,119,88,10,170,101,Medium,61,13,Yes,Yes 66 | 7.8,100,67,12,184,104,Medium,32,16,No,Yes 67 | 4.9,122,26,0,197,128,Medium,55,13,No,No 68 | 8.85,127,92,0,508,91,Medium,56,18,Yes,No 69 | 9.01,126,61,14,152,115,Medium,47,16,Yes,Yes 70 | 13.39,149,69,20,366,134,Good,60,13,Yes,Yes 71 | 7.99,127,59,0,339,99,Medium,65,12,Yes,No 72 | 9.46,89,81,15,237,99,Good,74,12,Yes,Yes 73 | 6.5,148,51,16,148,150,Medium,58,17,No,Yes 74 | 5.52,115,45,0,432,116,Medium,25,15,Yes,No 75 | 12.61,118,90,10,54,104,Good,31,11,No,Yes 76 | 6.2,150,68,5,125,136,Medium,64,13,No,Yes 77 | 8.55,88,111,23,480,92,Bad,36,16,No,Yes 78 | 10.64,102,87,10,346,70,Medium,64,15,Yes,Yes 79 | 7.7,118,71,12,44,89,Medium,67,18,No,Yes 80 | 4.43,134,48,1,139,145,Medium,65,12,Yes,Yes 81 | 9.14,134,67,0,286,90,Bad,41,13,Yes,No 82 | 8.01,113,100,16,353,79,Bad,68,11,Yes,Yes 83 | 7.52,116,72,0,237,128,Good,70,13,Yes,No 84 | 11.62,151,83,4,325,139,Good,28,17,Yes,Yes 85 | 4.42,109,36,7,468,94,Bad,56,11,Yes,Yes 86 | 2.23,111,25,0,52,121,Bad,43,18,No,No 87 | 8.47,125,103,0,304,112,Medium,49,13,No,No 88 | 8.7,150,84,9,432,134,Medium,64,15,Yes,No 89 | 11.7,131,67,7,272,126,Good,54,16,No,Yes 90 | 6.56,117,42,7,144,111,Medium,62,10,Yes,Yes 91 | 7.95,128,66,3,493,119,Medium,45,16,No,No 92 | 5.33,115,22,0,491,103,Medium,64,11,No,No 93 | 4.81,97,46,11,267,107,Medium,80,15,Yes,Yes 94 | 4.53,114,113,0,97,125,Medium,29,12,Yes,No 95 | 8.86,145,30,0,67,104,Medium,55,17,Yes,No 96 | 8.39,115,97,5,134,84,Bad,55,11,Yes,Yes 97 | 5.58,134,25,10,237,148,Medium,59,13,Yes,Yes 98 | 9.48,147,42,10,407,132,Good,73,16,No,Yes 99 | 7.45,161,82,5,287,129,Bad,33,16,Yes,Yes 100 | 12.49,122,77,24,382,127,Good,36,16,No,Yes 101 | 4.88,121,47,3,220,107,Bad,56,16,No,Yes 102 | 4.11,113,69,11,94,106,Medium,76,12,No,Yes 103 | 6.2,128,93,0,89,118,Medium,34,18,Yes,No 104 | 5.3,113,22,0,57,97,Medium,65,16,No,No 105 | 5.07,123,91,0,334,96,Bad,78,17,Yes,Yes 106 | 4.62,121,96,0,472,138,Medium,51,12,Yes,No 107 | 5.55,104,100,8,398,97,Medium,61,11,Yes,Yes 108 | 0.16,102,33,0,217,139,Medium,70,18,No,No 109 | 8.55,134,107,0,104,108,Medium,60,12,Yes,No 110 | 3.47,107,79,2,488,103,Bad,65,16,Yes,No 111 | 8.98,115,65,0,217,90,Medium,60,17,No,No 112 | 9.0,128,62,7,125,116,Medium,43,14,Yes,Yes 113 | 6.62,132,118,12,272,151,Medium,43,14,Yes,Yes 114 | 6.67,116,99,5,298,125,Good,62,12,Yes,Yes 115 | 6.01,131,29,11,335,127,Bad,33,12,Yes,Yes 116 | 9.31,122,87,9,17,106,Medium,65,13,Yes,Yes 117 | 8.54,139,35,0,95,129,Medium,42,13,Yes,No 118 | 5.08,135,75,0,202,128,Medium,80,10,No,No 119 | 8.8,145,53,0,507,119,Medium,41,12,Yes,No 120 | 7.57,112,88,2,243,99,Medium,62,11,Yes,Yes 121 | 7.37,130,94,8,137,128,Medium,64,12,Yes,Yes 122 | 6.87,128,105,11,249,131,Medium,63,13,Yes,Yes 123 | 11.67,125,89,10,380,87,Bad,28,10,Yes,Yes 124 | 6.88,119,100,5,45,108,Medium,75,10,Yes,Yes 125 | 8.19,127,103,0,125,155,Good,29,15,No,Yes 126 | 8.87,131,113,0,181,120,Good,63,14,Yes,No 127 | 9.34,89,78,0,181,49,Medium,43,15,No,No 128 | 11.27,153,68,2,60,133,Good,59,16,Yes,Yes 129 | 6.52,125,48,3,192,116,Medium,51,14,Yes,Yes 130 | 4.96,133,100,3,350,126,Bad,55,13,Yes,Yes 131 | 4.47,143,120,7,279,147,Bad,40,10,No,Yes 132 | 8.41,94,84,13,497,77,Medium,51,12,Yes,Yes 133 | 6.5,108,69,3,208,94,Medium,77,16,Yes,No 134 | 9.54,125,87,9,232,136,Good,72,10,Yes,Yes 135 | 7.62,132,98,2,265,97,Bad,62,12,Yes,Yes 136 | 3.67,132,31,0,327,131,Medium,76,16,Yes,No 137 | 6.44,96,94,14,384,120,Medium,36,18,No,Yes 138 | 5.17,131,75,0,10,120,Bad,31,18,No,No 139 | 6.52,128,42,0,436,118,Medium,80,11,Yes,No 140 | 10.27,125,103,12,371,109,Medium,44,10,Yes,Yes 141 | 12.3,146,62,10,310,94,Medium,30,13,No,Yes 142 | 6.03,133,60,10,277,129,Medium,45,18,Yes,Yes 143 | 6.53,140,42,0,331,131,Bad,28,15,Yes,No 144 | 7.44,124,84,0,300,104,Medium,77,15,Yes,No 145 | 0.53,122,88,7,36,159,Bad,28,17,Yes,Yes 146 | 9.09,132,68,0,264,123,Good,34,11,No,No 147 | 8.77,144,63,11,27,117,Medium,47,17,Yes,Yes 148 | 3.9,114,83,0,412,131,Bad,39,14,Yes,No 149 | 10.51,140,54,9,402,119,Good,41,16,No,Yes 150 | 7.56,110,119,0,384,97,Medium,72,14,No,Yes 151 | 11.48,121,120,13,140,87,Medium,56,11,Yes,Yes 152 | 10.49,122,84,8,176,114,Good,57,10,No,Yes 153 | 10.77,111,58,17,407,103,Good,75,17,No,Yes 154 | 7.64,128,78,0,341,128,Good,45,13,No,No 155 | 5.93,150,36,7,488,150,Medium,25,17,No,Yes 156 | 6.89,129,69,10,289,110,Medium,50,16,No,Yes 157 | 7.71,98,72,0,59,69,Medium,65,16,Yes,No 158 | 7.49,146,34,0,220,157,Good,51,16,Yes,No 159 | 10.21,121,58,8,249,90,Medium,48,13,No,Yes 160 | 12.53,142,90,1,189,112,Good,39,10,No,Yes 161 | 9.32,119,60,0,372,70,Bad,30,18,No,No 162 | 4.67,111,28,0,486,111,Medium,29,12,No,No 163 | 2.93,143,21,5,81,160,Medium,67,12,No,Yes 164 | 3.63,122,74,0,424,149,Medium,51,13,Yes,No 165 | 5.68,130,64,0,40,106,Bad,39,17,No,No 166 | 8.22,148,64,0,58,141,Medium,27,13,No,Yes 167 | 0.37,147,58,7,100,191,Bad,27,15,Yes,Yes 168 | 6.71,119,67,17,151,137,Medium,55,11,Yes,Yes 169 | 6.71,106,73,0,216,93,Medium,60,13,Yes,No 170 | 7.3,129,89,0,425,117,Medium,45,10,Yes,No 171 | 11.48,104,41,15,492,77,Good,73,18,Yes,Yes 172 | 8.01,128,39,12,356,118,Medium,71,10,Yes,Yes 173 | 12.49,93,106,12,416,55,Medium,75,15,Yes,Yes 174 | 9.03,104,102,13,123,110,Good,35,16,Yes,Yes 175 | 6.38,135,91,5,207,128,Medium,66,18,Yes,Yes 176 | 0.0,139,24,0,358,185,Medium,79,15,No,No 177 | 7.54,115,89,0,38,122,Medium,25,12,Yes,No 178 | 5.61,138,107,9,480,154,Medium,47,11,No,Yes 179 | 10.48,138,72,0,148,94,Medium,27,17,Yes,Yes 180 | 10.66,104,71,14,89,81,Medium,25,14,No,Yes 181 | 7.78,144,25,3,70,116,Medium,77,18,Yes,Yes 182 | 4.94,137,112,15,434,149,Bad,66,13,Yes,Yes 183 | 7.43,121,83,0,79,91,Medium,68,11,Yes,No 184 | 4.74,137,60,4,230,140,Bad,25,13,Yes,No 185 | 5.32,118,74,6,426,102,Medium,80,18,Yes,Yes 186 | 9.95,132,33,7,35,97,Medium,60,11,No,Yes 187 | 10.07,130,100,11,449,107,Medium,64,10,Yes,Yes 188 | 8.68,120,51,0,93,86,Medium,46,17,No,No 189 | 6.03,117,32,0,142,96,Bad,62,17,Yes,No 190 | 8.07,116,37,0,426,90,Medium,76,15,Yes,No 191 | 12.11,118,117,18,509,104,Medium,26,15,No,Yes 192 | 8.79,130,37,13,297,101,Medium,37,13,No,Yes 193 | 6.67,156,42,13,170,173,Good,74,14,Yes,Yes 194 | 7.56,108,26,0,408,93,Medium,56,14,No,No 195 | 13.28,139,70,7,71,96,Good,61,10,Yes,Yes 196 | 7.23,112,98,18,481,128,Medium,45,11,Yes,Yes 197 | 4.19,117,93,4,420,112,Bad,66,11,Yes,Yes 198 | 4.1,130,28,6,410,133,Bad,72,16,Yes,Yes 199 | 2.52,124,61,0,333,138,Medium,76,16,Yes,No 200 | 3.62,112,80,5,500,128,Medium,69,10,Yes,Yes 201 | 6.42,122,88,5,335,126,Medium,64,14,Yes,Yes 202 | 5.56,144,92,0,349,146,Medium,62,12,No,No 203 | 5.94,138,83,0,139,134,Medium,54,18,Yes,No 204 | 4.1,121,78,4,413,130,Bad,46,10,No,Yes 205 | 2.05,131,82,0,132,157,Bad,25,14,Yes,No 206 | 8.74,155,80,0,237,124,Medium,37,14,Yes,No 207 | 5.68,113,22,1,317,132,Medium,28,12,Yes,No 208 | 4.97,162,67,0,27,160,Medium,77,17,Yes,Yes 209 | 8.19,111,105,0,466,97,Bad,61,10,No,No 210 | 7.78,86,54,0,497,64,Bad,33,12,Yes,No 211 | 3.02,98,21,11,326,90,Bad,76,11,No,Yes 212 | 4.36,125,41,2,357,123,Bad,47,14,No,Yes 213 | 9.39,117,118,14,445,120,Medium,32,15,Yes,Yes 214 | 12.04,145,69,19,501,105,Medium,45,11,Yes,Yes 215 | 8.23,149,84,5,220,139,Medium,33,10,Yes,Yes 216 | 4.83,115,115,3,48,107,Medium,73,18,Yes,Yes 217 | 2.34,116,83,15,170,144,Bad,71,11,Yes,Yes 218 | 5.73,141,33,0,243,144,Medium,34,17,Yes,No 219 | 4.34,106,44,0,481,111,Medium,70,14,No,No 220 | 9.7,138,61,12,156,120,Medium,25,14,Yes,Yes 221 | 10.62,116,79,19,359,116,Good,58,17,Yes,Yes 222 | 10.59,131,120,15,262,124,Medium,30,10,Yes,Yes 223 | 6.43,124,44,0,125,107,Medium,80,11,Yes,No 224 | 7.49,136,119,6,178,145,Medium,35,13,Yes,Yes 225 | 3.45,110,45,9,276,125,Medium,62,14,Yes,Yes 226 | 4.1,134,82,0,464,141,Medium,48,13,No,No 227 | 6.68,107,25,0,412,82,Bad,36,14,Yes,No 228 | 7.8,119,33,0,245,122,Good,56,14,Yes,No 229 | 8.69,113,64,10,68,101,Medium,57,16,Yes,Yes 230 | 5.4,149,73,13,381,163,Bad,26,11,No,Yes 231 | 11.19,98,104,0,404,72,Medium,27,18,No,No 232 | 5.16,115,60,0,119,114,Bad,38,14,No,No 233 | 8.09,132,69,0,123,122,Medium,27,11,No,No 234 | 13.14,137,80,10,24,105,Good,61,15,Yes,Yes 235 | 8.65,123,76,18,218,120,Medium,29,14,No,Yes 236 | 9.43,115,62,11,289,129,Good,56,16,No,Yes 237 | 5.53,126,32,8,95,132,Medium,50,17,Yes,Yes 238 | 9.32,141,34,16,361,108,Medium,69,10,Yes,Yes 239 | 9.62,151,28,8,499,135,Medium,48,10,Yes,Yes 240 | 7.36,121,24,0,200,133,Good,73,13,Yes,No 241 | 3.89,123,105,0,149,118,Bad,62,16,Yes,Yes 242 | 10.31,159,80,0,362,121,Medium,26,18,Yes,No 243 | 12.01,136,63,0,160,94,Medium,38,12,Yes,No 244 | 4.68,124,46,0,199,135,Medium,52,14,No,No 245 | 7.82,124,25,13,87,110,Medium,57,10,Yes,Yes 246 | 8.78,130,30,0,391,100,Medium,26,18,Yes,No 247 | 10.0,114,43,0,199,88,Good,57,10,No,Yes 248 | 6.9,120,56,20,266,90,Bad,78,18,Yes,Yes 249 | 5.04,123,114,0,298,151,Bad,34,16,Yes,No 250 | 5.36,111,52,0,12,101,Medium,61,11,Yes,Yes 251 | 5.05,125,67,0,86,117,Bad,65,11,Yes,No 252 | 9.16,137,105,10,435,156,Good,72,14,Yes,Yes 253 | 3.72,139,111,5,310,132,Bad,62,13,Yes,Yes 254 | 8.31,133,97,0,70,117,Medium,32,16,Yes,No 255 | 5.64,124,24,5,288,122,Medium,57,12,No,Yes 256 | 9.58,108,104,23,353,129,Good,37,17,Yes,Yes 257 | 7.71,123,81,8,198,81,Bad,80,15,Yes,Yes 258 | 4.2,147,40,0,277,144,Medium,73,10,Yes,No 259 | 8.67,125,62,14,477,112,Medium,80,13,Yes,Yes 260 | 3.47,108,38,0,251,81,Bad,72,14,No,No 261 | 5.12,123,36,10,467,100,Bad,74,11,No,Yes 262 | 7.67,129,117,8,400,101,Bad,36,10,Yes,Yes 263 | 5.71,121,42,4,188,118,Medium,54,15,Yes,Yes 264 | 6.37,120,77,15,86,132,Medium,48,18,Yes,Yes 265 | 7.77,116,26,6,434,115,Medium,25,17,Yes,Yes 266 | 6.95,128,29,5,324,159,Good,31,15,Yes,Yes 267 | 5.31,130,35,10,402,129,Bad,39,17,Yes,Yes 268 | 9.1,128,93,12,343,112,Good,73,17,No,Yes 269 | 5.83,134,82,7,473,112,Bad,51,12,No,Yes 270 | 6.53,123,57,0,66,105,Medium,39,11,Yes,No 271 | 5.01,159,69,0,438,166,Medium,46,17,Yes,No 272 | 11.99,119,26,0,284,89,Good,26,10,Yes,No 273 | 4.55,111,56,0,504,110,Medium,62,16,Yes,No 274 | 12.98,113,33,0,14,63,Good,38,12,Yes,No 275 | 10.04,116,106,8,244,86,Medium,58,12,Yes,Yes 276 | 7.22,135,93,2,67,119,Medium,34,11,Yes,Yes 277 | 6.67,107,119,11,210,132,Medium,53,11,Yes,Yes 278 | 6.93,135,69,14,296,130,Medium,73,15,Yes,Yes 279 | 7.8,136,48,12,326,125,Medium,36,16,Yes,Yes 280 | 7.22,114,113,2,129,151,Good,40,15,No,Yes 281 | 3.42,141,57,13,376,158,Medium,64,18,Yes,Yes 282 | 2.86,121,86,10,496,145,Bad,51,10,Yes,Yes 283 | 11.19,122,69,7,303,105,Good,45,16,No,Yes 284 | 7.74,150,96,0,80,154,Good,61,11,Yes,No 285 | 5.36,135,110,0,112,117,Medium,80,16,No,No 286 | 6.97,106,46,11,414,96,Bad,79,17,No,No 287 | 7.6,146,26,11,261,131,Medium,39,10,Yes,Yes 288 | 7.53,117,118,11,429,113,Medium,67,18,No,Yes 289 | 6.88,95,44,4,208,72,Bad,44,17,Yes,Yes 290 | 6.98,116,40,0,74,97,Medium,76,15,No,No 291 | 8.75,143,77,25,448,156,Medium,43,17,Yes,Yes 292 | 9.49,107,111,14,400,103,Medium,41,11,No,Yes 293 | 6.64,118,70,0,106,89,Bad,39,17,Yes,No 294 | 11.82,113,66,16,322,74,Good,76,15,Yes,Yes 295 | 11.28,123,84,0,74,89,Good,59,10,Yes,No 296 | 12.66,148,76,3,126,99,Good,60,11,Yes,Yes 297 | 4.21,118,35,14,502,137,Medium,79,10,No,Yes 298 | 8.21,127,44,13,160,123,Good,63,18,Yes,Yes 299 | 3.07,118,83,13,276,104,Bad,75,10,Yes,Yes 300 | 10.98,148,63,0,312,130,Good,63,15,Yes,No 301 | 9.4,135,40,17,497,96,Medium,54,17,No,Yes 302 | 8.57,116,78,1,158,99,Medium,45,11,Yes,Yes 303 | 7.41,99,93,0,198,87,Medium,57,16,Yes,Yes 304 | 5.28,108,77,13,388,110,Bad,74,14,Yes,Yes 305 | 10.01,133,52,16,290,99,Medium,43,11,Yes,Yes 306 | 11.93,123,98,12,408,134,Good,29,10,Yes,Yes 307 | 8.03,115,29,26,394,132,Medium,33,13,Yes,Yes 308 | 4.78,131,32,1,85,133,Medium,48,12,Yes,Yes 309 | 5.9,138,92,0,13,120,Bad,61,12,Yes,No 310 | 9.24,126,80,19,436,126,Medium,52,10,Yes,Yes 311 | 11.18,131,111,13,33,80,Bad,68,18,Yes,Yes 312 | 9.53,175,65,29,419,166,Medium,53,12,Yes,Yes 313 | 6.15,146,68,12,328,132,Bad,51,14,Yes,Yes 314 | 6.8,137,117,5,337,135,Bad,38,10,Yes,Yes 315 | 9.33,103,81,3,491,54,Medium,66,13,Yes,No 316 | 7.72,133,33,10,333,129,Good,71,14,Yes,Yes 317 | 6.39,131,21,8,220,171,Good,29,14,Yes,Yes 318 | 15.63,122,36,5,369,72,Good,35,10,Yes,Yes 319 | 6.41,142,30,0,472,136,Good,80,15,No,No 320 | 10.08,116,72,10,456,130,Good,41,14,No,Yes 321 | 6.97,127,45,19,459,129,Medium,57,11,No,Yes 322 | 5.86,136,70,12,171,152,Medium,44,18,Yes,Yes 323 | 7.52,123,39,5,499,98,Medium,34,15,Yes,No 324 | 9.16,140,50,10,300,139,Good,60,15,Yes,Yes 325 | 10.36,107,105,18,428,103,Medium,34,12,Yes,Yes 326 | 2.66,136,65,4,133,150,Bad,53,13,Yes,Yes 327 | 11.7,144,69,11,131,104,Medium,47,11,Yes,Yes 328 | 4.69,133,30,0,152,122,Medium,53,17,Yes,No 329 | 6.23,112,38,17,316,104,Medium,80,16,Yes,Yes 330 | 3.15,117,66,1,65,111,Bad,55,11,Yes,Yes 331 | 11.27,100,54,9,433,89,Good,45,12,Yes,Yes 332 | 4.99,122,59,0,501,112,Bad,32,14,No,No 333 | 10.1,135,63,15,213,134,Medium,32,10,Yes,Yes 334 | 5.74,106,33,20,354,104,Medium,61,12,Yes,Yes 335 | 5.87,136,60,7,303,147,Medium,41,10,Yes,Yes 336 | 7.63,93,117,9,489,83,Bad,42,13,Yes,Yes 337 | 6.18,120,70,15,464,110,Medium,72,15,Yes,Yes 338 | 5.17,138,35,6,60,143,Bad,28,18,Yes,No 339 | 8.61,130,38,0,283,102,Medium,80,15,Yes,No 340 | 5.97,112,24,0,164,101,Medium,45,11,Yes,No 341 | 11.54,134,44,4,219,126,Good,44,15,Yes,Yes 342 | 7.5,140,29,0,105,91,Bad,43,16,Yes,No 343 | 7.38,98,120,0,268,93,Medium,72,10,No,No 344 | 7.81,137,102,13,422,118,Medium,71,10,No,Yes 345 | 5.99,117,42,10,371,121,Bad,26,14,Yes,Yes 346 | 8.43,138,80,0,108,126,Good,70,13,No,Yes 347 | 4.81,121,68,0,279,149,Good,79,12,Yes,No 348 | 8.97,132,107,0,144,125,Medium,33,13,No,No 349 | 6.88,96,39,0,161,112,Good,27,14,No,No 350 | 12.57,132,102,20,459,107,Good,49,11,Yes,Yes 351 | 9.32,134,27,18,467,96,Medium,49,14,No,Yes 352 | 8.64,111,101,17,266,91,Medium,63,17,No,Yes 353 | 10.44,124,115,16,458,105,Medium,62,16,No,Yes 354 | 13.44,133,103,14,288,122,Good,61,17,Yes,Yes 355 | 9.45,107,67,12,430,92,Medium,35,12,No,Yes 356 | 5.3,133,31,1,80,145,Medium,42,18,Yes,Yes 357 | 7.02,130,100,0,306,146,Good,42,11,Yes,No 358 | 3.58,142,109,0,111,164,Good,72,12,Yes,No 359 | 13.36,103,73,3,276,72,Medium,34,15,Yes,Yes 360 | 4.17,123,96,10,71,118,Bad,69,11,Yes,Yes 361 | 3.13,130,62,11,396,130,Bad,66,14,Yes,Yes 362 | 8.77,118,86,7,265,114,Good,52,15,No,Yes 363 | 8.68,131,25,10,183,104,Medium,56,15,No,Yes 364 | 5.25,131,55,0,26,110,Bad,79,12,Yes,Yes 365 | 10.26,111,75,1,377,108,Good,25,12,Yes,No 366 | 10.5,122,21,16,488,131,Good,30,14,Yes,Yes 367 | 6.53,154,30,0,122,162,Medium,57,17,No,No 368 | 5.98,124,56,11,447,134,Medium,53,12,No,Yes 369 | 14.37,95,106,0,256,53,Good,52,17,Yes,No 370 | 10.71,109,22,10,348,79,Good,74,14,No,Yes 371 | 10.26,135,100,22,463,122,Medium,36,14,Yes,Yes 372 | 7.68,126,41,22,403,119,Bad,42,12,Yes,Yes 373 | 9.08,152,81,0,191,126,Medium,54,16,Yes,No 374 | 7.8,121,50,0,508,98,Medium,65,11,No,No 375 | 5.58,137,71,0,402,116,Medium,78,17,Yes,No 376 | 9.44,131,47,7,90,118,Medium,47,12,Yes,Yes 377 | 7.9,132,46,4,206,124,Medium,73,11,Yes,No 378 | 16.27,141,60,19,319,92,Good,44,11,Yes,Yes 379 | 6.81,132,61,0,263,125,Medium,41,12,No,No 380 | 6.11,133,88,3,105,119,Medium,79,12,Yes,Yes 381 | 5.81,125,111,0,404,107,Bad,54,15,Yes,No 382 | 9.64,106,64,10,17,89,Medium,68,17,Yes,Yes 383 | 3.9,124,65,21,496,151,Bad,77,13,Yes,Yes 384 | 4.95,121,28,19,315,121,Medium,66,14,Yes,Yes 385 | 9.35,98,117,0,76,68,Medium,63,10,Yes,No 386 | 12.85,123,37,15,348,112,Good,28,12,Yes,Yes 387 | 5.87,131,73,13,455,132,Medium,62,17,Yes,Yes 388 | 5.32,152,116,0,170,160,Medium,39,16,Yes,No 389 | 8.67,142,73,14,238,115,Medium,73,14,No,Yes 390 | 8.14,135,89,11,245,78,Bad,79,16,Yes,Yes 391 | 8.44,128,42,8,328,107,Medium,35,12,Yes,Yes 392 | 5.47,108,75,9,61,111,Medium,67,12,Yes,Yes 393 | 6.1,153,63,0,49,124,Bad,56,16,Yes,No 394 | 4.53,129,42,13,315,130,Bad,34,13,Yes,Yes 395 | 5.57,109,51,10,26,120,Medium,30,17,No,Yes 396 | 5.35,130,58,19,366,139,Bad,33,16,Yes,Yes 397 | 12.57,138,108,17,203,128,Good,33,14,Yes,Yes 398 | 6.14,139,23,3,37,120,Medium,55,11,No,Yes 399 | 7.41,162,26,12,368,159,Medium,40,18,Yes,Yes 400 | 5.94,100,79,7,284,95,Bad,50,12,Yes,Yes 401 | 9.71,134,37,0,27,120,Good,49,16,Yes,Yes 402 | -------------------------------------------------------------------------------- /notebooks/data/Heart.csv: -------------------------------------------------------------------------------- 1 | "","Age","Sex","ChestPain","RestBP","Chol","Fbs","RestECG","MaxHR","ExAng","Oldpeak","Slope","Ca","Thal","AHD" 2 | "1",63,1,"typical",145,233,1,2,150,0,2.3,3,0,"fixed","No" 3 | "2",67,1,"asymptomatic",160,286,0,2,108,1,1.5,2,3,"normal","Yes" 4 | "3",67,1,"asymptomatic",120,229,0,2,129,1,2.6,2,2,"reversable","Yes" 5 | "4",37,1,"nonanginal",130,250,0,0,187,0,3.5,3,0,"normal","No" 6 | "5",41,0,"nontypical",130,204,0,2,172,0,1.4,1,0,"normal","No" 7 | "6",56,1,"nontypical",120,236,0,0,178,0,0.8,1,0,"normal","No" 8 | "7",62,0,"asymptomatic",140,268,0,2,160,0,3.6,3,2,"normal","Yes" 9 | "8",57,0,"asymptomatic",120,354,0,0,163,1,0.6,1,0,"normal","No" 10 | "9",63,1,"asymptomatic",130,254,0,2,147,0,1.4,2,1,"reversable","Yes" 11 | "10",53,1,"asymptomatic",140,203,1,2,155,1,3.1,3,0,"reversable","Yes" 12 | "11",57,1,"asymptomatic",140,192,0,0,148,0,0.4,2,0,"fixed","No" 13 | "12",56,0,"nontypical",140,294,0,2,153,0,1.3,2,0,"normal","No" 14 | "13",56,1,"nonanginal",130,256,1,2,142,1,0.6,2,1,"fixed","Yes" 15 | "14",44,1,"nontypical",120,263,0,0,173,0,0,1,0,"reversable","No" 16 | "15",52,1,"nonanginal",172,199,1,0,162,0,0.5,1,0,"reversable","No" 17 | "16",57,1,"nonanginal",150,168,0,0,174,0,1.6,1,0,"normal","No" 18 | "17",48,1,"nontypical",110,229,0,0,168,0,1,3,0,"reversable","Yes" 19 | "18",54,1,"asymptomatic",140,239,0,0,160,0,1.2,1,0,"normal","No" 20 | "19",48,0,"nonanginal",130,275,0,0,139,0,0.2,1,0,"normal","No" 21 | "20",49,1,"nontypical",130,266,0,0,171,0,0.6,1,0,"normal","No" 22 | "21",64,1,"typical",110,211,0,2,144,1,1.8,2,0,"normal","No" 23 | "22",58,0,"typical",150,283,1,2,162,0,1,1,0,"normal","No" 24 | "23",58,1,"nontypical",120,284,0,2,160,0,1.8,2,0,"normal","Yes" 25 | "24",58,1,"nonanginal",132,224,0,2,173,0,3.2,1,2,"reversable","Yes" 26 | "25",60,1,"asymptomatic",130,206,0,2,132,1,2.4,2,2,"reversable","Yes" 27 | "26",50,0,"nonanginal",120,219,0,0,158,0,1.6,2,0,"normal","No" 28 | "27",58,0,"nonanginal",120,340,0,0,172,0,0,1,0,"normal","No" 29 | "28",66,0,"typical",150,226,0,0,114,0,2.6,3,0,"normal","No" 30 | "29",43,1,"asymptomatic",150,247,0,0,171,0,1.5,1,0,"normal","No" 31 | "30",40,1,"asymptomatic",110,167,0,2,114,1,2,2,0,"reversable","Yes" 32 | "31",69,0,"typical",140,239,0,0,151,0,1.8,1,2,"normal","No" 33 | "32",60,1,"asymptomatic",117,230,1,0,160,1,1.4,1,2,"reversable","Yes" 34 | "33",64,1,"nonanginal",140,335,0,0,158,0,0,1,0,"normal","Yes" 35 | "34",59,1,"asymptomatic",135,234,0,0,161,0,0.5,2,0,"reversable","No" 36 | "35",44,1,"nonanginal",130,233,0,0,179,1,0.4,1,0,"normal","No" 37 | "36",42,1,"asymptomatic",140,226,0,0,178,0,0,1,0,"normal","No" 38 | "37",43,1,"asymptomatic",120,177,0,2,120,1,2.5,2,0,"reversable","Yes" 39 | "38",57,1,"asymptomatic",150,276,0,2,112,1,0.6,2,1,"fixed","Yes" 40 | "39",55,1,"asymptomatic",132,353,0,0,132,1,1.2,2,1,"reversable","Yes" 41 | "40",61,1,"nonanginal",150,243,1,0,137,1,1,2,0,"normal","No" 42 | "41",65,0,"asymptomatic",150,225,0,2,114,0,1,2,3,"reversable","Yes" 43 | "42",40,1,"typical",140,199,0,0,178,1,1.4,1,0,"reversable","No" 44 | "43",71,0,"nontypical",160,302,0,0,162,0,0.4,1,2,"normal","No" 45 | "44",59,1,"nonanginal",150,212,1,0,157,0,1.6,1,0,"normal","No" 46 | "45",61,0,"asymptomatic",130,330,0,2,169,0,0,1,0,"normal","Yes" 47 | "46",58,1,"nonanginal",112,230,0,2,165,0,2.5,2,1,"reversable","Yes" 48 | "47",51,1,"nonanginal",110,175,0,0,123,0,0.6,1,0,"normal","No" 49 | "48",50,1,"asymptomatic",150,243,0,2,128,0,2.6,2,0,"reversable","Yes" 50 | "49",65,0,"nonanginal",140,417,1,2,157,0,0.8,1,1,"normal","No" 51 | "50",53,1,"nonanginal",130,197,1,2,152,0,1.2,3,0,"normal","No" 52 | "51",41,0,"nontypical",105,198,0,0,168,0,0,1,1,"normal","No" 53 | "52",65,1,"asymptomatic",120,177,0,0,140,0,0.4,1,0,"reversable","No" 54 | "53",44,1,"asymptomatic",112,290,0,2,153,0,0,1,1,"normal","Yes" 55 | "54",44,1,"nontypical",130,219,0,2,188,0,0,1,0,"normal","No" 56 | "55",60,1,"asymptomatic",130,253,0,0,144,1,1.4,1,1,"reversable","Yes" 57 | "56",54,1,"asymptomatic",124,266,0,2,109,1,2.2,2,1,"reversable","Yes" 58 | "57",50,1,"nonanginal",140,233,0,0,163,0,0.6,2,1,"reversable","Yes" 59 | "58",41,1,"asymptomatic",110,172,0,2,158,0,0,1,0,"reversable","Yes" 60 | "59",54,1,"nonanginal",125,273,0,2,152,0,0.5,3,1,"normal","No" 61 | "60",51,1,"typical",125,213,0,2,125,1,1.4,1,1,"normal","No" 62 | "61",51,0,"asymptomatic",130,305,0,0,142,1,1.2,2,0,"reversable","Yes" 63 | "62",46,0,"nonanginal",142,177,0,2,160,1,1.4,3,0,"normal","No" 64 | "63",58,1,"asymptomatic",128,216,0,2,131,1,2.2,2,3,"reversable","Yes" 65 | "64",54,0,"nonanginal",135,304,1,0,170,0,0,1,0,"normal","No" 66 | "65",54,1,"asymptomatic",120,188,0,0,113,0,1.4,2,1,"reversable","Yes" 67 | "66",60,1,"asymptomatic",145,282,0,2,142,1,2.8,2,2,"reversable","Yes" 68 | "67",60,1,"nonanginal",140,185,0,2,155,0,3,2,0,"normal","Yes" 69 | "68",54,1,"nonanginal",150,232,0,2,165,0,1.6,1,0,"reversable","No" 70 | "69",59,1,"asymptomatic",170,326,0,2,140,1,3.4,3,0,"reversable","Yes" 71 | "70",46,1,"nonanginal",150,231,0,0,147,0,3.6,2,0,"normal","Yes" 72 | "71",65,0,"nonanginal",155,269,0,0,148,0,0.8,1,0,"normal","No" 73 | "72",67,1,"asymptomatic",125,254,1,0,163,0,0.2,2,2,"reversable","Yes" 74 | "73",62,1,"asymptomatic",120,267,0,0,99,1,1.8,2,2,"reversable","Yes" 75 | "74",65,1,"asymptomatic",110,248,0,2,158,0,0.6,1,2,"fixed","Yes" 76 | "75",44,1,"asymptomatic",110,197,0,2,177,0,0,1,1,"normal","Yes" 77 | "76",65,0,"nonanginal",160,360,0,2,151,0,0.8,1,0,"normal","No" 78 | "77",60,1,"asymptomatic",125,258,0,2,141,1,2.8,2,1,"reversable","Yes" 79 | "78",51,0,"nonanginal",140,308,0,2,142,0,1.5,1,1,"normal","No" 80 | "79",48,1,"nontypical",130,245,0,2,180,0,0.2,2,0,"normal","No" 81 | "80",58,1,"asymptomatic",150,270,0,2,111,1,0.8,1,0,"reversable","Yes" 82 | "81",45,1,"asymptomatic",104,208,0,2,148,1,3,2,0,"normal","No" 83 | "82",53,0,"asymptomatic",130,264,0,2,143,0,0.4,2,0,"normal","No" 84 | "83",39,1,"nonanginal",140,321,0,2,182,0,0,1,0,"normal","No" 85 | "84",68,1,"nonanginal",180,274,1,2,150,1,1.6,2,0,"reversable","Yes" 86 | "85",52,1,"nontypical",120,325,0,0,172,0,0.2,1,0,"normal","No" 87 | "86",44,1,"nonanginal",140,235,0,2,180,0,0,1,0,"normal","No" 88 | "87",47,1,"nonanginal",138,257,0,2,156,0,0,1,0,"normal","No" 89 | "88",53,0,"nonanginal",128,216,0,2,115,0,0,1,0,NA,"No" 90 | "89",53,0,"asymptomatic",138,234,0,2,160,0,0,1,0,"normal","No" 91 | "90",51,0,"nonanginal",130,256,0,2,149,0,0.5,1,0,"normal","No" 92 | "91",66,1,"asymptomatic",120,302,0,2,151,0,0.4,2,0,"normal","No" 93 | "92",62,0,"asymptomatic",160,164,0,2,145,0,6.2,3,3,"reversable","Yes" 94 | "93",62,1,"nonanginal",130,231,0,0,146,0,1.8,2,3,"reversable","No" 95 | "94",44,0,"nonanginal",108,141,0,0,175,0,0.6,2,0,"normal","No" 96 | "95",63,0,"nonanginal",135,252,0,2,172,0,0,1,0,"normal","No" 97 | "96",52,1,"asymptomatic",128,255,0,0,161,1,0,1,1,"reversable","Yes" 98 | "97",59,1,"asymptomatic",110,239,0,2,142,1,1.2,2,1,"reversable","Yes" 99 | "98",60,0,"asymptomatic",150,258,0,2,157,0,2.6,2,2,"reversable","Yes" 100 | "99",52,1,"nontypical",134,201,0,0,158,0,0.8,1,1,"normal","No" 101 | "100",48,1,"asymptomatic",122,222,0,2,186,0,0,1,0,"normal","No" 102 | "101",45,1,"asymptomatic",115,260,0,2,185,0,0,1,0,"normal","No" 103 | "102",34,1,"typical",118,182,0,2,174,0,0,1,0,"normal","No" 104 | "103",57,0,"asymptomatic",128,303,0,2,159,0,0,1,1,"normal","No" 105 | "104",71,0,"nonanginal",110,265,1,2,130,0,0,1,1,"normal","No" 106 | "105",49,1,"nonanginal",120,188,0,0,139,0,2,2,3,"reversable","Yes" 107 | "106",54,1,"nontypical",108,309,0,0,156,0,0,1,0,"reversable","No" 108 | "107",59,1,"asymptomatic",140,177,0,0,162,1,0,1,1,"reversable","Yes" 109 | "108",57,1,"nonanginal",128,229,0,2,150,0,0.4,2,1,"reversable","Yes" 110 | "109",61,1,"asymptomatic",120,260,0,0,140,1,3.6,2,1,"reversable","Yes" 111 | "110",39,1,"asymptomatic",118,219,0,0,140,0,1.2,2,0,"reversable","Yes" 112 | "111",61,0,"asymptomatic",145,307,0,2,146,1,1,2,0,"reversable","Yes" 113 | "112",56,1,"asymptomatic",125,249,1,2,144,1,1.2,2,1,"normal","Yes" 114 | "113",52,1,"typical",118,186,0,2,190,0,0,2,0,"fixed","No" 115 | "114",43,0,"asymptomatic",132,341,1,2,136,1,3,2,0,"reversable","Yes" 116 | "115",62,0,"nonanginal",130,263,0,0,97,0,1.2,2,1,"reversable","Yes" 117 | "116",41,1,"nontypical",135,203,0,0,132,0,0,2,0,"fixed","No" 118 | "117",58,1,"nonanginal",140,211,1,2,165,0,0,1,0,"normal","No" 119 | "118",35,0,"asymptomatic",138,183,0,0,182,0,1.4,1,0,"normal","No" 120 | "119",63,1,"asymptomatic",130,330,1,2,132,1,1.8,1,3,"reversable","Yes" 121 | "120",65,1,"asymptomatic",135,254,0,2,127,0,2.8,2,1,"reversable","Yes" 122 | "121",48,1,"asymptomatic",130,256,1,2,150,1,0,1,2,"reversable","Yes" 123 | "122",63,0,"asymptomatic",150,407,0,2,154,0,4,2,3,"reversable","Yes" 124 | "123",51,1,"nonanginal",100,222,0,0,143,1,1.2,2,0,"normal","No" 125 | "124",55,1,"asymptomatic",140,217,0,0,111,1,5.6,3,0,"reversable","Yes" 126 | "125",65,1,"typical",138,282,1,2,174,0,1.4,2,1,"normal","Yes" 127 | "126",45,0,"nontypical",130,234,0,2,175,0,0.6,2,0,"normal","No" 128 | "127",56,0,"asymptomatic",200,288,1,2,133,1,4,3,2,"reversable","Yes" 129 | "128",54,1,"asymptomatic",110,239,0,0,126,1,2.8,2,1,"reversable","Yes" 130 | "129",44,1,"nontypical",120,220,0,0,170,0,0,1,0,"normal","No" 131 | "130",62,0,"asymptomatic",124,209,0,0,163,0,0,1,0,"normal","No" 132 | "131",54,1,"nonanginal",120,258,0,2,147,0,0.4,2,0,"reversable","No" 133 | "132",51,1,"nonanginal",94,227,0,0,154,1,0,1,1,"reversable","No" 134 | "133",29,1,"nontypical",130,204,0,2,202,0,0,1,0,"normal","No" 135 | "134",51,1,"asymptomatic",140,261,0,2,186,1,0,1,0,"normal","No" 136 | "135",43,0,"nonanginal",122,213,0,0,165,0,0.2,2,0,"normal","No" 137 | "136",55,0,"nontypical",135,250,0,2,161,0,1.4,2,0,"normal","No" 138 | "137",70,1,"asymptomatic",145,174,0,0,125,1,2.6,3,0,"reversable","Yes" 139 | "138",62,1,"nontypical",120,281,0,2,103,0,1.4,2,1,"reversable","Yes" 140 | "139",35,1,"asymptomatic",120,198,0,0,130,1,1.6,2,0,"reversable","Yes" 141 | "140",51,1,"nonanginal",125,245,1,2,166,0,2.4,2,0,"normal","No" 142 | "141",59,1,"nontypical",140,221,0,0,164,1,0,1,0,"normal","No" 143 | "142",59,1,"typical",170,288,0,2,159,0,0.2,2,0,"reversable","Yes" 144 | "143",52,1,"nontypical",128,205,1,0,184,0,0,1,0,"normal","No" 145 | "144",64,1,"nonanginal",125,309,0,0,131,1,1.8,2,0,"reversable","Yes" 146 | "145",58,1,"nonanginal",105,240,0,2,154,1,0.6,2,0,"reversable","No" 147 | "146",47,1,"nonanginal",108,243,0,0,152,0,0,1,0,"normal","Yes" 148 | "147",57,1,"asymptomatic",165,289,1,2,124,0,1,2,3,"reversable","Yes" 149 | "148",41,1,"nonanginal",112,250,0,0,179,0,0,1,0,"normal","No" 150 | "149",45,1,"nontypical",128,308,0,2,170,0,0,1,0,"normal","No" 151 | "150",60,0,"nonanginal",102,318,0,0,160,0,0,1,1,"normal","No" 152 | "151",52,1,"typical",152,298,1,0,178,0,1.2,2,0,"reversable","No" 153 | "152",42,0,"asymptomatic",102,265,0,2,122,0,0.6,2,0,"normal","No" 154 | "153",67,0,"nonanginal",115,564,0,2,160,0,1.6,2,0,"reversable","No" 155 | "154",55,1,"asymptomatic",160,289,0,2,145,1,0.8,2,1,"reversable","Yes" 156 | "155",64,1,"asymptomatic",120,246,0,2,96,1,2.2,3,1,"normal","Yes" 157 | "156",70,1,"asymptomatic",130,322,0,2,109,0,2.4,2,3,"normal","Yes" 158 | "157",51,1,"asymptomatic",140,299,0,0,173,1,1.6,1,0,"reversable","Yes" 159 | "158",58,1,"asymptomatic",125,300,0,2,171,0,0,1,2,"reversable","Yes" 160 | "159",60,1,"asymptomatic",140,293,0,2,170,0,1.2,2,2,"reversable","Yes" 161 | "160",68,1,"nonanginal",118,277,0,0,151,0,1,1,1,"reversable","No" 162 | "161",46,1,"nontypical",101,197,1,0,156,0,0,1,0,"reversable","No" 163 | "162",77,1,"asymptomatic",125,304,0,2,162,1,0,1,3,"normal","Yes" 164 | "163",54,0,"nonanginal",110,214,0,0,158,0,1.6,2,0,"normal","No" 165 | "164",58,0,"asymptomatic",100,248,0,2,122,0,1,2,0,"normal","No" 166 | "165",48,1,"nonanginal",124,255,1,0,175,0,0,1,2,"normal","No" 167 | "166",57,1,"asymptomatic",132,207,0,0,168,1,0,1,0,"reversable","No" 168 | "167",52,1,"nonanginal",138,223,0,0,169,0,0,1,NA,"normal","No" 169 | "168",54,0,"nontypical",132,288,1,2,159,1,0,1,1,"normal","No" 170 | "169",35,1,"asymptomatic",126,282,0,2,156,1,0,1,0,"reversable","Yes" 171 | "170",45,0,"nontypical",112,160,0,0,138,0,0,2,0,"normal","No" 172 | "171",70,1,"nonanginal",160,269,0,0,112,1,2.9,2,1,"reversable","Yes" 173 | "172",53,1,"asymptomatic",142,226,0,2,111,1,0,1,0,"reversable","No" 174 | "173",59,0,"asymptomatic",174,249,0,0,143,1,0,2,0,"normal","Yes" 175 | "174",62,0,"asymptomatic",140,394,0,2,157,0,1.2,2,0,"normal","No" 176 | "175",64,1,"asymptomatic",145,212,0,2,132,0,2,2,2,"fixed","Yes" 177 | "176",57,1,"asymptomatic",152,274,0,0,88,1,1.2,2,1,"reversable","Yes" 178 | "177",52,1,"asymptomatic",108,233,1,0,147,0,0.1,1,3,"reversable","No" 179 | "178",56,1,"asymptomatic",132,184,0,2,105,1,2.1,2,1,"fixed","Yes" 180 | "179",43,1,"nonanginal",130,315,0,0,162,0,1.9,1,1,"normal","No" 181 | "180",53,1,"nonanginal",130,246,1,2,173,0,0,1,3,"normal","No" 182 | "181",48,1,"asymptomatic",124,274,0,2,166,0,0.5,2,0,"reversable","Yes" 183 | "182",56,0,"asymptomatic",134,409,0,2,150,1,1.9,2,2,"reversable","Yes" 184 | "183",42,1,"typical",148,244,0,2,178,0,0.8,1,2,"normal","No" 185 | "184",59,1,"typical",178,270,0,2,145,0,4.2,3,0,"reversable","No" 186 | "185",60,0,"asymptomatic",158,305,0,2,161,0,0,1,0,"normal","Yes" 187 | "186",63,0,"nontypical",140,195,0,0,179,0,0,1,2,"normal","No" 188 | "187",42,1,"nonanginal",120,240,1,0,194,0,0.8,3,0,"reversable","No" 189 | "188",66,1,"nontypical",160,246,0,0,120,1,0,2,3,"fixed","Yes" 190 | "189",54,1,"nontypical",192,283,0,2,195,0,0,1,1,"reversable","Yes" 191 | "190",69,1,"nonanginal",140,254,0,2,146,0,2,2,3,"reversable","Yes" 192 | "191",50,1,"nonanginal",129,196,0,0,163,0,0,1,0,"normal","No" 193 | "192",51,1,"asymptomatic",140,298,0,0,122,1,4.2,2,3,"reversable","Yes" 194 | "193",43,1,"asymptomatic",132,247,1,2,143,1,0.1,2,NA,"reversable","Yes" 195 | "194",62,0,"asymptomatic",138,294,1,0,106,0,1.9,2,3,"normal","Yes" 196 | "195",68,0,"nonanginal",120,211,0,2,115,0,1.5,2,0,"normal","No" 197 | "196",67,1,"asymptomatic",100,299,0,2,125,1,0.9,2,2,"normal","Yes" 198 | "197",69,1,"typical",160,234,1,2,131,0,0.1,2,1,"normal","No" 199 | "198",45,0,"asymptomatic",138,236,0,2,152,1,0.2,2,0,"normal","No" 200 | "199",50,0,"nontypical",120,244,0,0,162,0,1.1,1,0,"normal","No" 201 | "200",59,1,"typical",160,273,0,2,125,0,0,1,0,"normal","Yes" 202 | "201",50,0,"asymptomatic",110,254,0,2,159,0,0,1,0,"normal","No" 203 | "202",64,0,"asymptomatic",180,325,0,0,154,1,0,1,0,"normal","No" 204 | "203",57,1,"nonanginal",150,126,1,0,173,0,0.2,1,1,"reversable","No" 205 | "204",64,0,"nonanginal",140,313,0,0,133,0,0.2,1,0,"reversable","No" 206 | "205",43,1,"asymptomatic",110,211,0,0,161,0,0,1,0,"reversable","No" 207 | "206",45,1,"asymptomatic",142,309,0,2,147,1,0,2,3,"reversable","Yes" 208 | "207",58,1,"asymptomatic",128,259,0,2,130,1,3,2,2,"reversable","Yes" 209 | "208",50,1,"asymptomatic",144,200,0,2,126,1,0.9,2,0,"reversable","Yes" 210 | "209",55,1,"nontypical",130,262,0,0,155,0,0,1,0,"normal","No" 211 | "210",62,0,"asymptomatic",150,244,0,0,154,1,1.4,2,0,"normal","Yes" 212 | "211",37,0,"nonanginal",120,215,0,0,170,0,0,1,0,"normal","No" 213 | "212",38,1,"typical",120,231,0,0,182,1,3.8,2,0,"reversable","Yes" 214 | "213",41,1,"nonanginal",130,214,0,2,168,0,2,2,0,"normal","No" 215 | "214",66,0,"asymptomatic",178,228,1,0,165,1,1,2,2,"reversable","Yes" 216 | "215",52,1,"asymptomatic",112,230,0,0,160,0,0,1,1,"normal","Yes" 217 | "216",56,1,"typical",120,193,0,2,162,0,1.9,2,0,"reversable","No" 218 | "217",46,0,"nontypical",105,204,0,0,172,0,0,1,0,"normal","No" 219 | "218",46,0,"asymptomatic",138,243,0,2,152,1,0,2,0,"normal","No" 220 | "219",64,0,"asymptomatic",130,303,0,0,122,0,2,2,2,"normal","No" 221 | "220",59,1,"asymptomatic",138,271,0,2,182,0,0,1,0,"normal","No" 222 | "221",41,0,"nonanginal",112,268,0,2,172,1,0,1,0,"normal","No" 223 | "222",54,0,"nonanginal",108,267,0,2,167,0,0,1,0,"normal","No" 224 | "223",39,0,"nonanginal",94,199,0,0,179,0,0,1,0,"normal","No" 225 | "224",53,1,"asymptomatic",123,282,0,0,95,1,2,2,2,"reversable","Yes" 226 | "225",63,0,"asymptomatic",108,269,0,0,169,1,1.8,2,2,"normal","Yes" 227 | "226",34,0,"nontypical",118,210,0,0,192,0,0.7,1,0,"normal","No" 228 | "227",47,1,"asymptomatic",112,204,0,0,143,0,0.1,1,0,"normal","No" 229 | "228",67,0,"nonanginal",152,277,0,0,172,0,0,1,1,"normal","No" 230 | "229",54,1,"asymptomatic",110,206,0,2,108,1,0,2,1,"normal","Yes" 231 | "230",66,1,"asymptomatic",112,212,0,2,132,1,0.1,1,1,"normal","Yes" 232 | "231",52,0,"nonanginal",136,196,0,2,169,0,0.1,2,0,"normal","No" 233 | "232",55,0,"asymptomatic",180,327,0,1,117,1,3.4,2,0,"normal","Yes" 234 | "233",49,1,"nonanginal",118,149,0,2,126,0,0.8,1,3,"normal","Yes" 235 | "234",74,0,"nontypical",120,269,0,2,121,1,0.2,1,1,"normal","No" 236 | "235",54,0,"nonanginal",160,201,0,0,163,0,0,1,1,"normal","No" 237 | "236",54,1,"asymptomatic",122,286,0,2,116,1,3.2,2,2,"normal","Yes" 238 | "237",56,1,"asymptomatic",130,283,1,2,103,1,1.6,3,0,"reversable","Yes" 239 | "238",46,1,"asymptomatic",120,249,0,2,144,0,0.8,1,0,"reversable","Yes" 240 | "239",49,0,"nontypical",134,271,0,0,162,0,0,2,0,"normal","No" 241 | "240",42,1,"nontypical",120,295,0,0,162,0,0,1,0,"normal","No" 242 | "241",41,1,"nontypical",110,235,0,0,153,0,0,1,0,"normal","No" 243 | "242",41,0,"nontypical",126,306,0,0,163,0,0,1,0,"normal","No" 244 | "243",49,0,"asymptomatic",130,269,0,0,163,0,0,1,0,"normal","No" 245 | "244",61,1,"typical",134,234,0,0,145,0,2.6,2,2,"normal","Yes" 246 | "245",60,0,"nonanginal",120,178,1,0,96,0,0,1,0,"normal","No" 247 | "246",67,1,"asymptomatic",120,237,0,0,71,0,1,2,0,"normal","Yes" 248 | "247",58,1,"asymptomatic",100,234,0,0,156,0,0.1,1,1,"reversable","Yes" 249 | "248",47,1,"asymptomatic",110,275,0,2,118,1,1,2,1,"normal","Yes" 250 | "249",52,1,"asymptomatic",125,212,0,0,168,0,1,1,2,"reversable","Yes" 251 | "250",62,1,"nontypical",128,208,1,2,140,0,0,1,0,"normal","No" 252 | "251",57,1,"asymptomatic",110,201,0,0,126,1,1.5,2,0,"fixed","No" 253 | "252",58,1,"asymptomatic",146,218,0,0,105,0,2,2,1,"reversable","Yes" 254 | "253",64,1,"asymptomatic",128,263,0,0,105,1,0.2,2,1,"reversable","No" 255 | "254",51,0,"nonanginal",120,295,0,2,157,0,0.6,1,0,"normal","No" 256 | "255",43,1,"asymptomatic",115,303,0,0,181,0,1.2,2,0,"normal","No" 257 | "256",42,0,"nonanginal",120,209,0,0,173,0,0,2,0,"normal","No" 258 | "257",67,0,"asymptomatic",106,223,0,0,142,0,0.3,1,2,"normal","No" 259 | "258",76,0,"nonanginal",140,197,0,1,116,0,1.1,2,0,"normal","No" 260 | "259",70,1,"nontypical",156,245,0,2,143,0,0,1,0,"normal","No" 261 | "260",57,1,"nontypical",124,261,0,0,141,0,0.3,1,0,"reversable","Yes" 262 | "261",44,0,"nonanginal",118,242,0,0,149,0,0.3,2,1,"normal","No" 263 | "262",58,0,"nontypical",136,319,1,2,152,0,0,1,2,"normal","Yes" 264 | "263",60,0,"typical",150,240,0,0,171,0,0.9,1,0,"normal","No" 265 | "264",44,1,"nonanginal",120,226,0,0,169,0,0,1,0,"normal","No" 266 | "265",61,1,"asymptomatic",138,166,0,2,125,1,3.6,2,1,"normal","Yes" 267 | "266",42,1,"asymptomatic",136,315,0,0,125,1,1.8,2,0,"fixed","Yes" 268 | "267",52,1,"asymptomatic",128,204,1,0,156,1,1,2,0,NA,"Yes" 269 | "268",59,1,"nonanginal",126,218,1,0,134,0,2.2,2,1,"fixed","Yes" 270 | "269",40,1,"asymptomatic",152,223,0,0,181,0,0,1,0,"reversable","Yes" 271 | "270",42,1,"nonanginal",130,180,0,0,150,0,0,1,0,"normal","No" 272 | "271",61,1,"asymptomatic",140,207,0,2,138,1,1.9,1,1,"reversable","Yes" 273 | "272",66,1,"asymptomatic",160,228,0,2,138,0,2.3,1,0,"fixed","No" 274 | "273",46,1,"asymptomatic",140,311,0,0,120,1,1.8,2,2,"reversable","Yes" 275 | "274",71,0,"asymptomatic",112,149,0,0,125,0,1.6,2,0,"normal","No" 276 | "275",59,1,"typical",134,204,0,0,162,0,0.8,1,2,"normal","Yes" 277 | "276",64,1,"typical",170,227,0,2,155,0,0.6,2,0,"reversable","No" 278 | "277",66,0,"nonanginal",146,278,0,2,152,0,0,2,1,"normal","No" 279 | "278",39,0,"nonanginal",138,220,0,0,152,0,0,2,0,"normal","No" 280 | "279",57,1,"nontypical",154,232,0,2,164,0,0,1,1,"normal","Yes" 281 | "280",58,0,"asymptomatic",130,197,0,0,131,0,0.6,2,0,"normal","No" 282 | "281",57,1,"asymptomatic",110,335,0,0,143,1,3,2,1,"reversable","Yes" 283 | "282",47,1,"nonanginal",130,253,0,0,179,0,0,1,0,"normal","No" 284 | "283",55,0,"asymptomatic",128,205,0,1,130,1,2,2,1,"reversable","Yes" 285 | "284",35,1,"nontypical",122,192,0,0,174,0,0,1,0,"normal","No" 286 | "285",61,1,"asymptomatic",148,203,0,0,161,0,0,1,1,"reversable","Yes" 287 | "286",58,1,"asymptomatic",114,318,0,1,140,0,4.4,3,3,"fixed","Yes" 288 | "287",58,0,"asymptomatic",170,225,1,2,146,1,2.8,2,2,"fixed","Yes" 289 | "288",58,1,"nontypical",125,220,0,0,144,0,0.4,2,NA,"reversable","No" 290 | "289",56,1,"nontypical",130,221,0,2,163,0,0,1,0,"reversable","No" 291 | "290",56,1,"nontypical",120,240,0,0,169,0,0,3,0,"normal","No" 292 | "291",67,1,"nonanginal",152,212,0,2,150,0,0.8,2,0,"reversable","Yes" 293 | "292",55,0,"nontypical",132,342,0,0,166,0,1.2,1,0,"normal","No" 294 | "293",44,1,"asymptomatic",120,169,0,0,144,1,2.8,3,0,"fixed","Yes" 295 | "294",63,1,"asymptomatic",140,187,0,2,144,1,4,1,2,"reversable","Yes" 296 | "295",63,0,"asymptomatic",124,197,0,0,136,1,0,2,0,"normal","Yes" 297 | "296",41,1,"nontypical",120,157,0,0,182,0,0,1,0,"normal","No" 298 | "297",59,1,"asymptomatic",164,176,1,2,90,0,1,2,2,"fixed","Yes" 299 | "298",57,0,"asymptomatic",140,241,0,0,123,1,0.2,2,0,"reversable","Yes" 300 | "299",45,1,"typical",110,264,0,0,132,0,1.2,2,0,"reversable","Yes" 301 | "300",68,1,"asymptomatic",144,193,1,0,141,0,3.4,2,2,"reversable","Yes" 302 | "301",57,1,"asymptomatic",130,131,0,0,115,1,1.2,2,1,"reversable","Yes" 303 | "302",57,0,"nontypical",130,236,0,2,174,0,0,2,1,"normal","Yes" 304 | "303",38,1,"nonanginal",138,175,0,0,173,0,0,1,NA,"normal","No" 305 | -------------------------------------------------------------------------------- /notebooks/data/Hitters_X_test.csv: -------------------------------------------------------------------------------- 1 | "","AtBat","Hits","HmRun","Runs","RBI","Walks","Years","CAtBat","CHits","CHmRun","CRuns","CRBI","CWalks","LeagueN","DivisionW","PutOuts","Assists","Errors","NewLeagueN" 2 | "-Darryl Strawberry",475,123,27,76,93,72,4,1810,471,108,292,343,267,1,0,226,10,6,1 3 | "-Glenn Wilson",584,158,15,70,84,42,5,2358,636,58,265,316,134,1,0,331,20,4,1 4 | "-Leon Durham",484,127,20,66,65,67,7,3006,844,116,436,458,377,1,0,1231,80,7,1 5 | "-Tony Gwynn",642,211,14,107,59,52,5,2364,770,27,352,230,193,1,1,337,19,4,1 6 | "-Dave Concepcion",311,81,3,42,30,26,17,8247,2198,100,950,909,690,1,1,153,223,10,1 7 | "-Tom Brookens",281,76,3,42,25,20,8,2658,657,48,324,300,179,0,0,106,144,7,0 8 | "-Tim Laudner",193,47,10,21,29,24,6,1136,256,42,129,139,106,0,1,299,13,5,0 9 | "-Mike Marshall",330,77,19,47,53,27,6,1928,516,90,247,288,161,1,1,149,8,6,1 10 | "-Marty Barrett",625,179,4,94,60,65,5,1696,476,12,216,163,166,0,0,303,450,14,0 11 | "-Buddy Biancalana",190,46,2,24,8,15,5,479,102,5,65,23,39,0,1,102,177,16,0 12 | "-Willie McGee",497,127,7,65,48,37,5,2703,806,32,379,311,138,1,0,325,9,3,1 13 | "-Cal Ripken",627,177,25,98,81,70,6,3210,927,133,529,472,313,0,0,240,482,13,0 14 | "-Mike Schmidt",20,1,0,0,0,0,2,41,9,2,6,7,4,1,0,78,220,6,1 15 | "-Gary Ward",380,120,5,54,51,31,8,3118,900,92,444,419,240,0,1,237,8,1,0 16 | "-Rafael Belliard",309,72,0,33,31,26,5,354,82,0,41,32,26,1,0,117,269,12,1 17 | "-Jim Presley",616,163,27,83,107,32,3,1437,377,65,181,227,82,0,1,110,308,15,0 18 | "-Mookie Wilson",381,110,9,61,45,32,7,3015,834,40,451,249,168,1,0,228,7,5,1 19 | "-Tony Pena",510,147,10,56,52,53,7,2872,821,63,307,340,174,1,0,810,99,18,1 20 | "-Gary Redus",340,84,11,62,33,47,5,1516,376,42,284,141,219,1,0,185,8,4,0 21 | "-Pat Sheridan",236,56,6,41,19,21,5,1257,329,24,166,125,105,0,0,172,1,4,0 22 | "-Steve Lombardozzi",453,103,8,53,33,52,2,507,123,8,63,39,58,0,1,289,407,6,0 23 | "-Darnell Coles",521,142,20,67,86,45,4,815,205,22,99,103,78,0,0,107,242,23,0 24 | "-Larry Sheets",338,92,18,42,60,21,3,682,185,36,88,112,50,0,0,0,0,0,0 25 | "-Bob Melvin",268,60,5,24,25,15,2,350,78,5,34,29,18,1,1,442,59,6,1 26 | "-Dwayne Murphy",329,83,9,50,39,56,9,3828,948,145,575,528,635,0,1,276,6,2,0 27 | "-Graig Nettles",354,77,16,36,55,41,20,8716,2172,384,1172,1267,1057,1,1,83,174,16,1 28 | "-Andres Galarraga",321,87,10,39,42,30,2,396,101,12,48,46,33,1,0,805,40,4,1 29 | "-Gary Matthews",370,96,21,49,46,60,15,6986,1972,231,1070,955,921,1,0,137,5,9,1 30 | "-Rick Manning",205,52,8,31,27,17,12,5134,1323,56,643,445,459,0,0,155,3,2,0 31 | "-George Bell",641,198,31,101,108,41,5,2129,610,92,297,319,117,0,0,269,17,10,0 32 | "-Jody Davis",528,132,21,61,74,41,6,2641,671,97,273,383,226,1,0,885,105,8,1 33 | "-Keith Hernandez",551,171,13,94,83,94,13,6090,1840,128,969,900,917,1,0,1199,149,5,1 34 | "-Julio Franco",599,183,10,80,74,32,5,2482,715,27,330,326,158,0,0,231,374,18,0 35 | "-Carmelo Martinez",244,58,9,28,25,35,4,1335,333,49,164,179,194,1,1,142,14,2,1 36 | "-Tom Paciorek",213,61,4,17,22,3,17,4061,1145,83,488,491,244,0,1,178,45,4,0 37 | "-Lee Lacy",491,141,11,77,47,37,15,4291,1240,84,615,430,340,0,0,239,8,2,0 38 | "-Ozzie Guillen",547,137,2,58,47,12,2,1038,271,3,129,80,24,0,1,261,459,22,0 39 | "-Bill Doran",550,152,6,92,37,81,5,2308,633,32,349,182,308,1,1,262,329,16,1 40 | "-Mike Diaz",209,56,12,22,36,19,2,216,58,12,24,37,19,1,0,201,6,3,1 41 | "-Gary Pettis",539,139,5,93,58,69,5,1469,369,12,247,126,198,0,1,462,9,7,0 42 | "-Ozzie Virgil",359,80,15,45,48,63,7,1493,359,61,176,202,175,1,1,682,93,13,1 43 | "-Kevin Mitchell",328,91,12,51,43,33,2,342,94,12,51,44,33,1,0,145,59,8,1 44 | "-Mike Scioscia",374,94,5,36,26,62,7,1968,519,26,181,199,288,1,1,756,64,15,1 45 | "-John Moses",399,102,3,56,34,34,5,670,167,4,89,48,54,0,1,211,9,3,0 46 | "-Johnny Grubb",210,70,13,32,51,28,15,4040,1130,97,544,462,551,0,0,0,0,0,0 47 | "-Tim Wallach",480,112,18,50,71,44,7,3031,771,110,338,406,239,1,0,94,270,16,1 48 | "-Al Newman",185,37,1,23,8,21,2,214,42,1,30,9,24,1,0,76,127,7,0 49 | "-Harry Spilman",143,39,5,18,30,15,9,639,151,16,80,97,61,1,1,138,15,1,1 50 | "-Terry Kennedy",19,4,1,2,3,1,1,19,4,1,2,3,1,1,1,692,70,8,0 51 | "-Kurt Stillwell",279,64,0,31,26,30,1,279,64,0,31,26,30,1,1,107,205,16,1 52 | "-Hal McRae",278,70,7,22,37,18,18,7186,2081,190,935,1088,643,0,1,0,0,0,0 53 | "-Ozzie Smith",514,144,0,67,54,79,9,4739,1169,13,583,374,528,1,0,229,453,15,1 54 | "-Shawon Dunston",581,145,17,66,68,21,2,831,210,21,106,86,40,1,0,320,465,32,1 55 | "-Tito Landrum",205,43,2,24,17,20,7,854,219,12,105,99,71,1,0,131,6,1,1 56 | "-Buddy Bell",568,158,20,89,75,73,15,8068,2273,177,1045,993,732,1,1,105,290,10,1 57 | "-Bill Buckner",629,168,18,73,102,40,18,8424,2464,164,1008,1072,402,0,0,1067,157,14,0 58 | "-Dan Pasqua",280,82,16,44,45,47,2,428,113,25,61,70,63,0,0,148,4,2,0 59 | "-Juan Beniquez",343,103,6,48,36,40,15,4338,1193,70,581,421,325,0,0,211,56,13,0 60 | "-Kevin Bass",591,184,20,83,79,38,5,1689,462,40,219,195,82,1,1,303,12,5,1 61 | "-Greg Brock",325,76,16,33,52,37,5,1506,351,71,195,219,214,1,1,726,87,3,0 62 | "-Phil Garner",313,83,9,43,41,30,14,5885,1543,104,751,714,535,1,1,58,141,23,1 63 | "-Donnie Hill",339,96,4,37,29,23,4,1064,290,11,123,108,55,0,1,104,213,9,0 64 | "-Ron Roenicke",275,68,5,42,42,61,6,961,238,16,128,104,172,1,0,181,3,2,1 65 | "-Darrell Porter",155,41,12,21,29,22,16,5409,1338,181,746,805,875,0,1,165,9,1,0 66 | "-Juan Samuel",591,157,16,90,78,26,4,2020,541,52,310,226,91,1,0,290,440,25,1 67 | "-Ronn Reynolds",126,27,3,8,10,5,4,239,49,3,16,13,14,1,0,190,2,9,1 68 | "-Garry Templeton",510,126,2,42,44,35,11,5562,1578,44,703,519,256,1,1,207,358,20,1 69 | "-Len Dykstra",431,127,8,77,45,58,2,667,187,9,117,64,88,1,0,283,8,3,1 70 | "-Bruce Bochy",127,32,8,16,22,14,8,727,180,24,67,82,56,1,1,202,22,2,1 71 | "-Wade Boggs",580,207,8,107,71,105,5,2778,978,32,474,322,417,0,0,121,267,19,0 72 | "-Ron Oester",523,135,8,52,44,52,9,3368,895,39,377,284,296,1,1,367,475,19,1 73 | "-Mike Davis",489,131,19,77,55,34,7,2051,549,62,300,263,153,0,1,310,9,9,0 74 | "-Rickey Henderson",608,160,28,130,74,89,8,4071,1182,103,862,417,708,0,0,426,4,6,0 75 | "-Tommy Herr",559,141,2,48,61,73,8,3162,874,16,421,349,359,1,0,352,414,9,1 76 | "-Tom Foley",263,70,1,26,23,30,4,888,220,9,83,82,86,1,0,81,147,4,1 77 | "-Mike Kingery",209,54,3,25,14,12,1,209,54,3,25,14,12,0,1,102,6,3,0 78 | "-Ted Simmons",127,32,4,14,25,12,19,8396,2402,242,1048,1348,819,1,1,167,18,6,1 79 | "-Denny Walling",382,119,13,54,58,36,12,2133,594,41,287,294,227,1,1,59,156,9,1 80 | "-Sid Bream",522,140,16,73,77,60,4,730,185,22,93,106,86,1,0,1320,166,17,1 81 | "-Mitch Webster",576,167,8,89,49,57,4,822,232,19,132,83,79,1,0,325,12,8,1 82 | "-Tony Fernandez",687,213,10,91,65,27,4,1518,448,15,196,137,89,0,0,294,445,13,0 83 | "-Ron Hassey",341,110,9,45,49,46,9,2331,658,50,249,322,274,0,0,251,9,4,0 84 | "-Ray Knight",486,145,11,51,76,40,11,3967,1102,67,410,497,284,1,0,88,204,16,0 85 | "-Dave Henderson",388,103,15,59,47,39,6,2174,555,80,285,274,186,0,1,182,9,4,0 86 | "-Tim Flannery",368,103,3,48,28,54,8,1897,493,9,207,162,198,1,1,209,246,3,1 87 | "-Chili Davis",526,146,13,71,70,84,6,2648,715,77,352,342,289,1,1,303,9,9,1 88 | "-Jeff Reed",165,39,2,13,9,16,3,196,44,2,18,10,18,0,1,332,19,2,1 89 | "-Brett Butler",587,163,4,92,51,70,6,2695,747,17,442,198,317,0,0,434,9,3,0 90 | "-Steve Sax",633,210,6,91,56,59,6,3070,872,19,420,230,274,1,1,367,432,16,1 91 | "-Steve Garvey",557,142,21,58,81,23,18,8759,2583,271,1138,1299,478,1,1,1160,53,7,1 92 | "-Candy Maldonado",405,102,18,49,85,20,6,950,231,29,99,138,64,1,1,161,10,3,1 93 | "-Alex Trevino",202,53,4,31,26,27,9,1876,467,15,192,186,161,1,1,304,45,11,1 94 | "-Joe Carter",663,200,29,108,121,32,4,1447,404,57,210,222,68,0,0,241,8,6,0 95 | "-Rick Schu",208,57,8,32,25,18,3,653,170,17,98,54,62,1,0,42,94,13,1 96 | "-Joel Skinner",315,73,5,23,37,16,4,450,108,6,38,46,28,0,1,227,15,3,0 97 | "-Jose Uribe",453,101,3,46,43,61,3,948,218,6,96,72,91,1,1,249,444,16,1 98 | "-Eddie Murray",495,151,17,61,84,78,10,5624,1679,275,884,1015,709,0,0,1045,88,13,0 99 | "-Don Slaught",314,83,13,39,46,16,5,1457,405,28,156,159,76,0,1,533,40,4,0 100 | "-Paul Molitor",437,123,9,62,55,40,9,4139,1203,79,676,390,364,0,0,82,170,15,0 101 | "-Hubie Brooks",306,104,14,50,58,25,7,2954,822,55,313,377,187,1,0,116,222,15,1 102 | "-Rance Mulliniks",348,90,11,50,45,43,10,2288,614,43,295,273,269,0,0,60,176,6,0 103 | "-Dan Gladden",351,97,4,55,29,39,4,1258,353,16,196,110,117,1,1,226,7,3,0 104 | "-Craig Reynolds",313,78,6,32,41,12,12,3742,968,35,409,321,170,1,1,106,206,7,1 105 | "-Lou Whitaker",584,157,20,95,73,63,10,4704,1320,93,724,522,576,0,0,276,421,11,0 106 | "-Howard Johnson",220,54,10,30,39,31,5,1185,299,40,145,154,128,1,0,50,136,20,1 107 | "-Chris Bando",254,68,2,28,26,22,6,999,236,21,108,117,118,0,0,359,30,4,0 108 | "-Rey Quinones",312,68,2,32,22,24,1,312,68,2,32,22,24,0,0,86,150,15,0 109 | "-Eric Davis",415,115,27,97,71,68,3,711,184,45,156,119,99,1,1,274,2,7,1 110 | "-Phil Bradley",526,163,12,88,50,77,4,1556,470,38,245,167,174,0,1,250,11,1,0 111 | "-Reggie Jackson",419,101,18,65,58,92,20,9528,2510,548,1509,1659,1342,0,1,0,0,0,0 112 | "-Wayne Tolleson",475,126,3,61,43,52,6,1700,433,7,217,93,146,0,1,37,113,7,0 113 | "-Jose Cruz",479,133,10,48,72,55,17,7472,2147,153,980,1032,854,1,1,237,5,4,1 114 | "-Doug DeCinces",512,131,26,69,96,52,14,5347,1397,221,712,815,548,0,1,119,216,12,0 115 | "-Dave Parker",637,174,31,89,116,56,14,6727,2024,247,978,1093,495,1,1,278,9,9,1 116 | "-Bob Dernier",324,73,4,32,18,22,7,1931,491,13,291,108,180,1,0,222,3,3,1 117 | "-Alvin Davis",479,130,18,66,72,76,3,1624,457,63,224,266,263,0,1,880,82,14,0 118 | "-Jesse Barfield",589,170,40,107,108,69,6,2325,634,128,371,376,238,0,0,368,20,3,0 119 | "-Vance Law",360,81,5,37,44,37,7,2268,566,41,279,257,246,1,0,170,284,3,1 120 | "-Will Clark",408,117,11,66,41,34,1,408,117,11,66,41,34,1,1,942,72,11,1 121 | "-Len Matuszek",199,52,9,26,28,21,6,805,191,30,113,119,87,1,1,235,22,5,1 122 | "-Ken Landreaux",283,74,4,34,29,22,10,3919,1062,85,505,456,283,1,1,145,5,7,1 123 | "-Dale Sveum",317,78,7,35,35,32,1,317,78,7,35,35,32,0,0,45,122,26,0 124 | "-Mel Hall",442,131,18,68,77,33,6,1416,398,47,210,203,136,0,0,233,7,7,0 125 | "-Scott Bradley",220,66,5,20,28,13,3,290,80,5,27,31,15,0,1,281,21,3,0 126 | "-Herm Winningham",185,40,4,23,11,18,3,524,125,7,58,37,47,1,0,97,2,2,1 127 | "-Dale Murphy",614,163,29,89,83,75,11,5017,1388,266,813,822,617,1,1,303,6,6,1 128 | "-Kevin McReynolds",560,161,26,89,96,66,4,1789,470,65,233,260,155,1,1,332,9,8,1 129 | "-Bob Kearney",204,49,6,23,25,12,7,1309,308,27,126,132,66,0,1,419,46,5,0 130 | "-Tim Hulett",520,120,17,53,44,21,4,927,227,22,106,80,52,0,1,70,144,11,0 131 | "-Ryne Sandberg",627,178,14,68,76,46,6,3146,902,74,494,345,242,1,0,309,492,5,1 132 | "-Mike Heath",288,65,8,30,36,27,9,2815,698,55,315,325,189,1,0,259,30,10,0 133 | -------------------------------------------------------------------------------- /notebooks/data/Hitters_X_train.csv: -------------------------------------------------------------------------------- 1 | "","AtBat","Hits","HmRun","Runs","RBI","Walks","Years","CAtBat","CHits","CHmRun","CRuns","CRBI","CWalks","LeagueN","DivisionW","PutOuts","Assists","Errors","NewLeagueN" 2 | "-Alan Ashby",315,81,7,24,38,39,14,3449,835,69,321,414,375,1,1,632,43,10,1 3 | "-Andre Dawson",496,141,20,65,78,37,11,5628,1575,225,828,838,354,1,0,200,11,3,1 4 | "-Alfredo Griffin",594,169,4,74,51,35,11,4408,1133,19,501,336,194,0,1,282,421,25,0 5 | "-Argenis Salazar",298,73,0,24,24,7,3,509,108,0,41,37,12,0,1,121,283,9,0 6 | "-Andres Thomas",323,81,6,26,32,8,2,341,86,6,32,34,8,1,1,143,290,19,1 7 | "-Andre Thornton",401,92,17,49,66,65,13,5206,1332,253,784,890,866,0,0,0,0,0,0 8 | "-Alan Trammell",574,159,21,107,75,59,10,4631,1300,90,702,504,488,0,0,238,445,22,0 9 | "-Andy VanSlyke",418,113,13,48,61,47,4,1512,392,41,205,204,203,1,0,211,11,7,1 10 | "-Alan Wiggins",239,60,0,30,11,22,6,1941,510,4,309,103,207,0,0,121,151,6,0 11 | "-Bill Almon",196,43,7,29,27,30,13,3231,825,36,376,290,238,1,0,80,45,8,1 12 | "-Barry Bonds",413,92,16,72,48,65,1,413,92,16,72,48,65,1,0,280,9,5,1 13 | "-Bobby Bonilla",426,109,3,55,43,62,1,426,109,3,55,43,62,0,1,361,22,2,1 14 | "-Bob Brenly",472,116,16,60,62,74,6,1924,489,67,242,251,240,1,1,518,55,3,1 15 | "-Bo Diaz",474,129,10,50,56,40,10,2331,604,61,246,327,166,1,1,732,83,13,1 16 | "-Brian Downing",513,137,20,90,95,90,14,5201,1382,166,763,734,784,0,1,267,5,3,0 17 | "-Billy Hatcher",419,108,6,55,36,22,3,591,149,8,80,46,31,1,1,226,7,4,1 18 | "-Brook Jacoby",583,168,17,83,80,56,5,1646,452,44,219,208,136,0,0,109,292,25,0 19 | "-Bill Madlock",379,106,10,38,60,30,14,6207,1906,146,859,803,571,1,1,72,170,24,1 20 | "-BillyJo Robidoux",181,41,1,15,21,33,2,232,50,4,20,29,45,0,0,326,29,5,0 21 | "-Bill Schroeder",217,46,7,32,19,9,4,694,160,32,86,76,32,0,0,307,25,1,0 22 | "-Chris Brown",416,132,7,57,49,33,3,932,273,24,113,121,80,1,1,73,177,18,1 23 | "-Carmen Castillo",205,57,8,34,32,9,5,756,192,32,117,107,51,0,0,58,4,4,0 24 | "-Carlton Fisk",457,101,14,42,63,22,17,6521,1767,281,1003,977,619,0,1,389,39,4,0 25 | "-Curt Ford",214,53,2,30,29,23,2,226,59,2,32,32,27,1,0,109,7,3,1 26 | "-Carney Lansford",591,168,19,80,72,39,9,4478,1307,113,634,563,319,0,1,67,147,4,0 27 | "-Chet Lemon",403,101,12,45,53,39,12,5150,1429,166,747,666,526,0,0,316,6,5,0 28 | "-Cory Snyder",416,113,24,58,69,16,1,416,113,24,58,69,16,0,0,203,70,10,0 29 | "-Chris Speier",155,44,6,21,23,15,16,6631,1634,98,698,661,777,1,0,53,88,3,1 30 | "-Curt Wilkerson",236,56,0,27,15,11,4,1115,270,1,116,64,57,0,1,125,199,13,0 31 | "-Dave Anderson",216,53,1,31,15,22,4,926,210,9,118,69,114,1,1,73,152,11,1 32 | "-Don Baylor",585,139,31,93,94,62,17,7546,1982,315,1141,1179,727,0,0,0,0,0,0 33 | "-Daryl Boston",199,53,5,29,22,21,3,514,120,8,57,40,39,0,1,152,3,5,0 34 | "-Darrell Evans",507,122,29,78,85,91,18,7761,1947,347,1175,1152,1380,0,0,808,108,2,0 35 | "-Dwight Evans",529,137,26,86,97,97,15,6661,1785,291,1082,949,989,0,0,280,10,5,0 36 | "-Damaso Garcia",424,119,6,57,46,13,9,3651,1046,32,461,301,112,0,0,224,286,8,1 37 | "-Davey Lopes",255,70,7,49,35,43,15,6311,1661,154,1019,608,820,1,0,51,54,8,1 38 | "-Don Mattingly",677,238,31,117,113,53,5,2223,737,93,349,401,171,0,0,1377,100,6,0 39 | "-Dick Schofield",458,114,13,67,57,48,4,1350,298,28,160,123,122,0,1,246,389,18,0 40 | "-Danny Tartabull",511,138,25,76,96,61,3,592,164,28,87,110,71,0,1,157,7,8,0 41 | "-Dave Winfield",565,148,24,90,104,77,14,7287,2083,305,1135,1234,791,0,0,292,9,5,0 42 | "-Eddie Milner",424,110,15,70,47,36,7,2130,544,38,335,174,258,1,1,292,6,3,1 43 | "-Ed Romero",233,49,2,41,23,18,8,1350,336,7,166,122,106,0,0,102,132,10,0 44 | "-Frank White",566,154,22,76,84,43,14,6100,1583,131,743,693,300,0,1,316,439,10,0 45 | "-Glenn Braggs",215,51,4,19,18,11,1,215,51,4,19,18,11,0,0,116,5,12,0 46 | "-George Brett",441,128,16,70,73,80,14,6675,2095,209,1072,1050,695,0,1,97,218,16,0 47 | "-Gary Carter",490,125,24,81,105,62,13,6063,1646,271,847,999,680,1,0,869,62,8,1 48 | "-Glenn Davis",574,152,31,91,101,64,3,985,260,53,148,173,95,1,1,1253,111,11,1 49 | "-Gary Gaetti",596,171,34,91,108,52,6,2862,728,107,361,401,224,0,1,118,334,21,0 50 | "-Greg Gagne",472,118,12,63,54,30,4,793,187,14,102,80,50,0,1,228,377,26,0 51 | "-George Hendrick",283,77,14,45,47,26,16,6840,1910,259,915,1067,546,0,1,144,6,5,0 52 | "-Glenn Hubbard",408,94,4,42,36,66,9,3573,866,59,429,365,410,1,1,282,487,19,1 53 | "-Garth Iorg",327,85,3,30,44,20,8,2140,568,16,216,208,93,0,0,91,185,12,0 54 | "-Greg Walker",282,78,13,37,51,29,5,1649,453,73,211,280,138,0,1,670,57,5,0 55 | "-Harold Baines",570,169,21,72,88,38,7,3754,1077,140,492,589,263,0,1,295,15,5,0 56 | "-Harold Reynolds",445,99,1,46,24,29,4,618,129,1,72,31,48,0,1,278,415,16,0 57 | "-John Cangelosi",438,103,2,65,32,71,2,440,103,2,67,32,71,0,1,276,7,9,1 58 | "-Jose Canseco",600,144,33,85,117,65,2,696,173,38,101,130,69,0,1,319,4,14,0 59 | "-Jack Clark",232,55,9,34,23,45,12,4405,1213,194,702,705,625,1,0,623,35,3,1 60 | "-Jim Dwyer",160,39,8,18,31,22,14,2128,543,56,304,268,298,0,0,33,3,0,0 61 | "-Jim Gantner",497,136,7,58,38,26,11,3871,1066,40,450,367,241,0,0,304,347,10,0 62 | "-Jack Howell",151,41,4,26,21,19,2,288,68,9,45,39,35,0,1,28,56,2,0 63 | "-John Kruk",278,86,4,33,38,45,1,278,86,4,33,38,45,1,1,102,4,2,1 64 | "-Jeffrey Leonard",341,95,6,48,42,20,10,2964,808,81,379,428,221,1,1,158,4,5,1 65 | "-Jim Morrison",537,147,23,58,88,47,10,2744,730,97,302,351,174,1,0,92,257,20,1 66 | "-Jerry Mumphrey",309,94,5,37,32,26,13,4618,1330,57,616,522,436,1,0,161,3,3,1 67 | "-Johnny Ray",579,174,7,67,78,58,6,3053,880,32,366,337,218,1,0,280,479,5,1 68 | "-Jim Rice",618,200,20,98,110,62,13,7127,2163,351,1104,1289,564,0,0,330,16,8,0 69 | "-Jerry Royster",257,66,5,31,26,32,14,3910,979,33,518,324,382,1,1,87,166,14,0 70 | "-John Russell",315,76,13,35,60,25,3,630,151,24,68,94,55,1,0,498,39,13,1 71 | "-John Shelby",404,92,11,54,49,18,6,1354,325,30,188,135,63,0,0,222,5,5,0 72 | "-Jim Sundberg",429,91,12,41,42,57,13,5590,1397,83,578,579,644,0,1,686,46,4,1 73 | "-Joel Youngblood",184,47,5,20,28,18,11,3327,890,74,419,382,304,1,1,49,2,0,1 74 | "-Kal Daniels",181,58,6,34,23,22,1,181,58,6,34,23,22,1,1,88,0,3,1 75 | "-Kirk Gibson",441,118,28,84,86,68,8,2723,750,126,433,420,309,0,0,190,2,2,0 76 | "-Ken Griffey",490,150,21,69,58,35,14,6126,1839,121,983,707,600,0,0,96,5,3,1 77 | "-Kent Hrbek",550,147,29,85,91,71,6,2816,815,117,405,474,319,0,1,1218,104,10,0 78 | "-Keith Moreland",586,159,12,72,79,53,9,3082,880,83,363,477,295,1,0,181,13,4,1 79 | "-Ken Oberkfell",503,136,5,62,48,83,10,3423,970,20,408,303,414,1,1,65,258,8,1 80 | "-Ken Phelps",344,85,24,69,64,88,7,911,214,64,150,156,187,0,1,0,0,0,0 81 | "-Kirby Puckett",680,223,31,119,96,34,3,1928,587,35,262,201,91,0,1,429,8,6,0 82 | "-Larry Herndon",283,70,8,33,37,27,12,4479,1222,94,557,483,307,0,0,156,2,2,0 83 | "-Lloyd Moseby",589,149,21,89,86,64,7,3558,928,102,513,471,351,0,0,371,6,6,0 84 | "-Lance Parrish",327,84,22,53,62,38,10,4273,1123,212,577,700,334,0,0,483,48,6,1 85 | "-Larry Parrish",464,128,28,67,94,52,13,5829,1552,210,740,840,452,0,1,0,0,0,0 86 | "-Mike Aldrete",216,54,2,27,25,33,1,216,54,2,27,25,33,1,1,317,36,1,1 87 | "-Mariano Duncan",407,93,8,47,30,30,2,969,230,14,121,69,68,1,1,172,317,25,1 88 | "-Mike Easler",490,148,14,64,78,49,13,3400,1000,113,445,491,301,0,0,0,0,0,1 89 | "-Mike LaValliere",303,71,3,18,30,36,3,344,76,3,20,36,45,1,0,468,47,6,1 90 | "-Mike Pagliarulo",504,120,28,71,71,54,3,1085,259,54,150,167,114,0,0,103,283,19,0 91 | "-Mark Salas",258,60,8,28,33,18,3,638,170,17,80,75,36,0,1,358,32,8,0 92 | "-Mickey Tettleton",211,43,10,26,35,39,3,498,116,14,59,55,78,0,1,463,32,8,0 93 | "-Milt Thompson",299,75,6,38,23,26,3,580,160,8,71,33,44,1,0,212,1,2,1 94 | "-Marvell Wynne",288,76,7,34,37,15,4,1644,408,16,198,120,113,1,1,203,3,3,1 95 | "-Mike Young",369,93,9,43,42,49,5,1258,323,54,181,177,157,0,0,149,1,6,0 96 | "-Oddibe McDowell",572,152,18,105,49,65,2,978,249,36,168,91,101,0,1,325,13,3,0 97 | "-Pete Incaviglia",540,135,30,82,88,55,1,540,135,30,82,88,55,0,1,157,6,14,0 98 | "-Pete Rose",237,52,0,15,25,30,24,14053,4256,160,2165,1314,1566,1,1,523,43,6,1 99 | "-Pat Tabler",473,154,6,61,48,29,6,1966,566,29,250,252,178,0,0,846,84,9,0 100 | "-Rick Burleson",271,77,5,35,29,33,12,4933,1358,48,630,435,403,0,1,62,90,3,0 101 | "-Randy Bush",357,96,7,50,45,39,5,1394,344,43,178,192,136,0,1,167,2,4,0 102 | "-Rick Cerone",216,56,4,22,18,15,12,2796,665,43,266,304,198,0,0,391,44,4,0 103 | "-Ron Cey",256,70,13,42,36,44,16,7058,1845,312,965,1128,990,1,0,41,118,8,0 104 | "-Rob Deer",466,108,33,75,86,72,3,652,142,44,102,109,102,0,0,286,8,8,0 105 | "-Rick Dempsey",327,68,13,42,29,45,18,3949,939,78,438,380,466,0,0,659,53,7,0 106 | "-Ron Kittle",376,82,21,42,60,35,5,1770,408,115,238,299,157,0,1,0,0,0,0 107 | "-Rick Leach",246,76,5,35,39,13,6,912,234,12,102,96,80,0,0,44,0,1,0 108 | "-Rafael Ramirez",496,119,8,57,33,21,7,3358,882,36,365,280,165,1,1,155,371,29,1 109 | "-Rafael Santana",394,86,1,38,28,36,4,1089,267,3,94,71,76,1,0,203,369,16,1 110 | "-Ruben Sierra",382,101,16,50,55,22,1,382,101,16,50,55,22,0,1,200,7,6,0 111 | "-Roy Smalley",459,113,20,59,57,68,12,5348,1369,155,713,660,735,0,1,0,0,0,0 112 | "-Robby Thompson",549,149,7,73,47,42,1,549,149,7,73,47,42,1,1,255,450,17,1 113 | "-Rob Wilfong",288,63,3,25,33,16,10,2682,667,38,315,259,204,0,1,135,257,7,0 114 | "-Robin Yount",522,163,9,82,46,62,13,7037,2019,153,1043,827,535,0,0,352,9,1,0 115 | "-Steve Balboni",512,117,29,54,88,43,6,1750,412,100,204,276,155,0,1,1236,98,18,0 116 | "-Steve Buechele",461,112,18,54,54,35,2,680,160,24,76,75,49,0,1,111,226,11,0 117 | "-Scott Fletcher",530,159,3,82,50,47,6,1619,426,11,218,149,163,0,1,196,354,15,0 118 | "-Steve Jeltz",439,96,0,44,36,65,4,711,148,1,68,56,99,1,0,229,406,22,1 119 | "-Spike Owen",528,122,1,67,45,51,4,1716,403,12,211,146,155,0,1,209,372,17,0 120 | "-Tony Bernazard",562,169,17,88,73,53,8,3181,841,61,450,342,373,0,0,351,442,17,0 121 | "-Tom Brunansky",593,152,23,69,75,53,6,2765,686,133,369,384,321,0,1,315,10,6,0 122 | "-Terry Harper",265,68,8,26,30,29,7,1337,339,32,135,163,128,1,1,92,5,3,0 123 | "-Terry Pendleton",578,138,1,56,59,34,3,1399,357,7,149,161,87,1,0,133,371,20,1 124 | "-Tony Phillips",441,113,5,76,52,76,5,1546,397,17,226,149,191,0,1,160,290,11,0 125 | "-Terry Puhl",172,42,3,17,14,15,10,4086,1150,57,579,363,406,1,1,65,0,0,1 126 | "-Tim Teufel",279,69,4,35,31,32,4,1359,355,31,180,148,158,1,0,133,173,9,1 127 | "-Vince Coleman",600,139,0,94,29,60,2,1236,309,1,201,69,110,1,0,300,12,9,1 128 | "-Von Hayes",610,186,19,107,98,74,6,2728,753,69,399,366,286,1,0,1182,96,13,1 129 | "-Wally Backman",387,124,1,67,27,36,7,1775,506,6,272,125,194,1,0,186,290,17,1 130 | "-Wally Joyner",593,172,22,82,100,57,1,593,172,22,82,100,57,0,1,1222,139,15,0 131 | "-Willie Randolph",492,136,5,76,50,94,12,5511,1511,39,897,451,875,0,0,313,381,20,0 132 | "-Willie Upshaw",573,144,9,85,60,78,8,3198,857,97,470,420,332,0,0,1314,131,12,0 133 | "-Willie Wilson",631,170,9,77,44,31,11,4908,1457,30,775,357,249,0,1,408,4,3,0 134 | -------------------------------------------------------------------------------- /notebooks/data/Hitters_y_test.csv: -------------------------------------------------------------------------------- 1 | "","x" 2 | "1",1220 3 | "2",662.5 4 | "3",1183.333 5 | "4",740 6 | "5",320 7 | "6",341.667 8 | "7",245 9 | "8",670 10 | "9",575 11 | "10",175 12 | "11",700 13 | "12",1350 14 | "13",2127.333 15 | "14",600 16 | "15",130 17 | "16",200 18 | "17",800 19 | "18",1150 20 | "19",400 21 | "20",190 22 | "21",105 23 | "22",105 24 | "23",145 25 | "24",90 26 | "25",600 27 | "26",200 28 | "27",91.5 29 | "28",733.333 30 | "29",400 31 | "30",1175 32 | "31",1008.333 33 | "32",1800 34 | "33",775 35 | "34",340 36 | "35",235 37 | "36",525 38 | "37",175 39 | "38",625 40 | "39",90 41 | "40",400 42 | "41",700 43 | "42",125 44 | "43",875 45 | 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113 | "112",773.333 114 | "113",850 115 | "114",1041.667 116 | "115",708.333 117 | "116",480 118 | "117",1237.5 119 | "118",525 120 | "119",120 121 | "120",265 122 | "121",737.5 123 | "122",70 124 | "123",550 125 | "124",90 126 | "125",90 127 | "126",1900 128 | "127",625 129 | "128",300 130 | "129",185 131 | "130",740 132 | "131",650 133 | -------------------------------------------------------------------------------- /notebooks/data/Hitters_y_train.csv: -------------------------------------------------------------------------------- 1 | "","x" 2 | "1",475 3 | "2",500 4 | "3",750 5 | "4",100 6 | "5",75 7 | "6",1100 8 | "7",517.143 9 | "8",550 10 | "9",700 11 | "10",240 12 | "11",100 13 | "12",115 14 | "13",600 15 | "14",750 16 | "15",900 17 | "16",110 18 | "17",612.5 19 | "18",850 20 | "19",67.5 21 | "20",180 22 | "21",215 23 | "22",247.5 24 | "23",875 25 | "24",70 26 | "25",1200 27 | "26",675 28 | "27",90 29 | "28",275 30 | "29",230 31 | "30",225 32 | "31",950 33 | "32",75 34 | 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103 | "102",1050 104 | "103",215 105 | "104",400 106 | "105",425 107 | "106",250 108 | "107",875 109 | "108",250 110 | "109",97.5 111 | "110",740 112 | "111",140 113 | "112",341.667 114 | "113",1000 115 | "114",100 116 | "115",135 117 | "116",475 118 | "117",150 119 | "118",350 120 | "119",530 121 | "120",940 122 | "121",425 123 | "122",160 124 | "123",425 125 | "124",900 126 | "125",277.5 127 | "126",160 128 | "127",1300 129 | "128",550 130 | "129",165 131 | "130",875 132 | "131",960 133 | "132",1000 134 | -------------------------------------------------------------------------------- /notebooks/data/NCI60_y.csv: -------------------------------------------------------------------------------- 1 | "","x" 2 | "1","CNS" 3 | "2","CNS" 4 | "3","CNS" 5 | "4","RENAL" 6 | "5","BREAST" 7 | "6","CNS" 8 | "7","CNS" 9 | "8","BREAST" 10 | "9","NSCLC" 11 | "10","NSCLC" 12 | "11","RENAL" 13 | "12","RENAL" 14 | "13","RENAL" 15 | "14","RENAL" 16 | "15","RENAL" 17 | "16","RENAL" 18 | "17","RENAL" 19 | "18","BREAST" 20 | "19","NSCLC" 21 | "20","RENAL" 22 | "21","UNKNOWN" 23 | "22","OVARIAN" 24 | "23","MELANOMA" 25 | "24","PROSTATE" 26 | "25","OVARIAN" 27 | "26","OVARIAN" 28 | "27","OVARIAN" 29 | "28","OVARIAN" 30 | "29","OVARIAN" 31 | "30","PROSTATE" 32 | "31","NSCLC" 33 | "32","NSCLC" 34 | "33","NSCLC" 35 | "34","LEUKEMIA" 36 | "35","K562B-repro" 37 | "36","K562A-repro" 38 | "37","LEUKEMIA" 39 | "38","LEUKEMIA" 40 | "39","LEUKEMIA" 41 | "40","LEUKEMIA" 42 | "41","LEUKEMIA" 43 | "42","COLON" 44 | "43","COLON" 45 | "44","COLON" 46 | "45","COLON" 47 | "46","COLON" 48 | "47","COLON" 49 | "48","COLON" 50 | "49","MCF7A-repro" 51 | "50","BREAST" 52 | "51","MCF7D-repro" 53 | "52","BREAST" 54 | "53","NSCLC" 55 | "54","NSCLC" 56 | "55","NSCLC" 57 | "56","MELANOMA" 58 | "57","BREAST" 59 | "58","BREAST" 60 | "59","MELANOMA" 61 | "60","MELANOMA" 62 | "61","MELANOMA" 63 | "62","MELANOMA" 64 | "63","MELANOMA" 65 | "64","MELANOMA" 66 | -------------------------------------------------------------------------------- /notebooks/data/Portfolio.csv: -------------------------------------------------------------------------------- 1 | X,Y 2 | -0.895250889142,-0.234923525765 3 | -1.56245432748,-0.885175993045 4 | -0.417089883126,0.27188801805 5 | 1.04435572527,-0.734197504068 6 | -0.315568406681,0.841983429961 7 | -1.73712384902,-2.03719104075 8 | 1.96641315717,1.45295666192 9 | 2.15286789801,-0.43413862818 10 | -0.0812080267603,1.45080850219 11 | -0.891781794029,0.82101623454 12 | -0.29320170201,-1.04239112184 13 | 0.505779171069,0.608477825847 14 | 0.526751254093,-0.222493343283 15 | 1.06646932095,1.23135667526 16 | 0.294015895064,0.628589480036 17 | 0.0425492997634,-1.26757361755 18 | 1.83096958062,-0.572751605499 19 | -0.326937498878,-0.487472465046 20 | 0.521480415807,2.56598528732 21 | 1.39986834733,-0.357836127482 22 | -0.645447596469,-1.4124313895 23 | -0.90435187845,-0.568304791042 24 | 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"State","Murder","Assault","UrbanPop","Rape" 2 | "Alabama",13.2,236,58,21.2 3 | "Alaska",10,263,48,44.5 4 | "Arizona",8.1,294,80,31 5 | "Arkansas",8.8,190,50,19.5 6 | "California",9,276,91,40.6 7 | "Colorado",7.9,204,78,38.7 8 | "Connecticut",3.3,110,77,11.1 9 | "Delaware",5.9,238,72,15.8 10 | "Florida",15.4,335,80,31.9 11 | "Georgia",17.4,211,60,25.8 12 | "Hawaii",5.3,46,83,20.2 13 | "Idaho",2.6,120,54,14.2 14 | "Illinois",10.4,249,83,24 15 | "Indiana",7.2,113,65,21 16 | "Iowa",2.2,56,57,11.3 17 | "Kansas",6,115,66,18 18 | "Kentucky",9.7,109,52,16.3 19 | "Louisiana",15.4,249,66,22.2 20 | "Maine",2.1,83,51,7.8 21 | "Maryland",11.3,300,67,27.8 22 | "Massachusetts",4.4,149,85,16.3 23 | "Michigan",12.1,255,74,35.1 24 | "Minnesota",2.7,72,66,14.9 25 | "Mississippi",16.1,259,44,17.1 26 | "Missouri",9,178,70,28.2 27 | "Montana",6,109,53,16.4 28 | "Nebraska",4.3,102,62,16.5 29 | "Nevada",12.2,252,81,46 30 | "New Hampshire",2.1,57,56,9.5 31 | "New Jersey",7.4,159,89,18.8 32 | "New Mexico",11.4,285,70,32.1 33 | "New York",11.1,254,86,26.1 34 | "North Carolina",13,337,45,16.1 35 | "North Dakota",0.8,45,44,7.3 36 | "Ohio",7.3,120,75,21.4 37 | "Oklahoma",6.6,151,68,20 38 | "Oregon",4.9,159,67,29.3 39 | "Pennsylvania",6.3,106,72,14.9 40 | "Rhode Island",3.4,174,87,8.3 41 | "South Carolina",14.4,279,48,22.5 42 | "South Dakota",3.8,86,45,12.8 43 | "Tennessee",13.2,188,59,26.9 44 | "Texas",12.7,201,80,25.5 45 | "Utah",3.2,120,80,22.9 46 | "Vermont",2.2,48,32,11.2 47 | "Virginia",8.5,156,63,20.7 48 | "Washington",4,145,73,26.2 49 | "West Virginia",5.7,81,39,9.3 50 | "Wisconsin",2.6,53,66,10.8 51 | "Wyoming",6.8,161,60,15.6 52 | -------------------------------------------------------------------------------- /notebooks/utils/__pycache__/lecture03.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/notebooks/utils/__pycache__/lecture03.cpython-39.pyc -------------------------------------------------------------------------------- /notebooks/utils/__pycache__/lecture07.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/notebooks/utils/__pycache__/lecture07.cpython-39.pyc -------------------------------------------------------------------------------- /notebooks/utils/lecture03.py: -------------------------------------------------------------------------------- 1 | # Remove warnings 2 | import warnings 3 | warnings.filterwarnings('ignore') 4 | 5 | # Import 6 | import pandas as pd 7 | import numpy as np 8 | import seaborn as sns 9 | import statsmodels.api as sm 10 | 11 | from scipy.stats import norm 12 | from statsmodels.nonparametric.kernel_regression import KernelReg 13 | from pygam import LinearGAM, s, f, LogisticGAM 14 | from sklearn.preprocessing import PolynomialFeatures, LabelEncoder 15 | from patsy import dmatrix 16 | 17 | # Import matplotlib for graphs 18 | import matplotlib.pyplot as plt 19 | from mpl_toolkits.mplot3d import axes3d 20 | 21 | # Set global parameters 22 | plt.style.use('seaborn-white') 23 | plt.rcParams['lines.linewidth'] = 3 24 | plt.rcParams['figure.figsize'] = (10,6) 25 | plt.rcParams['figure.titlesize'] = 20 26 | plt.rcParams['axes.titlesize'] = 18 27 | plt.rcParams['axes.labelsize'] = 14 28 | plt.rcParams['legend.fontsize'] = 14 29 | 30 | 31 | # Figure 7.1 32 | def plot_predictions(X, y, x_grid, y01, y_hat1, y01_hat1, title): 33 | 34 | # Init figure 35 | fig, (ax1, ax2) = plt.subplots(1,2, figsize=(12,5)) 36 | fig.suptitle(title) 37 | 38 | # Scatter plot with polynomial regression line 39 | ax1.scatter(X, y, facecolor='None', edgecolor='k', alpha=0.2) 40 | ax1.plot(x_grid, y_hat1, color='b') 41 | ax1.set_ylim(ymin=0) 42 | 43 | # Logistic regression showing Pr(wage>250) for the age range. 44 | ax2.plot(x_grid, y01_hat1, color='b') 45 | 46 | # Run plot showing the distribution of wage>250 in the training data. 47 | ax2.scatter(X, y01/5, s=30, c='grey', marker='|', alpha=0.7) 48 | 49 | ax2.set_ylim(-0.01,0.21) 50 | ax2.set_xlabel('age') 51 | ax2.set_ylabel('Pr(wage>250|age)'); 52 | 53 | # Figure 7.3 54 | def plot_splines(df_short, x_grid, preds): 55 | 56 | # Init figure 57 | fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(12,10)) 58 | fig.suptitle('Figure 7.3') 59 | 60 | # Discontinuous piecewise cubic 61 | ax1.plot(x_grid, preds[0], color='b') 62 | ax1.set_title('Discontinuous piecewise cubic') 63 | # Continuous piecewise cubi 64 | ax2.plot(x_grid, preds[1], color='g') 65 | ax2.set_title('Continuous piecewise cubic') 66 | # Cubic (continuous) 67 | ax3.plot(x_grid, preds[2], color='r') 68 | ax3.set_title('Cubic') 69 | # Continuous piecewise linear 70 | ax4.plot(x_grid, preds[3], color='y') 71 | ax4.set_title('Continuous piecewise linear') 72 | 73 | for ax in (ax1,ax2,ax3,ax4): 74 | ax.scatter(df_short.age, df_short.wage, facecolor='None', edgecolor='k', alpha=0.3) 75 | ax.axvline(x=50, color='k', linestyle='--', alpha=0.5) 76 | 77 | 78 | # Figure 7.4 79 | def compare_predictions(X, y, x_grid, preds, labels): 80 | 81 | # Init figure 82 | fig, ax = plt.subplots(1,1) 83 | fig.suptitle('Figure 7.4') 84 | 85 | # Scatter 86 | ax.scatter(X, y, facecolor='None', edgecolor='k', alpha=0.1) 87 | for pred, label in zip(preds, labels): 88 | ax.plot(x_grid, pred, label=label) 89 | [ax.vlines(i , 0, 350, linestyles='dashed', lw=2, colors='k') for i in [25,40,60]] 90 | ax.legend(bbox_to_anchor=(1.5, 1.0)) 91 | ax.set_xlabel('age'), ax.set_ylabel('wage'); 92 | 93 | 94 | # Make new figure 1 95 | def plot_simulated_data(X_sim, y_sim, X_grid, y_grid): 96 | 97 | # Init 98 | fig, ax = plt.subplots(1,1) 99 | fig.suptitle('Simulated data'); 100 | 101 | # Plot 102 | ax.scatter(X_sim, y_sim, facecolor='None', edgecolor='k', alpha=0.5, label='data'); 103 | ax.plot(X_grid, y_grid, label='True relationship'); 104 | ax.set_xlabel('X'); ax.set_ylabel('y'); 105 | ax.legend(); 106 | return fig, ax 107 | 108 | 109 | # Figure 7.9a 110 | def make_figure_7_9a(fig, ax, X_sim, y_hat): 111 | ax.plot(X_sim, y_hat[0], label='LLN Estimate'); 112 | ax.legend(); 113 | return fig, ax 114 | 115 | 116 | # Figure 7.9b 117 | def make_figure_7_9b(fig, ax, X_tilde, y_tilde, X_grid_tilde, y_grid_tilde, x_i, y_i_hat): 118 | 119 | # Add local details 120 | ax.scatter(X_tilde, y_tilde, facecolor='orange', edgecolor='None', alpha=0.5); 121 | ax.scatter(x_i, y_i_hat, facecolor='r', alpha=1); 122 | ax.plot(X_grid_tilde, y_grid_tilde, color='r', label='Local OLS'); 123 | 124 | # Legend 125 | ax.legend(); 126 | ax.annotate("$x_i$", (x_i, y_i_hat-0.2), color='r', fontsize=20); 127 | ax.set_xlabel('X'); ax.set_ylabel('y'); 128 | 129 | 130 | # Figure 7.9 - d 131 | def make_figure_7_9d(X_sim, y_sim, w, results, X_grid, x_i, y_i_hat): 132 | 133 | # Init 134 | fig, ax = plt.subplots() 135 | fig.suptitle('Local Weighted Least Squares'); 136 | 137 | # Zoom in 138 | points = ax.scatter(X_sim, y_sim, c=w, cmap="YlOrRd", edgecolors='lightgrey', alpha=.7, s=80); 139 | plt.colorbar(points, label='gaussian weights') 140 | ax.plot(X_grid, results.params[0] + results.params[1]*X_grid, color='r') 141 | ax.scatter(x_i, y_i_hat, facecolor='r', alpha=1); 142 | ax.annotate("$x_i$", (x_i, y_i_hat-0.1), color='r', fontsize=20); 143 | ax.set_xlabel('local X'); ax.set_ylabel('local y'); 144 | 145 | 146 | # Figure 7.13 147 | def plot_gam(gam): 148 | 149 | # Init 150 | fig, axs = plt.subplots(1,3,figsize=(15,5)); 151 | fig.suptitle('Figure 7.13') 152 | 153 | # Plot 154 | titles = ['year', 'age', 'education'] 155 | for i, ax in enumerate(axs): 156 | XX = gam.generate_X_grid(term=i) 157 | pdep, confi = gam.partial_dependence(term=i, width=.95) 158 | ax.plot(XX[:, i], pdep) 159 | ax.plot(XX[:, i], confi, c='k', ls=':', alpha=0.5) 160 | if i == 0: 161 | ax.set_ylim(-40,40) 162 | ax.set_xlabel(titles[i]); -------------------------------------------------------------------------------- /notebooks/utils/lecture07.py: -------------------------------------------------------------------------------- 1 | # Remove warnings 2 | import warnings 3 | warnings.filterwarnings('ignore') 4 | 5 | # Import 6 | import pandas as pd 7 | import numpy as np 8 | import seaborn as sns 9 | import pydot 10 | from IPython.display import Image 11 | from six import StringIO 12 | 13 | from sklearn.model_selection import train_test_split, cross_val_score 14 | from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier, plot_tree 15 | from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, BaggingRegressor, RandomForestRegressor, GradientBoostingRegressor, GradientBoostingClassifier 16 | from sklearn.metrics import mean_squared_error,confusion_matrix, classification_report 17 | from sklearn.model_selection import train_test_split, KFold, cross_val_score 18 | from sklearn.metrics import confusion_matrix, mean_squared_error 19 | 20 | 21 | # Import matplotlib for graphs 22 | import matplotlib.pyplot as plt 23 | from mpl_toolkits.mplot3d import axes3d 24 | 25 | # Set global parameters 26 | plt.style.use('seaborn-white') 27 | plt.rcParams['lines.linewidth'] = 3 28 | plt.rcParams['figure.figsize'] = (12,7) 29 | plt.rcParams['figure.titlesize'] = 20 30 | plt.rcParams['axes.titlesize'] = 18 31 | plt.rcParams['axes.labelsize'] = 14 32 | plt.rcParams['legend.fontsize'] = 14 -------------------------------------------------------------------------------- /notebooks/utils/lecture10.py: -------------------------------------------------------------------------------- 1 | # Remove warnings 2 | import warnings 3 | warnings.filterwarnings('ignore') 4 | 5 | # General packages 6 | import pandas as pd 7 | import numpy as np 8 | import seaborn as sns 9 | import time 10 | from scipy.stats import multivariate_normal 11 | 12 | # Sklean 13 | from sklearn.preprocessing import scale 14 | from sklearn.decomposition import PCA 15 | from sklearn.cluster import KMeans 16 | from scipy.cluster import hierarchy 17 | from scipy.cluster.hierarchy import linkage, dendrogram, cut_tree 18 | 19 | # Import matplotlib for graphs 20 | import matplotlib.pyplot as plt 21 | from matplotlib.patches import Ellipse 22 | from mpl_toolkits.mplot3d import axes3d 23 | from IPython.display import clear_output 24 | 25 | # Set global parameters 26 | plt.style.use('seaborn-white') 27 | plt.rcParams['lines.linewidth'] = 3 28 | plt.rcParams['figure.figsize'] = (10,6) 29 | plt.rcParams['figure.titlesize'] = 20 30 | plt.rcParams['axes.titlesize'] = 18 31 | plt.rcParams['axes.labelsize'] = 14 32 | plt.rcParams['legend.fontsize'] = 14 33 | 34 | # Figure 10.1 a 35 | def make_figure_10_1a(df_dim2, df_weights): 36 | 37 | # Init 38 | fig, ax1 = plt.subplots(figsize=(8,8)) 39 | ax1.set_title('Figure 10.1'); 40 | 41 | # Plot Principal Components 1 and 2 42 | for i in df_dim2.index: 43 | ax1.annotate(i, (df_dim2.PC1.loc[i], -df_dim2.PC2.loc[i]), ha='center', fontsize=14) 44 | 45 | # Plot reference lines 46 | m = np.max(np.abs(df_dim2.values))*1.2 47 | ax1.hlines(0,-m,m, linestyles='dotted', colors='grey') 48 | ax1.vlines(0,-m,m, linestyles='dotted', colors='grey') 49 | ax1.set_xlabel('First Principal Component') 50 | ax1.set_ylabel('Second Principal Component') 51 | ax1.set_xlim(-m,m); ax1.set_ylim(-m,m) 52 | 53 | # Plot Principal Component loading vectors, using a second y-axis. 54 | ax1b = ax1.twinx().twiny() 55 | ax1b.set_ylim(-1,1); ax1b.set_xlim(-1,1) 56 | for i in df_weights[['PC1', 'PC2']].index: 57 | ax1b.annotate(i, (df_weights.PC1.loc[i]*1.05, -df_weights.PC2.loc[i]*1.05), color='orange', fontsize=16) 58 | ax1b.arrow(0,0,df_weights.PC1[i], -df_weights.PC2[i], color='orange', lw=2) 59 | 60 | 61 | # Figure 10.1 b 62 | def make_figure_10_1b(df_dim2, df_dim2_u, df_weights, df_weights_u): 63 | 64 | # Init 65 | fig, (ax1,ax2) = plt.subplots(1,2,figsize=(18,9)) 66 | 67 | # Scaled PCA 68 | for i in df_dim2.index: 69 | ax1.annotate(i, (df_dim2.PC1.loc[i], -df_dim2.PC2.loc[i]), ha='center', fontsize=14) 70 | ax1b = ax1.twinx().twiny() 71 | ax1b.set_ylim(-1,1); ax1b.set_xlim(-1,1) 72 | for i in df_weights[['PC1', 'PC2']].index: 73 | ax1b.annotate(i, (df_weights.PC1.loc[i]*1.05, -df_weights.PC2.loc[i]*1.05), color='orange', fontsize=16) 74 | ax1b.arrow(0,0,df_weights.PC1[i], -df_weights.PC2[i], color='orange', lw=2) 75 | ax1.set_title('Scaled') 76 | 77 | # Unscaled PCA 78 | for i in df_dim2_u.index: 79 | ax2.annotate(i, (df_dim2_u.PC1.loc[i], -df_dim2_u.PC2.loc[i]), ha='center', fontsize=14) 80 | ax2b = ax2.twinx().twiny() 81 | ax2b.set_ylim(-1,1); ax2b.set_xlim(-1,1) 82 | for i in df_weights_u[['PC1', 'PC2']].index: 83 | ax2b.annotate(i, (df_weights_u.PC1.loc[i]*1.05, -df_weights_u.PC2.loc[i]*1.05), color='orange', fontsize=16) 84 | ax2b.arrow(0,0,df_weights_u.PC1[i], -df_weights_u.PC2[i], color='orange', lw=2) 85 | ax2.set_title('Unscaled') 86 | 87 | # Plot reference lines 88 | for ax,df in zip((ax1,ax2), (df_dim2,df_dim2_u)): 89 | m = np.max(np.abs(df.values))*1.2 90 | ax.hlines(0,-m,m, linestyles='dotted', colors='grey') 91 | ax.vlines(0,-m,m, linestyles='dotted', colors='grey') 92 | ax.set_xlabel('First Principal Component') 93 | ax.set_ylabel('Second Principal Component') 94 | ax.set_xlim(-m,m); ax.set_ylim(-m,m) 95 | 96 | 97 | # Figure 10.2 98 | def make_figure_10_2(pca4): 99 | 100 | # Init 101 | fig, (ax1, ax2) = plt.subplots(1,2, figsize=(12,5)) 102 | fig.suptitle('Figure 10.2'); 103 | 104 | # Relative 105 | ax1.plot([1,2,3,4], pca4.explained_variance_ratio_) 106 | ax1.set_ylabel('Prop. Variance Explained') 107 | ax1.set_xlabel('Principal Component'); 108 | 109 | # Cumulative 110 | ax2.plot([1,2,3,4], np.cumsum(pca4.explained_variance_ratio_)) 111 | ax2.set_ylabel('Cumulative Variance Explained'); 112 | ax2.set_xlabel('Principal Component'); 113 | 114 | 115 | # Figure new 1 116 | def make_new_figure_1(X): 117 | 118 | # Init 119 | fig, ax = plt.subplots(figsize=(6, 5)) 120 | fig.suptitle("Baseline") 121 | 122 | # Plot 123 | ax.scatter(X[:,0], X[:,1], s=50, alpha=0.5, c='k') 124 | ax.set_xlabel('X0'); ax.set_ylabel('X1'); 125 | 126 | 127 | # Figure new 2 128 | def make_new_figure_2(X, clusters0): 129 | 130 | # Init 131 | fig, ax = plt.subplots(figsize=(6, 5)) 132 | fig.suptitle("Random assignment") 133 | 134 | # Plot 135 | ax.scatter(X[clusters0==0,0], X[clusters0==0,1], s=50, alpha=0.5) 136 | ax.scatter(X[clusters0==1,0], X[clusters0==1,1], s=50, alpha=0.5) 137 | ax.set_xlabel('X0'); ax.set_ylabel('X1'); 138 | 139 | 140 | # Plot assignment 141 | def plot_assignment(X, centroids, clusters, d, i): 142 | clear_output(wait=True) 143 | fig, ax = plt.subplots(figsize=(6, 5)) 144 | fig.suptitle("Iteration %.0f: inertia=%.1f" % (i,d)) 145 | 146 | # Plot 147 | ax.clear() 148 | colors = plt.rcParams['axes.prop_cycle'].by_key()['color']; 149 | K = np.size(centroids,0) 150 | for k in range(K): 151 | ax.scatter(X[clusters==k,0], X[clusters==k,1], s=50, c=colors[k], alpha=0.5) 152 | ax.scatter(centroids[k,0], centroids[k,1], marker = '*', s=300, color=colors[k]) 153 | ax.set_xlabel('X0'); ax.set_ylabel('X1'); 154 | 155 | # Show 156 | plt.show(); 157 | 158 | # Figure new 3 159 | def make_new_figure_3(d): 160 | 161 | # Init 162 | plt.figure(figsize=(25, 10)) 163 | plt.title('Hierarchical Clustering Dendrogram') 164 | 165 | # calculate full dendrogram 166 | plt.xlabel('sample index') 167 | plt.ylabel('distance') 168 | d 169 | plt.show() 170 | 171 | 172 | # Figure new 4 173 | def make_new_figure_4(linkages, titles): 174 | 175 | # Init 176 | fig, (ax1,ax2,ax3) = plt.subplots(1,3, figsize=(15,6)) 177 | 178 | # Plot 179 | for linkage, t, ax in zip(linkages, titles, (ax1,ax2,ax3)): 180 | dendrogram(linkage, ax=ax) 181 | ax.set_title(t) 182 | 183 | 184 | def get_cov_ellipse(distr, nstd, **kwargs): 185 | """ 186 | Return a matplotlib Ellipse patch representing a standard distribution around the mean 187 | """ 188 | 189 | # Find and sort eigenvalues and eigenvectors into descending order 190 | eigvals, eigvecs = np.linalg.eigh(distr.cov) 191 | order = eigvals.argsort()[::-1] 192 | eigvals, eigvecs = eigvals[order], eigvecs[:, order] 193 | 194 | # The anti-clockwise angle to rotate our ellipse by 195 | vx, vy = eigvecs[:,0][0], eigvecs[:,0][1] 196 | theta = np.arctan2(vy, vx) 197 | 198 | # Width and height of ellipse to draw 199 | width, height = 2 * nstd * np.sqrt(eigvals) 200 | return Ellipse(xy=distr.mean, width=width, height=height, 201 | angle=np.degrees(theta), **kwargs) 202 | 203 | 204 | # Plot assignment 205 | def plot_assignment_gmm(X, clusters, distr, i, logL): 206 | clear_output(wait=True) 207 | fig, ax = plt.subplots(figsize=(6, 5)) 208 | fig.suptitle(f"Iteration {i}, logL={logL:.2}") 209 | 210 | # Plot 211 | ax.clear() 212 | colors = plt.rcParams['axes.prop_cycle'].by_key()['color']; 213 | K = len(distr) 214 | for k in range(K): 215 | ax.scatter(X[clusters==k,0], X[clusters==k,1], s=50, c=colors[k], alpha=0.5) 216 | ax.scatter(distr[k].mean[0], distr[k].mean[1], marker = '*', s=300, color=colors[k]) 217 | for i in [0.5, 1, 2]: 218 | ax.add_artist(get_cov_ellipse(distr[k], nstd=i, color=colors[k], alpha=0.05)) 219 | ax.set_xlabel('X0'); ax.set_ylabel('X1'); 220 | 221 | # Show 222 | plt.show(); --------------------------------------------------------------------------------