├── .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 |
4 |
5 |
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 |
188 |
189 |
190 |
191 |
192 |
193 |
194 |
195 |
196 |
197 |
198 |
199 |
200 |
201 |
202 |
203 |
204 |
205 |
206 |
207 |
208 |
209 |
210 |
211 |
212 |
213 | 1642854050585
214 |
215 |
216 | 1642854050585
217 |
218 |
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,
61 | .reveal h2,
62 | .reveal h3,
63 | .reveal h4,
64 | .reveal h5,
65 | .reveal h6 {
66 | margin: 0 0 20px 0;
67 | color: #33474b;
68 | font-family: "Fira Sans";
69 | font-weight: normal;
70 | line-height: 1;
71 | letter-spacing: normal;
72 | text-transform: none;
73 | text-shadow: none;
74 | word-wrap: break-word; }
75 |
76 | .reveal h1 {
77 | font-size: 1.77em; }
78 |
79 | .reveal h2 {
80 | font-size: 1.30em; }
81 |
82 | .reveal h3 {
83 | font-size: .95em; }
84 |
85 | .reveal h4 {
86 | font-size: .75em; }
87 |
88 | .reveal h1 {
89 | text-shadow: none; }
90 |
91 | h1.subtitle {
92 | font-size: 1em;
93 | padding-bottom:15px;
94 | border-bottom: 2px solid #EB811B;
95 | }
96 |
97 | h2.author, h3.date {
98 | font-size: .6em;
99 | }
100 |
101 | h1.title {
102 | font-variant: small-caps;
103 | }
104 |
105 | .level2 h1 {
106 | font-size:1.57em;
107 | font-variant: small-caps;
108 | text-transform: lowercase;
109 | }
110 |
111 | .titleslide h1 {
112 | font-variant: small-caps;
113 | font-size:1.67em;
114 | margin-left:25px;
115 | margin-right:25px;
116 | padding-bottom: 10px;
117 | border-bottom: 2px solid #EB811B;
118 | }
119 |
120 |
121 | /*********************************************
122 | * OTHER
123 | *********************************************/
124 | .reveal p {
125 | margin: 20px 0;
126 | line-height: 1.3; }
127 |
128 | /* Ensure certain elements are never larger than the slide itself */
129 | .reveal img,
130 | .reveal video,
131 | .reveal iframe {
132 | max-width: 95%;
133 | max-height: 95%; }
134 |
135 | .reveal strong,
136 | .reveal b {
137 | font-weight: bold; }
138 |
139 | .reveal em {
140 | font-style: italic; }
141 |
142 | .reveal ol,
143 | .reveal dl,
144 | .reveal ul {
145 | display: inline-block;
146 | text-align: left;
147 | margin: 0 0 0 1em; }
148 |
149 | .reveal ol {
150 | list-style-type: decimal; }
151 |
152 | .reveal ul {
153 | list-style-type: disc; }
154 |
155 | .reveal ul ul {
156 | list-style-type: square; }
157 |
158 | .reveal ul ul ul {
159 | list-style-type: circle; }
160 |
161 | .reveal ul ul,
162 | .reveal ul ol,
163 | .reveal ol ol,
164 | .reveal ol ul {
165 | display: block;
166 | margin-left: 40px; }
167 |
168 | .reveal dt {
169 | font-weight: bold; }
170 |
171 | .reveal dd {
172 | margin-left: 40px; }
173 |
174 | .reveal q,
175 | .reveal blockquote {
176 | quotes: none; }
177 |
178 | .reveal blockquote {
179 | display: block;
180 | position: relative;
181 | width: 70%;
182 | margin: 20px auto;
183 | padding: 5px;
184 | font-style: italic;
185 | background: rgba(255, 255, 255, 0.05);
186 | box-shadow: 0px 0px 2px rgba(0, 0, 0, 0.2); }
187 |
188 | .reveal blockquote p:first-child,
189 | .reveal blockquote p:last-child {
190 | display: inline-block; }
191 |
192 | .reveal q {
193 | font-style: italic; }
194 |
195 | .reveal pre {
196 | display: block;
197 | position: relative;
198 | width: 90%;
199 | margin: 20px auto;
200 | text-align: left;
201 | font-size: 0.55em;
202 | font-family: monospace;
203 | line-height: 1.2em;
204 | word-wrap: break-word;
205 | box-shadow: 0px 0px 6px rgba(0, 0, 0, 0.3); }
206 |
207 | .reveal code {
208 | font-family: monospace; }
209 |
210 | .reveal pre code {
211 | display: block;
212 | padding: 5px;
213 | overflow: auto;
214 | max-height: 400px;
215 | word-wrap: normal; }
216 |
217 | .reveal table {
218 | margin: auto;
219 | border-collapse: collapse;
220 | border-spacing: 0; }
221 |
222 | .reveal table th {
223 | font-weight: bold; }
224 |
225 | .reveal table th,
226 | .reveal table td {
227 | text-align: left;
228 | padding: 0.2em 0.5em 0.2em 0.5em;
229 | border-bottom: 1px solid; }
230 |
231 | .reveal table th[align="center"],
232 | .reveal table td[align="center"] {
233 | text-align: center; }
234 |
235 | .reveal table th[align="right"],
236 | .reveal table td[align="right"] {
237 | text-align: right; }
238 |
239 | .reveal table tr:last-child td {
240 | border-bottom: none; }
241 |
242 | .reveal sup {
243 | vertical-align: super; }
244 |
245 | .reveal sub {
246 | vertical-align: sub; }
247 |
248 | .reveal small {
249 | display: inline-block;
250 | font-size: 0.6em;
251 | line-height: 1.2em;
252 | vertical-align: top; }
253 |
254 | .reveal small * {
255 | vertical-align: top; }
256 |
257 | /*********************************************
258 | * LINKS
259 | *********************************************/
260 | .reveal a {
261 | color: #51483D;
262 | text-decoration: none;
263 | -webkit-transition: color 0.15s ease;
264 | -moz-transition: color 0.15s ease;
265 | transition: color 0.15s ease; }
266 |
267 | .reveal a:hover {
268 | color: #8b7c69;
269 | text-shadow: none;
270 | border: none; }
271 |
272 | .reveal .roll span:after {
273 | color: #fff;
274 | background: #25211c; }
275 |
276 | /*********************************************
277 | * IMAGES
278 | *********************************************/
279 | .reveal section img {
280 | margin: 15px 0px;
281 | background: rgba(255, 255, 255, 0.12);
282 | border: 4px solid #000;
283 | box-shadow: 0 0 10px rgba(0, 0, 0, 0.15); }
284 |
285 | .reveal section img.plain {
286 | border: 0;
287 | box-shadow: none; }
288 |
289 | .reveal a img {
290 | -webkit-transition: all 0.15s linear;
291 | -moz-transition: all 0.15s linear;
292 | transition: all 0.15s linear; }
293 |
294 | .reveal a:hover img {
295 | background: rgba(255, 255, 255, 0.2);
296 | border-color: #51483D;
297 | box-shadow: 0 0 20px rgba(0, 0, 0, 0.55); }
298 |
299 | /*********************************************
300 | * NAVIGATION CONTROLS
301 | *********************************************/
302 | .reveal .controls .navigate-left,
303 | .reveal .controls .navigate-left.enabled {
304 | border-right-color: #51483D; }
305 |
306 | .reveal .controls .navigate-right,
307 | .reveal .controls .navigate-right.enabled {
308 | border-left-color: #51483D; }
309 |
310 | .reveal .controls .navigate-up,
311 | .reveal .controls .navigate-up.enabled {
312 | border-bottom-color: #51483D; }
313 |
314 | .reveal .controls .navigate-down,
315 | .reveal .controls .navigate-down.enabled {
316 | border-top-color: #51483D; }
317 |
318 | .reveal .controls .navigate-left.enabled:hover {
319 | border-right-color: #8b7c69; }
320 |
321 | .reveal .controls .navigate-right.enabled:hover {
322 | border-left-color: #8b7c69; }
323 |
324 | .reveal .controls .navigate-up.enabled:hover {
325 | border-bottom-color: #8b7c69; }
326 |
327 | .reveal .controls .navigate-down.enabled:hover {
328 | border-top-color: #8b7c69; }
329 |
330 | /*********************************************
331 | * PROGRESS BAR
332 | *********************************************/
333 | .reveal .progress {
334 | background: rgba(235, 129, 27, .4); }
335 |
336 | .reveal .progress span {
337 | background: rgba(235, 129, 27, 1);
338 | -webkit-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
339 | -moz-transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985);
340 | transition: width 800ms cubic-bezier(0.26, 0.86, 0.44, 0.985); }
341 |
--------------------------------------------------------------------------------
/figures/cnn1.gif:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/figures/cnn1.gif
--------------------------------------------------------------------------------
/figures/cnn1.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/figures/cnn1.png
--------------------------------------------------------------------------------
/figures/impurity.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/figures/impurity.png
--------------------------------------------------------------------------------
/figures/nn1.jpeg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/figures/nn1.jpeg
--------------------------------------------------------------------------------
/figures/nn2.jpeg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/figures/nn2.jpeg
--------------------------------------------------------------------------------
/figures/nn3.jpeg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/figures/nn3.jpeg
--------------------------------------------------------------------------------
/figures/nonlinearities.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/figures/nonlinearities.jpg
--------------------------------------------------------------------------------
/figures/ridgelasso.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/figures/ridgelasso.png
--------------------------------------------------------------------------------
/figures/rnn1.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/figures/rnn1.png
--------------------------------------------------------------------------------
/figures/rnn2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/figures/rnn2.png
--------------------------------------------------------------------------------
/figures/rnn3.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/figures/rnn3.png
--------------------------------------------------------------------------------
/img/01_regression_114_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_114_0.png
--------------------------------------------------------------------------------
/img/01_regression_120_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_120_0.png
--------------------------------------------------------------------------------
/img/01_regression_128_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_128_0.png
--------------------------------------------------------------------------------
/img/01_regression_131_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_131_0.png
--------------------------------------------------------------------------------
/img/01_regression_141_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_141_0.png
--------------------------------------------------------------------------------
/img/01_regression_150_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_150_0.png
--------------------------------------------------------------------------------
/img/01_regression_156_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_156_0.png
--------------------------------------------------------------------------------
/img/01_regression_164_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_164_0.png
--------------------------------------------------------------------------------
/img/01_regression_167_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_167_0.png
--------------------------------------------------------------------------------
/img/01_regression_170_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_170_0.png
--------------------------------------------------------------------------------
/img/01_regression_178_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_178_0.png
--------------------------------------------------------------------------------
/img/01_regression_23_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_23_0.png
--------------------------------------------------------------------------------
/img/01_regression_29_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_29_0.png
--------------------------------------------------------------------------------
/img/01_regression_34_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_34_0.png
--------------------------------------------------------------------------------
/img/01_regression_43_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_43_0.png
--------------------------------------------------------------------------------
/img/01_regression_48_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_48_0.png
--------------------------------------------------------------------------------
/img/01_regression_50_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_50_0.png
--------------------------------------------------------------------------------
/img/01_regression_52_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_52_0.png
--------------------------------------------------------------------------------
/img/01_regression_79_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/01_regression_79_0.png
--------------------------------------------------------------------------------
/img/02_iv_28_1.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/02_iv_28_1.png
--------------------------------------------------------------------------------
/img/02_iv_43_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/02_iv_43_0.png
--------------------------------------------------------------------------------
/img/02_iv_9_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/02_iv_9_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_100_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_100_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_106_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_106_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_108_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_108_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_10_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_10_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_112_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_112_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_115_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_115_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_120_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_120_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_122_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_122_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_125_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_125_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_129_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_129_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_12_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_12_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_130_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_130_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_135_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_135_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_140_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_140_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_147_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_147_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_39_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_39_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_44_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_44_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_60_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_60_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_69_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_69_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_71_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_71_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_78_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_78_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_85_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_85_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_90_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_90_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_93_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_93_0.png
--------------------------------------------------------------------------------
/img/03_nonparametric_98_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/03_nonparametric_98_0.png
--------------------------------------------------------------------------------
/img/04_crossvalidation_15_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/04_crossvalidation_15_0.png
--------------------------------------------------------------------------------
/img/04_crossvalidation_26_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/04_crossvalidation_26_0.png
--------------------------------------------------------------------------------
/img/04_crossvalidation_37_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/04_crossvalidation_37_0.png
--------------------------------------------------------------------------------
/img/04_crossvalidation_52_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/04_crossvalidation_52_0.png
--------------------------------------------------------------------------------
/img/05_regularization_100_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/05_regularization_100_0.png
--------------------------------------------------------------------------------
/img/05_regularization_108_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/05_regularization_108_0.png
--------------------------------------------------------------------------------
/img/05_regularization_117_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/05_regularization_117_0.png
--------------------------------------------------------------------------------
/img/05_regularization_131_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/05_regularization_131_0.png
--------------------------------------------------------------------------------
/img/05_regularization_19_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/05_regularization_19_0.png
--------------------------------------------------------------------------------
/img/05_regularization_31_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/05_regularization_31_0.png
--------------------------------------------------------------------------------
/img/05_regularization_40_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/05_regularization_40_0.png
--------------------------------------------------------------------------------
/img/05_regularization_44_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/05_regularization_44_0.png
--------------------------------------------------------------------------------
/img/05_regularization_52_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/05_regularization_52_0.png
--------------------------------------------------------------------------------
/img/05_regularization_62_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/05_regularization_62_0.png
--------------------------------------------------------------------------------
/img/05_regularization_76_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/05_regularization_76_0.png
--------------------------------------------------------------------------------
/img/05_regularization_86_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/05_regularization_86_0.png
--------------------------------------------------------------------------------
/img/06_convexity_101_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_101_0.png
--------------------------------------------------------------------------------
/img/06_convexity_18_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_18_0.png
--------------------------------------------------------------------------------
/img/06_convexity_24_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_24_0.png
--------------------------------------------------------------------------------
/img/06_convexity_27_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_27_0.png
--------------------------------------------------------------------------------
/img/06_convexity_35_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_35_0.png
--------------------------------------------------------------------------------
/img/06_convexity_37_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_37_0.png
--------------------------------------------------------------------------------
/img/06_convexity_41_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_41_0.png
--------------------------------------------------------------------------------
/img/06_convexity_43_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_43_0.png
--------------------------------------------------------------------------------
/img/06_convexity_54_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_54_0.png
--------------------------------------------------------------------------------
/img/06_convexity_57_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_57_0.png
--------------------------------------------------------------------------------
/img/06_convexity_60_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_60_0.png
--------------------------------------------------------------------------------
/img/06_convexity_66_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_66_0.png
--------------------------------------------------------------------------------
/img/06_convexity_67_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_67_0.png
--------------------------------------------------------------------------------
/img/06_convexity_73_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_73_0.png
--------------------------------------------------------------------------------
/img/06_convexity_75_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_75_0.png
--------------------------------------------------------------------------------
/img/06_convexity_77_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_77_0.png
--------------------------------------------------------------------------------
/img/06_convexity_82_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_82_0.png
--------------------------------------------------------------------------------
/img/06_convexity_91_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_91_0.png
--------------------------------------------------------------------------------
/img/06_convexity_99_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/06_convexity_99_0.png
--------------------------------------------------------------------------------
/img/07_trees_101_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_101_0.png
--------------------------------------------------------------------------------
/img/07_trees_103_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_103_0.png
--------------------------------------------------------------------------------
/img/07_trees_108_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_108_0.png
--------------------------------------------------------------------------------
/img/07_trees_10_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_10_0.png
--------------------------------------------------------------------------------
/img/07_trees_110_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_110_0.png
--------------------------------------------------------------------------------
/img/07_trees_111_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_111_0.png
--------------------------------------------------------------------------------
/img/07_trees_113_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_113_0.png
--------------------------------------------------------------------------------
/img/07_trees_120_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_120_0.png
--------------------------------------------------------------------------------
/img/07_trees_122_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_122_0.png
--------------------------------------------------------------------------------
/img/07_trees_12_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_12_0.png
--------------------------------------------------------------------------------
/img/07_trees_131_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_131_0.png
--------------------------------------------------------------------------------
/img/07_trees_133_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_133_0.png
--------------------------------------------------------------------------------
/img/07_trees_14_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_14_0.png
--------------------------------------------------------------------------------
/img/07_trees_16_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_16_0.png
--------------------------------------------------------------------------------
/img/07_trees_19_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_19_0.png
--------------------------------------------------------------------------------
/img/07_trees_21_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_21_0.png
--------------------------------------------------------------------------------
/img/07_trees_45_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_45_0.png
--------------------------------------------------------------------------------
/img/07_trees_47_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_47_0.png
--------------------------------------------------------------------------------
/img/07_trees_48_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_48_0.png
--------------------------------------------------------------------------------
/img/07_trees_50_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_50_0.png
--------------------------------------------------------------------------------
/img/07_trees_55_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_55_0.png
--------------------------------------------------------------------------------
/img/07_trees_57_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_57_0.png
--------------------------------------------------------------------------------
/img/07_trees_72_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_72_0.png
--------------------------------------------------------------------------------
/img/07_trees_74_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_74_0.png
--------------------------------------------------------------------------------
/img/07_trees_78_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_78_0.png
--------------------------------------------------------------------------------
/img/07_trees_80_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_80_0.png
--------------------------------------------------------------------------------
/img/07_trees_94_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_94_0.png
--------------------------------------------------------------------------------
/img/07_trees_96_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/07_trees_96_0.png
--------------------------------------------------------------------------------
/img/08_neuralnets_103_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/08_neuralnets_103_0.png
--------------------------------------------------------------------------------
/img/08_neuralnets_113_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/08_neuralnets_113_0.png
--------------------------------------------------------------------------------
/img/08_neuralnets_119_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/08_neuralnets_119_0.png
--------------------------------------------------------------------------------
/img/08_neuralnets_124_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/08_neuralnets_124_0.png
--------------------------------------------------------------------------------
/img/08_neuralnets_127_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/08_neuralnets_127_0.png
--------------------------------------------------------------------------------
/img/08_neuralnets_135_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/08_neuralnets_135_0.png
--------------------------------------------------------------------------------
/img/08_neuralnets_141_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/08_neuralnets_141_0.png
--------------------------------------------------------------------------------
/img/08_neuralnets_147_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/08_neuralnets_147_0.png
--------------------------------------------------------------------------------
/img/08_neuralnets_56_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/08_neuralnets_56_0.png
--------------------------------------------------------------------------------
/img/08_neuralnets_66_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/08_neuralnets_66_0.png
--------------------------------------------------------------------------------
/img/08_neuralnets_76_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/08_neuralnets_76_0.png
--------------------------------------------------------------------------------
/img/08_neuralnets_82_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/08_neuralnets_82_0.png
--------------------------------------------------------------------------------
/img/08_neuralnets_93_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/08_neuralnets_93_0.png
--------------------------------------------------------------------------------
/img/09_postdoubleselection_36_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/09_postdoubleselection_36_0.png
--------------------------------------------------------------------------------
/img/09_postdoubleselection_41_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/09_postdoubleselection_41_0.png
--------------------------------------------------------------------------------
/img/09_postdoubleselection_44_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/09_postdoubleselection_44_0.png
--------------------------------------------------------------------------------
/img/09_postdoubleselection_48_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/09_postdoubleselection_48_0.png
--------------------------------------------------------------------------------
/img/09_postdoubleselection_62_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/09_postdoubleselection_62_0.png
--------------------------------------------------------------------------------
/img/09_postdoubleselection_65_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/09_postdoubleselection_65_0.png
--------------------------------------------------------------------------------
/img/09_postdoubleselection_69_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/09_postdoubleselection_69_0.png
--------------------------------------------------------------------------------
/img/09_postdoubleselection_73_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/09_postdoubleselection_73_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_102_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_102_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_103_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_103_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_106_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_106_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_107_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_107_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_112_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_112_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_113_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_113_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_114_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_114_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_125_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_125_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_126_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_126_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_134_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_134_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_137_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_137_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_138_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_138_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_144_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_144_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_145_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_145_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_154_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_154_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_155_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_155_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_161_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_161_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_162_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_162_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_166_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_166_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_167_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_167_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_168_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_168_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_169_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_169_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_171_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_171_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_172_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_172_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_19_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_19_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_26_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_26_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_30_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_30_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_38_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_38_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_46_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_46_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_49_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_49_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_56_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_56_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_58_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_58_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_61_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_61_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_65_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_65_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_68_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_68_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_70_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_70_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_71_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_71_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_72_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_72_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_74_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_74_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_75_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_75_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_78_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_78_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_81_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_81_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_82_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_82_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_85_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_85_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_87_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_87_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_88_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_88_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_92_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_92_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_93_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_93_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_95_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_95_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_96_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_96_0.png
--------------------------------------------------------------------------------
/img/10_unsupervised_97_0.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/img/10_unsupervised_97_0.png
--------------------------------------------------------------------------------
/notebooks/__pycache__/utils.cpython-39.pyc:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/matteocourthoud/Machine-Learning-for-Economic-Analysis/e19a1643372aafd13d8b7f7c1f5e6e863b552ec1/notebooks/__pycache__/utils.cpython-39.pyc
--------------------------------------------------------------------------------
/notebooks/data/AJR02.csv:
--------------------------------------------------------------------------------
1 | "","GDP","Exprop","Mort","Latitude","Neo","Africa","Asia","Namer","Samer","logMort","Latitude2"
2 | "1",8.39,6.5,78.2,0.3111,0,1,0,0,0,4.35926964755127,0.09678321
3 | "2",7.77,5.36,280,0.1367,0,1,0,0,0,5.63478960316925,0.01868689
4 | "3",9.13,6.39,68.9,0.3778,0,0,0,0,1,4.23265617801961,0.14273284
5 | "4",9.9,9.32,8.55,0.3,1,0,0,0,0,2.14593128294867,0.09
6 | "5",9.29,7.5,85,0.2683,0,0,0,1,0,4.44265125649032,0.07198489
7 | "6",6.88,5.14,71.41,0.2667,0,0,1,0,0,4.26843791556102,0.07112889
8 | "7",7.93,5.64,71,0.1889,0,0,0,0,1,4.26267987704132,0.03568321
9 | "8",8.73,7.91,71,0.1111,0,0,0,0,1,4.26267987704132,0.01234321
10 | "9",6.85,4.45,280,0.1444,0,1,0,0,0,5.63478960316925,0.02085136
11 | "10",7.5,6.45,280,0.6667,0,1,0,0,0,5.63478960316925,0.44448889
12 | "11",9.99,9.73,16.1,0.6667,1,0,0,1,0,2.77881927199042,0.44448889
13 | "12",9.34,7.82,68.9,0.3333,0,0,0,0,1,4.23265617801961,0.11108889
14 | "13",8.81,7.32,71,0.0444,0,0,0,0,1,4.26267987704132,0.00197136
15 | "14",7.42,4.68,240,0.0111,0,1,0,0,0,5.48063892334199,0.00012321
16 | "15",8.79,7.05,78.1,0.1111,0,0,0,1,0,4.35799005684564,0.01234321
17 | "16",7.44,7,668,0.0889,0,1,0,0,0,6.50428817353665,0.00790321
18 | "17",8.36,6.18,130,0.2111,0,0,0,1,0,4.86753445045558,0.04456321
19 | "18",8.47,6.55,71,0.0222,0,0,0,0,1,4.26267987704132,0.00049284
20 | "19",7.95,6.77,67.8,0.3,0,1,0,0,0,4.21656219494635,0.09
21 | "20",7.95,5,78.1,0.15,0,0,0,1,0,4.35799005684564,0.0225
22 | "21",6.11,5.73,26,0.0889,0,1,0,0,0,3.25809653802148,0.00790321
23 | "22",8.9,7.82,280,0.0111,0,1,0,0,0,5.63478960316925,0.00012321
24 | "23",7.27,8.27,1470,0.1476,0,1,0,0,0,7.29301767977278,0.02178576
25 | "24",7.37,6.27,668,0.0889,0,1,0,0,0,6.50428817353665,0.00790321
26 | "25",8.29,5.14,71,0.17,0,0,0,1,0,4.26267987704132,0.0289
27 | "26",7.49,6.55,483,0.1222,0,1,0,0,0,6.18001665365257,0.01493284
28 | "27",7.9,5.89,32.18,0.0556,0,0,0,0,1,3.47134514156424,0.00309136
29 | "28",7.15,3.73,130,0.2111,0,0,0,1,0,4.86753445045558,0.04456321
30 | "29",7.69,5.32,78.1,0.1667,0,0,0,1,0,4.35799005684564,0.02778889
31 | "30",10.05,8.14,14.9,0.2461,0,0,1,0,0,2.70136121295141,0.06056521
32 | "31",7.33,8.27,48.63,0.2222,0,0,1,0,0,3.88424062441569,0.04937284
33 | "32",8.07,7.59,170,0.0556,0,0,1,0,0,5.13579843705026,0.00309136
34 | "33",8.19,7.09,130,0.2017,0,0,0,1,0,4.86753445045558,0.04068289
35 | "34",7.06,6.05,145,0.0111,0,1,0,0,0,4.97673374242057,0.00012321
36 | "35",6.84,4.45,536.04,0.2222,0,1,0,0,0,6.28420878515203,0.04937284
37 | "36",8.89,7.95,17.7,0.0256,0,0,1,0,0,2.87356463957978,0.00065536
38 | "37",6.57,4,2940,0.1889,0,1,0,0,0,7.98616486033273,0.03568321
39 | "38",9.43,7.23,16.3,0.3944,0,0,0,0,0,2.79116510781272,0.15555136
40 | "39",8.94,7.5,71,0.2556,0,0,0,1,0,4.26267987704132,0.06533136
41 | "40",8.04,7.09,78.2,0.3556,0,1,0,0,0,4.35926964755127,0.12645136
42 | "41",9.76,9.73,8.55,0.4556,1,0,0,0,0,2.14593128294867,0.20757136
43 | "42",7.54,5.23,163.3,0.1444,0,0,0,1,0,5.09558899997642,0.02085136
44 | "43",6.73,5,400,0.1778,0,1,0,0,0,5.99146454710798,0.03161284
45 | "44",6.81,5.55,2004,0.1111,0,1,0,0,0,7.60290046220476,0.01234321
46 | "45",7.35,6.05,36.99,0.3333,0,0,1,0,0,3.61064760584436,0.11108889
47 | "46",8.84,5.91,163.3,0.1,0,0,0,1,0,5.09558899997642,0.01
48 | "47",8.21,6.95,78.1,0.2556,0,0,0,0,1,4.35799005684564,0.06533136
49 | "48",8.4,5.77,71,0.1111,0,0,0,0,1,4.26267987704132,0.01234321
50 | "49",7.4,6,164.66,0.1556,0,1,0,0,0,5.10388274187028,0.02421136
51 | "50",6.25,5.82,483,0.0922,0,1,0,0,0,6.18001665365257,0.00850084
52 | "51",10.15,9.32,17.7,0.0136,0,0,1,0,0,2.87356463957978,0.00018496
53 | "52",8.89,6.86,15.5,0.3222,0,1,0,0,0,2.7408400239252,0.10381284
54 | "53",7.73,6.05,69.8,0.0778,0,0,1,0,0,4.24563400976833,0.00605284
55 | "54",7.31,4,88.2,0.1667,0,1,0,0,0,4.47960696301275,0.02778889
56 | "55",6.25,6.64,145,0.0667,0,1,0,0,0,4.97673374242057,0.00444889
57 | "56",7.22,6.91,668,0.0889,0,1,0,0,0,6.50428817353665,0.00790321
58 | "57",8.77,7.45,85,0.1222,0,0,0,1,0,4.44265125649032,0.01493284
59 | "58",8.48,6.45,63,0.3778,0,1,0,0,0,4.14313472639153,0.14273284
60 | "59",6.97,4.45,280,0.0111,0,1,0,0,0,5.63478960316925,0.00012321
61 | "60",9.03,7,71,0.3667,0,0,0,0,1,4.26267987704132,0.13446889
62 | "61",10.22,10,15,0.4222,1,0,0,1,0,2.70805020110221,0.17825284
63 | "62",9.07,7.14,78.1,0.0889,0,0,0,0,1,4.35799005684564,0.00790321
64 | "63",7.28,6.41,140,0.1778,0,0,1,0,0,4.9416424226093,0.03161284
65 | "64",6.87,3.5,240,0,0,1,0,0,0,5.48063892334199,0
66 |
--------------------------------------------------------------------------------
/notebooks/data/Advertising.csv:
--------------------------------------------------------------------------------
1 | "","TV","Radio","Newspaper","Sales"
2 | "1",230.1,37.8,69.2,22.1
3 | "2",44.5,39.3,45.1,10.4
4 | "3",17.2,45.9,69.3,9.3
5 | "4",151.5,41.3,58.5,18.5
6 | "5",180.8,10.8,58.4,12.9
7 | "6",8.7,48.9,75,7.2
8 | "7",57.5,32.8,23.5,11.8
9 | "8",120.2,19.6,11.6,13.2
10 | "9",8.6,2.1,1,4.8
11 | "10",199.8,2.6,21.2,10.6
12 | "11",66.1,5.8,24.2,8.6
13 | "12",214.7,24,4,17.4
14 | "13",23.8,35.1,65.9,9.2
15 | "14",97.5,7.6,7.2,9.7
16 | "15",204.1,32.9,46,19
17 | "16",195.4,47.7,52.9,22.4
18 | "17",67.8,36.6,114,12.5
19 | "18",281.4,39.6,55.8,24.4
20 | "19",69.2,20.5,18.3,11.3
21 | "20",147.3,23.9,19.1,14.6
22 | "21",218.4,27.7,53.4,18
23 | "22",237.4,5.1,23.5,12.5
24 | "23",13.2,15.9,49.6,5.6
25 | "24",228.3,16.9,26.2,15.5
26 | "25",62.3,12.6,18.3,9.7
27 | "26",262.9,3.5,19.5,12
28 | "27",142.9,29.3,12.6,15
29 | "28",240.1,16.7,22.9,15.9
30 | "29",248.8,27.1,22.9,18.9
31 | "30",70.6,16,40.8,10.5
32 | "31",292.9,28.3,43.2,21.4
33 | "32",112.9,17.4,38.6,11.9
34 | "33",97.2,1.5,30,9.6
35 | "34",265.6,20,0.3,17.4
36 | "35",95.7,1.4,7.4,9.5
37 | "36",290.7,4.1,8.5,12.8
38 | "37",266.9,43.8,5,25.4
39 | "38",74.7,49.4,45.7,14.7
40 | "39",43.1,26.7,35.1,10.1
41 | "40",228,37.7,32,21.5
42 | "41",202.5,22.3,31.6,16.6
43 | "42",177,33.4,38.7,17.1
44 | "43",293.6,27.7,1.8,20.7
45 | "44",206.9,8.4,26.4,12.9
46 | "45",25.1,25.7,43.3,8.5
47 | "46",175.1,22.5,31.5,14.9
48 | "47",89.7,9.9,35.7,10.6
49 | "48",239.9,41.5,18.5,23.2
50 | "49",227.2,15.8,49.9,14.8
51 | "50",66.9,11.7,36.8,9.7
52 | "51",199.8,3.1,34.6,11.4
53 | "52",100.4,9.6,3.6,10.7
54 | "53",216.4,41.7,39.6,22.6
55 | "54",182.6,46.2,58.7,21.2
56 | "55",262.7,28.8,15.9,20.2
57 | "56",198.9,49.4,60,23.7
58 | "57",7.3,28.1,41.4,5.5
59 | "58",136.2,19.2,16.6,13.2
60 | "59",210.8,49.6,37.7,23.8
61 | "60",210.7,29.5,9.3,18.4
62 | "61",53.5,2,21.4,8.1
63 | "62",261.3,42.7,54.7,24.2
64 | "63",239.3,15.5,27.3,15.7
65 | "64",102.7,29.6,8.4,14
66 | "65",131.1,42.8,28.9,18
67 | "66",69,9.3,0.9,9.3
68 | "67",31.5,24.6,2.2,9.5
69 | "68",139.3,14.5,10.2,13.4
70 | "69",237.4,27.5,11,18.9
71 | "70",216.8,43.9,27.2,22.3
72 | "71",199.1,30.6,38.7,18.3
73 | "72",109.8,14.3,31.7,12.4
74 | "73",26.8,33,19.3,8.8
75 | "74",129.4,5.7,31.3,11
76 | "75",213.4,24.6,13.1,17
77 | "76",16.9,43.7,89.4,8.7
78 | "77",27.5,1.6,20.7,6.9
79 | "78",120.5,28.5,14.2,14.2
80 | "79",5.4,29.9,9.4,5.3
81 | "80",116,7.7,23.1,11
82 | "81",76.4,26.7,22.3,11.8
83 | "82",239.8,4.1,36.9,12.3
84 | "83",75.3,20.3,32.5,11.3
85 | "84",68.4,44.5,35.6,13.6
86 | "85",213.5,43,33.8,21.7
87 | "86",193.2,18.4,65.7,15.2
88 | "87",76.3,27.5,16,12
89 | "88",110.7,40.6,63.2,16
90 | "89",88.3,25.5,73.4,12.9
91 | "90",109.8,47.8,51.4,16.7
92 | "91",134.3,4.9,9.3,11.2
93 | "92",28.6,1.5,33,7.3
94 | "93",217.7,33.5,59,19.4
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 | "44",80
46 | "45",365
47 | "46",750
48 | "47",70
49 | "48",175
50 | "49",920
51 | "50",75
52 | "51",325
53 | "52",1940
54 | "53",155
55 | "54",286.667
56 | "55",775
57 | "56",776.667
58 | "57",110
59 | "58",430
60 | "59",630
61 | "60",385
62 | "61",450
63 | "62",275
64 | "63",191
65 | "64",260
66 | "65",640
67 | "66",190
68 | "67",737.5
69 | "68",202.5
70 | "69",135
71 | "70",1600
72 | "71",750
73 | "72",780
74 | "73",1670
75 | "74",925
76 | "75",250
77 | "76",68
78 | "77",500
79 | "78",595
80 | "79",200
81 | "80",210
82 | "81",350
83 | "82",560
84 | "83",500
85 | "84",325
86 | "85",326.667
87 | "86",815
88 | "87",75
89 | "88",765
90 | "89",90
91 | "90",1450
92 | "91",415
93 | "92",512.5
94 | "93",250
95 | "94",140
96 | "95",110
97 | "96",195
98 | "97",2460
99 | "98",431.5
100 | "99",1260
101 | "100",750
102 | "101",450
103 | "102",210
104 | "103",416.667
105 | "104",420
106 | "105",297.5
107 | "106",305
108 | "107",70
109 | "108",300
110 | "109",750
111 | "110",487.5
112 | "111",385
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 | "33",535
35 | "34",933.333
36 | "35",850
37 | "36",450
38 | "37",1975
39 | "38",475
40 | "39",145
41 | "40",1861.46
42 | "41",490
43 | "42",375
44 | "43",750
45 | "44",70
46 | "45",1500
47 | "46",1925.571
48 | "47",215
49 | "48",900
50 | "49",155
51 | "50",700
52 | "51",535
53 | "52",362.5
54 | "53",500
55 | "54",950
56 | "55",87.5
57 | "56",100
58 | "57",165
59 | "58",1300
60 | "59",275
61 | "60",850
62 | "61",95
63 | "62",110
64 | "63",100
65 | "64",277.5
66 | "65",600
67 | "66",657
68 | "67",2412.5
69 | "68",250
70 | "69",155
71 | "70",300
72 | "71",825
73 | "72",450
74 | "73",86.5
75 | "74",1300
76 | "75",1000
77 | "76",1310
78 | "77",1043.333
79 | "78",725
80 | "79",300
81 | "80",365
82 | "81",225
83 | "82",787.5
84 | "83",800
85 | "84",587.5
86 | "85",75
87 | "86",150
88 | "87",700
89 | "88",100
90 | "89",175
91 | "90",137
92 | "91",120
93 | "92",140
94 | "93",240
95 | "94",350
96 | "95",200
97 | "96",172
98 | "97",750
99 | "98",580
100 | "99",450
101 | "100",300
102 | "101",250
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 | -1.76458606962,-0.746272562068
25 | -1.81048463819,0.493747359351
26 | -1.16989891378,-2.72528149494
27 | -0.685375735369,-0.457615734339
28 | 1.09091803184,0.0144945075275
29 | -0.432340114041,-0.399831023509
30 | 0.268814775371,-0.201608350198
31 | -0.851840753541,-1.74182928585
32 | -1.49708417204,-0.826033329438
33 | 0.0887747459974,-0.887360712724
34 | -1.60172430963,-0.695299045953
35 | -1.24685724026,-1.52958488449
36 | -1.06298912831,-0.110637447364
37 | -0.26628305531,0.0451634696289
38 | 1.67658383263,2.52005288263
39 | 0.119572571441,0.535542781034
40 | -0.0860079872691,1.36359582806
41 | 0.368080289749,1.72937250997
42 | -0.27149420694,1.37926732742
43 | -0.0859264618788,-0.127662573751
44 | -0.190750153683,-0.461333357788
45 | -0.781679768391,1.02239787731
46 | 0.792436346461,-0.814298088655
47 | -0.282869886234,-1.03846880699
48 | -0.236625531903,0.928450553143
49 | 1.17183009101,1.72983145003
50 | 0.496942768505,-0.925139825949
51 | -0.887370979477,-2.28349795939
52 | -1.30695315836,-2.38160058115
53 | -2.4327641204,-2.02554558512
54 | -0.40718896096,-0.335098643325
55 | -0.285665299455,-1.30878131267
56 | 1.5222148831,1.20100315335
57 | -0.998106907438,-0.946268900068
58 | -0.289973726127,0.206256579941
59 | -1.236839243,-0.675447507317
60 | -0.359506962064,-2.70015447022
61 | 0.543559153033,0.422547552093
62 | -0.403647282895,-0.0543899228706
63 | 1.30330893266,1.32896747385
64 | -0.717117243406,1.33137979804
65 | -1.01270788406,-0.924769230819
66 | 0.831992902159,2.24774586895
67 | 1.33764359604,0.868256457488
68 | 0.601693509867,-0.198217563055
69 | 1.30285098047,1.10466637602
70 | -0.881700578927,-1.54068478518
71 | -0.824529071305,-1.3370078772
72 | -0.984356518466,-1.13916026592
73 | -1.38499150721,0.702699932949
74 | -0.358842560436,-1.69451276978
75 | -0.226618229456,0.801938547571
76 | -0.941077436691,-0.733188708932
77 | 2.46033594813,-0.0483728170022
78 | 0.716797281413,0.602336759898
79 | -0.248087023209,-1.01849037379
80 | 1.01077288944,0.0529780222229
81 | 2.31304863448,1.75235887916
82 | 0.835179797449,0.985714875658
83 | -1.07190333914,-1.24729787324
84 | -1.65052614385,0.215464529577
85 | -0.600485690305,-0.420940526974
86 | -0.0585293830471,0.127620874053
87 | 0.0757267446339,-0.522149221026
88 | -1.15783156137,0.590893742239
89 | 1.67360608794,0.114623316085
90 | -1.04398823978,-0.418944284341
91 | 0.014687476592,-0.558746620673
92 | 0.675321970429,1.48262978763
93 | 1.77834230986,0.942774111448
94 | -1.29576363941,-1.0852038131
95 | 0.0796020218475,-0.539100814054
96 | 2.26085771442,0.673224840267
97 | 0.479090923234,1.45477446091
98 | -0.535019997433,-0.399174811276
99 | -0.773129330645,-0.957174849521
100 | 0.403634339015,1.39603816899
101 | -0.588496438718,-0.497285090818
102 |
--------------------------------------------------------------------------------
/notebooks/data/USArrests.csv:
--------------------------------------------------------------------------------
1 | "State","Murder","Assault","UrbanPop","Rape"
2 | "Alabama",13.2,236,58,21.2
3 | "Alaska",10,263,48,44.5
4 | "Arizona",8.1,294,80,31
5 | "Arkansas",8.8,190,50,19.5
6 | "California",9,276,91,40.6
7 | "Colorado",7.9,204,78,38.7
8 | "Connecticut",3.3,110,77,11.1
9 | "Delaware",5.9,238,72,15.8
10 | "Florida",15.4,335,80,31.9
11 | "Georgia",17.4,211,60,25.8
12 | "Hawaii",5.3,46,83,20.2
13 | "Idaho",2.6,120,54,14.2
14 | "Illinois",10.4,249,83,24
15 | "Indiana",7.2,113,65,21
16 | "Iowa",2.2,56,57,11.3
17 | "Kansas",6,115,66,18
18 | "Kentucky",9.7,109,52,16.3
19 | "Louisiana",15.4,249,66,22.2
20 | "Maine",2.1,83,51,7.8
21 | "Maryland",11.3,300,67,27.8
22 | "Massachusetts",4.4,149,85,16.3
23 | "Michigan",12.1,255,74,35.1
24 | "Minnesota",2.7,72,66,14.9
25 | "Mississippi",16.1,259,44,17.1
26 | "Missouri",9,178,70,28.2
27 | "Montana",6,109,53,16.4
28 | "Nebraska",4.3,102,62,16.5
29 | "Nevada",12.2,252,81,46
30 | "New Hampshire",2.1,57,56,9.5
31 | "New Jersey",7.4,159,89,18.8
32 | "New Mexico",11.4,285,70,32.1
33 | "New York",11.1,254,86,26.1
34 | "North Carolina",13,337,45,16.1
35 | "North Dakota",0.8,45,44,7.3
36 | "Ohio",7.3,120,75,21.4
37 | "Oklahoma",6.6,151,68,20
38 | "Oregon",4.9,159,67,29.3
39 | "Pennsylvania",6.3,106,72,14.9
40 | "Rhode Island",3.4,174,87,8.3
41 | "South Carolina",14.4,279,48,22.5
42 | "South Dakota",3.8,86,45,12.8
43 | "Tennessee",13.2,188,59,26.9
44 | "Texas",12.7,201,80,25.5
45 | "Utah",3.2,120,80,22.9
46 | "Vermont",2.2,48,32,11.2
47 | "Virginia",8.5,156,63,20.7
48 | "Washington",4,145,73,26.2
49 | "West Virginia",5.7,81,39,9.3
50 | "Wisconsin",2.6,53,66,10.8
51 | "Wyoming",6.8,161,60,15.6
52 |
--------------------------------------------------------------------------------
/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();
--------------------------------------------------------------------------------