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diff --git a/.nojekyll b/.nojekyll
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diff --git a/labs/06/scf.pdf b/labs/06/scf.pdf
diff --git a/labs/09/collab_with_git.pdf b/labs/09/collab_with_git.pdf
diff --git a/labs/lab0-setup.pdf b/labs/lab0-setup.pdf
diff --git a/labs/lab1-submission.pdf b/labs/lab1-submission.pdf
diff --git a/labs/lab2-testing.pdf b/labs/lab2-testing.pdf
diff --git a/labs/lab3-debugging.pdf b/labs/lab3-debugging.pdf
diff --git a/labs/lab5-codereview.pdf b/labs/lab5-codereview.pdf
diff --git a/labs/py_vs_R.pdf b/labs/py_vs_R.pdf
diff --git a/project/baseball.dat b/project/baseball.dat
diff --git a/project/project.html b/project/project.html
@@ -280,7 +280,7 @@ <h1 class="title">Final Project</h1>
 <p>Please use standard citation practices to cite any papers/code/online resources/people whose ideas you make use of. Please do not consult with class members who are not in your group.</p>
 <section id="problem" class="level1">
 <h1>Problem</h1>
-<p>Your task is to implement and experiment with the use of genetic algorithms for variable selection, including both linear regression and GLMs (though the ideas and implemention would readily generalize to other statistics/ML/DS methods). Some details on genetic algorithms are available in <a href="https://onlinelibrary.wiley.com/doi/book/10.1002/9781118555552">Section 3.4 of the Givens and Hoeting book on Computational Statistics</a> and the baseball data discussed there are in the class GitHub repository as <code>project/baseball.dat</code>. You should also be able to find plenty of other information about genetic algorithms online. The result should be an Python package that I can easily use (in particular, I will be testing your code on my own test cases). My grading will be largely based on the following items.</p>
+<p>Your task is to implement and experiment with the use of genetic algorithms for variable selection, including both linear regression and GLMs (though the ideas and implemention would readily generalize to other statistics/ML/DS methods). Some details on genetic algorithms are available in <a href="https://onlinelibrary.wiley.com/doi/book/10.1002/9781118555552">Section 3.4 of the Givens and Hoeting book on Computational Statistics</a> and the baseball data discussed there are in the class GitHub repository as <a href="./baseball.dat"><code>project/baseball.dat</code></a>. You should also be able to find plenty of other information about genetic algorithms online. The result should be an Python package that I can easily use (in particular, I will be testing your code on my own test cases). My grading will be largely based on the following items.</p>
 <ol type="1">
 <li><p>Your solution should allow the user to provide sensible inputs. Key inputs of course are the dataset and the type of regression, which you should just be able to pass along to relevant functions from the <code>statsmodels</code> or <code>scikit-learn</code> packages. By default you should just use AIC as your objective criterion but allow users to provide their own objective function. Similarly you can use the genetic operators described in Givens and Hoeting for variable selection, but ideally your code should be general enough that a user could provide additional operators. For various inputs that control the behavior of the genetic algorithm you should try to come up with good defaults (based in part on point 4 below), but also give users flexibility.</p></li>
 <li><p>Your solution should involve modular code, with functions or OOP methods that implement discrete tasks. You should have an overall design and style that is consistent across the components.</p></li>

diff --git a/project/project.pdf b/project/project.pdf
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diff --git a/units/test.pdf b/units/test.pdf
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