class 10 updates

Author: justmarkham

Diff for: README.md

@@ -286,7 +286,7 @@ Tuesday | Thursday
 
 ### Class 10: Linear Regression
 * Machine learning exercise ([article](http://blog.dominodatalab.com/10-interesting-uses-of-data-science/))
-* Linear regression
+* Linear regression ([notebook](http://nbviewer.ipython.org/github/justmarkham/DAT8/blob/master/notebooks/10_linear_regression.ipynb))
     * [Capital Bikeshare dataset](data/bikeshare.csv) used in a Kaggle competition
     * [Data dictionary](https://www.kaggle.com/c/bike-sharing-demand/data)
 * Feature engineering example: [Predicting User Engagement in Corporate Collaboration Network](http://nbviewer.ipython.org/github/mikeyea/DAT7_project/blob/master/final%20project/Class_Presention_MYea.ipynb)
@@ -330,12 +330,13 @@ Tuesday | Thursday
 ### Class 12: Logistic Regression
 * Yelp votes homework due (solution)
 * Logistic regression ([notebook](http://nbviewer.ipython.org/github/justmarkham/DAT8/blob/master/notebooks/12_logistic_regression.ipynb))
-* Exercise with Titanic data ([instructions](homework/12_titanic.md), [solution](code/12_titanic.py))
-* Confusion matrix ([slides](slides/12_confusion_matrix.pdf))
+    * [Glass identification dataset](https://archive.ics.uci.edu/ml/datasets/Glass+Identification)
+* Exercise with Titanic data ([notebook](http://nbviewer.ipython.org/github/justmarkham/DAT8/blob/master/notebooks/12_titanic_confusion.ipynb), [data](data/titanic.csv), [data dictionary](https://www.kaggle.com/c/titanic/data))
+* Confusion matrix ([slides](slides/12_confusion_matrix.pdf), [notebook](http://nbviewer.ipython.org/github/justmarkham/DAT8/blob/master/notebooks/12_titanic_confusion.ipynb))
 
 **Homework:**
 * If you aren't yet comfortable with all of the confusion matrix terminology, watch Rahul Patwari's videos on [Intuitive sensitivity and specificity](https://www.youtube.com/watch?v=U4_3fditnWg) (9 minutes) and [The tradeoff between sensitivity and specificity](https://www.youtube.com/watch?v=vtYDyGGeQyo) (13 minutes).
-* Video assignment on [ROC curves and AUC](homework/13_roc_auc.md)
+* Video/reading assignment on [ROC curves and AUC](homework/13_roc_auc.md)
 * Video/reading assignment on [cross-validation](homework/13_cross_validation.md)
 
 **Resources:**

Diff for: homework/10_yelp_votes.md

@@ -22,4 +22,4 @@ This assignment uses a small subset of the data from Kaggle's [Yelp Business Rat
 7. **Bonus:** Think of some new features you could create from the existing data that might be predictive of the response. Figure out how to create those features in Pandas, add them to your model, and see if the RMSE improves.
 8. **Bonus:** Compare your best RMSE on the testing set with the RMSE for the "null model", which is the model that ignores all features and simply predicts the mean response value in the testing set.
 9. **Bonus:** Instead of treating this as a regression problem, treat it as a classification problem and see what testing accuracy you can achieve with KNN.
-10. **Bonus:** Figure out how to use linear regression for classification, and compare its classification accuracy to KNN.
+10. **Bonus:** Figure out how to use linear regression for classification, and compare its classification accuracy with KNN's accuracy.