fixing some typos

CODE_OF_CONDUCT.md

@@ -34,7 +34,7 @@ This Code of Conduct applies both within project spaces and in public spaces whe
 
 ## Enforcement
 
-Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at hadley@rstudio.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
+Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at tiffany.timbers@stat.ubc.ca. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
 
 Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.
 

src/breast_cancer_eda.Rmd

@@ -98,7 +98,7 @@ kable(bind_rows(train_counts, test_counts),
 
 There is a minor class imbalance, but it is not so great that we will plan to immediately start our modeling plan with over- or under-sampling. If during initial tuning, there are indicators that it may in fact be a greater problem than anticipated (e.g., if the confusion matrix indicates that the model makes a lot more mistakes on the minority class, here malignant cases) then we will only then start to explore whether empoying techniques to address class imbalance may be of help to improving model performance in regards to predicting the minority class.
 
-## Exploratory analysis on the training data set
+# Exploratory analysis on the training data set
 
 To look at whether each of the predictors might be useful to predict the tumour class, we plotted the distributions of each predictor from the training data set and coloured the distribution by class (benign: blue and malignant: orange). In doing this we see that class distributions for all of the mean and max predictors for all the measurements overlap somewhat, but do show quite a difference in their centres and spreads. This is less so for the standard error (se) predictors. In particular, the standard errors of fractal dimension, smoothness, symmetry and texture look very similar in both the distribution centre and spread. Thus, we might choose to omit these from our model.
 

src/breast_cancer_eda.md

@@ -49,7 +49,7 @@ to explore whether empoying techniques to address class imbalance may be
 of help to improving model performance in regards to predicting the
 minority class.
 
-## Exploratory analysis on the training data set
+# Exploratory analysis on the training data set
 
 To look at whether each of the predictors might be useful to predict the
 tumour class, we plotted the distributions of each predictor from the