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lisds · Dec 11, 2023 · ad0013e · ad0013e
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diff --git a/logistic_regression.Rmd b/logistic_regression.Rmd
@@ -715,7 +715,7 @@ Let's demonstrate this in python:
 (1 - p) == 80/100
 ```
 
-When you are interpretting odds:  
+When you are interpreting odds:  
 
 **Odds of 1 indicate that the event of interest is equally as likely as events NOT of interest.**
 
@@ -1264,7 +1264,7 @@ pd.DataFrame({'Log Odds Prediction': log_odds_predictions,
 ```
 
 Remember, the probability predictions are the predicted probabilities of `appetite_d == 1`. 
-Let's name the predictions appropraitely to remind ourselves:
+Let's name the predictions appropriately to remind ourselves:
 
 ```{python}
 predicted_prob_of_1 = sigmoid_p_predictions

diff --git a/naive_bayes.Rmd b/naive_bayes.Rmd
@@ -24,7 +24,7 @@ pd.set_option('mode.copy_on_write', True)
 ```
 
 Also see: [Naive Bayes
-classifers](https://jakevdp.github.io/PythonDataScienceHandbook/05.05-naive-bayes.html)
+classifiers](https://jakevdp.github.io/PythonDataScienceHandbook/05.05-naive-bayes.html)
 in the [Python Data Science
 Handbook](https://jakevdp.github.io/PythonDataScienceHandbook).
 
@@ -311,7 +311,7 @@ np.mean(df['sklearn_predictions'] == df['species'])
 df
 ```
 
-## Puting the naïve into "naïve Bayes"
+## Putting the naïve into "naïve Bayes"
 
 Now, let's also consider the `bill_depth_mm` values: