dataset to data set

Author: trevorcampbell

source/classification1.md

@@ -332,7 +332,7 @@ points_df = pd.DataFrame(
 )
 perim_concav_with_new_point_df = pd.concat((cancer, points_df), ignore_index=True)
 # Find the euclidean distances from the new point to each of the points
-# in the orginal dataset
+# in the orginal data set
 my_distances = euclidean_distances(perim_concav_with_new_point_df[attrs])[
     len(cancer)
 ][:-1]
@@ -430,7 +430,7 @@ points_df2 = pd.DataFrame(
 )
 perim_concav_with_new_point_df2 = pd.concat((cancer, points_df2), ignore_index=True)
 # Find the euclidean distances from the new point to each of the points
-# in the orginal dataset
+# in the orginal data set
 my_distances2 = euclidean_distances(perim_concav_with_new_point_df2[attrs])[
     len(cancer)
 ][:-1]
@@ -783,7 +783,7 @@ points_df4 = pd.DataFrame(
 )
 perim_concav_with_new_point_df4 = pd.concat((cancer, points_df4), ignore_index=True)
 # Find the euclidean distances from the new point to each of the points
-# in the orginal dataset
+# in the orginal data set
 my_distances4 = euclidean_distances(perim_concav_with_new_point_df4[attrs])[
     len(cancer)
 ][:-1]

source/clustering.md

@@ -792,7 +792,7 @@ Total WSSD for K clusters ranging from 1 to 9.
 We can perform K-means in Python using a workflow similar to those
 in the earlier classification and regression chapters. We will begin
 by reading the original (i.e., unstandardized) subset of 18 observations
-from the penguins dataset.
+from the penguins data set.
 
 ```{code-cell} ipython3
 :tags: [remove-cell]
@@ -1056,7 +1056,7 @@ and guidance that the worksheets provide will function as intended.
   clustering for when you expect there to be subgroups, and then subgroups within
   subgroups, etc., in your data. In the realm of more general unsupervised
   learning, it covers *principal components analysis (PCA)*, which is a very
-  popular technique for reducing the number of predictors in a dataset.
+  popular technique for reducing the number of predictors in a data set.
 
 ## References
 

source/regression1.md

@@ -54,7 +54,7 @@ By the end of the chapter, readers will be able to do the following:
 * Recognize situations where a simple regression analysis would be appropriate for making predictions.
 * Explain the K-nearest neighbor (KNN) regression algorithm and describe how it differs from KNN classification.
 * Interpret the output of a KNN regression.
-* In a dataset with two or more variables, perform K-nearest neighbor regression in Python using a `scikit-learn` workflow.
+* In a data set with two or more variables, perform K-nearest neighbor regression in Python using a `scikit-learn` workflow.
 * Execute cross-validation in Python to choose the number of neighbors.
 * Evaluate KNN regression prediction accuracy in Python using a test data set and the root mean squared prediction error (RMSPE).
 * In the context of KNN regression, compare and contrast goodness of fit and prediction properties (namely RMSE vs RMSPE).
@@ -795,8 +795,8 @@ In this case the orange line becomes extremely smooth, and actually becomes flat
 once $K$ is equal to the number of datapoints in the entire data set.
 This happens because our predicted values for a given x value (here, home
 size), depend on many neighboring observations; in the case where $K$ is equal
-to the size of the dataset, the prediction is just the mean of the house prices
-in the dataset (completely ignoring the house size).
+to the size of the data set, the prediction is just the mean of the house prices
+in the data set (completely ignoring the house size).
 In contrast to the $K=1$ example,
 the smooth, inflexible orange line does not follow the training observations very closely.
 In other words, the model is *not influenced enough* by the training data.

source/viz.md

@@ -183,7 +183,7 @@ than 5,000 rows. The simplest way to plot larger data sets is to enable the
 `vegafusion` data transformer right after you import the `altair` package:
 `alt.data_transformers.enable("vegafusion")`. This will allow you to plot up to
 100,000 graphical objects (e.g., a scatter plot with 100,000 points). To
-visualize *even larger* datasets, see [the `altair` documentation](https://altair-viz.github.io/user_guide/large_datasets).
+visualize *even larger* data sets, see [the `altair` documentation](https://altair-viz.github.io/user_guide/large_datasets).
 ```
 
 ### Scatter plots and line plots: the Mauna Loa CO$_{\text{2}}$ data set
@@ -277,7 +277,7 @@ There are a few basic aspects of a plot that we need to specify:
     - Here, we use the `mark_point` function to visualize our data as a scatter plot.
 - The **encoding channels**, which tells `altair` how the columns in the data frame map to visual properties in the chart.
     - To create an encoding, we use the `encode` function.
-    - The `encode` method builds a key-value mapping between encoding channels (such as x, y) to fields in the dataset, accessed by field name (column names)
+    - The `encode` method builds a key-value mapping between encoding channels (such as x, y) to fields in the data set, accessed by field name (column names)
     - Here, we set the `x` axis of the plot to the `date_measured` variable,
       and on the `y` axis, we plot the `ppm` variable.
     - For the y-axis, we also provided the method