Correct spelling

Author: Jared Weed

projects/boston_housing/boston_housing.ipynb

@@ -22,7 +22,7 @@
     "## Getting Started\n",
     "In this project, you will evaluate the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts. A model trained on this data that is seen as a *good fit* could then be used to make certain predictions about a home — in particular, its monetary value. This model would prove to be invaluable for someone like a real estate agent who could make use of such information on a daily basis.\n",
     "\n",
-    "The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Housing). The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts. For the purposes of this project, the following preoprocessing steps have been made to the dataset:\n",
+    "The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Housing). The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts. For the purposes of this project, the following preprocessing steps have been made to the dataset:\n",
     "- 16 data points have an `'MDEV'` value of 50.0. These data points likely contain **missing or censored values** and have been removed.\n",
     "- 1 data point has an `'RM'` value of 8.78. This data point can be considered an **outlier** and has been removed.\n",
     "- The features `'RM'`, `'LSTAT'`, `'PTRATIO'`, and `'MDEV'` are essential. The remaining **non-relevant features** have been excluded.\n",
@@ -278,7 +278,7 @@
    "metadata": {},
    "source": [
     "### Learning Curves\n",
-    "The following code cell produces four graphs for a decision tree model with different maximum depths. Each graph visualizes the learning curves of the model for both training and testing as the size of the training set is increased. Note that the shaded reigon of a learning curve denotes the uncertainty of that curve (measured as the standard deviation). The model is scored on both the training and testing sets using R<sup>2</sup>, the coefficient of determination.  \n",
+    "The following code cell produces four graphs for a decision tree model with different maximum depths. Each graph visualizes the learning curves of the model for both training and testing as the size of the training set is increased. Note that the shaded region of a learning curve denotes the uncertainty of that curve (measured as the standard deviation). The model is scored on both the training and testing sets using R<sup>2</sup>, the coefficient of determination.  \n",
     "\n",
     "Run the code cell below and use these graphs to answer the following question."
    ]

projects/creating_customer_segments/customer_segments.ipynb

@@ -367,10 +367,10 @@
    },
    "outputs": [],
    "source": [
-    "# TODO: Apply PCA to the good data with the same number of dimensions as features\n",
+    "# TODO: Apply PCA by fitting the good data with the same number of dimensions as features\n",
     "pca = None\n",
     "\n",
-    "# TODO: Apply a PCA transformation to the sample log-data\n",
+    "# TODO: Transform the sample log-data using the PCA fit above\n",
     "pca_samples = None\n",
     "\n",
     "# Generate PCA results plot\n",
@@ -436,13 +436,13 @@
    },
    "outputs": [],
    "source": [
-    "# TODO: Fit PCA to the good data using only two dimensions\n",
+    "# TODO: Apply PCA by fitting the good data with only two dimensions\n",
     "pca = None\n",
     "\n",
-    "# TODO: Apply a PCA transformation the good data\n",
+    "# TODO: Transform the good data using the PCA fit above\n",
     "reduced_data = None\n",
     "\n",
-    "# TODO: Apply a PCA transformation to the sample log-data\n",
+    "# TODO: Transform the sample log-data using the PCA fit above\n",
     "pca_samples = None\n",
     "\n",
     "# Create a DataFrame for the reduced data\n",