From 1afa116d61babd400d68e26a5e349153bfafe6b5 Mon Sep 17 00:00:00 2001
From: Brian Wright <brianwright@virginia.edu>
Date: Sun, 30 Nov 2025 20:02:16 +0000
Subject: [PATCH] update comments

---
 .../notebooks/Final_Project_Notebook.ipynb    | 28 +++++++++++--------
 1 file changed, 17 insertions(+), 11 deletions(-)

diff --git a/ds1001_final/notebooks/Final_Project_Notebook.ipynb b/ds1001_final/notebooks/Final_Project_Notebook.ipynb
index 0069340..f4688e7 100644
--- a/ds1001_final/notebooks/Final_Project_Notebook.ipynb
+++ b/ds1001_final/notebooks/Final_Project_Notebook.ipynb
@@ -68,7 +68,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Activate the finalproject environment\n",
+    "# Activate the finalproj environment\n",
     "!source ds1001_final/ds1001_final/finalproj/bin/activate"
    ]
   },
@@ -96,6 +96,14 @@
     "# ensure all packages are installed)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "aae8b41a",
+   "metadata": {},
+   "source": [
+    "### Check !pip list again to confirm installations"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -311,15 +319,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "d6f008fe",
-   "metadata": {
-    "vscode": {
-     "languageId": "markdown"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Divide the dataset into features and target\n",
-    "target = \"xx\"['TargetVariable']  # Replace 'TargetVariable' with your actual target column name\n",
+    "target = \"xx\"['TargetVariable']  # Replace 'TargetVariable' with your actual target column name and \"xx\" with your dataframe name\n",
     "features = \"xx\".drop(columns=[target])\n"
    ]
   },
@@ -344,10 +348,11 @@
     "\n",
     "accuracy_results = {}\n",
     "\n",
-    "for k in range(x, x):  # Replace x with your desired range values\n",
+    "for k in range(x, x):  # Replace x with your desired range values, explain what is happening in this loop\n",
     "    knn_model = KNeighborsClassifier(n_neighbors=k)\n",
     "    knn_model.fit(X_train, y_train)\n",
-    "    accuracy = knn_model.score(X_test, y_test)"
+    "    accuracy = knn_model.score(X_test, y_test)\n",
+    "    accuracy_results[k] = accuracy"
    ]
   },
   {
@@ -376,7 +381,7 @@
    "outputs": [],
    "source": [
     "# using the hyperparameter k that gave the best accuracy, rerun the model and generate \n",
-    "# predictions on the test set.\n",
+    "# predictions on the test set. Explain why you choose this k value.\n",
     "best_k = 'xx'  # Replace 'xx' with the best k value found\n",
     "knn_model = KNeighborsClassifier(n_neighbors=best_k)\n",
     "knn_model.fit(X_train, y_train)\n",
@@ -398,7 +403,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# create a confusion matrix for your model's predictions.\n",
+    "# create a confusion matrix for your model's predictions. \n",
+    "# What does the confusion matrix tell you about your model's performance?\n",
     "cm = confusion_matrix(y_test, y_pred)\n",
     "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
     "disp.plot()\n",