add in-class activities

Author: gozsari

3-Supervised-1-Regression.ipynb

@@ -328,11 +328,22 @@
     "\n",
     "Up next, we’ll explore **Classification** techniques!\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## In-class Activity:\n",
+    "- Implement another regression algorithm using a different dataset.\n",
+    "- Use scikit-learn to choose dataset and new regression algorithm.\n",
+    "- Datasets: [Toy datasets](https://scikit-learn.org/1.5/datasets/toy_dataset.html)\n",
+    "- Algorithms: [Scikit-learn algorithms](https://scikit-learn.org/stable/supervised_learning.html#supervised-learning)"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -346,7 +357,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,

3-Supervised-2-Classification.ipynb

@@ -909,67 +909,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 📏 How Do We Evaluate Classification Models?\n",
-    "\n",
-    "We evaluate classification models using various metrics to understand their performance. Common evaluation metrics include accuracy, precision, recall, F1-score and Mathew's correlation coefficient (MCC) based on the confusion matrix.\n",
-    "\n",
-    "### Confusion Matrix:\n",
-    "- A table summarizing true positives, false positives, true negatives, and false negatives.\n",
-    "\n",
-    "|                | Predicted Positive | Predicted Negative |\n",
-    "|----------------|---------------------|---------------------|\n",
-    "| **Actual Positive** | True Positive (TP)     | False Negative (FN)     |\n",
-    "| **Actual Negative** | False Positive (FP)    | True Negative (TN)      |\n",
-    "\n",
-    "**True Positive (TP)**: Correctly predicted positive samples.\n",
-    "\n",
-    "**False Positive (FP)**: Incorrectly predicted positive samples.\n",
-    "\n",
-    "**True Negative (TN)**: Correctly predicted negative samples.\n",
-    "\n",
-    "**False Negative (FN)**: Incorrectly predicted negative samples.\n",
-    "\n",
-    "### 1️⃣ Accuracy:\n",
-    "- Percentage of correctly predicted samples.\n",
-    "- Formula: Accuracy = \\(\\frac{TP + TN}{TP + TN + FP + FN}\\)\n",
-    "- Limitation: Can be misleading for imbalanced datasets.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "### 2️⃣ Precision:\n",
-    "- Of all the predicted positives, how many are truly positive?\n",
-    "- Formula:\n",
-    "  \\[\n",
-    "  \\text{Precision} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Positives}}\n",
-    "  \\]\n",
-    "\n",
-    "---\n",
-    "\n",
-    "### 3️⃣ Recall (Sensitivity):\n",
-    "- Of all the actual positives, how many were correctly predicted?\n",
-    "- Formula:\n",
-    "  \\[\n",
-    "  \\text{Recall} = \\frac{\\text{True Positives}}{\\text{True Positives} + \\text{False Negatives}}\n",
-    "  \\]\n",
-    "\n",
-    "---\n",
-    "\n",
-    "### 4️⃣ F1-Score:\n",
-    "- Harmonic mean of precision and recall. Useful when you want to balance both.\n",
-    "- Formula:\n",
-    "  \\[\n",
-    "  \\text{F1-Score} = 2 \\times \\frac{\\text{Precision} \\times \\text{Recall}}{\\text{Precision} + \\text{Recall}}\n",
-    "  \\]\n",
-    "\n",
-    "---\n",
-    "\n",
-    "### 5️⃣ Confusion Matrix:\n",
-    "- A table summarizing true positives, false positives, true negatives, and false negatives.\n",
-    "\n",
-    "|                | Predicted Positive | Predicted Negative |\n",
-    "|----------------|---------------------|---------------------|\n",
-    "| **Actual Positive** | True Positive (TP)     | False Negative (FN)     |\n",
-    "| **Actual Negative** | False Positive (FP)    | True Negative (TN)      |\n"
+    "## In-class Activity:\n",
+    "- Implement another classification algorithm using a different dataset.\n",
+    "- Use scikit-learn to choose dataset and new classification algorithm.\n",
+    "- Datasets: [Toy datasets](https://scikit-learn.org/1.5/datasets/toy_dataset.html)\n",
+    "- Algorithms: [Scikit-learn algorithms](https://scikit-learn.org/stable/supervised_learning.html#supervised-learning)"
    ]
   }
  ],
@@ -989,7 +933,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,

4-Unsupervised-1-Clustering.ipynb

@@ -683,6 +683,15 @@
     "\n",
     "For more information on clustering, refer to the [Scikit-learn documentation](https://scikit-learn.org/stable/modules/clustering.html)."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## In-class Activity: Implementing another Clustering with the dataset.\n",
+    "- Choose an unsupervised learning technique (e.g., hierarchical clustering) and apply it to the dataset. \n",
+    "- See scikit-learn's documentation for more information on [clustering algorithms](https://scikit-learn.org/stable/modules/clustering.html)."
+   ]
   }
  ],
  "metadata": {
@@ -701,7 +710,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,