A4-report

A4/A4_Classification.ipynb

@@ -1290,7 +1290,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,

A4/A4_Regression.ipynb

@@ -1414,7 +1414,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,

A4/report.ipynb

@@ -95,6 +95,16 @@
     "- pytest integrated into CI\n",
     "- Tests run before deployment\n",
     "\n",
+    "\n",
+    "The implemented tests validate the full ML pipeline, including:\n",
+    "- Regression model loading\n",
+    "- Regression prediction functionality\n",
+    "- Classification model loading\n",
+    "- Classification prediction functionality\n",
+    "- Model artifact structure validation\n",
+    "- Error handling for incorrect inputs and failures\n",
+    "\n",
+    "\n",
     "### Git LFS support\n",
     "- Models tracked using Git LFS\n",
     "- Ensures version-controlled model artifacts\n",
@@ -267,6 +277,56 @@
     "The current pipeline provides the foundation for these improvements.\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A4 – Classification Task\n",
+    "\n",
+    "Two datasets were merged into a single dataset containing 41 features (including movement angles and weak-link indicators). For each data point, the weakest link was identified by selecting the column with the maximum score.\n",
+    "\n",
+    "Initially, a 14-class classifier was used. An alternative approach was then explored by separating features into upper-body and lower-body regions, following lab guidance and feedback. Models were trained separately for body regions and then combined to evaluate performance improvements.\n",
+    "\n",
+    "- 5-fold cross-validation was applied  \n",
+    "- Weighted averages were used due to class imbalance  \n",
+    "\n",
+    "Body-region classification models tested:\n",
+    "- Logistic Regression  \n",
+    "- LDA  \n",
+    "- QDA  \n",
+    "- Naive Bayes  \n",
+    "- KNN (best performer with k = 7)\n",
+    "\n",
+    "For the 14-class weak-link classification:\n",
+    "- LDA performed best initially (F1 = 0.57)\n",
+    "\n",
+    "Following feedback, a two-step approach was tested:\n",
+    "1. Predict body region using KNN  \n",
+    "2. Apply LDA for upper/lower classification  \n",
+    "\n",
+    "This did not improve performance (F1 ≈ 0.54).  \n",
+    "Applying Random Forest improved results:\n",
+    "\n",
+    "- Baseline (LDA): F1 = 0.57  \n",
+    "- After feedback adjustments: F1 = 0.54  \n",
+    "- Random Forest: F1 = 0.61 (best performance)\n",
+    "\n",
+    "The A4_Classification notebook extends A3 with these improvements.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## A4 – Regression Task\n",
+    "\n",
+    "The regression setup remains consistent with A2, with Random Forest introduced to improve performance.\n",
+    "\n",
+    "- Baseline model R²: 0.54  \n",
+    "- Random Forest R²: 0.65  \n",
+    "\n",
+    "This represents a direct improvement over the earlier regression pipeline.\n",
+    "\n",
+    "The A4_Regression notebook is an enhanced version of A2_ModelBuilding.ipynb, while A4_Classification extends A3 based on feedback and model experimentation.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -276,11 +336,24 @@
   }
  ],
  "metadata": {
-  "language_info": {
-   "name": "python"
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "orig_nbformat": 4
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }