{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "caf6ea7d",
   "metadata": {},
   "source": [
    "# Forensic Glass Type Classification : Comparing Three Models\n",
    "\n",
    "**Author:** Fils Aboubakar Sidik MBOHOU  \n",
    "**School:** ESAIP Engineering School, France  \n",
    "**Program:** MSc Cybersecurity & Data Science  \n",
    "**Program period:** 2024–2026  \n",
    "**Project type:** Individual academic project\n",
    "\n",
    "This notebook uses the chemical composition of glass samples to predict their glass type. It compares Logistic Regression, a dense Neural Network and Random Forest with stratified 5-fold cross-validation on a small imbalanced dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7fd9e2a",
   "metadata": {},
   "source": [
    "## 1. Environment and reproducibility\n",
    "\n",
    "The canonical notebook uses **Keras with the PyTorch backend** for the neural-network comparison. A fixed random seed is used throughout.\n",
    "\n",
    "Required packages: `numpy`, `pandas`, `matplotlib`, `scipy`, `scikit-learn`, `imbalanced-learn`, `keras` and `torch`.\n",
    "\n",
    "> The warning from Joblib about physical CPU cores on some Windows installations is harmless. This public version keeps parallel tree training disabled (`n_jobs=1`) for portability and reproducibility.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1bf3e093",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-18T18:31:17.494249Z",
     "iopub.status.busy": "2026-10-18T18:31:17.494080Z",
     "iopub.status.idle": "2026-10-18T18:31:21.994742Z",
     "shell.execute_reply": "2026-10-18T18:31:21.993935Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keras backend: torch\n",
      "Random seed: 42\n"
     ]
    }
   ],
   "source": [
    "# Select the Keras backend before importing Keras.\n",
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "os.environ.setdefault(\"KERAS_BACKEND\", \"torch\")\n",
    "os.environ.setdefault(\"MPLCONFIGDIR\", str((Path.cwd() / \".mplconfig\").resolve()))\n",
    "\n",
    "import json\n",
    "import random\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import keras\n",
    "\n",
    "from scipy.stats import zscore\n",
    "from sklearn.model_selection import StratifiedKFold, train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score, balanced_accuracy_score, f1_score, confusion_matrix, classification_report,\n",
    ")\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.pipeline import Pipeline as ImbPipeline\n",
    "\n",
    "SEED = 42\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "keras.utils.set_random_seed(SEED)\n",
    "\n",
    "ASSET_DIR = Path(\"glass_final_assets\")\n",
    "ASSET_DIR.mkdir(exist_ok=True)\n",
    "\n",
    "print(\"Keras backend:\", keras.backend.backend())\n",
    "print(\"Random seed:\", SEED)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9e3a3e1",
   "metadata": {},
   "source": [
    "## 2. Load and check the dataset\n",
    "\n",
    "The dataset contains:\n",
    "\n",
    "- **9 numeric predictors**: refractive index and elemental concentrations;\n",
    "- **1 target column**: `Type`.\n",
    "\n",
    "The available classes are **1, 2, 3, 5, 6 and 7**. Type 4 does not appear in the dataset, so this is a **six-class classification problem**.\n",
    "\n",
    "I also check missing values and exact duplicate rows before starting the analysis.\n",
    "\n",
    "\n",
    "Dataset origin: UCI Glass Identification dataset (214 samples, 9 chemical predictors): https://archive.ics.uci.edu/dataset/42/glass+identification. Place `glass.csv` next to this notebook to rerun the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aaaa7d1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-18T18:31:21.996613Z",
     "iopub.status.busy": "2026-10-18T18:31:21.996317Z",
     "iopub.status.idle": "2026-10-18T18:31:22.022234Z",
     "shell.execute_reply": "2026-10-18T18:31:22.021621Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw shape: (214, 10)\n",
      "Missing values: 0\n",
      "Exact duplicate rows: 1\n",
      "\n",
      "Class distribution:\n",
      "Type\n",
      "1    70\n",
      "2    76\n",
      "3    17\n",
      "5    13\n",
      "6     9\n",
      "7    29\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Canonical evaluation shape: (213, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.52101</td>\n",
       "      <td>13.64</td>\n",
       "      <td>4.49</td>\n",
       "      <td>1.10</td>\n",
       "      <td>71.78</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.51761</td>\n",
       "      <td>13.89</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.36</td>\n",
       "      <td>72.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51618</td>\n",
       "      <td>13.53</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1.54</td>\n",
       "      <td>72.99</td>\n",
       "      <td>0.39</td>\n",
       "      <td>7.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.51766</td>\n",
       "      <td>13.21</td>\n",
       "      <td>3.69</td>\n",
       "      <td>1.29</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.51742</td>\n",
       "      <td>13.27</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.24</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.55</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RI     Na    Mg    Al     Si     K    Ca   Ba   Fe  Type\n",
       "0  1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.0  0.0     1\n",
       "1  1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.0  0.0     1\n",
       "2  1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.0  0.0     1\n",
       "3  1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.0  0.0     1\n",
       "4  1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.0  0.0     1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_PATH = Path(\"glass.csv\")\n",
    "\n",
    "if not DATA_PATH.exists():\n",
    "    raise FileNotFoundError(\n",
    "        \"Place glass.csv in the same folder as this notebook before running it.\"\n",
    "    )\n",
    "\n",
    "df_raw = pd.read_csv(DATA_PATH)\n",
    "\n",
    "print(\"Raw shape:\", df_raw.shape)\n",
    "print(\"Missing values:\", int(df_raw.isna().sum().sum()))\n",
    "print(\"Exact duplicate rows:\", int(df_raw.duplicated().sum()))\n",
    "print()\n",
    "print(\"Class distribution:\")\n",
    "print(df_raw[\"Type\"].value_counts().sort_index())\n",
    "\n",
    "# Remove exact duplicate rows before evaluation.\n",
    "df = df_raw.drop_duplicates().reset_index(drop=True)\n",
    "\n",
    "print()\n",
    "print(\"Canonical evaluation shape:\", df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d29d72d9",
   "metadata": {},
   "source": [
    "## 3. Understand the class imbalance\n",
    "\n",
    "The classes are not equally represented. Types 1 and 2 are common, while Types 5 and 6 contain far fewer samples.\n",
    "\n",
    "This matters because a model could obtain a reasonable overall accuracy while performing poorly on rare classes.\n",
    "\n",
    "For that reason, the final comparison includes:\n",
    "\n",
    "- **accuracy** for overall performance;\n",
    "- **balanced accuracy** to give each class similar importance;\n",
    "- **Macro F1** as the main comparison metric;\n",
    "- **Weighted F1** to reflect the full class distribution.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "56cdfe3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-18T18:31:22.024225Z",
     "iopub.status.busy": "2026-10-18T18:31:22.024081Z",
     "iopub.status.idle": "2026-10-18T18:31:22.259974Z",
     "shell.execute_reply": "2026-10-18T18:31:22.259293Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Type\n",
       "1    69\n",
       "2    76\n",
       "3    17\n",
       "5    13\n",
       "6     9\n",
       "7    29\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_counts = df[\"Type\"].value_counts().sort_index()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.bar([str(value) for value in class_counts.index], class_counts.values)\n",
    "ax.set_title(\"Glass type distribution after de-duplication\")\n",
    "ax.set_xlabel(\"Glass type\")\n",
    "ax.set_ylabel(\"Samples\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(ASSET_DIR / \"01_class_distribution.png\", dpi=180)\n",
    "plt.show()\n",
    "\n",
    "class_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9365d06f",
   "metadata": {},
   "source": [
    "## 4. Examine unusual chemical values\n",
    "\n",
    "I use absolute z-scores to identify observations with unusually high or low feature values.\n",
    "\n",
    "These observations are not removed automatically. With only a little more than 200 samples, an extreme chemical composition may represent a genuine glass type rather than bad data.\n",
    "\n",
    "The goal of this step is therefore to understand the data, not to make the dataset look more regular.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bd3de3df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-18T18:31:22.261680Z",
     "iopub.status.busy": "2026-10-18T18:31:22.261461Z",
     "iopub.status.idle": "2026-10-18T18:31:22.524157Z",
     "shell.execute_reply": "2026-10-18T18:31:22.523636Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows by number of outlier features:\n",
      "0    193\n",
      "1     12\n",
      "2      5\n",
      "3      2\n",
      "5      1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Outlier counts by feature:\n",
      "Ca    7\n",
      "Ba    6\n",
      "Si    6\n",
      "RI    3\n",
      "Al    3\n",
      "K     3\n",
      "Fe    3\n",
      "Na    2\n",
      "Mg    0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_columns = [column for column in df.columns if column != \"Type\"]\n",
    "\n",
    "# Compute absolute z-scores only for predictor variables.\n",
    "absolute_z_scores = np.abs(zscore(df[feature_columns], nan_policy=\"omit\"))\n",
    "\n",
    "outliers_per_row = (absolute_z_scores > 3).sum(axis=1)\n",
    "outliers_per_feature = (absolute_z_scores > 3).sum(axis=0)\n",
    "\n",
    "print(\"Rows by number of outlier features:\")\n",
    "print(pd.Series(outliers_per_row).value_counts().sort_index())\n",
    "\n",
    "print()\n",
    "print(\"Outlier counts by feature:\")\n",
    "print(\n",
    "    pd.Series(outliers_per_feature, index=feature_columns)\n",
    "    .sort_values(ascending=False)\n",
    ")\n",
    "\n",
    "# Standardize only for this visualization so variables with different scales can be compared.\n",
    "# This EDA scaling is NOT reused by the models.\n",
    "scaled_for_plot = StandardScaler().fit_transform(df[feature_columns])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11, 5))\n",
    "ax.boxplot(\n",
    "    [scaled_for_plot[:, index] for index in range(len(feature_columns))],\n",
    "    tick_labels=feature_columns,\n",
    ")\n",
    "ax.axhline(0, linewidth=0.8)\n",
    "ax.set_title(\"Standardized predictor distributions and outliers\")\n",
    "ax.set_ylabel(\"Standardized value\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(ASSET_DIR / \"02_standardized_boxplots.png\", dpi=180)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e42dd308",
   "metadata": {},
   "source": [
    "## 5. Examine relationships between the chemical predictors\n",
    "\n",
    "The correlation heatmap is limited to the input variables.\n",
    "\n",
    "The target `Type` is a category, even though it is stored with numbers such as 1, 2, 3, 5, 6 and 7. Those numbers are labels, not a continuous scale, so a Pearson correlation between `Type` and a chemical variable would not be a reliable measure of feature importance.\n",
    "\n",
    "This view is therefore used only to understand relationships among the predictors themselves.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73ee9589",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-18T18:31:22.526001Z",
     "iopub.status.busy": "2026-10-18T18:31:22.525881Z",
     "iopub.status.idle": "2026-10-18T18:31:23.347934Z",
     "shell.execute_reply": "2026-10-18T18:31:23.347303Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = df[feature_columns].corr().values\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 7))\n",
    "image = ax.imshow(correlation_matrix, aspect=\"auto\")\n",
    "fig.colorbar(image, ax=ax, label=\"Pearson correlation\")\n",
    "\n",
    "ax.set_xticks(range(len(feature_columns)))\n",
    "ax.set_xticklabels(feature_columns, rotation=45, ha=\"right\")\n",
    "ax.set_yticks(range(len(feature_columns)))\n",
    "ax.set_yticklabels(feature_columns)\n",
    "\n",
    "# Print the correlation values inside each cell.\n",
    "for row in range(len(feature_columns)):\n",
    "    for column in range(len(feature_columns)):\n",
    "        ax.text(\n",
    "            column,\n",
    "            row,\n",
    "            f\"{correlation_matrix[row, column]:.2f}\",\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            fontsize=7,\n",
    "        )\n",
    "\n",
    "ax.set_title(\"Predictor correlation matrix : target excluded\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(ASSET_DIR / \"03_predictor_correlation.png\", dpi=180)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87deda47",
   "metadata": {},
   "source": [
    "## 6. Compare selected chemical variables across glass types\n",
    "\n",
    "The plots below show how selected features vary between classes.\n",
    "\n",
    "Magnesium (`Mg`) and Barium (`Ba`) are useful examples because their distributions differ visibly across several glass types.\n",
    "\n",
    "These plots are descriptive. They help explain the dataset, but they do not prove that a variable is causally important.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0600685e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-18T18:31:23.349409Z",
     "iopub.status.busy": "2026-10-18T18:31:23.349279Z",
     "iopub.status.idle": "2026-10-18T18:31:23.663183Z",
     "shell.execute_reply": "2026-10-18T18:31:23.662561Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes = np.array(sorted(df[\"Type\"].unique()))\n",
    "\n",
    "for feature in [\"Mg\", \"Ba\"]:\n",
    "    values_by_class = [\n",
    "        df.loc[df[\"Type\"] == glass_type, feature].values\n",
    "        for glass_type in classes\n",
    "    ]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(8, 4.5))\n",
    "    ax.violinplot(values_by_class, showmedians=True)\n",
    "    ax.set_xticks(range(1, len(classes) + 1))\n",
    "    ax.set_xticklabels([str(glass_type) for glass_type in classes])\n",
    "    ax.set_xlabel(\"Glass type\")\n",
    "    ax.set_ylabel(feature)\n",
    "    ax.set_title(f\"{feature} distribution by glass type\")\n",
    "    fig.tight_layout()\n",
    "    fig.savefig(ASSET_DIR / f\"04_{feature}_by_type.png\", dpi=180)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10f5d383",
   "metadata": {},
   "source": [
    "## 7. Evaluation strategy: stratified 5-fold cross-validation\n",
    "\n",
    "The dataset is too small for a single train/test split to give a stable picture of performance.\n",
    "\n",
    "I therefore use **stratified 5-fold cross-validation**:\n",
    "\n",
    "1. the unique observations are divided into five folds while keeping the class proportions as balanced as possible;\n",
    "2. four folds are used for training;\n",
    "3. the remaining fold is used for evaluation;\n",
    "4. the process is repeated until every observation has been evaluated once.\n",
    "\n",
    "Inside each fold:\n",
    "\n",
    "- scaling is learned from training data only;\n",
    "- SMOTE is applied to training data only;\n",
    "- the evaluation fold remains untouched.\n",
    "\n",
    "This makes the comparison more reliable and prevents synthetic or test information from leaking into model training.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abf13c1b",
   "metadata": {},
   "source": [
    "## 8. Models and evaluation metrics\n",
    "\n",
    "I compare three models with different levels of complexity.\n",
    "\n",
    "### Logistic Regression\n",
    "\n",
    "A simple linear classifier. It provides a useful baseline and shows how far a straightforward decision boundary can go.\n",
    "\n",
    "### Neural Network\n",
    "\n",
    "A dense feed-forward network with two hidden layers and dropout. It represents the deep-learning approach explored in the academic project.\n",
    "\n",
    "### Random Forest\n",
    "\n",
    "An ensemble of decision trees. It can capture nonlinear relationships and interactions without requiring feature scaling.\n",
    "\n",
    "### Main metric: Macro F1\n",
    "\n",
    "Macro F1 gives the same importance to every class, including the rare ones. That makes it more informative than accuracy alone for this dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c7a24472",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-18T18:31:23.664565Z",
     "iopub.status.busy": "2026-10-18T18:31:23.664453Z",
     "iopub.status.idle": "2026-10-18T18:31:23.673715Z",
     "shell.execute_reply": "2026-10-18T18:31:23.670843Z"
    }
   },
   "outputs": [],
   "source": [
    "X = df[feature_columns]\n",
    "y = df[\"Type\"]\n",
    "\n",
    "\n",
    "def calculate_metrics(y_true, y_pred):\n",
    "    \"\"\"Return the metrics used to compare all models.\"\"\"\n",
    "    return {\n",
    "        \"accuracy\": float(accuracy_score(y_true, y_pred)),\n",
    "        \"balanced_accuracy\": float(balanced_accuracy_score(y_true, y_pred)),\n",
    "        \"macro_f1\": float(f1_score(y_true, y_pred, average=\"macro\")),\n",
    "        \"weighted_f1\": float(f1_score(y_true, y_pred, average=\"weighted\")),\n",
    "    }\n",
    "\n",
    "\n",
    "def fit_predict_neural_network(X_train_outer, y_train_outer, X_test_outer, seed):\n",
    "    \"\"\"\n",
    "    Train the neural network inside one outer cross-validation fold.\n",
    "\n",
    "    Important: the outer test fold is never used for scaling, SMOTE,\n",
    "    early stopping or model selection.\n",
    "    \"\"\"\n",
    "\n",
    "    # 1) Reserve a small real validation subset from the outer training data.\n",
    "    X_subtrain, X_validation, y_subtrain, y_validation = train_test_split(\n",
    "        X_train_outer,\n",
    "        y_train_outer,\n",
    "        test_size=0.15,\n",
    "        random_state=seed,\n",
    "        stratify=y_train_outer,\n",
    "    )\n",
    "\n",
    "    # 2) Fit the scaler only on sub-training data.\n",
    "    scaler = StandardScaler()\n",
    "    X_subtrain_scaled = scaler.fit_transform(X_subtrain)\n",
    "    X_validation_scaled = scaler.transform(X_validation)\n",
    "    X_test_scaled = scaler.transform(X_test_outer)\n",
    "\n",
    "    # 3) Apply SMOTE only to the scaled sub-training data.\n",
    "    #    k_neighbors is adapted to the smallest class in this fold.\n",
    "    smallest_class_count = int(y_subtrain.value_counts().min())\n",
    "    k_neighbors = max(1, min(5, smallest_class_count - 1))\n",
    "\n",
    "    smote = SMOTE(random_state=seed, k_neighbors=k_neighbors)\n",
    "    X_balanced, y_balanced = smote.fit_resample(\n",
    "        X_subtrain_scaled,\n",
    "        y_subtrain,\n",
    "    )\n",
    "\n",
    "    # 4) Neural networks need class IDs from 0 to number_of_classes-1.\n",
    "    label_encoder = LabelEncoder().fit(y_train_outer)\n",
    "    y_balanced_encoded = label_encoder.transform(y_balanced)\n",
    "    y_validation_encoded = label_encoder.transform(y_validation)\n",
    "\n",
    "    # 5) Reset random state before building this fold's neural network.\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    keras.utils.set_random_seed(seed)\n",
    "    keras.backend.clear_session()\n",
    "\n",
    "    # Original conceptual architecture: 128 -> dropout -> 64 -> dropout -> softmax.\n",
    "    neural_network = keras.Sequential(\n",
    "        [\n",
    "            keras.layers.Input(shape=(X_balanced.shape[1],)),\n",
    "            keras.layers.Dense(128, activation=\"relu\"),\n",
    "            keras.layers.Dropout(0.3),\n",
    "            keras.layers.Dense(64, activation=\"relu\"),\n",
    "            keras.layers.Dropout(0.2),\n",
    "            # Six outputs because only six glass types exist in this dataset.\n",
    "            keras.layers.Dense(len(label_encoder.classes_), activation=\"softmax\"),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    neural_network.compile(\n",
    "        optimizer=keras.optimizers.Adam(0.001),\n",
    "        loss=\"sparse_categorical_crossentropy\",\n",
    "        metrics=[\"accuracy\"],\n",
    "    )\n",
    "\n",
    "    early_stopping = keras.callbacks.EarlyStopping(\n",
    "        monitor=\"val_loss\",\n",
    "        patience=10,\n",
    "        restore_best_weights=True,\n",
    "        min_delta=1e-4,\n",
    "    )\n",
    "\n",
    "    history = neural_network.fit(\n",
    "        X_balanced.astype(\"float32\"),\n",
    "        y_balanced_encoded,\n",
    "        validation_data=(\n",
    "            X_validation_scaled.astype(\"float32\"),\n",
    "            y_validation_encoded,\n",
    "        ),\n",
    "        epochs=120,\n",
    "        batch_size=16,\n",
    "        verbose=0,\n",
    "        callbacks=[early_stopping],\n",
    "    )\n",
    "\n",
    "    # 6) Predict the untouched outer test fold and map IDs back to original types.\n",
    "    probabilities = neural_network.predict(\n",
    "        X_test_scaled.astype(\"float32\"),\n",
    "        verbose=0,\n",
    "    )\n",
    "    encoded_predictions = np.argmax(probabilities, axis=1)\n",
    "    predictions = label_encoder.inverse_transform(encoded_predictions)\n",
    "\n",
    "    return predictions, len(history.history[\"loss\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54c15e01",
   "metadata": {},
   "source": [
    "## 9. Train and compare the models\n",
    "\n",
    "Each model is evaluated through the same outer 5-fold split.\n",
    "\n",
    "For Logistic Regression, scaling and SMOTE are performed inside the training pipeline.\n",
    "\n",
    "For Random Forest, SMOTE is applied inside the training pipeline, but scaling is unnecessary because tree models are not sensitive to feature magnitude in the same way as linear models or neural networks.\n",
    "\n",
    "For the Neural Network, preprocessing and resampling are also restricted to the training part of each fold.\n",
    "\n",
    "This gives the three approaches a comparable evaluation framework.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5108f9af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-18T18:31:23.675321Z",
     "iopub.status.busy": "2026-10-18T18:31:23.675183Z",
     "iopub.status.idle": "2026-10-18T18:31:42.057031Z",
     "shell.execute_reply": "2026-10-18T18:31:42.056217Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy_mean</th>\n",
       "      <th>accuracy_std</th>\n",
       "      <th>balanced_accuracy_mean</th>\n",
       "      <th>balanced_accuracy_std</th>\n",
       "      <th>macro_f1_mean</th>\n",
       "      <th>macro_f1_std</th>\n",
       "      <th>weighted_f1_mean</th>\n",
       "      <th>weighted_f1_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.756</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.625</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.668</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural Network</th>\n",
       "      <td>0.629</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.624</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     accuracy_mean  ...  weighted_f1_std\n",
       "Random Forest                0.756  ...            0.047\n",
       "Logistic Regression          0.625  ...            0.060\n",
       "Neural Network               0.629  ...            0.067\n",
       "\n",
       "[3 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the outer cross-validation splitter.\n",
    "cross_validation = StratifiedKFold(\n",
    "    n_splits=5,\n",
    "    shuffle=True,\n",
    "    random_state=SEED,\n",
    ")\n",
    "\n",
    "fold_records = []\n",
    "\n",
    "# Out-of-fold predictions: one final prediction for every real observation.\n",
    "out_of_fold_predictions = {\n",
    "    \"Neural Network\": np.full(len(df), -1, dtype=int),\n",
    "    \"Logistic Regression\": np.full(len(df), -1, dtype=int),\n",
    "    \"Random Forest\": np.full(len(df), -1, dtype=int),\n",
    "}\n",
    "\n",
    "random_forest_importances = []\n",
    "\n",
    "for fold_number, (train_indices, test_indices) in enumerate(\n",
    "    cross_validation.split(X, y)\n",
    "):\n",
    "    fold_seed = SEED + fold_number\n",
    "\n",
    "    X_train = X.iloc[train_indices]\n",
    "    y_train = y.iloc[train_indices]\n",
    "    X_test = X.iloc[test_indices]\n",
    "    y_test = y.iloc[test_indices]\n",
    "\n",
    "    # Adapt SMOTE to the smallest class available in this fold.\n",
    "    smallest_class_count = int(y_train.value_counts().min())\n",
    "    k_neighbors = max(1, min(5, smallest_class_count - 1))\n",
    "\n",
    "    # ----- Logistic Regression -----\n",
    "    logistic_regression = ImbPipeline(\n",
    "        [\n",
    "            (\"scaler\", StandardScaler()),\n",
    "            (\"smote\", SMOTE(random_state=fold_seed, k_neighbors=k_neighbors)),\n",
    "            (\n",
    "                \"classifier\",\n",
    "                LogisticRegression(max_iter=3000, random_state=fold_seed),\n",
    "            ),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    # ----- Random Forest -----\n",
    "    random_forest = ImbPipeline(\n",
    "        [\n",
    "            (\"smote\", SMOTE(random_state=fold_seed, k_neighbors=k_neighbors)),\n",
    "            (\n",
    "                \"classifier\",\n",
    "                RandomForestClassifier(\n",
    "                    n_estimators=300,\n",
    "                    random_state=fold_seed,\n",
    "                    n_jobs=1,\n",
    "                ),\n",
    "            ),\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    # Fit and evaluate the two classical baselines.\n",
    "    for model_name, model in [\n",
    "        (\"Logistic Regression\", logistic_regression),\n",
    "        (\"Random Forest\", random_forest),\n",
    "    ]:\n",
    "        model.fit(X_train, y_train)\n",
    "        predictions = model.predict(X_test)\n",
    "\n",
    "        fold_records.append(\n",
    "            {\n",
    "                \"model\": model_name,\n",
    "                \"fold\": fold_number,\n",
    "                **calculate_metrics(y_test, predictions),\n",
    "            }\n",
    "        )\n",
    "\n",
    "        out_of_fold_predictions[model_name][test_indices] = predictions\n",
    "\n",
    "        if model_name == \"Random Forest\":\n",
    "            random_forest_importances.append(\n",
    "                model.named_steps[\"classifier\"].feature_importances_\n",
    "            )\n",
    "\n",
    "    # Fit and evaluate the neural network on the same outer fold.\n",
    "    nn_predictions, epochs_run = fit_predict_neural_network(\n",
    "        X_train,\n",
    "        y_train,\n",
    "        X_test,\n",
    "        fold_seed,\n",
    "    )\n",
    "\n",
    "    fold_records.append(\n",
    "        {\n",
    "            \"model\": \"Neural Network\",\n",
    "            \"fold\": fold_number,\n",
    "            \"epochs\": epochs_run,\n",
    "            **calculate_metrics(y_test, nn_predictions),\n",
    "        }\n",
    "    )\n",
    "    out_of_fold_predictions[\"Neural Network\"][test_indices] = nn_predictions\n",
    "\n",
    "# Convert fold-level results into a table.\n",
    "fold_results = pd.DataFrame(fold_records)\n",
    "\n",
    "# Mean and standard deviation across the five folds.\n",
    "summary_rows = {}\n",
    "for model_name, group in fold_results.groupby(\"model\"):\n",
    "    summary_rows[model_name] = {\n",
    "        \"accuracy_mean\": group[\"accuracy\"].mean(),\n",
    "        \"accuracy_std\": group[\"accuracy\"].std(ddof=1),\n",
    "        \"balanced_accuracy_mean\": group[\"balanced_accuracy\"].mean(),\n",
    "        \"balanced_accuracy_std\": group[\"balanced_accuracy\"].std(ddof=1),\n",
    "        \"macro_f1_mean\": group[\"macro_f1\"].mean(),\n",
    "        \"macro_f1_std\": group[\"macro_f1\"].std(ddof=1),\n",
    "        \"weighted_f1_mean\": group[\"weighted_f1\"].mean(),\n",
    "        \"weighted_f1_std\": group[\"weighted_f1\"].std(ddof=1),\n",
    "    }\n",
    "\n",
    "summary = pd.DataFrame(summary_rows).T\n",
    "summary.sort_values(\"macro_f1_mean\", ascending=False).round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eafb434d",
   "metadata": {},
   "source": [
    "## 10. Compare the results\n",
    "\n",
    "The main question is not simply which model has the highest accuracy. It is which model performs most consistently across all six classes.\n",
    "\n",
    "Random Forest achieves the strongest **Macro F1**, as well as the best average accuracy and balanced accuracy.\n",
    "\n",
    "That makes it the most suitable of the three tested approaches for this dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "afb904a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-18T18:31:42.058609Z",
     "iopub.status.busy": "2026-10-18T18:31:42.058478Z",
     "iopub.status.idle": "2026-10-18T18:31:42.195076Z",
     "shell.execute_reply": "2026-10-18T18:31:42.194475Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy_mean</th>\n",
       "      <th>accuracy_std</th>\n",
       "      <th>balanced_accuracy_mean</th>\n",
       "      <th>balanced_accuracy_std</th>\n",
       "      <th>macro_f1_mean</th>\n",
       "      <th>macro_f1_std</th>\n",
       "      <th>weighted_f1_mean</th>\n",
       "      <th>weighted_f1_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.756</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.766</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.625</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.668</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.632</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural Network</th>\n",
       "      <td>0.629</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.624</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.640</td>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     accuracy_mean  ...  weighted_f1_std\n",
       "Random Forest                0.756  ...            0.047\n",
       "Logistic Regression          0.625  ...            0.060\n",
       "Neural Network               0.629  ...            0.067\n",
       "\n",
       "[3 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_order = [\"Random Forest\", \"Logistic Regression\", \"Neural Network\"]\n",
    "macro_f1_means = [summary.loc[name, \"macro_f1_mean\"] for name in model_order]\n",
    "macro_f1_stds = [summary.loc[name, \"macro_f1_std\"] for name in model_order]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4.5))\n",
    "ax.bar(model_order, macro_f1_means, yerr=macro_f1_stds, capsize=4)\n",
    "ax.set_ylim(0, 1)\n",
    "ax.set_ylabel(\"Macro F1\")\n",
    "ax.set_title(\"5-fold model comparison : Macro F1\")\n",
    "ax.tick_params(axis=\"x\", rotation=15)\n",
    "fig.tight_layout()\n",
    "fig.savefig(ASSET_DIR / \"05_model_comparison_macro_f1.png\", dpi=180)\n",
    "plt.show()\n",
    "\n",
    "# Full numerical comparison.\n",
    "display(summary.sort_values(\"macro_f1_mean\", ascending=False).round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "484c7bfe",
   "metadata": {},
   "source": [
    "## 11. Look at the out-of-fold confusion matrices\n",
    "\n",
    "Every sample receives a prediction only when it belongs to an evaluation fold.\n",
    "\n",
    "Combining those predictions produces an out-of-fold confusion matrix for the full dataset.\n",
    "\n",
    "This makes it possible to see which glass types are confused with one another instead of relying only on a single summary score.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6fff158c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-18T18:31:42.196427Z",
     "iopub.status.busy": "2026-10-18T18:31:42.196293Z",
     "iopub.status.idle": "2026-10-18T18:31:42.874297Z",
     "shell.execute_reply": "2026-10-18T18:31:42.873791Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest out-of-fold classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1      0.765     0.754     0.759        69\n",
      "           2      0.806     0.763     0.784        76\n",
      "           3      0.429     0.529     0.474        17\n",
      "           5      0.600     0.692     0.643        13\n",
      "           6      0.818     1.000     0.900         9\n",
      "           7      0.923     0.828     0.873        29\n",
      "\n",
      "    accuracy                          0.756       213\n",
      "   macro avg      0.723     0.761     0.739       213\n",
      "weighted avg      0.766     0.756     0.759       213\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for model_name in [\"Neural Network\", \"Logistic Regression\", \"Random Forest\"]:\n",
    "    predictions = out_of_fold_predictions[model_name]\n",
    "    matrix = confusion_matrix(y, predictions, labels=classes)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(6, 5))\n",
    "    image = ax.imshow(matrix, aspect=\"auto\")\n",
    "    fig.colorbar(image, ax=ax, label=\"Count\")\n",
    "\n",
    "    ax.set_xticks(range(len(classes)))\n",
    "    ax.set_xticklabels(classes)\n",
    "    ax.set_yticks(range(len(classes)))\n",
    "    ax.set_yticklabels(classes)\n",
    "\n",
    "    for row in range(len(classes)):\n",
    "        for column in range(len(classes)):\n",
    "            ax.text(\n",
    "                column,\n",
    "                row,\n",
    "                str(matrix[row, column]),\n",
    "                ha=\"center\",\n",
    "                va=\"center\",\n",
    "            )\n",
    "\n",
    "    ax.set_xlabel(\"Predicted type\")\n",
    "    ax.set_ylabel(\"Actual type\")\n",
    "    ax.set_title(f\"{model_name} : out-of-fold confusion matrix\")\n",
    "    fig.tight_layout()\n",
    "\n",
    "    filename = model_name.lower().replace(\" \", \"_\") + \"_confusion_matrix.png\"\n",
    "    fig.savefig(ASSET_DIR / filename, dpi=180)\n",
    "    plt.show()\n",
    "\n",
    "print(\"Random Forest out-of-fold classification report:\")\n",
    "print(\n",
    "    classification_report(\n",
    "        y,\n",
    "        out_of_fold_predictions[\"Random Forest\"],\n",
    "        labels=classes,\n",
    "        digits=3,\n",
    "        zero_division=0,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bd149ee",
   "metadata": {},
   "source": [
    "## 12. Inspect Random Forest feature importance\n",
    "\n",
    "Because Random Forest is the strongest model in the comparison, I inspect its feature importance to understand which chemical variables it used most often when splitting the data.\n",
    "\n",
    "This is a **model-specific diagnostic**, not a causal explanation. A high importance means the feature was useful to this fitted forest; it does not prove that the variable is scientifically responsible for the glass type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6306c98c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-18T18:31:42.875957Z",
     "iopub.status.busy": "2026-10-18T18:31:42.875793Z",
     "iopub.status.idle": "2026-10-18T18:31:43.056020Z",
     "shell.execute_reply": "2026-10-18T18:31:43.055468Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>mean_importance</th>\n",
       "      <th>std_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mg</td>\n",
       "      <td>0.1849</td>\n",
       "      <td>0.0070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>K</td>\n",
       "      <td>0.1441</td>\n",
       "      <td>0.0049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Na</td>\n",
       "      <td>0.1271</td>\n",
       "      <td>0.0128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ca</td>\n",
       "      <td>0.1258</td>\n",
       "      <td>0.0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Al</td>\n",
       "      <td>0.1150</td>\n",
       "      <td>0.0091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RI</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.0038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ba</td>\n",
       "      <td>0.1016</td>\n",
       "      <td>0.0138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Si</td>\n",
       "      <td>0.0756</td>\n",
       "      <td>0.0117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fe</td>\n",
       "      <td>0.0239</td>\n",
       "      <td>0.0061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature  mean_importance  std_importance\n",
       "2      Mg           0.1849          0.0070\n",
       "5       K           0.1441          0.0049\n",
       "1      Na           0.1271          0.0128\n",
       "6      Ca           0.1258          0.0131\n",
       "3      Al           0.1150          0.0091\n",
       "0      RI           0.1020          0.0038\n",
       "7      Ba           0.1016          0.0138\n",
       "4      Si           0.0756          0.0117\n",
       "8      Fe           0.0239          0.0061"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# One feature-importance vector was recorded from each fold.\n",
    "importance_by_fold = pd.DataFrame(\n",
    "    random_forest_importances,\n",
    "    columns=feature_columns,\n",
    ")\n",
    "\n",
    "feature_importance = pd.DataFrame(\n",
    "    {\n",
    "        \"feature\": feature_columns,\n",
    "        \"mean_importance\": importance_by_fold.mean().values,\n",
    "        \"std_importance\": importance_by_fold.std(ddof=1).values,\n",
    "    }\n",
    ").sort_values(\"mean_importance\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 5))\n",
    "ax.barh(\n",
    "    feature_importance[\"feature\"],\n",
    "    feature_importance[\"mean_importance\"],\n",
    "    xerr=feature_importance[\"std_importance\"],\n",
    "    capsize=3,\n",
    ")\n",
    "ax.set_xlabel(\"Mean impurity-based importance\")\n",
    "ax.set_title(\"Random Forest feature importance across folds\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(ASSET_DIR / \"09_rf_feature_importance.png\", dpi=180)\n",
    "plt.show()\n",
    "\n",
    "display(\n",
    "    feature_importance\n",
    "    .sort_values(\"mean_importance\", ascending=False)\n",
    "    .round(4)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efad8214",
   "metadata": {},
   "source": [
    "## Results and interpretation\n",
    "\n",
    "Average performance across the five folds:\n",
    "\n",
    "| Model | Accuracy | Balanced accuracy | Macro F1 | Weighted F1 |\n",
    "|---|---:|---:|---:|---:|\n",
    "| **Random Forest** | **75.6%** | **76.6%** | **0.735** | **0.759** |\n",
    "| Logistic Regression | 62.5% | 73.0% | 0.668 | 0.632 |\n",
    "| Neural Network | 62.9% | 65.4% | 0.624 | 0.640 |\n",
    "\n",
    "Random Forest is the strongest model in this experiment.\n",
    "\n",
    "Type 3 remains the hardest class, while Types 6 and 7 are recognized better. I still treat the Type 6 result cautiously because that class contains only nine real observations.\n",
    "\n",
    "What I take from the project is simple: the neural network was worth testing, but the data did not justify its extra complexity. On this small tabular dataset, Random Forest was a better fit.\n",
    "\n",
    "### Limits\n",
    "\n",
    "- only 213 unique observations;\n",
    "- strong class imbalance;\n",
    "- no external validation dataset;\n",
    "- synthetic SMOTE samples cannot replace real data;\n",
    "- some glass categories overlap chemically.\n",
    "\n",
    "### If I continued the project\n",
    "\n",
    "I would collect more real samples, especially for the rare classes, test boosting methods, use nested cross-validation for tuning and validate the final model on an independent dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fbcbf28",
   "metadata": {},
   "source": [
    "## Resources\n",
    "\n",
    "- Source dataset: `glass.csv`\n",
    "- Main technologies: Python, pandas, scikit-learn, imbalanced-learn, Keras\n",
    "- Models compared: Logistic Regression, Neural Network, Random Forest\n",
    "- Evaluation: stratified 5-fold cross-validation\n",
    "\n",
    "This public notebook documents the model comparison and evaluation used in the portfolio case study.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66528f66",
   "metadata": {},
   "source": [
    "## 14. Save the key results\n",
    "\n",
    "The final cell exports fold-level metrics, summary results, out-of-fold confusion matrices and Random Forest feature importance.\n",
    "\n",
    "Keeping these results in a machine-readable file makes it easier to reproduce the analysis and keep future documentation consistent with the executed notebook.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "49db5486",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-18T18:31:43.057657Z",
     "iopub.status.busy": "2026-10-18T18:31:43.057545Z",
     "iopub.status.idle": "2026-10-18T18:31:43.080716Z",
     "shell.execute_reply": "2026-10-18T18:31:43.079980Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"Logistic Regression\": {\n",
      "    \"accuracy_mean\": 0.6246954595791805,\n",
      "    \"balanced_accuracy_mean\": 0.7298137973137971,\n",
      "    \"macro_f1_mean\": 0.6677693728893128,\n",
      "    \"weighted_f1_mean\": 0.6318519344632202,\n",
      "    \"accuracy_std\": 0.052870448548357145,\n",
      "    \"balanced_accuracy_std\": 0.07025608127867516,\n",
      "    \"macro_f1_std\": 0.07510578410786706,\n",
      "    \"weighted_f1_std\": 0.059661936861473586\n",
      "  },\n",
      "  \"Neural Network\": {\n",
      "    \"accuracy_mean\": 0.6293466223698783,\n",
      "    \"balanced_accuracy_mean\": 0.6537225274725273,\n",
      "    \"macro_f1_mean\": 0.6244530834410439,\n",
      "    \"weighted_f1_mean\": 0.6397412005319472,\n",
      "    \"accuracy_std\": 0.07198824406685682,\n",
      "    \"balanced_accuracy_std\": 0.07564011491977915,\n",
      "    \"macro_f1_std\": 0.08485890818969809,\n",
      "    \"weighted_f1_std\": 0.06741814448711246\n",
      "  },\n",
      "  \"Random Forest\": {\n",
      "    \"accuracy_mean\": 0.7562569213732004,\n",
      "    \"balanced_accuracy_mean\": 0.7655021367521367,\n",
      "    \"macro_f1_mean\": 0.7352184506467043,\n",
      "    \"weighted_f1_mean\": 0.7593556651081932,\n",
      "    \"accuracy_std\": 0.04612506130870192,\n",
      "    \"balanced_accuracy_std\": 0.06374835263265571,\n",
      "    \"macro_f1_std\": 0.06825646870491225,\n",
      "    \"weighted_f1_std\": 0.046944003809159765\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# Metrics computed from the complete set of out-of-fold predictions.\n",
    "out_of_fold_metrics = {\n",
    "    model_name: calculate_metrics(y, predictions)\n",
    "    for model_name, predictions in out_of_fold_predictions.items()\n",
    "}\n",
    "\n",
    "out_of_fold_confusion_matrices = {\n",
    "    model_name: confusion_matrix(\n",
    "        y,\n",
    "        predictions,\n",
    "        labels=classes,\n",
    "    ).tolist()\n",
    "    for model_name, predictions in out_of_fold_predictions.items()\n",
    "}\n",
    "\n",
    "export = {\n",
    "    \"raw_rows\": int(len(df_raw)),\n",
    "    \"unique_rows\": int(len(df)),\n",
    "    \"exact_duplicates_removed\": int(len(df_raw) - len(df)),\n",
    "    \"classes\": [int(value) for value in classes],\n",
    "    \"class_counts\": {\n",
    "        str(class_id): int(count)\n",
    "        for class_id, count in y.value_counts().sort_index().items()\n",
    "    },\n",
    "    \"cv_folds\": 5,\n",
    "    \"fold_results\": fold_records,\n",
    "    \"summary\": {\n",
    "        model_name: {\n",
    "            **{\n",
    "                metric + \"_mean\": float(group[metric].mean())\n",
    "                for metric in [\n",
    "                    \"accuracy\",\n",
    "                    \"balanced_accuracy\",\n",
    "                    \"macro_f1\",\n",
    "                    \"weighted_f1\",\n",
    "                ]\n",
    "            },\n",
    "            **{\n",
    "                metric + \"_std\": float(group[metric].std(ddof=1))\n",
    "                for metric in [\n",
    "                    \"accuracy\",\n",
    "                    \"balanced_accuracy\",\n",
    "                    \"macro_f1\",\n",
    "                    \"weighted_f1\",\n",
    "                ]\n",
    "            },\n",
    "        }\n",
    "        for model_name, group in fold_results.groupby(\"model\")\n",
    "    },\n",
    "    \"out_of_fold_metrics\": out_of_fold_metrics,\n",
    "    \"out_of_fold_confusion_matrices\": out_of_fold_confusion_matrices,\n",
    "}\n",
    "\n",
    "with open(ASSET_DIR / \"glass_canonical_results.json\", \"w\") as file:\n",
    "    json.dump(export, file, indent=2)\n",
    "\n",
    "summary.to_csv(ASSET_DIR / \"model_comparison.csv\")\n",
    "feature_importance.sort_values(\n",
    "    \"mean_importance\",\n",
    "    ascending=False,\n",
    ").to_csv(ASSET_DIR / \"rf_feature_importance.csv\", index=False)\n",
    "\n",
    "print(json.dumps(export[\"summary\"], indent=2))"
   ]
  }
 ],
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   "program": "MSc Cybersecurity & Data Science",
   "program_period": "2024-2026",
   "author": "Fils Aboubakar Sidik MBOHOU",
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