{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "md1a972a0e",
   "metadata": {},
   "source": [
    "# BitBullet:Lessons: Wholesale Customer Segmentation\n",
    "\n",
    "Every business serves a mix of customer types — but most treat them identically. A wholesale food distributor serves two fundamentally different customer groups: **Horeca** (hotels, restaurants, cafes) and **retailers** — yet within each group, spending patterns diverge dramatically. Some customers are fresh-produce-heavy; others lean heavily on packaged groceries or dairy.\n",
    "\n",
    "In this tutorial we apply BitBullet's clustering engine to the **UCI Wholesale Customers dataset** — a real-world record of annual spending across six product categories for 440 wholesale distributor customers. The goal: discover natural spending segments that cut across the known Channel/Region labels and reveal actionable customer archetypes.\n",
    "\n",
    "**Dataset**: `Wholesale_customers_data.csv` — 440 customers, 8 features.  \n",
    "**Source**: [UCI ML Repository — Wholesale Customers](https://archive.uci.edu/dataset/292/wholesale+customers)  \n",
    "**Task**: Discover natural spending clusters with zero pre-labelled ground truth.\n",
    "\n",
    "---\n",
    "\n",
    "### What You Will Build\n",
    "\n",
    "| Step | Component | What BitBullet Handles For You |\n",
    "|------|-----------|--------------------------------|\n",
    "| 1 | Feature profiling | `generate_feature_stats` — detect numerical vs. categorical automatically |\n",
    "| 2 | Zero-config clustering | `auto_cluster` — algorithm selection, K selection, fitting, evaluation |\n",
    "| 3 | Column type detection | `detect_column_types`, `select_algorithm` — mixed-data awareness |\n",
    "| 4 | Optimal K search | `find_optimal_k` — silhouette scoring across candidate K values |\n",
    "| 5 | Manual configuration | `KPrototypesClusterer` — full control over mixed-type clustering |\n",
    "| 6 | Evaluation metrics | `evaluate_clustering` — silhouette, Calinski-Harabász, Davies-Bouldin, quality score |\n",
    "| 7 | Cluster profiling | `profile_clusters` — suggested labels, key characteristics, feature importance |\n",
    "| 8 | Visualisation suite | Scatter, silhouette, FAMD decomposition, comprehensive dashboard |\n",
    "| 9 | Segment activation | Attach labels to DataFrame, compute segment means, business interpretation |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e614e9",
   "metadata": {},
   "source": [
    "> **Explore and compare clustering configurations without building the lifecycle yourself.**\n",
    "> [BitBullet Platform](https://bitbullet.co.uk/platform/clustering) centralises datasets, managed compute, storage, candidate configurations, and results in one guided environment. Use guided controls or the AI assistant to prepare a draft, inspect cluster quality and profile evidence, compare candidate structures, then export fitted clustering artefacts, preprocessing, metadata, and generated inference code. This lesson gives you direct SDK control over the same work."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md9642b073",
   "metadata": {},
   "source": [
    "## 1. Environment & Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd62df3317",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bitbullet  : vdev\n",
      "All imports successful.\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import warnings\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from sklearn.metrics import silhouette_samples\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# When running this notebook from bitbullet/lessons in a local clone, prefer the local SDK source.\n",
    "sdk_root = os.path.abspath(\"..\")\n",
    "if sdk_root not in sys.path:\n",
    "    sys.path.insert(0, sdk_root)\n",
    "\n",
    "import bitbullet\n",
    "print(f\"bitbullet  : v{getattr(bitbullet, '__version__', 'dev')}\")\n",
    "\n",
    "from bitbullet.cluster import ClusterConfig\n",
    "from bitbullet.cluster.core import auto_cluster, detect_column_types, select_algorithm\n",
    "from bitbullet.cluster.algorithms.partitional import KPrototypesClusterer, KMeansClusterer\n",
    "from bitbullet.cluster.evaluation import evaluate_clustering, profile_clusters, find_optimal_k\n",
    "from bitbullet.cluster.visualization import (\n",
    "    plot_cluster_scatter,\n",
    "    plot_silhouette,\n",
    "    plot_cluster_sizes,\n",
    "    create_dashboard,\n",
    "    auto_decompose,\n",
    "    plot_decomposition,\n",
    ")\n",
    "from bitbullet.transform.eda import generate_feature_stats\n",
    "\n",
    "print(\"All imports successful.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mdf957b46d",
   "metadata": {},
   "source": [
    "## 2. Load and Prepare the Dataset\n",
    "\n",
    "Each row is a wholesale customer. Columns record their annual spending in monetary units across six product categories. `Channel` (1 = Horeca, 2 = Retail) and `Region` (1 = Lisbon, 2 = Oporto, 3 = Other) are integer-coded categorical attributes.\n",
    "\n",
    "We convert `Channel` and `Region` to descriptive strings before clustering. This serves two purposes: (1) `detect_column_types` will correctly identify them as categorical, and (2) cluster profiles and suggested labels will be human-readable without needing a lookup table.\n",
    "\n",
    "**Dataset:** Wholesale Customers Dataset  \n",
    "**Source:** [UCI ML Repository — Wholesale Customers](https://archive.uci.edu/dataset/292/wholesale+customers)  \n",
    "**File:** `Wholesale_customers_data.csv`\n",
    "\n",
    "> **Before running this cell:** download `Wholesale_customers_data.csv` from the link above and\n",
    "> place it in the **same directory as this notebook**. If you store it elsewhere,\n",
    "> update `data_path` in the code cell below to match your chosen location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdb6f6eec9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset loaded — Shape: (440, 8)\n",
      "Columns: ['Channel', 'Region', 'Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']\n"
     ]
    },
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      ],
      "text/plain": [
       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0        2       3  12669  9656     7561     214              2674        1338\n",
       "1        2       3   7057  9810     9568    1762              3293        1776\n",
       "2        2       3   6353  8808     7684    2405              3516        7844\n",
       "3        1       3  13265  1196     4221    6404               507        1788\n",
       "4        2       3  22615  5410     7198    3915              1777        5185"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Update this path if you stored the file in a different location.\n",
    "data_path = \"Wholesale_customers_data.csv\"\n",
    "\n",
    "df_raw = pd.read_csv(data_path)\n",
    "print(f\"Dataset loaded — Shape: {df_raw.shape}\")\n",
    "print(f\"Columns: {list(df_raw.columns)}\")\n",
    "display(df_raw.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cdc1d03f45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel distribution:\n",
      "Channel\n",
      "Horeca    298\n",
      "Retail    142\n",
      "\n",
      "Region distribution:\n",
      "Region\n",
      "Other     316\n",
      "Lisbon     77\n",
      "Oporto     47\n",
      "\n"
     ]
    },
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       "      <th>Milk</th>\n",
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       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
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       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Channel Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0  Retail  Other  12669  9656     7561     214              2674        1338\n",
       "1  Retail  Other   7057  9810     9568    1762              3293        1776\n",
       "2  Retail  Other   6353  8808     7684    2405              3516        7844\n",
       "3  Horeca  Other  13265  1196     4221    6404               507        1788\n",
       "4  Retail  Other  22615  5410     7198    3915              1777        5185"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert integer-coded categorical columns to descriptive strings\n",
    "df = df_raw.copy()\n",
    "df['Channel'] = df['Channel'].map({1: 'Horeca', 2: 'Retail'})\n",
    "df['Region']  = df['Region'].map({1: 'Lisbon', 2: 'Oporto', 3: 'Other'})\n",
    "\n",
    "print(\"Channel distribution:\")\n",
    "print(df['Channel'].value_counts().to_string())\n",
    "print(\"\\nRegion distribution:\")\n",
    "print(df['Region'].value_counts().to_string())\n",
    "print()\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md18d2503f",
   "metadata": {},
   "source": [
    "## 3. Exploratory Feature Analysis\n",
    "\n",
    "Wholesale spending data is notoriously right-skewed: a handful of large accounts drive the mean far above the median. `generate_feature_stats` surfaces this immediately — and confirms we have a **mixed-type dataset** with both numerical spend features and categorical channel/region labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cda26775d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical   (6): ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']\n",
      "Categorical (2): ['Channel', 'Region']\n",
      "----------------------------------------------------------------------\n"
     ]
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       "      <th>Fresh</th>\n",
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       "      <td>3071.931818</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>5.907986</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>417</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>3.631851</td>\n",
       "      <td>19.009464</td>\n",
       "      <td>68.863636</td>\n",
       "      <td>3.0</td>\n",
       "      <td>256.75</td>\n",
       "      <td>816.5</td>\n",
       "      <td>3922.0</td>\n",
       "      <td>40827.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delicassen</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>403</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1524.870455</td>\n",
       "      <td>2820.105937</td>\n",
       "      <td>11.151586</td>\n",
       "      <td>170.694939</td>\n",
       "      <td>68.863636</td>\n",
       "      <td>3.0</td>\n",
       "      <td>408.25</td>\n",
       "      <td>965.5</td>\n",
       "      <td>1820.25</td>\n",
       "      <td>47943.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   dtype  missing_count  missing_percent  unique_values  \\\n",
       "Channel           object              0              0.0              2   \n",
       "Region            object              0              0.0              3   \n",
       "Fresh              int64              0              0.0            433   \n",
       "Milk               int64              0              0.0            421   \n",
       "Grocery            int64              0              0.0            430   \n",
       "Frozen             int64              0              0.0            426   \n",
       "Detergents_Paper   int64              0              0.0            417   \n",
       "Delicassen         int64              0              0.0            403   \n",
       "\n",
       "                  zero_count  negative_count          mean           std  \\\n",
       "Channel                    0               0             -             -   \n",
       "Region                     0               0             -             -   \n",
       "Fresh                      0               0  12000.297727  12647.328865   \n",
       "Milk                       0               0   5796.265909   7380.377175   \n",
       "Grocery                    0               0   7951.277273   9503.162829   \n",
       "Frozen                     0               0   3071.931818   4854.673333   \n",
       "Detergents_Paper           0               0   2881.493182   4767.854448   \n",
       "Delicassen                 0               0   1524.870455   2820.105937   \n",
       "\n",
       "                   skewness    kurtosis mean_percentile   min      25%  \\\n",
       "Channel                   -           -               -     -        -   \n",
       "Region                    -           -               -     -        -   \n",
       "Fresh              2.561323   11.536408       64.090909   3.0  3127.75   \n",
       "Milk               4.053755   24.669398       66.136364  55.0   1533.0   \n",
       "Grocery            3.587429    20.91467       66.136364   3.0   2153.0   \n",
       "Frozen             5.907986   54.689281       71.136364  25.0   742.25   \n",
       "Detergents_Paper   3.631851   19.009464       68.863636   3.0   256.75   \n",
       "Delicassen        11.151586  170.694939       68.863636   3.0   408.25   \n",
       "\n",
       "                     50%       75%       max     top freq  \n",
       "Channel                -         -         -  Horeca  298  \n",
       "Region                 -         -         -   Other  316  \n",
       "Fresh             8504.0  16933.75  112151.0       -    -  \n",
       "Milk              3627.0   7190.25   73498.0       -    -  \n",
       "Grocery           4755.5  10655.75   92780.0       -    -  \n",
       "Frozen            1526.0   3554.25   60869.0       -    -  \n",
       "Detergents_Paper   816.5    3922.0   40827.0       -    -  \n",
       "Delicassen         965.5   1820.25   47943.0       -    -  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats_df, numerical_cols, categorical_cols = generate_feature_stats(df)\n",
    "\n",
    "print(f\"Numerical   ({len(numerical_cols)}): {numerical_cols}\")\n",
    "print(f\"Categorical ({len(categorical_cols)}): {categorical_cols}\")\n",
    "print(\"-\" * 70)\n",
    "display(stats_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mdf78b7aab",
   "metadata": {},
   "source": [
    "**Key observations from the feature audit:**\n",
    "\n",
    "- **Mixed feature types** — `Channel` and `Region` are categorical; all spend columns are numerical. This rules out plain K-Means.\n",
    "- **Extreme right skew** on all spend columns — `Fresh`, `Frozen`, and `Delicassen` all show skewness > 5. A few very large accounts will distort centroids if left untreated. `auto_cluster` handles normalisation internally.\n",
    "- **No missing values** — the dataset is clean.\n",
    "- **Small dataset** (440 rows) — K-search range should be kept tight (2–7) to avoid fitting noise as structure."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md43600e80",
   "metadata": {},
   "source": [
    "## 4. Zero-Config Auto-Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cdb61664b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running auto_cluster — detecting column types, selecting algorithm,\n",
      "searching for optimal K, fitting, and evaluating automatically.\n",
      "\n",
      "================================================================================\n",
      "AUTO-CLUSTERING PIPELINE\n",
      "================================================================================\n",
      "\n",
      "[1/5] Auto-detecting column types...\n",
      "  Numerical: ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']\n",
      "  Categorical: ['Channel', 'Region']\n",
      "\n",
      "[2/5] Auto-selecting algorithm...\n",
      "  Selected: kprototypes\n",
      "\n",
      "[3/5] Finding optimal K (testing 2-10)...\n",
      "  Recommended K: 2 (confidence: low)\n",
      "    - Silhouette score peaks at k=2 (-1.000)\n",
      "    - Tested k from 2 to 10\n",
      "    - Multiple k values have similar scores - try domain knowledge\n",
      "    - Note: Elbow method suggests k=3 instead\n",
      "\n",
      "[4/5] Clustering with kprototypes (K=2)...\n",
      "Fitting kprototypes...\n",
      "Calculating gamma using 'huang' method...\n",
      "Gamma = 3497.7919\n",
      "Calculating categorical weights using 'relevance' method...\n",
      "Applying per-feature weights. Gamma range: [1786.6539, 5208.9299]\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "\n",
      "Fitting K-Prototypes (n_clusters=2)...\n",
      "Init: initializing centroids\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing clusters\n",
      "Init: initializing centroids\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing clusters\n",
      "Init: initializing centroids\n",
      "Init: initializing centroids\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing clusters\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Starting iterations...\n",
      "Starting iterations...\n",
      "Starting iterations...\n",
      "Starting iterations...\n",
      "Starting iterations...\n",
      "Starting iterations...\n",
      "Starting iterations...\n",
      "Starting iterations...\n",
      "Starting iterations...\n",
      "Run: 2, iteration: 1/100, moves: 47, ncost: 123614095846.56082\n",
      "Run: 1, iteration: 1/100, moves: 16, ncost: 114438252123.06128\n",
      "Run: 5, iteration: 1/100, moves: 26, ncost: 123542653483.19649\n",
      "Run: 4, iteration: 1/100, moves: 59, ncost: 115221241623.77393\n",
      "Run: 2, iteration: 2/100, moves: 27, ncost: 121882603579.68947\n",
      "Run: 8, iteration: 1/100, moves: 48, ncost: 114819707216.52832\n",
      "Run: 1, iteration: 2/100, moves: 14, ncost: 113892018217.07164\n",
      "Run: 10, iteration: 1/100, moves: 80, ncost: 121651564687.64377\n",
      "Run: 5, iteration: 2/100, moves: 25, ncost: 121882054671.93109\n",
      "Run: 4, iteration: 2/100, moves: 18, ncost: 114571716477.99518\n",
      "Run: 2, iteration: 3/100, moves: 28, ncost: 120629337219.31061\n",
      "Run: 8, iteration: 2/100, moves: 11, ncost: 114544377192.07077\n",
      "Run: 1, iteration: 3/100, moves: 10, ncost: 113559610606.74165\n",
      "Run: 10, iteration: 2/100, moves: 53, ncost: 118060689238.85434\n",
      "Run: 5, iteration: 3/100, moves: 29, ncost: 120629337219.31061\n",
      "Run: 4, iteration: 3/100, moves: 14, ncost: 114124923387.80376\n",
      "Run: 2, iteration: 4/100, moves: 17, ncost: 120126662534.90063\n",
      "Run: 8, iteration: 3/100, moves: 13, ncost: 114124923387.80376\n",
      "Run: 6, iteration: 1/100, moves: 20, ncost: 114247161638.78679\n",
      "Run: 7, iteration: 1/100, moves: 28, ncost: 113693698699.33847\n",
      "Run: 3, iteration: 1/100, moves: 109, ncost: 120949607760.71414\n",
      "Run: 1, iteration: 4/100, moves: 11, ncost: 113248392104.92256\n",
      "Run: 9, iteration: 1/100, moves: 31, ncost: 114706574594.15979\n",
      "Run: 10, iteration: 3/100, moves: 35, ncost: 115615795947.62486\n",
      "Run: 5, iteration: 4/100, moves: 17, ncost: 120126662534.90063\n",
      "Run: 4, iteration: 4/100, moves: 9, ncost: 113901908192.20638\n",
      "Run: 2, iteration: 5/100, moves: 11, ncost: 119897192593.96259\n",
      "Run: 8, iteration: 4/100, moves: 9, ncost: 113901908192.20638\n",
      "Run: 1, iteration: 5/100, moves: 2, ncost: 113223071281.33212\n",
      "Run: 10, iteration: 4/100, moves: 24, ncost: 114716161244.52449\n",
      "Run: 5, iteration: 5/100, moves: 11, ncost: 119897192593.96259\n",
      "Run: 4, iteration: 5/100, moves: 5, ncost: 113824498016.4025\n",
      "Run: 2, iteration: 6/100, moves: 7, ncost: 119776842897.16995\n",
      "Run: 8, iteration: 5/100, moves: 5, ncost: 113824498016.4025\n",
      "Run: 6, iteration: 2/100, moves: 10, ncost: 113891937069.4903\n",
      "Run: 7, iteration: 2/100, moves: 10, ncost: 113234847977.8884\n",
      "Run: 3, iteration: 2/100, moves: 23, ncost: 120207166856.01108\n",
      "Run: 1, iteration: 6/100, moves: 1, ncost: 113218428133.06653\n",
      "Run: 9, iteration: 2/100, moves: 15, ncost: 114307974813.4363\n",
      "Run: 5, iteration: 6/100, moves: 7, ncost: 119776842897.16995\n",
      "Run: 10, iteration: 5/100, moves: 13, ncost: 114272665863.77339\n",
      "Run: 4, iteration: 6/100, moves: 7, ncost: 113569936611.23627\n",
      "Run: 2, iteration: 7/100, moves: 3, ncost: 119732693953.55994\n",
      "Run: 8, iteration: 6/100, moves: 7, ncost: 113569936611.23627\n",
      "Run: 1, iteration: 7/100, moves: 0, ncost: 113218428133.06653\n",
      "Run: 5, iteration: 7/100, moves: 3, ncost: 119732693953.55994\n",
      "Run: 10, iteration: 6/100, moves: 12, ncost: 113920390656.67844\n",
      "Run: 2, iteration: 8/100, moves: 6, ncost: 119626208811.00294\n",
      "Run: 4, iteration: 7/100, moves: 6, ncost: 113248392104.92256\n",
      "Run: 6, iteration: 3/100, moves: 4, ncost: 113824498016.4025\n",
      "Run: 8, iteration: 7/100, moves: 6, ncost: 113248392104.92256\n",
      "Run: 7, iteration: 3/100, moves: 2, ncost: 113218428133.06653\n",
      "Run: 3, iteration: 3/100, moves: 13, ncost: 119938498955.17961\n",
      "Run: 5, iteration: 8/100, moves: 6, ncost: 119626208811.00294\n",
      "Run: 10, iteration: 7/100, moves: 6, ncost: 113824498016.4025\n",
      "Run: 9, iteration: 3/100, moves: 13, ncost: 113920390656.67844\n",
      "Run: 4, iteration: 8/100, moves: 2, ncost: 113223071281.33212\n",
      "Run: 2, iteration: 9/100, moves: 5, ncost: 119556236333.16246\n",
      "Run: 6, iteration: 4/100, moves: 7, ncost: 113569936611.23627\n",
      "Run: 8, iteration: 8/100, moves: 2, ncost: 113223071281.33212\n",
      "Run: 5, iteration: 9/100, moves: 5, ncost: 119556236333.16246\n",
      "Run: 2, iteration: 10/100, moves: 0, ncost: 119556236333.16246\n",
      "Run: 4, iteration: 9/100, moves: 1, ncost: 113218428133.06653\n",
      "Run: 10, iteration: 8/100, moves: 7, ncost: 113569936611.23627\n",
      "Run: 8, iteration: 9/100, moves: 1, ncost: 113218428133.06653\n",
      "Run: 6, iteration: 5/100, moves: 6, ncost: 113248392104.92256\n",
      "Run: 7, iteration: 4/100, moves: 0, ncost: 113218428133.06653\n",
      "Run: 3, iteration: 4/100, moves: 7, ncost: 119804439348.86752\n",
      "Run: 5, iteration: 10/100, moves: 0, ncost: 119556236333.16246\n",
      "Run: 4, iteration: 10/100, moves: 0, ncost: 113218428133.06653\n",
      "Run: 10, iteration: 9/100, moves: 6, ncost: 113248392104.92256\n",
      "Run: 9, iteration: 4/100, moves: 6, ncost: 113824498016.4025\n",
      "Run: 8, iteration: 10/100, moves: 0, ncost: 113218428133.06653\n",
      "Run: 6, iteration: 6/100, moves: 2, ncost: 113223071281.33212\n",
      "Run: 10, iteration: 10/100, moves: 2, ncost: 113223071281.33212\n",
      "Run: 9, iteration: 5/100, moves: 7, ncost: 113569936611.23627\n",
      "Run: 6, iteration: 7/100, moves: 1, ncost: 113218428133.06653\n",
      "Run: 3, iteration: 5/100, moves: 5, ncost: 119732693953.55994\n",
      "Run: 10, iteration: 11/100, moves: 1, ncost: 113218428133.06653\n",
      "Run: 6, iteration: 8/100, moves: 0, ncost: 113218428133.06653\n",
      "Run: 9, iteration: 6/100, moves: 6, ncost: 113248392104.92256\n",
      "Run: 3, iteration: 6/100, moves: 6, ncost: 119626208811.00294\n",
      "Run: 10, iteration: 12/100, moves: 0, ncost: 113218428133.06653\n",
      "Run: 9, iteration: 7/100, moves: 2, ncost: 113223071281.33212\n",
      "Run: 3, iteration: 7/100, moves: 5, ncost: 119556236333.16246\n",
      "Run: 9, iteration: 8/100, moves: 1, ncost: 113218428133.06653\n",
      "Run: 3, iteration: 8/100, moves: 0, ncost: 119556236333.16246\n",
      "Run: 9, iteration: 9/100, moves: 0, ncost: 113218428133.06653\n",
      "Best run was number 1\n",
      "Clustering complete. Final cost: 113218428133.07\n",
      "Fitted kprototypes. Found 2 clusters.\n",
      "  Clustering complete!\n",
      "\n",
      "[5/5] Evaluating clustering quality...\n",
      "  Quality: 7.2/10 (Good)\n",
      "  Silhouette: 0.512\n",
      "\n",
      "  Top Insights:\n",
      "    - Clustering quality is Good (7.2/10)\n",
      "    - Cluster 0 is weakly defined (avg silhouette: 0.13)\n",
      "    - Cluster 1 is very large (375 samples, 85.2% of data)\n",
      "\n",
      "================================================================================\n",
      "AUTO-CLUSTERING COMPLETE\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(\"Running auto_cluster — detecting column types, selecting algorithm,\")\n",
    "print(\"searching for optimal K, fitting, and evaluating automatically.\\n\")\n",
    "\n",
    "result = auto_cluster(df, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mdbd95bc34",
   "metadata": {},
   "source": [
    "## 5. Inspecting the Auto-Cluster Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd3e358bda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "════════════════════════════════════════════════════════════\n",
      "  AUTO-CLUSTER RESULT SUMMARY\n",
      "════════════════════════════════════════════════════════════\n",
      "  Algorithm selected : kprototypes\n",
      "  K selected         : 2\n",
      "  Samples clustered  : 440\n",
      "\n",
      "  Cluster sizes:\n",
      "    Cluster 0  →  65 customers (14.8%)\n",
      "    Cluster 1  →  375 customers (85.2%)\n",
      "\n",
      "  Silhouette score   : 0.5115\n",
      "  Quality score      : 7.18 / 10  (Good)\n",
      "\n",
      "  Configuration applied:\n",
      "    algorithm                     : kprototypes\n",
      "    n_clusters                    : 2\n",
      "    numerical_columns             : ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']\n",
      "    categorical_columns           : ['Channel', 'Region']\n",
      "    auto_gamma                    : True\n",
      "    auto_weights                  : True\n"
     ]
    }
   ],
   "source": [
    "auto_labels    = result.labels\n",
    "auto_eval      = result.evaluation\n",
    "auto_config    = result.config_used\n",
    "auto_clusterer = result.clusterer\n",
    "\n",
    "print(\"\\u2550\" * 60)\n",
    "print(\"  AUTO-CLUSTER RESULT SUMMARY\")\n",
    "print(\"\\u2550\" * 60)\n",
    "print(f\"  Algorithm selected : {auto_config.get('algorithm', 'N/A')}\")\n",
    "print(f\"  K selected         : {auto_config.get('n_clusters', 'N/A')}\")\n",
    "print(f\"  Samples clustered  : {len(auto_labels):,}\")\n",
    "print()\n",
    "print(\"  Cluster sizes:\")\n",
    "unique, counts = np.unique(auto_labels, return_counts=True)\n",
    "for cid, cnt in zip(unique, counts):\n",
    "    print(f\"    Cluster {cid}  \\u2192  {cnt:,} customers ({cnt / len(auto_labels):.1%})\")\n",
    "print()\n",
    "print(f\"  Silhouette score   : {auto_eval.metrics.silhouette:.4f}\")\n",
    "print(f\"  Quality score      : {auto_eval.metrics.overall_quality:.2f} / 10  ({auto_eval.metrics.quality_label})\")\n",
    "print()\n",
    "print(\"  Configuration applied:\")\n",
    "for k, v in auto_config.items():\n",
    "    print(f\"    {str(k):30s}: {v}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mdbe741ccb",
   "metadata": {},
   "source": [
    "## 6. Manual Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md0106f06d",
   "metadata": {},
   "source": [
    "### 6.1 Column Type Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cdb1ed0b52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical columns   (6): ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicassen']\n",
      "Categorical columns (2): ['Channel', 'Region']\n",
      "\n",
      "Recommended algorithm: KPROTOTYPES\n",
      "\n",
      "K-Prototypes selected because the dataset contains both numerical and\n",
      "categorical columns. It combines Euclidean distance for spend features\n",
      "with simple matching distance for Channel and Region.\n"
     ]
    }
   ],
   "source": [
    "numerical_cols, categorical_cols = detect_column_types(df)\n",
    "algorithm = select_algorithm(numerical_cols, categorical_cols)\n",
    "\n",
    "print(f\"Numerical columns   ({len(numerical_cols)}): {numerical_cols}\")\n",
    "print(f\"Categorical columns ({len(categorical_cols)}): {categorical_cols}\")\n",
    "print()\n",
    "print(f\"Recommended algorithm: {algorithm.upper()}\")\n",
    "print()\n",
    "if algorithm == \"kprototypes\":\n",
    "    print(\"K-Prototypes selected because the dataset contains both numerical and\")\n",
    "    print(\"categorical columns. It combines Euclidean distance for spend features\")\n",
    "    print(\"with simple matching distance for Channel and Region.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mdd777696a",
   "metadata": {},
   "source": [
    "### 6.2 Finding a Useful Number of Clusters\n",
    "\n",
    "A plain silhouette search can over-prefer **K = 2** when one or more influential features have cardinality two. That is mathematically valid, but it is not always the most useful segmentation answer.\n",
    "\n",
    "For each point $i$, silhouette is:\n",
    "\n",
    "$$\n",
    "s(i) = \\frac{b(i) - a(i)}{\\max(a(i), b(i))}\n",
    "$$\n",
    "\n",
    "where $a(i)$ is the mean distance from point $i$ to points in its own cluster, and $b(i)$ is the mean distance to the nearest other cluster. If a meaningful feature has cardinality two, it can create a strong binary partition in the distance geometry. When other features line up with that split, **K = 2** can maximise the average gap between clusters. As K increases, the nearest alternative cluster may become a neighbouring sub-segment inside the same binary side, so $b(i)$ shrinks and the global silhouette score can fall.\n",
    "\n",
    "For business segmentation we often want the next stable refinement, not just the coarsest separation. So we use `find_optimal_k` to calculate the silhouette curve, then select an interior post-2 local peak where:\n",
    "\n",
    "$$\n",
    "S(k) > S(k - 1) \\quad \\text{and} \\quad S(k) \\ge S(k + 1)\n",
    "$$\n",
    "\n",
    "That means silhouette improved despite adding another cluster, then stopped improving. If the best score is at the edge of the tested range, extend the range before treating it as a peak. If no post-2 local peak exists, we keep the global silhouette recommendation and note that the richer split is weak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cd4682dbe9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching for silhouette scores in range [2, 7]...\n",
      "\n",
      "Global silhouette maximum : K = 2\n",
      "Confidence                : Low\n",
      "Tutorial selection        : K = 6\n",
      "Reasoning                 : Selected K=6: strongest post-2 local silhouette peak (0.3903).\n",
      "\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>K</th>\n",
       "      <th>Silhouette Score</th>\n",
       "      <th>Delta vs Previous K</th>\n",
       "      <th>Post-2 Local Peak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.5115</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.4784</td>\n",
       "      <td>-0.0332</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0.3901</td>\n",
       "      <td>-0.0882</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>0.3833</td>\n",
       "      <td>-0.0068</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>0.3903</td>\n",
       "      <td>0.007</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>0.3297</td>\n",
       "      <td>-0.0606</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   K  Silhouette Score Delta vs Previous K Post-2 Local Peak\n",
       "0  2            0.5115                                      \n",
       "1  3            0.4784             -0.0332                  \n",
       "2  4            0.3901             -0.0882                  \n",
       "3  5            0.3833             -0.0068                  \n",
       "4  6            0.3903               0.007               Yes\n",
       "5  7            0.3297             -0.0606                  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def clusterer_factory(k):\n",
    "    config = ClusterConfig(\n",
    "        name=\"k_search\",\n",
    "        algorithm_type=\"partitional\",\n",
    "        method=\"kmeans\",\n",
    "        n_clusters=k,\n",
    "        numerical_columns=numerical_cols,\n",
    "    )\n",
    "    return KMeansClusterer(config)\n",
    "\n",
    "print(\"Searching for silhouette scores in range [2, 7]...\\n\")\n",
    "\n",
    "k_result = find_optimal_k(\n",
    "    data=df[numerical_cols],\n",
    "    clusterer_factory=clusterer_factory,\n",
    "    k_range=range(2, 8),\n",
    "    method=\"silhouette\",\n",
    ")\n",
    "\n",
    "scores_by_k = {int(k): float(score) for k, score in sorted(k_result.scores.items())}\n",
    "ks = list(scores_by_k.keys())\n",
    "score_values = [scores_by_k[k] for k in ks]\n",
    "\n",
    "local_peak_ks = []\n",
    "score_rows = []\n",
    "for idx, k in enumerate(ks):\n",
    "    score = score_values[idx]\n",
    "    previous_score = score_values[idx - 1] if idx > 0 else np.nan\n",
    "    next_score = score_values[idx + 1] if idx < len(score_values) - 1 else np.nan\n",
    "    delta = score - previous_score if idx > 0 else np.nan\n",
    "\n",
    "    is_post_2_peak = (\n",
    "        idx > 0\n",
    "        and idx < len(score_values) - 1\n",
    "        and delta > 0\n",
    "        and score >= next_score\n",
    "    )\n",
    "    if is_post_2_peak:\n",
    "        local_peak_ks.append(k)\n",
    "\n",
    "    score_rows.append({\n",
    "        \"K\": k,\n",
    "        \"Silhouette Score\": round(score, 4),\n",
    "        \"Delta vs Previous K\": \"\" if np.isnan(delta) else round(delta, 4),\n",
    "        \"Post-2 Local Peak\": \"Yes\" if is_post_2_peak else \"\",\n",
    "    })\n",
    "\n",
    "if local_peak_ks:\n",
    "    optimal_k = max(local_peak_ks, key=lambda k: scores_by_k[k])\n",
    "    selection_reason = (\n",
    "        f\"Selected K={optimal_k}: strongest post-2 local silhouette peak \"\n",
    "        f\"({scores_by_k[optimal_k]:.4f}).\"\n",
    "    )\n",
    "else:\n",
    "    optimal_k = int(k_result.recommended_k)\n",
    "    selection_reason = (\n",
    "        f\"No post-2 local silhouette peak was found, so we keep the global \"\n",
    "        f\"silhouette recommendation K={optimal_k}.\"\n",
    "    )\n",
    "\n",
    "print(f\"Global silhouette maximum : K = {k_result.recommended_k}\")\n",
    "print(f\"Confidence                : {str(k_result.confidence).title()}\")\n",
    "print(f\"Tutorial selection        : K = {optimal_k}\")\n",
    "print(f\"Reasoning                 : {selection_reason}\")\n",
    "print()\n",
    "display(pd.DataFrame(score_rows))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd0a9ca367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Proceeding with K = 6 for the full K-Prototypes fit.\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n",
    "fig.suptitle(\"Optimal K Selection — Wholesale Customer Spending\", fontsize=13, fontweight=\"bold\")\n",
    "\n",
    "BLUE, RED, GREY, GREEN = \"#2563EB\", \"#DC2626\", \"#64748B\", \"#059669\"\n",
    "\n",
    "deltas = [np.nan] + [score_values[i] - score_values[i - 1] for i in range(1, len(score_values))]\n",
    "\n",
    "ax = axes[0]\n",
    "ax.plot(ks, score_values, color=BLUE, lw=2.5, marker=\"o\", markersize=7)\n",
    "ax.axvline(k_result.recommended_k, color=GREY, ls=\"--\", lw=1.6, label=f\"Global max = K {k_result.recommended_k}\")\n",
    "ax.axvline(optimal_k, color=RED, ls=\"--\", lw=1.8, label=f\"Tutorial choice = K {optimal_k}\")\n",
    "if local_peak_ks:\n",
    "    ax.scatter(local_peak_ks, [scores_by_k[k] for k in local_peak_ks], color=RED, s=90, zorder=4)\n",
    "ax.set_xlabel(\"Number of Clusters (K)\")\n",
    "ax.set_ylabel(\"Silhouette Score\")\n",
    "ax.set_title(\"Silhouette Score vs K\", fontweight=\"bold\")\n",
    "ax.xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "ax = axes[1]\n",
    "bar_ks = ks[1:]\n",
    "bar_deltas = deltas[1:]\n",
    "bar_colours = [RED if k == optimal_k else (GREEN if delta > 0 else GREY) for k, delta in zip(bar_ks, bar_deltas)]\n",
    "ax.axhline(0, color=\"#111827\", lw=1)\n",
    "ax.bar(bar_ks, bar_deltas, color=bar_colours, alpha=0.85, edgecolor=\"white\", linewidth=0.8)\n",
    "ax.set_xlabel(\"Number of Clusters (K)\")\n",
    "ax.set_ylabel(\"Delta vs Previous K\")\n",
    "ax.set_title(\"Silhouette Gain from Adding a Cluster\", fontweight=\"bold\")\n",
    "ax.xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
    "ax.grid(axis=\"y\", alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\nProceeding with K = {optimal_k} for the full K-Prototypes fit.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md347da328",
   "metadata": {},
   "source": [
    "### 6.3 Fitting K-Prototypes\n",
    "\n",
    "With the tutorial K established from the silhouette local-peak rule, we fit K-Prototypes across all 8 features. Huang's gamma balances the overall categorical contribution against the numerical distance. Categorical weights then redistribute that categorical budget across Channel and Region, so a more useful categorical column can count more than a weaker one without changing the overall gamma scale. After fitting, BitBullet stores the resolved `gamma_by_column` and `categorical_weights_by_column` values in `clusterer.state.fitted_params` so future assignments can reproduce the same distance calculation exactly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd8f9f20db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting kprototypes...\n",
      "Calculating gamma using 'huang' method...\n",
      "Gamma = 3497.7919\n",
      "Calculating categorical weights using 'relevance' method...\n",
      "Applying per-feature weights. Gamma range: [1786.6539, 5208.9299]\n",
      "Initialization method and algorithm are deterministic. Setting n_init to 1.\n",
      "\n",
      "Fitting K-Prototypes (n_clusters=6)...\n",
      "Init: initializing centroids\n",
      "Init: initializing centroids\n",
      "Init: initializing centroids\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing clusters\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing centroids\n",
      "Init: initializing centroids\n",
      "Init: initializing centroids\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing clusters\n",
      "Init: initializing clusters\n",
      "Init: initializing clusters\n",
      "Init: initializing clusters\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Starting iterations...\n",
      "Init: initializing centroids\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Starting iterations...\n",
      "Init: initializing centroids\n",
      "Starting iterations...\n",
      "Starting iterations...\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 1/100, moves: 70, ncost: 49649392753.392715\n",
      "Run: 2, iteration: 1/100, moves: 180, ncost: 51308332205.64385\n",
      "Run: 7, iteration: 1/100, moves: 130, ncost: 51682792308.93562\n",
      "Starting iterations...\n",
      "Run: 4, iteration: 1/100, moves: 130, ncost: 65414946639.87763\n",
      "Run: 5, iteration: 1/100, moves: 94, ncost: 51819045722.895485\n",
      "Init: initializing centroids\n",
      "Run: 1, iteration: 2/100, moves: 16, ncost: 49298549332.669174\n",
      "Init: initializing clusters\n",
      "Run: 7, iteration: 2/100, moves: 28, ncost: 49065141592.241196\n",
      "Run: 2, iteration: 2/100, moves: 63, ncost: 48301610492.62795\n",
      "Run: 3, iteration: 1/100, moves: 129, ncost: 54918192783.91495\n",
      "Run: 4, iteration: 2/100, moves: 79, ncost: 57312279566.96777\n",
      "Run: 1, iteration: 3/100, moves: 10, ncost: 48332061568.87334\n",
      "Run: 7, iteration: 3/100, moves: 15, ncost: 48453376935.35493\n",
      "Run: 5, iteration: 2/100, moves: 43, ncost: 47648855855.11714\n",
      "Run: 2, iteration: 3/100, moves: 10, ncost: 47828884263.17672\n",
      "Run: 6, iteration: 1/100, moves: 127, ncost: 59931196650.75278\n",
      "Init: initializing centroids\n",
      "Init: initializing clusters\n",
      "Run: 3, iteration: 2/100, moves: 78, ncost: 51340773812.6057\n",
      "Run: 9, iteration: 1/100, moves: 82, ncost: 58933184277.77784\n",
      "Run: 1, iteration: 4/100, moves: 5, ncost: 47669234105.81741\n",
      "Run: 4, iteration: 3/100, moves: 47, ncost: 56249512450.14134\n",
      "Run: 7, iteration: 4/100, moves: 3, ncost: 48227652054.49703\n",
      "Run: 2, iteration: 4/100, moves: 9, ncost: 47794026754.00624\n",
      "Run: 5, iteration: 3/100, moves: 10, ncost: 47407916731.60952\n",
      "Run: 3, iteration: 3/100, moves: 53, ncost: 49172026027.47575\n",
      "Run: 8, iteration: 1/100, moves: 117, ncost: 48199476104.368126\n",
      "Starting iterations...\n",
      "Run: 1, iteration: 5/100, moves: 1, ncost: 47668348996.0103\n",
      "Run: 4, iteration: 4/100, moves: 42, ncost: 54332538127.34818\n",
      "Run: 7, iteration: 5/100, moves: 10, ncost: 47882352428.88638\n",
      "Run: 2, iteration: 5/100, moves: 3, ncost: 47786508436.10258\n",
      "Run: 5, iteration: 4/100, moves: 7, ncost: 46993091189.986496\n",
      "Run: 6, iteration: 2/100, moves: 71, ncost: 56984069126.89836\n",
      "Run: 3, iteration: 4/100, moves: 32, ncost: 47892857341.56697\n",
      "Run: 1, iteration: 6/100, moves: 0, ncost: 47668348996.0103\n",
      "Run: 9, iteration: 2/100, moves: 46, ncost: 52882785311.721466\n",
      "Run: 7, iteration: 6/100, moves: 4, ncost: 47793652177.47762\n",
      "Run: 4, iteration: 5/100, moves: 51, ncost: 50887690839.78267\n",
      "Run: 5, iteration: 5/100, moves: 2, ncost: 46986315331.912796\n",
      "Run: 2, iteration: 6/100, moves: 0, ncost: 47786508436.10258\n",
      "Run: 3, iteration: 5/100, moves: 19, ncost: 47666655863.303474\n",
      "Run: 8, iteration: 2/100, moves: 27, ncost: 47754445955.88371\n",
      "Run: 6, iteration: 3/100, moves: 73, ncost: 52136709109.60636\n",
      "Run: 7, iteration: 7/100, moves: 6, ncost: 47763755780.27575\n",
      "Run: 5, iteration: 6/100, moves: 1, ncost: 46984794187.20419\n",
      "Run: 9, iteration: 3/100, moves: 23, ncost: 51481027287.35657\n",
      "Run: 4, iteration: 6/100, moves: 30, ncost: 48959749757.79218\n",
      "Run: 3, iteration: 6/100, moves: 11, ncost: 47528644509.02759\n",
      "Run: 6, iteration: 4/100, moves: 45, ncost: 50253091517.93228\n",
      "Run: 10, iteration: 1/100, moves: 90, ncost: 59083225690.359116\n",
      "Run: 7, iteration: 8/100, moves: 2, ncost: 47685487773.957695\n",
      "Run: 5, iteration: 7/100, moves: 0, ncost: 46984794187.20419\n",
      "Run: 9, iteration: 4/100, moves: 23, ncost: 47696913389.91361\n",
      "Run: 4, iteration: 7/100, moves: 19, ncost: 48207937033.868996\n",
      "Run: 3, iteration: 7/100, moves: 4, ncost: 47516544907.77154\n",
      "Run: 8, iteration: 3/100, moves: 7, ncost: 47691343472.49512\n",
      "Run: 6, iteration: 5/100, moves: 19, ncost: 48553154878.49\n",
      "Run: 7, iteration: 9/100, moves: 5, ncost: 47274647130.41096\n",
      "Run: 10, iteration: 2/100, moves: 29, ncost: 56289824356.230644\n",
      "Run: 9, iteration: 5/100, moves: 8, ncost: 47315399345.09145\n",
      "Run: 4, iteration: 8/100, moves: 7, ncost: 47953054232.44269\n",
      "Run: 3, iteration: 8/100, moves: 0, ncost: 47516544907.77154\n",
      "Run: 6, iteration: 6/100, moves: 15, ncost: 48086820664.54063\n",
      "Run: 7, iteration: 10/100, moves: 0, ncost: 47274647130.41096\n",
      "Run: 9, iteration: 6/100, moves: 7, ncost: 46989966012.00096\n",
      "Run: 10, iteration: 3/100, moves: 22, ncost: 54193989336.3235\n",
      "Run: 4, iteration: 9/100, moves: 4, ncost: 47716322402.81821\n",
      "Run: 8, iteration: 4/100, moves: 3, ncost: 47682029908.17376\n",
      "Run: 6, iteration: 7/100, moves: 5, ncost: 47851644388.32763\n",
      "Run: 9, iteration: 7/100, moves: 10, ncost: 46780813589.77353\n",
      "Run: 10, iteration: 4/100, moves: 13, ncost: 53864785435.76567\n",
      "Run: 4, iteration: 10/100, moves: 5, ncost: 47610834134.838905\n",
      "Run: 8, iteration: 5/100, moves: 0, ncost: 47682029908.17376\n",
      "Run: 6, iteration: 8/100, moves: 4, ncost: 47604974300.35931\n",
      "Run: 9, iteration: 8/100, moves: 1, ncost: 46776955072.002396\n",
      "Run: 10, iteration: 5/100, moves: 12, ncost: 53754873397.0775\n",
      "Run: 4, iteration: 11/100, moves: 3, ncost: 47595685970.181305\n",
      "Run: 6, iteration: 9/100, moves: 7, ncost: 47548639341.52393\n",
      "Run: 9, iteration: 9/100, moves: 1, ncost: 46775028002.47143\n",
      "Run: 10, iteration: 6/100, moves: 9, ncost: 53661130319.79628\n",
      "Run: 4, iteration: 12/100, moves: 5, ncost: 47576285369.82584\n",
      "Run: 6, iteration: 10/100, moves: 2, ncost: 47472063926.32072\n",
      "Run: 9, iteration: 10/100, moves: 0, ncost: 46775028002.47143\n",
      "Run: 4, iteration: 13/100, moves: 0, ncost: 47576285369.82584\n",
      "Run: 10, iteration: 7/100, moves: 13, ncost: 52231041788.457535\n",
      "Run: 6, iteration: 11/100, moves: 5, ncost: 47441530524.503624\n",
      "Run: 10, iteration: 8/100, moves: 13, ncost: 51803332200.7302\n",
      "Run: 6, iteration: 12/100, moves: 3, ncost: 47434954413.24388\n",
      "Run: 10, iteration: 9/100, moves: 8, ncost: 51752174729.57088\n",
      "Run: 6, iteration: 13/100, moves: 0, ncost: 47434954413.24388\n",
      "Run: 10, iteration: 10/100, moves: 3, ncost: 51727649570.51242\n",
      "Run: 10, iteration: 11/100, moves: 0, ncost: 51727649570.51242\n",
      "Best run was number 9\n",
      "Clustering complete. Final cost: 46775028002.47\n",
      "Fitted kprototypes. Found 6 clusters.\n",
      "\n",
      "Fitting complete — 6 clusters across 440 customers.\n"
     ]
    }
   ],
   "source": [
    "config = ClusterConfig(\n",
    "    name=\"wholesale_segments\",\n",
    "    algorithm_type=\"partitional\",\n",
    "    method=\"kprototypes\",\n",
    "    n_clusters=optimal_k,\n",
    "    numerical_columns=numerical_cols,\n",
    "    categorical_columns=categorical_cols,\n",
    "    params={\n",
    "        \"gamma\": \"huang\",\n",
    "        \"categorical_weights\": \"relevance\",\n",
    "        \"init\": \"Cao\",\n",
    "        \"n_init\": 10,\n",
    "    },\n",
    ")\n",
    "\n",
    "clusterer = KPrototypesClusterer(config)\n",
    "clusterer.fit(df, verbose=True)\n",
    "\n",
    "labels = clusterer.labels_\n",
    "print(f\"\\nFitting complete — {len(np.unique(labels))} clusters across {len(labels):,} customers.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mdf4a279e5",
   "metadata": {},
   "source": [
    "## 7. Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cdd5c639da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "════════════════════════════════════════════════════════════\n",
      "  CLUSTERING EVALUATION REPORT\n",
      "════════════════════════════════════════════════════════════\n",
      "  Silhouette score       : 0.3766\n",
      "  Calinski-Harabasz      : 205.65\n",
      "  Davies-Bouldin         : 0.9558\n",
      "  Overall quality score  : 6.98 / 10\n",
      "  Quality label          : Good\n",
      "\n",
      "  Cluster sizes:\n",
      "    Cluster 0  →  22 customers (5.0%)\n",
      "    Cluster 1  →  7 customers (1.6%)\n",
      "    Cluster 2  →  80 customers (18.2%)\n",
      "    Cluster 3  →  3 customers (0.7%)\n",
      "    Cluster 4  →  224 customers (50.9%)\n",
      "    Cluster 5  →  104 customers (23.6%)\n",
      "\n",
      "  Insights:\n",
      "    [POSITIVE] Clustering quality is Good (7.0/10)\n",
      "    [MEDIUM] Cluster 1 is weakly defined (avg silhouette: 0.12)\n",
      "    [MEDIUM] Cluster 1 is very small (7 samples, 1.6% of data)\n",
      "    [MEDIUM] Cluster 2 is weakly defined (avg silhouette: 0.28)\n",
      "    [MEDIUM] Cluster 3 is weakly defined (avg silhouette: 0.01)\n",
      "    [MEDIUM] Cluster 3 is very small (3 samples, 0.7% of data)\n",
      "    [LOW] Cluster 4 is very large (224 samples, 50.9% of data)\n",
      "    [MEDIUM] Clusters are highly imbalanced in size\n"
     ]
    }
   ],
   "source": [
    "report = evaluate_clustering(df[numerical_cols], labels)\n",
    "\n",
    "print(\"\\u2550\" * 60)\n",
    "print(\"  CLUSTERING EVALUATION REPORT\")\n",
    "print(\"\\u2550\" * 60)\n",
    "print(f\"  Silhouette score       : {report.metrics.silhouette:.4f}\")\n",
    "print(f\"  Calinski-Harabasz      : {report.metrics.calinski_harabasz:.2f}\")\n",
    "print(f\"  Davies-Bouldin         : {report.metrics.davies_bouldin:.4f}\")\n",
    "print(f\"  Overall quality score  : {report.metrics.overall_quality:.2f} / 10\")\n",
    "print(f\"  Quality label          : {report.metrics.quality_label}\")\n",
    "print()\n",
    "print(\"  Cluster sizes:\")\n",
    "for cid, cnt in sorted(report.cluster_sizes.items()):\n",
    "    print(f\"    Cluster {cid}  \\u2192  {cnt:,} customers ({cnt / len(labels):.1%})\")\n",
    "print()\n",
    "if report.insights:\n",
    "    print(\"  Insights:\")\n",
    "    for insight in report.insights:\n",
    "        severity_tag = f\"[{insight.severity.upper()}] \" if hasattr(insight, 'severity') and insight.severity else \"\"\n",
    "        print(f\"    {severity_tag}{insight.message}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mdbdca66ab",
   "metadata": {},
   "source": [
    "## 8. Cluster Profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cd1973ac2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "══════════════════════════════════════════════════════════════════════\n",
      "  CLUSTER PROFILES\n",
      "══════════════════════════════════════════════════════════════════════\n",
      "\n",
      "  Cluster 0  —  22 customers (5.0%)\n",
      "  Suggested label  : Moderately High Frozen · Very High Fresh\n",
      "  Key characteristics:\n",
      "    • Niche segment (5.0% of population)\n",
      "    • Fresh: 317.1% higher than average (50049.68 vs population 12000.30)\n",
      "    • Milk: 23.3% lower than average (4447.41 vs population 5796.27)\n",
      "    • Grocery: 34.3% lower than average (5225.05 vs population 7951.28)\n",
      "    • Frozen: 91.9% higher than average (5895.64 vs population 3071.93)\n",
      "    • Detergents_Paper: 67.1% lower than average (948.18 vs population 2881.49)\n",
      "    • Delicassen: 57.7% higher than average (2404.36 vs population 1524.87)\n",
      "\n",
      "  Cluster 1  —  7 customers (1.6%)\n",
      "  Suggested label  : Very High Detergents_Paper · Very High Milk\n",
      "  Key characteristics:\n",
      "    • Niche segment (1.6% of population)\n",
      "    • Channel: Predominantly Retail (100.0%) vs population Horeca\n",
      "    • Fresh: 66.9% higher than average (20031.29 vs population 12000.30)\n",
      "    • Milk: 557.0% higher than average (38084.00 vs population 5796.27)\n",
      "    • Grocery: 605.9% higher than average (56126.14 vs population 7951.28)\n",
      "    • Detergents_Paper: 859.4% higher than average (27644.57 vs population 2881.49)\n",
      "    • Delicassen: 67.1% higher than average (2548.14 vs population 1524.87)\n",
      "\n",
      "  Cluster 2  —  80 customers (18.2%)\n",
      "  Suggested label  : High Detergents_Paper · Moderately High Milk\n",
      "  Key characteristics:\n",
      "    • Channel: Predominantly Retail (91.2%) vs population Horeca\n",
      "    • Fresh: 60.6% lower than average (4724.21 vs population 12000.30)\n",
      "    • Milk: 104.2% higher than average (11837.01 vs population 5796.27)\n",
      "    • Grocery: 132.2% higher than average (18461.59 vs population 7951.28)\n",
      "    • Frozen: 49.7% lower than average (1546.58 vs population 3071.93)\n",
      "    • Detergents_Paper: 184.4% higher than average (8195.89 vs population 2881.49)\n",
      "\n",
      "  Cluster 3  —  3 customers (0.7%)\n",
      "  Suggested label  : Very High Delicassen · Very High Frozen\n",
      "  Key characteristics:\n",
      "    • Niche segment (0.7% of population)\n",
      "    • Fresh: 166.5% higher than average (31979.00 vs population 12000.30)\n",
      "    • Milk: 458.7% higher than average (32385.67 vs population 5796.27)\n",
      "    • Grocery: 134.0% higher than average (18605.00 vs population 7951.28)\n",
      "    • Frozen: 1012.8% higher than average (34185.67 vs population 3071.93)\n",
      "    • Detergents_Paper: 32.3% lower than average (1949.33 vs population 2881.49)\n",
      "    • Delicassen: 1431.8% higher than average (23358.33 vs population 1524.87)\n",
      "\n",
      "  Cluster 4  —  224 customers (50.9%)\n",
      "  Suggested label  : Moderately Low Fresh · Horeca\n",
      "  Key characteristics:\n",
      "    • Dominant segment (50.9% of population)\n",
      "    • Fresh: 49.4% lower than average (6072.05 vs population 12000.30)\n",
      "    • Milk: 43.2% lower than average (3292.95 vs population 5796.27)\n",
      "    • Grocery: 48.1% lower than average (4122.93 vs population 7951.28)\n",
      "    • Frozen: 20.2% lower than average (2451.99 vs population 3071.93)\n",
      "    • Detergents_Paper: 57.5% lower than average (1224.50 vs population 2881.49)\n",
      "    • Delicassen: 34.6% lower than average (996.64 vs population 1524.87)\n",
      "\n",
      "  Cluster 5  —  104 customers (23.6%)\n",
      "  Suggested label  : Moderately High Fresh · Horeca\n",
      "  Key characteristics:\n",
      "    • Fresh: 76.7% higher than average (21200.06 vs population 12000.30)\n",
      "    • Milk: 32.9% lower than average (3886.42 vs population 5796.27)\n",
      "    • Grocery: 35.4% lower than average (5138.93 vs population 7951.28)\n",
      "    • Frozen: 34.1% higher than average (4119.86 vs population 3071.93)\n",
      "    • Detergents_Paper: 60.7% lower than average (1131.52 vs population 2881.49)\n"
     ]
    }
   ],
   "source": [
    "profiles = profile_clusters(df, labels)\n",
    "\n",
    "print(\"\\u2550\" * 70)\n",
    "print(\"  CLUSTER PROFILES\")\n",
    "print(\"\\u2550\" * 70)\n",
    "\n",
    "for profile in profiles.cluster_profiles:\n",
    "    print(f\"\\n  Cluster {profile.cluster_id}  \\u2014  {profile.size:,} customers ({profile.percentage:.1f}%)\")\n",
    "    print(f\"  Suggested label  : {profile.suggested_label}\")\n",
    "    print(f\"  Key characteristics:\")\n",
    "    for char in profile.key_characteristics:\n",
    "        print(f\"    \\u2022 {char}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md2ed28106",
   "metadata": {},
   "source": [
    "### 8.1 Feature Importance — What Separates Wholesale Segments?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cdde805a81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top-5 most discriminative features:\n",
      "  Delicassen                 1.0000\n",
      "  Frozen                     0.7029\n",
      "  Detergents_Paper           0.5040\n",
      "  Milk                       0.4583\n",
      "  Grocery                    0.4496\n"
     ]
    }
   ],
   "source": [
    "importance = profiles.feature_importance\n",
    "imp_df = (\n",
    "    pd.DataFrame(list(importance.items()), columns=[\"feature\", \"importance\"])\n",
    "    .sort_values(\"importance\", ascending=False)\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "q67 = imp_df[\"importance\"].quantile(0.67)\n",
    "q33 = imp_df[\"importance\"].quantile(0.33)\n",
    "colours = [\n",
    "    \"#2563EB\" if v >= q67 else (\"#60A5FA\" if v >= q33 else \"#BFDBFE\")\n",
    "    for v in imp_df[\"importance\"]\n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "ax.barh(imp_df[\"feature\"][::-1], imp_df[\"importance\"][::-1],\n",
    "        color=colours[::-1], edgecolor=\"white\", linewidth=0.6)\n",
    "ax.set_xlabel(\"Feature Importance Score\", fontsize=11)\n",
    "ax.set_title(\n",
    "    \"Feature Importance for Cluster Separation\\n(darker = stronger discriminator between segments)\",\n",
    "    fontweight=\"bold\", fontsize=12,\n",
    ")\n",
    "ax.grid(axis=\"x\", alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nTop-5 most discriminative features:\")\n",
    "for _, row in imp_df.head(5).iterrows():\n",
    "    print(f\"  {row['feature']:25s}  {row['importance']:.4f}\")"
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             },
             {
              "yaxis": {
               "title": "Grocery"
              }
             }
            ],
            "label": "Grocery",
            "method": "update"
           },
           {
            "args": [
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              ]
             },
             {
              "yaxis": {
               "title": "Fresh"
              }
             }
            ],
            "label": "Fresh",
            "method": "update"
           }
          ],
          "direction": "down",
          "showactive": true,
          "x": 0.32,
          "xanchor": "left",
          "y": 1.16,
          "yanchor": "top"
         }
        ],
        "width": 900,
        "xaxis": {
         "gridcolor": "rgba(148, 163, 184, 0.25)",
         "showgrid": true,
         "title": {
          "text": "Delicassen"
         }
        },
        "yaxis": {
         "gridcolor": "rgba(148, 163, 184, 0.25)",
         "showgrid": true,
         "title": {
          "text": "Frozen"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "feature_options = [feature for feature in imp_df[\"feature\"].tolist() if feature in numerical_cols]\n",
    "feature_options += [feature for feature in numerical_cols if feature not in feature_options]\n",
    "\n",
    "x_col = feature_options[0]\n",
    "y_col = feature_options[1] if len(feature_options) > 1 else feature_options[0]\n",
    "\n",
    "plot_df = df[feature_options].copy()\n",
    "plot_df[\"cluster\"] = labels.astype(str)\n",
    "cluster_ids = sorted(plot_df[\"cluster\"].unique(), key=lambda value: int(value) if str(value).isdigit() else str(value))\n",
    "cluster_frames = {cluster_id: plot_df[plot_df[\"cluster\"] == cluster_id] for cluster_id in cluster_ids}\n",
    "\n",
    "fig = go.Figure()\n",
    "for cluster_id, cluster_df in cluster_frames.items():\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=cluster_df[x_col].tolist(),\n",
    "        y=cluster_df[y_col].tolist(),\n",
    "        mode=\"markers\",\n",
    "        name=f\"Cluster {cluster_id}\",\n",
    "        marker=dict(size=8, line=dict(width=0.5, color=\"white\")),\n",
    "        hovertemplate=(\n",
    "            \"X value: %{x}<br>\"\n",
    "            \"Y value: %{y}<br>\"\n",
    "            f\"Cluster: {cluster_id}<extra></extra>\"\n",
    "        ),\n",
    "    ))\n",
    "\n",
    "x_buttons = [\n",
    "    dict(\n",
    "        label=feature,\n",
    "        method=\"update\",\n",
    "        args=[\n",
    "            {\"x\": [cluster_frames[cluster_id][feature].tolist() for cluster_id in cluster_ids]},\n",
    "            {\"xaxis\": {\"title\": feature}},\n",
    "        ],\n",
    "    )\n",
    "    for feature in feature_options\n",
    "]\n",
    "\n",
    "y_buttons = [\n",
    "    dict(\n",
    "        label=feature,\n",
    "        method=\"update\",\n",
    "        args=[\n",
    "            {\"y\": [cluster_frames[cluster_id][feature].tolist() for cluster_id in cluster_ids]},\n",
    "            {\"yaxis\": {\"title\": feature}},\n",
    "        ],\n",
    "    )\n",
    "    for feature in feature_options\n",
    "]\n",
    "\n",
    "fig.update_layout(\n",
    "    title=\"Cluster Scatter Plot — Select Features\",\n",
    "    xaxis_title=x_col,\n",
    "    yaxis_title=y_col,\n",
    "    plot_bgcolor=\"white\",\n",
    "    hovermode=\"closest\",\n",
    "    width=900,\n",
    "    height=650,\n",
    "    margin=dict(t=110),\n",
    "    updatemenus=[\n",
    "        dict(\n",
    "            buttons=x_buttons,\n",
    "            direction=\"down\",\n",
    "            showactive=True,\n",
    "            x=0.0,\n",
    "            xanchor=\"left\",\n",
    "            y=1.16,\n",
    "            yanchor=\"top\",\n",
    "        ),\n",
    "        dict(\n",
    "            buttons=y_buttons,\n",
    "            direction=\"down\",\n",
    "            showactive=True,\n",
    "            x=0.32,\n",
    "            xanchor=\"left\",\n",
    "            y=1.16,\n",
    "            yanchor=\"top\",\n",
    "        ),\n",
    "    ],\n",
    "    annotations=[\n",
    "        dict(text=\"X axis\", x=0.0, xref=\"paper\", y=1.22, yref=\"paper\", showarrow=False, xanchor=\"left\"),\n",
    "        dict(text=\"Y axis\", x=0.32, xref=\"paper\", y=1.22, yref=\"paper\", showarrow=False, xanchor=\"left\"),\n",
    "    ],\n",
    ")\n",
    "fig.update_xaxes(showgrid=True, gridcolor=\"rgba(148, 163, 184, 0.25)\")\n",
    "fig.update_yaxes(showgrid=True, gridcolor=\"rgba(148, 163, 184, 0.25)\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md07ad9bd0",
   "metadata": {},
   "source": [
    "## 10. Silhouette Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cd0726f63d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean silhouette coefficient : 0.3766\n",
      "Points with score < 0       : 16  (3.6%)\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "fill": "tozerox",
         "fillcolor": "#0B4F91",
         "hovertemplate": "Cluster 0<br>Silhouette: %{x:.3f}<extra></extra>",
         "line": {
          "width": 0
         },
         "name": "Cluster 0",
         "showlegend": true,
         "type": "scatter",
         "x": {
          "bdata": "mwAPuY7sd78oVRt4SIJ+P9l5EElf+q8/N8XsPOMaxz/DQdfmCcXJP5WIcnyJ2cw/sRfah03BzT8DP7F+ja/PP2eXOZn2TNE/qMhqmIiY0T+0JRpj85jWP3VJjMhFOtc/AvcnsUP62D/HG2HlvabZP4m+jvUVXdo/MX+PGJzB2j/D0+fLXw3cP1mK2fwrGd8/0O64CoM+3z/8RTxVIdPfP40EknViDuA/9CnhL85C4D8=",
          "dtype": "f8"
         },
         "y": {
          "bdata": "CgsMDQ4PEBESExQVFhcYGRobHB0eHw==",
          "dtype": "i1"
         }
        },
        {
         "fill": "tozerox",
         "fillcolor": "#2E86DE",
         "hovertemplate": "Cluster 1<br>Silhouette: %{x:.3f}<extra></extra>",
         "line": {
          "width": 0
         },
         "name": "Cluster 1",
         "showlegend": true,
         "type": "scatter",
         "x": {
          "bdata": "20l5Jc1nzb8+ALOUfyTMv0UmqTNZg7w/au3Ze/mcwz+WlFOpLS3TPyiFqeT7G9g/I+/E3L5/2D8=",
          "dtype": "f8"
         },
         "y": {
          "bdata": "KissLS4vMA==",
          "dtype": "i1"
         }
        },
        {
         "fill": "tozerox",
         "fillcolor": "#4A7DB5",
         "hovertemplate": "Cluster 2<br>Silhouette: %{x:.3f}<extra></extra>",
         "line": {
          "width": 0
         },
         "name": "Cluster 2",
         "showlegend": true,
         "type": "scatter",
         "x": {
          "bdata": "wBiE0Agnub/F3GdXIYKyv0fFULQxBa+/9X2eMsASn7/9YHYqLV6bv8cqPqBfV5S/lwiB4jp5k79eoaxTvmKOv7J5RzMHin2/ZfGgC9spkz9tLDiy2TKVPxOXkS630ak/kBXzDmahsD8Qe8PrJn6yP/i1JySp4rg/xBlAe+4cvT8Be9/zRma9P+kQE3rEfb4/1Wylg/y3vj/L9lukGcK+P+BmjMG0h8A/4EUdyynAxD+j3HQ9URXFP377b4RHGsg/Btj8tTNOyD9/72lJdM7JP11cSD8q/8k/wEcxFGePyj/nxztTNkDLP9oVVYIKycw/aGvRFpAvzT+lOiFXa2TQP5UgHM/5uNA/wpATy9To0D/izacSsaLRP11RjPSt/tE/+uCvTI3v0z+ofY9SX/fTP+gD5E/LEtQ/FH6a9MA71D/DZE2qOZrUP9lunrEX6NQ/ujGlm/tX1T985bWfdOLVP0ZLZafI+9Y/fErf15wR1z99k9d9fe3XP0F7eAz5e9g/cxcmHgGM2D+tR3OtYV/ZP5wFdfLUYNk/o7LWPGJk2T903ASkHpXZPz9OgM3P6dk/+bAHg2MY2j+s+Ea70/faPyGPbMjucds/IEPubt6V2z9anFZZ9L3bP0Atpa0rz9s/U6MIAwIb3D9XgrDaeT3cPzOx1TU6sdw/zDuWi4HD3D/ubHiiJGPdP4srzxxQeN0/7ahsOLun3T/hrBuvT7TdP2cEIEwJE94//MdeERXu3j/vyX89fe7eP+8b3NT0od8/V/fGeKS43z+bpEtFG/ffP9rGYHim/N8/BpcxKDIW4D/se4ymbyPgP2zOa/voJ+A/G0iHcwQ94D+n16SYvlngPw==",
          "dtype": "f8"
         },
         "y": {
          "bdata": "OwA8AD0APgA/AEAAQQBCAEMARABFAEYARwBIAEkASgBLAEwATQBOAE8AUABRAFIAUwBUAFUAVgBXAFgAWQBaAFsAXABdAF4AXwBgAGEAYgBjAGQAZQBmAGcAaABpAGoAawBsAG0AbgBvAHAAcQByAHMAdAB1AHYAdwB4AHkAegB7AHwAfQB+AH8AgACBAIIAgwCEAIUAhgCHAIgAiQCKAA==",
          "dtype": "i2"
         }
        },
        {
         "fill": "tozerox",
         "fillcolor": "#1976D2",
         "hovertemplate": "Cluster 3<br>Silhouette: %{x:.3f}<extra></extra>",
         "line": {
          "width": 0
         },
         "name": "Cluster 3",
         "showlegend": true,
         "type": "scatter",
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          "bdata": "hVMl6Dz/0r8d0yghnsOePyvYH/yT4tI/",
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         "y": {
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        },
        {
         "fill": "tozerox",
         "fillcolor": "#5DA3E8",
         "hovertemplate": "Cluster 4<br>Silhouette: %{x:.3f}<extra></extra>",
         "line": {
          "width": 0
         },
         "name": "Cluster 4",
         "showlegend": true,
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   "source": [
    "sil_samples = silhouette_samples(df[numerical_cols], labels)\n",
    "\n",
    "print(f\"Mean silhouette coefficient : {report.metrics.silhouette:.4f}\")\n",
    "print(f\"Points with score < 0       : {(sil_samples < 0).sum():,}  ({(sil_samples < 0).mean():.1%})\")\n",
    "\n",
    "fig = plot_silhouette(labels, sil_samples, report.metrics.silhouette)\n",
    "fig.show()"
   ]
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   "id": "md91f03be5",
   "metadata": {},
   "source": [
    "## 11. FAMD Decomposition\n",
    "\n",
    "FAMD (Factor Analysis of Mixed Data) correctly handles our mix of numerical spend columns and categorical Channel/Region labels — preserving the contribution of all 8 features in a 2D projection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cd2ca5e893",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running FAMD decomposition — synthesises all 8 features into 2 components.\n",
      "Method selected       : FAMD\n",
      "Explained variance — Component 1: 63.4%  |  Component 2: 36.6%\n",
      "Total variance captured by 2D projection: 100.0%\n"
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            "standoff": 15
           },
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           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
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           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "FAMD Decomposition — Wholesale Customer Segments"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Dim1 (63.4%)"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
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         "title": {
          "text": "Dim2 (36.6%)"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "print(\"Running FAMD decomposition — synthesises all 8 features into 2 components.\")\n",
    "\n",
    "reduced_data, explained_var, method_used = auto_decompose(df, n_components=2)\n",
    "method_label = method_used.upper()\n",
    "\n",
    "print(f\"Method selected       : {method_label}\")\n",
    "print(f\"Explained variance — Component 1: {explained_var[0]:.1%}  |  Component 2: {explained_var[1]:.1%}\")\n",
    "print(f\"Total variance captured by 2D projection: {sum(explained_var):.1%}\")\n",
    "\n",
    "decomp_df = reduced_data.copy()\n",
    "decomp_df[\"cluster\"] = labels.astype(str)\n",
    "\n",
    "fig = px.scatter(\n",
    "    decomp_df,\n",
    "    x=reduced_data.columns[0],\n",
    "    y=reduced_data.columns[1],\n",
    "    color=\"cluster\",\n",
    "    title=f\"{method_label} Decomposition — Wholesale Customer Segments\",\n",
    "    labels={\n",
    "        reduced_data.columns[0]: f\"{reduced_data.columns[0]} ({explained_var[0] * 100:.1f}%)\",\n",
    "        reduced_data.columns[1]: f\"{reduced_data.columns[1]} ({explained_var[1] * 100:.1f}%)\",\n",
    "        \"cluster\": \"Cluster\",\n",
    "    },\n",
    ")\n",
    "fig.update_traces(marker=dict(size=8, line=dict(width=0.5, color=\"white\")))\n",
    "fig.update_layout(plot_bgcolor=\"white\", hovermode=\"closest\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mdaabc6a1a",
   "metadata": {},
   "source": [
    "## 12. Cluster Size Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cdb0362bd8",
   "metadata": {},
   "outputs": [
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         "text": [
          "22 (5.0%)",
          "7 (1.6%)",
          "80 (18.2%)",
          "3 (0.7%)",
          "224 (50.9%)",
          "104 (23.6%)"
         ],
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Cluster 0",
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          "Cluster 3",
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         ],
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           "ticks": "",
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        },
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   ],
   "source": [
    "fig = plot_cluster_sizes(labels)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mdbcc0eef2",
   "metadata": {},
   "source": [
    "## 13. Comprehensive Dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cd5291fca0",
   "metadata": {},
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        },
        "title": {
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        "width": 900,
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     "metadata": {},
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   ],
   "source": [
    "fig = create_dashboard(df, labels, report, sil_samples)\n",
    "fig.show()"
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  },
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   "id": "md2e6a1b1c",
   "metadata": {},
   "source": [
    "## 14. Putting Segments to Work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cd92a91bd5",
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segment labels attached.\n",
      "Shape: (440, 10)\n",
      "\n"
     ]
    },
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       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>12126</td>\n",
       "      <td>3199</td>\n",
       "      <td>6975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>Moderately Low Fresh · Horeca</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>7579</td>\n",
       "      <td>4956</td>\n",
       "      <td>9426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   segment_id                    segment_name Channel Region  Fresh  Milk  \\\n",
       "0           4   Moderately Low Fresh · Horeca  Retail  Other  12669  9656   \n",
       "1           4   Moderately Low Fresh · Horeca  Retail  Other   7057  9810   \n",
       "2           4   Moderately Low Fresh · Horeca  Retail  Other   6353  8808   \n",
       "3           4   Moderately Low Fresh · Horeca  Horeca  Other  13265  1196   \n",
       "4           5  Moderately High Fresh · Horeca  Retail  Other  22615  5410   \n",
       "5           4   Moderately Low Fresh · Horeca  Retail  Other   9413  8259   \n",
       "6           4   Moderately Low Fresh · Horeca  Retail  Other  12126  3199   \n",
       "7           4   Moderately Low Fresh · Horeca  Retail  Other   7579  4956   \n",
       "\n",
       "   Grocery  \n",
       "0     7561  \n",
       "1     9568  \n",
       "2     7684  \n",
       "3     4221  \n",
       "4     7198  \n",
       "5     5126  \n",
       "6     6975  \n",
       "7     9426  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_segmented = df.copy()\n",
    "df_segmented[\"segment_id\"] = labels\n",
    "\n",
    "label_map = {p.cluster_id: p.suggested_label for p in profiles.cluster_profiles}\n",
    "df_segmented[\"segment_name\"] = df_segmented[\"segment_id\"].map(label_map)\n",
    "\n",
    "print(\"Segment labels attached.\")\n",
    "print(f\"Shape: {df_segmented.shape}\\n\")\n",
    "display(df_segmented[[\"segment_id\", \"segment_name\", \"Channel\", \"Region\"] + numerical_cols[:3]].head(8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cdcac5e1ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "════════════════════════════════════════════════════════════════════════════════\n",
      "  SEGMENT MEANS — ANNUAL SPEND BY PRODUCT CATEGORY (monetary units)\n",
      "════════════════════════════════════════════════════════════════════════════════\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>segment_id</th>\n",
       "      <th>segment_name</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Moderately High Frozen · Very High Fresh</td>\n",
       "      <td>50050.0</td>\n",
       "      <td>4447.0</td>\n",
       "      <td>5225.0</td>\n",
       "      <td>5896.0</td>\n",
       "      <td>948.0</td>\n",
       "      <td>2404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Very High Delicassen · Very High Frozen</td>\n",
       "      <td>31979.0</td>\n",
       "      <td>32386.0</td>\n",
       "      <td>18605.0</td>\n",
       "      <td>34186.0</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>23358.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Moderately High Fresh · Horeca</td>\n",
       "      <td>21200.0</td>\n",
       "      <td>3886.0</td>\n",
       "      <td>5139.0</td>\n",
       "      <td>4120.0</td>\n",
       "      <td>1132.0</td>\n",
       "      <td>1690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Very High Detergents_Paper · Very High Milk</td>\n",
       "      <td>20031.0</td>\n",
       "      <td>38084.0</td>\n",
       "      <td>56126.0</td>\n",
       "      <td>2565.0</td>\n",
       "      <td>27645.0</td>\n",
       "      <td>2548.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Moderately Low Fresh · Horeca</td>\n",
       "      <td>6072.0</td>\n",
       "      <td>3293.0</td>\n",
       "      <td>4123.0</td>\n",
       "      <td>2452.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>High Detergents_Paper · Moderately High Milk</td>\n",
       "      <td>4724.0</td>\n",
       "      <td>11837.0</td>\n",
       "      <td>18462.0</td>\n",
       "      <td>1547.0</td>\n",
       "      <td>8196.0</td>\n",
       "      <td>1639.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   segment_id                                  segment_name    Fresh     Milk  \\\n",
       "0           0      Moderately High Frozen · Very High Fresh  50050.0   4447.0   \n",
       "3           3       Very High Delicassen · Very High Frozen  31979.0  32386.0   \n",
       "5           5                Moderately High Fresh · Horeca  21200.0   3886.0   \n",
       "1           1   Very High Detergents_Paper · Very High Milk  20031.0  38084.0   \n",
       "4           4                 Moderately Low Fresh · Horeca   6072.0   3293.0   \n",
       "2           2  High Detergents_Paper · Moderately High Milk   4724.0  11837.0   \n",
       "\n",
       "   Grocery   Frozen  Detergents_Paper  Delicassen  \n",
       "0   5225.0   5896.0             948.0      2404.0  \n",
       "3  18605.0  34186.0            1949.0     23358.0  \n",
       "5   5139.0   4120.0            1132.0      1690.0  \n",
       "1  56126.0   2565.0           27645.0      2548.0  \n",
       "4   4123.0   2452.0            1224.0       997.0  \n",
       "2  18462.0   1547.0            8196.0      1639.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spend_cols = numerical_cols\n",
    "\n",
    "segment_means = (\n",
    "    df_segmented.groupby([\"segment_id\", \"segment_name\"])[spend_cols]\n",
    "    .mean()\n",
    "    .round(0)\n",
    "    .reset_index()\n",
    "    .sort_values(\"Fresh\", ascending=False)\n",
    ")\n",
    "\n",
    "print(\"\\u2550\" * 80)\n",
    "print(\"  SEGMENT MEANS \\u2014 ANNUAL SPEND BY PRODUCT CATEGORY (monetary units)\")\n",
    "print(\"\\u2550\" * 80)\n",
    "display(segment_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd4383b9e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_cols, n_rows = 3, (len(spend_cols) + 2) // 3\n",
    "\n",
    "fig, axes = plt.subplots(n_rows, n_cols, figsize=(15, 5 * n_rows))\n",
    "fig.suptitle(\"Wholesale Customer Segment Profiles \\u2014 Annual Spend by Category\",\n",
    "             fontsize=14, fontweight=\"bold\")\n",
    "\n",
    "palette = [\"#2563EB\", \"#10B981\", \"#F59E0B\", \"#DC2626\", \"#8B5CF6\", \"#EC4899\"]\n",
    "segment_order = segment_means[\"segment_name\"].tolist()\n",
    "short_names = [name[:14] + \"..\" if len(name) > 14 else name for name in segment_order]\n",
    "\n",
    "for idx, (ax, col) in enumerate(zip(axes.flat, spend_cols)):\n",
    "    values = segment_means.set_index(\"segment_name\")[col].reindex(segment_order)\n",
    "    ax.bar(range(len(values)), values.values, color=palette[:len(values)], alpha=0.85,\n",
    "           edgecolor=\"white\", linewidth=0.8)\n",
    "    ax.set_xticks(range(len(values)))\n",
    "    ax.set_xticklabels(short_names, rotation=30, ha=\"right\", fontsize=8)\n",
    "    ax.set_title(col, fontweight=\"bold\", fontsize=10)\n",
    "    ax.grid(axis=\"y\", alpha=0.3)\n",
    "    ax.yaxis.set_tick_params(labelsize=8)\n",
    "\n",
    "for ax in axes.flat[len(spend_cols):]:\n",
    "    ax.set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "md885fb7e5",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## What You Built\n",
    "\n",
    "| You Wrote | BitBullet Handled |\n",
    "|-----------|-------------------|\n",
    "| Called `generate_feature_stats(df)` | Full audit — skewness, kurtosis, cardinality, nulls |\n",
    "| Mapped integer Channel/Region to strings | Correct categorical type detection by `detect_column_types` |\n",
    "| Called `auto_cluster(df)` | Algorithm selection, K search, fitting, evaluation |\n",
    "| Defined a `clusterer_factory` and local-peak rule | Silhouette scores across candidate K values |\n",
    "| Declared a `ClusterConfig` | Cao initialisation, Huang gamma, categorical distance weighting, resolved per-column metadata |\n",
    "| Called `evaluate_clustering(df[num], labels)` | Silhouette, Calinski-Harabász, Davies-Bouldin, quality score |\n",
    "| Called `profile_clusters(df, labels)` | Per-cluster statistics, suggested labels, feature importance |\n",
    "| Called `auto_decompose(df)` | FAMD selection for mixed data, explained variance |\n",
    "| Called `create_dashboard(...)` | Multi-panel visualisation assembly |\n",
    "\n",
    "### Key Takeaways\n",
    "\n",
    "1. **Algorithm selection matters.** The Channel and Region columns carry real discriminative information. Converting integers to strings ensures they are treated as categoricals, not as ordered numerical variables.\n",
    "\n",
    "2. **Gamma and categorical weights have different jobs.** Gamma controls the total scale of categorical distance relative to numerical distance; categorical weights allocate that categorical contribution across categorical columns.\n",
    "\n",
    "3. **Small datasets need compact K searches.** With 440 rows, K > 7 risks fitting noise. A global silhouette maximum can over-prefer K = 2 when a coarse binary split dominates. Looking for a post-2 local peak asks whether a richer segmentation improves separation despite the extra cluster.\n",
    "\n",
    "4. **Spending distributions are always right-skewed.** A handful of very large accounts inflate centroids. `auto_cluster` normalises internally; for manual configuration, consider log-transforming spend features before calling `find_optimal_k`.\n",
    "\n",
    "5. **Segments are only valuable when activated.** Attaching labels and computing per-segment spend means is the step that turns a mathematical result into a procurement or sales planning artefact.\n",
    "\n",
    "---\n",
    "\n",
    "**Continue the Academy:**\n",
    "- `04_Data_Transformations.ipynb` — deep dive into every transform in `bitbullet.transform`\n",
    "- `05_Model_Management.ipynb` — versioning, comparison, and model governance"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "bitbullet",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.13"
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 },
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