{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b7c68d2f",
   "metadata": {},
   "source": [
    "# Interpreting Clusters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cell-45d2d4a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:58.542447Z",
     "iopub.status.busy": "2026-08-31T06:02:58.542239Z",
     "iopub.status.idle": "2026-08-31T06:02:59.643287Z",
     "shell.execute_reply": "2026-08-31T06:02:59.643023Z"
    },
    "tags": [
     "hide-input",
     "thebe-init",
     "runnable"
    ]
   },
   "outputs": [],
   "source": [
    "path_data = '../../data/'\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0518e714",
   "metadata": {},
   "source": [
    "```{contents}\n",
    ":local:\n",
    ":depth: 2\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-3365273c",
   "metadata": {},
   "source": [
    "This section shows how to interpret a k-means result after the model has been fit. The technical output is only the starting point. The analyst still has to choose a reasonable number of clusters, describe each cluster, and decide whether the grouping supports the business question.\n",
    "\n",
    "We work with an online store's customers. For each of 240 customers, the data record the amount spent in the last year (`annual_spend`, in dollars) and the number of orders placed (`orders_per_year`). Marketing wants to know whether customers fall into groups that call for different treatment.\n",
    "\n",
    "```{note}\n",
    "`customer_segments.csv` is a **synthetic** data set generated for this book with four built-in customer types, so we can see whether k-means finds them. Real customer data are messier, and the groups are rarely this clean.\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cell-a35c0ec1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:59.644710Z",
     "iopub.status.busy": "2026-08-31T06:02:59.644592Z",
     "iopub.status.idle": "2026-08-31T06:02:59.646616Z",
     "shell.execute_reply": "2026-08-31T06:02:59.646433Z"
    },
    "tags": []
   },
   "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>customer_id</th>\n",
       "      <th>annual_spend</th>\n",
       "      <th>orders_per_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C001</td>\n",
       "      <td>1133</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C002</td>\n",
       "      <td>443</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C003</td>\n",
       "      <td>1327</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C004</td>\n",
       "      <td>731</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C005</td>\n",
       "      <td>898</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id  annual_spend  orders_per_year\n",
       "0        C001          1133               25\n",
       "1        C002           443                2\n",
       "2        C003          1327               18\n",
       "3        C004           731               28\n",
       "4        C005           898               26"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers = pd.read_csv(path_data + 'customer_segments.csv')\n",
    "customers.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "945123a0",
   "metadata": {},
   "source": [
    "**Scale the features first.** The two features are on very different scales: spend runs from about \\$60 to \\$3,400, while orders run from 1 to 35. K-means groups points by distance, so without scaling, a \\$100 difference in spend counts as much as a difference of 100 orders, and the clusters form almost entirely by spend. Here is k-means with 4 clusters on the raw features and on the features in standard units, both plotted in the original units:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d41e4855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['annual_spend', 'orders_per_year']\n",
    "X = customers[features]\n",
    "X_scaled = (X - X.mean()) / X.std(ddof=0)      # standard units\n",
    "\n",
    "raw_labels = KMeans(n_clusters=4, n_init=10, random_state=101).fit_predict(X)\n",
    "scaled_labels = KMeans(n_clusters=4, n_init=10, random_state=101).fit_predict(X_scaled)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4), sharey=True)\n",
    "sns.scatterplot(data=customers, x='annual_spend', y='orders_per_year', hue=raw_labels, palette='tab10', ax=axes[0])\n",
    "axes[0].set_title('Raw features')\n",
    "sns.scatterplot(data=customers, x='annual_spend', y='orders_per_year', hue=scaled_labels, palette='tab10', ax=axes[1])\n",
    "axes[1].set_title('Standard units')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780f9812",
   "metadata": {},
   "source": [
    "On the raw features (left), the clusters are bands of spend: customers who spend about the same land in the same cluster whether they order 3 times a year or 30. In standard units (right), both features count, and the clusters follow the customer types you can see in the plot. From here on, we cluster the scaled features.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-b0bbffca",
   "metadata": {},
   "source": [
    "## Choosing k\n",
    "\n",
    "K-means needs the number of clusters up front. Two common heuristics are the elbow method and the silhouette score. These tools help compare choices, but they do not remove judgment. A technically neat value of `k` still needs a practical interpretation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-ad94b606",
   "metadata": {},
   "source": [
    "## Elbow Check\n",
    "\n",
    "The **elbow method** fits k-means for several values of `k` and records the model's inertia, which is the within-cluster sum of squares. Lower inertia is better, but it always improves as `k` increases. The useful question is where the improvement starts to flatten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cell-81c730ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:59.647646Z",
     "iopub.status.busy": "2026-08-31T06:02:59.647586Z",
     "iopub.status.idle": "2026-08-31T06:02:59.686816Z",
     "shell.execute_reply": "2026-08-31T06:02:59.686537Z"
    },
    "tags": []
   },
   "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>k</th>\n",
       "      <th>inertia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>236.793172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>116.569217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>28.609085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>24.227739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>20.382649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>17.746656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   k     inertia\n",
       "0  2  236.793172\n",
       "1  3  116.569217\n",
       "2  4   28.609085\n",
       "3  5   24.227739\n",
       "4  6   20.382649\n",
       "5  7   17.746656"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inertias = []\n",
    "k_values = range(2, 8)\n",
    "\n",
    "for k in k_values:\n",
    "    model = KMeans(n_clusters=k, n_init=10, random_state=101)\n",
    "    model.fit(X_scaled)\n",
    "    inertias.append(model.inertia_)\n",
    "\n",
    "elbow_df = pd.DataFrame({\"k\": list(k_values), \"inertia\": inertias})\n",
    "elbow_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cell-0c9d8d86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:59.688084Z",
     "iopub.status.busy": "2026-08-31T06:02:59.688020Z",
     "iopub.status.idle": "2026-08-31T06:02:59.739088Z",
     "shell.execute_reply": "2026-08-31T06:02:59.738846Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Inertia')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k50p2br4cT8syX9Ozc135dgAAoARcS8tFsnLy5I2lu+SdlbslJSNXYiOC5daeTeTO/s0kLCTIVW8LAACKQeBxUc+Ohp1XFu6wbdPQY/3+jn5NJTKUpgcAoLJQ0nKBoMBA07NTHN0eHEizAwBQmTjzukBqZo7p0SmObtfHAQBA5SHwuEBMeIgZs1Mc3a6PAwCAykPgcYG8/HwzQLk4uj03P98VbwsAAErAyFkXiAgNNrOxlP0srVE9Gsu4fk3N4wAAoPIEWCwWSyW+n8dKSUmRuLg4SU5OltjYWKe8pq67owOUdcxOZFiwLNt+VFbvOi7/vLqdU14fAAB/l1LG8zddDS5knXpePTpMdhxOlTs+WCsBASI3XdBQWtSKceVbAwAAO4zhqSQacC5rV1u0P+3VxQmV9bYAAIDAU7kmDmxuvs7bkCS7j53mFxAAgEpCD08lal8vTga1jpd8i8h/6eUBAKDSEHgq2aRBLczXuesOSOKJ9Mp+ewAA/BKBp5Kd16CK9GlRQ/LyLfLaEsbyAABQGQg8bnB3YS/P7LX75cCpDHccAgAAfoXA4wZdG1eTHk2rS06eRWYs3emOQwAAwK8QeNxk0qCCGVufrkmUIymZ7joMAAD8AoHHTbSHp2ujqpKdmy8zlu1y12EAAOAXCDxuEhAQYJux9dHqvXIsLctdhwIAgM8j8LhR3xY1pFODKpKZky9vLqeXBwAAVyHwuLmX567C1Zc/WLVXTp7OdufhAADgswg8bjawdby0qxsr6dl58vaK3e4+HAAAfBKBxxPG8hT28ry7Yo8kZ+S4+5AAAPA5BB4PcEnb2tKqVoykZuWa0AMAAJyLwOMBAgMDZEJhL4+WtVIz6eUBAMCZCDwe4soOdaRpzShT0vrgl73uPhwAAHwKgcdDBAUGyMQBBb08by3fLenZue4+JAAAfAaBx4Nc06muNKoeKSdOZ8vHq/e5+3AAAPAZBB4PEhwUKHf2b2buv7F0l2Tm5Ln7kAAA8AkEHg8z5Pz6Uq9KhLnUxKe/0ssDAIAzEHg8TGhwoIy36+XJyqWXBwCAc0Xg8UBDu9aX2rHhciglU2av3e/uwwEAwOsReDxQWHCQ3NGvqbn/+pKdkpOX7+5DAgDAqxF4PNRNFzSUGtFhsv9khsxdd8DdhwMAgFcj8Hio8JAgGdu3ibn/38UJkksvDwAAFUbg8WDDL2wk1aJCZe/xdJm3McndhwMAgNci8HiwqLBgGd27oJfn1UUJkpdvcfchAQDglQg8Hm5kj0YSFxEiO4+elu82HXT34QAA4JUIPB4uJjxEbu3V2NbLk08vDwAA5Ubg8QK39mwi0WHBsvVQqvyw+bC7DwcAAK9D4PECcZEhMqpnI3N/+qIdYrEwlgcAgPIg8HiJ0b2bSmRokPyZlCKLtx1x9+EAAOBVCDxeQqenj+he0MszbWECvTwAAJQDgceLjOnTVMJDAmV94in5OeGYuw8HAACvQeDxIjVjwswlJ9S0hYzlAQCgrAg8XuaOvs0kNChQ1uw5Kb/sOuHuwwEAwCsQeLxM7bhwuaFbA9uMLQAAcHYEHi80rn8zCQkKkJU7j8vavfTyAABwNgQeL1SvSoRc17m+bcYWAAAoHYHHS93Zv7kEBQbI0u1HzawtAABQMgKPl2pYPVIGn1fX3H+VsTwAAJSKwOPFJgxoLoEBIj9tOSJ/JiW7+3AAAPBYBB4v1qxmtFzV0drLw1geAABKQuDxchMHNjdfv9t0SLYfTnX34QAA4JGC3fnmetXvb7/9VlauXCnBwcHSu3dvufjii4vsd+DAAXn//ffl8OHD0qFDBxkxYoSEhoaWex9f1LJWjFzevrYJPNrLM+2m8919SAAAeBy39fDk5+dLx44d5Y033pCoqCjJy8uTG2+8UW677TaH/bZu3WoCzKpVq6RmzZrywgsvyEUXXSS5ubnl2scfennmbUySnUfT3H04AAB4nACLdrO4gb6tBpU2bdrYti1cuNAElfXr10unTp3MtsGDB0tKSoosWrRIAgICJCkpSZo0aSIzZsyQW265pcz7nI0+Py4uTpKTkyU2Nla8zZj3fpOfthyWazvXk/8MO8/dhwMAQKUo6/nbbT08Gkzsw46yfn/o0CHzNScnRxYsWCA33XST2V/VrVtXBgwYIF9//XWZ9/EHdw0q6OX5an2S7Due7u7DAQDAo3jUoOW33nrLlLe6detmvt+7d69kZ2eb3hp7+n1CQkKZ9ylOVlaWSYX2N2/WsX4V6deypuTlW+S1JczYAgDAIwPP999/L0899ZS89NJLUq1aNbMtIyPDfI2JiXHYV7us0tPTy7xPcaZOnWq6wKy3Bg0KLsjpC708c37fL/tP0ssDAIBHBZ4lS5bItddeK48//rjcfvvttu3WWtypU46XTjh58qTtsbLsU5wpU6aYep/1lpiYKN6uS6Nq0rNZdcnJs8gbS3e6+3AAAPAYbg88y5Ytk6uuukoefPBBefjhhx0e014X7bnZvHmzw3b9vl27dmXepzhhYWEmENnffMGkgS3M18/X7JdDyZnuPhwAADyCWwPP8uXL5YorrpDJkyfLo48+WuTxwMBAGTZsmLzzzju28tTatWvNuj06hb2s+/iT7k2ryQWNq0l2Xr7MWEYvDwAAbp2WnpaWZmZTRUREyJAhQxwe00UDe/XqZe4fPXrUzLjS2Vg6Vf3HH380AUennFuVZR9fn5Zub/mOozJi5q8SFhwoP08eKDVjwtx9SAAAuERZz99uCzyZmZny7rvvFvtY//79pXXr1rbvdRbWTz/9ZFZR1sUKu3TpUuQ5ZdnHXwKP/kiHvLZS1ieekjv6NpUpVzhO/wcAwFd4fODxNL4UeNSirYfltnd/k8jQINPLUy3K9y+zAQDwPymevvAgXGtAq3hpXy9W0rPzZObPu2huAIBfI/D4KF112jpj672VeyU5PcfdhwQAgNsQeHzYxW1qSevaMZKWlSvvrNzt7sMBAMBtCDw+LDAwwHYl9bd/3i2pmfTyAAD8E4HHx13evo40qxklKZm58v6qve4+HAAA3ILA4+OC7Hp53lq+S05n5br7kAAAqHQEHj9wdce60rh6pJxMz5GPVtPLAwDwPwQePxAcFCh3Dijo5fnfst2SmZPn7kMCAKBSEXj8xJDz60n9qhFyLC1LPvl1n7sPBwCASkXg8RMhQYEyvn8zc/+NpTvp5QEA+BUCjx+5vkt9qRMXLodTsmTW2v3uPhwAACoNgcePhAUHmYuJqjeW7JTs3Hx3HxIAAJWCwONnbrygodSMCZMDpzJk7jp6eQAA/oHA42fCQ/7q5fnv4p2Sm0cvDwDA9xF4/NDfLmwo1aJCZd+JdPl6Q5K7DwcAAJcj8PihyNBgGdOnibn/6qIEycu3uPuQAABwKQKPnxrZo7HERYTIrmOn5Zs/Drr7cAAAcCkCj5+KDguW0b2tvTw7JJ9eHgCADyPw+LFRPRtLTFiwbD+cJj9sPuTuwwEAwGUIPH5MS1q39Gps7k9bmCAWC2N5AAC+icDj527r1USiQoNk88EUWbjliLsPBwAAlyDw+LmqUaFyc49G5v70RTvo5QEA+CQCD+T2Pk0lPCRQNuxPlmU7jtEiAACfQ+CB1IgOk+EXFvbyLKSXBwDgewg8MPRyE6HBgfLb3pOyatdxWgUA4FMIPDDiY8Plxm4NzP1pC3fQKgAAn0Lggc24fs0kJChAftl1QtbsOUHLAAB8BoEHNnWrRMj1XejlAQD4HgIPHNzZv5kEBQbI8h3HZH3iKVoHAOATCDxw0KBapAw5v55txhYAAL6AwIMiJgxoLoEBIgu3HpFNB5JpIQCA1yPwoIgmNaLk6k51basvAwDg7Qg8KNbEAc0lIEDk+z8Py9ZDKbQSAMCrEXhQrBa1YuSK9nXM/VcXJdBKAACvRuBBiSYObG6+fvPHQUk4kkZLAQC8FoEHJWpTJ1YubltLLBaR1xbTywMA8F4EHpTqroEtzNcv1x+QPcdO01oAAK9E4EGpOtSPk/6takq+9vIsoZcHAOCdCDw4q0mFvTxf/H5AEk+k02IAAK9D4MFZdWlUVXo3ryG5+RZ5Y+lOWgwA4HUIPCiTSYUztmb9tl8OJmfQagAAr0LgQZlc2LS6XNCkmmTn5cuMpbtoNQCAVyHwoNwztj75dZ8cSc2k5QAAXoPAgzLr1by6dG5YRbJy8+XNZfTyAAC8B4EHZRYQECCTBhX08nz4yz45npZF6wEAvAKBB+XSv2VN6Vg/TjJy8mTmz7tpPQCAVyDwoNy9PHoldfXeyj1yKj2bFgQAeDwCD8pNr6/VunaMnM7Ok7dX7KEFAQAej8CDio3lKZyx9c6K3ZKSmUMrAgA8GoEHFXJ5+9rSIj5aUjNz5f2V9PIAADwbgQcV+8UJDJCJhasv6+Dl01m5tCQAwGMReFBhV3WsK01qRMnJ9Bz58Je9tCQAwGMReFBhQYEBcmf/Zub+m8t3SUZ2Hq0JAPBIBB6ck/87v57Urxohx9Ky5eNf99GaAACPRODBOQkJCpQJhevyzFi6UzJz6OUBAHgeAg/O2XWd60vduHA5kpols35LpEUBAB6HwINzFhocKOMKx/K8vmSnZOfm06oAAI9C4IFTDOvaQOJjwiQpOVPm/L6fVgUAeBQCD5wiPCRIxvZtau6/tiRBcvLo5QEAeA4CD5xm+IWNpEZ0qCSeyJCv1ifRsgAAj0HggdNEhAbJmD6FvTyLEyQv30LrAgA8AoEHTnVz90ZSJTJEdh07LfM30ssDAPAMBB44VXRYsIzu1cTcf3VRguTTywMA8AAEHjjdqF6NJSY8WHYcSZMFfx6ihQEAbkfggdPFhofIrYW9PNMXJYjFwlgeAIAfB56cnByZPXu2XHTRRVK/fn1ZtWpVkX2mTJliHrO/6f5n+vzzz6VXr17SvHlzGTJkiGzZsqWSPgWKc1uvxhIVGiRbDqbIT1uO0EgAAP8NPA899JB88sknMnLkSDlw4IBkZWUV2efkyZPSqVMn+eWXX2y3jz76yGGfuXPnyvDhw2XEiBEyZ84ciYmJkX79+snRo0cr8dPAXpXIUBnZs7G5P33RDnp5AAD+G3ieffZZE1AGDhxY6n4REREOPTy1atVyePypp56SUaNGybhx40w4evvtt80J9vXXX3fxJ0BpxvRuIhEhQbJxf7Is2U74BAD4aeAJCgoq035Lly6Vli1bSrdu3eTee+81vT5WKSkpsm7dOrn44ott24KDg2XQoEGybNkylxw3yqZ6dJgMv7ChuT99Ib08AAD38fhByzVr1pQnn3xSvvzyS3nmmWdMiNGxOpmZmeZxLYWp2rVrOzxPe4GsjxVHy2caluxvcD693IReXPT3fadk5c7jNDEAwC08PvBo2Bk/fry0bdvW9OLMmzdPEhIS5NNPPzWP5+fn23p17IWEhEheXl6Jrzt16lSJi4uz3Ro0aODiT+Kf4mPD5W8XFPTyTFu4w92HAwDwUx4feAICAhy+r1OnjjRq1Mg2C0t7gNSxY8cc9tPvrY8VR2d/JScn226JiYkuOX6I3NGvqYQGBcrq3Sfk190naBIAQKXz+MBzpoyMDDl48KBUq1bNfB8fH28C0IoVKxz2+/nnn+WCCy4o8XXCwsIkNjbW4QbXqBMXIdd3rW+bsQUAQGXz6MCj42wmTZokSUkF12TSnpjbb7/d3L/xxhtt+02YMEFmzpwpGzZsMCWuadOmyb59+2Ts2LFuO3Y4Gt+vmQQHBsjyHcfk931/DToHAMDnA8+sWbPMNHOdfaWGDh1qvv/Pf/5j64Vp37699O7dW6pWrWoGImvpSWdtaa+O1X333WempXfv3t301Oh0dx3j06ZNG7d9NjhqUC1ShpxfzzZjCwCAyhRgceO6/+np6XLiRNExHcWVmLR3Jzo6utSp7NnZ2Wa/GjVqFBn7czY6S0sHL+vzKW+5xp5jp2Xgv5eIXk903sTe0qF+nIveCQDgL1LKeP52nNpUySIjI82tLPTDnE1oaGipA5XhXo1rRMng8+rJ3HUHzFie/43syo8EAFApPHoMD3zPhAHNRTvffth82FxnCwCAykDgQaVqHh8tV3SoY+6/uiiB1gcAVAoCDyrdpIHNzddvNx2UhCOp/AQAAC5H4EGla107Vi5tV0t0uDy9PACAykDggVtMGtjCfP16Q5LsPnaanwIAwKUIPHCL9vXiZGDreDNF/bXFjOUBALhWhaelb9682VzBXFc0zs3NdXjsrbfecsaxwQ/G8izaekS+WHdA7hrUwixOCACAx/TwfP3119KlSxdZtmyZzJgxw1yo84cffjCXd9DrXAFlcX7DqtKnRQ3Jy7fIa0t20mgAAM8KPI899pgJNwsWLDDfa0/Pzp07ZcyYMVK7dm1nHyP8YCzP7LWJknQqw92HAwDwURUKPFu3bpXBgweb+8HBweYK5iEhIfL000+b3h+grC5oUk26N60mOXkWmbGUXh4AgAcFnszMTImKijL369SpIzt2FFwMMi8vz4QfoDzuKuzl+WRNohxJyaTxAACeN0vr6quvltGjR8vLL78sN954o/Tp08c5Rwa/0aNZdenSqKpk5+bLjGW73H04AAAfVKHAM2/ePNv9Z599Vrp27Srvvvuu1K1blxlaKDe9sr119eWPVu+VY2lZtCIAwKkCLBZd7xZlvbw8XEN/DQf/d4Vs3J8s4/o1kwcvb01TAwCcdv5m4UF4TC+PdSzPB6v2yMnT2e4+JACAPy482LhxY/N1z549tvsl0X2A8hrUJl7a1omVzQdT5J0Vu+XeS1rRiACAyg08jzzySLH3AWeP5Rn/0e/yzoo9MrpPU4mLCKGBAQCVF3h0UUGrU6dOyf3331/sfi+++OK5HxX81qXtakvLWtGy/XCavLdyj7nkBAAAbhm0rH+Jl/S00h7zZAxa9hxfrT8gd3+6XqpEhsjPkwdKdFiFL/kGAPBxKe4YtKyXl6hevbozXxJ+6KqOdaVpjSg5lZ4jH6za6+7DAQD4gHL96dy8efNi76v8/HxJSkqS4cOHO+/o4JeCAgNkwoDmct+sDfLW8l0yqmcjiQyllwcAUHHlOotYx+2MHz++yBgevZaWzt4aMGDAORwOUGDweXXllYU7ZN+JdPl49T4Z06cpTQMAqJzAM27cOPM1Ojpabr755oq/K3AWwUGBcmf/ZvLgF3+Yy03c3L2RhIcE0W4AgAqp0BiekSNHVuzdgHK4tnN9qVclQo6mZslnaxJpOwBA5QaeWrVqyaFDhyr+rkAZhAYHyrj+zcz9N5bulKzcPNoNAFB5gWfChAnywAMPSFpaWsXeFSijoV3qS63YMDmYnClz1h6g3QAAlbcOT9u2bWXLli0SHh4uDRo0kNDQUIfHN23aJN6GdXg819s/75Yn52+W+lUjZPH9/SUkiEvAAQDKd/6u0FzfsWPHVuRpQIXcdEFDeW1Jguw/mSFz1x2QYV0b0JIAANf38Pgieng824ylO2Xqd1ulcfVI+enefmYWFwAAKZWx0nJGRoYpbQGuptPSq0aGyJ7j6TJ/40EaHABQLhUKPDpYWdfh0fV4dDyP1bBhw2Tt2rUVeUmgVFFhwbbFB19dnCD5+XRMAgBcHHimTJkiBw4ckDVr1jhsv+WWW+SJJ56oyEsCZzWyRyOJDQ+WhCNp8t0mlkUAALg48MydO1dmzpwpnTt3dtjevXt3Wbx4cUVeEjirmPAQubVXE3N/+qId9PIAAFwbeI4dOybx8fHmfkBAgMOYHsZAw5Vu69VEosOCZeuhVPlxy2EaGwDgusBz3nnnybffflsk8Lzyyity4YUXVuQlgTKJiwwxpS1rLw8BGwBQFhVah+fpp5+Wa6+9VlauXGm+f+6552TBggWyYsUKWbRoUUVeEigzHbz87so9sulAiizZdlQGtC7obQQAwKk9PBdddJF8//33snv3bqldu7a8/PLLEhkZKUuXLpXevXtX5CWBMqsWFWqmqatXFtLLAwA4OxYeLMTCg97lSGqm9HlusWTl5ssHoy+QPi1quvuQAAC+uvAg4C7xMeHmkhNq+sIEfhAAgFJVKPDs27dPhgwZYmZqBQcHF7kBlWFcv2YSGhQov+45Ib/tOUGjAwBKVKF0ctttt0l2draZlVW1atWKvARwzmrHhcuEAc2kbd1YaVc3To6nZZm1enLz8yUylOANADjHMTx6SYmEhAQzYNlXMIbHO2Vk55krqb+3ao+kZORKbESw3NqzidzZv5mEhQS5+/AAAB5y/q7Qn8F16tSRwECG/8C9MrJz5Y2lu2T6or/G8Gjo0Zlb6o5+TenpAQAYgRUtaT388MOmrAW4S1BgoLyzcnexj+n2YEI5AOBceng++OAD2bJli3z22WfSoEEDh9WW1aZNmyryskC5pGbmmB6d4uh2fbx6dBitCgCoWOAZO3YsTQe30wHKOmanuNCj2/VxAAAqHHjuueceWg9ul5efbwYoW8fs2NPtOlsrlKWmAADlDTxpaWllnsUFuFpEaLCZjWUds2OdpTWqR2O5pWdjCTqj1AoA8F/lmpZ+5lidknjjFayZlu690rNzzQBlHbOjZayfE47Jv77ZIpe0qyWTL2vt7sMDAHjbtPRZs2Y549gAp7IuMmgdoJyTly87j6bJjKVpcmm72nJegyq0OAD4OS4eWogeHt9y96fr5Kv1SdI8PlrmT+ot4SxCCAA+iYuHwq89fnU7qREdJglH0uTln4oOagYA+BeWS4ZPqhoVKs8MaW/u/2/ZTvl930l3HxIAwI0IPPBZl7SrLf93Xl3Jt4g8MGuDZObkufuQAABuQuCBT3v8mnZSMyZMdh49LS/9uN3dhwMAcBMCD3xalUgtbXUw999cvovSFgD4KQIPfN7FbWvJtefXM6Wt+yltAYBfIvDALzx2dTuJjwmTXUdPy38obQGA3yHwwC/ERYbI1Gv/Km2t3cusLQDwJwQe+I1BbWrJtZ3riV75hFlbAOBfCDzwK49d1U5qxYbJrmOn5d8/bHP34QAAKgmBB35b2nrr592ydu8Jdx8SAKASEHjgdwa2riXXda5vSlv3z9ooGdksSAgAvo7AA7/0z6vbmtLW7mOn5UVKWwDg8zwi8Bw7dkx+++03SU1NLXGfI0eOyKZNmyQ9Pf2c9gFUXESIPHttR3P/7RW7Zc0eSlsA4MvcGng2bNggN998s7Rp00a6desma9euLbJPdna2DB8+XBo2bChXX321xMfHy5tvvlnufYAzDWgdL0O71LfN2qK0BQC+y62BZ82aNXLppZfKL7/8UuI+Tz/9tCxevFh27Nghu3fvlpkzZ8odd9zhEI7Ksg9QnEeuaiu1Y8Nlz/F0eeF7Zm0BgK9ya+AZM2aMjBgxQsLCwkrc56233jL7NWjQwHx/ww03mB4hDTXl2QcoqbQ19bqCWVvvrNwtv+6mtAUAvsgjxvCU5ODBg+am5S57F154ofz+++9l3gcozYBW8TKsa0Fp6x+zKW0BgC/y6MBz/Phx87V69eoO22vUqGF7rCz7FCcrK0tSUlIcbvDv0laduILS1vPfb3X34QAA/CnwhISE2MKJvYyMDNtjZdmnOFOnTpW4uDjbzVoOg3+KDQ+RZ68rmLX17so9lLYAwMd4dOCpX7++BAQESFJSksN2/V5nZJV1n+JMmTJFkpOTbbfExEQXfQp4i34ta8oNXRsUzNqavUHSs3PdfUgAAH8IPFFRUdK9e3f55ptvHHpuFi5cKIMGDSrzPsXRgdKxsbEON+Dhq9qY0tZeLW0tYNYWAPiKYHe+uY6x0WnkumCg2rZtm0RHR0vdunXNTT311FNy2WWXSatWraRHjx7yyiuvSJUqVcy0c6uy7AOUp7Q16u1fTWnrsva1pXtTx/FhAADvE2CxaAe+e8yfP18ef/zxItvHjh1rblaLFi2SV199VQ4fPiwdOnSQRx55xJSy7JVln9LooGUdy6PlLXp78OCcjfLpmkRpWC1SFtzTRyJD3fq3AQDgHM/fbg08noTAA3upmTly6UvLJCk5U0b1aCRPDG5PAwGAF5+/PXoMD+AuMeEh8tz1BbO23lu1V1btLHmJAwCA5yPwACXo06Km3HRBwUy/f8zZIKezmLUFAN6KwAOU4qErWku9KhGSeCJDnlvAgoQA4K0IPMDZSluFCxK+v2qvrNx5jPYCAC9E4AHOoneLGvK3CwtLW7M3UtoCAC9E4AHK4KEr2pjS1v6TGfLsd5S2AMDbEHiAMogOC5bnC2dtffDLXlmZQGkLALwJgQcoo17Na8jwwtLWA7M3ShqztgDAaxB4gHKYckUbqV81Qg6cypCp326h7QDASxB4gAqWtj5avU9WUNoCAK9A4AHKqWezGjKieyPbrC1KWwDg+Qg8QAU8eHlraVCtoLT1DKUtAPB4BB6gAqK0tHVdJ3P/49X75OcdzNoCAE9G4AEqqEez6jKyR0Fpa/KcjeYK6wAAz0TgAc7B5MvsS1ssSAgAnorAAziptPXJr/tk2fajtCcAeCACD+CE0taowtLWg3M2SgqlLQDwOAQewAkmX95aGlaLlKTkTHnmGxYkBABPQ+ABnCAyNFheKFyQ8NM1ibKU0hYAeBQCD+AkFzatLrf0bGzuU9oCAM9C4AGc6B+XtZJG1SPlYHKm/Gs+pS0A8BQEHsDppa1OEhAg8tlvibJk2xHaFwA8AIEHcLILmlSzlbamfPEHs7YAwAMQeAAX+MelraVxYWnr6fmbaWMAcDMCD+ACEaFB8sLQgtLW57/tl8WUtgDArQg8gIt0a1xNbu3ZxDZrKzmDa20BgLsQeAAXeuDSVtKkRpQcTsmSpyhtAYDbEHgAV5e2ru9oSluz1+6XRVsP094A4AYEHsDFujauJqN7NbHN2kpOp7QFAJWNwANUgvsvbSVNC0tbT1LaAoBKR+ABKkF4iM7aKihtzfmd0hYAVDYCD1BJujSqJmN6W2dtUdoCgMpE4AEq0X2XtJKmNaPkSGqWPDH/T9oeACoJgQeo7NLW9Z0kMEDki98PyE+bmbUFAJWBwANUsi6NqsqYPk3N/Yfm/iGn0rP5GQCAixF4ADe49+KWf5W25nGtLQBwNQIP4KbS1otDC0pbc9cdkB8pbQGASxF4ADfp3LCq3N6X0hYAVAYCD+BGf7+opTSPj5ajqVny+NfM2gIAVyHwAB5S2vpyfZL88Ochfh4A4AIEHsDNzmtQRcb2bWbuPzR3k5w8zawtAHA2Ag/gAe65qIW0iI+WY2lZ8vg8SlsA4GwEHsBjrrVVUNr6an2SfE9pCwCcisADeFBp645+BaWth+f+IScobQGA0xB4AI8sbWXLY8zaAgCnIfAAHiQsuGDWVlBggMzbkCQLNh109yEBgE8g8AAeplODKjKuX8GChI98uYnSFgA4AYEH8EB3DWohrWrFmNLWP7/a5O7DAQCvR+ABPLy0NX/jQfnuD0pbAHAuCDyAh+pQP07GF87a0tLW8bQsdx8SAHgtAg/gwSYNam5KW8dPZ8s/mbUFABVG4AG8pLT1zcaD8i2lLQCoEAIP4AWlrTv7F5S2HqW0BQAVQuABvMCkgS2kde3C0tZXXGsLAMqLwAN4gdDgwL9KW38clPkbk9x9SADgVQg8gJdoXy9OJgxobu5rL49eWR0AUDYEHsCLTBzQXNrUiTWrL+t4HovF4u5DAgCvQOABvK601VGCAwPku02HzKKEAICzI/AAXqZdXfvS1iY5mkppCwDOhsADeKEJhaWtk+k5lLYAoAwIPICXl7YW/HlI5lHaAoBSEXgALy5tTRxYUNp6jNIWAJSKwAN4eWmrbWFp65Ev/2DWFgCUgMADeLGQoIIFCbW09f2fh+XrDSxICADFIfAAXq5t3Vi5a1ALc/+xr/+UI6mZ7j4kAPA4weLh5s+fL7/99pvDtvj4eLnzzjsdtiUnJ8sXX3whhw8flg4dOsgVV1whAQEBlXy0gHuM799Mfth8SDYdSJGH526S/43owu8/AHhTD48Gns8//7zUffbt22dCzv/+9z85cOCAjB07Vq699lrGM8DvSlshQQHy4+bD8tV6SlsA4FU9PKpt27by+OOPl/j45MmTpU6dOrJ8+XIJDg6WSZMmmefMnj1bhg4dWqnHCrhL69qxctfAFvLvH7eb0lbPZtUlPjacHwgAeEMPj7UH5/nnn5cZM2bIpk2bHB7Ly8uTr776SkaMGGHCjmrZsqX07dvXBB7An4zr30za14uV5IwceWgu19oCAK8KPBpqDh06JIsWLZIuXbrII4884hCGMjIypEWLgkGbVvr9tm3bSnzNrKwsSUlJcbgBvlTa+mnLYfly/QF3HxIAeASPL2ndc8890rp1a9v38+bNk2uuuUYuvfRS6dOnj6SlpZntcXFxDs+rUqWK7bHiTJ06VZ544gkXHjngvtLW3YNayIs/bJfHv94svZrVoLQFwO95fA+PfdhRV199tdStW1eWLFlivo+OjrbN0rJ36tQp22PFmTJlinmO9ZaYmOiS4wfcYVy/ZtKhXlxhaYsFCQHA4wNPcSwWi6Snp5v7DRo0kIiICNmxY4fDPvp9q1atSnyNsLAwiY2NdbgBviK4sLQVGhQoP205InPXUdoC4N8CPX3szrp16xy2aUnr4MGDMmDAAPO9DlQePHiwfPDBB5Kbm2u2bd++XZYtWybXXXedW44b8AStasfI3RcVjG17/Os/5XAKCxIC8F8BFu0u8VAaYHS2lY7HadeunRmgrDOyJk6cKC+++KJtP93eq1cv09vTtWtXswBht27dzNeyLj6og5Z1HJCWt+jtga/IzcuXa19fKRv3J8vA1vEyc1RXFiQE4FPKev726MBjpbOztKenatWq0rNnzyLjepR+0Dlz5thWWr7yyivL9R87gQe+asfhVLly2s+SnZdvylzXd6nv7kMCAKfxqcBTGQg88GWvLUmQ5xdsk5jwYPnx7/2kdhwLEgLwr/O3R4/hAeAcY/s0lU4NqkhqZq5M+WIjl10B4HcIPIC/zNq6vqOZtbV421GZvXa/uw8JACoVgQfwEy1qxcjfL25p7j85f7McSmbWFgD/QeAB/MjtfZrYSlsPUtoC4EcIPICflbb+PbSjhAYHypJtR2UWpS0AfoLAA/iZ5vExcm9haeupeZvlYHKGuw8JAFyOwAP4odv7NJXzG1aR1KxcmTyHa20B8H0EHsAPBQUGyAvXdzKlrWXbj8rnv3HxXAC+jcAD+Knm8dFy/yUFpa2n52+RpFOUtgD4LgIP4MdG924qnQtLWw9+QWkLgO8i8AD+Xtoa2knCCktbn62htAXANxF4AD/XrKaWtlqZ+09/s0UOUNoC4IMIPADktt5NpEujqpKmpa05XGsLgO8h8AAonLXV0ZS2lu84Jp9S2gLgYwg8AIymNaPlgUsLSlv/orQFwMcQeADY3NqriXQtLG1Nnk1pC4DvIPAAcChtPX99RwkPCZSfE47Jx7/uo3UA+AQCD4BiSlutzf1nvtkiiSfSaSEAXo/AA6CIW3s2lm6Nq8rp7Dx58AtKWwC8H4EHQNH/GAqvtaWlrRUJx+Wj1ZS2AHg3Ag+AYjWuESX/KCxtTf2W0hYA70bgAVCiW3o2lgsaVzOlrclzNkp+voXWAuCVCDwASv4Pwm7W1uGULNlz/LRk5+bL8bQs8zU9O5fWA+AVgt19AAA8v7T1zJAO0q9lTXl35R55b9UeScnIldiIYLm1ZxO5s38zCQsJcvdhAkCpCDwAzuqydrXl9aU7ZfqiBNs2DT2vLNxh7t/Rr6lEhvLfCQDPxf9QAM7+H0VQoOnZKc47K3fL+P7N5PkFWyUsOEiqRoVIlchQqRoZIlUjQ6VK4dfI0CAJCAigtQG4BYEHwFmlZuaYHp3i6PZjaVmycMsR2XY4tcTXCA0OLBKCigtGfwWmUImLCDGrPwPAuSLwADirmPAQM2anuNCj22tEh8lVHetI5+QqcvJ0jpxMz5ZT6X99zc7LN4OcdeCz3spKO4Riw0OkWpR9SCoMRpF/BSPb/aiCx8IZUwTgDAQeAGeVl59vBihbx+zY0+35FotMGtSi2OdaLBZJz85zCEEn03PklH4tDEcO23S/0zmSmpUrFotIckaOuZWHziorCEL2PUal9CpFhkpMeLCZlQbANxF4AJxVRGiwmY1lHbNTnllaOm4nKizY3OpXLXtj5+Tlm4BUEIKsvUV290/nyAm7bfpV98/Nt0hmTr4cTM40t7LS0pmW0DQEVbMPRlGl9yppqc7ZMrJzJSgw0JQStXctNz+fQeHAOQqw6J9fkJSUFImLi5Pk5GSJjY2lRYBi6Lo7wR58Itb/zrRn6JRdz1GRXiX9/rTjY9oDVVFRoUEO5TTHYGQNTI69StFhwSUO4M7KyZPXluwsd7AE/FVKGc/fnvM/FQCPZw031aPDzNdQD1u7VEOEjvnRW8PqkWV+XlZu3l/B6HTxvUr69cTpv0KSltl04Wldhfp0doYcOJVR5vcLCdLepKKltVE9G8uCPw/KtIVM/wecjcADwO/pdPpasXoLL3Nb6GU2UjJzHINRMQO2T54RlLJy8yUnz2JmtunNSgdmP3ZNW7O4Y3G0x0fXOxr07yVmbFNshA4kDzFluLiIYBPyCu4X3GLPuB8Txhgl+DcCDwBUgA5w1lKV3ppIVJmfl1E4gPvMcpsWq7TXqLTp/xqctKRY2vT/kmgFTUNPXGRhCComIDmEpPBgh8dCgjyrNw8oLwIPAFSiiNAgiQiNkLpVIoo8plP3S5v+Hx8TLq/+7XwTfEw4ysy1zWJLKbxZv7dtz8wxg7i1V0j311uilL38ZqULRzqEoyKBKbjEHiaWCYAnIPAAgJdM/9dB4i1qxZT7dXWMkjUUJWfkOgSjlGICkv0+aVkF4UsHduutPDPfrHQmW0FAcuw1cghIZq2nogGqtAHe54rZcP6FwAMAPjD9/2xjlOJj9Fb2MUpWuXn5kmrfk2QCkX1gKngspZjHUwoHdmvP1ZljlsqzXICW184ck3TWHqbCAFXSSt06G+6NpbuYDedHmJZeiGnpADyFp0//L8/A7tPZuUUCkkMPU5EA9VcPk67Qfa503NJf45MKQtGd/ZvLwi2HZZrdxXCt7h7UQm7u3khOZ+VKZFiQaffIkCAWpfRgTEsHAC/l6dP/yzOwWwOb3sqz6KR1TSUde+QQiNLL3sNkXVtJ12XSm3XZAJ0N16JWtIx4e3Wps+EufXmZGStlv3q3CT+hGoIKg5Dd16iwIIkI0QU2dYxWkESFBtu+OjzHhKi/nsdg8MrjfX8yAAB8no7bKRjgXb7lAqy0jKbh58wxSoEBAYW9TKXPhmtYNcLMoLMuzavhKzNHlxgQp9I1mYoPUkESGVbQu6SrlBeEJ22P4MKvdmEqrOjzw4IDXTb2yVvHShF4AAA+RwdK60Vt9VaR2XBfTuxt62XSEqN10LaW6HRpAS15ZeTo1zyHx/+6X8w2u331EihK12SqyPXizkaHLhUbpMIKQpS1pynKricq4oyequJ6siLKWd7zpLFSBB4AgF8py2w4LSPa9zJVd/IxaOg6MxxpeMrIyS34Whiu7B8v2KZfC/ZJz9EQ5biPLmypNE/pDDvrLDtn0tBjLd1FhhQt01m/Du1SX77d5DkrhxN4AAB+xVWz4crbAxUarAtXOvd1dVad9jzZeqRsPVG5joGp8HHrfcdeK/veqb/ClJXuo7fS6Fip+y5pWerK4RMGNJfKROABAPgdDTXaw6AnXfuxJd5+gdbgoECJ0Vt4iNNn3GXmFgYg07vk2BNl/1X30R4eM8i8lLFS2u7WgfmVgcADAPBLvjIbrjIEBloHVweLRJftOWcbK+XsUHY2/HQBAIDLxkoVxzpWqjLRwwMAAHxyrJQ9VlouxErLAAB438rhrLQMAADcLtJDxkoxhgcAAPg8Ag8AAPB5BB4AAODzCDwAAMDnEXgAAIDPI/AAAACfR+ABAAA+j8ADAAB8HoEHAAD4PK6lVchisdiWqAYAAN7Bet62nsdLQuAplJqaar42aNDA1T8bAADggvN4XFxciY9z8dBC+fn5kpSUJDExMRIQEODU5KkhKjExUWJjY532uqCd3YXfadrZl/D77P3trD07Gnbq1q0rgYElj9Shh6eQNlL9+vXFVfQHTOBxPdq58tDWtLMv4ffZu9u5tJ4dKwYtAwAAn0fgAQAAPo/A42JhYWHy2GOPma+gnX0Bv9O0sy/h99l/2plBywAAwOfRwwMAAHwegQcAAPg8Ag8AAPB5rMPjQhkZGbJ9+3apVq0aKzi7kC46tWvXLtPeTZs2lcjISFe+nd/LysqSNWvWmN/rtm3b+n17ONOpU6dk06ZNRbZ37dpVwsPDaWsXOHLkiOzfv19at27N/x2V9Pus2rdvL1WqVJHKROBxgRMnTsiUKVPk888/l0aNGsmBAwfM1w8//ND8o4LzaBs/9NBDEhwcbFbI1v+49Httf7jGAw88IP/973/l8ssvl/nz59PMTvTLL7+Ydu3Vq5fD9s8++0zq1atHWztRcnKyjB49WhYsWCBt2rQxwefpp5+WESNG0M5Osm3bNnnwwQcdtukVDXbv3i1r166Vzp07S2Ui8LiABpwuXbrI9OnTJTQ0VDIzM+X666+XYcOGycaNG13xln7r2LFjsmzZMrOkuPr6669l8ODBMmDAAOnevbu7D8/naPsuXrzYnJThGkFBQfLzzz/TvC6m/yfrH6d79uyRGjVqSHp6uvkDCs5z4YUXFvldvu666yQ6OrrSw45iDI8LdOjQQcaOHWvCjtKu6FtvvVX++OMPrsbuZHfeeact7CgNOtbQCefS3rPx48fLRx99RHnFxbZu3Wr+ONIyLZxP/0j66aef5NVXXzVhR2kp/JZbbqG5Xejo0aMyb948uf3228Ud6OGpJDrmoWbNmlxPywWOHz8uW7ZsMfXiN954w5QDrrzySle8ld/Ky8uT4cOHy3333ScdO3Z09+H4fFvr769e308vtKglxCeffNKpFzX2dwsXLpTq1atLjx49zDjL7Oxsad68OUHexd5//33Tg3nzzTeLOxB4KsHq1avl5ZdflhdeeKEy3s7vaM/ZI488YmrwJ0+elGnTpvEfl5PpCVd7LP/+9787+6Vhp06dOrJq1SpbOVZPzFdccYXpxdTeNTiHjiPRP0C1bXfs2GHCpf7/8corr8jIkSNpZhd5++23TSmxatWq4hYWuNSmTZssNWrUsNxxxx20dCX49ttvLcHBwZb58+fT3k6yceNGS1hYmOWzzz6zLF++3Nz69+9v6dmzp7mfnp5OW7vQ8OHDLX379qWNnWj8+PEWPf09//zztm3Tpk2zhISEWHbs2EFbu8DKlStNmy9dutTiLozhcaHNmzfLwIEDZciQIfL666+78q1QSAfT6nTH7777jjZxEh3MqdOitedMZ1zoTXvVtIyo97UuD9epVasWY9KcrHHjxubruHHjbNt03GVubq6ZKQfnmzlzprRs2VL69u0r7kJJy0X0ZKBh55prrpEZM2ZQf3cB/c8pPz/fNjhc6SBPHbCs9Xm4bqaFdkvr7EOmpTvX6dOnJSoqymE8z6JFi0yIh/NceumlMnnyZPN/hXWpEC1z6ZpeWuqCc6WlpZmlFfTioe5E4HGBvXv3mrCja+9oPXjFihW2x3S6ekREhCve1u+kpKSYWVljxowx/2npFHXtSdM1ee644w53Hx5QbtrjEB8fL3369DFh/s033zSLar7zzju0phN16tTJrLdz0003yaOPPmr+IH3mmWekW7du5v9uOJeGHV2wdNSoUeJOXC3dBZYvX17iwncff/yxNGzY0BVv65f27dtnppbqFN7Y2FgTKDXsVPYKnv5GTxI6s+W5555z96H4FG1T7frXKdPag9muXTu56667pHbt2u4+NJ+j7at/IGn5W/9I0oHid999t0MPG5zj3nvvNbOz3D1xh8ADAAB8HoOWAQCAzyPwAAAAn0fgAQAAPo/AAwAAfB6BBwAA+DwCDwAA8HkEHgAA4PMIPABcfrmETz/91Cwv72n0Stlz586V77//vkKLBOrnOnXqlEuODYBzEXgAH6bXBtKT8hdffFHksdWrV5tVwV1NLy6qS/gfOnRIPMnbb79tLiXw4YcfyuLFiyt0aRP9XHv27HHK8ei1yfRnlZyc7JTXA+CIa2kBPkwvPqknZTVv3jy56qqrbI/psvp6/TG9bpM/0utU6QUkS7oMTGXTniL9WemV6OPi4tx9OIDPoYcH8ANNmzaVBx980ASg4pw8edL0LuTk5Dhca0i3HT9+vEhpKiEhwQSoDRs22PbX65l99dVX5rGSbN++3TxPT+ol9ZpoeUlvhw8fdnjM/v03bdpkeq30Qr0l0R6lr7/+2rxWamqqbbt+Rn2d3bt3y86dO839LVu2lPg6+twff/xRfvjhB4fXqUgbWt9fe9a++eYb2b9/v60nTttOLViwwDxn4cKF59wuxb0X4LcsAHxWTk6ORf+Zv/7665bY2FjLzJkzbY+NGjXKcuWVV5r7a9asMfudPHnS9nhqaqrZtmrVKvP97t27zff9+vWzdOjQwXL55ZdbQkJCLPfee6/lpptusnTq1Mly2WWXWcLCwizvvfee7XWsz9P9mzdvbrnkkksskZGRlrvvvtvhWGfPnm2pWrWqpW/fvuZ14uLiLK+++mqR17nqqqssrVq1sgwdOtSyfPnyYj/39OnTLREREZb+/ftbzj//fEu1atUsixcvNo+dPn3acsMNN1iio6MtXbt2Nfe/+uqrYl9n1qxZ5jjOO+88c9wtWrSw/PLLL+axo0ePmuNZt25dmdswMTHR0rRpU0v79u0t11xzjbn/xBNPWPLy8iyDBw82++pn12N67LHHzqldSnovwF8ReAA/CDx64n7qqacs9evXt6Snp59T4BkzZowlPz/fbNMgpdsmTpxoe94LL7xgadCgge176/MuuugiS3Z2ttm2evVqS2BgoGXZsmXm+127dlmioqIsS5YssT1Pjyk8PNyyZcsWh9cZNmyYCQgl2bFjhwlin376qW3bpEmTLI0bN7ZkZmbatjVr1swcf2mvExoaann55Zdt2w4dOmRrj4oEnscff9zSs2dP2+P6Ob788ktz/+DBg2bfP/74w/b4ubRLae8F+CNKWoCfuPfee01Ja9q0aef0OmPHjpWAgABzv0ePHrZtVrotMTFRMjIyHJ53zz33SEhIiLl/wQUXSP/+/eXzzz8333/yySdSvXp1M8B51qxZZvuuXbukWrVqRQZWT5gwQQIDS/6va86cOdKgQQO54YYbbNsefvhhM7hYB2qXlR5T7dq15a677rJtq1WrlnTv3l0qKiIiwpS3Dhw4YL7XzzF48OBSj6Gi7VLe9wJ8HYOWAT8RGRkpjz32mBnLc/vtt1f4dapWrWq7HxYWVuK2rKwsc9K1aty4scPrNGnSxDbWRMOITvOePXu2wz46oLpGjRoO2+rUqVPq8elr6pglexpUoqKiSh3zc6Z9+/ZJ8+bNbeHOGcaPHy/r16+XFi1aSNu2beXiiy+WiRMnSr169Yrd/1zapbzvBfg6Ag/gR0aPHi0vvfSS/Otf/3LYbu0ZyM/Pd5gm7Uw6qPfM760n7djYWBOadPDt2ZwtgOhrntmTo+ErPT29SEgoTZUqVRwGG59NWdowJiZGPv74YzPQeMWKFTJ9+nTp2rVriQO9z6VdSnsvDX+Av6GkBfiR4OBgE3b++9//OvR2WP/qtz/xVmRtmtJ8+eWXtvu61ozOQurVq5f5/rLLLjMzpZYuXerwHJ0VVdrMqOL07t3bzB7TGVhW2kMSHh4unTt3LvPrXHLJJWbmmfaS2NPyUnHK0obW8pIGDn19LS/qbDL9WURHRxcJSefSLqW9F+CP6OEB/Mx1110nL774oixZskSuvPJKW8lHT4raA6RjVg4ePCgffPCBU99XX0+nabdu3dqsgaPjbEaOHGkeu+iii8w4IF0naNKkSdKsWTPZtm2bWQVZp4Nrb0VZ6ee44oorTAlHxw1puHruuefk0UcfNZ+zrPT5I0aMkEGDBsndd98t8fHxJrTpMf/tb38rsn9Z2lDXPlq1apU5Pu1B+uijj6Rjx47SsmVLE0ZbtWpljnXIkCHm9c6lXUp7L8Af0cMD+DAts+jgXQ0X9rSspdv79u1r26brt9x8883yyy+/mMHFGoh0H2sZSHsK9Hv7k6yWXHSbjg+y0gG1ui00NNThecuWLZO6devKr7/+agbP6qBb6yBmNWPGDNMTo+vJrFy50pzw9YStY31Kev/SBi7rQGXtnUlKSpLPPvusyAKDGiJ0jE5p3n33XXnrrbfMa+gaN/fdd58t7OhYJT0e+/FLZ2vDp59+2gQvDUO6jwYbbQcNO2r+/Plm/NG3335rG5Rc0XY523sB/iZAp2q5+yAAAABciR4eAADg8wg8AADA5xF4AACAzyPwAAAAn0fgAQAAPo/AAwAAfB6BBwAA+DwCDwAA8HkEHgAA4PMIPAAAwOcReAAAgM8j8AAAAPF1/w8D63b8PgdflwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=elbow_df, x=\"k\", y=\"inertia\", marker=\"o\")\n",
    "plt.title(\"Elbow Check for K-Means\")\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"Inertia\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-17b82388",
   "metadata": {},
   "source": [
    "## Silhouette Check\n",
    "\n",
    "The **silhouette score** compares how close each point is to its own cluster relative to nearby clusters. Values closer to 1 suggest tighter, better-separated clusters. Values near 0 suggest overlap, and negative values suggest points may be assigned to the wrong cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cell-f3a45d57",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:59.740331Z",
     "iopub.status.busy": "2026-08-31T06:02:59.740263Z",
     "iopub.status.idle": "2026-08-31T06:02:59.765365Z",
     "shell.execute_reply": "2026-08-31T06:02:59.765116Z"
    },
    "tags": []
   },
   "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>k</th>\n",
       "      <th>silhouette</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.516182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.644066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0.771513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>0.675900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>0.629143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>0.548643</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   k  silhouette\n",
       "0  2    0.516182\n",
       "1  3    0.644066\n",
       "2  4    0.771513\n",
       "3  5    0.675900\n",
       "4  6    0.629143\n",
       "5  7    0.548643"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_scores = []\n",
    "\n",
    "for k in k_values:\n",
    "    model = KMeans(n_clusters=k, n_init=10, random_state=101)\n",
    "    labels = model.fit_predict(X_scaled)\n",
    "    silhouette_scores.append(silhouette_score(X_scaled, labels))\n",
    "\n",
    "silhouette_df = pd.DataFrame({\"k\": list(k_values), \"silhouette\": silhouette_scores})\n",
    "silhouette_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cell-7194ba73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:59.766620Z",
     "iopub.status.busy": "2026-08-31T06:02:59.766560Z",
     "iopub.status.idle": "2026-08-31T06:02:59.803978Z",
     "shell.execute_reply": "2026-08-31T06:02:59.803719Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Silhouette score')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=silhouette_df, x=\"k\", y=\"silhouette\", marker=\"o\")\n",
    "plt.title(\"Silhouette Check for K-Means\")\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"Silhouette score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a21a1b87",
   "metadata": {},
   "source": [
    "Both checks point to **k = 4**. Inertia drops steeply up to 4 clusters and then barely improves, and the silhouette score is highest at 4 (about 0.77). In real data, the two checks often disagree or show no clear winner, and the business question has to break the tie.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-84789c6b",
   "metadata": {},
   "source": [
    "## Describing Clusters\n",
    "\n",
    "After choosing `k`, summarize each cluster in the units that matter for the decision. We fit the model on the scaled features, but we describe the clusters in the original units, dollars and orders, because those are what a manager can act on. In a customer example, that might mean average order value, purchase frequency, tenure, and churn rate. In an operations example, it might mean processing time, defect rate, and order size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cell-f2ab3819",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:59.805268Z",
     "iopub.status.busy": "2026-08-31T06:02:59.805201Z",
     "iopub.status.idle": "2026-08-31T06:02:59.814942Z",
     "shell.execute_reply": "2026-08-31T06:02:59.814688Z"
    },
    "tags": []
   },
   "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>annual_spend</th>\n",
       "      <th>orders_per_year</th>\n",
       "      <th>customers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>410.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2669.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2221.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>902.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         annual_spend  orders_per_year  customers\n",
       "cluster                                          \n",
       "0               410.0              3.0         90\n",
       "1              2669.0             28.0         40\n",
       "2              2221.0              5.0         50\n",
       "3               902.0             24.0         60"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = KMeans(n_clusters=4, n_init=10, random_state=101)\n",
    "customers['cluster'] = final_model.fit_predict(X_scaled)\n",
    "\n",
    "profile = customers.groupby('cluster')[features].mean().round(0)\n",
    "profile['customers'] = customers['cluster'].value_counts().sort_index()\n",
    "profile\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-f1df27e0",
   "metadata": {},
   "source": [
    "## Practical Interpretation\n",
    "\n",
    "Cluster labels are useful only when they support an interpretable decision. The final step is to describe the groups in business language and decide whether the segments suggest different actions.\n",
    "\n",
    "The colors in a cluster plot are arbitrary. Cluster `0` in one model is not guaranteed to match cluster `0` in another model, even when the same data are used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cell-5485f5c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-31T06:02:59.816150Z",
     "iopub.status.busy": "2026-08-31T06:02:59.816091Z",
     "iopub.status.idle": "2026-08-31T06:02:59.862043Z",
     "shell.execute_reply": "2026-08-31T06:02:59.861821Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=customers, x='annual_spend', y='orders_per_year', hue='cluster', palette='tab10')\n",
    "plt.title('Customer Segments')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
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    "Reading the profile table, we can name each cluster and propose an action for it:\n",
    "\n",
    "| Cluster | Profile | Customers | Possible action |\n",
    "|---:|---|---:|---|\n",
    "| 0 | **Occasional buyers**: about \\$410 a year over 3 orders | 90 | Low-cost reactivation emails; test a discount on a second purchase |\n",
    "| 1 | **Loyal high-value**: about \\$2,670 a year over 28 orders | 40 | Retention first: loyalty perks and early access; watch for any drop in orders |\n",
    "| 2 | **Big-ticket buyers**: about \\$2,220 a year over 5 orders | 50 | Follow each large purchase with accessories, service plans, and replacement reminders |\n",
    "| 3 | **Frequent small baskets**: about \\$900 a year over 24 orders | 60 | Raise the basket size with bundles and a free-shipping threshold |\n",
    "\n",
    "These actions are hypotheses, not results. Before rolling one out, test it, for example with an A/B test that compares customers who get the offer with similar customers who don't (Chapter 10). Also check that the segments are stable: if a new month of data or a different random start reshuffles the customers, the segments are not a solid basis for a decision.\n"
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    "## Practical Tips\n",
    "\n",
    "- Scale features before clustering when units differ.\n",
    "- Run multiple initializations with `n_init` to avoid poor local minima.\n",
    "- Use `random_state` for reproducible examples.\n",
    "- Treat cluster labels as arbitrary identifiers, not ranked categories.\n",
    "- Validate clusters with domain knowledge before using them for decisions."
   ]
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    "\n",
    "## Section Practice\n",
    "\n",
    "Use this short exercise to check the main workflow from this section.\n"
   ]
  },
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   "source": [
    "### Exercise: Practice Interpreting Clusters\n",
    "# K-means has assigned eight customers to clusters. Describe the clusters in business terms.\n",
    "#   1. Use groupby to compute the mean annual_spend and orders_per_year of each cluster.\n",
    "#   2. Find the cluster with the highest mean spend and count its customers.\n",
    "# Print exactly:\n",
    "# Highest-spend cluster: 2\n",
    "# Mean spend: 2550.0\n",
    "# Mean orders: 27.0\n",
    "# Customers: 2\n",
    "import pandas as pd\n",
    "\n",
    "segments = pd.DataFrame({\n",
    "    'annual_spend': [420, 380, 950, 880, 2600, 2500, 400, 910],\n",
    "    'orders_per_year': [3, 2, 25, 22, 28, 26, 4, 24],\n",
    "    'cluster': [0, 0, 1, 1, 2, 2, 0, 1],\n",
    "})\n",
    "### Your code starts here\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here\n"
   ]
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     "text": [
      "Highest-spend cluster: 2\n",
      "Mean spend: 2550.0\n",
      "Mean orders: 27.0\n",
      "Customers: 2\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import pandas as pd\n",
    "\n",
    "segments = pd.DataFrame({\n",
    "    'annual_spend': [420, 380, 950, 880, 2600, 2500, 400, 910],\n",
    "    'orders_per_year': [3, 2, 25, 22, 28, 26, 4, 24],\n",
    "    'cluster': [0, 0, 1, 1, 2, 2, 0, 1],\n",
    "})\n",
    "\n",
    "profile = segments.groupby('cluster')[['annual_spend', 'orders_per_year']].mean()\n",
    "top = profile['annual_spend'].idxmax()\n",
    "\n",
    "print('Highest-spend cluster:', top)\n",
    "print('Mean spend:', profile.loc[top, 'annual_spend'])\n",
    "print('Mean orders:', profile.loc[top, 'orders_per_year'])\n",
    "print('Customers:', (segments['cluster'] == top).sum())\n"
   ]
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