{
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   "source": [
    "# Lab\n",
    "\n",
    "- Use this assignment to practice the chapter's concepts and coding skills in runnable Python code.\n",
    "- Sign in from the account menu before you start working on the assignment.\n",
    "- Write and run your code directly in the coding cells.\n",
    "- Multiple submissions are allowed. The highest score is kept.\n",
    "\n",
    "<div data-lab-answers-release-at=\"2026-12-10T23:59-06:00\"></div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
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   "outputs": [],
   "source": [
    "### Question 1: The Assignment Step\n",
    "# Six customers (in standard units) and two current cluster centers are below.\n",
    "#   1. Assign each customer to the nearer center (0 or 1) using distance.\n",
    "#   2. Print the assignments as a list and the number of customers in each cluster.\n",
    "# Print exactly:\n",
    "# Assignments: [0, 0, 1, 1, 0, 1]\n",
    "# Cluster sizes: [3, 3]\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "points = pd.DataFrame({\n",
    "    \"spend_su\": [-1.0, -0.8, 1.1, 0.9, -1.2, 1.3],\n",
    "    \"orders_su\": [-0.5, -0.9, 0.8, 1.2, -0.7, 0.6],\n",
    "})\n",
    "centers = np.array([[-1.0, -1.0], [1.0, 1.0]])\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "hide-input",
     "lab-answer"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assignments: [0, 0, 1, 1, 0, 1]\n",
      "Cluster sizes: [3, 3]\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "points = pd.DataFrame({\n",
    "    \"spend_su\": [-1.0, -0.8, 1.1, 0.9, -1.2, 1.3],\n",
    "    \"orders_su\": [-0.5, -0.9, 0.8, 1.2, -0.7, 0.6],\n",
    "})\n",
    "centers = np.array([[-1.0, -1.0], [1.0, 1.0]])\n",
    "\n",
    "assignments = []\n",
    "for spend, orders in zip(points['spend_su'], points['orders_su']):\n",
    "    point = np.array([spend, orders])\n",
    "    d0 = np.sqrt(np.sum((point - centers[0]) ** 2))\n",
    "    d1 = np.sqrt(np.sum((point - centers[1]) ** 2))\n",
    "    if d0 <= d1:\n",
    "        assignments.append(0)\n",
    "    else:\n",
    "        assignments.append(1)\n",
    "\n",
    "print(\"Assignments:\", assignments)\n",
    "print(\"Cluster sizes:\", [assignments.count(0), assignments.count(1)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 2: The Update Step\n",
    "# Using the assignments from Question 1 (given below), move each center to the\n",
    "# mean of the customers assigned to it.\n",
    "#   1. Use groupby on the cluster column to compute the new centers.\n",
    "#   2. Print each new center rounded to 2 decimals, cluster 0 first.\n",
    "# Print exactly:\n",
    "# Center 0: [-1.0, -0.7]\n",
    "# Center 1: [1.1, 0.87]\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "points = pd.DataFrame({\n",
    "    \"spend_su\": [-1.0, -0.8, 1.1, 0.9, -1.2, 1.3],\n",
    "    \"orders_su\": [-0.5, -0.9, 0.8, 1.2, -0.7, 0.6],\n",
    "    \"cluster\": [0, 0, 1, 1, 0, 1],\n",
    "})\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "hide-input",
     "lab-answer"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Center 0: [-1.0, -0.7]\n",
      "Center 1: [1.1, 0.87]\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "points = pd.DataFrame({\n",
    "    \"spend_su\": [-1.0, -0.8, 1.1, 0.9, -1.2, 1.3],\n",
    "    \"orders_su\": [-0.5, -0.9, 0.8, 1.2, -0.7, 0.6],\n",
    "    \"cluster\": [0, 0, 1, 1, 0, 1],\n",
    "})\n",
    "\n",
    "new_centers = points.groupby('cluster').mean().round(2)\n",
    "print(\"Center 0:\", new_centers.loc[0].tolist())\n",
    "print(\"Center 1:\", new_centers.loc[1].tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 3: Raw vs. Scaled Features\n",
    "# Spend is in dollars and orders is a count, so spend dominates the distances.\n",
    "#   1. Fit KMeans(n_clusters=4, n_init=10, random_state=0) to the raw customers\n",
    "#      and, separately, to the features in standard units (scaled).\n",
    "#   2. Print the cluster sizes for each, sorted from smallest to largest.\n",
    "# Print exactly:\n",
    "# Raw sizes: [3, 3, 5, 5]\n",
    "# Scaled sizes: [4, 4, 4, 4]\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "customers = pd.DataFrame({\n",
    "    \"spend\": [300, 650, 450, 800, 500, 900, 700, 1100, 2000, 2500, 2900, 2300, 2100, 2700, 3000, 2400],\n",
    "    \"orders\": [3, 2, 4, 3, 24, 22, 26, 25, 5, 4, 6, 5, 28, 26, 30, 27],\n",
    "})\n",
    "scaled = (customers - customers.mean()) / customers.std(ddof=0)\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "hide-input",
     "lab-answer"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw sizes: [3, 3, 5, 5]\n",
      "Scaled sizes: [4, 4, 4, 4]\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "customers = pd.DataFrame({\n",
    "    \"spend\": [300, 650, 450, 800, 500, 900, 700, 1100, 2000, 2500, 2900, 2300, 2100, 2700, 3000, 2400],\n",
    "    \"orders\": [3, 2, 4, 3, 24, 22, 26, 25, 5, 4, 6, 5, 28, 26, 30, 27],\n",
    "})\n",
    "scaled = (customers - customers.mean()) / customers.std(ddof=0)\n",
    "\n",
    "raw_labels = KMeans(n_clusters=4, n_init=10, random_state=0).fit_predict(customers)\n",
    "scaled_labels = KMeans(n_clusters=4, n_init=10, random_state=0).fit_predict(scaled)\n",
    "\n",
    "print(\"Raw sizes:\", sorted(pd.Series(raw_labels).value_counts().tolist()))\n",
    "print(\"Scaled sizes:\", sorted(pd.Series(scaled_labels).value_counts().tolist()))\n",
    "# Without scaling, k-means splits customers mostly by spend and mixes frequent\n",
    "# and occasional buyers. With scaling, it finds the four customer types.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 4: Choosing k With the Silhouette Score\n",
    "#   1. For k = 2, 3, 4, and 5, fit KMeans(n_clusters=k, n_init=10, random_state=0)\n",
    "#      to scaled and compute silhouette_score(scaled, labels), rounded to 3 decimals.\n",
    "#   2. Print each score, then the k with the highest score.\n",
    "# Print exactly:\n",
    "# k=2: 0.457\n",
    "# k=3: 0.573\n",
    "# k=4: 0.746\n",
    "# k=5: 0.652\n",
    "# Best k: 4\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "customers = pd.DataFrame({\n",
    "    \"spend\": [300, 650, 450, 800, 500, 900, 700, 1100, 2000, 2500, 2900, 2300, 2100, 2700, 3000, 2400],\n",
    "    \"orders\": [3, 2, 4, 3, 24, 22, 26, 25, 5, 4, 6, 5, 28, 26, 30, 27],\n",
    "})\n",
    "scaled = (customers - customers.mean()) / customers.std(ddof=0)\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "hide-input",
     "lab-answer"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k=2: 0.457\n",
      "k=3: 0.573\n",
      "k=4: 0.746\n",
      "k=5: 0.652\n",
      "Best k: 4\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "customers = pd.DataFrame({\n",
    "    \"spend\": [300, 650, 450, 800, 500, 900, 700, 1100, 2000, 2500, 2900, 2300, 2100, 2700, 3000, 2400],\n",
    "    \"orders\": [3, 2, 4, 3, 24, 22, 26, 25, 5, 4, 6, 5, 28, 26, 30, 27],\n",
    "})\n",
    "scaled = (customers - customers.mean()) / customers.std(ddof=0)\n",
    "\n",
    "best_k = None\n",
    "best_score = -1\n",
    "for k in [2, 3, 4, 5]:\n",
    "    labels = KMeans(n_clusters=k, n_init=10, random_state=0).fit_predict(scaled)\n",
    "    score = round(silhouette_score(scaled, labels), 3)\n",
    "    print(\"k=\" + str(k) + \":\", score)\n",
    "    if score > best_score:\n",
    "        best_k = k\n",
    "        best_score = score\n",
    "print(\"Best k:\", best_k)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 5: Describing the Segments\n",
    "#   1. Fit KMeans(n_clusters=4, n_init=10, random_state=0) to scaled and add the\n",
    "#      labels to customers as a column named cluster.\n",
    "#   2. Use groupby to compute each cluster's mean spend and mean orders in the\n",
    "#      ORIGINAL units, and sort the clusters by mean spend.\n",
    "#   3. Print one line per cluster, lowest spend first, with both means rounded\n",
    "#      to 2 decimals.\n",
    "# Print exactly:\n",
    "# Spend 550.0, orders 3.0\n",
    "# Spend 800.0, orders 24.25\n",
    "# Spend 2425.0, orders 5.0\n",
    "# Spend 2550.0, orders 27.75\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "customers = pd.DataFrame({\n",
    "    \"spend\": [300, 650, 450, 800, 500, 900, 700, 1100, 2000, 2500, 2900, 2300, 2100, 2700, 3000, 2400],\n",
    "    \"orders\": [3, 2, 4, 3, 24, 22, 26, 25, 5, 4, 6, 5, 28, 26, 30, 27],\n",
    "})\n",
    "scaled = (customers - customers.mean()) / customers.std(ddof=0)\n",
    "### Your code starts here.\n",
    "\n",
    "\n",
    "\n",
    "### Your code ends here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "hide-input",
     "lab-answer"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spend 550.0, orders 3.0\n",
      "Spend 800.0, orders 24.25\n",
      "Spend 2425.0, orders 5.0\n",
      "Spend 2550.0, orders 27.75\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "customers = pd.DataFrame({\n",
    "    \"spend\": [300, 650, 450, 800, 500, 900, 700, 1100, 2000, 2500, 2900, 2300, 2100, 2700, 3000, 2400],\n",
    "    \"orders\": [3, 2, 4, 3, 24, 22, 26, 25, 5, 4, 6, 5, 28, 26, 30, 27],\n",
    "})\n",
    "scaled = (customers - customers.mean()) / customers.std(ddof=0)\n",
    "\n",
    "customers['cluster'] = KMeans(n_clusters=4, n_init=10, random_state=0).fit_predict(scaled)\n",
    "profile = customers.groupby('cluster')[['spend', 'orders']].mean().sort_values('spend')\n",
    "\n",
    "for spend, orders in zip(profile['spend'], profile['orders']):\n",
    "    print(\"Spend \" + str(round(spend, 2)) + \", orders \" + str(round(orders, 2)))\n",
    "# Occasional buyers, frequent small baskets, big-ticket buyers, and loyal\n",
    "# high-value customers. The cluster numbers themselves carry no meaning.\n"
   ]
  },
  {
   "cell_type": "markdown",
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    "        code: collectCodeCells(),\n",
    "        canvas_course_id: identity.canvas_course_id,\n",
    "        canvas_assignment_id: identity.canvas_assignment_id,\n",
    "        canvas_user_id: identity.canvas_user_id\n",
    "      })\n",
    "        .then(function (payload) {\n",
    "          var best = (payload.best_score === null || typeof payload.best_score === 'undefined') ? payload.score : payload.best_score;\n",
    "          var attempt = (payload.attempt_count === null || typeof payload.attempt_count === 'undefined') ? '' : ' Attempt ' + payload.attempt_count + '.';\n",
    "          applyFeedback(grader, payload.feedback || {});\n",
    "          result.textContent = 'Submission saved.' + attempt + ' Score: ' + payload.score + ' / ' + payload.max_score + '. Best score: ' + best + ' / ' + payload.max_score + '. Canvas sync: ' + payload.canvas_sync_status + '.';\n",
    "        })\n",
    "        .catch(function (error) {\n",
    "          result.textContent = 'Could not save this attempt: ' + error.message;\n",
    "        })\n",
    "        .finally(function () {\n",
    "          submit.disabled = false;\n",
    "        });\n",
    "    });\n",
    "\n",
    "    reset.addEventListener('click', function () {\n",
    "      clearFeedback(grader);\n",
    "      result.textContent = '';\n",
    "    });\n",
    "  }\n",
    "\n",
    "  if (document.readyState === 'loading') {\n",
    "    document.addEventListener('DOMContentLoaded', initLabGrader);\n",
    "  } else {\n",
    "    initLabGrader();\n",
    "  }\n",
    "})();\n",
    "</script>\n"
   ]
  }
 ],
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