{
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    "# 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=\"2026-12-01T23:59-06:00\"></div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 1: The Nearest Neighbor\n",
    "# A bank wants to predict whether a new applicant will repay a loan.\n",
    "#   1. Compute the distance from applicant to each past loan, using\n",
    "#      income_su and debt_su.\n",
    "#   2. Print the distance to the nearest loan (rounded to 3 decimals) and\n",
    "#      the 1-nearest-neighbor prediction (that loan's outcome).\n",
    "# Print exactly:\n",
    "# Nearest distance: 0.141\n",
    "# 1-NN prediction: repaid\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Past loans; both attributes are already in standard units.\n",
    "loans = pd.DataFrame({\n",
    "    \"income_su\": [1.2, 0.8, -0.3, -1.1, 0.4, -0.9, 1.5, -0.2, 0.1, -1.4],\n",
    "    \"debt_su\": [-0.9, -0.4, 0.6, 1.3, -0.1, 0.9, -1.2, 1.0, 0.3, 0.2],\n",
    "    \"outcome\": [\"repaid\", \"repaid\", \"default\", \"default\", \"repaid\",\n",
    "                \"default\", \"repaid\", \"default\", \"repaid\", \"default\"],\n",
    "})\n",
    "applicant = np.array([0.0, 0.4])\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": [
      "Nearest distance: 0.141\n",
      "1-NN prediction: repaid\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Past loans; both attributes are already in standard units.\n",
    "loans = pd.DataFrame({\n",
    "    \"income_su\": [1.2, 0.8, -0.3, -1.1, 0.4, -0.9, 1.5, -0.2, 0.1, -1.4],\n",
    "    \"debt_su\": [-0.9, -0.4, 0.6, 1.3, -0.1, 0.9, -1.2, 1.0, 0.3, 0.2],\n",
    "    \"outcome\": [\"repaid\", \"repaid\", \"default\", \"default\", \"repaid\",\n",
    "                \"default\", \"repaid\", \"default\", \"repaid\", \"default\"],\n",
    "})\n",
    "applicant = np.array([0.0, 0.4])\n",
    "\n",
    "attributes = loans[['income_su', 'debt_su']]\n",
    "loans['distance'] = np.sqrt(((attributes - applicant) ** 2).sum(axis=1))\n",
    "nearest = loans.sort_values('distance')\n",
    "\n",
    "print(\"Nearest distance:\", round(nearest['distance'].iloc[0], 3))\n",
    "print(\"1-NN prediction:\", nearest['outcome'].iloc[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 2: Majority Vote of 5 Neighbors\n",
    "#   1. Find the 5 loans nearest to applicant.\n",
    "#   2. Count the outcomes among them and predict the majority outcome.\n",
    "# Print exactly:\n",
    "# Default votes: 3\n",
    "# Repaid votes: 2\n",
    "# 5-NN prediction: default\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Past loans; both attributes are already in standard units.\n",
    "loans = pd.DataFrame({\n",
    "    \"income_su\": [1.2, 0.8, -0.3, -1.1, 0.4, -0.9, 1.5, -0.2, 0.1, -1.4],\n",
    "    \"debt_su\": [-0.9, -0.4, 0.6, 1.3, -0.1, 0.9, -1.2, 1.0, 0.3, 0.2],\n",
    "    \"outcome\": [\"repaid\", \"repaid\", \"default\", \"default\", \"repaid\",\n",
    "                \"default\", \"repaid\", \"default\", \"repaid\", \"default\"],\n",
    "})\n",
    "applicant = np.array([0.0, 0.4])\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": [
      "Default votes: 3\n",
      "Repaid votes: 2\n",
      "5-NN prediction: default\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Past loans; both attributes are already in standard units.\n",
    "loans = pd.DataFrame({\n",
    "    \"income_su\": [1.2, 0.8, -0.3, -1.1, 0.4, -0.9, 1.5, -0.2, 0.1, -1.4],\n",
    "    \"debt_su\": [-0.9, -0.4, 0.6, 1.3, -0.1, 0.9, -1.2, 1.0, 0.3, 0.2],\n",
    "    \"outcome\": [\"repaid\", \"repaid\", \"default\", \"default\", \"repaid\",\n",
    "                \"default\", \"repaid\", \"default\", \"repaid\", \"default\"],\n",
    "})\n",
    "applicant = np.array([0.0, 0.4])\n",
    "\n",
    "attributes = loans[['income_su', 'debt_su']]\n",
    "loans['distance'] = np.sqrt(((attributes - applicant) ** 2).sum(axis=1))\n",
    "votes = loans.sort_values('distance').head(5)['outcome'].value_counts()\n",
    "\n",
    "print(\"Default votes:\", votes['default'])\n",
    "print(\"Repaid votes:\", votes['repaid'])\n",
    "print(\"5-NN prediction:\", votes.idxmax())\n",
    "# The single nearest loan was repaid, but 3 of the 5 nearest defaulted:\n",
    "# the choice of k can change the prediction.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 3: Standardizing a New Case\n",
    "# The bank's training data are in original units. To compare a new applicant with\n",
    "# them, convert the applicant to standard units using the TRAINING set's mean and\n",
    "# SD (np.std, or .std(ddof=0)). Never use the new case to compute the scaling.\n",
    "#   1. Compute the applicant's income and balance in standard units, each rounded\n",
    "#      to 2 decimals.\n",
    "# Print exactly:\n",
    "# Income in standard units: -0.21\n",
    "# Balance in standard units: -0.17\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "training = pd.DataFrame({\n",
    "    \"income\": [42, 55, 61, 38, 70, 48],          # $1,000s\n",
    "    \"balance\": [1200, 300, 800, 2500, 150, 1900], # $\n",
    "})\n",
    "new_applicant = pd.Series({\"income\": 50, \"balance\": 1000})\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": [
      "Income in standard units: -0.21\n",
      "Balance in standard units: -0.17\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "training = pd.DataFrame({\n",
    "    \"income\": [42, 55, 61, 38, 70, 48],          # $1,000s\n",
    "    \"balance\": [1200, 300, 800, 2500, 150, 1900], # $\n",
    "})\n",
    "new_applicant = pd.Series({\"income\": 50, \"balance\": 1000})\n",
    "\n",
    "means = training.mean()\n",
    "sds = training.std(ddof=0)\n",
    "scaled = (new_applicant - means) / sds\n",
    "\n",
    "print(\"Income in standard units:\", round(scaled['income'], 2))\n",
    "print(\"Balance in standard units:\", round(scaled['balance'], 2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 4: A Classifier From Start to Finish\n",
    "#   1. Write classify(training, point, k): compute the distance from point to every\n",
    "#      row of training (using income_su and debt_su), take the k nearest rows, and\n",
    "#      return their most common outcome.\n",
    "#   2. Classify each test loan with k = 3 and print the predictions as a list.\n",
    "# Print exactly:\n",
    "# Predictions: ['repaid', 'default', 'default', 'default', 'repaid']\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Past loans; both attributes are already in standard units.\n",
    "loans = pd.DataFrame({\n",
    "    \"income_su\": [1.2, 0.8, -0.3, -1.1, 0.4, -0.9, 1.5, -0.2, 0.1, -1.4],\n",
    "    \"debt_su\": [-0.9, -0.4, 0.6, 1.3, -0.1, 0.9, -1.2, 1.0, 0.3, 0.2],\n",
    "    \"outcome\": [\"repaid\", \"repaid\", \"default\", \"default\", \"repaid\",\n",
    "                \"default\", \"repaid\", \"default\", \"repaid\", \"default\"],\n",
    "})\n",
    "test = pd.DataFrame({\n",
    "    \"income_su\": [0.9, -0.8, 0.0, -0.5, 0.6],\n",
    "    \"debt_su\": [-0.6, 0.8, 0.5, 0.1, 0.4],\n",
    "    \"outcome\": [\"repaid\", \"default\", \"repaid\", \"repaid\", \"default\"],\n",
    "})\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": [
      "Predictions: ['repaid', 'default', 'default', 'default', 'repaid']\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Past loans; both attributes are already in standard units.\n",
    "loans = pd.DataFrame({\n",
    "    \"income_su\": [1.2, 0.8, -0.3, -1.1, 0.4, -0.9, 1.5, -0.2, 0.1, -1.4],\n",
    "    \"debt_su\": [-0.9, -0.4, 0.6, 1.3, -0.1, 0.9, -1.2, 1.0, 0.3, 0.2],\n",
    "    \"outcome\": [\"repaid\", \"repaid\", \"default\", \"default\", \"repaid\",\n",
    "                \"default\", \"repaid\", \"default\", \"repaid\", \"default\"],\n",
    "})\n",
    "test = pd.DataFrame({\n",
    "    \"income_su\": [0.9, -0.8, 0.0, -0.5, 0.6],\n",
    "    \"debt_su\": [-0.6, 0.8, 0.5, 0.1, 0.4],\n",
    "    \"outcome\": [\"repaid\", \"default\", \"repaid\", \"repaid\", \"default\"],\n",
    "})\n",
    "\n",
    "def classify(training, point, k):\n",
    "    attributes = training[['income_su', 'debt_su']]\n",
    "    distances = np.sqrt(((attributes - point) ** 2).sum(axis=1))\n",
    "    nearest = training.assign(distance=distances).sort_values('distance').head(k)\n",
    "    return nearest['outcome'].value_counts().idxmax()\n",
    "\n",
    "predictions = []\n",
    "for income, debt in zip(test['income_su'], test['debt_su']):\n",
    "    predictions.append(classify(loans, np.array([income, debt]), 3))\n",
    "print(\"Predictions:\", predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "thebe-interactive"
    ]
   },
   "outputs": [],
   "source": [
    "### Question 5: Test Accuracy\n",
    "#   1. Using the predictions from Question 4 (given below), compute the accuracy:\n",
    "#      the proportion of test loans whose prediction matches the actual outcome.\n",
    "#   2. Compute the accuracy of always predicting \"repaid\", the more common outcome\n",
    "#      in the test set, as a baseline.\n",
    "# Print exactly:\n",
    "# Classifier accuracy: 0.4\n",
    "# Baseline accuracy: 0.6\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "test = pd.DataFrame({\n",
    "    \"outcome\": [\"repaid\", \"default\", \"repaid\", \"repaid\", \"default\"],\n",
    "    \"predicted\": [\"repaid\", \"default\", \"default\", \"default\", \"repaid\"],\n",
    "})\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": [
      "Classifier accuracy: 0.4\n",
      "Baseline accuracy: 0.6\n"
     ]
    }
   ],
   "source": [
    "### Solution\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "test = pd.DataFrame({\n",
    "    \"outcome\": [\"repaid\", \"default\", \"repaid\", \"repaid\", \"default\"],\n",
    "    \"predicted\": [\"repaid\", \"default\", \"default\", \"default\", \"repaid\"],\n",
    "})\n",
    "\n",
    "accuracy = (test['outcome'] == test['predicted']).mean()\n",
    "baseline = (test['outcome'] == 'repaid').mean()\n",
    "\n",
    "print(\"Classifier accuracy:\", accuracy)\n",
    "print(\"Baseline accuracy:\", baseline)\n",
    "# On these 5 test loans the classifier does worse than always guessing\n",
    "# \"repaid\". With so few test cases, check it on a much larger test set before trusting it.\n"
   ]
  },
  {
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    "    submit.addEventListener('click', function () {\n",
    "      clearFeedback(grader);\n",
    "      submit.disabled = true;\n",
    "      result.textContent = 'Submitting code.';\n",
    "\n",
    "      saveAttempt({\n",
    "        lab_id: labId,\n",
    "        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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