{
 "nbformat": 4,
 "nbformat_minor": 5,
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  },
  "language_info": {
   "name": "python",
   "version": "3.12",
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "nbconvert_exporter": "python"
  },
  "title": "Vehicle CO2: teaching, business, audit and national scenarios",
  "author": "Gareth Grant",
  "execution": {
   "date": "2026-10-04",
   "method": "Sequential Python cells; actual input CSV; captured Jupyter-compatible outputs",
   "code_cells_completed": 149,
   "source_sha256": "e832d820e42066d0946b02a58bce8bc1c7da9684f5724348ade9753b8eabec72",
   "dependencies": {
    "python": "3.12.14",
    "numpy": "2.3.5",
    "pandas": "2.2.3",
    "matplotlib": "3.10.8",
    "seaborn": "0.13.2",
    "scikit-learn": "1.8.0"
   }
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "# Vehicle CO₂ emissions: learn the code, explain the results, use the evidence\n\n**Gareth Grant · Python data-analysis portfolio · Verified run: 4 October 2026**\n\nThis notebook turns Canadian vehicle ratings into an understandable analysis and a set of\nbusiness, audit and policy demonstrations. Every numbered step has one purpose, a short code\ncell with comments, and, where useful, a separate interpretation cell. Interpretation outputs\nupdate when the calculation changes. Edit the Markdown explanations and the text passed to\n`explain()` to add your own comments.\n\nThe supplied CSV was used for the executed outputs. Numerical model results are measured\non held-out Make–Model groups. Company fleets, sales volumes, prices and tax scenarios are\nexplicitly labelled assumptions. The tax sources were checked on **4 October 2026**.\n\n**Read in three passes:** follow Sections 1–9 to learn the analysis; use Sections 10–12 for business\nand audit applications; finish with Section 13 for national sales scenarios and limitations.\n",
   "id": "co2-teaching-000"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## Executive result\n\nThe supplied CSV contains **7,385 vehicle-rating records**,\nwith **1,103 exact repeated rows** and no missing values.\n\n**Measured result:** Fuel-interaction Ridge achieved **RMSE 3.33 g/km**,\n**MAE 2.23 g/km** and **R² 0.9969** on\n**1,466 held-out records from 410 exact Make–Model groups**.\nThe selected specifications-only model had **RMSE 23.54 g/km**.\nFuel consumption and fuel type make prediction much easier because of their physical connection to CO₂.\n\n**Decision value:** the later sections demonstrate fleet budgets, jurisdiction-specific levy arithmetic,\ntraceable audit screening and sales-weighted national scenarios. Company activity and sales volumes\nare assumptions; the CSV alone cannot establish actual tax liability or a national emissions total.\n",
   "id": "co2-teaching-001"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 1. Start with decisions, not an algorithm\n\n| Reader | Decision supported | Evidence needed beyond this CSV |\n| --- | --- | --- |\n| Manufacturer or importer | Compare prospective product mixes and levy exposure | Applicable certificates, tax classification, effective date and unit sales |\n| Fleet manager | Compare rated fuel costs and tailpipe emissions | Actual mileage, fuel prices, routes, purchase cost and operating requirements |\n| Auditor | Identify records or calculations requiring evidence | VIN/variant identifiers, certificates, invoices, ledger totals and a documented materiality policy |\n| National policymaker | Explore the emissions effect of changes in new vehicle sales | Registrations/sales weights, vehicle-kilometres, fleet age, retirements and fuel statistics |\n\nThese are **potential uses inferred from the analysis**, not outcomes tested on a business,\naudit engagement or national registry. Where a valid measured rating is available, use it rather\nthan replacing it with a model estimate.\n\n### The question\n\nCan we estimate a vehicle's reported tailpipe CO₂ rating, and explain how that rating can inform\ndecisions? We examine two information settings: **specifications only**, and **fuel consumption\nalready known**. Those settings solve different practical problems.\n\n### The unit\n\nThe target is **g/km**: grams of CO₂ per kilometre. It is an emissions intensity, not annual tonnes.\nAn annual estimate needs a distance. It is also different from atmospheric CO₂ concentration\n(ppm), all greenhouse gases (CO₂e), and lifecycle emissions.\n",
   "id": "co2-teaching-002"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 2. Understand linear regression first\n\nFor one predictor, a fitted model has the form:\n\n$$\\widehat{CO_2}=b_0+b_1\\times\\text{engine size}.$$\n\nWith two predictors:\n\n$$\\widehat{CO_2}=b_0+b_1\\times\\text{engine size}+b_2\\times\\text{cylinders}.$$\n\nOrdinary least squares chooses coefficients to minimise the **sum of squared residuals**.\nA residual here is `actual − predicted`. Coefficients describe the fitted association, not an\nindependent causal effect. Engine size and cylinders are strongly related, so their separate\ncoefficients require cautious interpretation.\n\nRidge adds a penalty on coefficient size. Random forests fit multiple decision trees. We compare\nthem with simple alternatives; a more complex model must earn its place through validation.\n\n### What changed from the supplied v3 notebook?\n\nThe v3 notebook provides more explanations than the first upload, but it fits `X` and `y` after\ncreating a split, then evaluates predictions on the same full dataset. Its RMSE ≈29.96 and\nR² ≈0.738 are **in-sample** results. Its fitted best-fit line is also described as an ideal 1:1 line,\nand one example uses g/kg instead of g/km. This edition fits training partitions only, reports\nheld-out performance, and uses a true identity line for the prediction plot.\n",
   "id": "co2-teaching-003"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 3. Set up a reproducible notebook\n\nPlace `CO2 Emissions_Canada.csv` beside the notebook. In Jupyter, choose **Restart Kernel and\nRun All Cells**. The fallback `upload/` location is useful when working with the attached files.\nThe package includes `requirements.txt` with the analysis versions used for this run.\n\nImports are split by role. Shared helpers only format outputs or construct reusable pipelines;\nthe individual analysis steps remain visible below.\n",
   "id": "co2-teaching-004"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 1. Import data-handling tools\n\nThese tools load tables, create arrays and record the input fingerprint.\n",
   "id": "co2-teaching-005"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "from pathlib import Path\nimport hashlib\nimport json\nimport platform\nimport numpy as np\nimport pandas as pd\n",
   "execution_count": 1,
   "outputs": [],
   "id": "co2-teaching-006"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 2. Import plotting tools\n\nMatplotlib draws figures; seaborn provides readable statistical plots.\n",
   "id": "co2-teaching-007"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "import matplotlib\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n",
   "execution_count": 2,
   "outputs": [],
   "id": "co2-teaching-008"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 3. Import model tools\n\nEach estimator below will later be created in its own cell.\n",
   "id": "co2-teaching-009"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "import sklearn\nfrom sklearn.base import clone\nfrom sklearn.dummy import DummyRegressor\nfrom sklearn.linear_model import LinearRegression, Ridge\nfrom sklearn.ensemble import RandomForestRegressor\n",
   "execution_count": 3,
   "outputs": [],
   "id": "co2-teaching-010"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 4. Import preprocessing tools\n\nPipelines fit these transformations inside the appropriate training partition.\n",
   "id": "co2-teaching-011"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "from sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder, FunctionTransformer\n",
   "execution_count": 4,
   "outputs": [],
   "id": "co2-teaching-012"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 5. Import validation tools\n\nGrouped splitting keeps records of one exact vehicle model together.\n",
   "id": "co2-teaching-013"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "from sklearn.model_selection import GroupShuffleSplit, GroupKFold\nfrom sklearn.model_selection import cross_validate, cross_val_predict\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
   "execution_count": 5,
   "outputs": [],
   "id": "co2-teaching-014"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 6. Choose the input file\n\nOnly the supplied CSV is used. No replacement dataset is downloaded.\n",
   "id": "co2-teaching-015"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "# Read from the notebook folder, or the attachments folder.\nDATA_PATH = Path('CO2 Emissions_Canada.csv')\nif not DATA_PATH.is_file():\n    DATA_PATH = Path('upload') / 'CO2 Emissions_Canada.csv'\nif not DATA_PATH.is_file():\n    raise FileNotFoundError('Place the original CSV beside this notebook.')\n",
   "execution_count": 6,
   "outputs": [],
   "id": "co2-teaching-016"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 7. Record provenance\n\nThe exact source link comes from the supplied v3 notebook. Its licence was not verified.\n",
   "id": "co2-teaching-017"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "DATASET_URL = 'https://www.kaggle.com/datasets/karomatovdovudkhon/co2-emissions-canada'\nSOURCE_ACCESS_DATE = '1 April 2024, as cited in the supplied v3 notebook'\nSOURCE_LICENCE = 'Not independently verified'\nSOURCE_CHECKED_DATE = '2026-10-04'\nSEED = 42\nOUTPUT_DIR = Path('co2_outputs')\nSAVE_OUTPUTS = False  # Change to True to export local charts and result tables.\n",
   "execution_count": 7,
   "outputs": [],
   "id": "co2-teaching-018"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 8. Define the analysis settings\n\nThe test share is a share of model groups, not an exact share of rows.\n",
   "id": "co2-teaching-019"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "TEST_GROUP_SHARE = 0.20\nCV_FOLDS = 5\nN_BOOTSTRAPS = 1000\nFUEL_CODES = ['X', 'Z', 'D', 'E', 'N']\nFUEL_NAMES = {'X': 'Regular gasoline', 'Z': 'Premium gasoline',\n              'D': 'Diesel', 'E': 'Ethanol (E85)', 'N': 'Natural gas'}\n",
   "execution_count": 8,
   "outputs": [],
   "id": "co2-teaching-020"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 9. Set readable chart colours\n\nThe palette matches the portfolio; chart colours still retain a clear legend.\n",
   "id": "co2-teaching-021"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "NAVY, SCARLET, SLATE, SILVER = '#16324F', '#FF2400', '#667582', '#B8C2CC'\nFUEL_PALETTE = {'X': NAVY, 'Z': '#587C9B', 'D': '#20252B',\n                'E': SCARLET, 'N': '#967143'}\nsns.set_theme(style='whitegrid', context='notebook')\nplt.rcParams.update({'figure.dpi': 110, 'savefig.dpi': 220,\n                     'axes.spines.top': False, 'axes.spines.right': False})\n",
   "execution_count": 9,
   "outputs": [],
   "id": "co2-teaching-022"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 10. Create the interpretation helper\n\nJupyter can display Markdown. This helper keeps interpretation cells short and editable.\n",
   "id": "co2-teaching-023"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "def explain(message):\n    # Render a result paragraph using Jupyter's built-in display function.\n    display({'text/markdown': message}, raw=True)\n",
   "execution_count": 10,
   "outputs": [],
   "id": "co2-teaching-024"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 11. Create the figure helper\n\nOne helper handles layout and optional export; each chart gets its own analysis cell.\n",
   "id": "co2-teaching-025"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "def show_figure(fig, name):\n    fig.tight_layout()\n    if SAVE_OUTPUTS:\n        OUTPUT_DIR.mkdir(exist_ok=True)\n        fig.savefig(OUTPUT_DIR / f'{name}.png', bbox_inches='tight')\n        fig.savefig(OUTPUT_DIR / f'{name}.svg', bbox_inches='tight')\n    plt.show()\n",
   "execution_count": 11,
   "outputs": [],
   "id": "co2-teaching-026"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 4. Inspect the data before making claims\n\nRows are catalogue/rating entries. They are **not vehicles sold**, and their frequency is not market\nshare. The file does not include a model year, a sales quantity, annual distance, VIN, or tax\ncertificate identifier. Do not assume that these entries represent all vehicles on a country's roads.\n\nThe source names fuel consumption in L/100 km and mpg. We use L/100 km consistently and\navoid treating an inverse unit conversion as an independent physical measurement.\n",
   "id": "co2-teaching-027"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 12. Load the raw CSV\n\nKeep a raw copy so every later transformation can be traced.\n",
   "id": "co2-teaching-028"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "raw = pd.read_csv(DATA_PATH)\nsource_sha256 = hashlib.sha256(DATA_PATH.read_bytes()).hexdigest()\nprint('Input fingerprint:', source_sha256)\n",
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Input fingerprint: e832d820e42066d0946b02a58bce8bc1c7da9684f5724348ade9753b8eabec72\n"
    }
   ],
   "id": "co2-teaching-029"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 13. Count rows and columns\n\nStart by explaining what one row represents.\n",
   "id": "co2-teaching-030"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "n_rows, n_columns = raw.shape\ndisplay(pd.Series({'Catalogue records': n_rows, 'Variables': n_columns}).to_frame('Count'))\n",
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                   Count\nCatalogue records   7385\nVariables             12",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Catalogue records</th>\n      <td>7385</td>\n    </tr>\n    <tr>\n      <th>Variables</th>\n      <td>12</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-031"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** There are **{n_rows:,} catalogue records** and **{n_columns} variables**.\nThe row count is not a count of Canadian vehicle sales.\"\"\")\n",
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** There are **7,385 catalogue records** and **12 variables**.\nThe row count is not a count of Canadian vehicle sales."
     }
    }
   ],
   "id": "co2-teaching-032"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 14. Preview five records\n\nA small preview is more readable on a website than printing the entire dataset.\n",
   "id": "co2-teaching-033"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "display(raw.head(5))\n",
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "    Make       Model  ... Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)\n0  ACURA         ILX  ...                          33                  196\n1  ACURA         ILX  ...                          29                  221\n2  ACURA  ILX HYBRID  ...                          48                  136\n3  ACURA     MDX 4WD  ...                          25                  255\n4  ACURA     RDX AWD  ...                          27                  244\n\n[5 rows x 12 columns]",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Make</th>\n      <th>Model</th>\n      <th>Vehicle Class</th>\n      <th>Engine Size(L)</th>\n      <th>Cylinders</th>\n      <th>Transmission</th>\n      <th>Fuel Type</th>\n      <th>Fuel Consumption City (L/100 km)</th>\n      <th>Fuel Consumption Hwy (L/100 km)</th>\n      <th>Fuel Consumption Comb (L/100 km)</th>\n      <th>Fuel Consumption Comb (mpg)</th>\n      <th>CO2 Emissions(g/km)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>ACURA</td>\n      <td>ILX</td>\n      <td>COMPACT</td>\n      <td>2.0</td>\n      <td>4</td>\n      <td>AS5</td>\n      <td>Z</td>\n      <td>9.9</td>\n      <td>6.7</td>\n      <td>8.5</td>\n      <td>33</td>\n      <td>196</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ACURA</td>\n      <td>ILX</td>\n      <td>COMPACT</td>\n      <td>2.4</td>\n      <td>4</td>\n      <td>M6</td>\n      <td>Z</td>\n      <td>11.2</td>\n      <td>7.7</td>\n      <td>9.6</td>\n      <td>29</td>\n      <td>221</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>ACURA</td>\n      <td>ILX HYBRID</td>\n      <td>COMPACT</td>\n      <td>1.5</td>\n      <td>4</td>\n      <td>AV7</td>\n      <td>Z</td>\n      <td>6.0</td>\n      <td>5.8</td>\n      <td>5.9</td>\n      <td>48</td>\n      <td>136</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>ACURA</td>\n      <td>MDX 4WD</td>\n      <td>SUV - SMALL</td>\n      <td>3.5</td>\n      <td>6</td>\n      <td>AS6</td>\n      <td>Z</td>\n      <td>12.7</td>\n      <td>9.1</td>\n      <td>11.1</td>\n      <td>25</td>\n      <td>255</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>ACURA</td>\n      <td>RDX AWD</td>\n      <td>SUV - SMALL</td>\n      <td>3.5</td>\n      <td>6</td>\n      <td>AS6</td>\n      <td>Z</td>\n      <td>12.1</td>\n      <td>8.7</td>\n      <td>10.6</td>\n      <td>27</td>\n      <td>244</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-034"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 15. Check data types\n\nNumeric columns should be numeric before correlations and model fitting.\n",
   "id": "co2-teaching-035"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "display(raw.dtypes.astype(str).to_frame('Data type'))\n",
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                                 Data type\nMake                                object\nModel                               object\nVehicle Class                       object\nEngine Size(L)                     float64\nCylinders                            int64\nTransmission                        object\nFuel Type                           object\nFuel Consumption City (L/100 km)   float64\nFuel Consumption Hwy (L/100 km)    float64\nFuel Consumption Comb (L/100 km)   float64\nFuel Consumption Comb (mpg)          int64\nCO2 Emissions(g/km)                  int64",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Data type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Make</th>\n      <td>object</td>\n    </tr>\n    <tr>\n      <th>Model</th>\n      <td>object</td>\n    </tr>\n    <tr>\n      <th>Vehicle Class</th>\n      <td>object</td>\n    </tr>\n    <tr>\n      <th>Engine Size(L)</th>\n      <td>float64</td>\n    </tr>\n    <tr>\n      <th>Cylinders</th>\n      <td>int64</td>\n    </tr>\n    <tr>\n      <th>Transmission</th>\n      <td>object</td>\n    </tr>\n    <tr>\n      <th>Fuel Type</th>\n      <td>object</td>\n    </tr>\n    <tr>\n      <th>Fuel Consumption City (L/100 km)</th>\n      <td>float64</td>\n    </tr>\n    <tr>\n      <th>Fuel Consumption Hwy (L/100 km)</th>\n      <td>float64</td>\n    </tr>\n    <tr>\n      <th>Fuel Consumption Comb (L/100 km)</th>\n      <td>float64</td>\n    </tr>\n    <tr>\n      <th>Fuel Consumption Comb (mpg)</th>\n      <td>int64</td>\n    </tr>\n    <tr>\n      <th>CO2 Emissions(g/km)</th>\n      <td>int64</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-036"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 16. Check missing values once\n\n`isna()` and `isnull()` are aliases; repeating both adds no new evidence.\n",
   "id": "co2-teaching-037"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "missing_counts = raw.isna().sum()\ndisplay(missing_counts.to_frame('Missing values'))\n",
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                                  Missing values\nMake                                           0\nModel                                          0\nVehicle Class                                  0\nEngine Size(L)                                 0\nCylinders                                      0\nTransmission                                   0\nFuel Type                                      0\nFuel Consumption City (L/100 km)               0\nFuel Consumption Hwy (L/100 km)                0\nFuel Consumption Comb (L/100 km)               0\nFuel Consumption Comb (mpg)                    0\nCO2 Emissions(g/km)                            0",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Missing values</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Make</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Model</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Vehicle Class</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Engine Size(L)</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Cylinders</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Transmission</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Fuel Type</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Fuel Consumption City (L/100 km)</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Fuel Consumption Hwy (L/100 km)</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Fuel Consumption Comb (L/100 km)</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Fuel Consumption Comb (mpg)</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>CO2 Emissions(g/km)</th>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-038"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** The supplied file has **{int(missing_counts.sum()):,} missing cells**.\nThis does not prove that the remaining values are correct or unique.\"\"\")\n",
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** The supplied file has **0 missing cells**.\nThis does not prove that the remaining values are correct or unique."
     }
    }
   ],
   "id": "co2-teaching-039"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 17. Count exact repeated rows\n\nRepeated rows can influence both descriptive summaries and model fitting.\n",
   "id": "co2-teaching-040"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "raw_repeated_rows = int(raw.duplicated().sum())\nprint('Full-row repeats after the first occurrence:', raw_repeated_rows)\n",
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Full-row repeats after the first occurrence: 1103\n"
    }
   ],
   "id": "co2-teaching-041"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** There are **{raw_repeated_rows:,} exact repeated rows**.\nWithout model years, some repeats could be legitimate historical ratings. We retain them for\nthe main run, keep model identities together across splits, and later test removal as a sensitivity analysis.\"\"\")\n",
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** There are **1,103 exact repeated rows**.\nWithout model years, some repeats could be legitimate historical ratings. We retain them for\nthe main run, keep model identities together across splits, and later test removal as a sensitivity analysis."
     }
    }
   ],
   "id": "co2-teaching-042"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 18. Define shorter column names\n\nThe source names and units remain documented; shorter names make learning the code easier.\n",
   "id": "co2-teaching-043"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "rename_columns = {\n    'Make': 'make', 'Model': 'model', 'Vehicle Class': 'vehicle_class',\n    'Engine Size(L)': 'engine_l', 'Cylinders': 'cylinders',\n    'Transmission': 'transmission', 'Fuel Type': 'fuel_type',\n    'Fuel Consumption City (L/100 km)': 'city_l_100km',\n    'Fuel Consumption Hwy (L/100 km)': 'hwy_l_100km',\n    'Fuel Consumption Comb (L/100 km)': 'combined_l_100km',\n    'Fuel Consumption Comb (mpg)': 'combined_mpg',\n    'CO2 Emissions(g/km)': 'co2_g_km',\n}\n",
   "execution_count": 21,
   "outputs": [],
   "id": "co2-teaching-044"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 19. Check the expected schema\n\nStop if a different CSV has been supplied instead of silently using the wrong columns.\n",
   "id": "co2-teaching-045"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "missing_columns = sorted(set(rename_columns) - set(raw.columns))\nif missing_columns:\n    raise ValueError(f'Missing columns: {missing_columns}')\ndata = raw.rename(columns=rename_columns).copy()\n",
   "execution_count": 22,
   "outputs": [],
   "id": "co2-teaching-046"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 20. Trim text without discarding records\n\nLeading or trailing spaces can create false differences between model identities.\n",
   "id": "co2-teaching-047"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "text_columns = ['make', 'model', 'vehicle_class', 'transmission', 'fuel_type']\nfor column in text_columns:\n    data[column] = data[column].str.strip()\nnormalised_repeats = int(data.duplicated().sum())\ndata['source_row'] = np.arange(len(data))  # Zero-based CSV row index, excluding the header.\n",
   "execution_count": 23,
   "outputs": [],
   "id": "co2-teaching-048"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** After trimming text, **{normalised_repeats:,} rows** repeat a full normalised record.\nWe preserve the zero-based source row to locate a record in the original file.\"\"\")\n",
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** After trimming text, **1,104 rows** repeat a full normalised record.\nWe preserve the zero-based source row to locate a record in the original file."
     }
    }
   ],
   "id": "co2-teaching-049"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 21. Check numeric validity\n\nNegative or zero combustion-vehicle inputs require investigation before fitting.\n",
   "id": "co2-teaching-050"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "numeric_columns = ['engine_l', 'cylinders', 'city_l_100km', 'hwy_l_100km',\n                   'combined_l_100km', 'combined_mpg', 'co2_g_km']\ndomain_checks = pd.DataFrame({'Non-finite': (~np.isfinite(data[numeric_columns])).sum(),\n                              'Non-positive': data[numeric_columns].le(0).sum()})\ndisplay(domain_checks)\nassert np.isfinite(data[numeric_columns]).all().all()\nassert data[numeric_columns].gt(0).all().all()\nassert data['cylinders'].mod(1).eq(0).all()\n",
   "execution_count": 25,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                  Non-finite  Non-positive\nengine_l                   0             0\ncylinders                  0             0\ncity_l_100km               0             0\nhwy_l_100km                0             0\ncombined_l_100km           0             0\ncombined_mpg               0             0\nco2_g_km                   0             0",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Non-finite</th>\n      <th>Non-positive</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>engine_l</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>cylinders</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>city_l_100km</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>hwy_l_100km</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>combined_l_100km</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>combined_mpg</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>co2_g_km</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-051"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** This CSV passes the positive-value, finite-number and integer-cylinder checks.\nWe do not remove a large engine or high-emission record solely because it is unusual.\"\"\")\n",
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** This CSV passes the positive-value, finite-number and integer-cylinder checks.\nWe do not remove a large engine or high-emission record solely because it is unusual."
     }
    }
   ],
   "id": "co2-teaching-052"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 22. Count fuel categories\n\nSample size matters when interpreting averages or model reliability.\n",
   "id": "co2-teaching-053"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fuel_counts = data['fuel_type'].value_counts().reindex(FUEL_CODES, fill_value=0)\nfuel_count_table = fuel_counts.to_frame('Records')\nfuel_count_table['Meaning'] = fuel_count_table.index.map(FUEL_NAMES)\ndisplay(fuel_count_table)\n",
   "execution_count": 27,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "           Records           Meaning\nfuel_type                           \nX             3637  Regular gasoline\nZ             3202  Premium gasoline\nD              175            Diesel\nE              370     Ethanol (E85)\nN                1       Natural gas",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Records</th>\n      <th>Meaning</th>\n    </tr>\n    <tr>\n      <th>fuel_type</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>X</th>\n      <td>3637</td>\n      <td>Regular gasoline</td>\n    </tr>\n    <tr>\n      <th>Z</th>\n      <td>3202</td>\n      <td>Premium gasoline</td>\n    </tr>\n    <tr>\n      <th>D</th>\n      <td>175</td>\n      <td>Diesel</td>\n    </tr>\n    <tr>\n      <th>E</th>\n      <td>370</td>\n      <td>Ethanol (E85)</td>\n    </tr>\n    <tr>\n      <th>N</th>\n      <td>1</td>\n      <td>Natural gas</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-054"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** Natural gas has **{int(fuel_counts['N'])} record**; E85 has **{int(fuel_counts['E']):,}**;\ndiesel has **{int(fuel_counts['D']):,}**. A result for a sparse category should not be presented\nas established performance for an entire fuel market.\"\"\")\n",
   "execution_count": 28,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** Natural gas has **1 record**; E85 has **370**;\ndiesel has **175**. A result for a sparse category should not be presented\nas established performance for an entire fuel market."
     }
    }
   ],
   "id": "co2-teaching-055"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 23. Check the combined-rating arithmetic\n\nThe documented combined formula is 55% city and 45% highway.\n",
   "id": "co2-teaching-056"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "expected_combined = 0.55 * data['city_l_100km'] + 0.45 * data['hwy_l_100km']\ndata['combined_gap_l_100km'] = data['combined_l_100km'] - expected_combined\nRATING_TOLERANCE = 0.15  # A declared screening tolerance for rounded ratings.\ndata['combined_rating_review'] = data['combined_gap_l_100km'].abs() > RATING_TOLERANCE + 1e-8\nprint('Records above the screening tolerance:', int(data['combined_rating_review'].sum()))\n",
   "execution_count": 29,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Records above the screening tolerance: 45\n"
    }
   ],
   "id": "co2-teaching-057"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** **{int(data['combined_rating_review'].sum()):,} records** differ from the weighted\ncity/highway calculation by more than {RATING_TOLERANCE:.2f} L/100 km. This is a review flag,\nnot a finding of error or wrongdoing; source methods and rounding need checking.\"\"\")\n",
   "execution_count": 30,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** **45 records** differ from the weighted\ncity/highway calculation by more than 0.15 L/100 km. This is a review flag,\nnot a finding of error or wrongdoing; source methods and rounding need checking."
     }
    }
   ],
   "id": "co2-teaching-058"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 24. Summarise the target\n\nUse the distribution to give later prediction errors a meaningful scale.\n",
   "id": "co2-teaching-059"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "target_summary = data['co2_g_km'].describe()\ndisplay(target_summary.round(2).to_frame('CO₂ (g/km)'))\n",
   "execution_count": 31,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "       CO₂ (g/km)\ncount     7385.00\nmean       250.58\nstd         58.51\nmin         96.00\n25%        208.00\n50%        246.00\n75%        288.00\nmax        522.00",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CO₂ (g/km)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>7385.00</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>250.58</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>58.51</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>96.00</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>208.00</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>246.00</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>288.00</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>522.00</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-060"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** The catalogue mean is **{target_summary['mean']:.2f} g/km**, the median is\n**{target_summary['50%']:.0f} g/km**, and the range is **{target_summary['min']:.0f}–{target_summary['max']:.0f} g/km**.\nThis is an unweighted mean of recorded entries, not a sales-weighted national mean.\"\"\")\n",
   "execution_count": 32,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** The catalogue mean is **250.58 g/km**, the median is\n**246 g/km**, and the range is **96–522 g/km**.\nThis is an unweighted mean of recorded entries, not a sales-weighted national mean."
     }
    }
   ],
   "id": "co2-teaching-061"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 5. Protect the test set\n\nAn ordinary random row split may put repeated model entries on both sides. We instead hold out\ncomplete combinations of **Make + exact Model text**. This is an imperfect but useful separation:\nrelated trims and model families can still have different names. It does not simulate the next year\nof sales, because a verified year is unavailable.\n\nThe following cells split the data before model exploration. All exploratory plots use training\nrecords. The main selection uses grouped cross-validation inside that training partition.\n",
   "id": "co2-teaching-062"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 25. Create the grouping key\n\nThe two-column identity avoids collisions caused by simple string concatenation.\n",
   "id": "co2-teaching-063"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "group_ids, group_names = pd.factorize(\n    pd.MultiIndex.from_frame(data[['make', 'model']]), sort=True)\ndata['group_id'] = group_ids\nprint('Normalised Make–Model groups:', data['group_id'].nunique())\n",
   "execution_count": 33,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Normalised Make–Model groups: 2048\n"
    }
   ],
   "id": "co2-teaching-064"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 26. Create a grouped hold-out split\n\nA fixed seed makes the same group selection reproducible.\n",
   "id": "co2-teaching-065"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "splitter = GroupShuffleSplit(n_splits=1, test_size=TEST_GROUP_SHARE, random_state=SEED)\ntrain_indices, test_indices = next(splitter.split(data, groups=data['group_id']))\n",
   "execution_count": 34,
   "outputs": [],
   "id": "co2-teaching-066"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 27. Store training and test records\n\nUse copies so later plotting or audit columns do not alter the input unexpectedly.\n",
   "id": "co2-teaching-067"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "train = data.iloc[train_indices].copy()\ntest = data.iloc[test_indices].copy()\n",
   "execution_count": 35,
   "outputs": [],
   "id": "co2-teaching-068"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 28. Verify the separation\n\nA useful check tests the intended boundary directly.\n",
   "id": "co2-teaching-069"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "assert set(train['group_id']).isdisjoint(set(test['group_id']))\nsplit_summary = pd.DataFrame({\n    'Partition': ['Training', 'Held-out test'],\n    'Records': [len(train), len(test)],\n    'Groups': [train['group_id'].nunique(), test['group_id'].nunique()],\n})\ndisplay(split_summary)\n",
   "execution_count": 36,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "       Partition  Records  Groups\n0       Training     5919    1638\n1  Held-out test     1466     410",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Partition</th>\n      <th>Records</th>\n      <th>Groups</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Training</td>\n      <td>5919</td>\n      <td>1638</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Held-out test</td>\n      <td>1466</td>\n      <td>410</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-070"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** Training contains **{len(train):,} records** from **{train['group_id'].nunique():,} groups**.\nThe held-out test has **{len(test):,} records** from **{test['group_id'].nunique():,} groups**.\nNo exact Make–Model identity appears in both partitions.\"\"\")\n",
   "execution_count": 37,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** Training contains **5,919 records** from **1,638 groups**.\nThe held-out test has **1,466 records** from **410 groups**.\nNo exact Make–Model identity appears in both partitions."
     }
    }
   ],
   "id": "co2-teaching-071"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 29. Inspect fuel coverage across partitions\n\nA model cannot claim validated category performance when the category is absent from testing.\n",
   "id": "co2-teaching-072"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fuel_coverage = pd.DataFrame({\n    'Training': train['fuel_type'].value_counts(),\n    'Test': test['fuel_type'].value_counts(),\n}).fillna(0).astype(int).reindex(FUEL_CODES, fill_value=0)\ndisplay(fuel_coverage)\n",
   "execution_count": 38,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "           Training  Test\nfuel_type                \nX              2907   730\nZ              2561   641\nD               130    45\nE               320    50\nN                 1     0",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Training</th>\n      <th>Test</th>\n    </tr>\n    <tr>\n      <th>fuel_type</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>X</th>\n      <td>2907</td>\n      <td>730</td>\n    </tr>\n    <tr>\n      <th>Z</th>\n      <td>2561</td>\n      <td>641</td>\n    </tr>\n    <tr>\n      <th>D</th>\n      <td>130</td>\n      <td>45</td>\n    </tr>\n    <tr>\n      <th>E</th>\n      <td>320</td>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>N</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-073"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** Natural gas has **{fuel_coverage.loc['N', 'Training']} training record(s)** and\n**{fuel_coverage.loc['N', 'Test']} test record(s)**. The main score does not establish natural-gas prediction quality.\"\"\")\n",
   "execution_count": 39,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** Natural gas has **1 training record(s)** and\n**0 test record(s)**. The main score does not establish natural-gas prediction quality."
     }
    }
   ],
   "id": "co2-teaching-074"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 6. Explore one relationship at a time\n\nA useful chart answers a question. We use targeted plots rather than a large, difficult-to-read\npair plot. Association is not proof of causation, and a visual pattern is not a statistical test.\n",
   "id": "co2-teaching-075"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 30. Plot the distribution of training emissions\n\nThis shows where most ratings lie and whether a few large values extend the range.\n",
   "id": "co2-teaching-076"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fig, ax = plt.subplots(figsize=(8, 4.6))\nsns.histplot(data=train, x='co2_g_km', bins=30, color=NAVY, ax=ax)\nax.axvline(train['co2_g_km'].median(), color=SCARLET, linestyle='--', label='Median')\nax.set(title='Tailpipe CO₂ ratings — training records',\n       xlabel='CO₂ (g/km)', ylabel='Catalogue records')\nax.legend()\nshow_figure(fig, '01_training_distribution')\n",
   "execution_count": 40,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
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     }
    }
   ],
   "id": "co2-teaching-077"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 31. Calculate a correlation matrix\n\nPearson r measures the strength and direction of a linear association.\n",
   "id": "co2-teaching-078"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "corr_columns = ['engine_l', 'cylinders', 'combined_l_100km', 'combined_mpg', 'co2_g_km']\ncorrelations = train[corr_columns].corr()\ndisplay(correlations.round(3))\n",
   "execution_count": 41,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                  engine_l  cylinders  combined_l_100km  combined_mpg  co2_g_km\nengine_l             1.000      0.928             0.813        -0.751     0.846\ncylinders            0.928      1.000             0.777        -0.712     0.828\ncombined_l_100km     0.813      0.777             1.000        -0.925     0.914\ncombined_mpg        -0.751     -0.712            -0.925         1.000    -0.905\nco2_g_km             0.846      0.828             0.914        -0.905     1.000",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>engine_l</th>\n      <th>cylinders</th>\n      <th>combined_l_100km</th>\n      <th>combined_mpg</th>\n      <th>co2_g_km</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>engine_l</th>\n      <td>1.000</td>\n      <td>0.928</td>\n      <td>0.813</td>\n      <td>-0.751</td>\n      <td>0.846</td>\n    </tr>\n    <tr>\n      <th>cylinders</th>\n      <td>0.928</td>\n      <td>1.000</td>\n      <td>0.777</td>\n      <td>-0.712</td>\n      <td>0.828</td>\n    </tr>\n    <tr>\n      <th>combined_l_100km</th>\n      <td>0.813</td>\n      <td>0.777</td>\n      <td>1.000</td>\n      <td>-0.925</td>\n      <td>0.914</td>\n    </tr>\n    <tr>\n      <th>combined_mpg</th>\n      <td>-0.751</td>\n      <td>-0.712</td>\n      <td>-0.925</td>\n      <td>1.000</td>\n      <td>-0.905</td>\n    </tr>\n    <tr>\n      <th>co2_g_km</th>\n      <td>0.846</td>\n      <td>0.828</td>\n      <td>0.914</td>\n      <td>-0.905</td>\n      <td>1.000</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-079"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 32. Plot a readable heatmap\n\nRed is negative and blue is positive. A large negative correlation is still strong.\n",
   "id": "co2-teaching-080"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "heatmap_labels = ['Engine (L)', 'Cylinders', 'Fuel (L/100 km)', 'Fuel (mpg)', 'CO₂ (g/km)']\nfig, ax = plt.subplots(figsize=(7.2, 5.3))\nsns.heatmap(correlations, annot=True, fmt='.2f', cmap='RdBu', vmin=-1, vmax=1,\n            center=0, xticklabels=heatmap_labels, yticklabels=heatmap_labels,\n            cbar_kws={'label': 'Pearson r'}, ax=ax)\nax.set_title('Linear associations — training records')\nax.tick_params(axis='x', rotation=30)\nshow_figure(fig, '02_training_correlations')\n",
   "execution_count": 42,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "image/png": 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"
     }
    }
   ],
   "id": "co2-teaching-081"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** Engine size and cylinders have **r = {correlations.loc['engine_l', 'cylinders']:.2f}**.\nCombined fuel consumption and CO₂ have **r = {correlations.loc['combined_l_100km', 'co2_g_km']:.2f}**.\nThe inverse-unit mpg measure is related to the same fuel-use quantity; we exclude it from model inputs.\"\"\")\n",
   "execution_count": 43,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** Engine size and cylinders have **r = 0.93**.\nCombined fuel consumption and CO₂ have **r = 0.91**.\nThe inverse-unit mpg measure is related to the same fuel-use quantity; we exclude it from model inputs."
     }
    }
   ],
   "id": "co2-teaching-082"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 33. Plot engine size against emissions\n\nThis is the relationship used by the simplest regression model.\n",
   "id": "co2-teaching-083"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fig, ax = plt.subplots(figsize=(8, 4.8))\nax.scatter(train['engine_l'], train['co2_g_km'], s=14, alpha=0.24, color=NAVY)\nax.set(title='Engine size and reported CO₂ — training records',\n       xlabel='Engine size (L)', ylabel='CO₂ (g/km)')\nshow_figure(fig, '03_engine_and_emissions')\n",
   "execution_count": 44,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "image/png": 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"
     }
    }
   ],
   "id": "co2-teaching-084"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** Larger engines tend to have higher recorded emissions, but there is a wide\nspread at the same engine size. Engine size alone will not identify an exact certified rating.\"\"\")\n",
   "execution_count": 45,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** Larger engines tend to have higher recorded emissions, but there is a wide\nspread at the same engine size. Engine size alone will not identify an exact certified rating."
     }
    }
   ],
   "id": "co2-teaching-085"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 34. Plot fuel consumption by fuel type\n\nSeparate colours reveal whether a common fuel-consumption slope misses structure.\n",
   "id": "co2-teaching-086"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fig, ax = plt.subplots(figsize=(8.5, 5.0))\nsns.scatterplot(data=train, x='combined_l_100km', y='co2_g_km', hue='fuel_type',\n                hue_order=FUEL_CODES, palette=FUEL_PALETTE, s=20, alpha=0.33,\n                linewidth=0, ax=ax)\nax.set(title='Fuel consumption and CO₂ by fuel code — training records',\n       xlabel='Combined consumption (L/100 km)', ylabel='CO₂ (g/km)')\nax.legend(title='Fuel: X/Z gasoline, D diesel, E E85, N natural gas')\nshow_figure(fig, '04_fuel_relationship')\n",
   "execution_count": 46,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "image/png": 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"
     }
    }
   ],
   "id": "co2-teaching-087"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** The points form fuel-dependent bands. Different fuels need not have the\nsame emissions per litre. This motivates testing fuel-specific slopes. X and Z remain separate;\ntheir observational comparison does not isolate an octane effect.\"\"\")\n",
   "execution_count": 47,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** The points form fuel-dependent bands. Different fuels need not have the\nsame emissions per litre. This motivates testing fuel-specific slopes. X and Z remain separate;\ntheir observational comparison does not isolate an octane effect."
     }
    }
   ],
   "id": "co2-teaching-088"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 35. Summarise vehicle classes in training\n\nClass means describe this catalogue; show sample counts alongside them.\n",
   "id": "co2-teaching-089"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "training_classes = train.groupby('vehicle_class').agg(\n    records=('co2_g_km', 'size'), mean_co2_g_km=('co2_g_km', 'mean'),\n    median_co2_g_km=('co2_g_km', 'median'))\ndisplay(training_classes.sort_values('records', ascending=False).round(2))\n",
   "execution_count": 48,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                          records  mean_co2_g_km  median_co2_g_km\nvehicle_class                                                    \nSUV - SMALL                   994         235.06            232.0\nMID-SIZE                      883         220.62            212.0\nCOMPACT                       827         218.27            207.0\nSUV - STANDARD                602         303.15            305.0\nFULL-SIZE                     533         259.73            259.0\nSUBCOMPACT                    453         247.79            242.0\nPICKUP TRUCK - STANDARD       410         303.20            299.0\nTWO-SEATER                    357         280.24            264.0\nMINICOMPACT                   288         235.67            242.0\nSTATION WAGON - SMALL         184         198.92            198.0\nPICKUP TRUCK - SMALL          159         278.97            274.0\nMINIVAN                        65         261.68            262.0\nSPECIAL PURPOSE VEHICLE        58         232.86            232.0\nSTATION WAGON - MID-SIZE       43         245.88            230.0\nVAN - PASSENGER                43         395.70            414.0\nVAN - CARGO                    20         361.45            362.0",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>records</th>\n      <th>mean_co2_g_km</th>\n      <th>median_co2_g_km</th>\n    </tr>\n    <tr>\n      <th>vehicle_class</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>SUV - SMALL</th>\n      <td>994</td>\n      <td>235.06</td>\n      <td>232.0</td>\n    </tr>\n    <tr>\n      <th>MID-SIZE</th>\n      <td>883</td>\n      <td>220.62</td>\n      <td>212.0</td>\n    </tr>\n    <tr>\n      <th>COMPACT</th>\n      <td>827</td>\n      <td>218.27</td>\n      <td>207.0</td>\n    </tr>\n    <tr>\n      <th>SUV - STANDARD</th>\n      <td>602</td>\n      <td>303.15</td>\n      <td>305.0</td>\n    </tr>\n    <tr>\n      <th>FULL-SIZE</th>\n      <td>533</td>\n      <td>259.73</td>\n      <td>259.0</td>\n    </tr>\n    <tr>\n      <th>SUBCOMPACT</th>\n      <td>453</td>\n      <td>247.79</td>\n      <td>242.0</td>\n    </tr>\n    <tr>\n      <th>PICKUP TRUCK - STANDARD</th>\n      <td>410</td>\n      <td>303.20</td>\n      <td>299.0</td>\n    </tr>\n    <tr>\n      <th>TWO-SEATER</th>\n      <td>357</td>\n      <td>280.24</td>\n      <td>264.0</td>\n    </tr>\n    <tr>\n      <th>MINICOMPACT</th>\n      <td>288</td>\n      <td>235.67</td>\n      <td>242.0</td>\n    </tr>\n    <tr>\n      <th>STATION WAGON - SMALL</th>\n      <td>184</td>\n      <td>198.92</td>\n      <td>198.0</td>\n    </tr>\n    <tr>\n      <th>PICKUP TRUCK - SMALL</th>\n      <td>159</td>\n      <td>278.97</td>\n      <td>274.0</td>\n    </tr>\n    <tr>\n      <th>MINIVAN</th>\n      <td>65</td>\n      <td>261.68</td>\n      <td>262.0</td>\n    </tr>\n    <tr>\n      <th>SPECIAL PURPOSE VEHICLE</th>\n      <td>58</td>\n      <td>232.86</td>\n      <td>232.0</td>\n    </tr>\n    <tr>\n      <th>STATION WAGON - MID-SIZE</th>\n      <td>43</td>\n      <td>245.88</td>\n      <td>230.0</td>\n    </tr>\n    <tr>\n      <th>VAN - PASSENGER</th>\n      <td>43</td>\n      <td>395.70</td>\n      <td>414.0</td>\n    </tr>\n    <tr>\n      <th>VAN - CARGO</th>\n      <td>20</td>\n      <td>361.45</td>\n      <td>362.0</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-090"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 36. Plot the most frequent classes\n\nBoxplots show the spread within a class, which a mean alone would hide.\n",
   "id": "co2-teaching-091"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "most_frequent_classes = training_classes.nlargest(8, 'records').index\nfig, ax = plt.subplots(figsize=(9.2, 5.4))\nsns.boxplot(data=train[train['vehicle_class'].isin(most_frequent_classes)],\n            x='co2_g_km', y='vehicle_class', order=most_frequent_classes,\n            color='#D8E0E6', fliersize=2, ax=ax)\nax.set(title='Emissions within frequent classes — training records',\n       xlabel='CO₂ (g/km)', ylabel='Vehicle class')\nshow_figure(fig, '05_training_classes')\n",
   "execution_count": 49,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "image/png": 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avn27fvjhBx09elRjx47V8uXLc9yOI0rifJlZYa+VUsbnB1tiul+/fhoxYoTq1aun2NhYLViwQLNnz9bx48cLFVdWGzZsUExMjLp166Ynn3xSN998sy5fvqyjR48aZSZOnKjly5fLzc1NDz/8sPr376969erJYrFo27Zt+uqrr3TkyBE9/fTTWrRokWrWrGm3jfDwcL3wwguyWq2qWbOmRo8erU6dOql69epKTEzUvn37FBYWlq2nekpKip566int379fLi4uGjZsmIYOHapatWrp+PHjmj17tpYuXaqIiAi99NJLeSbYv/32W8XGxmrEiBEaPHiwateurZiYGOPHwLi4OD399NNKTExUxYoVNXbsWN11112qVKmSdu3apS+++ELvvvturnd2FfY8BVyzrAAAANexhQsXWv39/a3+/v7WNWvWWM1mc57/rl69mq2NRx991GjjjTfeyHE7Dz74oNXf39/atGlT6+23326Njo7OVmb9+vVGO+vXr8+2fuLEiVZ/f3/ro48+arc8MTHRqDdr1iyH9z0qKsqot3LlSofrWa1W61dffWX19/e33nnnndnWhYWFGe1++OGHOdb/4YcfjDLBwcHZ1t95553G+m+++SbHNkaPHm319/e3durUKdu6wYMHW/39/a1jxowp0H7l5/Tp00Zca9eutVs3ZcoUq7+/v/X111+3Nm3a1BoQEGC9ePGiXRnba+Wll17K1rZtn7/66qts6/I63pmdOHHCiK9JkybWzZs3Zytz+fJla6dOnaz+/v7WCRMmZFu/ceNGo40TJ05kW29b5+/vb128eHG29WlpadaBAwda/f39rffdd1+e8eYmvxisVvv3blBQkPX06dM5lktOTrZ27NjR6u/vb33xxRet6enpOZazPX8dOnSwJicn262zvdaaN29uPXDgQLa6Z8+eNbaR23OY1/Nrk/n527hxY7Hvh+0c4u/vb33iiSesFoslW/3M781Dhw5lW297DU+cODHX/XBEenq6tW/fvsbzl9O2bLLGmddxckRUVJS1SZMm1oCAAOvx48ft1oWHhxtt79u3z+E2Z8+ebbzvsr7v82N7vwwbNizHa4zVarXOnTvX6u/vb7311lutMTExdvtS2PP4tca2nwsXLsyzXObX+UMPPZTrMXWE2Wy29uzZ0+rv729dsGBBtvUlfb7M77Ve1Gvlv//+a9SfNGlSjvVnzJhhtx+Fkflz0oQJE3I9f9k+PwQGBma7xtokJCRY77rrLqu/v7/13XfftVt35coVa7t27az+/v7Wnj172r1Xssp6Xvnpp5/y/Sz13nvvGWVWrVqVbX3m4zR37txct21rJyAgwPr3339nW5+YmGi95557jLaynm8Le54CrlVMiAgAAMqNp556Sq1atcrzX063Y9t4eXnp5ZdfznFd3759JWWM6zhs2LAce8126tRJVapUkST9888/Dsed+Xbl/IbIKI56+VmwYIEkGT1MczJixAg1btxYkvTrr7/m2ladOnWMHrRZ2XqFnjt3TmfOnLFbZ7vNtTj3yxaPbSzerD2jbX93795dzZo1k9VqtStz9epVozd3bmMJF6c+ffqobdu22ZZXrFjRGMpk165dhW7/9ttv14ABA7Itd3Fx0aBBgyRJe/fuzXbLcUl44oknchw7VMroeX3+/Hl5eXnprbfeynWs0LFjx6pixYq6cOGCIiIijOUXLlzQ2rVrJUkPPfSQbrnllmx1a9euraeffroY9iR3Rd2PrCZNmpTj3RmDBw82Hhfl9ZGfv//+WwcPHpQkvfjii7r55ptzLVuQu0gcERAQoKZNm8pqtWrDhg1264p6PvXy8spxqJDcbNq0Sfv27ZPJZNK7776b6zjYDz30kNF79I8//ihyvOXFxIkTCzz2fWaVKlUyxurO+lopiJI+Xxb2WhkSEiJJ8vDwyPXzS17n14JydXXVq6++muv5yzYsSd++fXXHHXfkWKZy5crG+XbZsmV2d6wsWbLEGBrn7bffzvM9kfW8Yvvs4u/vr2HDhuVY56WXXjI+p+X12eXmm2/Www8/nOO6tLQ0LVq0SJLUs2dPdezYMVsZb29vvfjii7m2z/se5Q3JaQAAAAe1aNEi16REvXr1jMddunTJsYzJZDISn+fOnXN4u76+vrrxxhslZYzluHXrVofqNWrUyLhF+f3331dUVJTD28yN1WrV9u3bJUl33nlnrrdAm0wmI0F64MABJSQk5FiuY8eOuQ690ahRI+Nx1uNlGxs0JCRES5YssRtXt6iCgoIk2SenzWaz9u7dK1dXVwUFBRnjSWcus2PHDiMOZySnc3udSf87dufPny/R9i0WS67PbXHq1q1brutsCaWWLVvKxcVFly9fzvFfenq6EXfmpOyOHTuUnp4uSXlOKHfXXXcVw57krqj7kVm9evXs3j+ZValSxbiVvCivj/zY9sfd3T3bEA3FIT4+Xt99952GDRumjh07qlmzZgoICDD+2Y6NbaJQm4CAACNx9vrrr+vYsWMObS8wMFBSxrngjTfeUExMjEP1bMehfv36qlmzZq7P65UrV4xtZH5eS+I8fr2oUqVKrhMMZ5aamqrQ0FA9/fTT6tatm1q0aGH3Wvnvf/8rKftrpSBK+nxZ2Gul7Xrdtm3bXCcYdHV1VdeuXQsVV1aBgYGqXbt2juuSkpKMcdE7dOiQ63vh8uXLxo/b8fHxOnHihNGG7f1Uu3ZtderUyeG4Ll26ZPxY1rt371yT556ensZcErZjl5O8jteBAweM4X3yuqbccccd8vT0zHFdYc9TwLWKMacBAEC5MXv27CIlDfPqvZL5C4Yj5a5evVqgbU+cOFETJkzQ0aNH9cgjj6hmzZoKCgpS69at1alTpxx7ant5eWnChAn6+OOPtXPnTg0cOFB169a1q1fQ3lJms1mXLl2SJOPLY25s661Wq06fPp1jYt/RY2qbZNBm7NixWrlypcxms15++WW9/fbbat26tVq3bq327durRYsWuX75zE+7du20YMEC7d27V2azWd7e3tqyZYvS0tLUrFkzVa5cWe3atdP06dPtJkOyJar9/PzsfqwoKbklAKSM517KSAYUlqPPTVG24ai8jqdt4tHIyEhjzNr8ZJ6c7+TJk8bjvHr31q5dW5UrV85xTOHiUNT9yCy/nna210fW91VxsiWUGjRoYGyvuOzcuVPPPPNMrvufWdbnq379+nrkkUc0Z84crV69WqtXr1bjxo3VunVrtW3bVp06dcpxHNj27dvrzjvv1OrVq7Vw4UKFhISoadOmatWqlYKCgtSxY8ccJ1CzJTyPHTtWqOe1JM7jUkbPzJJ8/l1dXXNNvBUXR86zFy5c0KhRoxwam7co7+2SPl8W9lp56tQpScr1xyqbm266qVBxZZXXc3LixAlZLBZJGQnX119/3aE24+LijB/2becV2w85jjpz5ozRA9vRzy7x8fHGZ4Cs6tatm2t92zGX8r6muLm5qWHDhjn+4FTY8xRwrSI5DQAA4CBHJ9fLbQK7zKwFnFjt7rvv1k8//aTp06dr06ZNOnfunH7//Xf9/vvvkjJuKZ44caJuv/12u3ojR46Un5+f/vvf/+qff/7RyZMndfLkSYWEhMhkMqlz58567bXX8vwCldnly5eNx5UqVcqzbOb1metl5ugxzXq86tWrp9DQUE2bNk1//fWXLl++rHXr1mndunWSMhLE48aNsxvCwFG2HzDS0tK0efNmde/e3UhC29a1atVK7u7uOnz4sM6dO6eaNWsayWln9JqWHHudFUVhn5uSkFdy02w2F7i9zD3tr1y5YjzO7zVdsWLFEktOF3U/MisLz51tf/I7poVpd8yYMYqLi1O1atX0+OOPKygoSHXq1FHFihWN98WTTz6p7du3290eb/PGG28oICBAs2fP1sGDB3Xo0CEdOnRI8+fPl5ubm+666y69+uqr2X4A+uqrrzRr1izNmzdPJ0+e1J49e7Rnzx4FBwerQoUKGjRokF5++WVVrlzZqFOY10vW57W4z+OStHXrVg0fPrzAsTkqKChIwcHBJda+lPd5wWbixInas2eP3Nzc9NBDD6l79+6qX7++vL29jQk+p0+fru+++y7H14qjSvo9V9j2bee3nCaazSy/9Y7K6zkp7Lkz84/5hT2vZD6/FvSzS07J6bz2M/PnnaIc98Kep4BrEclpAACAa0T79u3Vvn17JSQkaMeOHdqxY4fWrVunPXv2aMeOHRo2bJjmzJmjli1b2tXr3bu3evfurQsXLmjHjh3atm2b1q5dq8OHD2v9+vXauXOnQkNDHeqFlvlLW+akXk4KkvQrjPr162vKlCl6//33tWvXLu3cuVORkZGKjIzUqVOn9Oqrryo+Pl6PP/54gdqtXbu2GjZsqKNHj2rTpk3q3r27kXi2Defh6empli1basuWLdq0aZN69Oihf//9V5LzktPIYPtyf9ddd2nq1KmFri9lJBXyGr82v9d8fvJKfhV1P8oaW0Intx+mCuuPP/7Q+fPn5eLiotmzZ+c4Rnh+2zWZTLr//vt1//3368yZM8Z5cfXq1Tp16pSWL1+unTt3avHixXZ3fHh4eGjUqFEaNWqUjh49qp07d2rLli1as2aNzp8/r/nz52vXrl367bffjPFubc/rrbfeaoz/WxjFeR4vL06cOKH169dLykj0PfTQQzmWc8bdH6XF9oNafvtY1HObIzJ/Dvj222/VvXv3ArdR2PNK5gRzSX92KeznpKwKe54CrkWMOQ0AAHCN8fHxUdeuXTVhwgSFhITop59+UoUKFWSxWPKc0LF69erq2bOnJk6cqOXLl+uTTz6RyWRSYmKiMUlRfry9vY1xK23jN+bGtt5kMhljZpcEDw8PtW7dWk888YS+//57hYWFqUGDBpIyvgAXpjecLcG8adMmxcfHKyoqSu7u7mrdunW2Mhs3btT27duN25VJTjuXLRl3/PjxQtXPfHv24cOHcy0XExOTZ88/27jAeQ2VEBsbm+u6ou5HWWO7Df/YsWPFmvyz3QIfEBCQa2I6JSVFR48edai9OnXq6J577tGbb76plStXGpOUnT59Os9kcsOGDTVo0CB98MEHWrNmjR555BFJGZPerV692ihne15PnjxpjG1eFMVxHpcyzlP79+8vsX8l3WvaEfv27TMe9+vXL9dyBw4ccEY4pcJ27c1vPO2ijLftKD8/P+POhsKe52znlYKOvV6nTh1jqC9HP7tUqVIlx17T+fHz8zMe53VNSU1NdXgs6cKep4BrBclpAACAa1yHDh2MHr22cWsdMWDAAOM2cEe/mJpMJmPc1DVr1uQ6rIDVatWff/4pSfL393dqr54bb7xR999/v6SMSZAuXLhQ4DZsCeaoqCiFhYXJarWqWbNmdj2iMk+KaOtZXb9+/UKN/2rrZVmU28rLq86dO0uS9u/fr0OHDhW4/u23324kTMLCwnIt99dff+XZTs2aNSXl/V6y9eLMSVH3o7gU12vRNlmZxWLRsmXLihyXje2ck1d8f/zxR4HH9ZcyhsoZNWqU0dvZ0fOpu7u7xo4da/yduZ7teb106ZIiIiIKHFN+CnMeL6uK+zyY+fqUW5unT592eJLha5Hter1ly5ZcJ2NMS0vT2rVrSzyWypUrGxNYLl++vFBt2M4rMTExxuSIjvD19TV+zMrrXJ6cnGz8uOToGPFZ+fv7G0P75HVNWbduXaF+uCvseQooy0hOAwAAlHFxcXG6ePFiruvT0tKMCXiqVq1qLI+Jicnz1tekpCSdO3dOUkYPIUfdd999RlxffPFFjmVsYyRK0gMPPOBw247KqzeS9L9eWa6urnbjvzoqKChIUkaS/dtvv5WUvUd0ixYt5OnpqePHjxvJt8L2mrY9b3FxcUpNTS1UG+XVgAEDVKNGDVmtVr366qv5jmt64sQJu6RV9erV1bVrV0nSvHnzcuxVFxsbq+nTp+fZri3psmHDhhx7SB8+fDjP3qRF3Y/iYnst5tXL2xEdOnSQv7+/JOnTTz/NsydzQV7ztp7uR44cyTEZGxMTo08//TTX+vkdt9jYWCNhlPm8ePTo0Tx7PmfuCZq5XqdOnRQQECBJeueddxQTE5NrG5J0/vx5Y9JZqeTO42VRcb32bDIPcRIeHp5tvcVi0RtvvHFd/yg4ZMgQSRnjNuf2vvjxxx91+vRpp8RjG2brn3/+yfNOLynj+ps18dqvXz9jIsB33nlH58+fz7V+1vOK7bPL/v37NXfu3BzrfP7554qPj5dU+M8urq6uGjRokCRp5cqVioyMzFbm8uXL+uyzz3Jto7DnKeBaRXIaAACUG8nJybp8+XK+/5wxwVtBHDx4UN26ddOLL76oZcuW6fDhw4qPj1dMTIwiIyP17LPPGj0t+/bta9T7+++/1bVrV02aNEl//fWXjh07pkuXLun06dNavXq1Hn/8cSMJkrlefnr06KE77rhDkvTDDz/otdde0+7duxUfH68DBw7oww8/1EcffSQpY5xVWy/m4tS3b1+NGDFCc+fO1e7du3XhwgXFxcVpz549mjx5sn799VcjVkcmzcqqZs2aRm9EW+Lf1lPaxsPDw5iA0lamsMnpZs2aScro6fftt9/qwoULSk1NVWpq6nWdOCkOXl5emjx5slxdXbVr1y4NHDhQwcHBOnTokNFzfs+ePZo/f76eeuop9e7dO9vkgy+//LI8PDyUnJys4cOH67ffflNMTIzOnz+vFStW6OGHH1ZKSkqedwAMGjRIrq6uSkpK0pNPPqnIyEjFx8fr5MmTmjt3rh555BHVqFGjRPejONhei9u2bdPKlStlNpuN12JBhqUwmUyaPHmyPDw8FB8fr/vvv18zZszQwYMHdenSJcXExCgiIkLvvfeeXa/j/PTu3Vuurq5KTU3V6NGjFRYWptjYWJ09e1YhISG6//77lZCQYHdrfWaLFi3SnXfeqffff19r1qzRyZMnlZCQoBMnTmj58uV6/PHHZbVa5eLioj59+hj1pk+frp49e+qzzz7T33//rTNnzujSpUs6duyYFixYoOeee05Sxhi/d955p91x+Pjjj+Xl5aWTJ09q4MCBmjFjhqKiohQfH6+4uDgdOHBAixYt0vjx43XnnXfaJbpL6jxeFtlee4sWLdKuXbuUnJxsvPYKc11u1qyZkaD+4IMPNHv2bB0/flxxcXFat26dhg0bpr///luNGzcu1v0oS2677TbjdTF//ny99NJL2r17ty5duqRDhw7po48+0meffWYMl1HSevfurf79+0vKSAQ/9dRTCg8P19mzZ5WYmKgzZ84oMjJSX331lfr06aOPP/7Yrr6Xl5c++OADmUwmHT16VEOGDNGcOXN09OhRJSQk6PTp0woPD9drr72md999167ugw8+qMDAQEnS+++/r48++kgHDx5UfHy8du/erVdeecUYGufOO+9Ut27dCr2fzz77rKpWrSqr1apnn31WP/zwg06ePGm89h599FEdO3Ys18kMC3ueAq5VTIgIAADKjaeeesqhcuHh4Xbj0JYFycnJWrZsWZ63xw8YMEAPP/yw3bLExEQtWLBACxYsyLXeU089VeCJib744guNGTNGGzduVEhISI5jHjZt2lTTp0+Xu7t7gdp2hNVqNSY/zE2TJk301ltvFXob7dq1M3poe3h45HiLb/v27e1iKEpyOigoSJs3b9a0adM0bdo0Y52fn59WrVpVqHbLizvuuEPffPONJk6cqFOnTun999/Ptayrq6tcXV3tlt1888364osv9PzzzysuLk5vvPGG3foKFSroP//5j957771cb42/+eabNX78eH3++efav3+/RowYkW39hx9+mGdvvKLuR3EYNGiQZs6cqbi4OI0ZM8Zu3eDBg40fnhzRrFkzzZw5U+PHj1d8fLw+//xzff7559nK2e5UcESDBg30wgsv6JNPPtGxY8eyJbYrVKigTz75RHPmzDF+NMrq/PnzCg4OzrUnu6urqyZNmqQmTZrYLT916pS+++47fffddznW8/T01CeffKJatWrZLW/SpIlmz56t8ePH6/Tp07keB5us58ySOo+XNY899pjWrFmj48ePa+jQoXbrJk+ebPQCdpSrq6smT56sJ598UpcvX9YHH3ygDz74wK7MyJEjVbFiRbtz7vXm3Xff1ZkzZ7R9+3YtXbpUS5cutVvfqVMn9e7dW//3f/9XIueUrCZPnixfX1/NmTNHa9euzXNIkZx+OOjevbs+++wzTZo0STExMXrvvfdyrDt48GC7vz08PPTdd99p1KhR2r9/v3788Uf9+OOP2ep16tQpz7svHFGtWjVNnz5dTz75pBITE/Xxxx/bJdpdXFz09ttva9myZbneTVHY8xRwLSI5DQAAUMbdfvvt+umnnxQZGalt27bpzJkzunDhgtLT01WzZk21aNFCQ4YMUZcuXezq9enTR9WqVVNkZKR27typmJgYXbhwQS4uLrrhhhvUqlUrPfDAA2rZsmWBY/L29tZPP/2kZcuWacmSJdqzZ48SEhJUqVIlBQQE6J577tHQoUONMUSLW0hIiCIjI7Vp0yYdP35c58+f19WrV+Xr66vAwEDdfffdGjRoUJES4+3atdPPP/8sSWrZsqUx4V3WMjaNGjXKlpQqiG+//VYzZ87UqlWrdPLkSSUlJZW5XvxlWbdu3bRy5Ur9+uuvWrt2rQ4ePKiEhAS5u7urZs2auuWWW9ShQwf17t3bmNQzs549e2rRokWaMWOGNmzYoPj4eFWrVk1BQUF68sknFRgYmGsSxGb06NG66aabNHv2bO3du1epqany8/PTPffco5EjRyouLq7E96OoqlWrpvnz5+vbb7/V5s2bFRsbW6ThQ9q3b6+wsDD9/PPPWr16taKjo3XlyhVVr15dderU0R133FHgHr9PPvmkbr75Zv3000/avXu3UlJSVLNmTbVv314jRoyQv7+/5syZk2Pdxx57TLfccosiIyO1e/duxcbG6uLFi3J3d5efn5+CgoL08MMPZ0uKvfTSS+rQoYM2btyoffv26dy5c4qPj1eFChXUoEEDdejQQY8++miuk7/edttt+uOPPxQaGqrw8HDt27dP8fHxcnFxUfXq1dW4cWMFBQWpd+/edr1YS/I8XtZ06NBBP/74o2bNmqXdu3fr4sWLRR7mqG3btvrtt980ffp0bdy4UQkJCapSpYqaNWumBx98UN26ddPUqVOLaQ/KJm9vbwUHB2vu3LlavHixjhw5IldXV2NSz4cffti41hVmAsCCcnd315tvvqn77rtPv/76q7Zs2aLTp08rKSlJlSpVUt26ddW8eXPdcccdxl1aWfXt21dt27bVnDlztH79ep04cUJXr15VzZo15efnp+7du+uee+7JVq927dpauHChFixYoOXLl+vAgQO6fPmyfH19deutt2rgwIG65557jMkTi6Jly5b6/fff9e2332rdunWKjY1VlSpV1KJFCz3++ONq06ZNrh0OCnueAq5VJiufeAEAAAAgX927d9epU6c0duxYjRs3rrTDAYBi8f777ys4OFj+/v7ZelYDQEljzGkAAAAAAIByKD09XatXr5b0v3G/AcCZSE4DAAAAAABch65cuZLn0DzfffedTp48KUlMrgegVDDmNAAAAAAAwHXo8OHDeu655/TAAw+oU6dO8vPzk9Vq1ZEjR/Trr79qyZIlkqQ2bdpkm7sCAJyB5DQAAAAAAMB16vTp0/riiy/0xRdf5Lg+MDBQX3zxRbFMBAgABUVyGgAAAAAA4Dp000036Z133tH69et18OBBxcXFKTk5WZUrV1ZgYKB69+6tIUOGyMPDo7RDBVBOmaxWq7W0gwAAAAAAAAAAlC9MiAgAAAAAAAAAcDqS0wAAAAAAAAAApyM5DQAAAAAAAABwOpLTAAAAAAAAAACnIzkNAAAAAAAAAHA6t9IOAABwfdq3b58sFovc3d3VpEmT0g4HAAAAAACUMSSnAQAlwmKxyGKxlHYYxS4lJUW7du2SJDVv3lweHh6lHBFwfeK9BpQ83mdAyeN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     }
    }
   ],
   "id": "co2-teaching-092"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 7. Learn fitting with two small linear models\n\nWe first fit engine-only and engine-plus-cylinders models on **training records**. These are\ndeclared learning comparators, not models chosen after inspecting test errors. Both join the\nvalidation comparison later.\n\nThe fitting and prediction steps have separate cells so their purpose is explicit. No full-data fit\nis used to report test performance.\n",
   "id": "co2-teaching-093"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 37. Choose the engine-only training inputs\n\n`X` is a table of predictors; `y` is the target series.\n",
   "id": "co2-teaching-094"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "engine_X_train = train[['engine_l']]\ny_train = train['co2_g_km']\n",
   "execution_count": 50,
   "outputs": [],
   "id": "co2-teaching-095"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 38. Create an engine-only estimator\n\nCreating a model does not fit it yet.\n",
   "id": "co2-teaching-096"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "engine_model = LinearRegression()\n",
   "execution_count": 51,
   "outputs": [],
   "id": "co2-teaching-097"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 39. Fit the engine-only model\n\nThe estimator learns coefficients from training observations only.\n",
   "id": "co2-teaching-098"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "engine_model.fit(engine_X_train, y_train)\n",
   "execution_count": 52,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 52,
     "metadata": {},
     "data": {
      "text/plain": "LinearRegression()"
     }
    }
   ],
   "id": "co2-teaching-099"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 40. Read the engine-only coefficients\n\nThe slope's unit is g/km for one additional litre of engine size.\n",
   "id": "co2-teaching-100"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "engine_intercept = float(engine_model.intercept_)\nengine_slope = float(engine_model.coef_[0])\ndisplay(pd.Series({'Intercept (g/km)': engine_intercept,\n                   'Slope (g/km per engine litre)': engine_slope}).round(3).to_frame('Estimate'))\n",
   "execution_count": 53,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                               Estimate\nIntercept (g/km)                134.821\nSlope (g/km per engine litre)    36.536",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Estimate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Intercept (g/km)</th>\n      <td>134.821</td>\n    </tr>\n    <tr>\n      <th>Slope (g/km per engine litre)</th>\n      <td>36.536</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-101"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** The fitted line is **CO₂ = {engine_intercept:.2f} + {engine_slope:.2f} × engine litres**.\nThe intercept refers to zero engine size, outside the observed range; it is not a typical vehicle's emissions.\"\"\")\n",
   "execution_count": 54,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** The fitted line is **CO₂ = 134.82 + 36.54 × engine litres**.\nThe intercept refers to zero engine size, outside the observed range; it is not a typical vehicle's emissions."
     }
    }
   ],
   "id": "co2-teaching-102"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 41. Create one engine-only prediction\n\nUse a DataFrame with the same feature name used during fitting.\n",
   "id": "co2-teaching-103"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "example_engine = pd.DataFrame({'engine_l': [3.5]})\nexample_engine_prediction = float(engine_model.predict(example_engine)[0])\nprint(f'Engine-only estimate for 3.5 L: {example_engine_prediction:.2f} g/km')\n",
   "execution_count": 55,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Engine-only estimate for 3.5 L: 262.70 g/km\n"
    }
   ],
   "id": "co2-teaching-104"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 42. Choose the two-predictor training inputs\n\nTwo related predictors can describe more of the observed variation.\n",
   "id": "co2-teaching-105"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "spec_X_train = train[['engine_l', 'cylinders']]\n",
   "execution_count": 56,
   "outputs": [],
   "id": "co2-teaching-106"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 43. Create a two-predictor estimator\n\nThis model keeps coefficients in the original units.\n",
   "id": "co2-teaching-107"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "two_variable_model = LinearRegression()\n",
   "execution_count": 57,
   "outputs": [],
   "id": "co2-teaching-108"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 44. Fit the two-predictor model\n\nUse the same training records as the other models.\n",
   "id": "co2-teaching-109"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "two_variable_model.fit(spec_X_train, y_train)\n",
   "execution_count": 58,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 58,
     "metadata": {},
     "data": {
      "text/plain": "LinearRegression()"
     }
    }
   ],
   "id": "co2-teaching-110"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 45. Read both coefficients\n\nEach coefficient holds the other predictor fixed in the fitted model.\n",
   "id": "co2-teaching-111"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "two_variable_intercept = float(two_variable_model.intercept_)\ntwo_variable_slopes = pd.Series(two_variable_model.coef_, index=spec_X_train.columns)\ndisplay(two_variable_slopes.round(3).to_frame('Coefficient'))\nprint(f'Intercept: {two_variable_intercept:.3f} g/km')\n",
   "execution_count": 59,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "           Coefficient\nengine_l        24.134\ncylinders        9.895",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Coefficient</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>engine_l</th>\n      <td>24.134</td>\n    </tr>\n    <tr>\n      <th>cylinders</th>\n      <td>9.895</td>\n    </tr>\n  </tbody>\n</table>"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Intercept: 118.547 g/km\n"
    }
   ],
   "id": "co2-teaching-112"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** The fitted equation is **CO₂ = {two_variable_intercept:.2f}\n+ {two_variable_slopes['engine_l']:.2f} × engine litres\n+ {two_variable_slopes['cylinders']:.2f} × cylinders**.\nBecause engine size and cylinders are correlated, these are conditional model coefficients,\nnot independent causal contributions.\"\"\")\n",
   "execution_count": 60,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** The fitted equation is **CO₂ = 118.55\n+ 24.13 × engine litres\n+ 9.90 × cylinders**.\nBecause engine size and cylinders are correlated, these are conditional model coefficients,\nnot independent causal contributions."
     }
    }
   ],
   "id": "co2-teaching-113"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 46. Predict for a two-predictor example\n\nAn estimate from two specifications is not a tax certificate.\n",
   "id": "co2-teaching-114"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "example_specifications = pd.DataFrame({'engine_l': [3.5], 'cylinders': [6]})\nexample_spec_prediction = float(two_variable_model.predict(example_specifications)[0])\nprint(f'Specification estimate, 3.5 L and 6 cylinders: {example_spec_prediction:.2f} g/km')\n",
   "execution_count": 61,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Specification estimate, 3.5 L and 6 cylinders: 262.39 g/km\n"
    }
   ],
   "id": "co2-teaching-115"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 8. Compare models fairly\n\n| Model | Information available | Reason for inclusion |\n| --- | --- | --- |\n| Mean baseline | None | Tests whether learning beats a constant prediction |\n| Engine only | Engine size | Transparent first model |\n| Engine + cylinders | Two numeric specifications | Corrected version of the supplied v3 approach |\n| Specifications Ridge | Engine, cylinders, fuel code, class | Models the specifications-only setting |\n| Fuel only | Combined fuel consumption | Tests the value of one closely related rating |\n| Fuel-aware Ridge | Fuel consumption + specifications | Adds fuel and class information |\n| Fuel-interaction Ridge | Consumption × fuel-code terms + specifications | Allows different consumption slopes by fuel |\n| Fuel-aware random forest | Same inputs as fuel-aware Ridge | Nonlinear comparator with the same information |\n\nAll candidate choices below are fixed before final test reporting. Specifications-only and\nfuel-known models use different information; their difference is not solely an algorithm effect.\nScaling is helpful for Ridge's penalty, and all fitted transformations stay inside pipelines.\n",
   "id": "co2-teaching-116"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 47. Create the common predictor tables\n\nMake and Model are grouping metadata; they never enter these model features.\n",
   "id": "co2-teaching-117"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "numeric_features = ['engine_l', 'cylinders', 'combined_l_100km']\ncategorical_features = ['fuel_type', 'vehicle_class']\nfeatures = numeric_features + categorical_features\nX_train = train[features].copy()\nX_test = test[features].copy()\ny_test = test['co2_g_km'].copy()\n",
   "execution_count": 62,
   "outputs": [],
   "id": "co2-teaching-118"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 48. Define numeric preprocessing\n\nMedian filling is a reusable safeguard; this verified CSV contains no missing predictors.\n",
   "id": "co2-teaching-119"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "numeric_preprocessing = Pipeline([\n    ('impute', SimpleImputer(strategy='median', keep_empty_features=True)),\n    ('scale', StandardScaler()),\n])\n",
   "execution_count": 63,
   "outputs": [],
   "id": "co2-teaching-120"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 49. Define categorical preprocessing\n\nOne-hot encoding creates indicator columns without imposing an artificial order.\n",
   "id": "co2-teaching-121"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "categorical_preprocessing = Pipeline([\n    ('impute', SimpleImputer(strategy='constant', fill_value='Unknown')),\n    ('encode', OneHotEncoder(handle_unknown='ignore', sparse_output=False)),\n])\n",
   "execution_count": 64,
   "outputs": [],
   "id": "co2-teaching-122"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 50. Create a numeric-model helper\n\nThis helper selects only the declared numeric inputs.\n",
   "id": "co2-teaching-123"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "def numeric_pipeline(columns, estimator):\n    selection = ColumnTransformer([('numeric', 'passthrough', columns)], remainder='drop')\n    return Pipeline([('select', selection), ('model', estimator)])\n",
   "execution_count": 65,
   "outputs": [],
   "id": "co2-teaching-124"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 51. Create a mixed-model helper\n\nThe helper creates a fresh fitted pipeline for each candidate and validation fold.\n",
   "id": "co2-teaching-125"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "def mixed_pipeline(numbers, estimator):\n    preprocessing = ColumnTransformer([\n        ('numeric', clone(numeric_preprocessing), numbers),\n        ('categorical', clone(categorical_preprocessing), categorical_features),\n    ])\n    return Pipeline([('preprocess', preprocessing), ('model', estimator)])\n",
   "execution_count": 66,
   "outputs": [],
   "id": "co2-teaching-126"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 52. Define fuel-consumption interactions\n\nA declared fuel-code indicator multiplies consumption; it uses no target values.\n",
   "id": "co2-teaching-127"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "def add_fuel_interactions(frame):\n    result = frame.copy()\n    for fuel in FUEL_CODES:\n        result[f'consumption_x_{fuel}'] = result['combined_l_100km'].where(\n            result['fuel_type'].eq(fuel), 0.0)\n    return result\n",
   "execution_count": 67,
   "outputs": [],
   "id": "co2-teaching-128"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 53. Create the constant baseline\n\nThe estimator ignores its input values and predicts the mean training target.\n",
   "id": "co2-teaching-129"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "models = {}\nmodels['Mean baseline'] = numeric_pipeline(['engine_l'], DummyRegressor(strategy='mean'))\n",
   "execution_count": 68,
   "outputs": [],
   "id": "co2-teaching-130"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 54. Create the engine-only candidate\n\nUse the same pipeline interface so all candidates share the same validation procedure.\n",
   "id": "co2-teaching-131"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "models['Engine only'] = numeric_pipeline(['engine_l'], LinearRegression())\n",
   "execution_count": 69,
   "outputs": [],
   "id": "co2-teaching-132"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 55. Create the engine-plus-cylinders candidate\n\nThis is the training-only version of the supplied notebook model.\n",
   "id": "co2-teaching-133"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "models['Engine + cylinders'] = numeric_pipeline(['engine_l', 'cylinders'], LinearRegression())\n",
   "execution_count": 70,
   "outputs": [],
   "id": "co2-teaching-134"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 56. Create the specifications Ridge candidate\n\nThis candidate does not need a fuel-consumption rating.\n",
   "id": "co2-teaching-135"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "models['Specifications Ridge'] = mixed_pipeline(['engine_l', 'cylinders'], Ridge(alpha=1.0))\n",
   "execution_count": 71,
   "outputs": [],
   "id": "co2-teaching-136"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 57. Create the fuel-only candidate\n\nThis tests whether one reported fuel rating already explains much of the target.\n",
   "id": "co2-teaching-137"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "models['Fuel only'] = numeric_pipeline(['combined_l_100km'], LinearRegression())\n",
   "execution_count": 72,
   "outputs": [],
   "id": "co2-teaching-138"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 58. Create the fuel-aware Ridge candidate\n\nRidge uses all declared numeric features and encoded fuel/class categories.\n",
   "id": "co2-teaching-139"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "models['Fuel-aware Ridge'] = mixed_pipeline(numeric_features, Ridge(alpha=1.0))\n",
   "execution_count": 73,
   "outputs": [],
   "id": "co2-teaching-140"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 59. Create the fuel-interaction candidate\n\nDifferent fuel-specific consumption terms allow different slopes.\n",
   "id": "co2-teaching-141"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "interaction_features = ['engine_l', 'cylinders'] + [f'consumption_x_{fuel}' for fuel in FUEL_CODES]\nmodels['Fuel-interaction Ridge'] = Pipeline([\n    ('interactions', FunctionTransformer(add_fuel_interactions, validate=False)),\n    ('preprocess', mixed_pipeline(interaction_features, Ridge()).named_steps['preprocess']),\n    ('model', Ridge(alpha=1.0)),\n])\n",
   "execution_count": 74,
   "outputs": [],
   "id": "co2-teaching-142"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 60. Create the random-forest candidate\n\nSettings are fixed demonstration choices, not a claim of optimal tuning.\n",
   "id": "co2-teaching-143"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "forest = RandomForestRegressor(n_estimators=150, min_samples_leaf=3,\n                               random_state=SEED, n_jobs=1)\nmodels['Fuel-aware random forest'] = mixed_pipeline(numeric_features, forest)\n",
   "execution_count": 75,
   "outputs": [],
   "id": "co2-teaching-144"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 61. Create grouped validation folds\n\nValidation groups also stay separate inside the training partition.\n",
   "id": "co2-teaching-145"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "training_groups = train['group_id'].to_numpy()\ngroup_cv = GroupKFold(n_splits=CV_FOLDS)\ncv_splits = list(group_cv.split(X_train, y_train, training_groups))\nfor fit_rows, validation_rows in cv_splits:\n    assert set(training_groups[fit_rows]).isdisjoint(training_groups[validation_rows])\n",
   "execution_count": 76,
   "outputs": [],
   "id": "co2-teaching-146"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 62. Define the scoring metrics\n\nErrors use g/km; R² compares squared error with predicting the validation mean.\n",
   "id": "co2-teaching-147"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "scoring = {'RMSE': 'neg_root_mean_squared_error',\n           'MAE': 'neg_mean_absolute_error', 'R2': 'r2'}\n",
   "execution_count": 77,
   "outputs": [],
   "id": "co2-teaching-148"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 63. Run the validation comparison\n\nScikit-learn negates error scores because its scoring API treats larger values as better.\n",
   "id": "co2-teaching-149"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "comparison_rows = []\nfor name, estimator in models.items():\n    scores = cross_validate(estimator, X_train, y_train, cv=cv_splits,\n                            scoring=scoring, n_jobs=1, error_score='raise')\n    comparison_rows.append({'Model': name,\n        'CV_RMSE_g_km': -scores['test_RMSE'].mean(),\n        'Fold_SD_g_km': scores['test_RMSE'].std(ddof=1),\n        'CV_MAE_g_km': -scores['test_MAE'].mean(),\n        'CV_R2': scores['test_R2'].mean()})\n",
   "execution_count": 78,
   "outputs": [],
   "id": "co2-teaching-150"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 64. Display the comparison table\n\nLower mean validation RMSE is the declared selection criterion.\n",
   "id": "co2-teaching-151"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "comparison = pd.DataFrame(comparison_rows).set_index('Model').sort_values('CV_RMSE_g_km')\ndisplay(comparison.round(3))\n",
   "execution_count": 79,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                          CV_RMSE_g_km  Fold_SD_g_km  CV_MAE_g_km  CV_R2\nModel                                                                   \nFuel-interaction Ridge           3.568         1.730        2.276  0.996\nFuel-aware random forest         4.895         2.075        2.567  0.992\nFuel-aware Ridge                 5.319         0.614        3.104  0.992\nFuel only                       23.668         1.186       14.596  0.833\nSpecifications Ridge            25.054         1.719       18.692  0.812\nEngine + cylinders              30.254         2.570       23.233  0.727\nEngine only                     30.978         2.668       23.554  0.713\nMean baseline                   58.166         3.837       46.254 -0.005",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CV_RMSE_g_km</th>\n      <th>Fold_SD_g_km</th>\n      <th>CV_MAE_g_km</th>\n      <th>CV_R2</th>\n    </tr>\n    <tr>\n      <th>Model</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Fuel-interaction Ridge</th>\n      <td>3.568</td>\n      <td>1.730</td>\n      <td>2.276</td>\n      <td>0.996</td>\n    </tr>\n    <tr>\n      <th>Fuel-aware random forest</th>\n      <td>4.895</td>\n      <td>2.075</td>\n      <td>2.567</td>\n      <td>0.992</td>\n    </tr>\n    <tr>\n      <th>Fuel-aware Ridge</th>\n      <td>5.319</td>\n      <td>0.614</td>\n      <td>3.104</td>\n      <td>0.992</td>\n    </tr>\n    <tr>\n      <th>Fuel only</th>\n      <td>23.668</td>\n      <td>1.186</td>\n      <td>14.596</td>\n      <td>0.833</td>\n    </tr>\n    <tr>\n      <th>Specifications Ridge</th>\n      <td>25.054</td>\n      <td>1.719</td>\n      <td>18.692</td>\n      <td>0.812</td>\n    </tr>\n    <tr>\n      <th>Engine + cylinders</th>\n      <td>30.254</td>\n      <td>2.570</td>\n      <td>23.233</td>\n      <td>0.727</td>\n    </tr>\n    <tr>\n      <th>Engine only</th>\n      <td>30.978</td>\n      <td>2.668</td>\n      <td>23.554</td>\n      <td>0.713</td>\n    </tr>\n    <tr>\n      <th>Mean baseline</th>\n      <td>58.166</td>\n      <td>3.837</td>\n      <td>46.254</td>\n      <td>-0.005</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-152"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 65. Select the fuel-known model\n\nThe winner is chosen without using any held-out test score.\n",
   "id": "co2-teaching-153"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fuel_known_candidates = ['Fuel only', 'Fuel-aware Ridge', 'Fuel-interaction Ridge',\n                         'Fuel-aware random forest', 'Mean baseline']\nselected_name = comparison.loc[fuel_known_candidates, 'CV_RMSE_g_km'].idxmin()\nprint('Selected fuel-known model:', selected_name)\n",
   "execution_count": 80,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Selected fuel-known model: Fuel-interaction Ridge\n"
    }
   ],
   "id": "co2-teaching-154"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** **{selected_name}** has the lowest mean grouped-validation RMSE in the\nfuel-known setting: **{comparison.loc[selected_name, 'CV_RMSE_g_km']:.2f} g/km**.\nThe fold standard deviation is **{comparison.loc[selected_name, 'Fold_SD_g_km']:.2f} g/km**;\nit describes fold variability and is **not a confidence interval**.\"\"\")\n",
   "execution_count": 81,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** **Fuel-interaction Ridge** has the lowest mean grouped-validation RMSE in the\nfuel-known setting: **3.57 g/km**.\nThe fold standard deviation is **1.73 g/km**;\nit describes fold variability and is **not a confidence interval**."
     }
    }
   ],
   "id": "co2-teaching-155"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 66. Select the specifications-only model\n\nSelect the strongest specifications-only candidate on the same training folds.\n",
   "id": "co2-teaching-156"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "specification_candidates = ['Mean baseline', 'Engine only', 'Engine + cylinders', 'Specifications Ridge']\nselected_spec_name = comparison.loc[specification_candidates, 'CV_RMSE_g_km'].idxmin()\nprint('Selected specifications-only model:', selected_spec_name)\n",
   "execution_count": 82,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Selected specifications-only model: Specifications Ridge\n"
    }
   ],
   "id": "co2-teaching-157"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 67. Plot validation errors\n\nThe plot makes the improvement over simple alternatives easy to compare.\n",
   "id": "co2-teaching-158"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fig, ax = plt.subplots(figsize=(9.8, 5.1))\npositions = np.arange(len(comparison))\ncolours = [SCARLET if name == selected_name else NAVY for name in comparison.index]\nax.barh(positions, comparison['CV_RMSE_g_km'], xerr=comparison['Fold_SD_g_km'],\n        color=colours, capsize=3)\nax.set_yticks(positions, comparison.index)\nax.invert_yaxis()\nax.set(title='Training-only grouped validation — lower is better',\n       xlabel='Mean RMSE (g/km); error bars show fold SD')\nshow_figure(fig, '06_validation_comparison')\n",
   "execution_count": 83,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
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"
     }
    }
   ],
   "id": "co2-teaching-159"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 9. Measure performance on held-out vehicles\n\nFit the selected models on all training records, then make the reserved test predictions.\nWe report the selected fuel-known model, the selected specifications-only model, the original\ntwo-feature approach and the mean baseline. The comparison is pre-declared; test errors do\nnot choose the winner.\n\n**MAE** is mean absolute error. **RMSE** emphasises larger errors. **R²** is a squared-error\ncomparison with a mean prediction; 0.997 is not “99.7% classification accuracy”. A high score\nwith fuel consumption available mostly reflects closely related physical ratings.\n",
   "id": "co2-teaching-160"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 68. Define one metric function\n\nA shared function prevents accidentally scoring different targets or units.\n",
   "id": "co2-teaching-161"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "def regression_metrics(actual, predicted):\n    residual = np.asarray(actual) - np.asarray(predicted)\n    return {'MAE_g_km': float(mean_absolute_error(actual, predicted)),\n            'RMSE_g_km': float(np.sqrt(mean_squared_error(actual, predicted))),\n            'R2': float(r2_score(actual, predicted)) if len(actual) > 1 else np.nan,\n            'Bias_g_km': float(residual.mean())}\n",
   "execution_count": 84,
   "outputs": [],
   "id": "co2-teaching-162"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 69. Fit the selected fuel-known model\n\nOnly training records enter `.fit()`.\n",
   "id": "co2-teaching-163"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "selected_model = clone(models[selected_name])\nselected_model.fit(X_train, y_train)\n",
   "execution_count": 85,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 85,
     "metadata": {},
     "data": {
      "text/plain": "Pipeline(steps=[('interactions',\n                 FunctionTransformer(func=<function add_fuel_interactions at 0x7f499a635ee0>)),\n                ('preprocess',\n                 ColumnTransformer(transformers=[('numeric',\n                                                  Pipeline(steps=[('impute',\n                                                                   SimpleImputer(keep_empty_features=True,\n                                                                                 strategy='median')),\n                                                                  ('scale',\n                                                                   StandardScaler())]),\n                                                  ['engine_l', 'cylinders',\n                                                   'consumption_x_X',\n                                                   'consumption_x_Z',\n                                                   'consumption_x_D',\n                                                   'consumption_x_E',\n                                                   'consumption_x_N']),\n                                                 ('categorical',\n                                                  Pipeline(steps=[('impute',\n                                                                   SimpleImputer(fill_value='Unknown',\n                                                                                 strategy='constant')),\n                                                                  ('encode',\n                                                                   OneHotEncoder(handle_unknown='ignore',\n                                                                                 sparse_output=False))]),\n                                                  ['fuel_type',\n                                                   'vehicle_class'])])),\n                ('model', Ridge())])"
     }
    }
   ],
   "id": "co2-teaching-164"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 70. Predict held-out ratings\n\nThese records have unseen exact Make–Model identities.\n",
   "id": "co2-teaching-165"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "y_pred = selected_model.predict(X_test)\n",
   "execution_count": 86,
   "outputs": [],
   "id": "co2-teaching-166"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 71. Score the selected fuel-known predictions\n\nCompare predictions with the reserved target in the original g/km unit.\n",
   "id": "co2-teaching-167"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "selected_metrics = regression_metrics(y_test, y_pred)\n",
   "execution_count": 87,
   "outputs": [],
   "id": "co2-teaching-168"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 72. Fit the specifications-only winner\n\nThis model uses no fuel-consumption rating.\n",
   "id": "co2-teaching-169"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "selected_spec_model = clone(models[selected_spec_name]).fit(X_train, y_train)\n",
   "execution_count": 88,
   "outputs": [],
   "id": "co2-teaching-170"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 73. Predict from specifications only\n\nThe fitted pipeline selects its declared inputs from the common test table.\n",
   "id": "co2-teaching-171"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "spec_predictions = selected_spec_model.predict(X_test)\n",
   "execution_count": 89,
   "outputs": [],
   "id": "co2-teaching-172"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 74. Score the specifications-only predictions\n\nUse the same target and metric function as the fuel-known model.\n",
   "id": "co2-teaching-173"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "spec_metrics = regression_metrics(y_test, spec_predictions)\n",
   "execution_count": 90,
   "outputs": [],
   "id": "co2-teaching-174"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 75. Fit the corrected two-feature comparator\n\nThis reproduces the v3 feature choice with a proper training-only fit.\n",
   "id": "co2-teaching-175"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "corrected_two_model = clone(models['Engine + cylinders']).fit(X_train, y_train)\n",
   "execution_count": 91,
   "outputs": [],
   "id": "co2-teaching-176"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 76. Predict with the two-feature comparator\n\nThis model uses only engine size and cylinders.\n",
   "id": "co2-teaching-177"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "two_predictions = corrected_two_model.predict(X_test)\n",
   "execution_count": 92,
   "outputs": [],
   "id": "co2-teaching-178"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 77. Score the two-feature comparator\n\nIts held-out error is distinct from the supplied v3 in-sample error.\n",
   "id": "co2-teaching-179"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "two_metrics = regression_metrics(y_test, two_predictions)\n",
   "execution_count": 93,
   "outputs": [],
   "id": "co2-teaching-180"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 78. Fit the constant baseline\n\nA score is more useful when a reader sees what a trivial prediction would achieve.\n",
   "id": "co2-teaching-181"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "baseline_model = clone(models['Mean baseline']).fit(X_train, y_train)\n",
   "execution_count": 94,
   "outputs": [],
   "id": "co2-teaching-182"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 79. Predict with the constant baseline\n\nEvery test record gets the mean of the training targets.\n",
   "id": "co2-teaching-183"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "baseline_predictions = baseline_model.predict(X_test)\n",
   "execution_count": 95,
   "outputs": [],
   "id": "co2-teaching-184"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 80. Score the constant baseline\n\nThis gives the test-set reference for the more informative models.\n",
   "id": "co2-teaching-185"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "baseline_metrics = regression_metrics(y_test, baseline_predictions)\n",
   "execution_count": 96,
   "outputs": [],
   "id": "co2-teaching-186"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 81. Compare held-out results\n\nThe table uses the same records, target and metric functions.\n",
   "id": "co2-teaching-187"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "heldout_results = pd.DataFrame({\n    f'Fuel known: {selected_name}': selected_metrics,\n    f'Specifications: {selected_spec_name}': spec_metrics,\n    'Corrected engine + cylinders': two_metrics,\n    'Mean baseline': baseline_metrics,\n}).T\ndisplay(heldout_results.round(3))\n",
   "execution_count": 97,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                                      MAE_g_km  RMSE_g_km     R2  Bias_g_km\nFuel known: Fuel-interaction Ridge       2.231      3.328  0.997     -0.040\nSpecifications: Specifications Ridge    18.147     23.541  0.844      1.323\nCorrected engine + cylinders            21.893     28.706  0.768      1.066\nMean baseline                           48.036     59.692 -0.001      2.171",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MAE_g_km</th>\n      <th>RMSE_g_km</th>\n      <th>R2</th>\n      <th>Bias_g_km</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Fuel known: Fuel-interaction Ridge</th>\n      <td>2.231</td>\n      <td>3.328</td>\n      <td>0.997</td>\n      <td>-0.040</td>\n    </tr>\n    <tr>\n      <th>Specifications: Specifications Ridge</th>\n      <td>18.147</td>\n      <td>23.541</td>\n      <td>0.844</td>\n      <td>1.323</td>\n    </tr>\n    <tr>\n      <th>Corrected engine + cylinders</th>\n      <td>21.893</td>\n      <td>28.706</td>\n      <td>0.768</td>\n      <td>1.066</td>\n    </tr>\n    <tr>\n      <th>Mean baseline</th>\n      <td>48.036</td>\n      <td>59.692</td>\n      <td>-0.001</td>\n      <td>2.171</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-188"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** With fuel consumption known, the selected model has **MAE\n{selected_metrics['MAE_g_km']:.2f} g/km**, **RMSE {selected_metrics['RMSE_g_km']:.2f} g/km** and\n**R² {selected_metrics['R2']:.4f}**. Without that rating, the selected specifications-only model\nhas **RMSE {spec_metrics['RMSE_g_km']:.2f} g/km**. This difference reflects the information available.\"\"\")\n",
   "execution_count": 98,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** With fuel consumption known, the selected model has **MAE\n2.23 g/km**, **RMSE 3.33 g/km** and\n**R² 0.9969**. Without that rating, the selected specifications-only model\nhas **RMSE 23.54 g/km**. This difference reflects the information available."
     }
    }
   ],
   "id": "co2-teaching-189"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 82. Store residuals with the source identity\n\nA positive residual means the model underpredicts the recorded rating.\n",
   "id": "co2-teaching-190"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "test_results = test[['source_row', 'group_id', 'make', 'model', 'vehicle_class',\n                     'fuel_type', 'combined_l_100km', 'co2_g_km']].copy()\ntest_results['predicted_g_km'] = y_pred\ntest_results['residual_g_km'] = test_results['co2_g_km'] - y_pred\ntest_results['absolute_error_g_km'] = test_results['residual_g_km'].abs()\n",
   "execution_count": 99,
   "outputs": [],
   "id": "co2-teaching-191"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 83. Plot actual versus predicted\n\nThe red diagonal is exactly `predicted = actual`, not a fitted best-fit line.\n",
   "id": "co2-teaching-192"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fig, ax = plt.subplots(figsize=(6.6, 6.0))\nlimits = [min(y_test.min(), y_pred.min()), max(y_test.max(), y_pred.max())]\nax.scatter(y_test, y_pred, color=NAVY, alpha=0.32, s=16, linewidths=0)\nax.plot(limits, limits, color=SCARLET, label='Identity: predicted = actual')\nax.set(xlim=limits, ylim=limits, xlabel='Actual CO₂ (g/km)',\n       ylabel='Predicted CO₂ (g/km)', title='Held-out fuel-known predictions')\nax.set_aspect('equal', adjustable='box')\nax.legend()\nshow_figure(fig, '07_actual_predicted')\n",
   "execution_count": 100,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "image/png": 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"
     }
    }
   ],
   "id": "co2-teaching-193"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 84. Plot residuals versus predictions\n\nLook for systematic trends, changing spread and exceptional observations.\n",
   "id": "co2-teaching-194"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fig, ax = plt.subplots(figsize=(8.3, 4.8))\nsns.scatterplot(data=test_results, x='predicted_g_km', y='residual_g_km',\n                hue='fuel_type', palette=FUEL_PALETTE, alpha=0.38, s=20, linewidth=0, ax=ax)\nax.axhline(0, color=SCARLET, linestyle='--')\nax.set(title='Held-out residuals by fuel type', xlabel='Predicted CO₂ (g/km)',\n       ylabel='Actual − predicted (g/km)')\nshow_figure(fig, '08_residuals')\n",
   "execution_count": 101,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "image/png": 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     }
    }
   ],
   "id": "co2-teaching-195"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 85. Summarise errors by fuel type\n\nThe sample count is part of the interpretation, especially for rare categories.\n",
   "id": "co2-teaching-196"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fuel_error_rows = []\nfor fuel, subset in test_results.groupby('fuel_type'):\n    fuel_error_rows.append({'Fuel': fuel, 'Test_records': len(subset),\n        **regression_metrics(subset['co2_g_km'], subset['predicted_g_km'])})\nfuel_errors = pd.DataFrame(fuel_error_rows).set_index('Fuel')\ndisplay(fuel_errors.round(3))\n",
   "execution_count": 102,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "      Test_records  MAE_g_km  RMSE_g_km     R2  Bias_g_km\nFuel                                                     \nD               45     1.095      1.432  0.998     -0.461\nE               50     6.203     11.359  0.952     -1.648\nX              730     1.974      2.370  0.998     -0.075\nZ              641     2.294      2.952  0.997      0.155",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Test_records</th>\n      <th>MAE_g_km</th>\n      <th>RMSE_g_km</th>\n      <th>R2</th>\n      <th>Bias_g_km</th>\n    </tr>\n    <tr>\n      <th>Fuel</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>D</th>\n      <td>45</td>\n      <td>1.095</td>\n      <td>1.432</td>\n      <td>0.998</td>\n      <td>-0.461</td>\n    </tr>\n    <tr>\n      <th>E</th>\n      <td>50</td>\n      <td>6.203</td>\n      <td>11.359</td>\n      <td>0.952</td>\n      <td>-1.648</td>\n    </tr>\n    <tr>\n      <th>X</th>\n      <td>730</td>\n      <td>1.974</td>\n      <td>2.370</td>\n      <td>0.998</td>\n      <td>-0.075</td>\n    </tr>\n    <tr>\n      <th>Z</th>\n      <td>641</td>\n      <td>2.294</td>\n      <td>2.952</td>\n      <td>0.997</td>\n      <td>0.155</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-197"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** E85 has test RMSE **{fuel_errors.loc['E', 'RMSE_g_km']:.2f} g/km** across\n**{int(fuel_errors.loc['E', 'Test_records'])} records**, compared with\n**{fuel_errors.loc['X', 'RMSE_g_km']:.2f} g/km** for regular gasoline.\nInvestigate category-specific errors instead of presenting only the overall average.\"\"\")\n",
   "execution_count": 103,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** E85 has test RMSE **11.36 g/km** across\n**50 records**, compared with\n**2.37 g/km** for regular gasoline.\nInvestigate category-specific errors instead of presenting only the overall average."
     }
    }
   ],
   "id": "co2-teaching-198"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 86. Summarise errors by vehicle class\n\nA class with very few held-out records provides weak evidence about future performance.\n",
   "id": "co2-teaching-199"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "class_error_rows = []\nfor vehicle_class, subset in test_results.groupby('vehicle_class'):\n    class_error_rows.append({'Class': vehicle_class, 'Test_records': len(subset),\n        **regression_metrics(subset['co2_g_km'], subset['predicted_g_km'])})\nclass_errors = pd.DataFrame(class_error_rows).set_index('Class')\ndisplay(class_errors.sort_values('Test_records', ascending=False).round(3))\n",
   "execution_count": 104,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                          Test_records  MAE_g_km  RMSE_g_km     R2  Bias_g_km\nClass                                                                        \nMID-SIZE                           250     2.063      2.381  0.998      0.111\nSUV - SMALL                        223     2.137      5.161  0.971     -0.773\nCOMPACT                            195     1.884      2.593  0.995     -0.358\nSUBCOMPACT                         153     1.896      2.375  0.998      0.546\nSUV - STANDARD                     133     3.123      3.930  0.993     -0.542\nPICKUP TRUCK - STANDARD            128     2.303      3.475  0.988      0.345\nFULL-SIZE                          106     2.400      2.924  0.997      0.510\nTWO-SEATER                         103     2.560      3.035  0.998     -0.103\nSTATION WAGON - SMALL               68     1.751      2.043  0.997      0.467\nMINICOMPACT                         38     1.543      1.804  0.997      0.380\nVAN - PASSENGER                     23     3.744      4.084  0.989      0.232\nSPECIAL PURPOSE VEHICLE             19     2.320      2.728  0.993      0.127\nMINIVAN                             15     3.401      3.605  0.868      2.146\nSTATION WAGON - MID-SIZE            10     3.200      3.450  0.998     -2.897\nVAN - CARGO                          2     0.687      0.720  0.998     -0.217",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Test_records</th>\n      <th>MAE_g_km</th>\n      <th>RMSE_g_km</th>\n      <th>R2</th>\n      <th>Bias_g_km</th>\n    </tr>\n    <tr>\n      <th>Class</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>MID-SIZE</th>\n      <td>250</td>\n      <td>2.063</td>\n      <td>2.381</td>\n      <td>0.998</td>\n      <td>0.111</td>\n    </tr>\n    <tr>\n      <th>SUV - SMALL</th>\n      <td>223</td>\n      <td>2.137</td>\n      <td>5.161</td>\n      <td>0.971</td>\n      <td>-0.773</td>\n    </tr>\n    <tr>\n      <th>COMPACT</th>\n      <td>195</td>\n      <td>1.884</td>\n      <td>2.593</td>\n      <td>0.995</td>\n      <td>-0.358</td>\n    </tr>\n    <tr>\n      <th>SUBCOMPACT</th>\n      <td>153</td>\n      <td>1.896</td>\n      <td>2.375</td>\n      <td>0.998</td>\n      <td>0.546</td>\n    </tr>\n    <tr>\n      <th>SUV - STANDARD</th>\n      <td>133</td>\n      <td>3.123</td>\n      <td>3.930</td>\n      <td>0.993</td>\n      <td>-0.542</td>\n    </tr>\n    <tr>\n      <th>PICKUP TRUCK - STANDARD</th>\n      <td>128</td>\n      <td>2.303</td>\n      <td>3.475</td>\n      <td>0.988</td>\n      <td>0.345</td>\n    </tr>\n    <tr>\n      <th>FULL-SIZE</th>\n      <td>106</td>\n      <td>2.400</td>\n      <td>2.924</td>\n      <td>0.997</td>\n      <td>0.510</td>\n    </tr>\n    <tr>\n      <th>TWO-SEATER</th>\n      <td>103</td>\n      <td>2.560</td>\n      <td>3.035</td>\n      <td>0.998</td>\n      <td>-0.103</td>\n    </tr>\n    <tr>\n      <th>STATION WAGON - SMALL</th>\n      <td>68</td>\n      <td>1.751</td>\n      <td>2.043</td>\n      <td>0.997</td>\n      <td>0.467</td>\n    </tr>\n    <tr>\n      <th>MINICOMPACT</th>\n      <td>38</td>\n      <td>1.543</td>\n      <td>1.804</td>\n      <td>0.997</td>\n      <td>0.380</td>\n    </tr>\n    <tr>\n      <th>VAN - PASSENGER</th>\n      <td>23</td>\n      <td>3.744</td>\n      <td>4.084</td>\n      <td>0.989</td>\n      <td>0.232</td>\n    </tr>\n    <tr>\n      <th>SPECIAL PURPOSE VEHICLE</th>\n      <td>19</td>\n      <td>2.320</td>\n      <td>2.728</td>\n      <td>0.993</td>\n      <td>0.127</td>\n    </tr>\n    <tr>\n      <th>MINIVAN</th>\n      <td>15</td>\n      <td>3.401</td>\n      <td>3.605</td>\n      <td>0.868</td>\n      <td>2.146</td>\n    </tr>\n    <tr>\n      <th>STATION WAGON - MID-SIZE</th>\n      <td>10</td>\n      <td>3.200</td>\n      <td>3.450</td>\n      <td>0.998</td>\n      <td>-2.897</td>\n    </tr>\n    <tr>\n      <th>VAN - CARGO</th>\n      <td>2</td>\n      <td>0.687</td>\n      <td>0.720</td>\n      <td>0.998</td>\n      <td>-0.217</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-200"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Check uncertainty and repeated-row sensitivity\n\nThese short extensions strengthen interpretation without hiding the basic workflow.\nThe group bootstrap resamples held-out model groups and does not refit the selected estimator.\nIts interval is conditional on this fitted model and an exchangeable-group assumption; it does\nnot include model-selection uncertainty or establish national representativeness.\n\nThe repeated-row sensitivity comparison uses **training-only validation**, leaving the held-out\ntest results untouched. Keep the main run and the sensitivity run clearly labelled.\n",
   "id": "co2-teaching-201"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 87. Index the held-out groups for resampling\n\nAll records from a sampled model group move together.\n",
   "id": "co2-teaching-202"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "test_group_values = test_results['group_id'].to_numpy()\nunique_test_groups = np.unique(test_group_values)\ngroup_positions = [np.flatnonzero(test_group_values == group) for group in unique_test_groups]\nsquared_errors = test_results['residual_g_km'].to_numpy() ** 2\nbootstrap_rng = np.random.default_rng(SEED)\n",
   "execution_count": 105,
   "outputs": [],
   "id": "co2-teaching-203"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 88. Run the group bootstrap\n\nThis estimates variability in RMSE across resampled held-out groups.\n",
   "id": "co2-teaching-204"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "bootstrap_errors = []\nfor _ in range(N_BOOTSTRAPS):\n    sample = bootstrap_rng.integers(0, len(group_positions), size=len(group_positions))\n    positions = np.concatenate([group_positions[index] for index in sample])\n    bootstrap_errors.append(np.sqrt(squared_errors[positions].mean()))\n",
   "execution_count": 106,
   "outputs": [],
   "id": "co2-teaching-205"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 89. Report the conditional interval\n\nA percentile interval is different from the standard deviation between cross-validation folds.\n",
   "id": "co2-teaching-206"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "rmse_lower, rmse_upper = np.quantile(bootstrap_errors, [0.025, 0.975])\nprint(f'Conditional 95% group-bootstrap RMSE interval: {rmse_lower:.2f}–{rmse_upper:.2f} g/km')\n",
   "execution_count": 107,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Conditional 95% group-bootstrap RMSE interval: 2.64–4.30 g/km\n"
    }
   ],
   "id": "co2-teaching-207"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 90. Create the deduplicated training view\n\nExclude tracking and audit columns when identifying repeated source records.\n",
   "id": "co2-teaching-208"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "source_columns = list(rename_columns.values())\nunique_training = train.drop_duplicates(subset=source_columns).copy()\nprint('Training records before/after repeat removal:', len(train), len(unique_training))\n",
   "execution_count": 108,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Training records before/after repeat removal: 5919 5035\n"
    }
   ],
   "id": "co2-teaching-209"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 91. Run validation on the sensitivity view\n\nUse the selected candidate settings, not a new search driven by test performance.\n",
   "id": "co2-teaching-210"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "dedup_cv = GroupKFold(n_splits=CV_FOLDS)\ndedup_scores = cross_validate(models[selected_name], unique_training[features],\n    unique_training['co2_g_km'], groups=unique_training['group_id'], cv=dedup_cv,\n    scoring=scoring, n_jobs=1, error_score='raise')\ndedup_rmse = float(-dedup_scores['test_RMSE'].mean())\n",
   "execution_count": 109,
   "outputs": [],
   "id": "co2-teaching-211"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 92. Compare the repeated-row policies\n\nRemoval changes observation weights and fold allocation, so this is sensitivity rather than a paired test.\n",
   "id": "co2-teaching-212"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "repeat_sensitivity = pd.Series({\n    'Retain repeats: grouped CV RMSE': comparison.loc[selected_name, 'CV_RMSE_g_km'],\n    'Remove normalised repeats: grouped CV RMSE': dedup_rmse,\n}).to_frame('RMSE (g/km)')\ndisplay(repeat_sensitivity.round(3))\n",
   "execution_count": 110,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                                            RMSE (g/km)\nRetain repeats: grouped CV RMSE                   3.568\nRemove normalised repeats: grouped CV RMSE        3.732",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>RMSE (g/km)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Retain repeats: grouped CV RMSE</th>\n      <td>3.568</td>\n    </tr>\n    <tr>\n      <th>Remove normalised repeats: grouped CV RMSE</th>\n      <td>3.732</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-213"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** Mean grouped-validation RMSE is **{comparison.loc[selected_name, 'CV_RMSE_g_km']:.2f} g/km**\nwith repeats retained and **{dedup_rmse:.2f} g/km** with repeats removed.\nNeither policy establishes the correct source history; restore model years before deciding how to weight records.\"\"\")\n",
   "execution_count": 111,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** Mean grouped-validation RMSE is **3.57 g/km**\nwith repeats retained and **3.73 g/km** with repeats removed.\nNeither policy establishes the correct source history; restore model years before deciding how to weight records."
     }
    }
   ],
   "id": "co2-teaching-214"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Compare with combustion arithmetic\n\nNatural Resources Canada reports approximately **2.3 kg CO₂ per litre of gasoline**.\nThat gives a rough gasoline-only rule:\n\n$$CO_2\\ (g/km)\\approx 23\\times\\text{combined fuel consumption (L/100 km)}.$$\n\nThe factor is approximate and not fitted to this CSV. We compare it with the selected estimator\non the **same X/Z gasoline rows**. It does not apply to diesel, E85 or natural gas.\n\nSource: [NRCan personal vehicles](https://natural-resources.canada.ca/energy-efficiency/transportation-energy-efficiency/personal-vehicles).\n",
   "id": "co2-teaching-215"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 93. Select the comparable gasoline subset\n\nBoth methods must be scored on the same observations.\n",
   "id": "co2-teaching-216"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "gasoline_test = test_results[test_results['fuel_type'].isin(['X', 'Z'])].copy()\ngasoline_rule_predictions = 23.0 * gasoline_test['combined_l_100km']\n",
   "execution_count": 112,
   "outputs": [],
   "id": "co2-teaching-217"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 94. Evaluate the gasoline benchmark\n\nA simple scientific rule is a useful alternative to a learned model.\n",
   "id": "co2-teaching-218"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "physical_results = pd.DataFrame({\n    'Approximate 23 × fuel-consumption rule': regression_metrics(\n        gasoline_test['co2_g_km'], gasoline_rule_predictions),\n    'Selected model on the same gasoline rows': regression_metrics(\n        gasoline_test['co2_g_km'], gasoline_test['predicted_g_km']),\n}).T\ndisplay(physical_results.round(3))\n",
   "execution_count": 113,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                                          MAE_g_km  RMSE_g_km     R2  Bias_g_km\nApproximate 23 × fuel-consumption rule       3.446      4.285  0.995      3.282\nSelected model on the same gasoline rows     2.123      2.658  0.998      0.032",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MAE_g_km</th>\n      <th>RMSE_g_km</th>\n      <th>R2</th>\n      <th>Bias_g_km</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Approximate 23 × fuel-consumption rule</th>\n      <td>3.446</td>\n      <td>4.285</td>\n      <td>0.995</td>\n      <td>3.282</td>\n    </tr>\n    <tr>\n      <th>Selected model on the same gasoline rows</th>\n      <td>2.123</td>\n      <td>2.658</td>\n      <td>0.998</td>\n      <td>0.032</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-219"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Interpretation:** On **{len(gasoline_test):,} gasoline test records**, the rough physical rule has\nRMSE **{physical_results.iloc[0]['RMSE_g_km']:.2f} g/km**, versus\n**{physical_results.iloc[1]['RMSE_g_km']:.2f} g/km** for the selected estimator.\nMuch of the predictive strength follows a known fuel/CO₂ relationship. It is not proof of a new mechanism.\"\"\")\n",
   "execution_count": 114,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Interpretation:** On **1,371 gasoline test records**, the rough physical rule has\nRMSE **4.29 g/km**, versus\n**2.66 g/km** for the selected estimator.\nMuch of the predictive strength follows a known fuel/CO₂ relationship. It is not proof of a new mechanism."
     }
    }
   ],
   "id": "co2-teaching-220"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 10. Business application: fleet operating scenarios\n\nFor a fleet manager, the useful question is often “What changes in annual fuel cost and tailpipe\nCO₂ if we change the vehicle mix?” The following demonstration uses **catalogue class means**\nas proxies, with full-row repeats removed for that summary. Those means are not matched\nreplacement vehicles, current South African ratings, or actual fleet performance.\n\nWe compare 100 vehicles at 25,000 km per vehicle per year. The fuel price is an **assumed R24/L**,\nnot a retrieved current price. All three fleet options have the same vehicle count and distance.\n\n$$\\text{Annual fuel litres}=N\\times km\\times\\frac{L/100\\,km}{100}$$\n$$\\text{Annual tailpipe tonnes}=N\\times km\\times\\frac{g/km}{1,000,000}.$$\n",
   "id": "co2-teaching-221"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 95. Create a unique catalogue summary\n\nThis view is descriptive and is created after the completed model evaluation.\n",
   "id": "co2-teaching-222"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "catalogue = data.drop_duplicates(subset=source_columns).copy()\ncatalogue_classes = catalogue.groupby('vehicle_class').agg(\n    entries=('co2_g_km', 'size'), mean_co2_g_km=('co2_g_km', 'mean'),\n    mean_fuel_l_100km=('combined_l_100km', 'mean'))\ndisplay(catalogue_classes.loc[['COMPACT', 'SUV - SMALL', 'SUV - STANDARD']].round(2))\n",
   "execution_count": 115,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                entries  mean_co2_g_km  mean_fuel_l_100km\nvehicle_class                                            \nCOMPACT             903         217.64               9.39\nSUV - SMALL        1005         236.44              10.25\nSUV - STANDARD      613         306.73              13.78",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>entries</th>\n      <th>mean_co2_g_km</th>\n      <th>mean_fuel_l_100km</th>\n    </tr>\n    <tr>\n      <th>vehicle_class</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>COMPACT</th>\n      <td>903</td>\n      <td>217.64</td>\n      <td>9.39</td>\n    </tr>\n    <tr>\n      <th>SUV - SMALL</th>\n      <td>1005</td>\n      <td>236.44</td>\n      <td>10.25</td>\n    </tr>\n    <tr>\n      <th>SUV - STANDARD</th>\n      <td>613</td>\n      <td>306.73</td>\n      <td>13.78</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-223"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 96. Declare the fleet assumptions\n\nChange one assumption at a time to see how the arithmetic responds.\n",
   "id": "co2-teaching-224"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "FLEET_VEHICLES = 100\nFLEET_ANNUAL_KM = 25000\nASSUMED_FUEL_PRICE_ZAR_PER_L = 24.0\nfleet_classes = ['COMPACT', 'SUV - SMALL', 'SUV - STANDARD']\nfleet_scenario = catalogue_classes.loc[fleet_classes].copy()\n",
   "execution_count": 116,
   "outputs": [],
   "id": "co2-teaching-225"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 97. Calculate annual fuel use\n\nThe units are litres; dividing by 100 converts the consumption rating to litres per kilometre.\n",
   "id": "co2-teaching-226"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fleet_scenario['annual_fuel_litres'] = (\n    FLEET_VEHICLES * FLEET_ANNUAL_KM * fleet_scenario['mean_fuel_l_100km'] / 100)\n",
   "execution_count": 117,
   "outputs": [],
   "id": "co2-teaching-227"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 98. Calculate the fuel budget\n\nThis is a scenario budget, not a quoted fleet operating cost.\n",
   "id": "co2-teaching-228"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fleet_scenario['annual_fuel_cost_zar'] = (\n    fleet_scenario['annual_fuel_litres'] * ASSUMED_FUEL_PRICE_ZAR_PER_L)\n",
   "execution_count": 118,
   "outputs": [],
   "id": "co2-teaching-229"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 99. Calculate annual tailpipe CO₂\n\nOne tonne contains one million grams.\n",
   "id": "co2-teaching-230"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fleet_scenario['annual_tailpipe_tonnes'] = (\n    FLEET_VEHICLES * FLEET_ANNUAL_KM * fleet_scenario['mean_co2_g_km'] / 1_000_000)\ndisplay(fleet_scenario.round(2))\n",
   "execution_count": 119,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                entries  ...  annual_tailpipe_tonnes\nvehicle_class            ...                        \nCOMPACT             903  ...                  544.10\nSUV - SMALL        1005  ...                  591.11\nSUV - STANDARD      613  ...                  766.83\n\n[3 rows x 6 columns]",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>entries</th>\n      <th>mean_co2_g_km</th>\n      <th>mean_fuel_l_100km</th>\n      <th>annual_fuel_litres</th>\n      <th>annual_fuel_cost_zar</th>\n      <th>annual_tailpipe_tonnes</th>\n    </tr>\n    <tr>\n      <th>vehicle_class</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>COMPACT</th>\n      <td>903</td>\n      <td>217.64</td>\n      <td>9.39</td>\n      <td>234833.89</td>\n      <td>5636013.29</td>\n      <td>544.10</td>\n    </tr>\n    <tr>\n      <th>SUV - SMALL</th>\n      <td>1005</td>\n      <td>236.44</td>\n      <td>10.25</td>\n      <td>256313.43</td>\n      <td>6151522.39</td>\n      <td>591.11</td>\n    </tr>\n    <tr>\n      <th>SUV - STANDARD</th>\n      <td>613</td>\n      <td>306.73</td>\n      <td>13.78</td>\n      <td>344588.09</td>\n      <td>8270114.19</td>\n      <td>766.83</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-231"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 100. Compare the two ends of the scenario\n\nDifferences are arithmetic consequences of these proxy ratings and assumptions.\n",
   "id": "co2-teaching-232"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fleet_cost_difference = float(fleet_scenario.loc['SUV - STANDARD', 'annual_fuel_cost_zar']\n                              - fleet_scenario.loc['COMPACT', 'annual_fuel_cost_zar'])\nfleet_co2_difference = float(fleet_scenario.loc['SUV - STANDARD', 'annual_tailpipe_tonnes']\n                             - fleet_scenario.loc['COMPACT', 'annual_tailpipe_tonnes'])\n",
   "execution_count": 120,
   "outputs": [],
   "id": "co2-teaching-233"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Business interpretation:** Under the declared assumptions, the standard-SUV class proxy uses\n**R{fleet_cost_difference:,.0f} more fuel budget per year** and produces\n**{fleet_co2_difference:,.1f} more tailpipe tonnes** than the compact class proxy for the 100-vehicle fleet.\nThis does not establish equivalent payload, safety, suitability, purchase cost, resale value or real-world economy.\"\"\")\n",
   "execution_count": 121,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Business interpretation:** Under the declared assumptions, the standard-SUV class proxy uses\n**R2,634,101 more fuel budget per year** and produces\n**222.7 more tailpipe tonnes** than the compact class proxy for the 100-vehicle fleet.\nThis does not establish equivalent payload, safety, suitability, purchase cost, resale value or real-world economy."
     }
    }
   ],
   "id": "co2-teaching-234"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 101. Plot the fleet scenario\n\nTwo charts keep currency and tonnes on separate axes.\n",
   "id": "co2-teaching-235"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fig, axes = plt.subplots(1, 2, figsize=(10.8, 4.6))\nlabels = ['Compact', 'Small SUV', 'Standard SUV']\naxes[0].bar(labels, fleet_scenario['annual_fuel_cost_zar'] / 1_000_000, color=NAVY)\naxes[1].bar(labels, fleet_scenario['annual_tailpipe_tonnes'], color=SCARLET)\naxes[0].set(title='Assumed fleet fuel budget', ylabel='R million / year')\naxes[1].set(title='Rated tailpipe scenario', ylabel='Tonnes CO₂ / year')\nfor ax in axes:\n    ax.tick_params(axis='x', rotation=15)\nshow_figure(fig, '09_fleet_scenario')\n",
   "execution_count": 122,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
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"
     }
    }
   ],
   "id": "co2-teaching-236"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### How a business would use this\n\nThe inferred business value is a repeatable comparison: reconcile actual vehicles, add distance\nand fuel prices, then compare options under equivalent operating requirements. Purchasing\ndecisions also require depreciation, maintenance, financing, charging/infrastructure and duty\ncycle. A vehicle supplier could use validated local ratings and forecast sales to compare product\nmixes and customer operating scenarios.\n\nUnder the GHG Protocol, combustion from owned/controlled vehicles can fall in **Scope 1**.\nA manufacturer's sold-product use phase is a different **Scope 3 Category 11** boundary,\nnormally assessed over expected lifetime use. The calculations here are tailpipe CO₂ only;\nthey are not a complete CO₂e or lifecycle inventory and do not handle the special accounting\nof biogenic carbon in E85.\n\nSources: [GHG Protocol Corporate Standard](https://ghgprotocol.org/sites/default/files/standards/ghg-protocol-revised.pdf),\n[Scope 3 calculation guidance, Category 11](https://ghgprotocol.org/sites/default/files/2023-03/Scope3_Calculation_Guidance_0%5B1%5D.pdf).\n",
   "id": "co2-teaching-237"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 11. Business application: tax exposure is jurisdiction-specific\n\n**South Africa illustration.** The SARS Schedule 1 Part 3D listing was checked on 4 October 2026\n(listed update: 1 April 2024). For a qualifying passenger vehicle in this simplified example, use\n**R146 per g/km above 95 g/km**. The 2025 SARS policy confirms the certificate-based calculation.\nActual liability requires the applicable classification, clearance date, recognised test report\nand any exclusions. We do not calculate VAT, other duties or non-passenger classifications.\n\nThe inputs below are **assumed locally valid rated emissions**. Historical Canadian catalogue\nratings and machine-learning estimates are not substitutes for the required South African report.\nIf the required test evidence is missing, the legal fallback applies; this model is not that fallback.\n\nSources: [SARS current schedules](https://www.sars.gov.za/legal-counsel/primary-legislation/schedules-to-the-customs-and-excise-act-1964/),\n[Part 3D](https://www.sars.gov.za/legal-lprim-ce-sch1p3d-schedule-no-1-part-3d/),\n[SARS motor-vehicle emissions policy](https://www.sars.gov.za/se-cd-02-environmental-levy-on-co2-emissions-new-motor-vehicles-manufactured-in-sa-external-policy/).\n",
   "id": "co2-teaching-238"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 102. Declare the South African illustration parameters\n\nFreeze the checked rate and threshold so the example can be reproduced.\n",
   "id": "co2-teaching-239"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "SA_PASSENGER_THRESHOLD_G_KM = 95.0\nSA_PASSENGER_RATE_ZAR = 146.0\nASSUMED_TAX_UNITS = 1000\nsa_tax_examples = pd.DataFrame({'Assumed_certified_g_km': [95, 110, 135, 150, 180]})\n",
   "execution_count": 123,
   "outputs": [],
   "id": "co2-teaching-240"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 103. Calculate the taxable excess\n\nOnly the rating above the threshold enters this simplified levy formula.\n",
   "id": "co2-teaching-241"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "sa_tax_examples['taxable_excess_g_km'] = (\n    sa_tax_examples['Assumed_certified_g_km'] - SA_PASSENGER_THRESHOLD_G_KM).clip(lower=0)\n",
   "execution_count": 124,
   "outputs": [],
   "id": "co2-teaching-242"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 104. Calculate levy per unit and scenario total\n\nThis is base levy arithmetic, with all eligibility assumptions declared above.\n",
   "id": "co2-teaching-243"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "sa_tax_examples['levy_per_vehicle_zar'] = sa_tax_examples['taxable_excess_g_km'] * SA_PASSENGER_RATE_ZAR\nsa_tax_examples['levy_for_assumed_units_zar'] = sa_tax_examples['levy_per_vehicle_zar'] * ASSUMED_TAX_UNITS\ndisplay(sa_tax_examples)\n",
   "execution_count": 125,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "   Assumed_certified_g_km  ...  levy_for_assumed_units_zar\n0                      95  ...                         0.0\n1                     110  ...                   2190000.0\n2                     135  ...                   5840000.0\n3                     150  ...                   8030000.0\n4                     180  ...                  12410000.0\n\n[5 rows x 4 columns]",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Assumed_certified_g_km</th>\n      <th>taxable_excess_g_km</th>\n      <th>levy_per_vehicle_zar</th>\n      <th>levy_for_assumed_units_zar</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>95</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>110</td>\n      <td>15.0</td>\n      <td>2190.0</td>\n      <td>2190000.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>135</td>\n      <td>40.0</td>\n      <td>5840.0</td>\n      <td>5840000.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>150</td>\n      <td>55.0</td>\n      <td>8030.0</td>\n      <td>8030000.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>180</td>\n      <td>85.0</td>\n      <td>12410.0</td>\n      <td>12410000.0</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-244"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 105. Measure sensitivity to a rating difference\n\nA rating error can matter financially even when a model has a small average error.\n",
   "id": "co2-teaching-245"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "sa_10g_sensitivity = 10 * SA_PASSENGER_RATE_ZAR * ASSUMED_TAX_UNITS\nsa_25g_scenario_difference = 25 * SA_PASSENGER_RATE_ZAR * ASSUMED_TAX_UNITS\n",
   "execution_count": 126,
   "outputs": [],
   "id": "co2-teaching-246"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Business interpretation:** A 10 g/km difference changes this scenario's base levy by\n**R{sa_10g_sensitivity:,.0f} across {ASSUMED_TAX_UNITS:,} units**, when both ratings are above the threshold.\nReducing an assumed valid rating from 135 to 110 g/km changes it by\n**R{sa_25g_scenario_difference:,.0f}**. These are scenario sensitivities, not tax amounts owed by a named business.\"\"\")\n",
   "execution_count": 127,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Business interpretation:** A 10 g/km difference changes this scenario's base levy by\n**R1,460,000 across 1,000 units**, when both ratings are above the threshold.\nReducing an assumed valid rating from 135 to 110 g/km changes it by\n**R3,650,000**. These are scenario sensitivities, not tax amounts owed by a named business."
     }
    }
   ],
   "id": "co2-teaching-247"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Canada uses a different tax measure\n\nCanada's Green Levy uses weighted fuel consumption: 55% city plus 45% highway. The official\ntable is CAD 0 below 13 L/100 km, CAD 1,000 for 13–<14, CAD 2,000 for 14–<15,\nCAD 3,000 for 15–<16, and CAD 4,000 at 16 or more. Eligibility excludes pickups and certain\nother vehicles. It is not a universal per-gram CO₂ tax.\n\nThe illustration below uses assumed eligible passenger vehicles, not every row in this catalogue.\nSource: [CRA current rates of excise taxes](https://www.canada.ca/en/revenue-agency/services/forms-publications/publications/currate/current-rates-excise-taxes.html), checked 4 October 2026.\n",
   "id": "co2-teaching-248"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 106. Create the Canadian tax scenarios\n\nThe examples are assumptions selected to demonstrate the slab boundaries.\n",
   "id": "co2-teaching-249"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "canada_examples = pd.DataFrame({'assumed_weighted_l_100km': [12.9, 13.0, 14.0, 15.0, 16.0]})\n",
   "execution_count": 128,
   "outputs": [],
   "id": "co2-teaching-250"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 107. Apply the Canadian rate bands\n\nDo not round a rating into another band without the applicable measurement rule.\n",
   "id": "co2-teaching-251"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "ratings = canada_examples['assumed_weighted_l_100km']\ncanada_examples['illustrative_levy_cad'] = np.select(\n    [ratings < 13, ratings < 14, ratings < 15, ratings < 16],\n    [0, 1000, 2000, 3000], default=4000)\ndisplay(canada_examples)\n",
   "execution_count": 129,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "   assumed_weighted_l_100km  illustrative_levy_cad\n0                      12.9                      0\n1                      13.0                   1000\n2                      14.0                   2000\n3                      15.0                   3000\n4                      16.0                   4000",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>assumed_weighted_l_100km</th>\n      <th>illustrative_levy_cad</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>12.9</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>13.0</td>\n      <td>1000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>14.0</td>\n      <td>2000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15.0</td>\n      <td>3000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>16.0</td>\n      <td>4000</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-252"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Keep the financial and environmental quantities separate\n\n| Quantity | Unit | Interpretation |\n| --- | --- | --- |\n| Vehicle rating | g CO₂/km | Tailpipe intensity under the rating method |\n| SA passenger illustration | ZAR per vehicle | A qualifying vehicle levy at a specified date/classification |\n| Canada Green Levy illustration | CAD per vehicle | An eligible vehicle's fuel-efficiency tax band |\n| Fleet annual scenario | tonnes CO₂/year | Rated intensity multiplied by assumed fleet kilometres |\n| Sold-product use phase | typically lifetime CO₂e | A separate corporate Scope 3 reporting boundary |\n| National road inventory | tonnes CO₂/year | A territorial inventory with fleet/activity/fuel evidence |\n\nNone of these amounts is atmospheric concentration in ppm. Do not add ZAR and CAD amounts,\nor confuse a one-vehicle fiscal charge with recurring tonnes of emissions.\n",
   "id": "co2-teaching-253"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 12. Audit application: screen, trace, corroborate\n\nAn audit perspective asks whether records are complete, correctly measured, properly classified\nand supported by evidence. Model residuals can prioritise review, but cannot establish fraud,\ncertify emissions, or independently verify the same physical rating system they were trained on.\n\nWe use training-only out-of-fold predictions to set a **99th-percentile screening threshold**.\nThe threshold is a declared analytics choice, not statutory materiality. It also reflects model\nselection on the training data and is not an independent confidence statement.\n",
   "id": "co2-teaching-254"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 108. Generate out-of-fold training predictions\n\nEach training row is predicted by an estimator fitted without its model group.\n",
   "id": "co2-teaching-255"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "oof_predictions = cross_val_predict(models[selected_name], X_train, y_train,\n                                    cv=cv_splits, n_jobs=1)\n",
   "execution_count": 130,
   "outputs": [],
   "id": "co2-teaching-256"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 109. Set a training-derived review threshold\n\nDo not choose a cutoff by inspecting the largest test errors.\n",
   "id": "co2-teaching-257"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "oof_absolute_errors = np.abs(y_train.to_numpy() - oof_predictions)\nAUDIT_REVIEW_QUANTILE = 0.99\naudit_threshold = float(np.quantile(oof_absolute_errors, AUDIT_REVIEW_QUANTILE))\nprint(f'Training-derived review threshold: {audit_threshold:.2f} g/km')\n",
   "execution_count": 131,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Training-derived review threshold: 7.16 g/km\n"
    }
   ],
   "id": "co2-teaching-258"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 110. Create a held-out review queue\n\nKeep source rows and vehicle identities so a reviewer can trace the input.\n",
   "id": "co2-teaching-259"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "audit_review = test_results[test_results['absolute_error_g_km'] > audit_threshold].copy()\naudit_review = audit_review.sort_values('absolute_error_g_km', ascending=False)\ndisplay(audit_review.head(10).round(2))\n",
   "execution_count": 132,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "      source_row  group_id  ... residual_g_km absolute_error_g_km\n4083        4083      1490  ...        -69.54               69.54\n3545        3545       514  ...         21.76               21.76\n2971        2971      1378  ...        -21.10               21.10\n7169        7169      1366  ...        -14.01               14.01\n6160        6160      1366  ...        -14.01               14.01\n2987        2987      1444  ...        -11.17               11.17\n2972        2972      1379  ...         -9.81                9.81\n4601        4601       514  ...          9.47                9.47\n2975        2975      1393  ...          9.25                9.25\n2981        2981      1423  ...          9.02                9.02\n\n[10 rows x 11 columns]",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>source_row</th>\n      <th>group_id</th>\n      <th>make</th>\n      <th>model</th>\n      <th>vehicle_class</th>\n      <th>fuel_type</th>\n      <th>combined_l_100km</th>\n      <th>co2_g_km</th>\n      <th>predicted_g_km</th>\n      <th>residual_g_km</th>\n      <th>absolute_error_g_km</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4083</th>\n      <td>4083</td>\n      <td>1490</td>\n      <td>MERCEDES-BENZ</td>\n      <td>GLA 250 4MATIC</td>\n      <td>SUV - SMALL</td>\n      <td>E</td>\n      <td>12.0</td>\n      <td>128</td>\n      <td>197.54</td>\n      <td>-69.54</td>\n      <td>69.54</td>\n    </tr>\n    <tr>\n      <th>3545</th>\n      <td>3545</td>\n      <td>514</td>\n      <td>CHEVROLET</td>\n      <td>SILVERADO 4WD FFV</td>\n      <td>PICKUP TRUCK - STANDARD</td>\n      <td>E</td>\n      <td>17.9</td>\n      <td>315</td>\n      <td>293.24</td>\n      <td>21.76</td>\n      <td>21.76</td>\n    </tr>\n    <tr>\n      <th>2971</th>\n      <td>2971</td>\n      <td>1378</td>\n      <td>MERCEDES-BENZ</td>\n      <td>AMG G 65</td>\n      <td>SUV - STANDARD</td>\n      <td>Z</td>\n      <td>21.4</td>\n      <td>476</td>\n      <td>497.10</td>\n      <td>-21.10</td>\n      <td>21.10</td>\n    </tr>\n    <tr>\n      <th>7169</th>\n      <td>7169</td>\n      <td>1366</td>\n      <td>MERCEDES-BENZ</td>\n      <td>AMG CLS 53 4MATIC+</td>\n      <td>COMPACT</td>\n      <td>Z</td>\n      <td>10.7</td>\n      <td>235</td>\n      <td>249.01</td>\n      <td>-14.01</td>\n      <td>14.01</td>\n    </tr>\n    <tr>\n      <th>6160</th>\n      <td>6160</td>\n      <td>1366</td>\n      <td>MERCEDES-BENZ</td>\n      <td>AMG CLS 53 4MATIC+</td>\n      <td>COMPACT</td>\n      <td>Z</td>\n      <td>10.7</td>\n      <td>235</td>\n      <td>249.01</td>\n      <td>-14.01</td>\n      <td>14.01</td>\n    </tr>\n    <tr>\n      <th>2987</th>\n      <td>2987</td>\n      <td>1444</td>\n      <td>MERCEDES-BENZ</td>\n      <td>C 450 AMG SPORT 4MATIC</td>\n      <td>COMPACT</td>\n      <td>Z</td>\n      <td>10.4</td>\n      <td>231</td>\n      <td>242.17</td>\n      <td>-11.17</td>\n      <td>11.17</td>\n    </tr>\n    <tr>\n      <th>2972</th>\n      <td>2972</td>\n      <td>1379</td>\n      <td>MERCEDES-BENZ</td>\n      <td>AMG GL 63 S</td>\n      <td>SUV - STANDARD</td>\n      <td>Z</td>\n      <td>16.3</td>\n      <td>369</td>\n      <td>378.81</td>\n      <td>-9.81</td>\n      <td>9.81</td>\n    </tr>\n    <tr>\n      <th>4601</th>\n      <td>4601</td>\n      <td>514</td>\n      <td>CHEVROLET</td>\n      <td>SILVERADO 4WD FFV</td>\n      <td>PICKUP TRUCK - STANDARD</td>\n      <td>E</td>\n      <td>18.3</td>\n      <td>309</td>\n      <td>299.53</td>\n      <td>9.47</td>\n      <td>9.47</td>\n    </tr>\n    <tr>\n      <th>2975</th>\n      <td>2975</td>\n      <td>1393</td>\n      <td>MERCEDES-BENZ</td>\n      <td>AMG GLE 63 S COUPE 4MATIC</td>\n      <td>SUV - STANDARD</td>\n      <td>Z</td>\n      <td>15.2</td>\n      <td>363</td>\n      <td>353.75</td>\n      <td>9.25</td>\n      <td>9.25</td>\n    </tr>\n    <tr>\n      <th>2981</th>\n      <td>2981</td>\n      <td>1423</td>\n      <td>MERCEDES-BENZ</td>\n      <td>AMG SL 63</td>\n      <td>TWO-SEATER</td>\n      <td>Z</td>\n      <td>12.4</td>\n      <td>298</td>\n      <td>288.98</td>\n      <td>9.02</td>\n      <td>9.02</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-260"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Audit interpretation:** **{len(audit_review):,} held-out records** exceed the training-derived\nscreening cutoff of **{audit_threshold:.2f} g/km**. These are records to corroborate, not confirmed misstatements.\"\"\")\n",
   "execution_count": 133,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Audit interpretation:** **15 held-out records** exceed the training-derived\nscreening cutoff of **7.16 g/km**. These are records to corroborate, not confirmed misstatements."
     }
    }
   ],
   "id": "co2-teaching-261"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 111. Examine the largest exception\n\nA numerical example shows why the review needs original supporting evidence.\n",
   "id": "co2-teaching-262"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "largest_exception = audit_review.iloc[0]\ndisplay(largest_exception.to_frame('Largest held-out exception'))\n",
   "execution_count": 134,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                    Largest held-out exception\nsource_row                                4083\ngroup_id                                  1490\nmake                             MERCEDES-BENZ\nmodel                           GLA 250 4MATIC\nvehicle_class                      SUV - SMALL\nfuel_type                                    E\ncombined_l_100km                          12.0\nco2_g_km                                   128\npredicted_g_km                      197.544233\nresidual_g_km                       -69.544233\nabsolute_error_g_km                  69.544233",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Largest held-out exception</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>source_row</th>\n      <td>4083</td>\n    </tr>\n    <tr>\n      <th>group_id</th>\n      <td>1490</td>\n    </tr>\n    <tr>\n      <th>make</th>\n      <td>MERCEDES-BENZ</td>\n    </tr>\n    <tr>\n      <th>model</th>\n      <td>GLA 250 4MATIC</td>\n    </tr>\n    <tr>\n      <th>vehicle_class</th>\n      <td>SUV - SMALL</td>\n    </tr>\n    <tr>\n      <th>fuel_type</th>\n      <td>E</td>\n    </tr>\n    <tr>\n      <th>combined_l_100km</th>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>co2_g_km</th>\n      <td>128</td>\n    </tr>\n    <tr>\n      <th>predicted_g_km</th>\n      <td>197.544233</td>\n    </tr>\n    <tr>\n      <th>residual_g_km</th>\n      <td>-69.544233</td>\n    </tr>\n    <tr>\n      <th>absolute_error_g_km</th>\n      <td>69.544233</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-263"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Observed exception:** Source row **{int(largest_exception['source_row'])}** lists\n**{largest_exception['make']} {largest_exception['model']}**, fuel code **{largest_exception['fuel_type']}**,\nconsumption **{largest_exception['combined_l_100km']:.1f} L/100 km**, and **{largest_exception['co2_g_km']:.0f} g/km**.\nThe model estimates **{largest_exception['predicted_g_km']:.1f} g/km**, a gap of\n**{largest_exception['absolute_error_g_km']:.1f} g/km**. Check the source row, variant, fuel code,\nrating method and original report before deciding whether the record is wrong.\"\"\")\n",
   "execution_count": 135,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Observed exception:** Source row **4083** lists\n**MERCEDES-BENZ GLA 250 4MATIC**, fuel code **E**,\nconsumption **12.0 L/100 km**, and **128 g/km**.\nThe model estimates **197.5 g/km**, a gap of\n**69.5 g/km**. Check the source row, variant, fuel code,\nrating method and original report before deciding whether the record is wrong."
     }
    }
   ],
   "id": "co2-teaching-264"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 112. Review the arithmetic flags separately\n\nA rating-formula discrepancy is a different control from a model residual.\n",
   "id": "co2-teaching-265"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "rating_review = data.loc[data['combined_rating_review'],\n    ['source_row', 'make', 'model', 'fuel_type', 'city_l_100km', 'hwy_l_100km',\n     'combined_l_100km', 'combined_gap_l_100km']].copy()\ndisplay(rating_review.reindex(rating_review['combined_gap_l_100km'].abs().sort_values(ascending=False).index).head(10))\n",
   "execution_count": 136,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "      source_row   make  ... combined_l_100km combined_gap_l_100km\n2641        2641   FORD  ...             12.4                0.510\n3725        3725   FORD  ...             12.4                0.465\n4796        4796   FORD  ...             12.4                0.465\n5057        5057  LEXUS  ...             11.2               -0.415\n2584        2584   FORD  ...             11.2               -0.415\n6087        6087  LEXUS  ...             11.2               -0.415\n6086        6086  LEXUS  ...             11.2               -0.415\n7103        7103  LEXUS  ...             11.2               -0.415\n7104        7104  LEXUS  ...             11.2               -0.415\n5058        5058  LEXUS  ...             11.2               -0.415\n\n[10 rows x 8 columns]",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>source_row</th>\n      <th>make</th>\n      <th>model</th>\n      <th>fuel_type</th>\n      <th>city_l_100km</th>\n      <th>hwy_l_100km</th>\n      <th>combined_l_100km</th>\n      <th>combined_gap_l_100km</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2641</th>\n      <td>2641</td>\n      <td>FORD</td>\n      <td>TAURUS FFV AWD</td>\n      <td>X</td>\n      <td>13.6</td>\n      <td>9.8</td>\n      <td>12.4</td>\n      <td>0.510</td>\n    </tr>\n    <tr>\n      <th>3725</th>\n      <td>3725</td>\n      <td>FORD</td>\n      <td>TAURUS FFV AWD</td>\n      <td>X</td>\n      <td>13.6</td>\n      <td>9.9</td>\n      <td>12.4</td>\n      <td>0.465</td>\n    </tr>\n    <tr>\n      <th>4796</th>\n      <td>4796</td>\n      <td>FORD</td>\n      <td>TAURUS FFV AWD</td>\n      <td>X</td>\n      <td>13.6</td>\n      <td>9.9</td>\n      <td>12.4</td>\n      <td>0.465</td>\n    </tr>\n    <tr>\n      <th>5057</th>\n      <td>5057</td>\n      <td>LEXUS</td>\n      <td>RC 300 AWD</td>\n      <td>Z</td>\n      <td>13.1</td>\n      <td>9.8</td>\n      <td>11.2</td>\n      <td>-0.415</td>\n    </tr>\n    <tr>\n      <th>2584</th>\n      <td>2584</td>\n      <td>FORD</td>\n      <td>F-150 (Payload Pkg)</td>\n      <td>X</td>\n      <td>13.1</td>\n      <td>9.8</td>\n      <td>11.2</td>\n      <td>-0.415</td>\n    </tr>\n    <tr>\n      <th>6087</th>\n      <td>6087</td>\n      <td>LEXUS</td>\n      <td>RC 350 AWD</td>\n      <td>Z</td>\n      <td>13.1</td>\n      <td>9.8</td>\n      <td>11.2</td>\n      <td>-0.415</td>\n    </tr>\n    <tr>\n      <th>6086</th>\n      <td>6086</td>\n      <td>LEXUS</td>\n      <td>RC 300 AWD</td>\n      <td>Z</td>\n      <td>13.1</td>\n      <td>9.8</td>\n      <td>11.2</td>\n      <td>-0.415</td>\n    </tr>\n    <tr>\n      <th>7103</th>\n      <td>7103</td>\n      <td>LEXUS</td>\n      <td>RC 300 AWD</td>\n      <td>Z</td>\n      <td>13.1</td>\n      <td>9.8</td>\n      <td>11.2</td>\n      <td>-0.415</td>\n    </tr>\n    <tr>\n      <th>7104</th>\n      <td>7104</td>\n      <td>LEXUS</td>\n      <td>RC 350 AWD</td>\n      <td>Z</td>\n      <td>13.1</td>\n      <td>9.8</td>\n      <td>11.2</td>\n      <td>-0.415</td>\n    </tr>\n    <tr>\n      <th>5058</th>\n      <td>5058</td>\n      <td>LEXUS</td>\n      <td>RC 350 AWD</td>\n      <td>Z</td>\n      <td>13.1</td>\n      <td>9.8</td>\n      <td>11.2</td>\n      <td>-0.415</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-266"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### What an auditor would request next\n\n| Assertion / control | Evidence or procedure |\n| --- | --- |\n| Completeness | Reconcile vehicle quantities to production/import records, stock movements, invoices and levy returns |\n| Accuracy | Match exact local variant and test method to a recognised laboratory report; trace spreadsheet formulas |\n| Classification | Confirm legal vehicle category and exemptions instead of mapping a catalogue class automatically |\n| Cutoff and rate | Match the clearance/reporting date to the effective regulation and rate |\n| Levy calculation | Reperform taxable excess × rate × eligible quantity; reconcile to the return and ledger |\n| Emissions boundary | Distinguish owned-fleet activity, lifetime sold-product use and territorial national emissions |\n| Data/model governance | Retain raw input hash, changes, group splits, model settings, assumptions and reviewer sign-off |\n\nFor the SA manufacturer example, SARS requires a report from an NRCS-recognised testing\nlaboratory and retained evidence. A model estimate can support planning and exception\nscreening; it cannot replace that evidence. Sources: [SARS motor-vehicle policy](https://www.sars.gov.za/se-cd-02-environmental-levy-on-co2-emissions-new-motor-vehicles-manufactured-in-sa-external-policy/),\n[GHG Protocol Corporate Standard](https://ghgprotocol.org/sites/default/files/standards/ghg-protocol-revised.pdf).\n",
   "id": "co2-teaching-267"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 13. Country perspective: use sales weights and activity\n\nTo explore what vehicle types being sold could imply, begin with a **sales/registration-weighted\nrating**, not the mean of catalogue entries:\n\n$$\\bar E_{sales}=\\frac{\\sum_i N_i E_i}{\\sum_i N_i}.$$\n\nFor a new-sales cohort driven a full assumed year:\n\n$$\\text{Cohort tonnes CO₂/year}=\\frac{\\sum_i N_i K_i E_i}{1,000,000}.$$\n\n`N` is vehicles, `K` is kilometres per vehicle and `E` is g/km. The following mixes are **hypothetical**.\nClass-average intensities come from the unique Canadian catalogue view; volumes and mileage\ndo not. No actual national sales, emissions, or market response is estimated here.\n",
   "id": "co2-teaching-268"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 113. Declare two hypothetical sales mixes\n\nBoth mixes have 100,000 vehicles so a volume difference does not explain their comparison.\n",
   "id": "co2-teaching-269"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "sales_mix = pd.DataFrame({\n    'Baseline mix': [25000, 45000, 30000],\n    'More compact mix': [50000, 35000, 15000],\n}, index=['COMPACT', 'SUV - SMALL', 'SUV - STANDARD'])\nCOHORT_ANNUAL_KM = 20000\ndisplay(sales_mix)\n",
   "execution_count": 137,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                Baseline mix  More compact mix\nCOMPACT                25000             50000\nSUV - SMALL            45000             35000\nSUV - STANDARD         30000             15000",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Baseline mix</th>\n      <th>More compact mix</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>COMPACT</th>\n      <td>25000</td>\n      <td>50000</td>\n    </tr>\n    <tr>\n      <th>SUV - SMALL</th>\n      <td>45000</td>\n      <td>35000</td>\n    </tr>\n    <tr>\n      <th>SUV - STANDARD</th>\n      <td>30000</td>\n      <td>15000</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-270"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 114. Attach the proxy rating for each class\n\nIn a real analysis, join registrations to matching local year/variant ratings.\n",
   "id": "co2-teaching-271"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "sales_proxy_intensity = catalogue_classes.loc[sales_mix.index, 'mean_co2_g_km']\ndisplay(sales_proxy_intensity.round(2).to_frame('Catalogue proxy g/km'))\n",
   "execution_count": 138,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                Catalogue proxy g/km\nCOMPACT                       217.64\nSUV - SMALL                   236.44\nSUV - STANDARD                306.73",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Catalogue proxy g/km</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>COMPACT</th>\n      <td>217.64</td>\n    </tr>\n    <tr>\n      <th>SUV - SMALL</th>\n      <td>236.44</td>\n    </tr>\n    <tr>\n      <th>SUV - STANDARD</th>\n      <td>306.73</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-272"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 115. Compute a sales-weighted rating\n\nThe weights are assumed vehicle counts, not catalogue record frequencies.\n",
   "id": "co2-teaching-273"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "cohort_counts = sales_mix.sum(axis=0)\nsales_weighted_g_km = sales_mix.mul(sales_proxy_intensity, axis=0).sum(axis=0) / cohort_counts\ndisplay(sales_weighted_g_km.round(2).to_frame('Assumed sales-weighted g/km'))\n",
   "execution_count": 139,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                  Assumed sales-weighted g/km\nBaseline mix                           252.83\nMore compact mix                       237.59",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Assumed sales-weighted g/km</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Baseline mix</th>\n      <td>252.83</td>\n    </tr>\n    <tr>\n      <th>More compact mix</th>\n      <td>237.59</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-274"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 116. Convert to full-year cohort tonnes\n\nThis is a full-year equivalent for the new cohort, not the observed year-of-sale total.\n",
   "id": "co2-teaching-275"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "cohort_annual_tonnes = sales_weighted_g_km * cohort_counts * COHORT_ANNUAL_KM / 1_000_000\ndisplay(cohort_annual_tonnes.round(1).to_frame('Full-year cohort tailpipe tonnes'))\n",
   "execution_count": 140,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                  Full-year cohort tailpipe tonnes\nBaseline mix                              505658.5\nMore compact mix                          475171.1",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Full-year cohort tailpipe tonnes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Baseline mix</th>\n      <td>505658.5</td>\n    </tr>\n    <tr>\n      <th>More compact mix</th>\n      <td>475171.1</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-276"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 117. Compare the hypothetical mixes\n\nHold distance and cohort size fixed while changing the assumed mix.\n",
   "id": "co2-teaching-277"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "cohort_difference_tonnes = float(cohort_annual_tonnes['Baseline mix'] - cohort_annual_tonnes['More compact mix'])\ncohort_difference_pct = cohort_difference_tonnes / cohort_annual_tonnes['Baseline mix'] * 100\n",
   "execution_count": 141,
   "outputs": [],
   "id": "co2-teaching-278"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Policy interpretation:** The more-compact mix is **{cohort_difference_tonnes:,.0f} tonnes/year lower**\nfor this assumed new cohort (**{cohort_difference_pct:.1f}%**). This is a scenario consequence of\nthe selected ratings, 100,000 vehicles and {COHORT_ANNUAL_KM:,} km each. It is not Canada's or\nSouth Africa's actual national reduction, and it does not estimate atmospheric ppm.\"\"\")\n",
   "execution_count": 142,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Policy interpretation:** The more-compact mix is **30,487 tonnes/year lower**\nfor this assumed new cohort (**6.0%**). This is a scenario consequence of\nthe selected ratings, 100,000 vehicles and 20,000 km each. It is not Canada's or\nSouth Africa's actual national reduction, and it does not estimate atmospheric ppm."
     }
    }
   ],
   "id": "co2-teaching-279"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 118. Show the distance sensitivity\n\nA lower rating does not guarantee a lower total if the fleet drives more kilometres.\n",
   "id": "co2-teaching-280"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "mileage_values = pd.Series([10000, 20000, 30000], name='Assumed km per vehicle')\ndistance_sensitivity = pd.DataFrame({\n    mix: sales_weighted_g_km[mix] * cohort_counts[mix] * mileage_values / 1_000_000\n    for mix in sales_mix.columns})\ndistance_sensitivity.index = mileage_values\ndisplay(distance_sensitivity.round(1))\n",
   "execution_count": 143,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "                        Baseline mix  More compact mix\nAssumed km per vehicle                                \n10000                       252829.3          237585.5\n20000                       505658.5          475171.1\n30000                       758487.8          712756.6",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Baseline mix</th>\n      <th>More compact mix</th>\n    </tr>\n    <tr>\n      <th>Assumed km per vehicle</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>10000</th>\n      <td>252829.3</td>\n      <td>237585.5</td>\n    </tr>\n    <tr>\n      <th>20000</th>\n      <td>505658.5</td>\n      <td>475171.1</td>\n    </tr>\n    <tr>\n      <th>30000</th>\n      <td>758487.8</td>\n      <td>712756.6</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-281"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 119. Plot the sales-cohort scenario\n\nThe title and axes explicitly identify the hypothetical nature and units.\n",
   "id": "co2-teaching-282"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "fig, ax = plt.subplots(figsize=(8.3, 4.8))\nax.bar(['Baseline mix', 'More compact mix'], cohort_annual_tonnes.values / 1000,\n       color=[NAVY, SCARLET])\nax.set(title='Hypothetical new-sales cohort — full-year tailpipe scenario',\n       ylabel='Thousand tonnes CO₂ / year')\nshow_figure(fig, '10_sales_cohort_scenario')\n",
   "execution_count": 144,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "image/png": 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vvvtOFy5c0IABA7R69ep0SWSrvXv3auXKlWrfvr369++vKlWqKDo6Wr6+vpo5c6YuXryozz//XF988UW6und7jbvdmTNn9Mknn6hu3boaMmSIateurfj4eB07dixH18P8rGvXrnriiSd06dIldenSRZI0a9YsNWrUyFbmTr3h84tnn31WO3fu1JEjRxQYGKiHHnoow3LLly+XJJUtWzbdPWjr1q0aOXKkDMNQw4YN9eqrr6pOnTpyd3fX+fPntXDhQq1evVpff/21ypQpky4x6eLionbt2umxxx5TtWrV5O3trYIFCyosLEz79+/Xjz/+qLNnz2ro0KH6448/VKBAgUzPZ9iwYXJ3d9dnn32m5s2by83NTUePHk3XWz8jr732mvr3769BgwYpICBATz/9dLovRG6/H9atW1cdO3ZUnTp1VKpUKRUvXlxRUVEKDAzUTz/9JD8/P40dO1Y1a9ZM11vXLG+99ZacnZ01YcIEtWrVSi4uLvL399cXX3yhkJAQTZgwQVWrVs0wQZUdEydO1JUrV/TGG2+oa9euKlasmE6dOqUZM2Zo9+7d+v3331WxYkWNHDkyXd0vvvhCP/zwgywWi7p3765nn33Wllw6cuSIvvnmGx06dEhvvfWWli1blun7LzPjxo1TeHi4PDw8NHz4cLVp00YlSpRQUlKSLly4oICAAK1ZsyZdvfj4eA0ePNh2f+jUqZNeeOEFPfTQQ3JycrLdi1euXJmu7rFjxzRgwADdunVLPj4+GjRokBo0aKBChQrp0qVL8vX11YIFC/TLL7+oZMmSGj58eIax37hxQ8OGDVO5cuU0bNgwPfLII7JYLNqxY4c+/fRTRUVF6f3339eGDRvS9ba0Pvu2atVKlStXVsmSJeXh4aGrV69q165dmjdvnoKCgvT222/rl19+yfT1u379ut555x2VL19ew4cPt32JZH2eyA6z7mcAsskAgPvU8uXLDR8fH8PHx8fYsmWLER0dneV/W7ZssZVfvnx5uvaeeuopw8fHxxg6dGiGx0tOTjbatm1r+Pj4GO+88066/dZ9Pj4+xujRozNsY+rUqbYyf/zxR5p9YWFhxsMPP2z4+PgYL7zwgnHz5s109Q8ePGjUrl3b8PHxMYYNG5Zu/9dff234+PgYbdu2zfD4VsHBwbY4atasafj7+6crExMTY7Ro0cLw8fExRowYkW7/+vXrDR8fH6NGjRrG1q1bMzxOVFSU0bFjR8PHx8cYP358uv3W1+zrr7/OMt7Uv+vbXbp0yfaaPPfcc8aNGzcybSchISHL42RU/sUXXzR8fHyMadOmpduf+nXcvXu3XW0bRtrzatWqlREWFpauzN9//20rs3jx4nT7J0yYYPj4+BgtWrQwQkJCMjzOP//8Y3tvrVu3Ls2+9957z/bapXbjxg2jVq1aRo0aNYyxY8caPj4+xieffJKmzJ49e2yxnTt3zt7TN4YOHWr4+PgYPXr0sLvunSxatMj2t5CcnJxuf+/evQ0fHx9j1KhR6fbNnTvX8PHxMR555BHjn3/+ybD9ixcvGk2bNjV8fHyMOXPmpNnXo0cPw8fHx3jzzTfNOZlUBg4caPj4+Bi1a9c29u7dm2m529/rx44ds/2uhg0bluFr8ueff9rKTJo0Kd1+62vm4+NjjB07NsPjfvLJJ7b4bv9bNOMaN2rUKFsML730knHr1q1MX4PsXg/vNdm57mR1zUzN+nr27t3b7jayup/eya1bt2x/PxMnTsywTHR0tPHII48YPj4+xtSpU9Psu3nzptG8eXPDx8fH+M9//pPh+9kwDOPzzz83fHx8jEcffTTD91tWoqOjjQ4dOhg+Pj7Gr7/+mm5/6tenSZMmxsWLF+1q/3ZZXZPsNWLECMPHx8f473//m+H+rH532X1P1KtXzzh16lS6MpcvX7b9brp06ZJlG7dL/d728fExfH1905VJSEgw+vTpY7vOXL58Oc3+v//+26hevbrh4+Nj/PLLLxmef3x8vPHSSy8ZPj4+xmuvvZZhmczcuHHDFt/8+fPtqjtr1ixb3dmzZ2daLjk52UhMTEyzrVu3boaPj4/Rp0+fTK971vte7dq1jStXrqTZZ70e+vj4GN26dTNiYmLS1U99D8jsuS4rly9fNho1amT4+PgYfn5+6fanjqFjx45GVFRUpm1l9T4x434GwD733texAHAXBg8erAYNGmT53+DBg7Ns4/nnn5ckbd68WREREen27969WxcuXJCU0tsiMwUKFNCoUaMy3DdkyBBbrzdrTwyrVatWKT4+XpI0ZsyYDHtH1KtXz9brYsOGDRnGaa9OnTqpcePG6bZ7enrahmkeOXIk3f758+dLSulp1rp16wzbLlSokF5//XVJ0m+//ZZuSJIZFi9erISEBFksFk2aNEkFCxbMtGxmcx1lVd7aM3XXrl05ivNO3nzzzQyHx9euXdvW4+T230NsbKxtns+33nor06FHNWvWtJ3H7b0orEOYjx49qujoaNv2gIAAJSYmqkaNGurYsaOktEOjU/+7bNmyeuCBB7J3oqkkJiZKkkN6gvbo0UNSyvDcs2fP2lV3wYIFklLmr8tsCGTZsmXVu3dvSelfU+t5eXt723XcOwkKCtK2bdskSQMGDEjTU+12t7/Xly1bZts+duzYDOdN7dixo1q1aiUp5fqUephzah4eHnr33Xcz3Gedry0hIUHHjx9Ps8/sa9yoUaMy7M2K/M/Nzc02THf16tXpprCQUoY5x8bGymKxpOtt+PvvvyssLEweHh768MMPM50HeOjQofL09FR4eLjdc8B6eXnZhhzf6fo/YMAAh83Fezesr+3OnTsddoxevXplOMS0dOnStvt+YGCgDh06dFft16lTx3YdT83FxUVjxoyRlHKduX0qloULF8owDDVo0MD2XHc7V1dXjRgxQlJKz9eoqKhsx5V66gV7r/HWe0uDBg00cODATMtZLJY0Pdr37NmjY8eOyWKxaPz48Zle91566SXbqJF169Zl2v4777yT4TyIHTp0sPXsy+i5705Kly5t66F6p7+Zt956664XnzLzfgYge0gsAkA2de3aVQUKFFBCQoJWr16dbr81EfjAAw+oSZMmmbbTuHHjTBcecHNzsw2zPnjwYJpE2759+ySlzD+V1XyEnTt3lpTycGvPsJHMWB+8MlK5cmVJUlhYWJrtcXFxtmM/+uijiomJyfS/atWqSUqZg+f2OeHMYH14feSRR1SlSpW7amP9+vV666231KFDBz3yyCOqXr267b/x48dLkm1RG0fJLDkrZf57OHjwoG1usSZNmmT5e8gsOWlNLFonZbfavXu3pJQhzg0bNpSrq6sCAwPTJHpyMr+iJFvSbtu2bVqwYEG6edLu5MyZM7bFBxo3bqxatWrZfm+PPPKIrZw9v7tz587ZvkBo1qxZlq+pj4+PpJS5Oq0Js9Tn5evrq9WrV6fZlxOpP6hl9IE7K9brS8OGDbP8MGy9vly/fl0nT57MsEy9evUyHVZmfa9KKfN+ZRSDGde4okWLql69epm2gfzvueeek5Qyt9uWLVvS7bfec5s0aZJuSHzq676Tk1Omf6PJycm292RGiZLExEStWLFCr7/+uh577DHVq1cvzfV/zpw5ku58DXnsscfsOncz+Pn5adSoUXryySdVv3591ahRwxa39YvU0NDQNF8Ymcn6hdOd9u3fv9/09qtXr65KlSpl2L71vdGiRYtsPZskJyenm584K0WKFLEtJjZ16lTbde1OTp06patXr0qy//ptPacHHnhApUqVyvScYmNjVaNGDUmZJwbd3NwyvWc7OTnpwQcflJT+ecMqLi5OixYt0oABA9SyZUvVqVMnzd+MNaGZ1d+MxWLJ8pnnTsy8nwHIHuZYBPCvYF0JMyu3r5x8uyJFiujxxx/Xb7/9puXLl6tfv362fdHR0Vq/fr2klAfCrFZJvtMk0db9N27c0PXr121JyIsXL0qS7WE3M6nnArImQHIiq1URrfPGxcXFpdkeHBxs62Hy/vvv6/3338/WsSIiIu6qZ1tWrMnKO02un5G4uDgNHTo0Wz1ZUk9y7ghZPRxn9ns4c+aM7eesPoSldnsPsDJlyujBBx/UuXPntHv3bttE9qmThtbVqffv36/du3erc+fOunnzpm2eqNv/9uLj4zPsgSSl9Oi19qbr16+ffH19denSJX366aeaMmWKHnnkETVq1EhNmjRRo0aNMu1lunjxYn322WeZHic1e353qV/T1NeArCQnJ+v69eu2npdDhw7Vhg0bFB0drXfffVcfffSRGjZsqIYNG6pZs2aqV6/eHVdaz4j1ve7h4WH7UJ1d2b2+pN5/4cIF24fU1LLzXpWUblJ+M69xmc296Ag3b9506AIx7u7udzXfZn4VFxeXae8gT09P23u/atWqql+/vg4cOKDly5enWZDk7NmztoRRRou2WP9O/fz8sr2Q2e3XvvDwcA0aNChbSaU7XUNy8/2YlJSkMWPGpFsgLjPR0dFZ9uS/W1k965QuXVqFChXSjRs37vo55U5fFFatWlVnz55N035MTIxtAbTp06dr+vTp2TqWvaM/Ro0apREjRujs2bN6+eWXVapUKTVp0kQNGzZUixYtMrw+nz9/3vZzRtfVrFiTdOfOnbvr97tV8eLFs5zD2noNz2hRlbNnz2rAgAEKCQm54/Gz+pspVqxYjt6TZt7PAGQPiUUAsMNzzz2n3377TSdPntSRI0dsvWp+++033bx5U05OTrahfpnJaHhJal5eXrafY2JibInFmJiYdPuzUz+n7mYRg7tNst26deuu6mXF2hvjTq9bRiZNmmRLKvbo0UNPPvmkqlSpokKFCtmGGq1atUoff/yxw1eevZvEwt38HjLqPde0aVOdO3fOlkyMiorSsWPH5OzsbBtu27RpU+3fv1979uxR586ddeDAAVtS7/bE4rhx4zL90GvtYShJhQsX1q+//qpvv/1Wa9asUVRUlPbs2aM9e/bom2++UYkSJTRw4ED169cvzfv04MGDGj9+vAzDUPXq1dWnTx/bIgYFChSQxWKxLeggya7fnRnv7YoVK2rFihWaMWOG/vrrL8XExGjbtm22Yczly5fXsGHD7O61kpP3upnXl+y+V2+f+sDMGFInMB1t0KBB8vf3d1j72fli7F7SpUuXTJNJt690+9xzz+nAgQPavn27QkND000VUqhQIT3xxBPp2rmbXni3X/tGjRqlo0ePysXFRS+99JLatWunBx54QAULFrQlXr777jvNmjXrjteQ3Hw/zpkzx3Z9bdu2rXr27Klq1aqpWLFitvvWvn37bL0WrVMzmO1Ozzqenp66cePGXT+n3Ok6YT1+6vbvtnemvc8mTz75pObNm6fvvvtOe/bsUWhoqH7//Xf9/vvvkqT69etr1KhRql+/foax2XsNN+teL9399TspKUnDhg1TSEiIPD091bdvX7Vs2VIVKlSQl5eXrd1x48bpt99+y/JvJqd/L3n1vAz8m5FYBAA7NG3a1NZ7a/ny5bbEoq+vr6SUoTVlypTJso07DedM/XCT+qHH+vPd1s9NqY87c+ZMtWvXLk/ikKSCBQsqMjLS7ofGuLg424ezQYMG6Z133smwnFnDWB0h9Qe7gICAu+4B0LRpUy1dulTHjh1TVFSU/P39lZycrHr16tnabNasmWbOnGkbIm39f4UKFTKd2zE7SpYsqXHjxmnMmDH6559/dOjQIe3evVvbt29XeHi4Jk+erIsXL2rs2LG2OosXL5ZhGKpQoYJ++eWXDD+kXL9+/a7iSf2arl69+q5XVH3ggQf0+eefa8KECTpy5IgOHjwoPz8/+fn56cKFCxo9erQiIyP16quvZrtN6+/ibj4geXl5KSoq6o7Xl9T7zb6+3EvXuHtddnvEOirplF2dOnXSp59+qpiYGK1cuVKDBg1SUlKSbVXcLl26ZLjStfXvtGPHjtnulZZacHCwtm/fLkkaO3asXnrppQzL3d5LPD9YvHixpJRhnlOnTs2wTG7ct2JjY7OcI8/6d363f8N3us5l1H7q6/fYsWPVp0+fuzp2djRr1kzNmjVTVFSUDhw4oAMHDmjbtm06evSoDhw4oD59+uinn36yTcuR+v5s7zXcel61a9e2PY/mNn9/f9tw4mnTpmU6lNneKU3uRn64nwH/NiQWAcAOFotFzz77rL744gv9/vvveu+99xQSEmKbfDyrRVusTp8+na39BQsWVJEiRWzby5cvr2PHjikwMDDL+qn35yShkxPly5eXk5OTkpOT0wzvyQsPPPCAIiMjdezYMbvqnTlzxtZLwbqwSUZOnDiRo/gcKfXwu+Dg4LsaDi7JNmdocnKy/P39M5w7sX79+ipQoIDOnj2rK1euZDm/4qRJkzRp0iS7YnB2dladOnVUp04d9e7dW9euXdMbb7yhAwcOaNGiRRoyZIiKFy8uSbbfdfv27TPt+XC38ynd/prebWLRys3NzTYMesCAAbp48aL69eunc+fOaebMmerbt2+2e5BYpxGIi4vT2bNn7RoOXb58eUVFReXp9eVeusaltnDhwrwOwW6pF8a5efNmhsk5SbY538y0adOmbJf19PTUU089pV9++UW+vr4aNGiQduzYYYsrs3tuxYoVdezYsbu+/6S+X2R1/c9v87JFRkbq0qVLklKSrpnJjfvW6dOn08xlm9qVK1dsvezu9m849bQUmR3/9vYLFSqkokWLKjIyMteeTQoXLqw2bdqoTZs2GjFihPz8/PTaa6/p1q1b+v777zVz5kxJss1dKKXMy1u3bt1sH8N6XwoJCVFycvJdjTTJKetiXEWKFMlyfsTc+JvJD/cz4N+GxVsAwE49evSQi4uLoqKitH79etuqu8WKFctWz7y9e/cqMjIyw33x8fHavHmzpJRETepeJdYhpyEhIVnO+WSdGNvZ2TnNMBvp/1eCdfSw3UKFCtkWTvjjjz/uuh0z4rWuQHjo0CG7Vv9N3aMjs+PHxMRo48aNdx2bozVp0sQ29M06BOtueHt72xY42L17d5qFW6wKFChg+xC5adMm/f3335LufuGWOylWrJheeeUVSSkJz3Pnztn2WX93Wa3yePtKodnl4+Njm0MwJ69pZsqVK2dbqfT69esKDw/Pdl3re11StudXs7JeX/bv35/ppPzS/19fihQpYlucxixmXOPskVvXw/wo9UrrmS2iEBUVpcOHD+dWSJmyLuJy5swZ23yLUsoCHZkt8tOyZUtJKQm0U6dO2X3M7Fz/L168mO2FOcyQnfdr6rgzu/4lJSXpt99+Mze4DPz111/Z2pfdOQFvZ53XOiMnTpyw3e9vb9/63li/fn2ejDh49NFHbffO1MnRqlWr2ua0tvbIzS7rOV2/ft3u1c3NYn0ts3p/BgQEZGv+xZzKD/cz4N+GxCIA2KlUqVK2BSx++eUXrVmzRpL09NNP25I4Wbl165YmT56c4b6ZM2faVkq9fUL6rl272tr/9NNPM3wgPnz4sJYtWyZJ6tChg60Hl1WxYsUkpUza7eghbtYhnIcOHdL333+fZVnDMDLsfWCNNye9Znr16iVXV1clJydr9OjRWQ4xSv2apJ7nK6PkoWEYmjBhwl0Pqc0NBQsWtCWp5s+fb0sIZubWrVuZzn1mTRBu3LhRgYGBcnV1TfeBzVpm9uzZtvkVUycf7XWn3r2pVxFPvdK6tffG9u3bM/w78fPzu+vhYhaLxfbeXrt2bYYrxKeWlJSUJukp3fm8rD1pnJ2dsxxKeLtKlSrZVp/98ccfs1wV/va/f2vPr4SEBE2YMCHd/FmStGHDBm3dulVSyvXJ7F4xZlzj7JGb18P8pmbNmrZ5AjNLQn/++ef5YqhvnTp1bIsqzJkzx9bjMav5jLt27aqSJUvKMAyNHj36jnPQBQcHp3m/pe6ZnNH1PyEhQWPHjs3VpHR27oclSpSwDYvN7Euvb7/99o6rWJth8eLFGV7rrl69qu+++05SykJMd7t6++HDhzNMwCUmJurTTz+VJLm6uqpbt25p9lsX3bIuCpbVF1DSna/Xt4uIiNC1a9cy3Z+UlGS7z1p/p1bWL8sCAgI0d+7cTNswDCPNe69Fixa23vMff/yxbYGazISFhZn+7GL9m4mOjs7wWSM6Oloff/yxqcfMTH64nwH/NvwFAcBdsPag8Pf3tyUCszMMWkpJWPn6+mrkyJE6cuSIIiMjdeLECX388cf69ttvJaV82/rkk0+mqVeiRAkNGzZMUspDZ9++fbVz505FRETowoULWrhwofr376+EhAQVLFhQ//3vf9Md++GHH5aU8s3yzJkzFR4ersTERCUmJpr+AemJJ57Q008/LUn68ssvNXjwYG3cuFGXL1/WjRs3dOnSJfn5+enrr79Wp06dMky2WuPduHGj9uzZo9jYWFu8d/owYFW6dGmNHj1aknTgwAE988wz8vX1VUhIiKKiohQcHKw//vhDw4cP16xZs2z1rKs4StKsWbM0Y8YMnTlzRteuXdPevXv1+uuvy9fX946rDua1kSNHqmrVqoqPj9eAAQP08ccfa9++fQoPD9f169d17tw5bdiwQR9//LHatGlj+wb/dtak4cWLF2UYhurWrZtumLG1jPVDU6VKlbJcVfxOBg0apOeff96WJAsNDdW1a9d08uRJffPNN/r6668lpcwrZe1RKaXMLSalrFD52muvKSAgQBERETpz5oxmzJih119/PU15e/Xp00dNmjSRYRh699139c4772jnzp26evWqoqKiFBISoq1bt2ry5Mlq37695s+fn6Z+ly5d1K9fPy1atEh///23wsPDFRERoaNHj2rixIlaunSppKyHcmdm3LhxKlq0qOLj49WvXz998cUX+ueffxQZGamwsDDt3btXX3zxRbo542rUqKEXX3xRUkrC9PXXX1dAQIAiIyN19uxZffPNNxo5cqQkqWzZsnrjjTfu9uXLlBnXOHvk5vUwvylYsKBt0ZMFCxZoxowZCgkJUWRkpAICAjR06FD9+uuvab5gyUvWe+769euVkJAgV1dXde3aNdPyHh4emjhxopydnXXkyBF169ZNCxcu1KlTp2w9gY8ePapffvlFgwcP1hNPPJFm8YyHH37Ylij59NNPtWDBAp0/f14RERHatm2b+vTpo507d+bq9d/6fg0ICLCtKn/7/dDZ2dn2e125cqUmTJigkydP6tq1azp8+LBGjx6tGTNm5ErcJUqUUJ8+ffTrr7/qypUrCgsL09q1a9WrVy9bL7L33nvvrtuvUKGCxo4dq2nTpikoKMh2bx4wYIBtKo4BAwakuwfVqVNHr7/+uiRpyZIl6tWrl/744w/bM8GVK1e0b98+zZo1Sz179tRbb71lV1yBgYF67LHH9J///Ee//fabTp8+rcjISF25ckV+fn4aMmSIrRft7cPV+/bta+v5P2nSJL399tvavXu3wsPDde3aNR09elRz587V008/bRvyLqV84TV58mR5eHgoJCRE3bp10/fff6/jx48rMjJSEREROnnypFauXKm33npLbdu2NX0oeKtWrWxfhP3nP//RypUrdfHiRYWGhmrdunV6/vnnFRgYmKN7b3blh/sZ8G/DHIsAcBdat26t0qVL274Vfvjhh7M911r37t0VHBysVatWZThM+KGHHtK0adMynFx/0KBBunbtmi3R0r9//3RlihYtqm+//TbDD4QPP/ywmjRpIn9/f82YMUMzZsyw7Stfvrxdc19lx8SJE1WkSBH99NNP2rp1q+3b4Yxk9EGnV69eWrZsmSIjI9W3b980+4YOHWpLQtxJ7969lZCQoClTpigoKCjTDzMPPfRQmn9/9NFH6tWrlyIjIzV9+vR0iwB07txZLVq00JgxY7IVR14oWLCgFixYoJEjR8rf31+LFy+2Te6fEWsvptvdPqQ5o56I9erVk6enp21CdDOGQR86dMg2h2lGypcvry+++CLNtu7du+uvv/7S5s2btWvXLu3atSvN/jJlymj69Onq1KnTXcXk6uqqmTNn6v3339e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"
     }
    }
   ],
   "id": "co2-teaching-283"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### What is required for a national estimate?\n\n| Missing evidence | Why it changes the answer |\n| --- | --- |\n| Actual sales/registrations by year, class and fuel | Establishes market weights and change over time |\n| Exact year and variant ratings | Prevents joining historical or mismatched model entries |\n| In-use vehicle stock, survival and scrappage | New sales are only part of the fleet operating this year |\n| Distance and real-world consumption by type | Converts intensity to activity; accounts for operating conditions |\n| Road-transport fuel sales and carbon factors | Reconciles totals with the national fuel-based inventory |\n| EV/PHEV, heavy vehicles and other missing types | Completes fleet coverage and distinguishes electricity from tailpipe activity |\n| Imports/exports, territory and inventory boundary | Aligns activity with the country and period being counted |\n\nStatistics Canada publishes registration data by fuel/type/geography; matching classifications\nand years would be a next step, not an automatic join to this file. IPCC guidance estimates road\nCO₂ primarily from fuel consumption and carbon content and recommends reconciling activity\ninformation. Biogenic carbon needs separate treatment. A sales-cohort scenario supplements\nthat work; it is not a replacement national inventory or a ppm model.\n\nSources: [Statistics Canada registration table 20-10-0025-01](https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=2010002501),\n[IPCC 2006 Guidelines, Volume 2 Chapter 3](https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/2_Volume2/V2_3_Ch3_Mobile_Combustion.pdf).\n",
   "id": "co2-teaching-284"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 14. Summarise the value without overstating it\n\nThe strongest portfolio evidence is the combination of reproducible code and clear judgement:\nrecognise the data's limits, choose validation that reduces repeated-model overlap, compare\nagainst simple alternatives, and connect metrics to a specific decision.\n\nThis notebook estimates **recorded vehicle ratings**. It does not certify new designs, prove\nfraud, calculate a named company's tax bill, establish premium-fuel causality, or measure a\ncountry's atmospheric CO₂ concentration. Current local certificates, activity and market data\nwould be needed to use the workflow operationally.\n",
   "id": "co2-teaching-285"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "interpretation"
   },
   "source": "explain(f\"\"\"**Verified model summary:** The selected fuel-known estimator is **{selected_name}**, with\nheld-out **MAE {selected_metrics['MAE_g_km']:.2f} g/km**, **RMSE {selected_metrics['RMSE_g_km']:.2f} g/km**\nand **R² {selected_metrics['R2']:.4f}** across **{len(test):,} records** in **{test['group_id'].nunique():,} groups**.\nThe corrected engine-and-cylinders comparator has **RMSE {two_metrics['RMSE_g_km']:.2f} g/km**.\n\n**Business value:** compare rated operating scenarios and jurisdiction-specific fiscal sensitivity.\n**Audit value:** prioritise traceable exceptions and require evidence before making conclusions.\n**Policy value:** demonstrate how sales weights and vehicle activity affect a new-cohort emissions scenario.\"\"\")\n",
   "execution_count": 145,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/markdown": "**Verified model summary:** The selected fuel-known estimator is **Fuel-interaction Ridge**, with\nheld-out **MAE 2.23 g/km**, **RMSE 3.33 g/km**\nand **R² 0.9969** across **1,466 records** in **410 groups**.\nThe corrected engine-and-cylinders comparator has **RMSE 28.71 g/km**.\n\n**Business value:** compare rated operating scenarios and jurisdiction-specific fiscal sensitivity.\n**Audit value:** prioritise traceable exceptions and require evidence before making conclusions.\n**Policy value:** demonstrate how sales weights and vehicle activity affect a new-cohort emissions scenario."
     }
    }
   ],
   "id": "co2-teaching-286"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Learning exercises\n\n1. Explain why a model can have high R² but still be unsuitable for certifying a tax rating.\n2. Change the fleet mileage assumption. Which outputs double if mileage doubles?\n3. Change only the fuel-price assumption. Why do estimated tonnes stay the same?\n4. Compare retained and removed repeated rows. What source information would settle the policy?\n5. Identify why a low-emission catalogue mean is different from a sales-weighted mean.\n6. Find the largest audit exception and list the original documents needed to resolve it.\n7. Explain why Scope 1, lifetime sold-product use and a national territorial total cannot be added blindly.\n\nFor your website, lead with the question and verified results. Show the fuel relationship,\nvalidation comparison, residual plot and one labelled business scenario. Make full code available\nthrough this notebook or GitHub, and keep assumptions next to their scenario results.\n",
   "id": "co2-teaching-287"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## 15. Optional exports and run record\n\n`SAVE_OUTPUTS=False` prevents the notebook from writing charts and tables. Set it to `True`\nbefore **Run All** to save the figures and the following traceable result files locally. Publishing\nthe notebook does not automatically update a hosted website or tax-rate snapshot.\n",
   "id": "co2-teaching-288"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 120. Record the runtime versions\n\nVersions, input hash and settings make a result easier to reproduce.\n",
   "id": "co2-teaching-289"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "versions = {'python': platform.python_version(), 'numpy': np.__version__,\n            'pandas': pd.__version__, 'matplotlib': matplotlib.__version__,\n            'seaborn': sns.__version__, 'scikit-learn': sklearn.__version__}\ndisplay(pd.Series(versions).to_frame('Version'))\n",
   "execution_count": 146,
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {},
     "data": {
      "text/plain": "              Version\npython        3.12.14\nnumpy           2.3.5\npandas          2.2.3\nmatplotlib     3.10.8\nseaborn        0.13.2\nscikit-learn    1.8.0",
      "text/html": "<table class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Version</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>python</th>\n      <td>3.12.14</td>\n    </tr>\n    <tr>\n      <th>numpy</th>\n      <td>2.3.5</td>\n    </tr>\n    <tr>\n      <th>pandas</th>\n      <td>2.2.3</td>\n    </tr>\n    <tr>\n      <th>matplotlib</th>\n      <td>3.10.8</td>\n    </tr>\n    <tr>\n      <th>seaborn</th>\n      <td>0.13.2</td>\n    </tr>\n    <tr>\n      <th>scikit-learn</th>\n      <td>1.8.0</td>\n    </tr>\n  </tbody>\n</table>"
     }
    }
   ],
   "id": "co2-teaching-290"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 121. Prepare a model-run record\n\nKeep input provenance and selection information with the measured results.\n",
   "id": "co2-teaching-291"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "run_record = {\n    'source_url': DATASET_URL, 'source_access_date': SOURCE_ACCESS_DATE,\n    'source_licence': SOURCE_LICENCE, 'source_sha256': source_sha256,\n    'versions': versions, 'seed': SEED, 'test_group_share': TEST_GROUP_SHARE,\n    'cv_folds': CV_FOLDS, 'selected_model': selected_name,\n    'specifications_model': selected_spec_name, 'test_records': len(test),\n    'test_groups': int(test['group_id'].nunique()), 'heldout_metrics': selected_metrics,\n    'tax_sources_checked': SOURCE_CHECKED_DATE,\n}\n",
   "execution_count": 147,
   "outputs": [],
   "id": "co2-teaching-292"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 122. Export the principal result tables if enabled\n\nThese exports have clear names and do not alter the original CSV.\n",
   "id": "co2-teaching-293"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "if SAVE_OUTPUTS:\n    OUTPUT_DIR.mkdir(exist_ok=True)\n    comparison.to_csv(OUTPUT_DIR / 'grouped_validation.csv')\n    heldout_results.to_csv(OUTPUT_DIR / 'heldout_metrics.csv')\n    test_results.to_csv(OUTPUT_DIR / 'heldout_predictions.csv', index=False)\n    fuel_errors.to_csv(OUTPUT_DIR / 'heldout_fuel_errors.csv')\n    class_errors.to_csv(OUTPUT_DIR / 'heldout_class_errors.csv')\n    audit_review.to_csv(OUTPUT_DIR / 'audit_review_queue.csv', index=False)\n",
   "execution_count": 148,
   "outputs": [],
   "id": "co2-teaching-294"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Step 123. Export the scenario tables if enabled\n\nEach scenario retains its own units and assumptions.\n",
   "id": "co2-teaching-295"
  },
  {
   "cell_type": "code",
   "metadata": {
    "purpose": "analysis"
   },
   "source": "if SAVE_OUTPUTS:\n    fleet_scenario.to_csv(OUTPUT_DIR / 'illustrative_fleet_scenario.csv')\n    sa_tax_examples.to_csv(OUTPUT_DIR / 'illustrative_sa_levy.csv', index=False)\n    canada_examples.to_csv(OUTPUT_DIR / 'illustrative_canadian_levy.csv', index=False)\n    sales_mix.to_csv(OUTPUT_DIR / 'hypothetical_sales_mix.csv')\n    cohort_annual_tonnes.to_csv(OUTPUT_DIR / 'hypothetical_cohort_tonnes.csv')\n    (OUTPUT_DIR / 'run_record.json').write_text(json.dumps(run_record, indent=2), encoding='utf-8')\n",
   "execution_count": 149,
   "outputs": [],
   "id": "co2-teaching-296"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## References and provenance\n\n**Dataset supplied by the user:** `CO2 Emissions_Canada.csv`.\nThe v3 notebook cites [Dovudkhon's Kaggle dataset](https://www.kaggle.com/datasets/karomatovdovudkhon/co2-emissions-canada),\naccessed 1 April 2024. The precise year range and licence are not independently verified here;\nmodel year is absent from the supplied CSV. Official NRCan data is background context, not a\nclaim that this local file was downloaded directly from the government portal.\n\n**Technical sources**\n\n- [scikit-learn: linear regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html).\n- [scikit-learn: grouped cross-validation](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-iterators-for-grouped-data).\n- [scikit-learn: preprocessing and leakage](https://scikit-learn.org/stable/common_pitfalls.html).\n- [scikit-learn: regression metrics and baselines](https://scikit-learn.org/stable/modules/model_evaluation.html).\n- [NRCan: personal vehicles and gasoline CO₂](https://natural-resources.canada.ca/energy-efficiency/transportation-energy-efficiency/personal-vehicles).\n\n**Business, tax, audit and national context — checked 4 October 2026**\n\n- [SARS: current Customs and Excise schedules](https://www.sars.gov.za/legal-counsel/primary-legislation/schedules-to-the-customs-and-excise-act-1964/), Schedule 1 Part 3D listing.\n- [SARS: Schedule 1 Part 3D](https://www.sars.gov.za/legal-lprim-ce-sch1p3d-schedule-no-1-part-3d/).\n- [SARS: motor-vehicle emissions external policy](https://www.sars.gov.za/se-cd-02-environmental-levy-on-co2-emissions-new-motor-vehicles-manufactured-in-sa-external-policy/), effective 30 June 2025.\n- [CRA: current rates of excise taxes](https://www.canada.ca/en/revenue-agency/services/forms-publications/publications/currate/current-rates-excise-taxes.html), Green Levy bands and exclusions.\n- [GHG Protocol: Corporate Standard](https://ghgprotocol.org/sites/default/files/standards/ghg-protocol-revised.pdf), reporting boundaries and verification context.\n- [GHG Protocol: Scope 3 calculation guidance](https://ghgprotocol.org/sites/default/files/2023-03/Scope3_Calculation_Guidance_0%5B1%5D.pdf), Category 11, expected use phase of sold products.\n- [IPCC: 2006 Guidelines, Volume 2 Chapter 3](https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/2_Volume2/V2_3_Ch3_Mobile_Combustion.pdf), road-transport methods and reconciliation.\n- [Statistics Canada: table 20-10-0025-01](https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=2010002501), new motor vehicle registrations.\n\nThe notebook proposes potential applications from those sources and the measured model\nresults. It does not claim that the proposed workflow has been validated for statutory tax,\nexternal assurance or national emissions accounting.\n",
   "id": "co2-teaching-297"
  }
 ]
}