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   "source": [
    "# Chapter Exercise\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea60c189",
   "metadata": {},
   "source": [
    "Apply the key ideas from this chapter in the connected task below. Work in your own notebook or local project. Keep your implementation, reasoning, and verification results together.\n"
   ]
  },
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   "id": "e4d25e1a",
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    "## Choose a regression model honestly\n",
    "\n",
    "1. Reuse the chapter's real housing data and define features, target, and a held-out evaluation set before fitting.\n",
    "\n",
    "2. Compare a baseline, a simple regression, and a multiple-feature model using the same evaluation metric and split. Select features using validation rather than the final test set.\n",
    "\n",
    "3. Report test performance, inspect coefficients and correlated predictors, and explain overfitting and interpretability. Record data/split settings and state what the fitted relationship does not establish.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7840910",
   "metadata": {},
   "source": [
    "Return to the chapter lessons as needed: [Regression with scikit-learn](1301-linear-regression.ipynb), [Multiple Regression](1302-multiple-regression.ipynb), [Choosing Features and Avoiding Overfitting](1303-choosing-features.ipynb).\n"
   ]
  }
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