{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ccd1edc5",
   "metadata": {},
   "source": [
    "### In order to create a regression model using the Linnerud dataset, you first load the data, then select one of the exercise variables (for example, sit-ups) as the input feature and one of the physiological variables (for example, waistline) as the output variable, fit a regression model such as LinearRegression, and then assess it and, if desired, plot the relationship; you carry out this procedure for each combination of exercise and physiological variables that you are interested in.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fc227b7",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_linnerud\n",
    "\n",
    "data = load_linnerud(as_frame=True)\n",
    "X = data.data        # exercises\n",
    "y = data.target      # physiological measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fad548fe",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [],
   "source": [
    "X_situps = X[[\"Situps\"]]   # 2D DataFrame\n",
    "y_waist = y[\"Waist\"]       # Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "325f344e",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_situps, y_waist, test_size=0.25, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "930b8b74",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71cee5ee",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2533995",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.scatter(X_situps, y_waist, label=\"Data\")\n",
    "x_vals = np.linspace(X_situps.min(), X_situps.max(), 100).reshape(-1, 1)\n",
    "plt.plot(x_vals, model.predict(x_vals), color=\"red\", label=\"Fit\")\n",
    "plt.xlabel(\"Situps\")\n",
    "plt.ylabel(\"Waist\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
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 "nbformat_minor": 5
}