4_Regresión Lineal - Predicción del coste de un incidente de seguridad.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/drive/1tnOvFNgXiLtktiSsltTr0s308LZSsEPJ\">Abre este Jupyter en Google Colab</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regresión Lineal: Coste de un incidente de seguridad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este ejercicio se explican los fundamentos básicos de la regresión lineal aplicada a un caso de uso sencillo relacionado con la Ciberseguridad."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enunciado del ejercicio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El ejercicio consiste en predecir el coste de un incidente de seguridad en base al número de equipos que se han visto afectados. El conjunto de datos es generado de manera aleatoria."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (2.2.0)\n",
      "Requirement already satisfied: numpy<2,>=1.23.2 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from pandas) (1.26.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from pandas) (2023.4)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Requirement already satisfied: numpy in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (1.26.3)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (3.8.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from matplotlib) (4.47.2)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from matplotlib) (1.4.5)\n",
      "Requirement already satisfied: numpy<2,>=1.21 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from matplotlib) (1.26.3)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from matplotlib) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from matplotlib) (3.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (1.4.0)\n",
      "Requirement already satisfied: numpy<2.0,>=1.19.5 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from scikit-learn) (1.26.3)\n",
      "Requirement already satisfied: scipy>=1.6.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from scikit-learn) (1.12.0)\n",
      "Requirement already satisfied: joblib>=1.2.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from scikit-learn) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from scikit-learn) (3.2.0)\n"
     ]
    }
   ],
   "source": [
    "# Instalacion de librerías externas\n",
    "!pip install pandas\n",
    "!pip install numpy\n",
    "!pip install matplotlib\n",
    "!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Generación del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La longitud del conjunto de datos es: 100\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "X = 2 * np.random.rand(100, 1)\n",
    "y = 4 + 3 * X + np.random.randn(100, 1)\n",
    "\n",
    "print(\"La longitud del conjunto de datos es:\", len(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Visualización del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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OpfE4yzqDvLsrS5sRdexqC/io2HfvLOsM7pQReSpAYRj5ABwW5HfvTnBiBCBIo0BRv99ACiMfgI+C+u7dCU6NAARpFIg8FaBwjHwADku98//gB6V9+4Lz7t2J0YoojgBE9bqB/hj5AHzS953/Jz8pvfCC/R2QU6MVUR0BcHKki+JqiApGPgCHBPEdsJNtDuL1O2m4eSqtrftrnJSWJgOaoCyRBiRGPgBfBPGdv5NtjnKuizS8PBWKqyFqRvjdACAsUpVK+7/zt7msudNtZhv64uQKAnkOEUaMfAAOCeI7fzfaHKSVKrYI+n48QKHI+QAcFqQaFSlBbHPYtLYmp1p6evYHgeR8IEgK6b8JPoAICGPpby+vyavHIghEkJFwCiAtjKW/vbwmLx+LKStEBSMfQB6COnIQxuWvXl5TGJ8/wC2MfAAOCvLIQT5LaYNW2MrLJc1BXD4NBAHBB5BD0OsvDLWKIldgZWtQ4uXKEFahAO4g+AByCPo731xLaXMFVjaP9ni5pDmIy6eBICDnA8jB6Tl/v3JHsq2iaGtLBhf9LV8uNTfbn+fg5coQVqEAQyPnA3CIk+98/RxNyLaKItuUgiR95SvBGO3xcmUIq1AAZzHyAeRhuO98bV010Xczs1xsaCsAuxXSf7O3C5CHWGx4Ha+te3c0NUkVFdLcuQO/l9rzhTwHAE4j+AA8YPOmc9OnZ2/bpk3Svn3kOQBwHjkfgAdsXjUxWNtOOok8BwDuIOcD8JDNqyZsbVtQq8sCUUPOB2Cp4eaOuMnGtvVNiC0tTY7QsNMrEHxMuwCwUtCrywIYHMEHUCBby46HTdCrywIYHMEHUIDBCoURkDjPiX1VuC+AnQg+gDwNNg2wdKkzlUvpKDMNd4VQ30Dx0EOlG290r60ACsNqFyBPg+2Fkq1GRqHVQEmsHFwxq3CyVZSVkgHI5Zc730YA7O0CuCLbNED/wEMqPC+BxMrcitlXJVu+iCQtXMjzCtiA4APIU7ZpgMWLh5+XQGKl8+rqpJKSgcd7e3leARsQfAAFaGpKTqm0tSU/X3HF8CuXOpFYiUyxmLRkycDjPK+AHSgyBhSofzGupiapsbH46qCpEZX585MjHjaVXg+yK65Ijn4sXMgGeYBtSDgFLGFrefO+gljqPAjPKxAGviecvvzyyzr//PN18MEHa8yYMTr22GP17LPPuvFQQFFsXNZaTGKllwarcWI7259XG1+LgNscDz7efPNNnXLKKRo5cqR+97vf6W9/+5t++MMf6sADD3T6oYCiuNGJhr0DYUWOO4Ia0AHD5fi0y5VXXqknnnhCf/zjH4v6eaZd4KZs9R+KqcvRVxRqdAxW46StLTmqgMK58VoE/OTrtMuDDz6oE088UZ/73Oc0btw4fexjH9Odd9456Pnd3d1KJBIZH4BbnF7WGpURAVbkOI8l1ogyx4OPf/3rX1q+fLnq6ur08MMP69JLL9XXvvY13XvvvVnPb2lpUVVVVfqjpqbG6SYhQPKdvih2msPpTjQqHchwS51jIAI6RJpx2MiRI820adMyjn31q181n/zkJ7Oe/+6775rOzs70x+7du40k09nZ6XTTYLm77jKmtNQYKfn5rruGd16uxykrS/58WVnhP9/X7t3725L6KCtLHi/29z36aPE/77bdu41pa7O3fUHj5GsR8FtnZ2fe/bfjOR+1tbWaMWOG7rrrrvSx5cuX6/rrr9fLL7885M+T8xFN+c5/OzVP7uTyy9bWgTU6isn5iELuCAZiKTDCopD+2/EiY6eccoq2b9+eceyf//ynamtrnX4ohEiu6Yu+/yHne95Q+hcKG47hFhmTBs8daWykQypWUGqSOPlaBILC8ZyPb3zjG3rqqaf0gx/8QDt37tSqVau0YsUKNTc3O/1QCJF8578/+MHsPz92rDvtytdwa0lEJXfEKyxhBezmePBx0kknae3atVq9erWOOeYYXXfddbrlllt03nnnOf1QCJF8Exr37s3+8/v2uds+t5F86JyorEACgsyVvV3OPvtsnX322W78aoRYPtMXqU66f85H0Dtp9ndxjlNTcwDcw8ZysMpQ899h7qSdyB1BeANUIEwIPhA4Ye6kST4cvjAHqEBYsKstgFBiCSvgLV+X2gKADRhFAuzl+GoXAPBL2HcXBsKC4AORRmcVHtT2AIKD4AORRWcVHtT2AIKF4AORRGcVLlSIBYKF4AORRGcVLlSIBYKF4AORRGcVLvmW5wdgB4IPRBKdVfg0NUkdHckE4o6O5NcA7ESRMVjHy63QKUQFAM4opP9m5ANW8XoFSiwm1dcTeACAlwg+YA1WoABANBB8wBqsQAGAaCD4gDVYgQIA0UDwAWuwAgUAooFdbWGVpiapsZEVKAAQZgQfsA5boQNAuDHtAgAAPEXwAQAAPEXw0U88nizPTG0JAADcQfDRh9fVNQEAiCKCj/dRXRN+Y9QNQFQQfLyP6prwU1hH3QioAGRD8PE+qmvCL2EddQtrQAVg+Ag+3kd1TfgljKNuYQ2oADiDImN9UF0TfkiNuvUNQII+6jZUQLVjR/K6+RsDoomRj35iMam+nv8U4Z0wjroNNo35pz8xFQNAKjHGGL8b0VcikVBVVZU6OztVWVnpd3MAz8Tj4Rp1a21NTrX09CQDj8WLpYULB47wdHSE43qBqCuk/2baBbCEU3vaxON2TGv0n8bMNRVD8AFEC8EHECKtrfsTPUtLk9M5TU3+tad/QBW23BYAxSHnAwgJ21eYhDG3BUBxGPkIOFuG2OG/IExrsKIMgMTIR6BRxAl9BaVQHivKABB8BJTtQ+zwHtMaAIKCaZeACsIQO7zHtAaAICD4CKgwVsWEM5xasgsAbmHaJaAYYgcABBUjHwHGEDsAIIgIPgKOIXYAQNAw7QIAADxF8AEAADxF8AEAADxF8AHAVfG41NZGATwA+xF8AHANWwAAyIbgA4Ar2AIAwGAIPgC4ItcWAACijeADgCuCsssuAO8RfABwBVsAABgMFU7huXg8OSRfV0dHFHZsAQAgG0Y+QszGJY6sfoieWEyqryfwALAfwUdI2djJe736wcbgCwBA8BFKti5x9HL1g43BFwAgieAjhGxd4ujV6gdbgy8AQBLBRwjZusTRq9UPtgZfAIAkgo8QsnmJY1OT1NGRzMXo6Eh+7TRbgy8AQBJLbUPK5iWOsZi77UkFX/PnJ0c8bAq+AABSiTHG+N2IvhKJhKqqqtTZ2anKykq/m4MAi8ftDL4AIIwK6b8Z+UBouT3CAgAoDjkfAADAUwQfFqI4FgAgzAg+LENxLABA2BF89OH3iAPFsQAAUeB68LF48WKVlJRowYIFbj/UsNgw4kBxLABAFLgafPzpT3/SHXfcoeOOO87Nhxk2W0YcKI4FAIgC14KPvXv36rzzztOdd96pAw880K2HcYQtIw42VyYFAMAprgUfzc3NmjVrlhoaGnKe193drUQikfHhNZtGHLwoPw4AgJ9cCT7WrFmj559/Xi0tLUOe29LSoqqqqvRHTU2NG03KybYRh1hMqq9nxAMAEE6Ol1ffvXu3TjzxRD3yyCPpXI/6+nqdcMIJuuWWWwac393dre7u7vTXiURCNTU1vpRXpxw3AADFKaS8uuPBx7p16/TZz35WZalhBEk9PT0qKSlRaWmpuru7M77XH3u7AAAQPL7u7XLGGWdo69atGccuuugiHX300Vq4cGHOwAMAAISf48FHRUWFjjnmmIxjY8eO1cEHHzzgOAAAiB4qnAIAAE85PvKRTXt7uxcP47p4PFkTpK6OhFQAAIrFyEeebCi/DgBAGBB85MGW8usAAIQBwUcebCm/DgBAGBB85MGm8uuDiceTJdkZjQEA2I7gIw+2lV/vj3wUAECQOF7hdLhsrnBqY/n1eDwZcPSdFiorS25KZ0sbAQDh52uFU5sNd6lsLGZfh54rH8W2tgIAIEVo2iWsUxNByEcBAKCvSAQfYV4qa3s+CgAA/UVi2iXsUxNNTVJjo335KAAAZBOJ4CM1NdE/KTNMUxM25qMAAJBNJKZdmJooHHVDAABuiUTwISWnJjo6kh1qR0fya2QX1uRcAIAdqPOBDNQNAQAUo5D+OzIjH8gP+9gAANxG8IEM1A0BALiN4AMZSM4FALgtEkttURjqhgAA3ETwgayoGwIAcAvTLgAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHwAAwFMEHy6Jx6W2tuRnAACwH8GHC1pbpdpa6fTTk59bW/1uEQAA9iD4cFg8Ll1yidTbm/y6t1eaP58REAAAUgg+HLZjx/7AI6WnR9q505/2AABgG4IPh9XVSaX9ntWyMmnyZH/aAwCAbQg+HBaLSStWJAMOKfn5jjuSxwEAgDTC7waEUVOT1NiYnGqZPJnAAwCAvgg+XBKLEXQAAJAN0y4AAMBTBB8AAMBTBB8AAMBTBB8AAMBTBB8AAMBTBB8AAMBTBB8AAMBTBB8AAMBTBB8AAMBTBB8AAMBTBB8AAMBTjgcfLS0tOumkk1RRUaFx48Zpzpw52r59u9MPAwAAAsrx4GPjxo1qbm7WU089pUceeUTvvfeezjzzTO3bt8/phwIAAAFUYowxbj7Av//9b40bN04bN27U//3f/w15fiKRUFVVlTo7O1VZWelm0wAAgEMK6b9dz/no7OyUJB100EFuPxQAAAiAEW7+8t7eXi1YsECnnHKKjjnmmKzndHd3q7u7O/11IpFws0kAAMBnro58NDc3a9u2bVqzZs2g57S0tKiqqir9UVNT42aTAACAz1zL+bjsssu0fv16PfbYY5o0adKg52Ub+aipqSHnAwCAACkk58PxaRdjjL761a9q7dq1am9vzxl4SFJ5ebnKy8udbgYAALCU48FHc3OzVq1apfXr16uiokJ79uyRJFVVVWnMmDFOPxwAAAgYx6ddSkpKsh6/5557dOGFFw758yy1BQAgeHyfdgEAABgMe7sAAABPEXwAAABPEXwAAABPEXwAAABPEXwAAABPEXwAAABPEXwAAABPEXz0E49LbW3JzwAAwHkEH320tkq1tdLppyc/t7b63SIAAMKH4ON98bh0ySVSb2/y695eaf58RkAAAHAawcf7duzYH3ik9PRIO3f60x4AAMKK4ON9dXVSab9no6xMmjzZn/YAABBWBB/vi8WkFSuSAYeU/HzHHcnjAADAOY7vahtkTU1SY2NyqmXyZAIPAADcQPDRTyxG0AEAgJuYdgEAAJ4i+AAAAJ4i+AAAAJ4i+AAAAJ4i+AAAAJ4i+AAAAJ4i+AAAAJ4i+AAAAJ4i+AAAAJ4i+AAAAJ4i+AAAAJ6ybm8XY4wkKZFI+NwSAACQr1S/nerHc7Eu+Ojq6pIk1dTU+NwSAABQqK6uLlVVVeU8p8TkE6J4qLe3V6+88ooqKipUUlIy7N+XSCRUU1Oj3bt3q7Ky0oEW2odrDL6wX5/ENYYF1xgOblyjMUZdXV2qrq5WaWnurA7rRj5KS0sVc2FP+8rKytC+iFK4xuAL+/VJXGNYcI3h4PQ1DjXikULCKQAA8BTBBwAA8FTog4/y8nItWrRI5eXlfjfFNVxj8IX9+iSuMSy4xnDw+xqtSzgFAADhFvqRDwAAYBeCDwAA4CmCDwAA4CmCDwAA4KlABh/Lli3TYYcdptGjR2vq1Kl65plncp7/85//XEcffbRGjx6tY489Vr/97W8zvm+M0TXXXKOJEydqzJgxamho0I4dO9y8hJwKub4777xTn/70p3XggQfqwAMPVENDw4DzL7zwQpWUlGR8nHXWWW5fRk6FXOPKlSsHtH/06NEZ59h2D6XCrrG+vn7ANZaUlGjWrFnpc2y6j4899pjOOeccVVdXq6SkROvWrRvyZ9rb2/Xxj39c5eXlmjx5slauXDngnEL/tt1U6DX+6le/0owZM/ShD31IlZWVmjZtmh5++OGMc773ve8NuIdHH320i1eRW6HX2N7envV1umfPnozzgnwfs/2dlZSUaMqUKelzbLqPLS0tOumkk1RRUaFx48Zpzpw52r59+5A/53e/GLjg42c/+5m++c1vatGiRXr++ed1/PHHq7GxUa+//nrW85988kl94QtfUFNTkzZv3qw5c+Zozpw52rZtW/qcG264Qbfeeqtuv/12Pf300xo7dqwaGxv17rvvenVZaYVeX3t7u77whS+ora1NmzZtUk1Njc4880y9/PLLGeedddZZevXVV9Mfq1ev9uJysir0GqVkFb6+7X/xxRczvm/TPZQKv8Zf/epXGde3bds2lZWV6XOf+1zGebbcx3379un444/XsmXL8jp/165dmjVrlk477TRt2bJFCxYs0MUXX5zRORfzunBTodf42GOPacaMGfrtb3+r5557TqeddprOOeccbd68OeO8KVOmZNzDxx9/3I3m56XQa0zZvn17xjWMGzcu/b2g38cf/ehHGde2e/duHXTQQQP+Fm25jxs3blRzc7OeeuopPfLII3rvvfd05plnat++fYP+jBX9ogmYk08+2TQ3N6e/7unpMdXV1aalpSXr+Z///OfNrFmzMo5NnTrVzJ8/3xhjTG9vr5kwYYK58cYb099/6623THl5uVm9erULV5BbodfX3//+9z9TUVFh7r333vSxefPmmdmzZzvd1KIVeo333HOPqaqqGvT32XYPjRn+fbz55ptNRUWF2bt3b/qYbfcxRZJZu3ZtznO+/e1vmylTpmQcmzt3rmlsbEx/PdznzE35XGM2H/3oR821116b/nrRokXm+OOPd65hDsrnGtva2owk8+abbw56Ttju49q1a01JSYnp6OhIH7P5Pr7++utGktm4ceOg59jQLwZq5OO///2vnnvuOTU0NKSPlZaWqqGhQZs2bcr6M5s2bco4X5IaGxvT5+/atUt79uzJOKeqqkpTp04d9He6pZjr6+/tt9/We++9p4MOOijjeHt7u8aNG6ejjjpKl156qd544w1H256vYq9x7969qq2tVU1NjWbPnq2//vWv6e/ZdA8lZ+5ja2urzj33XI0dOzbjuC33sVBD/R068ZzZpre3V11dXQP+Fnfs2KHq6modfvjhOu+88/TSSy/51MLinXDCCZo4caJmzJihJ554In08jPextbVVDQ0Nqq2tzThu633s7OyUpAGvu75s6BcDFXz85z//UU9Pj8aPH59xfPz48QPmHFP27NmT8/zU50J+p1uKub7+Fi5cqOrq6owXzVlnnaX77rtPGzZs0JIlS7Rx40bNnDlTPT09jrY/H8Vc41FHHaW7775b69ev1/3336/e3l5Nnz5d8Xhckl33UBr+fXzmmWe0bds2XXzxxRnHbbqPhRrs7zCRSOidd95x5LVvm6VLl2rv3r36/Oc/nz42depUrVy5Ug899JCWL1+uXbt26dOf/rS6urp8bGn+Jk6cqNtvv12//OUv9ctf/lI1NTWqr6/X888/L8mZ/8Ns8sorr+h3v/vdgL9FW+9jb2+vFixYoFNOOUXHHHPMoOfZ0C9at6stird48WKtWbNG7e3tGQmZ5557bvrfxx57rI477jgdccQRam9v1xlnnOFHUwsybdo0TZs2Lf319OnT9ZGPfER33HGHrrvuOh9b5o7W1lYde+yxOvnkkzOOB/0+RsmqVat07bXXav369Rn5EDNnzkz/+7jjjtPUqVNVW1urBx54QE1NTX40tSBHHXWUjjrqqPTX06dP1wsvvKCbb75ZP/nJT3xsmTvuvfdeHXDAAZozZ07GcVvvY3Nzs7Zt2+ZrHlG+AjXyccghh6isrEyvvfZaxvHXXntNEyZMyPozEyZMyHl+6nMhv9MtxVxfytKlS7V48WL9/ve/13HHHZfz3MMPP1yHHHKIdu7cOew2F2o415gycuRIfexjH0u336Z7KA3vGvft26c1a9bk9R+Yn/exUIP9HVZWVmrMmDGOvC5ssWbNGl188cV64IEHBgxt93fAAQfoyCOPDMQ9HMzJJ5+cbn+Y7qMxRnfffbcuuOACjRo1Kue5NtzHyy67TL/+9a/V1tamWCyW81wb+sVABR+jRo3SJz7xCW3YsCF9rLe3Vxs2bMh4Z9zXtGnTMs6XpEceeSR9/qRJkzRhwoSMcxKJhJ5++ulBf6dbirk+KZmVfN111+mhhx7SiSeeOOTjxONxvfHGG5o4caIj7S5EsdfYV09Pj7Zu3Zpuv033UBreNf785z9Xd3e3zj///CEfx8/7WKih/g6deF3YYPXq1brooou0evXqjGXSg9m7d69eeOGFQNzDwWzZsiXd/rDcRym5imTnzp15vRHw8z4aY3TZZZdp7dq1evTRRzVp0qQhf8aKftGRtFUPrVmzxpSXl5uVK1eav/3tb+aSSy4xBxxwgNmzZ48xxpgLLrjAXHnllenzn3jiCTNixAizdOlS8/e//90sWrTIjBw50mzdujV9zuLFi80BBxxg1q9fb/7yl7+Y2bNnm0mTJpl33nnH+utbvHixGTVqlPnFL35hXn311fRHV1eXMcaYrq4uc/nll5tNmzaZXbt2mT/84Q/m4x//uKmrqzPvvvuu59dXzDVee+215uGHHzYvvPCCee6558y5555rRo8ebf7617+mz7HpHhpT+DWmfOpTnzJz584dcNy2+9jV1WU2b95sNm/ebCSZm266yWzevNm8+OKLxhhjrrzySnPBBRekz//Xv/5lPvCBD5grrrjC/P3vfzfLli0zZWVl5qGHHkqfM9Rz5rVCr/GnP/2pGTFihFm2bFnG3+Jbb72VPudb3/qWaW9vN7t27TJPPPGEaWhoMIcccoh5/fXXPb8+Ywq/xptvvtmsW7fO7Nixw2zdutV8/etfN6WlpeYPf/hD+pyg38eU888/30ydOjXr77TpPl566aWmqqrKtLe3Z7zu3n777fQ5NvaLgQs+jDHmtttuM4ceeqgZNWqUOfnkk81TTz2V/t6pp55q5s2bl3H+Aw88YI488kgzatQoM2XKFPOb3/wm4/u9vb3m6quvNuPHjzfl5eXmjDPOMNu3b/fiUrIq5Ppqa2uNpAEfixYtMsYY8/bbb5szzzzTfOhDHzIjR440tbW15stf/rJv/xGkFHKNCxYsSJ87fvx485nPfMY8//zzGb/PtntoTOGv03/84x9Gkvn9738/4HfZdh9TSy77f6Suad68eebUU08d8DMnnHCCGTVqlDn88MPNPffcM+D35nrOvFboNZ566qk5zzcmubx44sSJZtSoUebDH/6wmTt3rtm5c6e3F9ZHode4ZMkSc8QRR5jRo0ebgw46yNTX15tHH310wO8N8n00JrmsdMyYMWbFihVZf6dN9zHbtUnK+PuysV8seb/xAAAAnghUzgcAAAg+gg8AAOApgg8AAOApgg8AAOApgg8AAOApgg8AAOApgg8AAOApgg8AAOApgg8AAOApgg8AAOApgg8AAOApgg8AAOCp/wcSsMixm1cfkwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, y, \"b.\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, y, \"b.\")\n",
    "plt.xlabel(\"Equipos afectados (u/1000)\")\n",
    "plt.ylabel(\"Coste del incidente (u/10000)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Modificación del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_equipos_afectados</th>\n",
       "      <th>coste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.442803</td>\n",
       "      <td>7.884102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.691606</td>\n",
       "      <td>6.874163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.996811</td>\n",
       "      <td>10.543628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.162554</td>\n",
       "      <td>7.981495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.047556</td>\n",
       "      <td>7.068049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.478438</td>\n",
       "      <td>5.445043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.831249</td>\n",
       "      <td>9.493411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.195255</td>\n",
       "      <td>7.317094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.053027</td>\n",
       "      <td>5.178226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.770266</td>\n",
       "      <td>9.898424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_equipos_afectados      coste\n",
       "0             1.442803   7.884102\n",
       "1             0.691606   6.874163\n",
       "2             1.996811  10.543628\n",
       "3             1.162554   7.981495\n",
       "4             1.047556   7.068049\n",
       "5             0.478438   5.445043\n",
       "6             1.831249   9.493411\n",
       "7             1.195255   7.317094\n",
       "8             0.053027   5.178226\n",
       "9             1.770266   9.898424"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'n_equipos_afectados': X.flatten(), 'coste': y.flatten()}\n",
    "df = pd.DataFrame(data)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_equipos_afectados</th>\n",
       "      <th>coste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1442</td>\n",
       "      <td>78841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>691</td>\n",
       "      <td>68741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1996</td>\n",
       "      <td>105436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1162</td>\n",
       "      <td>79814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1047</td>\n",
       "      <td>70680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>478</td>\n",
       "      <td>54450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1831</td>\n",
       "      <td>94934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1195</td>\n",
       "      <td>73170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>53</td>\n",
       "      <td>51782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1770</td>\n",
       "      <td>98984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_equipos_afectados   coste\n",
       "0                 1442   78841\n",
       "1                  691   68741\n",
       "2                 1996  105436\n",
       "3                 1162   79814\n",
       "4                 1047   70680\n",
       "5                  478   54450\n",
       "6                 1831   94934\n",
       "7                 1195   73170\n",
       "8                   53   51782\n",
       "9                 1770   98984"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Escalado del número de equipos afectados\n",
    "df['n_equipos_afectados'] = df['n_equipos_afectados'] * 1000\n",
    "df['n_equipos_afectados'] = df['n_equipos_afectados'].astype('int')\n",
    "# Escalado del coste\n",
    "df['coste'] = df['coste'] * 10000\n",
    "df['coste'] = df['coste'].astype('int')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Representación gráfica del conjunto de datos\n",
    "plt.plot(df['n_equipos_afectados'], df['coste'], \"b.\")\n",
    "plt.xlabel(\"Equipos afectados\")\n",
    "plt.ylabel(\"Coste del incidente\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Construcción del modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construcción del modelo y ajuste de la función hipótesis\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(df['n_equipos_afectados'].values.reshape(-1, 1), df['coste'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36728.17330415003"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Parámetro theta 0\n",
    "lin_reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([32.03370548])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Parámetro theta 1\n",
    "lin_reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicción para el valor mínimo y máximo del conjunto de datos de entrenamiento\n",
    "X_min_max = np.array([[df[\"n_equipos_afectados\"].min()], [df[\"n_equipos_afectados\"].max()]])\n",
    "y_train_pred = lin_reg.predict(X_min_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Representación gráfica de la función hipótesis generada\n",
    "plt.plot(X_min_max, y_train_pred, \"g-\")\n",
    "plt.plot(df['n_equipos_afectados'], df['coste'], \"b.\")\n",
    "plt.xlabel(\"Equipos afectados\")\n",
    "plt.ylabel(\"Coste del incidente\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Predicción de nuevos ejemplos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El coste del incidente sería: 78371 €\n"
     ]
    }
   ],
   "source": [
    "x_new = np.array([[1300]]) # 1300 equipos afectados\n",
    "\n",
    "# Predicción del coste que tendría el incidente\n",
    "coste = lin_reg.predict(x_new) \n",
    "\n",
    "print(\"El coste del incidente sería:\", int(coste[0]), \"€\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['n_equipos_afectados'], df['coste'], \"b.\")\n",
    "plt.plot(X_min_max, y_train_pred, \"g-\")\n",
    "plt.plot(x_new, coste, \"rx\")\n",
    "plt.xlabel(\"Equipos afectados\")\n",
    "plt.ylabel(\"Coste del incidente\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}