{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c9545a3d",
   "metadata": {},
   "source": [
    "# Ejercicio "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b0a03f",
   "metadata": {},
   "source": [
    "Empezamos cargando las librerias necesarias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9974177a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Lasso\n",
    "from sklearn import datasets\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20e5d459",
   "metadata": {},
   "source": [
    "Creamos un dataset y lo retocamos, estamos forzando un ejemplo concreto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7c7cb88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2         3         4         5         7  \\\n",
      "0  0.991136  1.630796 -1.900090 -0.111391 -1.232109  0.932722  2.798165   \n",
      "1 -0.325548 -0.538166 -0.261746 -0.220028  0.109686  0.252768  0.758303   \n",
      "2  1.937571  0.338847  1.876973  0.217793  0.086090  0.813308  2.439924   \n",
      "3 -0.960129  0.511255  0.853085 -1.216964 -1.547833 -0.213646 -0.640938   \n",
      "4 -1.352448 -0.613847 -1.060842 -0.222442  0.307362  0.087174  0.261521   \n",
      "\n",
      "          8  \n",
      "0 -0.616054  \n",
      "1  0.054843  \n",
      "2  0.043045  \n",
      "3 -0.773917  \n",
      "4  0.153681  \n"
     ]
    }
   ],
   "source": [
    "X, y = make_regression(n_samples=100,n_informative=6, n_features=6, coef=False,noise=100.0, random_state=33, bias=10.5)\n",
    "\n",
    "df = pd.DataFrame(X)\n",
    "df[7] = df[5] * 3\n",
    "df[4] = df[4] + df[3]\n",
    "df[8] = df[4] / 2\n",
    "\n",
    "X = df.to_numpy()\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "972a8ea8",
   "metadata": {},
   "source": [
    "Divide el conjunto en _entrenamiento_ y _test_ :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d900b247",
   "metadata": {},
   "source": [
    "Crea una regresión Lasso. Visualiza el valor de los coeficientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "343c068a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "76e5831a",
   "metadata": {},
   "source": [
    "Crea una regresión Lineal. Visualiza el valor de los coeficientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae1238e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b8b4b1ae",
   "metadata": {},
   "source": [
    "¿Qué puedes observar?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2828041",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "42214e8b",
   "metadata": {},
   "source": [
    "[![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/) <br/>\n",
    "Isaac Lera and Gabriel Moya <br/>\n",
    "Universitat de les Illes Balears <br/>\n",
    "isaac.lera@uib.edu, gabriel.moya@uib.edu"
   ]
  }
 ],
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