MPG/mpg_patiñodaniel.ipynb

{
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      "source": [
        "# MPG Analítica de datos\n",
        "**Daniel Enrique Patiño Jaimes**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 248,
      "metadata": {
        "id": "HmNCq0mvvkep"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7D2YHYdKGBj9"
      },
      "source": [
        "## Lectura del documento"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G39USxzxGF8f"
      },
      "source": [
        "Se le asigna nombre a cada columna para una mejor manipulación de la información"
      ]
    },
    {
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        "id": "HlAS5UbfvoER",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mpg</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>displacement</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>weight</th>\n",
              "      <th>acceleration</th>\n",
              "      <th>model year</th>\n",
              "      <th>origin</th>\n",
              "      <th>car name</th>\n",
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              "      <th>0</th>\n",
              "      <td>18.0</td>\n",
              "      <td>8</td>\n",
              "      <td>307.0</td>\n",
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              "      <td>3504</td>\n",
              "      <td>12.0</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>chevrolet chevelle malibu</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>11.5</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>buick skylark 320</td>\n",
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              "      <th>2</th>\n",
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              "      <td>amc rebel sst</td>\n",
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              "      <td>140.0</td>\n",
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              "      <td>2790</td>\n",
              "      <td>15.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>ford mustang gl</td>\n",
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              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>44.0</td>\n",
              "      <td>4</td>\n",
              "      <td>97.0</td>\n",
              "      <td>52</td>\n",
              "      <td>2130</td>\n",
              "      <td>24.6</td>\n",
              "      <td>82</td>\n",
              "      <td>2</td>\n",
              "      <td>vw pickup</td>\n",
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              "      <td>4</td>\n",
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              "      <td>11.6</td>\n",
              "      <td>82</td>\n",
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              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>ford ranger</td>\n",
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              "      <td>4</td>\n",
              "      <td>119.0</td>\n",
              "      <td>82</td>\n",
              "      <td>2720</td>\n",
              "      <td>19.4</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>chevy s-10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>398 rows × 9 columns</p>\n",
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            "text/plain": [
              "      mpg  cylinders  displacement horsepower  weight  acceleration  \\\n",
              "0    18.0          8         307.0        130    3504          12.0   \n",
              "1    15.0          8         350.0        165    3693          11.5   \n",
              "2    18.0          8         318.0        150    3436          11.0   \n",
              "3    16.0          8         304.0        150    3433          12.0   \n",
              "4    17.0          8         302.0        140    3449          10.5   \n",
              "..    ...        ...           ...        ...     ...           ...   \n",
              "393  27.0          4         140.0         86    2790          15.6   \n",
              "394  44.0          4          97.0         52    2130          24.6   \n",
              "395  32.0          4         135.0         84    2295          11.6   \n",
              "396  28.0          4         120.0         79    2625          18.6   \n",
              "397  31.0          4         119.0         82    2720          19.4   \n",
              "\n",
              "     model year  origin                   car name  \n",
              "0            70       1  chevrolet chevelle malibu  \n",
              "1            70       1          buick skylark 320  \n",
              "2            70       1         plymouth satellite  \n",
              "3            70       1              amc rebel sst  \n",
              "4            70       1                ford torino  \n",
              "..          ...     ...                        ...  \n",
              "393          82       1            ford mustang gl  \n",
              "394          82       2                  vw pickup  \n",
              "395          82       1              dodge rampage  \n",
              "396          82       1                ford ranger  \n",
              "397          82       1                 chevy s-10  \n",
              "\n",
              "[398 rows x 9 columns]"
            ]
          },
          "execution_count": 249,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = pd.read_csv('auto-mpgv2.csv')\n",
        "\n",
        "columnas = ['mpg','cylinders','displacement','horsepower','weight','acceleration','model year','origin','car name']\n",
        "\n",
        "data.columns=columnas\n",
        "\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P0sCy3xFxU0-"
      },
      "source": [
        "# Utilizando la base de datos de MPG(Millas por galón)obtener un modelo  de regresión con un 𝑟!superior al 88% utilizando una entrada o varias siguiendo los siguientes requerimientos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t2x-KDyR3wul"
      },
      "source": [
        "## Tienen que realizar un análisis de las variables para conocer si existen datos atípicos o no. En caso de confirmar su existencia deben procesarlos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "67Chp2q9AGhj"
      },
      "source": [
        "Se crea un nuevo data set en el cuál guardaremos los datos sin atípicos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 251,
      "metadata": {
        "id": "G9vV_uVxAEse"
      },
      "outputs": [],
      "source": [
        "new_dataset = data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1LasIL-4hKx"
      },
      "source": [
        "Se evidencia que la columna 'horsepower' no fue adjuntada en la gráfica de correlación, esto debido a que contaba con datos no numéricos ('?')"
      ]
    },
    {
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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "corr = data.corr()\n",
        "\n",
        "fig = plt.figure(figsize=(8,8))\n",
        "plt.matshow(corr,cmap = 'RdBu' ,fignum=fig.number)\n",
        "plt.xticks(range(len(corr.columns)),corr.columns,rotation='vertical');\n",
        "plt.yticks(range(len(corr.columns)),corr.columns);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OcVh_pI0HWOn"
      },
      "source": [
        "Se identifica la posición de los datos no numéricos mencionados anteriormente y estos son guardados en una variable para su posterior procesamiento"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 252,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "zdfZOiGe35p4",
        "outputId": "8bd000b1-b8b0-40e9-c327-5e94029ae400"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mpg</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>displacement</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>weight</th>\n",
              "      <th>acceleration</th>\n",
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              "      <th>car name</th>\n",
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              "      <td>19.0</td>\n",
              "      <td>71</td>\n",
              "      <td>1</td>\n",
              "      <td>ford pinto</td>\n",
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              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>21.0</td>\n",
              "      <td>6</td>\n",
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              "      <td>1</td>\n",
              "      <td>ford maverick</td>\n",
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              "    <tr>\n",
              "      <th>330</th>\n",
              "      <td>40.9</td>\n",
              "      <td>4</td>\n",
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              "      <td>2</td>\n",
              "      <td>renault lecar deluxe</td>\n",
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              "    <tr>\n",
              "      <th>336</th>\n",
              "      <td>23.6</td>\n",
              "      <td>4</td>\n",
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              "      <td>14.3</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "      <td>ford mustang cobra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>34.5</td>\n",
              "      <td>4</td>\n",
              "      <td>100.0</td>\n",
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              "      <td>2320</td>\n",
              "      <td>15.8</td>\n",
              "      <td>81</td>\n",
              "      <td>2</td>\n",
              "      <td>renault 18i</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>374</th>\n",
              "      <td>23.0</td>\n",
              "      <td>4</td>\n",
              "      <td>151.0</td>\n",
              "      <td>?</td>\n",
              "      <td>3035</td>\n",
              "      <td>20.5</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>amc concord dl</td>\n",
              "    </tr>\n",
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              "</table>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      mpg  cylinders  displacement horsepower  weight  acceleration  \\\n",
              "32   25.0          4          98.0          ?    2046          19.0   \n",
              "126  21.0          6         200.0          ?    2875          17.0   \n",
              "330  40.9          4          85.0          ?    1835          17.3   \n",
              "336  23.6          4         140.0          ?    2905          14.3   \n",
              "354  34.5          4         100.0          ?    2320          15.8   \n",
              "374  23.0          4         151.0          ?    3035          20.5   \n",
              "\n",
              "     model year  origin              car name  \n",
              "32           71       1            ford pinto  \n",
              "126          74       1         ford maverick  \n",
              "330          80       2  renault lecar deluxe  \n",
              "336          80       1    ford mustang cobra  \n",
              "354          81       2           renault 18i  \n",
              "374          82       1        amc concord dl  "
            ]
          },
          "execution_count": 252,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hp_null=data.loc[data['horsepower']== '?']\n",
        "hp_null"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pgT-xkZDHoRx"
      },
      "source": [
        "Se eliminan los datos faltantes mencionados anteriormente"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 253,
      "metadata": {
        "id": "SHHrqOfr36PX"
      },
      "outputs": [],
      "source": [
        "new_dataset.drop(new_dataset.loc[new_dataset.horsepower == '?'].index, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lR76g7zsHkgu"
      },
      "source": [
        "Se confirma que los datos fueron eliminados correctamente"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 254,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "muM_OgLM4CkI",
        "outputId": "644e0e59-3c51-469a-d685-10b0d315aeaf"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mpg</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>displacement</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>weight</th>\n",
              "      <th>acceleration</th>\n",
              "      <th>model year</th>\n",
              "      <th>origin</th>\n",
              "      <th>car name</th>\n",
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              "  </tbody>\n",
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              "        \n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "\n",
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              "\n",
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              "    }\n",
              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-435b7a61-b3dd-471a-9891-1ee991d6e5c4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-435b7a61-b3dd-471a-9891-1ee991d6e5c4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [mpg, cylinders, displacement, horsepower, weight, acceleration, model year, origin, car name]\n",
              "Index: []"
            ]
          },
          "execution_count": 254,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_dataset.loc[new_dataset['horsepower']== '?']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k0UhsG2jH2uC"
      },
      "source": [
        "Se analiza el tipo de dato que almacena la columna 'horsepower'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 255,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZIkmzuDN4FXJ",
        "outputId": "e7c62fe7-8937-4a70-cdd1-9b4694562c8a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<bound method NDFrame.describe of 0      130\n",
              "1      165\n",
              "2      150\n",
              "3      150\n",
              "4      140\n",
              "      ... \n",
              "393     86\n",
              "394     52\n",
              "395     84\n",
              "396     79\n",
              "397     82\n",
              "Name: horsepower, Length: 392, dtype: object>"
            ]
          },
          "execution_count": 255,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_dataset.horsepower.describe"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QIO1Ht_9IBwB"
      },
      "source": [
        "Se convierte a tipo float para poder graficar los datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 256,
      "metadata": {
        "id": "K_hzj6oN4HC4"
      },
      "outputs": [],
      "source": [
        "new_dataset['horsepower'] = new_dataset['horsepower'].astype('float64')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxZlNfHxTEFu"
      },
      "source": [
        "Se realizan los gráficos de cajas y bigotes para identificar que columnas cuentan con datos atípicos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 257,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "tj0z3-B93zK0",
        "outputId": "dde091ec-3dc2-41d4-f74e-18a573aa5ec3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "mpg                AxesSubplot(0.125,0.536818;0.168478x0.343182)\n",
              "cylinders       AxesSubplot(0.327174,0.536818;0.168478x0.343182)\n",
              "displacement    AxesSubplot(0.529348,0.536818;0.168478x0.343182)\n",
              "horsepower      AxesSubplot(0.731522,0.536818;0.168478x0.343182)\n",
              "weight                AxesSubplot(0.125,0.125;0.168478x0.343182)\n",
              "acceleration       AxesSubplot(0.327174,0.125;0.168478x0.343182)\n",
              "model year         AxesSubplot(0.529348,0.125;0.168478x0.343182)\n",
              "origin             AxesSubplot(0.731522,0.125;0.168478x0.343182)\n",
              "dtype: object"
            ]
          },
          "execution_count": 257,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1008x576 with 8 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "boxplots_cols = ['mpg','cylinders','displacement','horsepower','weight','acceleration','model year','origin']\n",
        "new_dataset[boxplots_cols].plot(kind='box',subplots=True,layout=(2,4),figsize=(14,8))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LD-FEUGtIfCN"
      },
      "source": [
        "Se eliminan los atípicos de las columnas que tienen, es decir 'horsepower', 'acceleration'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 258,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "TNnRkNPA48CT",
        "outputId": "ce53b16d-8518-416d-84da-6dcba44271c0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ee5dde5b-1adc-4310-af41-ed705ae40dff\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>mpg</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>displacement</th>\n",
              "      <th>horsepower</th>\n",
              "      <th>weight</th>\n",
              "      <th>acceleration</th>\n",
              "      <th>model year</th>\n",
              "      <th>origin</th>\n",
              "      <th>car name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18.0</td>\n",
              "      <td>8</td>\n",
              "      <td>307.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>3504</td>\n",
              "      <td>12.0</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>chevrolet chevelle malibu</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15.0</td>\n",
              "      <td>8</td>\n",
              "      <td>350.0</td>\n",
              "      <td>165.0</td>\n",
              "      <td>3693</td>\n",
              "      <td>11.5</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>buick skylark 320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18.0</td>\n",
              "      <td>8</td>\n",
              "      <td>318.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3436</td>\n",
              "      <td>11.0</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>plymouth satellite</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16.0</td>\n",
              "      <td>8</td>\n",
              "      <td>304.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>3433</td>\n",
              "      <td>12.0</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>amc rebel sst</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17.0</td>\n",
              "      <td>8</td>\n",
              "      <td>302.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>3449</td>\n",
              "      <td>10.5</td>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>ford torino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>27.0</td>\n",
              "      <td>4</td>\n",
              "      <td>151.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>2950</td>\n",
              "      <td>17.3</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>chevrolet camaro</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>27.0</td>\n",
              "      <td>4</td>\n",
              "      <td>140.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>2790</td>\n",
              "      <td>15.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>ford mustang gl</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>32.0</td>\n",
              "      <td>4</td>\n",
              "      <td>135.0</td>\n",
              "      <td>84.0</td>\n",
              "      <td>2295</td>\n",
              "      <td>11.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>dodge rampage</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>28.0</td>\n",
              "      <td>4</td>\n",
              "      <td>120.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>2625</td>\n",
              "      <td>18.6</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>ford ranger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>31.0</td>\n",
              "      <td>4</td>\n",
              "      <td>119.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>2720</td>\n",
              "      <td>19.4</td>\n",
              "      <td>82</td>\n",
              "      <td>1</td>\n",
              "      <td>chevy s-10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>373 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee5dde5b-1adc-4310-af41-ed705ae40dff')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ee5dde5b-1adc-4310-af41-ed705ae40dff');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
              "0    18.0          8         307.0       130.0    3504          12.0   \n",
              "1    15.0          8         350.0       165.0    3693          11.5   \n",
              "2    18.0          8         318.0       150.0    3436          11.0   \n",
              "3    16.0          8         304.0       150.0    3433          12.0   \n",
              "4    17.0          8         302.0       140.0    3449          10.5   \n",
              "..    ...        ...           ...         ...     ...           ...   \n",
              "392  27.0          4         151.0        90.0    2950          17.3   \n",
              "393  27.0          4         140.0        86.0    2790          15.6   \n",
              "395  32.0          4         135.0        84.0    2295          11.6   \n",
              "396  28.0          4         120.0        79.0    2625          18.6   \n",
              "397  31.0          4         119.0        82.0    2720          19.4   \n",
              "\n",
              "     model year  origin                   car name  \n",
              "0            70       1  chevrolet chevelle malibu  \n",
              "1            70       1          buick skylark 320  \n",
              "2            70       1         plymouth satellite  \n",
              "3            70       1              amc rebel sst  \n",
              "4            70       1                ford torino  \n",
              "..          ...     ...                        ...  \n",
              "392          82       1           chevrolet camaro  \n",
              "393          82       1            ford mustang gl  \n",
              "395          82       1              dodge rampage  \n",
              "396          82       1                ford ranger  \n",
              "397          82       1                 chevy s-10  \n",
              "\n",
              "[373 rows x 9 columns]"
            ]
          },
          "execution_count": 258,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "caracteristicas = ['horsepower','acceleration']\n",
        "for i in caracteristicas:\n",
        "  Q1 = new_dataset[i].quantile(0.25)\n",
        "  Q3 = new_dataset[i].quantile(0.75)\n",
        "  IQR = Q3 - Q1\n",
        "  # Entre más grande el número, más permisiva es la detección de atípicos\n",
        "  u_limit = Q3 + 1.5 * IQR\n",
        "  l_limit = Q1 - 1.5 * IQR\n",
        "  ubicacionNoAtipicos= (new_dataset[i] >= l_limit) & (new_dataset[i]<= u_limit)\n",
        "  new_dataset = new_dataset[ubicacionNoAtipicos]\n",
        "\n",
        "new_dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vsoV_GUE5O1Y"
      },
      "source": [
        "## Deben identificar el lugar exacto de estos atípicos y guardar sus índices en una variable externa."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKRIXTemTSDu"
      },
      "source": [
        "### Se buscan los atípicos en el dataframe original, el cuál no ha recibido ningun cambio en los datos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jItld6BaThNe"
      },
      "source": [
        "Se identifican los límites superior e inferior para comprobar que los datos atípicos encontrados hayan sido categorizados de manera correcta"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 259,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7lS0DjN17rzl",
        "outputId": "45bddb64-d365-4b94-9650-95f3769ec260"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Limite superior:  21.899999999999995\n",
            "Limite inferior:  8.900000000000002\n"
          ]
        }
      ],
      "source": [
        "Q1 = data['acceleration'].quantile(0.25)\n",
        "Q3 = data['acceleration'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "# Entre más grande el número, más permisiva es la detección de atípicos\n",
        "u_limit_a = Q3 + 1.5 * IQR\n",
        "l_limit_a = Q1 - 1.5 * IQR\n",
        "\n",
        "print('Limite superior: ',u_limit_a)\n",
        "print('Limite inferior: ',l_limit_a)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8sQiGs2Trx_"
      },
      "source": [
        "Ubicación de los datos atípicos en la columna de 'acceleration'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 260,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cifGKoOk5Sl6",
        "outputId": "e28b0e43-a90b-4101-f8b5-d2714e0094b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(array([ 58, 193, 194, 207, 297, 298, 324, 388]),)\n",
            "(array([ 7,  9, 11]),)\n"
          ]
        }
      ],
      "source": [
        "print(np.where(data['acceleration']>u_limit_a))\n",
        "print(np.where(data['acceleration']<l_limit_a))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 261,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nM7ml-Lv8PSa",
        "outputId": "a1fd03f3-9f0a-4fe0-a0f6-49645f33c7fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Limite superior:  202.5\n",
            "Limite inferior:  -1.5\n"
          ]
        }
      ],
      "source": [
        "Q1 = data['horsepower'].quantile(0.25)\n",
        "Q3 = data['horsepower'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "# Entre más grande el número, más permisiva es la detección de atípicos\n",
        "u_limit_h = Q3 + 1.5 * IQR\n",
        "l_limit_h = Q1 - 1.5 * IQR\n",
        "\n",
        "print('Limite superior: ',u_limit_h)\n",
        "print('Limite inferior: ',l_limit_h)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62QUtx_kTwj3"
      },
      "source": [
        "Ubicación de los datos atípicos en la columna de 'horsepower'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 262,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HeWN42wQSg3-",
        "outputId": "98f6f55e-08b9-4fa5-91cf-c6aa32f594b9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(array([  6,   7,   8,  13,  25,  27,  66,  93,  94, 115]),)\n",
            "(array([], dtype=int64),)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([ 32, 126, 330, 336, 354, 374])"
            ]
          },
          "execution_count": 262,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(np.where(data['horsepower']>u_limit_h))\n",
        "print(np.where(data['horsepower']<l_limit_h))\n",
        "hp_null.index.values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3u-p9Fqf4zdI"
      },
      "source": [
        "## El entrenamiento y prueba tiene que estar dividido 60% y 40% respectivamente. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 263,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHMNFoei4ubl",
        "outputId": "6ea8389a-43f9-4893-f968-20b9bf22f107"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "El r2 es  0.9121418804672214\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "df_caracteristica = new_dataset[['displacement','weight']]\n",
        "df_resultado = new_dataset['cylinders']\n",
        "\n",
        "x_train,x_test,y_train,y_test = train_test_split(df_caracteristica,df_resultado, train_size= 0.6) #test_size = porcentaje de datos para testear\n",
        "modelo=LinearRegression()\n",
        "modelo.fit(X=np.array(x_train), y=y_train) #Solo funciona con 'X' y 'y'\n",
        "predicciones = modelo.predict(X = np.array(x_test))\n",
        "r2 = r2_score(y_true = y_test, y_pred = predicciones)\n",
        "print('El r2 es ',r2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J3chXcI38qPZ"
      },
      "source": [
        "## Cuantificar la cantidad de registros que existen por vehículo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 264,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "esKvjIzw86yq",
        "outputId": "0ccfe18f-5370-4301-b018-7f04c34e0869"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "ford pinto        5\n",
              "amc matador       5\n",
              "toyota corolla    5\n",
              "ford maverick     4\n",
              "amc hornet        4\n",
              "                 ..\n",
              "amc pacer         1\n",
              "volvo 244dl       1\n",
              "fiat 131          1\n",
              "capri ii          1\n",
              "chevy s-10        1\n",
              "Name: car name, Length: 289, dtype: int64"
            ]
          },
          "execution_count": 264,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_dataset['car name'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGBC_QzSDVhe"
      },
      "source": [
        "# Seleccionando 5 vehículos a elección obtener una regresión por cada uno de ellos, buscando el mejor rendimiento posible. Además:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 265,
      "metadata": {
        "id": "fUhPV3PjEqne"
      },
      "outputs": [],
      "source": [
        "carro1 = new_dataset.loc[new_dataset['car name']== 'ford pinto']\n",
        "carro2 = new_dataset.loc[new_dataset['car name']== 'amc matador']\n",
        "carro3 = new_dataset.loc[new_dataset['car name']== 'toyota corolla']\n",
        "carro4 = new_dataset.loc[new_dataset['car name']== 'ford maverick']\n",
        "carro5 = new_dataset.loc[new_dataset['car name']== 'amc hornet']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6B7Hb4CDZeU"
      },
      "source": [
        "## Comparar visualmente las respuestas de todas las regresiones usando un gráfico de línea o dispersión."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJnEDfjSPfts"
      },
      "source": [
        "### Carro 1: Ford pinto"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 266,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Dn3hzAHwDkPE",
        "outputId": "0d5e45e8-a960-4c5b-f51f-973e7ae53417"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "El r2 es  -0.06166648250304063\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f536364b730>]"
            ]
          },
          "execution_count": 266,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_caracteristica = carro1['model year']\n",
        "df_resultado = carro1['displacement']\n",
        "\n",
        "\n",
        "x_train,x_test,y_train,y_test1 = train_test_split(df_caracteristica,df_resultado, train_size= 0.6) #test_size = porcentaje de datos para testear\n",
        "\n",
        "modelo=LinearRegression()\n",
        "modelo.fit(X=np.array(x_train).reshape(-1,1), y=y_train) #Solo funciona con 'X' y 'y'\n",
        "\n",
        "predicciones1 = modelo.predict(X = np.array(x_test).reshape(-1,1))\n",
        "r2 = r2_score(y_true = y_test1, y_pred = predicciones1)\n",
        "print('El r2 es ',r2)\n",
        "plt.plot(y_test1,predicciones1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2sl6PVr7PlAl"
      },
      "source": [
        "### Carro 2: Amc matador"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 267,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "4Eo77wYqFVpM",
        "outputId": "465bf362-761a-43ee-c3f8-d52c083252db"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "El r2 es  0.0\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f536362a820>]"
            ]
          },
          "execution_count": 267,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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6Rzw/AG5b7B7mq1fgzm6/7qd36u13F7uHOej1PcA/0vsPbB/wG6O8X4ftc5T36Uz9duv+Hlg/xTYLul/9+gFJatB5fVpGknR2DHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUoP8DqtLS3/5n7PQAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_caracteristica = carro2['cylinders']\n",
        "df_resultado = carro2['displacement']\n",
        "\n",
        "\n",
        "x_train,x_test,y_train,y_test2 = train_test_split(df_caracteristica,df_resultado, train_size= 0.6) #test_size = porcentaje de datos para testear\n",
        "\n",
        "modelo=LinearRegression()\n",
        "modelo.fit(X=np.array(x_train).reshape(-1,1), y=y_train) #Solo funciona con 'X' y 'y'\n",
        "\n",
        "predicciones2 = modelo.predict(X = np.array(x_test).reshape(-1,1))\n",
        "r2 = r2_score(y_true = y_test2, y_pred = predicciones2)\n",
        "print('El r2 es ',r2)\n",
        "plt.plot(y_test2,predicciones2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4k7V51FPq1s"
      },
      "source": [
        "### Carro 3: Toyota corolla"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 268,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "FSboiiMoFWvM",
        "outputId": "bd7f26c9-221f-4855-b598-5ff46efa3533"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "El r2 es  0.038039079662391395\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5363587970>]"
            ]
          },
          "execution_count": 268,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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AGGNmg4ErgEvdvbC4se5+e5H93AOEjg15ESleYaHz5pw1PPfBUgx4/Pou3NG3JRXUhExOUmmu6JsCbwTz9BWAt919spnlA2uBOcG0zAR3f9zMQsD97j64zKoWSXArtuUwYnwm89fu5sL2yTx9Y1dS6qkJmZwaC99UEztCoZDPnz8/2mWIREVeQSFjZ67i+Y+WU71KRR69ujM39WyuJmRyQmaW4e7F3tmod1WIxIjsjXsZlp7Jos37uLpbU0Zf14XkWlWiXZYkAAW9SJQdzivg+Y+XM3bmKurXqMwrd/RiYNcm0S5LEoiCXiSKvlqzi+HpmazacYDvhVIYeVVn6lRPinZZkmAU9CJRkJObz3PTlvDmnLWk1KvG33/Yl/PbNYx2WZKgFPQiZ9iMpdsYOSGLzfsOc+95rfjl5R2ooSZkUoZ0domcIbsPHOGJyYuY8M1G0hrVJP3+c+nVsl60y5JyQEEvUsbcnSlZWxg1KZs9B/P4Wf80/qN/GlUqqQmZnBkKepEytG3fYR55J5sPF22lW/M6vHlfXzo3qx3tsqScUdCLlAF359/zN/DE+4s4kl/IQ1d25Ifnt6aSmpBJFCjoRSJs3c5wE7LZK3bQp3V9nr2pG23UhEyiSEEvEiEFhc5fv1jDbz5YSsUKxpM3dOW2PqlqQiZRp6AXiYDlW/czbHwm36zbwyUdknnqxm40q1st2mWJAAp6kdNyJL+QVz5byQufrKBGlYr84fs9uL5HMzUhk5iioBc5RZkb9jAsPZMlW/Zz7VnNGHVtZxrWVBMyiT0KepGTdDivgN9PX8ars1aRXKsKr94VYkDnxtEuS6RECnqRk/Dlqp2MGJ/Jmp0H+UGfFoy4shN1qqkJmcQ2Bb1IKew/nMezU5cwbu46UutX563BfTk3TU3IJD4o6EVO4JMlWxk5MZut+w4z+PzW/L/LO1CtstoXSPxQ0IuUYNeBIzz+3kLeWbCJ9o1r8tLt53J2qpqQSfxR0Iscw915L3MzoyctZP/hPB64tB3/cUkalSupfYHEJwW9SBFb9oabkH20eCtnpdRhzM196dhETcgkvp0w6M2sKjATqBJsn+7uo8xsHBAC8oB5wFB3zztmbA/gZaA2UAA85e7/iugrEIkAd+efX63n6fcXk1dYyMirOnHf+a2pqPYFkgBKc0WfC/R39xwzSwJmm9lUYBxwR7DNW8BgwqFe1EHgLndfbmbNgAwz+8Dd90SmfJHTt3bnAUaMz2LOqp2c06Y+z97UnVYNa0S7LJGIOWHQu7sDOcHTpODh7j7l6DZmNg9IKWbssiIfbzKzbUAysOf0yhY5fQWFzuufr+Y3Hy4lqUIFnrmpG7f2bqH2BZJwSjVHb2YVgQwgDXjR3ecWWZcE3Ak8cIJ99AEqAyuLWTcEGAKQmppa2tpFTtnSLeEmZN+u38NlnRrx5A3daFKnarTLEikTpQp6dy8AephZXWCimXV19+xg9UvATHefVdJ4M2sK/A24290Li9n/WGAsQCgU8pN7CSKldyS/kJc+XcGLM1ZQq2oSf/zB2Vzbvamu4iWhndRdN+6+x8xmAAOBbDMbRXgqZmhJY8ysNvA+MNLdvzydYkVOx4L1exiensnSrfu5vkczRl3bhfo1Kke7LJEyV5q7bpKBvCDkqwEDgDFmNhi4Ari0uKv0YGxlYCLwprunR7BukVI7dKSA3364lL98vppGtary57tDXNpJTcik/CjNFX1T4I1gnr4C8La7TzazfGAtMCf4sXeCuz9uZiHgfncfDHwPuBBoYGb3BPu7x90XRPh1iBTri5U7GDE+i3W7DnJ731SGX9mR2lXVhEzKFwvfVBM7QqGQz58/P9plSJzbdziPZ6Ys5h/z1tOqQXWeHdSdc9o0iHZZImXGzDLcPVTcOr0zVhLOR4u2MvKdLLbvz2XohW34+WXt1YRMyjUFvSSMHTm5PPbeIt77dhMdm9Ti1btCdE+pG+2yRKJOQS9xz915d8EmHntvITm5+fxiQHvuv6itmpCJBBT0Etc27TnEI+9k88mSbZydWpcxg7rTvnGtaJclElMU9BKXCgudt+at49mpSygodH51TWfuPreVmpCJFENBL3Fn9Y4DjBifydzVuzgvrQHP3Nid1AbVo12WSMxS0EvcyC8o5M+zV/O76cuoXKkCzw3qzi2hFLUvEDkBBb3EhUWb9jF8fCZZG/cyoHNjnryhK41rqwmZSGko6CWm5eYX8MInK3j505XUrZ7Ei7f15KpuTXQVL3ISFPQSszLW7mb4+ExWbMvhprOb8+g1namnJmQiJ01BLzHn4JF8fv3BUv76xRqa1q7K6/f25pIOjaJdlkjcUtBLTJm9fAcjJmSyYfch7urXkmEDO1Kzik5TkdOhryCJCXsP5vHUlEW8PX8DrRvW4O2h/ejTun60yxJJCAp6ibpp2Vt49N1sdh04wo8vbssDl7ajapKakIlEioJeomb7/lxGT1rI+1mb6dS0Nn+5uzfdUupEuyyRhKOglzPO3Znw9UYen7yIQ0cKePCKDgy5sA1JFdWETKQsKOjljNq45xAPT8jis2Xb6dWyHmMGdSetUc1olyWS0BT0ckYUFjp/n7uWMVOX4MDoaztzV79WVFATMpEyp6CXMrdyew4jxmfy1ZrdXNCuIU/f2I0W9dWETORMUdBLmckrKOTVWav4w0fLqVqpAr++uTs391ITMpEzTUEvZSJ7416Gj89k4aZ9DOzShMdv6EKjWmpCJhINJwx6M6sKzASqBNunu/soMxsHhIA8YB4w1N3zihl/N/BI8PRJd38jUsVL7DmcV8CfPlnOK5+tol71yrx8e0+u7NY02mWJlGuluaLPBfq7e46ZJQGzzWwqMA64I9jmLWAw8HLRgWZWHxhF+BuCAxlmNsndd0fqBUjsmL9mF8PGZ7Jq+wFu7pXCI1d3om51NSETibYTBr27O5ATPE0KHu7uU45uY2bzgJRihl8BTHf3XcF204GBwD9Os26JIQdyw03I3pizhmZ1qvHmfX24sH1ytMsSkUCp5ujNrCKQAaQBL7r73CLrkoA7gQeKGdocWF/k+YZg2bH7HwIMAUhNTS1t7RIDPlu2nYcnZLFp7yHu7teKB6/oQA01IROJKaX6inT3AqCHmdUFJppZV3fPDla/BMx091mnWoS7jwXGAoRCIT/V/ciZs+fgEZ6YvJjxX2+gTXIN/j20H6FWakImEotO6tLL3feY2QzC0y/ZZjYKSAaGljBkI3BxkecpwKcnX6bEkqlZm3n03YXsPniEn16Sxk/7p6kJmUgMK81dN8lAXhDy1YABwBgzG0x4Dv5Sdy8sYfgHwNNmVi94fjnwUATqlijYtu8wv3p3IdMWbqFLs9q8cV9vujRTEzKRWFeaK/qmwBvBPH0F4G13n2xm+cBaYE7wBpgJ7v64mYWA+919sLvvMrMngK+CfT1+9BezEj/cnfSMDTwxeRGH8wsZPrAjP7qgNZXUhEwkLlj4pprYEQqFfP78+dEuQwLrdx3k4YlZzFq+g96t6vHsoO60TVYTMpFYY2YZ7h4qbp1uj5BiFRQ6b85Zw68/WIoBT1zfhdv7tlQTMpE4pKCX71ixbT/Dx2eRsXY3F7VP5qkbu5JST03IROKVgl7+R15BIf/92Ur++PEKqlepyO++dxY3nt1cTchE4pyCXoBwE7IH0zNZvHkfV3dvyuhru5Bcq0q0yxKRCFDQl3OH8wr4w0fLeXXWKurXqMx/39mLK7o0iXZZIhJBCvpybN7qXYwYn8mqHQf4fqgFD1/ViTrVk6JdlohEmIK+HNp/OI/npi3lb1+uJaVeNf7+w76c365htMsSkTKioC9nZizdxsgJWWzed5j7zmvNL69oT/XKOg1EEpm+wsuJ3QeO8MTkRUz4ZiPtGtUk/f5z6dWy3okHikjcU9AnOHfn/azNjHp3IXsP5fGz/mn8R/80qlRSEzKR8kJBn8C27jvMI+9kM33RVro1r8PfB/elU9Pa0S5LRM4wBX0Ccnfenr+eJ99fzJH8Qh66siM/PF9NyETKKwV9glm38yAjJmTyxcqd9GldnzGDutO6YY1olyUiUaSgTxAFhc5fv1jDbz5YSsUKxpM3dOW2PqlqQiYiCvpEsGzrfoalZ7Jg/R76d2zEkzd0pVndatEuS0RihII+jh3JL+SVz1byp0+WU7NKJZ6/tQfXndVMTchE5P9Q0Mepb9fvYfj4TJZs2c+1ZzVj9LWdaVBTTchE5LsU9HHm0JECfv/RMl6btYrkWlV49a4QAzo3jnZZIhLDFPRxZM7KnTw0IZM1Ow/ygz4teOiqTtSuqiZkInJ8Cvo4sO9wHs9OXcJbc9eRWr86bw3uy7lpakImIqVzwqA3s6rATKBKsH26u48ys58CPwfaAsnuvqOE8c8BVwMVgOnAAx5rf5E8hn2yZCsPT8hm2/7D/OiC1vxiQAeqVVb7AhEpvdJc0ecC/d09x8ySgNlmNhX4HJgMfFrSQDM7FzgP6B4smg1cdLwxErYzJ5fHJy/i3QWb6NC4Fq/c2YseLepGuywRiUMnDPrg6jsneJoUPNzdvwFOdCufA1WByoAFY7eeRr0Jz92Z9O0mHntvEfsP5/Hzy9rxk4vTqFxJ7QtE5NSUao7ezCoCGUAa8KK7zy3NOHefY2YzgM2Eg/4Fd19czP6HAEMAUlNTS1l64tm89xCPTMzm4yXbOKtFXZ4b1J0OTWpFuywRiXOlCnp3LwB6mFldYKKZdXX37BONM7M0oBOQEiyabmYXuPusY/Y/FhgLEAqFyt38fWGh88+v1vPMlMXkFRbyyNWduPe81lRU+wIRiYCTuuvG3fcEV+gDgRMGPXAj8KW75wAEc/v9gFnHHVWOrNlxgBETMvly1S76tWnAs4O60bKBmpCJSOSccOLXzJKDK3nMrBowAFhSyv2vAy4ys0rBL3IvAr4zdVMeFRQ6r85cxcDnZ7Jw4z6evakbb/2or0JeRCKuNFf0TYE3gnn6CsDb7j7ZzH4GDAOaAJlmNsXdB5tZCLjf3QcD6UB/IIvwL2anuft7ZfJK4siSLfsYnp7Jtxv2clmnRjx5Qzea1Kka7bJEJEFZrN3SHgqFfP78+dEuo0zk5hfw4oyVvDRjBXWqJTH6ui5c072pmpCJyGkzswx3DxW3Tu+MPUO+Wbeb4eMzWbY1hxt6NONX13ahfo3K0S5LRMoBBX0ZO3gkn99+uIy/fL6aJrWr8pd7QvTvqCZkInLmKOjL0BcrdjBiQhbrdh3k9r6pjLiyI7XUhExEzjAFfRnYeyiPZ6Ys5p9fradVg+r8c8g5nNOmQbTLEpFySkEfYdMXbeWRd7LYvj+XoRe14b8ua0/VJDUhE5HoUdBHyI6cXEZPWsjkzM10bFKLV+8K0T2lbrTLEhFR0J8ud+edBRt57L1FHMwt4P8NaM/Qi9qqCZmIxAwF/WnYtOcQIydmMWPpds5ODTcha9dYTchEJLYo6E9BYaEzbt46xkxdQkGh86trOnP3ua3UhExEYpKC/iSt2p7DiAlZzFu9i/PTGvLMTd1oUb96tMsSESmRgr6U8gsKeW32an4/fRmVK1XguUHduSWUovYFIhLzFPSlsGjTPoaN/5bsjfu4vHNjnrihK41rqwmZiMQHBf1x5OYX8MInK3j505XUrZ7Ei7f15KpuTXQVLyJxRUFfgoy14SZkK7blcFPP5jx6dWfqqQmZiMQhBf0xDuTm85sPl/LXL9bQrE41/npvby7u0CjaZYmInDIFfRGzlm/noQlZbNh9iLv6tWTYwI7UrKJDJCLxTSkG7D2Yx5PvL+LfGRto07AGbw/tR5/W9aNdlohIRJT7oJ+WvYVH381m14Ej/PjitjxwaTs1IRORhFJug37b/sOMnrSQKVlb6Ny0Nq/f05uuzetEuywRkYgrd0Hv7kz4eiOPT17EobwCHryiA0MubENSRTUhE5HEVK6CfsPugzw8MZuZy7bTq2U9xgzqTlqjmtEuS0SkTJ0w6M2sKjATqBJsn+7uo8zsp8DPgbZAsrvvKGF8KvAa0AJw4Cp3XxOR6kupsND525drGTNtCQCPXdeFO89pSQU1IRORcqA0V/S5QH93zzGzJGC2mU0FPgcmA5+eYPybwFPuPt3MagKFp1PwyVq5PYfh6ZnMX7ubC9o15Okb1YRMRMqXEwa9uzuQEzxNCh7u7t8Ax20HYGadgUruPj3YV06JG0dYXkEhY2eu4vmPl1MtqSK/ueUsBvVsrvYFIlLulGqO3swqAhlAGvCiu88t5f7bA3vMbALQGvgIGOHuBcfsfwgwBCA1NbWUuy5Z9sa9DB+fycJN+7iyaxMeu74LjWqpCZmIlE+lCvogmHuYWV1gopl1dffsUu7/AuBsYB3wL+Ae4M/H7H8sMBYgFAp5aYs/1uG8Av748XL+e+Yq6lWvzMu39+TKbk1PdXciIgnhpO66cfc9ZjYDGAiUJug3AAvcfRWAmb0DnMMxQR8J63cd5O7X57Fq+wFu6ZXCI1d3pk71pEh/GhGRuFOau26Sgbwg5KsBA4Axpdz/V0BdM0t29+1Af2D+KVd7HI1rV6VVgxqMvrYLF7ZPLotPISISl0rzLqGmwAwzyyQc3NPdfbKZ/czMNgApQKaZvQZgZqGjHwdTPr8EPjazLMCAV8vihVSuVIG/3NNbIS8icgwL31QTO0KhkM+fXyYX/SIiCcvMMtw9VNw6ve9fRCTBKehFRBKcgl5EJMEp6EVEEpyCXkQkwSnoRUQSnIJeRCTBxdx99Ga2HVhbhp+iIVBs7/wYEy91QvzUqjojK17qhPip9XTqbOnuxb5jNOaCvqyZ2fyS3lQQS+KlToifWlVnZMVLnRA/tZZVnZq6ERFJcAp6EZEEVx6Dfmy0CyileKkT4qdW1RlZ8VInxE+tZVJnuZujFxEpb8rjFb2ISLmioBcRSXBxHfRmVtXM5pnZt2a20MweC5aPM7OlZpZtZn8xs+/8TUEz62Fmc4JxmWb2/SLr/mpmq81sQfDoEa06g+0KitQyqcjy1mY218xWmNm/zKxytOo0s0uK1LjAzA6b2Q3BuogezxPU+udgWaaZpZtZzRLGPxQct6VmdkWR5QODZSvMbEQ06zSzAWaWYWZZwb/9i6z7NKjz6DFtFMU6W5nZoSK1vFJkXa+g/hVm9kczsyjWefsx52jh0XMx0sfzeLUWWf9HM8s5zvjInaPuHrcPwn+xqmbwcRIwl/DfpL0qWGfAP4AfFzO2PdAu+LgZsBmoGzz/K3BzLNQZjMkpYfnbwK3Bx6+UNP5M1VlkP/WBXUD1sjieJ6i1dpFtfgeMKGZsZ+BboArQGlgJVAweK4E2QOVgm85RrPNsoFnwcVdgY5F1nwKhGDmerYDsEvY7L9iPAVOBK6NV5zH76QasLKvjebxag+ch4G/H+dqO6Dka11f0Hnb0O2JS8HB3nxKsc8InWkoxY5e5+/Lg403ANqBM/g7h6dRZkuDKqD+QHix6A7ghRuq8GZjq7gdPp55TrHUf/M/xqQYUd7fB9cA/3T3X3VcDK4A+wWOFu69y9yPAP4Nto1Knu38TnJsAC4FqZlbldOopizpLYmZNCQfwl8G58yZld46ebJ0/IPz/W2ZKqtXMKgK/BoYdZ3hEz9G4DnoAM6toZgsIB/V0d59bZF0ScCcw7QT76EP4u+PKIoufCn4M/H0kvrhOs86qZjbfzL60YDoEaADscff84PkGoHmU6zzqVsJX/kVF9Hger1Yzex3YAnQE/lTM0ObA+iLPjx67kpZHq86iBgFfu3tukWWvB9MMj57ulEgE6mxtZt+Y2WdmdkGwrDnhY3hULB3P7/PdczSix/M4tf4UmOTum48zNKLnaNwHvbsXuHsPwleZfcysa5HVLwEz3X1WSeODq46/Afe6e2Gw+CHCJ0tvwtMQw6NcZ0sPvy36NuAPZtb2dOspozqPHs9uwAdFFkf8eB6vVne/l/B03GLCX9BRdbp1mlkXYAwwtMji2929G3BB8LgzinVuBlLd/WzgF8BbZlb7dOspgzoBMLO+wEF3zy6yOOLHs4RaLwRu4cTfiCIq7oP+KHffA8wABgKY2SjCUzG/KGlMcDK+D4x09y+L7Gtz8GNXLvA64R+Xolanu28M/l1FeC7xbGAnUNfMKgWbpQAbo1ln4HvARHfPK7KvMjuexdUaLCsg/GPtoGKGbARaFHl+9NiVtDxadWJmKcBE4C53X1lk3NFzYj/wFmV4jp6ozmB6YWfwcQbhn4zbEz52Raf5on48A9/5ibMsj+cxtV4CpAErzGwNUN3MVhQzJKLnaFwHvZklm1nd4ONqwABgiZkNBq4AflDkKv3YsZUJfwG96e7px6xrGvxrhOcUs7+zgzNXZ72jUx1m1hA4D1gUzHnOIDwfDnA38G606iziBxzzRRTp43mcWpeaWVqRz3UdsKSY4ZOAW82sipm1BtoR/t3DV0A7C9/NVJlwIEwqZvwZqTMY9z7hXyx+XmR5peBcODqddg1lc46Wts7kYN4ZM2tD+HiuCqYm9pnZOcH4uyibc7S0/++YWQXCFyP/LLIs4sfzOLVmuHsTd2/l7q0I/2SRVszwyJ6jHsHfMp/pB9Ad+AbIJPwf86tgeT7hq4oFwePo8hDwWvDxHUBekW0WAD2CdZ8AWcE+/07wm/Mo1XluUMu3wb8/LLLfNsF//grg30CVaNUZPG9F+OqiwjH7jejxLKlWwhcunxf5XOMI7sYg/MX/eJHxI4PXtJQid4IQvsNoWbBuZDTrBB4BDhxzjjYCagAZwT4XAs8DFaNY56CgjgXA18C1RfYbCsauBF4geDd+FP/fLwa+PGafET+ex/t6OmabnCIfl9k5qhYIIiIJLq6nbkRE5MQU9CIiCU5BLyKS4BT0IiIJTkEvIpLgFPQiIglOQS8ikuD+P2nxdLNe07qcAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_caracteristica = carro3['model year']\n",
        "df_resultado = carro3['mpg']\n",
        "\n",
        "x_train,x_test,y_train,y_test3 = train_test_split(df_caracteristica,df_resultado, train_size= 0.6) #test_size = porcentaje de datos para testear\n",
        "\n",
        "modelo=LinearRegression()\n",
        "modelo.fit(X=np.array(x_train).reshape(-1,1), y=y_train) #Solo funciona con 'X' y 'y'\n",
        "\n",
        "predicciones3 = modelo.predict(X = np.array(x_test).reshape(-1,1))\n",
        "r2 = r2_score(y_true = y_test3, y_pred = predicciones3)\n",
        "print('El r2 es ',r2)\n",
        "plt.plot(y_test3,predicciones3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J0VuV1WAPwmE"
      },
      "source": [
        "### Carro 4: Ford maverick"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 269,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "CjhgnjfSFXLe",
        "outputId": "7679f6cc-0c83-4016-d3fc-d8257b8c6173"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "El r2 es  -3.1744444444444246\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f53634f8070>]"
            ]
          },
          "execution_count": 269,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_caracteristica = carro4['acceleration']\n",
        "df_resultado = carro4['horsepower']\n",
        "\n",
        "\n",
        "x_train,x_test,y_train,y_test4 = train_test_split(df_caracteristica,df_resultado, train_size= 0.6) #test_size = porcentaje de datos para testear\n",
        "\n",
        "modelo=LinearRegression()\n",
        "modelo.fit(X=np.array(x_train).reshape(-1,1), y=y_train) #Solo funciona con 'X' y 'y'\n",
        "\n",
        "predicciones4 = modelo.predict(X = np.array(x_test).reshape(-1,1))\n",
        "r2 = r2_score(y_true = y_test4, y_pred = predicciones4)\n",
        "print('El r2 es ',r2)\n",
        "plt.plot(y_test4,predicciones4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ALwaclvAP1dk"
      },
      "source": [
        "### Carro 5: Amc hornet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 270,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "b0tGcZIoFXoc",
        "outputId": "676625cc-d14b-4387-e43d-46583812fce6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "El r2 es  0.0\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f53634db130>]"
            ]
          },
          "execution_count": 270,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_caracteristica = carro5['weight']\n",
        "df_resultado = carro5['acceleration']\n",
        "\n",
        "\n",
        "x_train,x_test,y_train,y_test5 = train_test_split(df_caracteristica,df_resultado, train_size= 0.6) #test_size = porcentaje de datos para testear\n",
        "\n",
        "modelo=LinearRegression()\n",
        "modelo.fit(X=np.array(x_train).reshape(-1,1), y=y_train) #Solo funciona con 'X' y 'y'\n",
        "\n",
        "predicciones5 = modelo.predict(X = np.array(x_test).reshape(-1,1))\n",
        "r2 = r2_score(y_true = y_test5, y_pred = predicciones5)\n",
        "print('El r2 es ',r2)\n",
        "plt.plot(y_test5,predicciones5)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}