real_estate_analysis_ML_pt1.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"DMWND0KW8FXh"},"source":["# **0: Connecting to Google Drive and Github**"]},{"cell_type":"code","execution_count":105,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1981,"status":"ok","timestamp":1725905399550,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"r2hEU6xtN6-g","outputId":"32d71c96-6f57-4821-9aa0-101bbbfd3533"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":106,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1725905399550,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"tAjJUWPZiZDa","outputId":"4b948c46-64b9-43c2-aae2-163a520437e0"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/Github\n"]}],"source":["%cd /content/drive/MyDrive/Github/"]},{"cell_type":"markdown","source":["##### Github"],"metadata":{"id":"fRtyRX0rolGz"}},{"cell_type":"code","source":["# !git init real_estate_uni_bivariable"],"metadata":{"id":"iUS-g_9IYrJ5","executionInfo":{"status":"ok","timestamp":1725905399550,"user_tz":-120,"elapsed":8,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"execution_count":107,"outputs":[]},{"cell_type":"code","source":["# %cd real_estate_uni_bivariable"],"metadata":{"id":"Wi_ga0NAYvcX","executionInfo":{"status":"ok","timestamp":1725905399550,"user_tz":-120,"elapsed":7,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"execution_count":108,"outputs":[]},{"cell_type":"code","source":["# %ls -a"],"metadata":{"id":"6NrLLf93YxFP","executionInfo":{"status":"ok","timestamp":1725905399550,"user_tz":-120,"elapsed":7,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"execution_count":109,"outputs":[]},{"cell_type":"code","source":["# !git status"],"metadata":{"id":"kbri0oDMY0Yc","executionInfo":{"status":"ok","timestamp":1725905399550,"user_tz":-120,"elapsed":7,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"execution_count":110,"outputs":[]},{"cell_type":"code","source":["# !git add ."],"metadata":{"id":"zEmS7fmhY1i-","executionInfo":{"status":"ok","timestamp":1725905399550,"user_tz":-120,"elapsed":7,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"execution_count":111,"outputs":[]},{"cell_type":"code","source":["# !git commit -m \"new commit\""],"metadata":{"id":"OXqaHny1Y4kl","executionInfo":{"status":"ok","timestamp":1725905399550,"user_tz":-120,"elapsed":7,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"execution_count":112,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"jvWhYSf0kkHy"},"source":["# **Dataset 1: Página web de anuncios inmobiliarios -> ASKING PRICE**"]},{"cell_type":"markdown","metadata":{"id":"ZUNIGszOxQuP"},"source":["## **Consideraciones iniciales o punto de partida**\n","\n","**VARIABLE DEPENDIENTE:** el precio de venta (asking-price). La variable se llama \"PRICE\".\n","\n","Este análisis se realiza para conocer el precio de venta de apartamentos (también conocido como Asking Price) que los vendedores están publicando en los anuncios de una página web de anuncios inmobiliarios. El objetivo es poder predecir el precio de venta de un inmueble concreto en función de sus características.\n","*   Se ha extraído una muestra de la población de datos que se quiere analizar. La muestra coincide con las características del inmueble que se quiere predecir. Se ha de tener en cuenta que el mercado inmobiliario de esta región es muy particular y existen grandes variaciones en el precio entre localidades cercanas.\n","*   En cuanto al tipo de inmueble, no se han analizado casas individuales ni adosados, sólo apartamentos (ya sean estudios, apartamentos de una habitación, de dos habitaciones, duplex o triplex. También apartamentos de nueva construcción).\n","*   Respecto al precio, se ha establecido un rango entre 0 y 300.000, ya que es el rango de precios que nos interesa, además, debido a problemas con el web scraping, no hemos podido ampliar esta lista para incluir otro tipo de inmuebles o aquellos con un precio superior. Es por ello que no ha sido necesario hacer un estudio de los outliers.\n","*   El dataset es muy pequeño (unas 180 líneas) ya que la zona geográfica está muy restringida. Se han recopilado datos durante 2 meses, desde el 29 de junio hasta el 29 de agosto, y se han añadido 3 nuevas columnas al final del dataset para gestionar los movimientos ocurridos a lo largo del tiempo: sold (y/n), selling_time (se ha calculado manualmente el tiempo transcurrido entre la aparición del anuncio y su desaparición) y old_price (se han registrado los cambios en el precio de venta, al alza o a la baja).\n","*   Al tener tan pocas filas, corro el riesgo real (y así se confirma desde las primeras pruebas) de incurrir en overfitting. Es por ello que voy a simplificar el algunas columnas, aquellas que salen de la variable general \"tags\", para intentar llegar a la parte de ejecución de los modelos con menos columnas. En concreto hago que las variables \"chambres\" (habitaciones) y \"étages\" (niveles) pasen de ser variables categóricas a variables numéricas."]},{"cell_type":"markdown","metadata":{"id":"cishLoCMOHLB"},"source":["## **1. Importar archivo**\n","---"]},{"cell_type":"code","execution_count":113,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":142},"executionInfo":{"elapsed":305,"status":"ok","timestamp":1725905399848,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"pNfjPCgLOGgs","outputId":"2ce548c9-865b-4703-9e84-bfa0bf03a22e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   area  contact_is_private_seller  district dpe estate_type ges is_exclusive  \\\n","0    20                          0         3   E  Appartment   B            f   \n","1    23                          0         1   F      Studio   C            f   \n","\n","    price  rooms sold  ... price_decrease_percent  bedrooms level balcony  \\\n","0  103000      2    n  ...                   6,36         1     3       0   \n","1  120000      1    n  ...                   0,00         0     3       0   \n","\n","   terrace  parking  elevator  box  garden  swimming_pool  \n","0        0        0         1    0       0              0  \n","1        0        0         0    0       0              0  \n","\n","[2 rows x 23 columns]"],"text/html":["\n","  <div id=\"df-63bbe182-0583-4dfc-a37c-e0029dc056b4\" 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>area</th>\n","      <th>contact_is_private_seller</th>\n","      <th>district</th>\n","      <th>dpe</th>\n","      <th>estate_type</th>\n","      <th>ges</th>\n","      <th>is_exclusive</th>\n","      <th>price</th>\n","      <th>rooms</th>\n","      <th>sold</th>\n","      <th>...</th>\n","      <th>price_decrease_percent</th>\n","      <th>bedrooms</th>\n","      <th>level</th>\n","      <th>balcony</th>\n","      <th>terrace</th>\n","      <th>parking</th>\n","      <th>elevator</th>\n","      <th>box</th>\n","      <th>garden</th>\n","      <th>swimming_pool</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>20</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>E</td>\n","      <td>Appartment</td>\n","      <td>B</td>\n","      <td>f</td>\n","      <td>103000</td>\n","      <td>2</td>\n","      <td>n</td>\n","      <td>...</td>\n","      <td>6,36</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>23</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>F</td>\n","      <td>Studio</td>\n","      <td>C</td>\n","      <td>f</td>\n","      <td>120000</td>\n","      <td>1</td>\n","      <td>n</td>\n","      <td>...</td>\n","      <td>0,00</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 23 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-63bbe182-0583-4dfc-a37c-e0029dc056b4')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-63bbe182-0583-4dfc-a37c-e0029dc056b4 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-63bbe182-0583-4dfc-a37c-e0029dc056b4');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-6f4e5c67-f5cb-4ed1-9814-53e8fa736e2e\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f4e5c67-f5cb-4ed1-9814-53e8fa736e2e')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-6f4e5c67-f5cb-4ed1-9814-53e8fa736e2e button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df"}},"metadata":{},"execution_count":113}],"source":["import pandas as pd\n","\n","df = pd.read_csv(\"/content/drive/MyDrive/Github/real_estate_analysis/df_real_estate_cleaned.csv\", encoding='utf-8')\n","df.head(2)"]},{"cell_type":"code","source":["df_a_predecir = pd.read_csv(\"/content/drive/MyDrive/Github/real_estate_analysis/df_real_estate_cleaned_predict.csv\", encoding='utf-8')\n","df_a_predecir.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":110},"id":"jnjCI1Mkv6h1","executionInfo":{"status":"ok","timestamp":1725905399848,"user_tz":-120,"elapsed":7,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}},"outputId":"3a805af9-9ed6-4601-f9df-bd47803c3409"},"execution_count":114,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   area  contact_is_private_seller  district dpe  estate_type ges  \\\n","0    42                          1         3   C  Appartement   A   \n","\n","  is_exclusive  price  rooms sold  ...  price_decrease_percent  bedrooms  \\\n","0            t    NaN      2    n  ...                       0         1   \n","\n","  level  balcony  terrace  parking  elevator  box  garden  swimming_pool  \n","0     1        0        1        1         1    0       0              0  \n","\n","[1 rows x 23 columns]"],"text/html":["\n","  <div id=\"df-a0d78a08-398c-49ca-86b7-b678de16af75\" 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>area</th>\n","      <th>contact_is_private_seller</th>\n","      <th>district</th>\n","      <th>dpe</th>\n","      <th>estate_type</th>\n","      <th>ges</th>\n","      <th>is_exclusive</th>\n","      <th>price</th>\n","      <th>rooms</th>\n","      <th>sold</th>\n","      <th>...</th>\n","      <th>price_decrease_percent</th>\n","      <th>bedrooms</th>\n","      <th>level</th>\n","      <th>balcony</th>\n","      <th>terrace</th>\n","      <th>parking</th>\n","      <th>elevator</th>\n","      <th>box</th>\n","      <th>garden</th>\n","      <th>swimming_pool</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>42</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>C</td>\n","      <td>Appartement</td>\n","      <td>A</td>\n","      <td>t</td>\n","      <td>NaN</td>\n","      <td>2</td>\n","      <td>n</td>\n","      <td>...</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1 rows × 23 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0d78a08-398c-49ca-86b7-b678de16af75')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-a0d78a08-398c-49ca-86b7-b678de16af75 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-a0d78a08-398c-49ca-86b7-b678de16af75');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df_a_predecir"}},"metadata":{},"execution_count":114}]},{"cell_type":"markdown","source":["Convertimos rápidamente las variables categóricas en numéricas (alternativamente podríamos hacer get_dummies pero preferimos no optar por ello ya que el dataset tiene pocas filas):"],"metadata":{"id":"LBQfshBIzIT9"}},{"cell_type":"code","source":["df.replace([\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\"],  [1, 2, 3, 4, 5, 6, 7], inplace=True)\n","df_a_predecir.replace([\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\"],  [1, 2, 3, 4, 5, 6, 7], inplace=True)\n","\n","df.replace([\"f\", \"t\"], [0, 1], inplace=True)\n","df_a_predecir.replace([\"f\", \"t\"], [0, 1], inplace=True)\n","\n","df.replace([\"n\", \"y\"], [0, 1], inplace=True)\n","df_a_predecir.replace([\"n\", \"y\"], [0, 1], inplace=True)\n","\n","df.replace([\"Studio\", \"Appartment\", \"Duplex\", \"Triplex\", \"New Appartment\"], [1, 2, 3, 4, 5], inplace=True)\n","df_a_predecir.replace([\"Studio\", \"Appartement\", \"Duplex\", \"Triplex\", \"New Appartment\"], [1, 2, 3, 4, 5], inplace=True)\n","\n","df.replace([\"1 week\", \"2 weeks\", \"1 month\", \"2 months\"], [7, 15, 30, 60], inplace=True)\n","# df_a_predecir.replace([\"1 week\", \"2 weeks\", \"1 month\", \"2 months\"], [7, 15, 30, 60], inplace=True\n","\n","df.fillna(0, inplace=True)\n","df_a_predecir.fillna(0, inplace=True)\n","\n","df = df.replace({',': '.'}, regex=True)\n","df_a_predecir = df_a_predecir.replace({',': '.'}, regex=True)\n","df.price_decrease_percent = df.price_decrease_percent.astype(\"float\")\n","df_a_predecir.price_decrease_percent = df_a_predecir.price_decrease_percent.astype(\"float\")\n","\n","df.drop(columns=[\"appartment_size\"], inplace=True)\n","df_a_predecir.drop(columns=[\"appartment_size\"], inplace=True)"],"metadata":{"id":"ysPish_7zhVW","executionInfo":{"status":"ok","timestamp":1725905399848,"user_tz":-120,"elapsed":6,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"execution_count":115,"outputs":[]},{"cell_type":"code","source":["df.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":142},"id":"h796_vyQ0isg","executionInfo":{"status":"ok","timestamp":1725905400193,"user_tz":-120,"elapsed":351,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}},"outputId":"cbd0340b-0670-4841-8b15-d4f90983aaa5"},"execution_count":116,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   area  contact_is_private_seller  district  dpe  estate_type  ges  \\\n","0    20                          0         3    5            2    2   \n","1    23                          0         1    6            1    3   \n","\n","   is_exclusive   price  rooms  sold  ...  price_decrease_percent  bedrooms  \\\n","0             0  103000      2     0  ...                    6.36         1   \n","1             0  120000      1     0  ...                    0.00         0   \n","\n","   level  balcony  terrace  parking  elevator  box  garden  swimming_pool  \n","0      3        0        0        0         1    0       0              0  \n","1      3        0        0        0         0    0       0              0  \n","\n","[2 rows x 22 columns]"],"text/html":["\n","  <div id=\"df-816161ca-34c1-4ad5-9be0-8069a889d37d\" 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>area</th>\n","      <th>contact_is_private_seller</th>\n","      <th>district</th>\n","      <th>dpe</th>\n","      <th>estate_type</th>\n","      <th>ges</th>\n","      <th>is_exclusive</th>\n","      <th>price</th>\n","      <th>rooms</th>\n","      <th>sold</th>\n","      <th>...</th>\n","      <th>price_decrease_percent</th>\n","      <th>bedrooms</th>\n","      <th>level</th>\n","      <th>balcony</th>\n","      <th>terrace</th>\n","      <th>parking</th>\n","      <th>elevator</th>\n","      <th>box</th>\n","      <th>garden</th>\n","      <th>swimming_pool</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>20</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>103000</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>6.36</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>23</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>6</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>120000</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0.00</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 22 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-816161ca-34c1-4ad5-9be0-8069a889d37d')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-816161ca-34c1-4ad5-9be0-8069a889d37d 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-816161ca-34c1-4ad5-9be0-8069a889d37d');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-416135f7-8e31-4794-be76-2a55374a0384\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-416135f7-8e31-4794-be76-2a55374a0384')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-416135f7-8e31-4794-be76-2a55374a0384 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df"}},"metadata":{},"execution_count":116}]},{"cell_type":"code","source":["df_a_predecir.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":110},"id":"tCgUzQVE7U3Z","executionInfo":{"status":"ok","timestamp":1725905400193,"user_tz":-120,"elapsed":14,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}},"outputId":"fdbdc3c4-9ea5-4e1f-9cbc-952b9d217d84"},"execution_count":117,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   area  contact_is_private_seller  district  dpe  estate_type  ges  \\\n","0    42                          1         3    3            2    1   \n","\n","   is_exclusive  price  rooms  sold  ...  price_decrease_percent  bedrooms  \\\n","0             1   0.00      2     0  ...                    0.00         1   \n","\n","   level  balcony  terrace  parking  elevator  box  garden  swimming_pool  \n","0      1        0        1        1         1    0       0              0  \n","\n","[1 rows x 22 columns]"],"text/html":["\n","  <div id=\"df-ce17a3eb-e453-4bf5-8541-4d937a2a916b\" 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>area</th>\n","      <th>contact_is_private_seller</th>\n","      <th>district</th>\n","      <th>dpe</th>\n","      <th>estate_type</th>\n","      <th>ges</th>\n","      <th>is_exclusive</th>\n","      <th>price</th>\n","      <th>rooms</th>\n","      <th>sold</th>\n","      <th>...</th>\n","      <th>price_decrease_percent</th>\n","      <th>bedrooms</th>\n","      <th>level</th>\n","      <th>balcony</th>\n","      <th>terrace</th>\n","      <th>parking</th>\n","      <th>elevator</th>\n","      <th>box</th>\n","      <th>garden</th>\n","      <th>swimming_pool</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>42</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.00</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0.00</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1 rows × 22 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce17a3eb-e453-4bf5-8541-4d937a2a916b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-ce17a3eb-e453-4bf5-8541-4d937a2a916b 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-ce17a3eb-e453-4bf5-8541-4d937a2a916b');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df_a_predecir"}},"metadata":{},"execution_count":117}]},{"cell_type":"markdown","metadata":{"id":"-la_GMeMj-Dm"},"source":["## **3. Análisis Manual con algoritmos de regresión de Machine Learning**\n","---\n","\n"]},{"cell_type":"markdown","metadata":{"id":"ss3gQkEZnnQG"},"source":["### Separar la tabla en dos: Entrenamiento y Prueba\n","\n","Se ha decidido no separar la tabla en tres partes como se debería hacer (train, test y validation) porque el dataset sólo dispone de unas 180 líneas (lo cual es poco).\n","\n","Este paso es muy importante por varios motivos:\n","1. Evaluar el rendimiento del modelo en datos no vistos\n","2. Evitar el overfitting (evitar que el modelo \"memorice\" los datos de entrenamiento)\n","3. Garantizar la fiabilidad de las métricas de evaluación\n","4. Mejorar la comparación entre varios modelos\n","5. Simulación de entorno real"]},{"cell_type":"code","execution_count":118,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1725905400194,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"E5wcZSa2lIt3","outputId":"9909aa35-a9c1-4d8b-af76-12dc525708f6"},"outputs":[{"output_type":"stream","name":"stdout","text":["['area', 'contact_is_private_seller', 'district', 'dpe', 'estate_type', 'ges', 'is_exclusive', 'rooms', 'sold', 'selling_time', 'old_price', 'price_decrease_percent', 'bedrooms', 'level', 'balcony', 'terrace', 'parking', 'elevator', 'box', 'garden', 'swimming_pool']\n"]}],"source":["X = df.drop(columns=[\"price\"])\n","y = df[\"price\"]\n","\n","predecir_X = df_a_predecir.drop(columns=[\"price\"])\n","# predecir_y = df_a_predecir[\"price\"]\n","\n","column_names = X.columns\n","\n","print(column_names.tolist())\n","# print(predecir_y)"]},{"cell_type":"code","execution_count":119,"metadata":{"id":"iQo5S7T_kjyc","executionInfo":{"status":"ok","timestamp":1725905400194,"user_tz":-120,"elapsed":8,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"]},{"cell_type":"code","execution_count":120,"metadata":{"id":"LeQj3_ovqe1S","executionInfo":{"status":"ok","timestamp":1725905400194,"user_tz":-120,"elapsed":8,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["X_train.replace([False, True],  [0, 1], inplace=True)\n","X_test.replace([False, True],  [0, 1], inplace=True)\n","predecir_X.replace([False, True],  [0, 1], inplace=True)"]},{"cell_type":"code","execution_count":121,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"collapsed":true,"executionInfo":{"elapsed":344,"status":"ok","timestamp":1725905400531,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"ek70kH9hk1ov","outputId":"3e6ee410-5b20-4b1e-cdc5-28e5668fff4d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["     area  contact_is_private_seller  district  dpe  estate_type  ges  \\\n","93     76                          0         4    4            2    5   \n","141    62                          0         4    4            2    1   \n","108    88                          0         1    7            2    3   \n","0      20                          0         3    5            2    2   \n","73     80                          1         3    3            2    1   \n","..    ...                        ...       ...  ...          ...  ...   \n","71     44                          0         1    4            1    2   \n","106    77                          0         4    5            2    3   \n","14     31                          1         1    5            2    2   \n","92     50                          0         2    4            2    4   \n","102    44                          0         2    1            5    1   \n","\n","     is_exclusive  rooms  sold  selling_time  ...  price_decrease_percent  \\\n","93              1      4     0          0.00  ...                    5.24   \n","141             1      3     0          0.00  ...                    0.00   \n","108             0      4     0          0.00  ...                    0.00   \n","0               0      2     0          0.00  ...                    6.36   \n","73              0      4     1         15.00  ...                    0.00   \n","..            ...    ...   ...           ...  ...                     ...   \n","71              0      2     0          0.00  ...                    0.00   \n","106             1      4     0          0.00  ...                    0.00   \n","14              0      3     1         30.00  ...                    6.29   \n","92              1      2     1         30.00  ...                    0.00   \n","102             0      2     0          0.00  ...                    0.00   \n","\n","     bedrooms  level  balcony  terrace  parking  elevator  box  garden  \\\n","93          3      1        1        1        0         1    0       0   \n","141         2      3        0        1        1         0    0       0   \n","108         3      2        0        1        0         0    1       0   \n","0           1      3        0        0        0         1    0       0   \n","73          3      2        0        1        0         1    1       0   \n","..        ...    ...      ...      ...      ...       ...  ...     ...   \n","71          0      2        0        1        1         0    0       0   \n","106         3      3        1        1        1         0    0       0   \n","14          1      2        0        1        0         0    0       1   \n","92          1      1        0        0        1         0    0       0   \n","102         0      0        0        1        0         0    1       0   \n","\n","     swimming_pool  \n","93               0  \n","141              0  \n","108              0  \n","0                0  \n","73               0  \n","..             ...  \n","71               0  \n","106              0  \n","14               0  \n","92               0  \n","102              0  \n","\n","[103 rows x 21 columns]"],"text/html":["\n","  <div id=\"df-073e3845-324d-4caf-9d15-1261e32ce95d\" 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>area</th>\n","      <th>contact_is_private_seller</th>\n","      <th>district</th>\n","      <th>dpe</th>\n","      <th>estate_type</th>\n","      <th>ges</th>\n","      <th>is_exclusive</th>\n","      <th>rooms</th>\n","      <th>sold</th>\n","      <th>selling_time</th>\n","      <th>...</th>\n","      <th>price_decrease_percent</th>\n","      <th>bedrooms</th>\n","      <th>level</th>\n","      <th>balcony</th>\n","      <th>terrace</th>\n","      <th>parking</th>\n","      <th>elevator</th>\n","      <th>box</th>\n","      <th>garden</th>\n","      <th>swimming_pool</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>93</th>\n","      <td>76</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>0.00</td>\n","      <td>...</td>\n","      <td>5.24</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>141</th>\n","      <td>62</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0.00</td>\n","      <td>...</td>\n","      <td>0.00</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>108</th>\n","      <td>88</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>7</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>0.00</td>\n","      <td>...</td>\n","      <td>0.00</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>20</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0.00</td>\n","      <td>...</td>\n","      <td>6.36</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>73</th>\n","      <td>80</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>15.00</td>\n","      <td>...</td>\n","      <td>0.00</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</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","      <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","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>71</th>\n","      <td>44</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0.00</td>\n","      <td>...</td>\n","      <td>0.00</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>106</th>\n","      <td>77</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>0.00</td>\n","      <td>...</td>\n","      <td>0.00</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>31</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>30.00</td>\n","      <td>...</td>\n","      <td>6.29</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>92</th>\n","      <td>50</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>30.00</td>\n","      <td>...</td>\n","      <td>0.00</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>102</th>\n","      <td>44</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0.00</td>\n","      <td>...</td>\n","      <td>0.00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>103 rows × 21 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-073e3845-324d-4caf-9d15-1261e32ce95d')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-073e3845-324d-4caf-9d15-1261e32ce95d 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-073e3845-324d-4caf-9d15-1261e32ce95d');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-99553f80-eb87-4a1c-96d2-a9a275ff3073\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-99553f80-eb87-4a1c-96d2-a9a275ff3073')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-99553f80-eb87-4a1c-96d2-a9a275ff3073 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","  <div id=\"id_6ed7c4a8-8111-48ca-8c42-c54c270d0e39\">\n","    <style>\n","      .colab-df-generate {\n","        background-color: #E8F0FE;\n","        border: none;\n","        border-radius: 50%;\n","        cursor: pointer;\n","        display: none;\n","        fill: #1967D2;\n","        height: 32px;\n","        padding: 0 0 0 0;\n","        width: 32px;\n","      }\n","\n","      .colab-df-generate:hover {\n","        background-color: #E2EBFA;\n","        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-generate {\n","        background-color: #3B4455;\n","        fill: #D2E3FC;\n","      }\n","\n","      [theme=dark] .colab-df-generate:hover {\n","        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","    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_train')\"\n","            title=\"Generate code using this dataframe.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n","  </svg>\n","    </button>\n","    <script>\n","      (() => {\n","      const buttonEl =\n","        document.querySelector('#id_6ed7c4a8-8111-48ca-8c42-c54c270d0e39 button.colab-df-generate');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      buttonEl.onclick = () => {\n","        google.colab.notebook.generateWithVariable('X_train');\n","      }\n","      })();\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"X_train"}},"metadata":{},"execution_count":121}],"source":["X_train"]},{"cell_type":"code","execution_count":122,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":110},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1725905400532,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"3QoTpuuwiJG7","outputId":"8de2b3e7-bcfb-4810-ca8f-61cb07d784b3","collapsed":true},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   area  contact_is_private_seller  district  dpe  estate_type  ges  \\\n","0    42                          1         3    3            2    1   \n","\n","   is_exclusive  rooms  sold  selling_time  ...  price_decrease_percent  \\\n","0             1      2     0             0  ...                    0.00   \n","\n","   bedrooms  level  balcony  terrace  parking  elevator  box  garden  \\\n","0         1      1        0        1        1         1    0       0   \n","\n","   swimming_pool  \n","0              0  \n","\n","[1 rows x 21 columns]"],"text/html":["\n","  <div id=\"df-7ca29675-d6b7-4aeb-aa96-0a981c63b8a4\" 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>area</th>\n","      <th>contact_is_private_seller</th>\n","      <th>district</th>\n","      <th>dpe</th>\n","      <th>estate_type</th>\n","      <th>ges</th>\n","      <th>is_exclusive</th>\n","      <th>rooms</th>\n","      <th>sold</th>\n","      <th>selling_time</th>\n","      <th>...</th>\n","      <th>price_decrease_percent</th>\n","      <th>bedrooms</th>\n","      <th>level</th>\n","      <th>balcony</th>\n","      <th>terrace</th>\n","      <th>parking</th>\n","      <th>elevator</th>\n","      <th>box</th>\n","      <th>garden</th>\n","      <th>swimming_pool</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>42</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>0.00</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1 rows × 21 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ca29675-d6b7-4aeb-aa96-0a981c63b8a4')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-7ca29675-d6b7-4aeb-aa96-0a981c63b8a4 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-7ca29675-d6b7-4aeb-aa96-0a981c63b8a4');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","  <div id=\"id_0ba65687-5a37-4fa1-ad38-4bf419fed62b\">\n","    <style>\n","      .colab-df-generate {\n","        background-color: #E8F0FE;\n","        border: none;\n","        border-radius: 50%;\n","        cursor: pointer;\n","        display: none;\n","        fill: #1967D2;\n","        height: 32px;\n","        padding: 0 0 0 0;\n","        width: 32px;\n","      }\n","\n","      .colab-df-generate:hover {\n","        background-color: #E2EBFA;\n","        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-generate {\n","        background-color: #3B4455;\n","        fill: #D2E3FC;\n","      }\n","\n","      [theme=dark] .colab-df-generate:hover {\n","        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","    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('predecir_X')\"\n","            title=\"Generate code using this dataframe.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n","  </svg>\n","    </button>\n","    <script>\n","      (() => {\n","      const buttonEl =\n","        document.querySelector('#id_0ba65687-5a37-4fa1-ad38-4bf419fed62b button.colab-df-generate');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      buttonEl.onclick = () => {\n","        google.colab.notebook.generateWithVariable('predecir_X');\n","      }\n","      })();\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"predecir_X"}},"metadata":{},"execution_count":122}],"source":["predecir_X"]},{"cell_type":"code","execution_count":123,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":458},"collapsed":true,"executionInfo":{"elapsed":11,"status":"ok","timestamp":1725905400532,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"DTMmHNzVk8Jf","outputId":"f5d0ddc2-68dd-4a53-e317-a3564aa50b77"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["93     235000\n","141    295000\n","108    258800\n","0      103000\n","73     220000\n","        ...  \n","71     219000\n","106    257000\n","14     149000\n","92     235000\n","102    250000\n","Name: price, Length: 103, dtype: int64"],"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>price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>93</th>\n","      <td>235000</td>\n","    </tr>\n","    <tr>\n","      <th>141</th>\n","      <td>295000</td>\n","    </tr>\n","    <tr>\n","      <th>108</th>\n","      <td>258800</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>103000</td>\n","    </tr>\n","    <tr>\n","      <th>73</th>\n","      <td>220000</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>71</th>\n","      <td>219000</td>\n","    </tr>\n","    <tr>\n","      <th>106</th>\n","      <td>257000</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>149000</td>\n","    </tr>\n","    <tr>\n","      <th>92</th>\n","      <td>235000</td>\n","    </tr>\n","    <tr>\n","      <th>102</th>\n","      <td>250000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>103 rows × 1 columns</p>\n","</div><br><label><b>dtype:</b> int64</label>"]},"metadata":{},"execution_count":123}],"source":["y_train"]},{"cell_type":"markdown","metadata":{"id":"yAhlziqlnfJc"},"source":["### Feature Scaling o convertir todos los numeros a un rango similar\n","\n","Vamos a utilizar dos estrategias para realizar el feature scaling, MinMaxScaler y StandardScaler. No obstante, el resto del análisis se realizará con los datos obtenidos por el StandardScaler ya que es menos sensible a los outliers y proporciona datos más coherentes."]},{"cell_type":"markdown","metadata":{"id":"IDsweoydjGgq"},"source":["#### MinMaxScaler o Normalización\n","\n","Ajusta los datos para que se encuentren en un rango entre 0 y 1.\n","\n","![image.png](data:image/png;base64,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)\n","\n","*   *X* es el valor original de la característica.\n","*   *Xmin* es el valor mínimo de la característica en el conjunto de datos.\n","*   *Xmax* es el valor máximo de la característica en el conjunto de datos.\n","*   *Xscaled* es el valor escalado."]},{"cell_type":"code","execution_count":124,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1725905400532,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"vWU8gyNwiiW5","outputId":"6e42d81d-dd7b-4746-99df-df9f47891519"},"outputs":[{"output_type":"stream","name":"stdout","text":["[[0.82352941 0.         1.         ... 0.         0.         0.        ]\n"," [0.61764706 0.         1.         ... 0.         0.         0.        ]\n"," [1.         0.         0.         ... 1.         0.         0.        ]\n"," ...\n"," [0.16176471 1.         0.         ... 0.         1.         0.        ]\n"," [0.44117647 0.         0.33333333 ... 0.         0.         0.        ]\n"," [0.35294118 0.         0.33333333 ... 1.         0.         0.        ]]\n","[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"]}],"source":["import numpy as np\n","from sklearn.preprocessing import MinMaxScaler\n","\n","scaler = MinMaxScaler()\n","\n","data_train = X_train.select_dtypes(include=[np.number]).values\n","data_test = X_test.select_dtypes(include=[np.number]).values\n","data_prediction = predecir_X.select_dtypes(include=[np.number]).values\n","\n","X_train_minmaxscaled = scaler.fit_transform(data_train)\n","X_test_minmaxscaled = scaler.fit_transform(data_test)\n","X_prediction_minmaxscaled = scaler.fit_transform(data_prediction)\n","\n","print(X_train_minmaxscaled)\n","print(X_prediction_minmaxscaled)"]},{"cell_type":"code","source":["data_train.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SkbKzwy19Tim","executionInfo":{"status":"ok","timestamp":1725905400532,"user_tz":-120,"elapsed":9,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}},"outputId":"94e557b2-3753-4efb-f01c-cb2257573784"},"execution_count":125,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(103, 21)"]},"metadata":{},"execution_count":125}]},{"cell_type":"code","source":["predecir_X.select_dtypes(include=\"object\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":64},"id":"XoFVkgW79VLw","executionInfo":{"status":"ok","timestamp":1725905400532,"user_tz":-120,"elapsed":7,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}},"outputId":"a79cde7e-39d3-4d31-b7ca-46cce318c43e"},"execution_count":126,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Empty DataFrame\n","Columns: []\n","Index: [0]"],"text/html":["\n","  <div id=\"df-bef39748-71fd-4d74-97b7-0c99a92b40ad\" 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","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bef39748-71fd-4d74-97b7-0c99a92b40ad')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-bef39748-71fd-4d74-97b7-0c99a92b40ad 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-bef39748-71fd-4d74-97b7-0c99a92b40ad');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n  \"name\": \"predecir_X\",\n  \"rows\": 1,\n  \"fields\": []\n}"}},"metadata":{},"execution_count":126}]},{"cell_type":"markdown","metadata":{"id":"6wsIt41SjDEY"},"source":["#### StandardScaler o Z-Score Normalization\n","\n","Ajusta los datos para que tengan una media de 0 y una desviación típica de 1.\n","\n","![image.png](data:image/png;base64,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)\n","\n","*    X es el valor original de la característica.\n","*    μ es la media de la característica en el conjunto de datos.\n","*    σ es la desviación estándar de la característica en el conjunto de datos.\n","*    X scaled es el valor escalado."]},{"cell_type":"code","execution_count":127,"metadata":{"id":"O8wfcHz0mfJr","executionInfo":{"status":"ok","timestamp":1725905400799,"user_tz":-120,"elapsed":9,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["import numpy as np\n","from sklearn.preprocessing import StandardScaler\n","\n","scaler = StandardScaler()\n","\n","data_train = X_train.select_dtypes(include=[np.number]).values\n","data_test = X_test.select_dtypes(include=[np.number]).values\n","data_prediction = predecir_X.select_dtypes(include=[np.number]).values\n","\n","X_train_scaled = scaler.fit_transform(data_train)\n","X_test_scaled = scaler.transform(data_test)\n","X_prediction_scaled = scaler.fit_transform(data_prediction)"]},{"cell_type":"code","execution_count":128,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1725905400799,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"jZeL6Y87ATI8","outputId":"af519ae8-003e-4d2f-c936-b9a18d920c29"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-1.22050542, -0.248708  , -0.99825077,  0.62630254, -1.33033001,\n","         0.18397059, -0.99033794, -1.48666153, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -1.46452235, -1.55412834, -0.65616732,\n","         0.95257934,  1.3938641 , -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-0.03347609, -0.248708  , -0.08021658, -0.73178507,  0.02634317,\n","        -1.03854366,  1.00975633, -0.35239384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -0.26733345, -0.84268292,  1.52400152,\n","        -1.04978132,  1.3938641 , -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [ 0.75787679, -0.248708  ,  0.83781761,  0.62630254,  0.02634317,\n","         2.01774197, -0.99033794,  0.78187384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299,  0.92985546, -0.84268292,  1.52400152,\n","        -1.04978132, -0.71743005, -0.71743005,  2.03715488, -0.30942637,\n","        -0.22587698],\n","       [-0.42915253, -0.248708  ,  1.75585181,  0.62630254,  0.02634317,\n","        -0.42728654,  1.00975633, -0.35239384,  1.63663418,  3.33576541,\n","         1.46926638,  0.91781628, -0.26733345, -0.84268292,  1.52400152,\n","        -1.04978132, -0.71743005, -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [ 2.05795653, -0.248708  ,  0.83781761,  1.30534634,  0.02634317,\n","         2.01774197,  1.00975633,  1.91614153,  1.63663418,  1.39461379,\n","        -0.43992572, -0.27059299,  2.12704437,  1.29165333, -0.65616732,\n","         0.95257934, -0.71743005, -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-1.0509298 , -0.248708  ,  0.83781761, -0.73178507, -1.33033001,\n","        -1.03854366, -0.99033794, -0.35239384, -0.61101009, -0.54653783,\n","         1.35471627,  0.43135791, -1.46452235, -0.84268292, -0.65616732,\n","        -1.04978132,  1.3938641 , -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-0.37262733, -0.248708  , -0.99825077, -0.73178507,  0.02634317,\n","        -1.03854366, -0.99033794, -0.35239384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -0.26733345, -0.84268292, -0.65616732,\n","         0.95257934,  1.3938641 ,  1.3938641 , -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-0.37262733, -0.248708  ,  1.75585181,  1.30534634,  0.02634317,\n","         2.01774197,  1.00975633, -0.35239384,  1.63663418,  1.39461379,\n","        -0.43992572, -0.27059299, -0.26733345, -0.1312375 , -0.65616732,\n","         0.95257934, -0.71743005, -0.71743005,  2.03715488, -0.30942637,\n","        -0.22587698],\n","       [ 0.07957432, -0.248708  ,  0.83781761,  0.62630254,  0.02634317,\n","        -0.42728654, -0.99033794,  0.78187384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -0.26733345, -0.84268292, -0.65616732,\n","        -1.04978132, -0.71743005, -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-0.1465265 , -0.248708  , -0.99825077,  1.30534634,  0.02634317,\n","         0.18397059, -0.99033794,  0.78187384,  1.63663418,  1.39461379,\n","        -0.43992572, -0.27059299,  0.92985546,  2.00309874, -0.65616732,\n","        -1.04978132, -0.71743005, -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [ 1.43617926, -0.248708  ,  1.75585181, -0.05274127,  0.02634317,\n","        -1.03854366,  1.00975633,  0.78187384,  1.63663418,  1.39461379,\n","        -0.43992572, -0.27059299,  0.92985546, -0.1312375 , -0.65616732,\n","         0.95257934,  1.3938641 , -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-0.65525336, -0.248708  , -0.99825077, -0.05274127,  0.02634317,\n","        -0.42728654, -0.99033794, -0.35239384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -0.26733345, -0.1312375 , -0.65616732,\n","         0.95257934,  1.3938641 , -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [ 1.26660364, -0.248708  ,  1.75585181,  0.62630254,  0.02634317,\n","         1.40648485, -0.99033794,  1.91614153,  1.63663418,  0.42403798,\n","        -0.43992572, -0.27059299,  2.12704437,  1.29165333, -0.65616732,\n","         0.95257934,  1.3938641 , -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [ 1.71880529, -0.248708  ,  1.75585181,  1.30534634,  0.02634317,\n","         2.01774197, -0.99033794,  1.91614153, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299,  0.92985546,  1.29165333,  1.52400152,\n","        -1.04978132, -0.71743005, -0.71743005,  2.03715488, -0.30942637,\n","        -0.22587698],\n","       [-0.71177856, -0.248708  , -0.99825077, -0.73178507,  0.02634317,\n","         0.18397059,  1.00975633, -0.35239384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -0.26733345, -0.1312375 , -0.65616732,\n","         0.95257934,  1.3938641 ,  1.3938641 , -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [ 0.02304911, -0.248708  , -0.99825077, -0.73178507,  0.02634317,\n","        -1.03854366, -0.99033794, -0.35239384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -0.26733345,  0.58020791, -0.65616732,\n","        -1.04978132, -0.71743005,  1.3938641 , -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-0.54220295, -0.248708  ,  0.83781761,  1.30534634, -1.33033001,\n","         2.01774197,  1.00975633, -1.48666153, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -1.46452235,  1.29165333,  1.52400152,\n","        -1.04978132, -0.71743005,  1.3938641 , -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-0.76830377, -0.248708  , -0.08021658, -0.73178507,  1.38301635,\n","        -1.03854366,  1.00975633, -0.35239384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -0.26733345, -0.1312375 , -0.65616732,\n","         0.95257934, -0.71743005,  1.3938641 ,  2.03715488, -0.30942637,\n","        -0.22587698],\n","       [-0.93787939, -0.248708  , -0.99825077,  1.30534634, -1.33033001,\n","        -0.42728654, -0.99033794, -1.48666153,  1.63663418,  1.39461379,\n","        -0.43992572, -0.27059299, -1.46452235, -0.1312375 , -0.65616732,\n","         0.95257934, -0.71743005,  1.3938641 , -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [ 0.64482638, -0.248708  ,  0.83781761,  1.30534634,  0.02634317,\n","         2.01774197, -0.99033794,  0.78187384,  1.63663418,  1.39461379,\n","        -0.43992572, -0.27059299,  0.92985546,  0.58020791, -0.65616732,\n","         0.95257934, -0.71743005, -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-1.16398021, -0.248708  , -0.99825077, -0.05274127, -1.33033001,\n","        -0.42728654,  1.00975633, -1.48666153, -0.61101009, -0.54653783,\n","         1.62627394,  0.58284275, -1.46452235, -0.84268292,  1.52400152,\n","         0.95257934, -0.71743005,  1.3938641 , -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [ 1.88838091, -0.248708  ,  1.75585181,  1.30534634,  0.02634317,\n","         0.18397059, -0.99033794,  1.91614153, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299,  2.12704437,  1.29165333, -0.65616732,\n","        -1.04978132, -0.71743005,  1.3938641 , -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-0.88135418, -0.248708  , -0.99825077, -0.73178507, -1.33033001,\n","         0.18397059,  1.00975633, -1.48666153, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -1.46452235,  0.58020791,  1.52400152,\n","        -1.04978132, -0.71743005,  1.3938641 , -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [ 0.30567514,  4.02077936, -0.08021658, -0.73178507,  0.02634317,\n","         0.79522772, -0.99033794,  0.78187384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299,  0.92985546, -0.1312375 ,  1.52400152,\n","         0.95257934,  1.3938641 , -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-0.25957692, -0.248708  ,  1.75585181,  0.62630254,  0.02634317,\n","        -0.42728654,  1.00975633, -0.35239384, -0.61101009, -0.54653783,\n","         2.21661669,  2.57348162, -0.26733345, -0.1312375 , -0.65616732,\n","         0.95257934, -0.71743005,  1.3938641 ,  2.03715488, -0.30942637,\n","        -0.22587698],\n","       [-0.42915253, -0.248708  ,  0.83781761, -0.05274127, -1.33033001,\n","        -0.42728654, -0.99033794, -1.48666153, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -1.46452235, -0.84268292,  1.52400152,\n","         0.95257934,  1.3938641 , -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-0.37262733, -0.248708  ,  0.83781761, -0.73178507,  0.02634317,\n","        -1.03854366, -0.99033794, -0.35239384, -0.61101009, -0.54653783,\n","         2.32287839,  2.05501941, -0.26733345, -0.1312375 , -0.65616732,\n","         0.95257934,  1.3938641 ,  1.3938641 , -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [ 0.64482638, -0.248708  ,  0.83781761, -2.08987268,  4.0963627 ,\n","        -1.03854366, -0.99033794,  0.78187384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -1.46452235, -1.55412834, -0.65616732,\n","         0.95257934, -0.71743005, -0.71743005, -0.49088069,  3.23178657,\n","        -0.22587698],\n","       [ 0.58830117, -0.248708  , -0.99825077, -0.05274127,  0.02634317,\n","        -1.03854366,  1.00975633,  0.78187384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299,  0.92985546, -0.1312375 ,  1.52400152,\n","        -1.04978132, -0.71743005, -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-1.22050542, -0.248708  , -0.08021658,  1.98439014, -1.33033001,\n","         0.18397059, -0.99033794, -1.48666153, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -1.46452235, -0.84268292,  1.52400152,\n","        -1.04978132, -0.71743005, -0.71743005,  2.03715488, -0.30942637,\n","        -0.22587698],\n","       [-1.22050542, -0.248708  , -0.99825077,  0.62630254, -1.33033001,\n","         0.18397059, -0.99033794, -1.48666153, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -1.46452235, -0.1312375 , -0.65616732,\n","         0.95257934,  1.3938641 , -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-0.65525336, -0.248708  , -0.08021658, -0.05274127, -1.33033001,\n","         0.79522772,  1.00975633, -1.48666153, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -1.46452235, -0.1312375 , -0.65616732,\n","        -1.04978132,  1.3938641 , -0.71743005, -0.49088069, -0.30942637,\n","         4.42718872],\n","       [-1.107455  , -0.248708  , -0.99825077, -0.05274127, -1.33033001,\n","        -0.42728654, -0.99033794, -1.48666153,  1.63663418, -0.09360246,\n","        -0.43992572, -0.27059299, -1.46452235, -0.1312375 , -0.65616732,\n","         0.95257934,  1.3938641 , -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-1.22050542, -0.248708  , -0.08021658,  1.98439014, -1.33033001,\n","         0.18397059, -0.99033794, -1.48666153,  1.63663418,  1.39461379,\n","        -0.43992572, -0.27059299, -1.46452235, -0.84268292,  1.52400152,\n","        -1.04978132, -0.71743005, -0.71743005,  2.03715488, -0.30942637,\n","        -0.22587698],\n","       [-0.93787939, -0.248708  ,  0.83781761,  0.62630254,  0.02634317,\n","        -0.42728654, -0.99033794, -0.35239384, -0.61101009, -0.54653783,\n","         1.15399973,  0.83673989, -0.26733345,  1.29165333, -0.65616732,\n","        -1.04978132, -0.71743005,  1.3938641 , -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [ 0.02304911, -0.248708  , -0.08021658, -0.73178507,  0.02634317,\n","        -1.03854366, -0.99033794,  0.78187384, -0.61101009, -0.54653783,\n","         3.02176157,  0.493232  ,  0.92985546, -0.1312375 , -0.65616732,\n","        -1.04978132,  1.3938641 , -0.71743005,  2.03715488, -0.30942637,\n","        -0.22587698],\n","       [-0.31610212, -0.248708  , -0.99825077,  1.30534634,  0.02634317,\n","         2.01774197, -0.99033794, -0.35239384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -0.26733345,  0.58020791,  1.52400152,\n","         0.95257934, -0.71743005,  1.3938641 , -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [ 3.13193544, -0.248708  ,  1.75585181,  1.98439014,  0.02634317,\n","         0.79522772, -0.99033794,  1.91614153, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299,  2.12704437,  1.29165333,  1.52400152,\n","         0.95257934,  1.3938641 ,  1.3938641 , -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-1.107455  , -0.248708  , -0.08021658,  0.62630254,  0.02634317,\n","        -0.42728654, -0.99033794, -0.35239384,  1.63663418,  1.39461379,\n","        -0.43992572, -0.27059299, -0.26733345, -0.84268292, -0.65616732,\n","         0.95257934,  1.3938641 , -0.71743005, -0.49088069, -0.30942637,\n","         4.42718872],\n","       [-0.65525336, -0.248708  , -0.99825077, -0.73178507, -1.33033001,\n","        -1.03854366, -0.99033794, -1.48666153, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -1.46452235,  2.00309874, -0.65616732,\n","        -1.04978132, -0.71743005,  1.3938641 , -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-0.88135418, -0.248708  ,  1.75585181, -2.08987268, -1.33033001,\n","        -1.03854366,  1.00975633, -1.48666153, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -1.46452235,  1.29165333,  1.52400152,\n","        -1.04978132, -0.71743005, -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [-0.31610212, -0.248708  ,  1.75585181, -0.73178507,  0.02634317,\n","        -1.03854366, -0.99033794, -0.35239384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -0.26733345,  0.58020791, -0.65616732,\n","         0.95257934,  1.3938641 ,  1.3938641 ,  2.03715488, -0.30942637,\n","        -0.22587698],\n","       [-0.20305171, -0.248708  , -0.99825077, -0.73178507,  0.02634317,\n","         0.18397059, -0.99033794, -0.35239384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -0.26733345,  1.29165333,  1.52400152,\n","        -1.04978132, -0.71743005, -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698],\n","       [ 0.70135158, -0.248708  ,  0.83781761, -0.05274127,  4.0963627 ,\n","        -1.03854366, -0.99033794,  0.78187384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299,  0.92985546, -1.55412834, -0.65616732,\n","         0.95257934, -0.71743005, -0.71743005, -0.49088069,  3.23178657,\n","        -0.22587698],\n","       [ 0.75787679, -0.248708  ,  1.75585181,  1.30534634,  0.02634317,\n","         2.01774197,  1.00975633, -0.35239384, -0.61101009, -0.54653783,\n","        -0.43992572, -0.27059299, -0.26733345,  1.29165333, -0.65616732,\n","        -1.04978132, -0.71743005, -0.71743005, -0.49088069, -0.30942637,\n","        -0.22587698]])"]},"metadata":{},"execution_count":128}],"source":["X_test_scaled"]},{"cell_type":"markdown","metadata":{"id":"0fiKm41KOzGQ"},"source":["#### PCA\n","\n","Alternativamente se puede realizar un Análisis de Componentes Principales para seleccionar los componentes (factores subyacentes) con mayor potencial predictivo (que explican aproximadamente lo mismo que las variables originales) y aligerar así el dataframe que vamos a pasar por los modelos de ML."]},{"cell_type":"code","execution_count":129,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1725905400800,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"qJctRCxoO443","outputId":"21cc501b-9776-4cdd-ddcf-4fb61879ca6e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Explained Variance Ratio: [0.15435832 0.11473742 0.08935936 0.08599365 0.06958755 0.0615002\n"," 0.05358684 0.05257603 0.04647468 0.04468223 0.03735303 0.03524924\n"," 0.029933   0.02469907 0.02108074 0.01934396 0.01878566 0.01513539\n"," 0.01230241 0.00740112 0.00371552 0.00214458]\n","Cumulative Explained Variance Ratio: [0.15435832 0.26909574 0.3584551  0.44444875 0.5140363  0.5755365\n"," 0.62912334 0.68169937 0.72817405 0.77285628 0.81020931 0.84545855\n"," 0.87539155 0.90009063 0.92117137 0.94051532 0.95930098 0.97443637\n"," 0.98673878 0.9941399  0.99785542 1.        ]\n"]}],"source":["from sklearn.decomposition import PCA\n","\n","features = df.select_dtypes(include=[np.number])\n","\n","scaler = StandardScaler()\n","scaled_features = scaler.fit_transform(features)\n","\n","pca = PCA()\n","pca.fit(scaled_features)\n","\n","explained_variance = pca.explained_variance_ratio_\n","cumulative_variance = np.cumsum(explained_variance)\n","\n","print(\"Explained Variance Ratio:\", explained_variance)\n","print(\"Cumulative Explained Variance Ratio:\", cumulative_variance)"]},{"cell_type":"code","execution_count":130,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"executionInfo":{"elapsed":1447,"status":"ok","timestamp":1725905402243,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"yCxVKhpEPiav","outputId":"af7794c1-4022-4f40-b216-abc6e275ac59"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import matplotlib.pyplot as plt\n","\n","plt.figure(figsize=(10, 6))\n","plt.plot(range(1, len(explained_variance) + 1), cumulative_variance, marker='o', linestyle='--')\n","plt.xlabel('Number of Principal Components')\n","plt.ylabel('Cumulative Explained Variance')\n","plt.title('PCA - Explained Variance')\n","plt.grid(True)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"2undtR7LPw5z"},"source":["Vemos que el primer componente principal sólo constituye el 21% de la varianza, necesitamos sumar los 4 primeros para llegar al 50% y los 8 primeros para llegar al 80%. Esto significa que no hay dos o tres variables que influyan de manera importante en la predicción sino que todas las variables aportan más o menos la misma \"cantidad\" de información. Por lo tanto no podemos seguir por esta vía ya que no tiene sentido elegir los 8 primeros componentes sobre 15 para poder obtener un resultado coherente. Lo comprobamos haciendo cross-validation con los componentes principales:"]},{"cell_type":"code","source":["from sklearn.model_selection import cross_val_score\n","from sklearn.decomposition import PCA\n","from sklearn.linear_model import LogisticRegression\n","\n","# PCA with 7 components\n","pca_7 = PCA(n_components=7)\n","X_train_pca_7 = pca_7.fit_transform(X_train_scaled)\n","\n","model = LogisticRegression()\n","scores_7 = cross_val_score(model, X_train_pca_7, y_train, cv=3)\n","\n","print(f\"Mean cross-validation score with 8 components: {scores_7.mean():.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i01OUTN3vpV7","executionInfo":{"status":"ok","timestamp":1725905402243,"user_tz":-120,"elapsed":3,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}},"outputId":"98e3c94b-27ca-41df-9e51-983642d51666"},"execution_count":131,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py:737: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=3.\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["Mean cross-validation score with 8 components: 0.0387\n"]}]},{"cell_type":"code","source":["# PCA with 12 components\n","pca_12 = PCA(n_components=12)\n","X_train_pca_12 = pca_12.fit_transform(X_train_scaled)\n","\n","scores_12 = cross_val_score(model, X_train_pca_12, y_train, cv=3)\n","\n","print(f\"Mean cross-validation score with 12 components: {scores_12.mean():.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9B3aHl1LvySy","executionInfo":{"status":"ok","timestamp":1725905402642,"user_tz":-120,"elapsed":401,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}},"outputId":"6848fd97-1343-40a8-e490-4677ba736df1"},"execution_count":132,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py:737: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=3.\n","  warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["Mean cross-validation score with 12 components: 0.0387\n"]}]},{"cell_type":"markdown","source":["Estas mean-scores tan sumamente bajos predicen que el modelo funcionará MUY mal con los datos de test."],"metadata":{"id":"Uc5Wo41mwQpa"}},{"cell_type":"markdown","metadata":{"id":"WltHXJL1oSFo"},"source":["### Crear y entrenar Caja Negra"]},{"cell_type":"markdown","metadata":{"id":"98hGimLVbHnX"},"source":["#### Última verificación para asegurarnos que no hay valores nulos"]},{"cell_type":"code","execution_count":133,"metadata":{"executionInfo":{"elapsed":3,"status":"ok","timestamp":1725905402642,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"p03rlZ06ondS","colab":{"base_uri":"https://localhost:8080/"},"outputId":"3e2ad40d-1aa3-42b6-8707-c1d2b3ac4ce9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Columnas con valores nulos en X_train: Index([], dtype='object')\n","Columnas con valores nulos en X_test: Index([], dtype='object')\n"]}],"source":["import numpy as np\n","\n","columns_with_nan_X_train = np.where(np.isnan(X_train).any(axis=0))[0]\n","columns_with_nan_names_X_train = column_names[columns_with_nan_X_train]\n","# print(X_train[:, columns_with_nan_X_train])\n","print(f\"Columnas con valores nulos en X_train: {columns_with_nan_names_X_train}\")\n","\n","columns_with_nan_X_test = np.where(np.isnan(X_test).any(axis=0))[0]\n","columns_with_nan_names_X_test = column_names[columns_with_nan_X_test]\n","# print(X_test[:, columns_with_nan_X_test])\n","print(f\"Columnas con valores nulos en X_test: {columns_with_nan_names_X_test}\")"]},{"cell_type":"markdown","metadata":{"id":"2ZENayJsV9cA"},"source":["#### Cheatsheet para interpretar los resultados obtenidos por cada algoritmo"]},{"cell_type":"markdown","metadata":{"id":"rTInwrPfdncg"},"source":["De manera general, se puede decir que:\n","\n","**Sesgo:** Un sesgo elevado indica que el modelo no se ajusta lo suficiente, es decir, que es demasiado simple para captar los patrones subyacentes en los datos.\n","\n","**Varianza:** Una varianza elevada indica que el modelo está sobreajustado, es decir, que es demasiado complejo y capta el ruido junto con la señal.\n","\n","---\n","\n","**Sesgo Bajo y Varianza Alta:**\n","- Error de Entrenamiento:  1% (**bajo**)\n","- Error de Prueba: 10% (**mucho mayor al de entrenamiento**)\n","\n","**Sesgo Alto y Varianza Baja:**\n","- Error de Entrenamiento: 15% (**alto**)\n","- Error de Prueba: 16% (**similar al de entrenamiento**)\n","\n","**Sesgo Alto y Varianza Alta:**\n","- Error de Entrenamiento: 15% (**alto**)\n","- Error de Prueba: 30% (**mucho mayor al de entrenamiento**)\n","\n","**Sesgo Bajo y Varianza Baja (lo que buscamos):**\n","- Error de Entrenamiento:  0.5% (**bajo**)\n","- Error de Prueba:  1.0% (**bajo**)\n","\n","El objetivo es obtener los errores de Entrenamiento y Prueba bajos.\n","\n","- El **Error de Entrenamiento alto** (tambien conocido como **Underfitting** en ingles) puede estar dado por una de las siguientes condiciones:\n","  - Necesitamos mas datos para entrenar nuestra caja negra\n","  - Necesitamos una caja negra que pueda predecir datos complejos, por ejemplo los ensambles pueden predecir datos mas complejos que una regresion lineal porque no asumen ninguna hipotesis respecto a los datos.\n","- El **Error de Prueba alto** (tambien conocido como **Overfitting** en ingles) puede estar dado por una de las siguientes condiciones:\n","  - Necesitamos mas datos para entrenar nuestra caja negra\n","  - Necesitamos una caja negra mas simple o un metodo para evitar que la caja negra se ajuste demasiado a los datos de prueba. Estos metodos se llaman de **regularizacion**\n","\n","---\n","\n","**CÓMO INTERPRETAR EL MSE, EL RMSE Y EL R2**\n","\n","El **Mean Squared Error (MSE)** es una métrica que mide el promedio de los errores cuadrados entre las predicciones del modelo y los valores reales. Se calcula como:\n","\n","  ![image.png](data:image/png;base64,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)\n","\n","Donde:\n","\n","*   𝑦𝑖 es el valor real,\n","*   𝑦^𝑖 es el valor predicho por el modelo,\n","*   𝑛 es el número total de observaciones.\n","\n","Un MSE más alto indica un peor desempeño del modelo, ya que significa que los errores de predicción son más grandes en promedio. En general, un MSE bajo es deseable.\n","\n","El **Root Mean Squared Error (RMSE)** es la raíz cuadrada del MSE y proporciona una medida de error en las mismas unidades que la variable dependiente. Se calcula como:\n","\n","![image.png](data:image/png;base64,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)\n","\n","Para interpretar el RMSE, es útil compararlo con el rango de los datos y la escala de los valores. Si el RMSE es una fracción significativa del rango de los valores, podría indicar un problema con el modelo. Sin embargo, si es pequeño en comparación con el rango de los datos, el modelo puede estar funcionando bien.\n","\n","El **R² (Coeficiente de Determinación)** es una métrica utilizada para evaluar la calidad de un modelo de regresión. A diferencia del MSE y RMSE, que miden la magnitud del error, el R² indica qué proporción de la variabilidad en la variable dependiente (objetivo) es explicada por el modelo. Se calcula como:\n","\n","![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJsAAAA0CAYAAACdK0q3AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAEnQAABJ0Ad5mH3gAAAtDSURBVHhe7ZsPTFvXFYd/25BwlRJHShWYutQVpYG1CU63BKtda7SQOooWiLoQmo7I1aDQSpSkCbjtRtjakKgtJNJq6Fr+dC0m3TQ7W4vRGuFMqU2lVHa0zI8pLaZq5NeGyZ6CZkeg5yiR7u6zr8EYQuwkvEJ2P+vJ5553bfm9e3zPOffc9x1CAYejAN9l7xzOgsONjaMY3Ng4isGNjaMY3Ng4isGNjaMY3Ng4isGNjaMYfFH3NiAyakf777oxNAao78yHdlcNjHdZYc1oRv1G1glheDpbYP7Yh7BKjVX36PFs4xaI73ugf2knslmvBUU2NsWY8BOvy0v8IYkpODfNhX5StekgcYZYmxJwHCQlWVmk1cMURCLeIyWkss/P2pTQCOmtySNZRisJMNVCo5AbjcDz5vNo7PFA/M8Q2irXYd3TXRAm2WnODSN81AabYQuK1UxByX7chOa9CXNVeAh9r+Zg+081TEFR58P4cjMMrKkEihhb+OQBtGTU4MjenSgrr0fHn3tQdroR5c0OOrlzbobLEwIgBpLuowq6TRVMpkyEEYCIwDhrx8ktRtk9TFaC2AS3sLhfzyJZWXmk9SxT0GndeUDWNZDBhOmfkz7+vh3Re1v1jntmeHJFItIVJktOcjCP3m/qbvt9gWk9RZpQLqRJPUGYFOH5hx8Sa87Jinuxfo0GahVrMyLDNrQNSCh7wQjtspjOc3g5Nr9Ri+PfHIEhwQVw0mTSg7ayzWg5w9q5RTA+tQf1z5UhP+G+isersbnKhmC0lY38rdtRU2dCrV6R1CBG1ORS4VwvqSzbRrb9bAPJo8FnFj3WbqJtWRc/dDTgpP+yDb80E/dF9rk58RLzT7JI3r5BsrgnthAZsTWQkpp+xYLoG+JKgDj7fk3qEsYmS0u9RtIYhM71E/O+SlKiZX3oWG17x8vOLjzpu9Gvesm26A+d2wWGTrdGM6GsvCrSf4Epk/D/qZJO6fTzIlMsIqQLXuK09xLzoQayIz4oCmZsNw11n36WjT7acW1Dki56Y9mogqFM2glC2DcMlyyU6mZM03HUD1fg2VIqBKnr/IgGr0lEzrShujsfA1bqPpUMTlPk8r8FDJ8PQ11YgcMtJqZdvAidNKtncpQMFTRyNtqQDcEzGnObQQe6TsQcaBzVSi3LRunnR6kiEkY4TI9IhIphRK7G+uFqBOKwC57RhBQkEoQw5IEvKCKYnHTMQ9rG5jvbFX3XPrIGCYl0AiEExJgkTFyOCXG+tqGxQ4Wj9mYUr6Rt+cJiZxYN6o1G1O+th7G0CPk5ScHnIuRyaBjirAFXoeBBHbBSTSXK1TBGRwPRMzPILYAOWqgy6VCM++E4sg0bytsxYKvDujepCcvxYG07gho6sYx1YXcn1Y070HYsBK2+CDk+C6zn2XelQJrGJsB9IiYZfqSNCcmcF+AajonGB/Jjgoz8w9+WUP/7+ukk4WgbWFfODWOBuceTtPQRhvuUG7WP6xB3PoEOM2xfs0acYTfsGyugL6TmebcWeq0Gut0V2PlMD/61Vwvxw1YM/rgY+aCTRqEWmr4hCOEAhJNW2E4KCD1oREUu+64USM/YxkbhjlpHLXQPRDWzEAa64aDv2bvehamUXeqkgPaqVogrwnD0tKP9TfloQedYgTJlktuaImy524v9Tz6P9uMOuIYcsJh2o/u+D3Bo63Sck1m6HtLb5ahutsA+5IJroA3ltT6Y3qN/ftYnCnXD8syoyqDed8yBnGXsOzJ0ePFvNdDmGvHWCxqMnKDhUEk1LF+n4ZtY7JYSoY8bYgHzL6wkofDBoJlbXx1ZK2c4L1vJyARTU7xH5ECUBduJxwEnWdSFK09r7Hcu4gQhdDEe3Usk5HMTp8tNRi4m3VUpRELx8YiWDJ3EKfhnrLfJBGyVpNI2faUhRwNZ+7qbtWjb4yb+s+bpMpjYSyoTzl+PtArxnjfux+bDNNB8vBaH9SximxDh9vjg+9wPTWk9ap9/EobcOTKHm8T3/m40fnij9QY1dr52jLp11kwVmswsL2kBnngXX/YqVKz+lgifoe64W/ZKBlQ8QePWrfL4ymXGRvSvLIMhOwwfnUVrV1ixuU+NF8tpiPSFA76iZtQ/lFpsm4axUVf46GNoom7U9JdvsKeIqSMBuI81oe5VAQV1b6HjNcM1EoebhGZF4cmkhCNlMqFOXmlOhf8jY5sXeu8jzLVOMUkzVupi07mrqRvbmA27f1gNO43XZq/6R+AwrUJ5J00KPvgGHfFYbalzw8YWgXjGDf+85Za5ueNeHYruSW0Ily9fzqTFwaVLl5g0NyknCGE5c5GFOdfXVFhxV0yyfO6LCZwFRx7cxXRcj5RntlgtE9C+9ik+rUte9gjC9vT9qP6Qik1/x6WX4j72FsLd6JInRWNLiNcGL6H5YaaOE3GhaVUp2qlY9ocvcaz81g8LTxCuRQS+gXa09wxBpNepvk+LiueMyLFZ6R+/nob0jHEPug6bMfBVGJl3rsK9+mdh2irC4tHDtADjNSeysV2X69RDydnWqQLwdOosEf8pJ/Ev5U25S2DpI/DXKlJyyDm9oeFKgAy+UkJ/dyuZWpSQvKR1UyXpTahFh871kqq8xPFaeOaP2WjGIdfLhFP2WD30YQ1WQNYlLeR9T4UcJk4hlzU6RDmcW1rI7lquEdJDFFndLfRfBJhu1rV/qwiwHrVhi6F4qlKAjGwYGptRnzBZhT/pQ8sPtmNLQi1a/YARv2lScp/ufAlC0I7q76/G6tWr8dh+uSZA+awJm2l79YY2uBLveWENjjbFJmy3ox+OITva9luQf6BiYZZBFpJ/tkevWT7WVVliuk8a8RjTrd4zwPaELQYuI0JDGzGQFF4s08Gwi8mUyGQA+CqA5OqoRl+m7PiwGe6WIIle4nQNkkGHk3gXq9+5rfCT3p9TN59XRTo9fjLjOaKEHbiS62A0zCl5pZ+MBBI7SURSMMy5pcbGUR6JxpXR/YPsWLupjrTaR5I2pfqJNbp3jfXL20B27OskToUnBG5stwMBJ+k9UMd2SjOja0zeBR0iI3YzaTCWkLVTRreNdArstMzFAJkx8c2DdCGQ9P3Xhxvb7caEn2WjjxJzoiHNQCIhIZaNZiVszZdO9RLrNXZXJ+N/r3c6202RNPezcRYXArrkDY2JLNPA8FIzTNkC3KOxVCZ4oguOGVmNCupCmo3+lmajPQKiNZ+ICOv7DkjJGfdcO3XHXbD0iAjJfdN49pcb25LmMsKCOPvZW1UBCh4BcpaxdafJUfjmSKE1+TqgUIVMRBD8QoAYCkP0eeE9H6YairzhtXkAWFOMwgwHmn7loJl4GL5zPgSHg/ALtO9Y6gvt3NiWOsfM6PosacDH3XCdroWhKL76FoC5wwYx/lwBQzhtR9FTenljOLIf0qFghRoFRcUofig7ujzq6ahEpGwnNLShyt2J7XfWwXJGjXz9emjoa72e9l2T+qYLbmxLnY1bkD28H+V72mE76YLrpAWNT3dD88dDMMjPeUTJxHathK7KajQds8M15IL9aDme8Znw7qw6NyUiz2tBiJ8HZ24ryghCEJOmyGjfFGGxG2dJEiKh+LOhEs02PU7i9IzMXG+jSBdDUzuiY2uhTuIVk9POALEaK6MJQsBmjQb/I++UkIaP4+lDiAzuKyGd52TZTVpZOcxN+6a6gpLWTl3O7Y3QWY5uGFGkzkHFriKoJn2wHLHijkf0UI8PwX21Aqbd+dTFhuEwVcOtM0KTqYWxNLU6BDc2zkwmqVuMJxZx5EcuVapZZe4I7atK7jsP3Ng4isETBI5icGPjKAY3No5icGPjKAY3No5icGPjKAY3No5icGPjKAY3No5CAP8DU/bkSkiKdLQAAAAASUVORK5CYII=)\n","\n","Donde:\n","\n","**SSres (Sum of Squares of Residuals):** Es la suma de los cuadrados de las diferencias entre los valores observados y los valores predichos por el modelo.\n","**SStot (Total Sum of Squares):** Es la suma de los cuadrados de las diferencias entre los valores observados y la media de los valores observados.\n","\n","*   R² = 1: Indica que el modelo explica el 100% de la variabilidad en la variable dependiente. Las predicciones del modelo coinciden perfectamente con los valores observados. Sin embargo, no siempre un R² alto es sinónimo de un buen modelo, especialmente si el modelo está sobreajustado (overfitting).\n","*   R² = 0: Indica que el modelo no explica ninguna variabilidad en la variable dependiente. El modelo no es mejor que simplemente predecir la media de los valores observados. Un valor bajo de R² (cercano a 0) indica que el modelo no captura bien la variabilidad de los datos, lo que podría significar que el modelo es demasiado simple o que las relaciones entre las variables no son lineales.\n","*   R² < 0: Puede ocurrir si el modelo es peor que una simple media (por ejemplo, si el modelo predice muy mal los datos). En este caso, el modelo está subajustando (underfitting).\n","\n","**Limitaciones del R²**\n","\n","No Detecta Sobreajuste: Un R² alto puede ser engañoso, ya que no indica si el modelo está sobreajustado. Por ejemplo, añadir variables irrelevantes al modelo puede aumentar el R² sin mejorar realmente la capacidad de generalización del modelo.\n","No Aplicable a Modelos No Lineales: R² se interpreta mejor en modelos lineales. En modelos no lineales, su interpretación puede ser menos directa.\n","\n","**R² Ajustado**\n","\n","Una versión ajustada del R² tiene en cuenta el número de predictores en el modelo. Esto es útil cuando se comparan modelos con diferente número de variables, ya que penaliza la adición de variables que no mejoran significativamente el modelo.\n"]},{"cell_type":"markdown","metadata":{"id":"1PP4SL7D451l"},"source":["### 🔶 **RANDOM FOREST REGRESSOR** 🔶"]},{"cell_type":"markdown","metadata":{"id":"yAyYIeng27CJ"},"source":["#### ENTRENAR MODELO: Random Forest Regressor\n","\n","(Voy a ignorar los parámetros siguientes ya que parece que no me aportan demasiado y ralentizan mucho el grid search: min_samples_split=3, min_samples_leaf=2 y max_features='sqrt')"]},{"cell_type":"code","execution_count":134,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"elapsed":1535,"status":"ok","timestamp":1725905404503,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"W5jrIrBwnI3G","outputId":"e08762de-2215-4b6e-937b-110e15f428f3"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["RandomForestRegressor(max_depth=20, n_estimators=200)"],"text/html":["<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=20, n_estimators=200)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=20, n_estimators=200)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":134}],"source":["from sklearn.model_selection import GridSearchCV\n","from sklearn.ensemble import RandomForestRegressor\n","\n","# param_grid = {\n","#     'n_estimators': [100, 200, 300, 400, 500],\n","#     'max_depth': [20, 25, 30, 35, 40]\n","# }\n","\n","# grid_search = GridSearchCV(estimator=model_rf, param_grid=param_grid, cv=5, n_jobs=-1, scoring='neg_mean_squared_error')\n","# grid_search.fit(X_train, y_train)\n","\n","# print(\"Best parameters:\", grid_search.best_params_)\n","# print(\"Best score:\", grid_search.best_score_)\n","\n","model_rf = RandomForestRegressor(n_estimators=200, max_depth=20)\n","model_rf.fit(X_train_scaled, y_train)"]},{"cell_type":"markdown","metadata":{"id":"oISuM3d-ThTW"},"source":["#### Clasificacion de Cajas Negras por Algoritmo utilizado"]},{"cell_type":"markdown","metadata":{"id":"9Iqyzl0_smT1"},"source":["##### Realizar predicción"]},{"cell_type":"markdown","metadata":{"id":"RJlemfWuaq6A"},"source":["Predicción de tabla de entrenamiento"]},{"cell_type":"code","execution_count":135,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1725905404503,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"afj0axncauN2","colab":{"base_uri":"https://localhost:8080/"},"outputId":"6b82cf84-ec0e-4b93-c6b6-edd7566c6f62"},"outputs":[{"output_type":"stream","name":"stdout","text":["[239253.75  272933.375 253552.75  117640.    228434.    206701.645\n"," 187326.5   191679.56  198602.79  170079.5   190148.125 152485.\n"," 259402.925 238429.    274581.405 204535.375 151041.5   235192.935\n"," 169176.    214121.75  258882.78  196685.5   245749.75  256738.\n"," 183002.645 236671.03  222322.    216719.895 266126.    206628.5\n"," 204083.375 154948.435 266705.09  246174.53  182438.5   188646.975\n"," 234237.25  223107.935 185782.145 257575.53  157103.645 191866.645\n"," 206199.935 231446.16  208712.    265476.75  271933.05  229737.5\n"," 161435.5   162463.    259263.195 267222.25  151379.5   173783.56\n"," 184677.435 225608.25  273301.155 137107.    261637.5   247597.125\n"," 131180.5   207683.79  209840.555 265681.5   189470.125 246818.5\n"," 190943.5   262464.75  206856.    269957.53  215410.    206471.145\n"," 248017.75  188350.395 216156.5   211761.03  191052.595 239262.25\n"," 250720.625 179019.765 205970.625 220680.34  164979.5   272397.215\n"," 268820.25  183655.145 253043.5   133440.    210251.645 273396.5\n"," 227250.645 239323.145 258979.02  230489.875 218523.435 269504.78\n"," 266497.075 173883.895 227832.985 253225.125 159893.75  225402.\n"," 247168.125]\n"]}],"source":["train_pred = model_rf.predict(X_train_scaled)\n","print(train_pred)"]},{"cell_type":"markdown","metadata":{"id":"XfxEtX8Ias0Q"},"source":["Predicción de tabla de prueba"]},{"cell_type":"code","execution_count":136,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1725905404503,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"L1i5SjXastNb","colab":{"base_uri":"https://localhost:8080/"},"outputId":"382ec3a2-dae2-4850-8482-b8593f38de0c"},"outputs":[{"output_type":"stream","name":"stdout","text":["[242052.25 250890.5  243011.   246690.03 235609.25 260880.78 246957.75\n"," 233164.5  242879.25 230987.5  244777.75 241764.5  241721.25 236358.\n"," 241905.5  231974.75 230183.5  235553.   221405.5  234042.5  246216.28\n"," 234482.5  226199.5  244186.25 245171.28 247747.5  254660.78 245409.\n"," 236510.   237592.5  235911.25 241358.5  233493.75 236171.5  243155.53\n"," 256565.53 229762.75 242999.75 246676.75 222953.75 229823.5  245250.5\n"," 230221.25 246751.75 233825.  ]\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n","  warnings.warn(\n"]}],"source":["test_pred = model_rf.predict(X_test)\n","print(test_pred)"]},{"cell_type":"markdown","metadata":{"id":"d8ALcLeAdgEB"},"source":["##### Calcular errores"]},{"cell_type":"markdown","metadata":{"id":"TRrbFB55a9pb"},"source":["###### Error de entrenamiento"]},{"cell_type":"markdown","metadata":{"id":"LqOL0-yJcpzc"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":137,"metadata":{"executionInfo":{"elapsed":10,"status":"ok","timestamp":1725905404503,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"oTlbti73bAUr","colab":{"base_uri":"https://localhost:8080/"},"outputId":"f12c788d-eeba-401e-b866-44e6f085ea93"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 205280647.46\n","Root Mean Squared Error (RMSE): 14327.62\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_train = mean_squared_error(y_train, train_pred).round(2)\n","rmse_train = np.sqrt(mse_train).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_train}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_train}\")"]},{"cell_type":"markdown","metadata":{"id":"XOlToYEgczSU"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":138,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1725905404503,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"-WjfyCa9c02p","colab":{"base_uri":"https://localhost:8080/"},"outputId":"308dc441-2953-4cfa-cc1b-5e7394cb51a0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R2: 0.92\n"]}],"source":["from sklearn import metrics\n","\n","r2_train = metrics.r2_score(y_train, train_pred).round(2)\n","\n","print(f\"Coeficiente de Determinación R2: {r2_train}\")"]},{"cell_type":"code","execution_count":139,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1725905404504,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"I1u8_lgpmsuQ","colab":{"base_uri":"https://localhost:8080/"},"outputId":"9acdece2-df21-4a41-9727-29725b381b64"},"outputs":[{"output_type":"stream","name":"stdout","text":["           MSE     RMSE   R2\n","0 205280647.46 14327.62 0.92\n"]}],"source":["errors_train = pd.DataFrame(\n","    {\"MSE\":[mse_train],\n","     \"RMSE\":[rmse_train],\n","     \"R2\":[r2_train]})\n","\n","print(errors_train)"]},{"cell_type":"markdown","metadata":{"id":"8RXvRvgvaXnm"},"source":["###### Error de prueba"]},{"cell_type":"markdown","metadata":{"id":"6IgD1Cz3ZT6r"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":140,"metadata":{"executionInfo":{"elapsed":429,"status":"ok","timestamp":1725905404930,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"W_CXWsonYiZJ","colab":{"base_uri":"https://localhost:8080/"},"outputId":"d1fa0d8c-8206-4efb-c3c9-29697aa07b2c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 3492367888.3\n","Root Mean Squared Error (RMSE): 59096.26\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_test = mean_squared_error(y_test, test_pred).round(2)\n","rmse_test = np.sqrt(mse_test).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_test}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_test}\")"]},{"cell_type":"markdown","metadata":{"id":"r-KkisTGZXQ8"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":141,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1725905404930,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"RYK9cbYDZTCQ","colab":{"base_uri":"https://localhost:8080/"},"outputId":"232615bc-5ebb-416a-a8e2-2659ef1b49e0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R2: -0.17\n"]}],"source":["from sklearn import metrics\n","\n","r2_test = metrics.r2_score(y_test, test_pred).round(2)\n","\n","print(f\"Coeficiente de Determinación R2: {r2_test}\")"]},{"cell_type":"code","execution_count":142,"metadata":{"id":"WQHZjogOm00V","executionInfo":{"status":"ok","timestamp":1725905404930,"user_tz":-120,"elapsed":5,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["errors_test = pd.DataFrame(\n","    {\"MSE\":[mse_test],\n","     \"RMSE\":[rmse_test],\n","     \"R2\":[r2_test]})"]},{"cell_type":"markdown","metadata":{"id":"R6njH1OeVfo3"},"source":["#### Comprobar que la Caja Negra esta bien entrenada - Sesgo y Varianza (Bias y Variance)"]},{"cell_type":"code","execution_count":143,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1725905404930,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"FXzRiGQ2m9qZ","colab":{"base_uri":"https://localhost:8080/","height":112},"outputId":"bf607122-e734-4f94-ed16-5fdd97ed2a2a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                     Model           MSE     RMSE    R2    set\n","0  Random Forest Regressor  205280647.46 14327.62  0.92  train\n","1  Random Forest Regressor 3492367888.30 59096.26 -0.17   test"],"text/html":["\n","  <div id=\"df-46ec1ede-e1e9-4f50-94a8-e21fc9df683a\" 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>Model</th>\n","      <th>MSE</th>\n","      <th>RMSE</th>\n","      <th>R2</th>\n","      <th>set</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Random Forest Regressor</td>\n","      <td>205280647.46</td>\n","      <td>14327.62</td>\n","      <td>0.92</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Random Forest Regressor</td>\n","      <td>3492367888.30</td>\n","      <td>59096.26</td>\n","      <td>-0.17</td>\n","      <td>test</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-46ec1ede-e1e9-4f50-94a8-e21fc9df683a')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-46ec1ede-e1e9-4f50-94a8-e21fc9df683a 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-46ec1ede-e1e9-4f50-94a8-e21fc9df683a');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-1c3cc5f2-a1f7-4cde-a142-b78e081b254e\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1c3cc5f2-a1f7-4cde-a142-b78e081b254e')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-1c3cc5f2-a1f7-4cde-a142-b78e081b254e button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"random_forest_df","summary":"{\n  \"name\": \"random_forest_df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Random Forest Regressor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2324321678.349742,\n        \"min\": 205280647.46,\n        \"max\": 3492367888.3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3492367888.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31656.20892849932,\n        \"min\": 14327.62,\n        \"max\": 59096.26,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          59096.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7707463914933369,\n        \"min\": -0.17,\n        \"max\": 0.92,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -0.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"set\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"test\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":143}],"source":["errors_train['set'] = 'train'\n","errors_test['set'] = 'test'\n","\n","random_forest_df = pd.concat([errors_train, errors_test], axis=0, ignore_index=True)\n","random_forest_df.insert(0, 'Model', 'Random Forest Regressor')\n","random_forest_df.head(2)"]},{"cell_type":"markdown","metadata":{"id":"NK2B0099__qg"},"source":["###### Cross Validation"]},{"cell_type":"code","execution_count":144,"metadata":{"executionInfo":{"elapsed":2752,"status":"ok","timestamp":1725905407678,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"4jeEw2JWAByF","colab":{"base_uri":"https://localhost:8080/"},"outputId":"23c38c6f-d350-45e7-a1a3-ac18c686db17"},"outputs":[{"output_type":"stream","name":"stdout","text":["Cross-validation scores: [0.40473132 0.36688368 0.51830673 0.42289095 0.27081578]\n","Mean score: 0.3967256923267922\n","Standard deviation: 0.08034240492095178\n"]}],"source":["from sklearn.model_selection import cross_val_score\n","\n","scores = cross_val_score(model_rf, X_train_scaled, y_train, cv=5)  # cv=5 indica 5-fold cross-validation\n","\n","print(\"Cross-validation scores:\", scores)\n","print(\"Mean score:\", scores.mean())\n","print(\"Standard deviation:\", scores.std())"]},{"cell_type":"markdown","metadata":{"id":"vYKj1ehLfMWl"},"source":["<font color=\"red\">Escribir conclusiones aquí.</font>"]},{"cell_type":"markdown","metadata":{"id":"RsKH7KzN6EnI"},"source":["### 🔶 **DECISSION TREE REGRESSOR** 🔶"]},{"cell_type":"markdown","metadata":{"id":"Cla6tAho6EnV"},"source":["#### ENTRENAR MODELO: Decission Tree Regressor"]},{"cell_type":"code","execution_count":145,"metadata":{"executionInfo":{"elapsed":22,"status":"ok","timestamp":1725905407678,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"iNNDfZPU6EnV","colab":{"base_uri":"https://localhost:8080/","height":75},"outputId":"9c511ee4-6bf5-42f5-a543-d3809a2e8c14"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeRegressor(max_depth=2)"],"text/html":["<style>#sk-container-id-9 {color: black;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=2)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=2)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":145}],"source":["from sklearn.tree import DecisionTreeRegressor\n","from sklearn.model_selection import GridSearchCV\n","\n","# param_grid = {\n","#     'max_depth': [3, 5, 7, 10],\n","#     'min_samples_split': [2, 5, 10],\n","#     'min_samples_leaf': [1, 5, 10],\n","#     'max_features': ['auto', 'sqrt', 'log2']\n","# }\n","\n","# grid_search = GridSearchCV(estimator=model_dt, param_grid=param_grid, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)\n","\n","# grid_search.fit(X_train, y_train)\n","\n","# print(\"Best parameters:\", grid_search.best_params_)\n","# print(\"Best score:\", -grid_search.best_score_)\n","\n","model_dt = DecisionTreeRegressor(\n","    max_depth=2,\n","    # max_depth=5,\n","    # max_features='auto\n","    # max_features='sqrt',\n","    # min_samples_leaf=10,\n","    # min_samples_split=20\n",")\n","\n","model_dt.fit(X_train_scaled, y_train)"]},{"cell_type":"markdown","metadata":{"id":"4yNvM9U36EnV"},"source":["#### Clasificacion de Cajas Negras por Algoritmo utilizado"]},{"cell_type":"markdown","metadata":{"id":"X6NukZ5t6EnV"},"source":["##### Realizar predicción"]},{"cell_type":"markdown","metadata":{"id":"8sKdYq9n6EnV"},"source":["Predicción de tabla de entrenamiento"]},{"cell_type":"code","execution_count":146,"metadata":{"executionInfo":{"elapsed":21,"status":"ok","timestamp":1725905407678,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"XIJKqASd6EnW","colab":{"base_uri":"https://localhost:8080/"},"outputId":"89a17d7f-c1f4-443a-86c1-0eb5befcd70c"},"outputs":[{"output_type":"stream","name":"stdout","text":["[248990.32258065 248990.32258065 248990.32258065 138125.\n"," 248990.32258065 223189.11111111 248990.32258065 223189.11111111\n"," 187852.63157895 187852.63157895 248990.32258065 138125.\n"," 223189.11111111 248990.32258065 248990.32258065 223189.11111111\n"," 138125.         223189.11111111 187852.63157895 223189.11111111\n"," 223189.11111111 248990.32258065 248990.32258065 223189.11111111\n"," 187852.63157895 223189.11111111 187852.63157895 223189.11111111\n"," 248990.32258065 187852.63157895 223189.11111111 187852.63157895\n"," 248990.32258065 223189.11111111 187852.63157895 223189.11111111\n"," 248990.32258065 223189.11111111 223189.11111111 223189.11111111\n"," 187852.63157895 223189.11111111 187852.63157895 223189.11111111\n"," 187852.63157895 248990.32258065 223189.11111111 223189.11111111\n"," 187852.63157895 138125.         223189.11111111 248990.32258065\n"," 187852.63157895 223189.11111111 223189.11111111 248990.32258065\n"," 248990.32258065 138125.         248990.32258065 248990.32258065\n"," 138125.         223189.11111111 223189.11111111 248990.32258065\n"," 187852.63157895 223189.11111111 187852.63157895 248990.32258065\n"," 248990.32258065 248990.32258065 223189.11111111 223189.11111111\n"," 248990.32258065 223189.11111111 223189.11111111 223189.11111111\n"," 223189.11111111 223189.11111111 223189.11111111 223189.11111111\n"," 248990.32258065 223189.11111111 138125.         248990.32258065\n"," 248990.32258065 187852.63157895 187852.63157895 138125.\n"," 223189.11111111 223189.11111111 223189.11111111 248990.32258065\n"," 223189.11111111 248990.32258065 187852.63157895 248990.32258065\n"," 223189.11111111 223189.11111111 223189.11111111 248990.32258065\n"," 187852.63157895 223189.11111111 223189.11111111]\n"]}],"source":["train_pred = model_dt.predict(X_train_scaled)\n","print(train_pred)"]},{"cell_type":"markdown","metadata":{"id":"VuiQqf8X6EnW"},"source":["Predicción de tabla de prueba"]},{"cell_type":"code","execution_count":147,"metadata":{"executionInfo":{"elapsed":20,"status":"ok","timestamp":1725905407678,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"YdD_PIq-6EnW","colab":{"base_uri":"https://localhost:8080/"},"outputId":"9665d5ce-588c-4b02-83c4-4185d3de5671"},"outputs":[{"output_type":"stream","name":"stdout","text":["[248990.32258065 248990.32258065 248990.32258065 248990.32258065\n"," 248990.32258065 248990.32258065 248990.32258065 248990.32258065\n"," 248990.32258065 248990.32258065 248990.32258065 248990.32258065\n"," 248990.32258065 248990.32258065 248990.32258065 248990.32258065\n"," 248990.32258065 248990.32258065 248990.32258065 248990.32258065\n"," 248990.32258065 248990.32258065 248990.32258065 248990.32258065\n"," 248990.32258065 248990.32258065 248990.32258065 248990.32258065\n"," 248990.32258065 248990.32258065 248990.32258065 248990.32258065\n"," 248990.32258065 248990.32258065 248990.32258065 248990.32258065\n"," 248990.32258065 248990.32258065 248990.32258065 248990.32258065\n"," 248990.32258065 248990.32258065 248990.32258065 248990.32258065\n"," 248990.32258065]\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:458: UserWarning: X has feature names, but DecisionTreeRegressor was fitted without feature names\n","  warnings.warn(\n"]}],"source":["test_pred = model_dt.predict(X_test)\n","print(test_pred)"]},{"cell_type":"markdown","metadata":{"id":"sfmEDebK6EnW"},"source":["##### Calcular errores"]},{"cell_type":"markdown","metadata":{"id":"rNnj3-P26EnW"},"source":["###### Error de entrenamiento"]},{"cell_type":"markdown","metadata":{"id":"Nihe5NZ56EnW"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":148,"metadata":{"executionInfo":{"elapsed":18,"status":"ok","timestamp":1725905407678,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"H7PAFIY26EnW","colab":{"base_uri":"https://localhost:8080/"},"outputId":"c3b73e18-f9d9-442e-8657-56da4c28a067"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 1467821941.08\n","Root Mean Squared Error (RMSE): 38312.16\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_train = mean_squared_error(y_train, train_pred).round(2)\n","rmse_train = np.sqrt(mse_train).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_train}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_train}\")"]},{"cell_type":"markdown","metadata":{"id":"07eZsq196EnW"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":149,"metadata":{"executionInfo":{"elapsed":17,"status":"ok","timestamp":1725905407678,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"PZzIFWXD6EnW","colab":{"base_uri":"https://localhost:8080/"},"outputId":"08594a4d-e136-4d10-ba2a-a7fe8d1ddc58"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R2: 0.4\n"]}],"source":["from sklearn import metrics\n","\n","r2_train = metrics.r2_score(y_train, train_pred).round(2)\n","\n","print(f\"Coeficiente de Determinación R2: {r2_train}\")"]},{"cell_type":"code","execution_count":150,"metadata":{"id":"2NDsEbIF6EnX","executionInfo":{"status":"ok","timestamp":1725905407678,"user_tz":-120,"elapsed":15,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["errors_train = pd.DataFrame(\n","    {\"MSE\":[mse_train],\n","     \"RMSE\":[rmse_train],\n","     \"R2\":[r2_train]})"]},{"cell_type":"markdown","metadata":{"id":"6g8zWe016EnX"},"source":["###### Error de prueba"]},{"cell_type":"markdown","metadata":{"id":"o6iilDHR6EnX"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":151,"metadata":{"executionInfo":{"elapsed":15,"status":"ok","timestamp":1725905407678,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"_Ui8k-fw6EnX","colab":{"base_uri":"https://localhost:8080/"},"outputId":"f8dbc9d7-2725-4b74-c399-ec302d3ff820"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 3988822637.11\n","Root Mean Squared Error (RMSE): 63157.13\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_test = mean_squared_error(y_test, test_pred).round(2)\n","rmse_test = np.sqrt(mse_test).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_test}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_test}\")"]},{"cell_type":"markdown","metadata":{"id":"K1K8fSay6EnX"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":152,"metadata":{"executionInfo":{"elapsed":14,"status":"ok","timestamp":1725905407679,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"-3_teTNa6EnX","colab":{"base_uri":"https://localhost:8080/"},"outputId":"bafc05d6-eddd-4ffe-aae2-d953dae57afd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R²: -0.33\n","Coeficiente de Determinación R² Ajustado: -1.54\n"]}],"source":["from sklearn import metrics\n","\n","r2_test = metrics.r2_score(y_test, test_pred).round(2)\n","\n","# Cálculo de R² Ajustado\n","n = len(y_test)  # Número de muestras\n","p = X_test.shape[1]  # Número de predictores (columnas en X_train)\n","\n","r2_adjusted_test = 1 - (1 - r2_test) * (n - 1) / (n - p - 1)\n","\n","# Redondeo de los resultados para presentarlos\n","r2_test = r2_test.round(2)\n","r2_adjusted_test = r2_adjusted_test.round(2)\n","\n","print(f\"Coeficiente de Determinación R²: {r2_test}\")\n","print(f\"Coeficiente de Determinación R² Ajustado: {r2_adjusted_test}\")"]},{"cell_type":"code","execution_count":153,"metadata":{"id":"AYLVGwtr6EnY","executionInfo":{"status":"ok","timestamp":1725905407679,"user_tz":-120,"elapsed":11,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["errors_test = pd.DataFrame(\n","    {\"MSE\":[mse_test],\n","     \"RMSE\":[rmse_test],\n","     \"R2\":[r2_test]})"]},{"cell_type":"markdown","metadata":{"id":"ra9h7EK-6EnY"},"source":["#### Comprobar que la Caja Negra esta bien entrenada - Sesgo y Varianza (Bias y Variance)"]},{"cell_type":"code","execution_count":154,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1725905407679,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"8061KxuS6EnY","colab":{"base_uri":"https://localhost:8080/","height":112},"outputId":"a3d53e37-4a00-4dd9-88f1-e0b2e8f21cf8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                      Model           MSE     RMSE    R2    set\n","0  Decission Tree Regressor 1467821941.08 38312.16  0.40  train\n","1  Decission Tree Regressor 3988822637.11 63157.13 -0.33   test"],"text/html":["\n","  <div id=\"df-177494fc-1a23-4103-adb6-f64409e9e3b5\" 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>Model</th>\n","      <th>MSE</th>\n","      <th>RMSE</th>\n","      <th>R2</th>\n","      <th>set</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Decission Tree Regressor</td>\n","      <td>1467821941.08</td>\n","      <td>38312.16</td>\n","      <td>0.40</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Decission Tree Regressor</td>\n","      <td>3988822637.11</td>\n","      <td>63157.13</td>\n","      <td>-0.33</td>\n","      <td>test</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-177494fc-1a23-4103-adb6-f64409e9e3b5')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-177494fc-1a23-4103-adb6-f64409e9e3b5 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-177494fc-1a23-4103-adb6-f64409e9e3b5');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-b15db215-8d7e-4111-aa98-6b4b9f4ea448\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b15db215-8d7e-4111-aa98-6b4b9f4ea448')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-b15db215-8d7e-4111-aa98-6b4b9f4ea448 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"decission_tree_df","summary":"{\n  \"name\": \"decission_tree_df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Decission Tree Regressor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1782616687.5388193,\n        \"min\": 1467821941.08,\n        \"max\": 3988822637.11,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3988822637.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17568.046765376333,\n        \"min\": 38312.16,\n        \"max\": 63157.13,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          63157.13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5161879502661797,\n        \"min\": -0.33,\n        \"max\": 0.4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -0.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"set\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"test\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":154}],"source":["errors_train['set'] = 'train'\n","errors_test['set'] = 'test'\n","\n","decission_tree_df = pd.concat([errors_train, errors_test], axis=0, ignore_index=True)\n","decission_tree_df.insert(0, 'Model', 'Decission Tree Regressor')\n","decission_tree_df.head(2)"]},{"cell_type":"markdown","metadata":{"id":"NMSubggW6EnY"},"source":["<font color=\"red\">Escribir conclusiones aquí.</font>"]},{"cell_type":"markdown","metadata":{"id":"VRe8hyzUTCdM"},"source":["### 🔶 **KNEIGHBORS REGRESSOR** 🔶"]},{"cell_type":"markdown","metadata":{"id":"JS4sLnpbTCdW"},"source":["#### ENTRENAR MODELO: KNeighbors Regressor"]},{"cell_type":"code","execution_count":155,"metadata":{"executionInfo":{"elapsed":10,"status":"ok","timestamp":1725905407679,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"uBxIctM-TCdX","colab":{"base_uri":"https://localhost:8080/","height":75},"outputId":"d400c1ae-6f24-4a00-9ade-de8d19cafc67"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["KNeighborsRegressor()"],"text/html":["<style>#sk-container-id-10 {color: black;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":155}],"source":["from sklearn.neighbors import KNeighborsRegressor\n","\n","model_knr = KNeighborsRegressor(\n","    n_neighbors=5       # Number of neighbors\n","   # weights='distance',    Use distance-weighted predictions\n","   # algorithm='auto',      Choose the algorithm\n","   # p=5                    Use Euclidean distance\n",")\n","model_knr.fit(X_train, y_train)"]},{"cell_type":"markdown","metadata":{"id":"QbZjGj4ZKPks"},"source":["#### Validación cruzada (Cross Validation)\n","\n","Decido realizar un análisis de Validación Cruzada ya que este algoritmo me está proporcionando resultados muy extraños. La validación cruzada divide el conjunto de datos en varios subconjuntos (pliegues) y entrena y prueba el modelo varias veces, utilizando cada vez un subconjunto diferente como conjunto de prueba y los subconjuntos restantes como conjunto de entrenamiento."]},{"cell_type":"code","execution_count":156,"metadata":{"executionInfo":{"elapsed":332,"status":"ok","timestamp":1725905408002,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"lh7vHG3IKFys","colab":{"base_uri":"https://localhost:8080/"},"outputId":"7d34db35-f7f4-4baa-b22a-8b939eb63c84"},"outputs":[{"output_type":"stream","name":"stdout","text":["Cross-Validation mean: 1778818235.9226105\n","Cross-Validation St_dev: 109708391.48900527\n","Confidence Interval: (0.2851925538854942, 0.5082588307680902)\n"]}],"source":["import numpy as np\n","from scipy import stats\n","from sklearn.model_selection import cross_val_score\n","from sklearn.metrics import mean_squared_error\n","\n","cv_scores = cross_val_score(model_knr, X_train, y_train, cv=3, scoring='neg_mean_squared_error')\n","std_cv_score = cross_val_score(model_knr, X_train_scaled, y_train, cv=3, scoring='neg_mean_squared_error').std()\n","\n","mean_cv_score = -cv_scores.mean()\n","confidence_interval = stats.t.interval(0.95, len(scores)-1, loc=np.mean(scores), scale=stats.sem(scores))\n","\n","print(f\"Cross-Validation mean: {mean_cv_score}\")\n","print(f\"Cross-Validation St_dev: {std_cv_score}\")\n","print(f\"Confidence Interval: {confidence_interval}\")"]},{"cell_type":"markdown","metadata":{"id":"h3g_xhk7C9gm"},"source":["Interpretación:\n","\n","*   Promedio alto, desviación típica baja: el modelo es preciso y coherente, lo que sugiere una buena generalización.\n","*   Promedio alto, desviación típica alta: el modelo es preciso, pero puede estar sobreajustándose o ser sensible a subconjuntos específicos de datos.\n","*   Promedio bajo, desviación típica baja: el modelo tiene un rendimiento sistemáticamente bajo, lo que sugiere que podría estar ajustándose mal.\n","*   Promedio bajo, desviación típica alta: el modelo no es preciso ni coherente, lo que puede indicar problemas con la elección del modelo, la calidad de los datos o la necesidad de un mejor preprocesamiento."]},{"cell_type":"markdown","metadata":{"id":"mdzQGUXNTCdX"},"source":["#### Clasificacion de Cajas Negras por Algoritmo utilizado"]},{"cell_type":"markdown","metadata":{"id":"7UfkMxgNTCdX"},"source":["##### Realizar predicción"]},{"cell_type":"markdown","metadata":{"id":"JqSdk-H4TCdX"},"source":["Predicción de tabla de entrenamiento"]},{"cell_type":"code","execution_count":157,"metadata":{"executionInfo":{"elapsed":27,"status":"ok","timestamp":1725905408003,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"2QKBs2cvTCdX","colab":{"base_uri":"https://localhost:8080/"},"outputId":"584d8a88-1989-43a6-9b37-4546353b83fc"},"outputs":[{"output_type":"stream","name":"stdout","text":["[136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400.]\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n","  warnings.warn(\n"]}],"source":["train_pred = model_knr.predict(X_train_scaled)\n","print(train_pred)"]},{"cell_type":"markdown","metadata":{"id":"M1y58z0iTCdX"},"source":["Predicción de tabla de prueba"]},{"cell_type":"code","execution_count":158,"metadata":{"executionInfo":{"elapsed":26,"status":"ok","timestamp":1725905408004,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"XvnFeWqETCdX","colab":{"base_uri":"https://localhost:8080/"},"outputId":"68cb49df-7e56-4a1a-8968-da8453e91226"},"outputs":[{"output_type":"stream","name":"stdout","text":["[136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.\n"," 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400. 136400.]\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n","  warnings.warn(\n"]}],"source":["test_pred = model_knr.predict(X_test_scaled)\n","print(test_pred)"]},{"cell_type":"markdown","metadata":{"id":"nQsXoLGUTCdY"},"source":["##### Calcular errores"]},{"cell_type":"markdown","metadata":{"id":"tK1qc5eJTCdY"},"source":["###### Error de entrenamiento"]},{"cell_type":"markdown","metadata":{"id":"CttB5tuKTCdY"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":159,"metadata":{"executionInfo":{"elapsed":22,"status":"ok","timestamp":1725905408004,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"rztQpxgmTCdY","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5cae2d03-bcc3-4437-8c90-f75c850f70af"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 9062517493.22\n","Root Mean Squared Error (RMSE): 95197.26\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_train = mean_squared_error(y_train, train_pred).round(2)\n","rmse_train = np.sqrt(mse_train).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_train}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_train}\")"]},{"cell_type":"markdown","metadata":{"id":"APe8Qx-xTCdY"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":160,"metadata":{"executionInfo":{"elapsed":20,"status":"ok","timestamp":1725905408004,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"RK2JFRFkTCdY","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b927e48d-30c1-475d-a562-4e6ce88dcdf8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R²: -2.73\n","Coeficiente de Determinación R² Ajustado: -3.7\n"]}],"source":["from sklearn import metrics\n","\n","r2_train = metrics.r2_score(y_train, train_pred).round(2)\n","\n","# Cálculo de R² Ajustado\n","n = len(y_train)  # Número de muestras\n","p = X_train.shape[1]  # Número de predictores (columnas en X_train)\n","\n","r2_adjusted_train = 1 - (1 - r2_train) * (n - 1) / (n - p - 1)\n","\n","# Redondeo de los resultados para presentarlos\n","r2_train = r2_train.round(2)\n","r2_adjusted_train = r2_adjusted_train.round(2)\n","\n","print(f\"Coeficiente de Determinación R²: {r2_train}\")\n","print(f\"Coeficiente de Determinación R² Ajustado: {r2_adjusted_train}\")"]},{"cell_type":"code","execution_count":161,"metadata":{"id":"dzM7LeJeTCdY","executionInfo":{"status":"ok","timestamp":1725905408004,"user_tz":-120,"elapsed":19,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["errors_train = pd.DataFrame(\n","    {\"MSE\":[mse_train],\n","     \"RMSE\":[rmse_train],\n","     \"R2\":[r2_train]})"]},{"cell_type":"markdown","metadata":{"id":"k95QLXQCTCdY"},"source":["###### Error de prueba"]},{"cell_type":"markdown","metadata":{"id":"LWjvvFg-TCdY"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":162,"metadata":{"executionInfo":{"elapsed":19,"status":"ok","timestamp":1725905408004,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"qIEWA8BsTCdY","colab":{"base_uri":"https://localhost:8080/"},"outputId":"dbdc42e1-eaaa-426b-e28a-bdd30642b29a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 9541921171.84\n","Root Mean Squared Error (RMSE): 97682.76\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_test = mean_squared_error(y_test, test_pred).round(2)\n","rmse_test = np.sqrt(mse_test).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_test}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_test}\")"]},{"cell_type":"markdown","metadata":{"id":"s4I2CHh4TCdZ"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":163,"metadata":{"executionInfo":{"elapsed":18,"status":"ok","timestamp":1725905408004,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"yRXV2P6tTCdZ","colab":{"base_uri":"https://localhost:8080/"},"outputId":"7e60f98b-895a-480e-ad48-42d1114dac09"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R²: -2.19\n","Coeficiente de Determinación R² Ajustado: -5.1\n"]}],"source":["from sklearn import metrics\n","\n","r2_test = metrics.r2_score(y_test, test_pred).round(2)\n","\n","# Cálculo de R² Ajustado\n","n = len(y_test)  # Número de muestras\n","p = X_test_scaled.shape[1]  # Número de predictores (columnas en X_train)\n","\n","r2_adjusted_test = 1 - (1 - r2_test) * (n - 1) / (n - p - 1)\n","\n","# Redondeo de los resultados para presentarlos\n","r2_test = r2_test.round(2)\n","r2_adjusted_test = r2_adjusted_test.round(2)\n","\n","print(f\"Coeficiente de Determinación R²: {r2_test}\")\n","print(f\"Coeficiente de Determinación R² Ajustado: {r2_adjusted_test}\")"]},{"cell_type":"code","execution_count":164,"metadata":{"id":"8CjPgsZ5TCda","executionInfo":{"status":"ok","timestamp":1725905408004,"user_tz":-120,"elapsed":16,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["errors_test = pd.DataFrame(\n","    {\"MSE\":[mse_test],\n","     \"RMSE\":[rmse_test],\n","     \"R2\":[r2_test]})"]},{"cell_type":"markdown","metadata":{"id":"EY2bvBppTCda"},"source":["#### Comprobar que la Caja Negra esta bien entrenada - Sesgo y Varianza (Bias y Variance)"]},{"cell_type":"code","execution_count":165,"metadata":{"executionInfo":{"elapsed":16,"status":"ok","timestamp":1725905408004,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"KEqYr6whTCda","colab":{"base_uri":"https://localhost:8080/","height":112},"outputId":"db8808ba-30bb-4437-d788-20f31fc027b2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                  Model           MSE     RMSE    R2    set\n","0  KNeighbors Regressor 9062517493.22 95197.26 -2.73  train\n","1  KNeighbors Regressor 9541921171.84 97682.76 -2.19   test"],"text/html":["\n","  <div id=\"df-3c422323-3c94-486d-aa4e-a470b8fb0ad2\" 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>Model</th>\n","      <th>MSE</th>\n","      <th>RMSE</th>\n","      <th>R2</th>\n","      <th>set</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>KNeighbors Regressor</td>\n","      <td>9062517493.22</td>\n","      <td>95197.26</td>\n","      <td>-2.73</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>KNeighbors Regressor</td>\n","      <td>9541921171.84</td>\n","      <td>97682.76</td>\n","      <td>-2.19</td>\n","      <td>test</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c422323-3c94-486d-aa4e-a470b8fb0ad2')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-3c422323-3c94-486d-aa4e-a470b8fb0ad2 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-3c422323-3c94-486d-aa4e-a470b8fb0ad2');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-3fbf9527-9359-4531-8697-6fee3ab488bb\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3fbf9527-9359-4531-8697-6fee3ab488bb')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-3fbf9527-9359-4531-8697-6fee3ab488bb button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"kneighbors_regressor_df","summary":"{\n  \"name\": \"kneighbors_regressor_df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"KNeighbors Regressor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 338989592.0779789,\n        \"min\": 9062517493.22,\n        \"max\": 9541921171.84,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          9541921171.84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1757.513904639164,\n        \"min\": 95197.26,\n        \"max\": 97682.76,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          97682.76\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3818376618407357,\n        \"min\": -2.73,\n        \"max\": -2.19,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -2.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"set\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"test\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":165}],"source":["errors_train['set'] = 'train'\n","errors_test['set'] = 'test'\n","\n","kneighbors_regressor_df = pd.concat([errors_train, errors_test], axis=0, ignore_index=True)\n","kneighbors_regressor_df.insert(0, 'Model', 'KNeighbors Regressor')\n","kneighbors_regressor_df.head(2)"]},{"cell_type":"markdown","metadata":{"id":"DagD7V7_TCdb"},"source":["<font color=\"red\">Escribir conclusiones aquí.</font>"]},{"cell_type":"markdown","metadata":{"id":"-sazFRJKUxCt"},"source":["### 🔶 **RIDGE REGRESSION** 🔶"]},{"cell_type":"markdown","metadata":{"id":"2jACnzvMUxC1"},"source":["#### ENTRENAR MODELO: Ridge Regression"]},{"cell_type":"code","execution_count":166,"metadata":{"executionInfo":{"elapsed":15,"status":"ok","timestamp":1725905408004,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"UetDUtbLUxC1","colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"efeee8c4-2898-44b8-ffee-f1bc69db366a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Ridge(alpha=1)"],"text/html":["<style>#sk-container-id-11 {color: black;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=1)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":166}],"source":["from sklearn import linear_model\n","model_rr = linear_model.Ridge(alpha=1)\n","model_rr.fit(X_train_scaled, y_train)"]},{"cell_type":"markdown","metadata":{"id":"oO6fZCwGUxC1"},"source":["#### Clasificacion de Cajas Negras por Algoritmo utilizado"]},{"cell_type":"markdown","metadata":{"id":"60W_CRHiUxC1"},"source":["##### Realizar predicción"]},{"cell_type":"markdown","metadata":{"id":"edlGQ4-VUxC2"},"source":["Predicción de tabla de entrenamiento"]},{"cell_type":"code","execution_count":167,"metadata":{"executionInfo":{"elapsed":14,"status":"ok","timestamp":1725905408004,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"GdCM3MKFUxC2","colab":{"base_uri":"https://localhost:8080/"},"outputId":"51c17804-dc3b-483e-c689-2173b8d86a1b"},"outputs":[{"output_type":"stream","name":"stdout","text":["[261790.03706395 215457.37065722 256388.36665606 142578.52465171\n"," 282716.54246665 186623.28619557 188945.86971965 193632.18832778\n"," 157297.57379394 174209.78659893 245085.4312253  165153.06861394\n"," 226708.30446666 243699.30072605 262216.28115028 207710.4658459\n"," 175218.95656164 216515.2504191  143914.98107776 213130.53111342\n"," 229579.61794501 194668.41745245 235215.43061415 213660.04600937\n"," 186082.75740066 234360.74187441 198594.56437688 234228.51156239\n"," 240441.04718645 164331.24640852 197091.26968892 193197.89222899\n"," 252168.65919086 230211.47495699 195390.9489572  232226.92238814\n"," 250064.19124305 203377.40879268 200760.49611191 229378.39601843\n"," 165171.24531647 210838.53072249 216638.99545525 196669.72558861\n"," 204433.50662741 282593.53577764 236269.49873106 229600.0633085\n"," 161706.9056384  181748.4702825  207740.20915826 226083.23532996\n"," 176284.70572672 192212.20547039 175972.83623459 275870.431873\n"," 261184.34819338 157605.76430072 270301.8740395  247659.6025458\n"," 169453.19203802 239758.66918246 193300.28470153 261080.73234833\n"," 192924.21056341 237271.20088162 202126.37460416 269076.79761288\n"," 218956.75899913 242481.0367818  208613.6823115  184585.41411844\n"," 269946.5661331  202628.24040808 260264.07405024 212509.23451359\n"," 191013.89181858 246576.7484865  254560.14806395 228087.22475978\n"," 188594.86158641 241281.8346025  189444.72338407 235965.05425547\n"," 233994.08667896 216757.86001778 228516.40711479 149648.56735872\n"," 202218.24397002 235251.55293518 200499.99239188 259919.13312725\n"," 251653.66511588 259487.64336015 226379.45375212 295116.75515703\n"," 239973.9282484  200575.79824666 214604.19431409 263411.04696524\n"," 185485.07651883 219878.09380362 261929.69665824]\n"]}],"source":["train_pred = model_rr.predict(X_train_scaled)\n","print(train_pred)"]},{"cell_type":"markdown","metadata":{"id":"1NwSJ9BZUxC2"},"source":["Predicción de tabla de prueba"]},{"cell_type":"code","execution_count":168,"metadata":{"executionInfo":{"elapsed":13,"status":"ok","timestamp":1725905408004,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"s7gbdM6BUxC2","colab":{"base_uri":"https://localhost:8080/"},"outputId":"d5ce9b09-1dc2-4436-9574-42266643a0e0"},"outputs":[{"output_type":"stream","name":"stdout","text":["[184974.08516279 231992.83475857 260420.99240746 166329.2874541\n"," 248412.34439395 164076.21861185 226019.57496096 193779.69976031\n"," 178978.11769516 186377.34892979 222593.53841542 213366.81363265\n"," 244306.56292834 265344.64792833 230671.22864775 212009.36386543\n"," 196548.15638905 213870.62187679 159247.25530436 217720.48775071\n"," 190265.93588379 216146.77152084 216183.93211965 301062.25531156\n"," 173703.39656329 213918.65435385 197902.78619922 250185.66250461\n"," 231999.4091086  167805.19843247 186351.99830016 229805.03579691\n"," 187751.01125184 163584.28718848 153749.26452898 212964.33219448\n"," 240820.89353625 285883.86538035 213888.82157686 187868.86455492\n"," 197517.73310493 217733.6599005  242302.33509211 221407.40584248\n"," 200665.27955958]\n"]}],"source":["test_pred = model_rr.predict(X_test_scaled)\n","print(test_pred)"]},{"cell_type":"markdown","metadata":{"id":"q9Txhc9bUxC2"},"source":["##### Calcular errores"]},{"cell_type":"markdown","metadata":{"id":"X2j7ZAuFUxC3"},"source":["###### Error de entrenamiento"]},{"cell_type":"markdown","metadata":{"id":"21LRJS-vUxC3"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":169,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1725905408004,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"b9w06fWFUxC3","colab":{"base_uri":"https://localhost:8080/"},"outputId":"0393a1f3-2cca-408f-a2c3-68a4a796662d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 1250426356.22\n","Root Mean Squared Error (RMSE): 35361.37\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_train = mean_squared_error(y_train, train_pred).round(2)\n","rmse_train = np.sqrt(mse_train).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_train}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_train}\")"]},{"cell_type":"markdown","metadata":{"id":"XAThWm0aUxC3"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":170,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1725905408004,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"FiVYYF26UxC3","colab":{"base_uri":"https://localhost:8080/"},"outputId":"95ac2af9-e9f9-405f-e062-e72ed0200116"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R2: 0.49\n"]}],"source":["from sklearn import metrics\n","\n","r2_train = metrics.r2_score(y_train, train_pred).round(2)\n","\n","print(f\"Coeficiente de Determinación R2: {r2_train}\")"]},{"cell_type":"code","execution_count":171,"metadata":{"id":"jz05y9M0UxC3","executionInfo":{"status":"ok","timestamp":1725905408004,"user_tz":-120,"elapsed":11,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["errors_train = pd.DataFrame(\n","    {\"MSE\":[mse_train],\n","     \"RMSE\":[rmse_train],\n","     \"R2\":[r2_train]})"]},{"cell_type":"markdown","metadata":{"id":"YPbVxKtPUxC3"},"source":["###### Error de prueba"]},{"cell_type":"markdown","metadata":{"id":"5wNhwvzvUxC3"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":172,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1725905408004,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"uY_x7PkNUxC3","colab":{"base_uri":"https://localhost:8080/"},"outputId":"8f835a1c-dd45-42c3-fae2-596d155ae8a5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 2241060088.9\n","Root Mean Squared Error (RMSE): 47339.84\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_test = mean_squared_error(y_test, test_pred).round(2)\n","rmse_test = np.sqrt(mse_test).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_test}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_test}\")"]},{"cell_type":"markdown","metadata":{"id":"enOMdzKeUxC4"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":173,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1725905408005,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"2L2NHNKrUxC4","colab":{"base_uri":"https://localhost:8080/"},"outputId":"9bed3ae4-67e2-40a2-e750-dba34b272a11"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R2: 0.25\n"]}],"source":["from sklearn import metrics\n","\n","r2_test = metrics.r2_score(y_test, test_pred).round(2)\n","\n","print(f\"Coeficiente de Determinación R2: {r2_test}\")"]},{"cell_type":"code","execution_count":174,"metadata":{"id":"06n5O0xxUxC4","executionInfo":{"status":"ok","timestamp":1725905408005,"user_tz":-120,"elapsed":11,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["errors_test = pd.DataFrame(\n","    {\"MSE\":[mse_test],\n","     \"RMSE\":[rmse_test],\n","     \"R2\":[r2_test]})"]},{"cell_type":"markdown","metadata":{"id":"CSgTYG1YUxC4"},"source":["#### Comprobar que la Caja Negra esta bien entrenada - Sesgo y Varianza (Bias y Variance)"]},{"cell_type":"code","execution_count":175,"metadata":{"executionInfo":{"elapsed":405,"status":"ok","timestamp":1725905408399,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"D0O6FsiKUxC4","colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"95834807-10c1-4f0a-dc5b-54c76cce6dde"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["              Model           MSE     RMSE   R2    set\n","0  Ridge Regression 1250426356.22 35361.37 0.49  train\n","1  Ridge Regression 2241060088.90 47339.84 0.25   test"],"text/html":["\n","  <div id=\"df-cda3a7d0-7b98-40a1-9e49-d464e36ab543\" 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>Model</th>\n","      <th>MSE</th>\n","      <th>RMSE</th>\n","      <th>R2</th>\n","      <th>set</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Ridge Regression</td>\n","      <td>1250426356.22</td>\n","      <td>35361.37</td>\n","      <td>0.49</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Ridge Regression</td>\n","      <td>2241060088.90</td>\n","      <td>47339.84</td>\n","      <td>0.25</td>\n","      <td>test</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cda3a7d0-7b98-40a1-9e49-d464e36ab543')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-cda3a7d0-7b98-40a1-9e49-d464e36ab543 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-cda3a7d0-7b98-40a1-9e49-d464e36ab543');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-b702e594-7859-41ed-8e70-e36b9462ab23\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b702e594-7859-41ed-8e70-e36b9462ab23')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-b702e594-7859-41ed-8e70-e36b9462ab23 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"ridge_regression_df","summary":"{\n  \"name\": \"ridge_regression_df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Ridge Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 700483830.0501696,\n        \"min\": 1250426356.22,\n        \"max\": 2241060088.9,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2241060088.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8470.05736523962,\n        \"min\": 35361.37,\n        \"max\": 47339.84,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          47339.84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.16970562748477142,\n        \"min\": 0.25,\n        \"max\": 0.49,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"set\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"test\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":175}],"source":["errors_train['set'] = 'train'\n","errors_test['set'] = 'test'\n","\n","ridge_regression_df = pd.concat([errors_train, errors_test], axis=0, ignore_index=True)\n","ridge_regression_df.insert(0, 'Model', 'Ridge Regression')\n","ridge_regression_df.head(2)"]},{"cell_type":"markdown","metadata":{"id":"dLuDnm9DUxC4"},"source":["<font color=\"red\">Escribir conclusiones aquí.</font>"]},{"cell_type":"code","execution_count":175,"metadata":{"id":"0ShYmAYYBxQS","executionInfo":{"status":"ok","timestamp":1725905408399,"user_tz":-120,"elapsed":23,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"NEEKBMEzB0p_"},"source":[]},{"cell_type":"markdown","metadata":{"id":"fYbZQ4uzB1TM"},"source":["### 🔶 **LASSO REGRESSION** 🔶"]},{"cell_type":"markdown","metadata":{"id":"bAve1W4sB1TN"},"source":["#### ENTRENAR MODELO: Lasso Regression"]},{"cell_type":"code","execution_count":176,"metadata":{"executionInfo":{"elapsed":23,"status":"ok","timestamp":1725905408399,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"oLpaAuN9B1TO","colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"3782ee68-c2a7-43e8-8135-2ea20c88cd74"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Lasso(alpha=0.1)"],"text/html":["<style>#sk-container-id-12 {color: black;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=0.1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.1)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":176}],"source":["from sklearn.linear_model import Lasso\n","\n","model_lasso = Lasso(alpha=0.1)\n","model_lasso.fit(X_train_scaled, y_train)"]},{"cell_type":"markdown","metadata":{"id":"aDxiCiJSB1TP"},"source":["#### Clasificacion de Cajas Negras por Algoritmo utilizado"]},{"cell_type":"markdown","metadata":{"id":"cu1TdaRTB1TP"},"source":["##### Realizar predicción"]},{"cell_type":"markdown","metadata":{"id":"jWUlhW_9B1TQ"},"source":["Predicción de tabla de entrenamiento"]},{"cell_type":"code","execution_count":177,"metadata":{"executionInfo":{"elapsed":22,"status":"ok","timestamp":1725905408399,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"qi90pZ1lB1TQ","colab":{"base_uri":"https://localhost:8080/"},"outputId":"60fc3136-7353-4c19-a891-abd247d096ef"},"outputs":[{"output_type":"stream","name":"stdout","text":["[261657.62467445 215437.27802135 256486.58464306 140875.01940349\n"," 282880.55150931 185960.97634705 188495.71704446 193280.83566957\n"," 156276.14109962 174239.43771735 246050.84977693 164722.40378051\n"," 226767.0005242  244541.94130072 262325.29061005 208917.88525683\n"," 175306.00633429 216890.81990243 143367.76952277 213346.11476923\n"," 229550.49967037 194158.54828817 235210.87865257 214067.82419827\n"," 185383.92615474 233820.83259035 198604.80104165 234208.64931353\n"," 239982.51896374 163495.88723108 196722.29927125 193662.04758913\n"," 252020.51139801 230099.67321968 195310.98925779 232858.86226025\n"," 250278.26037573 203486.76441462 200520.40057636 229703.66687178\n"," 164170.83266529 210896.65322357 216645.13684103 195868.38667387\n"," 204266.72444631 282706.53477826 237228.05395103 230062.17797726\n"," 161070.0189143  181675.34815625 208328.42634109 225198.52440619\n"," 175668.77516366 192032.45804134 175092.71968369 276098.90588469\n"," 261293.96586479 157076.40021617 271287.2511496  247352.5681385\n"," 168777.61379408 240343.28709341 192963.29666688 262153.48603244\n"," 192163.90166429 237194.04752736 201766.0901503  269503.81698316\n"," 219022.48076854 243586.68026684 208683.85554897 184119.39386659\n"," 270644.3227806  202506.09184636 260863.39636112 212198.87521753\n"," 191034.57383916 246894.9029503  255059.30472459 228138.06174294\n"," 188138.05585528 241744.0644651  188867.13957666 236730.94408331\n"," 234157.60524809 216853.7901933  229434.72200263 148927.46480464\n"," 201670.10354699 235245.56425007 200024.97088029 260056.96062091\n"," 251835.91273633 259990.34117728 226857.10210418 296225.55572994\n"," 240445.44779914 200665.12725092 214849.3129949  263567.49393687\n"," 184538.78822089 220306.19681665 262665.90411657]\n"]}],"source":["train_pred = model_lasso.predict(X_train_scaled)\n","print(train_pred)"]},{"cell_type":"markdown","metadata":{"id":"teNffQSmB1TR"},"source":["Predicción de tabla de prueba"]},{"cell_type":"code","execution_count":178,"metadata":{"executionInfo":{"elapsed":22,"status":"ok","timestamp":1725905408399,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"Mg_iHx-qB1TR","colab":{"base_uri":"https://localhost:8080/"},"outputId":"ddad243c-7daa-4076-a8f2-75073d192517"},"outputs":[{"output_type":"stream","name":"stdout","text":["[184730.5612184  232114.7988382  260657.88607567 165825.15537629\n"," 248961.51314719 162950.80497112 225971.43577819 193772.99500721\n"," 177696.36611943 185458.94734775 222718.43228504 213278.95635687\n"," 243966.85826921 265627.64083454 231080.30227517 211974.23307892\n"," 196918.93177681 213760.73577834 158493.46884884 217651.82497888\n"," 189887.6112564  215451.783729   216708.71770735 302183.63658442\n"," 173164.73911633 214171.84207255 197528.12707931 250561.32892564\n"," 231685.54513257 166828.32016874 186343.5020848  231096.68125579\n"," 187180.12311992 162499.30403788 152449.69767945 212191.89364377\n"," 241062.7436438  286484.25434135 213536.79040636 188145.12860296\n"," 197980.72562374 217787.06087896 242812.26119202 220876.98975649\n"," 201256.56915554]\n"]}],"source":["test_pred = model_lasso.predict(X_test_scaled)\n","print(test_pred)"]},{"cell_type":"markdown","metadata":{"id":"L2571USgB1TR"},"source":["##### Calcular errores"]},{"cell_type":"markdown","metadata":{"id":"UjJgteGyB1TS"},"source":["###### Error de entrenamiento"]},{"cell_type":"markdown","metadata":{"id":"CofgtjM3B1TS"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":179,"metadata":{"executionInfo":{"elapsed":21,"status":"ok","timestamp":1725905408399,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"XB7Ve4ONB1TS","colab":{"base_uri":"https://localhost:8080/"},"outputId":"1f649712-407e-478f-ff03-38fa08513094"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 1250121619.47\n","Root Mean Squared Error (RMSE): 35357.06\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_train = mean_squared_error(y_train, train_pred).round(2)\n","rmse_train = np.sqrt(mse_train).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_train}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_train}\")"]},{"cell_type":"markdown","metadata":{"id":"YLM3A7c5B1TT"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":180,"metadata":{"executionInfo":{"elapsed":20,"status":"ok","timestamp":1725905408399,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"54WPgTh3B1TT","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b3471102-7ef2-4648-e42b-432057b2b8ab"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R2: 0.49\n"]}],"source":["from sklearn import metrics\n","\n","r2_train = metrics.r2_score(y_train, train_pred).round(2)\n","\n","print(f\"Coeficiente de Determinación R2: {r2_train}\")"]},{"cell_type":"code","execution_count":181,"metadata":{"id":"Yl3OOWB5B1TU","executionInfo":{"status":"ok","timestamp":1725905408399,"user_tz":-120,"elapsed":18,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["errors_train = pd.DataFrame(\n","    {\"MSE\":[mse_train],\n","     \"RMSE\":[rmse_train],\n","     \"R2\":[r2_train]})"]},{"cell_type":"markdown","metadata":{"id":"E-zeaEjbB1TU"},"source":["###### Error de prueba"]},{"cell_type":"markdown","metadata":{"id":"wWwA8w00B1TU"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":182,"metadata":{"executionInfo":{"elapsed":18,"status":"ok","timestamp":1725905408400,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"O8Q6ITDlB1TU","colab":{"base_uri":"https://localhost:8080/"},"outputId":"2ad6490e-acc0-4420-bcdf-452756318074"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 2258083907.19\n","Root Mean Squared Error (RMSE): 47519.3\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_test = mean_squared_error(y_test, test_pred).round(2)\n","rmse_test = np.sqrt(mse_test).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_test}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_test}\")"]},{"cell_type":"markdown","metadata":{"id":"LXa2IMdoB1TV"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":183,"metadata":{"executionInfo":{"elapsed":17,"status":"ok","timestamp":1725905408400,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"NqMprlD1B1TV","colab":{"base_uri":"https://localhost:8080/"},"outputId":"99ac1528-3954-4103-8b77-5487968b7e68"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R2: 0.24\n"]}],"source":["from sklearn import metrics\n","\n","r2_test = metrics.r2_score(y_test, test_pred).round(2)\n","\n","print(f\"Coeficiente de Determinación R2: {r2_test}\")"]},{"cell_type":"code","execution_count":184,"metadata":{"id":"s5R3NOx8B1TV","executionInfo":{"status":"ok","timestamp":1725905408400,"user_tz":-120,"elapsed":17,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["errors_test = pd.DataFrame(\n","    {\"MSE\":[mse_test],\n","     \"RMSE\":[rmse_test],\n","     \"R2\":[r2_test]})"]},{"cell_type":"markdown","metadata":{"id":"T53uD1_KB1TV"},"source":["#### Comprobar que la Caja Negra esta bien entrenada - Sesgo y Varianza (Bias y Variance)"]},{"cell_type":"code","execution_count":185,"metadata":{"executionInfo":{"elapsed":16,"status":"ok","timestamp":1725905408400,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"0u0YeUQAB1TV","colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"9cd86c93-7861-4a4f-fd0c-79380662189a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["              Model           MSE     RMSE   R2    set\n","0  Lasso Regression 1250121619.47 35357.06 0.49  train\n","1  Lasso Regression 2258083907.19 47519.30 0.24   test"],"text/html":["\n","  <div id=\"df-e380965c-0941-4ec1-a03d-8123713f83f0\" 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>Model</th>\n","      <th>MSE</th>\n","      <th>RMSE</th>\n","      <th>R2</th>\n","      <th>set</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Lasso Regression</td>\n","      <td>1250121619.47</td>\n","      <td>35357.06</td>\n","      <td>0.49</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Lasso Regression</td>\n","      <td>2258083907.19</td>\n","      <td>47519.30</td>\n","      <td>0.24</td>\n","      <td>test</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e380965c-0941-4ec1-a03d-8123713f83f0')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-e380965c-0941-4ec1-a03d-8123713f83f0 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-e380965c-0941-4ec1-a03d-8123713f83f0');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-e8f5043a-0087-44b0-aa0d-85eb8dc257d8\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e8f5043a-0087-44b0-aa0d-85eb8dc257d8')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-e8f5043a-0087-44b0-aa0d-85eb8dc257d8 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"model_lasso_df","summary":"{\n  \"name\": \"model_lasso_df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Lasso Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 712736968.8271179,\n        \"min\": 1250121619.47,\n        \"max\": 2258083907.19,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2258083907.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8600.00237841828,\n        \"min\": 35357.06,\n        \"max\": 47519.3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          47519.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1767766952966369,\n        \"min\": 0.24,\n        \"max\": 0.49,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"set\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"test\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":185}],"source":["errors_train['set'] = 'train'\n","errors_test['set'] = 'test'\n","\n","model_lasso_df = pd.concat([errors_train, errors_test], axis=0, ignore_index=True)\n","model_lasso_df.insert(0, 'Model', 'Lasso Regression')\n","model_lasso_df.head(2)"]},{"cell_type":"markdown","metadata":{"id":"lREE6cMqB1TW"},"source":["<font color=\"red\">Escribir conclusiones aquí.</font>"]},{"cell_type":"markdown","metadata":{"id":"_YwsttBtCVew"},"source":["### 🔶 **ELASTIC NET REGRESSION** 🔶"]},{"cell_type":"markdown","metadata":{"id":"LZqKqJiWCVex"},"source":["#### ENTRENAR MODELO: Elastic Net Regression"]},{"cell_type":"code","execution_count":186,"metadata":{"executionInfo":{"elapsed":16,"status":"ok","timestamp":1725905408400,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"vICFFivgCVex","colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"c10648a7-2d07-4506-b395-a4dd1700e4ed"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["ElasticNet(alpha=0.1)"],"text/html":["<style>#sk-container-id-13 {color: black;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ElasticNet(alpha=0.1)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet(alpha=0.1)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":186}],"source":["from sklearn.linear_model import ElasticNet\n","\n","model_enet = ElasticNet(alpha=0.1, l1_ratio=0.5)\n","model_enet.fit(X_train_scaled, y_train)"]},{"cell_type":"markdown","metadata":{"id":"ka8E0VaUCVex"},"source":["#### Clasificacion de Cajas Negras por Algoritmo utilizado"]},{"cell_type":"markdown","metadata":{"id":"8kOi73ShCVey"},"source":["##### Realizar predicción"]},{"cell_type":"markdown","metadata":{"id":"siNMzeOwCVey"},"source":["Predicción de tabla de entrenamiento"]},{"cell_type":"code","execution_count":187,"metadata":{"executionInfo":{"elapsed":15,"status":"ok","timestamp":1725905408400,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"4fXBmX0ACVey","colab":{"base_uri":"https://localhost:8080/"},"outputId":"02a70e70-75e3-4d67-8409-37dbb2e32453"},"outputs":[{"output_type":"stream","name":"stdout","text":["[262031.78294168 215797.89036189 256309.85441167 148509.02915508\n"," 281726.01925255 188687.88002482 190571.81655071 195595.55083171\n"," 160968.02684362 174334.90845288 241973.88065835 166737.48814774\n"," 226486.6032917  240869.41296183 261270.02823804 203958.69420597\n"," 175037.49405461 215141.11695451 146159.02633879 212487.70711692\n"," 229478.43261301 196728.29057615 234936.27663183 212353.10193162\n"," 188465.35011181 235951.65093995 198598.00675609 234109.80701092\n"," 241925.85952523 167465.01787513 198538.63311654 192292.40426801\n"," 252557.01582001 230400.05859196 195668.9592208  230122.09155902\n"," 249839.19741415 203279.64610369 201612.59352338 228344.94712538\n"," 168645.25982707 210631.08345824 216450.49425874 199441.31083708\n"," 204880.67265221 281663.5003484  232915.06719921 228054.35648303\n"," 164168.01917645 182002.71985936 206473.79954361 229122.02235038\n"," 178485.55936576 192958.71253486 178982.97787856 274957.71629359\n"," 260605.05245247 159490.78769564 266942.81106482 248305.79072503\n"," 171753.49444258 237616.65386998 194611.32427516 257561.64045636\n"," 195071.40805875 237343.06317345 203320.93200547 267274.5798991\n"," 218958.39572938 238872.50982737 208593.82748663 186350.31294112\n"," 267413.50354069 203204.03399757 258302.50808235 213406.65905217\n"," 191162.98493179 245435.45631542 252883.79602804 227837.34093459\n"," 190463.43126192 239510.0047573  191325.05564994 233353.43678838\n"," 233454.00779161 216308.5538785  225172.01749235 152326.48970833\n"," 204071.57779318 235080.10257861 202189.55167049 259029.6182578\n"," 250743.96901809 257717.30801794 224235.5991303  291094.03364187\n"," 238163.06867025 200528.39854647 213444.58096008 262871.93272724\n"," 188357.72392557 218445.60372106 259050.24345046]\n"]}],"source":["train_pred = model_enet.predict(X_train_scaled)\n","print(train_pred)"]},{"cell_type":"markdown","metadata":{"id":"fFzorwE-CVez"},"source":["Predicción de tabla de prueba"]},{"cell_type":"code","execution_count":188,"metadata":{"executionInfo":{"elapsed":14,"status":"ok","timestamp":1725905408400,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"YEdygR9GCVez","colab":{"base_uri":"https://localhost:8080/"},"outputId":"157579ba-436a-458d-9a18-1e1b19f1ab50"},"outputs":[{"output_type":"stream","name":"stdout","text":["[185825.50189917 231474.44018662 259426.63734385 168897.67620488\n"," 246700.83765561 167536.77783722 226118.15732594 193965.22713417\n"," 183106.88967616 189596.69666382 222441.68944071 213653.51748732\n"," 245080.28408123 264008.54780338 229137.96331702 212226.73293538\n"," 195388.45930991 214302.00534305 162016.17787429 218036.00919063\n"," 191432.6876142  218827.47788239 214250.9843572  296805.07995031\n"," 175888.31974536 213072.99981993 199284.04236187 248343.62898802\n"," 232978.1018507  171224.45588237 186460.78381352 225333.64253808\n"," 189120.50572577 167331.01448057 158397.4728032  215548.65669723\n"," 239821.04845417 284073.96191454 214871.82755786 187135.4041404\n"," 195952.38732701 217697.51672763 240399.8737251  223581.48929519\n"," 199069.07941075]\n"]}],"source":["test_pred = model_enet.predict(X_test_scaled)\n","print(test_pred)"]},{"cell_type":"markdown","metadata":{"id":"ONT_YoE3CVez"},"source":["##### Calcular errores"]},{"cell_type":"markdown","metadata":{"id":"ZeFsY6L5CVez"},"source":["###### Error de entrenamiento"]},{"cell_type":"markdown","metadata":{"id":"s81lUFDJCVez"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":189,"metadata":{"executionInfo":{"elapsed":14,"status":"ok","timestamp":1725905408400,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"D_ZyDBDNCVez","colab":{"base_uri":"https://localhost:8080/"},"outputId":"7bacc817-1505-4bfe-bf04-b2bc7e63be4a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 1256187145.06\n","Root Mean Squared Error (RMSE): 35442.73\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_train = mean_squared_error(y_train, train_pred).round(2)\n","rmse_train = np.sqrt(mse_train).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_train}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_train}\")"]},{"cell_type":"markdown","metadata":{"id":"4EkpYuoxCVe0"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":190,"metadata":{"executionInfo":{"elapsed":14,"status":"ok","timestamp":1725905408400,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"_zKQfhGYCVe0","colab":{"base_uri":"https://localhost:8080/"},"outputId":"12435e74-ce81-4b8b-e5b5-211112161165"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R2: 0.48\n"]}],"source":["from sklearn import metrics\n","\n","r2_train = metrics.r2_score(y_train, train_pred).round(2)\n","\n","print(f\"Coeficiente de Determinación R2: {r2_train}\")"]},{"cell_type":"code","execution_count":191,"metadata":{"id":"zAcLBhu2CVe0","executionInfo":{"status":"ok","timestamp":1725905408400,"user_tz":-120,"elapsed":13,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["errors_train = pd.DataFrame(\n","    {\"MSE\":[mse_train],\n","     \"RMSE\":[rmse_train],\n","     \"R2\":[r2_train]})"]},{"cell_type":"markdown","metadata":{"id":"uqJQ9HiJCVe0"},"source":["###### Error de prueba"]},{"cell_type":"markdown","metadata":{"id":"e0rJM6_dCVe1"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":192,"metadata":{"executionInfo":{"elapsed":13,"status":"ok","timestamp":1725905408400,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"_C3tTCjeCVe1","colab":{"base_uri":"https://localhost:8080/"},"outputId":"dfc2f517-2678-40a8-931b-e48ee3834458"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 2190287069.03\n","Root Mean Squared Error (RMSE): 46800.5\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_test = mean_squared_error(y_test, test_pred).round(2)\n","rmse_test = np.sqrt(mse_test).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_test}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_test}\")"]},{"cell_type":"markdown","metadata":{"id":"aig4mv1-CVe1"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":193,"metadata":{"executionInfo":{"elapsed":13,"status":"ok","timestamp":1725905408400,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"fl-PLBgaCVe1","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b2f73e04-addd-4c05-d8b6-b1526f5ddef9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R2: 0.27\n"]}],"source":["from sklearn import metrics\n","\n","r2_test = metrics.r2_score(y_test, test_pred).round(2)\n","\n","print(f\"Coeficiente de Determinación R2: {r2_test}\")"]},{"cell_type":"code","execution_count":194,"metadata":{"id":"Hyj9uCPQCVe2","executionInfo":{"status":"ok","timestamp":1725905408400,"user_tz":-120,"elapsed":12,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["errors_test = pd.DataFrame(\n","    {\"MSE\":[mse_test],\n","     \"RMSE\":[rmse_test],\n","     \"R2\":[r2_test]})"]},{"cell_type":"markdown","metadata":{"id":"7I9RI5c_CVe2"},"source":["#### Comprobar que la Caja Negra esta bien entrenada - Sesgo y Varianza (Bias y Variance)"]},{"cell_type":"code","execution_count":195,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1725905408400,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"mK4nCVlpCVe2","colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"d0ed9108-ee7f-4584-a089-d8efc5272780"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                    Model           MSE     RMSE   R2    set\n","0  Elastic Net Regression 1256187145.06 35442.73 0.48  train\n","1  Elastic Net Regression 2190287069.03 46800.50 0.27   test"],"text/html":["\n","  <div id=\"df-92342951-ce9a-482f-ac75-1f564d9cd292\" 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>Model</th>\n","      <th>MSE</th>\n","      <th>RMSE</th>\n","      <th>R2</th>\n","      <th>set</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Elastic Net Regression</td>\n","      <td>1256187145.06</td>\n","      <td>35442.73</td>\n","      <td>0.48</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Elastic Net Regression</td>\n","      <td>2190287069.03</td>\n","      <td>46800.50</td>\n","      <td>0.27</td>\n","      <td>test</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-92342951-ce9a-482f-ac75-1f564d9cd292')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-92342951-ce9a-482f-ac75-1f564d9cd292 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-92342951-ce9a-482f-ac75-1f564d9cd292');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-d3a1a6df-360a-49e0-8214-2bcd97d69a85\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d3a1a6df-360a-49e0-8214-2bcd97d69a85')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-d3a1a6df-360a-49e0-8214-2bcd97d69a85 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"elastic_net_df","summary":"{\n  \"name\": \"elastic_net_df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Elastic Net Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 660508390.5450257,\n        \"min\": 1256187145.06,\n        \"max\": 2190287069.03,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2190287069.03\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8031.156186157132,\n        \"min\": 35442.73,\n        \"max\": 46800.5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          46800.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14849242404917495,\n        \"min\": 0.27,\n        \"max\": 0.48,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"set\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"test\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":195}],"source":["errors_train['set'] = 'train'\n","errors_test['set'] = 'test'\n","\n","elastic_net_df = pd.concat([errors_train, errors_test], axis=0, ignore_index=True)\n","elastic_net_df.insert(0, 'Model', 'Elastic Net Regression')\n","elastic_net_df.head(2)"]},{"cell_type":"markdown","metadata":{"id":"NVcVnnk0CVe2"},"source":["<font color=\"red\">Escribir conclusiones aquí.</font>"]},{"cell_type":"markdown","metadata":{"id":"zoBq-vI1Wt04"},"source":["### 🔶 **LINEAR REGRESSION** 🔶"]},{"cell_type":"markdown","metadata":{"id":"4wusIbS9Wt1D"},"source":["#### ENTRENAR MODELO: Linear Regression"]},{"cell_type":"code","execution_count":196,"metadata":{"executionInfo":{"elapsed":31,"status":"ok","timestamp":1725905408875,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"90spy9J2Wt1E","colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"95089513-22e7-4c83-feef-b8708d2d4f33"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["LinearRegression()"],"text/html":["<style>#sk-container-id-14 {color: black;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" 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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":196}],"source":["from sklearn.linear_model import LinearRegression\n","\n","model_lr = LinearRegression()\n","model_lr.fit(X_train_scaled, y_train)"]},{"cell_type":"markdown","metadata":{"id":"OcFvCM_JWt1E"},"source":["#### Clasificacion de Cajas Negras por Algoritmo utilizado"]},{"cell_type":"markdown","metadata":{"id":"bnPzhK4YWt1E"},"source":["##### Realizar predicción"]},{"cell_type":"markdown","metadata":{"id":"SWj6zXh1Wt1E"},"source":["Predicción de tabla de entrenamiento"]},{"cell_type":"code","execution_count":197,"metadata":{"executionInfo":{"elapsed":28,"status":"ok","timestamp":1725905408875,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"0NZ0wL8dWt1E","colab":{"base_uri":"https://localhost:8080/"},"outputId":"518209e8-b7a5-4226-9443-e13b8e12690a"},"outputs":[{"output_type":"stream","name":"stdout","text":["[261656.38439111 215437.24544149 256487.0544969  140873.9295094\n"," 282881.08689673 185959.0052828  188494.83189935 193281.63844323\n"," 156274.79060849 174239.13941971 246050.93741573 164721.70397162\n"," 226767.43623683 244542.31724693 262325.2575728  208919.03733595\n"," 175305.98824811 216890.89217834 143367.20734711 213346.45196227\n"," 229548.8855008  194157.55664361 235210.33477011 214068.36654156\n"," 185383.54887677 233821.12309414 198605.56723629 234208.45810492\n"," 239982.26106867 163494.87754764 196721.92603676 193663.52454198\n"," 252019.32440073 230099.78325803 195310.76129339 232859.59340531\n"," 250279.77349064 203487.13297186 200520.30602145 229704.95602575\n"," 164169.9540742  210897.30330137 216644.98150042 195867.84767636\n"," 204267.01566318 282707.47518603 237229.01240621 230062.23189501\n"," 161069.73551365 181674.10028195 208330.99924254 225197.62203541\n"," 175668.55706421 192032.7249083  175090.76598698 276099.72135711\n"," 261294.84705196 157075.45283518 271287.95124772 247350.58973729\n"," 168776.83247763 240343.55260765 192961.90117682 262153.5048899\n"," 192162.2913087  237194.61376791 201765.99880377 269502.07582003\n"," 219022.33042688 243587.44099461 208684.39634066 184119.08379308\n"," 270644.58330132 202506.1671956  260864.49500372 212198.51888072\n"," 191035.15788337 246895.16360113 255060.44307848 228137.89809409\n"," 188137.9774904  241744.52599534 188867.2657935  236731.09827204\n"," 234157.31210496 216853.94533882 229435.26802767 148926.74187679\n"," 201669.34924495 235245.60272559 200025.21149251 260057.561793\n"," 251836.57989708 259991.52767661 226856.8223912  296226.80596159\n"," 240446.47305088 200664.86058927 214849.63014916 263567.96901447\n"," 184538.77842902 220305.71283897 262667.24469972]\n"]}],"source":["train_pred = model_lr.predict(X_train_scaled)\n","print(train_pred)"]},{"cell_type":"markdown","metadata":{"id":"cRoII1xQWt1E"},"source":["Predicción de tabla de prueba"]},{"cell_type":"code","execution_count":198,"metadata":{"executionInfo":{"elapsed":26,"status":"ok","timestamp":1725905408875,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"JyQucw0hWt1F","colab":{"base_uri":"https://localhost:8080/"},"outputId":"be95b114-0c18-4a60-d5ba-4c743a1dd1e5"},"outputs":[{"output_type":"stream","name":"stdout","text":["[184730.05749118 232114.76056796 260658.54427541 165825.49404184\n"," 248961.64169308 162949.39777572 225971.74668075 193772.59872421\n"," 177695.37399198 185458.91842912 222717.66589646 213279.51240432\n"," 243965.98327171 265628.70406597 231081.10626979 211974.36832346\n"," 196918.50997229 213761.37643963 158492.0111893  217651.63642204\n"," 189886.11109867 215451.33079998 216708.89771345 302186.76140539\n"," 173163.58325231 214171.45593233 197527.31471607 250561.3776235\n"," 231685.51836357 166827.40526913 186343.51569827 231097.24982397\n"," 187177.57243514 162497.92588993 152448.60008932 212191.10622972\n"," 241063.401306   286484.56194652 213536.82426514 188145.36651083\n"," 197980.80514888 217787.82398781 242813.368237   220876.16889795\n"," 201256.18885671]\n"]}],"source":["test_pred = model_lr.predict(X_test_scaled)\n","print(test_pred)"]},{"cell_type":"markdown","metadata":{"id":"rMwMWyKSWt1F"},"source":["##### Calcular errores"]},{"cell_type":"markdown","metadata":{"id":"WiUbSy3iWt1F"},"source":["###### Error de entrenamiento"]},{"cell_type":"markdown","metadata":{"id":"WH5nlX5uWt1F"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":199,"metadata":{"executionInfo":{"elapsed":25,"status":"ok","timestamp":1725905408875,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"V5d8TVr3Wt1F","colab":{"base_uri":"https://localhost:8080/"},"outputId":"349364af-60f3-42fc-fde6-5586e3d6e17f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 1250121618.76\n","Root Mean Squared Error (RMSE): 35357.06\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_train = mean_squared_error(y_train, train_pred).round(2)\n","rmse_train = np.sqrt(mse_train).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_train}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_train}\")"]},{"cell_type":"markdown","metadata":{"id":"qnPhmt2FWt1F"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":200,"metadata":{"executionInfo":{"elapsed":24,"status":"ok","timestamp":1725905408875,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"DIxpI2PZWt1F","colab":{"base_uri":"https://localhost:8080/"},"outputId":"a6e39004-eba4-4474-f236-3ad16bb4862a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R2: 0.49\n"]}],"source":["from sklearn import metrics\n","\n","r2_train = metrics.r2_score(y_train, train_pred).round(2)\n","\n","print(f\"Coeficiente de Determinación R2: {r2_train}\")"]},{"cell_type":"code","execution_count":201,"metadata":{"id":"47gJOsngWt1G","executionInfo":{"status":"ok","timestamp":1725905408875,"user_tz":-120,"elapsed":24,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["errors_train = pd.DataFrame(\n","    {\"MSE\":[mse_train],\n","     \"RMSE\":[rmse_train],\n","     \"R2\":[r2_train]})"]},{"cell_type":"markdown","metadata":{"id":"NyKvB-sAWt1G"},"source":["###### Error de prueba"]},{"cell_type":"markdown","metadata":{"id":"mYcikyB5Wt1G"},"source":["MSE y RMSE"]},{"cell_type":"code","execution_count":202,"metadata":{"executionInfo":{"elapsed":23,"status":"ok","timestamp":1725905408875,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"4kRsaYbDWt1G","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b9f0be4a-7959-442d-94e0-59205dc64ece"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mean Squared Error (MSE): 2258108651.13\n","Root Mean Squared Error (RMSE): 47519.56\n"]}],"source":["from sklearn.metrics import mean_squared_error\n","\n","mse_test = mean_squared_error(y_test, test_pred).round(2)\n","rmse_test = np.sqrt(mse_test).round(2)\n","\n","print(f\"Mean Squared Error (MSE): {mse_test}\")\n","print(f\"Root Mean Squared Error (RMSE): {rmse_test}\")"]},{"cell_type":"markdown","metadata":{"id":"Y_fCyrRTWt1G"},"source":["Coeficiente de Determinación R2"]},{"cell_type":"code","execution_count":203,"metadata":{"executionInfo":{"elapsed":24,"status":"ok","timestamp":1725905408876,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"ydh6wMVPWt1G","colab":{"base_uri":"https://localhost:8080/"},"outputId":"0991cf24-a236-4db2-b84d-e52c456ee355"},"outputs":[{"output_type":"stream","name":"stdout","text":["Coeficiente de Determinación R2: 0.24\n"]}],"source":["from sklearn import metrics\n","\n","r2_test = metrics.r2_score(y_test, test_pred).round(2)\n","\n","print(f\"Coeficiente de Determinación R2: {r2_test}\")"]},{"cell_type":"code","execution_count":204,"metadata":{"id":"pr1RZP0WWt1G","executionInfo":{"status":"ok","timestamp":1725905408876,"user_tz":-120,"elapsed":24,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}}},"outputs":[],"source":["errors_test = pd.DataFrame(\n","    {\"MSE\":[mse_test],\n","     \"RMSE\":[rmse_test],\n","     \"R2\":[r2_test]})"]},{"cell_type":"markdown","metadata":{"id":"GbYD_8goWt1G"},"source":["#### Comprobar que la Caja Negra esta bien entrenada - Sesgo y Varianza (Bias y Variance)"]},{"cell_type":"code","execution_count":205,"metadata":{"executionInfo":{"elapsed":23,"status":"ok","timestamp":1725905408876,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"MuBfveFNWt1G","colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"ae5bfdaf-fc8e-461d-c2d8-cc80c3ab6aa2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["               Model           MSE     RMSE   R2    set\n","0  Linear Regression 1250121618.76 35357.06 0.49  train\n","1  Linear Regression 2258108651.13 47519.56 0.24   test"],"text/html":["\n","  <div id=\"df-3f03eced-f049-442e-b293-2dbc4f7e1417\" 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>Model</th>\n","      <th>MSE</th>\n","      <th>RMSE</th>\n","      <th>R2</th>\n","      <th>set</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Linear Regression</td>\n","      <td>1250121618.76</td>\n","      <td>35357.06</td>\n","      <td>0.49</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Linear Regression</td>\n","      <td>2258108651.13</td>\n","      <td>47519.56</td>\n","      <td>0.24</td>\n","      <td>test</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f03eced-f049-442e-b293-2dbc4f7e1417')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-3f03eced-f049-442e-b293-2dbc4f7e1417 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-3f03eced-f049-442e-b293-2dbc4f7e1417');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-d43613fb-c5e3-41b2-a646-210e517eb4da\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d43613fb-c5e3-41b2-a646-210e517eb4da')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-d43613fb-c5e3-41b2-a646-210e517eb4da button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"linear_regression_df","summary":"{\n  \"name\": \"linear_regression_df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Linear Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 712754465.9369311,\n        \"min\": 1250121618.76,\n        \"max\": 2258108651.13,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2258108651.13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8600.186226181384,\n        \"min\": 35357.06,\n        \"max\": 47519.56,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          47519.56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1767766952966369,\n        \"min\": 0.24,\n        \"max\": 0.49,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"set\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"test\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":205}],"source":["errors_train['set'] = 'train'\n","errors_test['set'] = 'test'\n","\n","linear_regression_df = pd.concat([errors_train, errors_test], axis=0, ignore_index=True)\n","linear_regression_df.insert(0, 'Model', 'Linear Regression')\n","linear_regression_df.head(2)"]},{"cell_type":"markdown","metadata":{"id":"RGXOREBZWt1G"},"source":["<font color=\"red\">Escribir conclusiones aquí.</font>"]},{"cell_type":"markdown","metadata":{"id":"22HHzM0yZlTl"},"source":["### Recapitulación de resultados de los diferentes tests"]},{"cell_type":"code","execution_count":206,"metadata":{"executionInfo":{"elapsed":23,"status":"ok","timestamp":1725905408876,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"biIRQusYZtSK","colab":{"base_uri":"https://localhost:8080/","height":488},"outputId":"c8ecccfe-dd05-4797-e12a-3ce0ac43bdcd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                       Model           MSE     RMSE    R2    set\n","0    Random Forest Regressor  205280647.46 14327.62  0.92  train\n","1    Random Forest Regressor 3492367888.30 59096.26 -0.17   test\n","2   Decission Tree Regressor 1467821941.08 38312.16  0.40  train\n","3   Decission Tree Regressor 3988822637.11 63157.13 -0.33   test\n","4       KNeighbors Regressor 9062517493.22 95197.26 -2.73  train\n","5       KNeighbors Regressor 9541921171.84 97682.76 -2.19   test\n","6           Ridge Regression 1250426356.22 35361.37  0.49  train\n","7           Ridge Regression 2241060088.90 47339.84  0.25   test\n","8           Lasso Regression 1250121619.47 35357.06  0.49  train\n","9           Lasso Regression 2258083907.19 47519.30  0.24   test\n","10    Elastic Net Regression 1256187145.06 35442.73  0.48  train\n","11    Elastic Net Regression 2190287069.03 46800.50  0.27   test\n","12         Linear Regression 1250121618.76 35357.06  0.49  train\n","13         Linear Regression 2258108651.13 47519.56  0.24   test"],"text/html":["\n","  <div id=\"df-fd3d2bd9-0e41-4aad-8014-98dddae0fd98\" 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>Model</th>\n","      <th>MSE</th>\n","      <th>RMSE</th>\n","      <th>R2</th>\n","      <th>set</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Random Forest Regressor</td>\n","      <td>205280647.46</td>\n","      <td>14327.62</td>\n","      <td>0.92</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Random Forest Regressor</td>\n","      <td>3492367888.30</td>\n","      <td>59096.26</td>\n","      <td>-0.17</td>\n","      <td>test</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Decission Tree Regressor</td>\n","      <td>1467821941.08</td>\n","      <td>38312.16</td>\n","      <td>0.40</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Decission Tree Regressor</td>\n","      <td>3988822637.11</td>\n","      <td>63157.13</td>\n","      <td>-0.33</td>\n","      <td>test</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>KNeighbors Regressor</td>\n","      <td>9062517493.22</td>\n","      <td>95197.26</td>\n","      <td>-2.73</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>KNeighbors Regressor</td>\n","      <td>9541921171.84</td>\n","      <td>97682.76</td>\n","      <td>-2.19</td>\n","      <td>test</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Ridge Regression</td>\n","      <td>1250426356.22</td>\n","      <td>35361.37</td>\n","      <td>0.49</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Ridge Regression</td>\n","      <td>2241060088.90</td>\n","      <td>47339.84</td>\n","      <td>0.25</td>\n","      <td>test</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Lasso Regression</td>\n","      <td>1250121619.47</td>\n","      <td>35357.06</td>\n","      <td>0.49</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Lasso Regression</td>\n","      <td>2258083907.19</td>\n","      <td>47519.30</td>\n","      <td>0.24</td>\n","      <td>test</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Elastic Net Regression</td>\n","      <td>1256187145.06</td>\n","      <td>35442.73</td>\n","      <td>0.48</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Elastic Net Regression</td>\n","      <td>2190287069.03</td>\n","      <td>46800.50</td>\n","      <td>0.27</td>\n","      <td>test</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Linear Regression</td>\n","      <td>1250121618.76</td>\n","      <td>35357.06</td>\n","      <td>0.49</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Linear Regression</td>\n","      <td>2258108651.13</td>\n","      <td>47519.56</td>\n","      <td>0.24</td>\n","      <td>test</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd3d2bd9-0e41-4aad-8014-98dddae0fd98')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      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-fd3d2bd9-0e41-4aad-8014-98dddae0fd98 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-fd3d2bd9-0e41-4aad-8014-98dddae0fd98');\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","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","<div id=\"df-467267f0-e1bd-4a86-9d9c-c1e32a715fc1\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-467267f0-e1bd-4a86-9d9c-c1e32a715fc1')\"\n","            title=\"Suggest charts\"\n","            style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","     width=\"24px\">\n","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-467267f0-e1bd-4a86-9d9c-c1e32a715fc1 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","  <div id=\"id_dd4e7f80-f57a-42cd-94f9-d0d05966a6e4\">\n","    <style>\n","      .colab-df-generate {\n","        background-color: #E8F0FE;\n","        border: none;\n","        border-radius: 50%;\n","        cursor: pointer;\n","        display: none;\n","        fill: #1967D2;\n","        height: 32px;\n","        padding: 0 0 0 0;\n","        width: 32px;\n","      }\n","\n","      .colab-df-generate:hover {\n","        background-color: #E2EBFA;\n","        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-generate {\n","        background-color: #3B4455;\n","        fill: #D2E3FC;\n","      }\n","\n","      [theme=dark] .colab-df-generate:hover {\n","        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","    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('result_df')\"\n","            title=\"Generate code using this dataframe.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n","  </svg>\n","    </button>\n","    <script>\n","      (() => {\n","      const buttonEl =\n","        document.querySelector('#id_dd4e7f80-f57a-42cd-94f9-d0d05966a6e4 button.colab-df-generate');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","      buttonEl.onclick = () => {\n","        google.colab.notebook.generateWithVariable('result_df');\n","      }\n","      })();\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"result_df","summary":"{\n  \"name\": \"result_df\",\n  \"rows\": 14,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Random Forest Regressor\",\n          \"Decission Tree Regressor\",\n          \"Elastic Net Regression\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2848504356.875264,\n        \"min\": 205280647.46,\n        \"max\": 9541921171.84,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          2258083907.19,\n          2190287069.03,\n          205280647.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22981.433713418177,\n        \"min\": 14327.62,\n        \"max\": 97682.76,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          46800.5,\n          47519.3,\n          14327.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.056119009694155,\n        \"min\": -2.73,\n        \"max\": 0.92,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          -2.19,\n          0.92,\n          0.48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"set\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"test\",\n          \"train\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":206}],"source":["result_df = pd.concat([random_forest_df, decission_tree_df, kneighbors_regressor_df, ridge_regression_df, model_lasso_df, elastic_net_df, linear_regression_df], axis=0, ignore_index=True)\n","result_df[['MSE', 'RMSE', 'R2']] = result_df[['MSE', 'RMSE', 'R2']].round(2)\n","pd.options.display.float_format = '{:.2f}'.format\n","result_df"]},{"cell_type":"markdown","metadata":{"id":"bGIuEsy-BUL5"},"source":["Conclusiones generales: Vemos que incurrimos en Overfitting con todos los modelos, probablemente debido al tamaño demasiado reducido del dataset. Para mejorar estos resultados se puede jugar con los distintos parámetros de cada algoritmo o, también, se pueden implementar diferentes técnicas existentes:\n","\n"]},{"cell_type":"code","source":["print(X_train.shape[1])\n","X_train_scaled.shape\n","\n","X_train.columns.tolist()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dViQzr6w7pmB","executionInfo":{"status":"ok","timestamp":1725905408876,"user_tz":-120,"elapsed":22,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"}},"outputId":"fa8501b1-2511-4d84-9313-c484bb62e023"},"execution_count":207,"outputs":[{"output_type":"stream","name":"stdout","text":["21\n"]},{"output_type":"execute_result","data":{"text/plain":["['area',\n"," 'contact_is_private_seller',\n"," 'district',\n"," 'dpe',\n"," 'estate_type',\n"," 'ges',\n"," 'is_exclusive',\n"," 'rooms',\n"," 'sold',\n"," 'selling_time',\n"," 'old_price',\n"," 'price_decrease_percent',\n"," 'bedrooms',\n"," 'level',\n"," 'balcony',\n"," 'terrace',\n"," 'parking',\n"," 'elevator',\n"," 'box',\n"," 'garden',\n"," 'swimming_pool']"]},"metadata":{},"execution_count":207}]},{"cell_type":"markdown","metadata":{"id":"FlvchcyECtAa"},"source":["#### Predicción del modelo RANDOM FOREST"]},{"cell_type":"code","execution_count":208,"metadata":{"executionInfo":{"elapsed":20,"status":"ok","timestamp":1725905408876,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"dCjPOZWBgE-j","colab":{"base_uri":"https://localhost:8080/"},"outputId":"cad35419-c9bb-4a79-e367-9f4da9150f21"},"outputs":[{"output_type":"stream","name":"stdout","text":["[202078.455]\n"]}],"source":["predictions = model_rf.predict(X_prediction_scaled)\n","print(predictions)"]},{"cell_type":"markdown","metadata":{"id":"ktF39fwkHAyv"},"source":["#### Predicción del modelo DECISSION TREE REGRESSOR"]},{"cell_type":"code","execution_count":209,"metadata":{"executionInfo":{"elapsed":19,"status":"ok","timestamp":1725905408876,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"jv4biAGJHJvV","colab":{"base_uri":"https://localhost:8080/"},"outputId":"86ee22e1-62c6-4feb-a9c7-ce9dc4bd9f07"},"outputs":[{"output_type":"stream","name":"stdout","text":["[223189.11111111]\n"]}],"source":["predictions = model_dt.predict(X_prediction_scaled)\n","print(predictions)"]},{"cell_type":"markdown","metadata":{"id":"RlIdtS1mOB4k"},"source":["#### Predicción del modelo K-NEIGHBORS REGRESSOR"]},{"cell_type":"code","execution_count":210,"metadata":{"executionInfo":{"elapsed":17,"status":"ok","timestamp":1725905408876,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"sKJz967GOI_C","colab":{"base_uri":"https://localhost:8080/"},"outputId":"3c910ecd-0c04-4410-c103-0e46d211d6eb"},"outputs":[{"output_type":"stream","name":"stdout","text":["[136400.]\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n","  warnings.warn(\n"]}],"source":["predictions = model_knr.predict(X_prediction_scaled)\n","print(predictions)"]},{"cell_type":"markdown","metadata":{"id":"OVMP9p0HN5tb"},"source":["#### Predicción del modelo RIDGE REGRESSION"]},{"cell_type":"code","execution_count":211,"metadata":{"executionInfo":{"elapsed":16,"status":"ok","timestamp":1725905408876,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"bEd5SGxj1n6j","colab":{"base_uri":"https://localhost:8080/"},"outputId":"f38e4f18-61a4-4fe4-f16d-91db073af4b5"},"outputs":[{"output_type":"stream","name":"stdout","text":["[217829.22330097]\n"]}],"source":["predictions = model_rr.predict(X_prediction_scaled)\n","print(predictions)"]},{"cell_type":"markdown","metadata":{"id":"3L2Y0hF8OSMX"},"source":["#### Predicción del modelo LINEAR REGRESSION"]},{"cell_type":"code","execution_count":212,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1725905408876,"user":{"displayName":"Maite Lizarraga","userId":"18236912764583760108"},"user_tz":-120},"id":"53OtU5R21pyv","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b814ccab-89e2-4dd4-a6eb-b4d392a3454b"},"outputs":[{"output_type":"stream","name":"stdout","text":["[217829.22330097]\n"]}],"source":["predictions = model_lr.predict(X_prediction_scaled)\n","print(predictions)"]}],"metadata":{"colab":{"collapsed_sections":["-sazFRJKUxCt","fYbZQ4uzB1TM","_YwsttBtCVew","zoBq-vI1Wt04"],"provenance":[{"file_id":"11FHtQpL_6giFvLbTK7e7r11bgwopLPmw","timestamp":1724957139947}],"toc_visible":true,"authorship_tag":"ABX9TyPOxZLJRwiZY5OU9AlSUIJa"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}