N38) Regularización L1 y L2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ¿Qué variables destacadas influyen en el precio de las viviendas?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Librerias\n",
    "from IPython.display import Image\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn import linear_model\n",
    "\n",
    "# This line is needed to display plots inline in Jupyter Notebook\n",
    "#%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduccion\n",
    "\n",
    "**Contexto comercial.** El negocio inmobiliario moderno está cada vez más dominado por grandes cantidades de datos. La inversión inmobiliaria requiere un buen juicio y una predicción precisa del precio para determinar si una propiedad es una inversión adecuada. Dada la naturaleza ilíquida de las inversiones inmobiliarias, existen una serie de complicaciones que pueden surgir al fijar el precio de una propiedad. Por lo tanto, es de gran interés para los inversionistas inmobiliarios comprender qué variables afectan los precios de las viviendas para que puedan planificar sus carteras de inversión con mayor claridad y confianza. Recientemente se unió a la división de crecimiento de la vivienda en una corporación multinacional y se le ha asignado la tarea de identificar los factores que afectan los precios de la vivienda.\n",
    "\n",
    "**Problema empresarial.** Su tarea es responder a la siguiente pregunta: **\"¿Qué variables son las más importantes para predecir los precios de las viviendas?\"**\n",
    "\n",
    "**Contexto analítico.** El caso procederá de la siguiente manera: 1) exploraremos los datos para obtener una idea de las características útiles; 2) aplicaremos la regularización L1 para reducir el número de variables en su modelo lineal inicial; 3) aplicar la regularización L2 para lidiar con la multicolinealidad y reducir aún más el conteo de variables; y finalmente 4) usaremos la regularización L1 y L2 juntas a través de la regresión de red elástica para obtener los beneficios de ambos en un modelo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entendiendo los datos de Saratoga \n",
    "\n",
    "Tenemos cerca de 15 variables para predecir el ```price``` de las viviendad\n",
    "\n",
    "Cantidad de interes:\n",
    "1. price (millions of US dollars)\n",
    "\n",
    "Variables Numericas no-categoricas:\n",
    "1. **lotSize**: size of the lot in acres\n",
    "2. **age**: age of the house in years\n",
    "3. **landValue**: value of the land in US dollars\n",
    "4. **livingArea**: living area in square feet\n",
    "5. **pctCollege**: percent of the neighborhood that graduated college\n",
    "\n",
    "Variables Numericas categoricas:\n",
    "1. **bedrooms**: number of bedrooms\n",
    "2. **fireplaces**: number of fireplaces\n",
    "3. **bathrooms**: number of bathrooms\n",
    "4. **rooms**: number of rooms\n",
    "\n",
    "Variables No-numericas categoricas:\n",
    "1. **heating**: what type of heating the house uses\n",
    "2. **fuel**: what type of fuel the house uses\n",
    "3. **sewer**: what type of sewer system the house has\n",
    "4. **waterfront**: whether or not the house has a waterfront\n",
    "5. **newConstruction**: whether or not the house has new construction\n",
    "6. **centralAir**: whether or not the house has a central air system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>lotSize</th>\n",
       "      <th>age</th>\n",
       "      <th>landValue</th>\n",
       "      <th>livingArea</th>\n",
       "      <th>pctCollege</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>fireplaces</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>rooms</th>\n",
       "      <th>heating</th>\n",
       "      <th>fuel</th>\n",
       "      <th>sewer</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>newConstruction</th>\n",
       "      <th>centralAir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.132500</td>\n",
       "      <td>0.09</td>\n",
       "      <td>42</td>\n",
       "      <td>50000</td>\n",
       "      <td>906</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>electric</td>\n",
       "      <td>electric</td>\n",
       "      <td>septic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.181115</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0</td>\n",
       "      <td>22300</td>\n",
       "      <td>1953</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>6</td>\n",
       "      <td>hot water/steam</td>\n",
       "      <td>gas</td>\n",
       "      <td>septic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.109000</td>\n",
       "      <td>0.19</td>\n",
       "      <td>133</td>\n",
       "      <td>7300</td>\n",
       "      <td>1944</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8</td>\n",
       "      <td>hot water/steam</td>\n",
       "      <td>gas</td>\n",
       "      <td>public/commercial</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.155000</td>\n",
       "      <td>0.41</td>\n",
       "      <td>13</td>\n",
       "      <td>18700</td>\n",
       "      <td>1944</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5</td>\n",
       "      <td>hot air</td>\n",
       "      <td>gas</td>\n",
       "      <td>septic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.086060</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0</td>\n",
       "      <td>15000</td>\n",
       "      <td>840</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>hot air</td>\n",
       "      <td>gas</td>\n",
       "      <td>public/commercial</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  lotSize  age  landValue  livingArea  pctCollege  bedrooms  \\\n",
       "0  0.132500     0.09   42      50000         906          35         2   \n",
       "1  0.181115     0.92    0      22300        1953          51         3   \n",
       "2  0.109000     0.19  133       7300        1944          51         4   \n",
       "3  0.155000     0.41   13      18700        1944          51         3   \n",
       "4  0.086060     0.11    0      15000         840          51         2   \n",
       "\n",
       "   fireplaces  bathrooms  rooms          heating      fuel              sewer  \\\n",
       "0           1        1.0      5         electric  electric             septic   \n",
       "1           0        2.5      6  hot water/steam       gas             septic   \n",
       "2           1        1.0      8  hot water/steam       gas  public/commercial   \n",
       "3           1        1.5      5          hot air       gas             septic   \n",
       "4           0        1.0      3          hot air       gas  public/commercial   \n",
       "\n",
       "  waterfront newConstruction centralAir  \n",
       "0         No              No         No  \n",
       "1         No              No         No  \n",
       "2         No              No         No  \n",
       "3         No              No         No  \n",
       "4         No             Yes        Yes  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cargando la data\n",
    "df = pd.read_csv('saratoga-houses_clean.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explorando relaciones entre datos por visualizaciones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comencemos visualizando la relación entre las variables y los precios de la vivienda a través de diagramas de dispersión 2D, comenzando con las características numéricas no categóricas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# variables Numericas no-catgoricas\n",
    "plt.figure(figsize=(24,20))\n",
    "varstolook = ['lotSize', 'age', 'landValue', 'livingArea', 'pctCollege']\n",
    "for i,feature in enumerate(varstolook):\n",
    "    plt.subplot(3,2,i+1)\n",
    "    colvalues = df[feature]\n",
    "    plt.scatter(colvalues.values, df.price.values, alpha=0.20, edgecolor=None, color='red')\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('price')\n",
    "    plt.title(\"Price vs. \" + feature, fontsize=20, verticalalignment='bottom');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algunas observaciones son evidentes. Primero, ```livingArea```, ```landValue``` y ```lotSize``` están correlacionados positivamente con los precios de la vivienda. A continuación, la edad de una casa está inversamente correlacionada con el precio hasta unos 50 años, después de lo cual la relación precio-edad se vuelve plana. Curiosamente, la variable ```pctCollege``` muestra grupos alrededor de ciertos porcentajes; sin embargo, no parece haber una relación sólida entre el precio y ```pctCollege```.\n",
    "\n",
    "Veamos las características categóricas numéricas para ver si podemos notar algún patrón significativo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Variables Numericas categoricas\n",
    "plt.figure(figsize=(24,20))\n",
    "varstolook = ['bedrooms', 'fireplaces', 'bathrooms', 'rooms']\n",
    "for i,feature in enumerate(varstolook):\n",
    "    plt.subplot(3,2,i+1)\n",
    "    pl2 = sns.boxplot(x=feature, y = \"price\", data = df, palette=\"Blues\")\n",
    "    pl2.set_xticklabels(pl2.get_xticklabels(), rotation=40, ha=\"right\");\n",
    "    plt.title(\"Price distribution across: \" + feature, fontsize=20, verticalalignment='bottom');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inmediatamente notamos la fuerte relación que tienen las ```rooms```, ```bathrooms``` y ```fireplaces``` con los precios de las viviendas. Esto se espera ya que las casas más grandes naturalmente tienen más habitaciones y exigen un precio de venta más alto. Con respecto a la característica ```bedrooms```, la tendencia de precios crecientes también está presente a medida que aumenta el número de dormitorios, pero es más débil en un número bajo (1) y alto (7) de dormitorios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejercicio 1\n",
    "\n",
    "Cree diagramas de caja para visualizar cada función categórica no numérica frente a los precios de cada valor de categoría dentro de estas variables categóricas. Debe tener seis diagramas con diagramas de caja para cada valor de categoría, uno para cada característica categórica no numérica:\n",
    "\n",
    "1. `heating`\n",
    "2. `fuel`\n",
    "3. `sewer`\n",
    "4. `waterfront`\n",
    "5. `newConstruction`\n",
    "6. `centralAir`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Asi lo puedo hacer\n",
    "plt.figure(figsize=(24,20))\n",
    "varstolook = ['heating', 'fuel','sewer', 'waterfront', 'newConstruction', 'centralAir']\n",
    "for i,feature in enumerate(varstolook):\n",
    "    plt.subplot(2,3,i+1)\n",
    "    pl2 = sns.boxplot(x=feature, y = \"price\", data = df, palette=\"Greens\")\n",
    "    pl2.set_xticklabels(pl2.get_xticklabels(), rotation=0, ha=\"center\");\n",
    "    plt.title(\"Dist. de Price distribution en: \" + feature, fontsize=20, verticalalignment='bottom');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como era de esperar, una propiedad frente al mar, unidades de nueva construcción y casas con aire acondicionado central van acompañadas de precios más altos. Si bien es más débil, existen algunas diferencias entre las variables de calefacción, combustible y alcantarillado. La calefacción de aire caliente, el combustible de gas y los sistemas de alcantarillado público/comercial cobran precios de vivienda más altos que sus valores categóricos adyacentes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejercicio 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hubo algunas relaciones que no se esperaban, como la débil relación entre ```pctCollege``` y los precios de las viviendas. ¿Cuál de los siguientes puede ser un posible impulsor externo de esta débil relación?\n",
    "\n",
    "A. Los vecindarios están ubicados cerca de excelentes servicios, lo que eleva todos los precios de las viviendas.\n",
    "\n",
    "B. Los residentes del vecindario son diversos y tienen una distribución uniforme de niveles de ingresos y educación\n",
    "\n",
    "C. La gente tiende a vivir en barrios donde viven sus amigos.\n",
    "\n",
    "D. El procedimiento de recopilación de datos para ```pctCollege``` se realizó por teléfono"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Respuesta.** (B). La explicación se da a continuación.\n",
    "\n",
    "La respuesta (A) no lleva inmediatamente a ninguna conclusión sobre por qué existe una relación débil entre el precio de la vivienda y el porcentaje de educación universitaria. Todo lo que (A) implica es que todas las casas tienen un precio más alto debido a la proximidad a servicios atractivos y nada sobre la distribución de esos precios de casas.\n",
    "\n",
    "La respuesta (B) es la opción ideal, ya que tener residentes diversos en cada vecindario, en términos de ingresos y nivel de educación, conducirá a relaciones débiles al predecir los precios de las viviendas a partir de estas cantidades. Si bien puede ser cierto que los residentes de ingresos más altos poseen viviendas más caras, la variable de porcentaje universitario es relativa a un vecindario, por lo que un efecto promedio debilitará la relación con el precio de cualquier vivienda si el vecindario está bien representado desde el punto de vista educativo.\n",
    "\n",
    "La respuesta (C) probablemente conduciría a una relación más fuerte entre el porcentaje de precios universitarios y de vivienda si fuera cierto en este conjunto de datos. Los amigos a menudo tienen intereses similares y pueden tener antecedentes educativos similares, lo que podría influir en los porcentajes universitarios promedio del vecindario para que se correlacionen más con los precios de las viviendas.\n",
    "\n",
    "En cuanto a la respuesta (D), no nos dice nada sobre la relación entre el porcentaje de universidad y el precio, por lo que es incorrecta. Sin embargo, es importante entender que los datos en este caso fueron adquiridos por algún método. Por lo tanto, puede haber errores de datos o sesgos en los datos proporcionados. Si bien no exploraremos los métodos de adquisición de datos en este caso, si los datos se recopilaron por teléfono, sería importante conocer la tasa de respuesta por ubicación para determinar si cada vecindario estuvo igualmente representado."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nuestro primer modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparando la data\n",
    "\n",
    "Antes de comenzar a modelar los datos, hagamos algunas transformaciones simples. Primero, transformemos las variables categóricas con solo dos categorías en valores binarios donde 1 indica que el efecto está presente y cero indica que el efecto no está presente. Esto permitirá una fácil estandarización de cada característica presente en los datos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding para variables categoricos no numericos\n",
    "df2 = pd.get_dummies(df, columns=['heating', 'fuel'], drop_first=False)\n",
    "df2 = pd.get_dummies(df2, columns=['sewer'], drop_first=True)\n",
    "df2.replace({'waterfront': {\"No\":0, \"Yes\":1},\n",
    "             'newConstruction': {\"No\":0, \"Yes\":1},\n",
    "             'centralAir': {\"No\":0, \"Yes\":1}}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>lotSize</th>\n",
       "      <th>age</th>\n",
       "      <th>landValue</th>\n",
       "      <th>livingArea</th>\n",
       "      <th>pctCollege</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>fireplaces</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>rooms</th>\n",
       "      <th>...</th>\n",
       "      <th>newConstruction</th>\n",
       "      <th>centralAir</th>\n",
       "      <th>heating_electric</th>\n",
       "      <th>heating_hot air</th>\n",
       "      <th>heating_hot water/steam</th>\n",
       "      <th>fuel_electric</th>\n",
       "      <th>fuel_gas</th>\n",
       "      <th>fuel_oil</th>\n",
       "      <th>sewer_public/commercial</th>\n",
       "      <th>sewer_septic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.132500</td>\n",
       "      <td>0.09</td>\n",
       "      <td>42</td>\n",
       "      <td>50000</td>\n",
       "      <td>906</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.181115</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0</td>\n",
       "      <td>22300</td>\n",
       "      <td>1953</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.109000</td>\n",
       "      <td>0.19</td>\n",
       "      <td>133</td>\n",
       "      <td>7300</td>\n",
       "      <td>1944</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.155000</td>\n",
       "      <td>0.41</td>\n",
       "      <td>13</td>\n",
       "      <td>18700</td>\n",
       "      <td>1944</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.086060</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0</td>\n",
       "      <td>15000</td>\n",
       "      <td>840</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  lotSize  age  landValue  livingArea  pctCollege  bedrooms  \\\n",
       "0  0.132500     0.09   42      50000         906          35         2   \n",
       "1  0.181115     0.92    0      22300        1953          51         3   \n",
       "2  0.109000     0.19  133       7300        1944          51         4   \n",
       "3  0.155000     0.41   13      18700        1944          51         3   \n",
       "4  0.086060     0.11    0      15000         840          51         2   \n",
       "\n",
       "   fireplaces  bathrooms  rooms  ...  newConstruction  centralAir  \\\n",
       "0           1        1.0      5  ...                0           0   \n",
       "1           0        2.5      6  ...                0           0   \n",
       "2           1        1.0      8  ...                0           0   \n",
       "3           1        1.5      5  ...                0           0   \n",
       "4           0        1.0      3  ...                1           1   \n",
       "\n",
       "   heating_electric  heating_hot air  heating_hot water/steam  fuel_electric  \\\n",
       "0                 1                0                        0              1   \n",
       "1                 0                0                        1              0   \n",
       "2                 0                0                        1              0   \n",
       "3                 0                1                        0              0   \n",
       "4                 0                1                        0              0   \n",
       "\n",
       "   fuel_gas  fuel_oil  sewer_public/commercial  sewer_septic  \n",
       "0         0         0                        0             1  \n",
       "1         1         0                        0             1  \n",
       "2         1         0                        1             0  \n",
       "3         1         0                        0             1  \n",
       "4         1         0                        1             0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación, dividamos los datos en un conjunto de entrenamiento y un conjunto de prueba. Elegimos retener el 25 por ciento de los datos como datos de prueba:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Separando para regresion\n",
    "X = df2.drop(['price'], axis=1)\n",
    "y = df2[['price']].values.ravel()\n",
    "\n",
    "# Split train/test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente, vamos a normalizar los datos. Como hemos visto con los algoritmos de agrupamiento, las escalas inconsistentes entre las variables pueden afectar negativamente el rendimiento de varias técnicas de aprendizaje automático. Resulta que la regularización es una de esas técnicas (discutiremos esto más adelante):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtener la media dy sd de train\n",
    "training_mean = X_train.mean()\n",
    "training_std =  X_train.std()\n",
    "\n",
    "# Normalizar la data (practica comun cuando usas tecnicas de regularizacion)\n",
    "X_train = (X_train - training_mean) / training_std #Usamos train por ser generalizable\n",
    "X_test = (X_test - training_mean) / training_std #Usamos train por ser generalizable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ajustar modelo de regresion multiple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comencemos con un modelo de regresión lineal simple usando todas las características dadas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.659\n",
      "Model:                            OLS   Adj. R-squared:                  0.654\n",
      "Method:                 Least Squares   F-statistic:                     137.1\n",
      "Date:                Mon, 09 Jan 2023   Prob (F-statistic):          1.22e-282\n",
      "Time:                        12:24:39   Log-Likelihood:                 1838.2\n",
      "No. Observations:                1296   AIC:                            -3638.\n",
      "Df Residuals:                    1277   BIC:                            -3540.\n",
      "Df Model:                          18                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "const                       0.2120      0.002    129.337      0.000       0.209       0.215\n",
      "lotSize                     0.0037      0.002      2.039      0.042       0.000       0.007\n",
      "age                        -0.0052      0.002     -2.593      0.010      -0.009      -0.001\n",
      "landValue                   0.0336      0.002     16.839      0.000       0.030       0.037\n",
      "livingArea                  0.0442      0.003     12.941      0.000       0.038       0.051\n",
      "pctCollege                 -0.0001      0.002     -0.070      0.944      -0.004       0.003\n",
      "bedrooms                   -0.0056      0.002     -2.276      0.023      -0.010      -0.001\n",
      "fireplaces                  0.0007      0.002      0.360      0.719      -0.003       0.005\n",
      "bathrooms                   0.0143      0.003      5.450      0.000       0.009       0.019\n",
      "rooms                       0.0070      0.003      2.643      0.008       0.002       0.012\n",
      "waterfront                  0.0097      0.002      5.776      0.000       0.006       0.013\n",
      "newConstruction            -0.0108      0.002     -5.842      0.000      -0.014      -0.007\n",
      "centralAir                  0.0049      0.002      2.477      0.013       0.001       0.009\n",
      "heating_electric            0.0006      0.004      0.136      0.892      -0.008       0.009\n",
      "heating_hot air             0.0018      0.002      0.806      0.421      -0.003       0.006\n",
      "heating_hot water/steam    -0.0028      0.002     -1.350      0.177      -0.007       0.001\n",
      "fuel_electric              -0.0019      0.004     -0.486      0.627      -0.010       0.006\n",
      "fuel_gas                    0.0019      0.002      0.817      0.414      -0.003       0.006\n",
      "fuel_oil                   -0.0004      0.002     -0.182      0.856      -0.005       0.004\n",
      "sewer_public/commercial    -0.0141      0.011     -1.286      0.199      -0.036       0.007\n",
      "sewer_septic               -0.0117      0.011     -1.068      0.286      -0.033       0.010\n",
      "==============================================================================\n",
      "Omnibus:                      448.615   Durbin-Watson:                   2.012\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2976.998\n",
      "Skew:                           1.442   Prob(JB):                         0.00\n",
      "Kurtosis:                       9.842   Cond. No.                     6.22e+15\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 1.59e-28. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "# Ajustar el modelo\n",
    "model_linear = sm.OLS(y_train, sm.add_constant(X_train))\n",
    "original_linear = model_linear.fit()\n",
    "print(original_linear.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aquí, vemos que las características de calefacción, combustible y alcantarillado no son estadísticamente significativas, mientras que las variables de área habitable, valor de la tierra, frente al mar y nueva construcción son significativas. Estos resultados son consistentes con la fuerza de las relaciones que observamos en nuestras visualizaciones de datos anteriores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Como la regularizacion ayuda al modelo?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ya hemos mencionado brevemente la **regularización** y cómo es una especie de \"penalización\" por tener demasiadas variables inútiles en un modelo. Tu objetivo es mejorar las predicciones del modelo fuera de la muestra al reducir la complejidad del modelo y evitar el sobreajuste. Un modelo equilibrado solo usará la complejidad suficiente para capturar la tendencia general de los datos; es decir, el número mínimo de parámetros del modelo posibles para obtener un ajuste aceptable.\n",
    "\n",
    "Motivados por la gran cantidad de variables insignificantes presentes en la regresión, nos gustaría reducir nuestra posibilidad de sobreajuste y eliminar algunas de las variables menos útiles. Emplearemos una herramienta llamada **regularización L1** para ayudarnos en este objetivo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aplicar feature selection LASSO \n",
    "\n",
    "Un modelo de regresión que utiliza la regularización L1 se denomina modelo de regresión **LASSO (Operador de selección y contracción mínima absoluta- Least Absolute Shrinkage and Selection Operator)**. La regularización L1 agrega la magnitud absoluta de los coeficientes del modelo de regresión ($\\beta_j$) a la función de pérdida de mínimos cuadrados ordinarios (OLS), multiplicada por un término de escala $\\lambda$:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\sum_{i=1}^N{\\big(y_i - \\sum_{j=1}^p{x_{ij}\\beta_j}}\\big)^2 + \\color{red}{\\lambda \\sum_{j=1}^{p}\\big| \\beta_j \\big|} .$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recuerde que la regresión lineal múltiple busca minimizar la función objetivo $Loss(Data\\ |\\ Model)$, donde la pérdida generalmente se define como la diferencia al cuadrado entre los valores de $y$ predichos y verdaderos (en este caso, $y$ son los precios de la vivienda ). Extender la regresión lineal múltiple para usar la regularización L1 requiere agregar un término adicional ${\\lambda \\sum_{j=1}^{p}\\big| \\beta_j \\big|}$ para penalizar la mayor complejidad del modelo.\n",
    "\n",
    "La regularización L1 reduce los coeficientes $\\beta_j$ hacia cero, ya que cualquier $\\beta_j$ grande aumentará en gran medida la función objetivo anterior, que buscamos minimizar. El valor de $\\lambda$ establece la fuerza de la regularización: los valores más grandes de $\\lambda$ afectarán cada vez más a los coeficientes $\\beta_j$.\n",
    "\n",
    "Ajustemos una regresión lineal múltiple con un término de regularización L1. En el método ```linear_model.Lasso()```, la notación ```alpha``` se refiere al valor $\\lambda$ que definimos arriba. Primero elegimos un $\\lambda$ pequeño para realizar solo una regularización débil. Si alguno de los coeficientes se establece en cero bajo esta regularización débil, entonces existe una buena posibilidad de que no sea significativo para las predicciones de precios de la vivienda."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ajustar el modelo L1\n",
    "model_l1 = linear_model.Lasso(alpha=0.00001, fit_intercept=True) # higher alpha for stronger regularization\n",
    "results_l1 = model_l1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación se muestran los nombres de los coeficientes del modelo seguidos de los valores $\\beta$ de ajuste. Los coeficientes están ordenados de menor a mayor para facilitar la visualización. Estamos buscando los coeficientes que son aproximadamente cero, ya que esto indica que la regularización L1 ha eliminado estas características de la predicción:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('sewer_public/commercial', -0.013232781041673554),\n",
       " ('sewer_septic', -0.01078764887503001),\n",
       " ('newConstruction', -0.010751135709249775),\n",
       " ('bedrooms', -0.005578301773366263),\n",
       " ('age', -0.005197381192771838),\n",
       " ('heating_hot water/steam', -0.003271105350930489),\n",
       " ('fuel_electric', -0.0013733902247770405),\n",
       " ('pctCollege', -0.00011477706533155957),\n",
       " ('heating_electric', -0.0),\n",
       " ('fuel_oil', -0.0),\n",
       " ('fireplaces', 0.0006947901157847505),\n",
       " ('heating_hot air', 0.0011599235531580066),\n",
       " ('fuel_gas', 0.002415840776398991),\n",
       " ('lotSize', 0.003732010644161486),\n",
       " ('centralAir', 0.004876517833592989),\n",
       " ('rooms', 0.006969691924555492),\n",
       " ('waterfront', 0.00972975250165226),\n",
       " ('bathrooms', 0.01429092599717739),\n",
       " ('landValue', 0.03358163077689344),\n",
       " ('livingArea', 0.04421952433968511)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(X_train.columns, results_l1.coef_), key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los resultados de la regularización L1 muestran que los coeficientes de **calefacción_eléctrica** y **combustible_aceite** son cero. Esta es la idea de la regularización L1 aplicando la selección de características. Es decir, agregar la restricción de complejidad del modelo a la función objetivo ha obligado al procedimiento de ajuste a establecer algunos de los coeficientes en cero (dichos coeficientes los consideramos sin importancia).\n",
    "\n",
    "Eliminemos estas dos variables en el futuro y ajustemos un modelo de regresión lineal múltiple (sin regularización):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reducir el numero de variables basados en reusltados L1\n",
    "cols_to_drop = ['heating_electric','fuel_oil']\n",
    "X_train_simplified = X_train.drop(columns=cols_to_drop)\n",
    "X_test_simplified = X_test.drop(columns=cols_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.659\n",
      "Model:                            OLS   Adj. R-squared:                  0.654\n",
      "Method:                 Least Squares   F-statistic:                     137.1\n",
      "Date:                Mon, 09 Jan 2023   Prob (F-statistic):          1.22e-282\n",
      "Time:                        12:39:10   Log-Likelihood:                 1838.2\n",
      "No. Observations:                1296   AIC:                            -3638.\n",
      "Df Residuals:                    1277   BIC:                            -3540.\n",
      "Df Model:                          18                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "const                       0.2120      0.002    129.337      0.000       0.209       0.215\n",
      "lotSize                     0.0037      0.002      2.039      0.042       0.000       0.007\n",
      "age                        -0.0052      0.002     -2.593      0.010      -0.009      -0.001\n",
      "landValue                   0.0336      0.002     16.839      0.000       0.030       0.037\n",
      "livingArea                  0.0442      0.003     12.941      0.000       0.038       0.051\n",
      "pctCollege                 -0.0001      0.002     -0.070      0.944      -0.004       0.003\n",
      "bedrooms                   -0.0056      0.002     -2.276      0.023      -0.010      -0.001\n",
      "fireplaces                  0.0007      0.002      0.360      0.719      -0.003       0.005\n",
      "bathrooms                   0.0143      0.003      5.450      0.000       0.009       0.019\n",
      "rooms                       0.0070      0.003      2.643      0.008       0.002       0.012\n",
      "waterfront                  0.0097      0.002      5.776      0.000       0.006       0.013\n",
      "newConstruction            -0.0108      0.002     -5.842      0.000      -0.014      -0.007\n",
      "centralAir                  0.0049      0.002      2.477      0.013       0.001       0.009\n",
      "heating_hot air             0.0011      0.007      0.146      0.884      -0.013       0.015\n",
      "heating_hot water/steam    -0.0034      0.006     -0.567      0.571      -0.015       0.008\n",
      "fuel_electric              -0.0015      0.006     -0.236      0.814      -0.014       0.011\n",
      "fuel_gas                    0.0024      0.003      0.878      0.380      -0.003       0.008\n",
      "sewer_public/commercial    -0.0141      0.011     -1.286      0.199      -0.036       0.007\n",
      "sewer_septic               -0.0117      0.011     -1.068      0.286      -0.033       0.010\n",
      "==============================================================================\n",
      "Omnibus:                      448.615   Durbin-Watson:                   2.012\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2976.998\n",
      "Skew:                           1.442   Prob(JB):                         0.00\n",
      "Kurtosis:                       9.842   Cond. No.                         19.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Reajustar el modelo (no regularization)\n",
    "model_linear = sm.OLS(y_train, sm.add_constant(X_train_simplified))\n",
    "res_linear = model_linear.fit()\n",
    "print(res_linear.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos que el nuevo modelo tiene los mismos valores de R-cuadrado (a tres dígitos significativos). Esto es importante ya que indica que al eliminar las dos características **calefacción_eléctrica** y **combustible_aceite**, hemos perdido un poder de predicción insignificante en términos de explicación de la variación en los datos. La simplificación de modelos como este es muy útil en la práctica y puede conducir a una mejor comprensión de los efectos que tiene cada característica en la variable de predicción."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejercicio 3\n",
    "\n",
    "Reflexionando sobre el uso de L1 anterior, ¿cuál de los siguientes sería un caso útil para la regularización de L1?\n",
    "\n",
    "A. Tiene datos que exhiben grandes características numéricas\n",
    "\n",
    "B. Tiene una gran cantidad de funciones y su objetivo es seleccionar una cantidad menor de funciones destacadas.\n",
    "\n",
    "C. Tiene una pequeña cantidad de características muy importantes\n",
    "\n",
    "D. Le gustaría evitar el desajuste"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Respuesta.** (B). La explicación se da a continuación.\n",
    "\n",
    "La respuesta (A) es incorrecta ya que cuando preparamos los datos eliminamos la media de cada característica. Esto se hizo específicamente porque cuando se usa la regularización, la magnitud de los coeficientes $\\beta$ es importante. El término agregado para la complejidad del modelo en la función objetivo del modelo funciona mejor cuando las características son todas de magnitud similar, de modo que ninguna característica reciba un tratamiento preferencial en el procedimiento de optimización del ajuste.\n",
    "\n",
    "La respuesta (B) es correcta y es cómo hemos usado la regularización L1 en este caso. Encontramos dos características cuyos coeficientes se establecieron en cero al agregar una restricción L1 al modelo de regresión múltiple. Por lo tanto, hemos realizado la selección de características.\n",
    "\n",
    "La respuesta (C) es incorrecta ya que un pequeño número de características muy importantes no indica la necesidad de regularización de L1. La regularización L1 se aplica regularmente para la selección de características para reducir algunos de los coeficientes a cero. Si se sabe que todas las características son muy importantes, la regularización L1 puede no ser la mejor opción.\n",
    "\n",
    "La respuesta (D) confunde subajuste con sobreajuste. Se aplican técnicas de regularización para evitar el sobreajuste al reducir la complejidad del modelo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identificar multicolinealidad y remover features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación, echemos un vistazo y veamos si la multicolinealidad está presente en las características. Recuerde que la multicolinealidad suele ser una fuente de complejidad innecesaria en un modelo que puede conducir a un ajuste excesivo, por lo que tiene sentido detectarla. Echemos un vistazo a las correlaciones entre las características numéricas no categóricas a través de una matriz de correlación:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nt1LrGjXGGOMIqpouIvcD84Bg4D1V3Swi97iXjweeAyaKyEZc7czHVDUpv/VaIjTGGFMk/HGJNVWdwyl3P3InwBPP9wKX/5N1WiI0xhhTJErktUaNMcYYrzkzD1oiNMYYUzSc2iK0UaPGGGNKNZ+3CNd9/bKv38LRmnYZHOgQiq2fv3010CEUW62qRQY6hGItPuWvQIdQKjm1RWhdo8YYY4qEJUJjjDGlmiVCY4wxpZsz86ANljHGGFO6WYvQGGNMkbCuUWOMMaWaJUJjjDGlmlMToR0jNMYYU6pZi9AYY0zRcGaD0BKhMcaYouHUrlFLhMYYY4qEJUJjjDGlmlMToQ2WMcYYU6pZi9AYY0yRcGqL0BKhMcaYouHMPGiJ0BhjTNGwFqExxphSzamJ0AbLGGOMKdWsRWiMMaZIOLRBaInQGGNM0XBq16glQmOMMUXCoXnQjhEaY4wp3axFaIwxpkhY16gxxphSzaF58J8lQhGpoKpHfRWMMcYY5woKcmYm9OoYoYi0FJGfgS3u6fNEZJxPIzPGGOMoIoV7BIq3g2X+B3QGkgFUdT3Q1ldBGWOMMf7i9ahRVd19yqyMIo6lUNasWMq9N19Nv97dmT75vRzLVZUJr71Ev97deeD26/j91y1Zy14bOZw+PS7l/tt6+jNkvxk/7CZ2fv8iq6cNybPMqME92TRzGCs/fYKm9Wtmze/UsgHrP3+aTTOHMej2Tv4I1+9UlXH/G8nt13Xlnlt68tsvW3Itt39vHA/ddRN3XN+NEU8/SlpaGgDLFy/gnlt6ct+t1/HAHTeyaf1af4bvc6rKyBHP0/WKTvS8phtbft6ca7kpkz+i6xWdOK/RORw8eCBr/uHDh3ngvnvodU13rul+FV98PsNfofucqjL2vyO5rddV3N3nWn775edcy+3bG8cDfXtz23VdecFj3znhl583cUXrpiya/40/wvYZESnUI1C8TYS7RaQloCJymogMwt1NWhxkZGTw1uiRDHt5DGMnzWDR93PZteP3bGXWrFjC3rhdvDV5Jv0HPcWb/x2Rtaxjl24Mf2Wsv8P2mw9n/0iP/nlvX+fWDalTK4rGPZ7h/uen8PqQGwBXf//ox6+jx/3jOP/a5+l1RXPqn1XNX2H7zarlrn3jvU9n89DgoYx59flcy7375mtcc/3NvPfpbMIqVmLel58D0LT5Rbw5aRrjJk1l4JBnGD3yGX+G73NLFi9i184dzP76G4YOf47nnx2ea7mmzZrx1rvvU6NGbLb5n06ZzFl16jDt81m8O/FDRr38Eml//+37wP1g1fIl7InbyftTv2TAY0N5/ZU89p1xo/nP9X2YOPVLwipWYu7sz7KWZWRk8M64/9H8opb+CttnSnrX6D1AfyAWiAOauqeLhd+2bKJ67OlUq1GT0NBQ2lzamRVLfshWZsWShXTo3BURoX6jJhw9cpgDyYkAND6vOWEVKwcgcv9YuvZ3DqQcy3N513ZN+PjLlQCs3LiDyhXLUS2yEhc2rs3vu5PYsSeZtPQMps1bS9f2TfwVtt8sX7KAjld0Q0Ro0LgJRw4fJjkpMVsZVWX9mpW0ae9qFV92ZXeWLZoPQLny5bN+zR4//qdjh5DnZcH87+nW/WpEhCbnNeXw4VQSExNylGvQoCGxsTVzzBcRjh09iqpy7NhRKleuTHBIyRiwvmzxAjpl7TvncfRI7vvOujUradvBte906tKdZYsWZC2fOf1j2nToRJXwqn6N3RdKdItQVZNU9SZVjVHVaFW9WVWTfR2ct5KTEoiMjsmajoyKybEzJiclEBV9sjUTERVDci4f5tKoRnQV4vYfzJreE3+IGtFVqBFdmbh4z/kHiY0qeT8YkhMTiPLYf6Kic+4bqSmHqBBWMesLPOqU/Wfpwu/pe2MPhg66n4FDSlaLMCEhnphqJz87MTHVSIiP9/r1N/S+ie3bf+ey9m3oeXV3Bj/xJEFBJeNaHsmJCUTFnKybyFy+V1JTDhHmse9ERseQlOiqv6TEeJYunM9VV/fyX9A+5NRE6NXPMhF5PZfZKcBqVZ2ZS/l+QD+AZ15+g+v73FGoIAuimnNejirNpVBJ++X+b+VWDaqK5HKXzVyq2vFy3X9OqRQtYP9p1a4jrdp1ZOO6NXzw9lhGvjahyOMMmEJ+dpYtWUL9+g145/0P2L1rF3ffdTvNml9AWFhYUUYZELntF6d+oPLbd94c/TJ97xtAcHCwT+Iz3vG2f6IsUB+Y5p6+FtgM3CkiHVR1gGdhVZ0ATAD4Zf8xn393RkZFk5Rw8hdqUmI8VSOjspWJiIohMWF/1nRyLmVKqz3xh6hZLTxrOjamCvsSUzgtNISaMZ7zw9mbmBKIEIvcrBmfMHeW6zhNvQaNSPTYfxITcu4blauEc/TIYTLS0wkOCSExj/3n3KbN2bdnNymHDlK5SniO5U7xyceT+Wz6VAAaNT6X+P0nPzvx8fuJio72el0zv/iMO/r2Q0SodcYZxMbW5I/t2zm3iTO72WfN+IQ5s1wDfs6p34jE+JN1k5QYT0Qu+84Rj30nKSGeiEhX/f26dTMjhj4GQErKQVYuW0xwcAit2l3qp60pWk5tW3jbP3E2cKmqvqGqbwCXAQ2Aa4DLfRWct+rWb8TeuF3s37eHtLQ0Fs+fx0Wt2mcr06JVOxbM+xJVZevmDZSvEEbVCEuEAF8t3Ejvri0AaHFubVKP/Mn+pFRWb97J2bWiOKNGBKEhwfTq3IyvftgQ4GiLRvdrb2DcpKmMmzSVS9p24Pu5s1FVtmzaQIWwsBxfZiJCk2YXsviHbwH4bs4sLmnTAYC9cbuyfvX/9ssW0tPSqFS5il+3p6jd0Psmpn42k6mfzaRDx8uYPesLVJUN69cRFlaRqCjvE2G16tVZ8eNyAJKTktix4w9qnp7zWKJTdL/2BsZPmsb4SdNo2fZSvs3ad9ZToULFXPed85pdyKIFrn3n269ncUmb9gB8OGMuH37merTp0IkHBj3p2CQIJbxrFNcgmQq4ukNxP6+hqhki8pdPIvsHgkNCuHvAYwwfdB+ZmZlcdmUPap1Zh69nuhqwXXr04oKLW7PmxyXc3bs7ZcqU5cHHh2e9/pVnHmfTujWkphzi9p6dufH2e7j8qmsCtDVFb9KLt9GmeV0iq4Sxbe5zPDd+DqEhrq6Yd6YvYe6SzXRu3YjNs4Zx7Hgadw//CICMjEwGvjSV2eP6ExwkTJr5I1u278/vrRypxSVtWLV8CXdc15UyZcvy8JBns5Y9/Uh/Bjw+jIioaO68dwAvDhvMpAljqVOvPp27uvaRJT98x3dfzyYkJJTTypThiWdfLlHd7m3atmPJooV07dKJsmXL8ezzJ0dc97/nLoY9+zzR0TFM/ugDJr73DslJSfS6pjut27Zj+LMv0O+e+3j6ySe49upuqCoDHh5EeAkYGALQomUbVi5fzG29rqJM2bIMevK5rGVPPnIfDz8+nIioaPreN5ARQwczacIY6tSrzxXd/hPAqH3Hqbu95NrHfWohkTuBp4AfcB1+awuMAKYAw1X10bxe64+uUSdr2mVwoEMotn7+9tVAh1BsVa9SNtAhFGvxKQH/fV5snRFRxmfpqtmz8wv1fb926KUBSaVetQhV9V0R+RroA2wFvgHi3NcdzTMJGmOMKT2c2hPi7ajRvsBDQE1gHXAxsBxwbme2McaYIuXQPOj1YJmHgAuBnaraATgfSMz/JcYYY0qTkj5Y5riqHncHW0ZVt4rIOT6NzBhjjKM4tUXobSKME5EqwBfAtyJyENjrq6CMMcYYf/F2sMyJcwmGi8gCoDIw12dRGWOMcRx/dG+KyBXAa0Aw8I6qjsylTHtgNBAKJKlqu/zW+Y+vfKuqC//pa4wxxpR8vs6DIhIMjAU64boBxCoRmaWqP3uUqQKMA65Q1V0iUuDVH0rGJeCNMcYEnB9ahC2Abaq63f1+nwA9AM8bQfYGPlPVXQCqWuDdFUrGJeCNMcYEXGHvRygi/URktcej3ylvEQt43iQ+zj3PUz0gXER+EJE1InJLQXFbi9AYY0yx4HnDhjzk1uQ89Wo2IUBzoCNQDlguIj+q6q95rdQSoTHGmCLhh67ROOB0j+ma5DyDIQ7XAJmjwFERWQScB+SZCK1r1BhjTJEobNeoF1YBdUXkTBE5DbgBmHVKmZlAGxEJEZHywEXAlvxWai1CY4wxRcLXLUJVTReR+4F5uE6feE9VN4vIPe7l41V1i4jMBTYAmbhOsdiU33otERpjjHEMVZ0DzDll3vhTpl8BXvF2nZYIjTHGFIkSffcJY4wxpiAOzYOWCI0xxhQNaxEaY4wp1RyaB+30CWOMMaWbtQiNMcYUCesaNcYYU6o5NA9aIjTGGFM0ghyaCS0RGmOMKRIOzYM2WMYYY0zpZi1CY4wxRcIGyxhjjCnVgpyZBy0RGmOMKRpObRHaMUJjjDGlms9bhGnpmb5+C0f7+dtXAx1CsdWw06BAh1BsXT+4X6BDKNauahAZ6BCKrTMiavhs3Q5tEFrXqDHGmKIhODMTWiI0xhhTJGywjDHGmFLNBssYY4wxDmQtQmOMMUXCoQ1CS4TGGGOKhl102xhjTKnm0DxoxwiNMcaUbtYiNMYYUyScOmrUEqExxpgi4dA86F3XqIjEiMi7IvK1e7qhiNzp29CMMcY4SZBIoR4Bi9vLchOBecCJi9T9CgzwQTzGGGMcSgr5CBRvE2Gkqk4FMgFUNR3I8FlUxhhjjJ94e4zwqIhEAAogIhcDKT6LyhhjjOOU9MEyDwOzgDoishSIAnr6LCpjjDGOU6Ivuq2qa0WkHXAOrq7cX1Q1zaeRGWOMcZQS3SIUkVtOmdVMRFDVD3wQkzHGGAdyaB70umv0Qo/nZYGOwFrAEqExxhhH87Zr9AHPaRGpDHzok4iMMcY4UonuGs3FMaBuUQZijDHG2Ur0YBkRmY371Alc5x42BKb6KihjjDHOU9JbhK96PE8HdqpqnA/iMcYYY/zK22OEC30diDHGGGdzZnuwgEQoIoc52SWabRGgqlrJJ1EZY4xxnBJ5h3pVreivQIwxxjibQ/PgPxs1KiLRuM4jBEBVdxV5RMYYYxzJqYNlvL0fYXcR+Q34A1gI7AC+9mFcxhhjjF942yJ8DrgY+E5VzxeRDsCNvgvrn/tp5TLeG/sqmZkZdLzyav5z4+3Zlqsq7419hbUrlnJambI8MHg4Z9VrkLU8IyODx+7rQ9WIKIaMeM3f4fucqvLm6JdYtXwJZcqW5ZEnn6PuOQ1ylNu/N44Xhz3G4dRUzq5Xn0eHjiA0NJTlixcw6e2xBEkQwcHB3P3QozQ+r1kAtqTojR92E13aNibxwGEu6DUi1zKjBvekc6tGHDv+N/2Gfci6ra5B051aNuDVR3sSHBTExC+W8er73/ozdL84t3oYvZvFEiSw6PcDfLUlMddyZ1Ytx9Odzmbcsl2s3u26Oc2r3erzZ3oGqpCRqTzzzTZ/hu5zv65byZyJY8jMzKD5pVfR7ure2ZavW/wti2d9AsBpZcvR/c4BVK99NoeSEpgx9kWOHDqABAkXdOxKyyudfx8DhzYIvb4fYZqqJgNBIhKkqguApr4L65/JyMjg7ddH8uSLrzP6veksmT+P3Tu2ZyuzduVS9sXtZswHX3Dvw08x4bUXsy3/6rMpxNaq7ceo/WvV8iXsjdvFe5/O5qHBQxnz6vO5lnv3zde45vqbee/T2YRVrMS8Lz8HoGnzi3hz0jTGTZrKwCHPMHrkM/4M36c+nP0jPfqPzXN559YNqVMrisY9nuH+56fw+pAbAAgKEkY/fh097h/H+dc+T68rmlP/rGr+CtsvRKBP81j++8MfDJnzKxedUYUalcrkWq5X0+ps3H84x7KXvt/O0Lm/lbgkmJmZwez3XuOWJ0by4H8nsnHp9yTE7chWpmp0dfoOG80Dr7xLh//0YebbowAIDg6mS597eeh/k7j7+XGs+GZmjtc6UUm/Q/0hEQkDFgGTReQ1XOcTFgvbtm6mWuzpVKtRk9DQUFp3uJxVy37IVmbV0oW0u/wqRIR6Dc/l6JEjHEx2/bJNToxn7YolXHbl1f4P3k+WL1lAxyu6ISI0aNyEI4cPk5yU/Ze9qrJ+zUratO8EwGVXdmfZovkAlCtfPqv///jxPx17LCA3S9f+zoGUY3ku79quCR9/uRKAlRt3ULliOapFVuLCxrX5fXcSO/Ykk5aewbR5a+navom/wvaLs6qWJ/7I3yQe/ZuMTGXFrkOcXzPnYPFO9SJZszuFw8eLzdeCz8Vt20pETA2qxtQgJCSUc1teypZVS7OVqXVOY8qFucYcnl63ISnJSQBUDI+gxln1AChTrjxRsbVIPZDk3w3wAZHCPQIl30QoIj1FpCzQA9dl1QYCc4HfgW6+D887B5ISiIyKyZquGhWT40v+1DIRUdFZZd4bO4o+/R5CxNvfBc6TnJhAVPTJ7Y+KjiE5MSFbmdSUQ1QIq0hwiKvHPCoqe5mlC7+n7409GDrofgYOKTktwoLUiK5C3P6DWdN74g9RI7oKNaIrExfvOf8gsVGVAxGiz4SXD+XAsZN3XDt4LI3wcqHZylQpF0KzmpWYvy05x+sVGNThLIZ3Ppt2dar6Oly/Sj2QROWI6KzpShFRpB7MO5mtWTCHek1b5Jh/MGE/+/7YRs2zcx6qcBoRKdQjUAr65r8J2AW8CXTGde7gJFV93d1VWixoLqc6nlqpeZVZvXwRlcPDqVPP+TthfjSXs0Fz1FEuhTzLtGrXkXemzGTYyNF88HbeXYklTW6fT1VFcjl9OLeTbp3Mm6+mm5rVYNq6/bnuYy98t43h835j1A9/0LFuBPWiKhR5jAGT2+cljxrbvukn1syfQ+eb+mWb/9fxP5ny36FceWt/ypYvQXXjQyJyhYj8IiLbROTxfMpdKCIZIlLgwdeCziO8RkQqAdcADwLvishMYIqqLsongH5AP4ChI1+j1013FBRHoURExpCUGJ81fSAxnqoRkfmWSU5MoGpEJMsXfceqZYtYu2IpaX//zbFjR3htxFM8NCT3Y2hOMmvGJ8yd9RkA9Ro0IjHh5PYnJsRTNTIqW/nKVcI5euQwGenpBIeEkJiYswzAuU2bs2/PblIOHaRylXDfbkQxsCf+EDWrndzO2Jgq7EtM4bTQEGrGeM4PZ29iSiBC9JkDx9KoWv5kCzC8fCgH/8x+T+7aVctzb8taAISVCaZJjUpkZipr96Ry6E9XV+nhvzJYG5fKWRHl+DXxqP82wIcqRUSRknyyxyQ1OZGK4RE5yu3f+TufT3iVWx8fSfmKJ3sMMtLTmTJqKOe1voxGF7X1S8y+5us+NREJBsYCnYA4YJWIzFLVn3Mp9xIwz5v1Fhi3qqa6W4FdgHOBdcAbIrI7n9dMUNULVPUCXydBgLPrN2Tfnt3E79tDWloaSxZ8wwUt22Urc2HLtiz85itUlV9/3kj5CmGER0Rxc98HePvTrxn/8ZcMfGoE5za9sEQkQYDu197AuElTGTdpKpe07cD3c2ejqmzZtIEKYWFEnJLkRIQmzS5k8Q+ukY/fzZnFJW06ALA3bldWi/G3X7aQnpZGpcpV/Lo9gfLVwo307urq0mpxbm1Sj/zJ/qRUVm/eydm1ojijRgShIcH06tyMr37YEOBoi9YfB44RU/E0IiuEEhwkXFSrCj/FpWYr8+jsrQxyP1bvTuGD1XtYuyeV04KFsiGur5jTgoVG1cLYk3I8EJvhE7F16pO8fw8HEvaRnp7GxmXzqX9By2xlDiXF8/GoofTq/wSRNU7Pmq+qfD7+ZaJiz6BV1+v8HbrP+KFrtAWwTVW3q+rfwCe4Dt2d6gFgBpCQy7IcvD6hXkTCgf8A1wNV3W9SLAQHh9D3gcE899j9ZGZmcGmXHtSqXYd5s6cD0LlbT5pd1Jq1K5bSv08PypQtS/9Hhwc2aD9rcUkbVi1fwh3XdaVM2bI8POTZrGVPP9KfAY8PIyIqmjvvHcCLwwYzacJY6tSrT+eu1wCw5Ifv+O7r2YSEhHJamTI88ezLJWbAzKQXb6NN87pEVglj29zneG78HEJDggF4Z/oS5i7ZTOfWjdg8axjHjqdx9/CPAMjIyGTgS1OZPa4/wUHCpJk/smX7/kBuSpHLVPho9V4GtT+LIIHF2w+yN/UvOpztOt63YNuBPF9buWwoD7Q5A4DgIOHHHYfYuO+IX+L2h+DgYLre8SCTRgwmMzOT5u27EHP6maz8dhYALTp1Z8H0Dzh2JJVZ744GICg4mPtefIudv2xi3eJvial1FmMG9wWg0419Oef8iwO1OUXCD7dhigU8G2FxwEWeBUQkFlcv5qVkv6l8niS340IeK6wIXI3rnMFmwCxcGXiB5vdCD5vijpS0wyZFqkLZf3tLyJKvYadBgQ6h2Lp+cL+CC5ViVzWILLhQKdWraQ2fpasBM7cW6vv+tasb3I37sJrbBFWdcGJCRHoBnVW1r3u6D9DC8+bxIjINGKWqP4rIROBLVZ2e3/sW9C38B64+1jeBuaqaVkB5Y4wxpVRhW4TupDchnyJxwOke0zWBvaeUuQD4xN1jFQlcKSLpqvpFXistKBHWUtW8T7Ayxhhj3PxwuGQVUFdEzgT2ADcA2S7no6pnesQzEVeL8Iv8VlpQIlwhInk2dVW1ZJ09bIwx5l/z9TFCVU0Xkftx9VQGA++p6mYRuce9fPy/WW9BibCr+//+7v8/dP9/E64T7I0xxhjAP1eHUdU5wJxT5uWaAFX1Nm/WWdB5hDsBRKSVqrbyWPS4iCwFns39lcYYY4wzeHv+YwURaX1iQkRaAnYZBGOMMVmcetFtb8fu3wm8JyInLotwCPD9mfLGGGMcw6lXa/YqEarqGuA89+XWRFVL1nWkjDHGFJpTr7HhVSIUkTLAtUBtIOTEEFlVtWOExhhjAALavVkY3naNzgRSgDXAX74LxxhjjPEvbxNhTVW9wqeRGGOMcTSHNgi9Pra5TETO9WkkxhhjHC1ICvcIFG9bhK2B20TkD1xdo4LrJr12ZRljjDFAyT9G2MWnURhjjDEB4u3pEyeuMBMNlPVpRMYYYxzJoQ1Cr0+f6A6MAmrguuPvGcAWoJHvQjPGGOMkgTzOVxjeDpZ5DrgY+NV9i4uOwFKfRWWMMcZxpJD/AsXbRJimqslAkIgEqeoCoKnvwjLGGOM0JX3U6CERCQMWAZNFJAFI911YxhhjjH942yLsAfwJDATmAr8D3XwVlDHGGOcp0S1CVT3qMTnJR7EYY4xxMHHosNF8E6GIHAY0t0W4Tqiv5JOojDHGOI5TR40WdIf6iv4KxBhjjLM5tEHo2PsoGmOMMUXC21GjxhhjTL5K+rVGjTHGmHyVyGOExhhjjLcc2iC0Y4TGGGNKN5+3COOPHPf1Wzhaq2qRgQ6h2Lp+cL9Ah1BsffryhECHUKx1nfhkoEMolYICeL3QwrCuUWOMMUXCqV2jlgiNMcYUCRssY4wxplRz6ukTNljGGGNMqWYtQmOMMUXCoQ1CS4TGGGOKhlO7Ri0RGmOMKRIOzYOWCI0xxhQNpw46cWrcxhhjTJGwFqExxpgiUSLvUO9JRBoDDYGyJ+ap6ge+CMoYY4zzODMNepkIRWQY0B5XIpwDdAGWAJYIjTHGAM4dNertMcKeQEdgv6reDpwHlPFZVMYYY4yfeNs1+qeqZopIuohUAhKAs3wYlzHGGIdxZnvQ+0S4WkSqAG8Da4AjwEpfBWWMMcZ5HNoz6l0iVNX73E/Hi8hcoJKqbvBdWMYYY5zGqaNGvTpGKC43i8hQVd0BHBKRFr4NzRhjjJMEFfIRKN6+9zjgEuBG9/RhYKxPIjLGGGP8yNtjhBepajMR+QlAVQ+KyGk+jMsYY4zDOLVr1NtEmCYiwYACiEgUkOmzqIwxxjiOM9Og94nwdeBzIFpEXsB1XuFTPovKGGOM45TYFqGIBAF/AINxnVQvwNWqusXHsRljjDE+V2AidJ9IP0pVLwG2+iEmY4wxDuSPkZ8icgXwGhAMvKOqI09ZfhPwmHvyCHCvqq7Pb53exv2NiFwrTm33GmOM8TkRKdTDi/UH4zpjoQuua1/fKCINTyn2B9BOVZsAzwETClqvt8cIHwYqAOkichxX96iqaiUvX2+MMaaE80NLqQWwTVW3A4jIJ0AP4OcTBVR1mUf5H4GaBa3U2yvLVPxHoRpjjCl1CttnKCL9gH4esyaoqmeLLhbY7TEdB1yUzyrvBL4u6H3/8Y15RaQOcANwo6o2/qevN8YYY3LjTnr5dWXmlmo114IiHXAlwtYFva+3l1irLiIDRWQlsBlXAr2xgJcZY4wpRYKQQj28EAec7jFdE9h7aiERaQK8A/RQ1eSCVppvi1BE7sKV8GoCU4G+wExVfcabiP1p89ofmfb2aDQzk5adutG5Z59sy/fH7eTD119g9++/0u3mfnS6pnfWsmNHDjN5zEj27toOIvR5YAhn1S9ZjV1V5aUXX2DJooWULVeW514YSYOGjXKUmzL5IyZ/OIndu3fxw5LlhIdXBeDw4cMMeexR9u/bS3pGBrfefgdXX3OtvzfDJ86tHkbvZrEECSz6/QBfbUnMtdyZVcvxdKezGbdsF6t3pwDwarf6/JmegSpkZCrPfLPNn6H73PhhN9GlbWMSDxzmgl4jci0zanBPOrdqxLHjf9Nv2Ies2xoHQKeWDXj10Z4EBwUx8YtlvPr+t/4M3S9+XbeCr94fQ2ZmBhd0vIp2V9+Ubfm6xd+yaOYUAMqULUf3vgOpXvtsDiUlMH3sCI4cOoBIEBde1pWWV/YMxCYUKT8Mp1wF1BWRM4E9uHone3sWEJFawGdAH1X91ZuVFtQ1OhZYDvRW1dXuN8m1GRpImRkZfPrWKB58ZjRVIqJ5aVBfmrRoTfVaZ2aVqRBWiV53DWT9j4tyvH7aO6Np2Owi7nr8BdLT0vj7r+P+DN8vlixexK6dO5j99Tds3LCe558dzuRPpuUo17RZM9q2b0/f227JNv/TKZM5q04d3hg3ngMHDtDjqiu46qpuhJ7m7CvtiUCf5rG8suAPDvyZxrDLz+anPansTf0rR7leTauzcf/hHOt46fvtHPk7w18h+9WHs39k/KcLeee5W3Jd3rl1Q+rUiqJxj2docW5tXh9yA21veZWgIGH049dx1b1j2BN/iCWTH+XLhRvZun2/n7fAdzIzM5j97mvc/tSrVIqI4s0n7qHBBa2Irlk7q0x4dHXuGv4a5cIq8stPK/hiwijuHfEmQcHBdOlzH7Fn1eOvP48x9vF+nN3kgmyvdSLx8XAZVU0XkfuBebhOn3hPVTeLyD3u5eOBoUAEMM49EjVdVS/Ib70FdY3WAD4B/isiv4jIc0Bo4Tal6O34bQtR1WoSWS2WkNBQmrfpyPqVi7OVqVglnNp1GxAckj33/3nsKNs2r6dlp24AhISGUj6s5I0NWjD/e7p1vxoRocl5TTl8OJXExIQc5Ro0aEhsbM5BViLCsaNHUVWOHTtK5cqVc9SlE51VtTzxR/4m8ejfZGQqK3Yd4vyaOQdDd6oXyZrdKRw+nh6AKANn6drfOZByLM/lXds14eMvXbcmXblxB5UrlqNaZCUubFyb33cnsWNPMmnpGUybt5au7Zv4K2y/iNu2larVYqkaU4OQkFCatLyULauWZitzxjmNKef+PqlVtyEpya7ehkrhEcSeVQ+AMuXKExV7BqkHkvy7AT4gUriHN1R1jqrWU9U6qvqCe954dxJEVfuqariqNnU/8k2CUEAiVNUkVX1TVdviuqpMCpAgIltEJPd+kgA4lJxIeGR01nR4RHTWDleQpP17CKtchQ9ff4ERA27jozde5K/jf/oq1IBJSIgnplq1rOmYmGokxMd7/fobet/E9u2/c1n7NvS8ujuDn3iSoKBA3jilaISXD+XAsbSs6YPH0ggvl/23XpVyITSrWYn523IealBgUIezGN75bNrVqerrcIudGtFViNt/MGt6T/whakRXoUZ0ZeLiPecfJDaqciBC9JnUA4lUjojKmq4UEUXKgby/d1bP/4p65+e8e93BhH3s++M3ap7dwCdxmoJ5/U2mqnGq+qqqNsd13sZfeZUVkX4islpEVn859YOiiLOg6HILwqtXZmZksPv3X2lzxTUMGT2R08qW45sZHxZxfMWA5qyjf3J9hGVLllC/fgO++2ExU2d8wYsvPMuRI0eKMsKA8KYGbmpWg2nr9udWhbzw3TaGz/uNUT/8Qce6EdSLqlDkMRZnue1CqpprF1mxO6ZSSLntD3l9prZv+ok1C+ZwxU13Z5v/1/FjfDxqGFfddj9lyzt/3/HDYBmf8KpvS0T+k8vsJSISrao5+tc8h8B+vzXJ5/t/lYhoDiadDONgcgKVq0Z699rIaKpERnHmOa6BI81atmfejI98Eqe/ffLxZD6bPhWARo3PJX7/yeMz8fH7iYqOzuulOcz84jPu6NsPEaHWGWcQG1uTP7Zv59wmzu7uOnAsjarlT7YAw8uHcvDPtGxlalctz70tawEQViaYJjUqkZmprN2TyqE/XV2lh//KYG1cKmdFlOPXxKP+24AA2xN/iJrVwrOmY2OqsC8xhdNCQ6gZ4zk/nL2JKYEI0WcqR0Rl63lKTU6kUnjO7539O3/n87de4dYnXqJ8xZOt4oz0dD4eNYzz2lxGo4va+iVmX3Pqtce8bRHeiWso6k3ux9u4rjazVET65PdCfzijbn0S9sWRFL+X9LQ01iz+niYtCjx1BIDK4RGER0YTH7cTgK0b1lD99No+jNZ/buh9E1M/m8nUz2bSoeNlzJ71BarKhvXrCAurSFSU94mwWvXqrPhxOQDJSUns2PEHNU8v8IINxd4fB44RU/E0IiuEEhwkXFSrCj/FpWYr8+jsrQxyP1bvTuGD1XtYuyeV04KFsiGuj9BpwUKjamHsSSl5A63y89XCjfTu6urua3FubVKP/Mn+pFRWb97J2bWiOKNGBKEhwfTq3IyvftgQ4GiLVmydc0jeF8eBhH2kp6exYdl86l/QMluZQ0nxTH71aXreP4TIGidH/asqn41/mejYWrTuep2/Q/cZfxwj9AVvRztkAg1UNR5ARGKAN3Gd0b8ICGhfYnBwCNf3G8iY4Q+TmZnBJR27UqPWWSz6+nMA2na5hpSDybz0yJ0cP3YUCQpiweypPD1mMuXKV+C6uwby/n+fIT09nchqNbjlwSGB3ByfaNO2HUsWLaRrl06ULVuOZ58/eYi3/z13MezZ54mOjmHyRx8w8b13SE5Kotc13Wndth3Dn32Bfvfcx9NPPsG1V3dDVRnw8KCsUyucLFPho9V7GdT+LIIEFm8/yN7Uv+hwtmvbFmw7kOdrK5cN5YE2ZwAQHCT8uOMQG/c5v7vY06QXb6NN87pEVglj29zneG78HEJDggF4Z/oS5i7ZTOfWjdg8axjHjqdx93BXb0pGRiYDX5rK7HH9CQ4SJs38kS0laMQouL53ut3xEBNfeBTNzKRZhy7EnH4mK76ZCcBFl/dg/vRJHDuSyqx3/gdAUHAw/UdOYOcvG1m36Btiap3FG4/eCcDlN97FOc0uDtj2lGaiuXV0n1pIZKOqnusxLcBGVW0sIj+p6vl5vdYfXaNO1ups77pwS6N7ppWsFkRR+vTlAq8jXKp9OPHJQIdQbPU8r7rP2l7fbinc932nBpEBaRd62yJcLCJfAidOPLsWWCQiFYBDvgjMGGOMswQ59Biht4mwP67k1wrXQLsPgBnqak528FFsxhhjHMTXJ9T7ird3n1BguvthjDHG5FCiR42KyH9E5DcRSRGRVBE5LCKpBb/SGGOMKd687Rp9Geimqlt8GYwxxhjnKtFdo0C8JUFjjDH5KemDZVaLyKfAF3hcWk1VP/NFUMYYY5ynpLcIKwHHgMs95imuez4ZY4wxjh0s4+2o0dt9HYgxxhgTCAXdoX6wqr4sIm+Qy8XjVfVBn0VmjDHGURzaICywRXhigMxqXwdijDHG2YIc2jeabyJU1dnupxtU9Sc/xGOMMcahnJkGvb8N039FZKuIPCcijXwakTHGGONHXiVCVe0AtAcSgQkislFEnvJlYMYYYxxGCvkIEG9bhKjqflV9HbgHWAcM9VVQxhhjnEcK+S9QvDp9QkQaANcDPYFk4BPgER/GZYwxxmEcOlbG6xPq3wemAJer6l4fxmOMMcahHJoHvT6h/mJfB2KMMcYEQkEn1E9V1etEZCPZT6gXXLcpbOLT6IwxxjiHQ5uEBbUIH3L/39XXgRhjjHG2EnnRbVXd5/5/p3/CMcYY41QlcrCMiBwml2uMcrJrtJJPojLGGOM4Ds2DBbYIK/orEGOMMSYQvD19whhjjMmfQ5uElgiNMcYUiRI5WMYYY4zxllMHy3h9rVFjjDGmJLIWoTHGmCLh0AYhoprb2RFFZ0fycd++gcM5tU/dH1buTg50CMWWOLUPyk/63PZCoEMotv78aYzPdp71uw8X6vv+vNMrBmTHthahMcaYIuHUH/aWCI0xxhQJp3ZU2GAZY4wxpZq1CI0xxhQJhzYILREaY4wpIg7NhJYIjTHGFAkbLGOMMaZUK/GDZUTkDBG5zP28nIjYnSmMMcY4nleJUETuAqYDb7ln1QS+8FFMxhhjHEgK+QgUb1uE/YFWQCqAqv4GRPsqKGOMMQ7k0Ezo7THCv1T17xOXdRKREHK/c70xxphSyqmDZbxtES4UkSFAORHpBEwDZvsuLGOMMSYnEblCRH4RkW0i8nguy0VEXncv3yAizQpap7eJ8HEgEdgI3A3MAZ76J8EbY4wp2UQK9yh4/RIMjAW6AA2BG0Wk4SnFugB13Y9+wJsFrderrlFVzQTedj+MMcaYHPzQMdoC2Kaq2wFE5BOgB/CzR5kewAfqurXSjyJSRUSqq+q+vFbqVSIUkY3kPCaYAqwGnldVu1+OMcaUdr7PhLHAbo/pOOAiL8rEAoVLhMDXQAbwsXv6Bvf/qcBEoJuX6zHGGFNCFXawjIj0w9WdecIEVZ2Q7S1yOrWR5k2ZbLxNhK1UtZXH9EYRWaqqrUTkZi/XYYwxxuTJnfQm5FMkDjjdY7omsPdflMnG28EyYSKS1fwUkRZAmHsy3ct1GGOMKcF8PVgGWAXUFZEzReQ0XL2Ts04pMwu4xT169GIgJb/jg+B9i7Av8J6InEh+h4G+IlIBeNHLdRhjjCnBfH2IUFXTReR+YB4QDLynqptF5B738vG4zmq4EtgGHANuL2i93o4aXQWcKyKVAVHVQx6Lp/6TDTHGGFNC+WHYqKrOwZXsPOeN93iuuK6G5jVvrzUaIyLvAp+o6iERaSgid/6TNzLGGFOySSH/BYq3xwgn4mqK1nBP/woM8EE8xhhjjF95mwgjVXUqkAmuflpcp1MYY4wxgF8Gy/iEt4NljopIBO5zMU6MxPFZVMYYYxzHmZfc9j4RPoxrSGodEVkKRAE9fRaVMcYY53FoJvR21OhaEWkHnINrU39R1TSfRmaMMcb4Qb6JUET+k8eieiKCqn7mg5iMMcY4kFPvR1hQizC/a4gqUGwSoary5v9eYuXyJZQtW5ZHnnqOuuc0yFFu/944Rgx9jMOpqZx9Tn0GDx1BaGho1vJfft7EgH59GPLsy7S5tJM/N8GnVJVx/3uJVcsXU6ZsWQY99Rx1zzn17iWwb28cI4YO5nBqKnXPaZBr/TzU72aGPPsybS+93J+b4DO/rlvJnIljyMzMoPmlV9Hu6t7Zlq9b/C2LZ30CwGlly9H9zgFUr302h5ISmDH2RY4cOoAECRd07ErLK0veEYNf163gq/dd9XNBx6tod/VN2ZavW/wti2ZOAaBM2XJ07zswq36mjx3hqh8J4sLLSl79jB92E13aNibxwGEu6DUi1zKjBvekc6tGHDv+N/2Gfci6rXEAdGrZgFcf7UlwUBATv1jGq+9/68/QfSKQA14KI99Ro6p6ez6PO/wVpDdWLV/CnrhdvD91Ng89NpQ3Xnk+13LvjHuN/1x/M+9PnU1YxUrMnf151rKMjAzeHTea5he19FfYfuOqn528P/VLBjw2lNfzqJ93x43mP9f3YeLUL931c/K3TkZGBu+M+1+Jqp/MzAxmv/catzwxkgf/O5GNS78nIW5HtjJVo6vTd9hoHnjlXTr8pw8z3x4FQHBwMF363MtD/5vE3c+PY8U3M3O81ukyMzOY/e5r3DrkJR763yQ2LJ2fYxvDo6tz1/DXePDV92h/7S18McFVP0HBwXTpcx8D/vcB97wwjh/nfVHi6ufD2T/So//YPJd3bt2QOrWiaNzjGe5/fgqvD3HdryAoSBj9+HX0uH8c51/7PL2uaE79s6r5K2yfkUI+AiXfRCgiD+f38FeQ3li+eAGXXdENEaFB4yYcPXKY5KTEbGVUlfVrVtKmg6ul16lLd5Yvmp+1fOb0KbTucBlVwqv6NXZ/WLZ4AZ2y6ue8POtn3ZqVtPWon2WLFmQtnzn9Y9p06FSi6idu21YiYmpQNaYGISGhnNvyUrasWpqtTK1zGlMurCIAp9dtSEpyEgAVwyOocVY9AMqUK09UbC1SDyT5dwN8LG7bVqpWi82qnya51M8ZHvVTq25DUpJd+1Wl8Ahis9XPGSWufpau/Z0DKcfyXN61XRM+/nIlACs37qByxXJUi6zEhY1r8/vuJHbsSSYtPYNp89bStX0Tf4XtM049faKg8wgrFvAoNpISE4iKicmajoyKITkxIVuZ1JRDVAirSHCIq0c4MjqGJHeZpMR4li2cz1VX9/Jf0H6UnJhAVMzJX5x51U9YjvqJB1z1s7QE1k/qgSQqR0RnTVeKiCL1YN5f1msWzKFe0xY55h9M2M++P7ZR8+yc3fFOlnogkcoRUVnTlSKiSDmQmGf51fO/ot75udXPPvb98VuJq5+C1IiuQtz+g1nTe+IPUSO6CjWiKxMX7zn/ILFRlQMRoqGAY4Sq+sy/WannPaVeGDWG3rf64WpsudxtSk75ieG6BF3uZcaPfoU77xtAcHCwT8ILtNy2/dSfYPnVz5ujX6ZvSayf3LY5j06a7Zt+Ys38Odz17OvZ5v91/E+m/HcoV97an7LlK/gkzEDJfbfJp34WzKHfs29km//X8WN8PGoYV912f4mrn4LkVlWqmus+lu8N8xzDmQcJvb1DfU3gDaAVrr/XEuAhVY3LrbznPaV2JB/32d931oxP+HqW6xhWvfqNSIyPz1qWlBhP1ciobOUrVwnn6JHDZKSnExwSQlJCPBHuMr9u3cyLQx8DICXlICuXLSY4OJiW7S71Vfg+N2vGJ8yZNQOAc+o3IjF+f9aypMST235C5SrhHMlRP67W0q9bNzMiR/2E0MrB9QPuFk7yyZZxanIiFcMjcpTbv/N3Pp/wKrc+PpLyFU/+cs9IT2fKqKGc1/oyGl3U1i8x+1PliKisrk5w1U+l8Mgc5fbv/J3P33qFW594KUf9fDxqGOe1KZn1U5A98YeoWS08azo2pgr7ElM4LTSEmjGe88PZm+j8a5SUyMEyHt7HdUJ9DVy3vJ/tnhdQ3a+9gTcnTeXNSVNp2bYD382djaqyZdMGylcIy/FFLyKc1+xCFi9wjc769utZXNKmAwAfzPiaDz5zPdp06MQDg550dBIEV/2MnzSN8ZOm0bLtpXybVT/rqVChYp71syhb/bQH4MMZc/nwM9fjRP04PQkCxNapT/L+PRxI2Ed6ehobl82n/gXZBwMdSorn41FD6dX/CSJrnLzfp6ry+fiXiYo9g1Zdr/N36H4RW+cckvfFZdXPhjzqZ/KrT9Pz/iE56uez8S8THVuL1iW0fgry1cKN9O7q6ipucW5tUo/8yf6kVFZv3snZtaI4o0YEoSHB9OrcjK9+2BDgaAvPqYNlvL2yTJSqeia+iSIywAfx/GstWrZh1fIl3N6rK2XKluWRJ5/NWvbUI/0Z+PgwIqKiufO+AYwYOpiJE8Zydr36dO52TQCj9p8WLduwcvlibut1lev0iSefy1r25CP38fDjw4mIiqbvfQMZMXQwkyaMoU69+lzRLa9TSUuG4OBgut7xIJNGDCYzM5Pm7bsQc/qZrPzWda/PFp26s2D6Bxw7ksqsd0cDrtGQ9734Fjt/2cS6xd8SU+ssxgzuC0CnG/tyzvkXB2pzilxwcAjd7niIiS88imZm0qyDq35WfDMTgIsu78H86ZNc9fPO/wBX/fQfOYGdv2xk3aJviKl1Fm886jo8cvmNd3FOs5JTP5NevI02zesSWSWMbXOf47nxcwgNcR0+eGf6EuYu2Uzn1o3YPGsYx46ncffwjwDIyMhk4EtTmT2uP8FBwqSZP7Jl+/783soRnNoilFyPHZ1aSOQ7XHegmOKedSNwu6p2LOi1vuwaLQmcegKqP6zcnRzoEIqtvI7TGZc+t70Q6BCKrT9/GuOznWdfyt+F+r6vXvm0gOzY3naN3gFcB+wH9uG6zmixOo/QGGNMYDn1foTeXmt0F9Ddx7EYY4xxMod2VBR0Qv3LInJPLvMHishLvgvLGGOM0zh1sExBXaNdcZ8GcYrXgKuKPhxjjDHGvwrqGlVVzcxlZqbY0XpjjDEenJoVCmoRHhORuqfOdM/70zchGWOMcaKSOlhmKPC1iDwPrHHPuwB4Ahjgw7iMMcY4jUNbhAVda/RrEbkaeBR4wD17E3Ctqm70cWzGGGMcxKF5sODTJ1R1k4h8qaq3es4XkV6qOs13oRljjDG+5+0J9U94Oc8YY0wp5dT7EebbIhSRLsCVQKyIeN57phKQ7svAjDHGOItTLxlZUNfoXmA1rqvKrPGYfxgY6KugjDHGOI9TT58oaLDMemC9iHwOHFXVDAARCQbK+CE+Y4wxxqe8PUb4DVDOY7oc8F3Rh2OMMcb4l7f3IyyrqkdOTKjqEREp76OYjDHGOJBTu0a9bREeFZFmJyZE5ALsyjLGGGM8lNQry5wwAJgmInsBBWoA1/sqKGOMMc5T0luEG4HxwF9AEvAWsNlXQRljjDH+4m2L8AMgFXjBPX0j8CHQyxdBGWOMcR6HNgi9ToTnqOp5HtMLRGS9LwIyxhjjUA7NhN52jf4kIhefmBCRi4ClvgnJGGOME5X0wTIXAbeIyC73dC1gi4hsxHXz3iY+ic4YY4xjOHWwjLeJ8AqfRmGMMcYEiFeJUFV3+joQY4wxzubQBqHXLUJjjDEmfw7NhJYIjTHGFAmn3obJ21GjxhhjTIlkLUJjjDFFwqmjRkVVAx2DX4lIP1WdEOg4iiOrm/xZ/eTN6iZ/Vj/FW2nsGu0X6ACKMaub/Fn95M3qJn9WP8VYaUyExhhjTBZLhMYYY0q10pgIrZ8+b1Y3+bP6yZvVTf6sfoqxUjdYxhhjjPFUGluExhhjTBbHJUIROVLA8toi0ttjuryITBaRjSKySUSWiEiYe9kyX8dr/KegfeMfrKe2e1+pICLJIlL5lOVfiMh1vo6jKJ2ISURqiMj0Asp2F5HHC/l+USKSJiJ3F2Y9xYmINBWRK0+Z10VEVovIFhHZKiKvFrCO4SIyyP18ooj09GXMxjuOS4ReqA309ph+CIhX1XNVtTFwJ5AGoKot/R+ecQpVPQp8A1x9Yp47KbYGvgxQWIWiqntVNd8vX1WdpaojC/lWvYAfgRvzKiAiwYV8D39rCmQlQhFpDIwBblbVBkBjYHtgQjOF4dhEKC6vuH+5bxSR692LRgJtRGSdiAwEqgN7TrxOVX9R1b/c6zjxK/lZd/l1IrJHRN53z79ZRFa657/lwA9uDu7WzBoR2Swi/dzz7hSRX0XkBxF5W0TGuOdHicgMEVnlfrQKbPTeEZEwEfleRNa6940e7vm13b/c33Zv/zciUs69rLmIrBeR5UB/j9VNAW7wmL4GmAsE5fYep8TRXkS+9JgeIyK3ebzfQvffYp6IVC/yisjFidau+/kKEWnksewHd1y3eewDE0XkdRFZJiLbT7RgRCRIRMa56/FLEZlzSuvmRuARoKaIxHq8xxH3520FcElenzERedPd0tosIs/4uD62isgkEdkgItPF1Yt0oXub17vjqww8C1zvjvV6YDDwgqpuBVDVdFUd517vGe79Y4P7/1oFxJHr/uCOY4OILD/xfeeeH+yeXuVeXmJa3gGhqo56AEfc/18LfAsEAzHALlxJrz3wpUf5pkACsBx4Hqh76ro8pisDG4DmQANgNhDqXjYOuCXQ218E9VfV/X85YBMQC+wAqgKhwGJgjLvMx0Br9/NawJZAx+/lvhECVHI/jwS24boufm0gHWjqXjYV16953H/3du7nrwCb3M9Pc+8/Ee7pucBVeb3HKXGcui+OAW5z1/MyIMo9/3rgPT/VTW2PbRsIPON+Xh341f38No99YCIwDdeP5obANvf8nsAc9/xqwEGgp3vZ6cBv7ucjgIc94lDgOvfzPD9jHvtpMPAD0MRH9VLbHVMr9/R7uBLcduBC97xK7r93Vr24568FzstjvbOBW93P7wC+cD8fDgzyqNue+e0PuD6jLd3PR3r87foBT7mflwFWA2cG+jPo1IeTrzXaGpiiqhlAvIgsBC4EUj0Lqeo6ETkLuBy4DFglIpeo6hbPciIiwGTgf6q6RkTux5UQV7kWUQ7XF6LTPSgi17ifnw70ARaq6gEAEZkG1HMvvwxoKCcvIFhJRCqq6mF/BvwvCDBCRNoCmbiSfYx72R+qus79fA1Q2/1rv4qqLnTP/xDoAqCqf4vILKCniMzA9cPqm3zeY78X8Z2DqxvtW3fdBgP7/vXW/ntTcf2YHAZchyvh5eYLVc0EfhaRE/XYGpjmnr9fRBZ4lL/BvW6AT4B3gf+6pzOAGe7nHcn7M3adu8ciBFeSbojrx4ov7FbVpe7nHwFPAvtUdRWAqqYCyD+7kOYlwH/czz8EXs6nbK77g4hUASqq6omxDB8DXd3PLweaeLTCKwN1gT/+SZDGxcmJ0Ou9UlWPAJ8Bn4lIJq5+/i2nFBsOxKnq+x7rn6SqTxRBrMWCiLTHldwuUdVjIvID8AuuX+a5CXKX/dMvARadm4AooLmqponIDqCse9lfHuUycH35Cq5WQV6mAE+5y810r/O2fN7jhHSyH344sVyAzap6yT/criKlqnvENRioCa5WSF7da551Jqf8n5sbgRgRuck9XUNE6qrqb8Bx94/XE+vI8RkTkTOBQbhaZAdFZCI567Yonfq3T8XVyirIZlyJfP2/eA9Pue4PIhJewGseUNV5Xry3KYBjjxECi3D11weLSBTQFlgJHAYqnigkIq1O7FAichquX5Y7PVckIl2BTsCDHrO/x9UKiHaXqSoiZ/hwe/yhMnDQnQTrAxcD5YF2IhIuIiG4upxP+Aa4/8SEiDT1Z7CFUBlIcCeoDkC+fzdVPQSkiEhr96ybTimyANev7f64kqK377ETV4u6jLvV2dE9/xcgSkQuARCRUM9jdX72Ca6uwMqquvEfvG4JcK37WGEMrm5gROQcoIKqxqpqbVWtDbxI9uOsJ+T1GasEHMX1N4nB3Tr3oVon/ha4kviPuJL3he64Kro/G9m+W3B1oQ8RkXruckEi8rB72TJObvNNuOorL7nuD6p6EDgsIhe7y3nW4TzgXhEJdb+mnohU+MdbbgBnJ8LPcXWVrAfmA4NVdb97Xrr7IPdAoA6wUEQ2Aj/h6kufccq6HgFqACcO2j+rqj/jagV8IyIbcHUh+WVAgw/NBULc2/Mcrg/8HlzHcVYA3wE/Aynu8g8CF7gPxv8M3OP/kP+VybjiXo3rS2irF6+5HRgrrsEy2VrA7u6/GUAErh9gXr2Hqu7G1UW4wV3+J/f8v3EdG3pJRNYD64BAjWCeTvauTG/NAOJwHcN6C9f+k4IrkXyeS9kco0fz+oyp6npcdbUZ1zG7pae+tohtAW51x1AVeANXC/kN99/nW1wt0gW4ftisE5HrVXUDMACYIiJbcNXFie+IB4Hb3evsg2v0eq4K2B/uBCa490vh5GfzHVyf1bXuATRv4ewevoCyK8sYRCRMVY+4f/V+jutA/alfZsZk47HfRODqjWnl/jHqGCJSG9eApsaBjiU3J+rY/fxxXD8U8kyq5t+xXxAGYLiIXIbrV+83wBeBDcc4xJfuAR2nAc85LQk6xFUi8gSu7+qduEaumiJmLUJjjDGlmpOPERpjjDGFZonQGGNMqWaJ0BhjTKlmidAYY0ypZonQGGNMqWaJ0BhjTKn2f0P7fhSVjSXgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matriz de correlacion\n",
    "features = ['lotSize', 'age', 'landValue', 'livingArea', 'pctCollege']\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "corr = X_train_simplified[features].corr()\n",
    "sns.heatmap(corr, xticklabels=corr.columns,yticklabels=corr.columns, cmap=\"Blues\", annot=True, fmt=\".2f\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La mayoría de las correlaciones que no están a lo largo de la diagonal principal son bajas, pero vemos que existe una correlación considerable entre el valor de la tierra y la superficie habitable. Si bien no es una correlación terriblemente alta de 0.45, esto no es ideal desde el punto de vista de la regresión lineal e indica que existe multicolinealidad.\n",
    "\n",
    "La regularización L1 a menudo sufre con las predicciones cuando los datos exhiben multicolinealidad ya que el algoritmo de optimización no funciona tan bien. Afortunadamente, existe otro tipo de regularización, la **regularización L2**, que es muy buena para abordar la multicolinealidad. Procedamos a aplicar la regularización L2 al conjunto actual de características."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aplicar Regularizacion L2 para lidiar con multicolinealidad\n",
    "\n",
    "Un modelo de regresión lineal que utiliza la regularización L2 se denomina **regresión Ridge**. La regularización L2 agrega la magnitud al cuadrado de los coeficientes del modelo de regresión ($\\beta_j$) a la función de pérdida OLS:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\sum_{i=1}^N{\\big(y_i - \\sum_{j=1}^p{x_{ij}\\beta_j}}\\big)^2 + \\color{red}{\\lambda \\sum_{j=1}^{p}\\beta_j^2} .$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La regularización L2 también reduce los coeficientes $\\beta_j$ hacia cero, sin embargo, los efectos de la reducción suelen ser menores que los de la regresión LASSO. Ajustemos el modelo y examinemos los nuevos coeficientes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ajustar el modelo\n",
    "model_l2 = linear_model.Ridge(alpha=28.0, fit_intercept=True) # higher alpha for stronger regularization\n",
    "results_l2 = model_l2.fit(X_train_simplified, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación se muestran los nombres de los coeficientes del modelo seguidos de los valores $\\beta$ de ajuste. Los coeficientes están ordenados de menor a mayor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('newConstruction', -0.01009750431707218),\n",
       " ('sewer_public/commercial', -0.005645526782576365),\n",
       " ('age', -0.005192786144608388),\n",
       " ('bedrooms', -0.004616405925118881),\n",
       " ('sewer_septic', -0.0033133856339937354),\n",
       " ('heating_hot water/steam', -0.002837562657934486),\n",
       " ('fuel_electric', -0.0011615800651063823),\n",
       " ('pctCollege', 6.581924953498744e-05),\n",
       " ('fireplaces', 0.001204279320315348),\n",
       " ('heating_hot air', 0.0016736767194721583),\n",
       " ('fuel_gas', 0.0022699413825540866),\n",
       " ('lotSize', 0.003852235288187124),\n",
       " ('centralAir', 0.005066594564126576),\n",
       " ('rooms', 0.00771891534068925),\n",
       " ('waterfront', 0.009718016717086627),\n",
       " ('bathrooms', 0.014790009073400256),\n",
       " ('landValue', 0.03313699635793963),\n",
       " ('livingArea', 0.041428583014597876)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(X_train_simplified.columns, results_l2.coef_), key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aquí, vemos que ```livingArea``` y ```landValue``` tienen los coeficientes más grandes. Esto era de esperar dado que la exploración de visualización de datos original de este análisis mostró una relación significativa entre estas variables y los precios de la vivienda.\n",
    "\n",
    "Sin embargo, es importante preguntarse por qué elegimos usar $\\lambda = 28.0$ en este ajuste. El valor de $\\lambda$ puede afectar significativamente los coeficientes del modelo, por lo que debe elegirse con cuidado. La siguiente sección explora un método para elegir gráficamente un valor $\\lambda$ óptimo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mirando los efectos de  $\\lambda$ en underfitting y overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El error cuadrático medio (MSE) del modelo se calcula comparando las predicciones del modelo con los valores reales. En este caso, esto significa comparar los precios de la vivienda previstos del modelo regularizado L2 con los precios reales de la vivienda. Los valores más bajos de error cuadrático medio indican un mejor rendimiento del modelo.\n",
    "\n",
    "Repasamos los valores de $\\lambda$ y veamos los efectos en el rendimiento del modelo. Compararemos el rendimiento de los datos de entrenamiento y los datos de prueba. Recuerde que los valores más altos de $\\lambda$ conducen a una regularización más fuerte:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hacer un loop por distintos valores de lambda\n",
    "lambdas = np.arange(0.1, 100.0, 0.1)\n",
    "\n",
    "coefs = []\n",
    "mse_train = []\n",
    "mse_test = []\n",
    "for l in lambdas:\n",
    "    ridge = linear_model.Ridge(alpha=l, fit_intercept=True)\n",
    "    ridge.fit(X_train_simplified, y_train)\n",
    "    mse_train.append(mean_squared_error(y_train, ridge.predict(X_train_simplified))) # train data\n",
    "    mse_test.append(mean_squared_error(y_test, ridge.predict(X_test_simplified))) # test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JJ7weq1mz4JJLQnpoiTDqsYow55wDBQoosRIRSSsn405y95S7mbppKv1v688DlR4I6fE//xzefRcefhhuuy2kh5YIZM45v2OIKrly5XKHDx8O6zmqVPESrEWLwnoaEZEMLzY+llZTWvHxho95q85b9KjcI6TH/+MPr6xC/vywfHl03u1tZkecc7n8jiNahLXHysxuM7PNZhZjZk8m87mZ2YDA5+vMrMLp2prZC4F915jZXDMrkOSYl5nZITN7NNG2RYFjrQk8Lg5sb2dmuxNtD90sxbNQpAj8/rvfUYiIpG+x8bG0mdqGjzd8zBu13wh5UhUfD+3awcGDMH58dCZVErywJVZmlhl4D6gLlAJamlmpJLvVBYoHHp2Bwalo+7pzrqxzrjzwGfBckmO+DcxKJqRWzrnygceuRNsnJtr+4Rl+3ZAqUsQrtxAX53ckIiLpU1x8HO2mtWP8+vG8Vus1HrnxkZCfY8AAmDMH3noLSiX930/SrXD2WFUCYpxzvzjnTgATgIZJ9mkIjHaeb4A8ZpY/pbbOuQOJ2ucC/hnLNLNGwC/AhjB9pzRRpAjExsLOnX5HIiKS/sTFx3Hv9HsZ+/1YXq75Mo9XeTzk51izxpuw3qABdOkS8sNLBAtnYlUQSFzmcltgW2r2SbGtmb1kZluBVgR6rMwsF/AE0PcU8YwIDPc9a/afG12bBoYWJ5tZ4VR/uzBSyQURkfCId/F0nNGR0WtH80KNF+h1c6+Qn+PIEbj7brjoIhg2TKUVMppwJlbJ/VVKOlP+VPuk2NY597RzrjAwFugW2NwXeNs5dyiZtq2cc1cDNwce9wS2zwCKOufKAvOAUcl+EbPOZrbKzFbFxsYmt0tIKbESEQm9eBdPp+mdGLlmJH2q9eGZqs+E5TyPPAIbN8Lo0ZA3b1hOIREsnInVNiBxD1AhYEcq90lNW4BxQNPA6+uBfma2BegOPGVm3QCcc9sDzwcDbSoF3v/tnDseaP8BcG1yX8Q5N9Q5V9E5VzFLlvCX/lJiJSISWvEuni6fdWH4muE8V/U5elfvHZbzTJsGQ4bAY49BrVphOYVEuHAmViuB4mZ2uZllBVoA05PsMx1oE7g78AZgv3NuZ0ptzax4ovYNgE0AzrmbnXNFnXNFgXeAl51zA80si5nlDbQ9B7gdWB94nz/JsTaG7uufuVy5vC5kJVYiImfPOccDnz/AB999wNM3P02f6n3Ccp7t26FDB6hQAV58MSyniFhpWQXAzGqb2bdm9n3guWaiNtcGtscEzmeB7dnMbGJg+3IzKxquaxG2xMo5F4s3TDcHL2H52Dm3wcy6mFnCVL6ZeJPNY/B6jO5PqW2gzatmtt7M1gF1gIdPE0o2YE5g/zXA9sC5AB4ysw1mthZ4CGh3dt86dIoUUWIlInK2nHN0m9mNId8O4ckqT/JCjRewMEx6io+Htm3h2DEYNw6yZg35KSKWD1UA/gLuCEzxaQuMSXSewYHjJ5wroSRrB2Cvc+5KvOoBr539N09eWMe1nHMz8ZKnxNuGJHrtgGRL3CbXNrC9aTK7J92nT6LXhzn1EF8vIPQzF0OgSBHYvNnvKEREopdzju6zuzNo1SAeu/ExXr7l5bAkVQBvvgnz58OHH0KJEmE5RST7505+ADNLuJP/h0T7/FMFAPjGzBKqABQ9VdtTVQFwzq1OtH0DkN3MsgEXAuc555YFjjUaaIRXgqkh0CfQZjIw0MzMhaFKupa0iVCXXeb1WKkwvohI8JxzPDz7YQasGEDPG3ryWq3XwpZUrVoFTz0FTZvCvfeG5RSRLk2rACTRFFgdmC9dMNA+uWP9c57AqNh+4KJUfLegKbGKUEWKwOHDsGeP35GIiEQX5xwPznqQd1e8yyOVH+GNOm+ELak6dMgrrXDppTB0aLotrZAl4c74wKNzks/TugqAd0Cz0nhDevelIo7UxBgS4b/FTc5I4jsDLwpLTi0ikv7Eu3i6zezG4FWDeezGx8LaUwXQvTvExMDChXDhhWE7jd9inXMVU/j8bKoAZE1FW/Du6P8c6A1gZoWAqUAb59zPic5R6BTHSjj/NjPLApwPhKXrQj1WEUolF0REghPv4rn/8/sZvGowT1R5IuxJ1eTJXgHQXr2gWrWwnSYapGkVADPLg5dk9XLOLU3YIXC8g2Z2Q+BuwDbAp4nO3zbwuhmwIBzzq0A9VhErIbHassXXMEREokJCnaoPvvuAp256ihdrvhjWpGrLFujYESpVgj59wnaaqOCciw3UjZwDZAaGJ1QBCHw+BO9mtHp4VQCOAO1Tahs49KtmVgKIB34DEioKdAOuBJ41s2cD2+oE1gHuCowEcuBNWk9YO3gYMMbMYvB6qlqE/EIEWJgStnQrV65c7vDhw2E/j3Nw/vneyugDBoT9dCIiUSvexdN5RmeGrR7GMzc/w/M1ng9rUnXypNdDtWGDtybg5ZeH7VQRwcyOOOdy+R1HtFCPVYQyg2LF4Ndf/Y5ERCRyxbt4Ok7vyIg1I3iu6nP0qd4nrEkVQO/esGwZTJiQ/pMqCZ7mWEWwYsXgl1/8jkJEJDLFxcfRYXoHRqwZQZ9qfehbo2/Yk6p58+DVV71hwObNw3oqiVJKrCJYQmKl0VoRkf+Ki4+j/aftGblmJH2r9w3b2n+J7doF99wDJUtC//5hP51EKSVWEaxYMW95hD/+8DsSEZHIERcfR9tpbRmzbgwv1niR56olVzcytBKWrNm7FyZOhJw5w35KiVJKrCJYsWLes4YDRUQ8sfGxtJnWhrHfj+Xlmi/zdNWn0+S8b70Fs2fD22/D1VenySklSimximBKrERE/hUbH8s9U+9h3PfjePWWV+l1c9os9bpypVerqkkT6NLl9PtLxqa7AiNYkSLe3YFKrEQkozsZd5LWU1vz8YaP6VerH49VeSxNznvgALRoAQUKeAssp9MlaySElFhFsGzZoGBBJVYikrGdjDvJ3VPuZvIPk3mj9hs8cuMjaXJe57weqt9+g8WL4YIL0uS0EuWUWEU4lVwQkYzsZNxJWn7Skk82fsJbdd6iR+UeaXbukSNh/Hh48UWoUiXNTitRTnOsIpwSKxHJqI7HHqfZpGZ8svET3rn1nTRNqjZuhG7doEYNePLJNDutpAPqsYpwxYrBjh1e2YXs2f2ORkQkbRw9eZQmHzdhdsxsBtUbRNfruqbZuY8d8+ZV5cwJH30EmTOn2aklHVCPVYRLuDNQizGLSEZx+MRhbh9/O3Ni5vDhHR+maVIF8OijsG4djBrlTVoXCYYSqwinkgsikpEcPH6QumPrsmjLIkY3Hk2HCh3S9PzTpsF770HPnlCvXpqeWtIJDQVGOCVWIpJR7Du2j7pj67Jy+0rGNx3PXaXvStPzb90K994L114Lr7ySpqeWdESJVYS7+GJvnF+JlYikZ38f+Zs6H9Xh+z+/Z/Jdk2lUslGanj82Fu6+G06ehAkTIGvWND29pCNKrCKcme4MFJH0bdfhXdQeU5vNf21mWotp1Cue9mNwzz8PS5Z4k9WvvDLNTy/piBKrKFCsGPz8s99RiIiE3s6DO6k1pha/7v2Vz+7+jFrFaqV5DPPmebWq2rWDVq3S/PSSzmjyehS44gqvx8o5vyMREQmdbQe2UW1kNX7b9xuzWs3yJanaudNLpq66CgYOTPPTSzqkHqsoULw4HDni1bMqWNDvaEREzt6WfVuoOaomfx/9m7n3zOXGwjemeQxxcV5SdfAgLFgAuXKleQiSDqnHKgoUL+49//STv3GIiIRCzJ4Yqo6oyt5je5l3zzxfkirwhv8WLvTKK5Qu7UsIkg4psYoCSqxEJL3Y9Ncmqo2sxpGTR1jYdiHXFbzOlzgWLIC+faFNG29ulUioaCgwChQuDNmywY8/+h2JiMiZW79rPbeMvgXDWNRuEWUuLuNLHH/+6Q0Blijh9VaZ+RKGpFNKrKJApkzeBHb1WIlItFq9czW1x9QmW5ZsLGizgBJ5S/gSR1wctG4N+/fDF19A7ty+hCHpmBKrKFG8uBIrEYlO32z7hrpj63JetvNY0GYBV1x4hW+xvPyyV17hww+hjD8dZpLOaY5VlChe3KtlFR/vdyQiIqm38NeF1Bpdi4tyXMSX7b70NalatAj69PGGAe+917cwJJ1TYhUliheH48e9taxERKLB5z9+Tr1x9Siapyhftf+KInmK+BbLrl3ekjXFi8OQIZpXJeGjxCpK/O9/3rOGA0UkGkzaMIlGExtRKl8pFrVbRP5z8/sWS3w83HMP7N0LH3+seVUSXkqsooRKLohItBi5ZiQtPmnB9QWvZ0GbBeTNmdfXeF59FebOhf79oWxZX0ORDECJVZQoUABy5lRiJSKR7b0V79H+0/bccvktzGk9h/Ozn+9rPF9+Cc8+Cy1bQqdOvoYiGYQSqyhh5q24rsRKRCLVq0tepdusbjQo0YDpLaeTK6u/a8Ts3u0lVFdcAe+/r3lVkjaUWEURlVwQkUjknOOZBc/Qa34vWpZpyeQ7J5M9S3ZfY0qYV/X33968qnPP9TUcyUCUWEWRhJILsbF+RyIi4nHO0WNOD1766iU6XtORMY3HcE7mc/wOi379YM4ceOcdKF/e72gkI1FiFUWKF/eSqt9+8zsSERGIi4+j04xO9F/en+7Xd2foHUPJnCmz32Hx1VfwzDNw111w331+RyMZjRKrKKKSCyISKU7GnaTVlFYMWz2MZ6s+y1u3voVFwCSmP/+E5s2hWDH44APNq5K0p8QqipQILK21aZO/cYhIxnYs9hhNP27KxA0T6VerH8/XeD4ikqq4OK8I6N69MHkynHee3xFJRqS1AqNI3rxw4YVKrETEP4dOHKLRhEbM/3U+79V7j/uvu9/vkP7Rpw8sWAAjRqhelfhHiVUUMYOrroKNG/2OREQyon3H9lF/XH2+2fYNIxuOpG35tn6H9I9Zs+DFF701ANu18zsaycjCOhRoZreZ2WYzizGzJ5P53MxsQODzdWZW4XRtzeyFwL5rzGyumRVIcszLzOyQmT2aaNuiwLHWBB4XB7ZnM7OJgXMsN7OiYbkQIVSypHqsRCTt/XHoD6qPrM7K7SuZ2GxiRCVVv/8OrVt7vVQDB/odjWR0YUuszCwz8B5QFygFtDSzUkl2qwsUDzw6A4NT0fZ151xZ51x54DPguSTHfBuYlUxIrZxz5QOPXYFtHYC9zrkrA+1eO9Pvm1auuspbTHTPHr8jEZGMYsu+Ldw84mZ+2vMTn939Gc1KNfM7pH+cOAF33undMT15MuTI4XdEktGFs8eqEhDjnPvFOXcCmAA0TLJPQ2C083wD5DGz/Cm1dc4dSNQ+F+AS3phZI+AXYEMqY2wIjAq8ngzcYpEwAzMFJUt6z+q1EpG08MPuH6gyvAp/H/mbeffMo84VdfwO6T8efRRWrPDmVSWsqSrip3AmVgWBrYnebwtsS80+KbY1s5fMbCvQikCPlZnlAp4A+p4inhGBYcBnEyVP/5zHORcL7AcuSu0X9MNVV3nPmmclIuG2YvsKbh5xM/EunsXtFlO5cGW/Q/qPiRPh3XehRw9o0sTvaEQ84Uyskuv5cancJ8W2zrmnnXOFgbFAt8DmvsDbzrlDybRt5Zy7Grg58LgniBgxs85mtsrMVsX6XPa8SBHIlk2JlYiE1/xf5lNzVE3yZM/D0nuXcvUlV/sd0n9s3gwdO8KNN8JrET+JQzKScCZW24DCid4XAnakcp/UtAUYBzQNvL4e6GdmW4DuwFNm1g3AObc98Hww0KZS0vObWRbgfOD/zV5yzg11zlV0zlXMksXfGykzZ/bqWWkoUETCZerGqdQbV4/LL7icJe2XUOyCYn6H9B+HD0PTppA9u9drdY7/K+iI/COcidVKoLiZXW5mWYEWwPQk+0wH2gTuDrwB2O+c25lSWzNLPIreANgE4Jy72TlX1DlXFHgHeNk5N9DMsphZ3kDbc4DbgfWJzp9wa0szYIFz7v/1WEUalVwQkXAZsXoEzSY1o0L+Cixut5j85+b3O6T/cA66doUffoCxY6FQIb8jEvmvsHW/OOdiAz1Gc4DMwHDn3AYz6xL4fAgwE6gHxABHgPYptQ0c+lUzKwHEA78BXU4TSjZgTiCpygzMAz4IfDYMGGNmMXg9VS3O/puHX8mS3mrtx455v7GJiITCW8ve4pG5j1DnijpMuWsKubLm8juk/+fDD2HMGK8YaJ3ImkcvAoBFQQdNRMmVK5c7fPiwrzFMnAgtWsDataouLCJnzznHswuf5aWvXuLOUncypvEYsmXJ5ndY/8/q1VC5MlSt6hUEzez/es8Zgpkdcc5FXpYdobRWYBRSyQURCZV4F88DMx/gpa9eolOFToxvOj4ik6p9+6BZM29pr7FjlVRJ5NKSNlHof//zlrfRPCsRORsn4k7QdlpbJqyfwBNVnuCVW16JiMWUk3IO2rf3KqwvXgz58vkdkcipKbGKQjlyQNGiSqxE5MwdOXmEZh83Y1bMLF6r9RqPV3nc75BO6Y03YNo0ePttr7yCSCRTYhWlSpXy7ooREQnWvmP7uGP8HSz9fSlDbx9Kp2s7+R3SKS1YAE8+6Q0DPvyw39GInJ7mWEWpMmW8OVYnT/odiYhEkx0Hd1B1RFWWb1vOxGYTIzqp2rbNu1Hnf/+D4cO9KRAikU6JVZQqU8ZLqn76ye9IRCRa/Pj3j9w47EZ+3fcrM1vN5M7Sd/od0ikdP+71Uh09ClOnwrnn+h2RSOoosYpSZcp4z+vXp7yfiAjAyu0rqTK8CkdOHmFR20XUKlbL75BS1KMHLF8OI0f+eye0SDRQYhWlSpaETJmUWInI6c39eS41RtXg3KznsvTepVxb4Fq/Q0rRqFEweDA89pi3dI1INFFiFaWyZ4fixZVYiUjKxn0/jvrj6nPlhVfydYevKX5R8dM38tGaNdClC9SoAS+/7Hc0IsFTYhXFypRRYiUip/bON+/QakorbrrsJha3W8yluS/1O6QU7d0LTZrARRfBhAng85r3EgQzu83MNptZjJk9mcznZmYDAp+vM7MKp2trZi8E9l1jZnPNrEBg+0VmttDMDpnZwCTnaR5os8HM+iXa3s7MdgeOtcbMOobnSiiximpXXw0xMd7kThGRBM45npz3JD3m9KDpVU2Z1WoW52c/3++wUhQfD61be3cCTp4MF1/sd0SSWmaWGXgPqAuUAlqaWakku9UFigcenYHBqWj7unOurHOuPPAZ8Fxg+zHgWeDRJHFcBLwO3OKcKw1cYma3JNplonOufODx4dl/8+SdMrGyvvZ4otd3JvlMHbQRoEwZryKx6lmJSILY+FjunX4vry19jS7XdmFis4lkzxL5q7W/8ALMnAn9+8MNN/gdjQSpEhDjnPvFOXcCmAA0TLJPQ2C083wD5DGz/Cm1dc4dSNQ+F+AC2w8755bgJViJFQN+dM7tDryfB6T5LL2UeqxaJHrdK8lnt4UhFgmS7gwUkcSOnDxC44mNGblmJH2q9WFQ/UFkzhT5i+rNnAl9+0Lbtt78Kok4WcxsVaJH5ySfFwS2Jnq/LbAtNfuk2NbMXjKzrUAr/u2xOpUYoKSZFTWzLEAjoHCiz5sGhgknm1nhZI8QAiklVnaK18m9Fx9ccQVky6bESkRgz9E91B5Tm89//JzB9QfTu3rviFz3L6lffoFWraBcOe9OwCgIOSOKdc5VTPQYmuTz5P7UXCr3SbGtc+5p51xhYCzQLaUgnXN7ga7AROArYAsQG/h4BlDUOVcWrydrVNL2Zpx3qmObcVlK504spcTKneJ1cu/FB1mywFVXKbESyei2HdjGzSNuZtWOVUy6cxJdKkZHt8+RI/+WU/jkE28dVIlK2/hvz1AhYEcq90lNW4BxpGJYzzk3wzl3vXOuMrAZ+Cmw/W/n3PHAbh8AydUcWZTwwoz5ST6bdrpzJ0jpnoty1tcO4GWTOQKvCbyP/AH7DKJMGVi0yO8oRMQvG3dv5NaPbmX/8f3MaT2H6kWr+x1SqjgHXbvC2rXw2WdQrJjfEclZWAkUN7PLge14U4nuTrLPdKCbmU0Argf2O+d2mtnuU7U1s+LOuYT1RRoAm04XiJld7JzbZWYXAPcDdwW253fO7Ux0rI3JNU/0+sIUPkvRKRMr19tF/sC8UKYMfPQR7NsHefL4HY2IpKWvt37NHePvIGvmrCxut5jyl5b3O6RUe/99GD0a+vSBevX8jkbOhnMu1sy6AXOAzMBw59wGM+sS+HwIMBOohzcP6gjQPqW2gUO/amYlgHjgN+Cfrlgz2wKcB2Q1s0ZAHefcD0B/MysX2O1559yPgdcPmVkDvKHBPUC75L7KKV4n9/6UzLnk97W+lhM46Xq7k4H3JfAuyhbX201N7QnSm1y5crnDhw/7HcY/Zs6E+vXhyy/h5pv9jkZE0srUjVO5e8rdFD6vMLNbz6bYBdHT5bNsGVSrBrVrw4wZ3ioSErnM7IhzLpffcYSbGduAt/B6p3oEXhN43905UjXhPaW/zrOBogDW164EluHdytjN+tqrZxa2hFr58t7z2rW+hiEiaWjgioE0/bgp5S8tz9cdvo6qpGrHDm9eVeHCMGaMkiqJKB8A5wK5E71OeJ/qulcpzbG6wPX+Z2yzLTDe9XYPWl/LCnwL/L/KqpL28ueHfPm8ZSBEJH2Ld/H0mteLfl/3o2GJhoxrOo6c5+T0O6xUO34cmjWDAwdgzhy4MOksFhEfOUffUBwntXcF1gS+AHC93Qm88U6JAGZer5USK5H07Xjsce6Zeg/9vu5H14pd+eSuT6IqqQJ48EFvGHDkSG/lCJFIYkYnM4oHXpsZw83Yb8Y6M65J7XFS6rFaZ33tDbxZ+lcCcwGsr+U5i7glDMqXhwED4ORJOOccv6MRkVDbf2w/jSc2ZuGWhbx6y6s8XuXxqKhRldj778MHH0CvXl6vlUgEehgYGXjdEiiHNwXqGmAAkKqZzCn1WHUC/sKbZ1XH9XZHAttLAW8EHa6ETblyXhf75s1+RyIioZZQo2rJ70sY03gMT9z0RNQlVUuXer1Vdet6S9eIRKhY5zgZeH07MNo5/naOeXhL6qTKKe8KlORF2l2BABs2eGUXxozxFjEVkfRh/a711B1bl/3H9jO1+VRuKXbL6RtFmO3boWJFyJ0bVqyACy7wOyIJVga6K/A7oD6wF6+8Q03n2BD4bKNzXJWa45xyKND62rqUGrrermzqw5VwKlHCW9pmzRolViLpxcJfF9J4YmNyZc3FV+2/otyl5U7fKMIcP+7dAXjoEMybp6RKIt5zwCq8elrTEyVV1YBfUnuQlOZYxeNNYB+Ht8bO0TMOVcIqSxZvIqgmsIukDxPWT6DttLZceeGVzGo1i8vOT/UyZRHDOXjgAVi+3FuupnRpvyMSSZlzfGZGEeBc59ib6KNVQPPUHielyuvlra+VxJvANQ74IfA81/V2sadqJ/4oXx6mTvV+mEXZ9AsRCXDO8eayN3nsi8eoWqQq05pP44Ic0dnNM2QIDBsGTz8NTZr4HY3I6ZnRJNHr5ExJ1XFSO8fK+lpz4D3gNdfbvZ6qRulQJM6xAnjvPejWDbZuhUKF/I5GRIIVFx9Hzzk9GbBiAHeVvotRjUaRPUt0Lsv61VdQsybceit8+ilk1gJpUS0DzbGKB9YEHvDf9QGdc9ybmuOkNBSI9bWCeAsiNsabzNUDyLDL2USyhArsa9YosRKJNkdOHuGeqfcwZeMUet7Qk9frvE4mi86S5Nu2eeUULr/cW8dUSZVEkaZ4Q35lgU+B8c4RE+xBTvkv1/raYry5VefgLVbYFvgcyGp9TfVyI0zZwK0EmmclEl3+PPQnNUbVYOrGqbxz6zu8eeubUZtUHTvmDfsdOQLTpmlheIkuzjHVOVoA1YCfgTfNWBKYvJ5qKfVYFcGbvH4f0DnRdgtsj57FqTKAc8+FK6+E777zOxIRSa0fdv9A/XH12XV4F1ObT6VhyYZ+h3TGnIP774eVK735nqVK+R2RyBk7BuwHDgCXAUGNyac0eb3oWYUlaa5iRa8Qn4hEvgW/LqDJxCZkz5Kdxe0WU7FARb9DOivvvQcjRsBzz0GjRn5HIxI8M2rg3bBXCZgH9HeOVUEfRwVCgxOpk9cB3noLHnkE/vgDLrnE72hE5FRGrRlFxxkdKXFRCT6/+3OK5Cnid0hnZf58b6J6vXreEGCm6BzJlFPIYJPX1wFL8Ebm/pMgOcdDqTlOipPXJbpUDPzCu2oV1K/vbywi8v855+i9qDcvfPkCtYrVYvKdkzk/+/l+h3VWYmLgzjuhZEkYO1ZJlUS19qE4iBKrdKRCBa/2hhIrkchzPPY4HaZ3YOz3Y+lwTQcG1x/MOZmje9X0/fuhQQMvmZo+3ZvrKRKtnGNUKI6TqsTK+lpm4JLE+7ve7vdQBCChkzs3XHWVN3lURCLHnqN7aDyxMV/+9iUv13yZJ296MuoWUk4qLg5atYKffoIvvoBiup1JBEhFYmV97UGgN/An3jI34I07aq3ACFSxIsyZowrsIpEiZk8M9cfVZ8u+LYxvOp4WZVr4HVJIPPUUfP45DBoE1av7HY1I5EhNj9XDQAnX2/0d7mDk7F13HYwe7RXpK1zY72hEMravt35NwwkNcc4xv818brrsJr9DCokxY6BfP+ja1XuIpCdmVHGOpafbdiqpmWa4Fa+eg0SBxBPYRcQ/H2/4mJqjapInex6WdViWbpKq5cuhUyeoUQP69/c7GpGweDeV25KVmh6rX4BF1tc+B44nbHS93VupPYmknXLlIEsWL7Fq3NjvaEQyHuccry19jV7ze1GlcBWmtZhG3px5/Q4rJLZv936uFCgAkybBOdE9917kP8yoDNwI5DOjZ6KPzgNSvThTahKr3wOPrIGHRLAcOaBMGU1gF/HD8djjdP6sM6PXjqZlmZYMbzg8ahdSTuroUa/w58GD3mT1iy7yOyKRkMsK5MbLjRLf43oAaJbag6hAaJAiuUBogk6d4JNP4O+/NYFdJK3sPrybJh83YcnvS3i++vM8U/WZqL/zL4FzcPfdMHEifPop3HGH3xFJWsooBUITmFHEOX4LvM4E5HaOA6ltf8oeK+tr77jerrv1tRkkqT4K4Hq7BmcSsITfddfBhx/CL7/AFVf4HY1I+rdh1wbuGH8HOw/tZGKzidxV+i6/QwqpV1+FCRPglVeUVEmG8IoZXYA44FvgfDPeco7XU9M4pcnrYwLPbwBvJvM4LTO7zcw2m1mMmT2ZzOdmZgMCn68zswqna2tmLwT2XWNmc82sQJJjXmZmh8zs0WTON93M1id6387MdgeOtcbMOqbme0U6TWAXSTuzY2Zz4/AbORp7lMXtFqe7pGr6dHj6aa/H6okn/I5GJE2UCvRQNQJm4i3EfE9qG4dtKNDMMgM/ArWBbcBKoKVz7odE+9QDHgTqAdcD/Z1z16fU1szOc84dCLR/CCjlnOuS6Jif4NXbWu6ceyPR9iZ4Y6RlnXNlAtvaARWdc91S+72iYSjwxAk4/3zvNui3dIuBSNgMXDGQh2c/zNUXX82MljMofH76qnGyfj1UruwVHl682JvDKRlPBhwK3ACUB8YBA51jsRlrnaNcatqnpkBoceAVoBTwzyxM19udrs5uJSDGOfeLF6hNABoCPyTapyEw2nnZ3TdmlsfM8gNFT9U2IakKyEWiYUoza4R3F+N/Mh8zyw30BDoDH5/uO0e7rFnh2mth2TK/IxFJn07GnaT77O4MWjWIhiUa8lGTj8idNbffYYXUrl1w++3eMjVTpyqpkgzlfWALsBb40owikPo5VqmpYzUCGAzEAjWA0fw7TJiSgng1sBJsC2xLzT4ptjWzl8xsK9AKeC6wLRfwBNA3mVhewBu+PJLMZ00DQ4uTzSzd/Lp5443w3Xdw7JjfkYikL/uO7aP+uPoMWjWIx298nCnNp6S7pOrYMe8OwF27YMYMKJj0J7dIOuYcA5yjoHPUcw4XmMheI7XtU5NY5XC93XzAXG/3m+vt+gA1U9Euudthko47nmqfFNs65552zhUGxgIJw3h9gbedc4f+cwKz8sCVzrmpyRxzBlDUOVcWmAfJL8BoZp3NbJWZrYqNjU1ul4hTubI3JPjdd35HIpJ+xOyJofKwyizasojhDYbzWu3XyGSp+TEaPZyDe+/1erw/+sjr/RbJSMy4xIxhZswKvC8FtE1t+9TUsTpmfS0T8JP1tW7AduDiVLTbBiTuASoE7EjlPllT0Ra88c/P8dYyvB5oZmb9gDxAvJkdw5vVf62ZbcH7vheb2SLnXHXn/rNMzwfAa8l9EefcUGAoeHOsTvF9I0rlyt7z1197vVcicnYWb1lMk4+bADCvzTyqFqnqc0Th8fzzMH68dwdgkyZ+RyPii5F4o3VPB97/CEwEhqWmcWp+1eoO5AQeAq4FWpO6zG0lUNzMLjezrEALYHqSfaYDbQJ3B94A7HfO7UyprZkVT9S+AbAJwDl3s3OuqHOuKPAO8LJzbqBzbrBzrkBg+03Aj8656oFj5U9yrI2p+F5R4dJLvdXmNc9K5OyNWD2C2mNqc3Gui1nRcUW6TarGj4c+faBdO90BKBmP2T+dTXmd42O8G+Fwjli8TppUSbHHyvpaZuAu19s9BhwC2qf2wM65WDPrBszBKwU/3Dm3wcy6BD4fgncbYz0gBm/+U/uU2gYO/aqZlcD7wr8BXThzD5lZA7z5Y3uAdmdxrIhTuTLMn+917aeTOoUiaSo2PpYnvniCt755i9rFavPxnR+TJ3sev8MKi2XLoH17qFoV3n9fPzMkQ1oBVAAOm3ERgSlIZtxAEGsmn7LcgvW1LK63i7W+tgC4xfVWiXaIjnILCQYNggcegF9/haJF/Y5GJLrsPbqXFp+0YO7Pc3mw0oO8detbZMmUmtkT0WfLFqhUCc47z1tkWcvVSGIZpdyCGaud4xozKuAtulwGWA/kA5o5x7rUHCelnxIJmdtq4FPra5NIVMbA9XZTzjR4SRuJ51kpsRJJvY27N9JwQkO27NvCh3d8SIcKHfwOKWwOHPDKKpw8CZ9/rqRKMrTEiy9PxRtVM+A4UAvOPrFKcCHwN96dgAl37DlAiVWEu/pqyJXL6+K/+26/oxGJDjN/mknLT1qSPUt2FrZdSJXLqvgdUtjExkLz5rB5M8yeDSVK+B2RiK8y4y3CnHQgPGcwB0kpsbrY+lpPvG6wpCUQNCwYBbJk8br3v/7a70hEIp9zjn5L+9Frfi/KX1qeaS2mcdn5l/kdVlj17OklVEOHwi23+B2NiO92OsfzZ3uQlO4KTMjccgPnJnqd8JAocOONsHYtRMm0MBFfHD15lNZTW/Pk/Ce5q/RdLLl3SbpPqt57D959Fx55BDp18jsakYgQkls2Uuqx2ul6u7PO3MRflStDXBysWAE1Ul03ViTj2HZgG40nNubbHd/yUs2X6HVTLyyd3xI3ezY89BA0aACvJVu9TyRDCkm/bUqJVfr+yZJB3Hijd9v0kiVKrESS+mbbNzSe2JhDJw4xrcU0GpRo4HdIYff99968qquvhrFjIXNmvyMSiQzOsScUx0lpKFAj7unABRdA2bLeyvQi8q+Ra0ZSbWQ1cp6Tk286fJMhkqodO6B+fcid21sDMLcmdYiE3CkTK9fbhSRzE/9Vq+ZNYD9xwu9IRPwXGx9Lzzk9af9pe2667CZWdFxB6YtL+x1W2B086CVVe/d6ZRUKp5sl50UiS/paPVSSVa0aHD0Kq1b5HYmIv/4+8jf1xtbj7W/e5qFKDzGn9Rwuypn+CzfFxsJdd3nDgJMmQfnyfkckkn4pscoAqgaWNdNwoGRka/5YQ8UPKrL4t8V8eMeH9K/bP91WUk/MOW8FhtmzYfBguO02vyMSSd+UWGUAefNC6dJKrCTjGvf9OG4cdiMn407yZbsv03Ul9aT69fPqVPXqpbIKImlBiVUGUa0aLF3qDQmIZBQJ86laTWlFxQIV+bbzt1xf6Hq/w0ozEybAk09Cixbw4ot+RyOSMSixyiCqVYNDh+C77/yORCRt7Dq8i9pjav8zn2p+m/lckvsSv8NKM199BW3bws03w8iRkEk/7UXSRPqfYCDAf+dZVarkbywi4bZqxyqaTGzC7iO7Gd1oNPeUu8fvkNLU5s3QsCFcfjlMmwbZsvkdkUjGod9hMohLL/UWWNU8K0nvRq4ZyU3Db8LMWHrv0gyXVO3aBXXremuFzpwJF17od0QiGYsSqwykalVveCAuzu9IRELvRNwJHvj8gX/qU33b+Vsq5K/gd1hp6sgRuOMO+OMPrwBosWJ+RySS8SixykCqV4cDB2D1ar8jEQmtPw79Qc1RNRm0ahCPVn6U2a1nkzdnXr/DSlNxcdC6NaxcCePGwfUZZ46+SETRHKsMpGZN73nePKhY0d9YRELlm23f0PTjpuw7to/xTcfTokwLv0NKc85B9+4wdSq8/TY0auR3RCIZl3qsMpBLL/UWXv3iC78jETl7zjneW/EeVUdUJXuW7CzrsCxDJlUAr70GAwdCz55egiUi/lFilcHUrg1LlnhzMUSi1aETh2g1pRXdZnWjzhV1WNlpJWUvKet3WL4YNcor/tmyJbz+ut/RiIgSqwymdm1vMeavvvI7EpEzs+mvTVz/4fVM3DCRl2q+xPSW07kwR8a89W3WLOjQAW65RbWqxF9mdpuZbTazGDN7MpnPzcwGBD5fZ2YVTtfWzF4I7LvGzOaaWYHA9ovMbKGZHTKzgUnO0zzQZoOZ9Uu0PZuZTQycY7mZFQ3LhUCJVYZTtSpkzarhQIlOkzZM4roPrmPX4V3MaT2Hp25+ikyWMX+MrVgBzZp5w/tTpnj/rkX8YGaZgfeAukApoKWZlUqyW12geODRGRiciravO+fKOufKA58BzwW2HwOeBR5NEsdFwOvALc650sAlZnZL4OMOwF7n3JXA28BrIfjqycqYP5EysJw5oUoVJVYSXU7GnaTH7B7cNfkurr74albft5paxWr5HZZvfvoJ6teHSy7xeq3OO8/viCSDqwTEOOd+cc6dACYADZPs0xAY7TzfAHnMLH9KbZ1zBxK1zwW4wPbDzrkleAlWYsWAH51zuwPv5wFNE51/VOD1ZOAWM7Oz+tanoMQqA6pdG9atgz//9DsSkdPbfmA71UdV553l7/BQpYdY1G4Rhc4r5HdYvvnjD7j1Vu/17NneTSkiPisIbE30fltgW2r2SbGtmb1kZluBVvzbY3UqMUBJMytqZlmARkDhpOd3zsUC+4GLTvfFzoQSqwyodm3ved48f+MQOZ0Fvy7gmvevYe0fa5nQdAL96/Yna+aMO+Z18KDXU/Xnn/DZZ/C///kdkWQQWcxsVaJH5ySfJ9fz41K5T4ptnXNPO+cKA2OBbikF6ZzbC3QFJgJfAVuA2CBiDAklVhnQNdd4y1xoOFAiVbyL55WvXqH2mNrkzZmXlZ1W0rxMc7/D8tWJE9C0KaxdC5MmqQCopKlY51zFRI+hST7fxr89QwCFgB2p3Cc1bQHG8e+w3ik552Y45653zlUGNgM/JT1/oDfrfGDP6Y53JpRYZUCZM3vFQr/4wissKBJJ9h7dS8MJDXlqwVPcVfouVnRawVX5rvI7LF/Fx8O993r/Zj/8EOrV8zsikf9YCRQ3s8vNLCvQApieZJ/pQJvA3YE3APudcztTamtmxRO1bwBsOl0gZnZx4PkC4H7gw0Tnbxt43QxY4Fx4/gdU5fUM6tZbYfJkWL/eu6tIJBKs3L6S5pObs+3ANgbcNoBulboRpvmlUeWJJ2DsWHjpJWjXzu9oRP7LORdrZt2AOUBmYLhzboOZdQl8PgSYCdTDmwd1BGifUtvAoV81sxJAPPAb0CXhnGa2BTgPyGpmjYA6zrkfgP5mVi6w2/POuR8Dr4cBY8wsBq+nKmzVhC1MCVu6lStXLnf48GG/wzhrO3ZAwYLw8stecUERPznnGLB8AI998Rj5z83PxGYTuaHQDX6HFRFefx0efxweeADefReUZ0paM7MjzrlcfscRLZRYBSm9JFYAFSp45ReWLPE7EsnI9h7dy73T72Xapmk0KNGAEQ1HZNiCn0kNGwYdO0Lz5l6PVebMfkckGZESq+BojlUGdvvtsGwZ/P2335FIRrVi+wquef8aPvvxM96q8xbTmk9TUhUwZQp07gy33QajRyupEokWSqwysPr1vUmxc+b4HYlkNM453l72NlWGVwFgSfsl9KjcQ/OpAubN89b+u+EGby6kqqqLRA8lVhnYdddBvnzw+ed+RyIZyZ6je2g0sRE95/akfvH6rL5vNdcXUu2ABCtWQKNGUKKEV6sqlwZgRKKK7grMwDJlgrp1YcYMiI2FLPrbIGH2zbZvaD65OTsP7uSdW9/hoesfUi9VIj/84P2bvOQSryf5ggv8jkhEgqUeqwzu9tth71745hu/I5H0zDnHm1+/yc0jbiaTZWLpvUt5+IaHlVQl8ttvUKfOv4uk58/vd0QiciaUWGVwdep4PVUaDpRw+fvI3zSc0JBHv3iUO/53B6vvW811Ba/zO6yI8uef3lJThw/D3LlQrJjfEYnImVJilcGdfz7cdJM3l0Mk1BZvWUy5IeWYHTOb/rf155O7PiFP9jx+hxVR9u/37vzbvt37BUcFe0WimxIroWFDrwL7Tz+dfl+R1IiNj+W5hc9RY1QNcp6Tk2Udlmk+VTKOHoUGDWDDBq+8wo03+h2RiJwtJVZC48be89Sp/sYh6cOWfVuoNrIaL3z5Am3Lt+W7+77j2gLX+h1WxDl+3FtU+auvYMwYb5kpEYl+qrwepPRUeT2x667zChBqErucjYnrJ3LfZ/cR7+IZcvsQ7r76br9DikixsV419SlT4IMPvOrqIpFKldeDox4rAaBJE1i+HLZt8zsSiUaHTxymw6cdaPFJC0rmLcmaLmuUVJ1CXJy3kPKUKdC/v5IqkfRGiZUAXmIFGg6U4K3euZprh17LiDUjeOqmp/iq/VcUu0C3tSXHOeja1Vv37+WX4aGH/I5IREJNiZUAXpXnUqW836JFUsM5xzvfvMMNw27g4ImDzGszj5dueYlzMp/jd2gRyTno3t0b+nv6aejVy++IRCQcwppYmdltZrbZzGLM7MlkPjczGxD4fJ2ZVThdWzN7IbDvGjOba2YFkhzzMjM7ZGaPJnO+6Wa2PtH7bGY2MXCO5WZWNGRfPgo1aQJffgm7d/sdiUS6XYd3UX9cfXrM6cFtV97G2i5rqXl5Tb/DimhPPw0DBkCPHvDCC35HIyLhErbEyswyA+8BdYFSQEszK5Vkt7pA8cCjMzA4FW1fd86Vdc6VBz4DnktyzLeBWcnE0wQ4lGRzB2Cvc+7KQLvXgv+m6UeTJt6izNOn+x2JRLKZP82k7OCyLPh1AQPrDmRa82nkzZnX77Ai2ksvwSuvwH33wZtvgqpOiKRf4eyxqgTEOOd+cc6dACYADZPs0xAY7TzfAHnMLH9KbZ1zBxK1zwX8c1ujmTUCfgE2JD6JmeUGegIvJnP+UYHXk4FbLAMX2ilfHooWhcmT/Y5EItGRk0e4//P7qT+uPvly5WNlp5U8UOkB1aY6jXfegWeegXvugUGDlFSJpHfhTKwKAlsTvd8W2JaafVJsa2YvmdlWoBWBHiszywU8AfRNJpYXgDeBI6c6v3MuFtgPXHT6r5Y+mXm3gH/xhYYD5b9W7VjFNe9fw+BVg+l5Q09WdlrJ1ZeoRPjpDB3qDf01awbDh3sLn4tI+hbOf+bJ/V6WtGjWqfZJsa1z7mnnXGFgLNAtsLkv8LZz7j/DfWZWHrjSOZfc/W6piREz62xmq8xsVWxsbDJN0o+WLb3bwdVrJeBVUH/xyxepPKwyR04eYX6b+bx565tkz5Ld79Ai3qhR0KUL1K/v3QWYJYvfEYlIWgjnP/VtQOFE7wsBO1K5T9ZUtAUYB3wO9AauB5qZWT8gDxBvZseAOOBaM9uC930vNrNFzrnqic6/zcyyAOcDe5KexDk3FBgKXoHQ03zvqFa2rHd34Lhx3m3hknH9vOdn7pl6D8u2LaNlmZa8V+89Lshxgd9hRYUxY6B9e7jlFpg0CbJm9TsiEUkr4eyxWgkUN7PLzSwr0AJIOi16OtAmcHfgDcB+59zOlNqaWfFE7RsAmwCcczc754o654oC7wAvO+cGOucGO+cKBLbfBPwYSKoSzt828LoZsMBl8FL0Zl6v1ZIl8PvvfkcjfnDOMXz1cMq/X54fdv/A2CZjGdd0nJKqVProI2jbFmrWhE8/hRw5/I5IRNJS2BKrwJylbsAcYCPwsXNug5l1MbMugd1m4k02jwE+AO5PqW2gzatmtt7M1gF1gIfPIsxhwEVmFoM3uf3/lYTIiFq29J4nTvQ3Dkl7fx35iyYfN6HD9A5ULFCRdV3XqYJ6EMaN85Kq6tW9u2tz5vQ7IhFJa1orMEjpda3ApK6/Hk6cgNWr/Y5E0sqsn2Zx7/R72XN0Dy/XfJkelXuQyTTbOrUmTIBWraBqVfjsM8illdUkndBagcHRT01J1t13w5o1sHGj35FIuB08fpCun3Wl3rh65M2Zl5WdVvLIjY8oqQrCxx97SdVNNympEsno9JNTknXXXd6t4ePG+R2JhNPiLYspN6Qc73/7Po9UfoSVnVZS9pKyfocVVSZP9n4RqVIFPv9cSZVIRqfESpKVPz/UqgWjR3vV2CV9OXLyCN1nd6f6qOpkskx82f5L3qjzhsooBOmTT6BFC6hcGWbOhNy5/Y5IRPymxEpOqX17787ABQv8jkRCadnWZZQfUp7+y/vT7bpurO2ylpsuu8nvsKLO1KleUnX99UqqRORfSqzklBo1gjx5YMQIvyORUDgWe4wnvniCm0bcxIm4Eyxos4B3671LrqwauwrWpEnecHnFijBrFpx7rt8RiUikUC1gOaXs2b25I8OHw759XpIl0enbHd/SZlobftj9A50qdOLNOm9ybjZlA2di7Fho0wZuvNGbU3XeeX5HJCKRRD1WkqL27eHYMe9Wcok+J+JO0Hthb67/8Hr2H9vPrFazGHrHUCVVZ2jECG8x5WrVvJ4qJVUikpTqWAUpo9SxSuAclCvnVY9evtzvaCQYa/9YS7tP27HmjzW0KdeG/rf1J0/2PH6HFbXef99b+69OHW9+lYp/SkahOlbBUY+VpMjM67VasQLWr/c7GkmN47HHeWbBM1T8oCI7D+7k0xafMqrRKCVVZ2HAgH8XVP70UyVVInJqSqzktFq3hnPOgQ8/9DsSOZ1lW5dxzfvX8NJXL9G6bGt+eOAHGpRo4HdYUe311+Hhh6FxY5gyxZt7KCJyKkqs5LTy5YNmzWDkSMhAo6BR5fCJw3Sf3Z0qw6tw+ORhZreazYiGI7gwx4V+hxbVXnwRHn8cmjf31s7MmtXviEQk0imxklS5/37Yvx/Gj/c7Eklq/i/zuXrw1fRf3p/7r7uf9V3Xc+uVt/odVlRzDp591nvccw989JHXaysicjqavB6kjDZ5PUHCJPbMmeG777y5V+Kvfcf28djcx/hw9YcUv7A4wxoM4+YiN/sdVtSLj4eePaF/f7j3Xhg61Pt7L5JRafJ6cNRjJali5vVarVkD33zjdzQyffN0Sg8qzfA1w3n8xsdZ22WtkqoQiI2FDh28pKp7d/jgAyVVIhIcJVaSaq1aeRWmBw3yO5KMa9fhXbT8pCUNJzTkohwXsbzjcl6r/Ro5zsnhd2hR7/hxr5r6yJHQty+89Za3ELmISDD0Y0NS7dxzoW1b+Phj2L3b72gylngXz7DvhlFyYEk++eET+lbvy6rOq6hYoKLfoaULhw7B7bd79an694fnntNwt4icGSVWEpT774cTJ2DIEL8jyTg27t5I9ZHV6TijI2UuLsPaLmt5rtpzZM2sW9RCYc8eqF3bW2x81Ch46CG/IxKRaKbJ60HKqJPXE6tXD779Fn77TTV9wulY7DFe+eoVXlnyCrmz5ub12q/T/pr2ZDL9PhQqf/zhVVLfvNlbtqlxY78jEok8mrweHP2ElqA9+ijs2uXdgi7hsfDXhZQbUo7nv3yeu0rfxaZum+hQoYOSqhDasgVuugl++cVbTFlJlYiEgnqsgqQeK6/0QoUK3uLMGzZogm8o/XXkLx6d+yij1o6i2AXFGFx/MHWuqON3WOnOunVw223e3+GZM+GGG/yOSCRyqccqOPovUYJmBo88Aps2waxZfkeTPjjnGLVmFCUHlmTs92PpdVMv1nddr6QqDBYtgptv9n4h+PJLJVUiElrqsQqSeqw8J0/C5ZfD//7nTfqVM7dx90YemPkAC7cspHKhyrx/+/tcfcnVfoeVLk2a5K19eeWVMHs2FC7sd0QikU89VsFRj5WckXPO8RamXbgQVqzwO5rodOjEIZ744gnKDinL6j9WM7j+YJbcu0RJVZi8+6635l+lSvDVV0qqRCQ81GMVJPVY/evgQShaFG68EWbM8Dua6OGc45ONn9BjTg+2HdjGveXv5dVar5IvVz6/Q0uXnIOnnoJXX/UmqI8dCzlUT1Uk1dRjFRz1WMkZO/dc6NEDPvsMVq/2O5rosPmvzdz60a3cOelOLspxEUvvXcqwhsOUVIXJyZPQrp2XVHXp4g0FKqkSkXBSj1WQ1GP1X/v3Q5EiULMmTJnidzSR6/CJw7z81cu8/vXr5DgnBy/WeJGu13UlS6YsfoeWbh06BHfe6c2leuEFePppVVMXORPqsQqOfqrLWTn/fG+u1fPPe7ewly3rd0SRxTnHtE3T6D6nO7/v/5025drQr1Y/Lsl9id+hpWs7dnhL1Kxb5y2k3LGj3xGJSEahHqsgqcfq/9uzx5trddtt3jqC4vnp7594ePbDzIqZRZmLyzCo3iBuLnKz32Gle2vXeknVvn0wcaK3UoCInDn1WAVHc6zkrF14ITz4oDd/Zc0av6Px3/5j+3ls7mOUHlSaJb8v4a06b/Fd5++UVKWBmTO9auoAS5YoqRKRtKceqyCpxyp5+/ZBsWJescWZM/2Oxh9x8XGMXDOSpxY8xe7Du2lfvj0v3fISl+a+1O/QMoRBg7wEv1w574aKAgX8jkgkfVCPVXDUYyUhkScP9OrlVWJfvNjvaNLekt+XUOnDSnSc0ZErL7ySFZ1WMKzhMCVVaSAuzrs79YEHoH59r5q6kioR8Yt6rIKkHqtTO3oUihf3Ci9+/XXGuANr6/6tPDHvCcavH0+h8wrRr1Y/WpRpgWWELx8BDh+Gu++G6dO9myjefBMyZ/Y7KpH0RT1WwVGPlYRMjhzQpw988w18+qnf0YTXkZNH6LuoLyUGlmDqpqk8V/U5Nj2wiZZXt1RSlUa2bvXW/PvsM6+q+jvvKKkS8YuZ3WZmm80sxsyeTOZzM7MBgc/XmVmF07U1sxcC+64xs7lmViCw/SIzW2hmh8xsYJLztDSz7wPtZptZ3sD2dma2O3CsNWYWtnuF1WMVJPVYpSw2Fq6+2qt2/f333tI36Um8i2fi+ok8Of9Jft//O3eVvot+tfpRJE8Rv0PLUL7+2quifuwYjB+vSeoi4XS6Hiszywz8CNQGtgErgZbOuR8S7VMPeBCoB1wP9HfOXZ9SWzM7zzl3IND+IaCUc66LmeUCrgHKAGWcc90C+2QBdgT2+8vM+gFHnHN9zKwdUDFh33BSj5WEVJYs8MYbsHkzDBx4+v2jyZe/fckNH97A3VPu5sIcF7K43WImNpuopCqNDR8O1avDeed5vaNKqkR8VwmIcc794pw7AUwAGibZpyEw2nm+AfKYWf6U2iYkVQG5ABfYftg5twQ4luQcFnjkMm/o4Dy8RCtNKbGSkKtXz6tp1acP7NrldzRn78e/f6TxxMZUG1mNHQd3MLLhSFZ1WkXVIlX9Di1DiY2F7t2hQwcvsVqxAq66yu+oRDKELGa2KtGjc5LPCwJbE73fFtiWmn1SbGtmL5nZVqAV8FxKQTrnTgJdge8J9FwBwxLt0jQwRDjZzMK2DLsSKwk5M3j7bThyxFtGJFrtPrybB2c+SOlBpZn3yzxerPEiPz74I23LtyVzJk3mSUt79kDdutC/v5dczZwJF1zgd1QiGUasc65iosfQJJ8nN7E06TyjU+2TYlvn3NPOucLAWCDFYTwzOwcvsboGKACsA3oFPp4BFHXOlQXmAaNSOtbZUGIlYVGyJDz0EAwbBt9+63c0wTl68iivLnmVK9+9ksGrBtOpQidiHozh6apPk/OcnH6Hl+H88ANUquSVURg+3Evas2gxLpFIsg1I3ANUiP8/BHeqfVLTFmAc0PQ0cZQHcM797LwJ5B8DNwa2/e2cOx7Y7wPg2tMc64wpsZKwee45yJfPqy8UF+d3NKcX7+L5aN1HlBhYgl7ze1GtSDW+7/o9g+oP0tp+Ppk0Ca6/3ltQeeFCaN/e74hEJBkrgeJmdrmZZQVaANOT7DMdaBO4O/AGYL9zbmdKbc2seKL2DYBNp4ljO1DKzPIF3tcGNgaOlT/JsTYG+yVTS7/3Sdicfz689Ra0bg2DB0O3sN+LcWacc8z5eQ5PzX+K1X+spkL+CoxqNIoal9fwO7QM6+RJeOIJr3fqhhu8BKtQIb+jEpHkOOdizawbMAfIDAx3zm0wsy6Bz4cAM/HuCIwBjgDtU2obOPSrZlYCiAd+A7oknNPMtuBNTs9qZo2AOoE7CfsCX5rZyUCbdoEmD5lZAyAW2JNoe8ip3EKQVG4hOM55c2OWLvWGdAqHbbrgmVm2dRm95vdi8W+LKZqnKC/UeIG7r76bTKbOXL/88QfcdRd89ZWXjL/5JmTN6ndUIhmXCoQGR4lVkJRYBe/XX6F0aahVyyscGgn1M9fvWs/TC55m+ubpXJLrEp6p+gydr+1M1sz6H9xPS5bAnXfCgQMwdCi0auV3RCKixCo4+rVcwu7yy+H552HGDJg82d9Yft37K22mtqHs4LIs2rKIF2u8SMxDMXSr1E1JlY+c8yqn16gBuXN79amUVIlINAprYpWWJe4TfX5ZoMz9o4m2zTaztWa2wcyGBCq9pmmJ+4yue3eoWBG6doWdO9P+/H8e+pMHZz5IiYElmPTDJB698VF+eegXnq76NLmz5k77gOQf+/Z5Q389esDtt8OqVV71fhGRaBS2ocC0LnGf6Jif4E10W+6ceyOw7Tzn3IFAJdbJwCTn3IQzKXGvocAzt2kTXHMN1Kzpre+WFkOCe47u4a1lb/HON+9wLPYYHa7pwHPVnqPgeUlr14kfli+HFi1g2zZ46SV47LHIGCoWkX9pKDA44eyxStMS9wCBOwN+ATYk2idxmyxAVv5/4TJJAyVLQr9+XnHHDz4I77n2HdtH74W9ubz/5bz01Uvc/r/b2fjARt6/430lVREgPh5efx1uuskbBvzqK3j8cSVVIhL9wplYpWmJ+8CijE8AfZMLxszmALuAg3i9VgnSpMS9eB54wJvE3rMn/PRT6I+//9h++i7qS9F3ivL8l89Tu1ht1nVZx4RmEyh+UfHTH0DCbtcuqF/fS6QaNoQ1a7ySCiIi6UE4E6u0LnHfF3jbOXcouWCcc7cC+YFsQM3A5lSVuDezzglrJMXGxia3i6RSpkwwYoR3+/xdd8GxpEtonqEDxw/wwuIXKNq/KH0W96Hm5TVZc98aJt81masv0YSdSLFwIZQv7z0PGuTVp8qTx++oRERCJ5yJVVqXuL8e6BcoGtYdeCpQdOwfzrljeBVdE4YVU1Xi3jk3NGGNpCxaS+OsFSoEo0d7PRU9epzdsQ4cP8BLX75E0XeK8tyi56hWpBrfdf6OKc2nUO7SciGJV87e8eNeD9Utt8B553lzq7p21dCfiKQ/4cwS/ilTj1dmvgVwd5J9pgPdzGwCXmK03zm308x2n6qtmRV3ziUMIv1T4t45d3PCQc2sD3DIOTfQzHID5waOmwVvovxXgf3yB0rqJxwrbCXu5b9uv92rrP3aa1C1KrRsGVz7A8cP8N6K93hj2RvsObqHO/53B32q96FC/gqnbyxp6vvvver769ZB585ewc/cuhFTRNKpsCVWfpS4P4VcwHQzyxY41gJgSOCzNCtxL//fiy96Fdk7dYKyZb0ioqfz15G/6P9Nf95d8S77j++nXvF69KnWh+sKXhf+gCUo8fHekjRPPeUN982Y4SXUIiLpmSqvB0nlFkJr+3avvlXOnN7wUN68ye+34+AO3vz6TYZ8O4QjJ4/Q5Kom9LqpFxULVEzbgCVVfvsN2rWDRYugUSOvinq+fKdpJCIRSeUWgqPEKkhKrEJv+XKoVs27M2zu3P+uC/fr3l95belrjFgzgrj4OFpe3ZJeN/WiVL5S/gUsp+QcDBsGjzzi9VgNGOAlWJpLJRK9lFgFR4lVkJRYhcfYsd48nM6dYcgQ2PjXD7y65FXGfT+OzJky0758ex6v8jjFLijmd6hyCr/+6g3rzp/vzZsbMQKK6Y9LJOopsQqObnGTiNCqFWzYAK+8Al/tG8emUq3JcU4OHrr+IR6p/IiKekawuDgYONCbS5U5Mwwe7CXImbQSqYhkQEqsxHdx8XHM+HEGi4q9AeU6sfHjttTtnovRL1Uhb85TTLqSiLBpE3ToAF9/DXXrwvvvQ2GV2RWRDEy/U4pvjp48ypBVQ7jqvatoPLExOw9v5+33DnFr3Vhm92/IoplKqiLVsWPw/PNesc+NG726ZJ9/rqRKREQ9VpLmdh/ezaCVgxi4ciB/HfmLigUqMrHZRJpc1YQsmbLQeTLUru0ND+bODbfd5nfEktjcud7SRDExXvX8/v3h0kv9jkpEJDJo8nqQNHn9zP3494+8vextRq4dybHYY9zxvzt49MZHufmym7Ekt43t3etV6d6wAaZM8daWE39t3+5Vyp80CYoXh/fe8xJgEUnfNHk9OEqsgqTEKjjxLp45MXMYsGIAs2NmkzVzVtqUbUPPyj25Kt9VKbbdswfq1PEqdk+a5C3YK2nvxAlvcnrv3hAbC08/DY89Btmy+R2ZiKQFJVbBUWIVJCVWqXPg+AFGrhnJwBUD+WnPT+TPnZ+uFbvS+drOXJL7klQfZ98+byjw22/ho4+gefPwxSz/5RxMn+4lUT/95E1OHzhQJRREMholVsHRHCsJqc1/bWbgioGMXDuSQycOUblQZZ6v8TxNrmpC1sxZT3+AJPLk8eb01K8PLVrA1q1e8UkVnAyvNWugZ09YuBBKlvQmptetq+suInI6SqzkrMXFxzHn5zm8u+Ldf4b7mpduzoOVHgzJGn7nnQdffAFt2ni9J1u2eBOmM2c++9jlv3buhGee8Yp7Xnih10PVuTOcc47fkYmIRAclVnLGdhzcwfDVw/nguw/4ff/v5M+dn+erPx/0cF9qZM8OEyZAkSLwxhtecvXRR16Plpy9v/+Gfv3g3Xe9eVQ9engJ1gUX+B2ZiEh00RyrIGX0OVbxLp4vfv6C9799n+mbpxPn4rjl8lu479r7aFiy4RkN9wVr8GB46CG47DLvjsFy5cJ+ynTrwAF4+2146y04eBDuvhv69IErr/Q7MhGJFJpjFRwlVkHKqInVH4f+YMTqEXzw3Qf8uu9X8ubMS/vy7elUoRPFLyqe5vEsWwbNmnl3Dg4eDG3bav5PMA4e9K5bv35eb1WTJl7Bz9Kl/Y5MRCKNEqvgKLEKUkZKrE7GnWRWzCxGrhnJjB9nEBsfS/Wi1bnv2vtoXLIx2bL4e7/9rl3ehPaFC+HOO2HQIMirYu0p+usvGDDAmzu1d693x+WLL8K11/odmYhEKiVWwVFiFaSMkFit/WMto9aO4qN1H7H7yG4uznUx95S9h04VOlEibwm/w/uPuDh4/XV47jlvsvWHH8Ltt/sdVeTZtg3efBOGDoUjR6BxY+jVC647+3sLRCSdU2IVHCVWQUqvidXuw7sZ9/04Rq4dyZo/1nBOpnNoUKIB7cq349YrbuWczJF9W9jatXDPPfD9916tqzffhIIF/Y7Kf8uXez1UkyZBfLy3TNATT0CpUn5HJiLRQolVcJRYBSk9JVaHThxixuYZjF8/nlkxs4iNj+Xa/NfSrnw7WpZpyUU5L/I7xKAcPw6vvAKvvgpZsniVwh9+GLKGfz59RDl+HD7+2LvDb+VKr1xF+/benX5FivgdnYhEGyVWwVFiFaRoT6yOxR5j1k+zmLBhAjM2z+Bo7FEKnluQFmVa0LZcW66+5Gq/Qzxrv/wC3bvDjBlw+eXeXW6tWqX/ulfr18PIkTBmjDf/rGRJePBBryfv3HP9jk5EopUSq+AosQpSNCZWR08eZd4v8/hk4ydM3TSVA8cPkC9nPu4sdSctyrSgymVVyGSZ/A4z5GbPhqeegtWrvSSjd2/vTsIs6ah62969MH68V9Bz1Srvu91xB3TtCrVq6U5JETl7SqyCo8QqSNGSWO05uofPf/ycaZunMTtmNkdOHuH8bOfT5KomtCjTgpqX1yRLpnSUYZxCfDxMnQrPPgsbN0Lhwl4NrI4do7e46N698OmnMHmyt9zPyZNeLa/27b06VPny+R2hiKQnSqyCo8QqSJGaWDnn2LB7A3N/nstnP37Gl799SZyLo8C5BWhYoiGNSjaietHqaVLAMxLFx8Nnn3nFMBctgpw5oWlTr/5VjRqQKcI77H791euBmz4d5s3zqqMXKeJ9h9at4Zpr/I5QRNIrJVbBUWIVpEhKrP489Cfzf53P3J/nMvfnuew8tBOAUvlK0ahEIxqVbMS1Ba5Nl8N8Z2P1ahgyBCZOhP37vV6sJk28IbSqVSNjXbw9e+Drr701EmfPhh9/9LYXK+YNZzZrBhUraqhPRMJPiVVwlFgFya/EKi4+jk1/bWLp1qV8vfVrlm5dSsyeGAAuynERtYrVos4VdahdrDaFzy+c5vFFo6NHvSG1sWO9XqBjx+D886F6dbjpJrj5ZqhQIfyJ1vHjsGkTfPedl0wtXeoNWwLkyOHFc9tt3qN4cSVTIpK2lFgFR4lVkMKdWB04foDf9v3G7/t/J2ZPDOv+XMe6XevYsGsDR2OPApAvZz6qXFaFKoWrUL1odSrkr6BeqbN0+LCXXH32mTdUGOPlrGTP7tV8Kl0aypSB//0PChXyamRdfHHq7jR0zluT788/4bff/n389JNXd2vzZq/QKXiLHleuDFWqwI03wvXXe8mViIhflFgFR4lVkEKZWE3fPJ0Xv3yRgycOcujEIfYf28/BEwf/s0++nPkod2k5yl5clnKXlqNyocpceeGVmLotwmrnTliyBL75xitjsH497Njx330yZ/Z6uHLn9h45c3pzueLivOejR72J5nv3eu8Ty5TJmyNVpgxcfbX3KFcOSpSI/PleIpKxKLEKTvq/LSyCZcucjQtzXEiRPEU4N+u5nJv1XAqeV5Ai5xehSJ4iXJ7nci7JfYnfYWZI+fN76w/eeee/2/buhZ9/hu3b/33s3w+HDnmPI0e8pChzZu85e3avByrhkS8fFC3qJVQFC0bGXC4REQkt9VgFKZImr4uIiISbeqyCo0EHERERkRBRYiUiIiISIkqsREREREJEiZWIiIhIiCixEhEREQkRJVYiIiIiIaLESkRERCRElFiJiIiIhIgSKxEREZEQUWIlIiIiEiJKrERERERCRImViIiISIgosRIREREJEXPO+R1DVDGzeOBoCA+ZBYgN4fEyIl3D0NB1PHu6hmdP1zA0Qnkdczjn1BGTSkqsfGZmq5xzFf2OI5rpGoaGruPZ0zU8e7qGoaHr6B9loCIiIiIhosRKREREJESUWPlvqN8BpAO6hqGh63j2dA3Pnq5haOg6+kRzrERERERCRD1WIiIiIiGixMonZnabmW02sxgze9LveKKFmRU2s4VmttHMNpjZw4HtF5rZF2b2U+D5Ar9jjXRmltnMVpvZZ4H3uoZBMLM8ZjbZzDYF/j5W1jUMnpn1CPxbXm9m480su65jysxsuJntMrP1ibad8pqZWa/A/zWbzexWf6LOOJRY+cDMMgPvAXWBUkBLMyvlb1RRIxZ4xDl3FXAD8EDg2j0JzHfOFQfmB95Lyh4GNiZ6r2sYnP7AbOdcSaAc3rXUNQyCmRUEHgIqOufKAJmBFug6ns5I4LYk25K9ZoGfjy2A0oE2gwL/B0mYKLHyRyUgxjn3i3PuBDABaOhzTFHBObfTOfdd4PVBvP/MCuJdv1GB3UYBjXwJMEqYWSGgPvBhos26hqlkZucBVYFhAM65E865feganoksQA4zywLkBHag65gi59yXwJ4km091zRoCE5xzx51zvwIxeP8HSZgosfJHQWBrovfbAtskCGZWFLgGWA5c4pzbCV7yBVzsY2jR4B3gcSA+0TZdw9QrBuwGRgSGUz80s1zoGgbFObcdeAP4HdgJ7HfOzUXX8Uyc6prp/5s0psTKH5bMNt2eGQQzyw18AnR3zh3wO55oYma3A7ucc9/6HUsUywJUAAY7564BDqPhqqAF5gE1BC4HCgC5zKy1v1GlO/r/Jo0psfLHNqBwoveF8Lq/JRXM7By8pGqsc25KYPOfZpY/8Hl+YJdf8UWBKkADM9uCNwxd08w+QtcwGNuAbc655YH3k/ESLV3D4NQCfnXO7XbOnQSmADei63gmTnXN9P9NGlNi5Y+VQHEzu9zMsuJNLJzuc0xRwcwMb17LRufcW4k+mg60DbxuC3ya1rFFC+dcL+dcIedcUby/ewucc63RNUw159wfwFYzKxHYdAvwA7qGwfoduMHMcgb+bd+CN29S1zF4p7pm04EWZpbNzC4HigMrfIgvw1CBUJ+YWT28eS6ZgeHOuZf8jSg6mNlNwFfA9/w7P+gpvHlWHwOX4f2wvtM5l3RypyRhZtWBR51zt5vZRegappqZlceb/J8V+AVoj/fLqq5hEMysL9Ac747f1UBHIDe6jqdkZuOB6kBe4E+gNzCNU1wzM3sauBfvGnd3zs1K+6gzDiVWIiIiIiGioUARERGREFFiJSIiIhIiSqxEREREQkSJlYiIiEiIKLESERERCRElViIiIiIhosRKREREJESUWIlIxDOzq83sNzPr6ncsIiIpUWIlIhHPOfc93vI7bfyORUQkJUqsRCRa7AJK+x2EiEhKlFiJSLR4FchmZkX8DkRE5FSUWIlIxDOz24BcwOeo10pEIpgSKxGJaGaWHegH3A98D5TxNyIRkVNTYiUike4ZYLRzbgtKrEQkwimxEpGIZWYlgNrAO4FNSqxEJKKZc87vGERERETSBfVYiYiIiISIEisRERGREFFiJSIiIhIiSqxEREREQkSJlYiIiEiIKLESERERCRElViIiIiIhosRKREREJET+DywzsWY4bhjMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafiquemos los resultados\n",
    "fig, ax1 = plt.subplots(figsize=(8,6))\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(lambdas, mse_train, 'g-')\n",
    "ax2.plot(lambdas, mse_test, 'b-')\n",
    "\n",
    "ax1.set_xlabel('$\\lambda$')\n",
    "ax1.set_ylabel('Train MSE', color='g')\n",
    "ax2.set_ylabel('Test MSE', color='b')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aquí, vemos que el modelo se sobreajusta ligeramente cuando $\\lambda$ es pequeño. El MSE de prueba es mucho más alto que el MSE de train y el modelo en su mayoría no está regularizado (es decir, el modelo es una regresión lineal sin términos adicionales en la función de optimización).\n",
    "\n",
    "El modelo no se ajusta bien cuando $\\lambda$ es grande. El efecto de un $\\lambda$ grande es aumentar la fuerza de la regularización, lo que efectivamente hace que el modelo esté restringido en cuanto a los coeficientes que puede usar para ajustar los datos. Con un $\\lambda$ grande, vemos que el modelo tiene problemas tanto en los datos del train como en los de la prueba (MSE alto tanto para los datos del tren como en los de la prueba), lo que indica que el modelo no se ajusta bien.\n",
    "\n",
    "Por lo tanto, debemos elegir una lambda que equilibre el ajuste excesivo y el ajuste insuficiente. A partir de la curva MSE de prueba, vemos que el mínimo se alcanza alrededor de $\\lambda = 28,0$. Esta fue la motivación para elegir $\\lambda$ en la sección anterior. En general, te recomiendo usar un método gráfico de este tipo para comprender los efectos que tiene la regularización en un modelo de regresión múltiple."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejercicio 4\n",
    "\n",
    "En la regularización L2, a medida que $\\lambda$ se acerca a cero, ¿cómo se comportarían las predicciones del modelo de precios de la vivienda? Compare con el caso en el que ajustamos un modelo de regresión lineal múltiple sin regularización.\n",
    "\n",
    "A. Las predicciones del precio de la vivienda se volverían cada vez más sesgadas\n",
    "\n",
    "B. Las predicciones del precio de la vivienda se volverían menos resistentes a la multicolinealidad presente en las características\n",
    "\n",
    "C. Las predicciones del precio de la vivienda no cambian a medida que cambia $\\lambda$\n",
    "\n",
    "D. El modelo se volvería menos complejo, lo que conduciría a una menor variación en las predicciones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Respuesta.** (B). La explicación se da a continuación.\n",
    "\n",
    "Explicación:\n",
    "La respuesta (A) es incorrecta ya que a medida que $\\lambda$ se acerca a cero, esto reduce efectivamente la fuerza de la regularización (eliminando así el sesgo del modelo).\n",
    "\n",
    "La respuesta (B) es correcta ya que la reducción de $\\lambda$ reduce la fuerza de la regularización. La regularización L2 se utiliza principalmente para reducir los efectos de la multicolinealidad mediante el control de la complejidad del modelo. Los valores más altos de $\\lambda$ conducen a un modelo más regularizado, por lo que los valores más bajos de $\\lambda$ se volverán menos resistentes a la multicolinealidad.\n",
    "\n",
    "La respuesta (C) es incorrecta ya que las predicciones cambiarán a medida que cambie $\\lambda$ ya que la regularización es parte de la función objetivo del modelo. La función objetivo se minimiza durante el ajuste del modelo, y debido a que $\\lambda$ es parte del procedimiento de optimización, cambiar su valor también cambiará el modelo resultante y, por lo tanto, cambiará las estimaciones del modelo.\n",
    "\n",
    "Con respecto a la respuesta (D), la disminución de $\\lambda$ conduce a un modelo más complejo. Al aumentar $\\lambda$, el modelo se vuelve menos complejo, lo que ayuda a evitar el sobreajuste."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejercicio 5\n",
    "\n",
    "Ajustamos una regresión lineal múltiple regularizada L2 para predecir los datos de viviendas y observamos cómo el $\\lambda$ elegido afecta el train y el error de prueba. También nos gustaría ver cómo los coeficientes se ven afectados por la elección de $\\lambda$. Escriba un script para cambiar el valor de $\\lambda$ y observe cómo cambian los coeficientes de regresión de Ridge ajustados. Es decir, escriba un guión para producir la siguiente trama. Cada línea de la gráfica corresponde al coeficiente de una sola característica a medida que cambia el valor de $\\lambda$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"ridge_coef_plot.png\", width=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mi solucion !!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Iterar sobre distintos valores de lambda \n",
    "lambdas = np.logspace(-1, 7, 200)\n",
    "\n",
    "coefs = []\n",
    "for l in lambdas:\n",
    "    ridge = linear_model.Ridge(alpha=l, fit_intercept=True)\n",
    "    ridge.fit(X_train_simplified, y_train)\n",
    "    coefs.append(ridge.coef_)\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(8,6))\n",
    "ax.plot(lambdas, coefs)\n",
    "ax.set_xscale('log')\n",
    "ax.set_xlim(ax.get_xlim())  # reverse axis\n",
    "plt.xlabel('$\\lambda$')\n",
    "plt.ylabel(\"Valores de Coeficiente\")\n",
    "plt.title('Coeficientes Ridge coefficients en funcion de regularizacion')\n",
    "plt.axis('tight')\n",
    "plt.show()\n",
    "#plt.savefig('ridge_coef_plot.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cada línea anterior corresponde a la ruta de un coeficiente a medida que $\\lambda$ aumenta de izquierda a derecha. Vemos que a medida que aumenta la fuerza de regularización, todos los coeficientes eventualmente se vuelven cero. Este es el efecto de reducción de la regularización, donde un $\\lambda$ muy grande obligará a todos los coeficientes del modelo a cero (debido al procedimiento de optimización que minimiza la función objetivo). Hacer que todos los coeficientes lleguen a cero conducirá a un ajuste insuficiente ya que las predicciones del modelo se vuelven demasiado restringidas.\n",
    "\n",
    "En términos de disminuir $\\lambda$ a valores muy pequeños, está claro que los coeficientes del modelo finalmente alcanzan un valor constante para valores pequeños de $\\lambda$. Esto ilustra que a medida que $\\lambda$ se vuelve pequeño, la regularización esencialmente desaparece y los coeficientes serán los mismos que si no se aplicara ninguna regularización. Dependiendo de los datos, esto podría conducir a un sobreajuste.\n",
    "\n",
    "Por lo tanto, existe una compensación entre el ajuste excesivo y el ajuste insuficiente al elegir un valor $\\lambda$ óptimo. Una $\\lambda$ demasiado alta puede no ajustarse correctamente, mientras que una $\\lambda$ demasiado baja puede sobreajustarse. Al elegir el $\\lambda$ apropiado a través de una cuidadosa observación y análisis, las técnicas de regularización pueden ser una herramienta poderosa para prevenir y controlar el sobreajuste."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularizacion Elastic Net regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora que hemos aplicado la regularización L1 y L2 individualmente, ¿hay algún método por el cual podamos obtener sus beneficios juntos? Afortunadamente, los beneficios de escasez de L1 y los beneficios de robustez y prevención contra el sobreajuste de L2 se pueden compartir en un método denominado **Regresión Elastic Net**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La regularización L1 suma los valores absolutos de las betas a la función objetivo:\n",
    "\n",
    "$$Loss(Data\\ |\\ Model) + \\color{red}{\\lambda \\sum_{j=1}^{p}\\big| \\beta_j \\big|} .$$\n",
    "\n",
    "Por otro lado, la regularización L2 suma las magnitudes al cuadrado de las betas a la función objetivo:\n",
    "\n",
    "$$Loss(Data\\ |\\ Model) + \\color{red}{\\lambda \\sum_{j=1}^{p}\\beta_j^2} .$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La red elástica puede ser un método poderoso para fomentar tanto una solución dispersa como una solución robusta frente a características perjudiciales como la multicolinealidad. Dados los datos $(y_1, ..., y_n)$ y las características observadas para cada $y_i$, a saber, $(x_{i1}, ... ,x_{ip})$, un modelo de regresión con $p$ los parámetros $(\\beta_0, ..., \\beta_p)$ toman la forma,\n",
    "\n",
    "$$y_i = \\sum_{j=1}^{p} x_{ij}\\beta_j .$$\n",
    "\n",
    "La regularización Elastic Net combina las penalizaciones de regularización L1 y L2 para incluir tanto el valor absoluto como la magnitud al cuadrado de los coeficientes beta en la función de pérdida:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\sum_{i=1}^{n} (y_i - \\sum_{j=1}^{p} x_{ij} \\beta_j)^2 + \\color{red}{\\alpha \\rho \\sum_{j=1}^{p}\\big| \\beta_j \\big|} + \\color{red}{\\alpha(1-\\rho) \\sum_{j=1}^{p}\\beta_j^2}.$$\n",
    "\n",
    "\n",
    "Aquí, $\\alpha$ es la tasa de regularización y $\\rho$ controla el equilibrio entre la regularización L1 y L2 en la función de pérdida. Es decir, $\\rho = 1$ da como resultado una penalización L1 pura, $\\rho = 0$ da como resultado una penalización L2 pura, mientras que $0 < \\rho < 1$ da como resultado una regularización Elastic Net mixta."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar a cómo calibramos $\\lambda$ en nuestra discusión sobre la regularización L2, es útil construir gráficos en función de la tasa de regularización elastic Net $\\alpha$. Recorramos una lista de diferentes valores de $\\alpha$ y tracemos los coeficientes como una función de $\\alpha$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterar sobre distintos valores de alpha\n",
    "alpha_list = np.logspace(-5, 1, 50)\n",
    "\n",
    "coefs = []\n",
    "for a in alpha_list:\n",
    "    enet = linear_model.ElasticNet(random_state=0, alpha=a, l1_ratio=0.5, fit_intercept=True)\n",
    "    enet.fit(X_train_simplified, y_train)\n",
    "    coefs.append(enet.coef_.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "ax.plot(alpha_list, coefs)\n",
    "ax.set_xscale('log')\n",
    "ax.set_xlim(ax.get_xlim())  # reverse axis\n",
    "plt.xlabel(r'$\\alpha$')\n",
    "plt.ylabel('Coeficientes de Parametros')\n",
    "plt.title(r'Coeficientes de Elastic net en funcion de $\\alpha$')\n",
    "plt.axis('tight');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De la gráfica anterior, vemos que a medida que $\\alpha$ aumenta más allá de ~0.1, todos los coeficientes se vuelven cero. Esto indica que la regularización en tal alfa es demasiado fuerte. Por otro lado, valores de $\\alpha$ muy pequeños conducen a una regularización débil. Podemos ver que a medida que los valores de $\\alpha$ disminuyen de derecha a izquierda, las estimaciones de los parámetros comienzan a verse afectadas de manera más significativa cerca de $10^{-4}$. Esto indica que en ese punto la regularización comienza a tener un efecto en el ajuste del modelo.\n",
    "\n",
    "Ahora que tenemos una idea de cómo los coeficientes se ven afectados por $\\alpha$, echemos un vistazo a cómo elegir el valor óptimo de $\\alpha$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Un metodo para elegir el valor optimo de  $\\alpha$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Elegir un $\\alpha$ óptimo es importante. Si elige un $\\alpha$ que sea demasiado grande, los coeficientes del modelo estarán demasiado restringidos y se reducirá la precisión del modelo fuera de la muestra. Por otro lado, elegir un $\\alpha$ que sea demasiado pequeño puede resultar en que la regularización tenga poco efecto, reduciendo así el propósito principal de la regularización como control contra el sobreajuste.\n",
    "\n",
    "Un método para seleccionar un $\\alpha$ óptimo es observar el R-cuadrado del modelo como una función de $\\alpha$ y, posteriormente, seleccionar el $\\alpha$ que proporcione el valor más alto de R-cuadrado del conjunto de prueba. Ajustemos el modelo de regresión lineal ElasticNet para diferentes valores de $\\alpha$ y veamos los efectos en el R-cuadrado para los conjuntos de entrenamiento y prueba. Tenga en cuenta que estableceremos la relación de regularización L1 y L2 (indicada como el parámetro `l1_ratio` en `linear_model.ElasticNet()`, y a menudo indicada matemáticamente como $\\rho$) en 0,5 en este análisis, con la idea de equilibrar Efectos de regularización L1 y L2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valor optimo de alpha como parametro regularizacion : 0.000518\n"
     ]
    }
   ],
   "source": [
    "# Comparar la progresion del train y los errore sde test errors a medida que alpha varia\n",
    "alphas = np.logspace(-5, 1, 50)\n",
    "enet = linear_model.ElasticNet(random_state=0, l1_ratio=0.5, fit_intercept=True)\n",
    "train_errors = list()\n",
    "test_errors = list()\n",
    "for alpha in alphas:\n",
    "    enet.set_params(alpha=alpha)\n",
    "    enet.fit(X_train_simplified, y_train)\n",
    "    train_errors.append(enet.score(X_train_simplified, y_train))\n",
    "    test_errors.append(enet.score(X_test_simplified, y_test))\n",
    "\n",
    "i_alpha_optim = np.argmax(test_errors)\n",
    "alpha_optim = alphas[i_alpha_optim]\n",
    "print(\"Valor optimo de alpha como parametro regularizacion : %.6f\" % alpha_optim)\n",
    "\n",
    "# Estimar el coef_ optimo\n",
    "enet.set_params(alpha=alpha_optim)\n",
    "coef_ = enet.fit(X, y).coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veamos cómo evoluciona el R-cuadrado de los datos de entrenamiento y prueba a medida que se cambia el parámetro $\\alpha$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficar el R2 en funcion de alpha\n",
    "fig,ax = plt.subplots(figsize=(10,5))\n",
    "plt.semilogx(alphas, train_errors, label='Train')\n",
    "plt.semilogx(alphas, test_errors, label='Test')\n",
    "plt.vlines(alpha_optim, plt.ylim()[0], np.max(test_errors), color='k',linewidth=3, label=r'Optimum $\\alpha$')\n",
    "plt.legend(loc='lower left')\n",
    "plt.ylim([0, 0.8])\n",
    "plt.title(r'Performance de Elastic net en funcion del parametro $\\alpha$')\n",
    "plt.xlabel(r'$\\alpha$')\n",
    "plt.ylabel(r'$R^2$');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El valor optimo de $\\alpha$ es ~0.0005."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejercicio 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al elegir un $\\alpha$ óptimo, dividimos los datos en dos conjuntos, entrenamos y probamos. El conjunto de trenes se usó para ajustar el modelo de regresión lineal regularizado de red elástica, mientras que el conjunto de prueba se usó para calcular el R-cuadrado para cada valor diferente de $\\alpha$. ¿Cuál es la razón principal de dividir en datos de entrenamiento y prueba al elegir un $\\alpha$ óptimo para esta tarea de regresión de red elástica?\n",
    "\n",
    "(A) Al elegir $\\alpha$ utilizando datos de la muestra, reducimos el riesgo de sobreajuste\n",
    "\n",
    "(B) La complejidad del modelo disminuye, lo que conduce a una menor probabilidad de sobreajuste\n",
    "\n",
    "(C) Para ayudar en la precisión de la predicción del modelo fuera de la muestra, $\\alpha$ se selecciona usando los datos de prueba que no están involucrados en el proceso de ajuste\n",
    "\n",
    "(D) Dado que $\\alpha$ es una sola variable para optimizar, la división en tren y prueba reduce el tiempo de cálculo y ayuda en el desarrollo del modelo.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Respuesta.** (C). La explicación se da a continuación.\n",
    "\n",
    "La respuesta (A) es incorrecta ya que elegimos $\\alpha$ usando datos de prueba fuera de la muestra, no los datos del tren dentro de la muestra que se usaron para ajustar el modelo lineal.\n",
    "\n",
    "La respuesta (B) es incorrecta ya que la complejidad del modelo no disminuye al dividirse en conjuntos de entrenamiento y prueba. La complejidad del modelo permanece sin cambios, ya que los datos en los que se ajusta o prueba el modelo no están involucrados en la determinación de la complejidad del modelo.\n",
    "\n",
    "La división en datos de entrenamiento y prueba nos permite ajustar utilizando los datos de la muestra y luego probar los datos fuera de la muestra para garantizar que el modelo no se sobreajuste. Por lo tanto, al seleccionar $\\alpha$ usando los datos de prueba que no están involucrados en el ajuste del modelo (por lo tanto, datos de prueba fuera de la muestra), esto ayuda a tener una mejor precisión de predicción fuera de la muestra. Por lo tanto, la respuesta (C) es la respuesta correcta.\n",
    "\n",
    "Aunque la división en datos de entrenamiento y prueba puede reducir los tiempos de entrenamiento en el desarrollo del modelo, esta no es una razón por la que dividimos los datos de entrenamiento y prueba para elegir un $\\alpha$ óptimo. Por lo tanto, la respuesta (D) es incorrecta."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejercicio 7\n",
    "\n",
    "Escriba un script para cambiar $\\rho$ y vea cómo cambia el $\\alpha$ óptimo (basado en el conjunto de prueba máximo R-cuadrado como se ve arriba). Es decir, ¿cuáles son los valores óptimos de $\\alpha$ usando para los siguientes valores de $\\rho$: [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]? Comente qué sucede cuando $\\rho$ es 0 y cuando $\\rho$ es 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mi solucion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal alpha for rho = 0.100000: 0.002121\n",
      "Optimal alpha for rho = 0.200000: 0.001207\n",
      "Optimal alpha for rho = 0.300000: 0.000910\n",
      "Optimal alpha for rho = 0.400000: 0.000687\n",
      "Optimal alpha for rho = 0.500000: 0.000518\n",
      "Optimal alpha for rho = 0.600000: 0.000518\n",
      "Optimal alpha for rho = 0.700000: 0.000391\n",
      "Optimal alpha for rho = 0.800000: 0.000391\n",
      "Optimal alpha for rho = 0.900000: 0.000295\n"
     ]
    }
   ],
   "source": [
    "# Comparar la progresion del train y los errores de test errors a medida que rho varia\n",
    "alphas = np.logspace(-5, 1, 50)\n",
    "rhos = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
    "list_alpha_optim = []\n",
    "for rho in rhos:\n",
    "    train_errors = []\n",
    "    test_errors = []\n",
    "    enet = linear_model.ElasticNet(random_state=0, l1_ratio=0.5, fit_intercept=True)\n",
    "    for alpha in alphas:\n",
    "        enet.set_params(alpha=alpha, l1_ratio=rho)\n",
    "        enet.fit(X_train_simplified, y_train)\n",
    "        train_errors.append(enet.score(X_train_simplified, y_train))\n",
    "        test_errors.append(enet.score(X_test_simplified, y_test))\n",
    "\n",
    "    i_alpha_optim = np.argmax(test_errors)\n",
    "    alpha_optim = alphas[i_alpha_optim]\n",
    "    print(\"Optimal alpha for rho = %.6f: %.6f\" % (rho, alpha_optim))\n",
    "    list_alpha_optim.append(alpha_optim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Encontramos que el alfa óptimo disminuye a medida que aumenta $\\rho$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insights del modelo final elastic net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para predecir los precios de las viviendas con una porción equilibrada de regularización L1 y L2, encontramos un $\\alpha = 0.000518$ óptimo para usar en el modelo de red elástica. Echemos un vistazo a los resultados de nuestro modelo final y veamos si hay alguna idea que podamos extraer:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('newConstruction', -0.01032689222658409),\n",
       " ('age', -0.00521669164615804),\n",
       " ('bedrooms', -0.004675685635988088),\n",
       " ('sewer_public/commercial', -0.002246222923318455),\n",
       " ('heating_hot water/steam', -0.0015928929103743324),\n",
       " ('pctCollege', -0.0),\n",
       " ('fuel_electric', -0.0),\n",
       " ('sewer_septic', -0.0),\n",
       " ('fireplaces', 0.0005434853927543259),\n",
       " ('fuel_gas', 0.001967178310464714),\n",
       " ('heating_hot air', 0.0030686697632882507),\n",
       " ('lotSize', 0.003513891569413089),\n",
       " ('centralAir', 0.004870730698973052),\n",
       " ('rooms', 0.0064860340858468295),\n",
       " ('waterfront', 0.009525337302894229),\n",
       " ('bathrooms', 0.014121489970889516),\n",
       " ('landValue', 0.03345216803179955),\n",
       " ('livingArea', 0.04392189129273643)]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Miremos los coeficientes del modelo final\n",
    "final_elastic_net_model = linear_model.ElasticNet(random_state=0, alpha=0.000518, l1_ratio=0.5, fit_intercept=True)\n",
    "final_elastic_net_model.fit(X_train_simplified, y_train)\n",
    "sorted(zip(X_train_simplified.columns, final_elastic_net_model.coef_), key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dado que elegimos realizar la estandarización de los datos antes del ajuste (para hacer que las características estén en la misma escala para no influir negativamente en los coeficientes de ajuste), debemos tener cuidado al interpretar el resultado del modelo.\n",
    "\n",
    "Los resultados de este modelo ElasticNet han empujado algunos de los coeficientes hacia cero, al tiempo que han reducido por completo algunas de las características menos importantes para tener coeficientes cero (**fuel_electric**, **sewer_septic**, **pctCollege**).\n",
    "\n",
    "Vemos que los precios de la vivienda están muy influenciados positivamente por **livingArea** y **landValue**. Esto sigue nuestra intuición ya que ambas cantidades deberían conducir naturalmente a un precio de vivienda deseado más alto.\n",
    "\n",
    "Además, las variables **nuevaConstrucción** y **edad** tienen grandes betas negativas. Si bien la edad sigue lo que esperamos (las casas más antiguas pueden tener características obsoletas y, por lo tanto, pueden bajar de precio), la característica de nueva construcción puede justificar un análisis más detallado de por qué la variable conduce a un coeficiente negativo. Puede haber algunas variables ocultas en juego para las que carecemos de datos; por ejemplo, la nueva construcción puede provenir de una empresa contratista que no es de confianza, lo que podría conducir a precios más bajos de la vivienda."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusiones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso, nos propusimos determinar variables sobresalientes que pudieran ayudar a explicar los precios de las viviendas. A través de modelos de regresión lineal múltiple, L1, L2 y regularización ElasticNet, hemos encontrado una serie de variables útiles y no útiles para el análisis.\n",
    "\n",
    "Una serie de variables que resultaron inútiles incluyen **combustible_aceite** y **calefacción_eléctrica**. De hecho, muchas de las variables categóricas no tenían valores $p$ significativos, por lo que un análisis futuro podría investigar por qué sucedió esto.\n",
    "\n",
    "Las variables destacadas del análisis de regresión incluyeron **landValue** y **livingArea**. Ambas variables mostraron una fuerte relación con el precio en la sección original de análisis de datos exploratorios, cada una tenía valores $p$ significativos en la sección de regresión lineal múltiple, y ambas se utilizaron a través de las secciones L1, L2 y regularización elasticNet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Para llevar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "En este caso, hemos aplicado los fundamentos de la regularización del modelo a la predicción del precio de la vivienda. Cubrimos la regularización de L1, la regularización de L2, el sobreajuste y el ajuste insuficiente, la regularización de ElasticNet y completamos una serie de análisis de regresión múltiple.\n",
    "\n",
    "Sobre la base de este conocimiento, puede utilizar técnicas de regularización para ayudar a sus modelos contra el sobreajuste y mejorar la precisión de la predicción. Las predicciones sólidas son increíblemente importantes no solo para mejorar las estimaciones del modelo, sino también para ayudar a mantener la confianza en las predicciones del modelo fuera de la muestra y en entornos de producción. Las decisiones comerciales impulsadas cuantitativamente dependen de estimaciones de calidad, y las técnicas de regularización son una herramienta útil para lograr ese objetivo."
   ]
  }
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