{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ¿Cuánto cree la IA que vale tu casa?\n",
    "\n",
    "Comencemos con un ejemplo típico dentro del mundo de la machine-learning (¡gracias, Andrew Ng!): predecir cuánto vale una casa en base a sus características ('features'). Las características son el conjunto de atributos de la casa sobre la que queremos predecir el precio: por ejemplo 'm²', 'n_plantas', 'n_habitaciones' , etc... \n",
    "\n",
    "¿Has notado que solo he mencionado características numéricas? Esto es por que los modelos de ML 'entienden' mucho mejor los números, que los datos en otro formato. De hecho lo que mejor admiten son arrays de números normalizados (en el rango [0,1]). Quizás estás pensando que hay factores de naturaleza no numérica, que previsiblemente influirán en el precio de la casa que estamos prediciendo. Por ejemplo: la localización (¿En qué país está la casa?, ¿en qué barrio está?, ¿cuánto vale el metro cuadrado en esa zona?). El proceso para transformar estos datos en numéricos, se llama 'codificación' o 'encoding'.\n",
    "\n",
    "Pero vamos a empezar por el principio: hemos elegido un dataset (conjunto de datos necesario para entrenar al modelo) de [Kaggle](https://www.kaggle.com/datasets/yasserh/housing-prices-dataset)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploración de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd                      # librerías básicas para la manipulación de datos en ML\n",
    "import numpy  as np                      # librerías básicas para la manipulación de datos en ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('dataset.csv')     # lectura del dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['price',\n",
       " 'area',\n",
       " 'bedrooms',\n",
       " 'bathrooms',\n",
       " 'stories',\n",
       " 'mainroad',\n",
       " 'guestroom',\n",
       " 'basement',\n",
       " 'hotwaterheating',\n",
       " 'airconditioning',\n",
       " 'parking',\n",
       " 'prefarea',\n",
       " 'furnishingstatus']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dataset.columns)                    # lista de 13 características"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(545, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape                            # tamaño del dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tenemos un dataset de 545 registros, y 13 características para cada registro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
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       "      <td>1</td>\n",
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       "      <td>no</td>\n",
       "      <td>no</td>\n",
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       "      <td>no</td>\n",
       "      <td>semi-furnished</td>\n",
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       "      <th>542</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3620</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
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       "      <td>no</td>\n",
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       "      <td>1750000</td>\n",
       "      <td>2910</td>\n",
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       "      <td>1750000</td>\n",
       "      <td>3850</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
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       "      <td>unfurnished</td>\n",
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       "</table>\n",
       "<p>545 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0    13300000  7420         4          2        3      yes        no       no   \n",
       "1    12250000  8960         4          4        4      yes        no       no   \n",
       "2    12250000  9960         3          2        2      yes        no      yes   \n",
       "3    12215000  7500         4          2        2      yes        no      yes   \n",
       "4    11410000  7420         4          1        2      yes       yes      yes   \n",
       "..        ...   ...       ...        ...      ...      ...       ...      ...   \n",
       "540   1820000  3000         2          1        1      yes        no      yes   \n",
       "541   1767150  2400         3          1        1       no        no       no   \n",
       "542   1750000  3620         2          1        1      yes        no       no   \n",
       "543   1750000  2910         3          1        1       no        no       no   \n",
       "544   1750000  3850         3          1        2      yes        no       no   \n",
       "\n",
       "    hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0                no             yes        2      yes        furnished  \n",
       "1                no             yes        3       no        furnished  \n",
       "2                no              no        2      yes   semi-furnished  \n",
       "3                no             yes        3      yes        furnished  \n",
       "4                no             yes        2       no        furnished  \n",
       "..              ...             ...      ...      ...              ...  \n",
       "540              no              no        2       no      unfurnished  \n",
       "541              no              no        0       no   semi-furnished  \n",
       "542              no              no        0       no      unfurnished  \n",
       "543              no              no        0       no        furnished  \n",
       "544              no              no        0       no      unfurnished  \n",
       "\n",
       "[545 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¡Wow! Tenemos un montón de información. Comencemos con las columnas numéricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>543</th>\n",
       "      <td>1750000</td>\n",
       "      <td>2910</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3850</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  area  bedrooms  bathrooms  stories  parking\n",
       "0    13300000  7420         4          2        3        2\n",
       "1    12250000  8960         4          4        4        3\n",
       "2    12250000  9960         3          2        2        2\n",
       "3    12215000  7500         4          2        2        3\n",
       "4    11410000  7420         4          1        2        2\n",
       "..        ...   ...       ...        ...      ...      ...\n",
       "540   1820000  3000         2          1        1        2\n",
       "541   1767150  2400         3          1        1        0\n",
       "542   1750000  3620         2          1        1        0\n",
       "543   1750000  2910         3          1        1        0\n",
       "544   1750000  3850         3          1        2        0\n",
       "\n",
       "[545 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_dataset = dataset.select_dtypes(include='number')\n",
    "numeric_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estaría bien poder hacer un resumen de los datos..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
       "      <td>5.450000e+02</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.766729e+06</td>\n",
       "      <td>5150.541284</td>\n",
       "      <td>2.965138</td>\n",
       "      <td>1.286239</td>\n",
       "      <td>1.805505</td>\n",
       "      <td>0.693578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.870440e+06</td>\n",
       "      <td>2170.141023</td>\n",
       "      <td>0.738064</td>\n",
       "      <td>0.502470</td>\n",
       "      <td>0.867492</td>\n",
       "      <td>0.861586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.750000e+06</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.430000e+06</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.340000e+06</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.740000e+06</td>\n",
       "      <td>6360.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.330000e+07</td>\n",
       "      <td>16200.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price          area    bedrooms   bathrooms     stories  \\\n",
       "count  5.450000e+02    545.000000  545.000000  545.000000  545.000000   \n",
       "mean   4.766729e+06   5150.541284    2.965138    1.286239    1.805505   \n",
       "std    1.870440e+06   2170.141023    0.738064    0.502470    0.867492   \n",
       "min    1.750000e+06   1650.000000    1.000000    1.000000    1.000000   \n",
       "25%    3.430000e+06   3600.000000    2.000000    1.000000    1.000000   \n",
       "50%    4.340000e+06   4600.000000    3.000000    1.000000    2.000000   \n",
       "75%    5.740000e+06   6360.000000    3.000000    2.000000    2.000000   \n",
       "max    1.330000e+07  16200.000000    6.000000    4.000000    4.000000   \n",
       "\n",
       "          parking  \n",
       "count  545.000000  \n",
       "mean     0.693578  \n",
       "std      0.861586  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      1.000000  \n",
       "max      3.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "¿Hay algún valor nulo que nos vaya a jo**r la inferencia?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 545 entries, 0 to 544\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype\n",
      "---  ------     --------------  -----\n",
      " 0   price      545 non-null    int64\n",
      " 1   area       545 non-null    int64\n",
      " 2   bedrooms   545 non-null    int64\n",
      " 3   bathrooms  545 non-null    int64\n",
      " 4   stories    545 non-null    int64\n",
      " 5   parking    545 non-null    int64\n",
      "dtypes: int64(6)\n",
      "memory usage: 25.7 KB\n"
     ]
    }
   ],
   "source": [
    "numeric_dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Separar Características y objetivo\n",
    "\n",
    "¿Qué queremos predecir? El precio\n",
    "\n",
    "¿Qué tenemos para predecirlo? El resto de columnas. Comencemos comparando precio vs área:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt                                                                 # import por defecto para plots y visualización de datos\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(dataset['area'], dataset['price'], color='blue', alpha=0.5)\n",
    "plt.title('Comparación de Precio vs Área')\n",
    "plt.xlabel('Área')\n",
    "plt.ylabel('Precio')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Enfoque Naive \n",
    "Como podemos ver hay una correlación lineal entre el área y el precio de una casa, un enfoque básico sería intentar hallar la línea recta que minimiza la suma de las distancias cuadráticas (errores cuadráticos) de todos los registros. Queremos encontrar los parámetros óptimos de la ecuación de una recta $y = bx + a + \\epsilon $ o, en términos de parámetros, $$y = \\theta_1 \\cdot x + \\theta_0 + \\epsilon$$\n",
    "\n",
    "Vectorialmente, esta relación lineal se puede representar como $y = \\mathbf{\\theta}^T \\cdot [x, 1]$, donde $\\mathbf{\\theta} = [\\theta_1, \\theta_0]^T$ es el vector de parámetros y $[x, 1]$ representa el vector de características ampliado con un término constante para incorporar $\\theta_0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ¿Cómo podemos resolver el sistema?\n",
    "# https://es.wikipedia.org/wiki/M%C3%ADnimos_cuadrados_ordinarios#Modelo_de_regresi%C3%B3n_simple\n",
    "\n",
    "X_mean = np.mean(dataset['area'])                                                                                    # características con las que predecimos\n",
    "Y_mean = np.mean(dataset['price'])                                                                                   # característica a predecir\n",
    "beta_1 = np.sum((dataset['area'] - X_mean) * (dataset['price'] - Y_mean)) / np.sum((dataset['area'] - X_mean) ** 2)  # pendiente\n",
    "beta_0 = Y_mean - beta_1 * X_mean                                                                                    # valor cuando x=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "461.9748942727835"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2387308.48239643"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Función para la línea de regresión\n",
    "def regression_line(x):\n",
    "    return beta_0 + beta_1 * x\n",
    "\n",
    "# Valores para la línea de regresión\n",
    "x_values = np.linspace(dataset['area'].min(), dataset['area'].max(), 100)  # 100 puntos entre el min y max de 'area'\n",
    "y_values = regression_line(x_values)\n",
    "\n",
    "# Creando la gráfica\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(dataset['area'], dataset['price'], color='blue', label='Datos')  # Datos reales\n",
    "plt.plot(x_values, y_values, color='red', label='Línea de regresión')  # Línea de regresión\n",
    "plt.title('Relación Área-Precio con Regresión Lineal')\n",
    "plt.xlabel('Área')\n",
    "plt.ylabel('Precio')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2488861398180.6567"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcular el error cuadrático medio\n",
    "predictions = beta_0 + beta_1 * dataset['area']\n",
    "mse_train = np.mean((dataset['price'] - predictions) ** 2)\n",
    "mse_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parece que funciona, pero esto es sólo con los datos de entrenamiento. ¿Qué tal geralizará a en los otros conjuntos? Por otro lado, ya que los hemos calculado, para algo habrá que usarlos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Función para la línea de regresión\n",
    "def regression_line(x):\n",
    "    return beta_0 + beta_1 * x\n",
    "\n",
    "# Valores para la línea de regresión\n",
    "x_values = np.linspace(dataset['area'].min(), dataset['area'].max(), 100)  # 100 puntos entre el min y max de 'area'\n",
    "y_values = regression_line(x_values)\n",
    "\n",
    "# Creando la gráfica\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(dataset['area'], dataset['price'], color='blue', label='Datos')  # Datos reales\n",
    "plt.plot(x_values, y_values, color='red', label='Línea de regresión')  # Línea de regresión\n",
    "plt.title('Relación Área-Precio con Regresión Lineal')\n",
    "plt.xlabel('Área')\n",
    "plt.ylabel('Precio')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2488861398180.6567"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcular el error cuadrático medio\n",
    "predictions = beta_0 + beta_1 * dataset['area']\n",
    "mse_test = np.mean((dataset['price'] - predictions) ** 2)\n",
    "mse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Función para la línea de regresión\n",
    "def regression_line(x):\n",
    "    return beta_0 + beta_1 * x\n",
    "\n",
    "# Valores para la línea de regresión\n",
    "x_values = np.linspace(dataset['area'].min(), dataset['area'].max(), 100)  # 100 puntos entre el min y max de 'area'\n",
    "y_values = regression_line(x_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2488861398180.6567"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcular el error cuadrático medio\n",
    "predictions = beta_0 + beta_1 * dataset['area']\n",
    "mse_test = np.mean((dataset['price'] - predictions) ** 2)\n",
    "mse_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es un enfoque simplista, pero permite estimar si una vivienda esta infravalorada o sobrevalorada, en base a su precio y su área"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Interpolación polinómica\n",
    "Para incrementar la precisión en la predicción del valor de una propiedad, podemos optar por polinomios de grado superior, lo que nos lleva a funciones no lineales. En un modelo lineal, la relación entre la variable dependiente $y$ (el precio) y la independiente $x$ (la superficie) se describe mediante la ecuación de una recta $y = mx + b$. En cambio, para un modelo polinomial de grado $n$, la relación se expresa como $y = b_0 + b_1x + b_2x^2 + ... + b_nx^n$, una función no lineal, donde $b_0, b_1, ..., b_n$ son los coeficientes.\n",
    "\n",
    "El ajuste de este modelo polinómico se realiza a través del método de mínimos cuadrados, que busca minimizar la suma de los cuadrados de las diferencias entre los valores observados y los predichos. La función de costo asociada se define como $J(b_0, b_1, ..., b_n) = \\frac{1}{2N} \\sum_{i=1}^{N} (y_i - (b_0 + b_1x_i + b_2x_i^2 + ... + b_nx_i^n))^2$.\n",
    "\n",
    "La meta es identificar los parámetros idóneos para nuestra función $f' = a x^2 + b x + c$ de tal manera que $f'(\\text{area})$ se aproxime estrechamente al $\\text{precio}$.\n",
    "\n",
    "## Ejemplo: Ajuste a un Polinomio de Segundo Grado\n",
    "Nos proponemos ajustar nuestros datos a una función de la forma $y = ax^2 + bx + c$, o en otras palabras, nuestro objetivo es resolver el sistema $y = X\\beta$, donde $\\beta$ representa el vector de coeficientes $[c, b, a]^T$.\n",
    "### Procedimiento para aislar $\\beta$\n",
    "Para determinar los coeficientes $\\beta$, reordenamos los términos de la siguiente forma:\n",
    "1. Partimos de la ecuación inicial $y = X\\beta$.\n",
    "2. Multiplicamos ambos lados por $X^T$, la matriz transpuesta de $X$, obteniendo $(X^T y) = (X^T X)\\beta$.\n",
    "3. Aplicamos la matriz inversa de $(X^T X)$ a ambos lados, resultando en $(X^T X)^{-1} (X^T y) = \\beta$.\n",
    "\n",
    "Es importante recordar que la multiplicación de una matriz por su inversa nos da la matriz identidad $I$, y por ende, $(X^T X)^{-1} (X^T X) = I$. Utilizando esta propiedad, simplificamos la expresión a:\n",
    "\n",
    "$ \\beta = (X^T X)^{-1} X^T y $\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.95440758e+05,  1.03518489e+03, -4.35645185e-02])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x =  dataset['area']                                            # datos de ejemplo\n",
    "y =  dataset['price']                                           # datos de ejemplo\n",
    "X = np.column_stack((np.ones(x.shape[0]), x, x**2))             # calculamos x⁰, x¹, y x²\n",
    "beta = np.linalg.inv(X.T @ X) @ X.T @ y                         # resolvemos el sistema y calculamos coeficientes\n",
    "beta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ya tenemos nuestra función $f'$ con coeficientes incluidos, vamos a plotearla con los datos y ver qué tal se ajusta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def polinomio(x):\n",
    "    return beta[0] + beta[1]*x + beta[2]*(x**2)\n",
    "\n",
    "area = dataset['area']\n",
    "precio = dataset['price']\n",
    "\n",
    "x_line = np.linspace(area.min(), area.max(), 400)\n",
    "y_line = polinomio(x_line)\n",
    "\n",
    "# Crear la gráfica\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(area, precio, color='blue', label='Datos Reales')\n",
    "plt.plot(x_line, y_line, color='red', label='Ajuste Polinómico de 2° Orden')\n",
    "plt.title('Precio vs. Área')\n",
    "plt.xlabel('Área')\n",
    "plt.ylabel('Precio')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Recursos\n",
    "[Andrew Ng](https://www.coursera.org/specializations/machine-learning-introduction)\n",
    "\n",
    "[GPT3.5](https://chat.openai.com)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}