scikit-learn-tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit learn tutorial\n",
    "\n",
    "Paquete de machine learning más popular con algoritmos preconstruidos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instalación en Jupyter\n",
    "\n",
    "* Con anaconda:\n",
    "```bash\n",
    "conda install scikit-learn\n",
    "```\n",
    "* Sin anaconda\n",
    "```bash\n",
    "pip install scikit-learn\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regresión lineal con python\n",
    "\n",
    "Se trata de un algoritmo de aprendizaje supervisado que se utiliza para Machine Learning y en estadistica.\n",
    "\n",
    "La regresion lineal es una aproximación para modelar la relación entre una variable escalar dependiente 'y' y una o más variables explicativas'x'\n",
    "\n",
    "La idea es **dibujar una recta que indicará la tendencia del conjunto de datos** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Obtenemos los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importamos los módulos que necesitamos\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cargamos un dataset de la página 'Kaggle.com' que contiene datasets gratis\n",
    "casas = pd.read_csv('datasets/USA_Housing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Area Income</th>\n",
       "      <th>Avg. Area House Age</th>\n",
       "      <th>Avg. Area Number of Rooms</th>\n",
       "      <th>Avg. Area Number of Bedrooms</th>\n",
       "      <th>Area Population</th>\n",
       "      <th>Price</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79545.458574</td>\n",
       "      <td>5.682861</td>\n",
       "      <td>7.009188</td>\n",
       "      <td>4.09</td>\n",
       "      <td>23086.800503</td>\n",
       "      <td>1.059034e+06</td>\n",
       "      <td>208 Michael Ferry Apt. 674\\nLaurabury, NE 3701...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79248.642455</td>\n",
       "      <td>6.002900</td>\n",
       "      <td>6.730821</td>\n",
       "      <td>3.09</td>\n",
       "      <td>40173.072174</td>\n",
       "      <td>1.505891e+06</td>\n",
       "      <td>188 Johnson Views Suite 079\\nLake Kathleen, CA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61287.067179</td>\n",
       "      <td>5.865890</td>\n",
       "      <td>8.512727</td>\n",
       "      <td>5.13</td>\n",
       "      <td>36882.159400</td>\n",
       "      <td>1.058988e+06</td>\n",
       "      <td>9127 Elizabeth Stravenue\\nDanieltown, WI 06482...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63345.240046</td>\n",
       "      <td>7.188236</td>\n",
       "      <td>5.586729</td>\n",
       "      <td>3.26</td>\n",
       "      <td>34310.242831</td>\n",
       "      <td>1.260617e+06</td>\n",
       "      <td>USS Barnett\\nFPO AP 44820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59982.197226</td>\n",
       "      <td>5.040555</td>\n",
       "      <td>7.839388</td>\n",
       "      <td>4.23</td>\n",
       "      <td>26354.109472</td>\n",
       "      <td>6.309435e+05</td>\n",
       "      <td>USNS Raymond\\nFPO AE 09386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>80175.754159</td>\n",
       "      <td>4.988408</td>\n",
       "      <td>6.104512</td>\n",
       "      <td>4.04</td>\n",
       "      <td>26748.428425</td>\n",
       "      <td>1.068138e+06</td>\n",
       "      <td>06039 Jennifer Islands Apt. 443\\nTracyport, KS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>64698.463428</td>\n",
       "      <td>6.025336</td>\n",
       "      <td>8.147760</td>\n",
       "      <td>3.41</td>\n",
       "      <td>60828.249085</td>\n",
       "      <td>1.502056e+06</td>\n",
       "      <td>4759 Daniel Shoals Suite 442\\nNguyenburgh, CO ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>78394.339278</td>\n",
       "      <td>6.989780</td>\n",
       "      <td>6.620478</td>\n",
       "      <td>2.42</td>\n",
       "      <td>36516.358972</td>\n",
       "      <td>1.573937e+06</td>\n",
       "      <td>972 Joyce Viaduct\\nLake William, TN 17778-6483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>59927.660813</td>\n",
       "      <td>5.362126</td>\n",
       "      <td>6.393121</td>\n",
       "      <td>2.30</td>\n",
       "      <td>29387.396003</td>\n",
       "      <td>7.988695e+05</td>\n",
       "      <td>USS Gilbert\\nFPO AA 20957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>81885.927184</td>\n",
       "      <td>4.423672</td>\n",
       "      <td>8.167688</td>\n",
       "      <td>6.10</td>\n",
       "      <td>40149.965749</td>\n",
       "      <td>1.545155e+06</td>\n",
       "      <td>Unit 9446 Box 0958\\nDPO AE 97025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
       "0      79545.458574             5.682861                   7.009188   \n",
       "1      79248.642455             6.002900                   6.730821   \n",
       "2      61287.067179             5.865890                   8.512727   \n",
       "3      63345.240046             7.188236                   5.586729   \n",
       "4      59982.197226             5.040555                   7.839388   \n",
       "5      80175.754159             4.988408                   6.104512   \n",
       "6      64698.463428             6.025336                   8.147760   \n",
       "7      78394.339278             6.989780                   6.620478   \n",
       "8      59927.660813             5.362126                   6.393121   \n",
       "9      81885.927184             4.423672                   8.167688   \n",
       "\n",
       "   Avg. Area Number of Bedrooms  Area Population         Price  \\\n",
       "0                          4.09     23086.800503  1.059034e+06   \n",
       "1                          3.09     40173.072174  1.505891e+06   \n",
       "2                          5.13     36882.159400  1.058988e+06   \n",
       "3                          3.26     34310.242831  1.260617e+06   \n",
       "4                          4.23     26354.109472  6.309435e+05   \n",
       "5                          4.04     26748.428425  1.068138e+06   \n",
       "6                          3.41     60828.249085  1.502056e+06   \n",
       "7                          2.42     36516.358972  1.573937e+06   \n",
       "8                          2.30     29387.396003  7.988695e+05   \n",
       "9                          6.10     40149.965749  1.545155e+06   \n",
       "\n",
       "                                             Address  \n",
       "0  208 Michael Ferry Apt. 674\\nLaurabury, NE 3701...  \n",
       "1  188 Johnson Views Suite 079\\nLake Kathleen, CA...  \n",
       "2  9127 Elizabeth Stravenue\\nDanieltown, WI 06482...  \n",
       "3                          USS Barnett\\nFPO AP 44820  \n",
       "4                         USNS Raymond\\nFPO AE 09386  \n",
       "5  06039 Jennifer Islands Apt. 443\\nTracyport, KS...  \n",
       "6  4759 Daniel Shoals Suite 442\\nNguyenburgh, CO ...  \n",
       "7     972 Joyce Viaduct\\nLake William, TN 17778-6483  \n",
       "8                          USS Gilbert\\nFPO AA 20957  \n",
       "9                   Unit 9446 Box 0958\\nDPO AE 97025  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casas.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Avg. Area Income', 'Avg. Area House Age', 'Avg. Area Number of Rooms',\n",
       "       'Avg. Area Number of Bedrooms', 'Area Population', 'Price', 'Address'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casas.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 7 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   Avg. Area Income              5000 non-null   float64\n",
      " 1   Avg. Area House Age           5000 non-null   float64\n",
      " 2   Avg. Area Number of Rooms     5000 non-null   float64\n",
      " 3   Avg. Area Number of Bedrooms  5000 non-null   float64\n",
      " 4   Area Population               5000 non-null   float64\n",
      " 5   Price                         5000 non-null   float64\n",
      " 6   Address                       5000 non-null   object \n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 273.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# nos da información como el número de columnas y los tipo de datos de cada una de las columnas\n",
    "casas.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg. Area Income</th>\n",
       "      <th>Avg. Area House Age</th>\n",
       "      <th>Avg. Area Number of Rooms</th>\n",
       "      <th>Avg. Area Number of Bedrooms</th>\n",
       "      <th>Area Population</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>68583.108984</td>\n",
       "      <td>5.977222</td>\n",
       "      <td>6.987792</td>\n",
       "      <td>3.981330</td>\n",
       "      <td>36163.516039</td>\n",
       "      <td>1.232073e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10657.991214</td>\n",
       "      <td>0.991456</td>\n",
       "      <td>1.005833</td>\n",
       "      <td>1.234137</td>\n",
       "      <td>9925.650114</td>\n",
       "      <td>3.531176e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17796.631190</td>\n",
       "      <td>2.644304</td>\n",
       "      <td>3.236194</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>172.610686</td>\n",
       "      <td>1.593866e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>61480.562388</td>\n",
       "      <td>5.322283</td>\n",
       "      <td>6.299250</td>\n",
       "      <td>3.140000</td>\n",
       "      <td>29403.928702</td>\n",
       "      <td>9.975771e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>68804.286404</td>\n",
       "      <td>5.970429</td>\n",
       "      <td>7.002902</td>\n",
       "      <td>4.050000</td>\n",
       "      <td>36199.406689</td>\n",
       "      <td>1.232669e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75783.338666</td>\n",
       "      <td>6.650808</td>\n",
       "      <td>7.665871</td>\n",
       "      <td>4.490000</td>\n",
       "      <td>42861.290769</td>\n",
       "      <td>1.471210e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>107701.748378</td>\n",
       "      <td>9.519088</td>\n",
       "      <td>10.759588</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>69621.713378</td>\n",
       "      <td>2.469066e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
       "count       5000.000000          5000.000000                5000.000000   \n",
       "mean       68583.108984             5.977222                   6.987792   \n",
       "std        10657.991214             0.991456                   1.005833   \n",
       "min        17796.631190             2.644304                   3.236194   \n",
       "25%        61480.562388             5.322283                   6.299250   \n",
       "50%        68804.286404             5.970429                   7.002902   \n",
       "75%        75783.338666             6.650808                   7.665871   \n",
       "max       107701.748378             9.519088                  10.759588   \n",
       "\n",
       "       Avg. Area Number of Bedrooms  Area Population         Price  \n",
       "count                   5000.000000      5000.000000  5.000000e+03  \n",
       "mean                       3.981330     36163.516039  1.232073e+06  \n",
       "std                        1.234137      9925.650114  3.531176e+05  \n",
       "min                        2.000000       172.610686  1.593866e+04  \n",
       "25%                        3.140000     29403.928702  9.975771e+05  \n",
       "50%                        4.050000     36199.406689  1.232669e+06  \n",
       "75%                        4.490000     42861.290769  1.471210e+06  \n",
       "max                        6.500000     69621.713378  2.469066e+06  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nos aporta algunos datos como el total de filas, la media de cada columna, desviación típica, precio mínimo, el 25%...\n",
    "# por ejemplo, el precio medio es de 1.232073e+06 \n",
    "casas.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.059034e+06\n",
       "1       1.505891e+06\n",
       "2       1.058988e+06\n",
       "3       1.260617e+06\n",
       "4       6.309435e+05\n",
       "            ...     \n",
       "4995    1.060194e+06\n",
       "4996    1.482618e+06\n",
       "4997    1.030730e+06\n",
       "4998    1.198657e+06\n",
       "4999    1.298950e+06\n",
       "Name: Price, Length: 5000, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# para coger los datos de una columna\n",
    "casas['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c10cf1d10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Realizamos un gráfico histograma de la distribución del precio medio de nuestro dataset:\n",
    "# Coincide con que la punta de valores se encuentra en el precio medio (es de 1.232073e+06)\n",
    "sns.distplot(casas['Price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dividimos los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Avg. Area Income', 'Avg. Area House Age', 'Avg. Area Number of Rooms',\n",
       "       'Avg. Area Number of Bedrooms', 'Area Population', 'Price', 'Address'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# momstramos las columnas\n",
    "casas.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eje X = eje de características (todas las columnas menos el precio) que contienen los valores que caracterizan\n",
    "# Eje Y = variable objetivo (precio), que será la variable que queremos predecir\n",
    "x = casas[['Avg. Area Income', 'Avg. Area House Age', 'Avg. Area Number of Rooms',\n",
    "       'Avg. Area Number of Bedrooms', 'Area Population']]\n",
    "y = casas['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# con el parámetro 'test_size' separamos el porcentaje de datos que van a ir destinados al test\n",
    "# de 5000 (100%): 70% entrenamiento, 30% tests\n",
    "# random_state=42 valor que vamos a darle para que coja los valores de entrenamiento de forma aleatoria\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.3, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Avg. Area Income</th>\n",
       "      <th>Avg. Area House Age</th>\n",
       "      <th>Avg. Area Number of Rooms</th>\n",
       "      <th>Avg. Area Number of Bedrooms</th>\n",
       "      <th>Area Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>55245.337288</td>\n",
       "      <td>3.965745</td>\n",
       "      <td>8.961106</td>\n",
       "      <td>4.38</td>\n",
       "      <td>43557.943435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2115</th>\n",
       "      <td>62305.638407</td>\n",
       "      <td>6.490769</td>\n",
       "      <td>7.647362</td>\n",
       "      <td>3.19</td>\n",
       "      <td>47066.617420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4437</th>\n",
       "      <td>77345.472379</td>\n",
       "      <td>5.407515</td>\n",
       "      <td>8.243178</td>\n",
       "      <td>3.11</td>\n",
       "      <td>26706.911029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>65846.171039</td>\n",
       "      <td>6.385374</td>\n",
       "      <td>6.804131</td>\n",
       "      <td>3.18</td>\n",
       "      <td>28214.363551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2486</th>\n",
       "      <td>69350.793357</td>\n",
       "      <td>6.910415</td>\n",
       "      <td>8.288048</td>\n",
       "      <td>4.29</td>\n",
       "      <td>36779.058567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4426</th>\n",
       "      <td>76223.561256</td>\n",
       "      <td>6.371627</td>\n",
       "      <td>5.342217</td>\n",
       "      <td>2.42</td>\n",
       "      <td>30165.337445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>56685.014442</td>\n",
       "      <td>6.958045</td>\n",
       "      <td>7.502115</td>\n",
       "      <td>3.38</td>\n",
       "      <td>43322.166854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3092</th>\n",
       "      <td>66195.337714</td>\n",
       "      <td>6.507971</td>\n",
       "      <td>6.611861</td>\n",
       "      <td>3.14</td>\n",
       "      <td>37288.923574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3772</th>\n",
       "      <td>58694.515017</td>\n",
       "      <td>7.394768</td>\n",
       "      <td>9.269453</td>\n",
       "      <td>4.32</td>\n",
       "      <td>49960.977236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>61162.580254</td>\n",
       "      <td>5.896316</td>\n",
       "      <td>7.880521</td>\n",
       "      <td>6.04</td>\n",
       "      <td>36033.701431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3500 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
       "1840      55245.337288             3.965745                   8.961106   \n",
       "2115      62305.638407             6.490769                   7.647362   \n",
       "4437      77345.472379             5.407515                   8.243178   \n",
       "1146      65846.171039             6.385374                   6.804131   \n",
       "2486      69350.793357             6.910415                   8.288048   \n",
       "...                ...                  ...                        ...   \n",
       "4426      76223.561256             6.371627                   5.342217   \n",
       "466       56685.014442             6.958045                   7.502115   \n",
       "3092      66195.337714             6.507971                   6.611861   \n",
       "3772      58694.515017             7.394768                   9.269453   \n",
       "860       61162.580254             5.896316                   7.880521   \n",
       "\n",
       "      Avg. Area Number of Bedrooms  Area Population  \n",
       "1840                          4.38     43557.943435  \n",
       "2115                          3.19     47066.617420  \n",
       "4437                          3.11     26706.911029  \n",
       "1146                          3.18     28214.363551  \n",
       "2486                          4.29     36779.058567  \n",
       "...                            ...              ...  \n",
       "4426                          2.42     30165.337445  \n",
       "466                           3.38     43322.166854  \n",
       "3092                          3.14     37288.923574  \n",
       "3772                          4.32     49960.977236  \n",
       "860                           6.04     36033.701431  \n",
       "\n",
       "[3500 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Avg. Area Income</th>\n",
       "      <th>Avg. Area House Age</th>\n",
       "      <th>Avg. Area Number of Rooms</th>\n",
       "      <th>Avg. Area Number of Bedrooms</th>\n",
       "      <th>Area Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>61907.593345</td>\n",
       "      <td>7.017838</td>\n",
       "      <td>6.440256</td>\n",
       "      <td>3.25</td>\n",
       "      <td>43828.947207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2586</th>\n",
       "      <td>57160.202243</td>\n",
       "      <td>6.893260</td>\n",
       "      <td>6.921532</td>\n",
       "      <td>3.13</td>\n",
       "      <td>43467.147035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2653</th>\n",
       "      <td>70190.796445</td>\n",
       "      <td>6.745054</td>\n",
       "      <td>6.662567</td>\n",
       "      <td>2.01</td>\n",
       "      <td>29215.136112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>69316.796889</td>\n",
       "      <td>6.300409</td>\n",
       "      <td>7.873576</td>\n",
       "      <td>4.28</td>\n",
       "      <td>24448.211461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>72991.481649</td>\n",
       "      <td>3.412866</td>\n",
       "      <td>6.494081</td>\n",
       "      <td>2.48</td>\n",
       "      <td>50626.495426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3563</th>\n",
       "      <td>74208.124644</td>\n",
       "      <td>6.259782</td>\n",
       "      <td>8.666717</td>\n",
       "      <td>3.38</td>\n",
       "      <td>25507.336340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>71112.350296</td>\n",
       "      <td>5.612677</td>\n",
       "      <td>7.419542</td>\n",
       "      <td>4.26</td>\n",
       "      <td>24134.413996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1837</th>\n",
       "      <td>63665.394418</td>\n",
       "      <td>6.729105</td>\n",
       "      <td>7.732959</td>\n",
       "      <td>3.43</td>\n",
       "      <td>44029.681816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2380</th>\n",
       "      <td>56073.892443</td>\n",
       "      <td>6.576733</td>\n",
       "      <td>6.959056</td>\n",
       "      <td>4.40</td>\n",
       "      <td>64149.680213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>66774.181946</td>\n",
       "      <td>5.476416</td>\n",
       "      <td>8.060545</td>\n",
       "      <td>3.12</td>\n",
       "      <td>23793.337043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1500 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
       "1501      61907.593345             7.017838                   6.440256   \n",
       "2586      57160.202243             6.893260                   6.921532   \n",
       "2653      70190.796445             6.745054                   6.662567   \n",
       "1055      69316.796889             6.300409                   7.873576   \n",
       "705       72991.481649             3.412866                   6.494081   \n",
       "...                ...                  ...                        ...   \n",
       "3563      74208.124644             6.259782                   8.666717   \n",
       "1538      71112.350296             5.612677                   7.419542   \n",
       "1837      63665.394418             6.729105                   7.732959   \n",
       "2380      56073.892443             6.576733                   6.959056   \n",
       "1912      66774.181946             5.476416                   8.060545   \n",
       "\n",
       "      Avg. Area Number of Bedrooms  Area Population  \n",
       "1501                          3.25     43828.947207  \n",
       "2586                          3.13     43467.147035  \n",
       "2653                          2.01     29215.136112  \n",
       "1055                          4.28     24448.211461  \n",
       "705                           2.48     50626.495426  \n",
       "...                            ...              ...  \n",
       "3563                          3.38     25507.336340  \n",
       "1538                          4.26     24134.413996  \n",
       "1837                          3.43     44029.681816  \n",
       "2380                          4.40     64149.680213  \n",
       "1912                          3.12     23793.337043  \n",
       "\n",
       "[1500 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obtenemos el modelo de entrenamiento "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrn = LinearRegression()\n",
    "lrn.fit(x_train, y_train) # entrenamos el modelo!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# del 30% de valores restantes, obetenemos las prediciones del precio\n",
    "predicciones = lrn.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluamos el modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>precios predecidos</th>\n",
       "      <th>precios reales</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>1.308536e+06</td>\n",
       "      <td>1.339096e+06</td>\n",
       "      <td>30559.941319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2586</th>\n",
       "      <td>1.237123e+06</td>\n",
       "      <td>1.251794e+06</td>\n",
       "      <td>14671.451372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2653</th>\n",
       "      <td>1.243836e+06</td>\n",
       "      <td>1.340095e+06</td>\n",
       "      <td>96259.338035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>1.229242e+06</td>\n",
       "      <td>1.431508e+06</td>\n",
       "      <td>202265.920788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>1.059353e+06</td>\n",
       "      <td>1.042374e+06</td>\n",
       "      <td>-16978.994123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1.542359e+06</td>\n",
       "      <td>1.555321e+06</td>\n",
       "      <td>12961.788393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>1.095211e+06</td>\n",
       "      <td>1.250882e+06</td>\n",
       "      <td>155671.675730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2468</th>\n",
       "      <td>8.326763e+05</td>\n",
       "      <td>1.039381e+06</td>\n",
       "      <td>206704.400853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2413</th>\n",
       "      <td>7.889063e+05</td>\n",
       "      <td>8.324752e+05</td>\n",
       "      <td>43568.856786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600</th>\n",
       "      <td>1.468528e+06</td>\n",
       "      <td>1.420648e+06</td>\n",
       "      <td>-47879.449178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      precios predecidos  precios reales          error\n",
       "1501        1.308536e+06    1.339096e+06   30559.941319\n",
       "2586        1.237123e+06    1.251794e+06   14671.451372\n",
       "2653        1.243836e+06    1.340095e+06   96259.338035\n",
       "1055        1.229242e+06    1.431508e+06  202265.920788\n",
       "705         1.059353e+06    1.042374e+06  -16978.994123\n",
       "106         1.542359e+06    1.555321e+06   12961.788393\n",
       "589         1.095211e+06    1.250882e+06  155671.675730\n",
       "2468        8.326763e+05    1.039381e+06  206704.400853\n",
       "2413        7.889063e+05    8.324752e+05   43568.856786\n",
       "1600        1.468528e+06    1.420648e+06  -47879.449178"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comparamos en una tabla los precios predecidos VS precios reales\n",
    "diccionario = {'precios predecidos':predicciones, 'precios reales':y_test, 'error':y_test-predicciones}\n",
    "dataframe = pd.DataFrame(diccionario)\n",
    "dataframe.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f4c0dc0ff90>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vamos a ver los valores graficados: al estar todos juntos quiere decir que se ha hecho bien\n",
    "plt.scatter(y_test,predicciones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c0c46f350>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# el diagrama debería tener forma de distribución normal (en forma de capana invertida) para dar por valido el modelo\n",
    "sns.distplot(y_test-predicciones)"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}