Prediccion_Clasificacion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import plotly.offline as py\n",
    "import plotly.graph_objs as go\n",
    "import warnings\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place</th>\n",
       "      <th>Location</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Toilets</th>\n",
       "      <th>Area</th>\n",
       "      <th>Air Conditioning</th>\n",
       "      <th>Built-in Wardrobes</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Heating</th>\n",
       "      <th>Garage</th>\n",
       "      <th>Terrace</th>\n",
       "      <th>Furnished</th>\n",
       "      <th>Balcony</th>\n",
       "      <th>Garden</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ático</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2000</td>\n",
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       "      <th>2</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1200</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Place    Location  Rooms  Toilets  Area  Air Conditioning  \\\n",
       "0   Piso  Arganzuela      2        2    70                 1   \n",
       "1  Ático  Arganzuela      2        2    86                 1   \n",
       "2   Piso  Arganzuela      1        1    74                 1   \n",
       "3   Piso  Arganzuela      1        1    28                 1   \n",
       "4   Piso  Arganzuela      2        2    65                 1   \n",
       "\n",
       "   Built-in Wardrobes  Elevator  Heating  Garage  Terrace  Furnished  Balcony  \\\n",
       "0                   1         1        1       0        0          1        0   \n",
       "1                   1         1        1       0        1          1        0   \n",
       "2                   1         1        1       1        0          0        0   \n",
       "3                   1         1        1       0        1          1        0   \n",
       "4                   0         1        1       1        1          1        1   \n",
       "\n",
       "   Garden  Pool  Price  \n",
       "0       0     0   3500  \n",
       "1       0     1   2000  \n",
       "2       1     1   1200  \n",
       "3       1     1   1400  \n",
       "4       1     1   2490  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lectura del DataFrame\n",
    "df = pd.read_csv(r'C:\\Users\\Diana\\Desktop\\Formacion\\dsft-jun23-Diana\\Desafio\\houses_rent_info2 (1).csv', sep = \",\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Place</th>\n",
       "      <th>Location</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Toilets</th>\n",
       "      <th>Area</th>\n",
       "      <th>Air Conditioning</th>\n",
       "      <th>Built-in Wardrobes</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Heating</th>\n",
       "      <th>Garage</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5325</td>\n",
       "      <td>5325</td>\n",
       "      <td>5325.000000</td>\n",
       "      <td>5325.000000</td>\n",
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       "      <td>5325.000000</td>\n",
       "      <td>5325.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Salamanca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4792</td>\n",
       "      <td>1156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.244883</td>\n",
       "      <td>1.826479</td>\n",
       "      <td>109.899718</td>\n",
       "      <td>0.783099</td>\n",
       "      <td>0.770704</td>\n",
       "      <td>0.856526</td>\n",
       "      <td>0.795493</td>\n",
       "      <td>0.248451</td>\n",
       "      <td>0.355681</td>\n",
       "      <td>0.647700</td>\n",
       "      <td>0.260469</td>\n",
       "      <td>0.141972</td>\n",
       "      <td>0.185164</td>\n",
       "      <td>2636.454836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.190896</td>\n",
       "      <td>0.976976</td>\n",
       "      <td>71.719901</td>\n",
       "      <td>0.412174</td>\n",
       "      <td>0.420419</td>\n",
       "      <td>0.350589</td>\n",
       "      <td>0.403379</td>\n",
       "      <td>0.432155</td>\n",
       "      <td>0.478764</td>\n",
       "      <td>0.477732</td>\n",
       "      <td>0.438932</td>\n",
       "      <td>0.349054</td>\n",
       "      <td>0.388467</td>\n",
       "      <td>1750.519076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20000.000000</td>\n",
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      ],
      "text/plain": [
       "       Place   Location        Rooms      Toilets         Area  \\\n",
       "count   5325       5325  5325.000000  5325.000000  5325.000000   \n",
       "unique     4         21          NaN          NaN          NaN   \n",
       "top     Piso  Salamanca          NaN          NaN          NaN   \n",
       "freq    4792       1156          NaN          NaN          NaN   \n",
       "mean     NaN        NaN     2.244883     1.826479   109.899718   \n",
       "std      NaN        NaN     1.190896     0.976976    71.719901   \n",
       "min      NaN        NaN     1.000000     1.000000    13.000000   \n",
       "25%      NaN        NaN     1.000000     1.000000    64.000000   \n",
       "50%      NaN        NaN     2.000000     2.000000    90.000000   \n",
       "75%      NaN        NaN     3.000000     2.000000   134.000000   \n",
       "max      NaN        NaN    40.000000    11.000000  1400.000000   \n",
       "\n",
       "        Air Conditioning  Built-in Wardrobes     Elevator      Heating  \\\n",
       "count        5325.000000         5325.000000  5325.000000  5325.000000   \n",
       "unique               NaN                 NaN          NaN          NaN   \n",
       "top                  NaN                 NaN          NaN          NaN   \n",
       "freq                 NaN                 NaN          NaN          NaN   \n",
       "mean            0.783099            0.770704     0.856526     0.795493   \n",
       "std             0.412174            0.420419     0.350589     0.403379   \n",
       "min             0.000000            0.000000     0.000000     0.000000   \n",
       "25%             1.000000            1.000000     1.000000     1.000000   \n",
       "50%             1.000000            1.000000     1.000000     1.000000   \n",
       "75%             1.000000            1.000000     1.000000     1.000000   \n",
       "max             1.000000            1.000000     1.000000     1.000000   \n",
       "\n",
       "             Garage      Terrace    Furnished      Balcony       Garden  \\\n",
       "count   5325.000000  5325.000000  5325.000000  5325.000000  5325.000000   \n",
       "unique          NaN          NaN          NaN          NaN          NaN   \n",
       "top             NaN          NaN          NaN          NaN          NaN   \n",
       "freq            NaN          NaN          NaN          NaN          NaN   \n",
       "mean       0.248451     0.355681     0.647700     0.260469     0.141972   \n",
       "std        0.432155     0.478764     0.477732     0.438932     0.349054   \n",
       "min        0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%        0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%        0.000000     0.000000     1.000000     0.000000     0.000000   \n",
       "75%        0.000000     1.000000     1.000000     1.000000     0.000000   \n",
       "max        1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "               Pool         Price  \n",
       "count   5325.000000   5325.000000  \n",
       "unique          NaN           NaN  \n",
       "top             NaN           NaN  \n",
       "freq            NaN           NaN  \n",
       "mean       0.185164   2636.454836  \n",
       "std        0.388467   1750.519076  \n",
       "min        0.000000    400.000000  \n",
       "25%        0.000000   1500.000000  \n",
       "50%        0.000000   2240.000000  \n",
       "75%        0.000000   3200.000000  \n",
       "max        1.000000  20000.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estadísticos\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5325 entries, 0 to 5324\n",
      "Data columns (total 16 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   Place               5325 non-null   object\n",
      " 1   Location            5325 non-null   object\n",
      " 2   Rooms               5325 non-null   int64 \n",
      " 3   Toilets             5325 non-null   int64 \n",
      " 4   Area                5325 non-null   int64 \n",
      " 5   Air Conditioning    5325 non-null   int64 \n",
      " 6   Built-in Wardrobes  5325 non-null   int64 \n",
      " 7   Elevator            5325 non-null   int64 \n",
      " 8   Heating             5325 non-null   int64 \n",
      " 9   Garage              5325 non-null   int64 \n",
      " 10  Terrace             5325 non-null   int64 \n",
      " 11  Furnished           5325 non-null   int64 \n",
      " 12  Balcony             5325 non-null   int64 \n",
      " 13  Garden              5325 non-null   int64 \n",
      " 14  Pool                5325 non-null   int64 \n",
      " 15  Price               5325 non-null   int64 \n",
      "dtypes: int64(14), object(2)\n",
      "memory usage: 665.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Tipos de los datos y missings\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>COL_N</th>\n",
       "      <th>Place</th>\n",
       "      <th>Location</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Toilets</th>\n",
       "      <th>Area</th>\n",
       "      <th>Air Conditioning</th>\n",
       "      <th>Built-in Wardrobes</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Heating</th>\n",
       "      <th>Garage</th>\n",
       "      <th>Terrace</th>\n",
       "      <th>Furnished</th>\n",
       "      <th>Balcony</th>\n",
       "      <th>Garden</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DATA_TYPE</th>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MISSINGS (%)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNIQUE_VALUES</th>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>318</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARDIN (%)</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.17</td>\n",
       "      <td>5.97</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.04</td>\n",
       "      <td>14.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "COL_N           Place Location  Rooms Toilets   Area Air Conditioning  \\\n",
       "DATA_TYPE      object   object  int64   int64  int64            int64   \n",
       "MISSINGS (%)      0.0      0.0    0.0     0.0    0.0              0.0   \n",
       "UNIQUE_VALUES       4       21     10       9    318                2   \n",
       "CARDIN (%)       0.08     0.39   0.19    0.17   5.97             0.04   \n",
       "\n",
       "COL_N         Built-in Wardrobes Elevator Heating Garage Terrace Furnished  \\\n",
       "DATA_TYPE                  int64    int64   int64  int64   int64     int64   \n",
       "MISSINGS (%)                 0.0      0.0     0.0    0.0     0.0       0.0   \n",
       "UNIQUE_VALUES                  2        2       2      2       2         2   \n",
       "CARDIN (%)                  0.04     0.04    0.04   0.04    0.04      0.04   \n",
       "\n",
       "COL_N         Balcony Garden   Pool  Price  \n",
       "DATA_TYPE       int64  int64  int64  int64  \n",
       "MISSINGS (%)      0.0    0.0    0.0    0.0  \n",
       "UNIQUE_VALUES       2      2      2    761  \n",
       "CARDIN (%)       0.04   0.04   0.04  14.29  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def data_report(df):\n",
    "    # Sacamos los NOMBRES\n",
    "    cols = pd.DataFrame(df.columns.values, columns=[\"COL_N\"])\n",
    "\n",
    "    # Sacamos los TIPOS\n",
    "    types = pd.DataFrame(df.dtypes.values, columns=[\"DATA_TYPE\"])\n",
    "\n",
    "    # Sacamos los MISSINGS\n",
    "    percent_missing = round(df.isnull().sum() * 100 / len(df), 2)\n",
    "    percent_missing_df = pd.DataFrame(percent_missing.values, columns=[\"MISSINGS (%)\"])\n",
    "\n",
    "    # Sacamos los VALORES UNICOS\n",
    "    unicos = pd.DataFrame(df.nunique().values, columns=[\"UNIQUE_VALUES\"])\n",
    "    \n",
    "    percent_cardin = round(unicos['UNIQUE_VALUES']*100/len(df), 2)\n",
    "    percent_cardin_df = pd.DataFrame(percent_cardin.values, columns=[\"CARDIN (%)\"])\n",
    "\n",
    "    concatenado = pd.concat([cols, types, percent_missing_df, unicos, percent_cardin_df], axis=1, sort=False)\n",
    "    concatenado.set_index('COL_N', drop=True, inplace=True)\n",
    "\n",
    "    return concatenado.T\n",
    "        \n",
    "data_report(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Place      \n",
       "Piso           4792\n",
       "Ático           406\n",
       "Dúplex          119\n",
       "Apartamento       8\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Place']].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "\n",
    "# Usamos un scatter plot para mostrar la relación entre 'Location' y 'Price'\n",
    "sns.scatterplot(x='Place', y='Price', data=df)\n",
    "\n",
    "# Rotamos las etiquetas del eje x para mejorar la legibilidad\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Agregamos un título y etiquetas a los ejes\n",
    "plt.title('Relación entre Localización y Precio')\n",
    "plt.xlabel('Localización')\n",
    "plt.ylabel('Precio')\n",
    "\n",
    "# Mostramos el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25, 8))\n",
    "\n",
    "# Usamos un scatter plot para mostrar la relación entre 'Location' y 'Price'\n",
    "sns.scatterplot(x='Location', y='Price', data=df)\n",
    "\n",
    "# Rotamos las etiquetas del eje x para mejorar la legibilidad\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Agregamos un título y etiquetas a los ejes\n",
    "plt.title('Relación entre Localización y Precio')\n",
    "plt.xlabel('Localización')\n",
    "plt.ylabel('Precio')\n",
    "\n",
    "# Mostramos el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5283"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_obs_menor_igual_5000 = df[df['Price'] <= 10000].shape[0]\n",
    "num_obs_menor_igual_5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2240.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Price'].agg('median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Salamanca</th>\n",
       "      <td>3620.451557</td>\n",
       "      <td>845</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chamberí</th>\n",
       "      <td>3045.518400</td>\n",
       "      <td>800</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chamartín</th>\n",
       "      <td>2919.549683</td>\n",
       "      <td>850</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retiro</th>\n",
       "      <td>2791.245033</td>\n",
       "      <td>800</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Centro</th>\n",
       "      <td>2575.994247</td>\n",
       "      <td>675</td>\n",
       "      <td>14000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moncloa</th>\n",
       "      <td>2339.254717</td>\n",
       "      <td>740</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hortaleza</th>\n",
       "      <td>2027.715640</td>\n",
       "      <td>650</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tetuán</th>\n",
       "      <td>2002.106383</td>\n",
       "      <td>720</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuencarral</th>\n",
       "      <td>1776.464000</td>\n",
       "      <td>625</td>\n",
       "      <td>3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ciudad Lineal</th>\n",
       "      <td>1767.070312</td>\n",
       "      <td>750</td>\n",
       "      <td>5300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arganzuela</th>\n",
       "      <td>1626.436047</td>\n",
       "      <td>790</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usera</th>\n",
       "      <td>1449.196429</td>\n",
       "      <td>690</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Villaverde</th>\n",
       "      <td>1446.710843</td>\n",
       "      <td>700</td>\n",
       "      <td>2310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Blas</th>\n",
       "      <td>1325.696970</td>\n",
       "      <td>790</td>\n",
       "      <td>4375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barajas</th>\n",
       "      <td>1300.500000</td>\n",
       "      <td>825</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latina</th>\n",
       "      <td>1272.000000</td>\n",
       "      <td>800</td>\n",
       "      <td>2715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vicálvaro</th>\n",
       "      <td>1191.000000</td>\n",
       "      <td>740</td>\n",
       "      <td>1750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carabanchel</th>\n",
       "      <td>1160.291667</td>\n",
       "      <td>750</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puente de Vallecas</th>\n",
       "      <td>1150.186441</td>\n",
       "      <td>550</td>\n",
       "      <td>6543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moratalaz</th>\n",
       "      <td>1099.642857</td>\n",
       "      <td>400</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Villa de Vallecas</th>\n",
       "      <td>976.526316</td>\n",
       "      <td>700</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           mean  min    max\n",
       "Location                                   \n",
       "Salamanca           3620.451557  845  20000\n",
       "Chamberí            3045.518400  800  20000\n",
       "Chamartín           2919.549683  850  15000\n",
       "Retiro              2791.245033  800  15000\n",
       "Centro              2575.994247  675  14000\n",
       "Moncloa             2339.254717  740   9000\n",
       "Hortaleza           2027.715640  650   7000\n",
       "Tetuán              2002.106383  720   6000\n",
       "Fuencarral          1776.464000  625   3900\n",
       "Ciudad Lineal       1767.070312  750   5300\n",
       "Arganzuela          1626.436047  790   3500\n",
       "Usera               1449.196429  690   3500\n",
       "Villaverde          1446.710843  700   2310\n",
       "San Blas            1325.696970  790   4375\n",
       "Barajas             1300.500000  825   1800\n",
       "Latina              1272.000000  800   2715\n",
       "Vicálvaro           1191.000000  740   1750\n",
       "Carabanchel         1160.291667  750   2000\n",
       "Puente de Vallecas  1150.186441  550   6543\n",
       "Moratalaz           1099.642857  400   1400\n",
       "Villa de Vallecas    976.526316  700   1480"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precios_localizacion = df.groupby('Location')['Price'].agg(['mean', 'min', 'max'])\n",
    "resultados_sorted = precios_localizacion.sort_values(by='mean', ascending=False)\n",
    "resultados_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''df.loc[df['Price'] > 5000, 'Price'] = df['Price'].agg('median')'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "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>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Moncloa</th>\n",
       "      <td>2267.179245</td>\n",
       "      <td>740</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retiro</th>\n",
       "      <td>2422.536424</td>\n",
       "      <td>800</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Centro</th>\n",
       "      <td>2356.531160</td>\n",
       "      <td>675</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chamartín</th>\n",
       "      <td>2417.837209</td>\n",
       "      <td>850</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chamberí</th>\n",
       "      <td>2702.358400</td>\n",
       "      <td>800</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tetuán</th>\n",
       "      <td>1983.436170</td>\n",
       "      <td>720</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salamanca</th>\n",
       "      <td>2800.881488</td>\n",
       "      <td>845</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hortaleza</th>\n",
       "      <td>1955.014218</td>\n",
       "      <td>650</td>\n",
       "      <td>4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Blas</th>\n",
       "      <td>1325.696970</td>\n",
       "      <td>790</td>\n",
       "      <td>4375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ciudad Lineal</th>\n",
       "      <td>1743.164062</td>\n",
       "      <td>750</td>\n",
       "      <td>4300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuencarral</th>\n",
       "      <td>1776.464000</td>\n",
       "      <td>625</td>\n",
       "      <td>3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usera</th>\n",
       "      <td>1449.196429</td>\n",
       "      <td>690</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arganzuela</th>\n",
       "      <td>1626.436047</td>\n",
       "      <td>790</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latina</th>\n",
       "      <td>1272.000000</td>\n",
       "      <td>800</td>\n",
       "      <td>2715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puente de Vallecas</th>\n",
       "      <td>1077.254237</td>\n",
       "      <td>550</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Villaverde</th>\n",
       "      <td>1446.710843</td>\n",
       "      <td>700</td>\n",
       "      <td>2310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carabanchel</th>\n",
       "      <td>1160.291667</td>\n",
       "      <td>750</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barajas</th>\n",
       "      <td>1300.500000</td>\n",
       "      <td>825</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vicálvaro</th>\n",
       "      <td>1191.000000</td>\n",
       "      <td>740</td>\n",
       "      <td>1750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Villa de Vallecas</th>\n",
       "      <td>976.526316</td>\n",
       "      <td>700</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moratalaz</th>\n",
       "      <td>1099.642857</td>\n",
       "      <td>400</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           mean  min   max\n",
       "Location                                  \n",
       "Moncloa             2267.179245  740  5000\n",
       "Retiro              2422.536424  800  5000\n",
       "Centro              2356.531160  675  5000\n",
       "Chamartín           2417.837209  850  5000\n",
       "Chamberí            2702.358400  800  5000\n",
       "Tetuán              1983.436170  720  5000\n",
       "Salamanca           2800.881488  845  5000\n",
       "Hortaleza           1955.014218  650  4700\n",
       "San Blas            1325.696970  790  4375\n",
       "Ciudad Lineal       1743.164062  750  4300\n",
       "Fuencarral          1776.464000  625  3900\n",
       "Usera               1449.196429  690  3500\n",
       "Arganzuela          1626.436047  790  3500\n",
       "Latina              1272.000000  800  2715\n",
       "Puente de Vallecas  1077.254237  550  2500\n",
       "Villaverde          1446.710843  700  2310\n",
       "Carabanchel         1160.291667  750  2000\n",
       "Barajas             1300.500000  825  1800\n",
       "Vicálvaro           1191.000000  740  1750\n",
       "Villa de Vallecas    976.526316  700  1480\n",
       "Moratalaz           1099.642857  400  1400"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''precios_localizacion = df.groupby('Location')['Price'].agg(['mean', 'min', 'max'])\n",
    "resultados_sorted = precios_localizacion.sort_values(by='max', ascending=False)\n",
    "resultados_sorted'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "df['Location_num'] = le.fit_transform(df['Location'])\n",
    "df['Place_num'] = le.fit_transform(df['Place'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alquileres menores a 1500€ 1257\n",
      "Alquileres entre 1501€ y 2500€ 1781\n",
      "Alquileres mayores a 2501€ 2152\n"
     ]
    }
   ],
   "source": [
    "precios_menor_1500 = df[df['Price'] < 1500]['Price'].count()\n",
    "precios_entre_1501_y_2500 = df[(df['Price'] >= 1501) & (df['Price'] <= 2500)]['Price'].count()\n",
    "precios_mayor_2501 = df[df['Price'] > 2501]['Price'].count()\n",
    "\n",
    "print('Alquileres menores a 1500€',precios_menor_1500)\n",
    "print('Alquileres entre 1501€ y 2500€',precios_entre_1501_y_2500)\n",
    "print('Alquileres mayores a 2501€',precios_mayor_2501)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>Place</th>\n",
       "      <th>Location</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Toilets</th>\n",
       "      <th>Area</th>\n",
       "      <th>Air Conditioning</th>\n",
       "      <th>Built-in Wardrobes</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Heating</th>\n",
       "      <th>Garage</th>\n",
       "      <th>Terrace</th>\n",
       "      <th>Furnished</th>\n",
       "      <th>Balcony</th>\n",
       "      <th>Garden</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Price</th>\n",
       "      <th>Location_num</th>\n",
       "      <th>Place_num</th>\n",
       "      <th>Precio Rango</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3500</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>&gt; 2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ático</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1500 - 2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt; 1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1400</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt; 1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2490</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1500 - 2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5320</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Villaverde</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>75</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>0</td>\n",
       "      <td>840</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt; 1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5321</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Villaverde</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt; 1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5322</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Villaverde</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1650</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1500 - 2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5323</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Villaverde</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71</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",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1650</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1500 - 2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5324</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Villaverde</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>840</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt; 1500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5325 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Place    Location  Rooms  Toilets  Area  Air Conditioning  \\\n",
       "0      Piso  Arganzuela      2        2    70                 1   \n",
       "1     Ático  Arganzuela      2        2    86                 1   \n",
       "2      Piso  Arganzuela      1        1    74                 1   \n",
       "3      Piso  Arganzuela      1        1    28                 1   \n",
       "4      Piso  Arganzuela      2        2    65                 1   \n",
       "...     ...         ...    ...      ...   ...               ...   \n",
       "5320   Piso  Villaverde      3        1    75                 0   \n",
       "5321   Piso  Villaverde      1        1    40                 0   \n",
       "5322   Piso  Villaverde      1        2    67                 1   \n",
       "5323   Piso  Villaverde      1        1    71                 1   \n",
       "5324   Piso  Villaverde      3        1    56                 0   \n",
       "\n",
       "      Built-in Wardrobes  Elevator  Heating  Garage  Terrace  Furnished  \\\n",
       "0                      1         1        1       0        0          1   \n",
       "1                      1         1        1       0        1          1   \n",
       "2                      1         1        1       1        0          0   \n",
       "3                      1         1        1       0        1          1   \n",
       "4                      0         1        1       1        1          1   \n",
       "...                  ...       ...      ...     ...      ...        ...   \n",
       "5320                   0         1        0       0        1          0   \n",
       "5321                   0         0        0       0        1          0   \n",
       "5322                   0         1        0       1        1          1   \n",
       "5323                   0         1        0       1        0          1   \n",
       "5324                   0         0        0       0        1          0   \n",
       "\n",
       "      Balcony  Garden  Pool  Price  Location_num  Place_num Precio Rango  \n",
       "0           0       0     0   3500             0          2       > 2500  \n",
       "1           0       0     1   2000             0          3  1500 - 2500  \n",
       "2           0       1     1   1200             0          2       < 1500  \n",
       "3           0       1     1   1400             0          2       < 1500  \n",
       "4           1       1     1   2490             0          2  1500 - 2500  \n",
       "...       ...     ...   ...    ...           ...        ...          ...  \n",
       "5320        0       0     0    840            20          2       < 1500  \n",
       "5321        0       0     0    700            20          2       < 1500  \n",
       "5322        0       0     0   1650            20          2  1500 - 2500  \n",
       "5323        1       0     0   1650            20          2  1500 - 2500  \n",
       "5324        0       0     0    840            20          2       < 1500  \n",
       "\n",
       "[5325 rows x 19 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 1500, 2500, np.inf]\n",
    "labels = ['< 1500', '1500 - 2500', '> 2500']\n",
    "df['Precio Rango'] = pd.cut(df['Price'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Place</th>\n",
       "      <th>Location</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Toilets</th>\n",
       "      <th>Area</th>\n",
       "      <th>Air Conditioning</th>\n",
       "      <th>Built-in Wardrobes</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Heating</th>\n",
       "      <th>Garage</th>\n",
       "      <th>Terrace</th>\n",
       "      <th>Furnished</th>\n",
       "      <th>Balcony</th>\n",
       "      <th>Garden</th>\n",
       "      <th>Pool</th>\n",
       "      <th>Price</th>\n",
       "      <th>Location_num</th>\n",
       "      <th>Place_num</th>\n",
       "      <th>Precio Rango</th>\n",
       "      <th>Precio Rango Numérico</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3500</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>&gt; 2500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ático</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1500 - 2500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt; 1500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1400</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt; 1500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Arganzuela</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2490</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1500 - 2500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5320</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Villaverde</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>75</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>0</td>\n",
       "      <td>840</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt; 1500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5321</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Villaverde</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>700</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt; 1500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5322</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Villaverde</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1650</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1500 - 2500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5323</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Villaverde</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71</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",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1650</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1500 - 2500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5324</th>\n",
       "      <td>Piso</td>\n",
       "      <td>Villaverde</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>840</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt; 1500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5325 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Place    Location  Rooms  Toilets  Area  Air Conditioning  \\\n",
       "0      Piso  Arganzuela      2        2    70                 1   \n",
       "1     Ático  Arganzuela      2        2    86                 1   \n",
       "2      Piso  Arganzuela      1        1    74                 1   \n",
       "3      Piso  Arganzuela      1        1    28                 1   \n",
       "4      Piso  Arganzuela      2        2    65                 1   \n",
       "...     ...         ...    ...      ...   ...               ...   \n",
       "5320   Piso  Villaverde      3        1    75                 0   \n",
       "5321   Piso  Villaverde      1        1    40                 0   \n",
       "5322   Piso  Villaverde      1        2    67                 1   \n",
       "5323   Piso  Villaverde      1        1    71                 1   \n",
       "5324   Piso  Villaverde      3        1    56                 0   \n",
       "\n",
       "      Built-in Wardrobes  Elevator  Heating  Garage  Terrace  Furnished  \\\n",
       "0                      1         1        1       0        0          1   \n",
       "1                      1         1        1       0        1          1   \n",
       "2                      1         1        1       1        0          0   \n",
       "3                      1         1        1       0        1          1   \n",
       "4                      0         1        1       1        1          1   \n",
       "...                  ...       ...      ...     ...      ...        ...   \n",
       "5320                   0         1        0       0        1          0   \n",
       "5321                   0         0        0       0        1          0   \n",
       "5322                   0         1        0       1        1          1   \n",
       "5323                   0         1        0       1        0          1   \n",
       "5324                   0         0        0       0        1          0   \n",
       "\n",
       "      Balcony  Garden  Pool  Price  Location_num  Place_num Precio Rango  \\\n",
       "0           0       0     0   3500             0          2       > 2500   \n",
       "1           0       0     1   2000             0          3  1500 - 2500   \n",
       "2           0       1     1   1200             0          2       < 1500   \n",
       "3           0       1     1   1400             0          2       < 1500   \n",
       "4           1       1     1   2490             0          2  1500 - 2500   \n",
       "...       ...     ...   ...    ...           ...        ...          ...   \n",
       "5320        0       0     0    840            20          2       < 1500   \n",
       "5321        0       0     0    700            20          2       < 1500   \n",
       "5322        0       0     0   1650            20          2  1500 - 2500   \n",
       "5323        1       0     0   1650            20          2  1500 - 2500   \n",
       "5324        0       0     0    840            20          2       < 1500   \n",
       "\n",
       "     Precio Rango Numérico  \n",
       "0                        2  \n",
       "1                        1  \n",
       "2                        0  \n",
       "3                        0  \n",
       "4                        1  \n",
       "...                    ...  \n",
       "5320                     0  \n",
       "5321                     0  \n",
       "5322                     1  \n",
       "5323                     1  \n",
       "5324                     0  \n",
       "\n",
       "[5325 rows x 20 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rangos = {'< 1500': 0, '1500 - 2500': 1, '> 2500': 2}\n",
    "df['Precio Rango Numérico'] = df['Precio Rango'].map(rangos)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''nuevo_df = df.iloc[:, :-2]'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_df = df[['Rooms','Toilets','Area','Air Conditioning','Built-in Wardrobes','Elevator','Heating','Garage','Terrace','Furnished','Balcony','Garden','Pool','Location_num','Place_num']]\n",
    "y_df = df.iloc[:,-1]\n",
    "\n",
    "X_train_df, X_test_df, y_train_df, y_test_df = train_test_split(X_df,y_df, test_size=0.2, random_state= 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rango de X_train_df (4260, 15)\n",
      "Rango de X_test_df (1065, 15)\n",
      "Rango de y_train_df (4260,)\n",
      "Rango de y_test_df (1065,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Rango de X_train_df\", X_train_df.shape)\n",
    "print(\"Rango de X_test_df\", X_test_df.shape)\n",
    "print(\"Rango de y_train_df\", y_train_df.shape)\n",
    "print(\"Rango de y_test_df\", y_test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "smote = SMOTE(random_state=42)\n",
    "X_res, y_res = smote.fit_resample(X_train_df, y_train_df)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaler_df = scaler.fit_transform(X_res)\n",
    "X_test_scaler_df = scaler.transform(X_test_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4260"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5478\n"
     ]
    }
   ],
   "source": [
    "num_filas = y_res.shape[0]\n",
    "print(num_filas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Score             Feature\n",
      "0   0.3390             Toilets\n",
      "1   0.2322                Area\n",
      "2   0.1264        Location_num\n",
      "3   0.0691           Furnished\n",
      "4   0.0337               Rooms\n",
      "5   0.0262    Air Conditioning\n",
      "6   0.0235             Heating\n",
      "7   0.0228             Terrace\n",
      "8   0.0226             Balcony\n",
      "9   0.0210  Built-in Wardrobes\n",
      "10  0.0198              Garage\n",
      "11  0.0184              Garden\n",
      "12  0.0165           Place_num\n",
      "13  0.0165            Elevator\n",
      "14  0.0122                Pool\n"
     ]
    }
   ],
   "source": [
    "#convertir variables categóricas en numéricas\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=100)\n",
    "\n",
    "# Entrenar el modelo\n",
    "rf.fit(X_train_df, y_train_df)\n",
    "\n",
    "# Obtener la importancia de las características\n",
    "scores = sorted(zip(map(lambda x: round(x, 4), rf.feature_importances_), X_train_df.columns), reverse=True)\n",
    "df_scores = pd.DataFrame(scores, columns=['Score', 'Feature'])\n",
    "\n",
    "# Imprimir las características y sus importancias\n",
    "print(df_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['Toilets', 'Area', 'Location_num','Furnished','Rooms', 'Air Conditioning', 'Heating','Terrace','Balcony', 'Built-in Wardrobes', 'Garage', 'Garden', 'Place_num', 'Elevator', 'Pool']]\n",
    "y = df.iloc[:,-1]\n",
    "\n",
    "X_train_random, X_test_random, y_train_random, y_test_random = train_test_split(X,y, test_size=0.2, random_state= 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE(random_state=42)\n",
    "X_res_random, y_res_random = smote.fit_resample(X_train_random, y_train_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaler_random = scaler.fit_transform(X_res_random)\n",
    "X_test_scaler_random = scaler.transform(X_test_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = RepeatedKFold(n_splits=5, n_repeats = 2, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejor modelo: Pipeline(steps=[('reglog', LogisticRegression(C=0.5, max_iter=250, tol=1e-05))])\n",
      "Puntuación de validación cruzada: 0.6966980801919809\n",
      "Puntuación en el conjunto de entrenamiento: 0.7000730193501278\n"
     ]
    }
   ],
   "source": [
    "#Regresión logística con datos balanceados\n",
    "reg_log = Pipeline([(\"reglog\", LogisticRegression(solver = 'lbfgs'))]) \n",
    "reg_log_param = {'reglog__C': [0.1,0.5,1,10,50,100], \n",
    "                'reglog__max_iter': [250], \n",
    "                'reglog__fit_intercept':[True],\n",
    "                'reglog__intercept_scaling':[1],\n",
    "                'reglog__penalty':['l2'],\n",
    "                'reglog__tol':[0.00001,0.0001,0.000001]}\n",
    "\n",
    "log_primal_Grid = GridSearchCV(reg_log,reg_log_param, cv=cv, refit=True, verbose=0, n_jobs=-1)\n",
    "log_primal_Grid.fit(X_train_scaler_random, y_res_random)\n",
    "\n",
    "best_model = log_primal_Grid.best_estimator_\n",
    "best_score = log_primal_Grid.best_score_\n",
    "train_score = best_model.score(X_train_scaler_random, y_res_random)\n",
    "\n",
    "print(\"Mejor modelo:\", best_model)\n",
    "print(\"Puntuación de validación cruzada:\", best_score)\n",
    "print(\"Puntuación en el conjunto de entrenamiento:\", train_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejor modelo: Pipeline(steps=[('reglog',\n",
      "                 LogisticRegression(C=50, max_iter=300, solver='liblinear'))])\n",
      "Puntuación de validación cruzada: 0.6750652434756524\n",
      "Puntuación en el conjunto de entrenamiento: 0.6778021175611537\n"
     ]
    }
   ],
   "source": [
    "#Regresión Logística con extensión polinómica con datos balanceados\n",
    "reg_log = Pipeline([(\"reglog\", LogisticRegression(solver = 'liblinear'))]) \n",
    "reg_log_param = {'reglog__C': [0.1,10,50], \n",
    "                'reglog__max_iter': [300,500], \n",
    "                'reglog__fit_intercept':[True],\n",
    "                'reglog__intercept_scaling':[1],\n",
    "                'reglog__penalty':['l2'],\n",
    "                'reglog__tol':[0.0001,0.000001]}\n",
    "\n",
    "poly2 = PolynomialFeatures(degree=2)\n",
    "df_train_pol2 = poly2.fit_transform(X_train_random)\n",
    "log_pol2_Grid = GridSearchCV(reg_log,reg_log_param, cv=cv, refit=True, verbose=0, n_jobs=-1)\n",
    "log_pol2_Grid.fit(X_train_scaler_random, y_res_random)\n",
    "\n",
    "best_model = log_pol2_Grid.best_estimator_\n",
    "best_score = log_pol2_Grid.best_score_\n",
    "train_score = best_model.score(X_train_scaler_random, y_res_random)\n",
    "\n",
    "print(\"Mejor modelo:\", best_model)\n",
    "print(\"Puntuación de validación cruzada:\", best_score)\n",
    "print(\"Puntuación en el conjunto de entrenamiento:\", train_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejor modelo: RandomForestClassifier(max_depth=9, max_features=4, min_samples_split=3,\n",
      "                       n_estimators=50)\n",
      "Puntuación de validación cruzada: 0.7636001399860014\n",
      "Puntuación en el conjunto de entrenamiento: 0.8218327856882074\n"
     ]
    }
   ],
   "source": [
    "#RandomForest con datos balanceados\n",
    "param_grid = {\n",
    "    'max_depth': [3,4,5,6,7,8,9],\n",
    "    'max_features': [2,4,6,7,8,9],\n",
    "    'n_estimators': [50, 100],\n",
    "    'min_samples_split': [3, 5, 6, 7]}\n",
    "\n",
    "\n",
    "RanFor = RandomForestClassifier()\n",
    "RanFor_grid = GridSearchCV(RanFor, param_grid, cv=cv, refit=True, verbose=0, n_jobs=-1)\n",
    "RanFor_grid.fit(X_train_scaler_random, y_res_random)\n",
    "\n",
    "best_model = RanFor_grid.best_estimator_\n",
    "best_score = RanFor_grid.best_score_\n",
    "train_score = best_model.score(X_train_scaler_random, y_res_random)\n",
    "\n",
    "print(\"Mejor modelo:\", best_model)\n",
    "print(\"Puntuación de validación cruzada:\", best_score)\n",
    "print(\"Puntuación en el conjunto de entrenamiento:\", train_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejor modelo: XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
      "              colsample_bylevel=None, colsample_bynode=None,\n",
      "              colsample_bytree=None, early_stopping_rounds=None,\n",
      "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "              gamma=0.001, gpu_id=None, grow_policy=None, importance_type=None,\n",
      "              interaction_constraints=None, learning_rate=0.3, max_bin=None,\n",
      "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "              max_delta_step=None, max_depth=8, max_leaves=None,\n",
      "              min_child_weight=1, missing=nan, monotone_constraints=None,\n",
      "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
      "              objective='multi:softprob', predictor=None, ...)\n",
      "Puntuación de validación cruzada: 0.8101654834516548\n",
      "Puntuación en el conjunto de entrenamiento: 0.9653158086893027\n"
     ]
    }
   ],
   "source": [
    "#XGBClassifier con datos balanceados\n",
    "param_grid = {\n",
    "    'max_depth': [5,6,7,8],\n",
    "    'gamma': [0.01,0.001,0.001],\n",
    "    'min_child_weight': [1,5,10],\n",
    "    'learning_rate': [0.05,0.1, 0.2, 0.3],\n",
    "    'n_estimators':[5,10,20,100] }\n",
    "\n",
    "\n",
    "xgb = XGBClassifier()\n",
    "xgb_grid = GridSearchCV(xgb, param_grid, cv=5, refit=True, verbose=0,n_jobs=-1)\n",
    "xgb_grid.fit(X_train_scaler_random, y_res_random)\n",
    "\n",
    "best_model = xgb_grid.best_estimator_\n",
    "best_score = xgb_grid.best_score_\n",
    "train_score = xgb_grid.score(X_train_scaler_random, y_res_random)\n",
    "\n",
    "print(\"Mejor modelo:\", best_model)\n",
    "print(\"Puntuación de validación cruzada:\", best_score)\n",
    "print(\"Puntuación en el conjunto de entrenamiento:\", train_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.7755868544600939\n",
      "Precisión 0.7699321452800492\n",
      "Recall 0.7716777964329888\n",
      "F1-Score 0.7707448154266352\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = xgb_grid.predict(X_test_scaler_random)\n",
    "c_mat = confusion_matrix(y_test_random,y_pred)\n",
    "\n",
    "\n",
    "sns.heatmap(c_mat/c_mat.sum(axis=1), annot=True);\n",
    "print('Accuracy',accuracy_score(y_test_random,y_pred))\n",
    "print('Precisión',precision_score(y_test_random, y_pred, average='macro'))\n",
    "print('Recall',recall_score(y_test_random, y_pred, average='macro'))\n",
    "print('F1-Score',f1_score(y_test_random, y_pred, average='macro'))\n"
   ]
  }
 ],
 "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": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}