Data_Suelo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importar Librerias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "import meteomatics.api as api\n",
    "from __future__ import print_function\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from lazypredict.Supervised import LazyClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, accuracy_score, f1_score, precision_score, recall_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import pickle\n",
    "from env import creds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "username = creds.username\n",
    "password = creds.password"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Establecer parametros para el llamado al API. En este caso se establece para un año en una coordenada en el municipio de General Teran en Nuevo Leon, ya que es una de las zonas con mayor agricultura del Estado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "now = dt.datetime.utcnow().replace(hour=0, minute=0, second=0, microsecond=0)\n",
    "startdate_ts = '2023-10-05T00:00:00+00:00'\n",
    "startdate_ts = dt.datetime.strptime(startdate_ts, '%Y-%m-%dT%H:%M:%S%z')\n",
    "enddate_ts = startdate_ts + dt.timedelta(days=365)\n",
    "interval_ts = dt.timedelta(hours=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "coordinates_ts = [(25.25863754474608, -99.68200619248428)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters_ts = ['grass_land_temperature_sum:C',\n",
    "                  'evapotranspiration_24h:mm', \n",
    "                  'leaf_wetness:idx', \n",
    "                  'soil_moisture_index_-5cm:idx',\n",
    "                    'volumetric_soil_water_-5cm:m3m3',\n",
    "                    'soil_moisture_index_-15cm:idx',\n",
    "                    'volumetric_soil_water_-15cm:m3m3', \n",
    "                    'soil_moisture_index_-50cm:idx', \n",
    "                    'volumetric_soil_water_-50cm:m3m3']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se hace el llamado al API de Meteomatics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame Agricultural Parameters:\n",
      "                                        grass_land_temperature_sum:C  \\\n",
      "lat   lon    validdate                                                 \n",
      "25.26 -99.68 2023-10-05 00:00:00+00:00                       6801.40   \n",
      "             2023-10-05 01:00:00+00:00                       6801.40   \n",
      "             2023-10-05 02:00:00+00:00                       6801.40   \n",
      "             2023-10-05 03:00:00+00:00                       6801.40   \n",
      "             2023-10-05 04:00:00+00:00                       6801.40   \n",
      "\n",
      "                                        evapotranspiration_24h:mm  \\\n",
      "lat   lon    validdate                                              \n",
      "25.26 -99.68 2023-10-05 00:00:00+00:00                       5.66   \n",
      "             2023-10-05 01:00:00+00:00                       5.72   \n",
      "             2023-10-05 02:00:00+00:00                       5.72   \n",
      "             2023-10-05 03:00:00+00:00                       5.73   \n",
      "             2023-10-05 04:00:00+00:00                       5.75   \n",
      "\n",
      "                                        leaf_wetness:idx  \\\n",
      "lat   lon    validdate                                     \n",
      "25.26 -99.68 2023-10-05 00:00:00+00:00              0.00   \n",
      "             2023-10-05 01:00:00+00:00              0.00   \n",
      "             2023-10-05 02:00:00+00:00              0.00   \n",
      "             2023-10-05 03:00:00+00:00              0.00   \n",
      "             2023-10-05 04:00:00+00:00              0.00   \n",
      "\n",
      "                                        soil_moisture_index_-5cm:idx  \\\n",
      "lat   lon    validdate                                                 \n",
      "25.26 -99.68 2023-10-05 00:00:00+00:00                          0.00   \n",
      "             2023-10-05 01:00:00+00:00                          0.00   \n",
      "             2023-10-05 02:00:00+00:00                          0.00   \n",
      "             2023-10-05 03:00:00+00:00                          0.00   \n",
      "             2023-10-05 04:00:00+00:00                          0.00   \n",
      "\n",
      "                                        volumetric_soil_water_-5cm:m3m3  \\\n",
      "lat   lon    validdate                                                    \n",
      "25.26 -99.68 2023-10-05 00:00:00+00:00                             0.15   \n",
      "             2023-10-05 01:00:00+00:00                             0.15   \n",
      "             2023-10-05 02:00:00+00:00                             0.15   \n",
      "             2023-10-05 03:00:00+00:00                             0.15   \n",
      "             2023-10-05 04:00:00+00:00                             0.15   \n",
      "\n",
      "                                        soil_moisture_index_-15cm:idx  \\\n",
      "lat   lon    validdate                                                  \n",
      "25.26 -99.68 2023-10-05 00:00:00+00:00                           0.03   \n",
      "             2023-10-05 01:00:00+00:00                           0.03   \n",
      "             2023-10-05 02:00:00+00:00                           0.03   \n",
      "             2023-10-05 03:00:00+00:00                           0.03   \n",
      "             2023-10-05 04:00:00+00:00                           0.03   \n",
      "\n",
      "                                        volumetric_soil_water_-15cm:m3m3  \\\n",
      "lat   lon    validdate                                                     \n",
      "25.26 -99.68 2023-10-05 00:00:00+00:00                              0.16   \n",
      "             2023-10-05 01:00:00+00:00                              0.16   \n",
      "             2023-10-05 02:00:00+00:00                              0.16   \n",
      "             2023-10-05 03:00:00+00:00                              0.16   \n",
      "             2023-10-05 04:00:00+00:00                              0.16   \n",
      "\n",
      "                                        soil_moisture_index_-50cm:idx  \\\n",
      "lat   lon    validdate                                                  \n",
      "25.26 -99.68 2023-10-05 00:00:00+00:00                           0.00   \n",
      "             2023-10-05 01:00:00+00:00                           0.00   \n",
      "             2023-10-05 02:00:00+00:00                           0.00   \n",
      "             2023-10-05 03:00:00+00:00                           0.00   \n",
      "             2023-10-05 04:00:00+00:00                           0.00   \n",
      "\n",
      "                                        volumetric_soil_water_-50cm:m3m3  \n",
      "lat   lon    validdate                                                    \n",
      "25.26 -99.68 2023-10-05 00:00:00+00:00                              0.15  \n",
      "             2023-10-05 01:00:00+00:00                              0.15  \n",
      "             2023-10-05 02:00:00+00:00                              0.15  \n",
      "             2023-10-05 03:00:00+00:00                              0.15  \n",
      "             2023-10-05 04:00:00+00:00                              0.15  \n"
     ]
    }
   ],
   "source": [
    "print(\"DataFrame Agricultural Parameters:\")\n",
    "try:\n",
    "    df = api.query_time_series(coordinates_ts, startdate_ts, enddate_ts, interval_ts,\n",
    "                                  parameters_ts, username, password)\n",
    "    print (df.head())\n",
    "except Exception as e:\n",
    "    print(\"Failed, the exception is {}\".format(e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8761, 9)"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>grass_land_temperature_sum:C</th>\n",
       "      <th>evapotranspiration_24h:mm</th>\n",
       "      <th>leaf_wetness:idx</th>\n",
       "      <th>soil_moisture_index_-5cm:idx</th>\n",
       "      <th>volumetric_soil_water_-5cm:m3m3</th>\n",
       "      <th>soil_moisture_index_-15cm:idx</th>\n",
       "      <th>volumetric_soil_water_-15cm:m3m3</th>\n",
       "      <th>soil_moisture_index_-50cm:idx</th>\n",
       "      <th>volumetric_soil_water_-50cm:m3m3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>validdate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">25.26</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">-99.68</th>\n",
       "      <th>2023-10-05 00:00:00+00:00</th>\n",
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       "    <tr>\n",
       "      <th>2023-10-05 01:00:00+00:00</th>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-05 02:00:00+00:00</th>\n",
       "      <td>6801.40</td>\n",
       "      <td>5.72</td>\n",
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       "    <tr>\n",
       "      <th>2023-10-05 03:00:00+00:00</th>\n",
       "      <td>6801.40</td>\n",
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       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-05 04:00:00+00:00</th>\n",
       "      <td>6801.40</td>\n",
       "      <td>5.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
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      "text/plain": [
       "                                        grass_land_temperature_sum:C  \\\n",
       "lat   lon    validdate                                                 \n",
       "25.26 -99.68 2023-10-05 00:00:00+00:00                       6801.40   \n",
       "             2023-10-05 01:00:00+00:00                       6801.40   \n",
       "             2023-10-05 02:00:00+00:00                       6801.40   \n",
       "             2023-10-05 03:00:00+00:00                       6801.40   \n",
       "             2023-10-05 04:00:00+00:00                       6801.40   \n",
       "\n",
       "                                        evapotranspiration_24h:mm  \\\n",
       "lat   lon    validdate                                              \n",
       "25.26 -99.68 2023-10-05 00:00:00+00:00                       5.66   \n",
       "             2023-10-05 01:00:00+00:00                       5.72   \n",
       "             2023-10-05 02:00:00+00:00                       5.72   \n",
       "             2023-10-05 03:00:00+00:00                       5.73   \n",
       "             2023-10-05 04:00:00+00:00                       5.75   \n",
       "\n",
       "                                        leaf_wetness:idx  \\\n",
       "lat   lon    validdate                                     \n",
       "25.26 -99.68 2023-10-05 00:00:00+00:00              0.00   \n",
       "             2023-10-05 01:00:00+00:00              0.00   \n",
       "             2023-10-05 02:00:00+00:00              0.00   \n",
       "             2023-10-05 03:00:00+00:00              0.00   \n",
       "             2023-10-05 04:00:00+00:00              0.00   \n",
       "\n",
       "                                        soil_moisture_index_-5cm:idx  \\\n",
       "lat   lon    validdate                                                 \n",
       "25.26 -99.68 2023-10-05 00:00:00+00:00                          0.00   \n",
       "             2023-10-05 01:00:00+00:00                          0.00   \n",
       "             2023-10-05 02:00:00+00:00                          0.00   \n",
       "             2023-10-05 03:00:00+00:00                          0.00   \n",
       "             2023-10-05 04:00:00+00:00                          0.00   \n",
       "\n",
       "                                        volumetric_soil_water_-5cm:m3m3  \\\n",
       "lat   lon    validdate                                                    \n",
       "25.26 -99.68 2023-10-05 00:00:00+00:00                             0.15   \n",
       "             2023-10-05 01:00:00+00:00                             0.15   \n",
       "             2023-10-05 02:00:00+00:00                             0.15   \n",
       "             2023-10-05 03:00:00+00:00                             0.15   \n",
       "             2023-10-05 04:00:00+00:00                             0.15   \n",
       "\n",
       "                                        soil_moisture_index_-15cm:idx  \\\n",
       "lat   lon    validdate                                                  \n",
       "25.26 -99.68 2023-10-05 00:00:00+00:00                           0.03   \n",
       "             2023-10-05 01:00:00+00:00                           0.03   \n",
       "             2023-10-05 02:00:00+00:00                           0.03   \n",
       "             2023-10-05 03:00:00+00:00                           0.03   \n",
       "             2023-10-05 04:00:00+00:00                           0.03   \n",
       "\n",
       "                                        volumetric_soil_water_-15cm:m3m3  \\\n",
       "lat   lon    validdate                                                     \n",
       "25.26 -99.68 2023-10-05 00:00:00+00:00                              0.16   \n",
       "             2023-10-05 01:00:00+00:00                              0.16   \n",
       "             2023-10-05 02:00:00+00:00                              0.16   \n",
       "             2023-10-05 03:00:00+00:00                              0.16   \n",
       "             2023-10-05 04:00:00+00:00                              0.16   \n",
       "\n",
       "                                        soil_moisture_index_-50cm:idx  \\\n",
       "lat   lon    validdate                                                  \n",
       "25.26 -99.68 2023-10-05 00:00:00+00:00                           0.00   \n",
       "             2023-10-05 01:00:00+00:00                           0.00   \n",
       "             2023-10-05 02:00:00+00:00                           0.00   \n",
       "             2023-10-05 03:00:00+00:00                           0.00   \n",
       "             2023-10-05 04:00:00+00:00                           0.00   \n",
       "\n",
       "                                        volumetric_soil_water_-50cm:m3m3  \n",
       "lat   lon    validdate                                                    \n",
       "25.26 -99.68 2023-10-05 00:00:00+00:00                              0.15  \n",
       "             2023-10-05 01:00:00+00:00                              0.15  \n",
       "             2023-10-05 02:00:00+00:00                              0.15  \n",
       "             2023-10-05 03:00:00+00:00                              0.15  \n",
       "             2023-10-05 04:00:00+00:00                              0.15  "
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['lat', 'lon', 'validdate', 'grass_land_temperature_sum:C',\n",
       "       'evapotranspiration_24h:mm', 'leaf_wetness:idx',\n",
       "       'soil_moisture_index_-5cm:idx', 'volumetric_soil_water_-5cm:m3m3',\n",
       "       'soil_moisture_index_-15cm:idx', 'volumetric_soil_water_-15cm:m3m3',\n",
       "       'soil_moisture_index_-50cm:idx', 'volumetric_soil_water_-50cm:m3m3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.reset_index()\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>validdate</th>\n",
       "      <th>grass_land_temperature_sum:C</th>\n",
       "      <th>evapotranspiration_24h:mm</th>\n",
       "      <th>leaf_wetness:idx</th>\n",
       "      <th>soil_moisture_index_-5cm:idx</th>\n",
       "      <th>volumetric_soil_water_-5cm:m3m3</th>\n",
       "      <th>soil_moisture_index_-15cm:idx</th>\n",
       "      <th>volumetric_soil_water_-15cm:m3m3</th>\n",
       "      <th>soil_moisture_index_-50cm:idx</th>\n",
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       "      <th>0</th>\n",
       "      <td>2023-10-05 00:00:00+00:00</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-10-05 02:00:00+00:00</td>\n",
       "      <td>6801.40</td>\n",
       "      <td>5.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-10-05 03:00:00+00:00</td>\n",
       "      <td>6801.40</td>\n",
       "      <td>5.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-10-05 04:00:00+00:00</td>\n",
       "      <td>6801.40</td>\n",
       "      <td>5.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  validdate  grass_land_temperature_sum:C  \\\n",
       "0 2023-10-05 00:00:00+00:00                       6801.40   \n",
       "1 2023-10-05 01:00:00+00:00                       6801.40   \n",
       "2 2023-10-05 02:00:00+00:00                       6801.40   \n",
       "3 2023-10-05 03:00:00+00:00                       6801.40   \n",
       "4 2023-10-05 04:00:00+00:00                       6801.40   \n",
       "\n",
       "   evapotranspiration_24h:mm  leaf_wetness:idx  soil_moisture_index_-5cm:idx  \\\n",
       "0                       5.66              0.00                          0.00   \n",
       "1                       5.72              0.00                          0.00   \n",
       "2                       5.72              0.00                          0.00   \n",
       "3                       5.73              0.00                          0.00   \n",
       "4                       5.75              0.00                          0.00   \n",
       "\n",
       "   volumetric_soil_water_-5cm:m3m3  soil_moisture_index_-15cm:idx  \\\n",
       "0                             0.15                           0.03   \n",
       "1                             0.15                           0.03   \n",
       "2                             0.15                           0.03   \n",
       "3                             0.15                           0.03   \n",
       "4                             0.15                           0.03   \n",
       "\n",
       "   volumetric_soil_water_-15cm:m3m3  soil_moisture_index_-50cm:idx  \\\n",
       "0                              0.16                           0.00   \n",
       "1                              0.16                           0.00   \n",
       "2                              0.16                           0.00   \n",
       "3                              0.16                           0.00   \n",
       "4                              0.16                           0.00   \n",
       "\n",
       "   volumetric_soil_water_-50cm:m3m3  \n",
       "0                              0.15  \n",
       "1                              0.15  \n",
       "2                              0.15  \n",
       "3                              0.15  \n",
       "4                              0.15  "
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns=['lat', 'lon'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>validdate</th>\n",
       "      <th>grass_land_temperature_sum:C</th>\n",
       "      <th>evapotranspiration_24h:mm</th>\n",
       "      <th>leaf_wetness:idx</th>\n",
       "      <th>soil_moisture_index_-5cm:idx</th>\n",
       "      <th>volumetric_soil_water_-5cm:m3m3</th>\n",
       "      <th>soil_moisture_index_-15cm:idx</th>\n",
       "      <th>volumetric_soil_water_-15cm:m3m3</th>\n",
       "      <th>soil_moisture_index_-50cm:idx</th>\n",
       "      <th>volumetric_soil_water_-50cm:m3m3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-10-05</td>\n",
       "      <td>6801.40</td>\n",
       "      <td>5.78</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-10-06</td>\n",
       "      <td>6832.10</td>\n",
       "      <td>3.88</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-10-07</td>\n",
       "      <td>6855.30</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-10-08</td>\n",
       "      <td>6878.60</td>\n",
       "      <td>2.07</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-10-09</td>\n",
       "      <td>6897.50</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    validdate  grass_land_temperature_sum:C  evapotranspiration_24h:mm  \\\n",
       "0  2023-10-05                       6801.40                       5.78   \n",
       "1  2023-10-06                       6832.10                       3.88   \n",
       "2  2023-10-07                       6855.30                       2.34   \n",
       "3  2023-10-08                       6878.60                       2.07   \n",
       "4  2023-10-09                       6897.50                       1.28   \n",
       "\n",
       "   leaf_wetness:idx  soil_moisture_index_-5cm:idx  \\\n",
       "0              0.08                          0.02   \n",
       "1              0.62                          1.16   \n",
       "2              0.12                          0.89   \n",
       "3              0.71                          0.93   \n",
       "4              0.33                          0.81   \n",
       "\n",
       "   volumetric_soil_water_-5cm:m3m3  soil_moisture_index_-15cm:idx  \\\n",
       "0                             0.15                           0.04   \n",
       "1                             0.38                           0.37   \n",
       "2                             0.33                           0.52   \n",
       "3                             0.33                           0.54   \n",
       "4                             0.31                           0.54   \n",
       "\n",
       "   volumetric_soil_water_-15cm:m3m3  soil_moisture_index_-50cm:idx  \\\n",
       "0                              0.16                           0.00   \n",
       "1                              0.23                           0.00   \n",
       "2                              0.26                           0.02   \n",
       "3                              0.26                           0.03   \n",
       "4                              0.27                           0.04   \n",
       "\n",
       "   volumetric_soil_water_-50cm:m3m3  \n",
       "0                              0.15  \n",
       "1                              0.15  \n",
       "2                              0.15  \n",
       "3                              0.15  \n",
       "4                              0.15  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2['validdate'] = pd.to_datetime(df2['validdate']).dt.date\n",
    "\n",
    "# Step 2: Group by the 'validdate' and calculate the mean for all other numeric columns\n",
    "df2 = df2.groupby('validdate').mean().reset_index()\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "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>Fecha</th>\n",
       "      <th>Grass Land Temperature</th>\n",
       "      <th>Evapotranspiration</th>\n",
       "      <th>Leaf Wetness</th>\n",
       "      <th>Soil Moisture Index -5cm</th>\n",
       "      <th>Volumetric Soil Water -5cm (m3/m3)</th>\n",
       "      <th>Soil Moisture Index -15cm</th>\n",
       "      <th>Volumetric Soil Water -15cm (m3/m3)</th>\n",
       "      <th>Soil Moisture Index -50cm</th>\n",
       "      <th>Volumetric Soil Water -50cm (m3/m3)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-10-05</td>\n",
       "      <td>6801.40</td>\n",
       "      <td>5.78</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.04</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-10-06</td>\n",
       "      <td>6832.10</td>\n",
       "      <td>3.88</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-10-07</td>\n",
       "      <td>6855.30</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-10-08</td>\n",
       "      <td>6878.60</td>\n",
       "      <td>2.07</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-10-09</td>\n",
       "      <td>6897.50</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fecha  Grass Land Temperature  Evapotranspiration  Leaf Wetness  \\\n",
       "0  2023-10-05                 6801.40                5.78          0.08   \n",
       "1  2023-10-06                 6832.10                3.88          0.62   \n",
       "2  2023-10-07                 6855.30                2.34          0.12   \n",
       "3  2023-10-08                 6878.60                2.07          0.71   \n",
       "4  2023-10-09                 6897.50                1.28          0.33   \n",
       "\n",
       "   Soil Moisture Index -5cm  Volumetric Soil Water -5cm (m3/m3)  \\\n",
       "0                      0.02                                0.15   \n",
       "1                      1.16                                0.38   \n",
       "2                      0.89                                0.33   \n",
       "3                      0.93                                0.33   \n",
       "4                      0.81                                0.31   \n",
       "\n",
       "   Soil Moisture Index -15cm  Volumetric Soil Water -15cm (m3/m3)  \\\n",
       "0                       0.04                                 0.16   \n",
       "1                       0.37                                 0.23   \n",
       "2                       0.52                                 0.26   \n",
       "3                       0.54                                 0.26   \n",
       "4                       0.54                                 0.27   \n",
       "\n",
       "   Soil Moisture Index -50cm  Volumetric Soil Water -50cm (m3/m3)  \n",
       "0                       0.00                                 0.15  \n",
       "1                       0.00                                 0.15  \n",
       "2                       0.02                                 0.15  \n",
       "3                       0.03                                 0.15  \n",
       "4                       0.04                                 0.15  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_suelo = df2.copy()\n",
    "df_suelo = df_suelo.rename(columns={'validdate': 'Fecha', 'grass_land_temperature_sum:C': 'Grass Land Temperature', 'evapotranspiration_24h:mm': 'Evapotranspiration', 'leaf_wetness:idx': 'Leaf Wetness', 'soil_moisture_index_-5cm:idx': 'Soil Moisture Index -5cm', 'volumetric_soil_water_-5cm:m3m3': 'Volumetric Soil Water -5cm (m3/m3)', 'soil_moisture_index_-15cm:idx': 'Soil Moisture Index -15cm', 'volumetric_soil_water_-15cm:m3m3': 'Volumetric Soil Water -15cm (m3/m3)', 'soil_moisture_index_-50cm:idx': 'Soil Moisture Index -50cm', 'volumetric_soil_water_-50cm:m3m3': 'Volumetric Soil Water -50cm (m3/m3)'})\n",
    "df_suelo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Grass Land Temperature</th>\n",
       "      <th>Evapotranspiration</th>\n",
       "      <th>Leaf Wetness</th>\n",
       "      <th>Soil Moisture Index -5cm</th>\n",
       "      <th>Volumetric Soil Water -5cm (m3/m3)</th>\n",
       "      <th>Soil Moisture Index -15cm</th>\n",
       "      <th>Volumetric Soil Water -15cm (m3/m3)</th>\n",
       "      <th>Soil Moisture Index -50cm</th>\n",
       "      <th>Volumetric Soil Water -50cm (m3/m3)</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>2024-09-30</td>\n",
       "      <td>6430.10</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>2024-10-01</td>\n",
       "      <td>6455.50</td>\n",
       "      <td>4.74</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2024-10-02</td>\n",
       "      <td>6481.30</td>\n",
       "      <td>4.96</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>2024-10-03</td>\n",
       "      <td>6507.10</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>2024-10-04</td>\n",
       "      <td>6532.10</td>\n",
       "      <td>4.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Fecha  Grass Land Temperature  Evapotranspiration  Leaf Wetness  \\\n",
       "361  2024-09-30                 6430.10                3.45          0.12   \n",
       "362  2024-10-01                 6455.50                4.74          0.29   \n",
       "363  2024-10-02                 6481.30                4.96          0.08   \n",
       "364  2024-10-03                 6507.10                4.55          0.04   \n",
       "365  2024-10-04                 6532.10                4.87          0.00   \n",
       "\n",
       "     Soil Moisture Index -5cm  Volumetric Soil Water -5cm (m3/m3)  \\\n",
       "361                      0.21                                0.20   \n",
       "362                      0.17                                0.19   \n",
       "363                      0.13                                0.18   \n",
       "364                      0.09                                0.17   \n",
       "365                      0.07                                0.17   \n",
       "\n",
       "     Soil Moisture Index -15cm  Volumetric Soil Water -15cm (m3/m3)  \\\n",
       "361                       0.43                                 0.24   \n",
       "362                       0.41                                 0.24   \n",
       "363                       0.37                                 0.23   \n",
       "364                       0.34                                 0.22   \n",
       "365                       0.32                                 0.22   \n",
       "\n",
       "     Soil Moisture Index -50cm  Volumetric Soil Water -50cm (m3/m3)  \n",
       "361                       0.43                                 0.24  \n",
       "362                       0.42                                 0.24  \n",
       "363                       0.42                                 0.24  \n",
       "364                       0.42                                 0.24  \n",
       "365                       0.41                                 0.23  "
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_suelo.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Emission M14</th>\n",
       "      <th>Land Surface Temperature Day</th>\n",
       "      <th>Land Surface Temp Night</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-10-05</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>298.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-10-06</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-10-07</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-10-08</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-10-09</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Emission M14  Land Surface Temperature Day  \\\n",
       "0  2023-10-05          0.49                          0.00   \n",
       "1  2023-10-06          0.49                          0.00   \n",
       "2  2023-10-07          0.49                          0.00   \n",
       "3  2023-10-08          0.49                          0.00   \n",
       "4  2023-10-09          0.49                          0.00   \n",
       "\n",
       "   Land Surface Temp Night  \n",
       "0                   298.40  \n",
       "1                     0.00  \n",
       "2                     0.00  \n",
       "3                     0.00  \n",
       "4                     0.00  "
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LSTemp = pd.read_csv('LSTemp&Emis.csv')\n",
    "LSTemp['Date'] = pd.to_datetime(LSTemp['Date']).dt.date\n",
    "LSTemp.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Grass Land Temperature</th>\n",
       "      <th>Evapotranspiration</th>\n",
       "      <th>Leaf Wetness</th>\n",
       "      <th>Soil Moisture Index -5cm</th>\n",
       "      <th>Volumetric Soil Water -5cm (m3/m3)</th>\n",
       "      <th>Soil Moisture Index -15cm</th>\n",
       "      <th>Volumetric Soil Water -15cm (m3/m3)</th>\n",
       "      <th>Soil Moisture Index -50cm</th>\n",
       "      <th>Volumetric Soil Water -50cm (m3/m3)</th>\n",
       "      <th>Date</th>\n",
       "      <th>Emission M14</th>\n",
       "      <th>Land Surface Temperature Day</th>\n",
       "      <th>Land Surface Temp Night</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-10-05</td>\n",
       "      <td>6801.40</td>\n",
       "      <td>5.78</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2023-10-05</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>298.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-10-06</td>\n",
       "      <td>6832.10</td>\n",
       "      <td>3.88</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2023-10-06</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-10-07</td>\n",
       "      <td>6855.30</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2023-10-07</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-10-08</td>\n",
       "      <td>6878.60</td>\n",
       "      <td>2.07</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2023-10-08</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-10-09</td>\n",
       "      <td>6897.50</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2023-10-09</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fecha  Grass Land Temperature  Evapotranspiration  Leaf Wetness  \\\n",
       "0  2023-10-05                 6801.40                5.78          0.08   \n",
       "1  2023-10-06                 6832.10                3.88          0.62   \n",
       "2  2023-10-07                 6855.30                2.34          0.12   \n",
       "3  2023-10-08                 6878.60                2.07          0.71   \n",
       "4  2023-10-09                 6897.50                1.28          0.33   \n",
       "\n",
       "   Soil Moisture Index -5cm  Volumetric Soil Water -5cm (m3/m3)  \\\n",
       "0                      0.02                                0.15   \n",
       "1                      1.16                                0.38   \n",
       "2                      0.89                                0.33   \n",
       "3                      0.93                                0.33   \n",
       "4                      0.81                                0.31   \n",
       "\n",
       "   Soil Moisture Index -15cm  Volumetric Soil Water -15cm (m3/m3)  \\\n",
       "0                       0.04                                 0.16   \n",
       "1                       0.37                                 0.23   \n",
       "2                       0.52                                 0.26   \n",
       "3                       0.54                                 0.26   \n",
       "4                       0.54                                 0.27   \n",
       "\n",
       "   Soil Moisture Index -50cm  Volumetric Soil Water -50cm (m3/m3)        Date  \\\n",
       "0                       0.00                                 0.15  2023-10-05   \n",
       "1                       0.00                                 0.15  2023-10-06   \n",
       "2                       0.02                                 0.15  2023-10-07   \n",
       "3                       0.03                                 0.15  2023-10-08   \n",
       "4                       0.04                                 0.15  2023-10-09   \n",
       "\n",
       "   Emission M14  Land Surface Temperature Day  Land Surface Temp Night  \n",
       "0          0.49                          0.00                   298.40  \n",
       "1          0.49                          0.00                     0.00  \n",
       "2          0.49                          0.00                     0.00  \n",
       "3          0.49                          0.00                     0.00  \n",
       "4          0.49                          0.00                     0.00  "
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = pd.merge(df_suelo, LSTemp, how='left', left_on= 'Fecha', right_on='Date')\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_final.drop(columns=['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_df = df_final.copy()\n",
    "corr_df = corr_df.drop(columns=['Fecha'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = corr_df.corr()\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, fmt=\".2f\", cmap='coolwarm', linewidths=.5)\n",
    "plt.title('Matriz de Correlacion', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se quitan los campos de Volumetric Soil Water, ya que estan altamente correlacionados con el campo de Soil Moisture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "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>Grass Land Temperature</th>\n",
       "      <th>Evapotranspiration</th>\n",
       "      <th>Leaf Wetness</th>\n",
       "      <th>Soil Moisture Index -5cm</th>\n",
       "      <th>Soil Moisture Index -15cm</th>\n",
       "      <th>Soil Moisture Index -50cm</th>\n",
       "      <th>Emission M14</th>\n",
       "      <th>Land Surface Temperature Day</th>\n",
       "      <th>Land Surface Temp Night</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6801.40</td>\n",
       "      <td>5.78</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>298.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6832.10</td>\n",
       "      <td>3.88</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6855.30</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6878.60</td>\n",
       "      <td>2.07</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6897.50</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Grass Land Temperature  Evapotranspiration  Leaf Wetness  \\\n",
       "0                 6801.40                5.78          0.08   \n",
       "1                 6832.10                3.88          0.62   \n",
       "2                 6855.30                2.34          0.12   \n",
       "3                 6878.60                2.07          0.71   \n",
       "4                 6897.50                1.28          0.33   \n",
       "\n",
       "   Soil Moisture Index -5cm  Soil Moisture Index -15cm  \\\n",
       "0                      0.02                       0.04   \n",
       "1                      1.16                       0.37   \n",
       "2                      0.89                       0.52   \n",
       "3                      0.93                       0.54   \n",
       "4                      0.81                       0.54   \n",
       "\n",
       "   Soil Moisture Index -50cm  Emission M14  Land Surface Temperature Day  \\\n",
       "0                       0.00          0.49                          0.00   \n",
       "1                       0.00          0.49                          0.00   \n",
       "2                       0.02          0.49                          0.00   \n",
       "3                       0.03          0.49                          0.00   \n",
       "4                       0.04          0.49                          0.00   \n",
       "\n",
       "   Land Surface Temp Night  \n",
       "0                   298.40  \n",
       "1                     0.00  \n",
       "2                     0.00  \n",
       "3                     0.00  \n",
       "4                     0.00  "
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df = corr_df.drop(columns=['Volumetric Soil Water -5cm (m3/m3)', 'Volumetric Soil Water -15cm (m3/m3)', 'Volumetric Soil Water -50cm (m3/m3)'])\n",
    "pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df['Leaf Wetness'] = pred_df['Leaf Wetness'].round(0)\n",
    "pred_df.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>Grass Land Temperature</th>\n",
       "      <th>Evapotranspiration</th>\n",
       "      <th>Leaf Wetness</th>\n",
       "      <th>Soil Moisture Index -5cm</th>\n",
       "      <th>Soil Moisture Index -15cm</th>\n",
       "      <th>Soil Moisture Index -50cm</th>\n",
       "      <th>Emission M14</th>\n",
       "      <th>Land Surface Temperature Day</th>\n",
       "      <th>Land Surface Temp Night</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6801.40</td>\n",
       "      <td>5.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>298.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6832.10</td>\n",
       "      <td>3.88</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6855.30</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6878.60</td>\n",
       "      <td>2.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6897.50</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>6430.10</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.98</td>\n",
       "      <td>316.42</td>\n",
       "      <td>294.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>6455.50</td>\n",
       "      <td>4.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.98</td>\n",
       "      <td>311.64</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>6481.30</td>\n",
       "      <td>4.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.98</td>\n",
       "      <td>313.26</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>6507.10</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.98</td>\n",
       "      <td>309.36</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>6532.10</td>\n",
       "      <td>4.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Grass Land Temperature  Evapotranspiration  Leaf Wetness  \\\n",
       "0                   6801.40                5.78          0.00   \n",
       "1                   6832.10                3.88          1.00   \n",
       "2                   6855.30                2.34          0.00   \n",
       "3                   6878.60                2.07          1.00   \n",
       "4                   6897.50                1.28          0.00   \n",
       "..                      ...                 ...           ...   \n",
       "361                 6430.10                3.45          0.00   \n",
       "362                 6455.50                4.74          0.00   \n",
       "363                 6481.30                4.96          0.00   \n",
       "364                 6507.10                4.55          0.00   \n",
       "365                 6532.10                4.87          0.00   \n",
       "\n",
       "     Soil Moisture Index -5cm  Soil Moisture Index -15cm  \\\n",
       "0                        0.02                       0.04   \n",
       "1                        1.16                       0.37   \n",
       "2                        0.89                       0.52   \n",
       "3                        0.93                       0.54   \n",
       "4                        0.81                       0.54   \n",
       "..                        ...                        ...   \n",
       "361                      0.21                       0.43   \n",
       "362                      0.17                       0.41   \n",
       "363                      0.13                       0.37   \n",
       "364                      0.09                       0.34   \n",
       "365                      0.07                       0.32   \n",
       "\n",
       "     Soil Moisture Index -50cm  Emission M14  Land Surface Temperature Day  \\\n",
       "0                         0.00          0.49                          0.00   \n",
       "1                         0.00          0.49                          0.00   \n",
       "2                         0.02          0.49                          0.00   \n",
       "3                         0.03          0.49                          0.00   \n",
       "4                         0.04          0.49                          0.00   \n",
       "..                         ...           ...                           ...   \n",
       "361                       0.43          0.98                        316.42   \n",
       "362                       0.42          0.98                        311.64   \n",
       "363                       0.42          0.98                        313.26   \n",
       "364                       0.42          0.98                        309.36   \n",
       "365                       0.41          0.00                          0.00   \n",
       "\n",
       "     Land Surface Temp Night  \n",
       "0                     298.40  \n",
       "1                       0.00  \n",
       "2                       0.00  \n",
       "3                       0.00  \n",
       "4                       0.00  \n",
       "..                       ...  \n",
       "361                   294.60  \n",
       "362                     0.00  \n",
       "363                     0.00  \n",
       "364                     0.00  \n",
       "365                     0.00  \n",
       "\n",
       "[366 rows x 9 columns]"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Leaf Wetness\n",
       "0.00    298\n",
       "1.00     68\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df['Leaf Wetness'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se dividen los datos en la variable que se va a predecir y las variables que van a ayudar a la prediccion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pred_df.drop(columns=['Leaf Wetness'])\n",
    "y = pred_df['Leaf Wetness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se dividen la informacion en datos de entrenamiento (80%) y prueba (20%)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se utiliza la libreria Lazy Classifier para ver todos los modelos de clasificacion y elegir el mejor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 29/29 [00:00<00:00, 48.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 53, number of negative: 239\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000061 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 571\n",
      "[LightGBM] [Info] Number of data points in the train set: 292, number of used features: 8\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.181507 -> initscore=-1.506172\n",
      "[LightGBM] [Info] Start training from score -1.506172\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
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      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "                               Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
      "Model                                                                           \n",
      "XGBClassifier                      0.92               0.87     0.87      0.92   \n",
      "GaussianNB                         0.84               0.87     0.87      0.85   \n",
      "LinearSVC                          0.92               0.85     0.85      0.92   \n",
      "LogisticRegression                 0.92               0.85     0.85      0.92   \n",
      "BernoulliNB                        0.82               0.84     0.84      0.84   \n",
      "NearestCentroid                    0.77               0.83     0.83      0.79   \n",
      "RandomForestClassifier             0.88               0.82     0.82      0.88   \n",
      "LinearDiscriminantAnalysis         0.88               0.82     0.82      0.88   \n",
      "LGBMClassifier                     0.88               0.82     0.82      0.88   \n",
      "RidgeClassifier                    0.91               0.82     0.82      0.90   \n",
      "RidgeClassifierCV                  0.91               0.82     0.82      0.90   \n",
      "QuadraticDiscriminantAnalysis      0.88               0.80     0.80      0.88   \n",
      "ExtraTreesClassifier               0.86               0.79     0.79      0.86   \n",
      "CalibratedClassifierCV             0.89               0.78     0.78      0.89   \n",
      "LabelSpreading                     0.85               0.78     0.78      0.85   \n",
      "LabelPropagation                   0.85               0.78     0.78      0.85   \n",
      "AdaBoostClassifier                 0.84               0.77     0.77      0.84   \n",
      "ExtraTreeClassifier                0.80               0.75     0.75      0.81   \n",
      "SGDClassifier                      0.82               0.74     0.74      0.83   \n",
      "KNeighborsClassifier               0.86               0.72     0.72      0.85   \n",
      "SVC                                0.84               0.70     0.70      0.83   \n",
      "DecisionTreeClassifier             0.77               0.68     0.68      0.78   \n",
      "BaggingClassifier                  0.80               0.67     0.67      0.79   \n",
      "PassiveAggressiveClassifier        0.76               0.52     0.52      0.72   \n",
      "DummyClassifier                    0.80               0.50     0.50      0.71   \n",
      "Perceptron                         0.73               0.48     0.48      0.69   \n",
      "\n",
      "                               Time Taken  \n",
      "Model                                      \n",
      "XGBClassifier                        0.05  \n",
      "GaussianNB                           0.01  \n",
      "LinearSVC                            0.01  \n",
      "LogisticRegression                   0.01  \n",
      "BernoulliNB                          0.01  \n",
      "NearestCentroid                      0.01  \n",
      "RandomForestClassifier               0.12  \n",
      "LinearDiscriminantAnalysis           0.01  \n",
      "LGBMClassifier                       0.03  \n",
      "RidgeClassifier                      0.01  \n",
      "RidgeClassifierCV                    0.01  \n",
      "QuadraticDiscriminantAnalysis        0.01  \n",
      "ExtraTreesClassifier                 0.08  \n",
      "CalibratedClassifierCV               0.02  \n",
      "LabelSpreading                       0.01  \n",
      "LabelPropagation                     0.01  \n",
      "AdaBoostClassifier                   0.10  \n",
      "ExtraTreeClassifier                  0.01  \n",
      "SGDClassifier                        0.01  \n",
      "KNeighborsClassifier                 0.01  \n",
      "SVC                                  0.01  \n",
      "DecisionTreeClassifier               0.01  \n",
      "BaggingClassifier                    0.03  \n",
      "PassiveAggressiveClassifier          0.01  \n",
      "DummyClassifier                      0.01  \n",
      "Perceptron                           0.01  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Inicializar LazyClassifier\n",
    "clf = LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=None)\n",
    "models, predictions = clf.fit(X_train, X_test, y_train, y_test)\n",
    "print(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8784\n",
      "F1 Score: 0.8798\n",
      "Precision: 0.8817\n",
      "Recall: 0.8784\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.92      0.92        59\n",
      "         1.0       0.69      0.73      0.71        15\n",
      "\n",
      "    accuracy                           0.88        74\n",
      "   macro avg       0.81      0.82      0.82        74\n",
      "weighted avg       0.88      0.88      0.88        74\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf_classifier = RandomForestClassifier(random_state=42)\n",
    "rf_classifier.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rf_classifier.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "precision = precision_score(y_test, y_pred, average='weighted')\n",
    "recall = recall_score(y_test, y_pred, average='weighted')\n",
    "\n",
    "# Print the classification report for more detailed metrics\n",
    "report = classification_report(y_test, y_pred)\n",
    "\n",
    "# Display the metrics\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(f\"F1 Score: {f1:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"Recall: {recall:.4f}\")\n",
    "print(\"\\nClassification Report:\")\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Plot the confusion matrix\n",
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\", cbar=False)\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Guardamos el modelo entrenado utilizando pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved successfully to Modelo_Pred_LeafWetness.pkl\n"
     ]
    }
   ],
   "source": [
    "# Specify the file path to save the model\n",
    "model_filename = 'Modelo_Pred_LeafWetness.pkl'\n",
    "\n",
    "# Save the trained model to a file using pickle\n",
    "with open(model_filename, 'wb') as file:\n",
    "    pickle.dump(rf_classifier, file)\n",
    "\n",
    "print(f\"Model saved successfully to {model_filename}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Realizamos un analisis prescriptivo de la informacion, identificando las variables que afectan en mayor medida a nuestra variable predictora."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}