starter-notebook_kaggle.ipynb

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    "# Regression Predict Student Solution\n",
    "\n",
    "© Explore Data Science Academy\n",
    "\n",
    "---\n",
    "### Honour Code\n",
    "\n",
    "I {**YOUR NAME, YOUR SURNAME**}, confirm - by submitting this document - that the solutions in this notebook are a result of my own work and that I abide by the [EDSA honour code](https://drive.google.com/file/d/1QDCjGZJ8-FmJE3bZdIQNwnJyQKPhHZBn/view?usp=sharing).\n",
    "\n",
    "Non-compliance with the honour code constitutes a material breach of contract.\n",
    "\n",
    "### Predict Overview: Spain Electricity Shortfall Challenge\n",
    "\n",
    "The government of Spain is considering an expansion of it's renewable energy resource infrastructure investments. As such, they require information on the trends and patterns of the countries renewable sources and fossil fuel energy generation. Your company has been awarded the contract to:\n",
    "\n",
    "- 1. analyse the supplied data;\n",
    "- 2. identify potential errors in the data and clean the existing data set;\n",
    "- 3. determine if additional features can be added to enrich the data set;\n",
    "- 4. build a model that is capable of forecasting the three hourly demand shortfalls;\n",
    "- 5. evaluate the accuracy of the best machine learning model;\n",
    "- 6. determine what features were most important in the model’s prediction decision, and\n",
    "- 7. explain the inner working of the model to a non-technical audience.\n",
    "\n",
    "Formally the problem statement was given to you, the senior data scientist, by your manager via email reads as follow:\n",
    "\n",
    "> In this project you are tasked to model the shortfall between the energy generated by means of fossil fuels and various renewable sources - for the country of Spain. The daily shortfall, which will be referred to as the target variable, will be modelled as a function of various city-specific weather features such as `pressure`, `wind speed`, `humidity`, etc. As with all data science projects, the provided features are rarely adequate predictors of the target variable. As such, you are required to perform feature engineering to ensure that you will be able to accurately model Spain's three hourly shortfalls.\n",
    " \n",
    "On top of this, she has provided you with a starter notebook containing vague explanations of what the main outcomes are. "
   ]
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    "<a id=\"cont\"></a>\n",
    "\n",
    "## Table of Contents\n",
    "\n",
    "<a href=#one>1. Importing Packages</a>\n",
    "\n",
    "<a href=#two>2. Loading Data</a>\n",
    "\n",
    "<a href=#three>3. Exploratory Data Analysis (EDA)</a>\n",
    "\n",
    "<a href=#four>4. Data Engineering</a>\n",
    "\n",
    "<a href=#five>5. Modeling</a>\n",
    "\n",
    "<a href=#six>6. Model Performance</a>\n",
    "\n",
    "<a href=#seven>7. Model Explanations</a>"
   ]
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    " <a id=\"one\"></a>\n",
    "## 1. Importing Packages\n",
    "<a href=#cont>Back to Table of Contents</a>\n",
    "\n",
    "---\n",
    "    \n",
    "| ⚡ Description: Importing Packages ⚡ |\n",
    "| :--------------------------- |\n",
    "| In this section you are required to import, and briefly discuss, the libraries that will be used throughout your analysis and modelling. |\n",
    "\n",
    "---"
   ]
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    "# Libraries for data loading, data manipulation and data visulisation\n",
    "import numpy as np # for linear algebra\n",
    "import pandas as pd # Data processing, CSV file importation\n",
    "\n",
    "# Libraries for data preparation and model building\n",
    "##Accuracy packages\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import *\n",
    "from sklearn.linear_model import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from tqdm import tqdm\n",
    "import math\n",
    "import random\n",
    "\n",
    "#Visualization Packages\n",
    "from scipy import stats\n",
    "from scipy.stats import norm, skew #for some statistics\n",
    "import seaborn as sns \n",
    "\n",
    "# Setting global constants to ensure notebook results are reproducible\n",
    "#PARAMETER_CONSTANT = ###"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "<a id=\"two\"></a>\n",
    "## 2. Loading the Data\n",
    "<a class=\"anchor\" id=\"1.1\"></a>\n",
    "<a href=#cont>Back to Table of Contents</a>\n",
    "\n",
    "---\n",
    "    \n",
    "| ⚡ Description: Loading the data ⚡ |\n",
    "| :--------------------------- |\n",
    "| In this section you are required to load the data from the `df_train` file into a DataFrame. |\n",
    "\n",
    "---"
   ]
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   "source": [
    "To load your data, you need to make sure that the raw data and the notebook file are on the same folder on your computer, \n",
    "Then navigate to the Jupyter Notebook"
   ]
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       "      Unnamed: 0                 time  Madrid_wind_speed Valencia_wind_deg  \\\n",
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       "4              4  2015-01-01 15:00:00           1.000000           level_7   \n",
       "...          ...                  ...                ...               ...   \n",
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       "\n",
       "      Bilbao_rain_1h  Valencia_wind_speed  Seville_humidity  Madrid_humidity  \\\n",
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       "1                0.0             1.666667         78.333333        64.666667   \n",
       "2                0.0             1.000000         71.333333        64.333333   \n",
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       "...              ...                  ...               ...              ...   \n",
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       "8761             0.2             7.333333         67.666667        79.000000   \n",
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       "\n",
       "      Bilbao_clouds_all  Bilbao_wind_speed  ...  Madrid_temp_max  \\\n",
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       "\n",
       "      Barcelona_temp  Bilbao_temp_min  Bilbao_temp  Barcelona_temp_min  \\\n",
       "0         281.013000       269.338615   269.338615          281.013000   \n",
       "1         280.561667       270.376000   270.376000          280.561667   \n",
       "2         281.583667       275.027229   275.027229          281.583667   \n",
       "3         283.434104       281.135063   281.135063          283.434104   \n",
       "4         284.213167       282.252063   282.252063          284.213167   \n",
       "...              ...              ...          ...                 ...   \n",
       "8758      281.276667       285.150000   287.573333          280.483333   \n",
       "8759      287.483333       286.483333   288.616667          287.150000   \n",
       "8760      289.816667       283.816667   285.330000          289.150000   \n",
       "8761      287.523333       278.816667   281.410000          286.816667   \n",
       "8762      287.483333       276.816667   281.020000          287.150000   \n",
       "\n",
       "      Bilbao_temp_max  Seville_temp_min Madrid_temp  Madrid_temp_min  \\\n",
       "0          269.338615        274.254667  265.938000       265.938000   \n",
       "1          270.376000        274.945000  266.386667       266.386667   \n",
       "2          275.027229        278.792000  272.708667       272.708667   \n",
       "3          281.135063        285.394000  281.895219       281.895219   \n",
       "4          282.252063        285.513719  280.678437       280.678437   \n",
       "...               ...               ...         ...              ...   \n",
       "8758       290.150000        284.816667  279.686667       278.483333   \n",
       "8759       291.150000        287.150000  282.400000       280.150000   \n",
       "8760       286.816667        289.150000  283.956667       281.150000   \n",
       "8761       284.150000        289.150000  282.666667       280.816667   \n",
       "8762       285.150000        287.483333  281.396667       280.483333   \n",
       "\n",
       "      load_shortfall_3h  \n",
       "0           6715.666667  \n",
       "1           4171.666667  \n",
       "2           4274.666667  \n",
       "3           5075.666667  \n",
       "4           6620.666667  \n",
       "...                 ...  \n",
       "8758         -28.333333  \n",
       "8759        2266.666667  \n",
       "8760         822.000000  \n",
       "8761        -760.000000  \n",
       "8762        2780.666667  \n",
       "\n",
       "[8763 rows x 49 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the train data\n",
    "df_train = pd.read_csv('Data/df_train.csv')\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "02ec4a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>time</th>\n",
       "      <th>Madrid_wind_speed</th>\n",
       "      <th>Valencia_wind_deg</th>\n",
       "      <th>Bilbao_rain_1h</th>\n",
       "      <th>Valencia_wind_speed</th>\n",
       "      <th>Seville_humidity</th>\n",
       "      <th>Madrid_humidity</th>\n",
       "      <th>Bilbao_clouds_all</th>\n",
       "      <th>Bilbao_wind_speed</th>\n",
       "      <th>...</th>\n",
       "      <th>Barcelona_temp_max</th>\n",
       "      <th>Madrid_temp_max</th>\n",
       "      <th>Barcelona_temp</th>\n",
       "      <th>Bilbao_temp_min</th>\n",
       "      <th>Bilbao_temp</th>\n",
       "      <th>Barcelona_temp_min</th>\n",
       "      <th>Bilbao_temp_max</th>\n",
       "      <th>Seville_temp_min</th>\n",
       "      <th>Madrid_temp</th>\n",
       "      <th>Madrid_temp_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>8763</td>\n",
       "      <td>2018-01-01 00:00:00</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>level_8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>71.333333</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>287.816667</td>\n",
       "      <td>280.816667</td>\n",
       "      <td>287.356667</td>\n",
       "      <td>276.150000</td>\n",
       "      <td>280.380000</td>\n",
       "      <td>286.816667</td>\n",
       "      <td>285.150000</td>\n",
       "      <td>283.150000</td>\n",
       "      <td>279.866667</td>\n",
       "      <td>279.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8764</td>\n",
       "      <td>2018-01-01 03:00:00</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>level_8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>284.816667</td>\n",
       "      <td>280.483333</td>\n",
       "      <td>284.190000</td>\n",
       "      <td>277.816667</td>\n",
       "      <td>281.010000</td>\n",
       "      <td>283.483333</td>\n",
       "      <td>284.150000</td>\n",
       "      <td>281.150000</td>\n",
       "      <td>279.193333</td>\n",
       "      <td>278.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8765</td>\n",
       "      <td>2018-01-01 06:00:00</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>level_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>284.483333</td>\n",
       "      <td>276.483333</td>\n",
       "      <td>283.150000</td>\n",
       "      <td>276.816667</td>\n",
       "      <td>279.196667</td>\n",
       "      <td>281.816667</td>\n",
       "      <td>282.150000</td>\n",
       "      <td>280.483333</td>\n",
       "      <td>276.340000</td>\n",
       "      <td>276.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8766</td>\n",
       "      <td>2018-01-01 09:00:00</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>level_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>82.666667</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>284.150000</td>\n",
       "      <td>277.150000</td>\n",
       "      <td>283.190000</td>\n",
       "      <td>279.150000</td>\n",
       "      <td>281.740000</td>\n",
       "      <td>282.150000</td>\n",
       "      <td>284.483333</td>\n",
       "      <td>279.150000</td>\n",
       "      <td>275.953333</td>\n",
       "      <td>274.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8767</td>\n",
       "      <td>2018-01-01 12:00:00</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>level_7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>65.333333</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>287.483333</td>\n",
       "      <td>281.150000</td>\n",
       "      <td>286.816667</td>\n",
       "      <td>281.816667</td>\n",
       "      <td>284.116667</td>\n",
       "      <td>286.150000</td>\n",
       "      <td>286.816667</td>\n",
       "      <td>284.483333</td>\n",
       "      <td>280.686667</td>\n",
       "      <td>280.150000</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>11678</td>\n",
       "      <td>2018-12-31 09:00:00</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>level_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>81.666667</td>\n",
       "      <td>49.666667</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>279.816667</td>\n",
       "      <td>281.483333</td>\n",
       "      <td>278.140000</td>\n",
       "      <td>270.816667</td>\n",
       "      <td>273.210000</td>\n",
       "      <td>276.483333</td>\n",
       "      <td>276.150000</td>\n",
       "      <td>279.816667</td>\n",
       "      <td>274.910000</td>\n",
       "      <td>271.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>11679</td>\n",
       "      <td>2018-12-31 12:00:00</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>level_8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>28.333333</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>286.483333</td>\n",
       "      <td>287.816667</td>\n",
       "      <td>286.150000</td>\n",
       "      <td>278.150000</td>\n",
       "      <td>278.443333</td>\n",
       "      <td>285.816667</td>\n",
       "      <td>278.816667</td>\n",
       "      <td>287.150000</td>\n",
       "      <td>283.156667</td>\n",
       "      <td>280.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>11680</td>\n",
       "      <td>2018-12-31 15:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>level_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>26.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>289.483333</td>\n",
       "      <td>288.816667</td>\n",
       "      <td>288.820000</td>\n",
       "      <td>284.150000</td>\n",
       "      <td>285.073333</td>\n",
       "      <td>288.150000</td>\n",
       "      <td>285.816667</td>\n",
       "      <td>290.816667</td>\n",
       "      <td>287.733333</td>\n",
       "      <td>286.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>11681</td>\n",
       "      <td>2018-12-31 18:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>level_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>52.666667</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>285.816667</td>\n",
       "      <td>285.150000</td>\n",
       "      <td>284.473333</td>\n",
       "      <td>280.150000</td>\n",
       "      <td>281.626667</td>\n",
       "      <td>283.150000</td>\n",
       "      <td>282.816667</td>\n",
       "      <td>287.483333</td>\n",
       "      <td>283.813333</td>\n",
       "      <td>282.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>11682</td>\n",
       "      <td>2018-12-31 21:00:00</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>level_10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>61.666667</td>\n",
       "      <td>69.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>283.816667</td>\n",
       "      <td>276.816667</td>\n",
       "      <td>281.133333</td>\n",
       "      <td>276.150000</td>\n",
       "      <td>276.450000</td>\n",
       "      <td>278.483333</td>\n",
       "      <td>276.816667</td>\n",
       "      <td>283.816667</td>\n",
       "      <td>276.623333</td>\n",
       "      <td>276.483333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2920 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                 time  Madrid_wind_speed Valencia_wind_deg  \\\n",
       "0           8763  2018-01-01 00:00:00           5.000000           level_8   \n",
       "1           8764  2018-01-01 03:00:00           4.666667           level_8   \n",
       "2           8765  2018-01-01 06:00:00           2.333333           level_7   \n",
       "3           8766  2018-01-01 09:00:00           2.666667           level_7   \n",
       "4           8767  2018-01-01 12:00:00           4.000000           level_7   \n",
       "...          ...                  ...                ...               ...   \n",
       "2915       11678  2018-12-31 09:00:00           0.333333           level_9   \n",
       "2916       11679  2018-12-31 12:00:00           0.333333           level_8   \n",
       "2917       11680  2018-12-31 15:00:00           1.000000           level_6   \n",
       "2918       11681  2018-12-31 18:00:00           1.000000           level_6   \n",
       "2919       11682  2018-12-31 21:00:00           1.333333          level_10   \n",
       "\n",
       "      Bilbao_rain_1h  Valencia_wind_speed  Seville_humidity  Madrid_humidity  \\\n",
       "0                0.0             5.000000         87.000000        71.333333   \n",
       "1                0.0             5.333333         89.000000        78.000000   \n",
       "2                0.0             5.000000         89.000000        89.666667   \n",
       "3                0.0             5.333333         93.333333        82.666667   \n",
       "4                0.0             8.666667         65.333333        64.000000   \n",
       "...              ...                  ...               ...              ...   \n",
       "2915             0.0             2.000000         81.666667        49.666667   \n",
       "2916             0.0             1.333333         61.000000        28.333333   \n",
       "2917             0.0             3.000000         47.000000        26.333333   \n",
       "2918             0.0             2.000000         52.666667        56.666667   \n",
       "2919             0.0             2.333333         61.666667        69.333333   \n",
       "\n",
       "      Bilbao_clouds_all  Bilbao_wind_speed  ...  Barcelona_temp_max  \\\n",
       "0             20.000000           3.000000  ...          287.816667   \n",
       "1              0.000000           3.666667  ...          284.816667   \n",
       "2              0.000000           2.333333  ...          284.483333   \n",
       "3             26.666667           5.666667  ...          284.150000   \n",
       "4             26.666667          10.666667  ...          287.483333   \n",
       "...                 ...                ...  ...                 ...   \n",
       "2915          80.000000           1.333333  ...          279.816667   \n",
       "2916          56.666667           1.000000  ...          286.483333   \n",
       "2917           0.000000           0.666667  ...          289.483333   \n",
       "2918           0.000000           0.666667  ...          285.816667   \n",
       "2919           0.000000           1.333333  ...          283.816667   \n",
       "\n",
       "      Madrid_temp_max  Barcelona_temp  Bilbao_temp_min  Bilbao_temp  \\\n",
       "0          280.816667      287.356667       276.150000   280.380000   \n",
       "1          280.483333      284.190000       277.816667   281.010000   \n",
       "2          276.483333      283.150000       276.816667   279.196667   \n",
       "3          277.150000      283.190000       279.150000   281.740000   \n",
       "4          281.150000      286.816667       281.816667   284.116667   \n",
       "...               ...             ...              ...          ...   \n",
       "2915       281.483333      278.140000       270.816667   273.210000   \n",
       "2916       287.816667      286.150000       278.150000   278.443333   \n",
       "2917       288.816667      288.820000       284.150000   285.073333   \n",
       "2918       285.150000      284.473333       280.150000   281.626667   \n",
       "2919       276.816667      281.133333       276.150000   276.450000   \n",
       "\n",
       "      Barcelona_temp_min  Bilbao_temp_max Seville_temp_min  Madrid_temp  \\\n",
       "0             286.816667       285.150000       283.150000   279.866667   \n",
       "1             283.483333       284.150000       281.150000   279.193333   \n",
       "2             281.816667       282.150000       280.483333   276.340000   \n",
       "3             282.150000       284.483333       279.150000   275.953333   \n",
       "4             286.150000       286.816667       284.483333   280.686667   \n",
       "...                  ...              ...              ...          ...   \n",
       "2915          276.483333       276.150000       279.816667   274.910000   \n",
       "2916          285.816667       278.816667       287.150000   283.156667   \n",
       "2917          288.150000       285.816667       290.816667   287.733333   \n",
       "2918          283.150000       282.816667       287.483333   283.813333   \n",
       "2919          278.483333       276.816667       283.816667   276.623333   \n",
       "\n",
       "      Madrid_temp_min  \n",
       "0          279.150000  \n",
       "1          278.150000  \n",
       "2          276.150000  \n",
       "3          274.483333  \n",
       "4          280.150000  \n",
       "...               ...  \n",
       "2915       271.150000  \n",
       "2916       280.483333  \n",
       "2917       286.483333  \n",
       "2918       282.150000  \n",
       "2919       276.483333  \n",
       "\n",
       "[2920 rows x 48 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the test data\n",
    "df_test = pd.read_csv('Data/df_test.csv')\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81132ab3",
   "metadata": {},
   "source": [
    "<a id=\"three\"></a>\n",
    "## 3. Exploratory Data Analysis (EDA)\n",
    "<a class=\"anchor\" id=\"1.1\"></a>\n",
    "<a href=#cont>Back to Table of Contents</a>\n",
    "\n",
    "---\n",
    "    \n",
    "| ⚡ Description: Exploratory data analysis ⚡ |\n",
    "| :--------------------------- |\n",
    "| In this section, you are required to perform an in-depth analysis of all the variables in the DataFrame. |\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d0e407",
   "metadata": {},
   "source": [
    "### Check the \"Shape\" of the data-sets\n",
    "Looking at the shape of both datasets, it is clear that the data has been split into 2 sets. 75% of the data is designated as the train data while 25% of the data is designated as the test data. The shape also shows that the training set has 49 columns while the test data set has only 48 coulmns. The missing column from the test set is the column that our model is to predict. We can identify that particular entity by simply identifying the entity(Column) that is missing from the test data set. \n",
    "From examining both datasets, that column can be identified as the load_shortfall_3hr column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f95921d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8763, 49), (2920, 48))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eab79a49",
   "metadata": {},
   "source": [
    "### THE COLUMN FUNCTION\n",
    "The column function returns the names of all the columns that are in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "622c78f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'time', 'Madrid_wind_speed', 'Valencia_wind_deg',\n",
       "       'Bilbao_rain_1h', 'Valencia_wind_speed', 'Seville_humidity',\n",
       "       'Madrid_humidity', 'Bilbao_clouds_all', 'Bilbao_wind_speed',\n",
       "       'Seville_clouds_all', 'Bilbao_wind_deg', 'Barcelona_wind_speed',\n",
       "       'Barcelona_wind_deg', 'Madrid_clouds_all', 'Seville_wind_speed',\n",
       "       'Barcelona_rain_1h', 'Seville_pressure', 'Seville_rain_1h',\n",
       "       'Bilbao_snow_3h', 'Barcelona_pressure', 'Seville_rain_3h',\n",
       "       'Madrid_rain_1h', 'Barcelona_rain_3h', 'Valencia_snow_3h',\n",
       "       'Madrid_weather_id', 'Barcelona_weather_id', 'Bilbao_pressure',\n",
       "       'Seville_weather_id', 'Valencia_pressure', 'Seville_temp_max',\n",
       "       'Madrid_pressure', 'Valencia_temp_max', 'Valencia_temp',\n",
       "       'Bilbao_weather_id', 'Seville_temp', 'Valencia_humidity',\n",
       "       'Valencia_temp_min', 'Barcelona_temp_max', 'Madrid_temp_max',\n",
       "       'Barcelona_temp', 'Bilbao_temp_min', 'Bilbao_temp',\n",
       "       'Barcelona_temp_min', 'Bilbao_temp_max', 'Seville_temp_min',\n",
       "       'Madrid_temp', 'Madrid_temp_min', 'load_shortfall_3h'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "219d8740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'time', 'Madrid_wind_speed', 'Valencia_wind_deg',\n",
       "       'Bilbao_rain_1h', 'Valencia_wind_speed', 'Seville_humidity',\n",
       "       'Madrid_humidity', 'Bilbao_clouds_all', 'Bilbao_wind_speed',\n",
       "       'Seville_clouds_all', 'Bilbao_wind_deg', 'Barcelona_wind_speed',\n",
       "       'Barcelona_wind_deg', 'Madrid_clouds_all', 'Seville_wind_speed',\n",
       "       'Barcelona_rain_1h', 'Seville_pressure', 'Seville_rain_1h',\n",
       "       'Bilbao_snow_3h', 'Barcelona_pressure', 'Seville_rain_3h',\n",
       "       'Madrid_rain_1h', 'Barcelona_rain_3h', 'Valencia_snow_3h',\n",
       "       'Madrid_weather_id', 'Barcelona_weather_id', 'Bilbao_pressure',\n",
       "       'Seville_weather_id', 'Valencia_pressure', 'Seville_temp_max',\n",
       "       'Madrid_pressure', 'Valencia_temp_max', 'Valencia_temp',\n",
       "       'Bilbao_weather_id', 'Seville_temp', 'Valencia_humidity',\n",
       "       'Valencia_temp_min', 'Barcelona_temp_max', 'Madrid_temp_max',\n",
       "       'Barcelona_temp', 'Bilbao_temp_min', 'Bilbao_temp',\n",
       "       'Barcelona_temp_min', 'Bilbao_temp_max', 'Seville_temp_min',\n",
       "       'Madrid_temp', 'Madrid_temp_min'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fab7420",
   "metadata": {},
   "source": [
    "### THE DESCRIBE FUNCTION\n",
    "The describe() function is used to generate descriptive statistics that summarize the central tendency, dispersion \n",
    "and shape of a dataset's distribution, excluding null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e805134e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-28T08:52:37.824204Z",
     "start_time": "2021-06-28T08:52:37.811206Z"
    }
   },
   "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>Unnamed: 0</th>\n",
       "      <th>Madrid_wind_speed</th>\n",
       "      <th>Bilbao_rain_1h</th>\n",
       "      <th>Valencia_wind_speed</th>\n",
       "      <th>Seville_humidity</th>\n",
       "      <th>Madrid_humidity</th>\n",
       "      <th>Bilbao_clouds_all</th>\n",
       "      <th>Bilbao_wind_speed</th>\n",
       "      <th>Seville_clouds_all</th>\n",
       "      <th>Bilbao_wind_deg</th>\n",
       "      <th>...</th>\n",
       "      <th>Madrid_temp_max</th>\n",
       "      <th>Barcelona_temp</th>\n",
       "      <th>Bilbao_temp_min</th>\n",
       "      <th>Bilbao_temp</th>\n",
       "      <th>Barcelona_temp_min</th>\n",
       "      <th>Bilbao_temp_max</th>\n",
       "      <th>Seville_temp_min</th>\n",
       "      <th>Madrid_temp</th>\n",
       "      <th>Madrid_temp_min</th>\n",
       "      <th>load_shortfall_3h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4381.000000</td>\n",
       "      <td>2.425729</td>\n",
       "      <td>0.135753</td>\n",
       "      <td>2.586272</td>\n",
       "      <td>62.658793</td>\n",
       "      <td>57.414717</td>\n",
       "      <td>43.469132</td>\n",
       "      <td>1.850356</td>\n",
       "      <td>13.714748</td>\n",
       "      <td>158.957511</td>\n",
       "      <td>...</td>\n",
       "      <td>289.540309</td>\n",
       "      <td>289.855459</td>\n",
       "      <td>285.017973</td>\n",
       "      <td>286.422929</td>\n",
       "      <td>288.447422</td>\n",
       "      <td>287.966027</td>\n",
       "      <td>291.633356</td>\n",
       "      <td>288.419439</td>\n",
       "      <td>287.202203</td>\n",
       "      <td>10673.857612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2529.804538</td>\n",
       "      <td>1.850371</td>\n",
       "      <td>0.374901</td>\n",
       "      <td>2.411190</td>\n",
       "      <td>22.621226</td>\n",
       "      <td>24.335396</td>\n",
       "      <td>32.551044</td>\n",
       "      <td>1.695888</td>\n",
       "      <td>24.272482</td>\n",
       "      <td>102.056299</td>\n",
       "      <td>...</td>\n",
       "      <td>9.752047</td>\n",
       "      <td>6.528111</td>\n",
       "      <td>6.705672</td>\n",
       "      <td>6.818682</td>\n",
       "      <td>6.102593</td>\n",
       "      <td>7.105590</td>\n",
       "      <td>8.178220</td>\n",
       "      <td>9.346796</td>\n",
       "      <td>9.206237</td>\n",
       "      <td>5218.046404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>264.983333</td>\n",
       "      <td>270.816667</td>\n",
       "      <td>264.483333</td>\n",
       "      <td>267.483333</td>\n",
       "      <td>269.483333</td>\n",
       "      <td>269.063000</td>\n",
       "      <td>270.150000</td>\n",
       "      <td>264.983333</td>\n",
       "      <td>264.983333</td>\n",
       "      <td>-6618.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2190.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>44.333333</td>\n",
       "      <td>36.333333</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>282.150000</td>\n",
       "      <td>284.973443</td>\n",
       "      <td>280.085167</td>\n",
       "      <td>281.374167</td>\n",
       "      <td>284.150000</td>\n",
       "      <td>282.836776</td>\n",
       "      <td>285.816667</td>\n",
       "      <td>281.404281</td>\n",
       "      <td>280.299167</td>\n",
       "      <td>7390.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4381.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>65.666667</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>288.116177</td>\n",
       "      <td>289.416667</td>\n",
       "      <td>284.816667</td>\n",
       "      <td>286.158333</td>\n",
       "      <td>288.150000</td>\n",
       "      <td>287.630000</td>\n",
       "      <td>290.816667</td>\n",
       "      <td>287.053333</td>\n",
       "      <td>286.083333</td>\n",
       "      <td>11114.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6571.500000</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>78.666667</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>296.816667</td>\n",
       "      <td>294.909000</td>\n",
       "      <td>289.816667</td>\n",
       "      <td>291.034167</td>\n",
       "      <td>292.966667</td>\n",
       "      <td>292.483333</td>\n",
       "      <td>297.150000</td>\n",
       "      <td>295.154667</td>\n",
       "      <td>293.884500</td>\n",
       "      <td>14498.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8762.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.666667</td>\n",
       "      <td>97.333333</td>\n",
       "      <td>359.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>314.483333</td>\n",
       "      <td>307.316667</td>\n",
       "      <td>309.816667</td>\n",
       "      <td>310.710000</td>\n",
       "      <td>304.816667</td>\n",
       "      <td>317.966667</td>\n",
       "      <td>314.816667</td>\n",
       "      <td>313.133333</td>\n",
       "      <td>310.383333</td>\n",
       "      <td>31904.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  Madrid_wind_speed  Bilbao_rain_1h  Valencia_wind_speed  \\\n",
       "count  8763.000000        8763.000000     8763.000000          8763.000000   \n",
       "mean   4381.000000           2.425729        0.135753             2.586272   \n",
       "std    2529.804538           1.850371        0.374901             2.411190   \n",
       "min       0.000000           0.000000        0.000000             0.000000   \n",
       "25%    2190.500000           1.000000        0.000000             1.000000   \n",
       "50%    4381.000000           2.000000        0.000000             1.666667   \n",
       "75%    6571.500000           3.333333        0.100000             3.666667   \n",
       "max    8762.000000          13.000000        3.000000            52.000000   \n",
       "\n",
       "       Seville_humidity  Madrid_humidity  Bilbao_clouds_all  \\\n",
       "count       8763.000000      8763.000000        8763.000000   \n",
       "mean          62.658793        57.414717          43.469132   \n",
       "std           22.621226        24.335396          32.551044   \n",
       "min            8.333333         6.333333           0.000000   \n",
       "25%           44.333333        36.333333          10.000000   \n",
       "50%           65.666667        58.000000          45.000000   \n",
       "75%           82.000000        78.666667          75.000000   \n",
       "max          100.000000       100.000000         100.000000   \n",
       "\n",
       "       Bilbao_wind_speed  Seville_clouds_all  Bilbao_wind_deg  ...  \\\n",
       "count        8763.000000         8763.000000      8763.000000  ...   \n",
       "mean            1.850356           13.714748       158.957511  ...   \n",
       "std             1.695888           24.272482       102.056299  ...   \n",
       "min             0.000000            0.000000         0.000000  ...   \n",
       "25%             0.666667            0.000000        73.333333  ...   \n",
       "50%             1.000000            0.000000       147.000000  ...   \n",
       "75%             2.666667           20.000000       234.000000  ...   \n",
       "max            12.666667           97.333333       359.333333  ...   \n",
       "\n",
       "       Madrid_temp_max  Barcelona_temp  Bilbao_temp_min  Bilbao_temp  \\\n",
       "count      8763.000000     8763.000000      8763.000000  8763.000000   \n",
       "mean        289.540309      289.855459       285.017973   286.422929   \n",
       "std           9.752047        6.528111         6.705672     6.818682   \n",
       "min         264.983333      270.816667       264.483333   267.483333   \n",
       "25%         282.150000      284.973443       280.085167   281.374167   \n",
       "50%         288.116177      289.416667       284.816667   286.158333   \n",
       "75%         296.816667      294.909000       289.816667   291.034167   \n",
       "max         314.483333      307.316667       309.816667   310.710000   \n",
       "\n",
       "       Barcelona_temp_min  Bilbao_temp_max  Seville_temp_min  Madrid_temp  \\\n",
       "count         8763.000000      8763.000000       8763.000000  8763.000000   \n",
       "mean           288.447422       287.966027        291.633356   288.419439   \n",
       "std              6.102593         7.105590          8.178220     9.346796   \n",
       "min            269.483333       269.063000        270.150000   264.983333   \n",
       "25%            284.150000       282.836776        285.816667   281.404281   \n",
       "50%            288.150000       287.630000        290.816667   287.053333   \n",
       "75%            292.966667       292.483333        297.150000   295.154667   \n",
       "max            304.816667       317.966667        314.816667   313.133333   \n",
       "\n",
       "       Madrid_temp_min  load_shortfall_3h  \n",
       "count      8763.000000        8763.000000  \n",
       "mean        287.202203       10673.857612  \n",
       "std           9.206237        5218.046404  \n",
       "min         264.983333       -6618.000000  \n",
       "25%         280.299167        7390.333333  \n",
       "50%         286.083333       11114.666667  \n",
       "75%         293.884500       14498.166667  \n",
       "max         310.383333       31904.000000  \n",
       "\n",
       "[8 rows x 46 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at data statistics for df_train\n",
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "35f62726",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Bilbao_temp</th>\n",
       "      <th>Barcelona_temp_min</th>\n",
       "      <th>Bilbao_temp_max</th>\n",
       "      <th>Seville_temp_min</th>\n",
       "      <th>Madrid_temp</th>\n",
       "      <th>Madrid_temp_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "      <td>2920.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10222.500000</td>\n",
       "      <td>2.457820</td>\n",
       "      <td>0.067517</td>\n",
       "      <td>3.012785</td>\n",
       "      <td>67.123516</td>\n",
       "      <td>62.644463</td>\n",
       "      <td>43.355422</td>\n",
       "      <td>2.283562</td>\n",
       "      <td>15.477283</td>\n",
       "      <td>162.643836</td>\n",
       "      <td>...</td>\n",
       "      <td>290.695462</td>\n",
       "      <td>288.888393</td>\n",
       "      <td>289.911289</td>\n",
       "      <td>284.920684</td>\n",
       "      <td>286.522375</td>\n",
       "      <td>289.124971</td>\n",
       "      <td>288.483641</td>\n",
       "      <td>290.152431</td>\n",
       "      <td>287.869763</td>\n",
       "      <td>286.618130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>843.075718</td>\n",
       "      <td>1.774838</td>\n",
       "      <td>0.153381</td>\n",
       "      <td>1.996340</td>\n",
       "      <td>20.611292</td>\n",
       "      <td>24.138393</td>\n",
       "      <td>30.486298</td>\n",
       "      <td>1.654787</td>\n",
       "      <td>25.289197</td>\n",
       "      <td>97.749873</td>\n",
       "      <td>...</td>\n",
       "      <td>7.113599</td>\n",
       "      <td>9.089699</td>\n",
       "      <td>7.119411</td>\n",
       "      <td>6.803424</td>\n",
       "      <td>6.492355</td>\n",
       "      <td>7.168049</td>\n",
       "      <td>6.221324</td>\n",
       "      <td>7.906915</td>\n",
       "      <td>8.977511</td>\n",
       "      <td>8.733163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8763.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.666667</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>273.816667</td>\n",
       "      <td>269.816667</td>\n",
       "      <td>272.650000</td>\n",
       "      <td>266.483333</td>\n",
       "      <td>268.120000</td>\n",
       "      <td>271.483333</td>\n",
       "      <td>270.138667</td>\n",
       "      <td>271.150000</td>\n",
       "      <td>268.713333</td>\n",
       "      <td>267.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9492.750000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>86.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>284.816667</td>\n",
       "      <td>281.483333</td>\n",
       "      <td>284.307500</td>\n",
       "      <td>280.150000</td>\n",
       "      <td>281.778333</td>\n",
       "      <td>283.483333</td>\n",
       "      <td>284.150000</td>\n",
       "      <td>284.483333</td>\n",
       "      <td>280.816667</td>\n",
       "      <td>279.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10222.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>70.333333</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>290.150000</td>\n",
       "      <td>287.483333</td>\n",
       "      <td>289.483333</td>\n",
       "      <td>284.483333</td>\n",
       "      <td>286.265000</td>\n",
       "      <td>288.816667</td>\n",
       "      <td>288.483333</td>\n",
       "      <td>289.150000</td>\n",
       "      <td>286.396667</td>\n",
       "      <td>285.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10952.250000</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>233.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>296.483333</td>\n",
       "      <td>295.483333</td>\n",
       "      <td>295.816667</td>\n",
       "      <td>289.816667</td>\n",
       "      <td>291.119167</td>\n",
       "      <td>295.150000</td>\n",
       "      <td>292.816667</td>\n",
       "      <td>295.150000</td>\n",
       "      <td>294.452500</td>\n",
       "      <td>293.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11682.000000</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>97.333333</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>309.483333</td>\n",
       "      <td>313.483333</td>\n",
       "      <td>308.150000</td>\n",
       "      <td>307.483333</td>\n",
       "      <td>308.966667</td>\n",
       "      <td>306.816667</td>\n",
       "      <td>310.816667</td>\n",
       "      <td>314.483333</td>\n",
       "      <td>312.223333</td>\n",
       "      <td>310.150000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0  Madrid_wind_speed  Bilbao_rain_1h  Valencia_wind_speed  \\\n",
       "count   2920.000000        2920.000000     2920.000000          2920.000000   \n",
       "mean   10222.500000           2.457820        0.067517             3.012785   \n",
       "std      843.075718           1.774838        0.153381             1.996340   \n",
       "min     8763.000000           0.000000        0.000000             0.000000   \n",
       "25%     9492.750000           1.333333        0.000000             1.666667   \n",
       "50%    10222.500000           2.000000        0.000000             2.333333   \n",
       "75%    10952.250000           3.333333        0.000000             4.000000   \n",
       "max    11682.000000          13.333333        1.600000            14.333333   \n",
       "\n",
       "       Seville_humidity  Madrid_humidity  Bilbao_clouds_all  \\\n",
       "count       2920.000000      2920.000000        2920.000000   \n",
       "mean          67.123516        62.644463          43.355422   \n",
       "std           20.611292        24.138393          30.486298   \n",
       "min           11.666667         8.000000           0.000000   \n",
       "25%           52.000000        43.000000          13.333333   \n",
       "50%           70.333333        63.000000          45.000000   \n",
       "75%           85.000000        84.000000          75.000000   \n",
       "max          100.000000       100.000000          97.333333   \n",
       "\n",
       "       Bilbao_wind_speed  Seville_clouds_all  Bilbao_wind_deg  ...  \\\n",
       "count        2920.000000         2920.000000      2920.000000  ...   \n",
       "mean            2.283562           15.477283       162.643836  ...   \n",
       "std             1.654787           25.289197        97.749873  ...   \n",
       "min             0.000000            0.000000         0.000000  ...   \n",
       "25%             1.000000            0.000000        86.666667  ...   \n",
       "50%             1.666667            0.000000       140.000000  ...   \n",
       "75%             3.333333           20.000000       233.333333  ...   \n",
       "max            10.666667           93.333333       360.000000  ...   \n",
       "\n",
       "       Barcelona_temp_max  Madrid_temp_max  Barcelona_temp  Bilbao_temp_min  \\\n",
       "count         2920.000000      2920.000000     2920.000000      2920.000000   \n",
       "mean           290.695462       288.888393      289.911289       284.920684   \n",
       "std              7.113599         9.089699        7.119411         6.803424   \n",
       "min            273.816667       269.816667      272.650000       266.483333   \n",
       "25%            284.816667       281.483333      284.307500       280.150000   \n",
       "50%            290.150000       287.483333      289.483333       284.483333   \n",
       "75%            296.483333       295.483333      295.816667       289.816667   \n",
       "max            309.483333       313.483333      308.150000       307.483333   \n",
       "\n",
       "       Bilbao_temp  Barcelona_temp_min  Bilbao_temp_max  Seville_temp_min  \\\n",
       "count  2920.000000         2920.000000      2920.000000       2920.000000   \n",
       "mean    286.522375          289.124971       288.483641        290.152431   \n",
       "std       6.492355            7.168049         6.221324          7.906915   \n",
       "min     268.120000          271.483333       270.138667        271.150000   \n",
       "25%     281.778333          283.483333       284.150000        284.483333   \n",
       "50%     286.265000          288.816667       288.483333        289.150000   \n",
       "75%     291.119167          295.150000       292.816667        295.150000   \n",
       "max     308.966667          306.816667       310.816667        314.483333   \n",
       "\n",
       "       Madrid_temp  Madrid_temp_min  \n",
       "count  2920.000000      2920.000000  \n",
       "mean    287.869763       286.618130  \n",
       "std       8.977511         8.733163  \n",
       "min     268.713333       267.816667  \n",
       "25%     280.816667       279.816667  \n",
       "50%     286.396667       285.483333  \n",
       "75%     294.452500       293.150000  \n",
       "max     312.223333       310.150000  \n",
       "\n",
       "[8 rows x 45 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at data statistics for df_test\n",
    "df_test.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0720390",
   "metadata": {},
   "source": [
    "### THE 'ISNULL' FUNCTION\n",
    "Identifying the column that has the null entries is important so it doesnt affect the performance of our model. \n",
    "this fuction shows us the number of null values present in the dataset. The function shows that only the Valencia_pressure has null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "044ec5ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                 0\n",
       "time                       0\n",
       "Madrid_wind_speed          0\n",
       "Valencia_wind_deg          0\n",
       "Bilbao_rain_1h             0\n",
       "Valencia_wind_speed        0\n",
       "Seville_humidity           0\n",
       "Madrid_humidity            0\n",
       "Bilbao_clouds_all          0\n",
       "Bilbao_wind_speed          0\n",
       "Seville_clouds_all         0\n",
       "Bilbao_wind_deg            0\n",
       "Barcelona_wind_speed       0\n",
       "Barcelona_wind_deg         0\n",
       "Madrid_clouds_all          0\n",
       "Seville_wind_speed         0\n",
       "Barcelona_rain_1h          0\n",
       "Seville_pressure           0\n",
       "Seville_rain_1h            0\n",
       "Bilbao_snow_3h             0\n",
       "Barcelona_pressure         0\n",
       "Seville_rain_3h            0\n",
       "Madrid_rain_1h             0\n",
       "Barcelona_rain_3h          0\n",
       "Valencia_snow_3h           0\n",
       "Madrid_weather_id          0\n",
       "Barcelona_weather_id       0\n",
       "Bilbao_pressure            0\n",
       "Seville_weather_id         0\n",
       "Valencia_pressure       2068\n",
       "Seville_temp_max           0\n",
       "Madrid_pressure            0\n",
       "Valencia_temp_max          0\n",
       "Valencia_temp              0\n",
       "Bilbao_weather_id          0\n",
       "Seville_temp               0\n",
       "Valencia_humidity          0\n",
       "Valencia_temp_min          0\n",
       "Barcelona_temp_max         0\n",
       "Madrid_temp_max            0\n",
       "Barcelona_temp             0\n",
       "Bilbao_temp_min            0\n",
       "Bilbao_temp                0\n",
       "Barcelona_temp_min         0\n",
       "Bilbao_temp_max            0\n",
       "Seville_temp_min           0\n",
       "Madrid_temp                0\n",
       "Madrid_temp_min            0\n",
       "load_shortfall_3h          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify colunm(s) that contain null values in df_train\n",
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "208df139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                0\n",
       "time                      0\n",
       "Madrid_wind_speed         0\n",
       "Valencia_wind_deg         0\n",
       "Bilbao_rain_1h            0\n",
       "Valencia_wind_speed       0\n",
       "Seville_humidity          0\n",
       "Madrid_humidity           0\n",
       "Bilbao_clouds_all         0\n",
       "Bilbao_wind_speed         0\n",
       "Seville_clouds_all        0\n",
       "Bilbao_wind_deg           0\n",
       "Barcelona_wind_speed      0\n",
       "Barcelona_wind_deg        0\n",
       "Madrid_clouds_all         0\n",
       "Seville_wind_speed        0\n",
       "Barcelona_rain_1h         0\n",
       "Seville_pressure          0\n",
       "Seville_rain_1h           0\n",
       "Bilbao_snow_3h            0\n",
       "Barcelona_pressure        0\n",
       "Seville_rain_3h           0\n",
       "Madrid_rain_1h            0\n",
       "Barcelona_rain_3h         0\n",
       "Valencia_snow_3h          0\n",
       "Madrid_weather_id         0\n",
       "Barcelona_weather_id      0\n",
       "Bilbao_pressure           0\n",
       "Seville_weather_id        0\n",
       "Valencia_pressure       454\n",
       "Seville_temp_max          0\n",
       "Madrid_pressure           0\n",
       "Valencia_temp_max         0\n",
       "Valencia_temp             0\n",
       "Bilbao_weather_id         0\n",
       "Seville_temp              0\n",
       "Valencia_humidity         0\n",
       "Valencia_temp_min         0\n",
       "Barcelona_temp_max        0\n",
       "Madrid_temp_max           0\n",
       "Barcelona_temp            0\n",
       "Bilbao_temp_min           0\n",
       "Bilbao_temp               0\n",
       "Barcelona_temp_min        0\n",
       "Bilbao_temp_max           0\n",
       "Seville_temp_min          0\n",
       "Madrid_temp               0\n",
       "Madrid_temp_min           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify colunm(s) that contain null values in df_test\n",
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2fb74182",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot relevant feature interactions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "727d09a5",
   "metadata": {},
   "source": [
    "### Evaluate the correlation between the columns of the dataset\n",
    "It is neccesary to evaluate the columns to see how the values within the columns correlate. if multiple columns show strong correlation, the correlating columns will have to be removed from the data set before it is used for model creation as these columns may not add any additional functionality or advantage to the model but will only serve to increase it's size and lead to slow performance. a correlation value of 1 represents a perfect positive correlation while a value equal to -1 indicates a perfect negative correlation. The further away the value is from 1 or -1, the weaker the correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1f73a65d",
   "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>Unnamed: 0</th>\n",
       "      <th>Madrid_wind_speed</th>\n",
       "      <th>Bilbao_rain_1h</th>\n",
       "      <th>Valencia_wind_speed</th>\n",
       "      <th>Seville_humidity</th>\n",
       "      <th>Madrid_humidity</th>\n",
       "      <th>Bilbao_clouds_all</th>\n",
       "      <th>Bilbao_wind_speed</th>\n",
       "      <th>Seville_clouds_all</th>\n",
       "      <th>Bilbao_wind_deg</th>\n",
       "      <th>...</th>\n",
       "      <th>Madrid_temp_max</th>\n",
       "      <th>Barcelona_temp</th>\n",
       "      <th>Bilbao_temp_min</th>\n",
       "      <th>Bilbao_temp</th>\n",
       "      <th>Barcelona_temp_min</th>\n",
       "      <th>Bilbao_temp_max</th>\n",
       "      <th>Seville_temp_min</th>\n",
       "      <th>Madrid_temp</th>\n",
       "      <th>Madrid_temp_min</th>\n",
       "      <th>load_shortfall_3h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109288</td>\n",
       "      <td>-0.249633</td>\n",
       "      <td>-0.090161</td>\n",
       "      <td>-0.079025</td>\n",
       "      <td>-0.103359</td>\n",
       "      <td>-0.068123</td>\n",
       "      <td>0.049197</td>\n",
       "      <td>-0.140583</td>\n",
       "      <td>-0.155738</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093378</td>\n",
       "      <td>0.068078</td>\n",
       "      <td>0.028698</td>\n",
       "      <td>0.081799</td>\n",
       "      <td>0.103436</td>\n",
       "      <td>0.146216</td>\n",
       "      <td>0.037487</td>\n",
       "      <td>0.088439</td>\n",
       "      <td>0.085197</td>\n",
       "      <td>0.192612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_wind_speed</th>\n",
       "      <td>-0.109288</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.259719</td>\n",
       "      <td>0.513092</td>\n",
       "      <td>-0.117892</td>\n",
       "      <td>-0.137092</td>\n",
       "      <td>0.244001</td>\n",
       "      <td>0.377854</td>\n",
       "      <td>0.191251</td>\n",
       "      <td>0.270950</td>\n",
       "      <td>...</td>\n",
       "      <td>0.091464</td>\n",
       "      <td>0.080133</td>\n",
       "      <td>0.077330</td>\n",
       "      <td>0.060467</td>\n",
       "      <td>0.089992</td>\n",
       "      <td>0.042114</td>\n",
       "      <td>0.110278</td>\n",
       "      <td>0.109572</td>\n",
       "      <td>0.098138</td>\n",
       "      <td>-0.150981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_rain_1h</th>\n",
       "      <td>-0.249633</td>\n",
       "      <td>0.259719</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.265864</td>\n",
       "      <td>0.069878</td>\n",
       "      <td>0.106765</td>\n",
       "      <td>0.370733</td>\n",
       "      <td>0.085398</td>\n",
       "      <td>0.081131</td>\n",
       "      <td>0.279350</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.148397</td>\n",
       "      <td>-0.138901</td>\n",
       "      <td>-0.141269</td>\n",
       "      <td>-0.182494</td>\n",
       "      <td>-0.106593</td>\n",
       "      <td>-0.219871</td>\n",
       "      <td>-0.120575</td>\n",
       "      <td>-0.135927</td>\n",
       "      <td>-0.116431</td>\n",
       "      <td>-0.152510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia_wind_speed</th>\n",
       "      <td>-0.090161</td>\n",
       "      <td>0.513092</td>\n",
       "      <td>0.265864</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.075227</td>\n",
       "      <td>-0.019388</td>\n",
       "      <td>0.210524</td>\n",
       "      <td>0.386478</td>\n",
       "      <td>0.163675</td>\n",
       "      <td>0.248643</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021970</td>\n",
       "      <td>-0.021456</td>\n",
       "      <td>-0.010166</td>\n",
       "      <td>-0.022676</td>\n",
       "      <td>0.001413</td>\n",
       "      <td>-0.033271</td>\n",
       "      <td>0.003618</td>\n",
       "      <td>-0.011982</td>\n",
       "      <td>-0.019742</td>\n",
       "      <td>-0.142791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_humidity</th>\n",
       "      <td>-0.079025</td>\n",
       "      <td>-0.117892</td>\n",
       "      <td>0.069878</td>\n",
       "      <td>-0.075227</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.799831</td>\n",
       "      <td>0.061680</td>\n",
       "      <td>-0.088180</td>\n",
       "      <td>0.399436</td>\n",
       "      <td>-0.087246</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.708937</td>\n",
       "      <td>-0.617797</td>\n",
       "      <td>-0.605493</td>\n",
       "      <td>-0.604733</td>\n",
       "      <td>-0.595065</td>\n",
       "      <td>-0.583718</td>\n",
       "      <td>-0.784850</td>\n",
       "      <td>-0.717171</td>\n",
       "      <td>-0.688664</td>\n",
       "      <td>-0.167290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_humidity</th>\n",
       "      <td>-0.103359</td>\n",
       "      <td>-0.137092</td>\n",
       "      <td>0.106765</td>\n",
       "      <td>-0.019388</td>\n",
       "      <td>0.799831</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.041878</td>\n",
       "      <td>-0.016808</td>\n",
       "      <td>0.374112</td>\n",
       "      <td>-0.069570</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.803260</td>\n",
       "      <td>-0.684867</td>\n",
       "      <td>-0.635760</td>\n",
       "      <td>-0.634825</td>\n",
       "      <td>-0.636777</td>\n",
       "      <td>-0.611395</td>\n",
       "      <td>-0.769591</td>\n",
       "      <td>-0.802605</td>\n",
       "      <td>-0.759633</td>\n",
       "      <td>-0.150536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_clouds_all</th>\n",
       "      <td>-0.068123</td>\n",
       "      <td>0.244001</td>\n",
       "      <td>0.370733</td>\n",
       "      <td>0.210524</td>\n",
       "      <td>0.061680</td>\n",
       "      <td>0.041878</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.031915</td>\n",
       "      <td>0.046737</td>\n",
       "      <td>0.280154</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048491</td>\n",
       "      <td>-0.023171</td>\n",
       "      <td>-0.067731</td>\n",
       "      <td>-0.114118</td>\n",
       "      <td>-0.010804</td>\n",
       "      <td>-0.158014</td>\n",
       "      <td>-0.077417</td>\n",
       "      <td>-0.046686</td>\n",
       "      <td>-0.046189</td>\n",
       "      <td>-0.127293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_wind_speed</th>\n",
       "      <td>0.049197</td>\n",
       "      <td>0.377854</td>\n",
       "      <td>0.085398</td>\n",
       "      <td>0.386478</td>\n",
       "      <td>-0.088180</td>\n",
       "      <td>-0.016808</td>\n",
       "      <td>0.031915</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127344</td>\n",
       "      <td>0.417534</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025367</td>\n",
       "      <td>0.018967</td>\n",
       "      <td>0.107302</td>\n",
       "      <td>0.142435</td>\n",
       "      <td>0.023711</td>\n",
       "      <td>0.177123</td>\n",
       "      <td>0.055077</td>\n",
       "      <td>0.031245</td>\n",
       "      <td>0.021425</td>\n",
       "      <td>-0.081602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_clouds_all</th>\n",
       "      <td>-0.140583</td>\n",
       "      <td>0.191251</td>\n",
       "      <td>0.081131</td>\n",
       "      <td>0.163675</td>\n",
       "      <td>0.399436</td>\n",
       "      <td>0.374112</td>\n",
       "      <td>0.046737</td>\n",
       "      <td>0.127344</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.053482</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.238895</td>\n",
       "      <td>-0.186463</td>\n",
       "      <td>-0.134249</td>\n",
       "      <td>-0.152393</td>\n",
       "      <td>-0.153412</td>\n",
       "      <td>-0.165567</td>\n",
       "      <td>-0.200453</td>\n",
       "      <td>-0.227094</td>\n",
       "      <td>-0.212758</td>\n",
       "      <td>-0.091804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_wind_deg</th>\n",
       "      <td>-0.155738</td>\n",
       "      <td>0.270950</td>\n",
       "      <td>0.279350</td>\n",
       "      <td>0.248643</td>\n",
       "      <td>-0.087246</td>\n",
       "      <td>-0.069570</td>\n",
       "      <td>0.280154</td>\n",
       "      <td>0.417534</td>\n",
       "      <td>0.053482</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028535</td>\n",
       "      <td>0.068701</td>\n",
       "      <td>0.095047</td>\n",
       "      <td>0.065396</td>\n",
       "      <td>0.086482</td>\n",
       "      <td>0.031187</td>\n",
       "      <td>0.062663</td>\n",
       "      <td>0.044102</td>\n",
       "      <td>0.048397</td>\n",
       "      <td>-0.120800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_wind_speed</th>\n",
       "      <td>-0.218781</td>\n",
       "      <td>0.294640</td>\n",
       "      <td>0.069997</td>\n",
       "      <td>0.347966</td>\n",
       "      <td>-0.138625</td>\n",
       "      <td>-0.058518</td>\n",
       "      <td>0.094019</td>\n",
       "      <td>0.275317</td>\n",
       "      <td>0.136591</td>\n",
       "      <td>0.177393</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084209</td>\n",
       "      <td>0.116240</td>\n",
       "      <td>0.123403</td>\n",
       "      <td>0.120361</td>\n",
       "      <td>0.109268</td>\n",
       "      <td>0.109692</td>\n",
       "      <td>0.130367</td>\n",
       "      <td>0.089365</td>\n",
       "      <td>0.084679</td>\n",
       "      <td>-0.103633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_wind_deg</th>\n",
       "      <td>-0.140449</td>\n",
       "      <td>-0.095380</td>\n",
       "      <td>-0.030723</td>\n",
       "      <td>-0.066071</td>\n",
       "      <td>0.164064</td>\n",
       "      <td>0.183725</td>\n",
       "      <td>-0.065120</td>\n",
       "      <td>-0.018225</td>\n",
       "      <td>-0.031373</td>\n",
       "      <td>-0.015481</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.193487</td>\n",
       "      <td>-0.189016</td>\n",
       "      <td>-0.182302</td>\n",
       "      <td>-0.164656</td>\n",
       "      <td>-0.226583</td>\n",
       "      <td>-0.147388</td>\n",
       "      <td>-0.195212</td>\n",
       "      <td>-0.211319</td>\n",
       "      <td>-0.211010</td>\n",
       "      <td>-0.116133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_clouds_all</th>\n",
       "      <td>-0.116928</td>\n",
       "      <td>0.230126</td>\n",
       "      <td>0.135524</td>\n",
       "      <td>0.221887</td>\n",
       "      <td>0.366602</td>\n",
       "      <td>0.484293</td>\n",
       "      <td>0.109788</td>\n",
       "      <td>0.239326</td>\n",
       "      <td>0.552414</td>\n",
       "      <td>0.085040</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.279732</td>\n",
       "      <td>-0.223940</td>\n",
       "      <td>-0.167008</td>\n",
       "      <td>-0.174079</td>\n",
       "      <td>-0.188578</td>\n",
       "      <td>-0.172921</td>\n",
       "      <td>-0.245756</td>\n",
       "      <td>-0.262908</td>\n",
       "      <td>-0.245475</td>\n",
       "      <td>-0.081623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_wind_speed</th>\n",
       "      <td>-0.093926</td>\n",
       "      <td>0.434104</td>\n",
       "      <td>0.140101</td>\n",
       "      <td>0.316035</td>\n",
       "      <td>-0.202449</td>\n",
       "      <td>-0.125680</td>\n",
       "      <td>0.075066</td>\n",
       "      <td>0.213420</td>\n",
       "      <td>0.144119</td>\n",
       "      <td>0.120378</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100081</td>\n",
       "      <td>0.085395</td>\n",
       "      <td>0.093079</td>\n",
       "      <td>0.082904</td>\n",
       "      <td>0.097407</td>\n",
       "      <td>0.070808</td>\n",
       "      <td>0.154254</td>\n",
       "      <td>0.108460</td>\n",
       "      <td>0.089832</td>\n",
       "      <td>-0.048433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_rain_1h</th>\n",
       "      <td>-0.075692</td>\n",
       "      <td>0.062758</td>\n",
       "      <td>0.052558</td>\n",
       "      <td>0.031804</td>\n",
       "      <td>-0.051022</td>\n",
       "      <td>-0.061265</td>\n",
       "      <td>0.052913</td>\n",
       "      <td>-0.026640</td>\n",
       "      <td>0.003590</td>\n",
       "      <td>0.026187</td>\n",
       "      <td>...</td>\n",
       "      <td>0.079558</td>\n",
       "      <td>0.060384</td>\n",
       "      <td>0.031765</td>\n",
       "      <td>0.034889</td>\n",
       "      <td>0.023582</td>\n",
       "      <td>0.032362</td>\n",
       "      <td>0.065312</td>\n",
       "      <td>0.075013</td>\n",
       "      <td>0.070146</td>\n",
       "      <td>-0.056633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_rain_1h</th>\n",
       "      <td>-0.075340</td>\n",
       "      <td>0.108413</td>\n",
       "      <td>0.092984</td>\n",
       "      <td>0.046085</td>\n",
       "      <td>0.227476</td>\n",
       "      <td>0.195455</td>\n",
       "      <td>0.041090</td>\n",
       "      <td>0.073080</td>\n",
       "      <td>0.408001</td>\n",
       "      <td>0.030082</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.125740</td>\n",
       "      <td>-0.098982</td>\n",
       "      <td>-0.072453</td>\n",
       "      <td>-0.080749</td>\n",
       "      <td>-0.078631</td>\n",
       "      <td>-0.085650</td>\n",
       "      <td>-0.113038</td>\n",
       "      <td>-0.115727</td>\n",
       "      <td>-0.101189</td>\n",
       "      <td>-0.031572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_snow_3h</th>\n",
       "      <td>-0.092377</td>\n",
       "      <td>0.071183</td>\n",
       "      <td>0.096730</td>\n",
       "      <td>0.115133</td>\n",
       "      <td>0.023556</td>\n",
       "      <td>0.031653</td>\n",
       "      <td>0.080180</td>\n",
       "      <td>-0.001642</td>\n",
       "      <td>0.001718</td>\n",
       "      <td>-0.041314</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.084406</td>\n",
       "      <td>-0.086561</td>\n",
       "      <td>-0.086115</td>\n",
       "      <td>-0.096477</td>\n",
       "      <td>-0.079382</td>\n",
       "      <td>-0.105006</td>\n",
       "      <td>-0.075545</td>\n",
       "      <td>-0.081209</td>\n",
       "      <td>-0.074892</td>\n",
       "      <td>-0.031721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_pressure</th>\n",
       "      <td>-0.040157</td>\n",
       "      <td>0.011134</td>\n",
       "      <td>0.052458</td>\n",
       "      <td>0.050282</td>\n",
       "      <td>0.021599</td>\n",
       "      <td>0.025198</td>\n",
       "      <td>0.037506</td>\n",
       "      <td>0.009572</td>\n",
       "      <td>0.020264</td>\n",
       "      <td>0.034220</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015984</td>\n",
       "      <td>-0.033221</td>\n",
       "      <td>-0.022514</td>\n",
       "      <td>-0.027410</td>\n",
       "      <td>-0.029612</td>\n",
       "      <td>-0.031854</td>\n",
       "      <td>-0.032889</td>\n",
       "      <td>-0.017886</td>\n",
       "      <td>-0.017819</td>\n",
       "      <td>-0.034385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_rain_3h</th>\n",
       "      <td>-0.082525</td>\n",
       "      <td>0.004795</td>\n",
       "      <td>0.016392</td>\n",
       "      <td>0.027637</td>\n",
       "      <td>0.034343</td>\n",
       "      <td>0.059958</td>\n",
       "      <td>0.009557</td>\n",
       "      <td>-0.026037</td>\n",
       "      <td>0.087240</td>\n",
       "      <td>0.006888</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048765</td>\n",
       "      <td>-0.041804</td>\n",
       "      <td>-0.021331</td>\n",
       "      <td>-0.032691</td>\n",
       "      <td>-0.029569</td>\n",
       "      <td>-0.043441</td>\n",
       "      <td>-0.032983</td>\n",
       "      <td>-0.043338</td>\n",
       "      <td>-0.035708</td>\n",
       "      <td>-0.032945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_rain_1h</th>\n",
       "      <td>-0.068980</td>\n",
       "      <td>0.150446</td>\n",
       "      <td>0.187423</td>\n",
       "      <td>0.093865</td>\n",
       "      <td>0.164019</td>\n",
       "      <td>0.250614</td>\n",
       "      <td>0.089281</td>\n",
       "      <td>0.088502</td>\n",
       "      <td>0.295499</td>\n",
       "      <td>0.057058</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.116052</td>\n",
       "      <td>-0.054825</td>\n",
       "      <td>-0.041161</td>\n",
       "      <td>-0.055851</td>\n",
       "      <td>-0.028900</td>\n",
       "      <td>-0.068176</td>\n",
       "      <td>-0.081794</td>\n",
       "      <td>-0.105667</td>\n",
       "      <td>-0.094601</td>\n",
       "      <td>-0.027159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_rain_3h</th>\n",
       "      <td>-0.104326</td>\n",
       "      <td>-0.014644</td>\n",
       "      <td>-0.001412</td>\n",
       "      <td>-0.037553</td>\n",
       "      <td>0.015555</td>\n",
       "      <td>0.017797</td>\n",
       "      <td>-0.041013</td>\n",
       "      <td>-0.038246</td>\n",
       "      <td>0.029194</td>\n",
       "      <td>0.007202</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.046967</td>\n",
       "      <td>-0.042970</td>\n",
       "      <td>-0.027060</td>\n",
       "      <td>-0.043223</td>\n",
       "      <td>-0.029398</td>\n",
       "      <td>-0.059089</td>\n",
       "      <td>-0.012016</td>\n",
       "      <td>-0.039565</td>\n",
       "      <td>-0.029094</td>\n",
       "      <td>-0.024720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia_snow_3h</th>\n",
       "      <td>-0.028130</td>\n",
       "      <td>0.021660</td>\n",
       "      <td>0.008269</td>\n",
       "      <td>0.058629</td>\n",
       "      <td>0.007351</td>\n",
       "      <td>0.012571</td>\n",
       "      <td>0.024339</td>\n",
       "      <td>-0.008114</td>\n",
       "      <td>-0.009782</td>\n",
       "      <td>-0.022680</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.024737</td>\n",
       "      <td>-0.029135</td>\n",
       "      <td>-0.028634</td>\n",
       "      <td>-0.031726</td>\n",
       "      <td>-0.027172</td>\n",
       "      <td>-0.034205</td>\n",
       "      <td>-0.019706</td>\n",
       "      <td>-0.023734</td>\n",
       "      <td>-0.021807</td>\n",
       "      <td>-0.021633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_weather_id</th>\n",
       "      <td>0.047202</td>\n",
       "      <td>-0.169358</td>\n",
       "      <td>-0.147768</td>\n",
       "      <td>-0.099056</td>\n",
       "      <td>-0.228442</td>\n",
       "      <td>-0.341407</td>\n",
       "      <td>-0.080837</td>\n",
       "      <td>-0.101497</td>\n",
       "      <td>-0.376157</td>\n",
       "      <td>-0.036532</td>\n",
       "      <td>...</td>\n",
       "      <td>0.149340</td>\n",
       "      <td>0.085587</td>\n",
       "      <td>0.056109</td>\n",
       "      <td>0.068572</td>\n",
       "      <td>0.056176</td>\n",
       "      <td>0.076601</td>\n",
       "      <td>0.110713</td>\n",
       "      <td>0.136829</td>\n",
       "      <td>0.123498</td>\n",
       "      <td>0.030868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_weather_id</th>\n",
       "      <td>0.034761</td>\n",
       "      <td>-0.099582</td>\n",
       "      <td>-0.120618</td>\n",
       "      <td>-0.037605</td>\n",
       "      <td>-0.050515</td>\n",
       "      <td>-0.051139</td>\n",
       "      <td>-0.124169</td>\n",
       "      <td>-0.003074</td>\n",
       "      <td>-0.099166</td>\n",
       "      <td>-0.053839</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027375</td>\n",
       "      <td>0.029239</td>\n",
       "      <td>0.053279</td>\n",
       "      <td>0.053303</td>\n",
       "      <td>0.046167</td>\n",
       "      <td>0.054720</td>\n",
       "      <td>0.020735</td>\n",
       "      <td>0.025671</td>\n",
       "      <td>0.026252</td>\n",
       "      <td>0.063868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_pressure</th>\n",
       "      <td>-0.022475</td>\n",
       "      <td>-0.231747</td>\n",
       "      <td>-0.054814</td>\n",
       "      <td>-0.096374</td>\n",
       "      <td>-0.099458</td>\n",
       "      <td>-0.053074</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>-0.122915</td>\n",
       "      <td>-0.330575</td>\n",
       "      <td>-0.107361</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.139887</td>\n",
       "      <td>-0.229760</td>\n",
       "      <td>-0.267415</td>\n",
       "      <td>-0.250325</td>\n",
       "      <td>-0.230942</td>\n",
       "      <td>-0.221529</td>\n",
       "      <td>-0.150221</td>\n",
       "      <td>-0.156223</td>\n",
       "      <td>-0.160023</td>\n",
       "      <td>0.039052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_weather_id</th>\n",
       "      <td>0.072472</td>\n",
       "      <td>-0.120014</td>\n",
       "      <td>-0.095723</td>\n",
       "      <td>-0.069092</td>\n",
       "      <td>-0.328265</td>\n",
       "      <td>-0.291095</td>\n",
       "      <td>-0.033825</td>\n",
       "      <td>-0.086691</td>\n",
       "      <td>-0.537924</td>\n",
       "      <td>-0.008937</td>\n",
       "      <td>...</td>\n",
       "      <td>0.193848</td>\n",
       "      <td>0.168608</td>\n",
       "      <td>0.130659</td>\n",
       "      <td>0.134535</td>\n",
       "      <td>0.150195</td>\n",
       "      <td>0.132564</td>\n",
       "      <td>0.180721</td>\n",
       "      <td>0.185768</td>\n",
       "      <td>0.174238</td>\n",
       "      <td>0.061104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia_pressure</th>\n",
       "      <td>0.267772</td>\n",
       "      <td>-0.157088</td>\n",
       "      <td>-0.206765</td>\n",
       "      <td>-0.041408</td>\n",
       "      <td>-0.088094</td>\n",
       "      <td>-0.168207</td>\n",
       "      <td>-0.079778</td>\n",
       "      <td>0.054035</td>\n",
       "      <td>-0.214600</td>\n",
       "      <td>-0.124372</td>\n",
       "      <td>...</td>\n",
       "      <td>0.123501</td>\n",
       "      <td>0.026211</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.074986</td>\n",
       "      <td>-0.067132</td>\n",
       "      <td>0.147187</td>\n",
       "      <td>0.027998</td>\n",
       "      <td>0.082532</td>\n",
       "      <td>0.035991</td>\n",
       "      <td>0.086022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_temp_max</th>\n",
       "      <td>0.039424</td>\n",
       "      <td>0.050043</td>\n",
       "      <td>-0.210323</td>\n",
       "      <td>-0.024045</td>\n",
       "      <td>-0.566426</td>\n",
       "      <td>-0.576413</td>\n",
       "      <td>-0.102322</td>\n",
       "      <td>0.103342</td>\n",
       "      <td>-0.181783</td>\n",
       "      <td>-0.076038</td>\n",
       "      <td>...</td>\n",
       "      <td>0.772369</td>\n",
       "      <td>0.693193</td>\n",
       "      <td>0.651814</td>\n",
       "      <td>0.703218</td>\n",
       "      <td>0.586923</td>\n",
       "      <td>0.726510</td>\n",
       "      <td>0.743801</td>\n",
       "      <td>0.740831</td>\n",
       "      <td>0.674561</td>\n",
       "      <td>0.109834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_pressure</th>\n",
       "      <td>0.385578</td>\n",
       "      <td>-0.136672</td>\n",
       "      <td>-0.298587</td>\n",
       "      <td>-0.158475</td>\n",
       "      <td>-0.103813</td>\n",
       "      <td>-0.121737</td>\n",
       "      <td>-0.135672</td>\n",
       "      <td>0.075541</td>\n",
       "      <td>-0.219365</td>\n",
       "      <td>-0.166636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.092253</td>\n",
       "      <td>0.010542</td>\n",
       "      <td>0.009734</td>\n",
       "      <td>0.092394</td>\n",
       "      <td>-0.068083</td>\n",
       "      <td>0.176838</td>\n",
       "      <td>0.037876</td>\n",
       "      <td>0.054819</td>\n",
       "      <td>0.014089</td>\n",
       "      <td>0.092638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia_temp_max</th>\n",
       "      <td>0.087002</td>\n",
       "      <td>0.133468</td>\n",
       "      <td>-0.135198</td>\n",
       "      <td>0.065815</td>\n",
       "      <td>-0.650643</td>\n",
       "      <td>-0.705918</td>\n",
       "      <td>0.002621</td>\n",
       "      <td>0.091352</td>\n",
       "      <td>-0.188804</td>\n",
       "      <td>0.069642</td>\n",
       "      <td>...</td>\n",
       "      <td>0.917838</td>\n",
       "      <td>0.912094</td>\n",
       "      <td>0.840573</td>\n",
       "      <td>0.854592</td>\n",
       "      <td>0.845057</td>\n",
       "      <td>0.837574</td>\n",
       "      <td>0.886449</td>\n",
       "      <td>0.911331</td>\n",
       "      <td>0.862683</td>\n",
       "      <td>0.144270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia_temp</th>\n",
       "      <td>0.135294</td>\n",
       "      <td>0.128726</td>\n",
       "      <td>-0.127953</td>\n",
       "      <td>0.072366</td>\n",
       "      <td>-0.663276</td>\n",
       "      <td>-0.706989</td>\n",
       "      <td>-0.000299</td>\n",
       "      <td>0.093919</td>\n",
       "      <td>-0.186982</td>\n",
       "      <td>0.069065</td>\n",
       "      <td>...</td>\n",
       "      <td>0.917820</td>\n",
       "      <td>0.914601</td>\n",
       "      <td>0.847584</td>\n",
       "      <td>0.854892</td>\n",
       "      <td>0.874447</td>\n",
       "      <td>0.833562</td>\n",
       "      <td>0.890915</td>\n",
       "      <td>0.915753</td>\n",
       "      <td>0.871563</td>\n",
       "      <td>0.173225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_weather_id</th>\n",
       "      <td>0.192485</td>\n",
       "      <td>-0.238128</td>\n",
       "      <td>-0.604616</td>\n",
       "      <td>-0.201379</td>\n",
       "      <td>-0.105088</td>\n",
       "      <td>-0.139686</td>\n",
       "      <td>-0.536205</td>\n",
       "      <td>-0.031661</td>\n",
       "      <td>-0.101888</td>\n",
       "      <td>-0.264719</td>\n",
       "      <td>...</td>\n",
       "      <td>0.153665</td>\n",
       "      <td>0.128188</td>\n",
       "      <td>0.140580</td>\n",
       "      <td>0.190635</td>\n",
       "      <td>0.098428</td>\n",
       "      <td>0.235183</td>\n",
       "      <td>0.145173</td>\n",
       "      <td>0.143126</td>\n",
       "      <td>0.124199</td>\n",
       "      <td>0.145875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_temp</th>\n",
       "      <td>0.042897</td>\n",
       "      <td>0.090861</td>\n",
       "      <td>-0.170009</td>\n",
       "      <td>-0.008508</td>\n",
       "      <td>-0.743126</td>\n",
       "      <td>-0.738819</td>\n",
       "      <td>-0.095003</td>\n",
       "      <td>0.080357</td>\n",
       "      <td>-0.208859</td>\n",
       "      <td>0.005110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.920908</td>\n",
       "      <td>0.852651</td>\n",
       "      <td>0.818454</td>\n",
       "      <td>0.839014</td>\n",
       "      <td>0.779252</td>\n",
       "      <td>0.828099</td>\n",
       "      <td>0.955160</td>\n",
       "      <td>0.917075</td>\n",
       "      <td>0.868453</td>\n",
       "      <td>0.159343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia_humidity</th>\n",
       "      <td>0.023917</td>\n",
       "      <td>-0.285787</td>\n",
       "      <td>-0.103868</td>\n",
       "      <td>-0.413017</td>\n",
       "      <td>0.464012</td>\n",
       "      <td>0.378980</td>\n",
       "      <td>-0.129684</td>\n",
       "      <td>-0.279825</td>\n",
       "      <td>0.097491</td>\n",
       "      <td>-0.230583</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.262919</td>\n",
       "      <td>-0.247168</td>\n",
       "      <td>-0.271621</td>\n",
       "      <td>-0.260946</td>\n",
       "      <td>-0.266820</td>\n",
       "      <td>-0.241397</td>\n",
       "      <td>-0.301694</td>\n",
       "      <td>-0.261356</td>\n",
       "      <td>-0.247574</td>\n",
       "      <td>0.043140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia_temp_min</th>\n",
       "      <td>0.185468</td>\n",
       "      <td>0.119657</td>\n",
       "      <td>-0.116835</td>\n",
       "      <td>0.075533</td>\n",
       "      <td>-0.657427</td>\n",
       "      <td>-0.689854</td>\n",
       "      <td>-0.003806</td>\n",
       "      <td>0.093177</td>\n",
       "      <td>-0.179211</td>\n",
       "      <td>0.069433</td>\n",
       "      <td>...</td>\n",
       "      <td>0.893368</td>\n",
       "      <td>0.892024</td>\n",
       "      <td>0.832773</td>\n",
       "      <td>0.832393</td>\n",
       "      <td>0.879687</td>\n",
       "      <td>0.806606</td>\n",
       "      <td>0.871738</td>\n",
       "      <td>0.897012</td>\n",
       "      <td>0.860347</td>\n",
       "      <td>0.201863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_temp_max</th>\n",
       "      <td>0.047417</td>\n",
       "      <td>0.061854</td>\n",
       "      <td>-0.157326</td>\n",
       "      <td>-0.040998</td>\n",
       "      <td>-0.606770</td>\n",
       "      <td>-0.691803</td>\n",
       "      <td>-0.034040</td>\n",
       "      <td>0.011151</td>\n",
       "      <td>-0.205060</td>\n",
       "      <td>0.049775</td>\n",
       "      <td>...</td>\n",
       "      <td>0.906248</td>\n",
       "      <td>0.974480</td>\n",
       "      <td>0.854620</td>\n",
       "      <td>0.867268</td>\n",
       "      <td>0.885911</td>\n",
       "      <td>0.844563</td>\n",
       "      <td>0.862597</td>\n",
       "      <td>0.896766</td>\n",
       "      <td>0.847888</td>\n",
       "      <td>0.164734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_temp_max</th>\n",
       "      <td>0.093378</td>\n",
       "      <td>0.091464</td>\n",
       "      <td>-0.148397</td>\n",
       "      <td>-0.021970</td>\n",
       "      <td>-0.708937</td>\n",
       "      <td>-0.803260</td>\n",
       "      <td>-0.048491</td>\n",
       "      <td>0.025367</td>\n",
       "      <td>-0.238895</td>\n",
       "      <td>0.028535</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900428</td>\n",
       "      <td>0.865596</td>\n",
       "      <td>0.872234</td>\n",
       "      <td>0.836680</td>\n",
       "      <td>0.846887</td>\n",
       "      <td>0.923481</td>\n",
       "      <td>0.988871</td>\n",
       "      <td>0.939147</td>\n",
       "      <td>0.182548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_temp</th>\n",
       "      <td>0.068078</td>\n",
       "      <td>0.080133</td>\n",
       "      <td>-0.138901</td>\n",
       "      <td>-0.021456</td>\n",
       "      <td>-0.617797</td>\n",
       "      <td>-0.684867</td>\n",
       "      <td>-0.023171</td>\n",
       "      <td>0.018967</td>\n",
       "      <td>-0.186463</td>\n",
       "      <td>0.068701</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900428</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.867434</td>\n",
       "      <td>0.865705</td>\n",
       "      <td>0.964089</td>\n",
       "      <td>0.831470</td>\n",
       "      <td>0.873170</td>\n",
       "      <td>0.903143</td>\n",
       "      <td>0.868222</td>\n",
       "      <td>0.182673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_temp_min</th>\n",
       "      <td>0.028698</td>\n",
       "      <td>0.077330</td>\n",
       "      <td>-0.141269</td>\n",
       "      <td>-0.010166</td>\n",
       "      <td>-0.605493</td>\n",
       "      <td>-0.635760</td>\n",
       "      <td>-0.067731</td>\n",
       "      <td>0.107302</td>\n",
       "      <td>-0.134249</td>\n",
       "      <td>0.095047</td>\n",
       "      <td>...</td>\n",
       "      <td>0.865596</td>\n",
       "      <td>0.867434</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.982936</td>\n",
       "      <td>0.831224</td>\n",
       "      <td>0.932219</td>\n",
       "      <td>0.847007</td>\n",
       "      <td>0.876185</td>\n",
       "      <td>0.845799</td>\n",
       "      <td>0.172920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_temp</th>\n",
       "      <td>0.081799</td>\n",
       "      <td>0.060467</td>\n",
       "      <td>-0.182494</td>\n",
       "      <td>-0.022676</td>\n",
       "      <td>-0.604733</td>\n",
       "      <td>-0.634825</td>\n",
       "      <td>-0.114118</td>\n",
       "      <td>0.142435</td>\n",
       "      <td>-0.152393</td>\n",
       "      <td>0.065396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.872234</td>\n",
       "      <td>0.865705</td>\n",
       "      <td>0.982936</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.810785</td>\n",
       "      <td>0.981624</td>\n",
       "      <td>0.842972</td>\n",
       "      <td>0.875271</td>\n",
       "      <td>0.838195</td>\n",
       "      <td>0.176411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_temp_min</th>\n",
       "      <td>0.103436</td>\n",
       "      <td>0.089992</td>\n",
       "      <td>-0.106593</td>\n",
       "      <td>0.001413</td>\n",
       "      <td>-0.595065</td>\n",
       "      <td>-0.636777</td>\n",
       "      <td>-0.010804</td>\n",
       "      <td>0.023711</td>\n",
       "      <td>-0.153412</td>\n",
       "      <td>0.086482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.836680</td>\n",
       "      <td>0.964089</td>\n",
       "      <td>0.831224</td>\n",
       "      <td>0.810785</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.764031</td>\n",
       "      <td>0.831689</td>\n",
       "      <td>0.853881</td>\n",
       "      <td>0.838985</td>\n",
       "      <td>0.195984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_temp_max</th>\n",
       "      <td>0.146216</td>\n",
       "      <td>0.042114</td>\n",
       "      <td>-0.219871</td>\n",
       "      <td>-0.033271</td>\n",
       "      <td>-0.583718</td>\n",
       "      <td>-0.611395</td>\n",
       "      <td>-0.158014</td>\n",
       "      <td>0.177123</td>\n",
       "      <td>-0.165567</td>\n",
       "      <td>0.031187</td>\n",
       "      <td>...</td>\n",
       "      <td>0.846887</td>\n",
       "      <td>0.831470</td>\n",
       "      <td>0.932219</td>\n",
       "      <td>0.981624</td>\n",
       "      <td>0.764031</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.808278</td>\n",
       "      <td>0.843633</td>\n",
       "      <td>0.802041</td>\n",
       "      <td>0.180934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_temp_min</th>\n",
       "      <td>0.037487</td>\n",
       "      <td>0.110278</td>\n",
       "      <td>-0.120575</td>\n",
       "      <td>0.003618</td>\n",
       "      <td>-0.784850</td>\n",
       "      <td>-0.769591</td>\n",
       "      <td>-0.077417</td>\n",
       "      <td>0.055077</td>\n",
       "      <td>-0.200453</td>\n",
       "      <td>0.062663</td>\n",
       "      <td>...</td>\n",
       "      <td>0.923481</td>\n",
       "      <td>0.873170</td>\n",
       "      <td>0.847007</td>\n",
       "      <td>0.842972</td>\n",
       "      <td>0.831689</td>\n",
       "      <td>0.808278</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.939079</td>\n",
       "      <td>0.907758</td>\n",
       "      <td>0.174718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_temp</th>\n",
       "      <td>0.088439</td>\n",
       "      <td>0.109572</td>\n",
       "      <td>-0.135927</td>\n",
       "      <td>-0.011982</td>\n",
       "      <td>-0.717171</td>\n",
       "      <td>-0.802605</td>\n",
       "      <td>-0.046686</td>\n",
       "      <td>0.031245</td>\n",
       "      <td>-0.227094</td>\n",
       "      <td>0.044102</td>\n",
       "      <td>...</td>\n",
       "      <td>0.988871</td>\n",
       "      <td>0.903143</td>\n",
       "      <td>0.876185</td>\n",
       "      <td>0.875271</td>\n",
       "      <td>0.853881</td>\n",
       "      <td>0.843633</td>\n",
       "      <td>0.939079</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.973641</td>\n",
       "      <td>0.186036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_temp_min</th>\n",
       "      <td>0.085197</td>\n",
       "      <td>0.098138</td>\n",
       "      <td>-0.116431</td>\n",
       "      <td>-0.019742</td>\n",
       "      <td>-0.688664</td>\n",
       "      <td>-0.759633</td>\n",
       "      <td>-0.046189</td>\n",
       "      <td>0.021425</td>\n",
       "      <td>-0.212758</td>\n",
       "      <td>0.048397</td>\n",
       "      <td>...</td>\n",
       "      <td>0.939147</td>\n",
       "      <td>0.868222</td>\n",
       "      <td>0.845799</td>\n",
       "      <td>0.838195</td>\n",
       "      <td>0.838985</td>\n",
       "      <td>0.802041</td>\n",
       "      <td>0.907758</td>\n",
       "      <td>0.973641</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>load_shortfall_3h</th>\n",
       "      <td>0.192612</td>\n",
       "      <td>-0.150981</td>\n",
       "      <td>-0.152510</td>\n",
       "      <td>-0.142791</td>\n",
       "      <td>-0.167290</td>\n",
       "      <td>-0.150536</td>\n",
       "      <td>-0.127293</td>\n",
       "      <td>-0.081602</td>\n",
       "      <td>-0.091804</td>\n",
       "      <td>-0.120800</td>\n",
       "      <td>...</td>\n",
       "      <td>0.182548</td>\n",
       "      <td>0.182673</td>\n",
       "      <td>0.172920</td>\n",
       "      <td>0.176411</td>\n",
       "      <td>0.195984</td>\n",
       "      <td>0.180934</td>\n",
       "      <td>0.174718</td>\n",
       "      <td>0.186036</td>\n",
       "      <td>0.174744</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Unnamed: 0  Madrid_wind_speed  Bilbao_rain_1h  \\\n",
       "Unnamed: 0              1.000000          -0.109288       -0.249633   \n",
       "Madrid_wind_speed      -0.109288           1.000000        0.259719   \n",
       "Bilbao_rain_1h         -0.249633           0.259719        1.000000   \n",
       "Valencia_wind_speed    -0.090161           0.513092        0.265864   \n",
       "Seville_humidity       -0.079025          -0.117892        0.069878   \n",
       "Madrid_humidity        -0.103359          -0.137092        0.106765   \n",
       "Bilbao_clouds_all      -0.068123           0.244001        0.370733   \n",
       "Bilbao_wind_speed       0.049197           0.377854        0.085398   \n",
       "Seville_clouds_all     -0.140583           0.191251        0.081131   \n",
       "Bilbao_wind_deg        -0.155738           0.270950        0.279350   \n",
       "Barcelona_wind_speed   -0.218781           0.294640        0.069997   \n",
       "Barcelona_wind_deg     -0.140449          -0.095380       -0.030723   \n",
       "Madrid_clouds_all      -0.116928           0.230126        0.135524   \n",
       "Seville_wind_speed     -0.093926           0.434104        0.140101   \n",
       "Barcelona_rain_1h      -0.075692           0.062758        0.052558   \n",
       "Seville_rain_1h        -0.075340           0.108413        0.092984   \n",
       "Bilbao_snow_3h         -0.092377           0.071183        0.096730   \n",
       "Barcelona_pressure     -0.040157           0.011134        0.052458   \n",
       "Seville_rain_3h        -0.082525           0.004795        0.016392   \n",
       "Madrid_rain_1h         -0.068980           0.150446        0.187423   \n",
       "Barcelona_rain_3h      -0.104326          -0.014644       -0.001412   \n",
       "Valencia_snow_3h       -0.028130           0.021660        0.008269   \n",
       "Madrid_weather_id       0.047202          -0.169358       -0.147768   \n",
       "Barcelona_weather_id    0.034761          -0.099582       -0.120618   \n",
       "Bilbao_pressure        -0.022475          -0.231747       -0.054814   \n",
       "Seville_weather_id      0.072472          -0.120014       -0.095723   \n",
       "Valencia_pressure       0.267772          -0.157088       -0.206765   \n",
       "Seville_temp_max        0.039424           0.050043       -0.210323   \n",
       "Madrid_pressure         0.385578          -0.136672       -0.298587   \n",
       "Valencia_temp_max       0.087002           0.133468       -0.135198   \n",
       "Valencia_temp           0.135294           0.128726       -0.127953   \n",
       "Bilbao_weather_id       0.192485          -0.238128       -0.604616   \n",
       "Seville_temp            0.042897           0.090861       -0.170009   \n",
       "Valencia_humidity       0.023917          -0.285787       -0.103868   \n",
       "Valencia_temp_min       0.185468           0.119657       -0.116835   \n",
       "Barcelona_temp_max      0.047417           0.061854       -0.157326   \n",
       "Madrid_temp_max         0.093378           0.091464       -0.148397   \n",
       "Barcelona_temp          0.068078           0.080133       -0.138901   \n",
       "Bilbao_temp_min         0.028698           0.077330       -0.141269   \n",
       "Bilbao_temp             0.081799           0.060467       -0.182494   \n",
       "Barcelona_temp_min      0.103436           0.089992       -0.106593   \n",
       "Bilbao_temp_max         0.146216           0.042114       -0.219871   \n",
       "Seville_temp_min        0.037487           0.110278       -0.120575   \n",
       "Madrid_temp             0.088439           0.109572       -0.135927   \n",
       "Madrid_temp_min         0.085197           0.098138       -0.116431   \n",
       "load_shortfall_3h       0.192612          -0.150981       -0.152510   \n",
       "\n",
       "                      Valencia_wind_speed  Seville_humidity  Madrid_humidity  \\\n",
       "Unnamed: 0                      -0.090161         -0.079025        -0.103359   \n",
       "Madrid_wind_speed                0.513092         -0.117892        -0.137092   \n",
       "Bilbao_rain_1h                   0.265864          0.069878         0.106765   \n",
       "Valencia_wind_speed              1.000000         -0.075227        -0.019388   \n",
       "Seville_humidity                -0.075227          1.000000         0.799831   \n",
       "Madrid_humidity                 -0.019388          0.799831         1.000000   \n",
       "Bilbao_clouds_all                0.210524          0.061680         0.041878   \n",
       "Bilbao_wind_speed                0.386478         -0.088180        -0.016808   \n",
       "Seville_clouds_all               0.163675          0.399436         0.374112   \n",
       "Bilbao_wind_deg                  0.248643         -0.087246        -0.069570   \n",
       "Barcelona_wind_speed             0.347966         -0.138625        -0.058518   \n",
       "Barcelona_wind_deg              -0.066071          0.164064         0.183725   \n",
       "Madrid_clouds_all                0.221887          0.366602         0.484293   \n",
       "Seville_wind_speed               0.316035         -0.202449        -0.125680   \n",
       "Barcelona_rain_1h                0.031804         -0.051022        -0.061265   \n",
       "Seville_rain_1h                  0.046085          0.227476         0.195455   \n",
       "Bilbao_snow_3h                   0.115133          0.023556         0.031653   \n",
       "Barcelona_pressure               0.050282          0.021599         0.025198   \n",
       "Seville_rain_3h                  0.027637          0.034343         0.059958   \n",
       "Madrid_rain_1h                   0.093865          0.164019         0.250614   \n",
       "Barcelona_rain_3h               -0.037553          0.015555         0.017797   \n",
       "Valencia_snow_3h                 0.058629          0.007351         0.012571   \n",
       "Madrid_weather_id               -0.099056         -0.228442        -0.341407   \n",
       "Barcelona_weather_id            -0.037605         -0.050515        -0.051139   \n",
       "Bilbao_pressure                 -0.096374         -0.099458        -0.053074   \n",
       "Seville_weather_id              -0.069092         -0.328265        -0.291095   \n",
       "Valencia_pressure               -0.041408         -0.088094        -0.168207   \n",
       "Seville_temp_max                -0.024045         -0.566426        -0.576413   \n",
       "Madrid_pressure                 -0.158475         -0.103813        -0.121737   \n",
       "Valencia_temp_max                0.065815         -0.650643        -0.705918   \n",
       "Valencia_temp                    0.072366         -0.663276        -0.706989   \n",
       "Bilbao_weather_id               -0.201379         -0.105088        -0.139686   \n",
       "Seville_temp                    -0.008508         -0.743126        -0.738819   \n",
       "Valencia_humidity               -0.413017          0.464012         0.378980   \n",
       "Valencia_temp_min                0.075533         -0.657427        -0.689854   \n",
       "Barcelona_temp_max              -0.040998         -0.606770        -0.691803   \n",
       "Madrid_temp_max                 -0.021970         -0.708937        -0.803260   \n",
       "Barcelona_temp                  -0.021456         -0.617797        -0.684867   \n",
       "Bilbao_temp_min                 -0.010166         -0.605493        -0.635760   \n",
       "Bilbao_temp                     -0.022676         -0.604733        -0.634825   \n",
       "Barcelona_temp_min               0.001413         -0.595065        -0.636777   \n",
       "Bilbao_temp_max                 -0.033271         -0.583718        -0.611395   \n",
       "Seville_temp_min                 0.003618         -0.784850        -0.769591   \n",
       "Madrid_temp                     -0.011982         -0.717171        -0.802605   \n",
       "Madrid_temp_min                 -0.019742         -0.688664        -0.759633   \n",
       "load_shortfall_3h               -0.142791         -0.167290        -0.150536   \n",
       "\n",
       "                      Bilbao_clouds_all  Bilbao_wind_speed  \\\n",
       "Unnamed: 0                    -0.068123           0.049197   \n",
       "Madrid_wind_speed              0.244001           0.377854   \n",
       "Bilbao_rain_1h                 0.370733           0.085398   \n",
       "Valencia_wind_speed            0.210524           0.386478   \n",
       "Seville_humidity               0.061680          -0.088180   \n",
       "Madrid_humidity                0.041878          -0.016808   \n",
       "Bilbao_clouds_all              1.000000           0.031915   \n",
       "Bilbao_wind_speed              0.031915           1.000000   \n",
       "Seville_clouds_all             0.046737           0.127344   \n",
       "Bilbao_wind_deg                0.280154           0.417534   \n",
       "Barcelona_wind_speed           0.094019           0.275317   \n",
       "Barcelona_wind_deg            -0.065120          -0.018225   \n",
       "Madrid_clouds_all              0.109788           0.239326   \n",
       "Seville_wind_speed             0.075066           0.213420   \n",
       "Barcelona_rain_1h              0.052913          -0.026640   \n",
       "Seville_rain_1h                0.041090           0.073080   \n",
       "Bilbao_snow_3h                 0.080180          -0.001642   \n",
       "Barcelona_pressure             0.037506           0.009572   \n",
       "Seville_rain_3h                0.009557          -0.026037   \n",
       "Madrid_rain_1h                 0.089281           0.088502   \n",
       "Barcelona_rain_3h             -0.041013          -0.038246   \n",
       "Valencia_snow_3h               0.024339          -0.008114   \n",
       "Madrid_weather_id             -0.080837          -0.101497   \n",
       "Barcelona_weather_id          -0.124169          -0.003074   \n",
       "Bilbao_pressure                0.000377          -0.122915   \n",
       "Seville_weather_id            -0.033825          -0.086691   \n",
       "Valencia_pressure             -0.079778           0.054035   \n",
       "Seville_temp_max              -0.102322           0.103342   \n",
       "Madrid_pressure               -0.135672           0.075541   \n",
       "Valencia_temp_max              0.002621           0.091352   \n",
       "Valencia_temp                 -0.000299           0.093919   \n",
       "Bilbao_weather_id             -0.536205          -0.031661   \n",
       "Seville_temp                  -0.095003           0.080357   \n",
       "Valencia_humidity             -0.129684          -0.279825   \n",
       "Valencia_temp_min             -0.003806           0.093177   \n",
       "Barcelona_temp_max            -0.034040           0.011151   \n",
       "Madrid_temp_max               -0.048491           0.025367   \n",
       "Barcelona_temp                -0.023171           0.018967   \n",
       "Bilbao_temp_min               -0.067731           0.107302   \n",
       "Bilbao_temp                   -0.114118           0.142435   \n",
       "Barcelona_temp_min            -0.010804           0.023711   \n",
       "Bilbao_temp_max               -0.158014           0.177123   \n",
       "Seville_temp_min              -0.077417           0.055077   \n",
       "Madrid_temp                   -0.046686           0.031245   \n",
       "Madrid_temp_min               -0.046189           0.021425   \n",
       "load_shortfall_3h             -0.127293          -0.081602   \n",
       "\n",
       "                      Seville_clouds_all  Bilbao_wind_deg  ...  \\\n",
       "Unnamed: 0                     -0.140583        -0.155738  ...   \n",
       "Madrid_wind_speed               0.191251         0.270950  ...   \n",
       "Bilbao_rain_1h                  0.081131         0.279350  ...   \n",
       "Valencia_wind_speed             0.163675         0.248643  ...   \n",
       "Seville_humidity                0.399436        -0.087246  ...   \n",
       "Madrid_humidity                 0.374112        -0.069570  ...   \n",
       "Bilbao_clouds_all               0.046737         0.280154  ...   \n",
       "Bilbao_wind_speed               0.127344         0.417534  ...   \n",
       "Seville_clouds_all              1.000000         0.053482  ...   \n",
       "Bilbao_wind_deg                 0.053482         1.000000  ...   \n",
       "Barcelona_wind_speed            0.136591         0.177393  ...   \n",
       "Barcelona_wind_deg             -0.031373        -0.015481  ...   \n",
       "Madrid_clouds_all               0.552414         0.085040  ...   \n",
       "Seville_wind_speed              0.144119         0.120378  ...   \n",
       "Barcelona_rain_1h               0.003590         0.026187  ...   \n",
       "Seville_rain_1h                 0.408001         0.030082  ...   \n",
       "Bilbao_snow_3h                  0.001718        -0.041314  ...   \n",
       "Barcelona_pressure              0.020264         0.034220  ...   \n",
       "Seville_rain_3h                 0.087240         0.006888  ...   \n",
       "Madrid_rain_1h                  0.295499         0.057058  ...   \n",
       "Barcelona_rain_3h               0.029194         0.007202  ...   \n",
       "Valencia_snow_3h               -0.009782        -0.022680  ...   \n",
       "Madrid_weather_id              -0.376157        -0.036532  ...   \n",
       "Barcelona_weather_id           -0.099166        -0.053839  ...   \n",
       "Bilbao_pressure                -0.330575        -0.107361  ...   \n",
       "Seville_weather_id             -0.537924        -0.008937  ...   \n",
       "Valencia_pressure              -0.214600        -0.124372  ...   \n",
       "Seville_temp_max               -0.181783        -0.076038  ...   \n",
       "Madrid_pressure                -0.219365        -0.166636  ...   \n",
       "Valencia_temp_max              -0.188804         0.069642  ...   \n",
       "Valencia_temp                  -0.186982         0.069065  ...   \n",
       "Bilbao_weather_id              -0.101888        -0.264719  ...   \n",
       "Seville_temp                   -0.208859         0.005110  ...   \n",
       "Valencia_humidity               0.097491        -0.230583  ...   \n",
       "Valencia_temp_min              -0.179211         0.069433  ...   \n",
       "Barcelona_temp_max             -0.205060         0.049775  ...   \n",
       "Madrid_temp_max                -0.238895         0.028535  ...   \n",
       "Barcelona_temp                 -0.186463         0.068701  ...   \n",
       "Bilbao_temp_min                -0.134249         0.095047  ...   \n",
       "Bilbao_temp                    -0.152393         0.065396  ...   \n",
       "Barcelona_temp_min             -0.153412         0.086482  ...   \n",
       "Bilbao_temp_max                -0.165567         0.031187  ...   \n",
       "Seville_temp_min               -0.200453         0.062663  ...   \n",
       "Madrid_temp                    -0.227094         0.044102  ...   \n",
       "Madrid_temp_min                -0.212758         0.048397  ...   \n",
       "load_shortfall_3h              -0.091804        -0.120800  ...   \n",
       "\n",
       "                      Madrid_temp_max  Barcelona_temp  Bilbao_temp_min  \\\n",
       "Unnamed: 0                   0.093378        0.068078         0.028698   \n",
       "Madrid_wind_speed            0.091464        0.080133         0.077330   \n",
       "Bilbao_rain_1h              -0.148397       -0.138901        -0.141269   \n",
       "Valencia_wind_speed         -0.021970       -0.021456        -0.010166   \n",
       "Seville_humidity            -0.708937       -0.617797        -0.605493   \n",
       "Madrid_humidity             -0.803260       -0.684867        -0.635760   \n",
       "Bilbao_clouds_all           -0.048491       -0.023171        -0.067731   \n",
       "Bilbao_wind_speed            0.025367        0.018967         0.107302   \n",
       "Seville_clouds_all          -0.238895       -0.186463        -0.134249   \n",
       "Bilbao_wind_deg              0.028535        0.068701         0.095047   \n",
       "Barcelona_wind_speed         0.084209        0.116240         0.123403   \n",
       "Barcelona_wind_deg          -0.193487       -0.189016        -0.182302   \n",
       "Madrid_clouds_all           -0.279732       -0.223940        -0.167008   \n",
       "Seville_wind_speed           0.100081        0.085395         0.093079   \n",
       "Barcelona_rain_1h            0.079558        0.060384         0.031765   \n",
       "Seville_rain_1h             -0.125740       -0.098982        -0.072453   \n",
       "Bilbao_snow_3h              -0.084406       -0.086561        -0.086115   \n",
       "Barcelona_pressure          -0.015984       -0.033221        -0.022514   \n",
       "Seville_rain_3h             -0.048765       -0.041804        -0.021331   \n",
       "Madrid_rain_1h              -0.116052       -0.054825        -0.041161   \n",
       "Barcelona_rain_3h           -0.046967       -0.042970        -0.027060   \n",
       "Valencia_snow_3h            -0.024737       -0.029135        -0.028634   \n",
       "Madrid_weather_id            0.149340        0.085587         0.056109   \n",
       "Barcelona_weather_id         0.027375        0.029239         0.053279   \n",
       "Bilbao_pressure             -0.139887       -0.229760        -0.267415   \n",
       "Seville_weather_id           0.193848        0.168608         0.130659   \n",
       "Valencia_pressure            0.123501        0.026211         0.001008   \n",
       "Seville_temp_max             0.772369        0.693193         0.651814   \n",
       "Madrid_pressure              0.092253        0.010542         0.009734   \n",
       "Valencia_temp_max            0.917838        0.912094         0.840573   \n",
       "Valencia_temp                0.917820        0.914601         0.847584   \n",
       "Bilbao_weather_id            0.153665        0.128188         0.140580   \n",
       "Seville_temp                 0.920908        0.852651         0.818454   \n",
       "Valencia_humidity           -0.262919       -0.247168        -0.271621   \n",
       "Valencia_temp_min            0.893368        0.892024         0.832773   \n",
       "Barcelona_temp_max           0.906248        0.974480         0.854620   \n",
       "Madrid_temp_max              1.000000        0.900428         0.865596   \n",
       "Barcelona_temp               0.900428        1.000000         0.867434   \n",
       "Bilbao_temp_min              0.865596        0.867434         1.000000   \n",
       "Bilbao_temp                  0.872234        0.865705         0.982936   \n",
       "Barcelona_temp_min           0.836680        0.964089         0.831224   \n",
       "Bilbao_temp_max              0.846887        0.831470         0.932219   \n",
       "Seville_temp_min             0.923481        0.873170         0.847007   \n",
       "Madrid_temp                  0.988871        0.903143         0.876185   \n",
       "Madrid_temp_min              0.939147        0.868222         0.845799   \n",
       "load_shortfall_3h            0.182548        0.182673         0.172920   \n",
       "\n",
       "                      Bilbao_temp  Barcelona_temp_min  Bilbao_temp_max  \\\n",
       "Unnamed: 0               0.081799            0.103436         0.146216   \n",
       "Madrid_wind_speed        0.060467            0.089992         0.042114   \n",
       "Bilbao_rain_1h          -0.182494           -0.106593        -0.219871   \n",
       "Valencia_wind_speed     -0.022676            0.001413        -0.033271   \n",
       "Seville_humidity        -0.604733           -0.595065        -0.583718   \n",
       "Madrid_humidity         -0.634825           -0.636777        -0.611395   \n",
       "Bilbao_clouds_all       -0.114118           -0.010804        -0.158014   \n",
       "Bilbao_wind_speed        0.142435            0.023711         0.177123   \n",
       "Seville_clouds_all      -0.152393           -0.153412        -0.165567   \n",
       "Bilbao_wind_deg          0.065396            0.086482         0.031187   \n",
       "Barcelona_wind_speed     0.120361            0.109268         0.109692   \n",
       "Barcelona_wind_deg      -0.164656           -0.226583        -0.147388   \n",
       "Madrid_clouds_all       -0.174079           -0.188578        -0.172921   \n",
       "Seville_wind_speed       0.082904            0.097407         0.070808   \n",
       "Barcelona_rain_1h        0.034889            0.023582         0.032362   \n",
       "Seville_rain_1h         -0.080749           -0.078631        -0.085650   \n",
       "Bilbao_snow_3h          -0.096477           -0.079382        -0.105006   \n",
       "Barcelona_pressure      -0.027410           -0.029612        -0.031854   \n",
       "Seville_rain_3h         -0.032691           -0.029569        -0.043441   \n",
       "Madrid_rain_1h          -0.055851           -0.028900        -0.068176   \n",
       "Barcelona_rain_3h       -0.043223           -0.029398        -0.059089   \n",
       "Valencia_snow_3h        -0.031726           -0.027172        -0.034205   \n",
       "Madrid_weather_id        0.068572            0.056176         0.076601   \n",
       "Barcelona_weather_id     0.053303            0.046167         0.054720   \n",
       "Bilbao_pressure         -0.250325           -0.230942        -0.221529   \n",
       "Seville_weather_id       0.134535            0.150195         0.132564   \n",
       "Valencia_pressure        0.074986           -0.067132         0.147187   \n",
       "Seville_temp_max         0.703218            0.586923         0.726510   \n",
       "Madrid_pressure          0.092394           -0.068083         0.176838   \n",
       "Valencia_temp_max        0.854592            0.845057         0.837574   \n",
       "Valencia_temp            0.854892            0.874447         0.833562   \n",
       "Bilbao_weather_id        0.190635            0.098428         0.235183   \n",
       "Seville_temp             0.839014            0.779252         0.828099   \n",
       "Valencia_humidity       -0.260946           -0.266820        -0.241397   \n",
       "Valencia_temp_min        0.832393            0.879687         0.806606   \n",
       "Barcelona_temp_max       0.867268            0.885911         0.844563   \n",
       "Madrid_temp_max          0.872234            0.836680         0.846887   \n",
       "Barcelona_temp           0.865705            0.964089         0.831470   \n",
       "Bilbao_temp_min          0.982936            0.831224         0.932219   \n",
       "Bilbao_temp              1.000000            0.810785         0.981624   \n",
       "Barcelona_temp_min       0.810785            1.000000         0.764031   \n",
       "Bilbao_temp_max          0.981624            0.764031         1.000000   \n",
       "Seville_temp_min         0.842972            0.831689         0.808278   \n",
       "Madrid_temp              0.875271            0.853881         0.843633   \n",
       "Madrid_temp_min          0.838195            0.838985         0.802041   \n",
       "load_shortfall_3h        0.176411            0.195984         0.180934   \n",
       "\n",
       "                      Seville_temp_min  Madrid_temp  Madrid_temp_min  \\\n",
       "Unnamed: 0                    0.037487     0.088439         0.085197   \n",
       "Madrid_wind_speed             0.110278     0.109572         0.098138   \n",
       "Bilbao_rain_1h               -0.120575    -0.135927        -0.116431   \n",
       "Valencia_wind_speed           0.003618    -0.011982        -0.019742   \n",
       "Seville_humidity             -0.784850    -0.717171        -0.688664   \n",
       "Madrid_humidity              -0.769591    -0.802605        -0.759633   \n",
       "Bilbao_clouds_all            -0.077417    -0.046686        -0.046189   \n",
       "Bilbao_wind_speed             0.055077     0.031245         0.021425   \n",
       "Seville_clouds_all           -0.200453    -0.227094        -0.212758   \n",
       "Bilbao_wind_deg               0.062663     0.044102         0.048397   \n",
       "Barcelona_wind_speed          0.130367     0.089365         0.084679   \n",
       "Barcelona_wind_deg           -0.195212    -0.211319        -0.211010   \n",
       "Madrid_clouds_all            -0.245756    -0.262908        -0.245475   \n",
       "Seville_wind_speed            0.154254     0.108460         0.089832   \n",
       "Barcelona_rain_1h             0.065312     0.075013         0.070146   \n",
       "Seville_rain_1h              -0.113038    -0.115727        -0.101189   \n",
       "Bilbao_snow_3h               -0.075545    -0.081209        -0.074892   \n",
       "Barcelona_pressure           -0.032889    -0.017886        -0.017819   \n",
       "Seville_rain_3h              -0.032983    -0.043338        -0.035708   \n",
       "Madrid_rain_1h               -0.081794    -0.105667        -0.094601   \n",
       "Barcelona_rain_3h            -0.012016    -0.039565        -0.029094   \n",
       "Valencia_snow_3h             -0.019706    -0.023734        -0.021807   \n",
       "Madrid_weather_id             0.110713     0.136829         0.123498   \n",
       "Barcelona_weather_id          0.020735     0.025671         0.026252   \n",
       "Bilbao_pressure              -0.150221    -0.156223        -0.160023   \n",
       "Seville_weather_id            0.180721     0.185768         0.174238   \n",
       "Valencia_pressure             0.027998     0.082532         0.035991   \n",
       "Seville_temp_max              0.743801     0.740831         0.674561   \n",
       "Madrid_pressure               0.037876     0.054819         0.014089   \n",
       "Valencia_temp_max             0.886449     0.911331         0.862683   \n",
       "Valencia_temp                 0.890915     0.915753         0.871563   \n",
       "Bilbao_weather_id             0.145173     0.143126         0.124199   \n",
       "Seville_temp                  0.955160     0.917075         0.868453   \n",
       "Valencia_humidity            -0.301694    -0.261356        -0.247574   \n",
       "Valencia_temp_min             0.871738     0.897012         0.860347   \n",
       "Barcelona_temp_max            0.862597     0.896766         0.847888   \n",
       "Madrid_temp_max               0.923481     0.988871         0.939147   \n",
       "Barcelona_temp                0.873170     0.903143         0.868222   \n",
       "Bilbao_temp_min               0.847007     0.876185         0.845799   \n",
       "Bilbao_temp                   0.842972     0.875271         0.838195   \n",
       "Barcelona_temp_min            0.831689     0.853881         0.838985   \n",
       "Bilbao_temp_max               0.808278     0.843633         0.802041   \n",
       "Seville_temp_min              1.000000     0.939079         0.907758   \n",
       "Madrid_temp                   0.939079     1.000000         0.973641   \n",
       "Madrid_temp_min               0.907758     0.973641         1.000000   \n",
       "load_shortfall_3h             0.174718     0.186036         0.174744   \n",
       "\n",
       "                      load_shortfall_3h  \n",
       "Unnamed: 0                     0.192612  \n",
       "Madrid_wind_speed             -0.150981  \n",
       "Bilbao_rain_1h                -0.152510  \n",
       "Valencia_wind_speed           -0.142791  \n",
       "Seville_humidity              -0.167290  \n",
       "Madrid_humidity               -0.150536  \n",
       "Bilbao_clouds_all             -0.127293  \n",
       "Bilbao_wind_speed             -0.081602  \n",
       "Seville_clouds_all            -0.091804  \n",
       "Bilbao_wind_deg               -0.120800  \n",
       "Barcelona_wind_speed          -0.103633  \n",
       "Barcelona_wind_deg            -0.116133  \n",
       "Madrid_clouds_all             -0.081623  \n",
       "Seville_wind_speed            -0.048433  \n",
       "Barcelona_rain_1h             -0.056633  \n",
       "Seville_rain_1h               -0.031572  \n",
       "Bilbao_snow_3h                -0.031721  \n",
       "Barcelona_pressure            -0.034385  \n",
       "Seville_rain_3h               -0.032945  \n",
       "Madrid_rain_1h                -0.027159  \n",
       "Barcelona_rain_3h             -0.024720  \n",
       "Valencia_snow_3h              -0.021633  \n",
       "Madrid_weather_id              0.030868  \n",
       "Barcelona_weather_id           0.063868  \n",
       "Bilbao_pressure                0.039052  \n",
       "Seville_weather_id             0.061104  \n",
       "Valencia_pressure              0.086022  \n",
       "Seville_temp_max               0.109834  \n",
       "Madrid_pressure                0.092638  \n",
       "Valencia_temp_max              0.144270  \n",
       "Valencia_temp                  0.173225  \n",
       "Bilbao_weather_id              0.145875  \n",
       "Seville_temp                   0.159343  \n",
       "Valencia_humidity              0.043140  \n",
       "Valencia_temp_min              0.201863  \n",
       "Barcelona_temp_max             0.164734  \n",
       "Madrid_temp_max                0.182548  \n",
       "Barcelona_temp                 0.182673  \n",
       "Bilbao_temp_min                0.172920  \n",
       "Bilbao_temp                    0.176411  \n",
       "Barcelona_temp_min             0.195984  \n",
       "Bilbao_temp_max                0.180934  \n",
       "Seville_temp_min               0.174718  \n",
       "Madrid_temp                    0.186036  \n",
       "Madrid_temp_min                0.174744  \n",
       "load_shortfall_3h              1.000000  \n",
       "\n",
       "[46 rows x 46 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate correlation for df_train\n",
    "df_train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2aed7e8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Madrid_wind_speed</th>\n",
       "      <th>Bilbao_rain_1h</th>\n",
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       "      <th>Bilbao_temp</th>\n",
       "      <th>Barcelona_temp_min</th>\n",
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       "      <td>-0.135484</td>\n",
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       "      <td>0.241640</td>\n",
       "      <td>0.246385</td>\n",
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       "      <th>Madrid_wind_speed</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.253975</td>\n",
       "      <td>0.577509</td>\n",
       "      <td>-0.107176</td>\n",
       "      <td>-0.055267</td>\n",
       "      <td>0.164097</td>\n",
       "      <td>0.404369</td>\n",
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       "      <td>-0.024364</td>\n",
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       "      <td>-0.022688</td>\n",
       "      <td>-0.056954</td>\n",
       "      <td>-0.023627</td>\n",
       "      <td>-0.023591</td>\n",
       "      <td>-0.046734</td>\n",
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       "      <td>-0.171216</td>\n",
       "      <td>0.253975</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.155717</td>\n",
       "      <td>0.103573</td>\n",
       "      <td>0.149643</td>\n",
       "      <td>0.414151</td>\n",
       "      <td>0.118086</td>\n",
       "      <td>0.080647</td>\n",
       "      <td>0.183078</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.207383</td>\n",
       "      <td>-0.223619</td>\n",
       "      <td>-0.202221</td>\n",
       "      <td>-0.221014</td>\n",
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       "      <td>-0.258778</td>\n",
       "      <td>-0.233939</td>\n",
       "      <td>-0.218465</td>\n",
       "      <td>-0.217309</td>\n",
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       "      <td>0.577509</td>\n",
       "      <td>0.155717</td>\n",
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       "      <td>-0.214738</td>\n",
       "      <td>-0.100565</td>\n",
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       "      <td>-0.004474</td>\n",
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       "      <td>0.028977</td>\n",
       "      <td>0.015834</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>-0.003910</td>\n",
       "      <td>0.030320</td>\n",
       "      <td>-0.006542</td>\n",
       "      <td>-0.033857</td>\n",
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       "      <td>0.103573</td>\n",
       "      <td>-0.214738</td>\n",
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       "      <td>-0.743369</td>\n",
       "      <td>-0.658135</td>\n",
       "      <td>-0.630085</td>\n",
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       "      <td>-0.100565</td>\n",
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       "      <td>-0.789474</td>\n",
       "      <td>-0.672803</td>\n",
       "      <td>-0.607507</td>\n",
       "      <td>-0.588370</td>\n",
       "      <td>-0.668369</td>\n",
       "      <td>-0.548738</td>\n",
       "      <td>-0.740434</td>\n",
       "      <td>-0.777081</td>\n",
       "      <td>-0.756214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_clouds_all</th>\n",
       "      <td>-0.192422</td>\n",
       "      <td>0.164097</td>\n",
       "      <td>0.414151</td>\n",
       "      <td>0.126358</td>\n",
       "      <td>0.033669</td>\n",
       "      <td>0.005968</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001085</td>\n",
       "      <td>0.010232</td>\n",
       "      <td>0.227288</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.075326</td>\n",
       "      <td>-0.102398</td>\n",
       "      <td>-0.071588</td>\n",
       "      <td>-0.128791</td>\n",
       "      <td>-0.167230</td>\n",
       "      <td>-0.067711</td>\n",
       "      <td>-0.215278</td>\n",
       "      <td>-0.132752</td>\n",
       "      <td>-0.104385</td>\n",
       "      <td>-0.108140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_wind_speed</th>\n",
       "      <td>-0.136715</td>\n",
       "      <td>0.404369</td>\n",
       "      <td>0.118086</td>\n",
       "      <td>0.423967</td>\n",
       "      <td>-0.172222</td>\n",
       "      <td>-0.043188</td>\n",
       "      <td>0.001085</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.138443</td>\n",
       "      <td>0.519614</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.025420</td>\n",
       "      <td>-0.001129</td>\n",
       "      <td>-0.022682</td>\n",
       "      <td>0.078474</td>\n",
       "      <td>0.097111</td>\n",
       "      <td>-0.019624</td>\n",
       "      <td>0.117940</td>\n",
       "      <td>0.060728</td>\n",
       "      <td>-0.004439</td>\n",
       "      <td>-0.030676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_clouds_all</th>\n",
       "      <td>-0.089696</td>\n",
       "      <td>0.166845</td>\n",
       "      <td>0.080647</td>\n",
       "      <td>0.080878</td>\n",
       "      <td>0.388829</td>\n",
       "      <td>0.327895</td>\n",
       "      <td>0.010232</td>\n",
       "      <td>0.138443</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007418</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.166132</td>\n",
       "      <td>-0.208761</td>\n",
       "      <td>-0.158508</td>\n",
       "      <td>-0.126255</td>\n",
       "      <td>-0.123033</td>\n",
       "      <td>-0.150160</td>\n",
       "      <td>-0.114845</td>\n",
       "      <td>-0.170409</td>\n",
       "      <td>-0.206495</td>\n",
       "      <td>-0.213871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_wind_deg</th>\n",
       "      <td>-0.135484</td>\n",
       "      <td>0.277087</td>\n",
       "      <td>0.183078</td>\n",
       "      <td>0.281658</td>\n",
       "      <td>-0.233416</td>\n",
       "      <td>-0.169288</td>\n",
       "      <td>0.227288</td>\n",
       "      <td>0.519614</td>\n",
       "      <td>0.007418</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.112969</td>\n",
       "      <td>0.132334</td>\n",
       "      <td>0.114064</td>\n",
       "      <td>0.157191</td>\n",
       "      <td>0.146133</td>\n",
       "      <td>0.114486</td>\n",
       "      <td>0.124797</td>\n",
       "      <td>0.145157</td>\n",
       "      <td>0.130515</td>\n",
       "      <td>0.112450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_wind_speed</th>\n",
       "      <td>0.095843</td>\n",
       "      <td>0.203092</td>\n",
       "      <td>0.030641</td>\n",
       "      <td>0.409237</td>\n",
       "      <td>-0.182931</td>\n",
       "      <td>-0.057157</td>\n",
       "      <td>0.036946</td>\n",
       "      <td>0.302084</td>\n",
       "      <td>0.045497</td>\n",
       "      <td>0.182693</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038945</td>\n",
       "      <td>0.015559</td>\n",
       "      <td>0.034937</td>\n",
       "      <td>0.069284</td>\n",
       "      <td>0.063306</td>\n",
       "      <td>0.031155</td>\n",
       "      <td>0.051541</td>\n",
       "      <td>0.068690</td>\n",
       "      <td>0.006909</td>\n",
       "      <td>-0.018323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_wind_deg</th>\n",
       "      <td>0.166713</td>\n",
       "      <td>-0.044620</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>-0.008091</td>\n",
       "      <td>0.212338</td>\n",
       "      <td>0.223869</td>\n",
       "      <td>-0.030848</td>\n",
       "      <td>0.004768</td>\n",
       "      <td>0.002125</td>\n",
       "      <td>-0.061825</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.251263</td>\n",
       "      <td>-0.261649</td>\n",
       "      <td>-0.263724</td>\n",
       "      <td>-0.219383</td>\n",
       "      <td>-0.208246</td>\n",
       "      <td>-0.274537</td>\n",
       "      <td>-0.189198</td>\n",
       "      <td>-0.247358</td>\n",
       "      <td>-0.263493</td>\n",
       "      <td>-0.256650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_clouds_all</th>\n",
       "      <td>-0.029719</td>\n",
       "      <td>0.290741</td>\n",
       "      <td>0.188485</td>\n",
       "      <td>0.240487</td>\n",
       "      <td>0.272806</td>\n",
       "      <td>0.525499</td>\n",
       "      <td>0.034425</td>\n",
       "      <td>0.221904</td>\n",
       "      <td>0.427129</td>\n",
       "      <td>0.041360</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.279512</td>\n",
       "      <td>-0.335006</td>\n",
       "      <td>-0.268556</td>\n",
       "      <td>-0.202018</td>\n",
       "      <td>-0.192392</td>\n",
       "      <td>-0.256132</td>\n",
       "      <td>-0.174887</td>\n",
       "      <td>-0.265770</td>\n",
       "      <td>-0.325586</td>\n",
       "      <td>-0.334828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_wind_speed</th>\n",
       "      <td>-0.145868</td>\n",
       "      <td>0.420553</td>\n",
       "      <td>0.071920</td>\n",
       "      <td>0.280553</td>\n",
       "      <td>-0.244655</td>\n",
       "      <td>-0.135591</td>\n",
       "      <td>0.026322</td>\n",
       "      <td>0.276280</td>\n",
       "      <td>0.153677</td>\n",
       "      <td>0.157465</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082035</td>\n",
       "      <td>0.129740</td>\n",
       "      <td>0.090166</td>\n",
       "      <td>0.093591</td>\n",
       "      <td>0.091015</td>\n",
       "      <td>0.097994</td>\n",
       "      <td>0.085206</td>\n",
       "      <td>0.185432</td>\n",
       "      <td>0.124168</td>\n",
       "      <td>0.098924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_rain_1h</th>\n",
       "      <td>-0.020585</td>\n",
       "      <td>0.118758</td>\n",
       "      <td>0.134627</td>\n",
       "      <td>0.070692</td>\n",
       "      <td>0.070347</td>\n",
       "      <td>0.117499</td>\n",
       "      <td>0.112632</td>\n",
       "      <td>-0.005558</td>\n",
       "      <td>0.030973</td>\n",
       "      <td>-0.018825</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.135158</td>\n",
       "      <td>-0.127575</td>\n",
       "      <td>-0.126440</td>\n",
       "      <td>-0.137987</td>\n",
       "      <td>-0.145530</td>\n",
       "      <td>-0.116929</td>\n",
       "      <td>-0.151702</td>\n",
       "      <td>-0.119155</td>\n",
       "      <td>-0.121625</td>\n",
       "      <td>-0.117493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_rain_1h</th>\n",
       "      <td>-0.046741</td>\n",
       "      <td>0.124355</td>\n",
       "      <td>0.069728</td>\n",
       "      <td>0.054037</td>\n",
       "      <td>0.236841</td>\n",
       "      <td>0.199270</td>\n",
       "      <td>-0.001283</td>\n",
       "      <td>0.101971</td>\n",
       "      <td>0.440222</td>\n",
       "      <td>0.023735</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.144542</td>\n",
       "      <td>-0.150750</td>\n",
       "      <td>-0.140226</td>\n",
       "      <td>-0.111571</td>\n",
       "      <td>-0.112303</td>\n",
       "      <td>-0.135314</td>\n",
       "      <td>-0.110392</td>\n",
       "      <td>-0.112015</td>\n",
       "      <td>-0.146426</td>\n",
       "      <td>-0.149502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_snow_3h</th>\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>...</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>Barcelona_pressure</th>\n",
       "      <td>0.301629</td>\n",
       "      <td>-0.527713</td>\n",
       "      <td>-0.292166</td>\n",
       "      <td>-0.370779</td>\n",
       "      <td>-0.070909</td>\n",
       "      <td>-0.065996</td>\n",
       "      <td>-0.224310</td>\n",
       "      <td>-0.274803</td>\n",
       "      <td>-0.303777</td>\n",
       "      <td>-0.209087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113815</td>\n",
       "      <td>0.140078</td>\n",
       "      <td>0.102866</td>\n",
       "      <td>0.136672</td>\n",
       "      <td>0.154764</td>\n",
       "      <td>0.091536</td>\n",
       "      <td>0.173764</td>\n",
       "      <td>0.118214</td>\n",
       "      <td>0.133247</td>\n",
       "      <td>0.141323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_rain_3h</th>\n",
       "      <td>-0.000187</td>\n",
       "      <td>0.012609</td>\n",
       "      <td>-0.008149</td>\n",
       "      <td>-0.018665</td>\n",
       "      <td>0.008871</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>0.012131</td>\n",
       "      <td>-0.014359</td>\n",
       "      <td>0.013071</td>\n",
       "      <td>-0.030802</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021016</td>\n",
       "      <td>0.009581</td>\n",
       "      <td>0.022171</td>\n",
       "      <td>0.019671</td>\n",
       "      <td>0.020314</td>\n",
       "      <td>0.023191</td>\n",
       "      <td>0.020828</td>\n",
       "      <td>0.002538</td>\n",
       "      <td>0.010262</td>\n",
       "      <td>0.011252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_rain_1h</th>\n",
       "      <td>-0.090666</td>\n",
       "      <td>0.224845</td>\n",
       "      <td>0.118811</td>\n",
       "      <td>0.109151</td>\n",
       "      <td>0.098910</td>\n",
       "      <td>0.229901</td>\n",
       "      <td>0.057843</td>\n",
       "      <td>0.158933</td>\n",
       "      <td>0.298645</td>\n",
       "      <td>0.050711</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.070354</td>\n",
       "      <td>-0.102865</td>\n",
       "      <td>-0.061732</td>\n",
       "      <td>-0.045784</td>\n",
       "      <td>-0.047521</td>\n",
       "      <td>-0.052838</td>\n",
       "      <td>-0.048350</td>\n",
       "      <td>-0.053510</td>\n",
       "      <td>-0.103824</td>\n",
       "      <td>-0.117917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_rain_3h</th>\n",
       "      <td>0.000708</td>\n",
       "      <td>0.010039</td>\n",
       "      <td>0.022245</td>\n",
       "      <td>-0.006045</td>\n",
       "      <td>-0.023903</td>\n",
       "      <td>-0.021271</td>\n",
       "      <td>0.002251</td>\n",
       "      <td>0.038782</td>\n",
       "      <td>-0.015761</td>\n",
       "      <td>0.038895</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022952</td>\n",
       "      <td>0.027214</td>\n",
       "      <td>0.025413</td>\n",
       "      <td>0.022363</td>\n",
       "      <td>0.020618</td>\n",
       "      <td>0.027711</td>\n",
       "      <td>0.017873</td>\n",
       "      <td>0.029488</td>\n",
       "      <td>0.028173</td>\n",
       "      <td>0.030366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia_snow_3h</th>\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>...</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>Madrid_weather_id</th>\n",
       "      <td>0.041953</td>\n",
       "      <td>-0.205462</td>\n",
       "      <td>-0.120980</td>\n",
       "      <td>-0.099254</td>\n",
       "      <td>-0.169767</td>\n",
       "      <td>-0.349022</td>\n",
       "      <td>-0.056189</td>\n",
       "      <td>-0.130108</td>\n",
       "      <td>-0.343825</td>\n",
       "      <td>-0.045245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.115963</td>\n",
       "      <td>0.160757</td>\n",
       "      <td>0.107372</td>\n",
       "      <td>0.074148</td>\n",
       "      <td>0.076326</td>\n",
       "      <td>0.098331</td>\n",
       "      <td>0.077904</td>\n",
       "      <td>0.105730</td>\n",
       "      <td>0.158728</td>\n",
       "      <td>0.169791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_weather_id</th>\n",
       "      <td>0.028856</td>\n",
       "      <td>-0.082191</td>\n",
       "      <td>-0.113650</td>\n",
       "      <td>-0.045271</td>\n",
       "      <td>-0.060415</td>\n",
       "      <td>-0.103705</td>\n",
       "      <td>-0.112166</td>\n",
       "      <td>-0.014993</td>\n",
       "      <td>-0.029149</td>\n",
       "      <td>-0.017847</td>\n",
       "      <td>...</td>\n",
       "      <td>0.110018</td>\n",
       "      <td>0.094544</td>\n",
       "      <td>0.100468</td>\n",
       "      <td>0.124639</td>\n",
       "      <td>0.131755</td>\n",
       "      <td>0.090382</td>\n",
       "      <td>0.137586</td>\n",
       "      <td>0.075990</td>\n",
       "      <td>0.085885</td>\n",
       "      <td>0.079353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_pressure</th>\n",
       "      <td>0.184644</td>\n",
       "      <td>-0.445457</td>\n",
       "      <td>-0.205045</td>\n",
       "      <td>-0.247379</td>\n",
       "      <td>-0.179860</td>\n",
       "      <td>-0.239304</td>\n",
       "      <td>0.011598</td>\n",
       "      <td>-0.278390</td>\n",
       "      <td>-0.410339</td>\n",
       "      <td>-0.107519</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114508</td>\n",
       "      <td>0.145411</td>\n",
       "      <td>0.101524</td>\n",
       "      <td>0.043387</td>\n",
       "      <td>0.044299</td>\n",
       "      <td>0.088089</td>\n",
       "      <td>0.042852</td>\n",
       "      <td>0.107113</td>\n",
       "      <td>0.133651</td>\n",
       "      <td>0.135395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_weather_id</th>\n",
       "      <td>0.082703</td>\n",
       "      <td>-0.116220</td>\n",
       "      <td>-0.089323</td>\n",
       "      <td>-0.048185</td>\n",
       "      <td>-0.329678</td>\n",
       "      <td>-0.273442</td>\n",
       "      <td>-0.016618</td>\n",
       "      <td>-0.063427</td>\n",
       "      <td>-0.540006</td>\n",
       "      <td>-0.000196</td>\n",
       "      <td>...</td>\n",
       "      <td>0.203044</td>\n",
       "      <td>0.202941</td>\n",
       "      <td>0.198250</td>\n",
       "      <td>0.157375</td>\n",
       "      <td>0.156216</td>\n",
       "      <td>0.192444</td>\n",
       "      <td>0.150801</td>\n",
       "      <td>0.171767</td>\n",
       "      <td>0.198197</td>\n",
       "      <td>0.199823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia_pressure</th>\n",
       "      <td>0.233345</td>\n",
       "      <td>-0.408045</td>\n",
       "      <td>-0.271538</td>\n",
       "      <td>-0.258368</td>\n",
       "      <td>-0.148450</td>\n",
       "      <td>-0.222737</td>\n",
       "      <td>-0.171009</td>\n",
       "      <td>-0.184119</td>\n",
       "      <td>-0.265257</td>\n",
       "      <td>-0.132985</td>\n",
       "      <td>...</td>\n",
       "      <td>0.249211</td>\n",
       "      <td>0.274004</td>\n",
       "      <td>0.238388</td>\n",
       "      <td>0.258130</td>\n",
       "      <td>0.277780</td>\n",
       "      <td>0.226030</td>\n",
       "      <td>0.298448</td>\n",
       "      <td>0.240775</td>\n",
       "      <td>0.269128</td>\n",
       "      <td>0.272155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_temp_max</th>\n",
       "      <td>0.271110</td>\n",
       "      <td>-0.038384</td>\n",
       "      <td>-0.238806</td>\n",
       "      <td>0.021728</td>\n",
       "      <td>-0.756395</td>\n",
       "      <td>-0.745308</td>\n",
       "      <td>-0.134453</td>\n",
       "      <td>0.057369</td>\n",
       "      <td>-0.189792</td>\n",
       "      <td>0.141944</td>\n",
       "      <td>...</td>\n",
       "      <td>0.890446</td>\n",
       "      <td>0.941157</td>\n",
       "      <td>0.891111</td>\n",
       "      <td>0.852849</td>\n",
       "      <td>0.848445</td>\n",
       "      <td>0.886765</td>\n",
       "      <td>0.823560</td>\n",
       "      <td>0.993506</td>\n",
       "      <td>0.941410</td>\n",
       "      <td>0.930016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_pressure</th>\n",
       "      <td>0.196598</td>\n",
       "      <td>-0.335964</td>\n",
       "      <td>-0.229570</td>\n",
       "      <td>-0.164802</td>\n",
       "      <td>-0.082909</td>\n",
       "      <td>-0.094984</td>\n",
       "      <td>-0.143983</td>\n",
       "      <td>-0.140326</td>\n",
       "      <td>-0.304788</td>\n",
       "      <td>-0.127463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074543</td>\n",
       "      <td>0.071967</td>\n",
       "      <td>0.058886</td>\n",
       "      <td>0.070196</td>\n",
       "      <td>0.096062</td>\n",
       "      <td>0.043027</td>\n",
       "      <td>0.126843</td>\n",
       "      <td>0.039849</td>\n",
       "      <td>0.058194</td>\n",
       "      <td>0.054514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia_temp_max</th>\n",
       "      <td>0.222733</td>\n",
       "      <td>0.030763</td>\n",
       "      <td>-0.194648</td>\n",
       "      <td>0.072974</td>\n",
       "      <td>-0.623489</td>\n",
       "      <td>-0.708881</td>\n",
       "      <td>-0.060637</td>\n",
       "      <td>0.020066</td>\n",
       "      <td>-0.155728</td>\n",
       "      <td>0.144888</td>\n",
       "      <td>...</td>\n",
       "      <td>0.950976</td>\n",
       "      <td>0.933500</td>\n",
       "      <td>0.952967</td>\n",
       "      <td>0.886459</td>\n",
       "      <td>0.880043</td>\n",
       "      <td>0.949459</td>\n",
       "      <td>0.852706</td>\n",
       "      <td>0.891294</td>\n",
       "      <td>0.931938</td>\n",
       "      <td>0.922374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia_temp</th>\n",
       "      <td>0.207437</td>\n",
       "      <td>0.035666</td>\n",
       "      <td>-0.191332</td>\n",
       "      <td>0.075390</td>\n",
       "      <td>-0.623704</td>\n",
       "      <td>-0.710969</td>\n",
       "      <td>-0.057513</td>\n",
       "      <td>0.020786</td>\n",
       "      <td>-0.153033</td>\n",
       "      <td>0.146447</td>\n",
       "      <td>...</td>\n",
       "      <td>0.951416</td>\n",
       "      <td>0.933877</td>\n",
       "      <td>0.953628</td>\n",
       "      <td>0.885423</td>\n",
       "      <td>0.878546</td>\n",
       "      <td>0.950316</td>\n",
       "      <td>0.850638</td>\n",
       "      <td>0.891049</td>\n",
       "      <td>0.932545</td>\n",
       "      <td>0.923087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_weather_id</th>\n",
       "      <td>0.186956</td>\n",
       "      <td>-0.197850</td>\n",
       "      <td>-0.762097</td>\n",
       "      <td>-0.103676</td>\n",
       "      <td>-0.137954</td>\n",
       "      <td>-0.164793</td>\n",
       "      <td>-0.495943</td>\n",
       "      <td>-0.046793</td>\n",
       "      <td>-0.066192</td>\n",
       "      <td>-0.167435</td>\n",
       "      <td>...</td>\n",
       "      <td>0.220114</td>\n",
       "      <td>0.234793</td>\n",
       "      <td>0.215948</td>\n",
       "      <td>0.234286</td>\n",
       "      <td>0.257355</td>\n",
       "      <td>0.210777</td>\n",
       "      <td>0.282505</td>\n",
       "      <td>0.259304</td>\n",
       "      <td>0.231817</td>\n",
       "      <td>0.230605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_temp</th>\n",
       "      <td>0.273018</td>\n",
       "      <td>-0.029730</td>\n",
       "      <td>-0.236915</td>\n",
       "      <td>0.026945</td>\n",
       "      <td>-0.749199</td>\n",
       "      <td>-0.743359</td>\n",
       "      <td>-0.134444</td>\n",
       "      <td>0.059857</td>\n",
       "      <td>-0.177401</td>\n",
       "      <td>0.144179</td>\n",
       "      <td>...</td>\n",
       "      <td>0.886854</td>\n",
       "      <td>0.939514</td>\n",
       "      <td>0.887801</td>\n",
       "      <td>0.850862</td>\n",
       "      <td>0.846113</td>\n",
       "      <td>0.883743</td>\n",
       "      <td>0.820877</td>\n",
       "      <td>0.998947</td>\n",
       "      <td>0.940348</td>\n",
       "      <td>0.929243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia_humidity</th>\n",
       "      <td>0.272480</td>\n",
       "      <td>-0.319646</td>\n",
       "      <td>-0.033854</td>\n",
       "      <td>-0.495787</td>\n",
       "      <td>0.459746</td>\n",
       "      <td>0.409074</td>\n",
       "      <td>-0.084185</td>\n",
       "      <td>-0.318187</td>\n",
       "      <td>0.050984</td>\n",
       "      <td>-0.282833</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.196006</td>\n",
       "      <td>-0.191074</td>\n",
       "      <td>-0.194542</td>\n",
       "      <td>-0.194563</td>\n",
       "      <td>-0.178466</td>\n",
       "      <td>-0.191965</td>\n",
       "      <td>-0.151424</td>\n",
       "      <td>-0.216399</td>\n",
       "      <td>-0.176728</td>\n",
       "      <td>-0.145049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valencia_temp_min</th>\n",
       "      <td>0.192403</td>\n",
       "      <td>0.040401</td>\n",
       "      <td>-0.187942</td>\n",
       "      <td>0.077663</td>\n",
       "      <td>-0.623296</td>\n",
       "      <td>-0.712309</td>\n",
       "      <td>-0.054462</td>\n",
       "      <td>0.021468</td>\n",
       "      <td>-0.150256</td>\n",
       "      <td>0.147814</td>\n",
       "      <td>...</td>\n",
       "      <td>0.950935</td>\n",
       "      <td>0.933358</td>\n",
       "      <td>0.953358</td>\n",
       "      <td>0.883575</td>\n",
       "      <td>0.876261</td>\n",
       "      <td>0.950241</td>\n",
       "      <td>0.847828</td>\n",
       "      <td>0.889971</td>\n",
       "      <td>0.932260</td>\n",
       "      <td>0.922917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_temp_max</th>\n",
       "      <td>0.248009</td>\n",
       "      <td>-0.034217</td>\n",
       "      <td>-0.207383</td>\n",
       "      <td>-0.003885</td>\n",
       "      <td>-0.587597</td>\n",
       "      <td>-0.673470</td>\n",
       "      <td>-0.075326</td>\n",
       "      <td>-0.025420</td>\n",
       "      <td>-0.166132</td>\n",
       "      <td>0.112969</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.929809</td>\n",
       "      <td>0.997079</td>\n",
       "      <td>0.900923</td>\n",
       "      <td>0.896933</td>\n",
       "      <td>0.988413</td>\n",
       "      <td>0.872245</td>\n",
       "      <td>0.883042</td>\n",
       "      <td>0.930620</td>\n",
       "      <td>0.924518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_temp_max</th>\n",
       "      <td>0.242473</td>\n",
       "      <td>-0.024364</td>\n",
       "      <td>-0.223619</td>\n",
       "      <td>-0.004474</td>\n",
       "      <td>-0.668035</td>\n",
       "      <td>-0.789474</td>\n",
       "      <td>-0.102398</td>\n",
       "      <td>-0.001129</td>\n",
       "      <td>-0.208761</td>\n",
       "      <td>0.132334</td>\n",
       "      <td>...</td>\n",
       "      <td>0.929809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.930543</td>\n",
       "      <td>0.893980</td>\n",
       "      <td>0.885806</td>\n",
       "      <td>0.925964</td>\n",
       "      <td>0.855189</td>\n",
       "      <td>0.936354</td>\n",
       "      <td>0.997189</td>\n",
       "      <td>0.987142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_temp</th>\n",
       "      <td>0.248321</td>\n",
       "      <td>-0.028548</td>\n",
       "      <td>-0.202221</td>\n",
       "      <td>-0.001078</td>\n",
       "      <td>-0.587903</td>\n",
       "      <td>-0.672803</td>\n",
       "      <td>-0.071588</td>\n",
       "      <td>-0.022682</td>\n",
       "      <td>-0.158508</td>\n",
       "      <td>0.114064</td>\n",
       "      <td>...</td>\n",
       "      <td>0.997079</td>\n",
       "      <td>0.930543</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900691</td>\n",
       "      <td>0.896205</td>\n",
       "      <td>0.997110</td>\n",
       "      <td>0.871073</td>\n",
       "      <td>0.884164</td>\n",
       "      <td>0.930849</td>\n",
       "      <td>0.924073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_temp_min</th>\n",
       "      <td>0.239508</td>\n",
       "      <td>-0.029330</td>\n",
       "      <td>-0.221014</td>\n",
       "      <td>0.028977</td>\n",
       "      <td>-0.562529</td>\n",
       "      <td>-0.607507</td>\n",
       "      <td>-0.128791</td>\n",
       "      <td>0.078474</td>\n",
       "      <td>-0.126255</td>\n",
       "      <td>0.157191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.900923</td>\n",
       "      <td>0.893980</td>\n",
       "      <td>0.900691</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992971</td>\n",
       "      <td>0.895276</td>\n",
       "      <td>0.961304</td>\n",
       "      <td>0.847719</td>\n",
       "      <td>0.894435</td>\n",
       "      <td>0.884779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_temp</th>\n",
       "      <td>0.262182</td>\n",
       "      <td>-0.041190</td>\n",
       "      <td>-0.239588</td>\n",
       "      <td>0.015834</td>\n",
       "      <td>-0.547807</td>\n",
       "      <td>-0.588370</td>\n",
       "      <td>-0.167230</td>\n",
       "      <td>0.097111</td>\n",
       "      <td>-0.123033</td>\n",
       "      <td>0.146133</td>\n",
       "      <td>...</td>\n",
       "      <td>0.896933</td>\n",
       "      <td>0.885806</td>\n",
       "      <td>0.896205</td>\n",
       "      <td>0.992971</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.890338</td>\n",
       "      <td>0.987021</td>\n",
       "      <td>0.842689</td>\n",
       "      <td>0.886292</td>\n",
       "      <td>0.877070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barcelona_temp_min</th>\n",
       "      <td>0.247846</td>\n",
       "      <td>-0.022688</td>\n",
       "      <td>-0.196089</td>\n",
       "      <td>0.001775</td>\n",
       "      <td>-0.585128</td>\n",
       "      <td>-0.668369</td>\n",
       "      <td>-0.067711</td>\n",
       "      <td>-0.019624</td>\n",
       "      <td>-0.150160</td>\n",
       "      <td>0.114486</td>\n",
       "      <td>...</td>\n",
       "      <td>0.988413</td>\n",
       "      <td>0.925964</td>\n",
       "      <td>0.997110</td>\n",
       "      <td>0.895276</td>\n",
       "      <td>0.890338</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.864937</td>\n",
       "      <td>0.880290</td>\n",
       "      <td>0.925747</td>\n",
       "      <td>0.918312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bilbao_temp_max</th>\n",
       "      <td>0.289018</td>\n",
       "      <td>-0.056954</td>\n",
       "      <td>-0.258778</td>\n",
       "      <td>-0.003910</td>\n",
       "      <td>-0.514154</td>\n",
       "      <td>-0.548738</td>\n",
       "      <td>-0.215278</td>\n",
       "      <td>0.117940</td>\n",
       "      <td>-0.114845</td>\n",
       "      <td>0.124797</td>\n",
       "      <td>...</td>\n",
       "      <td>0.872245</td>\n",
       "      <td>0.855189</td>\n",
       "      <td>0.871073</td>\n",
       "      <td>0.961304</td>\n",
       "      <td>0.987021</td>\n",
       "      <td>0.864937</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.817184</td>\n",
       "      <td>0.855787</td>\n",
       "      <td>0.847514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seville_temp_min</th>\n",
       "      <td>0.272777</td>\n",
       "      <td>-0.023627</td>\n",
       "      <td>-0.233939</td>\n",
       "      <td>0.030320</td>\n",
       "      <td>-0.743369</td>\n",
       "      <td>-0.740434</td>\n",
       "      <td>-0.132752</td>\n",
       "      <td>0.060728</td>\n",
       "      <td>-0.170409</td>\n",
       "      <td>0.145157</td>\n",
       "      <td>...</td>\n",
       "      <td>0.883042</td>\n",
       "      <td>0.936354</td>\n",
       "      <td>0.884164</td>\n",
       "      <td>0.847719</td>\n",
       "      <td>0.842689</td>\n",
       "      <td>0.880290</td>\n",
       "      <td>0.817184</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.937678</td>\n",
       "      <td>0.926939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_temp</th>\n",
       "      <td>0.241640</td>\n",
       "      <td>-0.023591</td>\n",
       "      <td>-0.218465</td>\n",
       "      <td>-0.006542</td>\n",
       "      <td>-0.658135</td>\n",
       "      <td>-0.777081</td>\n",
       "      <td>-0.104385</td>\n",
       "      <td>-0.004439</td>\n",
       "      <td>-0.206495</td>\n",
       "      <td>0.130515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.930620</td>\n",
       "      <td>0.997189</td>\n",
       "      <td>0.930849</td>\n",
       "      <td>0.894435</td>\n",
       "      <td>0.886292</td>\n",
       "      <td>0.925747</td>\n",
       "      <td>0.855787</td>\n",
       "      <td>0.937678</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madrid_temp_min</th>\n",
       "      <td>0.246385</td>\n",
       "      <td>-0.046734</td>\n",
       "      <td>-0.217309</td>\n",
       "      <td>-0.033857</td>\n",
       "      <td>-0.630085</td>\n",
       "      <td>-0.756214</td>\n",
       "      <td>-0.108140</td>\n",
       "      <td>-0.030676</td>\n",
       "      <td>-0.213871</td>\n",
       "      <td>0.112450</td>\n",
       "      <td>...</td>\n",
       "      <td>0.924518</td>\n",
       "      <td>0.987142</td>\n",
       "      <td>0.924073</td>\n",
       "      <td>0.884779</td>\n",
       "      <td>0.877070</td>\n",
       "      <td>0.918312</td>\n",
       "      <td>0.847514</td>\n",
       "      <td>0.926939</td>\n",
       "      <td>0.995269</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Unnamed: 0  Madrid_wind_speed  Bilbao_rain_1h  \\\n",
       "Unnamed: 0              1.000000          -0.235888       -0.171216   \n",
       "Madrid_wind_speed      -0.235888           1.000000        0.253975   \n",
       "Bilbao_rain_1h         -0.171216           0.253975        1.000000   \n",
       "Valencia_wind_speed    -0.229930           0.577509        0.155717   \n",
       "Seville_humidity       -0.012468          -0.107176        0.103573   \n",
       "Madrid_humidity        -0.032478          -0.055267        0.149643   \n",
       "Bilbao_clouds_all      -0.192422           0.164097        0.414151   \n",
       "Bilbao_wind_speed      -0.136715           0.404369        0.118086   \n",
       "Seville_clouds_all     -0.089696           0.166845        0.080647   \n",
       "Bilbao_wind_deg        -0.135484           0.277087        0.183078   \n",
       "Barcelona_wind_speed    0.095843           0.203092        0.030641   \n",
       "Barcelona_wind_deg      0.166713          -0.044620        0.000205   \n",
       "Madrid_clouds_all      -0.029719           0.290741        0.188485   \n",
       "Seville_wind_speed     -0.145868           0.420553        0.071920   \n",
       "Barcelona_rain_1h      -0.020585           0.118758        0.134627   \n",
       "Seville_rain_1h        -0.046741           0.124355        0.069728   \n",
       "Bilbao_snow_3h               NaN                NaN             NaN   \n",
       "Barcelona_pressure      0.301629          -0.527713       -0.292166   \n",
       "Seville_rain_3h        -0.000187           0.012609       -0.008149   \n",
       "Madrid_rain_1h         -0.090666           0.224845        0.118811   \n",
       "Barcelona_rain_3h       0.000708           0.010039        0.022245   \n",
       "Valencia_snow_3h             NaN                NaN             NaN   \n",
       "Madrid_weather_id       0.041953          -0.205462       -0.120980   \n",
       "Barcelona_weather_id    0.028856          -0.082191       -0.113650   \n",
       "Bilbao_pressure         0.184644          -0.445457       -0.205045   \n",
       "Seville_weather_id      0.082703          -0.116220       -0.089323   \n",
       "Valencia_pressure       0.233345          -0.408045       -0.271538   \n",
       "Seville_temp_max        0.271110          -0.038384       -0.238806   \n",
       "Madrid_pressure         0.196598          -0.335964       -0.229570   \n",
       "Valencia_temp_max       0.222733           0.030763       -0.194648   \n",
       "Valencia_temp           0.207437           0.035666       -0.191332   \n",
       "Bilbao_weather_id       0.186956          -0.197850       -0.762097   \n",
       "Seville_temp            0.273018          -0.029730       -0.236915   \n",
       "Valencia_humidity       0.272480          -0.319646       -0.033854   \n",
       "Valencia_temp_min       0.192403           0.040401       -0.187942   \n",
       "Barcelona_temp_max      0.248009          -0.034217       -0.207383   \n",
       "Madrid_temp_max         0.242473          -0.024364       -0.223619   \n",
       "Barcelona_temp          0.248321          -0.028548       -0.202221   \n",
       "Bilbao_temp_min         0.239508          -0.029330       -0.221014   \n",
       "Bilbao_temp             0.262182          -0.041190       -0.239588   \n",
       "Barcelona_temp_min      0.247846          -0.022688       -0.196089   \n",
       "Bilbao_temp_max         0.289018          -0.056954       -0.258778   \n",
       "Seville_temp_min        0.272777          -0.023627       -0.233939   \n",
       "Madrid_temp             0.241640          -0.023591       -0.218465   \n",
       "Madrid_temp_min         0.246385          -0.046734       -0.217309   \n",
       "\n",
       "                      Valencia_wind_speed  Seville_humidity  Madrid_humidity  \\\n",
       "Unnamed: 0                      -0.229930         -0.012468        -0.032478   \n",
       "Madrid_wind_speed                0.577509         -0.107176        -0.055267   \n",
       "Bilbao_rain_1h                   0.155717          0.103573         0.149643   \n",
       "Valencia_wind_speed              1.000000         -0.214738        -0.100565   \n",
       "Seville_humidity                -0.214738          1.000000         0.771534   \n",
       "Madrid_humidity                 -0.100565          0.771534         1.000000   \n",
       "Bilbao_clouds_all                0.126358          0.033669         0.005968   \n",
       "Bilbao_wind_speed                0.423967         -0.172222        -0.043188   \n",
       "Seville_clouds_all               0.080878          0.388829         0.327895   \n",
       "Bilbao_wind_deg                  0.281658         -0.233416        -0.169288   \n",
       "Barcelona_wind_speed             0.409237         -0.182931        -0.057157   \n",
       "Barcelona_wind_deg              -0.008091          0.212338         0.223869   \n",
       "Madrid_clouds_all                0.240487          0.272806         0.525499   \n",
       "Seville_wind_speed               0.280553         -0.244655        -0.135591   \n",
       "Barcelona_rain_1h                0.070692          0.070347         0.117499   \n",
       "Seville_rain_1h                  0.054037          0.236841         0.199270   \n",
       "Bilbao_snow_3h                        NaN               NaN              NaN   \n",
       "Barcelona_pressure              -0.370779         -0.070909        -0.065996   \n",
       "Seville_rain_3h                 -0.018665          0.008871         0.000784   \n",
       "Madrid_rain_1h                   0.109151          0.098910         0.229901   \n",
       "Barcelona_rain_3h               -0.006045         -0.023903        -0.021271   \n",
       "Valencia_snow_3h                      NaN               NaN              NaN   \n",
       "Madrid_weather_id               -0.099254         -0.169767        -0.349022   \n",
       "Barcelona_weather_id            -0.045271         -0.060415        -0.103705   \n",
       "Bilbao_pressure                 -0.247379         -0.179860        -0.239304   \n",
       "Seville_weather_id              -0.048185         -0.329678        -0.273442   \n",
       "Valencia_pressure               -0.258368         -0.148450        -0.222737   \n",
       "Seville_temp_max                 0.021728         -0.756395        -0.745308   \n",
       "Madrid_pressure                 -0.164802         -0.082909        -0.094984   \n",
       "Valencia_temp_max                0.072974         -0.623489        -0.708881   \n",
       "Valencia_temp                    0.075390         -0.623704        -0.710969   \n",
       "Bilbao_weather_id               -0.103676         -0.137954        -0.164793   \n",
       "Seville_temp                     0.026945         -0.749199        -0.743359   \n",
       "Valencia_humidity               -0.495787          0.459746         0.409074   \n",
       "Valencia_temp_min                0.077663         -0.623296        -0.712309   \n",
       "Barcelona_temp_max              -0.003885         -0.587597        -0.673470   \n",
       "Madrid_temp_max                 -0.004474         -0.668035        -0.789474   \n",
       "Barcelona_temp                  -0.001078         -0.587903        -0.672803   \n",
       "Bilbao_temp_min                  0.028977         -0.562529        -0.607507   \n",
       "Bilbao_temp                      0.015834         -0.547807        -0.588370   \n",
       "Barcelona_temp_min               0.001775         -0.585128        -0.668369   \n",
       "Bilbao_temp_max                 -0.003910         -0.514154        -0.548738   \n",
       "Seville_temp_min                 0.030320         -0.743369        -0.740434   \n",
       "Madrid_temp                     -0.006542         -0.658135        -0.777081   \n",
       "Madrid_temp_min                 -0.033857         -0.630085        -0.756214   \n",
       "\n",
       "                      Bilbao_clouds_all  Bilbao_wind_speed  \\\n",
       "Unnamed: 0                    -0.192422          -0.136715   \n",
       "Madrid_wind_speed              0.164097           0.404369   \n",
       "Bilbao_rain_1h                 0.414151           0.118086   \n",
       "Valencia_wind_speed            0.126358           0.423967   \n",
       "Seville_humidity               0.033669          -0.172222   \n",
       "Madrid_humidity                0.005968          -0.043188   \n",
       "Bilbao_clouds_all              1.000000           0.001085   \n",
       "Bilbao_wind_speed              0.001085           1.000000   \n",
       "Seville_clouds_all             0.010232           0.138443   \n",
       "Bilbao_wind_deg                0.227288           0.519614   \n",
       "Barcelona_wind_speed           0.036946           0.302084   \n",
       "Barcelona_wind_deg            -0.030848           0.004768   \n",
       "Madrid_clouds_all              0.034425           0.221904   \n",
       "Seville_wind_speed             0.026322           0.276280   \n",
       "Barcelona_rain_1h              0.112632          -0.005558   \n",
       "Seville_rain_1h               -0.001283           0.101971   \n",
       "Bilbao_snow_3h                      NaN                NaN   \n",
       "Barcelona_pressure            -0.224310          -0.274803   \n",
       "Seville_rain_3h                0.012131          -0.014359   \n",
       "Madrid_rain_1h                 0.057843           0.158933   \n",
       "Barcelona_rain_3h              0.002251           0.038782   \n",
       "Valencia_snow_3h                    NaN                NaN   \n",
       "Madrid_weather_id             -0.056189          -0.130108   \n",
       "Barcelona_weather_id          -0.112166          -0.014993   \n",
       "Bilbao_pressure                0.011598          -0.278390   \n",
       "Seville_weather_id            -0.016618          -0.063427   \n",
       "Valencia_pressure             -0.171009          -0.184119   \n",
       "Seville_temp_max              -0.134453           0.057369   \n",
       "Madrid_pressure               -0.143983          -0.140326   \n",
       "Valencia_temp_max             -0.060637           0.020066   \n",
       "Valencia_temp                 -0.057513           0.020786   \n",
       "Bilbao_weather_id             -0.495943          -0.046793   \n",
       "Seville_temp                  -0.134444           0.059857   \n",
       "Valencia_humidity             -0.084185          -0.318187   \n",
       "Valencia_temp_min             -0.054462           0.021468   \n",
       "Barcelona_temp_max            -0.075326          -0.025420   \n",
       "Madrid_temp_max               -0.102398          -0.001129   \n",
       "Barcelona_temp                -0.071588          -0.022682   \n",
       "Bilbao_temp_min               -0.128791           0.078474   \n",
       "Bilbao_temp                   -0.167230           0.097111   \n",
       "Barcelona_temp_min            -0.067711          -0.019624   \n",
       "Bilbao_temp_max               -0.215278           0.117940   \n",
       "Seville_temp_min              -0.132752           0.060728   \n",
       "Madrid_temp                   -0.104385          -0.004439   \n",
       "Madrid_temp_min               -0.108140          -0.030676   \n",
       "\n",
       "                      Seville_clouds_all  Bilbao_wind_deg  ...  \\\n",
       "Unnamed: 0                     -0.089696        -0.135484  ...   \n",
       "Madrid_wind_speed               0.166845         0.277087  ...   \n",
       "Bilbao_rain_1h                  0.080647         0.183078  ...   \n",
       "Valencia_wind_speed             0.080878         0.281658  ...   \n",
       "Seville_humidity                0.388829        -0.233416  ...   \n",
       "Madrid_humidity                 0.327895        -0.169288  ...   \n",
       "Bilbao_clouds_all               0.010232         0.227288  ...   \n",
       "Bilbao_wind_speed               0.138443         0.519614  ...   \n",
       "Seville_clouds_all              1.000000         0.007418  ...   \n",
       "Bilbao_wind_deg                 0.007418         1.000000  ...   \n",
       "Barcelona_wind_speed            0.045497         0.182693  ...   \n",
       "Barcelona_wind_deg              0.002125        -0.061825  ...   \n",
       "Madrid_clouds_all               0.427129         0.041360  ...   \n",
       "Seville_wind_speed              0.153677         0.157465  ...   \n",
       "Barcelona_rain_1h               0.030973        -0.018825  ...   \n",
       "Seville_rain_1h                 0.440222         0.023735  ...   \n",
       "Bilbao_snow_3h                       NaN              NaN  ...   \n",
       "Barcelona_pressure             -0.303777        -0.209087  ...   \n",
       "Seville_rain_3h                 0.013071        -0.030802  ...   \n",
       "Madrid_rain_1h                  0.298645         0.050711  ...   \n",
       "Barcelona_rain_3h              -0.015761         0.038895  ...   \n",
       "Valencia_snow_3h                     NaN              NaN  ...   \n",
       "Madrid_weather_id              -0.343825        -0.045245  ...   \n",
       "Barcelona_weather_id           -0.029149        -0.017847  ...   \n",
       "Bilbao_pressure                -0.410339        -0.107519  ...   \n",
       "Seville_weather_id             -0.540006        -0.000196  ...   \n",
       "Valencia_pressure              -0.265257        -0.132985  ...   \n",
       "Seville_temp_max               -0.189792         0.141944  ...   \n",
       "Madrid_pressure                -0.304788        -0.127463  ...   \n",
       "Valencia_temp_max              -0.155728         0.144888  ...   \n",
       "Valencia_temp                  -0.153033         0.146447  ...   \n",
       "Bilbao_weather_id              -0.066192        -0.167435  ...   \n",
       "Seville_temp                   -0.177401         0.144179  ...   \n",
       "Valencia_humidity               0.050984        -0.282833  ...   \n",
       "Valencia_temp_min              -0.150256         0.147814  ...   \n",
       "Barcelona_temp_max             -0.166132         0.112969  ...   \n",
       "Madrid_temp_max                -0.208761         0.132334  ...   \n",
       "Barcelona_temp                 -0.158508         0.114064  ...   \n",
       "Bilbao_temp_min                -0.126255         0.157191  ...   \n",
       "Bilbao_temp                    -0.123033         0.146133  ...   \n",
       "Barcelona_temp_min             -0.150160         0.114486  ...   \n",
       "Bilbao_temp_max                -0.114845         0.124797  ...   \n",
       "Seville_temp_min               -0.170409         0.145157  ...   \n",
       "Madrid_temp                    -0.206495         0.130515  ...   \n",
       "Madrid_temp_min                -0.213871         0.112450  ...   \n",
       "\n",
       "                      Barcelona_temp_max  Madrid_temp_max  Barcelona_temp  \\\n",
       "Unnamed: 0                      0.248009         0.242473        0.248321   \n",
       "Madrid_wind_speed              -0.034217        -0.024364       -0.028548   \n",
       "Bilbao_rain_1h                 -0.207383        -0.223619       -0.202221   \n",
       "Valencia_wind_speed            -0.003885        -0.004474       -0.001078   \n",
       "Seville_humidity               -0.587597        -0.668035       -0.587903   \n",
       "Madrid_humidity                -0.673470        -0.789474       -0.672803   \n",
       "Bilbao_clouds_all              -0.075326        -0.102398       -0.071588   \n",
       "Bilbao_wind_speed              -0.025420        -0.001129       -0.022682   \n",
       "Seville_clouds_all             -0.166132        -0.208761       -0.158508   \n",
       "Bilbao_wind_deg                 0.112969         0.132334        0.114064   \n",
       "Barcelona_wind_speed            0.038945         0.015559        0.034937   \n",
       "Barcelona_wind_deg             -0.251263        -0.261649       -0.263724   \n",
       "Madrid_clouds_all              -0.279512        -0.335006       -0.268556   \n",
       "Seville_wind_speed              0.082035         0.129740        0.090166   \n",
       "Barcelona_rain_1h              -0.135158        -0.127575       -0.126440   \n",
       "Seville_rain_1h                -0.144542        -0.150750       -0.140226   \n",
       "Bilbao_snow_3h                       NaN              NaN             NaN   \n",
       "Barcelona_pressure              0.113815         0.140078        0.102866   \n",
       "Seville_rain_3h                 0.021016         0.009581        0.022171   \n",
       "Madrid_rain_1h                 -0.070354        -0.102865       -0.061732   \n",
       "Barcelona_rain_3h               0.022952         0.027214        0.025413   \n",
       "Valencia_snow_3h                     NaN              NaN             NaN   \n",
       "Madrid_weather_id               0.115963         0.160757        0.107372   \n",
       "Barcelona_weather_id            0.110018         0.094544        0.100468   \n",
       "Bilbao_pressure                 0.114508         0.145411        0.101524   \n",
       "Seville_weather_id              0.203044         0.202941        0.198250   \n",
       "Valencia_pressure               0.249211         0.274004        0.238388   \n",
       "Seville_temp_max                0.890446         0.941157        0.891111   \n",
       "Madrid_pressure                 0.074543         0.071967        0.058886   \n",
       "Valencia_temp_max               0.950976         0.933500        0.952967   \n",
       "Valencia_temp                   0.951416         0.933877        0.953628   \n",
       "Bilbao_weather_id               0.220114         0.234793        0.215948   \n",
       "Seville_temp                    0.886854         0.939514        0.887801   \n",
       "Valencia_humidity              -0.196006        -0.191074       -0.194542   \n",
       "Valencia_temp_min               0.950935         0.933358        0.953358   \n",
       "Barcelona_temp_max              1.000000         0.929809        0.997079   \n",
       "Madrid_temp_max                 0.929809         1.000000        0.930543   \n",
       "Barcelona_temp                  0.997079         0.930543        1.000000   \n",
       "Bilbao_temp_min                 0.900923         0.893980        0.900691   \n",
       "Bilbao_temp                     0.896933         0.885806        0.896205   \n",
       "Barcelona_temp_min              0.988413         0.925964        0.997110   \n",
       "Bilbao_temp_max                 0.872245         0.855189        0.871073   \n",
       "Seville_temp_min                0.883042         0.936354        0.884164   \n",
       "Madrid_temp                     0.930620         0.997189        0.930849   \n",
       "Madrid_temp_min                 0.924518         0.987142        0.924073   \n",
       "\n",
       "                      Bilbao_temp_min  Bilbao_temp  Barcelona_temp_min  \\\n",
       "Unnamed: 0                   0.239508     0.262182            0.247846   \n",
       "Madrid_wind_speed           -0.029330    -0.041190           -0.022688   \n",
       "Bilbao_rain_1h              -0.221014    -0.239588           -0.196089   \n",
       "Valencia_wind_speed          0.028977     0.015834            0.001775   \n",
       "Seville_humidity            -0.562529    -0.547807           -0.585128   \n",
       "Madrid_humidity             -0.607507    -0.588370           -0.668369   \n",
       "Bilbao_clouds_all           -0.128791    -0.167230           -0.067711   \n",
       "Bilbao_wind_speed            0.078474     0.097111           -0.019624   \n",
       "Seville_clouds_all          -0.126255    -0.123033           -0.150160   \n",
       "Bilbao_wind_deg              0.157191     0.146133            0.114486   \n",
       "Barcelona_wind_speed         0.069284     0.063306            0.031155   \n",
       "Barcelona_wind_deg          -0.219383    -0.208246           -0.274537   \n",
       "Madrid_clouds_all           -0.202018    -0.192392           -0.256132   \n",
       "Seville_wind_speed           0.093591     0.091015            0.097994   \n",
       "Barcelona_rain_1h           -0.137987    -0.145530           -0.116929   \n",
       "Seville_rain_1h             -0.111571    -0.112303           -0.135314   \n",
       "Bilbao_snow_3h                    NaN          NaN                 NaN   \n",
       "Barcelona_pressure           0.136672     0.154764            0.091536   \n",
       "Seville_rain_3h              0.019671     0.020314            0.023191   \n",
       "Madrid_rain_1h              -0.045784    -0.047521           -0.052838   \n",
       "Barcelona_rain_3h            0.022363     0.020618            0.027711   \n",
       "Valencia_snow_3h                  NaN          NaN                 NaN   \n",
       "Madrid_weather_id            0.074148     0.076326            0.098331   \n",
       "Barcelona_weather_id         0.124639     0.131755            0.090382   \n",
       "Bilbao_pressure              0.043387     0.044299            0.088089   \n",
       "Seville_weather_id           0.157375     0.156216            0.192444   \n",
       "Valencia_pressure            0.258130     0.277780            0.226030   \n",
       "Seville_temp_max             0.852849     0.848445            0.886765   \n",
       "Madrid_pressure              0.070196     0.096062            0.043027   \n",
       "Valencia_temp_max            0.886459     0.880043            0.949459   \n",
       "Valencia_temp                0.885423     0.878546            0.950316   \n",
       "Bilbao_weather_id            0.234286     0.257355            0.210777   \n",
       "Seville_temp                 0.850862     0.846113            0.883743   \n",
       "Valencia_humidity           -0.194563    -0.178466           -0.191965   \n",
       "Valencia_temp_min            0.883575     0.876261            0.950241   \n",
       "Barcelona_temp_max           0.900923     0.896933            0.988413   \n",
       "Madrid_temp_max              0.893980     0.885806            0.925964   \n",
       "Barcelona_temp               0.900691     0.896205            0.997110   \n",
       "Bilbao_temp_min              1.000000     0.992971            0.895276   \n",
       "Bilbao_temp                  0.992971     1.000000            0.890338   \n",
       "Barcelona_temp_min           0.895276     0.890338            1.000000   \n",
       "Bilbao_temp_max              0.961304     0.987021            0.864937   \n",
       "Seville_temp_min             0.847719     0.842689            0.880290   \n",
       "Madrid_temp                  0.894435     0.886292            0.925747   \n",
       "Madrid_temp_min              0.884779     0.877070            0.918312   \n",
       "\n",
       "                      Bilbao_temp_max  Seville_temp_min  Madrid_temp  \\\n",
       "Unnamed: 0                   0.289018          0.272777     0.241640   \n",
       "Madrid_wind_speed           -0.056954         -0.023627    -0.023591   \n",
       "Bilbao_rain_1h              -0.258778         -0.233939    -0.218465   \n",
       "Valencia_wind_speed         -0.003910          0.030320    -0.006542   \n",
       "Seville_humidity            -0.514154         -0.743369    -0.658135   \n",
       "Madrid_humidity             -0.548738         -0.740434    -0.777081   \n",
       "Bilbao_clouds_all           -0.215278         -0.132752    -0.104385   \n",
       "Bilbao_wind_speed            0.117940          0.060728    -0.004439   \n",
       "Seville_clouds_all          -0.114845         -0.170409    -0.206495   \n",
       "Bilbao_wind_deg              0.124797          0.145157     0.130515   \n",
       "Barcelona_wind_speed         0.051541          0.068690     0.006909   \n",
       "Barcelona_wind_deg          -0.189198         -0.247358    -0.263493   \n",
       "Madrid_clouds_all           -0.174887         -0.265770    -0.325586   \n",
       "Seville_wind_speed           0.085206          0.185432     0.124168   \n",
       "Barcelona_rain_1h           -0.151702         -0.119155    -0.121625   \n",
       "Seville_rain_1h             -0.110392         -0.112015    -0.146426   \n",
       "Bilbao_snow_3h                    NaN               NaN          NaN   \n",
       "Barcelona_pressure           0.173764          0.118214     0.133247   \n",
       "Seville_rain_3h              0.020828          0.002538     0.010262   \n",
       "Madrid_rain_1h              -0.048350         -0.053510    -0.103824   \n",
       "Barcelona_rain_3h            0.017873          0.029488     0.028173   \n",
       "Valencia_snow_3h                  NaN               NaN          NaN   \n",
       "Madrid_weather_id            0.077904          0.105730     0.158728   \n",
       "Barcelona_weather_id         0.137586          0.075990     0.085885   \n",
       "Bilbao_pressure              0.042852          0.107113     0.133651   \n",
       "Seville_weather_id           0.150801          0.171767     0.198197   \n",
       "Valencia_pressure            0.298448          0.240775     0.269128   \n",
       "Seville_temp_max             0.823560          0.993506     0.941410   \n",
       "Madrid_pressure              0.126843          0.039849     0.058194   \n",
       "Valencia_temp_max            0.852706          0.891294     0.931938   \n",
       "Valencia_temp                0.850638          0.891049     0.932545   \n",
       "Bilbao_weather_id            0.282505          0.259304     0.231817   \n",
       "Seville_temp                 0.820877          0.998947     0.940348   \n",
       "Valencia_humidity           -0.151424         -0.216399    -0.176728   \n",
       "Valencia_temp_min            0.847828          0.889971     0.932260   \n",
       "Barcelona_temp_max           0.872245          0.883042     0.930620   \n",
       "Madrid_temp_max              0.855189          0.936354     0.997189   \n",
       "Barcelona_temp               0.871073          0.884164     0.930849   \n",
       "Bilbao_temp_min              0.961304          0.847719     0.894435   \n",
       "Bilbao_temp                  0.987021          0.842689     0.886292   \n",
       "Barcelona_temp_min           0.864937          0.880290     0.925747   \n",
       "Bilbao_temp_max              1.000000          0.817184     0.855787   \n",
       "Seville_temp_min             0.817184          1.000000     0.937678   \n",
       "Madrid_temp                  0.855787          0.937678     1.000000   \n",
       "Madrid_temp_min              0.847514          0.926939     0.995269   \n",
       "\n",
       "                      Madrid_temp_min  \n",
       "Unnamed: 0                   0.246385  \n",
       "Madrid_wind_speed           -0.046734  \n",
       "Bilbao_rain_1h              -0.217309  \n",
       "Valencia_wind_speed         -0.033857  \n",
       "Seville_humidity            -0.630085  \n",
       "Madrid_humidity             -0.756214  \n",
       "Bilbao_clouds_all           -0.108140  \n",
       "Bilbao_wind_speed           -0.030676  \n",
       "Seville_clouds_all          -0.213871  \n",
       "Bilbao_wind_deg              0.112450  \n",
       "Barcelona_wind_speed        -0.018323  \n",
       "Barcelona_wind_deg          -0.256650  \n",
       "Madrid_clouds_all           -0.334828  \n",
       "Seville_wind_speed           0.098924  \n",
       "Barcelona_rain_1h           -0.117493  \n",
       "Seville_rain_1h             -0.149502  \n",
       "Bilbao_snow_3h                    NaN  \n",
       "Barcelona_pressure           0.141323  \n",
       "Seville_rain_3h              0.011252  \n",
       "Madrid_rain_1h              -0.117917  \n",
       "Barcelona_rain_3h            0.030366  \n",
       "Valencia_snow_3h                  NaN  \n",
       "Madrid_weather_id            0.169791  \n",
       "Barcelona_weather_id         0.079353  \n",
       "Bilbao_pressure              0.135395  \n",
       "Seville_weather_id           0.199823  \n",
       "Valencia_pressure            0.272155  \n",
       "Seville_temp_max             0.930016  \n",
       "Madrid_pressure              0.054514  \n",
       "Valencia_temp_max            0.922374  \n",
       "Valencia_temp                0.923087  \n",
       "Bilbao_weather_id            0.230605  \n",
       "Seville_temp                 0.929243  \n",
       "Valencia_humidity           -0.145049  \n",
       "Valencia_temp_min            0.922917  \n",
       "Barcelona_temp_max           0.924518  \n",
       "Madrid_temp_max              0.987142  \n",
       "Barcelona_temp               0.924073  \n",
       "Bilbao_temp_min              0.884779  \n",
       "Bilbao_temp                  0.877070  \n",
       "Barcelona_temp_min           0.918312  \n",
       "Bilbao_temp_max              0.847514  \n",
       "Seville_temp_min             0.926939  \n",
       "Madrid_temp                  0.995269  \n",
       "Madrid_temp_min              1.000000  \n",
       "\n",
       "[45 rows x 45 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate correlation for df_test\n",
    "df_test.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "de51df85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# have a look at feature distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6cc06c04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.correlation import plot_corr\n",
    "\n",
    "fig = plt.figure(figsize=(15,15));\n",
    "ax = fig.add_subplot(111);\n",
    "plot_corr(df_test.corr(), xnames = df_test.corr().columns, ax = ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fa93ec6",
   "metadata": {},
   "source": [
    "<a id=\"four\"></a>\n",
    "## 4. Data Engineering\n",
    "<a class=\"anchor\" id=\"1.1\"></a>\n",
    "<a href=#cont>Back to Table of Contents</a>\n",
    "\n",
    "---\n",
    "    \n",
    "| ⚡ Description: Data engineering ⚡ |\n",
    "| :--------------------------- |\n",
    "| In this section you are required to: clean the dataset, and possibly create new features - as identified in the EDA phase. |\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c91674",
   "metadata": {},
   "source": [
    "### FIXING THE NULL VALUES\n",
    "Looking at the two dataset, everything seems to be in order except the \"Valencia_pressure\" data set which records 2,522 null entries.\n",
    "Note that the load_shortfall_3h has null entries because it is the value that is to be predicted by the model. After merging the train and  the test data-sets to simplify the work of pre-processing and data cleaning, any action carried out on the merged data set affects bothe the df_train and the df_test data sets as they are both one single dataframe now.So, you can carry out the actions neccesary for data cleaning only once on the merged data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "34db04da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ubasinachi\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Valencia_pressure'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df_train['Valencia_pressure'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "772b8def",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ubasinachi\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Valencia_pressure'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df_test['Valencia_pressure'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ee7257ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean\n",
      "1012.0514065222828\n",
      "Mode\n",
      "0    1018.0\n",
      "dtype: float64\n",
      "Median\n",
      "1015.0\n"
     ]
    }
   ],
   "source": [
    "print('Mean')\n",
    "print(df_train['Valencia_pressure'].mean())\n",
    "print('Mode')\n",
    "print(df_train['Valencia_pressure'].mode())\n",
    "print('Median')\n",
    "print(df_train['Valencia_pressure'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ff312571",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean\n",
      "1013.1483509056495\n",
      "Mode\n",
      "0    1016.0\n",
      "dtype: float64\n",
      "Median\n",
      "1014.6666666667\n"
     ]
    }
   ],
   "source": [
    "print('Mean')\n",
    "print(df_test['Valencia_pressure'].mean())\n",
    "print('Mode')\n",
    "print(df_test['Valencia_pressure'].mode())\n",
    "print('Median')\n",
    "print(df_test['Valencia_pressure'].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff445031",
   "metadata": {},
   "source": [
    "### CHOOSING THE VALUE TO REPLACE THE NULL VALUES\n",
    "To fix the null value problem, you can choose to either remove the entries with these nulls from your data set or fill in the  the null values by replacing the nulls with the mean, median or mode. It wouldn't really make too much of a difference which entry you choose to go with as these features are actually quite simmilar looking at the measures of centralization of the \"Valencia_pressure\" from the data set and the box-plot shown above. For the purpose of this model, we shalll fill out the Null values in \"Valencia pressure\" with the mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4b15447e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing the null values in valencia_pressure of the train dataset with the mode\n",
    "df_train_clean = df_train\n",
    "df_train_clean['Valencia_pressure'] = df_train_clean['Valencia_pressure'].fillna(df_train_clean['Valencia_pressure'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "21c2aab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing the null values in valencia_pressure of the test dataset with the mode\n",
    "df_test_clean = df_test\n",
    "df_test_clean['Valencia_pressure'] = df_test_clean['Valencia_pressure'].fillna(df_test_clean['Valencia_pressure'].mode()[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ec8118",
   "metadata": {},
   "source": [
    "### MERGING THE DATA SET\n",
    "Merging the dataset, the train and test data set helps to simplify your work, any action done affects both the the train and the test datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d38106da",
   "metadata": {
    "scrolled": true
   },
   "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>Unnamed: 0</th>\n",
       "      <th>Madrid_wind_speed</th>\n",
       "      <th>Bilbao_rain_1h</th>\n",
       "      <th>Valencia_wind_speed</th>\n",
       "      <th>Seville_humidity</th>\n",
       "      <th>Madrid_humidity</th>\n",
       "      <th>Bilbao_clouds_all</th>\n",
       "      <th>Bilbao_wind_speed</th>\n",
       "      <th>Seville_clouds_all</th>\n",
       "      <th>Bilbao_wind_deg</th>\n",
       "      <th>...</th>\n",
       "      <th>Madrid_temp_max</th>\n",
       "      <th>Barcelona_temp</th>\n",
       "      <th>Bilbao_temp_min</th>\n",
       "      <th>Bilbao_temp</th>\n",
       "      <th>Barcelona_temp_min</th>\n",
       "      <th>Bilbao_temp_max</th>\n",
       "      <th>Seville_temp_min</th>\n",
       "      <th>Madrid_temp</th>\n",
       "      <th>Madrid_temp_min</th>\n",
       "      <th>load_shortfall_3h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>11683.000000</td>\n",
       "      <td>8763.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5841.000000</td>\n",
       "      <td>2.433750</td>\n",
       "      <td>0.118698</td>\n",
       "      <td>2.692873</td>\n",
       "      <td>63.774687</td>\n",
       "      <td>58.721818</td>\n",
       "      <td>43.440712</td>\n",
       "      <td>1.958629</td>\n",
       "      <td>14.155268</td>\n",
       "      <td>159.878855</td>\n",
       "      <td>...</td>\n",
       "      <td>289.377372</td>\n",
       "      <td>289.869413</td>\n",
       "      <td>284.993657</td>\n",
       "      <td>286.447784</td>\n",
       "      <td>288.616766</td>\n",
       "      <td>288.095397</td>\n",
       "      <td>291.263219</td>\n",
       "      <td>288.282055</td>\n",
       "      <td>287.056222</td>\n",
       "      <td>10673.857612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3372.735932</td>\n",
       "      <td>1.831762</td>\n",
       "      <td>0.334918</td>\n",
       "      <td>2.321759</td>\n",
       "      <td>22.219380</td>\n",
       "      <td>24.390621</td>\n",
       "      <td>32.046215</td>\n",
       "      <td>1.696043</td>\n",
       "      <td>24.541321</td>\n",
       "      <td>101.005654</td>\n",
       "      <td>...</td>\n",
       "      <td>9.594562</td>\n",
       "      <td>6.680538</td>\n",
       "      <td>6.730076</td>\n",
       "      <td>6.738466</td>\n",
       "      <td>6.391989</td>\n",
       "      <td>6.898601</td>\n",
       "      <td>8.136234</td>\n",
       "      <td>9.258559</td>\n",
       "      <td>9.093455</td>\n",
       "      <td>5218.046404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>264.983333</td>\n",
       "      <td>270.816667</td>\n",
       "      <td>264.483333</td>\n",
       "      <td>267.483333</td>\n",
       "      <td>269.483333</td>\n",
       "      <td>269.063000</td>\n",
       "      <td>270.150000</td>\n",
       "      <td>264.983333</td>\n",
       "      <td>264.983333</td>\n",
       "      <td>-6618.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2920.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>46.333333</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>282.150000</td>\n",
       "      <td>284.816667</td>\n",
       "      <td>280.122526</td>\n",
       "      <td>281.483448</td>\n",
       "      <td>284.150000</td>\n",
       "      <td>283.150000</td>\n",
       "      <td>285.483333</td>\n",
       "      <td>281.250000</td>\n",
       "      <td>280.150000</td>\n",
       "      <td>7390.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5841.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>59.333333</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>145.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>287.843333</td>\n",
       "      <td>289.426667</td>\n",
       "      <td>284.816667</td>\n",
       "      <td>286.193333</td>\n",
       "      <td>288.276333</td>\n",
       "      <td>287.816667</td>\n",
       "      <td>290.382000</td>\n",
       "      <td>286.923333</td>\n",
       "      <td>285.816667</td>\n",
       "      <td>11114.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8761.500000</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>233.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>296.483333</td>\n",
       "      <td>295.150000</td>\n",
       "      <td>289.816667</td>\n",
       "      <td>291.040000</td>\n",
       "      <td>293.483333</td>\n",
       "      <td>292.483333</td>\n",
       "      <td>296.641333</td>\n",
       "      <td>294.993167</td>\n",
       "      <td>293.708917</td>\n",
       "      <td>14498.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11682.000000</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.666667</td>\n",
       "      <td>97.333333</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>314.483333</td>\n",
       "      <td>308.150000</td>\n",
       "      <td>309.816667</td>\n",
       "      <td>310.710000</td>\n",
       "      <td>306.816667</td>\n",
       "      <td>317.966667</td>\n",
       "      <td>314.816667</td>\n",
       "      <td>313.133333</td>\n",
       "      <td>310.383333</td>\n",
       "      <td>31904.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0  Madrid_wind_speed  Bilbao_rain_1h  Valencia_wind_speed  \\\n",
       "count  11683.000000       11683.000000    11683.000000         11683.000000   \n",
       "mean    5841.000000           2.433750        0.118698             2.692873   \n",
       "std     3372.735932           1.831762        0.334918             2.321759   \n",
       "min        0.000000           0.000000        0.000000             0.000000   \n",
       "25%     2920.500000           1.000000        0.000000             1.000000   \n",
       "50%     5841.000000           2.000000        0.000000             2.000000   \n",
       "75%     8761.500000           3.333333        0.100000             3.666667   \n",
       "max    11682.000000          13.333333        3.000000            52.000000   \n",
       "\n",
       "       Seville_humidity  Madrid_humidity  Bilbao_clouds_all  \\\n",
       "count      11683.000000     11683.000000       11683.000000   \n",
       "mean          63.774687        58.721818          43.440712   \n",
       "std           22.219380        24.390621          32.046215   \n",
       "min            8.333333         6.333333           0.000000   \n",
       "25%           46.333333        38.000000          12.000000   \n",
       "50%           67.000000        59.333333          45.000000   \n",
       "75%           83.000000        80.000000          75.000000   \n",
       "max          100.000000       100.000000         100.000000   \n",
       "\n",
       "       Bilbao_wind_speed  Seville_clouds_all  Bilbao_wind_deg  ...  \\\n",
       "count       11683.000000        11683.000000     11683.000000  ...   \n",
       "mean            1.958629           14.155268       159.878855  ...   \n",
       "std             1.696043           24.541321       101.005654  ...   \n",
       "min             0.000000            0.000000         0.000000  ...   \n",
       "25%             1.000000            0.000000        76.666667  ...   \n",
       "50%             1.333333            0.000000       145.666667  ...   \n",
       "75%             2.666667           20.000000       233.333333  ...   \n",
       "max            12.666667           97.333333       360.000000  ...   \n",
       "\n",
       "       Madrid_temp_max  Barcelona_temp  Bilbao_temp_min   Bilbao_temp  \\\n",
       "count     11683.000000    11683.000000     11683.000000  11683.000000   \n",
       "mean        289.377372      289.869413       284.993657    286.447784   \n",
       "std           9.594562        6.680538         6.730076      6.738466   \n",
       "min         264.983333      270.816667       264.483333    267.483333   \n",
       "25%         282.150000      284.816667       280.122526    281.483448   \n",
       "50%         287.843333      289.426667       284.816667    286.193333   \n",
       "75%         296.483333      295.150000       289.816667    291.040000   \n",
       "max         314.483333      308.150000       309.816667    310.710000   \n",
       "\n",
       "       Barcelona_temp_min  Bilbao_temp_max  Seville_temp_min   Madrid_temp  \\\n",
       "count        11683.000000     11683.000000      11683.000000  11683.000000   \n",
       "mean           288.616766       288.095397        291.263219    288.282055   \n",
       "std              6.391989         6.898601          8.136234      9.258559   \n",
       "min            269.483333       269.063000        270.150000    264.983333   \n",
       "25%            284.150000       283.150000        285.483333    281.250000   \n",
       "50%            288.276333       287.816667        290.382000    286.923333   \n",
       "75%            293.483333       292.483333        296.641333    294.993167   \n",
       "max            306.816667       317.966667        314.816667    313.133333   \n",
       "\n",
       "       Madrid_temp_min  load_shortfall_3h  \n",
       "count     11683.000000        8763.000000  \n",
       "mean        287.056222       10673.857612  \n",
       "std           9.093455        5218.046404  \n",
       "min         264.983333       -6618.000000  \n",
       "25%         280.150000        7390.333333  \n",
       "50%         285.816667       11114.666667  \n",
       "75%         293.708917       14498.166667  \n",
       "max         310.383333       31904.000000  \n",
       "\n",
       "[8 rows x 46 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the test and train data set to simplify your work \n",
    "merged_df = pd.concat([df_train_clean, df_test_clean])\n",
    "merged_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cab488f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11683, 49)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11aac09c",
   "metadata": {},
   "source": [
    "### CONFIRMING NO NULL VALUES IN THE MERGED DATASETS\n",
    "We do this to confirm there are no null values contained in the merged dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1e2a0c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                 0\n",
       "time                       0\n",
       "Madrid_wind_speed          0\n",
       "Valencia_wind_deg          0\n",
       "Bilbao_rain_1h             0\n",
       "Valencia_wind_speed        0\n",
       "Seville_humidity           0\n",
       "Madrid_humidity            0\n",
       "Bilbao_clouds_all          0\n",
       "Bilbao_wind_speed          0\n",
       "Seville_clouds_all         0\n",
       "Bilbao_wind_deg            0\n",
       "Barcelona_wind_speed       0\n",
       "Barcelona_wind_deg         0\n",
       "Madrid_clouds_all          0\n",
       "Seville_wind_speed         0\n",
       "Barcelona_rain_1h          0\n",
       "Seville_pressure           0\n",
       "Seville_rain_1h            0\n",
       "Bilbao_snow_3h             0\n",
       "Barcelona_pressure         0\n",
       "Seville_rain_3h            0\n",
       "Madrid_rain_1h             0\n",
       "Barcelona_rain_3h          0\n",
       "Valencia_snow_3h           0\n",
       "Madrid_weather_id          0\n",
       "Barcelona_weather_id       0\n",
       "Bilbao_pressure            0\n",
       "Seville_weather_id         0\n",
       "Valencia_pressure          0\n",
       "Seville_temp_max           0\n",
       "Madrid_pressure            0\n",
       "Valencia_temp_max          0\n",
       "Valencia_temp              0\n",
       "Bilbao_weather_id          0\n",
       "Seville_temp               0\n",
       "Valencia_humidity          0\n",
       "Valencia_temp_min          0\n",
       "Barcelona_temp_max         0\n",
       "Madrid_temp_max            0\n",
       "Barcelona_temp             0\n",
       "Bilbao_temp_min            0\n",
       "Bilbao_temp                0\n",
       "Barcelona_temp_min         0\n",
       "Bilbao_temp_max            0\n",
       "Seville_temp_min           0\n",
       "Madrid_temp                0\n",
       "Madrid_temp_min            0\n",
       "load_shortfall_3h       2920\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77df0aad",
   "metadata": {},
   "source": [
    "### CHECKING THE TAIL OF THE MERGED DATASET\n",
    "Checking the tail of the merged dataset helps us to confirm that the data represented at the tail is consistent with the data we get from the df_test_clean dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e788a41e",
   "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>Unnamed: 0</th>\n",
       "      <th>time</th>\n",
       "      <th>Madrid_wind_speed</th>\n",
       "      <th>Valencia_wind_deg</th>\n",
       "      <th>Bilbao_rain_1h</th>\n",
       "      <th>Valencia_wind_speed</th>\n",
       "      <th>Seville_humidity</th>\n",
       "      <th>Madrid_humidity</th>\n",
       "      <th>Bilbao_clouds_all</th>\n",
       "      <th>Bilbao_wind_speed</th>\n",
       "      <th>...</th>\n",
       "      <th>Madrid_temp_max</th>\n",
       "      <th>Barcelona_temp</th>\n",
       "      <th>Bilbao_temp_min</th>\n",
       "      <th>Bilbao_temp</th>\n",
       "      <th>Barcelona_temp_min</th>\n",
       "      <th>Bilbao_temp_max</th>\n",
       "      <th>Seville_temp_min</th>\n",
       "      <th>Madrid_temp</th>\n",
       "      <th>Madrid_temp_min</th>\n",
       "      <th>load_shortfall_3h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>11678</td>\n",
       "      <td>2018-12-31 09:00:00</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>level_9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>81.666667</td>\n",
       "      <td>49.666667</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>281.483333</td>\n",
       "      <td>278.140000</td>\n",
       "      <td>270.816667</td>\n",
       "      <td>273.210000</td>\n",
       "      <td>276.483333</td>\n",
       "      <td>276.150000</td>\n",
       "      <td>279.816667</td>\n",
       "      <td>274.910000</td>\n",
       "      <td>271.150000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>11679</td>\n",
       "      <td>2018-12-31 12:00:00</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>level_8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>28.333333</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>287.816667</td>\n",
       "      <td>286.150000</td>\n",
       "      <td>278.150000</td>\n",
       "      <td>278.443333</td>\n",
       "      <td>285.816667</td>\n",
       "      <td>278.816667</td>\n",
       "      <td>287.150000</td>\n",
       "      <td>283.156667</td>\n",
       "      <td>280.483333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>11680</td>\n",
       "      <td>2018-12-31 15:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>level_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>26.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>288.816667</td>\n",
       "      <td>288.820000</td>\n",
       "      <td>284.150000</td>\n",
       "      <td>285.073333</td>\n",
       "      <td>288.150000</td>\n",
       "      <td>285.816667</td>\n",
       "      <td>290.816667</td>\n",
       "      <td>287.733333</td>\n",
       "      <td>286.483333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>11681</td>\n",
       "      <td>2018-12-31 18:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>level_6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>52.666667</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>285.150000</td>\n",
       "      <td>284.473333</td>\n",
       "      <td>280.150000</td>\n",
       "      <td>281.626667</td>\n",
       "      <td>283.150000</td>\n",
       "      <td>282.816667</td>\n",
       "      <td>287.483333</td>\n",
       "      <td>283.813333</td>\n",
       "      <td>282.150000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>11682</td>\n",
       "      <td>2018-12-31 21:00:00</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>level_10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>61.666667</td>\n",
       "      <td>69.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>276.816667</td>\n",
       "      <td>281.133333</td>\n",
       "      <td>276.150000</td>\n",
       "      <td>276.450000</td>\n",
       "      <td>278.483333</td>\n",
       "      <td>276.816667</td>\n",
       "      <td>283.816667</td>\n",
       "      <td>276.623333</td>\n",
       "      <td>276.483333</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                 time  Madrid_wind_speed Valencia_wind_deg  \\\n",
       "2915       11678  2018-12-31 09:00:00           0.333333           level_9   \n",
       "2916       11679  2018-12-31 12:00:00           0.333333           level_8   \n",
       "2917       11680  2018-12-31 15:00:00           1.000000           level_6   \n",
       "2918       11681  2018-12-31 18:00:00           1.000000           level_6   \n",
       "2919       11682  2018-12-31 21:00:00           1.333333          level_10   \n",
       "\n",
       "      Bilbao_rain_1h  Valencia_wind_speed  Seville_humidity  Madrid_humidity  \\\n",
       "2915             0.0             2.000000         81.666667        49.666667   \n",
       "2916             0.0             1.333333         61.000000        28.333333   \n",
       "2917             0.0             3.000000         47.000000        26.333333   \n",
       "2918             0.0             2.000000         52.666667        56.666667   \n",
       "2919             0.0             2.333333         61.666667        69.333333   \n",
       "\n",
       "      Bilbao_clouds_all  Bilbao_wind_speed  ...  Madrid_temp_max  \\\n",
       "2915          80.000000           1.333333  ...       281.483333   \n",
       "2916          56.666667           1.000000  ...       287.816667   \n",
       "2917           0.000000           0.666667  ...       288.816667   \n",
       "2918           0.000000           0.666667  ...       285.150000   \n",
       "2919           0.000000           1.333333  ...       276.816667   \n",
       "\n",
       "      Barcelona_temp  Bilbao_temp_min  Bilbao_temp  Barcelona_temp_min  \\\n",
       "2915      278.140000       270.816667   273.210000          276.483333   \n",
       "2916      286.150000       278.150000   278.443333          285.816667   \n",
       "2917      288.820000       284.150000   285.073333          288.150000   \n",
       "2918      284.473333       280.150000   281.626667          283.150000   \n",
       "2919      281.133333       276.150000   276.450000          278.483333   \n",
       "\n",
       "      Bilbao_temp_max  Seville_temp_min Madrid_temp  Madrid_temp_min  \\\n",
       "2915       276.150000        279.816667  274.910000       271.150000   \n",
       "2916       278.816667        287.150000  283.156667       280.483333   \n",
       "2917       285.816667        290.816667  287.733333       286.483333   \n",
       "2918       282.816667        287.483333  283.813333       282.150000   \n",
       "2919       276.816667        283.816667  276.623333       276.483333   \n",
       "\n",
       "      load_shortfall_3h  \n",
       "2915                NaN  \n",
       "2916                NaN  \n",
       "2917                NaN  \n",
       "2918                NaN  \n",
       "2919                NaN  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9044c2ff",
   "metadata": {},
   "source": [
    "### CHECK DATA TYPE\n",
    "Now that we have taken care of the null values, we can check to identify the data types contained in the dataset. Machine learning models only work with numeric data, which means the data types for the models must be floats or integers in order to get the best predictions out of the models built from our data.The code bellow reveals the data types of the data contained in our data set. Note that the \"time\" data, the \"Valencia_wind_deg\" data as well as the \"Seville_pressure\" are all object data (also known as strings). These have to be converted to floats or integers for the model to be able to make use of them as inputs. Like the null values, you can handle this problem by simply dropping the colunms. This is not recommended as everytime you drop data, you are loosing pottentially valuable information that may be very useful for your model building efforts. A more beneficial approach will be to process this data by Transforming it to numeric form or encoding it to a form that the model can utilize. 3 non-numeric objects are observed from the merged_df_clean data set. they are \"time\", \"Valencia_wind_deg\" and \"Seville_pressure\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c913eff3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                int64\n",
       "time                     object\n",
       "Madrid_wind_speed       float64\n",
       "Valencia_wind_deg        object\n",
       "Bilbao_rain_1h          float64\n",
       "Valencia_wind_speed     float64\n",
       "Seville_humidity        float64\n",
       "Madrid_humidity         float64\n",
       "Bilbao_clouds_all       float64\n",
       "Bilbao_wind_speed       float64\n",
       "Seville_clouds_all      float64\n",
       "Bilbao_wind_deg         float64\n",
       "Barcelona_wind_speed    float64\n",
       "Barcelona_wind_deg      float64\n",
       "Madrid_clouds_all       float64\n",
       "Seville_wind_speed      float64\n",
       "Barcelona_rain_1h       float64\n",
       "Seville_pressure         object\n",
       "Seville_rain_1h         float64\n",
       "Bilbao_snow_3h          float64\n",
       "Barcelona_pressure      float64\n",
       "Seville_rain_3h         float64\n",
       "Madrid_rain_1h          float64\n",
       "Barcelona_rain_3h       float64\n",
       "Valencia_snow_3h        float64\n",
       "Madrid_weather_id       float64\n",
       "Barcelona_weather_id    float64\n",
       "Bilbao_pressure         float64\n",
       "Seville_weather_id      float64\n",
       "Valencia_pressure       float64\n",
       "Seville_temp_max        float64\n",
       "Madrid_pressure         float64\n",
       "Valencia_temp_max       float64\n",
       "Valencia_temp           float64\n",
       "Bilbao_weather_id       float64\n",
       "Seville_temp            float64\n",
       "Valencia_humidity       float64\n",
       "Valencia_temp_min       float64\n",
       "Barcelona_temp_max      float64\n",
       "Madrid_temp_max         float64\n",
       "Barcelona_temp          float64\n",
       "Bilbao_temp_min         float64\n",
       "Bilbao_temp             float64\n",
       "Barcelona_temp_min      float64\n",
       "Bilbao_temp_max         float64\n",
       "Seville_temp_min        float64\n",
       "Madrid_temp             float64\n",
       "Madrid_temp_min         float64\n",
       "load_shortfall_3h       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7846c35",
   "metadata": {},
   "source": [
    "### THE TIME COLUMN\n",
    "Let us take a more thorough look at the object elements in our data frame. We will start with the time column. We need to take that column and map it into a date_time format which is the form that is usable by models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8869ad05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2015-01-01 03:00:00\n",
       "1       2015-01-01 06:00:00\n",
       "2       2015-01-01 09:00:00\n",
       "3       2015-01-01 12:00:00\n",
       "4       2015-01-01 15:00:00\n",
       "               ...         \n",
       "2915    2018-12-31 09:00:00\n",
       "2916    2018-12-31 12:00:00\n",
       "2917    2018-12-31 15:00:00\n",
       "2918    2018-12-31 18:00:00\n",
       "2919    2018-12-31 21:00:00\n",
       "Name: time, Length: 11683, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4038b043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2015-01-01 03:00:00\n",
       "1      2015-01-01 06:00:00\n",
       "2      2015-01-01 09:00:00\n",
       "3      2015-01-01 12:00:00\n",
       "4      2015-01-01 15:00:00\n",
       "               ...        \n",
       "2915   2018-12-31 09:00:00\n",
       "2916   2018-12-31 12:00:00\n",
       "2917   2018-12-31 15:00:00\n",
       "2918   2018-12-31 18:00:00\n",
       "2919   2018-12-31 21:00:00\n",
       "Name: time, Length: 11683, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['time'] = pd.to_datetime(merged_df['time'])\n",
    "merged_df.time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68557ff0",
   "metadata": {},
   "source": [
    "# Creating new features\n",
    "\n",
    "### WEEK\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3adc3bcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
       "       52, 53], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['week'] = merged_df['time'].apply(lambda x: x.isocalendar()[1])\n",
    "merged_df['week'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36735d8d",
   "metadata": {},
   "source": [
    "### TIME OF THE DAY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c5afd9dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  6,  9, 12, 15, 18, 21,  0], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['hour'] = merged_df['time'].apply(lambda x: x.hour)\n",
    "merged_df['hour'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ca04081",
   "metadata": {},
   "source": [
    "### YEAR COLUMN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e234dd1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2015, 2016, 2017, 2018], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['year'] = merged_df['time'].apply(lambda x: x.year)\n",
    "merged_df['year'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f97adae",
   "metadata": {},
   "source": [
    "### The Valencia_wind_deg Column\n",
    "The next object in the dataset is the \"Valencia_wind_deg\". This is recorded as levels which are denoted by the string \"level\" followed by a number which describes that particular level. To encode this, we can simply extract the number from the column that identifies that level. This line of code can help us to achieve that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1886a3c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        level_5\n",
       "1       level_10\n",
       "2        level_9\n",
       "3        level_8\n",
       "4        level_7\n",
       "          ...   \n",
       "2915     level_9\n",
       "2916     level_8\n",
       "2917     level_6\n",
       "2918     level_6\n",
       "2919    level_10\n",
       "Name: Valencia_wind_deg, Length: 11683, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['Valencia_wind_deg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4f964642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5\n",
       "1       10\n",
       "2        9\n",
       "3        8\n",
       "4        7\n",
       "        ..\n",
       "2915     9\n",
       "2916     8\n",
       "2917     6\n",
       "2918     6\n",
       "2919    10\n",
       "Name: Valencia_wind_deg, Length: 11683, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['Valencia_wind_deg'] = merged_df['Valencia_wind_deg'].str.extract('(\\d+)')\n",
    "merged_df['Valencia_wind_deg']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7d2c74",
   "metadata": {},
   "source": [
    "As you can see above, the data has been reduced to a number without the string \"level\" to define it but there is still a problem. The data type is still an object. we can convert the object to numeric form by using the pandas numeric function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "39825c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        5\n",
       "1       10\n",
       "2        9\n",
       "3        8\n",
       "4        7\n",
       "        ..\n",
       "2915     9\n",
       "2916     8\n",
       "2917     6\n",
       "2918     6\n",
       "2919    10\n",
       "Name: Valencia_wind_deg, Length: 11683, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['Valencia_wind_deg'] = pd.to_numeric(merged_df['Valencia_wind_deg'])\n",
    "merged_df.Valencia_wind_deg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b7f4493",
   "metadata": {},
   "source": [
    "Repeat the process for Seville_pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5b336748",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df['Seville_pressure'] = merged_df['Seville_pressure'].str.extract('(\\d+)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a61ac8ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       25\n",
       "1       25\n",
       "2       25\n",
       "3       25\n",
       "4       25\n",
       "        ..\n",
       "2915    24\n",
       "2916    24\n",
       "2917    23\n",
       "2918    23\n",
       "2919    23\n",
       "Name: Seville_pressure, Length: 11683, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['Seville_pressure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8362861c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       25\n",
       "1       25\n",
       "2       25\n",
       "3       25\n",
       "4       25\n",
       "        ..\n",
       "2915    24\n",
       "2916    24\n",
       "2917    23\n",
       "2918    23\n",
       "2919    23\n",
       "Name: Seville_pressure, Length: 11683, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['Seville_pressure'] = pd.to_numeric(merged_df['Seville_pressure'])\n",
    "merged_df['Seville_pressure']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a39014",
   "metadata": {},
   "source": [
    "# Engineering existing features\n",
    "\n",
    "### Dropping unnecessary columns\n",
    "We need to drop columns that are not useful to the model. For now that would be the \"Unnamed:0\" and the \"time\" columns. Note that you can opt to add the time column to your model but that would require encoding the time data until it is in a form that is usable by the model. We see by running the .head function that the unwanted columns have indeed been dropped from our df. Your data is now clean and ready for use in model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c71db477",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_clean = merged_df\n",
    "merged_df_clean = merged_df_clean.drop(['Unnamed: 0', 'time'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1457ecca",
   "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>Madrid_wind_speed</th>\n",
       "      <th>Valencia_wind_deg</th>\n",
       "      <th>Bilbao_rain_1h</th>\n",
       "      <th>Valencia_wind_speed</th>\n",
       "      <th>Seville_humidity</th>\n",
       "      <th>Madrid_humidity</th>\n",
       "      <th>Bilbao_clouds_all</th>\n",
       "      <th>Bilbao_wind_speed</th>\n",
       "      <th>Seville_clouds_all</th>\n",
       "      <th>Bilbao_wind_deg</th>\n",
       "      <th>...</th>\n",
       "      <th>Bilbao_temp</th>\n",
       "      <th>Barcelona_temp_min</th>\n",
       "      <th>Bilbao_temp_max</th>\n",
       "      <th>Seville_temp_min</th>\n",
       "      <th>Madrid_temp</th>\n",
       "      <th>Madrid_temp_min</th>\n",
       "      <th>load_shortfall_3h</th>\n",
       "      <th>week</th>\n",
       "      <th>hour</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>74.333333</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>223.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>269.338615</td>\n",
       "      <td>281.013000</td>\n",
       "      <td>269.338615</td>\n",
       "      <td>274.254667</td>\n",
       "      <td>265.938000</td>\n",
       "      <td>265.938000</td>\n",
       "      <td>6715.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>78.333333</td>\n",
       "      <td>64.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>221.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>270.376000</td>\n",
       "      <td>280.561667</td>\n",
       "      <td>270.376000</td>\n",
       "      <td>274.945000</td>\n",
       "      <td>266.386667</td>\n",
       "      <td>266.386667</td>\n",
       "      <td>4171.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>71.333333</td>\n",
       "      <td>64.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>214.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>275.027229</td>\n",
       "      <td>281.583667</td>\n",
       "      <td>275.027229</td>\n",
       "      <td>278.792000</td>\n",
       "      <td>272.708667</td>\n",
       "      <td>272.708667</td>\n",
       "      <td>4274.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>65.333333</td>\n",
       "      <td>56.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>199.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>281.135063</td>\n",
       "      <td>283.434104</td>\n",
       "      <td>281.135063</td>\n",
       "      <td>285.394000</td>\n",
       "      <td>281.895219</td>\n",
       "      <td>281.895219</td>\n",
       "      <td>5075.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>282.252063</td>\n",
       "      <td>284.213167</td>\n",
       "      <td>282.252063</td>\n",
       "      <td>285.513719</td>\n",
       "      <td>280.678437</td>\n",
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       "      <td>6620.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Madrid_wind_speed  Valencia_wind_deg  Bilbao_rain_1h  Valencia_wind_speed  \\\n",
       "0           0.666667                  5             0.0             0.666667   \n",
       "1           0.333333                 10             0.0             1.666667   \n",
       "2           1.000000                  9             0.0             1.000000   \n",
       "3           1.000000                  8             0.0             1.000000   \n",
       "4           1.000000                  7             0.0             1.000000   \n",
       "\n",
       "   Seville_humidity  Madrid_humidity  Bilbao_clouds_all  Bilbao_wind_speed  \\\n",
       "0         74.333333        64.000000                0.0           1.000000   \n",
       "1         78.333333        64.666667                0.0           1.000000   \n",
       "2         71.333333        64.333333                0.0           1.000000   \n",
       "3         65.333333        56.333333                0.0           1.000000   \n",
       "4         59.000000        57.000000                2.0           0.333333   \n",
       "\n",
       "   Seville_clouds_all  Bilbao_wind_deg  ...  Bilbao_temp  Barcelona_temp_min  \\\n",
       "0                 0.0       223.333333  ...   269.338615          281.013000   \n",
       "1                 0.0       221.000000  ...   270.376000          280.561667   \n",
       "2                 0.0       214.333333  ...   275.027229          281.583667   \n",
       "3                 0.0       199.666667  ...   281.135063          283.434104   \n",
       "4                 0.0       185.000000  ...   282.252063          284.213167   \n",
       "\n",
       "   Bilbao_temp_max  Seville_temp_min  Madrid_temp  Madrid_temp_min  \\\n",
       "0       269.338615        274.254667   265.938000       265.938000   \n",
       "1       270.376000        274.945000   266.386667       266.386667   \n",
       "2       275.027229        278.792000   272.708667       272.708667   \n",
       "3       281.135063        285.394000   281.895219       281.895219   \n",
       "4       282.252063        285.513719   280.678437       280.678437   \n",
       "\n",
       "   load_shortfall_3h  week  hour  year  \n",
       "0        6715.666667     1     3  2015  \n",
       "1        4171.666667     1     6  2015  \n",
       "2        4274.666667     1     9  2015  \n",
       "3        5075.666667     1    12  2015  \n",
       "4        6620.666667     1    15  2015  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43b2d523",
   "metadata": {},
   "source": [
    "<a id=\"five\"></a>\n",
    "## 5. Modelling\n",
    "<a class=\"anchor\" id=\"1.1\"></a>\n",
    "<a href=#cont>Back to Table of Contents</a>\n",
    "\n",
    "---\n",
    "    \n",
    "| ⚡ Description: Modelling ⚡ |\n",
    "| :--------------------------- |\n",
    "| In this section, you are required to create one or more regression models that are able to accurately predict the thee hour load shortfall. |\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7ba1bc86",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Madrid_wind_speed</th>\n",
       "      <th>Valencia_wind_deg</th>\n",
       "      <th>Bilbao_rain_1h</th>\n",
       "      <th>Valencia_wind_speed</th>\n",
       "      <th>Seville_humidity</th>\n",
       "      <th>Madrid_humidity</th>\n",
       "      <th>Bilbao_clouds_all</th>\n",
       "      <th>Bilbao_wind_speed</th>\n",
       "      <th>Seville_clouds_all</th>\n",
       "      <th>Bilbao_wind_deg</th>\n",
       "      <th>...</th>\n",
       "      <th>Bilbao_temp</th>\n",
       "      <th>Barcelona_temp_min</th>\n",
       "      <th>Bilbao_temp_max</th>\n",
       "      <th>Seville_temp_min</th>\n",
       "      <th>Madrid_temp</th>\n",
       "      <th>Madrid_temp_min</th>\n",
       "      <th>load_shortfall_3h</th>\n",
       "      <th>week</th>\n",
       "      <th>hour</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>5.000000</td>\n",
       "      <td>87.000000</td>\n",
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       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>193.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>280.380000</td>\n",
       "      <td>286.816667</td>\n",
       "      <td>285.150000</td>\n",
       "      <td>283.150000</td>\n",
       "      <td>279.866667</td>\n",
       "      <td>279.150000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
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       "      <th>1</th>\n",
       "      <td>4.666667</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>89.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>143.333333</td>\n",
       "      <td>...</td>\n",
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       "      <td>283.483333</td>\n",
       "      <td>284.150000</td>\n",
       "      <td>281.150000</td>\n",
       "      <td>279.193333</td>\n",
       "      <td>278.150000</td>\n",
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       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>89.000000</td>\n",
       "      <td>89.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>279.196667</td>\n",
       "      <td>281.816667</td>\n",
       "      <td>282.150000</td>\n",
       "      <td>280.483333</td>\n",
       "      <td>276.340000</td>\n",
       "      <td>276.150000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.666667</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>82.666667</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>196.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>281.740000</td>\n",
       "      <td>282.150000</td>\n",
       "      <td>284.483333</td>\n",
       "      <td>279.150000</td>\n",
       "      <td>275.953333</td>\n",
       "      <td>274.483333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.000000</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>65.333333</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>233.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>284.116667</td>\n",
       "      <td>286.150000</td>\n",
       "      <td>286.816667</td>\n",
       "      <td>284.483333</td>\n",
       "      <td>280.686667</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.333333</td>\n",
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       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>276.483333</td>\n",
       "      <td>276.150000</td>\n",
       "      <td>279.816667</td>\n",
       "      <td>274.910000</td>\n",
       "      <td>271.150000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>28.333333</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>278.443333</td>\n",
       "      <td>285.816667</td>\n",
       "      <td>278.816667</td>\n",
       "      <td>287.150000</td>\n",
       "      <td>283.156667</td>\n",
       "      <td>280.483333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>14.666667</td>\n",
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       "      <td>286.483333</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>15</td>\n",
       "      <td>2018</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
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       "      <td>2.000000</td>\n",
       "      <td>52.666667</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>281.626667</td>\n",
       "      <td>283.150000</td>\n",
       "      <td>282.816667</td>\n",
       "      <td>287.483333</td>\n",
       "      <td>283.813333</td>\n",
       "      <td>282.150000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>1.333333</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>61.666667</td>\n",
       "      <td>69.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>276.450000</td>\n",
       "      <td>278.483333</td>\n",
       "      <td>276.816667</td>\n",
       "      <td>283.816667</td>\n",
       "      <td>276.623333</td>\n",
       "      <td>276.483333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2920 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Madrid_wind_speed  Valencia_wind_deg  Bilbao_rain_1h  \\\n",
       "0              5.000000                  8             0.0   \n",
       "1              4.666667                  8             0.0   \n",
       "2              2.333333                  7             0.0   \n",
       "3              2.666667                  7             0.0   \n",
       "4              4.000000                  7             0.0   \n",
       "...                 ...                ...             ...   \n",
       "2915           0.333333                  9             0.0   \n",
       "2916           0.333333                  8             0.0   \n",
       "2917           1.000000                  6             0.0   \n",
       "2918           1.000000                  6             0.0   \n",
       "2919           1.333333                 10             0.0   \n",
       "\n",
       "      Valencia_wind_speed  Seville_humidity  Madrid_humidity  \\\n",
       "0                5.000000         87.000000        71.333333   \n",
       "1                5.333333         89.000000        78.000000   \n",
       "2                5.000000         89.000000        89.666667   \n",
       "3                5.333333         93.333333        82.666667   \n",
       "4                8.666667         65.333333        64.000000   \n",
       "...                   ...               ...              ...   \n",
       "2915             2.000000         81.666667        49.666667   \n",
       "2916             1.333333         61.000000        28.333333   \n",
       "2917             3.000000         47.000000        26.333333   \n",
       "2918             2.000000         52.666667        56.666667   \n",
       "2919             2.333333         61.666667        69.333333   \n",
       "\n",
       "      Bilbao_clouds_all  Bilbao_wind_speed  Seville_clouds_all  \\\n",
       "0             20.000000           3.000000            0.000000   \n",
       "1              0.000000           3.666667            0.000000   \n",
       "2              0.000000           2.333333            6.666667   \n",
       "3             26.666667           5.666667            6.666667   \n",
       "4             26.666667          10.666667            0.000000   \n",
       "...                 ...                ...                 ...   \n",
       "2915          80.000000           1.333333            0.000000   \n",
       "2916          56.666667           1.000000            0.000000   \n",
       "2917           0.000000           0.666667            0.000000   \n",
       "2918           0.000000           0.666667            0.000000   \n",
       "2919           0.000000           1.333333            0.000000   \n",
       "\n",
       "      Bilbao_wind_deg  ...  Bilbao_temp  Barcelona_temp_min  Bilbao_temp_max  \\\n",
       "0          193.333333  ...   280.380000          286.816667       285.150000   \n",
       "1          143.333333  ...   281.010000          283.483333       284.150000   \n",
       "2          130.000000  ...   279.196667          281.816667       282.150000   \n",
       "3          196.666667  ...   281.740000          282.150000       284.483333   \n",
       "4          233.333333  ...   284.116667          286.150000       286.816667   \n",
       "...               ...  ...          ...                 ...              ...   \n",
       "2915        70.000000  ...   273.210000          276.483333       276.150000   \n",
       "2916        36.666667  ...   278.443333          285.816667       278.816667   \n",
       "2917        14.666667  ...   285.073333          288.150000       285.816667   \n",
       "2918        19.000000  ...   281.626667          283.150000       282.816667   \n",
       "2919       120.000000  ...   276.450000          278.483333       276.816667   \n",
       "\n",
       "      Seville_temp_min  Madrid_temp  Madrid_temp_min  load_shortfall_3h  week  \\\n",
       "0           283.150000   279.866667       279.150000                NaN     1   \n",
       "1           281.150000   279.193333       278.150000                NaN     1   \n",
       "2           280.483333   276.340000       276.150000                NaN     1   \n",
       "3           279.150000   275.953333       274.483333                NaN     1   \n",
       "4           284.483333   280.686667       280.150000                NaN     1   \n",
       "...                ...          ...              ...                ...   ...   \n",
       "2915        279.816667   274.910000       271.150000                NaN     1   \n",
       "2916        287.150000   283.156667       280.483333                NaN     1   \n",
       "2917        290.816667   287.733333       286.483333                NaN     1   \n",
       "2918        287.483333   283.813333       282.150000                NaN     1   \n",
       "2919        283.816667   276.623333       276.483333                NaN     1   \n",
       "\n",
       "      hour  year  \n",
       "0        0  2018  \n",
       "1        3  2018  \n",
       "2        6  2018  \n",
       "3        9  2018  \n",
       "4       12  2018  \n",
       "...    ...   ...  \n",
       "2915     9  2018  \n",
       "2916    12  2018  \n",
       "2917    15  2018  \n",
       "2918    18  2018  \n",
       "2919    21  2018  \n",
       "\n",
       "[2920 rows x 50 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_clean[len(df_train_clean):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2344b3e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data\n",
    "y = merged_df_clean[:len(df_train_clean)][['load_shortfall_3h']]\n",
    "x = merged_df_clean[:len(df_train_clean)].drop('load_shortfall_3h', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d0723d0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1a431924",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Madrid_wind_speed</th>\n",
       "      <th>Valencia_wind_deg</th>\n",
       "      <th>Bilbao_rain_1h</th>\n",
       "      <th>Valencia_wind_speed</th>\n",
       "      <th>Seville_humidity</th>\n",
       "      <th>Madrid_humidity</th>\n",
       "      <th>Bilbao_clouds_all</th>\n",
       "      <th>Bilbao_wind_speed</th>\n",
       "      <th>Seville_clouds_all</th>\n",
       "      <th>Bilbao_wind_deg</th>\n",
       "      <th>...</th>\n",
       "      <th>Bilbao_temp_min</th>\n",
       "      <th>Bilbao_temp</th>\n",
       "      <th>Barcelona_temp_min</th>\n",
       "      <th>Bilbao_temp_max</th>\n",
       "      <th>Seville_temp_min</th>\n",
       "      <th>Madrid_temp</th>\n",
       "      <th>Madrid_temp_min</th>\n",
       "      <th>week</th>\n",
       "      <th>hour</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>74.333333</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>223.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>269.338615</td>\n",
       "      <td>269.338615</td>\n",
       "      <td>281.013000</td>\n",
       "      <td>269.338615</td>\n",
       "      <td>274.254667</td>\n",
       "      <td>265.938000</td>\n",
       "      <td>265.938000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>78.333333</td>\n",
       "      <td>64.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>221.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>270.376000</td>\n",
       "      <td>270.376000</td>\n",
       "      <td>280.561667</td>\n",
       "      <td>270.376000</td>\n",
       "      <td>274.945000</td>\n",
       "      <td>266.386667</td>\n",
       "      <td>266.386667</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>71.333333</td>\n",
       "      <td>64.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>214.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>275.027229</td>\n",
       "      <td>275.027229</td>\n",
       "      <td>281.583667</td>\n",
       "      <td>275.027229</td>\n",
       "      <td>278.792000</td>\n",
       "      <td>272.708667</td>\n",
       "      <td>272.708667</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>65.333333</td>\n",
       "      <td>56.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>199.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>281.135063</td>\n",
       "      <td>281.135063</td>\n",
       "      <td>283.434104</td>\n",
       "      <td>281.135063</td>\n",
       "      <td>285.394000</td>\n",
       "      <td>281.895219</td>\n",
       "      <td>281.895219</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>282.252063</td>\n",
       "      <td>282.252063</td>\n",
       "      <td>284.213167</td>\n",
       "      <td>282.252063</td>\n",
       "      <td>285.513719</td>\n",
       "      <td>280.678437</td>\n",
       "      <td>280.678437</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Madrid_wind_speed  Valencia_wind_deg  Bilbao_rain_1h  Valencia_wind_speed  \\\n",
       "0           0.666667                  5             0.0             0.666667   \n",
       "1           0.333333                 10             0.0             1.666667   \n",
       "2           1.000000                  9             0.0             1.000000   \n",
       "3           1.000000                  8             0.0             1.000000   \n",
       "4           1.000000                  7             0.0             1.000000   \n",
       "\n",
       "   Seville_humidity  Madrid_humidity  Bilbao_clouds_all  Bilbao_wind_speed  \\\n",
       "0         74.333333        64.000000                0.0           1.000000   \n",
       "1         78.333333        64.666667                0.0           1.000000   \n",
       "2         71.333333        64.333333                0.0           1.000000   \n",
       "3         65.333333        56.333333                0.0           1.000000   \n",
       "4         59.000000        57.000000                2.0           0.333333   \n",
       "\n",
       "   Seville_clouds_all  Bilbao_wind_deg  ...  Bilbao_temp_min  Bilbao_temp  \\\n",
       "0                 0.0       223.333333  ...       269.338615   269.338615   \n",
       "1                 0.0       221.000000  ...       270.376000   270.376000   \n",
       "2                 0.0       214.333333  ...       275.027229   275.027229   \n",
       "3                 0.0       199.666667  ...       281.135063   281.135063   \n",
       "4                 0.0       185.000000  ...       282.252063   282.252063   \n",
       "\n",
       "   Barcelona_temp_min  Bilbao_temp_max  Seville_temp_min  Madrid_temp  \\\n",
       "0          281.013000       269.338615        274.254667   265.938000   \n",
       "1          280.561667       270.376000        274.945000   266.386667   \n",
       "2          281.583667       275.027229        278.792000   272.708667   \n",
       "3          283.434104       281.135063        285.394000   281.895219   \n",
       "4          284.213167       282.252063        285.513719   280.678437   \n",
       "\n",
       "   Madrid_temp_min  week  hour  year  \n",
       "0       265.938000     1     3  2015  \n",
       "1       266.386667     1     6  2015  \n",
       "2       272.708667     1     9  2015  \n",
       "3       281.895219     1    12  2015  \n",
       "4       280.678437     1    15  2015  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "a9cad2d0",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>load_shortfall_3h</th>\n",
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       "</table>\n",
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      "text/plain": [
       "   load_shortfall_3h\n",
       "0        6715.666667\n",
       "1        4171.666667\n",
       "2        4274.666667\n",
       "3        5075.666667\n",
       "4        6620.666667"
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     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "733c0462",
   "metadata": {},
   "source": [
    "### Create one or more ML models\n",
    "\n",
    "First we create a base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "00b75df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# declare the base model type\n",
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "a978a0c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit linear model\n",
    "lm.fit(x_train, y_train)\n",
    "preds = lm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4bbabc6c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>load_shortfall_3h</th>\n",
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       "      <th>3</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6978.450766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   load_shortfall_3h\n",
       "0        9884.608982\n",
       "1       10735.818984\n",
       "2       12646.776890\n",
       "3        8446.314215\n",
       "4        6978.450766"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_y = pd.DataFrame(preds, columns = ['load_shortfall_3h'])\n",
    "pred_y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "86fd7ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_col = pd.DataFrame({'time':df_test_clean['time']})\n",
    "team_submission = time_col.join(pred_y)\n",
    "team_submission.to_csv('team_submission2.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "fcfe79b9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>time</th>\n",
       "      <th>load_shortfall_3h</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01 00:00:00</td>\n",
       "      <td>9884.608982</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-01 03:00:00</td>\n",
       "      <td>10735.818984</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-01 06:00:00</td>\n",
       "      <td>12646.776890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-01 09:00:00</td>\n",
       "      <td>8446.314215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-01 12:00:00</td>\n",
       "      <td>6978.450766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>2018-12-31 09:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>2018-12-31 12:00:00</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>2018-12-31 15:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>2018-12-31 18:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>2018-12-31 21:00:00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2920 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     time  load_shortfall_3h\n",
       "0     2018-01-01 00:00:00        9884.608982\n",
       "1     2018-01-01 03:00:00       10735.818984\n",
       "2     2018-01-01 06:00:00       12646.776890\n",
       "3     2018-01-01 09:00:00        8446.314215\n",
       "4     2018-01-01 12:00:00        6978.450766\n",
       "...                   ...                ...\n",
       "2915  2018-12-31 09:00:00                NaN\n",
       "2916  2018-12-31 12:00:00                NaN\n",
       "2917  2018-12-31 15:00:00                NaN\n",
       "2918  2018-12-31 18:00:00                NaN\n",
       "2919  2018-12-31 21:00:00                NaN\n",
       "\n",
       "[2920 rows x 2 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6a466ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = merged_df_clean[:len(df_train_clean)].drop('load_shortfall_3h', axis = 1)\n",
    "x_test = merged_df_clean[len(df_train_clean):].drop('load_shortfall_3h', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "816b5a00",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ubasinachi\\AppData\\Local\\Temp\\ipykernel_19240\\2947321549.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  rf.fit(x_train, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=0)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor(n_estimators = 100, random_state = 0)\n",
    "rf.fit(x_train, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "ea0da854",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds2 = rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f5c28143",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10951.230000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   load_shortfall_3h\n",
       "0       13047.256667\n",
       "1        9423.590000\n",
       "2        9912.936667\n",
       "3       10597.876667\n",
       "4       10951.230000"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_y_1 = pd.DataFrame(preds2, columns = ['load_shortfall_3h'])\n",
    "pred_y_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4b802eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_col = pd.DataFrame({'time':df_test_clean['time']})\n",
    "team_submission = time_col.join(pred_y_1)\n",
    "team_submission.to_csv('team_submission3.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "046a0e52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>load_shortfall_3h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01 00:00:00</td>\n",
       "      <td>13047.256667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-01 03:00:00</td>\n",
       "      <td>9423.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-01 06:00:00</td>\n",
       "      <td>9912.936667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-01 09:00:00</td>\n",
       "      <td>10597.876667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-01 12:00:00</td>\n",
       "      <td>10951.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2915</th>\n",
       "      <td>2018-12-31 09:00:00</td>\n",
       "      <td>12206.293333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2916</th>\n",
       "      <td>2018-12-31 12:00:00</td>\n",
       "      <td>11323.306667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2917</th>\n",
       "      <td>2018-12-31 15:00:00</td>\n",
       "      <td>11858.040000</td>\n",
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       "    <tr>\n",
       "      <th>2918</th>\n",
       "      <td>2018-12-31 18:00:00</td>\n",
       "      <td>12322.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2919</th>\n",
       "      <td>2018-12-31 21:00:00</td>\n",
       "      <td>11540.310000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2920 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     time  load_shortfall_3h\n",
       "0     2018-01-01 00:00:00       13047.256667\n",
       "1     2018-01-01 03:00:00        9423.590000\n",
       "2     2018-01-01 06:00:00        9912.936667\n",
       "3     2018-01-01 09:00:00       10597.876667\n",
       "4     2018-01-01 12:00:00       10951.230000\n",
       "...                   ...                ...\n",
       "2915  2018-12-31 09:00:00       12206.293333\n",
       "2916  2018-12-31 12:00:00       11323.306667\n",
       "2917  2018-12-31 15:00:00       11858.040000\n",
       "2918  2018-12-31 18:00:00       12322.070000\n",
       "2919  2018-12-31 21:00:00       11540.310000\n",
       "\n",
       "[2920 rows x 2 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_submission"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f7f63f",
   "metadata": {},
   "source": [
    "### Evaluate base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "3225a3df",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.metrics import mean_squared_error\n",
    "#from math import sqrt\n",
    "\n",
    "#rmse = sqrt(mean_squared_error(y, pred_y_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8fee670a",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'rmse' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [112]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mrmse\u001b[49m(y_test, preds)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'rmse' is not defined"
     ]
    }
   ],
   "source": [
    "rmse(y_test, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e21c1a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bdf5c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "r2_score(y_test, preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b530251",
   "metadata": {},
   "source": [
    "<a id=\"six\"></a>\n",
    "## 6. Model Performance\n",
    "<a class=\"anchor\" id=\"1.1\"></a>\n",
    "<a href=#cont>Back to Table of Contents</a>\n",
    "\n",
    "---\n",
    "    \n",
    "| ⚡ Description: Model performance ⚡ |\n",
    "| :--------------------------- |\n",
    "| In this section you are required to compare the relative performance of the various trained ML models on a holdout dataset and comment on what model is the best and why. |\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a69b5a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compare model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3874a7c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose best model and motivate why it is the best choice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8ad0c0d",
   "metadata": {},
   "source": [
    "<a id=\"seven\"></a>\n",
    "## 7. Model Explanations\n",
    "<a class=\"anchor\" id=\"1.1\"></a>\n",
    "<a href=#cont>Back to Table of Contents</a>\n",
    "\n",
    "---\n",
    "    \n",
    "| ⚡ Description: Model explanation ⚡ |\n",
    "| :--------------------------- |\n",
    "| In this section, you are required to discuss how the best performing model works in a simple way so that both technical and non-technical stakeholders can grasp the intuition behind the model's inner workings. |\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ff741c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# discuss chosen methods logic"
   ]
  }
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