sklearn.ipynb

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    "# Scikit Learn Tutorial\n",
    "\n",
    "This tutorial is intended to get the reader up to speed with the data science workflow using Scikit Learn. \n",
    "\n",
    "The main areas we will cover are:\n",
    "* Feature engineering\n",
    "* Pre-processing data\n",
    "* Running and evaluating models\n",
    "\n",
    "The Scikit Learn API (application programming interface) makes it easy to quickly swap and compare models. For this reason the tutorial will cover linear regression and random forests, with the same overarching method being applicable to other models.\n",
    "\n",
    "Scikit Learn documentation -> https://scikit-learn.org/stable/\n",
    "\n",
    "<br>\n",
    "\n",
    "#### The Data Science Workflow\n",
    "\n",
    "0a) Feature engineering\n",
    "\n",
    "0b) Pre-processing data\n",
    "\n",
    "1) Separate data into test/train split\n",
    "\n",
    "2) Create model\n",
    "\n",
    "3) Evaluate model\n",
    "\n",
    "4) Iterate creation and evaluation until accuracy is good enough\n",
    "\n",
    "<img src='img/workflow.PNG' width='800'>\n",
    "\n",
    "<br>\n",
    "\n",
    "### Imports"
   ]
  },
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Reading in Data"
   ]
  },
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   "execution_count": 2,
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       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>price</th>\n",
       "      <th>demand</th>\n",
       "      <th>biomass</th>\n",
       "      <th>coal</th>\n",
       "      <th>gas</th>\n",
       "      <th>hydro</th>\n",
       "      <th>nuclear</th>\n",
       "      <th>solar</th>\n",
       "      <th>wind</th>\n",
       "      <th>dutch</th>\n",
       "      <th>french</th>\n",
       "      <th>irish</th>\n",
       "      <th>pumped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-31 00:30:00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>26.935</td>\n",
       "      <td>2.042</td>\n",
       "      <td>4.213</td>\n",
       "      <td>6.064</td>\n",
       "      <td>0.999</td>\n",
       "      <td>7.681</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.963</td>\n",
       "      <td>0.793</td>\n",
       "      <td>1.621</td>\n",
       "      <td>-0.044</td>\n",
       "      <td>-0.397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-31 01:00:00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>26.254</td>\n",
       "      <td>2.043</td>\n",
       "      <td>4.236</td>\n",
       "      <td>5.860</td>\n",
       "      <td>1.004</td>\n",
       "      <td>7.677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.986</td>\n",
       "      <td>0.794</td>\n",
       "      <td>1.622</td>\n",
       "      <td>0.103</td>\n",
       "      <td>-1.071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-31 01:30:00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>25.443</td>\n",
       "      <td>2.043</td>\n",
       "      <td>4.191</td>\n",
       "      <td>5.332</td>\n",
       "      <td>1.003</td>\n",
       "      <td>7.679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.172</td>\n",
       "      <td>0.806</td>\n",
       "      <td>1.621</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-1.405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-31 02:00:00</td>\n",
       "      <td>41.89</td>\n",
       "      <td>24.744</td>\n",
       "      <td>2.043</td>\n",
       "      <td>4.012</td>\n",
       "      <td>4.869</td>\n",
       "      <td>1.000</td>\n",
       "      <td>7.697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.781</td>\n",
       "      <td>1.621</td>\n",
       "      <td>-0.093</td>\n",
       "      <td>-1.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-31 02:30:00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>24.196</td>\n",
       "      <td>2.044</td>\n",
       "      <td>3.899</td>\n",
       "      <td>4.279</td>\n",
       "      <td>0.998</td>\n",
       "      <td>7.705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.339</td>\n",
       "      <td>0.773</td>\n",
       "      <td>1.621</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-1.399</td>\n",
       "    </tr>\n",
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       "             datetime  price  demand  biomass   coal    gas  hydro  nuclear   \\\n",
       "0 2015-12-31 00:30:00  40.00  26.935    2.042  4.213  6.064  0.999     7.681   \n",
       "1 2015-12-31 01:00:00  40.00  26.254    2.043  4.236  5.860  1.004     7.677   \n",
       "2 2015-12-31 01:30:00  45.00  25.443    2.043  4.191  5.332  1.003     7.679   \n",
       "3 2015-12-31 02:00:00  41.89  24.744    2.043  4.012  4.869  1.000     7.697   \n",
       "4 2015-12-31 02:30:00  42.00  24.196    2.044  3.899  4.279  0.998     7.705   \n",
       "\n",
       "   solar   wind  dutch  french  irish  pumped  \n",
       "0    0.0  3.963  0.793   1.621 -0.044  -0.397  \n",
       "1    0.0  3.986  0.794   1.622  0.103  -1.071  \n",
       "2    0.0  4.172  0.806   1.621  0.001  -1.405  \n",
       "3    0.0  4.216  0.781   1.621 -0.093  -1.402  \n",
       "4    0.0  4.339  0.773   1.621 -0.063  -1.399  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
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   ],
   "source": [
    "df_DAM = pd.read_csv('data/df_energy.csv', parse_dates=['datetime'])\n",
    "df_DAM.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Feature Engineering\n",
    "\n",
    "Feature engineering is often one of the most important parts of machine learning, it's where we create new variables from the ones we currently have.\n",
    "\n",
    "<br>\n",
    "\n",
    "Electricity is traded in half hour blocks called Settlement Periods (SP). To make the time component easier for the machine learning algorithm to understand we'll use the SP instead of time."
   ]
  },
  {
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       "      <th>demand</th>\n",
       "      <th>biomass</th>\n",
       "      <th>coal</th>\n",
       "      <th>gas</th>\n",
       "      <th>hydro</th>\n",
       "      <th>nuclear</th>\n",
       "      <th>solar</th>\n",
       "      <th>wind</th>\n",
       "      <th>dutch</th>\n",
       "      <th>french</th>\n",
       "      <th>irish</th>\n",
       "      <th>pumped</th>\n",
       "      <th>SP</th>\n",
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       "      <th>0</th>\n",
       "      <td>2015-12-31 00:30:00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>26.935</td>\n",
       "      <td>2.042</td>\n",
       "      <td>4.213</td>\n",
       "      <td>6.064</td>\n",
       "      <td>0.999</td>\n",
       "      <td>7.681</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.963</td>\n",
       "      <td>0.793</td>\n",
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       "      <td>2</td>\n",
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       "      <th>1</th>\n",
       "      <td>2015-12-31 01:00:00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>26.254</td>\n",
       "      <td>2.043</td>\n",
       "      <td>4.236</td>\n",
       "      <td>5.860</td>\n",
       "      <td>1.004</td>\n",
       "      <td>7.677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.986</td>\n",
       "      <td>0.794</td>\n",
       "      <td>1.622</td>\n",
       "      <td>0.103</td>\n",
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       "      <td>3</td>\n",
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       "      <th>2</th>\n",
       "      <td>2015-12-31 01:30:00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>25.443</td>\n",
       "      <td>2.043</td>\n",
       "      <td>4.191</td>\n",
       "      <td>5.332</td>\n",
       "      <td>1.003</td>\n",
       "      <td>7.679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.172</td>\n",
       "      <td>0.806</td>\n",
       "      <td>1.621</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-1.405</td>\n",
       "      <td>4</td>\n",
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       "      <th>3</th>\n",
       "      <td>2015-12-31 02:00:00</td>\n",
       "      <td>41.89</td>\n",
       "      <td>24.744</td>\n",
       "      <td>2.043</td>\n",
       "      <td>4.012</td>\n",
       "      <td>4.869</td>\n",
       "      <td>1.000</td>\n",
       "      <td>7.697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.781</td>\n",
       "      <td>1.621</td>\n",
       "      <td>-0.093</td>\n",
       "      <td>-1.402</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-31 02:30:00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>24.196</td>\n",
       "      <td>2.044</td>\n",
       "      <td>3.899</td>\n",
       "      <td>4.279</td>\n",
       "      <td>0.998</td>\n",
       "      <td>7.705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.339</td>\n",
       "      <td>0.773</td>\n",
       "      <td>1.621</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-1.399</td>\n",
       "      <td>6</td>\n",
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       "             datetime  price  demand  biomass   coal    gas  hydro  nuclear   \\\n",
       "0 2015-12-31 00:30:00  40.00  26.935    2.042  4.213  6.064  0.999     7.681   \n",
       "1 2015-12-31 01:00:00  40.00  26.254    2.043  4.236  5.860  1.004     7.677   \n",
       "2 2015-12-31 01:30:00  45.00  25.443    2.043  4.191  5.332  1.003     7.679   \n",
       "3 2015-12-31 02:00:00  41.89  24.744    2.043  4.012  4.869  1.000     7.697   \n",
       "4 2015-12-31 02:30:00  42.00  24.196    2.044  3.899  4.279  0.998     7.705   \n",
       "\n",
       "   solar   wind  dutch  french  irish  pumped  SP  \n",
       "0    0.0  3.963  0.793   1.621 -0.044  -0.397   2  \n",
       "1    0.0  3.986  0.794   1.622  0.103  -1.071   3  \n",
       "2    0.0  4.172  0.806   1.621  0.001  -1.405   4  \n",
       "3    0.0  4.216  0.781   1.621 -0.093  -1.402   5  \n",
       "4    0.0  4.339  0.773   1.621 -0.063  -1.399   6  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_hour = df_DAM.datetime.dt.hour\n",
    "s_minutes = df_DAM.datetime.dt.minute\n",
    "\n",
    "s_SP = s_hour*2 + s_minutes/30 + 1\n",
    "df_DAM['SP'] = s_SP\n",
    "df_DAM['SP'] = df_DAM['SP'].astype('int')\n",
    "\n",
    "df_DAM.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br> \n",
    "\n",
    "There's often a seasonal component in electricity prices so we'll also tell the algorithm whether it's winter or not. We'll define winter as being from October to March.\n",
    "\n",
    "We'll use a dictionary to 'map' the month values (1-12) to winter/summer. We'll use a 'dummy' variable where 1=Winter and 0=Summer.\n",
    "\n",
    "Dictionarys contain unique 'keys' which correspond to defined values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 'winter'}\n",
      "winter\n"
     ]
    }
   ],
   "source": [
    "dictionary = {\n",
    "    1 : 'winter'\n",
    "}\n",
    "\n",
    "print(dictionary)\n",
    "print(dictionary[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{10: 1, 11: 1, 12: 1, 1: 1, 2: 1, 3: 1, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0}"
      ]
     },
     "execution_count": 5,
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   ],
   "source": [
    "winter_months = [10, 11, 12, 1, 2, 3]\n",
    "summer_months = [4, 5, 6, 7, 8, 9]\n",
    "\n",
    "all_months = winter_months + summer_months\n",
    "seasons = [1]*6 + [0]*6                            # Here we're assigning winter '1' and summer '0'\n",
    "\n",
    "month_to_season = dict(zip(all_months, seasons))\n",
    "month_to_season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       "      <td>2015-12-31 00:30:00</td>\n",
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       "      <td>2015-12-31 01:30:00</td>\n",
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       "      <td>2.043</td>\n",
       "      <td>4.191</td>\n",
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       "      <td>2015-12-31 02:00:00</td>\n",
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       "      <td>24.744</td>\n",
       "      <td>2.043</td>\n",
       "      <td>4.012</td>\n",
       "      <td>4.869</td>\n",
       "      <td>1.000</td>\n",
       "      <td>7.697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.781</td>\n",
       "      <td>1.621</td>\n",
       "      <td>-0.093</td>\n",
       "      <td>-1.402</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-31 02:30:00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>24.196</td>\n",
       "      <td>2.044</td>\n",
       "      <td>3.899</td>\n",
       "      <td>4.279</td>\n",
       "      <td>0.998</td>\n",
       "      <td>7.705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.339</td>\n",
       "      <td>0.773</td>\n",
       "      <td>1.621</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-1.399</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  price  demand  biomass   coal    gas  hydro  nuclear   \\\n",
       "0 2015-12-31 00:30:00  40.00  26.935    2.042  4.213  6.064  0.999     7.681   \n",
       "1 2015-12-31 01:00:00  40.00  26.254    2.043  4.236  5.860  1.004     7.677   \n",
       "2 2015-12-31 01:30:00  45.00  25.443    2.043  4.191  5.332  1.003     7.679   \n",
       "3 2015-12-31 02:00:00  41.89  24.744    2.043  4.012  4.869  1.000     7.697   \n",
       "4 2015-12-31 02:30:00  42.00  24.196    2.044  3.899  4.279  0.998     7.705   \n",
       "\n",
       "   solar   wind  dutch  french  irish  pumped  SP  season  \n",
       "0    0.0  3.963  0.793   1.621 -0.044  -0.397   2       1  \n",
       "1    0.0  3.986  0.794   1.622  0.103  -1.071   3       1  \n",
       "2    0.0  4.172  0.806   1.621  0.001  -1.405   4       1  \n",
       "3    0.0  4.216  0.781   1.621 -0.093  -1.402   5       1  \n",
       "4    0.0  4.339  0.773   1.621 -0.063  -1.399   6       1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_season = df_DAM.datetime.dt.month.map(month_to_season)\n",
    "df_DAM['season'] = s_season\n",
    "\n",
    "df_DAM.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "Due to a phenomena called the 'Merit Order Effect' the amount of renewables on the grid are likely to play a large role in the price of electricity. This is because renewables have very low operating costs so bid into the market at the bottom of the merit order stack.\n",
    "\n",
    "<img src='img/merit_order.png' width='800'>\n",
    "\n",
    "To help our model capture this information we'll create two new columns. One will be the amount of generation provided by 'dispatchable' resources (demand - intermittent renewables), the other will be the percentage of demand met by renewables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>datetime</th>\n",
       "      <th>price</th>\n",
       "      <th>demand</th>\n",
       "      <th>biomass</th>\n",
       "      <th>coal</th>\n",
       "      <th>gas</th>\n",
       "      <th>hydro</th>\n",
       "      <th>nuclear</th>\n",
       "      <th>solar</th>\n",
       "      <th>wind</th>\n",
       "      <th>dutch</th>\n",
       "      <th>french</th>\n",
       "      <th>irish</th>\n",
       "      <th>pumped</th>\n",
       "      <th>SP</th>\n",
       "      <th>season</th>\n",
       "      <th>disp_gen</th>\n",
       "      <th>pct_renew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-31 00:30:00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>26.935</td>\n",
       "      <td>2.042</td>\n",
       "      <td>4.213</td>\n",
       "      <td>6.064</td>\n",
       "      <td>0.999</td>\n",
       "      <td>7.681</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.963</td>\n",
       "      <td>0.793</td>\n",
       "      <td>1.621</td>\n",
       "      <td>-0.044</td>\n",
       "      <td>-0.397</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>22.972</td>\n",
       "      <td>0.147132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-31 01:00:00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>26.254</td>\n",
       "      <td>2.043</td>\n",
       "      <td>4.236</td>\n",
       "      <td>5.860</td>\n",
       "      <td>1.004</td>\n",
       "      <td>7.677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.986</td>\n",
       "      <td>0.794</td>\n",
       "      <td>1.622</td>\n",
       "      <td>0.103</td>\n",
       "      <td>-1.071</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.268</td>\n",
       "      <td>0.151824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-31 01:30:00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>25.443</td>\n",
       "      <td>2.043</td>\n",
       "      <td>4.191</td>\n",
       "      <td>5.332</td>\n",
       "      <td>1.003</td>\n",
       "      <td>7.679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.172</td>\n",
       "      <td>0.806</td>\n",
       "      <td>1.621</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-1.405</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>21.271</td>\n",
       "      <td>0.163974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-31 02:00:00</td>\n",
       "      <td>41.89</td>\n",
       "      <td>24.744</td>\n",
       "      <td>2.043</td>\n",
       "      <td>4.012</td>\n",
       "      <td>4.869</td>\n",
       "      <td>1.000</td>\n",
       "      <td>7.697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.781</td>\n",
       "      <td>1.621</td>\n",
       "      <td>-0.093</td>\n",
       "      <td>-1.402</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>20.528</td>\n",
       "      <td>0.170385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-31 02:30:00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>24.196</td>\n",
       "      <td>2.044</td>\n",
       "      <td>3.899</td>\n",
       "      <td>4.279</td>\n",
       "      <td>0.998</td>\n",
       "      <td>7.705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.339</td>\n",
       "      <td>0.773</td>\n",
       "      <td>1.621</td>\n",
       "      <td>-0.063</td>\n",
       "      <td>-1.399</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>19.857</td>\n",
       "      <td>0.179327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             datetime  price  demand  biomass   coal    gas  hydro  nuclear   \\\n",
       "0 2015-12-31 00:30:00  40.00  26.935    2.042  4.213  6.064  0.999     7.681   \n",
       "1 2015-12-31 01:00:00  40.00  26.254    2.043  4.236  5.860  1.004     7.677   \n",
       "2 2015-12-31 01:30:00  45.00  25.443    2.043  4.191  5.332  1.003     7.679   \n",
       "3 2015-12-31 02:00:00  41.89  24.744    2.043  4.012  4.869  1.000     7.697   \n",
       "4 2015-12-31 02:30:00  42.00  24.196    2.044  3.899  4.279  0.998     7.705   \n",
       "\n",
       "   solar   wind  dutch  french  irish  pumped  SP  season  disp_gen  pct_renew  \n",
       "0    0.0  3.963  0.793   1.621 -0.044  -0.397   2       1    22.972   0.147132  \n",
       "1    0.0  3.986  0.794   1.622  0.103  -1.071   3       1    22.268   0.151824  \n",
       "2    0.0  4.172  0.806   1.621  0.001  -1.405   4       1    21.271   0.163974  \n",
       "3    0.0  4.216  0.781   1.621 -0.093  -1.402   5       1    20.528   0.170385  \n",
       "4    0.0  4.339  0.773   1.621 -0.063  -1.399   6       1    19.857   0.179327  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DAM['disp_gen'] = df_DAM['demand'] - ( df_DAM['solar'] + df_DAM['wind'] )\n",
    "df_DAM['pct_renew'] = ( df_DAM['solar'] + df_DAM['wind'] ) / df_DAM['demand']\n",
    "\n",
    "df_DAM.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Data Pre-Processing\n",
    "\n",
    "Now we've created the features for the machine learning (ML) model we need to prepare the data so that it's in the correct format.\n",
    "\n",
    "<br>\n",
    "\n",
    "One of the things sklearn won't accept is any NaN values. We can check there are none using df.isnull() and then using sum to count up all the true values.\n",
    "\n",
    "In this case we have no NaN values but when we do we need to decide how to handle them, the main choice is whether to drop the data or if you'll 'impute' it in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime     0\n",
       "price        0\n",
       "demand       0\n",
       "biomass      0\n",
       "coal         0\n",
       "gas          0\n",
       "hydro        0\n",
       "nuclear      0\n",
       "solar        0\n",
       "wind         0\n",
       "dutch        0\n",
       "french       0\n",
       "irish        0\n",
       "pumped       0\n",
       "SP           0\n",
       "season       0\n",
       "disp_gen     0\n",
       "pct_renew    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DAM.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "When you do have missing data that's in a time series format one of the best approaches is to use s.interpolate() which takes the mean of the known values around it, other common methods are s.ffill() and s.bfill() which forward and backfill.\n",
    "\n",
    "When you're not working with time series data things become a bit more complicated, one approach is to fill in missing values with the mean of the column, if possible (you have enough data) it is often best to simply drop NaN values using df.dropna()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    NaN\n",
       "2    NaN\n",
       "3    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1, np.nan, np.nan, 3])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.000000\n",
       "1    1.666667\n",
       "2    2.333333\n",
       "3    3.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    3.0\n",
       "3    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.bfill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    2.0\n",
       "3    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_val = s.mean()\n",
    "s.fillna(mean_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "3    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "The next step is to separate the X and y columns, then transform the dataframes into numpy arrays which will be accepted by sklearn, we can use df.values to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['demand', 'biomass', 'coal', 'gas', 'hydro', 'nuclear ', 'solar',\n",
       "       'wind', 'dutch', 'french', 'irish', 'pumped', 'SP', 'season',\n",
       "       'disp_gen', 'pct_renew'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DAM.drop(columns=['price', 'datetime']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34895, 16)\n",
      "(34895,)\n"
     ]
    }
   ],
   "source": [
    "X_cols = df_DAM.drop(columns=['price', 'datetime']).columns\n",
    "y_col = 'price'\n",
    "\n",
    "X = df_DAM[X_cols].values \n",
    "y = df_DAM[y_col].values\n",
    "\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "Now comes one of the most important parts in data science, properly separating test and train data. This ensures that if your model is overfitting to the training data you can find out how it will fare against unseen data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24426, 16)\n",
      "(10469, 16)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "Feature normalisation is one technique in the data science (DS) toolkit which can help our algorithms produce better results.\n",
    "\n",
    "There are a number of ways in which this is useful, one of the most common is related to 'distance' metrics. For example in k-means clustering if one dimension has a scale 10x as large as another then the algorithm will in effect put 10x as much weight on that variable.\n",
    "\n",
    "MinMaxScaler scales the data so that the max value of each column is 1 and it's minimum is 0.\n",
    "\n",
    "StandardScaler sets the mean to 0 and the standard deviation to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>24426.000000</td>\n",
       "      <td>24426.000000</td>\n",
       "      <td>24426.000000</td>\n",
       "      <td>24426.000000</td>\n",
       "      <td>24426.000000</td>\n",
       "      <td>24426.000000</td>\n",
       "      <td>24426.000000</td>\n",
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       "      <td>24426.000000</td>\n",
       "      <td>24426.000000</td>\n",
       "      <td>24426.000000</td>\n",
       "      <td>24426.000000</td>\n",
       "      <td>24426.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.612821</td>\n",
       "      <td>0.629637</td>\n",
       "      <td>0.189165</td>\n",
       "      <td>0.528911</td>\n",
       "      <td>0.297422</td>\n",
       "      <td>0.851679</td>\n",
       "      <td>0.129034</td>\n",
       "      <td>0.331322</td>\n",
       "      <td>0.879824</td>\n",
       "      <td>0.742165</td>\n",
       "      <td>0.599929</td>\n",
       "      <td>0.499244</td>\n",
       "      <td>0.499876</td>\n",
       "      <td>0.495865</td>\n",
       "      <td>0.563474</td>\n",
       "      <td>0.113821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.150984</td>\n",
       "      <td>0.192602</td>\n",
       "      <td>0.185439</td>\n",
       "      <td>0.187847</td>\n",
       "      <td>0.194213</td>\n",
       "      <td>0.072427</td>\n",
       "      <td>0.194328</td>\n",
       "      <td>0.211043</td>\n",
       "      <td>0.167454</td>\n",
       "      <td>0.281067</td>\n",
       "      <td>0.219069</td>\n",
       "      <td>0.178124</td>\n",
       "      <td>0.294412</td>\n",
       "      <td>0.499993</td>\n",
       "      <td>0.135015</td>\n",
       "      <td>0.063705</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.495960</td>\n",
       "      <td>0.528324</td>\n",
       "      <td>0.043943</td>\n",
       "      <td>0.383733</td>\n",
       "      <td>0.138275</td>\n",
       "      <td>0.804267</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154321</td>\n",
       "      <td>0.864697</td>\n",
       "      <td>0.615520</td>\n",
       "      <td>0.449777</td>\n",
       "      <td>0.376881</td>\n",
       "      <td>0.255319</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.458839</td>\n",
       "      <td>0.063842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.618472</td>\n",
       "      <td>0.727553</td>\n",
       "      <td>0.123450</td>\n",
       "      <td>0.526914</td>\n",
       "      <td>0.262295</td>\n",
       "      <td>0.861723</td>\n",
       "      <td>0.005269</td>\n",
       "      <td>0.297891</td>\n",
       "      <td>0.959028</td>\n",
       "      <td>0.860476</td>\n",
       "      <td>0.634487</td>\n",
       "      <td>0.575031</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.556835</td>\n",
       "      <td>0.106837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.716665</td>\n",
       "      <td>0.792678</td>\n",
       "      <td>0.285155</td>\n",
       "      <td>0.669681</td>\n",
       "      <td>0.424804</td>\n",
       "      <td>0.902698</td>\n",
       "      <td>0.213058</td>\n",
       "      <td>0.470442</td>\n",
       "      <td>0.969509</td>\n",
       "      <td>0.977880</td>\n",
       "      <td>0.758371</td>\n",
       "      <td>0.601057</td>\n",
       "      <td>0.744681</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651018</td>\n",
       "      <td>0.155036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0             1             2             3             4   \\\n",
       "count  24426.000000  24426.000000  24426.000000  24426.000000  24426.000000   \n",
       "mean       0.612821      0.629637      0.189165      0.528911      0.297422   \n",
       "std        0.150984      0.192602      0.185439      0.187847      0.194213   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.495960      0.528324      0.043943      0.383733      0.138275   \n",
       "50%        0.618472      0.727553      0.123450      0.526914      0.262295   \n",
       "75%        0.716665      0.792678      0.285155      0.669681      0.424804   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "                 5             6             7             8             9   \\\n",
       "count  24426.000000  24426.000000  24426.000000  24426.000000  24426.000000   \n",
       "mean       0.851679      0.129034      0.331322      0.879824      0.742165   \n",
       "std        0.072427      0.194328      0.211043      0.167454      0.281067   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.804267      0.000000      0.154321      0.864697      0.615520   \n",
       "50%        0.861723      0.005269      0.297891      0.959028      0.860476   \n",
       "75%        0.902698      0.213058      0.470442      0.969509      0.977880   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "                 10            11            12            13            14  \\\n",
       "count  24426.000000  24426.000000  24426.000000  24426.000000  24426.000000   \n",
       "mean       0.599929      0.499244      0.499876      0.495865      0.563474   \n",
       "std        0.219069      0.178124      0.294412      0.499993      0.135015   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.449777      0.376881      0.255319      0.000000      0.458839   \n",
       "50%        0.634487      0.575031      0.489362      0.000000      0.556835   \n",
       "75%        0.758371      0.601057      0.744681      1.000000      0.651018   \n",
       "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "                 15  \n",
       "count  24426.000000  \n",
       "mean       0.113821  \n",
       "std        0.063705  \n",
       "min        0.000000  \n",
       "25%        0.063842  \n",
       "50%        0.106837  \n",
       "75%        0.155036  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "pd.DataFrame(X_train_scaled).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Running the Model!\n",
    "\n",
    "One of Scikit Learn's strengths is it's relatively simple API, we can use the same process for a range of ML model types.\n",
    "\n",
    "<img src='img/sklearn_API.png' width='500'>\n",
    "\n",
    "We'll first do a multivariate linear regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()               # Instantiate the model\n",
    "lr.fit(X_train_scaled, y_train)       # Fit the model to the training data\n",
    "y_pred = lr.predict(X_test_scaled)    # Make predictions using the model on the test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "Evaluating the model is done using the r2 score by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score: 0.17487511312513437\n"
     ]
    }
   ],
   "source": [
    "sum_of_squares = ((y_test - y_pred) ** 2).sum()\n",
    "res_sum_of_squares = ((y_test - y_test.mean()) ** 2).sum()\n",
    "r2_score = 1 - (sum_of_squares / res_sum_of_squares)\n",
    "\n",
    "print(f'R2 score: {r2_score}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score: 0.17487511312513437\n"
     ]
    }
   ],
   "source": [
    "model_score = lr.score(X_test_scaled, y_test)\n",
    "print(f'R2 score: {model_score}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "We can look at other scoring measures using sklearns metrics module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 9.086932696441671\n",
      "MSE: 691.0908825032332\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "print(f'MAE: {mae}')\n",
    "print(f'MSE: {mse}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "Comparing the absolute and squared error it appears as though the model is unable to handle some of the more extreme values.\n",
    "\n",
    "We can check this by plotting the error distribution and a separate scatter plot to let us know where the errors are occuring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12accc43978>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_test - y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 55)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = X_test[:, 0]\n",
    "y = y_test\n",
    "c = y_test - y_pred\n",
    "\n",
    "## Plotting\n",
    "plt.figure(figsize=(10, 5), dpi=100)\n",
    "\n",
    "plt.scatter(x, y, c=c, s=5)\n",
    "plt.colorbar()\n",
    "\n",
    "plt.xlim(15, 55)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Random Forest Model\n",
    "\n",
    "One of the most used models at the moment in ML is the random forest, it's an ensemble of individual decision trees.\n",
    "\n",
    "<img src='img/decision_tree.png' width='500'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=25)  \n",
    "rf.fit(X_train_scaled, y_train)       \n",
    "y_pred = rf.predict(X_test_scaled)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score: 0.6996334581756483\n",
      "MAE: 4.781037654026172\n",
      "MSE: 251.57473949190947\n"
     ]
    }
   ],
   "source": [
    "r2 = rf.score(X_test_scaled, y_test)\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "print(f'R2 score: {r2}')\n",
    "print(f'MAE: {mae}')\n",
    "print(f'MSE: {mse}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 55)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x750 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = X_test[:, 0]\n",
    "y = y_test\n",
    "c = y_test - y_pred\n",
    "\n",
    "df_pred = pd.DataFrame({'x':x, 'y':y, 'c':c})\n",
    "df_pred_filt = df_pred[df_pred.y<200]\n",
    "\n",
    "## Plotting\n",
    "plt.figure(figsize=(10, 5), dpi=150)\n",
    "\n",
    "plt.scatter(df_pred_filt.x, df_pred_filt.y, c=df_pred_filt.c, s=5, )\n",
    "plt.colorbar()\n",
    "\n",
    "plt.xlim(15, 55)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "Feature importance is one of the ways in which random forest models can be explored, the importance is based on the number of decision nodes which use that feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.06345164, 0.06651208, 0.08990678, 0.12521   , 0.04459492,\n",
       "       0.04396061, 0.01907681, 0.03791889, 0.03211809, 0.06348096,\n",
       "       0.05686565, 0.16754522, 0.05455872, 0.01762676, 0.08419377,\n",
       "       0.03297913])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pumped</td>\n",
       "      <td>16.754522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gas</td>\n",
       "      <td>12.521000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>coal</td>\n",
       "      <td>8.990678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>disp_gen</td>\n",
       "      <td>8.419377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>biomass</td>\n",
       "      <td>6.651208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>french</td>\n",
       "      <td>6.348096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>demand</td>\n",
       "      <td>6.345164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>irish</td>\n",
       "      <td>5.686565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SP</td>\n",
       "      <td>5.455872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hydro</td>\n",
       "      <td>4.459492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nuclear</td>\n",
       "      <td>4.396061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>wind</td>\n",
       "      <td>3.791889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>pct_renew</td>\n",
       "      <td>3.297913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dutch</td>\n",
       "      <td>3.211809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>solar</td>\n",
       "      <td>1.907681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>season</td>\n",
       "      <td>1.762676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      feature  importance\n",
       "11     pumped   16.754522\n",
       "3         gas   12.521000\n",
       "2        coal    8.990678\n",
       "14   disp_gen    8.419377\n",
       "1     biomass    6.651208\n",
       "9      french    6.348096\n",
       "0      demand    6.345164\n",
       "10      irish    5.686565\n",
       "12         SP    5.455872\n",
       "4       hydro    4.459492\n",
       "5    nuclear     4.396061\n",
       "7        wind    3.791889\n",
       "15  pct_renew    3.297913\n",
       "8       dutch    3.211809\n",
       "6       solar    1.907681\n",
       "13     season    1.762676"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = rf.feature_importances_\n",
    "df_importances = pd.DataFrame({'feature':X_cols, 'importance':importances*100})\n",
    "df_importances.sort_values('importance', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Putting the Model Together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score: 0.6475956490154511\n",
      "MAE: 4.449329169054441\n",
      "MSE: 173.96823511679088\n"
     ]
    }
   ],
   "source": [
    "def random_forest_model(df, X_cols, y_col):\n",
    "    ## Separating X and y values\n",
    "    X = df_DAM[X_cols].values \n",
    "    y = df_DAM[y_col].values\n",
    "    \n",
    "    ## Splitting test and training data\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=0.1, random_state=42)\n",
    "    \n",
    "    ## Scaling data\n",
    "    scaler = MinMaxScaler()\n",
    "    scaler.fit(X_train)\n",
    "\n",
    "    X_train_scaled = scaler.transform(X_train)\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "    ## Running the model\n",
    "    rf = RandomForestRegressor(n_estimators=25)  \n",
    "    rf.fit(X_train_scaled, y_train)       \n",
    "    y_pred = rf.predict(X_test_scaled)\n",
    "    \n",
    "    ## Calculating metrics\n",
    "    r2 = rf.score(X_test_scaled, y_test)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "\n",
    "    print(f'R2 score: {r2}')\n",
    "    print(f'MAE: {mae}')\n",
    "    print(f'MSE: {mse}')\n",
    "    \n",
    "    return \n",
    "  \n",
    "## Defining input variables\n",
    "X_cols = df_DAM.drop(columns=['price', 'datetime']).columns\n",
    "\n",
    "y_col = 'price'\n",
    "    \n",
    "random_forest_model(df_DAM, X_cols, y_col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### What Next?\n",
    "\n",
    "With this model:\n",
    "\n",
    "* Try out different hyper-parameters and feature engineering methods to see how the model's accuracy changes.\n",
    "* Try predicting something like the amount of pumped hydro used based on the other features.\n",
    "* Try out different models to a random forest"
   ]
  }
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