forestfirePredictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forrest Fire Prediction using SVR, Random Forest, and Deep Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Available at: http://archive.ics.uci.edu/ml/datasets/Forest+Fires**\n",
    "\n",
    "\n",
    "X - x-axis spatial coordinate within the Montesinho park map: 1 to 9\n",
    "\n",
    "Y - y-axis spatial coordinate within the Montesinho park map: 2 to 9\n",
    "\n",
    "month - month of the year: \"jan\" to \"dec\"\n",
    "\n",
    "day - day of the week: \"mon\" to \"sun\"\n",
    "\n",
    "FFMC - FFMC index from the FWI system: 18.7 to 96.20\n",
    "\n",
    "DMC - DMC index from the FWI system: 1.1 to 291.3\n",
    "\n",
    "DC - DC index from the FWI system: 7.9 to 860.6\n",
    "\n",
    "ISI - ISI index from the FWI system: 0.0 to 56.10\n",
    "\n",
    "temp - temperature in Celsius degrees: 2.2 to 33.30\n",
    "\n",
    "RH - relative humidity in %: 15.0 to 100\n",
    "\n",
    "wind - wind speed in km/h: 0.40 to 9.40\n",
    "\n",
    "rain - outside rain in mm/m2 : 0.0 to 6.4\n",
    "\n",
    "area - the burned area of the forest (in ha): 0.00 to 1090.84\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>86.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>94.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>51</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>tue</td>\n",
       "      <td>90.6</td>\n",
       "      <td>35.4</td>\n",
       "      <td>669.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>sat</td>\n",
       "      <td>90.6</td>\n",
       "      <td>43.7</td>\n",
       "      <td>686.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>33</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>91.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>sun</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>92.3</td>\n",
       "      <td>85.3</td>\n",
       "      <td>488.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>29</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>aug</td>\n",
       "      <td>mon</td>\n",
       "      <td>92.3</td>\n",
       "      <td>88.9</td>\n",
       "      <td>495.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>24.1</td>\n",
       "      <td>27</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>aug</td>\n",
       "      <td>mon</td>\n",
       "      <td>91.5</td>\n",
       "      <td>145.4</td>\n",
       "      <td>608.2</td>\n",
       "      <td>10.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>86</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>sep</td>\n",
       "      <td>tue</td>\n",
       "      <td>91.0</td>\n",
       "      <td>129.5</td>\n",
       "      <td>692.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.1</td>\n",
       "      <td>63</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>sep</td>\n",
       "      <td>sat</td>\n",
       "      <td>92.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>698.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>22.8</td>\n",
       "      <td>40</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>sep</td>\n",
       "      <td>sat</td>\n",
       "      <td>92.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>698.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>17.8</td>\n",
       "      <td>51</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>sep</td>\n",
       "      <td>sat</td>\n",
       "      <td>92.8</td>\n",
       "      <td>73.2</td>\n",
       "      <td>713.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>19.3</td>\n",
       "      <td>38</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>63.5</td>\n",
       "      <td>70.8</td>\n",
       "      <td>665.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>72</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>sep</td>\n",
       "      <td>mon</td>\n",
       "      <td>90.9</td>\n",
       "      <td>126.5</td>\n",
       "      <td>686.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>42</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>sep</td>\n",
       "      <td>wed</td>\n",
       "      <td>92.9</td>\n",
       "      <td>133.3</td>\n",
       "      <td>699.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>26.4</td>\n",
       "      <td>21</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>sep</td>\n",
       "      <td>fri</td>\n",
       "      <td>93.3</td>\n",
       "      <td>141.2</td>\n",
       "      <td>713.9</td>\n",
       "      <td>13.9</td>\n",
       "      <td>22.9</td>\n",
       "      <td>44</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>mar</td>\n",
       "      <td>sat</td>\n",
       "      <td>91.7</td>\n",
       "      <td>35.8</td>\n",
       "      <td>80.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>15.1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>oct</td>\n",
       "      <td>mon</td>\n",
       "      <td>84.9</td>\n",
       "      <td>32.8</td>\n",
       "      <td>664.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.7</td>\n",
       "      <td>47</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>mar</td>\n",
       "      <td>wed</td>\n",
       "      <td>89.2</td>\n",
       "      <td>27.9</td>\n",
       "      <td>70.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>15.9</td>\n",
       "      <td>35</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>apr</td>\n",
       "      <td>sat</td>\n",
       "      <td>86.3</td>\n",
       "      <td>27.4</td>\n",
       "      <td>97.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>9.3</td>\n",
       "      <td>44</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>sep</td>\n",
       "      <td>tue</td>\n",
       "      <td>91.0</td>\n",
       "      <td>129.5</td>\n",
       "      <td>692.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.3</td>\n",
       "      <td>40</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>sep</td>\n",
       "      <td>mon</td>\n",
       "      <td>91.8</td>\n",
       "      <td>78.5</td>\n",
       "      <td>724.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>19.1</td>\n",
       "      <td>38</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>jun</td>\n",
       "      <td>sun</td>\n",
       "      <td>94.3</td>\n",
       "      <td>96.3</td>\n",
       "      <td>200.0</td>\n",
       "      <td>56.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>44</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>aug</td>\n",
       "      <td>sat</td>\n",
       "      <td>90.2</td>\n",
       "      <td>110.9</td>\n",
       "      <td>537.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>19.5</td>\n",
       "      <td>43</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>aug</td>\n",
       "      <td>sat</td>\n",
       "      <td>93.5</td>\n",
       "      <td>139.4</td>\n",
       "      <td>594.2</td>\n",
       "      <td>20.3</td>\n",
       "      <td>23.7</td>\n",
       "      <td>32</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X  Y month  day  FFMC    DMC     DC   ISI  temp  RH  wind  rain  area\n",
       "0   7  5   mar  fri  86.2   26.2   94.3   5.1   8.2  51   6.7   0.0   0.0\n",
       "1   7  4   oct  tue  90.6   35.4  669.1   6.7  18.0  33   0.9   0.0   0.0\n",
       "2   7  4   oct  sat  90.6   43.7  686.9   6.7  14.6  33   1.3   0.0   0.0\n",
       "3   8  6   mar  fri  91.7   33.3   77.5   9.0   8.3  97   4.0   0.2   0.0\n",
       "4   8  6   mar  sun  89.3   51.3  102.2   9.6  11.4  99   1.8   0.0   0.0\n",
       "5   8  6   aug  sun  92.3   85.3  488.0  14.7  22.2  29   5.4   0.0   0.0\n",
       "6   8  6   aug  mon  92.3   88.9  495.6   8.5  24.1  27   3.1   0.0   0.0\n",
       "7   8  6   aug  mon  91.5  145.4  608.2  10.7   8.0  86   2.2   0.0   0.0\n",
       "8   8  6   sep  tue  91.0  129.5  692.6   7.0  13.1  63   5.4   0.0   0.0\n",
       "9   7  5   sep  sat  92.5   88.0  698.6   7.1  22.8  40   4.0   0.0   0.0\n",
       "10  7  5   sep  sat  92.5   88.0  698.6   7.1  17.8  51   7.2   0.0   0.0\n",
       "11  7  5   sep  sat  92.8   73.2  713.0  22.6  19.3  38   4.0   0.0   0.0\n",
       "12  6  5   aug  fri  63.5   70.8  665.3   0.8  17.0  72   6.7   0.0   0.0\n",
       "13  6  5   sep  mon  90.9  126.5  686.5   7.0  21.3  42   2.2   0.0   0.0\n",
       "14  6  5   sep  wed  92.9  133.3  699.6   9.2  26.4  21   4.5   0.0   0.0\n",
       "15  6  5   sep  fri  93.3  141.2  713.9  13.9  22.9  44   5.4   0.0   0.0\n",
       "16  5  5   mar  sat  91.7   35.8   80.8   7.8  15.1  27   5.4   0.0   0.0\n",
       "17  8  5   oct  mon  84.9   32.8  664.2   3.0  16.7  47   4.9   0.0   0.0\n",
       "18  6  4   mar  wed  89.2   27.9   70.8   6.3  15.9  35   4.0   0.0   0.0\n",
       "19  6  4   apr  sat  86.3   27.4   97.1   5.1   9.3  44   4.5   0.0   0.0\n",
       "20  6  4   sep  tue  91.0  129.5  692.6   7.0  18.3  40   2.7   0.0   0.0\n",
       "21  5  4   sep  mon  91.8   78.5  724.3   9.2  19.1  38   2.7   0.0   0.0\n",
       "22  7  4   jun  sun  94.3   96.3  200.0  56.1  21.0  44   4.5   0.0   0.0\n",
       "23  7  4   aug  sat  90.2  110.9  537.4   6.2  19.5  43   5.8   0.0   0.0\n",
       "24  7  4   aug  sat  93.5  139.4  594.2  20.3  23.7  32   5.8   0.0   0.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('forestfires.csv')\n",
    "df.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.669246</td>\n",
       "      <td>4.299807</td>\n",
       "      <td>90.644681</td>\n",
       "      <td>110.872340</td>\n",
       "      <td>547.940039</td>\n",
       "      <td>9.021663</td>\n",
       "      <td>18.889168</td>\n",
       "      <td>44.288201</td>\n",
       "      <td>4.017602</td>\n",
       "      <td>0.021663</td>\n",
       "      <td>12.847292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.313778</td>\n",
       "      <td>1.229900</td>\n",
       "      <td>5.520111</td>\n",
       "      <td>64.046482</td>\n",
       "      <td>248.066192</td>\n",
       "      <td>4.559477</td>\n",
       "      <td>5.806625</td>\n",
       "      <td>16.317469</td>\n",
       "      <td>1.791653</td>\n",
       "      <td>0.295959</td>\n",
       "      <td>63.655818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>18.700000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>90.200000</td>\n",
       "      <td>68.600000</td>\n",
       "      <td>437.700000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>91.600000</td>\n",
       "      <td>108.300000</td>\n",
       "      <td>664.200000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>92.900000</td>\n",
       "      <td>142.400000</td>\n",
       "      <td>713.900000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>96.200000</td>\n",
       "      <td>291.300000</td>\n",
       "      <td>860.600000</td>\n",
       "      <td>56.100000</td>\n",
       "      <td>33.300000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>1090.840000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                X           Y        FFMC         DMC          DC         ISI  \\\n",
       "count  517.000000  517.000000  517.000000  517.000000  517.000000  517.000000   \n",
       "mean     4.669246    4.299807   90.644681  110.872340  547.940039    9.021663   \n",
       "std      2.313778    1.229900    5.520111   64.046482  248.066192    4.559477   \n",
       "min      1.000000    2.000000   18.700000    1.100000    7.900000    0.000000   \n",
       "25%      3.000000    4.000000   90.200000   68.600000  437.700000    6.500000   \n",
       "50%      4.000000    4.000000   91.600000  108.300000  664.200000    8.400000   \n",
       "75%      7.000000    5.000000   92.900000  142.400000  713.900000   10.800000   \n",
       "max      9.000000    9.000000   96.200000  291.300000  860.600000   56.100000   \n",
       "\n",
       "             temp          RH        wind        rain         area  \n",
       "count  517.000000  517.000000  517.000000  517.000000   517.000000  \n",
       "mean    18.889168   44.288201    4.017602    0.021663    12.847292  \n",
       "std      5.806625   16.317469    1.791653    0.295959    63.655818  \n",
       "min      2.200000   15.000000    0.400000    0.000000     0.000000  \n",
       "25%     15.500000   33.000000    2.700000    0.000000     0.000000  \n",
       "50%     19.300000   42.000000    4.000000    0.000000     0.520000  \n",
       "75%     22.800000   53.000000    4.900000    0.000000     6.570000  \n",
       "max     33.300000  100.000000    9.400000    6.400000  1090.840000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot scatterplots and distributions of numerical features to see how they may affect the output 'area'\n",
    "For this, first we need to transform the outcome 'area' by taking its logarithm (after adding 1 to avoid zeros)\n",
    "\n",
    "$$\\text{Log-area} = log_{10}(area+1)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "df['Log-area']=np.log10(df['area']+1)\n",
    "\n",
    "for i in df.describe().columns[:-2]:\n",
    "    df.plot.scatter(i,'Log-area',grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot boxplots of how the categorical features (month and day) affect the outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11894d1d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BlsnixYvp6emho6ODyZMn09HRQU9PD4sXL252aGZWALX0+N8KHAvcLWllWnca8EqAiDgPOAr4hKRNwLPA0ekww8ZgYGCABQsWbLVuwYIFDAwMNCkiMyuSERN/RCwHNEKZc4Fz6xVU2c2ePZvly5fT0dHx4rrly5cze/bsJkZlZkXhK3dbUHd3N11dXfT19bFp0yb6+vro6uqiu7u72aGZWQHUdHLXxtfgCdwTTzyRgYEBZs+ezeLFi31i18zqwom/RXV2dtLZ2Ul/fz/t7e3NDsfMCsRDPWZmJePEb2ZWMk78ZiPo7e1l7ty5LFy4kLlz59Lb29vskEpDUtVHR0fHkNtsZE78ZsMYvIr6nHPO4brrruOcc86hu7vbyX+cVLvdQESw98lXD3d7mZbWCh0Jn9y1uhlrb6uV/1nzV1EPnmjv6enhxBNP9CwrG7VWuR2Le/xWN0P1wCZyD81XUVs9tcrtWJz4zYYxeBV1nq+itrFqlY6EE7/ZMHwVtdVTq3QkPMZvNgxfRW31NNiRGBzjH+xIjPdQjxO/2Qh8FbXVS6t0JJz4zczGUSt0JDzGb2ZWMk78ZmYl48RvZlYyTvxmZiXjxG9mVjJO/GZmJePEb2ZWMk78ZmYl48RvZlYyIyZ+SXtJ6pM0IGmVpE9VKSNJX5O0WtJdkt7YmHDNzGx71XLLhk3ASRFxh6RpwApJ10fEvbkyhwH7pcebgW+mn2Zm1mJG7PFHxCMRcUd6/hQwAOxRUewIYElkbgGmS9q97tGamdl2G9UYv6RZwBuAWys27QE8nFtey7YfDmZm1gJqvjunpKnA94B/jIgnKzdXeck236knaRGwCKCtrY3+/v7aIx2DjRs3NryORitCGwZN9HYU4b0oQhsGTfR2NPO9qCnxS5pClvQvjYirqhRZC+yVW94TWFdZKCLOB84HmD9/fjT6lqRFuH96EdoAwI+umTDtGMuXxrf6dwcP8t9T62jp2zIr+y/oAQYi4l+HKLYM+GCa3fMWYENEPFLHOM3GTdG+MN6sUi09/rcCxwJ3S1qZ1p0GvBIgIs4DrgUOB1YDzwAfrn+oZmYTTyseQY6Y+CNiOdXH8PNlAji+XkGZmRXFUEl81inXsOasd45zNBlfuWtmVjJO/GZmJePEb2ZWMk78ZmYl48RvZlYyTvxmZiXjxG9mVjJO/GZmJePEb2ZWMk78ZmYlU/Ntma2xWvF+HmZWTO7xtwjfEdLMxosTv5lZyTjxm5mVjBO/mVnJOPGbmZWME7+ZWck48ZuZlUwh5vF7DrzZ1sbyPwH+vyiLQvT4PQfebGtj+Z/w/0V5FCLxm5lZ7Zz4zcxKxonfzKxkRkz8ki6Q9HtJ9wyxvV3SBkkr0+P0+odpZmb1UsusnguBc4Elw5S5OSLeVZeIzMysoUbs8UfETcBj4xCLmZmNg3qN8R8k6U5JP5Q0p077NDOzBqjHBVx3AHtHxEZJhwM/AParVlDSImARQFtbG/39/XWofnjjUUejFaENUIx2uA2towjtaFYbtjvxR8STuefXSvqGpBkRsb5K2fOB8wHmz58f7e3t21v98H50DQ2vo9GK0AYoRjvchtZRhHY0sQ3bPdQj6RVK14dLOjDt89Ht3a+ZmTXGiD1+Sb1AOzBD0lrgDGAKQEScBxwFfELSJuBZ4Ojwtd9mZi1rxMQfEZ0jbD+XbLqnmZlNAL5y18ysZJz4zcxKxonfzKxknPjNzErGid/MrGSc+M3MSsaJ38ysZJz4zcxKxonfzKxknPjNzErGid/MrGSc+M3MSsaJ38ysZJz4zcxKxonfzKxknPjNzErGid/MrGSc+M3MSsaJ38ysZJz4zcxKxonfzKxknPjNzErGid/MrGSc+M3MSmbExC/pAkm/l3TPENsl6WuSVku6S9Ib6x+mmZnVSy09/guBQ4fZfhiwX3osAr65/WGZmVmjjJj4I+Im4LFhihwBLInMLcB0SbvXK0AzM6uvyXXYxx7Aw7nltWndI5UFJS0iOyqgra2N/v7+OlQ/vPGoo9GK0AYoRjvchtZRhHY0qw31SPyqsi6qFYyI84HzAebPnx/t7e11qH4YP7qGhtfRaEVoAxSjHW5DQ7zu8z9mw7PPj/p1x/3o6ZrL7rLTFO484x2jrqOhmvhe1CPxrwX2yi3vCayrw37NrAQ2PPs8a85656he09/fP6qkOeuUa0YZVbHVYzrnMuCDaXbPW4ANEbHNMI+ZmbWGEXv8knqBdmCGpLXAGcAUgIg4D7gWOBxYDTwDfLhRwZqZ2fYbMfFHROcI2wM4vm4RWcsb65jsaA63W3JM1qwg6jHGbyXjMVmzic2J30prLEcuPmqxInDit9Ia7ZGLj1qsKHyTNjOzkplQPf4iHJo3ug3gIQYzG96ESvxFODRvdBvAQwxmzTCROqYTKvGbmbWqidQx9Ri/mVnJOPGbmZWMh3rMJjBPFrCxcOI3m8A8WcDGwkM9ZmYl48RvZlYyTvxmZiXjxG9mVjJO/GZmJePEb2ZWMk78ZmYl48RvZlYyTvxmZiXjxG9mVjJO/GZmJePEb2ZWMjUlfkmHSrpP0mpJp1TZfpykP0hamR4fqX+oZmZWDyPenVPSJODrwF8Da4HbJC2LiHsrin43Ik5oQIxmZlZHtfT4DwRWR8QDEfEn4DLgiMaGZWZmjVJL4t8DeDi3vDatq3SkpLskXSlpr7pEZ2ZmdVfLF7GoyrqoWP53oDcinpP0ceAi4O3b7EhaBCwCaGtro7+/f3TRwqhes3HjxlHXMZaYRqvRbRhtHWMx2v2X9b0oQhtGW8dY+O+p/vsfVkQM+wAOAq7LLZ8KnDpM+UnAhpH2O2/evBitvU++elTl+/r6Grr/sWh0G8ZSx3jsv4zvRRHaMJY6xmP/ZXwvxtIG4Paokn9rGeq5DdhP0j6SXgIcDSzLF5C0e27xb4GB7f1AMjOzxhhxqCciNkk6AbiOrDd/QUSskvQFsk+TZcAnJf0tsAl4DDiugTGbmdl2qOnL1iPiWuDainWn556fSjYEZGZmLc5X7pqZlYwTv5lZyTjxm5mVjBO/mVnJOPGbmZWME7+ZWck48ZuZlYwTv5lZyTjxm5mVTE1X7ppZa5o2+xQOuGibL8Ub3kWjrQPgnaN7kbU0J36zCeypgbNYc1btSbm/v5/29vZR1THrlGtGGZW1Oid+M7M6aPTRVz2PvJz4x5kPzc2KqdFHX/U88nLiH2c+NDfb2pg6Q9C03nIROPGbWVONtjMEze0tF4Gnc5qZlYwTv5lZyTjxm5mVjBO/mVnJTKiTuxNpnqyZWauaUIl/Is2TLTJPvzOb2CZU4rfW4Ol3ZhObx/jNzEqmpsQv6VBJ90laLWmbY3xJO0r6btp+q6RZ9Q7UzMzqY8TEL2kS8HXgMGB/oFPS/hXFuoDHI2Jf4KvA2fUO1MzM6qOWHv+BwOqIeCAi/gRcBhxRUeYItpy6uxJYKEn1C9PMzOqllsS/B/BwbnltWle1TERsAjYAL69HgGZmVl+1zOqp1nOPMZRB0iJgEUBbWxv9/f01VL+1arM9Hjr7XaPez94nX73Nup2nMKaYRquRbYDxaUe92gDNey/G47qQ/v6dR7f/MSjCezHULC7/b29R1zZExLAP4CDgutzyqcCpFWWuAw5KzycD6wENt9958+ZFo/X19TW8jkYrQhsiitEOt6F1FKEd49EG4Paokn9rGeq5DdhP0j6SXgIcDSyrKLMM+FB6fhRwY6rUzMxazIhDPRGxSdIJZL36ScAFEbFK0hfIPk2WAT3AxZJWA4+RfTiYmVkLqunK3Yi4Fri2Yt3pued/BN5X39DMzKwRfOWumVnJOPGbmZWME7+ZWck48ZuZlYwTv5lZyahZ0+0l/QF4qMHVzCC7mGwiK0IboBjtcBtaRxHaMR5t2Dsidqtc2bTEPx4k3R4R85sdx/YoQhugGO1wG1pHEdrRzDZ4qMfMrGSc+M3MSqboif/8ZgdQB0VoAxSjHW5D6yhCO5rWhkKP8ZuZ2baK3uM3M7MKhUr8kj4paUDSpRXr50v6WrPism1JOk7SzGbHUTSSpkv6h/S8XVL1b+tpcfl2lFGj37tCJX7gH4DDI+KYwRWSJkfE7RHxySbGZds6DnDir7/pZP8HE11R2tGSCpP4JZ0HvApYJmmDpPMl/RhY0oo9H0mzJP1K0rcl3SPpUkmHSPqppF9LOlDSrpJ+IOkuSbdIem167ZmSLpDUL+kBSS3xoSZpZ0nXSLozten9kk6XdFtaPl+Zo4D5wKWSVkraqdmxw5Dxr5E0I22fL6k/PW/J9wA4C3i1pJXAV4Cpkq5Mf2uXShLAUO1qIS+2I/39vPj/K+lcScel5/Mk/UTSCknXSdq9WQEPkvTZwb8HSV+VdGN6vlDSJZLeIennku6QdIWkqWn7oel9Wg68t6FBVvtaron6ANaQXQ13JrAC2CmtbweubnZ8FbHOAjYBB5B9AK8ALiD7/uIjgB8A5wBnpPJvB1am52cCPwN2TO19FJjSAm06EvhWbnkXYNfc8sXAu9PzfmB+s2OuIf41wIy0PB/ob/H3YBZwT3reDmwA9kx/Yz8HFqRtVdvVKo8q7bg6t+1csiPGKek92C2tfz/ZF0U1O/a3AFek5zcDv0ixngGcDNwE7Jy2nwycDrwUeBjYL+WAyxuZswrT469iWUQ82+wgRvBgRNwdES8Aq4AbIvtruJvsD38BWbIkIm4EXi5pl/TaayLiuYhYD/weaBv36Ld1N3CIpLMlHRwRG4AOSbdKupvsw2tOc0McVrX4h9OK70GlX0TE2vQ3tpLs76oo/hKYC1yfjnA+R/Yh12wrgHmSpgHPkX3gzgcOBp4F9gd+mmL+ELA38F/I8sGvUw64pJEB1vQNXBPU080OoAbP5Z6/kFt+gey92VTlNYPzb/Ov3UwLvJcR8Z+S5gGHA19OQ23Hk/XsH5Z0JlnPpiUNEf8mtgyJVsbecu9BFUPFOFy7Wk0+VtgSr4BVEXHQ+Ic0tIh4XtIa4MNkRyR3AR3Aq4EHgesjojP/GkmvZ8v/dsMVucdfBDcBx0B2lh9YHxFPNjWiYaRZOs9ExCXA/wTemDatT+OYR+WKPwVMG+cQhzVE/GuAeanIkU0KbTRq/b2uobXblW/HQ8D+knZMR7wL0/r7gN0kHQQgaYqkVjmivAn4dPp5M/BxsiOuW4C3StoXQNLLJL0G+BWwj6RXp9d3brvL+mnFHoptcSbwHUl3Ac+QHRa2sgOAr0h6AXge+ATwHrIhlDXAbbmyFwLnSXoWOKhFhuWqxb8T0CPpNODWZgZXi4h4NE0QuIdsWOF3QxT9PC3crop2/JBszPsu4NfAL1OZP6WJAl9LHwiTgf9NNmzabDcD3cDPI+JpSX8Ebo6IP6QT072SdkxlP5eONhcB10haDywnG8ZqCF+5a2ZWMh7qMTMrGSd+M7OSceI3MysZJ34zs5Jx4jczKxknfrMq0r14Pt3sOMwawYnfzKxknPjNEkndku6T9B9k94FB0kfT3SHvlPS9dKXlNEkPSpqSyvxZutvllKY2wKxGTvxmZLf3BY4G3kB2S9w3pU1XRcSbIuJ1wADQFRFPkd1d9J2pzNHA9yLi+fGN2mxsnPjNMgcD34+IZ9L9kJal9XMl3ZzuLnoMW+4u+m2ym3CRfn5nXKM12w5O/GZbVLt/yYXACRFxANn9bV4KEBE/BWZJehswKSLuGbcozbaTE79Z5ibg7yTtlO6j/u60fhrwSBq/P6biNUuAXtzbtwnGN2kzSyR1Ax8kuw3wWuBesu91+GxadzcwLSKOS+VfQXZ/9d0j4olmxGw2Fk78ZmOUbgl8REQc2+xYzEbD9+M3GwNJ5wCHkX1bl9mE4h6/mVnJ+OSumVnJOPGbmZWME7+ZWck48ZuZlYwTv5lZyTjxm5mVzP8Hw0OGQ6qHFxgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='Log-area',by='day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118f4be80>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='Log-area',by='month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data pre-processing, test/train split, REC function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Label encoder for the categorical feature (day and month\n",
    "enc = LabelEncoder()\n",
    "enc.fit(df['month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['apr', 'aug', 'dec', 'feb', 'jan', 'jul', 'jun', 'mar', 'may',\n",
       "       'nov', 'oct', 'sep'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area</th>\n",
       "      <th>Log-area</th>\n",
       "      <th>month_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>86.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>94.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>51</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>tue</td>\n",
       "      <td>90.6</td>\n",
       "      <td>35.4</td>\n",
       "      <td>669.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>sat</td>\n",
       "      <td>90.6</td>\n",
       "      <td>43.7</td>\n",
       "      <td>686.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>33</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>91.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>sun</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X  Y month  day  FFMC   DMC     DC  ISI  temp  RH  wind  rain  area  \\\n",
       "0  7  5   mar  fri  86.2  26.2   94.3  5.1   8.2  51   6.7   0.0   0.0   \n",
       "1  7  4   oct  tue  90.6  35.4  669.1  6.7  18.0  33   0.9   0.0   0.0   \n",
       "2  7  4   oct  sat  90.6  43.7  686.9  6.7  14.6  33   1.3   0.0   0.0   \n",
       "3  8  6   mar  fri  91.7  33.3   77.5  9.0   8.3  97   4.0   0.2   0.0   \n",
       "4  8  6   mar  sun  89.3  51.3  102.2  9.6  11.4  99   1.8   0.0   0.0   \n",
       "\n",
       "   Log-area  month_encoded  \n",
       "0       0.0              7  \n",
       "1       0.0             10  \n",
       "2       0.0             10  \n",
       "3       0.0              7  \n",
       "4       0.0              7  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['month_encoded']=enc.transform(df['month'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.fit(df['day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fri', 'mon', 'sat', 'sun', 'thu', 'tue', 'wed'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area</th>\n",
       "      <th>Log-area</th>\n",
       "      <th>month_encoded</th>\n",
       "      <th>day_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>86.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>94.3</td>\n",
       "      <td>5.1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>51</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>tue</td>\n",
       "      <td>90.6</td>\n",
       "      <td>35.4</td>\n",
       "      <td>669.1</td>\n",
       "      <td>6.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>oct</td>\n",
       "      <td>sat</td>\n",
       "      <td>90.6</td>\n",
       "      <td>43.7</td>\n",
       "      <td>686.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>33</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>91.7</td>\n",
       "      <td>33.3</td>\n",
       "      <td>77.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>97</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>mar</td>\n",
       "      <td>sun</td>\n",
       "      <td>89.3</td>\n",
       "      <td>51.3</td>\n",
       "      <td>102.2</td>\n",
       "      <td>9.6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>92.3</td>\n",
       "      <td>85.3</td>\n",
       "      <td>488.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>29</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>aug</td>\n",
       "      <td>mon</td>\n",
       "      <td>92.3</td>\n",
       "      <td>88.9</td>\n",
       "      <td>495.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>24.1</td>\n",
       "      <td>27</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>aug</td>\n",
       "      <td>mon</td>\n",
       "      <td>91.5</td>\n",
       "      <td>145.4</td>\n",
       "      <td>608.2</td>\n",
       "      <td>10.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>86</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>sep</td>\n",
       "      <td>tue</td>\n",
       "      <td>91.0</td>\n",
       "      <td>129.5</td>\n",
       "      <td>692.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.1</td>\n",
       "      <td>63</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>sep</td>\n",
       "      <td>sat</td>\n",
       "      <td>92.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>698.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>22.8</td>\n",
       "      <td>40</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>sep</td>\n",
       "      <td>sat</td>\n",
       "      <td>92.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>698.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>17.8</td>\n",
       "      <td>51</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>sep</td>\n",
       "      <td>sat</td>\n",
       "      <td>92.8</td>\n",
       "      <td>73.2</td>\n",
       "      <td>713.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>19.3</td>\n",
       "      <td>38</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>63.5</td>\n",
       "      <td>70.8</td>\n",
       "      <td>665.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>17.0</td>\n",
       "      <td>72</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>sep</td>\n",
       "      <td>mon</td>\n",
       "      <td>90.9</td>\n",
       "      <td>126.5</td>\n",
       "      <td>686.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>21.3</td>\n",
       "      <td>42</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>sep</td>\n",
       "      <td>wed</td>\n",
       "      <td>92.9</td>\n",
       "      <td>133.3</td>\n",
       "      <td>699.6</td>\n",
       "      <td>9.2</td>\n",
       "      <td>26.4</td>\n",
       "      <td>21</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X  Y month  day  FFMC    DMC     DC   ISI  temp  RH  wind  rain  area  \\\n",
       "0   7  5   mar  fri  86.2   26.2   94.3   5.1   8.2  51   6.7   0.0   0.0   \n",
       "1   7  4   oct  tue  90.6   35.4  669.1   6.7  18.0  33   0.9   0.0   0.0   \n",
       "2   7  4   oct  sat  90.6   43.7  686.9   6.7  14.6  33   1.3   0.0   0.0   \n",
       "3   8  6   mar  fri  91.7   33.3   77.5   9.0   8.3  97   4.0   0.2   0.0   \n",
       "4   8  6   mar  sun  89.3   51.3  102.2   9.6  11.4  99   1.8   0.0   0.0   \n",
       "5   8  6   aug  sun  92.3   85.3  488.0  14.7  22.2  29   5.4   0.0   0.0   \n",
       "6   8  6   aug  mon  92.3   88.9  495.6   8.5  24.1  27   3.1   0.0   0.0   \n",
       "7   8  6   aug  mon  91.5  145.4  608.2  10.7   8.0  86   2.2   0.0   0.0   \n",
       "8   8  6   sep  tue  91.0  129.5  692.6   7.0  13.1  63   5.4   0.0   0.0   \n",
       "9   7  5   sep  sat  92.5   88.0  698.6   7.1  22.8  40   4.0   0.0   0.0   \n",
       "10  7  5   sep  sat  92.5   88.0  698.6   7.1  17.8  51   7.2   0.0   0.0   \n",
       "11  7  5   sep  sat  92.8   73.2  713.0  22.6  19.3  38   4.0   0.0   0.0   \n",
       "12  6  5   aug  fri  63.5   70.8  665.3   0.8  17.0  72   6.7   0.0   0.0   \n",
       "13  6  5   sep  mon  90.9  126.5  686.5   7.0  21.3  42   2.2   0.0   0.0   \n",
       "14  6  5   sep  wed  92.9  133.3  699.6   9.2  26.4  21   4.5   0.0   0.0   \n",
       "\n",
       "    Log-area  month_encoded  day_encoded  \n",
       "0        0.0              7            0  \n",
       "1        0.0             10            5  \n",
       "2        0.0             10            2  \n",
       "3        0.0              7            0  \n",
       "4        0.0              7            3  \n",
       "5        0.0              1            3  \n",
       "6        0.0              1            1  \n",
       "7        0.0              1            1  \n",
       "8        0.0             11            5  \n",
       "9        0.0             11            2  \n",
       "10       0.0             11            2  \n",
       "11       0.0             11            2  \n",
       "12       0.0              1            0  \n",
       "13       0.0             11            1  \n",
       "14       0.0             11            6  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['day_encoded']=enc.transform(df['day'])\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_size=0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data=df.drop(['area','Log-area','month','day'],axis=1)\n",
    "y_data=df['Log-area']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=test_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y_train=y_train.values.reshape(y_train.size,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Regression Error Characteristic (REC) estimation**\n",
    "\n",
    "Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualizing and comparing classification results. Regression Error Characteristic (REC) curves generalize ROC curves to regression. REC curves plot the error tolerance on the $x-axis$ versus the percentage of points predicted within the tolerance on the $y-axis$. The resulting curve estimates the cumulative distribution function of the error. The REC curve visually presents commonly-useds tatistics. The area-over-the-curve (AOC) is a biased estimate of the expected error. The $R^2$ value can be estimated using the ratio of the AOC for a given model to the AOC for the nul-model. Users can quickly assess the relative merits of many regression functions by examining the relative position of their REC curves. The shape of the curve reveals additional information that can be used to guide modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rec(m,n,tol):\n",
    "    if type(m)!='numpy.ndarray':\n",
    "        m=np.array(m)\n",
    "    if type(n)!='numpy.ndarray':\n",
    "        n=np.array(n)\n",
    "    l=m.size\n",
    "    percent = 0\n",
    "    for i in range(l):\n",
    "        if np.abs(10**m[i]-10**n[i])<=tol:\n",
    "            percent+=1\n",
    "    return 100*(percent/l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the max tolerance limit for REC curve x-axis\n",
    "# For this problem this represents the absolute value of error in the prediction of the outcome i.e. area burned\n",
    "tol_max=20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gridsearch**\n",
    "\n",
    "\n",
    "Finding the right parameters for machine learning models is a tricky task! But luckily, Scikit-learn has the functionality of trying a bunch of combinations and see what works best, built in with GridSearchCV! The CV stands for cross-validation.\n",
    "\n",
    "**GridSearchCV takes a dictionary that describes the parameters that should be tried and a model to train. The grid of parameters is defined as a dictionary, where the keys are the parameters and the values are the settings to be tested.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Regressor (SVR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameter grid for the Grid Search\n",
    "param_grid = {'C': [0.01,0.1,1, 10], 'epsilon': [10,1,0.1,0.01,0.001,0.0001], 'kernel': ['rbf']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=SVR(C=1.0, cache_size=200, coef0=0.0, degree=3,\n",
       "                           epsilon=0.1, gamma='auto_deprecated', kernel='rbf',\n",
       "                           max_iter=-1, shrinking=True, tol=0.001,\n",
       "                           verbose=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'C': [0.01, 0.1, 1, 10],\n",
       "                         'epsilon': [10, 1, 0.1, 0.01, 0.001, 0.0001],\n",
       "                         'kernel': ['rbf']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_SVR = GridSearchCV(SVR(),param_grid,refit=True,verbose=0,cv=5)\n",
    "grid_SVR.fit(scaler.fit_transform(X_train),scaler.fit_transform(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters obtained by Grid Search: {'C': 0.01, 'epsilon': 1, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameters obtained by Grid Search:\",grid_SVR.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for Support Vector Regression: 0.670559760327513\n"
     ]
    }
   ],
   "source": [
    "a=grid_SVR.predict(X_test)\n",
    "print(\"RMSE for Support Vector Regression:\",np.sqrt(np.mean((y_test-a)**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a1babcfd0>"
      ]
     },
     "execution_count": 27,
     "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": [
    "plt.xlabel(\"Actual area burned\")\n",
    "plt.ylabel(\"Error\")\n",
    "plt.grid(True)\n",
    "plt.scatter(10**(y_test),10**(a)-10**(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   0.,   1.,   0.,   0.,   0.,   1.,   0.,   1.,   0.,   0.,\n",
       "          0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   2.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,\n",
       "          1.,   0.,   1.,   1.,   0.,   1.,   1.,   1.,   3.,   1.,   3.,\n",
       "          3.,   3.,   7.,  12.,  20., 141.]),\n",
       " array([-212.17342221, -207.91582221, -203.65822221, -199.40062221,\n",
       "        -195.14302221, -190.88542221, -186.62782221, -182.37022221,\n",
       "        -178.11262221, -173.85502221, -169.59742221, -165.33982221,\n",
       "        -161.08222221, -156.82462221, -152.56702221, -148.30942221,\n",
       "        -144.05182221, -139.79422221, -135.53662221, -131.27902221,\n",
       "        -127.02142221, -122.76382221, -118.50622221, -114.24862221,\n",
       "        -109.99102221, -105.73342221, -101.47582221,  -97.21822221,\n",
       "         -92.96062221,  -88.70302221,  -84.44542221,  -80.18782221,\n",
       "         -75.93022221,  -71.67262221,  -67.41502221,  -63.15742221,\n",
       "         -58.89982221,  -54.64222221,  -50.38462221,  -46.12702221,\n",
       "         -41.86942221,  -37.61182221,  -33.35422221,  -29.09662221,\n",
       "         -24.83902221,  -20.58142221,  -16.32382221,  -12.06622221,\n",
       "          -7.80862221,   -3.55102221,    0.70657779]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 28,
     "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": [
    "plt.title(\"Histogram of prediction errors\\n\",fontsize=18)\n",
    "plt.xlabel(\"Prediction error ($ha$)\",fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.hist(10**(a.reshape(a.size,))-10**(y_test),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1bd80390>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rec_SVR=[]\n",
    "for i in range(tol_max):\n",
    "    rec_SVR.append(rec(a,y_test,i))\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"REC curve for the Support Vector Regressor\\n\",fontsize=15)\n",
    "plt.xlabel(\"Absolute error (tolerance) in prediction ($ha$)\")\n",
    "plt.ylabel(\"Percentage of correct prediction\")\n",
    "plt.xticks([i*5 for i in range(tol_max+1)])\n",
    "plt.ylim(-10,100)\n",
    "plt.yticks([i*20 for i in range(6)])\n",
    "plt.grid(True)\n",
    "plt.plot(range(tol_max),rec_SVR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mae', max_depth=10, max_features=None,\n",
       "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_model = DecisionTreeRegressor(max_depth=10,criterion='mae')\n",
    "tree_model.fit(scaler.fit_transform(X_train),scaler.fit_transform(y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for Decision Tree: 1.417140730974592\n"
     ]
    }
   ],
   "source": [
    "\n",
    "a=tree_model.predict(X_test)\n",
    "print(\"RMSE for Decision Tree:\",np.sqrt(np.mean((y_test-a)**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a1c0ccb00>"
      ]
     },
     "execution_count": 33,
     "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": [
    "plt.xlabel(\"Actual area burned\")\n",
    "plt.ylabel(\"Error\")\n",
    "plt.grid(True)\n",
    "plt.scatter(10**(y_test),10**(a)-10**(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   0.,   1.,   0.,   0.,   0.,   1.,   0.,   1.,   0.,   0.,\n",
       "          0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   2.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,\n",
       "          1.,   0.,   1.,   1.,   0.,   1.,   1.,   1.,   3.,   1.,   3.,\n",
       "          3.,   3.,   7.,  12.,  20., 141.]),\n",
       " array([-213.70984407, -209.45224407, -205.19464407, -200.93704407,\n",
       "        -196.67944407, -192.42184407, -188.16424407, -183.90664407,\n",
       "        -179.64904407, -175.39144407, -171.13384407, -166.87624407,\n",
       "        -162.61864407, -158.36104407, -154.10344407, -149.84584407,\n",
       "        -145.58824407, -141.33064407, -137.07304407, -132.81544407,\n",
       "        -128.55784407, -124.30024407, -120.04264407, -115.78504407,\n",
       "        -111.52744407, -107.26984407, -103.01224407,  -98.75464407,\n",
       "         -94.49704407,  -90.23944407,  -85.98184407,  -81.72424407,\n",
       "         -77.46664407,  -73.20904407,  -68.95144407,  -64.69384407,\n",
       "         -60.43624407,  -56.17864407,  -51.92104407,  -47.66344407,\n",
       "         -43.40584407,  -39.14824407,  -34.89064407,  -30.63304407,\n",
       "         -26.37544407,  -22.11784407,  -17.86024407,  -13.60264407,\n",
       "          -9.34504407,   -5.08744407,   -0.82984407]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Histogram of prediction errors\\n\",fontsize=18)\n",
    "plt.xlabel(\"Prediction error ($ha$)\",fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.hist(10**(a.reshape(a.size,))-10**(y_test),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1c27bc50>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rec_DT=[]\n",
    "for i in range(tol_max):\n",
    "    rec_DT.append(rec(a,y_test,i))\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"REC curve for the single Decision Tree\\n\",fontsize=15)\n",
    "plt.xlabel(\"Absolute error (tolerance) in prediction ($ha$)\")\n",
    "plt.ylabel(\"Percentage of correct prediction\")\n",
    "plt.xticks([i for i in range(0,tol_max+1,5)])\n",
    "plt.ylim(-10,100)\n",
    "plt.yticks([i*20 for i in range(6)])\n",
    "plt.grid(True)\n",
    "plt.plot(range(tol_max),rec_DT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=RandomForestRegressor(bootstrap=True, criterion='mse',\n",
       "                                             max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators='warn', n_jobs=None,\n",
       "                                             oob_score=False, random_state=None,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'max_depth': [5, 10, 15, 20, 50],\n",
       "                         'max_leaf_nodes': [2, 5, 10],\n",
       "                         'min_samples_leaf': [2, 5, 10],\n",
       "                         'min_samples_split': [2, 5, 10]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'max_depth': [5,10,15,20,50], 'max_leaf_nodes': [2,5,10], 'min_samples_leaf': [2,5,10],\n",
    "             'min_samples_split':[2,5,10]}\n",
    "grid_RF = GridSearchCV(RandomForestRegressor(),param_grid,refit=True,verbose=0,cv=5)\n",
    "grid_RF.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters obtained by Grid Search: {'max_depth': 15, 'max_leaf_nodes': 2, 'min_samples_leaf': 2, 'min_samples_split': 2}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameters obtained by Grid Search:\",grid_RF.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for Random Forest: 0.6103179708949186\n"
     ]
    }
   ],
   "source": [
    "a=grid_RF.predict(X_test)\n",
    "rmse_rf=np.sqrt(np.mean((y_test-a)**2))\n",
    "print(\"RMSE for Random Forest:\",rmse_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a1c4cd2b0>"
      ]
     },
     "execution_count": 40,
     "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": [
    "plt.xlabel(\"Actual area burned\")\n",
    "plt.ylabel(\"Error\")\n",
    "plt.grid(True)\n",
    "plt.scatter(10**(y_test),10**(a)-10**(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   0.,   1.,   0.,   0.,   0.,   1.,   0.,   1.,   0.,   0.,\n",
       "          0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   2.,   0.,   0.,   0.,   0.,   1.,   0.,   1.,\n",
       "          0.,   0.,   1.,   1.,   0.,   2.,   0.,   3.,   2.,   1.,   3.,\n",
       "          3.,   4.,   8.,  18.,  32., 120.]),\n",
       " array([-2.11047747e+02, -2.06741726e+02, -2.02435704e+02, -1.98129682e+02,\n",
       "        -1.93823660e+02, -1.89517638e+02, -1.85211616e+02, -1.80905594e+02,\n",
       "        -1.76599573e+02, -1.72293551e+02, -1.67987529e+02, -1.63681507e+02,\n",
       "        -1.59375485e+02, -1.55069463e+02, -1.50763441e+02, -1.46457420e+02,\n",
       "        -1.42151398e+02, -1.37845376e+02, -1.33539354e+02, -1.29233332e+02,\n",
       "        -1.24927310e+02, -1.20621289e+02, -1.16315267e+02, -1.12009245e+02,\n",
       "        -1.07703223e+02, -1.03397201e+02, -9.90911793e+01, -9.47851575e+01,\n",
       "        -9.04791356e+01, -8.61731138e+01, -8.18670919e+01, -7.75610701e+01,\n",
       "        -7.32550482e+01, -6.89490264e+01, -6.46430045e+01, -6.03369827e+01,\n",
       "        -5.60309608e+01, -5.17249390e+01, -4.74189171e+01, -4.31128953e+01,\n",
       "        -3.88068735e+01, -3.45008516e+01, -3.01948298e+01, -2.58888079e+01,\n",
       "        -2.15827861e+01, -1.72767642e+01, -1.29707424e+01, -8.66472052e+00,\n",
       "        -4.35869867e+00, -5.26768227e-02,  4.25334502e+00]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 41,
     "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": [
    "plt.title(\"Histogram of prediction errors\\n\",fontsize=18)\n",
    "plt.xlabel(\"Prediction error ($ha$)\",fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.hist(10**(a.reshape(a.size,))-10**(y_test),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1c642da0>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rec_RF=[]\n",
    "for i in range(tol_max):\n",
    "    rec_RF.append(rec(a,y_test,i))\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"REC curve for the Random Forest\\n\",fontsize=15)\n",
    "plt.xlabel(\"Absolute error (tolerance) in prediction ($ha$)\")\n",
    "plt.ylabel(\"Percentage of correct prediction\")\n",
    "plt.xticks([i for i in range(0,tol_max+1,5)])\n",
    "plt.ylim(-10,100)\n",
    "plt.yticks([i*20 for i in range(6)])\n",
    "plt.grid(True)\n",
    "plt.plot(range(tol_max),rec_RF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep network (using Keras (TensorFlow backend))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "import keras.optimizers as opti\n",
    "from keras.layers import Dense, Activation,Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 100)               1300      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 50)                5050      \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 16,501\n",
      "Trainable params: 16,501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(100, input_dim=12))\n",
    "model.add(Activation('selu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(100))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Activation('selu'))\n",
    "model.add(Dense(50))\n",
    "model.add(Activation('elu'))\n",
    "model.add(Dense(1))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate=0.001\n",
    "optimizer = opti.RMSprop(lr=learning_rate)\n",
    "model.compile(optimizer=optimizer,loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1a36541ef0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=X_train\n",
    "target = y_train\n",
    "model.fit(data, target, epochs=100, batch_size=10,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE for Deep Network: 0.6113012165071641\n"
     ]
    }
   ],
   "source": [
    "a=model.predict(X_test)\n",
    "print(\"RMSE for Deep Network:\",np.sqrt(np.mean((y_test-a.reshape(a.size,))**2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a36237940>"
      ]
     },
     "execution_count": 48,
     "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": [
    "plt.xlabel(\"Actual area burned\")\n",
    "plt.ylabel(\"Error\")\n",
    "plt.grid(True)\n",
    "plt.scatter(10**(y_test),10**(a.reshape(a.size,))-10**(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.,   0.,   1.,   0.,   0.,   0.,   1.,   0.,   1.,   0.,   0.,\n",
       "          0.,   0.,   1.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n",
       "          0.,   0.,   0.,   2.,   0.,   0.,   0.,   0.,   1.,   0.,   0.,\n",
       "          1.,   0.,   1.,   1.,   0.,   1.,   1.,   1.,   3.,   1.,   3.,\n",
       "          3.,   3.,   7.,  12.,  20., 141.]),\n",
       " array([-210.26591313, -206.00782279, -201.74973245, -197.49164211,\n",
       "        -193.23355177, -188.97546143, -184.71737109, -180.45928075,\n",
       "        -176.2011904 , -171.94310006, -167.68500972, -163.42691938,\n",
       "        -159.16882904, -154.9107387 , -150.65264836, -146.39455802,\n",
       "        -142.13646767, -137.87837733, -133.62028699, -129.36219665,\n",
       "        -125.10410631, -120.84601597, -116.58792563, -112.32983529,\n",
       "        -108.07174495, -103.8136546 ,  -99.55556426,  -95.29747392,\n",
       "         -91.03938358,  -86.78129324,  -82.5232029 ,  -78.26511256,\n",
       "         -74.00702222,  -69.74893187,  -65.49084153,  -61.23275119,\n",
       "         -56.97466085,  -52.71657051,  -48.45848017,  -44.20038983,\n",
       "         -39.94229949,  -35.68420914,  -31.4261188 ,  -27.16802846,\n",
       "         -22.90993812,  -18.65184778,  -14.39375744,  -10.1356671 ,\n",
       "          -5.87757676,   -1.61948642,    2.63860393]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 49,
     "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": [
    "plt.title(\"Histogram of prediction errors\\n\",fontsize=18)\n",
    "plt.xlabel(\"Prediction error ($ha$)\",fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.hist(10**(a.reshape(a.size,))-10**(y_test),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a36967940>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rec_NN=[]\n",
    "for i in range(tol_max):\n",
    "    rec_NN.append(rec(a,y_test,i))\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.title(\"REC curve for the Deep Network\\n\",fontsize=15)\n",
    "plt.xlabel(\"Absolute error (tolerance) in prediction ($ha$)\")\n",
    "plt.ylabel(\"Percentage of correct prediction\")\n",
    "plt.xticks([i for i in range(0,tol_max+1,5)])\n",
    "plt.ylim(-10,100)\n",
    "plt.yticks([i*20 for i in range(6)])\n",
    "plt.grid(True)\n",
    "plt.plot(range(tol_max),rec_NN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relative performance of various models (REC curves)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x117c03b00>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.title(\"REC curve for various models\\n\",fontsize=20)\n",
    "plt.xlabel(\"Absolute error (tolerance) in prediction ($ha$)\",fontsize=15)\n",
    "plt.ylabel(\"Percentage of correct prediction\",fontsize=15)\n",
    "plt.xticks([i for i in range(0,tol_max+1,1)],fontsize=13)\n",
    "plt.ylim(-10,100)\n",
    "plt.xlim(-2,tol_max)\n",
    "plt.yticks([i*20 for i in range(6)],fontsize=18)\n",
    "plt.grid(True)\n",
    "plt.plot(range(tol_max),rec_SVR,'--',lw=3)\n",
    "plt.plot(range(tol_max),rec_DT,'*-',lw=3)\n",
    "plt.plot(range(tol_max),rec_RF,'o-',lw=3)\n",
    "plt.plot(range(tol_max),rec_NN,'k-',lw=3)\n",
    "plt.legend(['SVR','Decision Tree','Random Forest','Deep NN'],fontsize=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}