base_models_tests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 517 entries, 0 to 516\n",
      "Data columns (total 13 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   X       517 non-null    int64  \n",
      " 1   Y       517 non-null    int64  \n",
      " 2   month   517 non-null    object \n",
      " 3   day     517 non-null    object \n",
      " 4   FFMC    517 non-null    float64\n",
      " 5   DMC     517 non-null    float64\n",
      " 6   DC      517 non-null    float64\n",
      " 7   ISI     517 non-null    float64\n",
      " 8   temp    517 non-null    float64\n",
      " 9   RH      517 non-null    int64  \n",
      " 10  wind    517 non-null    float64\n",
      " 11  rain    517 non-null    float64\n",
      " 12  area    517 non-null    float64\n",
      "dtypes: float64(8), int64(3), object(2)\n",
      "memory usage: 52.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#importing the CSV file, original file can be found on GitHub page or at https://www.kaggle.com/datasets/sumitm004/forest-fire-area\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv(\"forestfires.csv\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>monthNum</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>3</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>10</td>\n",
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       "    <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>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>3</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>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>8</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>8</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>8</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>9</td>\n",
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       "    <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>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "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",
       "\n",
       "   monthNum  \n",
       "0         3  \n",
       "1        10  \n",
       "2        10  \n",
       "3         3  \n",
       "4         3  \n",
       "5         8  \n",
       "6         8  \n",
       "7         8  \n",
       "8         9  \n",
       "9         9  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from calendar import month_abbr\n",
    "#addidng a new column where month is displayed as the Int8 monthNum\n",
    "lower_ma = [m.lower() for m in month_abbr]\n",
    "\n",
    "df['monthNum'] = df['month'].str.lower().map(lambda m: lower_ma.index(m)).astype('Int8')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>monthNum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\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>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\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>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\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>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\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>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FFMC    DMC     DC   ISI  temp  RH  wind  rain  area  monthNum\n",
       "0  86.2   26.2   94.3   5.1   8.2  51   6.7   0.0   0.0         3\n",
       "1  90.6   35.4  669.1   6.7  18.0  33   0.9   0.0   0.0        10\n",
       "2  90.6   43.7  686.9   6.7  14.6  33   1.3   0.0   0.0        10\n",
       "3  91.7   33.3   77.5   9.0   8.3  97   4.0   0.2   0.0         3\n",
       "4  89.3   51.3  102.2   9.6  11.4  99   1.8   0.0   0.0         3\n",
       "5  92.3   85.3  488.0  14.7  22.2  29   5.4   0.0   0.0         8\n",
       "6  92.3   88.9  495.6   8.5  24.1  27   3.1   0.0   0.0         8\n",
       "7  91.5  145.4  608.2  10.7   8.0  86   2.2   0.0   0.0         8\n",
       "8  91.0  129.5  692.6   7.0  13.1  63   5.4   0.0   0.0         9\n",
       "9  92.5   88.0  698.6   7.1  22.8  40   4.0   0.0   0.0         9"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping the string values from the dataset as well as X and Y. X and Y were very specific to the Montesinho Natural Park and\n",
    "#we want the model to be usable in any location\n",
    "df = df.drop(columns=['X', 'Y','month','day'])\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1090.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>0.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skew</th>\n",
       "      <td>12.846934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               area\n",
       "min        0.000000\n",
       "max     1090.840000\n",
       "median     0.520000\n",
       "skew      12.846934"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#area is currently a float value however converting to integers makes many models easier to work with\n",
    "#as shown below, area has a wide range of values and the floats do not add much value to the model \n",
    "df.agg({\"area\": [\"min\", \"max\", \"median\", \"skew\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>monthNum</th>\n",
       "      <th>area_int</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\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>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\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>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\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>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\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>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\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>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\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>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FFMC    DMC     DC   ISI  temp  RH  wind  rain  area  monthNum  area_int\n",
       "0  86.2   26.2   94.3   5.1   8.2  51   6.7   0.0   0.0         3         0\n",
       "1  90.6   35.4  669.1   6.7  18.0  33   0.9   0.0   0.0        10         0\n",
       "2  90.6   43.7  686.9   6.7  14.6  33   1.3   0.0   0.0        10         0\n",
       "3  91.7   33.3   77.5   9.0   8.3  97   4.0   0.2   0.0         3         0\n",
       "4  89.3   51.3  102.2   9.6  11.4  99   1.8   0.0   0.0         3         0\n",
       "5  92.3   85.3  488.0  14.7  22.2  29   5.4   0.0   0.0         8         0\n",
       "6  92.3   88.9  495.6   8.5  24.1  27   3.1   0.0   0.0         8         0\n",
       "7  91.5  145.4  608.2  10.7   8.0  86   2.2   0.0   0.0         8         0\n",
       "8  91.0  129.5  692.6   7.0  13.1  63   5.4   0.0   0.0         9         0\n",
       "9  92.5   88.0  698.6   7.1  22.8  40   4.0   0.0   0.0         9         0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting area to area_int as this is both simple and will work for the model\n",
    "\n",
    "df['area_int'] = df['area'].astype({\"area\": int})\n",
    "#df = df.drop(columns=['area'])\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train test split\n",
    "\n",
    "#split the dataset into train and test sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = df[['FFMC','DMC','DC','ISI','temp','RH','wind','rain','monthNum']], df.area_int\n",
    "  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for decision tree on training data\n",
      "  Default settings\n",
      "Confusion Matrix\n",
      "[[191   0   0 ...   0   0   0]\n",
      " [ 15  10   0 ...   0   0   0]\n",
      " [  9   0  10 ...   0   0   0]\n",
      " ...\n",
      " [  1   0   0 ...   0   0   0]\n",
      " [  0   0   0 ...   0   1   0]\n",
      " [  1   0   0 ...   0   0   0]]\n",
      "Accuracy is   0.6980609418282548\n",
      "Precision is  0.6693039835923663\n",
      "Recall is     0.6980609418282548\n",
      "F1 is         0.6344893768793956\n",
      "\n",
      "Results for decision tree on test data\n",
      "  Default settings\n",
      "Confusion Matrix\n",
      "[[67  3  3 ...  0  0  0]\n",
      " [ 9  0  0 ...  0  0  0]\n",
      " [ 7  0  0 ...  0  0  0]\n",
      " ...\n",
      " [ 0  0  0 ...  0  0  0]\n",
      " [ 1  0  0 ...  0  0  0]\n",
      " [ 1  0  0 ...  0  0  0]]\n",
      "Accuracy is   0.42948717948717946\n",
      "Precision is  0.28517948717948716\n",
      "Recall is     0.42948717948717946\n",
      "F1 is         0.3427638067061144\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\krh52\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\krh52\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\krh52\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "  \n",
    "\n",
    "X = X_train[['DMC','DC','monthNum']]\n",
    "y = y_train\n",
    "\n",
    "X_test = X_test[['DMC','DC','monthNum']]\n",
    "\n",
    "tree_model = DecisionTreeClassifier()\n",
    "tree_model.fit(X,y)\n",
    "\n",
    "y_pred = tree_model.predict(X)\n",
    "print('Results for decision tree on training data')\n",
    "print('  Default settings')\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y, y_pred))\n",
    "print('Accuracy is  ', accuracy_score(y, y_pred))\n",
    "print('Precision is ', precision_score(y, y_pred, average='weighted'))\n",
    "print('Recall is    ', recall_score(y,y_pred, average='weighted'))\n",
    "print('F1 is        ', f1_score(y, y_pred, average='weighted'))\n",
    "print()\n",
    "\n",
    "y_test_pred = tree_model.predict(X_test)\n",
    "print('Results for decision tree on test data')\n",
    "print('  Default settings')\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y_test, y_test_pred))\n",
    "print('Accuracy is  ', accuracy_score(y_test, y_test_pred))\n",
    "print('Precision is ', precision_score(y_test, y_test_pred, average='weighted'))\n",
    "print('Recall is    ', recall_score(y_test,y_test_pred, average='weighted'))\n",
    "print('F1 is        ', f1_score(y_test, y_test_pred, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for Random Forest on train data\n",
      "  Default settings\n",
      "Confusion Matrix\n",
      "[[180   1   0 ...   0   0   0]\n",
      " [ 13   9   0 ...   0   0   0]\n",
      " [  7   0   8 ...   0   0   0]\n",
      " ...\n",
      " [  1   0   0 ...   0   0   0]\n",
      " [  0   0   0 ...   0   1   0]\n",
      " [  1   0   0 ...   0   0   0]]\n",
      "Accuracy is   0.6980609418282548\n",
      "Precision is  0.6764836780192202\n",
      "Recall is     0.6980609418282548\n",
      "F1 is         0.6494840761204482\n",
      "\n",
      "Results for Random Forest on test data\n",
      "  Default settings\n",
      "Confusion Matrix\n",
      "[[67  1  1 ...  0  0  0]\n",
      " [10  0  0 ...  0  0  0]\n",
      " [ 6  0  0 ...  0  0  0]\n",
      " ...\n",
      " [ 0  0  0 ...  0  0  0]\n",
      " [ 1  0  0 ...  0  0  0]\n",
      " [ 1  0  0 ...  0  0  0]]\n",
      "Accuracy is   0.4423076923076923\n",
      "Precision is  0.3337463190404367\n",
      "Recall is     0.4423076923076923\n",
      "F1 is         0.3715929285236216\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\krh52\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\krh52\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\krh52\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "\n",
    "X = X_train[['DMC','DC','monthNum']]\n",
    "y = y_train\n",
    "\n",
    "X_test = X_test[['DMC','DC','monthNum']]\n",
    "\n",
    "rf_model = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "rf_model.fit(X,y)\n",
    "\n",
    "y_pred = rf_model.predict(X)\n",
    "\n",
    "\n",
    "print('Results for Random Forest on train data')\n",
    "print('  Default settings')\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y, y_pred))\n",
    "print('Accuracy is  ', accuracy_score(y, y_pred))\n",
    "print('Precision is ', precision_score(y, y_pred, average='weighted'))\n",
    "print('Recall is    ', recall_score(y,y_pred, average='weighted'))\n",
    "print('F1 is        ', f1_score(y, y_pred, average='weighted'))\n",
    "print()\n",
    "\n",
    "y_test_pred = rf_model.predict(X_test)\n",
    "print('Results for Random Forest on test data')\n",
    "print('  Default settings')\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y_test, y_test_pred))\n",
    "print('Accuracy is  ', accuracy_score(y_test, y_test_pred))\n",
    "print('Precision is ', precision_score(y_test, y_test_pred, average='weighted'))\n",
    "print('Recall is    ', recall_score(y_test,y_test_pred, average='weighted'))\n",
    "print('F1 is        ', f1_score(y_test, y_test_pred, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for svc on training data\n",
      "  Default settings\n",
      "Confusion Matrix\n",
      "[[191   0   0 ...   0   0   0]\n",
      " [ 25   0   0 ...   0   0   0]\n",
      " [ 19   0   0 ...   0   0   0]\n",
      " ...\n",
      " [  1   0   0 ...   0   0   0]\n",
      " [  1   0   0 ...   0   0   0]\n",
      " [  1   0   0 ...   0   0   0]]\n",
      "Accuracy is   0.5290858725761773\n",
      "Precision is  0.27993186055969493\n",
      "Recall is     0.5290858725761773\n",
      "F1 is         0.36614275964510823\n",
      "\n",
      "Results for svc on test data\n",
      "  Default settings\n",
      "Confusion Matrix\n",
      "[[83  0  0 ...  0  0  0]\n",
      " [10  0  0 ...  0  0  0]\n",
      " [ 7  0  0 ...  0  0  0]\n",
      " ...\n",
      " [ 1  0  0 ...  0  0  0]\n",
      " [ 1  0  0 ...  0  0  0]\n",
      " [ 1  0  0 ...  0  0  0]]\n",
      "Accuracy is   0.532051282051282\n",
      "Precision is  0.2830785667324129\n",
      "Recall is     0.532051282051282\n",
      "F1 is         0.36954189464649717\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\krh52\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\krh52\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "  \n",
    "\n",
    "X = X_train[['DMC','DC','monthNum']]\n",
    "y = y_train\n",
    "\n",
    "X_test = X_test[['DMC','DC','monthNum']]\n",
    "\n",
    "svc_model = SVC()\n",
    "svc_model.fit(X,y)\n",
    "\n",
    "y_pred = svc_model.predict(X)\n",
    "print('Results for svc on training data')\n",
    "print('  Default settings')\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y, y_pred))\n",
    "print('Accuracy is  ', accuracy_score(y, y_pred))\n",
    "print('Precision is ', precision_score(y, y_pred, average='weighted'))\n",
    "print('Recall is    ', recall_score(y,y_pred, average='weighted'))\n",
    "print('F1 is        ', f1_score(y, y_pred, average='weighted'))\n",
    "print()\n",
    "\n",
    "y_test_pred = svc_model.predict(X_test)\n",
    "print('Results for svc on test data')\n",
    "print('  Default settings')\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y_test, y_test_pred))\n",
    "print('Accuracy is  ', accuracy_score(y_test, y_test_pred))\n",
    "print('Precision is ', precision_score(y_test, y_test_pred, average='weighted'))\n",
    "print('Recall is    ', recall_score(y_test,y_test_pred, average='weighted'))\n",
    "print('F1 is        ', f1_score(y_test, y_test_pred, average='weighted'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neural Network - MLP Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for NN on train data\n",
      "  Default settings\n",
      "Confusion Matrix\n",
      "[[191   0   0 ...   0   0   0]\n",
      " [ 25   0   0 ...   0   0   0]\n",
      " [ 19   0   0 ...   0   0   0]\n",
      " ...\n",
      " [  1   0   0 ...   0   0   0]\n",
      " [  1   0   0 ...   0   0   0]\n",
      " [  1   0   0 ...   0   0   0]]\n",
      "Accuracy is   0.5290858725761773\n",
      "Precision is  0.27993186055969493\n",
      "Recall is     0.5290858725761773\n",
      "F1 is         0.36614275964510823\n",
      "\n",
      "Results for NN on test data\n",
      "  Default settings\n",
      "Confusion Matrix\n",
      "[[83  0  0 ...  0  0  0]\n",
      " [10  0  0 ...  0  0  0]\n",
      " [ 7  0  0 ...  0  0  0]\n",
      " ...\n",
      " [ 1  0  0 ...  0  0  0]\n",
      " [ 1  0  0 ...  0  0  0]\n",
      " [ 1  0  0 ...  0  0  0]]\n",
      "Accuracy is   0.532051282051282\n",
      "Precision is  0.2830785667324129\n",
      "Recall is     0.532051282051282\n",
      "F1 is         0.36954189464649717\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\krh52\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\krh52\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "  \n",
    "\n",
    "X = X_train[['DMC','DC','monthNum']]\n",
    "y = y_train\n",
    "\n",
    "X_test = X_test[['DMC','DC','monthNum']]\n",
    "\n",
    "nn_model = MLPClassifier(hidden_layer_sizes=(50, 25, 10),\n",
    "                         solver='lbfgs')\n",
    "nn_model.fit(X,y)\n",
    "\n",
    "y_pred = nn_model.predict(X)\n",
    "\n",
    "\n",
    "print('Results for NN on train data')\n",
    "print('  Default settings')\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y, y_pred))\n",
    "print('Accuracy is  ', accuracy_score(y, y_pred))\n",
    "print('Precision is ', precision_score(y, y_pred, average='weighted'))\n",
    "print('Recall is    ', recall_score(y,y_pred, average='weighted'))\n",
    "print('F1 is        ', f1_score(y, y_pred, average='weighted'))\n",
    "print()\n",
    "\n",
    "y_test_pred = nn_model.predict(X_test)\n",
    "print('Results for NN on test data')\n",
    "print('  Default settings')\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y_test, y_test_pred))\n",
    "print('Accuracy is  ', accuracy_score(y_test, y_test_pred))\n",
    "print('Precision is ', precision_score(y_test, y_test_pred, average='weighted'))\n",
    "print('Recall is    ', recall_score(y_test,y_test_pred, average='weighted'))\n",
    "print('F1 is        ', f1_score(y_test, y_test_pred, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for ADA on train data\n",
      "  Default settings\n",
      "Confusion Matrix\n",
      "[[191   0   0 ...   0   0   0]\n",
      " [ 25   0   0 ...   0   0   0]\n",
      " [ 19   0   0 ...   0   0   0]\n",
      " ...\n",
      " [  1   0   0 ...   0   0   0]\n",
      " [  1   0   0 ...   0   0   0]\n",
      " [  1   0   0 ...   0   0   0]]\n",
      "Accuracy is   0.5290858725761773\n",
      "Precision is  0.27993186055969493\n",
      "Recall is     0.5290858725761773\n",
      "F1 is         0.36614275964510823\n",
      "\n",
      "Results for ADA on test data\n",
      "  Default settings\n",
      "Confusion Matrix\n",
      "[[83  0  0 ...  0  0  0]\n",
      " [10  0  0 ...  0  0  0]\n",
      " [ 7  0  0 ...  0  0  0]\n",
      " ...\n",
      " [ 1  0  0 ...  0  0  0]\n",
      " [ 1  0  0 ...  0  0  0]\n",
      " [ 1  0  0 ...  0  0  0]]\n",
      "Accuracy is   0.532051282051282\n",
      "Precision is  0.2830785667324129\n",
      "Recall is     0.532051282051282\n",
      "F1 is         0.36954189464649717\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\krh52\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\krh52\\Anaconda\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "  \n",
    "\n",
    "X = X_train[['DMC','DC','monthNum']]\n",
    "y = y_train\n",
    "\n",
    "X_test = X_test[['DMC','DC','monthNum']]\n",
    "\n",
    "ada_model = AdaBoostClassifier(n_estimators=100)\n",
    "\n",
    "ada_model.fit(X,y)\n",
    "\n",
    "y_pred = ada_model.predict(X)\n",
    "\n",
    "\n",
    "print('Results for ADA on train data')\n",
    "print('  Default settings')\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y, y_pred))\n",
    "print('Accuracy is  ', accuracy_score(y, y_pred))\n",
    "print('Precision is ', precision_score(y, y_pred, average='weighted'))\n",
    "print('Recall is    ', recall_score(y,y_pred, average='weighted'))\n",
    "print('F1 is        ', f1_score(y, y_pred, average='weighted'))\n",
    "print()\n",
    "\n",
    "y_test_pred = ada_model.predict(X_test)\n",
    "print('Results for ADA on test data')\n",
    "print('  Default settings')\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(y_test, y_test_pred))\n",
    "print('Accuracy is  ', accuracy_score(y_test, y_test_pred))\n",
    "print('Precision is ', precision_score(y_test, y_test_pred, average='weighted'))\n",
    "print('Recall is    ', recall_score(y_test,y_test_pred, average='weighted'))\n",
    "print('F1 is        ', f1_score(y_test, y_test_pred, average='weighted'))\n"
   ]
  }
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