SVM Forest fire.ipynb

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  {
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   "source": [
    "# Classify the Size_Categorie using SVM "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77146177",
   "metadata": {},
   "source": [
    "## Attributes:\n",
    "month\tmonth of the year: 'jan' to 'dec'\n",
    "    \n",
    "day\t:: day of the week: 'mon' to 'sun'\n",
    "    \n",
    "FFMC ::FFMC index from the FWI system: 18.7 to 96.20\n",
    "    \n",
    "DMC\t:: DMC index from the FWI system: 1.1 to 291.3\n",
    "    \n",
    "DC\t::DC index from the FWI system: 7.9 to 860.6\n",
    "    \n",
    "ISI\t::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\t::relative humidity in %: 15.0 to 100\n",
    "    \n",
    "wind\t::wind speed in km/h: 0.40 to 9.40\n",
    "    \n",
    "rain\t::outside rain in mm/m2 : 0.0 to 6.4 \n",
    "    \n",
    "Size_Categorie \tthe burned area of the forest ( Small , Large)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4f99615",
   "metadata": {},
   "source": [
    "## Support vector machines (SVMs) are\n",
    "        a set of supervised learning methods used for classification, regression and outliers detection.\n",
    "\n",
    "The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.\n",
    "\n",
    "SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme cases are called as support vectors, and hence algorithm is termed as Support Vector Machine. \n",
    "\n",
    "The dimensions of the hyperplane depend on the features present in the dataset, which means if there are 2 features (as shown in image), then hyperplane will be a straight line. And if there are 3 features, then hyperplane will be a 2-dimension plane.\n",
    "\n",
    "The data points or vectors that are the closest to the hyperplane and which affect the position of the hyperplane are termed as Support Vector. Since these vectors support the hyperplane, hence called a Support vector."
   ]
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     "end_time": "2022-11-15T07:02:58.352429Z",
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   "source": [
    "# Importing necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "\n",
    "from sklearn.preprocessing import StandardScaler , LabelEncoder\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV \n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
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       "      <th>rain</th>\n",
       "      <th>...</th>\n",
       "      <th>monthfeb</th>\n",
       "      <th>monthjan</th>\n",
       "      <th>monthjul</th>\n",
       "      <th>monthjun</th>\n",
       "      <th>monthmar</th>\n",
       "      <th>monthmay</th>\n",
       "      <th>monthnov</th>\n",
       "      <th>monthoct</th>\n",
       "      <th>monthsep</th>\n",
       "      <th>size_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>86.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>94.3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>oct</td>\n",
       "      <td>tue</td>\n",
       "      <td>90.6</td>\n",
       "      <td>35.4</td>\n",
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       "      <th>2</th>\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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>small</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "  month  day  FFMC   DMC     DC  ISI  temp  RH  wind  rain  ...  monthfeb  \\\n",
       "0   mar  fri  86.2  26.2   94.3  5.1   8.2  51   6.7   0.0  ...         0   \n",
       "1   oct  tue  90.6  35.4  669.1  6.7  18.0  33   0.9   0.0  ...         0   \n",
       "2   oct  sat  90.6  43.7  686.9  6.7  14.6  33   1.3   0.0  ...         0   \n",
       "3   mar  fri  91.7  33.3   77.5  9.0   8.3  97   4.0   0.2  ...         0   \n",
       "4   mar  sun  89.3  51.3  102.2  9.6  11.4  99   1.8   0.0  ...         0   \n",
       "\n",
       "   monthjan  monthjul  monthjun  monthmar  monthmay  monthnov  monthoct  \\\n",
       "0         0         0         0         1         0         0         0   \n",
       "1         0         0         0         0         0         0         1   \n",
       "2         0         0         0         0         0         0         1   \n",
       "3         0         0         0         1         0         0         0   \n",
       "4         0         0         0         1         0         0         0   \n",
       "\n",
       "   monthsep  size_category  \n",
       "0         0          small  \n",
       "1         0          small  \n",
       "2         0          small  \n",
       "3         0          small  \n",
       "4         0          small  \n",
       "\n",
       "[5 rows x 31 columns]"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#Loding data\n",
    "data = pd.read_csv('forestfires.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c94113d",
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    "ExecuteTime": {
     "end_time": "2022-11-15T07:02:58.416645Z",
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   "outputs": [
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       "      <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>size_category</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>mar</td>\n",
       "      <td>fri</td>\n",
       "      <td>86.2</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>oct</td>\n",
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       "      <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>small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>small</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\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>small</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  month  day  FFMC   DMC     DC  ISI  temp  RH  wind  rain  area size_category\n",
       "0   mar  fri  86.2  26.2   94.3  5.1   8.2  51   6.7   0.0   0.0         small\n",
       "1   oct  tue  90.6  35.4  669.1  6.7  18.0  33   0.9   0.0   0.0         small\n",
       "2   oct  sat  90.6  43.7  686.9  6.7  14.6  33   1.3   0.0   0.0         small\n",
       "3   mar  fri  91.7  33.3   77.5  9.0   8.3  97   4.0   0.2   0.0         small\n",
       "4   mar  sun  89.3  51.3  102.2  9.6  11.4  99   1.8   0.0   0.0         small"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dropping encoded columns\n",
    "raw_data = data[['month', 'day','FFMC', 'DMC', 'DC', 'ISI', 'temp', 'RH', 'wind', 'rain', 'area', 'size_category' ]]\n",
    "raw_data.head()          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8c76207f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:02:58.432553Z",
     "start_time": "2022-11-15T07:02:58.416645Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(517, 31)\n",
      "(517, 12)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(raw_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6bb08a0",
   "metadata": {},
   "source": [
    "# Descriptive Statistics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "010c595d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:02:58.448549Z",
     "start_time": "2022-11-15T07:02:58.432553Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff2e98c8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:02:58.480731Z",
     "start_time": "2022-11-15T07:02:58.448549Z"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FFMC</th>\n",
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       "      <td>517.000000</td>\n",
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       "      <td>517.000000</td>\n",
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       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\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>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>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>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>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>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>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": [
       "             FFMC         DMC          DC         ISI        temp          RH  \\\n",
       "count  517.000000  517.000000  517.000000  517.000000  517.000000  517.000000   \n",
       "mean    90.644681  110.872340  547.940039    9.021663   18.889168   44.288201   \n",
       "std      5.520111   64.046482  248.066192    4.559477    5.806625   16.317469   \n",
       "min     18.700000    1.100000    7.900000    0.000000    2.200000   15.000000   \n",
       "25%     90.200000   68.600000  437.700000    6.500000   15.500000   33.000000   \n",
       "50%     91.600000  108.300000  664.200000    8.400000   19.300000   42.000000   \n",
       "75%     92.900000  142.400000  713.900000   10.800000   22.800000   53.000000   \n",
       "max     96.200000  291.300000  860.600000   56.100000   33.300000  100.000000   \n",
       "\n",
       "             wind        rain         area  \n",
       "count  517.000000  517.000000   517.000000  \n",
       "mean     4.017602    0.021663    12.847292  \n",
       "std      1.791653    0.295959    63.655818  \n",
       "min      0.400000    0.000000     0.000000  \n",
       "25%      2.700000    0.000000     0.000000  \n",
       "50%      4.000000    0.000000     0.520000  \n",
       "75%      4.900000    0.000000     6.570000  \n",
       "max      9.400000    6.400000  1090.840000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "99a11d0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:02:58.496821Z",
     "start_time": "2022-11-15T07:02:58.480731Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 517 entries, 0 to 516\n",
      "Data columns (total 12 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   month          517 non-null    object \n",
      " 1   day            517 non-null    object \n",
      " 2   FFMC           517 non-null    float64\n",
      " 3   DMC            517 non-null    float64\n",
      " 4   DC             517 non-null    float64\n",
      " 5   ISI            517 non-null    float64\n",
      " 6   temp           517 non-null    float64\n",
      " 7   RH             517 non-null    int64  \n",
      " 8   wind           517 non-null    float64\n",
      " 9   rain           517 non-null    float64\n",
      " 10  area           517 non-null    float64\n",
      " 11  size_category  517 non-null    object \n",
      "dtypes: float64(8), int64(1), object(3)\n",
      "memory usage: 48.6+ KB\n"
     ]
    }
   ],
   "source": [
    "raw_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28f032e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:02:58.512837Z",
     "start_time": "2022-11-15T07:02:58.496821Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numeric features: ['FFMC', 'DMC', 'DC', 'ISI', 'temp', 'RH', 'wind', 'rain', 'area'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Numeric_variables\n",
    "\n",
    "numeric_features = [feature for feature in raw_data.columns if raw_data[feature].dtypes != 'O']\n",
    "print('numeric features:', numeric_features, '\\n') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fe212e76",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:02:58.528752Z",
     "start_time": "2022-11-15T07:02:58.512837Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "categorical_features: ['month', 'day', 'size_category']\n"
     ]
    }
   ],
   "source": [
    "#categorical_variables\n",
    "\n",
    "categorical_features = [feature for feature in raw_data.columns if raw_data[feature].dtypes == 'O']\n",
    "print('categorical_features:', categorical_features) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "71dbc34c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:02:58.544838Z",
     "start_time": "2022-11-15T07:02:58.528752Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area    1.000000\n",
       "temp    0.097844\n",
       "DMC     0.072994\n",
       "DC      0.049383\n",
       "FFMC    0.040122\n",
       "wind    0.012317\n",
       "ISI     0.008258\n",
       "rain   -0.007366\n",
       "RH     -0.075519\n",
       "Name: area, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Corrrelation\n",
    "\n",
    "raw_data.corr()['area'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3e0e8d3",
   "metadata": {},
   "source": [
    "# Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7c62770a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:03:00.060006Z",
     "start_time": "2022-11-15T07:02:58.544838Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x3600 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for numerical_features -- histogram\n",
    "fig, axes=plt.subplots(len(numeric_features),1,figsize=(14,50))\n",
    "\n",
    "for i in range (0, len(numeric_features)):\n",
    "    sns.histplot(data[numeric_features[i]],ax=axes[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04bcbfe4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:03:00.587884Z",
     "start_time": "2022-11-15T07:03:00.060006Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1800 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for outlier detection-- boxplot\n",
    "fig, axes=plt.subplots(len(numeric_features),1,figsize=(14,25))\n",
    "\n",
    "for i in range (0, len(numeric_features)):\n",
    "    sns.boxplot(data[numeric_features[i]],ax=axes[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "69990490",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:03:00.860198Z",
     "start_time": "2022-11-15T07:03:00.587884Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for categorical_features -- barplot\n",
    "fig, axes=plt.subplots(len(categorical_features),1,figsize=(14,15))\n",
    "\n",
    "for i in range (0, len(categorical_features)):\n",
    "    sns.countplot(raw_data[categorical_features[i]],ax=axes[i], order=raw_data[categorical_features[i]].value_counts().index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19bbb0a8",
   "metadata": {},
   "source": [
    "#### Forest fires (irrespective of small or big) are more frequent in August and Sept "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a0cf71e",
   "metadata": {},
   "source": [
    "Lets do univariate analysis to find out proportion of small and large forest fires separately that too monthwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1ec16847",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:03:01.020197Z",
     "start_time": "2022-11-15T07:03:00.860198Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1) in absolute numbers\n",
    "df = pd.DataFrame()\n",
    "\n",
    "df['small'] = raw_data[raw_data['size_category'] == 'small']['month'].value_counts()\n",
    "df['large'] = raw_data[raw_data['size_category'] == 'large']['month'].value_counts()\n",
    "    \n",
    "df.plot.bar(figsize=(14,6))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b3c615a6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:03:01.164375Z",
     "start_time": "2022-11-15T07:03:01.020197Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2) in percentage\n",
    "df['small'] = raw_data[raw_data['size_category'] == 'small']['month'].value_counts(normalize=True)\n",
    "df['large'] = raw_data[raw_data['size_category'] == 'large']['month'].value_counts(normalize=True)\n",
    "    \n",
    "df.plot.bar(figsize=(14,6))  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f499c7d7",
   "metadata": {},
   "source": [
    "# Data Pre-Processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f03d63b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:03:01.180474Z",
     "start_time": "2022-11-15T07:03:01.164375Z"
    }
   },
   "outputs": [],
   "source": [
    "x = data.copy()\n",
    "x.drop('size_category',axis=1, inplace =True)\n",
    "\n",
    "y = data[['size_category']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0960b3bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:03:01.196485Z",
     "start_time": "2022-11-15T07:03:01.180474Z"
    }
   },
   "outputs": [],
   "source": [
    "# encoding output \n",
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y) \n",
    "\n",
    "# small : 1, large: 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7e2b2d27",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:03:01.212601Z",
     "start_time": "2022-11-15T07:03:01.196485Z"
    }
   },
   "outputs": [],
   "source": [
    "#inputs are already encoded, just need to drop month and day columns\n",
    "x.drop(['month' , 'day'] , axis=1, inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "30cc23e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:03:01.228595Z",
     "start_time": "2022-11-15T07:03:01.212601Z"
    }
   },
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9e41b8d5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:03:01.244605Z",
     "start_time": "2022-11-15T07:03:01.228595Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train:  (361, 28)\n",
      "Shape of X_test:  (156, 28)\n",
      "Shape of y_train:  (361,)\n",
      "Shape of y_test (156,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of X_train: \",x_train.shape)\n",
    "print(\"Shape of X_test: \", x_test.shape)\n",
    "print(\"Shape of y_train: \",y_train.shape)\n",
    "print(\"Shape of y_test\",y_test.shape) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4e0f0c",
   "metadata": {},
   "source": [
    "# SVM with default hyper parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b303f3b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:03:01.268488Z",
     "start_time": "2022-11-15T07:03:01.244605Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = SVC()\n",
    "classifier.fit(x_train, y_train)\n",
    "\n",
    "pred = classifier.predict(x_test)\n",
    "\n",
    "accuracy_score(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e3c332d",
   "metadata": {},
   "source": [
    "# GridSearchCV for optimum hyper parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd3155bb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:04:12.718568Z",
     "start_time": "2022-11-15T07:03:01.268488Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVC(),\n",
       "             param_grid=[{&#x27;C&#x27;: [100, 90, 75, 55, 50, 45, 20, 10, 0.1, 0.001],\n",
       "                          &#x27;degree&#x27;: [2, 3, 4],\n",
       "                          &#x27;gamma&#x27;: [50, 5, 10, 0.5, 0.2, 0.1],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;, &#x27;linear&#x27;, &#x27;poly&#x27;]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=SVC(),\n",
       "             param_grid=[{&#x27;C&#x27;: [100, 90, 75, 55, 50, 45, 20, 10, 0.1, 0.001],\n",
       "                          &#x27;degree&#x27;: [2, 3, 4],\n",
       "                          &#x27;gamma&#x27;: [50, 5, 10, 0.5, 0.2, 0.1],\n",
       "                          &#x27;kernel&#x27;: [&#x27;rbf&#x27;, &#x27;linear&#x27;, &#x27;poly&#x27;]}])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid=[{'C': [100, 90, 75, 55, 50, 45, 20, 10, 0.1, 0.001],\n",
       "                          'degree': [2, 3, 4],\n",
       "                          'gamma': [50, 5, 10, 0.5, 0.2, 0.1],\n",
       "                          'kernel': ['rbf', 'linear', 'poly']}])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = SVC()\n",
    "param_grid = [{'kernel':['rbf', 'linear', 'poly'],'gamma':[50,5,10,0.5,0.2,0.1],\n",
    "               'C':[100,90,75,55,50,45,20,10,0.1,0.001],'degree': [2,3,4] }]\n",
    "\n",
    "gsv = GridSearchCV(classifier,param_grid)\n",
    "gsv.fit(x_train,y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bb5eabcc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:04:12.731161Z",
     "start_time": "2022-11-15T07:04:12.718568Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'C': 100, 'degree': 2, 'gamma': 50, 'kernel': 'poly'}, 0.9695205479452056)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsv.best_params_ , gsv.best_score_   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c25df5",
   "metadata": {},
   "source": [
    "# SVM with Optimised hyper parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "626c04a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:04:12.806750Z",
     "start_time": "2022-11-15T07:04:12.731161Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accurcy of SVM is: 0.9807692307692307\n"
     ]
    }
   ],
   "source": [
    "classifier = gsv.best_estimator_\n",
    "\n",
    "classifier.fit(x_train, y_train)\n",
    "\n",
    "pred = classifier.predict(x_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, pred) \n",
    "print(\"Accurcy of SVM is:\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a190f23",
   "metadata": {},
   "source": [
    "SVM is giving commendable test accuracy of 98% "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2e980d80",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:04:12.822682Z",
     "start_time": "2022-11-15T07:04:12.806750Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.98      0.97        45\n",
      "           1       0.99      0.98      0.99       111\n",
      "\n",
      "    accuracy                           0.98       156\n",
      "   macro avg       0.97      0.98      0.98       156\n",
      "weighted avg       0.98      0.98      0.98       156\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2c6d8ec5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:04:12.983549Z",
     "start_time": "2022-11-15T07:04:12.822682Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test, pred), annot=True, fmt='d') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "71001d5e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-15T07:04:12.991465Z",
     "start_time": "2022-11-15T07:04:12.983549Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC Score: 0.9798798798798798\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "auc = roc_auc_score(y_test,pred)\n",
    "print('AUC-ROC Score:',auc) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e76fa2",
   "metadata": {},
   "source": [
    "# Thank You "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}