{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Problem Statement \n", "\n", "To predict the fire forest burn area" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#Importing the necessary libraries\n", "\n", "import matplotlib.pyplot as plt\n", "import math\n", "import numpy as np\n", "import pandas as pd\n", "import random" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Loading the dataset\n", "db = pd.read_csv('forest_fires.csv')" ] }, { "cell_type": "code", "execution_count": 3, "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", " </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", " </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" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Printing the first 5 rows of the loaded Dataset\n", "db.head()" ] }, { "cell_type": "code", "execution_count": 4, "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": [ "# Extracting the dataset information\n", "db.info()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[<AxesSubplot:title={'center':'X'}>,\n", " <AxesSubplot:title={'center':'Y'}>,\n", " <AxesSubplot:title={'center':'FFMC'}>],\n", " [<AxesSubplot:title={'center':'DMC'}>,\n", " <AxesSubplot:title={'center':'DC'}>,\n", " <AxesSubplot:title={'center':'ISI'}>],\n", " [<AxesSubplot:title={'center':'temp'}>,\n", " <AxesSubplot:title={'center':'RH'}>,\n", " <AxesSubplot:title={'center':'wind'}>],\n", " [<AxesSubplot:title={'center':'rain'}>,\n", " <AxesSubplot:title={'center':'area'}>, <AxesSubplot:>]],\n", " dtype=object)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1440x1080 with 12 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## Plotting\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "plt.style.use('seaborn')\n", "db.hist(bins=30, figsize=(20,15))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Converting days and months into integers \n", "\n", "db.month.replace(('jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec'),(1,2,3,4,5,6,7,8,9,10,11,12), inplace=True)\n", "db.day.replace(('mon','tue','wed','thu','fri','sat','sun'),(1,2,3,4,5,6,7), inplace=True)" ] }, { "cell_type": "code", "execution_count": 8, "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", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>7</td>\n", " <td>5</td>\n", " <td>3</td>\n", " <td>5</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>10</td>\n", " <td>2</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>10</td>\n", " <td>6</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>3</td>\n", " <td>5</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>3</td>\n", " <td>7</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>8</td>\n", " <td>7</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>8</td>\n", " <td>1</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>8</td>\n", " <td>1</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>9</td>\n", " <td>2</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>9</td>\n", " <td>6</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", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " X Y month day FFMC DMC DC ISI temp RH wind rain area\n", "0 7 5 3 5 86.2 26.2 94.3 5.1 8.2 51 6.7 0.0 0.0\n", "1 7 4 10 2 90.6 35.4 669.1 6.7 18.0 33 0.9 0.0 0.0\n", "2 7 4 10 6 90.6 43.7 686.9 6.7 14.6 33 1.3 0.0 0.0\n", "3 8 6 3 5 91.7 33.3 77.5 9.0 8.3 97 4.0 0.2 0.0\n", "4 8 6 3 7 89.3 51.3 102.2 9.6 11.4 99 1.8 0.0 0.0\n", "5 8 6 8 7 92.3 85.3 488.0 14.7 22.2 29 5.4 0.0 0.0\n", "6 8 6 8 1 92.3 88.9 495.6 8.5 24.1 27 3.1 0.0 0.0\n", "7 8 6 8 1 91.5 145.4 608.2 10.7 8.0 86 2.2 0.0 0.0\n", "8 8 6 9 2 91.0 129.5 692.6 7.0 13.1 63 5.4 0.0 0.0\n", "9 7 5 9 6 92.5 88.0 698.6 7.1 22.8 40 4.0 0.0 0.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Printing after replacement\n", "db.head(10)" ] }, { "cell_type": "code", "execution_count": 9, "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", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>X</th>\n", " <td>1.000000</td>\n", " <td>0.539548</td>\n", " <td>-0.065003</td>\n", " <td>-0.024922</td>\n", " <td>-0.021039</td>\n", " <td>-0.048384</td>\n", " <td>-0.085916</td>\n", " <td>0.006210</td>\n", " <td>-0.051258</td>\n", " <td>0.085223</td>\n", " <td>0.018798</td>\n", " <td>0.065387</td>\n", " <td>0.063385</td>\n", " </tr>\n", " <tr>\n", " <th>Y</th>\n", " <td>0.539548</td>\n", " <td>1.000000</td>\n", " <td>-0.066292</td>\n", " <td>-0.005453</td>\n", " <td>-0.046308</td>\n", " <td>0.007782</td>\n", " <td>-0.101178</td>\n", " <td>-0.024488</td>\n", " <td>-0.024103</td>\n", " <td>0.062221</td>\n", " <td>-0.020341</td>\n", " <td>0.033234</td>\n", " <td>0.044873</td>\n", " </tr>\n", " <tr>\n", " <th>month</th>\n", " <td>-0.065003</td>\n", " <td>-0.066292</td>\n", " <td>1.000000</td>\n", " <td>-0.050837</td>\n", " <td>0.291477</td>\n", " <td>0.466645</td>\n", " <td>0.868698</td>\n", " <td>0.186597</td>\n", " <td>0.368842</td>\n", " <td>-0.095280</td>\n", " <td>-0.086368</td>\n", " <td>0.013438</td>\n", " <td>0.056496</td>\n", " </tr>\n", " <tr>\n", " <th>day</th>\n", " <td>-0.024922</td>\n", " <td>-0.005453</td>\n", " <td>-0.050837</td>\n", " <td>1.000000</td>\n", " <td>-0.041068</td>\n", " <td>0.062870</td>\n", " <td>0.000105</td>\n", " <td>0.032909</td>\n", " <td>0.052190</td>\n", " <td>0.092151</td>\n", " <td>0.032478</td>\n", " <td>-0.048340</td>\n", " <td>0.023226</td>\n", " </tr>\n", " <tr>\n", " <th>FFMC</th>\n", " <td>-0.021039</td>\n", " <td>-0.046308</td>\n", " <td>0.291477</td>\n", " <td>-0.041068</td>\n", " <td>1.000000</td>\n", " <td>0.382619</td>\n", " <td>0.330512</td>\n", " <td>0.531805</td>\n", " <td>0.431532</td>\n", " <td>-0.300995</td>\n", " <td>-0.028485</td>\n", " <td>0.056702</td>\n", " <td>0.040122</td>\n", " </tr>\n", " <tr>\n", " <th>DMC</th>\n", " <td>-0.048384</td>\n", " <td>0.007782</td>\n", " <td>0.466645</td>\n", " <td>0.062870</td>\n", " <td>0.382619</td>\n", " <td>1.000000</td>\n", " <td>0.682192</td>\n", " <td>0.305128</td>\n", " <td>0.469594</td>\n", " <td>0.073795</td>\n", " <td>-0.105342</td>\n", " <td>0.074790</td>\n", " <td>0.072994</td>\n", " </tr>\n", " <tr>\n", " <th>DC</th>\n", " <td>-0.085916</td>\n", " <td>-0.101178</td>\n", " <td>0.868698</td>\n", " <td>0.000105</td>\n", " <td>0.330512</td>\n", " <td>0.682192</td>\n", " <td>1.000000</td>\n", " <td>0.229154</td>\n", " <td>0.496208</td>\n", " <td>-0.039192</td>\n", " <td>-0.203466</td>\n", " <td>0.035861</td>\n", " <td>0.049383</td>\n", " </tr>\n", " <tr>\n", " <th>ISI</th>\n", " <td>0.006210</td>\n", " <td>-0.024488</td>\n", " <td>0.186597</td>\n", " <td>0.032909</td>\n", " <td>0.531805</td>\n", " <td>0.305128</td>\n", " <td>0.229154</td>\n", " <td>1.000000</td>\n", " <td>0.394287</td>\n", " <td>-0.132517</td>\n", " <td>0.106826</td>\n", " <td>0.067668</td>\n", " <td>0.008258</td>\n", " </tr>\n", " <tr>\n", " <th>temp</th>\n", " <td>-0.051258</td>\n", " <td>-0.024103</td>\n", " <td>0.368842</td>\n", " <td>0.052190</td>\n", " <td>0.431532</td>\n", " <td>0.469594</td>\n", " <td>0.496208</td>\n", " <td>0.394287</td>\n", " <td>1.000000</td>\n", " <td>-0.527390</td>\n", " <td>-0.227116</td>\n", " <td>0.069491</td>\n", " <td>0.097844</td>\n", " </tr>\n", " <tr>\n", " <th>RH</th>\n", " <td>0.085223</td>\n", " <td>0.062221</td>\n", " <td>-0.095280</td>\n", " <td>0.092151</td>\n", " <td>-0.300995</td>\n", " <td>0.073795</td>\n", " <td>-0.039192</td>\n", " <td>-0.132517</td>\n", " <td>-0.527390</td>\n", " <td>1.000000</td>\n", " <td>0.069410</td>\n", " <td>0.099751</td>\n", " <td>-0.075519</td>\n", " </tr>\n", " <tr>\n", " <th>wind</th>\n", " <td>0.018798</td>\n", " <td>-0.020341</td>\n", " <td>-0.086368</td>\n", " <td>0.032478</td>\n", " <td>-0.028485</td>\n", " <td>-0.105342</td>\n", " <td>-0.203466</td>\n", " <td>0.106826</td>\n", " <td>-0.227116</td>\n", " <td>0.069410</td>\n", " <td>1.000000</td>\n", " <td>0.061119</td>\n", " <td>0.012317</td>\n", " </tr>\n", " <tr>\n", " <th>rain</th>\n", " <td>0.065387</td>\n", " <td>0.033234</td>\n", " <td>0.013438</td>\n", " <td>-0.048340</td>\n", " <td>0.056702</td>\n", " <td>0.074790</td>\n", " <td>0.035861</td>\n", " <td>0.067668</td>\n", " <td>0.069491</td>\n", " <td>0.099751</td>\n", " <td>0.061119</td>\n", " <td>1.000000</td>\n", " <td>-0.007366</td>\n", " </tr>\n", " <tr>\n", " <th>area</th>\n", " <td>0.063385</td>\n", " <td>0.044873</td>\n", " <td>0.056496</td>\n", " <td>0.023226</td>\n", " <td>0.040122</td>\n", " <td>0.072994</td>\n", " <td>0.049383</td>\n", " <td>0.008258</td>\n", " <td>0.097844</td>\n", " <td>-0.075519</td>\n", " <td>0.012317</td>\n", " <td>-0.007366</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " X Y month day FFMC DMC DC \\\n", "X 1.000000 0.539548 -0.065003 -0.024922 -0.021039 -0.048384 -0.085916 \n", "Y 0.539548 1.000000 -0.066292 -0.005453 -0.046308 0.007782 -0.101178 \n", "month -0.065003 -0.066292 1.000000 -0.050837 0.291477 0.466645 0.868698 \n", "day -0.024922 -0.005453 -0.050837 1.000000 -0.041068 0.062870 0.000105 \n", "FFMC -0.021039 -0.046308 0.291477 -0.041068 1.000000 0.382619 0.330512 \n", "DMC -0.048384 0.007782 0.466645 0.062870 0.382619 1.000000 0.682192 \n", "DC -0.085916 -0.101178 0.868698 0.000105 0.330512 0.682192 1.000000 \n", "ISI 0.006210 -0.024488 0.186597 0.032909 0.531805 0.305128 0.229154 \n", "temp -0.051258 -0.024103 0.368842 0.052190 0.431532 0.469594 0.496208 \n", "RH 0.085223 0.062221 -0.095280 0.092151 -0.300995 0.073795 -0.039192 \n", "wind 0.018798 -0.020341 -0.086368 0.032478 -0.028485 -0.105342 -0.203466 \n", "rain 0.065387 0.033234 0.013438 -0.048340 0.056702 0.074790 0.035861 \n", "area 0.063385 0.044873 0.056496 0.023226 0.040122 0.072994 0.049383 \n", "\n", " ISI temp RH wind rain area \n", "X 0.006210 -0.051258 0.085223 0.018798 0.065387 0.063385 \n", "Y -0.024488 -0.024103 0.062221 -0.020341 0.033234 0.044873 \n", "month 0.186597 0.368842 -0.095280 -0.086368 0.013438 0.056496 \n", "day 0.032909 0.052190 0.092151 0.032478 -0.048340 0.023226 \n", "FFMC 0.531805 0.431532 -0.300995 -0.028485 0.056702 0.040122 \n", "DMC 0.305128 0.469594 0.073795 -0.105342 0.074790 0.072994 \n", "DC 0.229154 0.496208 -0.039192 -0.203466 0.035861 0.049383 \n", "ISI 1.000000 0.394287 -0.132517 0.106826 0.067668 0.008258 \n", "temp 0.394287 1.000000 -0.527390 -0.227116 0.069491 0.097844 \n", "RH -0.132517 -0.527390 1.000000 0.069410 0.099751 -0.075519 \n", "wind 0.106826 -0.227116 0.069410 1.000000 0.061119 0.012317 \n", "rain 0.067668 0.069491 0.099751 0.061119 1.000000 -0.007366 \n", "area 0.008258 0.097844 -0.075519 0.012317 -0.007366 1.000000 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Correlation analysis of database\n", "\n", "db.corr()" ] }, { "cell_type": "code", "execution_count": 10, "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", " </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", " <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>7.475822</td>\n", " <td>4.259188</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>2.275990</td>\n", " <td>2.072929</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>1.000000</td>\n", " <td>1.000000</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>7.000000</td>\n", " <td>2.000000</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>8.000000</td>\n", " <td>5.000000</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>9.000000</td>\n", " <td>6.000000</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>12.000000</td>\n", " <td>7.000000</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 month day FFMC DMC \\\n", "count 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 \n", "mean 4.669246 4.299807 7.475822 4.259188 90.644681 110.872340 \n", "std 2.313778 1.229900 2.275990 2.072929 5.520111 64.046482 \n", "min 1.000000 2.000000 1.000000 1.000000 18.700000 1.100000 \n", "25% 3.000000 4.000000 7.000000 2.000000 90.200000 68.600000 \n", "50% 4.000000 4.000000 8.000000 5.000000 91.600000 108.300000 \n", "75% 7.000000 5.000000 9.000000 6.000000 92.900000 142.400000 \n", "max 9.000000 9.000000 12.000000 7.000000 96.200000 291.300000 \n", "\n", " DC ISI temp RH wind rain \\\n", "count 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 \n", "mean 547.940039 9.021663 18.889168 44.288201 4.017602 0.021663 \n", "std 248.066192 4.559477 5.806625 16.317469 1.791653 0.295959 \n", "min 7.900000 0.000000 2.200000 15.000000 0.400000 0.000000 \n", "25% 437.700000 6.500000 15.500000 33.000000 2.700000 0.000000 \n", "50% 664.200000 8.400000 19.300000 42.000000 4.000000 0.000000 \n", "75% 713.900000 10.800000 22.800000 53.000000 4.900000 0.000000 \n", "max 860.600000 56.100000 33.300000 100.000000 9.400000 6.400000 \n", "\n", " area \n", "count 517.000000 \n", "mean 12.847292 \n", "std 63.655818 \n", "min 0.000000 \n", "25% 0.000000 \n", "50% 0.520000 \n", "75% 6.570000 \n", "max 1090.840000 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.describe()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "train_set, test_set = train_test_split(db, test_size=0.2, random_state=42)\n", "work_set = train_set.copy() " ] }, { "cell_type": "code", "execution_count": 13, "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", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>329</th>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>9</td>\n", " <td>6</td>\n", " <td>92.2</td>\n", " <td>102.3</td>\n", " <td>751.5</td>\n", " <td>8.4</td>\n", " <td>23.5</td>\n", " <td>27</td>\n", " <td>4.0</td>\n", " <td>0.0</td>\n", " <td>3.33</td>\n", " </tr>\n", " <tr>\n", " <th>173</th>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>90.9</td>\n", " <td>126.5</td>\n", " <td>686.5</td>\n", " <td>7.0</td>\n", " <td>17.7</td>\n", " <td>39</td>\n", " <td>2.2</td>\n", " <td>0.0</td>\n", " <td>3.07</td>\n", " </tr>\n", " <tr>\n", " <th>272</th>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>92.1</td>\n", " <td>152.6</td>\n", " <td>658.2</td>\n", " <td>14.3</td>\n", " <td>20.2</td>\n", " <td>47</td>\n", " <td>4.0</td>\n", " <td>0.0</td>\n", " <td>3.09</td>\n", " </tr>\n", " <tr>\n", " <th>497</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>96.1</td>\n", " <td>181.1</td>\n", " <td>671.2</td>\n", " <td>14.3</td>\n", " <td>32.3</td>\n", " <td>27</td>\n", " <td>2.2</td>\n", " <td>0.0</td>\n", " <td>14.68</td>\n", " </tr>\n", " <tr>\n", " <th>182</th>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>7</td>\n", " <td>86.8</td>\n", " <td>15.6</td>\n", " <td>48.3</td>\n", " <td>3.9</td>\n", " <td>12.4</td>\n", " <td>53</td>\n", " <td>2.2</td>\n", " <td>0.0</td>\n", " <td>6.38</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", "329 4 3 9 6 92.2 102.3 751.5 8.4 23.5 27 4.0 0.0 3.33\n", "173 4 4 9 1 90.9 126.5 686.5 7.0 17.7 39 2.2 0.0 3.07\n", "272 2 5 8 2 92.1 152.6 658.2 14.3 20.2 47 4.0 0.0 3.09\n", "497 3 4 8 2 96.1 181.1 671.2 14.3 32.3 27 2.2 0.0 14.68\n", "182 5 4 2 7 86.8 15.6 48.3 3.9 12.4 53 2.2 0.0 6.38" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_set.head()" ] }, { "cell_type": "code", "execution_count": 14, "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", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>304</th>\n", " <td>6</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>6</td>\n", " <td>85.1</td>\n", " <td>28.0</td>\n", " <td>113.8</td>\n", " <td>3.5</td>\n", " <td>11.3</td>\n", " <td>94</td>\n", " <td>4.9</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>501</th>\n", " <td>7</td>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>96.1</td>\n", " <td>181.1</td>\n", " <td>671.2</td>\n", " <td>14.3</td>\n", " <td>21.6</td>\n", " <td>65</td>\n", " <td>4.9</td>\n", " <td>0.8</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>441</th>\n", " <td>8</td>\n", " <td>6</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>92.1</td>\n", " <td>207.0</td>\n", " <td>672.6</td>\n", " <td>8.2</td>\n", " <td>25.5</td>\n", " <td>29</td>\n", " <td>1.8</td>\n", " <td>0.0</td>\n", " <td>1.23</td>\n", " </tr>\n", " <tr>\n", " <th>153</th>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>9</td>\n", " <td>5</td>\n", " <td>94.3</td>\n", " <td>85.1</td>\n", " <td>692.3</td>\n", " <td>15.9</td>\n", " <td>20.1</td>\n", " <td>47</td>\n", " <td>4.9</td>\n", " <td>0.0</td>\n", " <td>1.46</td>\n", " </tr>\n", " <tr>\n", " <th>503</th>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>3</td>\n", " <td>94.5</td>\n", " <td>139.4</td>\n", " <td>689.1</td>\n", " <td>20.0</td>\n", " <td>29.2</td>\n", " <td>30</td>\n", " <td>4.9</td>\n", " <td>0.0</td>\n", " <td>1.95</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", "304 6 5 5 6 85.1 28.0 113.8 3.5 11.3 94 4.9 0.0 0.00\n", "501 7 5 8 2 96.1 181.1 671.2 14.3 21.6 65 4.9 0.8 0.00\n", "441 8 6 8 1 92.1 207.0 672.6 8.2 25.5 29 1.8 0.0 1.23\n", "153 5 4 9 5 94.3 85.1 692.3 15.9 20.1 47 4.9 0.0 1.46\n", "503 2 4 8 3 94.5 139.4 689.1 20.0 29.2 30 4.9 0.0 1.95" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_set.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='X', ylabel='Y'>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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WVnR1dSGZTKKrqwupVAqDg4Po6Og47uePntMXhEacGzyZRqlLvljG4GARWoV3ghPnoU7kuB6yKTXS+pbKFoaHi9ArvHOeOM95Itt2UZqRivy8FUwLA4OliqObJ86pncj1BDThwQ1wsIsfcbm+4nROcsnxBUaCKCfqe8VyXBTG7Iqt54nznCfyhEAunUByig3NSOY5f/CDH8TTTz8NIQT6+vpQKpXQ2toa1teRBLLJJNw6u3Q8z0M2Ge1KSDPSad8jaQ9xPYEZ6WhbNQBg6Dq8Os+J8DwYevSLCcbl+orTOfnYR2dKVU49dFVF3WtyCRHYIjGhnd1zzjkHW7ZswcqVKyGEwG233QZNwhV6KDjJpIZcU8r3FCQAyDWlIl8TOZ1OoC2bQqmOVklbNiXFmtu6rqIplfA9/QgAmlIJKdZ3jsv1Fadz8sm/WIxHnq7/fbEMa26rqoKErsGt48E8oWuBrbkd6qPXP/3TP4VZPEloRouBN9/O+5ru4rgeZld5nzNd5nRm8fKbg76mU9m2i0Wz5FlnuyWTRN9wydfUHdcTmNEsz5rOcbm+4nROWrT6pkO1SNRmM5Ia8iXb13QqTwhkksGl1OgfvShW2luakPA5DzWhqdJsUnDyzBakfA5QTOmaVJtgZNNJ36PPdVWRasOFuFxfcTon9W5iIdMmGMmEBs3niG1NUQLdBIPJmQK3aG5bzXP9PE9g0dy2kCLy56wzZsKrsevRcz2cdUb0788mmn1Ctub3aUIIzD4hG1JE/sXl+orTOfn8hf6ueb/Hhak5m4RAjecFAs3ZYB+YmJwpcElNw+L57VAw3pVYieN6UAAsnt8u3a5B6UQCyz8wG6oY76quxLZdqAJY/oHZUu5Kpasq5pyYgwJUfafmegIKgDkn5qTcASku11eczsmyxYtrTrSy7kqlKgpasykoQNXNLDwxfl5asynfK4tNhltGNoBGrsvR++2qqoqO9gwGBovwGmy/3aP3c9ZUBW2tGQwNF+F6oqH3c1ZUFTM6Mjg4UIRowL2D43J9xemcHG8/56M16n7OUBR0zsii/2ABCGg/50pTqZicG0Ac6mJZLgqWhVwmhXyxjGwyGfmoWT/GxmwcHBtDUyqJUtnCjHRailHZfjiOB9Nx0JJrwki+BEPXpRgB7Edcrq84nZNHn92Jp5/vRd4an8f8sY/OlGJUth+eJ+B4HtrbMhgcKkJX1UBGZTM5NzjWRT5xqQfAusgoLvUAWJdq5U2mMR/JiIiIYozJmYiISDJMzkRERJJhciYiIpIMkzMREZFkJk3OYxJsFE9ERPRuNGlyvvDCC/HHP/5xOmMhIiIiVEjO//zP/4y1a9di/fr1sKwAdtMmIiKiKZk0OS9fvhyPPvoohBBYuXIl/vjHP2L//v2H/0dEREThqLj5ZFNTE6677jocOHAA11xzDZqbmyGEgKIo+M1vfjNdMRIREb2rVEzOTz31FP71X/8Vy5cvx+bNm5HNyrdVGRERUdxMmpyvvfZavPLKK/ja176GZcuWTWdMRERE72qTJufOzk48+uijSKfT0xkPERHRu96kyfkrX/nKdMZBRERE7+AKYURERJJhciYiIpIMkzMREZFkmJyJiIgkw+RMREQkmYqLkDQiy3JRsCzYwkO+WEY2mUQyqUUdli+7dw/hpf29yJtAzgDeP2smurraog7Ll0LBQl+hgH0DBVi2hROzWWSzyajDqtn2nX144Y03MWICLQaw9JS5WLL4xKjD8iVO98pjv3sVz/xh/+F7ZflHZuGC/+/UqMOqWVzuEyBe15fnCTieh5Jpw3Jc6KoKVVVC/U5FCCFC/YYp6u/P13X84EgJB0dM5EtlaKqK9vYMBgeLcD0PuaYUZrQYaG9pCijacD20+RVs7x7CSKEMAMhkDBSLJgCgJZvCkoVtWHnO+6IMccp2vTWIPX15DOZN6KqClpYMRkaKcDyB9pyB+SfmsODk9qjDrOoHP/8DXt5bQH78lCCdUjFW9gAAuRRw2rwsvvCpj0QY4dTF6V658VtPYsie/PdtCeBbN/7V9AXkU1zuEyBe15dluzAtF7bjQlEUzJiRxcGDBQghkNA1GEkNyYT/B47Oztykv2v45Gy5Ll5/cwi260HXjvTSt7Q0YWSkdPhnx/WQ0FQsmtuGpCbn09ueoSFs2LQdZtmFYaQO/7umKXDdI6fJNMswUhquvmwJ5rfJ2ZIuWBae2rIPZdtFInGkgyaTTqE4Vj78s207SCU0nP3hOcgm5WshvNbXh+/818soe4CRPPKkrCoKvKNuHdMSSKnA9Z89De89Uc6WdJzuld+/+ip+8MjUN+D5wt/OwlmnyteSjst9AsTr+vKEwGjBgisEVOXIfd/RkcHAQPGYz2mKguZs8pjPTVWl5NzQ75wt18XOPYMQwDEXw/HomgoBYOeeQViuOy3x1WLP0BC+f+9WQNGPSczHYxgpQNHx/Xu3Ys/Q0PQEWIOCZeHXv38THpRj/uAcTyKhw4OCX//+TRQk25r0tb4+fOs/X4aiK8ck5uMxkgoUXcG3/vNlvNbXN00RTl2c7pVaEzMA/OCR/fj9q6+GFJE/cblPgHhdX54QGC6UIYCqCVdVFAgAw4XyMQ/rQWjo5Pz6m0M19/urqoLX35QvoW3YtB2aXtsTsaYnsWHT9pAi8u+pLfug1vhErGoantqyL6SI/PnOf70MvUpSnkhPKvjOf70cUkT+xeleqTUx13tcWOJynwDxur5GCxYU1FYXBQpGC8E+NDVsch4cKcF2PV/H2q6HwaO6WaL20OZXYJb9PUGaZRcPbX4l4Ij82/XWIMq2v7qUbRe73hoMOCJ/fvDzP6Ds7/JC2Rs/XhZxuldu/NaTkR4flLjcJ0C8ri/LduH6bAG7QsDyeU6PJ9Tk/KlPfQqrV6/G6tWrsXbt2kDLPjhiVu0+mYyuqTg4YgYaTz22dw9V7cqejGGksL1bnqfPPX35ql10k0kkdOzpq29gYFBe3luo2pU9GSOp4OW9hYAj8i9O90qlwV/TcXxQ4nKfAPG6vkzL9fXuGBjv4jat4JJzaFOpyuUyhBDYuHFj4GVblot8qYykz4sbAPKlMizLjXxo/+7d46Oy/SZnABgplLF791Dk06wKBQuDeRNGKuG7jMG8iULBinT6yPadfciXAaOOEPLl8XKinmYVp3vlsd8F8874sd+9Guk0q7jcJ0C8ri/PE7AdF6rqv81qOy48TwQyzSq0lvOrr76KUqmEq666CldccQW2bt0aWNkFy4JWx39AAFBVVYqBFS/t75WqnHr0FQrQ67woNVVBXyHaVucLb7wpVTn1iNO98swfgnlnHFQ5fsXlPgHidX05ngfFZ6v5MEWB4/l8HzZBaC1nwzDwuc99DhdffDH27t2Lz3/+83j88ceh68f/yra2NHR9ak9OtvDQ3l79P0BLlbl0uUyq4lD26ZA3x+cxV6Npk180mYyBvFl5WP502DdQQEtLpurnMulKvQQpJBPJSOsyYo7PY66mUvdXOqVgRIJzErd7JahyoqxLXO4TIF7XV8m0YaSdqp/r6Kh87tIpHU2G/16RQ0JLzvPnz8fcuXOhKArmz5+P1tZW9Pf3Y+bMmcf9/NDQ2JTLzhfLGBwsQqvwnmPi3LqJHNdDNqXWvfhJvXIGUCyaFbu1J85znsg0y8gZ9S/kUi/LtjAyUoReoYtr4vzNiWzbgWU3RVqXFgMYK3sV3zlPnOc8kWkJtEhwTuJ2r5gBJOio75W43CdAvK4vy3FRGLMrtp4nznOeyBMCuXQCySk2NCOZ5/zQQw/hm9/8JgCgr68PhUIBnZ2dgZSdTSbh1tl14HmeFJP53z/r+A8rUZVTjxOzWThefXP9XE/gxGw2oIj8WXrKXKnKqUec7pXlH5klVTl+xeU+AeJ1femqirrX5BICep3d/IeElpxXrlyJfD6Pyy67DDfccAO+/vWvT9qlXatkUkOuyf8AKgDINaUiH4AAAF1dbWjJ1leXlmwq8sFgAJDNJtGeq95FX0l7zoh8kMuSxSciV98pQS6FyAeDAfG6V4IaxBX1mttxuU+AeF1fqqogMcUW72QSuhbYmtuhJedkMolvfetb2LRpE+6//34sXbo00PJntBhwfM6tc1wPM1rquzmCtGRhG0xz8i6sSkyzjCULo0/Mh8w/MQfbrv7e5nhs28H8E6N973TIafOyMC1/T9GmJXDavOhbNYfE6V5pq/NVXr3HByUu9wkQr+vLSGq+V/ryhIAR4ENGwy5C0t7ShITPuXUJTZVq4fWV57wPRsrfSTVSmlSbYCw4uR0pnwvBpxKaNIv7f+FTH8EUxoQdV0qFVJtgxOleqXcTC1k2wYjLfQLE6/pKJjRoPkdsa4pS1yYYEzVscgaARXPb4NX47sbzBBbNlaelecjVly2B69Q2ncB1LFx92ZKQIvLv7A/PgVfjmrme6+LsD88JKSJ/rv/saXBqbD07lsD1nz0tpIj8i9O98oW/9ffO2O9xYYnLfQLE6/pqziYhUFtdBASaA37N0NDJOalpWDy/HQpQtVvFcT0oABbPb5dyJ5T5bW1Y85kzAeFU7eI2zTIgHKz5zJlS7kqVTSZx7llzoUJU7bqzbQcqBM49a64Ug0KO9t4TT8SNf3cahCOqdnGbloBwBG78Ozl3pYrTvXLWqafWnGhl3JUqLvcJEK/rS1UUtGZTUICqXdyeEFAAtGZTvlcWm0zDbxl5yNF7iKqqio72DAYGi/AacA/RuO7nrKkKWlsyGB4pwm2wfWrjup9zo98rcdzPuZHvEyBe19fR+zlDUdA5I4v+gwWA+znXzrJcFCwLuUwK+WIZ2WRSipGAfuzePYSX9vcib47PzXz/rJlSjMr2o1Cw0FcoIJlIwrItnJjNSjHatFbbd/bhhTfexIg5Ph966SlzpRiV7Uec7pXHfvcqnvnD/sP3yvKPzIp8VLYfcblPgHhdX54n4Hge2tsyGBwqQlfVQEZlv6uS8yGdnbnIJ7UHhXWRT1zqAbAuMopLPQDWpVp5k2nod85ERERxxORMREQkGSZnIiIiyTA5ExERSYbJmYiISDJMzkRERJJhciYiIpIMkzMREZFkmJyJiIgkw+RMREQkGSZnIiIiyTA5ExERSYbJmYiISDJMzkRERJJhciYiIpIMkzMREZFkmJyJiIgkw+RMREQkGSZnIiIiyehRBxC0/fvzeOPtfphlBUZK4JQTOjFrVi7qsHx55bV+bN2zDyMm0GIAZ86fg/e9tzPqsHx5/A/deO6Ft5AvAbkmYNnSk3H+RxZGHVbNnnphL57bvvtIPZZ04eyl86IOy5fu7kHs6Nl/+Po6ffYsLFzYHnVYvoyOltE7Ooru3hG4roWZzc1obk5FHVbNfrllF5578c0j19cH5uITH14QdVi+eJ6A43komTYsx4WuqlBVJeqwfDFNB8OmiXzZQrFkodUwYBjhpk9FCCFC/YYp6u/P13X8L7fsxsvdQxjMl6AoAtlsBoVCEUIoaM814bSFbfjEh7sCijZc9zz2AnbsHUa+CAgAmbSO4pgDBUAuA5w+rxVXXrA06jCn5P9+/0n0Vji1M3PA19b81fQF5NO6Hz2JPf2T/35+J/CVz8lfDwD4ya9fwvZdQxgp2ACATCaFYrEMAGjJJrBkQRsuOff9UYY4Za/u6kf3gVEMjJjQNQXNzRmMjhbhuAIdLQYWntSMUxfI/0D7lbueRM/I5L+f3QKsu6Yxri/LdmFaLmzHhaIomDEji4MHCxBCIKFrMJIakgkt6jCn5MDBAnoHxzBcLENXFbS1ZTA0VITjCbRmUpjZnsZJM7K+y+/snLzh2PDJeX8+j3sefgWmZSGVMg7/eyKhwbbdwz+XyyaMZBJXrngfZuXkbEm/0d+POzbugGkBTcaRi1dVFXjekdNUMl0YSeDa1afjlE45//D8sbsbdz701pQ//8WVJ+NDC+VrSW/duxd3PLB7yp+/9tIunDlvXngB1WH34CDuvH8bzLKAYSQP/7umKXDdI9eXaVowUgq+uOoMdLXL2ZIeLZfxxLN7UbZdJBJHWjDpphTGSuXDP9u2g1RCw3l/MQ/NKfla0n/atQvf/+mbU/78movn4oML5GxJe0JgtGDBFQKqcqSF3NGRwcBA8ZjPaYqC5mzymM/JxHQcbH21H5brIqEf+VucyxnI583DP9uOi6Sm4cxTO2HotbekKyXnhn7nvD+fxw9/+hKEoh6TmI8nlTIgFBU//OlL2J+vr5Uehjf6+/Hte3ZAUbVjEvPxNBkaFFXDt+/ZgTf6KzTnIlJrYgaAOx96C3/s7g4pIn9qTcwAcMcDu7F1795wAqrD7sFBfHfjVkBJHJOYj8cwkoCSwHc3bsXuwcHpCbAGo+Uy/uf/7YYH5ZjEfDyJhA4PCv7n/+3GaLlc8bPTrdbEDADf/+mb+NOuXSFF5J8nBIYLZQigasJVFQUCwHChDE+OtuExTMfBn3b2QSg4JjEfT0LXIBTgTzv7YDpOoHE0dHK+5+FXoNX4tKLpOu55+JWQIvLvjo07oNfY1aMnNNyxcUdIEflXa2Ku97iw1JqY6z0uTHfevw2aXjkpT6TpSdx5/7aQIvLviWf3QtVqu1dUTcMTz+4NJyCfak3M9R4XptGCBQW1tYIVKBgtWCFF5N/WV/uhqrWlRlVVsfXVYBtKoSbngYEB/OVf/iV2hfCk98stu2Fa/k6saVn45RZ5/oDe89gLMH1eo6Y1frws/u/3n4z0+KCs+1F9cdR7fJB+8uuXYJb9tVDMssBPfv1SwBH59+qufpSPel1Vi7Lt4tVdcvQ0feWu+q6Peo8PkmW7cH22gF0hYPk8n2E4cLAAy/UXj+W6OHCwEFgsoSVn27Zx2223wTAqdzf79XL3UNWu7MmkUgZe7h4KOCL/duwdrtqVPZkmQ8OOvcPBBlSHSoO/puP4oFQa/DUdxwdp+66hql3ZkzGMJLbvkude6T4wWrUrezKJhI7uA6MBR+RPpcFf03F8kEzL9f3uWFUUmJY8ybl3cKxqV/ZkErqG3sGxwGIJLTmvX78el156KU444YTAy96/P4/BfKmuMgbzJezfH30meOW1fuSL1T9XSb44Xk7UHv9DMO+MgyrHr6de2CtVOfXo7h48PCrbr5GCje7u6N89j46WMTBiVv9gBQMjJkZHo333/MstwfQkBlVOPTxPwHbqS6624x4z4DUqpulguFjftTFcLMM0g3n3HEpyfvjhh9He3o6PfexjYRSPN97uh6LUdzIVReCNt6NPaFv37EO9l6V4p5yoPfdCMO+MgyrH9/dvD+aVR1Dl1GNHz36pyqlH7+godK2+0b2apqB3NNrW83MvBvPOOKhy6uF4HpR6R1wrChzPCyagOgybJvQ652FrqoJhs74HyENCmUX9s5/9DIqi4LnnnsPOnTtx880346677kJnhWk/bW1p6FPsTjDLCrLZTNXPJSoMsEokMjDLSsWh7NNhxByfx1xNpcn7mbSOEbPysPzpUGdnxjHlRFmXuNQDeOf6ylSfQqRVSHqZTEqK66u7dwTNzdXv+3RThfo2paBpSV5fASmZNox09ZZiR0fl85ZO6WgyEkGF5Uu+bKGtrfr1lctVfp2aaQrm+golOd93332H///q1avxL//yLxUTMwAMDU29r95ICRQKxYrvnCfOc56oXDZhpFrrXvykXi0GUBxzKr5znjjPeaKS6aLFqH8hl3rlmoDhAHoMc03R1iXXBBwI4J1e1PUA3rm+iuWK75wnznOeyDQtKa4v17UwOlqEXuGd88R5zhPZtgPXTUd+fQ0GkKBluL4sx0VhzK7Yep44z3kiTwjk0gkkfb7rDUqxZGFoqFixkThxnvNEtuNiRnNiyucldvOcTzmhE0LU1/0ghIJTToh+AY8z58+pcQLC/6a8U07Uli09WapyfH//kmBWkguqnHqcPnuWVOXUY2ZzM5wKDxFT4boCM5ubA4rIn2UfmCtVOfXQVRV1r2MlBPQapy6FodUw4NT57tv1BFoDGgQd+n+RjRs3YkHAK9rMmpVDe66prjLac01SrLn9vvd2Ile9J6WiXAZSrLkd1FrZUa+5HdRa2TKsub1wYTtasvV1F7ZkE1Ksud3cnEJHS31/+DpajMjX3A5qrWwZ1txWVcX36OZDEromxZrbhqGjdQqvgCppzaQCW3M7+scVn05b2IZy2d+L93LZxGkL2wKOyL/T57WiZPob8VgyXZw+rzXYgOows87nnXqPD8r8Op916j0+SEsWtMH0OZHeNC0sWSDPvbLwpGbYtr/RsLbtYOFJ0baaD5ndEu3xQTKSmu+VvjwhYCTlWWd7Znva9+hz23Exsz0dWCwNm5w/8eEuGEmfczeTSak2wbjygqXwOQ0VRhJSbYJR7yYWsmyCUe8mFjJtgnHJue+HkfLXMjFSilSbYJy6oBMpn5smpBKaNJtg1LuJhUybYCQTGjSfI7Y1RZFqE4yTZmSRrHH1uUOSmlbXJhgTNWxyBoArV7wPbo3rmbqOgytXvC+kiPy7dvXpcGpcKcexXVy7+vSQIvLviyv9vTP2e1xYrr3U3wOc3+PC9MVVZ8B1ams9u46FL646I6SI/DvvL+bBq3EVJ891cd5fzAsnIJ/WXOzvnbHf48LUnE1C1DgpVECgOeuzVRKiM0/thFfj1C7P83DmqcE++DV0cp6Vy+HvL34/FOFV7eIul00owsPfX/x+KXelOqWzEzdceTqE51bt4i6ZLoTn4oYr5dyV6kMLF9acaGXclerMefNqTrSy7krV1d6Of1h9JiDsql3cpmkBwsY/rD5Tyl2pmlMp/J+Pd0GFqNrFbdsOVAj8n493Sbcr1QcXLKg50cq6K5WqKGjNpqAAVbu4PSGgAGjNpqTclcrQdXxw8YlQBKp2cduOC0UAH1x8oq9dqSpp+C0jD+F+znLifs7yiet+zpqmoKU5g5HRIlzu5xyZo/dzhqKgc0YW/QcLQIPv56ypCtrbMhgcKo6PyuZ+zrXZvz+PN97uh1lWYKQETjmhU4pR2X688lo/tu7ZhxFzfL7qmfPnSDEq24/H/9CN5154C/nS+PzMZUtPjnxUth9PvbAXz23ffaQeS7qkGJXtR3f3IHb07D98fZ0+e5YUo7L9GB0to3d0FJqWhOtamNncHPmobD9+uWUXnnvxzSPX1wfmSjEq2w/PE3A873BC01VVilHZfpimg2HTRKYpiWLJQqthBDIq+12VnA/p7MxFPkE/KKyLfOJSD4B1kVFc6gGwLtXKm0xDv3MmIiKKIyZnIiIiyTA5ExERSYbJmYiISDJMzkRERJJhciYiIpIMkzMREZFkmJyJiIgkw+RMREQkGSZnIiIiyTA5ExERSYbJmYiISDJMzkRERJJhciYiIpIMkzMREZFkmJyJiIgkw+RMREQkGSZnIiIiyehRBxC04WETPcPDUN4ahPAszG5tRWurEXVYvniegON5KJk2LMeFrqpQVSXqsHyJS1127x7CS/t7kTeBnAG8f9ZMdHW1RR3Wu97rbwxg+74ejJhAiwEsmTMbi07piDqsmjmOB9NxoA2PoWBaMHQdut6Ybag41SUKsUnOO14/gNd6RjEwUoKuKcjlMsjni3DcPnS0NOG9s5tx+qKTog5zSizbhWm5sB0XiqLASDsojNkQQiChazCSGpIJLeowpyQudXlo8yvY3j2EkUIZAJDJGCgWTfwGB9CSTWHJwjasPOd9EUf57nP/E9uxffcQRgsuBIBMJoli0cKTfzqI5qyGJV1tWHXekqjDrKowZmGkaKFUtqGqKlxFxcBgCZ7noSmVQEsmiWw6GXWYUxKnukRJEUKIqIMAgP7+vK/jhk0Tjz21G2XbQSKROPzvTUYSJdM6/LNt20gldFxwdhdaDTlb0p4QGC1YcIWAqhxpVXZ0ZDAwUDzmc5qioDmbPOZzMolLXfYMDWHDpu0wyy4MI3X43zVNgeseuXVMswwjpeHqy5ZgfltjtaQ7O3O+77+o7BoYwPfu2wazDDQZR+57VVPgHXVeSqYNIwV86fIzsKBDvpa043noebsAxxPQjupJamtLY2ho7PDPriegqwpmn5CFrsrZ+oxTXSYT9L3S2Zmb9HeN9V9mgmHTxM9/0w0PyjGJ+XgSiQQ8KPj5b7oxbJrTFOHUeUJguFCGAKomKVVRIAAMF8rw5Hi2OkZc6rJnaAjfv3croOjHJObjMYwUoOj4/r1bsWdoaHoCfJfaNTCA2/97GxQlcUxiPp4mIwFFSeD2/96GXQMD0xTh1Dieh319eQjgmGR2PJo6fp/s68vD8bxpia8WcaqLLBo6OT/21G6oWm1doqqm4bGndocUkX+jBQsKams5KlAwWrCqf3CaxaUuGzZth6bX1v2m6Uls2LQ9pIgIAL533zboeuWkPJGuJ/C9+7aFFJE/PW8XoNTYW6QoCnreLoQUkX9xqossGjY573j9AMq24+vYsu1gx+sHAo7IP8t24fpsNbpCwLLdgCPyLy51eWjzKzDL/mIxyy4e2vxKwBERMP6O2Sz7O9Ysjx8vg8KYBcfzd584nkBhTJ4H2TjVRSahJWfXdbF27VpceumluOyyy/D6668HWv5rPaNVu7Ink0gk8FrPaKDx1MO0XN/vW1VFgWnJkdCA+NRle/dQ1a7syRhGCtu72bUdhu27h6p2ZU+myUhg+245zstI0ara/TsZTVUwUpQnocWpLjIJLTlv3rwZAPDAAw/g+uuvx7e//e3Ayh4eNjEwUqqrjIGREoaHo3/37HkCtlNfQrIdF57PJ9cgxaUuu3cfGZXt10ihjN2SJIK4eP2NAYwW6ru+RgsuXn8j2nfPjuOhVLbrKqNUtuE40b+vjVNdZBNacj733HOxbt06AMD+/fvR3NwcWNk9w8PQtfpG9mqagp7h4WACqoPjeTW/q/lfFEWKgRVxqctL+3ulKofGbd/Xg3of28Q75UTJdByodY5SVlQVpuPvtV6Q4lQX2YQ6z1nXddx888341a9+hTvuuKPiZ9va0tD1qQ3uUt4aRC6Xqfq5JmPywTxNRhKKmqw4lH06lEwbRrr6hdnRUbm+6ZTuu7svKHGpS94cn8dcjVbhATGTMZA3K0+VkEkjxDlijs9jrkateF6SGIn4vGjDY3CV6gmtrS1d8fct2RTaWyt/JmxxqstUTde1E/oiJOvXr8c//uM/4tOf/jT+53/+B+n08U/A0fPgqhGehXy+CL3CO+eJ85wnsm0bwstEPr/TclwUxuyKLc6Jc4Mn8oRALp1AcooPN2GJS11yBlAsmhXfOU+c5zyRaZaRM/zP359OjTLPucUAikWr4oPbxHnOE5VMGy0Rn5eCaWFgsFRxhbyJc4Mncj0BTXhwIx5AGae6TEUs5jk/8sgjuPvuuwEATU1NUBSl7u6PQ2a3tsKpcANOhesKzG5tDSSeeuiqirrXgRFCisn8canL+2fNlKocGrdkzuwaJ+j9b8o75UTJ0HV4db66EZ4HQ49+gcc41UU2of0V/MQnPoFXXnkFl19+OT73uc/hlltugRHQylytrQY6WprqKqOjpUmKNbdVVUGizlZiQtekWKc6LnXp6mpDS9bfSO1DWrIprrkdsEWndKA5W9/11ZzVIl9zW9dVNKXqe23TlEpIsU51nOoim9AeV9LpNG6//fawisd7Zzfj+dfe9jWdyrZtvFeiP5xGUkO+ZPuaguQJgUxSnqfOuNRlycI2PL31gK/pVKZZxofPbIx13BvNkq42PL39oK8xCSXTxoeWzAghqtq1ZJLoGy75moLkegIzmuVZmzpOdZFJwz6unL7oJKQS/v6QpxK6VJtgJBMaNJ+jnDVFkWrjiLjUZeU574OR8heLkdK4CUZIVp23BD6nn8NIQZpNMLLpJHSfPUS6qki1cUSc6iKThk3OAHDB2V3w3NoGEXiuiwvO7gopIv+as0mIGieKCAg0Z+W7sONSl6svWwLXqW2BBNexcPVlciSAuPrS5WfAcWqbW+s4Nr50+RkhReTP7BOyNY/REEJg9gnZkCLyL051kUVDJ+dWw8Cn/nohVAjYduWb1bZtqBD41F8vlHJXKlVR0JpNQQGqbgDhCQEFQGs2JeVOTnGpy/y2Nqz5zJmAcGBWWTPSNMuAcLDmM2c23K5UjWZBRweuu+IMCGGjZFa+70umDSFsXHeFfLtS6aqKOSfmoGC8e7cS1xu/T+acmIt8wOTxxKkusmj4LSMPOXo/Z01T0JzLYDRfhOuKht7PGYqCzhlZ9B8sAA22BzIQn7pMtp8zgIbfz7lRplIdz2T7OStAw+7nrKgqZnRkcHCgCNGAeyDHqS4TTedUqtgk50OGh030DA9DUZMQnoXZra1SjMr2w/MEHM9De1sGg0NF6Koa+Uhmv+JSl927h/DS/l7kzfH50O+fNbPhR2U3cnI+5PU3BrB9Xw9GzPH50EvmzI58VLYfjuPBdBy05Jowki/B0PWGHckcp7ocMp3JWY6hsQFqbTXQ2npSLP7gqKqCpKqhyYh+gZF6xaUuXV1t6Opqi8X1FSeLTunAolM6Gv686LqKrJ5Ee2u6IRblqCROdYlCYz/GEBERxRCTMxERkWSYnImIiCTD5ExERCQZJmciIiLJMDkTERFJhsmZiIhIMkzOREREkmFyJiIikgyTMxERkWSYnImIiCTD5ExERCQZJmciIiLJMDkTERFJhsmZiIhIMkzOREREkmFyJiIikgyTMxERkWSYnImIiCSjRx1A0A4cKGBX/0GUbRWphIcFnTNw0knZqMPy5fU3BrB9Xw9GTKDFAJbMmY1Fp3REHRYREYUsNsl58wt78fKuIQzkS1AVgWw2g0KhiF+L/ejINeG0BW04Z+m8qMOckvuf2I7tu4cwWnAhAGQySRSLFp7800E0ZzUs6WrDqvOWRB0mERGFpOGT84FCARt/sROmZSGZMmAYqcO/S6YMAEDR9vDMth78cWc/Vl+4GCdl5WxJ7xoYwPfu2wazDDQZCRjGkbcOTUYCAGA5wNPbD2LLa0/iS5efgQUdbEkTEcVNQ79zPlAo4D9/9jI8RT2ciCeTTBnwFBX/+bOXcaBQmKYIp27XwABu/+9tUJTE4UQ8mSYjAUVJ4Pb/3oZdAwPTFCEREU2Xhk7OG3+xE2qitsa/mtCx8Rc7Q4rIv+/dtw26XjkpT6TrCXzvvm0hRURERFFp2OS8+YW9MC3L17GmZWHzC3uDDagO9z+xHWbZ37Fmefx4IiKKj1CSs23buOmmm7Bq1SqsXLkSv/nNbwL/jpd3DVXtyp5MMmXg5V1DAUfk3/bdQ1W7sifTZCSwfbc8dSEiovqFkpwfffRRtLa24v7778cPf/hDrFu3LtDyDxwoYCBfqquMgXwJBw5E/+759TcGMFpw6ypjtODi9Tf47pmIKC5CSc7nn38+rrvuOgCAEAKapgVa/q7+g1AVUVcZqiKwq/9gQBH5t31fD+qrCSDeKYeIiOIhlKlUmUwGAFAoFHDttdfi+uuvr3pMW1sauj61JF62VWSzmaqfSyYmLy+ZyKBsq+jszE3pO8MyYo7PY65G1ZRJf5fJJDFiIvK6TFWjxFlNXOoBsC4yiks9ANbFj9DmOff29mLNmjVYtWoVLrjggqqfHxoam3LZqYSHQqFY8Z1zMqHBsifvLrbKJlKJVvT356f8vWFoMYBi0ar4zlnVFHju5O3rkmmjxUDkdZmKzs5cQ8RZTVzqAbAuMopLPQDWpVp5kwmlW/vgwYO46qqrcNNNN2HlypWBl7+gcwY8MXlLcio8oWBB54yAIvJvyZzZqK8mgPJOOUREFA+hJOcNGzZgdHQUd955J1avXo3Vq1fDNM3Ayj/ppCw6ck11ldGRa5Jize1Fp3SgOVvfO/nmrMY1t4mIYiSUbu1bb70Vt956axhFH3bagjY8s63H13Qqq2ziI6fK09Jc0tWGp7cf9DWdqmTa+NCS6HsAiIgoOA27CMk5S+fBSFYfSHU8RjIp1SYYq85bgqOWBK+JkQI3wSAiipmGTc4AsPrCxfBsp6ZjPNvB6gsXhxSRf1+6/Aw4jl3TMY5j40uXnxFSREREFJWGTs4nZbP4u4tOgyo8WOXK77StsglVePi7i06TcleqBR0duO6KMyCEjZJZOUmXTBtC2LjuCu5KRUQURw2/ZeRJ2SxuuvzD/2s/52QiA6tswhMKOnJN+Mips6Xqyj6eBR0d+Pa1f3Xc/ZxLpg0F44O/PrRkBruyiYhirOGT8yHnLJ2Hc5bOw4EDBezqP4iyrSKVaMWCzhlSjMquxarzlmAVxpf23L6vByPm+HzoJXNmc1Q2EdG7QGyS8yEnnZTFSSdlYzHxfdEpHVh0Skcs6kJERFPX0O+ciYiI4ojJmYiISDJMzkRERJJRhBD17lhIREREAWLLmYiISDJMzkRERJJhciYiIpIMkzMREZFkmJyJiIgkw+RMREQkmdgt3wkA27Ztw7//+79j48aNUYfim23buOWWW9DT0wPLsnDNNdfgr//6r6MOyxfXdXHrrbdiz549UBQFX/3qV7Fo0aKow6rLwMAAVqxYgR//+MdYsGBB1OH49qlPfQrZd3Zpe8973oNvfOMbEUfkz913340nn3wStm3jsssuw8UXXxx1SL48/PDD+PnPfw4AKJfL2LlzJ5599lk0NzdHHFntbNvGl7/8ZfT09EBVVaxbt65h7xXLsrB27Vrs27cP2WwWt912G+bNmxfqd8YuOf/Hf/wHHn30UTQ1NUUdSl0effRRtLa24t/+7d8wPDyMv/3bv23Y5Lx582YAwAMPPIDnn38e3/72t3HXXXdFHJV/tm3jtttug2EYUYdSl3K5DCFEQz/EAsDzzz+PF198EZs2bUKpVMKPf/zjqEPybcWKFVixYgUA4Ktf/SouuuiihkzMAPDb3/4WjuPggQcewLPPPovvfOc7+O53vxt1WL48+OCDSKfTePDBB7F7926sW7cOP/rRj0L9zth1a5988skNewEc7fzzz8d1110HABBCQNO0iCPy79xzz8W6desAAPv372/YPzaHrF+/HpdeeilOOOGEqEOpy6uvvopSqYSrrroKV1xxBbZu3Rp1SL4888wzWLRoEdasWYOrr74aZ599dtQh1W3Hjh3o7u7GJZdcEnUovs2fPx+u68LzPBQKBeh647YFu7u78fGPfxwA0NXVhV27doX+nY37X2sS5513Hv785z9HHUbdMpkMAKBQKODaa6/F9ddfH21AddJ1HTfffDN+9atf4Y477og6HN8efvhhtLe342Mf+xh+8IMfRB1OXQzDwOc+9zlcfPHF2Lt3Lz7/+c/j8ccfb7g/okNDQ9i/fz82bNiAP//5z7jmmmvw+OOPQ1GUqEPz7e6778aaNWuiDqMu6XQaPT09+Ju/+RsMDQ1hw4YNUYfk2+LFi7F582ace+652LZtG/r6+uC6bqiNpti1nOOkt7cXV1xxBS688EJccMEFUYdTt/Xr1+OJJ57AV77yFYyNjUUdji8/+9nP8Lvf/Q6rV6/Gzp07cfPNN6O/vz/qsHyZP38+PvnJT0JRFMyfPx+tra0NWZfW1lYsX74cyWQSXV1dSKVSGBwcjDos30ZHR7Fnzx6cddZZUYdSl3vuuQfLly/HE088gV/84hf48pe/jHK5HHVYvlx00UXIZrNYtWoVfvWrX+G0004LvTeTyVlSBw8exFVXXYWbbroJK1eujDqcujzyyCO4++67AQBNTU1QFAWq2piX3n333Yd7770XGzduxOLFi7F+/Xp0dnZGHZYvDz30EL75zW8CAPr6+lAoFBqyLh/84Afx9NNPQwiBvr4+lEoltLa2Rh2Wb1u2bMGyZcuiDqNuzc3NyOVyAICWlhY4jgPXdSOOyp8dO3Zg2bJl2LRpE84//3zMmTMn9O9srP6rd5ENGzZgdHQUd955J+68804A44PdGnEQ0ic+8QmsXbsWl19+ORzHwS233NKQ9YiblStXYu3atbjsssugKAq+/vWvN1yXNgCcc8452LJlC1auXAkhBG677baGHqOxZ88evOc974k6jLpdeeWVuOWWW7Bq1SrYto0bbrgB6XQ66rB8mTt3Lm6//XZs2LABuVwOX/va10L/Tu5KRUREJJnG7FskIiKKMSZnIiIiyTA5ExERSYbJmYiISDJMzkRERJJhciZ6l3n++eexfPlyDAwMHP63H/3oR/iHf/iHCKMioqMxORO9y3z0ox/FBRdcgFtvvRUAsHXrVvzkJz+ZlrmbRDQ1nOdM9C5kWRYuvvhiXHTRRbj33nuxfv16fOADH4g6LCJ6B5Mz0bvUG2+8gQsvvBBf+MIXGn5jFaK4Ybc20bvUCy+8gLa2Njz33HNwHCfqcIjoKEzORO9C3d3d+O53v4sHHngAyWQSd911V9QhEdFRmJyJ3mXK5TJuuOEG3HTTTZgzZw6++c1v4t5778XWrVujDo2I3sHkTPQu8/Wvfx2LFi3ChRdeCACYPXs21q5di5tuugnFYjHi6IgI4IAwIiIi6bDlTEREJBkmZyIiIskwORMREUmGyZmIiEgyTM5ERESSYXImIiKSDJMzERGRZJiciYiIJPP/A5pg2L15tH9LAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 576x396 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "work_set.plot(kind='scatter', x='X', y='Y', alpha=0.1, s=300)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<AxesSubplot:xlabel='X', ylabel='Y'>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x396 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "work_set.plot(kind='scatter', x='X', y='Y', alpha=0.2, s=20*work_set['area'])" ] }, { "cell_type": "code", "execution_count": 17, "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", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>329</th>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>9</td>\n", " <td>6</td>\n", " <td>92.2</td>\n", " <td>102.3</td>\n", " <td>751.5</td>\n", " <td>8.4</td>\n", " <td>23.5</td>\n", " <td>27</td>\n", " <td>4.0</td>\n", " <td>0.0</td>\n", " <td>3.33</td>\n", " </tr>\n", " <tr>\n", " <th>173</th>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>90.9</td>\n", " <td>126.5</td>\n", " <td>686.5</td>\n", " <td>7.0</td>\n", " <td>17.7</td>\n", " <td>39</td>\n", " <td>2.2</td>\n", " <td>0.0</td>\n", " <td>3.07</td>\n", " </tr>\n", " <tr>\n", " <th>272</th>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>92.1</td>\n", " <td>152.6</td>\n", " <td>658.2</td>\n", " <td>14.3</td>\n", " <td>20.2</td>\n", " <td>47</td>\n", " <td>4.0</td>\n", " <td>0.0</td>\n", " <td>3.09</td>\n", " </tr>\n", " <tr>\n", " <th>497</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>96.1</td>\n", " <td>181.1</td>\n", " <td>671.2</td>\n", " <td>14.3</td>\n", " <td>32.3</td>\n", " <td>27</td>\n", " <td>2.2</td>\n", " <td>0.0</td>\n", " <td>14.68</td>\n", " </tr>\n", " <tr>\n", " <th>182</th>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>7</td>\n", " <td>86.8</td>\n", " <td>15.6</td>\n", " <td>48.3</td>\n", " <td>3.9</td>\n", " <td>12.4</td>\n", " <td>53</td>\n", " <td>2.2</td>\n", " <td>0.0</td>\n", " <td>6.38</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>9</td>\n", " <td>5</td>\n", " <td>94.3</td>\n", " <td>85.1</td>\n", " <td>692.3</td>\n", " <td>15.9</td>\n", " <td>17.7</td>\n", " <td>37</td>\n", " <td>3.6</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>106</th>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>91.4</td>\n", " <td>30.7</td>\n", " <td>74.3</td>\n", " <td>7.5</td>\n", " <td>18.2</td>\n", " <td>29</td>\n", " <td>3.1</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>270</th>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>92.1</td>\n", " <td>152.6</td>\n", " <td>658.2</td>\n", " <td>14.3</td>\n", " <td>21.8</td>\n", " <td>56</td>\n", " <td>3.1</td>\n", " <td>0.0</td>\n", " <td>0.52</td>\n", " </tr>\n", " <tr>\n", " <th>435</th>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>6</td>\n", " <td>90.8</td>\n", " <td>84.7</td>\n", " <td>376.6</td>\n", " <td>5.6</td>\n", " <td>23.8</td>\n", " <td>51</td>\n", " <td>1.8</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " </tr>\n", " <tr>\n", " <th>102</th>\n", " <td>2</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>94.8</td>\n", " <td>108.3</td>\n", " <td>647.1</td>\n", " <td>17.0</td>\n", " <td>20.1</td>\n", " <td>40</td>\n", " <td>4.0</td>\n", " <td>0.0</td>\n", " <td>0.00</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>413 rows × 13 columns</p>\n", "</div>" ], "text/plain": [ " X Y month day FFMC DMC DC ISI temp RH wind rain area\n", "329 4 3 9 6 92.2 102.3 751.5 8.4 23.5 27 4.0 0.0 3.33\n", "173 4 4 9 1 90.9 126.5 686.5 7.0 17.7 39 2.2 0.0 3.07\n", "272 2 5 8 2 92.1 152.6 658.2 14.3 20.2 47 4.0 0.0 3.09\n", "497 3 4 8 2 96.1 181.1 671.2 14.3 32.3 27 2.2 0.0 14.68\n", "182 5 4 2 7 86.8 15.6 48.3 3.9 12.4 53 2.2 0.0 6.38\n", ".. .. .. ... ... ... ... ... ... ... .. ... ... ...\n", "71 4 5 9 5 94.3 85.1 692.3 15.9 17.7 37 3.6 0.0 0.00\n", "106 4 5 3 4 91.4 30.7 74.3 7.5 18.2 29 3.1 0.0 0.00\n", "270 2 2 8 2 92.1 152.6 658.2 14.3 21.8 56 3.1 0.0 0.52\n", "435 2 5 7 6 90.8 84.7 376.6 5.6 23.8 51 1.8 0.0 0.00\n", "102 2 4 8 2 94.8 108.3 647.1 17.0 20.1 40 4.0 0.0 0.00\n", "\n", "[413 rows x 13 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "work_set" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Extracting features form the dataset\n", "\n", "# converting to list \n", "\n", "x_values = list(work_set['X'])\n", "y_values = list(work_set['Y'])\n", "\n", "loc_values = []\n", "\n", "for index in range(0, len(x_values)):\n", " temp_values = []\n", " temp_values.append(x_values[index])\n", " temp_values.append(y_values[index])\n", " loc_values.append(temp_values)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Counting the instance location in dataset\n", "\n", "def count_points(x_points, y_points, scaling_factor):\n", " count_array = []\n", " \n", " for index in range(0, len(x_points)):\n", " temp_values = [x_values[index], y_points[index]]\n", " count = 0\n", " \n", " for value in loc_values:\n", " if(temp_values == value):\n", " count = count + 1\n", " count_array.append(count * scaling_factor)\n", " \n", " return count_array" ] }, { "cell_type": "code", "execution_count": 21, "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", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>329</th>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>9</td>\n", " <td>6</td>\n", " <td>92.2</td>\n", " <td>102.3</td>\n", " <td>751.5</td>\n", " <td>8.4</td>\n", " <td>23.5</td>\n", " <td>27</td>\n", " <td>4.0</td>\n", " <td>0.0</td>\n", " <td>3.33</td>\n", " </tr>\n", " <tr>\n", " <th>173</th>\n", " <td>4</td>\n", " <td>4</td>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>90.9</td>\n", " <td>126.5</td>\n", " <td>686.5</td>\n", " <td>7.0</td>\n", " <td>17.7</td>\n", " <td>39</td>\n", " <td>2.2</td>\n", " <td>0.0</td>\n", " <td>3.07</td>\n", " </tr>\n", " <tr>\n", " <th>272</th>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>92.1</td>\n", " <td>152.6</td>\n", " <td>658.2</td>\n", " <td>14.3</td>\n", " <td>20.2</td>\n", " <td>47</td>\n", " <td>4.0</td>\n", " <td>0.0</td>\n", " <td>3.09</td>\n", " </tr>\n", " <tr>\n", " <th>497</th>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>2</td>\n", " <td>96.1</td>\n", " <td>181.1</td>\n", " <td>671.2</td>\n", " <td>14.3</td>\n", " <td>32.3</td>\n", " <td>27</td>\n", " <td>2.2</td>\n", " <td>0.0</td>\n", " <td>14.68</td>\n", " </tr>\n", " <tr>\n", " <th>182</th>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>7</td>\n", " <td>86.8</td>\n", " <td>15.6</td>\n", " <td>48.3</td>\n", " <td>3.9</td>\n", " <td>12.4</td>\n", " <td>53</td>\n", " <td>2.2</td>\n", " <td>0.0</td>\n", " <td>6.38</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", "329 4 3 9 6 92.2 102.3 751.5 8.4 23.5 27 4.0 0.0 3.33\n", "173 4 4 9 1 90.9 126.5 686.5 7.0 17.7 39 2.2 0.0 3.07\n", "272 2 5 8 2 92.1 152.6 658.2 14.3 20.2 47 4.0 0.0 3.09\n", "497 3 4 8 2 96.1 181.1 671.2 14.3 32.3 27 2.2 0.0 14.68\n", "182 5 4 2 7 86.8 15.6 48.3 3.9 12.4 53 2.2 0.0 6.38" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "work_set.head()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[<AxesSubplot:xlabel='RH', ylabel='RH'>]], dtype=object)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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9DHsv42Tv/bUCsbKou/POOyMi4qc//WmccsopMTg4GGvWrImRkZG45ZZbqhoDAABgSqn8B6UsW7as6ksCAABMWT7SAAAAIDFRBwAAkJioAwAASEzUAQAAJCbqAAAAEhN1AAAAiYk6AACAxEQdAABAYqIOAAAgMVEHAACQmKgDAABITNQBAAAkJuoAAAASE3UAAACJiToAAIDERB0AAEBiog4AACAxUQcAAJCYqAMAAEhM1AEAACQm6gAAABITdQAAAImJOgAAgMREHQAAQGKiDgAAIDFRBwAAkJioAwAASEzUAQAAJCbqAAAAEhN1AAAAiYk6AACAxEQdAABAYqIOAAAgMVEHAACQmKgDAABITNQBAAAkJuoAAAASE3UAAACJNZUeAAAyWNG1vfQIqW28YUnpEQCmLCd1AAAAiYk6AACAxEQdAABAYqIOAAAgMVEHAACQmKgDAABITNQBAAAkJuoAAAASE3UAAACJiToAAIDERB0AAEBiog4AACAxUQcAAJCYqAMAAEhM1AEAACQm6gAAABITdQAAAImJOgAAgMREHQAAQGKiDgAAIDFRBwAAkJioAwAASEzUAQAAJCbqAAAAEhN1AAAAiYk6AACAxEQdAABAYqIOAAAgMVEHAACQmKgDAABITNQBAAAkJuoAAAASE3UAAACJiToAAIDERB0AAEBiog4AACAxUQcAAJCYqAMAAEhM1AEAACQm6gAAABITdQAAAImJOgAAgMREHQAAQGKiDgAAIDFRBwAAkFhT6QEAgKlvRdf20iOktvGGJaVHAN7GnNQBAAAkJuoAAAASE3UAAACJiToAAIDERB0AAEBiog4AACAxUQcAAJCYqAMAAEhM1AEAACQm6gAAABITdQAAAImJOgAAgMREHQAAQGJNVV3o2WefjUceeSRmzZoV8+bNi0OHDsWRI0dibGws1q5dGzNmzKhqFAAAgCmjspO6kZGRuPnmm2P16tXx7LPPxtjYWNx0001x8cUXx7Zt26oaAwAAYEqp7KTu4x//eExOTsY999wTF110UTQ0NEREREdHR+zevft1nz9nzsxoamqs9ZhptLW1lB4BAKjIVPt7f6r9+2Rh72VUsffKom5sbCzWrVsXl112WZx99tlxzz33REREb29vtLe3v+7zh4YO1XrENNraWmJgYLT0GABARabS3/v+P6YMey/jZO/9tQKxsqi77bbboru7O37yk59EY2NjzJ8/P2699dYYHx+PtWvXVjUGAADAlFJZ1K1fv76qSwEAANQNH2kAAACQWGUndQAAvDUruraXHiGtjTcsKT0C1JyTOgAAgMREHQAAQGKiDgAAIDFRBwAAkFjd/qAUbzgGAACmAid1AAAAiYk6AACAxEQdAABAYqIOAAAgMVEHAACQmKgDAABITNQBAAAkJuoAAAASE3UAAACJiToAAIDERB0AAEBiog4AACAxUQcAAJCYqAMAAEhM1AEAACQm6gAAABITdQAAAImJOgAAgMREHQAAQGKiDgAAIDFRBwAAkJioAwAASEzUAQAAJCbqAAAAEhN1AAAAiYk6AACAxEQdAABAYqIOAAAgMVEHAACQmKgDAABITNQBAAAkJuoAAAASE3UAAACJiToAAIDERB0AAEBiog4AACAxUQcAAJCYqAMAAEhM1AEAACQm6gAAABITdQAAAImJOgAAgMREHQAAQGKiDgAAIDFRBwAAkJioAwAASEzUAQAAJCbqAAAAEhN1AAAAiYk6AACAxEQdAABAYqIOAAAgMVEHAACQmKgDAABITNQBAAAkJuoAAAASE3UAAACJiToAAIDERB0AAEBiog4AACAxUQcAAJBYU+kBAACgVlZ0bS89Qmobb1hSegTeACd1AAAAiYk6AACAxEQdAABAYqIOAAAgMVEHAACQmKgDAABITNQBAAAkJuoAAAASE3UAAACJiToAAIDEmkoPAAAAvD2t6NpeeoTUtt5xeSXXcVIHAACQmKgDAABITNQBAAAkJuoAAAASE3UAAACJiToAAIDERB0AAEBiog4AACAxUQcAAJCYqAMAAEhM1AEAACQm6gAAABITdQAAAImJOgAAgMREHQAAQGKiDgAAIDFRBwAAkFhTyYv39fVFV1dXzJ49OxYsWBDXXHNNyXEAAADSKXpS98gjj8Ty5ctjzZo18dvf/jaOHj1achwAAIB0ip7UDQ4ORmdnZ0REtLa2xujoaMydO/e4f7atreWkXnvrHZef1K8HAADwf53sjjmeoid1nZ2d0dvbGxERBw8ejNbW1pLjAAAApNMwOTk5WeriAwMD0dXVFbNmzYr3v//9cfXVV5caBQAAIKWiUQcAAMB/x0caAAAAJCbqAAAAEhN1AAAAiYk6AACAxEQdAABAYqIOAADgJHrllVdieHg4JiYmKrleUyVX4b9y9OjRePzxx6O/vz/a29vj0ksvjcbGxtJjTXlDQ0OxZcuWGBgYiPb29vjc5z4Xzc3Npcea0uy8DPeYMuy9DHsvw/29enZezubNm+N3v/tdtLa2xsjISHziE5+Iq666qqbXdFKXwM033xyHDx+Oc889Nw4fPhzf+ta3So9UF9avXx8XXnhhLF++PC644IJYvXp16ZGmPDsvwz2mDHsvw97LcH+vnp2X87e//S3uv//+uP322+P++++P5557rubXdFKXQHNzcyxbtuzYr3ft2lVwmvrR3NwcixYtioiIs846K379618Xnmjqs/My3GPKsPcy7L0M9/fq2Xk5w8PDsXPnzujs7Iz9+/fH2NhYza8p6hKYMWNGrF69Ojo6OqKvry9mz55deqS6cMYZZ8RXvvKVmDdvXgwNDcXixYtLjzTlvXrnBw4ciA996EOlR6oLr77HvPjii9HW1lZ6pLrg3l6GvZfx7/v7nDlzYt++fXHJJZeUHmnKO+OMM2LlypUxf/78OHDgQHz4wx8uPVLd+MY3vhE//OEPY3BwME4//fT45je/WfNrevllAjNnzoyenp6YmJiI6667Lvr7+0uPVBfmzp0bR44ciXe9613R2NgYvb29pUea8jo7O+Oyyy6LxYsXx/j4eMybN6/0SHWhsbExzjnnnHjqqaeip6cnTj/99NIj1YVZs2bFeeedF08++WS88MILsWDBgtIj1YVf/epX8dnPfjYWL14c1113XXzta18rPVJdGBwcjAsuuCB6e3ujtbU1jh49WnqkKa+joyNmz54de/fujYiIU089tfBE9WPGjBlxyimnxPTp02PatGmV7F7UJfDSSy/FQw89FI2NjfGPf/wjXnnlldIj1YU///nPcd9998XTTz8dGzZs8BdQBbZv3x5PP/30sV0fOXKk8ET1YWBgIHbt2hXf//734+GHH46//OUvpUeqCwcPHozf//73sWnTpnj44Yfj6aefLj1SXbjooovil7/8ZTz++OOV/VQ6/mnPnj2xcePG2LBhw7HQoHZ27twZ3/72t2PhwoVx3333xc6dO0uPVDfWr18fH/jAB+Laa6+t7P2Moi6BsbGxOHToUFx33XWxdevW2LdvX+mR6sJLL70UfX19sW7duhgeHnZSV4Hbb7893v3ud8fExES85z3viaVLl5YeqS7s2rUrBgcHY3BwMPbt2xcHDhwoPVJdGBsbi7PPPjuef/75ePHFF2N0dLT0SHVh+vTpsWrVqrjqqqti48aN8cUvfrH0SHVhbGwsnnvuuejp6Yldu3aJugoMDAzEnj17Ynh4OHp6emJkZKT0SHWjubk5PvjBD8ZZZ50VixYtitbW1ppfs2FycnKy5lfhv/KHP/whRkZG4mMf+1iMj4/HbbfdFuvWrSs91pTX3d0dY2NjsXDhwti9e3ccOXIkzj///NJj1YXt27fH1q1b4zvf+U7pUerCvn374plnnonzzz8/du3aFWeddVYsXLiw9FhT3t69e2PTpk3R3d0dbW1tsXLlyjjnnHNKjzXlbd68Oa655prSY9Sdvr6+eOGFF6KjoyM2b94cy5Yti/e9732lx5rS/vjHP8bPf/7z+MIXvhD79++P+fPnx5lnnll6rLrw0EMPxTPPPBOnnXZaDA8Px6JFi2L58uU1vaaoAwAAOImOHDkSBw8ejHnz5sW0abV/caSffgkAAHCSfOYzn4mZM2fGzJkzjz32wAMP1PSaTuoAAABOku7u7vjxj38cq1atquyaog4AAOAkGhwcjNNOO62y64k6AACAxHykAQAAQGKiDgAAIDE//RIA/qWnpycuvfTSY58XNzExEePj47F06dJYtmxZXHvttbF9+/b/eM65554be/bsKTEuAESEqAOA/9De3h4/+9nPjv26r68vPvWpT8WnP/3pglMBwGvz8ksAOIGBgYGYnJyMoaGh0qMAwHE5qQOAV+nv74/LL788Dh8+HENDQ3HeeefF3XffHR0dHcd+DwDeTkQdALzKv19+OTExEV1dXbFnz55YvHhx9PX1/b+XZkb88z11AFCSl18CwHFMmzYtrr/++jhw4EBs3Lix9DgA8JpEHQC8hqamprj++uvj3nvvjcHBwdLjAMBxiToAOIGPfvSjceGFF8add95ZehQAOK6GycnJydJDAAAA8NY4qQMAAEhM1AEAACQm6gAAABITdQAAAImJOgAAgMREHQAAQGKiDgAAIDFRBwAAkNj/AOr9LnXU0eP7AAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 1080x720 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting the histogram for the RH attribute\n", "from pandas.plotting import scatter_matrix\n", "\n", "attributes = ['RH']\n", "scatter_matrix(work_set[attributes], figsize=(15,10))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[<AxesSubplot:xlabel='temp', ylabel='temp'>]], dtype=object)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x720 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting the histogram for the temp attribute\n", "from pandas.plotting import scatter_matrix\n", "\n", "attributes = ['temp']\n", "scatter_matrix(work_set[attributes], figsize=(15,10))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[<AxesSubplot:xlabel='DMC', ylabel='DMC'>]], dtype=object)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "<Figure size 1080x720 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting the histogram for the DMC attribute\n", "from pandas.plotting import scatter_matrix\n", "\n", "attributes = ['DMC']\n", "scatter_matrix(work_set[attributes], figsize=(15,10))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[<AxesSubplot:xlabel='area', ylabel='area'>]], dtype=object)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x720 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting the histogram for the area attribute\n", "from pandas.plotting import scatter_matrix\n", "\n", "attributes = ['area']\n", "scatter_matrix(work_set[attributes], figsize=(15,10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Finding the unique values in month , day and area ( the values could be repetitive)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 3, 10, 8, 9, 4, 6, 7, 2, 1, 12, 5, 11])" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db['month'].unique()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([5, 2, 6, 7, 1, 3, 4])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db['day'].unique()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.00000e+00, 3.60000e-01, 4.30000e-01, 4.70000e-01, 5.50000e-01,\n", " 6.10000e-01, 7.10000e-01, 7.70000e-01, 9.00000e-01, 9.50000e-01,\n", " 9.60000e-01, 1.07000e+00, 1.12000e+00, 1.19000e+00, 1.36000e+00,\n", " 1.43000e+00, 1.46000e+00, 1.56000e+00, 1.61000e+00, 1.63000e+00,\n", " 1.64000e+00, 1.69000e+00, 1.75000e+00, 1.90000e+00, 1.94000e+00,\n", " 1.95000e+00, 2.01000e+00, 2.14000e+00, 2.29000e+00, 2.51000e+00,\n", " 2.53000e+00, 2.55000e+00, 2.57000e+00, 2.69000e+00, 2.74000e+00,\n", " 3.07000e+00, 3.50000e+00, 4.53000e+00, 4.61000e+00, 4.69000e+00,\n", " 4.88000e+00, 5.23000e+00, 5.33000e+00, 5.44000e+00, 6.38000e+00,\n", " 6.83000e+00, 6.96000e+00, 7.04000e+00, 7.19000e+00, 7.30000e+00,\n", " 7.40000e+00, 8.24000e+00, 8.31000e+00, 8.68000e+00, 8.71000e+00,\n", " 9.41000e+00, 1.00100e+01, 1.00200e+01, 1.09300e+01, 1.10600e+01,\n", " 1.12400e+01, 1.13200e+01, 1.15300e+01, 1.21000e+01, 1.30500e+01,\n", " 1.37000e+01, 1.39900e+01, 1.45700e+01, 1.54500e+01, 1.72000e+01,\n", " 1.92300e+01, 2.34100e+01, 2.42300e+01, 2.60000e+01, 2.61300e+01,\n", " 2.73500e+01, 2.86600e+01, 2.94800e+01, 3.03200e+01, 3.17200e+01,\n", " 3.18600e+01, 3.20700e+01, 3.58800e+01, 3.68500e+01, 3.70200e+01,\n", " 3.77100e+01, 4.85500e+01, 4.93700e+01, 5.83000e+01, 6.41000e+01,\n", " 7.13000e+01, 8.84900e+01, 9.51800e+01, 1.03390e+02, 1.05660e+02,\n", " 1.54880e+02, 1.96480e+02, 2.00940e+02, 2.12880e+02, 1.09084e+03,\n", " 1.01300e+01, 2.87000e+00, 7.60000e-01, 9.00000e-02, 7.50000e-01,\n", " 2.47000e+00, 6.80000e-01, 2.40000e-01, 2.10000e-01, 1.52000e+00,\n", " 1.03400e+01, 8.02000e+00, 1.38000e+00, 8.85000e+00, 3.30000e+00,\n", " 4.25000e+00, 6.54000e+00, 7.90000e-01, 1.70000e-01, 4.40000e+00,\n", " 5.20000e-01, 9.27000e+00, 3.09000e+00, 8.98000e+00, 1.11900e+01,\n", " 5.38000e+00, 1.78500e+01, 1.07300e+01, 2.20300e+01, 9.77000e+00,\n", " 2.47700e+01, 1.10000e+00, 2.42400e+01, 8.00000e+00, 2.64000e+00,\n", " 8.64500e+01, 6.57000e+00, 3.52000e+00, 4.10000e-01, 5.18000e+00,\n", " 1.42900e+01, 1.58000e+00, 3.78000e+00, 4.41000e+00, 3.43600e+01,\n", " 7.21000e+00, 1.01000e+00, 2.18000e+00, 4.42000e+00, 3.33000e+00,\n", " 6.58000e+00, 1.56400e+01, 1.12200e+01, 2.13000e+00, 5.60400e+01,\n", " 7.48000e+00, 1.47000e+00, 3.93000e+00, 6.10000e+00, 5.83000e+00,\n", " 2.81900e+01, 3.71000e+00, 7.31000e+00, 2.03000e+00, 1.72000e+00,\n", " 5.97000e+00, 1.30600e+01, 1.26000e+00, 8.12000e+00, 1.09000e+00,\n", " 3.94000e+00, 2.93000e+00, 5.65000e+00, 2.00300e+01, 1.26400e+01,\n", " 1.83000e+01, 3.93500e+01, 1.74630e+02, 7.73000e+00, 1.63300e+01,\n", " 5.86000e+00, 4.28700e+01, 1.21800e+01, 1.60000e+01, 2.45900e+01,\n", " 2.87400e+01, 9.96000e+00, 3.01800e+01, 7.07600e+01, 5.17800e+01,\n", " 3.64000e+00, 3.63000e+00, 8.16000e+00, 4.95000e+00, 6.04000e+00,\n", " 3.95000e+00, 7.80000e+00, 4.62000e+00, 7.46280e+02, 7.02000e+00,\n", " 2.44000e+00, 3.05000e+00, 1.85760e+02, 6.30000e+00, 7.20000e-01,\n", " 4.96000e+00, 2.35000e+00, 3.20000e+00, 6.36000e+00, 1.53400e+01,\n", " 5.40000e-01, 6.43000e+00, 3.30000e-01, 1.23000e+00, 3.35000e+00,\n", " 9.71000e+00, 8.27500e+01, 3.32000e+00, 5.39000e+00, 6.84000e+00,\n", " 3.18000e+00, 5.55000e+00, 6.61000e+00, 6.11300e+01, 3.84800e+01,\n", " 7.03200e+01, 1.00800e+01, 3.19000e+00, 1.76000e+00, 7.36000e+00,\n", " 2.21000e+00, 2.78530e+02, 2.75000e+00, 1.29000e+00, 2.64300e+01,\n", " 2.07000e+00, 2.00000e+00, 1.64000e+01, 4.67000e+01, 4.33200e+01,\n", " 8.59000e+00, 2.77000e+00, 1.46800e+01, 4.05400e+01, 1.08200e+01,\n", " 4.95900e+01, 5.80000e+00, 2.17000e+00, 6.44000e+00, 5.42900e+01,\n", " 1.11600e+01])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db['area'].unique()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# defining the method for plotting the histogram\n", "def histogram_plot(db, title):\n", " plt.figure(figsize=(8, 6)) \n", " \n", " ax = plt.subplot() \n", " ax.spines[\"top\"].set_visible(False) \n", " ax.spines[\"bottom\"].set_visible(False) \n", " ax.spines[\"right\"].set_visible(False) \n", " ax.spines[\"left\"].set_visible(False)\n", " \n", " ax.get_xaxis().tick_bottom()\n", " ax.get_yaxis().tick_left() \n", " \n", " plt.title(title, fontsize = 22)\n", " plt.hist(db, edgecolor='black', linewidth=1.2)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Scattering the plot with the help of the location\n", "\n", "plt.figure(figsize=(8, 6)) \n", " \n", "ax = plt.subplot() \n", "ax.spines[\"top\"].set_visible(False) \n", "ax.spines[\"bottom\"].set_visible(False) \n", "ax.spines[\"right\"].set_visible(False) \n", "ax.spines[\"left\"].set_visible(False)\n", " \n", "ax.get_xaxis().tick_bottom()\n", "ax.get_yaxis().tick_left() \n", " \n", "plt.title(\"Fire location plot\", fontsize = 22)\n", "plt.scatter(x_values, y_values, s = count_points(x_values, y_values, 25), alpha = 0.3)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# Encoding the data using LabelEncoder\n", "\n", "from sklearn.preprocessing import LabelEncoder\n", "\n", "month_encoder = LabelEncoder()\n", "day_encoder = LabelEncoder()\n", "\n", "months = db['month']\n", "days = db['day']\n", "\n", "month_1hot = month_encoder.fit_transform(months) # label encoding month\n", "day_1hot = day_encoder.fit_transform(days) # label encoding day" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 2, 9, 9, 2, 2, 7, 7, 7, 8, 8, 8, 8, 7, 8, 8, 8, 2,\n", " 9, 2, 3, 8, 8, 5, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8,\n", " 8, 8, 9, 9, 9, 2, 6, 7, 7, 8, 8, 8, 8, 6, 2, 2, 8,\n", " 7, 7, 7, 7, 8, 8, 9, 1, 1, 2, 2, 7, 7, 7, 7, 8, 8,\n", " 8, 2, 2, 8, 2, 7, 8, 1, 1, 2, 7, 7, 7, 7, 7, 7, 7,\n", " 8, 8, 8, 8, 2, 7, 2, 7, 7, 7, 8, 1, 2, 7, 7, 7, 7,\n", " 7, 8, 0, 2, 2, 7, 8, 8, 2, 2, 8, 8, 2, 2, 2, 2, 2,\n", " 7, 7, 7, 8, 8, 8, 9, 2, 8, 9, 9, 1, 2, 2, 8, 2, 7,\n", " 8, 8, 6, 8, 8, 7, 7, 6, 7, 7, 2, 8, 7, 8, 5, 6, 6,\n", " 8, 8, 7, 8, 7, 7, 8, 2, 7, 2, 8, 8, 2, 7, 7, 2, 7,\n", " 8, 7, 7, 8, 7, 7, 3, 7, 8, 7, 8, 9, 1, 9, 7, 8, 2,\n", " 8, 2, 2, 2, 7, 7, 8, 7, 7, 3, 8, 8, 8, 8, 2, 1, 9,\n", " 2, 8, 7, 8, 8, 8, 9, 7, 8, 2, 2, 2, 8, 8, 8, 2, 7,\n", " 8, 2, 6, 8, 8, 9, 7, 8, 7, 8, 8, 8, 8, 8, 7, 8, 8,\n", " 8, 3, 3, 3, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n", " 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n", " 7, 11, 11, 11, 11, 11, 11, 11, 11, 11, 1, 1, 1, 6, 6, 6, 6,\n", " 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 8,\n", " 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n", " 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n", " 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,\n", " 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 7, 7,\n", " 8, 8, 7, 7, 2, 0, 6, 7, 7, 7, 7, 7, 8, 2, 7, 7, 1,\n", " 8, 8, 2, 1, 1, 8, 7, 7, 5, 5, 8, 7, 7, 8, 7, 8, 1,\n", " 8, 6, 1, 1, 6, 7, 7, 7, 6, 2, 7, 7, 7, 7, 6, 8, 7,\n", " 7, 7, 7, 7, 7, 8, 7, 7, 7, 7, 6, 7, 7, 7, 8, 8, 7,\n", " 3, 6, 8, 7, 7, 2, 8, 7, 7, 7, 7, 7, 7, 6, 7, 7, 7,\n", " 7, 7, 7, 8, 1, 1, 1, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5,\n", " 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n", " 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n", " 7, 7, 7, 7, 7, 7, 10])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "month_1hot" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([4, 1, 5, 4, 6, 6, 0, 0, 1, 5, 5, 5, 4, 0, 2, 4, 5, 0, 2, 5, 1, 0,\n", " 6, 5, 5, 6, 4, 0, 5, 6, 4, 0, 4, 6, 0, 1, 1, 4, 5, 1, 1, 5, 1, 5,\n", " 2, 2, 0, 0, 0, 0, 3, 6, 2, 2, 3, 3, 1, 6, 0, 4, 6, 6, 3, 6, 0, 3,\n", " 4, 4, 4, 4, 4, 4, 4, 1, 4, 3, 4, 0, 4, 1, 6, 6, 1, 2, 3, 3, 3, 3,\n", " 6, 5, 5, 4, 6, 6, 0, 6, 5, 5, 6, 6, 6, 1, 1, 5, 5, 4, 3, 6, 5, 0,\n", " 4, 4, 6, 0, 1, 1, 5, 5, 0, 3, 0, 0, 6, 1, 4, 6, 0, 4, 2, 6, 5, 0,\n", " 6, 3, 1, 5, 6, 0, 1, 1, 0, 2, 4, 5, 2, 3, 0, 1, 1, 3, 4, 6, 5, 4,\n", " 5, 6, 5, 2, 2, 4, 0, 3, 5, 5, 6, 3, 2, 2, 4, 3, 2, 2, 6, 0, 5, 5,\n", " 3, 6, 2, 1, 6, 0, 6, 0, 4, 1, 6, 0, 5, 6, 4, 3, 1, 2, 1, 4, 3, 3,\n", " 1, 0, 1, 6, 6, 2, 5, 3, 5, 1, 4, 3, 5, 5, 4, 0, 5, 5, 6, 0, 2, 0,\n", " 6, 4, 0, 4, 2, 6, 0, 5, 6, 5, 2, 6, 1, 1, 5, 6, 5, 1, 5, 6, 2, 4,\n", " 6, 6, 6, 6, 6, 2, 2, 2, 2, 2, 2, 3, 3, 3, 5, 5, 5, 5, 0, 4, 4, 4,\n", " 4, 1, 1, 1, 1, 1, 1, 1, 1, 6, 2, 3, 0, 0, 0, 0, 4, 1, 6, 2, 4, 6,\n", " 2, 5, 5, 5, 5, 5, 4, 1, 1, 6, 6, 6, 2, 5, 0, 0, 4, 4, 5, 6, 6, 6,\n", " 6, 6, 6, 6, 6, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 5, 5, 5, 5,\n", " 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4,\n", " 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 5, 6, 6, 2, 6, 3,\n", " 4, 5, 0, 5, 3, 6, 2, 3, 2, 3, 5, 6, 6, 3, 4, 4, 0, 4, 6, 1, 0, 6,\n", " 6, 6, 5, 2, 2, 3, 4, 3, 2, 1, 5, 5, 4, 1, 4, 4, 0, 5, 6, 3, 1, 2,\n", " 6, 6, 2, 2, 6, 5, 5, 3, 3, 0, 3, 6, 3, 5, 3, 6, 4, 5, 0, 5, 5, 4,\n", " 4, 0, 0, 4, 4, 6, 6, 2, 2, 6, 2, 4, 0, 3, 3, 0, 3, 2, 5, 5, 5, 5,\n", " 6, 1, 1, 5, 0, 2, 3, 6, 6, 4, 0, 5, 3, 3, 3, 6, 6, 0, 3, 3, 6, 6,\n", " 6, 0, 1, 1, 1, 2, 2, 3, 4, 4, 5, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 3,\n", " 4, 4, 4, 4, 4, 6, 6, 6, 6, 5, 1])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "day_1hot" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/vishwasmore/opt/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:617: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n", " return self.partial_fit(X, y)\n", "/Users/vishwasmore/opt/anaconda3/lib/python3.7/site-packages/sklearn/base.py:462: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n", " return self.fit(X, **fit_params).transform(X)\n" ] } ], "source": [ "# Standardizing the data (Feature Scaling) so that all the features are of the same scale\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "scaler = StandardScaler()\n", "\n", "numerical_features = db.drop(['month', 'day'], axis=1)\n", "scaled_features = scaler.fit_transform(numerical_features)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1.00831277, 0.56986043, -0.80595947, ..., 1.49861442,\n", " -0.07326831, -0.20201979],\n", " [ 1.00831277, -0.24400101, -0.00810203, ..., -1.74175564,\n", " -0.07326831, -0.20201979],\n", " [ 1.00831277, -0.24400101, -0.00810203, ..., -1.51828184,\n", " -0.07326831, -0.20201979],\n", " ...,\n", " [ 1.00831277, -0.24400101, -1.64008316, ..., 1.49861442,\n", " -0.07326831, -0.02653216],\n", " [-1.58736044, -0.24400101, 0.68095666, ..., -0.00983371,\n", " -0.07326831, -0.20201979],\n", " [ 0.57570057, -1.05786246, -2.02087875, ..., 0.26950853,\n", " -0.07326831, -0.20201979]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaled_features" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "from sklearn.base import BaseEstimator, TransformerMixin\n", "\n", "# defining the methods for the AttributeSelector\n", "class AttributeSelector(BaseEstimator, TransformerMixin):\n", " def __init__(self, attribute_names):\n", " self.attribute_names = attribute_names\n", "\n", " def fit(self, X, y=None):\n", " return self\n", "\n", " def transform(self, X):\n", " return X[self.attribute_names].values" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import MultiLabelBinarizer\n", "# defining the methods for the CustomBinarizer\n", "class CustomBinarizer(BaseEstimator, TransformerMixin):\n", " def __init__(self, class_labels):\n", " self.class_labels = class_labels\n", " def fit(self, X, y=None,**fit_params):\n", " return self\n", " def transform(self, X):\n", " return MultiLabelBinarizer(classes=self.class_labels).fit_transform(X)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "from sklearn.pipeline import Pipeline\n", "\n", "\n", "numerical_attributes = ['X', 'Y', 'FFMC', 'DMC', 'DC', 'ISI', 'temp', 'RH', 'wind', 'rain'] # Selecting the numerical columns\n", "categorical_attributes = ['month', 'day'] # # Selecting the categorical columns\n", "categorical_classes = np.concatenate((db['month'].unique(), db['day'].unique()), axis=0)\n", "\n", "# creating the separate numerical and categorical pipelines\n", "numerical_pipeline = Pipeline([\n", " ('selector', AttributeSelector(numerical_attributes)),\n", " ('standardize', StandardScaler()),\n", "])\n", "categorical_pipeline = Pipeline([\n", " ('selector', AttributeSelector(categorical_attributes)),\n", " ('encode', CustomBinarizer(categorical_classes)),\n", "])" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#FFMC distrubution\n", "# Creating Histogram based on FFMC attribute\n", "histogram_plot(db['FFMC'], title = \"FFMC distribution\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 576x432 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#DC distrubution\n", "# Creating Histogram based on DC attribute \n", "histogram_plot(db['DC'], title = \"DC distribution\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# Separating the features and labels into X and Y\n", "X = db.iloc[:,[0,1,2,3,4,5,6,7,8,9,10,11]].values\n", "Y = db.iloc[:, 11].values" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "# Separating the test and training set\n", "train_x, test_x, train_y, test_y = train_test_split(X,Y, test_size=0.3, random_state = 9)\n", "mse_values = []\n", "variance_score = []" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After all the data cleaning and modifications, we have our training and test sets ready. They can be easily consumed by the algorithm of our choice" ] }, { "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }