End-to-End Deploy-able Regression Pipeline Using ScikitLearn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f1d109784ee182dfe4de23e72429f93e35906d7b"
   },
   "source": [
    "# Prediction of Burn Area using Meteorological Data\n",
    "\n",
    "### My goal with this kernel is to provide a reusable end-to-end Regression framework that is deployable in real world. It is imperative that the users adapt it to the dataset in hand and make small necessary changes, but the overall process and class-objects given can adapt to any regression system.\n",
    "\n",
    "### Also, this kernel showcases how Scikit-Learn library can simplify and automate data preprocessing, model building and predicting for practical deployment.\n",
    "\n",
    "### I chose this dataset because this problem showcases several fundamental challenges in data science, that are not always easy to overcome: - such as highly left-right skewed data, extreme outliers, underfitting, overfitting, lack of enough examples etc. Many of these problems can be dealt with by collecting more data, meaningful feature engineering, and may be removing rare cases or modelling with different techniques that can handle outliers much better, but that is not the motivation behind this kernel.\n",
    "\n",
    "#### This dataset covers meteorological and spatiotemporal data for forest fires (between 2000 and 2003) in Portugal’s Montesinho Natural Park, with 13 attributes each for 517 such incidents. Our target attribute from these 13 is ‘area’ - total burned area in hectares (ha). The corresponding weather data is that which is registered by sensors when the fire was detected (or first broke out). We have: temp, RH (relative humidity), wind (speed), rain (accumulated precipitation over the last 30 minutes).\n",
    "\n",
    "#### We also have DMC, DC, ISI and FFMC are components of the Canadian Forest Fire Weather Index (FWI) System, which measures the effects of fuel moisture and wind on fire behavior. These have been calculated using consecutive daily observations of temperature, relative humidity, wind speed, and 24-hour rainfall - i.e. these are time-lagged and not instantaneous values, unlike the four weather variables. Roughly, the higher these components, the more the expected severity of the fire.\n",
    "\n",
    "#### Spatiotemporal data includes the month and day of the week that the incident occurred, and X and Y co-ordinates of the incident with respect to the park. Smaller fires are much more frequent. This is the case in this dataset, as well as with incidences of wildfires around the world, making this a difficult regression problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "#support python 2&3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "#common imports\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#make notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "#Pretty Figures\n",
    "%matplotlib inline\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "#Ignore useless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore',message='^internal gelsd')\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "9e817dd20fa03efe646ef977facf6aaafb61f03f"
   },
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to load data\n",
    "dataset_path = os.path.join('datasets', 'forestfire', 'forestfires.csv')\n",
    "def load_data(path):\n",
    "        return pd.read_csv(dataset_path)\n",
    "    \n",
    "fire = load_data(dataset_path)\n",
    "fire.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "collapsed": true
   },
   "source": [
    "#### the dataset doesn't have any missing values, so I am randomly removing some data points from FFMC attribute to showcase how to handle missing data problems.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "0a025a9b96d6a34863435645631411ff2c0deb8c"
   },
   "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",
      "X        517 non-null int64\n",
      "Y        517 non-null int64\n",
      "month    517 non-null object\n",
      "day      517 non-null object\n",
      "FFMC     502 non-null float64\n",
      "DMC      517 non-null float64\n",
      "DC       517 non-null float64\n",
      "ISI      517 non-null float64\n",
      "temp     517 non-null float64\n",
      "RH       517 non-null int64\n",
      "wind     517 non-null float64\n",
      "rain     517 non-null float64\n",
      "area     517 non-null float64\n",
      "dtypes: float64(8), int64(3), object(2)\n",
      "memory usage: 52.6+ KB\n"
     ]
    }
   ],
   "source": [
    "fire.FFMC[np.random.choice(fire.index,15)] = np.nan\n",
    "fire.info() #now we have 15 missing values for attribute FFMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "3a56cb4c94574592b0c4787efa397618a104572e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>517.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.669246</td>\n",
       "      <td>4.299807</td>\n",
       "      <td>90.653586</td>\n",
       "      <td>110.872340</td>\n",
       "      <td>547.940039</td>\n",
       "      <td>9.021663</td>\n",
       "      <td>18.889168</td>\n",
       "      <td>44.288201</td>\n",
       "      <td>4.017602</td>\n",
       "      <td>0.021663</td>\n",
       "      <td>12.847292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.313778</td>\n",
       "      <td>1.229900</td>\n",
       "      <td>5.499615</td>\n",
       "      <td>64.046482</td>\n",
       "      <td>248.066192</td>\n",
       "      <td>4.559477</td>\n",
       "      <td>5.806625</td>\n",
       "      <td>16.317469</td>\n",
       "      <td>1.791653</td>\n",
       "      <td>0.295959</td>\n",
       "      <td>63.655818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>18.700000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>90.200000</td>\n",
       "      <td>68.600000</td>\n",
       "      <td>437.700000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>91.600000</td>\n",
       "      <td>108.300000</td>\n",
       "      <td>664.200000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>92.900000</td>\n",
       "      <td>142.400000</td>\n",
       "      <td>713.900000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>96.200000</td>\n",
       "      <td>291.300000</td>\n",
       "      <td>860.600000</td>\n",
       "      <td>56.100000</td>\n",
       "      <td>33.300000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>1090.840000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                X           Y        FFMC         DMC          DC         ISI  \\\n",
       "count  517.000000  517.000000  502.000000  517.000000  517.000000  517.000000   \n",
       "mean     4.669246    4.299807   90.653586  110.872340  547.940039    9.021663   \n",
       "std      2.313778    1.229900    5.499615   64.046482  248.066192    4.559477   \n",
       "min      1.000000    2.000000   18.700000    1.100000    7.900000    0.000000   \n",
       "25%      3.000000    4.000000   90.200000   68.600000  437.700000    6.500000   \n",
       "50%      4.000000    4.000000   91.600000  108.300000  664.200000    8.400000   \n",
       "75%      7.000000    5.000000   92.900000  142.400000  713.900000   10.800000   \n",
       "max      9.000000    9.000000   96.200000  291.300000  860.600000   56.100000   \n",
       "\n",
       "             temp          RH        wind        rain         area  \n",
       "count  517.000000  517.000000  517.000000  517.000000   517.000000  \n",
       "mean    18.889168   44.288201    4.017602    0.021663    12.847292  \n",
       "std      5.806625   16.317469    1.791653    0.295959    63.655818  \n",
       "min      2.200000   15.000000    0.400000    0.000000     0.000000  \n",
       "25%     15.500000   33.000000    2.700000    0.000000     0.000000  \n",
       "50%     19.300000   42.000000    4.000000    0.000000     0.520000  \n",
       "75%     22.800000   53.000000    4.900000    0.000000     6.570000  \n",
       "max     33.300000  100.000000    9.400000    6.400000  1090.840000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fire.describe() #some attributes skewed to the right(mean>median) some to the left(mean<median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "ce28bde6ff4a8eeee4c190bdbe934f31c1b71e8e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "categorical_attributes: ['month', 'day']\n",
      "numerical_attributes: ['X', 'Y', 'FFMC', 'DMC', 'DC', 'ISI', 'temp', 'RH', 'wind', 'rain', 'area']\n"
     ]
    }
   ],
   "source": [
    "#let's name the categorical and numeical attributes \n",
    "categorical_attributes = list(fire.select_dtypes(include=['object']).columns)\n",
    "numerical_attributes = list(fire.select_dtypes(include=['float64', 'int64']).columns)\n",
    "print('categorical_attributes:', categorical_attributes)\n",
    "print('numerical_attributes:', numerical_attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "03e3eab0cb017643e265fce7c002d885987fa197"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "months\n",
      "aug    184\n",
      "sep    172\n",
      "mar     54\n",
      "jul     32\n",
      "feb     20\n",
      "jun     17\n",
      "oct     15\n",
      "apr      9\n",
      "dec      9\n",
      "jan      2\n",
      "may      2\n",
      "nov      1\n",
      "Name: month, dtype: int64\n",
      "\n",
      "\n",
      "days\n",
      "sun    95\n",
      "fri    85\n",
      "sat    84\n",
      "mon    74\n",
      "tue    64\n",
      "thu    61\n",
      "wed    54\n",
      "Name: day, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Here we face an uniqe issue where a few months have only 1 or 2 data points. I chose to get rid of these so that the glm models\n",
    "#can handle the tests properly. Let me know how you would have hanlded.\n",
    "print('months', fire.month.value_counts(), sep='\\n')\n",
    "print('\\n')\n",
    "print('days', fire.day.value_counts(), sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "b0db4b5d74dc7ebbf8c67d95e3ba273074fb2b93"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "aug    184\n",
       "sep    172\n",
       "mar     54\n",
       "jul     32\n",
       "feb     20\n",
       "jun     17\n",
       "oct     15\n",
       "apr      9\n",
       "dec      9\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months_to_remove = ['nov','jan','may']\n",
    "forest_fire = fire.drop(fire[fire.month.isin(months_to_remove)].index ,axis=0)\n",
    "forest_fire.month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "be2157d37358ad5a4bec16c7ecfe36080daa2995"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing distributions \n",
    "forest_fire.hist(bins=50, figsize=(15,10), ec='w')\n",
    "plt.show()\n",
    "#target-area-is heavily skewed, we have extreme outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "d40f26b727c8e58079f195d8d89a768ca2cbc0a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(800,100,'max: 1090.84')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(forest_fire.area, ec='w', bins=100, color='red')\n",
    "plt.text(800,100, 'max: '+str(forest_fire.area.max()), color='black', fontsize=14)\n",
    "#Burnt area attribute ranges from 0 to 1091."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "fc4aef3ad5b48ccf2ba32811cbeedb37bc059b16"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-5       362\n",
       "10-50      70\n",
       "5-10       56\n",
       "50-100     13\n",
       ">100       11\n",
       "Name: area_cat, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Grouping the the burnt area to get a better understanding\n",
    "forest_fire['area_cat'] = pd.cut(forest_fire['area'], bins=[0,5, 10, 50, 100, 1100], include_lowest=True, \n",
    "                                 labels=['0-5', '5-10', '10-50', '50-100', '>100'])\n",
    "forest_fire.area_cat.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "5ec8ce0858e058efde535a85c46ebe6f49f87820"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.85875"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Interquartile range\n",
    "Q1 = forest_fire.area.quantile(.25)\n",
    "Q3 = forest_fire.area.quantile(.75)\n",
    "IQR = 1.5*(Q3-Q1)\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "c1932e419278ba6925adcc54a2ff54e3fe1e191d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-5       362\n",
       "5-10       56\n",
       "10-50      37\n",
       "50-100      0\n",
       ">100        0\n",
       "Name: area_cat, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we are loosing quite a number of data points in already a small data set if we remove all outliers\n",
    "forest_fire.query('(@Q1 - 1.5 * @IQR) <= area <= (@Q3 + 1.5 * @IQR)').area_cat.value_counts(sort=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0445891b1107be03aff5c26c74029e5cf861ffac"
   },
   "source": [
    "#### I chose to remove any area greater than 100. You could do it differently, maybe with log transformations or simply removing every outlier example. It all depends how you state your problem. For me it is a matter of showcasing several tricks of Scikit-Learn, but I would have gone for a log-log model otherwise. Maybe in part2, where I will do a Regression framework using TensorFlow in eager."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "c23deed0df065aaa013a299a93f15c9ad8813ba1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-5       362\n",
       "10-50      70\n",
       "5-10       56\n",
       "50-100     13\n",
       ">100        0\n",
       "Name: area_cat, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove outliers\n",
    "forest_fire.drop(forest_fire[forest_fire.area>100].index,axis=0, inplace=True)\n",
    "forest_fire.area_cat.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "354f86c4ef8320a3907c3b1579f8aa57236bcc2f"
   },
   "source": [
    "#### Now the time for our assumptions. I remind you many assumptions we make during the iterative process might not make to the final model; As I already said, we make some assumptions, then during the model building process we might prove them right or wrong. But it always to helps to list the assumptions and take opinions of several domain experts but this is the luxury not every data scientist has.\n",
    "\n",
    "#### Assumption1- Temperature might be a great indicator of wildfires and it might be the case that high temperature may lead to more area burnt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "a14ee2061d34226869c72e03580ac0de3553ac72"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's understand what temp ranges we have here.\n",
    "plt.hist(forest_fire.temp, ec='w', bins=50, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "c99040bcb8a27ee49929982ac6129ad04bf226d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0-15     109\n",
       "15-20    167\n",
       "20-25    163\n",
       ">25       62\n",
       "Name: temp_bins, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_fire['temp_bins'] = pd.cut(forest_fire.temp, bins=[0, 15, 20, 25, 40], include_lowest=True, \n",
    "                                 labels=['0-15', '15-20', '20-25', '>25'])\n",
    "forest_fire.temp_bins.value_counts(sort=False)\n",
    "#so we have from very cold 0-15 degrees to hot >25 degree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f0edf47e7625f07bb0982c5c1498b8cb2570e30d"
   },
   "source": [
    "#### As, I have assumed temp is a critical indicator, to have a golden test dataset I would prefer that both train and test has equally distributed temperature conditions. Stratified shuffle split will help us maintain the required strata compositions here. My strata set is temperature categories I have created above. Do note while creating groups make sure that all the groups are well represented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "72c78d95e872aed96dcfadbd1411a20b6c7c5554"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(forest_fire.values, forest_fire.temp_bins.values):\n",
    "    st_train_set = forest_fire.iloc[train_index]\n",
    "    st_test_set = forest_fire.iloc[test_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "59a50b423a0a1d1a55bc362a2dce23e3c4e8f648"
   },
   "source": [
    "### Once you create the test data remember to not touch it until you finalize your model. Any exploration of test data would result in a biased perspective that might not generalize well to new data once the system is live."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "33a976fddb890aa201c65ecea700bf8245fc75e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0-15     0.217822\n",
      "15-20    0.336634\n",
      "20-25    0.326733\n",
      ">25      0.118812\n",
      "Name: temp_bins, dtype: float64\n",
      "0-15     0.217565\n",
      "15-20    0.333333\n",
      "20-25    0.325349\n",
      ">25      0.123752\n",
      "Name: temp_bins, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#this works like magic.\n",
    "print(st_test_set.temp_bins.value_counts(sort=False)/len(st_test_set), sep='\\n')\n",
    "print(forest_fire.temp_bins.value_counts(sort=False)/len(forest_fire), sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "4d584c4783b0d917cd94c3ceac0ee55b96ec9a57"
   },
   "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>359</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>sep</td>\n",
       "      <td>fri</td>\n",
       "      <td>92.5</td>\n",
       "      <td>122.0</td>\n",
       "      <td>789.7</td>\n",
       "      <td>10.2</td>\n",
       "      <td>21.1</td>\n",
       "      <td>39</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>sep</td>\n",
       "      <td>wed</td>\n",
       "      <td>92.6</td>\n",
       "      <td>115.4</td>\n",
       "      <td>777.1</td>\n",
       "      <td>8.8</td>\n",
       "      <td>19.7</td>\n",
       "      <td>41</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>oct</td>\n",
       "      <td>wed</td>\n",
       "      <td>91.4</td>\n",
       "      <td>37.9</td>\n",
       "      <td>673.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>20.2</td>\n",
       "      <td>37</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>sep</td>\n",
       "      <td>thu</td>\n",
       "      <td>92.8</td>\n",
       "      <td>119.0</td>\n",
       "      <td>783.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>21.6</td>\n",
       "      <td>28</td>\n",
       "      <td>6.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>aug</td>\n",
       "      <td>sun</td>\n",
       "      <td>90.2</td>\n",
       "      <td>99.6</td>\n",
       "      <td>631.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>21.5</td>\n",
       "      <td>34</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</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",
       "359  1  4   sep  fri  92.5  122.0  789.7  10.2  21.1  39   2.2   0.0   8.12\n",
       "314  4  4   sep  wed  92.6  115.4  777.1   8.8  19.7  41   1.8   0.0   1.58\n",
       "203  8  6   oct  wed  91.4   37.9  673.8   5.2  20.2  37   2.7   0.0  13.70\n",
       "319  5  4   sep  thu  92.8  119.0  783.5   7.5  21.6  28   6.3   0.0   4.41\n",
       "51   4  3   aug  sun  90.2   99.6  631.2   6.3  21.5  34   2.2   0.0   0.00"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#now lets drop the area and temp categories \n",
    "for _ in (st_train_set, st_test_set):\n",
    "    _.drop(['area_cat','temp_bins'], axis=1, inplace=True)\n",
    "    \n",
    "forest_fire = st_train_set.copy()\n",
    "forest_fire.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "4c6d2f733048f3b4fd646229920edada9930f87c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#December had a few incidents but all on the higher burnt area side. Is it becuase dry weather or because more tourists? \n",
    "ax = plt.figure(figsize=(12,8))\n",
    "ax = sns.boxplot(x='month', y='area', data=forest_fire, color='lightgrey', )\n",
    "ax = sns.stripplot(x='month', y='area', data=forest_fire, color='red', jitter=0.4, size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "dd42bf7c8ab5c313e87fd8afa0775902787fad0d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#There are more incidents on weekends - Friday/Sat/Sun, it might mean that campers vactioning might have caused/spotted fires.\n",
    "ax = plt.figure(figsize=(12,8))\n",
    "ax = sns.boxplot(x='day', y='area', data=forest_fire, color='lightgrey', )\n",
    "ax = sns.stripplot(x='day', y='area', data=forest_fire, color='red', jitter=0.4, size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "571a47a91a5cd90734a8782ef0dcbbafdfc657e7"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#I am checking the temparature distribution as per the forest cordinates during the month of december\n",
    "#it looks like the temps are low, so it could be a combinaton of dry weather and human made fire. But as data scientists,\n",
    "#we must make important discoveries to arrive at any conclusive evidence. Else the above statments are just my mind made \n",
    "#fantsies\n",
    "forest_fire[forest_fire.month=='dec'].plot(kind='scatter', x='X', y='Y', c='temp', cmap=plt.get_cmap('coolwarm'), colorbar=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8080d7f2020b1670d658876c983025d8b0c5f170"
   },
   "source": [
    "#### Spearman rank correlation is more robust to the effect of outliers than Pearson’s correlations coefficient. If you need to know why I will load a new kernel sometime. But do remember we are just checking linear correlations here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "373a4bc6d35840098b5e3ede229edadf40e2f1be"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>temp</th>\n",
       "      <th>RH</th>\n",
       "      <th>wind</th>\n",
       "      <th>rain</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.481958</td>\n",
       "      <td>-0.070729</td>\n",
       "      <td>-0.082882</td>\n",
       "      <td>-0.097020</td>\n",
       "      <td>0.003760</td>\n",
       "      <td>-0.060757</td>\n",
       "      <td>0.081566</td>\n",
       "      <td>0.016699</td>\n",
       "      <td>0.106047</td>\n",
       "      <td>0.038364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>0.481958</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027837</td>\n",
       "      <td>0.006697</td>\n",
       "      <td>-0.116588</td>\n",
       "      <td>-0.050865</td>\n",
       "      <td>-0.047194</td>\n",
       "      <td>0.075483</td>\n",
       "      <td>-0.056825</td>\n",
       "      <td>0.065329</td>\n",
       "      <td>0.062555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FFMC</th>\n",
       "      <td>-0.070729</td>\n",
       "      <td>-0.027837</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.494726</td>\n",
       "      <td>0.229370</td>\n",
       "      <td>0.795014</td>\n",
       "      <td>0.594105</td>\n",
       "      <td>-0.347661</td>\n",
       "      <td>-0.030324</td>\n",
       "      <td>0.118104</td>\n",
       "      <td>0.018090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DMC</th>\n",
       "      <td>-0.082882</td>\n",
       "      <td>0.006697</td>\n",
       "      <td>0.494726</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.562753</td>\n",
       "      <td>0.394178</td>\n",
       "      <td>0.468887</td>\n",
       "      <td>0.043702</td>\n",
       "      <td>-0.098967</td>\n",
       "      <td>0.166716</td>\n",
       "      <td>0.026109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>-0.097020</td>\n",
       "      <td>-0.116588</td>\n",
       "      <td>0.229370</td>\n",
       "      <td>0.562753</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.082584</td>\n",
       "      <td>0.274941</td>\n",
       "      <td>0.050014</td>\n",
       "      <td>-0.157062</td>\n",
       "      <td>0.033104</td>\n",
       "      <td>0.036140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISI</th>\n",
       "      <td>0.003760</td>\n",
       "      <td>-0.050865</td>\n",
       "      <td>0.795014</td>\n",
       "      <td>0.394178</td>\n",
       "      <td>0.082584</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.406165</td>\n",
       "      <td>-0.171347</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>0.133786</td>\n",
       "      <td>0.019129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>-0.060757</td>\n",
       "      <td>-0.047194</td>\n",
       "      <td>0.594105</td>\n",
       "      <td>0.468887</td>\n",
       "      <td>0.274941</td>\n",
       "      <td>0.406165</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.551008</td>\n",
       "      <td>-0.184548</td>\n",
       "      <td>0.056701</td>\n",
       "      <td>0.048286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH</th>\n",
       "      <td>0.081566</td>\n",
       "      <td>0.075483</td>\n",
       "      <td>-0.347661</td>\n",
       "      <td>0.043702</td>\n",
       "      <td>0.050014</td>\n",
       "      <td>-0.171347</td>\n",
       "      <td>-0.551008</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100768</td>\n",
       "      <td>0.187838</td>\n",
       "      <td>-0.043599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wind</th>\n",
       "      <td>0.016699</td>\n",
       "      <td>-0.056825</td>\n",
       "      <td>-0.030324</td>\n",
       "      <td>-0.098967</td>\n",
       "      <td>-0.157062</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.184548</td>\n",
       "      <td>0.100768</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.146433</td>\n",
       "      <td>0.049758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rain</th>\n",
       "      <td>0.106047</td>\n",
       "      <td>0.065329</td>\n",
       "      <td>0.118104</td>\n",
       "      <td>0.166716</td>\n",
       "      <td>0.033104</td>\n",
       "      <td>0.133786</td>\n",
       "      <td>0.056701</td>\n",
       "      <td>0.187838</td>\n",
       "      <td>0.146433</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.056283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>0.038364</td>\n",
       "      <td>0.062555</td>\n",
       "      <td>0.018090</td>\n",
       "      <td>0.026109</td>\n",
       "      <td>0.036140</td>\n",
       "      <td>0.019129</td>\n",
       "      <td>0.048286</td>\n",
       "      <td>-0.043599</td>\n",
       "      <td>0.049758</td>\n",
       "      <td>-0.056283</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             X         Y      FFMC       DMC        DC       ISI      temp  \\\n",
       "X     1.000000  0.481958 -0.070729 -0.082882 -0.097020  0.003760 -0.060757   \n",
       "Y     0.481958  1.000000 -0.027837  0.006697 -0.116588 -0.050865 -0.047194   \n",
       "FFMC -0.070729 -0.027837  1.000000  0.494726  0.229370  0.795014  0.594105   \n",
       "DMC  -0.082882  0.006697  0.494726  1.000000  0.562753  0.394178  0.468887   \n",
       "DC   -0.097020 -0.116588  0.229370  0.562753  1.000000  0.082584  0.274941   \n",
       "ISI   0.003760 -0.050865  0.795014  0.394178  0.082584  1.000000  0.406165   \n",
       "temp -0.060757 -0.047194  0.594105  0.468887  0.274941  0.406165  1.000000   \n",
       "RH    0.081566  0.075483 -0.347661  0.043702  0.050014 -0.171347 -0.551008   \n",
       "wind  0.016699 -0.056825 -0.030324 -0.098967 -0.157062  0.128069 -0.184548   \n",
       "rain  0.106047  0.065329  0.118104  0.166716  0.033104  0.133786  0.056701   \n",
       "area  0.038364  0.062555  0.018090  0.026109  0.036140  0.019129  0.048286   \n",
       "\n",
       "            RH      wind      rain      area  \n",
       "X     0.081566  0.016699  0.106047  0.038364  \n",
       "Y     0.075483 -0.056825  0.065329  0.062555  \n",
       "FFMC -0.347661 -0.030324  0.118104  0.018090  \n",
       "DMC   0.043702 -0.098967  0.166716  0.026109  \n",
       "DC    0.050014 -0.157062  0.033104  0.036140  \n",
       "ISI  -0.171347  0.128069  0.133786  0.019129  \n",
       "temp -0.551008 -0.184548  0.056701  0.048286  \n",
       "RH    1.000000  0.100768  0.187838 -0.043599  \n",
       "wind  0.100768  1.000000  0.146433  0.049758  \n",
       "rain  0.187838  0.146433  1.000000 -0.056283  \n",
       "area -0.043599  0.049758 -0.056283  1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = forest_fire.corr(method='spearman')\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "2d96650ee100dd63d67715a4f5a22d9f34bd2007"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure(figsize=(12,8))\n",
    "ax = sns.heatmap(corr_matrix, cmap='PiYG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "77b6abbd9e0e89c8b79ee830df0896b102317c8b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area    1.000000\n",
       "Y       0.062555\n",
       "wind    0.049758\n",
       "temp    0.048286\n",
       "X       0.038364\n",
       "DC      0.036140\n",
       "DMC     0.026109\n",
       "ISI     0.019129\n",
       "FFMC    0.018090\n",
       "RH     -0.043599\n",
       "rain   -0.056283\n",
       "Name: area, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#corrleation with area\n",
    "corr_matrix.area.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "df97fbabb8a61ff58c6ac45a1a3fe18417617453"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing relations of most related attributes\n",
    "attributes = ['area', 'wind', 'temp', 'rain', 'RH']\n",
    "sns.pairplot(forest_fire[attributes])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "348b0b35dcc82f31d188c6374bc1c3dadf061f9e"
   },
   "source": [
    "#### Not enough opportunity for feature engineering. we might take help of external data sources but need domain experts' input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "8f3272b4a2d2c64cb26c69ab9aba353070eac8f7"
   },
   "outputs": [],
   "source": [
    "#create a fresh copy of train to preprocess\n",
    "forest_fire = st_train_set.drop('area', axis=1)\n",
    "forest_fire_labels = st_train_set.area.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "bc35a65e091724ccddd4d3b71c60c8f4662be02f"
   },
   "source": [
    "#### I chose to remove coordinates X & Y before model building. The reason being here we are building a model that will use weather conditions to determine the severity of a wildfire based on area burnt, so keeping X&Y locations of one forest does ‘t generalize well to others.\n",
    "#### Now in the process of data prep, you might need to create or drop several columns. So, it's a good idea to automate the process. Below an example of what an Attribute/Feature deleter will look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "b5e552e654ca75e1385625d2e977ccf63fa04314"
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "class AttributeDeleter(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, delete=True):\n",
    "        self.delete = delete\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        return np.delete(X,[fire.columns.get_loc(i) for i in['X','Y','area']],axis=1)\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "823d42e76363923b778cf8d8f83c3b535bfdcb4f"
   },
   "source": [
    "#### With same reasoning, you need to do a lot of transformations depending on the task, so having a custom pipeline that helps you automate these tasks will simplify your deployment process in the end.\n",
    "\n",
    "#### To handle missing data, I chose to fill them with median values of the attribute you can choose to do differently, or once you finalize your model you can do a hyperparameter tuning to find out which imputing strategy works best using a grid search. Imputing simply means what value are you going to put in place of null values.\n",
    "\n",
    "#### I chose to create of dummies out of my categorical attributes, but you can do a low-level vector representation of them using embeddings. OneHotEncoder does that for me.\n",
    "\n",
    "#### And lastly, we have data attributes in many different scales. So, it's always a good idea to either standardize or normalize the scales. Reason being many instances based and model based learning have a distance based cost function to minimize to get the best possible similarity or model fit parameters. Having a standardized scale helps us find the best possible parameters in an efficient way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "85e284c07520634c81a01c54db0759caca1c76d7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: DeprecationWarning: in the future out of bounds indices will raise an error instead of being ignored by `numpy.delete`.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((400, 24), (400, 12))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "categorical_attributes = list(forest_fire.select_dtypes(include=['object']).columns)\n",
    "numerical_attributes = list(forest_fire.select_dtypes(include=['float64', 'int64']).columns)\n",
    "\n",
    "num_pipeline = Pipeline([('imputer', SimpleImputer(strategy='median')),\n",
    "                         ('drop_attributes', AttributeDeleter()),\n",
    "                         ('std_scaler', StandardScaler()),\n",
    "                        ])\n",
    "full_pipeline = ColumnTransformer([('num', num_pipeline, numerical_attributes),\n",
    "                                   ('cat', OneHotEncoder(), categorical_attributes),\n",
    "                                  ])\n",
    "\n",
    "train = full_pipeline.fit_transform(forest_fire)\n",
    "train_labels = forest_fire_labels\n",
    "train.shape ,forest_fire.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "db645d26b58670fe60955bc8321fb0bcd988cea9"
   },
   "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>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>apr</th>\n",
       "      <th>aug</th>\n",
       "      <th>...</th>\n",
       "      <th>mar</th>\n",
       "      <th>oct</th>\n",
       "      <th>sep</th>\n",
       "      <th>fri</th>\n",
       "      <th>mon</th>\n",
       "      <th>sat</th>\n",
       "      <th>sun</th>\n",
       "      <th>thu</th>\n",
       "      <th>tue</th>\n",
       "      <th>wed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.380562</td>\n",
       "      <td>0.142619</td>\n",
       "      <td>0.956717</td>\n",
       "      <td>0.231093</td>\n",
       "      <td>0.367208</td>\n",
       "      <td>-0.334216</td>\n",
       "      <td>-1.038205</td>\n",
       "      <td>-0.0819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.403855</td>\n",
       "      <td>0.041644</td>\n",
       "      <td>0.905476</td>\n",
       "      <td>-0.065316</td>\n",
       "      <td>0.125964</td>\n",
       "      <td>-0.209799</td>\n",
       "      <td>-1.265975</td>\n",
       "      <td>-0.0819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.124330</td>\n",
       "      <td>-1.144046</td>\n",
       "      <td>0.485378</td>\n",
       "      <td>-0.827509</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>-0.458633</td>\n",
       "      <td>-0.753492</td>\n",
       "      <td>-0.0819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.450443</td>\n",
       "      <td>0.096722</td>\n",
       "      <td>0.931503</td>\n",
       "      <td>-0.340552</td>\n",
       "      <td>0.453366</td>\n",
       "      <td>-1.018511</td>\n",
       "      <td>1.296439</td>\n",
       "      <td>-0.0819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.155195</td>\n",
       "      <td>-0.200083</td>\n",
       "      <td>0.312134</td>\n",
       "      <td>-0.594617</td>\n",
       "      <td>0.436134</td>\n",
       "      <td>-0.645259</td>\n",
       "      <td>-1.038205</td>\n",
       "      <td>-0.0819</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FFMC       DMC        DC       ISI      temp        RH      wind  \\\n",
       "0  0.380562  0.142619  0.956717  0.231093  0.367208 -0.334216 -1.038205   \n",
       "1  0.403855  0.041644  0.905476 -0.065316  0.125964 -0.209799 -1.265975   \n",
       "2  0.124330 -1.144046  0.485378 -0.827509  0.212122 -0.458633 -0.753492   \n",
       "3  0.450443  0.096722  0.931503 -0.340552  0.453366 -1.018511  1.296439   \n",
       "4 -0.155195 -0.200083  0.312134 -0.594617  0.436134 -0.645259 -1.038205   \n",
       "\n",
       "     rain  apr  aug ...   mar  oct  sep  fri  mon  sat  sun  thu  tue  wed  \n",
       "0 -0.0819  0.0  0.0 ...   0.0  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1 -0.0819  0.0  0.0 ...   0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "2 -0.0819  0.0  0.0 ...   0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "3 -0.0819  0.0  0.0 ...   0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "4 -0.0819  0.0  1.0 ...   0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the train data\n",
    "train_df = pd.DataFrame(train, columns= numerical_attributes[2:] + list(full_pipeline.named_transformers_.cat.categories_[0]) +\n",
    "             list(full_pipeline.named_transformers_.cat.categories_[1]))\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "856f0d3998ce39d2ea71727bbe212ea5a9fe6983"
   },
   "source": [
    "#### Now we can finally start the model selection process where we try out bunch of different models. Choose some and fine tune them before finalizing one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "12e43e91a057e13e2200f1e5a64ed5916a548de8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear_train_rmse 13.408351802213172\n",
      "Scores: [16.01181085 11.85147634 13.52834459 17.82237591 11.01185707 16.30916709\n",
      " 12.44789601 12.50009332 20.8719534   9.47630378]\n",
      "Mean: 14.18312783613998\n",
      "Standard deviation: 3.313136940693585\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(train, train_labels)\n",
    "\n",
    "area_predictions = lin_reg.predict(train)\n",
    "lin_mse = mean_squared_error(train_labels, area_predictions)\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "print('linear_train_rmse', lin_rmse) #model might be underfitting\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(lin_reg, train, train_labels, scoring='neg_mean_squared_error', cv=10)\n",
    "lin_rmse_scores = np.sqrt(-scores)\n",
    "\n",
    "def explain_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    " \n",
    "explain_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "2b2261fe403dda6274c0539b404dfba01416e869"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tree_train_rmse 3.7388655197800307\n",
      "Scores: [21.90990799 26.70966732 18.95440292 19.70253462 17.07366913 22.03832196\n",
      " 17.49653641 20.64671458 20.1917216  21.55456628]\n",
      "Mean: 20.627804281422478\n",
      "Standard deviation: 2.6031698518015642\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(train, train_labels)\n",
    "\n",
    "area_predictions = tree_reg.predict(train)\n",
    "tree_mse = mean_squared_error(train_labels, area_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "print('tree_train_rmse', tree_rmse) #model obviously overfitting\n",
    "\n",
    "scores = cross_val_score(tree_reg, train, train_labels, scoring='neg_mean_squared_error', cv=10)\n",
    "tree_rmse_scores = np.sqrt(-scores)\n",
    "explain_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "15a37f691287233e3a0815e912a25b076810a439"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:248: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rf_train_rmse 7.003737224422403\n",
      "Scores: [15.75734655 15.56646954 13.56197889 16.94072509 11.84132637 18.22956683\n",
      " 12.12908477 13.03174024 20.2997867  13.30045216]\n",
      "Mean: 15.065847713245489\n",
      "Standard deviation: 2.6492479031218896\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf_reg = RandomForestRegressor()\n",
    "rf_reg.fit(train, train_labels)\n",
    "\n",
    "area_predictions = rf_reg.predict(train)\n",
    "rf_mse = mean_squared_error(train_labels, area_predictions)\n",
    "rf_rmse = np.sqrt(rf_mse)\n",
    "print('rf_train_rmse', rf_rmse) #model is overfitting \n",
    "\n",
    "scores = cross_val_score(rf_reg, train, train_labels, scoring='neg_mean_squared_error', cv=10)\n",
    "rf_rmse_scores = np.sqrt(-scores)\n",
    "explain_scores(rf_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "ac4d7144f76aacd7b30262ec286c2c29be7964d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "svm_train_rmse 14.733283759783474\n",
      "Scores: [16.27139224 11.47787044 13.33049909 18.73071215 12.32326765 17.80343673\n",
      " 11.84191105 11.59399797 21.52286507  8.19784989]\n",
      "Mean: 14.309380229983105\n",
      "Standard deviation: 3.8906717758894844\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_reg = SVR(kernel='linear')\n",
    "svm_reg.fit(train, train_labels)\n",
    "\n",
    "area_predictions = svm_reg.predict(train)\n",
    "svm_mse = mean_squared_error(train_labels, area_predictions)\n",
    "svm_rmse = np.sqrt(svm_mse)\n",
    "print('svm_train_rmse', svm_rmse) #svm is generalizing well to crossvalidation set\n",
    "\n",
    "scores = cross_val_score(svm_reg, train, train_labels, scoring='neg_mean_squared_error', cv=10)\n",
    "svm_rmse_scores = np.sqrt(-scores)\n",
    "explain_scores(svm_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "25da8cef377368e92ab994c76c03ef90e0627155"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn_train_rmse 12.264732235560627\n",
      "Scores: [15.44431282 13.26958344 14.2681095  18.89717775 12.73927185 17.07379813\n",
      " 13.14335727 12.84071475 21.89010291 11.89887815]\n",
      "Mean: 15.146530657284037\n",
      "Standard deviation: 3.0547068841305847\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "knn_reg = KNeighborsRegressor()\n",
    "knn_reg.fit(train, train_labels)\n",
    "\n",
    "area_predictions = knn_reg.predict(train)\n",
    "knn_mse = mean_squared_error(train_labels, area_predictions)\n",
    "knn_rmse = np.sqrt(knn_mse)\n",
    "print('knn_train_rmse', knn_rmse) #overfiiting\n",
    "\n",
    "scores = cross_val_score(knn_reg, train, train_labels, scoring='neg_mean_squared_error', cv=10)\n",
    "knn_rmse_scores = np.sqrt(-scores)\n",
    "explain_scores(knn_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "019e1844a8a3d2f5acaf0c8f1c309c7d13feedc3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'bootstrap': [False, True], 'n_estimators': [75, 100, 125, 150, 200], 'max_features': [1, 2, 4, 6]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets improve the models with hyperparameter tuning\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [{'bootstrap':[False,True],'n_estimators':[75,100,125,150,200], 'max_features':[1,2,4,6]}]\n",
    "\n",
    "forest_reg = RandomForestRegressor()\n",
    "\n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5, scoring='neg_mean_squared_error', return_train_score=True)\n",
    "\n",
    "grid_search.fit(train, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "a9bd990f9d524d3f343341a0618aaa100f135555"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True, 'max_features': 1, 'n_estimators': 75}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "712466236493c941a3caf6b259234aebb56e8cb9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features=1, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=75, n_jobs=None, oob_score=False,\n",
       "           random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "a5aaf18d8cc61502f1ac467433add328271d7a3c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.17537734230385713, 'DC'),\n",
       " (0.15289995815855104, 'ISI'),\n",
       " (0.12051661256266065, 'temp'),\n",
       " (0.1044473334743039, 'FFMC'),\n",
       " (0.1020736868679805, 'Y'),\n",
       " (0.09592391686029453, 'X'),\n",
       " (0.07801463253867355, 'DMC'),\n",
       " (0.01926203556540669, 'thu'),\n",
       " (0.019087592939623085, 'mon'),\n",
       " (0.016744853911751525, 'fri'),\n",
       " (0.015413766996571099, 'feb'),\n",
       " (0.015405606429970608, 'rain'),\n",
       " (0.015001357975895167, 'oct'),\n",
       " (0.011750263345391514, 'dec'),\n",
       " (0.01124775597520119, 'sun'),\n",
       " (0.00972420140954538, 'mar'),\n",
       " (0.007844060566629608, 'sat'),\n",
       " (0.006499488778386858, 'sep'),\n",
       " (0.006432503527427631, 'jul'),\n",
       " (0.005636372658704237, 'jun'),\n",
       " (0.003525553895602018, 'wind'),\n",
       " (0.003359376028489187, 'aug'),\n",
       " (0.002706880489350845, 'apr'),\n",
       " (0.0011048467397320543, 'RH')]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "attributes = numerical_attributes + list(full_pipeline.named_transformers_.cat.categories_[0]) +\\\n",
    "             list(full_pipeline.named_transformers_.cat.categories_[1])\n",
    "    \n",
    "sorted(zip(feature_importances, attributes), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "f1ac7b33bd95e55bb0e5da639a73eddd91a5ce08"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "indices = np.argsort(feature_importances)\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices)), feature_importances[indices], color='lightblue', align='center')\n",
    "plt.yticks(range(len(indices)), [attributes[i] for i in indices])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "e8dae4e65bcf2924828306df1a521e931145bfb5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid='warn', n_iter=40, n_jobs=None,\n",
       "          param_distributions={'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000248974C1E80>, 'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002489895D7B8>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_squared_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets try with RandomizedSearchCV\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_dist = {'n_estimators': randint(low=10, high=250),\n",
    "              'max_features': randint(low=1, high=24),\n",
    "             }\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_dist, n_iter=40, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
    "\n",
    "rnd_search.fit(train, train_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_uuid": "b552485608d74b20e511bccc7c043fa0ec92d391"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 4, 'n_estimators': 98}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_uuid": "62d963dd75db733deb8813a2c998dbdc788bfeed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14.899327797269214, 14.797222091483913)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(-rnd_search.best_score_), np.sqrt(-grid_search.best_score_) #both have very similar scores even though they came up with \n",
    "#different best parameters. which one to use, I leave to you explain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_uuid": "79e50967840fe3e39ca234c6ad4e1f62b11091bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 21 candidates, totalling 105 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  45 tasks      | elapsed:   11.4s\n",
      "[Parallel(n_jobs=4)]: Done 105 out of 105 | elapsed:   11.9s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "14.279281809880542"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tuning svr\n",
    "\n",
    "param_grid = [{'kernel': ['linear'], 'C':[0.5,1,5,10,30]},\n",
    "              {'kernel':['rbf'], 'C':[5,10,15,20], 'gamma':[0.5,1.0,1.5,2.0]},\n",
    "             ]\n",
    "\n",
    "svm_reg = SVR()\n",
    "grid_search = GridSearchCV(svm_reg, param_grid, cv=5, scoring='neg_mean_squared_error', verbose=1, n_jobs=4)\n",
    "grid_search.fit(train,train_labels)\n",
    "\n",
    "negative_mse = grid_search.best_score_\n",
    "rmse = np.sqrt(-negative_mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_uuid": "7ccd58881f7a50ca5475be7e48e57df40a8d66fd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'gamma': 1.5, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_uuid": "32f1fcc1795e7b1e9aefebe0a7282eaf28da4300"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 108 tasks      | elapsed:    3.6s\n",
      "[Parallel(n_jobs=4)]: Done 500 out of 500 | elapsed:   18.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "14.287286738686518"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import expon, reciprocal\n",
    "\n",
    "param_dist = {'kernel':['linear','rbf'],\n",
    "                  'C':reciprocal(1,100),\n",
    "                  'gamma':expon(scale=1.0)}\n",
    "svm_reg = SVR()\n",
    "rnd_search = RandomizedSearchCV(svm_reg, param_distributions=param_dist, n_iter=100, cv=5,\n",
    "                               scoring='neg_mean_squared_error', verbose=2, n_jobs=4, random_state=42)\n",
    "rnd_search.fit(train, train_labels)\n",
    "\n",
    "negative_mse = rnd_search.best_score_\n",
    "rmse = np.sqrt(-negative_mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_uuid": "81985156f4e8e3647361711827ffefcb8f229064"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10.414253710293567, 'gamma': 1.0115156153275293, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_search.best_params_ #randomized search is able to find better parameters for rbf kernel in same number of iterations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6b09506cf04636bdb9a77bf6d5a430fd1af561de"
   },
   "source": [
    "#### At times it is a good idea to use only your best features in the model, but it is context dependent. Below sample code helps you add another transformer to your pipeline that chooses the best feature based on an estimated feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_uuid": "49850cbe0b9812922aeda297bbbdef746f49ee88"
   },
   "outputs": [],
   "source": [
    "def indices_top_features(impotance, top):\n",
    "    return np.sort(np.argpartition(np.array(impotance), -top)[-top:])\n",
    "\n",
    "class TopFeatureSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, feature_importnaces, top):\n",
    "        self.feature_importnaces = feature_importances\n",
    "        self.top = top\n",
    "    def fit(self, X, y=None):\n",
    "        self.feature_indcies_ = indices_top_features(self.feature_importnaces, self.top)\n",
    "        return self\n",
    "    def transform(self,X):\n",
    "        return X[:, self.feature_indcies_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_uuid": "0b18558bc24e4a4b1d5c55dfac22f447a615ce5a"
   },
   "outputs": [],
   "source": [
    "data_prep_feature_seletion_pipe = Pipeline([('prep', full_pipeline),\n",
    "                                            ('fe_select', TopFeatureSelector(feature_importances,5)) #here am choosing top 5 features you can choose others depending on \n",
    "                                           ])                                                        #on what you want to keep  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "9909a4b16fd7adf6b80b97b09a5ae2c0a1bc617b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: DeprecationWarning: in the future out of bounds indices will raise an error instead of being ignored by `numpy.delete`.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(400, 5)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_fe_selected = data_prep_feature_seletion_pipe.fit_transform(forest_fire)\n",
    "train_fe_selected.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "8f3c37ae27ba216afaeccc967b345eceb8007d1d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "knn_train_rmse 12.410643353186812\n",
      "Scores: [15.82385426 13.66648182 14.32809107 19.03754417 12.9068144  17.30360238\n",
      " 13.26014549 10.96969598 21.70251846  9.79899367]\n",
      "Mean: 14.879774169263836\n",
      "Standard deviation: 3.464077990130809\n"
     ]
    }
   ],
   "source": [
    "#now let's try knn with these reduced dimensions \n",
    "\n",
    "knn_reg = KNeighborsRegressor()\n",
    "knn_reg.fit(train_fe_selected, train_labels)\n",
    "\n",
    "area_predictions = knn_reg.predict(train_fe_selected)\n",
    "knn_mse = mean_squared_error(train_labels, area_predictions)\n",
    "knn_rmse = np.sqrt(knn_mse)\n",
    "print('knn_train_rmse', knn_rmse) #knn is generalizing well to crossvalidation set\n",
    "\n",
    "scores = cross_val_score(knn_reg, train_fe_selected, train_labels, scoring='neg_mean_squared_error', cv=10)\n",
    "knn_rmse_scores = np.sqrt(-scores)\n",
    "explain_scores(knn_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "d755807363ae47111bdb00e3f79ef9f503df9ef8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 14 candidates, totalling 70 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  70 out of  70 | elapsed:    0.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "14.12515060556197"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets tune KNN \n",
    "param_grid = {'weights': ['uniform', 'distance'], 'n_neighbors': list(range(1,36,5))}\n",
    "\n",
    "knn_reg= KNeighborsRegressor()\n",
    "knn_grid_search = GridSearchCV(knn_reg, param_grid, cv=5, scoring='neg_mean_squared_error', verbose=1, n_jobs=4)\n",
    "knn_grid_search.fit(train_fe_selected,train_labels)\n",
    "\n",
    "knn_negative_mse = knn_grid_search.best_score_\n",
    "knn_rmse = np.sqrt(-knn_negative_mse)\n",
    "knn_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_uuid": "bad5fc17332b100444e58eb220533a29a33cc414"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgb_train_rmse 9.510023888294025\n",
      "Scores: [15.10031759 17.85561218 13.47592171 17.49411289 12.31912452 16.28012249\n",
      " 12.59937269 12.42109479 21.30372835 12.44402188]\n",
      "Mean: 15.12934290926716\n",
      "Standard deviation: 2.8991427729342276\n"
     ]
    }
   ],
   "source": [
    "#and for final model I want to try GBM\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "xgb_reg = XGBRegressor()\n",
    "xgb_reg.fit(train_fe_selected, train_labels)\n",
    "\n",
    "area_predictions = xgb_reg.predict(train_fe_selected)\n",
    "xgb_mse = mean_squared_error(train_labels, area_predictions)\n",
    "xgb_rmse = np.sqrt(xgb_mse)\n",
    "print('xgb_train_rmse', xgb_rmse) #overfitting\n",
    "\n",
    "scores = cross_val_score(xgb_reg, train_fe_selected, train_labels, scoring='neg_mean_squared_error', cv=10)\n",
    "xgb_rmse_scores = np.sqrt(-scores)\n",
    "explain_scores(xgb_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_uuid": "0ac280e2f05355e71467eb0052faae991d317268"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 216 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done 508 tasks      | elapsed:    2.4s\n",
      "[Parallel(n_jobs=4)]: Done 1080 out of 1080 | elapsed:    5.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "14.279281809880542"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'objective':['reg:linear'],\n",
    "              'learning_rate': [0.02,0.03,0.04], \n",
    "              'max_depth': [1,2],\n",
    "              'min_child_weight': [2,3,4],\n",
    "              'silent': [1],\n",
    "              'subsample': [0.8],\n",
    "              'colsample_bytree': [0.2,0.3,0.4],\n",
    "              'n_estimators': [50,60,70,100]}\n",
    "\n",
    "xgb_reg = XGBRegressor()\n",
    "\n",
    "xgb_grid_search = GridSearchCV(xgb_reg, param_grid, cv=5, scoring='neg_mean_squared_error', verbose=1, n_jobs=4)\n",
    "xgb_grid_search.fit(train_fe_selected,train_labels)\n",
    "\n",
    "xgb_negative_mse = grid_search.best_score_\n",
    "xgb_rmse = np.sqrt(-xgb_negative_mse)\n",
    "xgb_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_uuid": "e498ac79eb8c252b8b6c8dcaa91b5ac08a2f00a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 0.2,\n",
       " 'learning_rate': 0.03,\n",
       " 'max_depth': 1,\n",
       " 'min_child_weight': 4,\n",
       " 'n_estimators': 60,\n",
       " 'objective': 'reg:linear',\n",
       " 'silent': 1,\n",
       " 'subsample': 0.8}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_uuid": "4922c95e3a16992987598435599080064a127460"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: DeprecationWarning: in the future out of bounds indices will raise an error instead of being ignored by `numpy.delete`.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "13.5845575382228"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = knn_grid_search.best_estimator_\n",
    "\n",
    "X_test = st_test_set.drop(['area'], axis=1)\n",
    "y_test = st_test_set['area'].copy()\n",
    "\n",
    "X_test_prepared = data_prep_feature_seletion_pipe.transform(X_test)\n",
    "\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "\n",
    "final_mse = mean_squared_error(y_test, final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b8cd42355186197b8e72bb3b2ded1479aedc141d"
   },
   "source": [
    "### Let's say, I determined to deploy XGB model. I will add a predictor to my previously defined Pipeline as the last element., which will take care of the entire process from preparing the data to predicting area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_uuid": "1ba2eff28a2891fd0b09752986917ce3fb3a0d17"
   },
   "outputs": [],
   "source": [
    "prepare_select_and_predict_pipeline = Pipeline([\n",
    "    ('preparation', full_pipeline),\n",
    "    ('feature_selection', TopFeatureSelector(feature_importances, 5)),\n",
    "    ('final_model', XGBRegressor(**xgb_grid_search.best_params_))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_uuid": "92a3ac99080093c4e966efbbbb0c51a918adc89e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: DeprecationWarning: in the future out of bounds indices will raise an error instead of being ignored by `numpy.delete`.\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('preparation', ColumnTransformer(n_jobs=None, remainder='drop', sparse_threshold=0.3,\n",
       "         transformer_weights=None,\n",
       "         transformers=[('num', Pipeline(memory=None,\n",
       "     steps=[('imputer', SimpleImputer(copy=True, fill_value=None, missing_values=nan,\n",
       "       strategy='median', verbos...,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None, silent=1,\n",
       "       subsample=0.8))])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepare_select_and_predict_pipeline.fit(forest_fire,forest_fire_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_uuid": "2c53411d0fe3b06b4553a37a900fee2cc0cfb9ce"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: DeprecationWarning: in the future out of bounds indices will raise an error instead of being ignored by `numpy.delete`.\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "final_predictions = prepare_select_and_predict_pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5e59f9e64e283d40c0a29d06aee62a79b6bd515e"
   },
   "source": [
    "### An example showing a small use case. Let's assume some_data here are the new data that our system will predict for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_uuid": "2d6d8b3c5cb70a90a8af5a4d62597cd974066b79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\t [4.4410906 4.4410906 4.581033  5.4276505]\n",
      "Labels:\t\t [8.12, 1.58, 13.7, 4.41]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: DeprecationWarning: in the future out of bounds indices will raise an error instead of being ignored by `numpy.delete`.\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "some_data = forest_fire[:4]\n",
    "some_labels = forest_fire_labels[:4]\n",
    "\n",
    "print(\"Predictions:\\t\", prepare_select_and_predict_pipeline.predict(some_data))\n",
    "print(\"Labels:\\t\\t\", list(some_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_uuid": "2c1ea28d5ef2c0021c8748b75c1ff85d366af796"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.67278737, 18.8395253 ])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Confidence Interval of our Predictions will help us better understand the ouput of our model\n",
    "from scipy import stats\n",
    "\n",
    "confidence = 0.95\n",
    "squared_errors = (final_predictions-y_test)**2\n",
    "np.sqrt(stats.t.interval(confidence, len(squared_errors)-1, loc=squared_errors.mean(), scale=stats.sem(squared_errors)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "40254096c6b647c83bb2d1187ec893856a621825"
   },
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}