Forest Fires.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forest Fire prediction using machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What does this article entails?\n",
    "Forest, bush or vegeterian fire, can be described as any uncontrolled and non-prescribed combustion or burning of plants in a natural setting such as a forest, grassland, etc. In this article we are not determining if a forest fire will take place or not, we are predicting the confidence of the forest fire based on some attributes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why we need a forest fire predictor?\n",
    "\n",
    "Well the first question arise that why we even need Machine learning to predict forest fire in that particular area?\n",
    "So, yes the question is valid that inspite of having the experienced forest department who have been dealing with these issues for a long time why is there need of ML, having said that answer is quite simple that the experienced forest department can check on 3-4 paramters from their human mind but ML on other hand can handle the numerous paramters whether it can be latitude, longitude, satellite, version and what not, so dealing with this multi-relationship of parameter that are responsible for fire in forest we do need ML for sure!  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of content\n",
    "\n",
    "1. Importing necessary libraries\n",
    "2. Exploratory data analysis\n",
    "3. Data cleaning\n",
    "4. Model development (RandomForestRegressor)\n",
    "5. Tuning the model (RandomSearchCV)\n",
    "6. bz2 module (big bonus) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "NWD9wAxO-EZz"
   },
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading dataset (.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 651,
     "status": "ok",
     "timestamp": 1624466626401,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "ZK5KeZCT-TXm",
    "outputId": "30acc1f1-e3db-4d90-9578-ee1ef12a7b1a"
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>brightness</th>\n",
       "      <th>scan</th>\n",
       "      <th>track</th>\n",
       "      <th>acq_date</th>\n",
       "      <th>acq_time</th>\n",
       "      <th>satellite</th>\n",
       "      <th>instrument</th>\n",
       "      <th>confidence</th>\n",
       "      <th>version</th>\n",
       "      <th>bright_t31</th>\n",
       "      <th>frp</th>\n",
       "      <th>daynight</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-11.8070</td>\n",
       "      <td>142.0583</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>56</td>\n",
       "      <td>Terra</td>\n",
       "      <td>MODIS</td>\n",
       "      <td>48</td>\n",
       "      <td>6.3</td>\n",
       "      <td>297.3</td>\n",
       "      <td>6.6</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-11.7924</td>\n",
       "      <td>142.0850</td>\n",
       "      <td>319.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>56</td>\n",
       "      <td>Terra</td>\n",
       "      <td>MODIS</td>\n",
       "      <td>71</td>\n",
       "      <td>6.3</td>\n",
       "      <td>297.3</td>\n",
       "      <td>11.3</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-12.8398</td>\n",
       "      <td>132.8744</td>\n",
       "      <td>311.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>57</td>\n",
       "      <td>Terra</td>\n",
       "      <td>MODIS</td>\n",
       "      <td>42</td>\n",
       "      <td>6.3</td>\n",
       "      <td>298.7</td>\n",
       "      <td>23.1</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-14.4306</td>\n",
       "      <td>143.3035</td>\n",
       "      <td>310.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>57</td>\n",
       "      <td>Terra</td>\n",
       "      <td>MODIS</td>\n",
       "      <td>33</td>\n",
       "      <td>6.3</td>\n",
       "      <td>296.1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-12.4953</td>\n",
       "      <td>131.4897</td>\n",
       "      <td>310.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>57</td>\n",
       "      <td>Terra</td>\n",
       "      <td>MODIS</td>\n",
       "      <td>36</td>\n",
       "      <td>6.3</td>\n",
       "      <td>298.8</td>\n",
       "      <td>27.6</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   latitude  longitude  brightness  scan  track    acq_date  acq_time  \\\n",
       "0  -11.8070   142.0583       313.0   1.0    1.0  2019-08-01        56   \n",
       "1  -11.7924   142.0850       319.3   1.0    1.0  2019-08-01        56   \n",
       "2  -12.8398   132.8744       311.6   3.1    1.7  2019-08-01        57   \n",
       "3  -14.4306   143.3035       310.1   1.1    1.1  2019-08-01        57   \n",
       "4  -12.4953   131.4897       310.3   4.0    1.9  2019-08-01        57   \n",
       "\n",
       "  satellite instrument  confidence  version  bright_t31   frp daynight  type  \n",
       "0     Terra      MODIS          48      6.3       297.3   6.6        D     0  \n",
       "1     Terra      MODIS          71      6.3       297.3  11.3        D     0  \n",
       "2     Terra      MODIS          42      6.3       298.7  23.1        D     0  \n",
       "3     Terra      MODIS          33      6.3       296.1   6.5        D     0  \n",
       "4     Terra      MODIS          36      6.3       298.8  27.6        D     0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = pd.read_csv('fire_archive.csv')\n",
    "\n",
    "forest.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "executionInfo": {
     "elapsed": 612,
     "status": "ok",
     "timestamp": 1624466629474,
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      "displayName": "siddharth patra",
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    },
    "id": "xygeQDpT-eFZ",
    "outputId": "15632d97-37a4-4bd7-f632-6ddd0e0b3666"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36011, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.shape\n",
    "# Here we can see that we have 36011 rows and 15 columns in our dataset, obviously we have to do lot of data cleaning but first let's explore this dataset more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['latitude', 'longitude', 'brightness', 'scan', 'track', 'acq_date',\n",
       "       'acq_time', 'satellite', 'instrument', 'confidence', 'version',\n",
       "       'bright_t31', 'frp', 'daynight', 'type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1624466630145,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "O5shOy6rDX21",
    "outputId": "f5ff013e-f17c-44fc-ecd0-2725dca947dc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "latitude      0\n",
       "longitude     0\n",
       "brightness    0\n",
       "scan          0\n",
       "track         0\n",
       "acq_date      0\n",
       "acq_time      0\n",
       "satellite     0\n",
       "instrument    0\n",
       "confidence    0\n",
       "version       0\n",
       "bright_t31    0\n",
       "frp           0\n",
       "daynight      0\n",
       "type          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.isnull().sum()\n",
    "# Fortunately we don't have any null values in this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 320
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1624466630554,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
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    "id": "E10UcHhyGlqu",
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>brightness</th>\n",
       "      <th>scan</th>\n",
       "      <th>track</th>\n",
       "      <th>acq_time</th>\n",
       "      <th>confidence</th>\n",
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       "      <th>frp</th>\n",
       "      <th>type</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>36011.000000</td>\n",
       "      <td>36011.000000</td>\n",
       "      <td>36011.000000</td>\n",
       "      <td>36011.000000</td>\n",
       "      <td>36011.000000</td>\n",
       "      <td>36011.000000</td>\n",
       "      <td>36011.000000</td>\n",
       "      <td>3.601100e+04</td>\n",
       "      <td>36011.000000</td>\n",
       "      <td>36011.000000</td>\n",
       "      <td>36011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-19.100962</td>\n",
       "      <td>138.931446</td>\n",
       "      <td>328.750696</td>\n",
       "      <td>1.620905</td>\n",
       "      <td>1.215281</td>\n",
       "      <td>600.025798</td>\n",
       "      <td>67.551387</td>\n",
       "      <td>6.300000e+00</td>\n",
       "      <td>301.339138</td>\n",
       "      <td>51.132176</td>\n",
       "      <td>0.019439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.265777</td>\n",
       "      <td>9.261400</td>\n",
       "      <td>18.992808</td>\n",
       "      <td>0.813764</td>\n",
       "      <td>0.247852</td>\n",
       "      <td>548.369111</td>\n",
       "      <td>23.179946</td>\n",
       "      <td>3.728625e-12</td>\n",
       "      <td>9.724462</td>\n",
       "      <td>92.280112</td>\n",
       "      <td>0.198326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-42.762800</td>\n",
       "      <td>114.104300</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.300000e+00</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-26.370250</td>\n",
       "      <td>131.072250</td>\n",
       "      <td>316.500000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>6.300000e+00</td>\n",
       "      <td>293.600000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-15.706500</td>\n",
       "      <td>136.738500</td>\n",
       "      <td>326.400000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>424.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>6.300000e+00</td>\n",
       "      <td>302.400000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-13.343600</td>\n",
       "      <td>147.477500</td>\n",
       "      <td>336.700000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>6.300000e+00</td>\n",
       "      <td>308.600000</td>\n",
       "      <td>52.800000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-10.072600</td>\n",
       "      <td>153.490400</td>\n",
       "      <td>504.400000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2359.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>6.300000e+00</td>\n",
       "      <td>400.100000</td>\n",
       "      <td>3679.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           latitude     longitude    brightness          scan         track  \\\n",
       "count  36011.000000  36011.000000  36011.000000  36011.000000  36011.000000   \n",
       "mean     -19.100962    138.931446    328.750696      1.620905      1.215281   \n",
       "std        7.265777      9.261400     18.992808      0.813764      0.247852   \n",
       "min      -42.762800    114.104300    300.000000      1.000000      1.000000   \n",
       "25%      -26.370250    131.072250    316.500000      1.100000      1.000000   \n",
       "50%      -15.706500    136.738500    326.400000      1.300000      1.100000   \n",
       "75%      -13.343600    147.477500    336.700000      1.900000      1.300000   \n",
       "max      -10.072600    153.490400    504.400000      4.800000      2.000000   \n",
       "\n",
       "           acq_time    confidence       version    bright_t31           frp  \\\n",
       "count  36011.000000  36011.000000  3.601100e+04  36011.000000  36011.000000   \n",
       "mean     600.025798     67.551387  6.300000e+00    301.339138     51.132176   \n",
       "std      548.369111     23.179946  3.728625e-12      9.724462     92.280112   \n",
       "min        0.000000      0.000000  6.300000e+00    267.000000      0.000000   \n",
       "25%      205.000000     52.000000  6.300000e+00    293.600000     13.800000   \n",
       "50%      424.000000     70.000000  6.300000e+00    302.400000     25.800000   \n",
       "75%      600.000000     86.000000  6.300000e+00    308.600000     52.800000   \n",
       "max     2359.000000    100.000000  6.300000e+00    400.100000   3679.500000   \n",
       "\n",
       "               type  \n",
       "count  36011.000000  \n",
       "mean       0.019439  \n",
       "std        0.198326  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        3.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 657
    },
    "executionInfo": {
     "elapsed": 2063,
     "status": "ok",
     "timestamp": 1624466632608,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "WfpZ0WbDHXQF",
    "outputId": "1338af7f-2682-4dbb-a59b-9e3bec3b0d33"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "sns.heatmap(forest.corr(),annot=True,cmap='viridis',linewidths=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "cWnCHKAUHvcg"
   },
   "outputs": [],
   "source": [
    "forest = forest.drop(['track'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "VtZ8k0O7_HCT"
   },
   "outputs": [],
   "source": [
    "# By the way from the the dataset we are not finding if the forest fire happens or not, we are trying to find the confidence of the forest\n",
    "# fire happening. They may seem to be the same thing but there is a very small difference between them, try to find that :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1624466632609,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "hOSBoB-q_cXe",
    "outputId": "77b88996-b6d6-4f60-a63d-47f85ee8137b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The scan column\n",
      "1.0    8284\n",
      "1.1    6000\n",
      "1.2    3021\n",
      "1.3    2412\n",
      "1.4    1848\n",
      "1.5    1610\n",
      "1.6    1451\n",
      "1.7    1281\n",
      "1.8    1041\n",
      "1.9     847\n",
      "2.0     707\n",
      "2.2     691\n",
      "2.1     649\n",
      "2.3     608\n",
      "2.5     468\n",
      "2.4     433\n",
      "2.8     422\n",
      "3.0     402\n",
      "2.7     366\n",
      "2.9     361\n",
      "2.6     347\n",
      "3.1     259\n",
      "3.2     244\n",
      "3.6     219\n",
      "3.4     203\n",
      "3.3     203\n",
      "3.8     189\n",
      "3.9     156\n",
      "4.7     149\n",
      "4.3     137\n",
      "3.5     134\n",
      "3.7     134\n",
      "4.1     120\n",
      "4.6     118\n",
      "4.5     116\n",
      "4.2     108\n",
      "4.0     103\n",
      "4.4     100\n",
      "4.8      70\n",
      "Name: scan, dtype: int64\n",
      "\n",
      "The aqc_time column\n",
      "506     851\n",
      "454     631\n",
      "122     612\n",
      "423     574\n",
      "448     563\n",
      "       ... \n",
      "1558      1\n",
      "635       1\n",
      "1153      1\n",
      "302       1\n",
      "1519      1\n",
      "Name: acq_time, Length: 662, dtype: int64\n",
      "\n",
      "The satellite column\n",
      "Aqua     20541\n",
      "Terra    15470\n",
      "Name: satellite, dtype: int64\n",
      "\n",
      "The instrument column\n",
      "MODIS    36011\n",
      "Name: instrument, dtype: int64\n",
      "\n",
      "The version column\n",
      "6.3    36011\n",
      "Name: version, dtype: int64\n",
      "\n",
      "The daynight column\n",
      "D    28203\n",
      "N     7808\n",
      "Name: daynight, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Finding categorical data\n",
    "print(\"The scan column\")\n",
    "print(forest['scan'].value_counts())\n",
    "print()\n",
    "print(\"The aqc_time column\")\n",
    "print(forest['acq_time'].value_counts())\n",
    "print()\n",
    "print(\"The satellite column\")\n",
    "print(forest['satellite'].value_counts())\n",
    "print()\n",
    "print(\"The instrument column\")\n",
    "print(forest['instrument'].value_counts())\n",
    "print()\n",
    "print(\"The version column\")\n",
    "print(forest['version'].value_counts())\n",
    "print()\n",
    "print(\"The daynight column\")\n",
    "print(forest['daynight'].value_counts())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "PlAMjUKyAEtz"
   },
   "outputs": [],
   "source": [
    "# From the above data we can see that some columns have just one value reccurring in them, meaning they are not valuable to us\n",
    "# So we will drop them altogether.\n",
    "# Thus only satellite and daynight column are the only categorical type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "NkhRx6gGAm_I"
   },
   "outputs": [],
   "source": [
    "# But we can even use the scan column to restructure it into a categorical data type column. Which we will be doing in just a while."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1624466633726,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "sR_mx8WiBAcR",
    "outputId": "b5307fde-f5bb-43c6-b0cc-c3308ec0bbe4"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
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       "      <td>48</td>\n",
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       "      <td>56</td>\n",
       "      <td>Terra</td>\n",
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       "      <td>2019-08-01</td>\n",
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       "      <td>Terra</td>\n",
       "      <td>36</td>\n",
       "      <td>298.8</td>\n",
       "      <td>27.6</td>\n",
       "      <td>D</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latitude  longitude  brightness  scan    acq_date  acq_time satellite  \\\n",
       "0  -11.8070   142.0583       313.0   1.0  2019-08-01        56     Terra   \n",
       "1  -11.7924   142.0850       319.3   1.0  2019-08-01        56     Terra   \n",
       "2  -12.8398   132.8744       311.6   3.1  2019-08-01        57     Terra   \n",
       "3  -14.4306   143.3035       310.1   1.1  2019-08-01        57     Terra   \n",
       "4  -12.4953   131.4897       310.3   4.0  2019-08-01        57     Terra   \n",
       "\n",
       "   confidence  bright_t31   frp daynight  type  \n",
       "0          48       297.3   6.6        D     0  \n",
       "1          71       297.3  11.3        D     0  \n",
       "2          42       298.7  23.1        D     0  \n",
       "3          33       296.1   6.5        D     0  \n",
       "4          36       298.8  27.6        D     0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = forest.drop(['instrument', 'version'], axis = 1)\n",
    "\n",
    "forest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1624466633727,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
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     "user_tz": -330
    },
    "id": "KBjrkSQlDSqN",
    "outputId": "d5819e18-1d12-48b2-e78d-ecccba5dee4f"
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "   latitude  longitude  brightness  scan    acq_date  acq_time  satellite  \\\n",
       "0  -11.8070   142.0583       313.0   1.0  2019-08-01        56          1   \n",
       "1  -11.7924   142.0850       319.3   1.0  2019-08-01        56          1   \n",
       "2  -12.8398   132.8744       311.6   3.1  2019-08-01        57          1   \n",
       "3  -14.4306   143.3035       310.1   1.1  2019-08-01        57          1   \n",
       "4  -12.4953   131.4897       310.3   4.0  2019-08-01        57          1   \n",
       "\n",
       "   confidence  bright_t31   frp  daynight  type  \n",
       "0          48       297.3   6.6         1     0  \n",
       "1          71       297.3  11.3         1     0  \n",
       "2          42       298.7  23.1         1     0  \n",
       "3          33       296.1   6.5         1     0  \n",
       "4          36       298.8  27.6         1     0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daynight_map = {\"D\": 1, \"N\": 0}\n",
    "satellite_map = {\"Terra\": 1, \"Aqua\": 0}\n",
    "\n",
    "forest['daynight'] = forest['daynight'].map(daynight_map)\n",
    "forest['satellite'] = forest['satellite'].map(satellite_map)\n",
    "\n",
    "forest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1624466634395,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "UdGTmFhfESJg",
    "outputId": "887f3074-6639-44a2-a7f9-adac7e9edb5e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    35666\n",
       "2      335\n",
       "3       10\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at another columns type\n",
    "forest['type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1624466634999,
     "user": {
      "displayName": "siddharth patra",
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    },
    "id": "pz9rHUAiEb18",
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      ],
      "text/plain": [
       "   latitude  longitude  brightness  scan    acq_date  acq_time  satellite  \\\n",
       "0  -11.8070   142.0583       313.0   1.0  2019-08-01        56          1   \n",
       "1  -11.7924   142.0850       319.3   1.0  2019-08-01        56          1   \n",
       "2  -12.8398   132.8744       311.6   3.1  2019-08-01        57          1   \n",
       "3  -14.4306   143.3035       310.1   1.1  2019-08-01        57          1   \n",
       "4  -12.4953   131.4897       310.3   4.0  2019-08-01        57          1   \n",
       "\n",
       "   confidence  bright_t31   frp  daynight  0  2  3  \n",
       "0          48       297.3   6.6         1  1  0  0  \n",
       "1          71       297.3  11.3         1  1  0  0  \n",
       "2          42       298.7  23.1         1  1  0  0  \n",
       "3          33       296.1   6.5         1  1  0  0  \n",
       "4          36       298.8  27.6         1  1  0  0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types = pd.get_dummies(forest['type'])\n",
    "forest = pd.concat([forest, types], axis=1)\n",
    "\n",
    "forest = forest.drop(['type'], axis = 1)\n",
    "forest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "FiyPSxSRE6J2"
   },
   "outputs": [],
   "source": [
    "# Renaming columns for better understanding\n",
    "\n",
    "forest = forest.rename(columns={0: 'type_0', 2: 'type_2', 3: 'type_3'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1624466635914,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "v6UXQ4-SFgPB",
    "outputId": "bce2e6a6-6007-47e8-fa8d-d8297ecee76b"
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
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       "      <td>-11.8070</td>\n",
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       "      <td>-12.8398</td>\n",
       "      <td>132.8744</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-12.4953</td>\n",
       "      <td>131.4897</td>\n",
       "      <td>310.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>298.8</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   latitude  longitude  brightness  scan    acq_date  acq_time  satellite  \\\n",
       "0  -11.8070   142.0583       313.0   1.0  2019-08-01        56          1   \n",
       "1  -11.7924   142.0850       319.3   1.0  2019-08-01        56          1   \n",
       "2  -12.8398   132.8744       311.6   3.1  2019-08-01        57          1   \n",
       "3  -14.4306   143.3035       310.1   1.1  2019-08-01        57          1   \n",
       "4  -12.4953   131.4897       310.3   4.0  2019-08-01        57          1   \n",
       "\n",
       "   confidence  bright_t31   frp  daynight  type_0  type_2  type_3 scan_binned  \n",
       "0          48       297.3   6.6         1       1       0       0           1  \n",
       "1          71       297.3  11.3         1       1       0       0           1  \n",
       "2          42       298.7  23.1         1       1       0       0           4  \n",
       "3          33       296.1   6.5         1       1       0       0           2  \n",
       "4          36       298.8  27.6         1       1       0       0           4  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now I mentioned we will be converting scan column to categorical type, we will be doing this using binning method.\n",
    "# Range for this columns was 1 to 4.8\n",
    "\n",
    "bins = [0, 1, 2, 3, 4, 5]\n",
    "labels = [1,2,3,4,5]\n",
    "forest['scan_binned'] = pd.cut(forest['scan'], bins=bins, labels=labels)\n",
    "\n",
    "forest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "fJIYxn2TIseV"
   },
   "outputs": [],
   "source": [
    "# Converting the datatype to datetype from string or numpy.\n",
    "\n",
    "forest['acq_date'] = pd.to_datetime(forest['acq_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "g-H-wnVKFjJe"
   },
   "outputs": [],
   "source": [
    "# Now we will be dropping scan column and handle date type data - we can extract useful information from these datatypes \n",
    "# just like we do with categorical data.\n",
    "\n",
    "forest = forest.drop(['scan'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "id": "oPT5eHoIJWhC",
    "outputId": "7ee91e1d-3ad5-4008-f5d6-a1d25b2c8a24"
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   "outputs": [
    {
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       "      <td>2019</td>\n",
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       "      <td>-12.4953</td>\n",
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       "      <td>310.3</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>298.8</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2019</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latitude  longitude  brightness   acq_date  acq_time  satellite  \\\n",
       "0  -11.8070   142.0583       313.0 2019-08-01        56          1   \n",
       "1  -11.7924   142.0850       319.3 2019-08-01        56          1   \n",
       "2  -12.8398   132.8744       311.6 2019-08-01        57          1   \n",
       "3  -14.4306   143.3035       310.1 2019-08-01        57          1   \n",
       "4  -12.4953   131.4897       310.3 2019-08-01        57          1   \n",
       "\n",
       "   confidence  bright_t31   frp  daynight  type_0  type_2  type_3 scan_binned  \\\n",
       "0          48       297.3   6.6         1       1       0       0           1   \n",
       "1          71       297.3  11.3         1       1       0       0           1   \n",
       "2          42       298.7  23.1         1       1       0       0           4   \n",
       "3          33       296.1   6.5         1       1       0       0           2   \n",
       "4          36       298.8  27.6         1       1       0       0           4   \n",
       "\n",
       "   year  \n",
       "0  2019  \n",
       "1  2019  \n",
       "2  2019  \n",
       "3  2019  \n",
       "4  2019  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest['year'] = forest['acq_date'].dt.year\n",
    "\n",
    "forest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "QB-9VFL7JlUL"
   },
   "outputs": [],
   "source": [
    "forest['month'] = forest['acq_date'].dt.month\n",
    "forest['day'] = forest['acq_date'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1624466638242,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "bCuQA0xzJ0zX",
    "outputId": "d7f05940-ddc9-4b07-aeef-8d4dd4cdf1d8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36011, 17)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "E8qfLwgjJ3I9"
   },
   "outputs": [],
   "source": [
    "# Separating our target varibale:\n",
    "\n",
    "y = forest['confidence']\n",
    "fin = forest.drop(['confidence', 'acq_date', 'acq_time', 'bright_t31', 'type_0'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "executionInfo": {
     "elapsed": 1350,
     "status": "ok",
     "timestamp": 1624466640812,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
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    },
    "id": "P2WANxh0g10m",
    "outputId": "c8006555-4616-4884-d3c7-e222d7d760de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "sns.heatmap(fin.corr(),annot=True,cmap='viridis',linewidths=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1624466640814,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "fpwoKDWee2YL",
    "outputId": "61470bd6-973c-46a2-b46e-654c230ff283"
   },
   "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>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>brightness</th>\n",
       "      <th>satellite</th>\n",
       "      <th>frp</th>\n",
       "      <th>daynight</th>\n",
       "      <th>type_2</th>\n",
       "      <th>type_3</th>\n",
       "      <th>scan_binned</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-11.8070</td>\n",
       "      <td>142.0583</td>\n",
       "      <td>313.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-11.7924</td>\n",
       "      <td>142.0850</td>\n",
       "      <td>319.3</td>\n",
       "      <td>1</td>\n",
       "      <td>11.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-12.8398</td>\n",
       "      <td>132.8744</td>\n",
       "      <td>311.6</td>\n",
       "      <td>1</td>\n",
       "      <td>23.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-14.4306</td>\n",
       "      <td>143.3035</td>\n",
       "      <td>310.1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-12.4953</td>\n",
       "      <td>131.4897</td>\n",
       "      <td>310.3</td>\n",
       "      <td>1</td>\n",
       "      <td>27.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latitude  longitude  brightness  satellite   frp  daynight  type_2  type_3  \\\n",
       "0  -11.8070   142.0583       313.0          1   6.6         1       0       0   \n",
       "1  -11.7924   142.0850       319.3          1  11.3         1       0       0   \n",
       "2  -12.8398   132.8744       311.6          1  23.1         1       0       0   \n",
       "3  -14.4306   143.3035       310.1          1   6.5         1       0       0   \n",
       "4  -12.4953   131.4897       310.3          1  27.6         1       0       0   \n",
       "\n",
       "  scan_binned  year  month  day  \n",
       "0           1  2019      8    1  \n",
       "1           1  2019      8    1  \n",
       "2           4  2019      8    1  \n",
       "3           2  2019      8    1  \n",
       "4           4  2019      8    1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fin.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the clean data into training and testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "2XnxIRf7KuVL"
   },
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(fin.iloc[:, :500], y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using RandomForestRegressor for model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "zzPfe5KeKzRW"
   },
   "outputs": [],
   "source": [
    "random_model = RandomForestRegressor(n_estimators=300, random_state = 42, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 39175,
     "status": "ok",
     "timestamp": 1624466681739,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "Xs3LXVH1K2DF",
    "outputId": "38fa2025-4ea4-464f-9402-6a8dccd46721"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.32 %\n"
     ]
    }
   ],
   "source": [
    "#Fit\n",
    "random_model.fit(Xtrain, ytrain)\n",
    "\n",
    "y_pred = random_model.predict(Xtest)\n",
    "\n",
    "#Checking the accuracy\n",
    "random_model_accuracy = round(random_model.score(Xtrain, ytrain)*100,2)\n",
    "print(round(random_model_accuracy, 2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 629,
     "status": "ok",
     "timestamp": 1624466682362,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "_bRcHW-TK5e5",
    "outputId": "d1d3d60d-69a6-4781-f8fa-7555058ec602"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65.32 %\n"
     ]
    }
   ],
   "source": [
    "#Checking the accuracy\n",
    "random_model_accuracy1 = round(random_model.score(Xtest, ytest)*100,2)\n",
    "print(round(random_model_accuracy1, 2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "OdHq4nfDvWE5"
   },
   "outputs": [],
   "source": [
    "# Save the trained model as a pickle string.\n",
    "import pickle \n",
    "\n",
    "saved_model = pickle.dump(random_model, open('ForestModelOld.pickle','wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "PkzWoNLULaos"
   },
   "outputs": [],
   "source": [
    "# The accuracy is not so great, plus the model is overfitting\n",
    "# So we use RandomCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1624460388906,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "vmuyDMGOMGpD",
    "outputId": "bf5680bc-94eb-48bc-9c5a-2244638c3eff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'criterion': 'mse',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'auto',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 300,\n",
       " 'n_jobs': -1,\n",
       " 'oob_score': False,\n",
       " 'random_state': 42,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_model.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "E3szICeuMJ4r"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "n_estimators = number of trees in the foreset\n",
    "max_features = max number of features considered for splitting a node\n",
    "max_depth = max number of levels in each decision tree\n",
    "min_samples_split = min number of data points placed in a node before the node is split\n",
    "min_samples_leaf = min number of data points allowed in a leaf node\n",
    "bootstrap = method for sampling data points (with or without replacement)\n",
    "\"\"\"\n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1624460388907,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "kRiXHtQUMOkq",
    "outputId": "b586a853-2408-4ad6-9d5e-fc9a66717348"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [300, 310, 321, 331, 342, 352, 363, 373, 384, 394, 405, 415, 426, 436, 447, 457, 468, 478, 489, 500], 'max_features': ['auto', 'sqrt'], 'max_depth': [15, 18, 21, 25, 28, 31, 35, None], 'min_samples_split': [2, 3, 5], 'min_samples_leaf': [1, 2, 4]}\n"
     ]
    }
   ],
   "source": [
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 300, stop = 500, num = 20)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(15, 35, num = 7)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 3, 5]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "                }\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3195792,
     "status": "ok",
     "timestamp": 1624463584692,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "Acyu2I_hMYx4",
    "outputId": "723b78c2-fc07-4f17-e34a-182de91fbad1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 50 candidates, totalling 150 fits\n",
      "[CV] n_estimators=415, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=415, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=18, total=  32.6s\n",
      "[CV] n_estimators=415, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=18 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   32.5s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=415, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=18, total=  27.3s\n",
      "[CV] n_estimators=415, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=18 \n",
      "[CV]  n_estimators=415, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=18, total=  29.7s\n",
      "[CV] n_estimators=436, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=28 \n",
      "[CV]  n_estimators=436, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=28, total=  36.1s\n",
      "[CV] n_estimators=436, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=28 \n",
      "[CV]  n_estimators=436, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=28, total=  34.7s\n",
      "[CV] n_estimators=436, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=28 \n",
      "[CV]  n_estimators=436, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=28, total=  35.9s\n",
      "[CV] n_estimators=478, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35 \n",
      "[CV]  n_estimators=478, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35, total=  14.7s\n",
      "[CV] n_estimators=478, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35 \n",
      "[CV]  n_estimators=478, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35, total=  14.5s\n",
      "[CV] n_estimators=478, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35 \n",
      "[CV]  n_estimators=478, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35, total=  14.9s\n",
      "[CV] n_estimators=310, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=28 \n",
      "[CV]  n_estimators=310, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=28, total=  19.3s\n",
      "[CV] n_estimators=310, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=28 \n",
      "[CV]  n_estimators=310, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=28, total=  19.1s\n",
      "[CV] n_estimators=310, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=28 \n",
      "[CV]  n_estimators=310, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=28, total=  19.4s\n",
      "[CV] n_estimators=447, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=447, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=25, total=  36.2s\n",
      "[CV] n_estimators=447, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=447, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=25, total=  37.2s\n",
      "[CV] n_estimators=447, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=447, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=25, total=  35.7s\n",
      "[CV] n_estimators=457, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=457, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=25, total=  29.2s\n",
      "[CV] n_estimators=457, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=457, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=25, total=  27.8s\n",
      "[CV] n_estimators=457, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=457, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=25, total=  29.8s\n",
      "[CV] n_estimators=384, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=384, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=25, total=  34.3s\n",
      "[CV] n_estimators=384, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=384, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=25, total=  40.8s\n",
      "[CV] n_estimators=384, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=384, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=25, total=  28.1s\n",
      "[CV] n_estimators=426, min_samples_split=3, min_samples_leaf=2, max_features=auto, max_depth=35 \n",
      "[CV]  n_estimators=426, min_samples_split=3, min_samples_leaf=2, max_features=auto, max_depth=35, total=  41.1s\n",
      "[CV] n_estimators=426, min_samples_split=3, min_samples_leaf=2, max_features=auto, max_depth=35 \n",
      "[CV]  n_estimators=426, min_samples_split=3, min_samples_leaf=2, max_features=auto, max_depth=35, total=  32.6s\n",
      "[CV] n_estimators=426, min_samples_split=3, min_samples_leaf=2, max_features=auto, max_depth=35 \n",
      "[CV]  n_estimators=426, min_samples_split=3, min_samples_leaf=2, max_features=auto, max_depth=35, total=  42.3s\n",
      "[CV] n_estimators=352, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=21 \n",
      "[CV]  n_estimators=352, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=21, total=  11.6s\n",
      "[CV] n_estimators=352, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=21 \n",
      "[CV]  n_estimators=352, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=21, total=  12.5s\n",
      "[CV] n_estimators=352, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=21 \n",
      "[CV]  n_estimators=352, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=21, total=  12.0s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=31 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=31, total=  40.4s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=31 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=31, total=  34.4s\n",
      "[CV] n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=31 \n",
      "[CV]  n_estimators=300, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=31, total=  26.3s\n",
      "[CV] n_estimators=447, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=447, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=  12.9s\n",
      "[CV] n_estimators=447, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=447, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=  16.9s\n",
      "[CV] n_estimators=447, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=447, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=  22.8s\n",
      "[CV] n_estimators=373, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=373, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=None, total=  20.9s\n",
      "[CV] n_estimators=373, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=373, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=None, total=  17.8s\n",
      "[CV] n_estimators=373, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=373, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=None, total=  24.7s\n",
      "[CV] n_estimators=384, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=18 \n",
      "[CV]  n_estimators=384, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=18, total=  38.4s\n",
      "[CV] n_estimators=384, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=18 \n",
      "[CV]  n_estimators=384, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=18, total=  27.6s\n",
      "[CV] n_estimators=384, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=18 \n",
      "[CV]  n_estimators=384, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=18, total=  28.0s\n",
      "[CV] n_estimators=300, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=28 \n",
      "[CV]  n_estimators=300, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=28, total=   6.7s\n",
      "[CV] n_estimators=300, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=28 \n",
      "[CV]  n_estimators=300, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=28, total=   7.3s\n",
      "[CV] n_estimators=300, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=28 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=300, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=28, total=   7.9s\n",
      "[CV] n_estimators=478, min_samples_split=3, min_samples_leaf=2, max_features=auto, max_depth=21 \n",
      "[CV]  n_estimators=478, min_samples_split=3, min_samples_leaf=2, max_features=auto, max_depth=21, total=  38.7s\n",
      "[CV] n_estimators=478, min_samples_split=3, min_samples_leaf=2, max_features=auto, max_depth=21 \n",
      "[CV]  n_estimators=478, min_samples_split=3, min_samples_leaf=2, max_features=auto, max_depth=21, total=  34.4s\n",
      "[CV] n_estimators=478, min_samples_split=3, min_samples_leaf=2, max_features=auto, max_depth=21 \n",
      "[CV]  n_estimators=478, min_samples_split=3, min_samples_leaf=2, max_features=auto, max_depth=21, total=  36.6s\n",
      "[CV] n_estimators=310, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=21 \n",
      "[CV]  n_estimators=310, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=21, total=  22.7s\n",
      "[CV] n_estimators=310, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=21 \n",
      "[CV]  n_estimators=310, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=21, total=  22.3s\n",
      "[CV] n_estimators=310, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=21 \n",
      "[CV]  n_estimators=310, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=21, total=  28.5s\n",
      "[CV] n_estimators=489, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=18 \n",
      "[CV]  n_estimators=489, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=18, total=  33.4s\n",
      "[CV] n_estimators=489, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=18 \n",
      "[CV]  n_estimators=489, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=18, total=  48.5s\n",
      "[CV] n_estimators=489, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=18 \n",
      "[CV]  n_estimators=489, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=18, total=  49.4s\n",
      "[CV] n_estimators=500, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=500, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=  16.4s\n",
      "[CV] n_estimators=500, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=500, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=  15.3s\n",
      "[CV] n_estimators=500, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=500, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=15, total=  12.3s\n",
      "[CV] n_estimators=415, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=415, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=15, total=   9.0s\n",
      "[CV] n_estimators=415, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=415, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=15, total=  11.5s\n",
      "[CV] n_estimators=415, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=415, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=15, total=  12.1s\n",
      "[CV] n_estimators=489, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=21 \n",
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      "[CV] n_estimators=489, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=21 \n",
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      "[CV] n_estimators=489, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=21 \n",
      "[CV]  n_estimators=489, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=21, total=  38.9s\n",
      "[CV] n_estimators=426, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=21 \n",
      "[CV]  n_estimators=426, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=21, total=  12.6s\n",
      "[CV] n_estimators=426, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=21 \n",
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      "[CV] n_estimators=426, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=21 \n",
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      "[CV] n_estimators=310, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=310, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=25, total=  23.6s\n",
      "[CV] n_estimators=310, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=310, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=25, total=  22.3s\n",
      "[CV] n_estimators=310, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=25 \n",
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      "[CV] n_estimators=394, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
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      "[CV] n_estimators=394, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=394, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=  15.9s\n",
      "[CV] n_estimators=394, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=394, min_samples_split=2, min_samples_leaf=1, max_features=sqrt, max_depth=25, total=  14.6s\n",
      "[CV] n_estimators=457, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35 \n",
      "[CV]  n_estimators=457, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35, total=  15.8s\n",
      "[CV] n_estimators=457, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35 \n",
      "[CV]  n_estimators=457, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35, total=  14.2s\n",
      "[CV] n_estimators=457, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35 \n",
      "[CV]  n_estimators=457, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35, total=  14.5s\n",
      "[CV] n_estimators=489, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=28 \n",
      "[CV]  n_estimators=489, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=28, total=  10.6s\n",
      "[CV] n_estimators=489, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=28 \n",
      "[CV]  n_estimators=489, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=28, total=  10.4s\n",
      "[CV] n_estimators=489, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=28 \n",
      "[CV]  n_estimators=489, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=28, total=  10.7s\n",
      "[CV] n_estimators=478, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=28 \n",
      "[CV]  n_estimators=478, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=28, total=  33.2s\n",
      "[CV] n_estimators=478, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=28 \n",
      "[CV]  n_estimators=478, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=28, total=  32.5s\n",
      "[CV] n_estimators=478, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=28 \n",
      "[CV]  n_estimators=478, min_samples_split=2, min_samples_leaf=2, max_features=auto, max_depth=28, total=  32.4s\n",
      "[CV] n_estimators=321, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=31 \n",
      "[CV]  n_estimators=321, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=31, total=   9.6s\n",
      "[CV] n_estimators=321, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=31 \n",
      "[CV]  n_estimators=321, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=31, total=   9.9s\n",
      "[CV] n_estimators=321, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=31 \n",
      "[CV]  n_estimators=321, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=31, total=  10.0s\n",
      "[CV] n_estimators=405, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=21 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=405, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=21, total=  10.0s\n",
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      "[CV]  n_estimators=426, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=28, total=   8.8s\n",
      "[CV] n_estimators=426, min_samples_split=2, min_samples_leaf=4, max_features=sqrt, max_depth=28 \n",
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      "[CV] n_estimators=447, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=31 \n",
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      "[CV] n_estimators=394, min_samples_split=5, min_samples_leaf=4, max_features=sqrt, max_depth=25 \n",
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      "[CV] n_estimators=342, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=28 \n",
      "[CV]  n_estimators=342, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=28, total=  21.1s\n",
      "[CV] n_estimators=342, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=28 \n",
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      "[CV] n_estimators=342, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=28 \n",
      "[CV]  n_estimators=342, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=28, total=  21.9s\n",
      "[CV] n_estimators=426, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35 \n",
      "[CV]  n_estimators=426, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35, total=  12.7s\n",
      "[CV] n_estimators=426, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35 \n",
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      "[CV] n_estimators=426, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35 \n",
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      "[CV] n_estimators=373, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=373, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=None, total=  25.9s\n",
      "[CV] n_estimators=373, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=373, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=None, total=  26.2s\n",
      "[CV] n_estimators=373, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=373, min_samples_split=5, min_samples_leaf=1, max_features=auto, max_depth=None, total=  26.9s\n",
      "[CV] n_estimators=426, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=31 \n",
      "[CV]  n_estimators=426, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=31, total=  25.9s\n",
      "[CV] n_estimators=426, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=31 \n",
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      "[CV] n_estimators=426, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=31 \n",
      "[CV]  n_estimators=426, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=31, total=  25.6s\n",
      "[CV] n_estimators=478, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=21 \n",
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      "[CV] n_estimators=478, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=21 \n",
      "[CV]  n_estimators=478, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=21, total=  28.4s\n",
      "[CV] n_estimators=478, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=21 \n",
      "[CV]  n_estimators=478, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=21, total=  28.8s\n",
      "[CV] n_estimators=500, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=35 \n",
      "[CV]  n_estimators=500, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=35, total=  30.0s\n",
      "[CV] n_estimators=500, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=35 \n",
      "[CV]  n_estimators=500, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=35, total=  29.8s\n",
      "[CV] n_estimators=500, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=35 \n",
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      "[CV] n_estimators=342, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=342, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=15, total=  19.1s\n",
      "[CV] n_estimators=342, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=342, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=15, total=  18.9s\n",
      "[CV] n_estimators=342, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=15 \n",
      "[CV]  n_estimators=342, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=15, total=  19.3s\n",
      "[CV] n_estimators=457, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=35 \n",
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      "[CV] n_estimators=457, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=35 \n",
      "[CV]  n_estimators=457, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=35, total=  30.1s\n",
      "[CV] n_estimators=457, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=35 \n",
      "[CV]  n_estimators=457, min_samples_split=5, min_samples_leaf=2, max_features=auto, max_depth=35, total=  30.1s\n",
      "[CV] n_estimators=415, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=415, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=  10.6s\n",
      "[CV] n_estimators=415, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=415, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=  10.4s\n",
      "[CV] n_estimators=415, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=None \n",
      "[CV]  n_estimators=415, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=None, total=  10.4s\n",
      "[CV] n_estimators=394, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=394, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=None, total=  24.9s\n",
      "[CV] n_estimators=394, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=None \n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  n_estimators=394, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=None, total=  23.9s\n",
      "[CV] n_estimators=394, min_samples_split=3, min_samples_leaf=4, max_features=auto, max_depth=None \n",
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      "[CV] n_estimators=426, min_samples_split=3, min_samples_leaf=1, max_features=auto, max_depth=28 \n",
      "[CV]  n_estimators=426, min_samples_split=3, min_samples_leaf=1, max_features=auto, max_depth=28, total=  31.4s\n",
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      "[CV] n_estimators=426, min_samples_split=3, min_samples_leaf=1, max_features=auto, max_depth=28 \n",
      "[CV]  n_estimators=426, min_samples_split=3, min_samples_leaf=1, max_features=auto, max_depth=28, total=  31.2s\n",
      "[CV] n_estimators=310, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=310, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=25, total=   7.9s\n",
      "[CV] n_estimators=310, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=310, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=25, total=   7.7s\n",
      "[CV] n_estimators=310, min_samples_split=2, min_samples_leaf=2, max_features=sqrt, max_depth=25 \n",
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      "[CV] n_estimators=405, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35 \n",
      "[CV]  n_estimators=405, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35, total=  12.1s\n",
      "[CV] n_estimators=405, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35 \n",
      "[CV]  n_estimators=405, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35, total=  12.1s\n",
      "[CV] n_estimators=405, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35 \n",
      "[CV]  n_estimators=405, min_samples_split=3, min_samples_leaf=1, max_features=sqrt, max_depth=35, total=  12.0s\n",
      "[CV] n_estimators=352, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=25 \n",
      "[CV]  n_estimators=352, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=25, total=   8.0s\n",
      "[CV] n_estimators=352, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=25 \n",
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      "[CV] n_estimators=352, min_samples_split=3, min_samples_leaf=4, max_features=sqrt, max_depth=25 \n",
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      "[CV] n_estimators=394, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=31 \n",
      "[CV]  n_estimators=394, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=31, total=  10.4s\n",
      "[CV] n_estimators=394, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=31 \n",
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      "[CV] n_estimators=394, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=31 \n",
      "[CV]  n_estimators=394, min_samples_split=5, min_samples_leaf=1, max_features=sqrt, max_depth=31, total=  10.5s\n",
      "[CV] n_estimators=331, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=31 \n",
      "[CV]  n_estimators=331, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=31, total=  26.3s\n",
      "[CV] n_estimators=331, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=31 \n",
      "[CV]  n_estimators=331, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=31, total=  26.0s\n",
      "[CV] n_estimators=331, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=31 \n",
      "[CV]  n_estimators=331, min_samples_split=2, min_samples_leaf=1, max_features=auto, max_depth=31, total=  26.1s\n",
      "[CV] n_estimators=468, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=468, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   9.9s\n",
      "[CV] n_estimators=468, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=468, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=  10.0s\n",
      "[CV] n_estimators=468, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=15 \n",
      "[CV]  n_estimators=468, min_samples_split=5, min_samples_leaf=2, max_features=sqrt, max_depth=15, total=   9.7s\n",
      "[CV] n_estimators=468, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=468, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=None, total=  27.9s\n",
      "[CV] n_estimators=468, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=468, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=None, total=  27.6s\n",
      "[CV] n_estimators=468, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=None \n",
      "[CV]  n_estimators=468, min_samples_split=5, min_samples_leaf=4, max_features=auto, max_depth=None, total=  28.1s\n",
      "[CV] n_estimators=436, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=436, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=25, total=  26.1s\n",
      "[CV] n_estimators=436, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=436, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=25, total=  26.2s\n",
      "[CV] n_estimators=436, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=25 \n",
      "[CV]  n_estimators=436, min_samples_split=2, min_samples_leaf=4, max_features=auto, max_depth=25, total=  26.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 54.5min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3,\n",
       "                   estimator=RandomForestRegressor(n_estimators=300, n_jobs=-1,\n",
       "                                                   random_state=42),\n",
       "                   n_iter=50,\n",
       "                   param_distributions={'max_depth': [15, 18, 21, 25, 28, 31,\n",
       "                                                      35, None],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 3, 5],\n",
       "                                        'n_estimators': [300, 310, 321, 331,\n",
       "                                                         342, 352, 363, 373,\n",
       "                                                         384, 394, 405, 415,\n",
       "                                                         426, 436, 447, 457,\n",
       "                                                         468, 478, 489, 500]},\n",
       "                   random_state=42, verbose=2)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "\n",
    "# n_iter, which controls the number of different combinations to try, and cv which is the number of folds to use for cross validation\n",
    "rf_random = RandomizedSearchCV(estimator = random_model, param_distributions = random_grid, n_iter = 50, cv = 3, verbose=2, random_state=42)\n",
    "# Fit the random search model\n",
    "rf_random.fit(Xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1624463584694,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "qtSGS68bMhtJ",
    "outputId": "1f0c6e6f-7e4b-4b26-a63a-14aeb7ba500d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 394,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 25}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "6WOIaGF8eGuZ"
   },
   "outputs": [],
   "source": [
    "random_new = RandomForestRegressor(n_estimators = 394, min_samples_split = 2, min_samples_leaf = 1, max_features = 'sqrt',\n",
    "                                      max_depth = 25, bootstrap = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 26080,
     "status": "ok",
     "timestamp": 1624463641706,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "AxPHSZeSeUEZ",
    "outputId": "63116d0c-106e-4dc0-c226-49575d2c2805"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.31 %\n"
     ]
    }
   ],
   "source": [
    "#Fit\n",
    "random_new.fit(Xtrain, ytrain)\n",
    "\n",
    "y_pred1 = random_new.predict(Xtest)\n",
    "\n",
    "#Checking the accuracy\n",
    "random_model_accuracy1 = round(random_new.score(Xtrain, ytrain)*100,2)\n",
    "print(round(random_model_accuracy1, 2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1393,
     "status": "ok",
     "timestamp": 1624463723323,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "JCdRpS1dehw6",
    "outputId": "2a4179a5-48e5-4e67-925e-1df531a2367f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67.39 %\n"
     ]
    }
   ],
   "source": [
    "#Checking the accuracy\n",
    "random_model_accuracy2 = round(random_new.score(Xtest, ytest)*100,2)\n",
    "print(round(random_model_accuracy2, 2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "mERn5dVVsKQq"
   },
   "outputs": [],
   "source": [
    "# Save the trained model as a pickle string.\n",
    "saved_model = pickle.dump(random_new, open('ForestModel.pickle','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Load the pickled model - \n",
    "reg_from_pickle = pickle.load(saved_model)\n",
    "# # Use the loaded pickled model to make predictions\n",
    "# # reg_from_pickle.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### bz2file\n",
    "\n",
    "Here comes the cherry on the cake part (bonus of this article). Let's understand what this bz2file module is all about.\n",
    "Let's get started !\n",
    "\n",
    "### What is bz2file\n",
    "\n",
    "bz2file is one of the modules in python which are responsible for compression and decompression of files, hence it can help in decreasing the serialized or deserialized file to smaller size which will be very helpful in long run when we have large datasets\n",
    "\n",
    "### How bz2file is helpful here?\n",
    "\n",
    "As we know that our dataset is 2.7+ MB and our Random forest model is whooping 700+ MB so we need to compress that so that that model won't be leading as an hectic situation for storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7641,
     "status": "ok",
     "timestamp": 1624463981747,
     "user": {
      "displayName": "siddharth patra",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gjnwsdj5DKa95WHRl25dlQz9L1Bbc10vap4xTNIiA=s64",
      "userId": "01966341811263607507"
     },
     "user_tz": -330
    },
    "id": "UhI5_3yBZbMI",
    "outputId": "2e93b522-fbfd-404c-8df8-226b9550dc7a",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting bz2file\n",
      "  Downloading bz2file-0.98.tar.gz (11 kB)\n",
      "Building wheels for collected packages: bz2file\n",
      "  Building wheel for bz2file (setup.py): started\n",
      "  Building wheel for bz2file (setup.py): finished with status 'done'\n",
      "  Created wheel for bz2file: filename=bz2file-0.98-py3-none-any.whl size=6888 sha256=6346c435b208d78941d2c6705178e89f969f3a093f8a9a753093202be4822178\n",
      "  Stored in directory: c:\\users\\pc\\appdata\\local\\pip\\cache\\wheels\\19\\88\\ce\\c9430af242507ffff602cf86c5ff6a1ae5205cba5aaf21f6cc\n",
      "Successfully built bz2file\n",
      "Installing collected packages: bz2file\n",
      "Successfully installed bz2file-0.98\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install bz2file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "DzxZCfNknswt"
   },
   "outputs": [],
   "source": [
    "# Hence I installed bz2file, which is used to compress data. This is a life saving package for those who have low spaces on\n",
    "# their disk but want to store or use large datasets. Now the pickled file was over 700 mb in size which when used bz2\n",
    "# compressed in into a file of size 93 mb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "L_M3_tIXmz0t"
   },
   "outputs": [],
   "source": [
    "import bz2\n",
    "\n",
    "compressionLevel = 9\n",
    "source_file = 'ForestModel.pickle' # this file can be in a different format, like .csv or others...\n",
    "destination_file = 'ForestModel.bz2'\n",
    "\n",
    "with open(source_file, 'rb') as data:\n",
    "    tarbz2contents = bz2.compress(data.read(), compressionLevel)\n",
    "    \n",
    "fh = open(destination_file, \"wb\")\n",
    "fh.write(tarbz2contents)\n",
    "fh.close()"
   ]
  }
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